biological clock research articles

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Cell Biol
• v.219(11); 2020 Nov 2

Keeping track of time: The fundamentals of cellular clocks

Gliech and Holland discuss the guiding design principles of biological clocks across a variety of model systems.

Biological timekeeping enables the coordination and execution of complex cellular processes such as developmental programs, day/night organismal changes, intercellular signaling, and proliferative safeguards. While these systems are often considered separately owing to a wide variety of mechanisms, time frames, and outputs, all clocks are built by calibrating or delaying the rate of biochemical reactions and processes. In this review, we explore the common themes and core design principles of cellular clocks, giving special consideration to the challenges associated with building timers from biochemical components. We also outline how evolution has coopted time to increase the reliability of a diverse range of biological systems.

To consider cellular processes outside the context of time is to lose touch with the physical universe in which cells reside. Indeed, temporal accounting has been tallied for every biochemical reaction in every cellular process by every cell. Rather than simply allowing all biological programs to proceed at their fastest possible pace, cells harness time to guide, sense, and modulate biological outcomes. This deliberate temporal usage has increased the fidelity and scope of cellular processes by enabling the sequential execution of events ( Pourquie, 2001 ; Delgado and Torres, 2016 ; Raff, 2007 ), timed responses ( Renner and Schmitz, 2009 ; Heinzel et al., 2017 ; Thornquist et al., 2020 ), selectivity to input signals ( Gerardin et al., 2019 ; Qian et al., 2019 ), delay sensing ( Lambrus and Holland, 2017 ; Hellmuth and Stemmann, 2020 ), and organismal synchronization with the environment ( Diernfellner and Brunner, 2020 ; Shalit-Kaneh et al., 2018 ; Buhr and Takahashi, 2013 ).

Biological clocks (see Definitions) can be broadly classified into directive and reactive clocks ( Fig. 1 A and Definitions). Directive clocks set temporal constraints on a process and serve to direct predetermined outcomes. Imagine for example the fuse on a firework. Once lit, the fuse delays the inevitable launch and explosion so that a reveler has time to escape unharmed. In a biological context, the negative feedback loops in the ERK1/2 MAPK signaling network act as a directive clock. Following receptor activation, a signal down-regulation program is set in motion through inhibitory phosphorylation, receptor internalization, transcription of negative regulators, and phosphatase activity to turn off the signal ( Lake et al., 2016 ). The time it takes to enact this program has been sculpted by evolution to allow for short or oscillatory bursts of signaling activity. In addition to negative feedback regulation in kinase signaling ( Lake et al., 2016 ; Renner and Schmitz, 2009 ), directive clocks are also found in developmental programs ( Raff, 2007 ), circadian biology ( Bell-Pedersen et al., 2005 ), and other instances in which biological processes require a strict time frame.

The fundamentals of cellular clocks. (A) Schematic of directive and reactive clocks. Directive clocks introduce a time delay between a trigger and its effected response. Reactive clocks create a time constraint that either promotes (top) or inhibits (bottom) an effected response from a secondary input. (B) Predictable timekeeper modulation generates effected cellular responses only after crossing a critical threshold. Clock duration is the time taken from the start of the timekeeper modulation to the triggering of the response. (C) Oscillatory clocks experience repeated cycles of timekeeper behavior. The downstream activity in oscillatory clocks is also gated by a critical effector threshold.

Reactive clocks are comparatively rare in biology and allow cells to make decisions based on the timing of inputs. Unlike the directive timers, the outcomes of these biological processes are not predetermined and rely on both temporal and external cues ( Fig. 1 A ). Imagine a contestant participating in a trivia game show. If the contestant answers the question correctly before time runs out, they win a prize. However, the wrong answer within the time frame, or the right answer outside of the set time frame, fails to produce the same reward. In this case, both the answer to the question and the time at which it is provided are accounted for. The mitotic surveillance pathway is an example of a reactive clock that acts to arrest cells only after experiencing abnormally long mitosis ( Uetake and Sluder, 2010 ; Lambrus and Holland, 2017 ). In this case, cell fate is determined by both the time on the clock and the cue of mitotic exit. Since extending mitosis increases the frequency of mitotic errors, this timing mechanism protects cell populations from the detrimental consequences arising as a result of erroneous cell divisions.

In this review, we discuss similarities and differences in the designs of timers (see Definitions) across a broad range of biological processes. We also consider the common strategies, hurdles, and modifications that have been developed to tailor cellular clocks to the needs of the system in which they operate.

The fundamentals of cellular clocks

Timekeepers.

Biological clocks use a timekeeper to measure duration (see Definitions). Like sand in an hourglass, the timekeeper serves as a physical manifestation of elapsed time. The dynamics of this element set the overall clock pace and length ( Fig. 1 B ). In principle, a timekeeper can be anything with the ability to predictably change over time. Ions ( Kohajda et al., 2020 ), metabolites ( Zhang et al., 2019 ), microRNAs ( Baudet et al., 2011 ), and promoter elements ( Heinzel et al., 2017 ) have all been proposed as timekeeper elements, but biology primarily assigns timekeeping duties to proteins ( Aly et al., 2018 ; Baker et al., 2012 ; Shalit-Kaneh et al., 2018 ; Buhr and Takahashi, 2013 ; Pickering et al., 2018 ; Fig. 2 ). In this case, the state of protein posttranslational modification, abundance, conformation, or complexing modulates over time to serve as a temporal marker.

Summary of biological clocks. A compilation of the clock examples from this review organized by function. Clock name, a brief description, timekeeper type, timer duration, and key references are listed for each entry.

Consider, for example, the clock that constrains Drosophila melanogaster ’s mating behavior ( Thornquist et al., 2020 ). In this clock, mating motivation is directly linked to the protein state of Ca 2+ /calmodulin-dependent protein kinase II (CaMKII) in the specialized Crz neurons. At the onset of mating, CaMKII rapidly activates and prevents Crz neurons from firing. Over the next 6 min, a gradual increase in CAMKII inhibitory autophosphorylation serves to suppress its own kinase activity. Once CAMKII activity is sufficiently depleted, neuronal inhibition is released, and sperm transfer takes place. Here, the timekeeper is CaMKII and the measured dynamic is kinase activity, a proxy for the state of protein posttranslational modification.

A second example comes from the clock that enforces a minimum duration in mammalian mitoses. This clock functions by generating a period at the onset of mitosis during which the activity of the protease Separase is toxic to cells ( Hellmuth and Stemmann, 2020 ). Separase is required to initiate chromosome separation at anaphase, and cells that reach anaphase prematurely induce apoptosis through the Separase-dependent cleavage of the antiapoptotic protein MCL1 (induced myeloid leukemia cell differentiation 1). The time-sensitive toxicity of Separase activity is controlled by ongoing phosphorylation of MCL1 by NEK2A (NimA-related protein kinase 2). Mitotic entry triggers the gradual depletion of NEK2A and subsequent shift of MCL1 to a dephosphorylated state that is resistant to Separase cleavage ( Fig. 3 A ). The duration of this timer is therefore tied to the abundance and degradation rate of the timekeeper NEK2A.

Minimum duration of mitosis clock and the mammalian circadian oscillator. The architecture of a reactive and directive clock. (A) Minimum duration of mitosis clock. The timekeeper NEK2A degrades at the onset of mitosis. Phosphorylation of the antiapoptotic protein MCL1 by NEK2A sensitizes MCL1 to Separase cleavage. (i) In the event of premature anaphase onset, Separase activation triggers apoptosis. (ii) In normal mitosis, degradation of NEK2A relieves MCL1 phosphorylation and the sensitivity to Separase activation, thereby enabling mitotic progression. (B) Simplified schematic of the core mammalian circadian oscillator. At dawn, the transcription factor CLOCK:BMAL1 initiates the transcription and accumulation of its negative regulators PER and CRY. In the evening, nuclear translocation of PER and CRY lead to CLOCK:BMAL1 repression and PER and CRY depletion. Depletion of PER and CRY de-represses CLOCK:BMAL1 and restarts the cycle. Dozens of gradual phosphorylations by CK1 promote the degradation, complexing, and nuclear translocation of PER and CRY. Light-induced transcription of PER and CRY is the primary mechanism for clock entrainment. In addition to what is shown, other feedback loops and kinases also help maintain the clock’s pace.

The end point of cellular clocks is determined by timekeeper modulation breaching an effector threshold ( Fig. 1 B ). To use the above examples, this would be the depletion of CaMKII activity until it can no longer inhibit neuronal firing or the degradation of NEK2A until it can no longer maintain MCL1 phosphorylation. In the case of directive clocks such as in Drosophila mating, the depletion of the timekeeper serves to commit cells to a specific response. With reactive clocks such as the minimum duration of mitosis, the time taken to deplete the timekeeper creates a window that licenses a stimulus to generate a response.

In special cases, clocks are characterized by repeated cycles of activity. These so-called oscillators (see Definitions) rely on a cyclical design with a built-in reset of the timekeeper ( Fig. 1 C ). Circadian oscillators ( Bell-Pedersen et al., 2005 ) and Cyclin/CDK cell cycle oscillators ( Örd and Loog, 2019 ) are perhaps the best known examples of this style of clock, but oscillators are also found in other contexts in which biological processes are regularly repeated or require periodicity. Take, for example, the formation of a new procentriole during centriole duplication in Drosophila embryos ( Aydogan et al., 2020 ). The amount of material integrated into the new procentriole is controlled by oscillations in the recruitment of the master regulator Polo-like kinase 4 (PLK4) by its receptor Asterless. Asterless keeps time in this system by limiting the residency of PLK4 at the parent centriole through the accumulation of inhibitory phosphorylations. Repeated cycles of phosphatase activation during mitosis dephosphorylate Asterless to reset the clock and coordinate centriole duplication with the cell cycle oscillator.

Timekeeper dynamics must be effected into a cellular response. Broadly speaking, timekeepers are either directly wired to the system they control, such as CaMKII controlling neuronal firing in Drosophila mating, or will feed into other existing cellular programs through the use of transcription factors. Transcription factors can also be used directly as the timekeeper, combining both schemes into a minimal clock architecture. In mammalian development, for example, segmentation of the presomitic mesoderm (eventual vertebrae, ribs, and skeletal muscle) is controlled by the oscillatory abundance of the HES7 (Hes family bHLH transcription factor 7) transcription factor ( Kageyama et al., 2007 ; Bessho et al., 2001 ). Delayed inhibitory feedback of HES7 on its own promoter leads to repetitive periodic transcription of HES7 mRNA and waves of protein abundance. However, HES7 also binds and represses the promoter of the gene LFNG , which itself is an inhibitor of Notch signaling. In this way, HES7 abundance indirectly dictates the period of Notch pathway activity to control segmentation.

In the case of reactive clocks, effectors must be sensitive to both timing and a secondary cue to trigger a cellular response. Both clocks that regulate the minimum and maximum duration of mammalian mitoses make use of this principle. To reiterate briefly, the minimum duration of mitosis clock activates apoptosis when cells experience a premature onset of anaphase. MCL1, the specialized effector of cell death, integrates both a timed cue (NEK2A levels) with a secondary input (anaphase-induced Separase activation). Cell death occurs only if both NEK2A levels are high and Separase is activated ( Fig. 3 A ). The second timer, the mitotic surveillance pathway, induces cell cycle arrest if cells have taken too long to complete mitosis ( Uetake and Sluder, 2010 ; Lambrus et al., 2016 ; Meitinger et al., 2016 ; Fong et al., 2016 ). Cells must exit mitosis within a certain time period to avoid activation of this pathway. Although the underlying molecular underpinnings are unclear, the activation of p53-mediated cell cycle arrest must be sensitive to a timed cue modulated by a mitotic timekeeper and a second input that depends on mitotic exit.

The most important consideration for biological clocks is maintaining agreement between the duration of the process being measured and the timekeeper dynamics. In other words, the process that drives a timer must be optimized to match the clock length (the time of day, for example, cannot be reliably pinpointed with an hourglass or a calendar). This constraint plays a major role in dictating the composition and architecture of the timing mechanism. At their shortest durations, biological clocks are limited by the maximum optimized rate of the underlying timekeeper reaction or process. At their longest, extrinsic noise and issues with robustness can overpower a timekeeper’s ability to behave predictably. Below, we discuss three examples of fast (seconds), medium (hours), and slow (days) timekeeping mechanisms that are well matched to the time frame of the biological process that they control.

A small cluster of cells called the sinoatrial node sets the rate of the beating heart. This process is tightly regulated by a pacemaker cellular clock. A human heart needs to beat on average once per second, and the fast pace of ion flow across membranes provides a well-suited timekeeping mechanism. The clock’s pace is set by specialized Na + ion channels and a Na + /Ca 2+ cation exchanger that work together to generate cyclical depolarizations of the neuron ( Kohajda et al., 2020 ; DiFrancesco, 2020 ; Carmeliet, 2019 ; Bean, 2007 ).

As one of many layers of regulation in apoptosis, the function of the Apoptotic protease-activating factor 1/Caspase-9 apoptosome holoenzyme is temporally constrained. To execute cell death, Caspase-9 must associate with the apoptosome to process the effector Caspase-3. However, the binding affinity of Caspase-9 to the apoptosome is extremely weak. As a result, the apoptosome relies on the high-affinity binding of the precursor procaspase-9 and its processing in situ. The newly activated Caspase-9 leads to a short burst of apoptosome activity before it rapidly dissociates from the complex. For sustained activity, the holoenzyme requires many cycles of procaspase-9 binding, processing, and dissociation. Once the reserve of procaspase-9 in the cell is depleted, the holoenzyme shuts off. Complete processing of cellular procaspase-9 through the apoptosome takes several hours, providing a maximal time window for the cell to execute programmed cell death ( Malladi et al., 2009 ; Li et al., 2017 ).

T and B lymphocytes experience a proliferative burst as part of the immune response to foreign invasion. This growth is constrained temporally by division destiny, a cellular clock that limits lymphocyte proliferation and prevents unbridled immune activation. Over the course of 10 d, the pro-proliferative protein Myc gradually depletes within lymphocytes until its level is no longer sufficient to promote cell division. While the exact mechanism is unclear, epigenetic changes at the MYC promoter have been proposed as the timekeeper in this system. The gradual loss of MYC transcriptional activity provides a defined time window for the expansion of immune cell populations ( Heinzel et al., 2017 ).

A thorough understanding of the time ranges accessible to the variety of timekeeper architectures can help develop better models for unknown timer mechanisms. Consider the mammalian circadian oscillator, which was originally thought to be timed purely from the cyclical negative feedback of the heterodimeric transcription factor CLOCK:BMAL1 (Clock circadian regulator: Brain and muscle ARNT-like 1) and its targets Period (PER) and Cryptochrome (CRY; Buhr and Takahashi, 2013 ). In a simplified form, CLOCK:BMAL1 promotes the transcription of the PER and CRY proteins ( Fig. 3 B ). These target proteins then serve to negatively regulate and direct the degradation of CLOCK:BMAL1. Subsequently, PER/CRY transcription is attenuated, and CLOCK:BMAL1 is de-repressed to restart the ∼24-h cycle.

Based on other known clocks, direct transcriptional feedback loops such as these usually operate within the time frame of a few hours ( Averbukh et al., 2018 ; Kageyama et al., 2007 ; Matsuda et al., 2020 ; Renner and Schmitz, 2009 ; Lake et al., 2016 ; Figs. 2 and ​ and4). 4 ). While extremely weak transcription factor binding could, in principle, extend the measured timer duration to a full 24-h cycle, such an architecture is likely to suffer from issues with intrinsic and extrinsic noise ( Balázsi et al., 2011 ; Singh and Soltani, 2013 ). Therefore, one might predict that an additional time delay exists to slow the accumulation of the inhibitory PER/CRY complex. Indeed, it was later found that PER and CRY complex formation is dependent on dozens of phosphorylation events over the course of many hours by the extremely inefficient kinases Casein kinase 1 (CK1), CK2, and Glycogen synthase kinase 3β ( Ode and Ueda, 2018 ). In addition, phosphorylation promotes PER degradation, which further serves to reduce the rate of accumulation of the PER/CRY complex. The mammalian circadian clock is therefore set by two nested timers: an abundance-based transcriptional timer containing within it a second phosphorylation-based delay timer ( Fig. 3 B ).

Time frames of biological clocks. Approximate time frames for which timekeepers are used in biological clocks. Lower bounds are illustrated as black lines to represent the minimum clock duration that is set by the fastest rate of the biological process. Upper limits are illustrated as arrows to represent the ambiguous upper bound set by timekeeper robustness and clock reliability.

Cellular clocks are challenged by the internal and external noise of the systems in which they reside ( Balázsi et al., 2011 ). For instance, the pace of a degradation-based timer may fluctuate based on the initial abundance of the timekeeper protein, the temperature-sensitive catalysis of polyubiquitylation, or competition for the proteasome by other substrates. Biological clocks have evolved to mitigate this variability and improve timekeeping robustness.

The degree of timing variability that can be tolerated will vary depending on the biological context. As such, not all clocks require immutable underpinnings. To draw from a previous example, it may not be critical for cells experiencing delayed divisions to undergo a cell cycle arrest after exactly 2 h in mitosis. Single-cell data show that the decision to arrest or continue proliferating varies between 1.5 and 2.5 h ( Uetake and Sluder, 2010 ; Lambrus et al., 2016 ; Meitinger et al., 2016 ). By contrast, a circadian clock that fluctuates between 18 and 30 h, a similar percentage deviation, would be ineffective.

While the nature of the confounding variables is unique to different clock architectures, the strategies used to steady timekeeping are generalizable. In this section, we provide an overview of molecular mechanisms that ensure robustness in cellular clocks.

Timer design in response to robustness

It is reasonable to assume that the type of timekeeping mechanism (e.g., protein degradation vs. phosphorylation) and the layout of those mechanisms (e.g., single vs. multiple feedback loops) can affect the ability of a clock to reliably measure time and effect a cellular response. Robustness therefore can apply important evolutionary pressure when determining the overall architecture of timers.

Computational modeling has proven to be a valuable tool to evaluate components that improve the reliability of clocks. For example, in an exhaustive in silico screen of three-node enzymatic networks used for kinetic filtering of noise in cell signaling, it was found that the most effective and robust architectures were frequently used in biological systems ( Gerardin et al., 2019 ). In other words, biological timers generally converge toward the most effective and robust layouts. In another example, different species of cyanobacteria rely on two closely related circadian clocks. The first is a free-running oscillator that can sustain periodicity for several days without external cues, while the loss of a single protein in the second has converted this oscillator to an hourglass-style clock that must be reset daily by environmental cues. Computational simulations of these two clocks revealed that their architectures make them particularly resistant to either intrinsic or extrinsic noise, respectively. The additional need for robustness in either category presumably played a role in determining which design was ultimately evolved by each species ( Pittayakanchit et al., 2018 ).

Safety in numbers: Intercellular synchronization

A major contributor to noise in biological clocks is intrinsic variability at the single-cell level ( Balázsi et al., 2011 ). Many cells running the same clock can mask this noise by using the averaging effect of large numbers. Interconnecting cellular oscillators therefore represent an appealing strategy for steadying clock pace. Whether setting the beating heart ( Kohajda et al., 2020 ), circadian oscillations in the mammalian brain ( Buhr and Takahashi, 2013 ; Yamaguchi et al., 2003 ), or the development of somites ( Dequéant and Pourquié, 2008 ; Oates, 2020 ), overall clock pace is set by groups of synchronized cellular oscillators. Coordination between cells is achieved primarily through gap junction channels in the first two cases and intercellular Notch signaling in the third. In this way, intrinsic noise within individual cells is simply averaged away as groups of cells converge to a single harmonious oscillation.

Entrainment

Circadian oscillators have the additional luxury and challenge of mimicking a preexisting environmental clock, namely the alternation of day and night. Without external cues, biological mimicry of a 24-h cycle with exact precision is all but impossible. Entrainment is the process of using one set of oscillations, such as the fluctuations of light and temperature, to set the pace of another oscillator such as the biological circadian clock ( Golombek and Rosenstein, 2010 ). Circadian oscillators then benefit organisms through their ability to physiologically anticipate cyclic changes in the environment ( Bell-Pedersen et al., 2005 ). Biological oscillators can also entrain each other to create an oscillator hierarchy. This is the case with the circadian oscillators in peripheral organs, which take their cue from the master circadian oscillator in the brain ( Brown et al., 2019 ), and the PLK4-driven centriole biogenesis oscillator, which is entrained by the cell-cycle oscillator ( Aydogan et al., 2020 ).

Direct compensation of clocks

When biological clocks reach the reliable limits of their timekeeping architecture, they resort to the inclusion of specialized targeted modifications to increase robustness. These compensatory mechanics serve to directly counterbalance specific environmental influences to maintain a more consistent clock pace. Unlike the other robustness measures that have been discussed, direct compensation acts to mitigate the effects of a single noisy environmental variable rather than reduce the influence of biological noise as a whole. This focused approach is therefore unique to each clock system. Circadian oscillators offer several examples of how a diverse set of direct compensations steady the pace of biological clocks in response to temperature fluctuations.

As previously mentioned, the pace of the mammalian circadian oscillator is set in large part by kinase activity. Under uncompensated conditions, high temperatures increase phosphorylation and protein degradation rates and threaten to shorten the clock’s period. While humans are homeothermic, the body still experiences minute temperature fluctuations throughout the day which could affect the pace of the circadian oscillator ( Kräuchi, 2002 ). Additionally, states of torpor and hibernation in mammals such as rodents can lower body temperature tens of degrees Celsius ( Körtner and Geiser, 2000 ). To counterbalance this effect, PER2, a key transcription factor in the circadian feedback loop, uses temperature to switch between slow and fast degradation pathways. By using two kinases that are differentially sensitive to temperature, phosphorylation of PER2 at either its β-TrCP (β-Transducin repeat–containing protein) or FASP (Familial advanced sleep phase) domain provides a phosphoswitch that commits the protein to faster or slower degradation rates, respectively. Temperature changes shift the population of PER2 using each kinetic pathway so that the bulk degradation of the protein maintains 24-h clock periodicity ( Narasimamurthy and Virshup, 2017 ).

The Arabidopsis circadian oscillator uses a similar counterbalancing principle, although at a different step in the transcription factor feedback cycle. Unlike homeothermic mammals, this plant requires stable timekeeping over a much wider temperature range. As with many sequence-specific protein–DNA interactions, the central transcription factor of the circadian oscillator Circadian clock associated 1 (CCA1) increases its affinity to transcriptional targets with elevated temperatures ( Liu et al., 2008 ). At the same time, increased temperature causes the antagonistic kinase CK1 to increase its phosphorylation of CCA1 and inhibit DNA binding. Consequently, the binding of CCA1 to downstream promoters is relatively uniform over a broad range of temperatures ( Portolés and Más, 2010 ).

The circadian rhythm of Cyanobacteria is generated by the cyclical phosphorylation and dephosphorylation of the kinase KaiC over 24 h ( Swan et al., 2018 ; Nakajima et al., 2005 ). Unlike previous examples, no transcription is required for timing. Instead, the pace of this clock is set by the rate of ATP hydrolysis in KaiC hexameric complexes, which would normally be hastened at warmer temperatures. This effect is counterbalanced, however, by temperature-sensitive inhibitory KaiC autophosphorylation ( Murakami et al., 2008 ). By negatively regulating its own activity through autoinhibition, this complex produces a stable ATP hydrolysis rate and phosphorylation kinetics to steady timer pace over a broad temperature range.

In each of these cases, compensation occurs at steps in the timing mechanism that are both critical for pace and particularly temperature labile. Taking note of the elements in biological clocks that are directly compensated can provide insight into which determinants play important roles in setting a timer’s pace.

Focusing on what is important: Timekeeper/effector integration

Some clocks circumvent the need for extreme precision in timing by directly using the timekeeper as the effector. For instance, developmental programs often prioritize the robust sequential execution of events rather than the specific time frame in which they occur. In one example, embryonic Drosophila neuroblasts rely on the sequential, timed decay of temporal transcription factors in neural progenitor cells to direct the proper patterning and differentiation of neurons ( Averbukh et al., 2018 ). The transcription of genes required for differentiation is directly induced by a relay of activating and repressive transcription factor timekeepers. Developmental biology is replete with transcription factor–based clocks, presumably because of evolutionary pressure favoring the reliability of sequential gene expression patterns over exact timing ( Delgado and Torres, 2016 ; Kageyama et al., 2007 ; Pickering et al., 2018 ; Aly et al., 2018 ; Roselló-Díez et al., 2014 ).

Cellular clocks are found throughout biology and operate over a vast range of timescales to control activities ranging from cell-autonomous processes such as mitotic timing to broad organismal changes such as developmental patterning. The pervasiveness of timer usage by cells serves as a testament to how clocks help biological systems become more efficient, diverse, and robust. Below, we explore how cellular clocks offer unique solutions to a variety of biological problems.

Clocks function as noise filters and information decoders

Biological noise presents a significant challenge for intercellular signaling networks. The errant binding of a lone growth factor molecule to a receptor, for example, must be prevented from triggering downstream signal transduction and amplification. To address this issue, cells make use of a principle called kinetic filtering, which places a time delay on pathway activation to ensure that the incoming signal is sustained and robust before enacting a downstream response ( Gerardin et al., 2019 ). These types of clocks erase signals from rapid and sporadic receptor activity, enabling cells to make better use of signaling networks for intentional communication.

Time encoding of intercellular communication can also be used to elicit differential responses. For instance, acute, oscillatory, or sustained activation of the nuclear factor-κB pathway sets in motion alternative transcriptional programs enabled by two clocks ( Lane et al., 2017 ). In one instance, transcription initiation in a subset of target genes is gated by a slow chromatin regulatory step that requires sustained pathway activation. In the other, stable mRNA transcripts from target genes gradually accumulate over time to generate high levels of translation only after a sustained response ( Sen et al., 2020 ; Purvis and Lahav, 2013 ; Fig. 5 A ).

Uses of biological clocks. (A) Clocks are used as noise filters and information decoders in intercellular signaling networks. Kinetic filtering and timed negative feedback programs convert noisy input signals into acute, sustained, or oscillatory pathway activation. These dynamic responses elicit different gene expression patterns. (B) Varied clock usage in development. (i) Clocks determine the outgrowth size of developing limbs. (ii) Clocks act in concert with morphogen gradients to specify digits as well as particular developmental zones. (iii) Temporal sequential expression and decay of transcription factors in neural progenitor cells lead to the specification of multiple sets of differentiated neurons. (iv) Oscillations of HES genes traveling along the developing paraxial mesoderm intersect with a wavefront to generate segmented somites. (C) Circadian oscillations direct a wide variety of behaviors such as sleep/wake, cell division, and flower blooming. (D) Clocks manage many basic cellular processes. (i) The cell cycle is driven by the cyclin/CDK oscillator: a minimum- and maximum-duration clock set the appropriate duration of mitosis. (ii) Clocks limit programmed proliferative bursts such as that which occurs in lymphocytes following infection. (iii) The procaspase-9 clock limits the duration of apoptosome activity in programmed cell death.

Clocks serve as developmental guides

During development, organisms orchestrate the size and architecture of their features through the encoded behavior of individual cells. This raises the challenge of translating the intricacies of the developing body into single-cell decisions to proliferate and differentiate. Consider that the cells at the end of a developing arm must know to stop dividing despite having no immediate way to ascertain arm length. Cells in the developing mammalian embryo must robustly generate segments that will become the spine, ribs, and skeletal muscle, without access to a master blueprint. Progenitor cells in the brain must produce the correct number of multiple specific cell types with no access to an ongoing cellular census. In these circumstances, time becomes a metric to approximate what single cells cannot otherwise sense. Outgrowth time is used as a proxy for limb size to constrain total growth ( Pickering et al., 2018 ; Sheeba et al., 2014 ). In development, temporally controlled transcription factor oscillations intersect with a morphogen gradient to inform cells of their location and direct the creation of segment boundaries ( Dequéant and Pourquié, 2008 ; Kageyama et al., 2007 ; Hubaud and Pourquié, 2014 ). Finally, proliferation time approximates the number of divisions to force oligodendrocyte precursors to differentiate and generate an appropriate population size ( Durand and Raff, 2000 ; Dugas et al., 2007 ).

In each instance, a cellular clock controls these programs by measuring the elapsed developmental time ( Fig. 5 B ). This solves the problem of informational downscaling by using temporal cues from within individual cells to achieve complex organization and patterning on an organismal scale.

Clocks direct physiology and behavior

Circadian oscillators serve as a link between many organisms and the day/night cycle of the planet. In humans, these oscillators alone account for changes in sleep schedule ( Jagannath et al., 2017 ), metabolism ( Zhu et al., 2017 ), body temperature ( Panda, 2016 ), and hormone levels ( Morris et al., 2012 ), among others. Disruption of circadian rhythms has been linked to a suite of pathologies, including decreased longevity, metabolic syndromes, immune dysfunction, cardiovascular disorders, and cancer ( Evans and Davidson, 2013 ; Fig. 5 C ). This wide array of circadian dysfunctions serves to highlight how heavily humans rely on the clock that synchronizes our diurnal species with cycles of day and night.

Across the tree of life, there are many examples of organisms tying their behavior to day/night cycles. The malarial parasite, for instance, makes use of an intrinsic circadian oscillator to coordinate its asexual cell cycle behavior with its mammalian host during infection ( Rijo-ferreira et al., 2020 ). The fungus Neurospora crassa uses circadian timing to coordinate cycles of fungal growth ( Baker et al., 2012 ), while cyanobacteria use it to control bioluminescence ( Murakami et al., 2008 ). Many varieties of plants use the circadian cycle to coordinate flower opening and closing ( Samach and Coupland, 2000 ). Plants also use circadian oscillators to measure the length of the day and relate it to seasonal changes that are used to direct flowering and fruiting behaviors ( Samach and Coupland, 2000 ; Singh et al., 2020 ). This wide variety of circadian applications demonstrates the central role of these oscillators in providing a robust basis for diverse behaviors.

Although rare, the timing of noncircadian behaviors in multicellular organisms can also be achieved using biological clocks. One example is the previously discussed clock that dictates motivation during Drosophila mating ( Thornquist et al., 2020 ). The rarity of this phenomenon suggests that clocks are generally ill suited for the direction of complex behaviors. Unlike the comparatively simple choice for single cells to proliferate, differentiate, or arrest, behavioral decisions in response to an unpredictable environment are better suited to neuronal networks.

Clocks manage and safeguard cellular processes

Clocks serve a specialized role in reining in the detrimental effects of runaway biological programs. This is achieved by setting strict temporal limits and using time as an indicator of processes gone awry. For instance, the body is shielded from lymphocyte hyperproliferation by two clocks: one that sets the lymphocyte proliferative potential and one that ensures eventual death ( Heinzel et al., 2017 ). Macrophage response to infection is metered by a lactate-driven clock to switch from anaerobic to aerobic processes ( Zhang et al., 2019 ). The apoptosome clock prevents cells from erroneously committing to programmed cell death ( Malladi et al., 2009 ), while the accumulation and degradation of cyclins set temporal constraints on much of the eukaryotic cell cycle ( Örd and Loog, 2019 ; Morgan, 1997 ). Finally, two clocks regulate the minimum and maximum allowable durations in mitosis to maintain genome stability ( Lambrus and Holland, 2017 ; Hellmuth and Stemmann, 2020 ; Fig. 5 D ).

These safeguards use one of two general mechanisms to regulate cells. In one instance, ongoing processes are strictly temporally constrained, as is the case with lymphocyte proliferation, the cell cycle oscillator, or the macrophage response. In the other instance, cells circumvent the need for direct sensing of errors by using temporal cues as a proxy. In this way, a single response pathway can serve to mitigate a wide variety of problems that all lead to a similar timing defect. For instance, the mitotic surveillance pathway can respond to extended mitoses caused by failed mitotic spindle formation or weak kinetochore–microtubule attachments alike.

Continued discoveries suggest that our census of biological clocks is far from complete. Additionally, while this review primarily focuses on systems for which we understand the molecular underpinnings, many clocks have been identified for which the driving mechanism remains to be determined. In several developmental clocks, for example, timekeeper proteins accumulate and deplete over the course of days to weeks. How such gradual and robust changes are possible remains unclear. Similarly, a more granular understanding of biological clocks may reveal potential therapeutic targets. For instance, bright light therapy in which the sun is simulated to better entrain the circadian clock to day/night cycles is widely used for treating mood disorders, despite providing only modestly beneficial outcomes ( Nussbaumer-Streit et al., 2019 ). Modulating the circadian clock directly at a molecular level would likely prove more effective and is an area of ongoing research ( Huang et al., 2020 ).

Biological clocks are as diverse in architecture as they are in function. This design plasticity indicates that timing mechanisms can be arrived at by many different means. However, while clock designs are often distinct, all timers follow the same generic blueprint: a dynamic timekeeper gradually modulates until it triggers a directive or reactive cellular response. Issues of pathway integration, time frame, and robustness all play a role in shaping the timer architecture to best suit the needs of the system in which they reside. The ubiquity of clocks underscores the broad influence timing has on the proper function of single cells and whole organisms. This suggests that the evolution of molecular timers represents a robust strategy for the directing and sensing of biological processes.

Biological clock: A complete biological system for measuring time and effecting a cellular response. Timer: The underlying components of a biological clock that measure time. Oscillator: A cyclical biological clock characterized by repeated cycles of activity. Timekeeper: The physical entity at the core of a timer whose state reports on elapsed time. Directive clock: A clock in which the cellular outcome is predetermined. Reactive clock: A clock in which the cellular outcome is dependent on both temporal and external cues.

Acknowledgments

This work was supported by National Institutes of Health grants R01GM114119 and R01GM133897, an American Cancer Society Scholar Grant (RSG-16-156-01-CCG), and an American Cancer Society Mission Boost Grant (MBG-19-173-01-MBG).

The authors declare no competing financial interests.

• Aly H., Akagi K., and Ueda H.. 2018. Proteasome activity determines pupation timing through the degradation speed of timer molecule Blimp-1 . Dev. Growth Differ . 60 :502–508. 10.1111/dgd.12569 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Averbukh I., Lai S.L., Doe C.Q., and Barkai N.. 2018. A repressor-decay timer for robust temporal patterning in embryonic Drosophila neuroblast lineages . eLife . 7 e38631 10.7554/eLife.38631 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aydogan M.G., Steinacker T.L., Mofatteh M., Wilmott Z.M., Zhou F.Y., Gartenmann L., Wainman A., Saurya S., Novak Z.A., Wong S.S., et al.. 2020. An autonomous oscillator times and executes centriole biogenesis . Cell . 181 :1566–1581.e27. 10.1016/j.cell.2020.05.018 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baker C.L., Loros J.J., and Dunlap J.C.. 2012. The circadian clock of Neurospora crassa . FEMS Microbiol. Rev . 36 :95–110. 10.1111/j.1574-6976.2011.00288.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Balázsi G., van Oudenaarden A., and Collins J.J.. 2011. Cellular decision making and biological noise: from microbes to mammals . Cell . 144 :910–925. 10.1016/j.cell.2011.01.030 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baudet M.L., Zivraj K.H., Abreu-Goodger C., Muldal A., Armisen J., Blenkiron C., Goldstein L.D., Miska E.A., and Holt C.E.. 2011. miR-124 acts through CoREST to control onset of Sema3A sensitivity in navigating retinal growth cones . Nat. Neurosci . 15 :29–38. 10.1038/nn.2979 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bean B.P. 2007. The action potential in mammalian central neurons . Nat. Rev. Neurosci . 8 :451–465. 10.1038/nrn2148 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bell-Pedersen D., Cassone V.M., Earnest D.J., Golden S.S., Hardin P.E., Thomas T.L., and Zoran M.J.. 2005. Circadian rhythms from multiple oscillators: lessons from diverse organisms . Nat. Rev. Genet . 6 :544–556. 10.1038/nrg1633 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bessho Y., Sakata R., Komatsu S., Shiota K., Yamada S., and Kageyama R.. 2001. Dynamic expression and essential functions of Hes7 in somite segmentation . Genes Dev . 15 :2642–2647. 10.1101/gad.930601 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brown A.J., Pendergast J.S., and Yamazaki S.. 2019. Peripheral circadian oscillators . Yale J. Biol. Med . 92 :327–335. 10.1016/b978-0-12-396971-2.00004-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buhr E.D., and Takahashi J.S.. 2013. Molecular components of the Mammalian circadian clock . Handb. Exp. Pharmacol . 217 :3–27. 10.1007/978-3-642-25950-0_1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carmeliet E. 2019. Pacemaking in cardiac tissue. From IK2 to a coupled-clock system . Physiol. Rep . 7 e13862 10.14814/phy2.13862 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Delgado I., and Torres M.. 2016. Gradients, waves and timers, an overview of limb patterning models . Semin. Cell Dev. Biol . 49 :109–115. 10.1016/j.semcdb.2015.12.016 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dequéant M.L., and Pourquié O.. 2008. Segmental patterning of the vertebrate embryonic axis . Nat. Rev. Genet . 9 :370–382. 10.1038/nrg2320 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Diernfellner A.C.R., and Brunner M.. 2020. Phosphorylation Timers in the Neurospora crassa Circadian Clock . J. Mol. Biol . 432 :3449–3465. 10.1016/j.jmb.2020.04.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DiFrancesco D. 2020. A Brief History of Pacemaking . Front. Physiol . 10 :1599 10.3389/fphys.2019.01599 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dugas J.C., Ibrahim A., and Barres B.A.. 2007. A crucial role for p57(Kip2) in the intracellular timer that controls oligodendrocyte differentiation . J. Neurosci . 27 :6185–6196. 10.1523/JNEUROSCI.0628-07.2007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Durand B., and Raff M.. 2000. A cell-intrinsic timer that operates during oligodendrocyte development . BioEssays . 22 :64–71. 10.1002/(SICI)1521-1878(200001)22:1<64::AID-BIES11>3.0.CO;2-Q [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Evans J.A., and Davidson A.J.. 2013. Health Consequences of Circadian Disruption in Humans and Animal Models In Prog. Mol. Biol. Transl. Sci . Vol. 119 pp. 283–323. [ PubMed ] [ Google Scholar ]
• Fong C.S., Mazo G., Das T., Goodman J., Kim M., O’Rourke B.P., Izquierdo D., and Tsou M.F.B.. 2016. 53BP1 and USP28 mediate p53-dependent cell cycle arrest in response to centrosome loss and prolonged mitosis . eLife . 5 e16270 10.7554/eLife.16270 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gerardin J., Reddy N.R., and Lim W.A.. 2019. The Design Principles of Biochemical Timers: Circuits that Discriminate between Transient and Sustained Stimulation . Cell Syst . 9 :297–308.e2. 10.1016/j.cels.2019.07.008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Golombek D.A., and Rosenstein R.E.. 2010. Physiology of circadian entrainment . Physiol. Rev . 90 :1063–1102. 10.1152/physrev.00009.2009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Heinzel S., Binh Giang T., Kan A., Marchingo J.M., Lye B.K., Corcoran L.M., and Hodgkin P.D.. 2017. A Myc-dependent division timer complements a cell-death timer to regulate T cell and B cell responses . Nat. Immunol . 18 :96–103. 10.1038/ni.3598 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hellmuth S., and Stemmann O.. 2020. Separase-triggered apoptosis enforces minimal length of mitosis . Nature . 580 :542–547. 10.1038/s41586-020-2187-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang S., Jiao X., Lu D., Pei X., Qi D., and Li Z.. 2020. Recent advances in modulators of circadian rhythms: an update and perspective . J. Enzyme Inhib. Med. Chem . 35 :1267–1286. 10.1080/14756366.2020.1772249 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hubaud A., and Pourquié O.. 2014. Signalling dynamics in vertebrate segmentation . Nat. Rev. Mol. Cell Biol . 15 :709–721. 10.1038/nrm3891 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jagannath A., Taylor L., Wakaf Z., Vasudevan S.R., and Foster R.G.. 2017. The genetics of circadian rhythms, sleep and health . Hum. Mol. Genet . 26 ( R2 ):R128–R138. 10.1093/hmg/ddx240 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kageyama R., Ohtsuka T., and Kobayashi T.. 2007. The Hes gene family: repressors and oscillators that orchestrate embryogenesis . Development . 134 :1243–1251. 10.1242/dev.000786 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kohajda Z., Loewe A., Tóth N., Varró A., and Nagy N.. 2020. The Cardiac Pacemaker Story-Fundamental Role of the Na + /Ca 2+ Exchanger in Spontaneous Automaticity . Front. Pharmacol . 11 :516 10.3389/fphar.2020.00516 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Körtner G., and Geiser F.. 2000. The temporal organization of daily torpor and hibernation: circadian and circannual rhythms . Chronobiol. Int . 17 :103–128. 10.1081/CBI-100101036 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kräuchi K. 2002. How is the circadian rhythm of core body temperature regulated? Clin. Auton. Res . 12 :147–149. 10.1007/s10286-002-0043-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lake D., Corrêa S.A.L., and Müller J.. 2016. Negative feedback regulation of the ERK1/2 MAPK pathway . Cell. Mol. Life Sci . 73 :4397–4413. 10.1007/s00018-016-2297-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lambrus B.G., and Holland A.J.. 2017. A New Mode of Mitotic Surveillance . Trends Cell Biol . 27 :314–321. 10.1016/j.tcb.2017.01.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lambrus B.G., Daggubati V., Uetake Y., Scott P.M., Clutario K.M., Sluder G., and Holland A.J.. 2016. A USP28-53BP1-p53-p21 signaling axis arrests growth after centrosome loss or prolonged mitosis . J. Cell Biol . 214 :143–153. 10.1083/jcb.201604054 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lane K., Van Valen D., DeFelice M.M., Macklin D.N., Kudo T., Jaimovich A., Carr A., Meyer T., Pe’er D., Boutet S.C., et al.. 2017. Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation . Cell Syst . 4 :458–469.e5. 10.1016/j.cels.2017.03.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Li Y., Zhou M., Hu Q., Bai X.C., Huang W., Scheres S.H.W., and Shi Y.. 2017. Mechanistic insights into caspase-9 activation by the structure of the apoptosome holoenzyme . Proc. Natl. Acad. Sci. USA . 114 :1542–1547. 10.1073/pnas.1620626114 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu C.C., Richard A.J., Datta K., and LiCata V.J.. 2008. Prevalence of temperature-dependent heat capacity changes in protein-DNA interactions . Biophys. J . 94 :3258–3265. 10.1529/biophysj.107.117697 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malladi S., Challa-Malladi M., Fearnhead H.O., and Bratton S.B.. 2009. The Apaf-1*procaspase-9 apoptosome complex functions as a proteolytic-based molecular timer . EMBO J . 28 :1916–1925. 10.1038/emboj.2009.152 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Matsuda M., Yamanaka Y., Uemura M., Osawa M., Saito M.K., Nagahashi A., Nishio M., Guo L., Ikegawa S., Sakurai S., et al.. 2020. Recapitulating the human segmentation clock with pluripotent stem cells . Nature . 580 :124–129. 10.1038/s41586-020-2144-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Meitinger F., Anzola J.V., Kaulich M., Richardson A., Stender J.D., Benner C., Glass C.K., Dowdy S.F., Desai A., Shiau A.K., et al.. 2016. 53BP1 and USP28 mediate p53 activation and G1 arrest after centrosome loss or extended mitotic duration . J. Cell Biol . 214 :155–166. 10.1083/jcb.201604081 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morgan D.O. 1997. Cyclin-Dependent Kinases: Engines, Clocks, and Microprocessors In Annu. Rev. Cell Dev. Biol . Vol. 13 pp. 261–291. [ PubMed ] [ Google Scholar ]
• Morris C.J., Aeschbach D., and Scheer F.A.J.L.. 2012. Circadian system, sleep and endocrinology . Mol. Cell. Endocrinol . 349 :91–104. 10.1016/j.mce.2011.09.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Murakami R., Miyake A., Iwase R., Hayashi F., Uzumaki T., and Ishiura M.. 2008. ATPase activity and its temperature compensation of the cyanobacterial clock protein KaiC . Genes Cells . 13 :387–395. 10.1111/j.1365-2443.2008.01174.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nakajima M., Imai K., Ito H., Nishiwaki T., Murayama Y., Iwasaki H., Oyama T., and Kondo T.. 2005. Reconstitution of circadian oscillation of cyanobacterial KaiC phosphorylation in vitro . Science . 308 :414–415. 10.1126/science.1108451 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Narasimamurthy R., and Virshup D.M.. 2017. Molecular mechanisms regulating temperature compensation of the circadian clock . Front. Neurol . 8 :161 10.3389/fneur.2017.00161 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nussbaumer-Streit B., Forneris C.A., Morgan L.C., Van Noord M.G., Gaynes B.N., Greenblatt A., Wipplinger J., Lux L.J., Winkler D., and Gartlehner G.. 2019. Light therapy for preventing seasonal affective disorder . Cochrane Database Syst. Rev . 3 CD011269 10.1002/14651858.CD011269.pub3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Oates A.C. 2020. Waiting on the Fringe: cell autonomy and signaling delays in segmentation clocks . Curr. Opin. Genet. Dev . 63 :61–70. 10.1016/j.gde.2020.04.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ode K.L., and Ueda H.R.. 2018. Design principles of phosphorylation-dependent timekeeping in eukaryotic circadian clocks . Cold Spring Harb. Perspect. Biol . 10 a028357 10.1101/cshperspect.a028357 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Örd M., and Loog M.. 2019. How the cell cycle clock ticks . Mol. Biol. Cell . 30 :169–172. 10.1091/mbc.E18-05-0272 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Panda S. 2016. Circadian physiology of metabolism . Science . 354 :1008–1015. 10.1126/science.aah4967 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pickering J., Rich C.A., Stainton H., Aceituno C., Chinnaiya K., Saiz-Lopez P., Ros M.A., and Towers M.. 2018. An intrinsic cell cycle timer terminates limb bud outgrowth . eLife . 7 e37429 10.7554/eLife.37429 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pittayakanchit W., Lu Z., Chew J., Rust M.J., and Murugan A.. 2018. Biophysical clocks face a trade-off between internal and external noise resistance . eLife . 7 e37624 10.7554/eLife.37624 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Portolés S., and Más P.. 2010. The functional interplay between protein kinase CK2 and CCA1 transcriptional activity is essential for clock temperature compensation in Arabidopsis . PLoS Genet . 6 e1001201 10.1371/journal.pgen.1001201 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pourquie O. 2001. The vertebrate segmentation clock . J. Anat . 199 :169–175. 10.1046/j.1469-7580.2001.19910169.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Purvis J.E., and Lahav G.. 2013. Encoding and decoding cellular information through signaling dynamics . Cell . 152 :945–956. 10.1016/j.cell.2013.02.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qian J., Gelens L., and Bollen M.. 2019. Coordination of Timers and Sensors in Cell Signaling . BioEssays . 41 e1800217 10.1002/bies.201800217 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Raff M. 2007. Intracellular developmental timers . Cold Spring Harb. Symp. Quant. Biol . 72 :431–435. 10.1101/sqb.2007.72.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Renner F., and Schmitz M.L.. 2009. Autoregulatory feedback loops terminating the NF-kappaB response . Trends Biochem. Sci . 34 :128–135. 10.1016/j.tibs.2008.12.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rijo-ferreira F., Acosta-rodriguez V.A., Abel J.H., and Kornblum I.. 2020. The malaria parasite has an intrinsic clock . Science . 368 :746–753. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Roselló-Díez A., Arques C.G., Delgado I., Giovinazzo G., and Torres M.. 2014. Diffusible signals and epigenetic timing cooperate in late proximo-distal limb patterning . Development . 141 :1534–1543. 10.1242/dev.106831 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Samach A., and Coupland G.. 2000. Time measurement and the control of flowering in plants . BioEssays . 22 :38–47. 10.1002/(SICI)1521-1878(200001)22:1<38::AID-BIES8>3.0.CO;2-L [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sen S., Cheng Z., Sheu K.M., Chen Y.H., and Hoffmann A.. 2020. Gene Regulatory Strategies that Decode the Duration of NFκB Dynamics Contribute to LPS- versus TNF-Specific Gene Expression . Cell Syst . 10 :169–182.e5. 10.1016/j.cels.2019.12.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shalit-Kaneh A., Kumimoto R.W., Filkov V., and Harmer S.L.. 2018. Multiple feedback loops of the Arabidopsis circadian clock provide rhythmic robustness across environmental conditions . Proc. Natl. Acad. Sci. USA . 115 :7147–7152. 10.1073/pnas.1805524115 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sheeba C.J., Andrade R.P., and Palmeirim I.. 2014. Limb patterning: from signaling gradients to molecular oscillations . J. Mol. Biol . 426 :780–784. 10.1016/j.jmb.2013.11.022 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Singh A., and Soltani M.. 2013. Quantifying intrinsic and extrinsic variability in stochastic gene expression models . PLoS One . 8 e84301 10.1371/journal.pone.0084301 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Singh R.K., Bhalerao R.P., and Eriksson M.E.. 2020. Growing in time: Exploring the molecular mechanisms of tree growth . Tree Physiol . tpaa065 10.1093/treephys/tpaa065 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swan J.A., Golden S.S., LiWang A., and Partch C.L.. 2018. Structure, function, and mechanism of the core circadian clock in cyanobacteria . J. Biol. Chem . 293 :5026–5034. 10.1074/jbc.TM117.001433 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thornquist S.C., Langer K., Zhang S.X., Rogulja D., and Crickmore M.A.. 2020. CaMKII Measures the Passage of Time to Coordinate Behavior and Motivational State . Neuron . 105 :334–345.e9. 10.1016/j.neuron.2019.10.018 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Uetake Y., and Sluder G.. 2010. Prolonged prometaphase blocks daughter cell proliferation despite normal completion of mitosis . Curr. Biol . 20 :1666–1671. 10.1016/j.cub.2010.08.018 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yamaguchi S., Isejima H., Matsuo T., Okura R., Yagita K., Kobayashi M., and Okamura H.. 2003. Synchronization of Cellular Clocks in the Suprachiasmatic Nucleus . Science . 302 :1408–1412. 10.1126/science.1089287 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhang D., Tang Z., Huang H., Zhou G., Cui C., Weng Y., Liu W., Kim S., Lee S., Perez-Neut M., et al.. 2019. Metabolic regulation of gene expression by histone lactylation . Nature . 574 :575–580. 10.1038/s41586-019-1678-1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhu B., Zhang Q., Pan Y., Mace E.M., York B., Antoulas A.C., Dacso C.C., and O’Malley B.W.. 2017. A Cell-Autonomous Mammalian 12 hr Clock Coordinates Metabolic and Stress Rhythms . Cell Metab . 25 :1305–1319.e9. 10.1016/j.cmet.2017.05.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Research article
• Open access
• Published: 21 December 2018

Biological clock function is linked to proactive and reactive personality types

• Christian Tudorache   ORCID: orcid.org/0000-0002-6208-9608 1 ,
• Hans Slabbekoorn 1 ,
• Yuri Robbers 2 ,
• Eline Hin 1 ,
• Johanna H. Meijer 2 ,
• Herman P. Spaink 1 &
• Marcel J. M. Schaaf 1  

BMC Biology volume  16 , Article number:  148 ( 2018 ) Cite this article

6737 Accesses

21 Citations

21 Altmetric

Metrics details

Many physiological processes in our body are controlled by the biological clock and show circadian rhythmicity. It is generally accepted that a robust rhythm is a prerequisite for optimal functioning and that a lack of rhythmicity can contribute to the pathogenesis of various diseases. Here, we tested in a heterogeneous laboratory zebrafish population whether and how variation in the rhythmicity of the biological clock is associated with the coping styles of individual animals, as assessed in a behavioural assay to reliably measure this along a continuum between proactive and reactive extremes.

Using RNA sequencing on brain samples, we demonstrated a prominent difference in the expression level of genes involved in the biological clock between proactive and reactive individuals. Subsequently, we tested whether this correlation between gene expression and coping style was due to a consistent change in the level of clock gene expression or to a phase shift or to altered amplitude of the circadian rhythm of gene expression. Our data show a remarkable individual variation in amplitude of the clock gene expression rhythms, which was also reflected in the fluctuating concentrations of melatonin and cortisol, and locomotor activity. This variation in rhythmicity showed a strong correlation with the coping style of the individual, ranging from robust rhythms with large amplitudes in proactive fish to a complete absence of rhythmicity in reactive fish. The rhythmicity of the proactive fish decreased when challenged with constant light conditions whereas the rhythmicity of reactive individuals was not altered.

These results shed new light on the role of the biological clock by demonstrating that large variation in circadian rhythmicity of individuals may occur within populations. The observed correlation between coping style and circadian rhythmicity suggests that the level of rhythmicity forms an integral part of proactive or reactive coping styles.

In humans, personality comprises consistent individual variation of correlated behavioural and habitual traits [ 1 ], such as extraversiveness, impulsivity or novelty-seeking [ 2 , 3 ]. These personality traits are also linked to the physiological and genetic makeup of an individual [ 4 , 5 , 6 , 7 , 8 ]. Similarly, in animals, behavioural traits such as risk-taking or aggressiveness [ 9 ] can be consistently correlated with physiological traits such as metabolic rate [ 10 ] or endocrine stress response [ 11 ]. These correlated sets of traits, termed coping styles (animal personalities), are recognised in a number of taxa [ 12 ] and vary along a proactive-reactive continuum, with proactive individuals being more risk-taker and aggressive and having higher baseline metabolic rates than reactive individuals [ 9 , 10 ]. Further, characterisation of coping styles is crucial to a fundamental understanding of the occurrence of behavioural polymorphisms [ 13 , 14 , 15 ] and may provide new perspectives on the application of personalised care and personality disorders in humans [ 6 , 16 , 17 , 18 , 19 , 20 , 21 ].

In experimental animals, coping styles can be assessed using the degree of risk-taking behaviour as a proxy, which is strongly correlated with a number of other behavioural and physiological traits [ 9 , 10 , 11 , 12 ]. In various fish species, risk-taking can be evaluated by an emergence test, a well-established test with high repeatability [ 9 , 10 , 11 , 22 ]. During this test, a group of individual fish is allowed to emerge from a familiar shelter into a novel and potentially dangerous environment. The order of emergence is considered a measure for the individual tendency of risk-taking [ 9 , 10 , 11 , 22 ].

In the present study, we have used this assay to assess the coping styles of individual zebrafish from a heterogeneous laboratory population (AB/TL strain). Subsequently, RNA sequencing was performed on brain samples from proactive and reactive zebrafish to further investigate molecular determinants of coping styles. Our results showed an overrepresentation of genes involved in the biological clock among the genes differentially expressed in the brains from proactive and reactive individuals. The biological clock is an endogenous timing mechanism which allows an organism to anticipate regular changes in the environment that result from the day/night cycle [ 23 , 24 , 25 , 26 , 27 ]. In vertebrates, the molecular basis of the biological clock is formed by feedback loops of gene expression. In the core loop, the transcription factors BMAL and CLOCK stimulate the expression of the genes encoding the corepressor proteins PER and CRY, which inhibit the activity of BMAL and CLOCK. Thereby, PER and CRY repress their own expression and create an oscillating pattern of gene expression. This oscillator is stabilised by a second feedback loop which involves the nuclear receptors REV-ERB and ROR. In addition, the biological clock can be synchronised with the actual day-night cycle by environmental cues like light and temperature. The suprachiasmatic nucleus (SCN) of the hypothalamus in mammals and the pineal gland in other vertebrates appear to act as ‘master clocks’. They coordinate the clock of peripheral cells and tissues through a set of systemic signals, including the cycling secretion patterns of the hormones melatonin and cortisol. This way, the biological clock creates diurnal rhythmicity which can be observed at several functional levels, from the oscillating expression of clock genes over endocrine secretion patterns to rhythmic behavioural activity [ 23 , 24 , 25 , 26 , 27 ].

The association between clock gene expression levels and coping styles observed in our RNAseq experiments led to three alternative hypotheses. The different gene expression levels may be generally higher or lower between individuals with different coping styles; the observed differences in gene expression may be due to a shift in phase of the biological clock (i.e. chronotype [ 4 , 23 , 24 , 25 , 28 ]) or to a different amplitude of the diurnal rhythm. We tested these hypotheses on three levels of biological function: on the molecular level by measuring gene expression, on the endocrine level by investigating hormone production and on the behavioural level by studying locomotion activity. Our results show that the amplitude of the circadian rhythm correlates with the coping style of the individual, ranging between high amplitudes in proactive fish and an absence of rhythmicity in reactive fish.

The emergence test as a proxy for coping style

We assessed different coping styles using a group emergence test, in which each zebrafish of a group of ten was allowed to emerge from a familiar (slightly darkened) shelter into a novel and potentially dangerous environment. The individual fish were ranked according to their latency to emerge (Fig.  1 a). We evaluated the temporal consistency of this emergence test and tested whether difference in light intensity was a confounding factor. First, the test was repeated in a single emergence setting and emergence times were measured. A positive correlation between single emergence times and group emergence rank was observed (Fig.  1 b; Spearman rank, N  = 187, ρ  = 0.82, p  < 0.0001). Second, single emergence tests were performed on two consecutive days at different times of the day. The results of this experiment show strong correlations between single emergence times of the same individual during two consecutive days at different times of the day (Additional file  1 : Figure S1A; Spearman rank, N  = 19–24, ρ  = 0.38–0.91, p  < 0.001 or p  < 0.0001). Third, to test the effect of light conditions on emergence behaviour, the emergence test was performed with similar light conditions in both the shelter and the novel environment compartment. A strong correlation was observed between the emergence time measured using this approach and the emergence rank in the initial test (Additional file  1 : Figure S1B; Spearman rank, N  = 72, ρ  = 0.77, p  < 0.0001). Fourth, when plotting all pooled emergence times from single emergence experiments over the group emergence times, a strong positive correlation confirmed the robustness of emergence time regardless the social and environmental settings (Additional file  1 : Figure S1C; Spearman rank, N  = 190, ρ  = 0.87, p  < 0.0001). In order to further evaluate risk-taking as a proxy for coping style, we tested and confirmed a correlation between emergence rank and frequency of aggressive behaviour during a mirror-biting test (Fig.  1 c; Spearman rank, N  = 59, ρ  = − 0.77, p  < 0.0001).

Behavioural testing in zebrafish. Behavioural testing in zebrafish. a Schematic overview of the group emergence test used in this study. A fish tank was used that consisted of two compartments separated by a wall with a closable hatch. A batch of ten fish was introduced into the darkened holding compartment of this tank. After a 10-min acclimation period, the hatch was opened and fish were allowed to emerge into the uncovered, well lit, second compartment, and the emergence time was recorded. After emergence, individual fish were collected and grouped according to emergence rank (1–10), with number 1 designated early emerger (EE) and number 10 late emerger (LE). The emergence order is considered a measure for risk-taking behaviour, which is a widely used proxy for coping style. b Behavioural traits are correlated across situation and time: single emergence time plotted against group emergence ranks. Data were collected from 24 batches of 10 adult zebrafish ( N  = 187) in the group emergence test (rank) and subsequent single emergence test (time), with a significant correlation between emergence rank in the group test and time in the single test (Spearman rank test). c Aggressiveness correlates with risk-taking behaviour within a coping style: individuals previously ranked during a group emergence test were subjected to a mirror-image stimulation. Upon exposure to their own mirror image, the number of aggressive behaviours (AGR; bites to the image, parallel swimming, circles and strikes) were counted and divided by the measuring period minus the duration of freezing bouts and approach latency. The resulting AGR frequency (s −1 ) was significantly correlated with emergence rank (Spearman rank test, N  = 59)

Since fish from the hybrid AB/TL line were used in these experiments, it could be hypothesised that the observed behavioural variation in this line originated from genetic differences between the original AB and TL lines. Since these two lines have previously been shown to perform differently in other behavioural tests [ 29 ], proactivity may have been inherited from one line and reactivity from the other. However, no difference was observed in the behaviour of fish from the AB and TL lines in the emergence test (Additional file  2 : Figure S2; X 2 test, N  = 6, p  > 0.05). We therefore conclude that genetic variation in the hybrid AB/TL line which may underlie the observed phenotypical variation most likely originates from genetic variation within the original AB and TL strains, [ 30 , 31 ]. However, the contribution of genetic variation between these lines (e.g. involving different genes affecting the phenotype in the two lines) can still not be excluded [ 30 , 32 ].

Diurnal rhythmicity of gene expression is associated with coping style

Subsequently, group emergence tests were performed with ten individuals and we collected individuals of rank 1 (early emergers, EE, highly risk-taking) and rank 10 (late emergers, LE, highly risk-avoiding). RNA was isolated from their brains and processed for transcriptome analysis by RNA sequencing. This yielded RNA sequences derived from 31,398 different genes. The expression of 1478 of these genes differed significantly between EE and LE fish (Additional file  3 : Table S1). Gene ontology analysis revealed that 20 of the top 100 most differentially expressed genes were involved in the control of the biological clock (and 43 of the total of 1478 differentially expressed genes, enrichment score 3.66; Fig.  2 and Additional file  3 : Table S1), with a fold change ranging from 4.34 to 20.07. These data indicate a strong association between coping styles and the regulation of the biological clock. Other enriched ontology groups to be found in the top 100 most differentially expressed genes were ‘oxygen binding and transport’ (enrichment score 5.81), ‘transcription factor activity’ (3.38) and ‘haematopoiesis’ (3.30).

Transcriptome analysis of brain samples from proactive (early emerging, EE) and reactive (late emerging, LE) zebrafish using RNA sequencing. Brains were collected from EE and LE fish ( n  = 4) at 7 hCT, and subsequently, RNA was isolated and used for RNA sequencing. Analysis of the EE and LE transcriptomes showed that differences in gene expression were found for 3% of genes involved in the regulation of the biological clock (see Additional file  3 : Table S1). This overview shows differential gene expression levels of 44 differently ( p  < 0.05) regulated genes, involved in the core loop, the stabilising loop and the entrainment of the biological clock. Rectangles represent genes, and differential gene expression between EE and LE fish is indicated by red (higher expression level in EE) and blue rectangles (higher level in LE fish). Circles represent regulating protein complexes. Inhibition is indicated by a red line with round head, and promotion by a black line with arrow head

Subsequently, we further investigated the relationship between coping style and the biological clock at different functional levels, i.e. gene expression, endocrinology and locomotion behaviour. First, we measured, by quantitative PCR, the expression patterns of 5 genes, i.e. one member from each of the four core clock genes and one stabilising loop gene that showed diurnal rhythmicity in its expression ( bmal1a , clock1a , per1a , cry1a and cipca ) and that were among the top 20 most differentially expressed clock genes. The observed expression patterns show that EE fish had higher peak values and a wider range of expression levels of these genes than LE fish (Fig.  3 ; Two-way ANOVA, Bonferroni post hoc, N  = 6, p  < 0.05). When fish of all emergence ranks were taken into account (i.e. 1 to 10, Additional file  4 : Figure S3), both the area under the curve (AUC, as a measure for net transcriptional activity) and the amplitude of expression (used as an indicator for rhythmicity) were negatively correlated with the emergence rank, for bmal1a and cipca regarding AUC (Spearman rank test, N  = 72, ρ  = − 0.4619, p  = 0.0102 and ρ  = − 0.4793, p  = 0.0074, respectively) and all genes regarding amplitude (Spearman rank test, N  = 72, ρ  = − 0.67 to − 0.45, p  < 0.0001 to p  = 0.0138). These results indicate a more robust circadian rhythmicity in individuals with proactive coping styles compared to individuals with reactive coping styles.

The expression patterns of five clock-related genes over time are correlated with the risk-taking behaviour. The brains were collected from EE and LE fish, at six different time points, i.e. 1, 7, 13, 15, 19 and 23 hCT, in a 14–10-h light to dark regime ( n  = 3), and subsequently, RNA was isolated and used for qPCR analysis. The relative expression of bmal1a , clock1a , per1a , cry1a and cipca was double-plotted over time from EE (red) and LE (blue) fish. Letters indicate significant difference (two-way ANOVA, with time and coping style as variables, Bonferroni post hoc test, p  < 0.05, N  = 6. Data shown are means ± SEM)

Diurnal rhythmicity of hormone secretion is associated with coping style

Subsequently, we studied the diurnal rhythmicity of levels of cortisol and melatonin, important endocrine regulators of diurnal activity patterns [ 23 ]. In EE fish, whole body melatonin and cortisol concentrations fluctuated significantly between light and dark phase, whereas LE fish had constantly high values for cortisol and intermediate values for melatonin, with no detectable rhythmicity (Fig.  4 ; two-way ANOVA, with time and coping style as variables, Bonferroni post hoc test, N  = 6, p  < 0.05). Additionally, taking the data of all emergence ranks into account, a positive correlation between emergence rank and net secretion activity (AUC), and a negative correlation between emergence rank and circadian rhythmicity (amplitude), was observed for both hormones (Additional file  5 : Figure S4; Spearman rank test, N  = 72; cortisol: ρ  = 0.81 and p  < 0.0001 (AUC), ρ  = − 0.64 and p  = 0.0002 (amplitude); melatonin: ρ  = 0.61 and p  = 0.0003 (AUC), ρ  = − 0.37 and p  = 0.0429 (amplitude)). This indicates a stronger rhythmicity in the secretion of melatonin and cortisol in proactive individuals compared to reactive individuals.

Diurnal patterns in cortisol and melatonin levels are associated with the risk-taking behaviour. Full body melatonin ( a ) and cortisol ( b ) concentrations (pg g −1 and ng g −1 body weight, respectively) were determined by ELISA in early (EE, red) and late emerging fish (LE, blue) over 24 h during a 14:10 light to dark cycle (indicated by white and grey bars, double plotted). Samples were taken at 1, 7, 13, 15, 19 and 23 h circadian time (hCT). Data shown are means ± SE ( N  = 3). Letters indicate significant difference (two-way ANOVA, with time and coping style as variables, Bonferroni post hoc test, p  < 0.05, N  = 6. Data shown are means ± SEM)

Diurnal rhythmicity of behavioural activity is associated with coping style

Finally, we monitored the locomotor activity during three consecutive diurnal cycles. Plotting average swimming velocity data of EE and LE fish revealed that EE fish reached higher peak activity levels (especially in the first hours of the light phase) and a wider range of activity levels than LE fish (Fig.  5 a; two-way ANOVA with time and coping style as variables, Sidack’s post hoc test, N  = 6, p  < 0.05). Further analysis (Spearman rank) of the activity data using fish of all emergence ranks showed that emergence rank was negatively correlated with rhythmicity (Additional file  6 : Figure S5A, N  = 72, ρ  = − 0.34 and p  < 0.0037 (AUC)), total behavioural activity (Additional file  6 : Figure S5B, N  = 72, ρ  = − 0.39 and p  < 0.0008 (amplitude)) and rhythm strength (Additional file  6 : Figure S5C, N  = 72, ρ  = − 0.53 and p  < 0.0001). However, maximum swimming speeds did not vary significantly with emergence ranks, suggesting that there was no difference in physiological capacity for locomotion between fish of different emergence ranks (Additional file  6 : Figure S5D). Again, these data demonstrate a more pronounced rhythmicity in the more proactive individuals.

Diurnal activity pattern is associated with emergence behaviour. Actogram of early (EE, emergence rank 1, red) and late emerging fish (LE, rank 10, blue) during 3 days under light-dark regime ( a ) and during three subsequent days in constant light ( b ). Locomotion activity is represented as average swimming velocities over 30 min ( V , mm s −1 ) as a function of time (hours circadian time, hCT). Activity recording started at 7 hCT of day 1 and ended at 7 hCT of day 6. The light regime switched at day 3 from light-dark (14:10) cycles to constant light, indicated by white and grey bars. Asterisks and horizontal bars indicate significant different time intervals between coping styles (two-way ANOVA with time and coping style as variables, Sidack’s post hoc test, N  = 6, p  < 0.05. Data shown are means ± SEM)

Finally, we subjected the fish to a constant light challenge and monitored their behaviour over three consecutive diurnal cycles. Under these constant light conditions, the differences between EE and LE fish in maximum values and range of activity level gradually decreased, with EE fish approaching the activity pattern observed for LE fish (Fig.  5 b; two-way ANOVA with time and coping style as variables, Sidack’s post hoc test, N  = 6, p  < 0.05). The activity data of fish of all emergence ranks showed that rhythm strength was correlated (Spearman rank) with emergence rank (Additional file  6 : Figure S5G; N  = 72, ρ  = − 0.61, p  < 0.0001), but not total activity or amplitude (Additional file  6 : Figure S5E-F, N  = 72, p  > 0.05). These results indicate that activity patterns of proactive individuals gradually lose rhythmicity in the absence of time-related cues and become similar to the arrhythmic behaviour of reactive individuals. This demonstrates that the strong diurnal rhythmicity of proactive individuals is dependent on external light cues.

In the present study, we have classified individual zebrafish from the same batch by their coping style, along a proactive-reactive continuum, using risk-taking behaviour as a proxy for coping style. Subsequently, we have demonstrated a strong association between coping style and the amplitude of the circadian rhythmicity at the molecular, endocrine and behavioural level: proactive individuals showed a robust diurnal rhythm with a large amplitude, whereas reactive individuals virtually lacked rhythmicity.

Transcriptome analysis using RNA sequencing on brain samples from proactive and reactive individuals showed an enrichment of genes involved in the regulation of the biological clock among the set of genes that was differentially expressed. In several recent studies, transcriptome analysis has also been performed to study differential gene expression related to coping styles in zebrafish [ 9 , 33 ]. In one of these studies [ 9 ], an approach to assess coping styles was used which was very similar to ours, and the RNA sequencing analysis on hindbrain samples demonstrated that genes involved in the biological clock were overrepresented among the differentially expressed genes. However, in other brain regions, no correlation between clock-related genes and coping style was observed. In another study [ 34 ], a microarray analysis was performed on whole brain samples from zebrafish strains with different behavioural phenotypes, which also revealed differential expression of biological clock-related genes.

The results from our RNA sequencing raised two issues, which we answered in additional control experiments. First, the time of the day may have affected the separation of behavioural phenotypes during the emergence test, and the observed differences in coping style could have been a result of phase-shifted diurnal rhythmicities (i.e. chronotypes [ 4 , 23 , 24 , 25 , 28 ]). However, the performance in the emergence test of individual fish at different times on two consecutive days appeared to be highly consistent, indicating that the observed behaviour in the emergence test was independent of the time of the day. Second, many of the differentially expressed clock genes are known to be light-sensitive [ 35 ]. Therefore, the issue arose whether light responsiveness was a confounding factor in the emergence test, since in the initial setup there was a difference in light intensity between the two compartments. When the test was repeated with identical light conditions on both sides of the hatch, the emergence order was not altered, which demonstrated that the emergence order was not affected by differences in light responsiveness between individual fish.

In addition, we tested the validity of risk-taking behaviour in the emergence test as a proxy for coping style by correlating it with aggressiveness measured using a mirror test. We found a strong correlation between these two behavioural traits, which is in line with previous studies using an evolutionary model [ 13 ] and an experimental approach [ 36 ] and has been reviewed elaborately [ 14 , 37 , 38 ]. The results of our extensive validation of the emergence test support the robustness of the emergence test and explain its wide use in coping style research [ 9 , 10 , 11 , 22 ].

In our population of zebrafish, we found large individual variation in diurnal rhythmicity within a continuum that ranges from very robust diurnal rhythms to a complete absence of rhythmicity. The spontaneous occurrence of individuals with a low level of rhythmicity within a healthy laboratory population has been shown before in Djungarian hamsters [ 39 ], and it will be interesting to study whether such individuals can be found under natural conditions as well. Under certain conditions, a lack of an internal rhythmicity can be advantageous for individuals, for example when external cues are absent or fluctuate unpredictably [ 23 , 24 , 25 , 28 ] or when competition with other individuals can be avoided by a larger flexibility in the timing of certain activities [ 40 ]. In humans, the enormous variation observed in the level and rhythm of the melatonin secretion [ 41 , 42 , 43 ] suggests that variability in circadian rhythmicity may be a more general phenomenon than previously assumed. Recognition of this diversity in individual phenotypes [ 12 , 13 , 14 , 15 ] will be of great relevance for our understanding of the regulation and function of the biological clock, but also for the personalization of diagnosis and treatment of psychiatric and metabolic disorders [ 23 , 24 , 25 , 28 , 44 ] and degenerative diseases [ 45 ] that are attributed to dysregulation of the biological clock.

The observed variation of circadian rhythmicity was associated with coping style, with proactive individuals showing robust rhythms and reactive fish displaying very low rhythmicity. We therefore suggest the diurnal rhythmicity to be an integral part of the coping style of an individual. In general, proactive individuals have been shown to be mainly intrinsically organised, i.e. routine-based and relatively resistant to the influence of external stimuli [ 12 , 13 , 14 ]. The biological clock itself is a proactive system, anticipating rhythmic changes in external conditions, so the stronger rhythmicity found in proactive zebrafish is in line with a more intrinsic regulation of diurnal rhythmicity. In contrast, for reactive individuals, a robust diurnal rhythm may be constraining the flexibility that characterises their reactive phenotype and would therefore be maladaptive in combination with this behavioural phenotype.

Although future research should indicate how generally this phenomenon occurs in nature, our study demonstrates that there are mechanisms that under certain circumstances link coping style and circadian rhythmicity. Possibly, this correlation between coping style and circadian rhythmicity may be a result of genetic correlation, i.e. one trait responding as a consequence of selection acting upon another trait with which it shares genes [ 46 ]. The main mechanism underlying genetic correlation is pleiotropy, in which one genetic variant affects more than one phenotype [ 47 ]. Genes related to the biological clock are often pleiotropic genes, which can influence both behaviour and physiology [ 23 , 24 , 25 , 27 , 28 , 48 ]. Other possible pleiotropic genes that are involved in the observed correlation between coping style and the biological clock are those involved in the biosynthesis and function of neurotransmitters and hormones. For example, cortisol has been found to be a ‘permissive cue’ for diurnal rhythmicity through a variety of molecular mechanism [ 49 ]. Additionally, glucocorticoids are responsible for many changes in behaviour related to coping style in both humans [ 50 ] and non-human vertebrates [ 51 ]. Besides this direct form of pleiotropy, an indirect form of pleiotropy exists, in which a genetic variant affects one phenotype, which lies on the causal path to a second phenotype. In our model, changes in circadian activity patterns may have dramatic effects on certain endocrine systems [ 52 , 53 ], which in turn may influence other behavioural traits.

Conclusions

It is generally accepted that a disturbed or weak diurnal rhythm compromises optimal biological functioning and is linked to psychiatric and metabolic disorders and degenerative diseases. However, here, we demonstrate large variation in diurnal rhythmicity within a single laboratory population of zebrafish, ranging from robust rhythms to a complete absence of rhythmicity. This variation is correlated with the coping style of the individual fish. This association suggests that the biological clock may be part of the complex phenotypes that represent individual survival strategies in nature as well.

Materials and methods

Zebrafish husbandry conditions.

Zebrafish were maintained and handled according to the guidelines from the Zebrafish Model Organism Database (ZFIN, http://zfin.org ) and in compliance with the directives of the local animal welfare committee of Leiden University (DEC number 14058). The wildtype zebrafish ( Danio rerio ) strain used in this study was an AB/TL strain, which originates from crossbreeding of the AB and Tüpfel Long Fin (TL) strains. This strain was originally obtained from the Hubrecht Laboratory (Utrecht, The Netherlands) and maintained in our laboratory for at least ten generations at the time of the experiments described here. New generations were generated by mating of fish from the previous generation, using 60–80 individuals (1:1 ratio male to female). The AB/TL line, considered a segregated hybrid line, was chosen because it contains large genetic variation. In addition to the genetic variation existing within the original AB and TL strains [ 30 , 31 ], the variation between the two original lines [ 30 , 32 ] also contributes to the genetic variation of the AB/TL line.

The fish were reared in densities of ± 40 individuals (male to female 1:1) per 7.5-l tanks in standardised recirculation systems (Fleuren & Nooijen, Nederweert, The Netherlands); water temperature was maintained at 28 ± 1 °C ( n  = 5), with a conductivity of 518 ± 12 μS ( n  = 5) and oxygen concentration of 7.9 ± 0.4 mg l −1 ( n  = 5). Light cycles were maintained at 14-h light to 10-h darkness cycle, with light periods from 8:00/7:00 (0 h Circadian Time, (hCT)) to 22:00/21:00 (14 hCT) summer time/winter time, with a linearly decreasing/increasing light intensity between 0 and 320 ± 21 ln m −2 ( N  = 3) over a period of 15 min. Fish were fed twice daily, at 1 ± 1 hCT and at 8 ± 1 hCT, with dry food (DuplaRinM, Gelsdorf, Germany) and frozen artemia (Dutch Select Food, Aquadistri BV, Klundert, The Netherlands). The fish used in the experiments were between 1 and 2 years old and had a standard length of 32.1 ± 2.3 mm and a body weight of 150.61 ± 17.99 mg (mean ± SD). There was no correlation between emergence rank and body weight (Spearman rank, N  = 144, p  > 0.05).

Behavioural tests

Group emergence.

The setup for the emergence test consisted of a Plexiglas tank (33 × 13 × 13 cm) with a volume of 3 l. It was divided into a darkened holding compartment and an uncovered novel area compartment, by a wall with a hatch (2 × 2 cm) at its mid bottom. The hatch was manually closable by means of a trap door. Ten fish of mixed sex were transferred from the housing tank into the holding compartment of the emergence setup where they were acclimated for 10 min. Thereafter, the trap door was opened, enabling the emergence of one fish at a time into the novel area compartment. After each emergence event, the trap door was closed and the emerged fish was manually transferred to holding tanks (33 × 13 × 13 cm), separated by emergence rank (1–10). The time of emergence from the moment of the opening of the trap door until the actual passage through the hatch was recorded for all individuals. The entire test did not last longer than 10 min and was performed between 5:00 to 8:00 hCT, unless otherwise indicated. Fish not emerging after 10 min were excluded from further analysis. Animals were kept overnight in the holding tanks until further experimentation the next day.

Individual emergence

In order to test the consistency of the degree of risk-taking over the diurnal cycle, individual emergence was investigated at three times of the day (1:00 h, 7:00 h and 13:00 hCT). Nine groups ( n  = 8) were created, each tested at a combination of two different times of the day on consecutive days (1:00/1:00, 1:00/7:00, 1:00/13:00, 7:00/1:00, 7:00/7:00, 7:00/13:00, 13:00/1:00, 13:00/7:00, 13:00/13:00 hCT). The individual emergence setup consisted of three vertical rows of eight Plexiglass emergence tanks, as used for the group emergence tests, with the trap doors connected per row. A flow through system (47.6 l h −1 per tank) provided each tank with fresh system water, and LED strips (Eurolite, Steinigke Showtechnic GmbH, Waldbüttelbrunn, Germany) were placed in front of the uncovered compartment, resulting in similar light intensity as in the husbandry facility (425 ± 11 ln m −2 ). The light intensity of the holding compartment was 5 ± 1 ln m −2 . This setup enabled testing the emergence of eight individuals simultaneously. Fish were placed individually in the holding compartment and acclimated overnight. At testing time, hatches were opened manually and emergence was recorded using a video camera (HDC-SD90, Panansonic Corporation, Kadoma Osaka, Japan) placed 1.2 m in front of the setup. The test was terminated when all fish had emerged. Fish were placed back into the holding compartment, and the test was repeated on the same individual the following day, according to the design described above.

In order to test the consistency of risk-taking behaviour at identical light intensities of the holding compartment and the novel environment compartment of the emergence test, a similar setup was used with light intensities of 5 ± 1 ln m −2 in both compartments. Here too, nine groups with eight fish each ( N  = 72) were created, each tested at 7:00 h ZT in the individual emergence setup according to the protocol described above.

A group emergence test was performed 1 day prior to the individual emergence tests to investigate the correlation between the behaviour in the two tests. Each row of 8 tanks in the individual emergence setup contained fish from the same group emergence test. Since only 8 out of the 10 individuals could be tested simultaneously, two individuals were randomly eliminated, using the excel randomisation function. The group emergence test was performed nine times, yielding 72 fish (9 groups of 8) to be tested in the individual emergence test. As indicated above, each group was tested at different times on two consecutive days. This combination of individual and group emergence test was performed three times, so a total of 216 fish was tested. Individual emergence times were determined by the observation from the video footage and were correlated with the emergence time of the group emergence test using Spearman rank correlation with significance accepted at p  < 0.05.

Emergence of mixed AB and TL groups

In order to test whether the observed behavioural variation in the degree of risk-taking originated from the difference between the AB and TL lines used to create the wildtype (AB/TL) line of this study, the emergence test was repeated with groups created by mixing AB and TL fish in a ratio of 1:1. Six groups of ten fish were created before the group emergence test was performed, and housed under the same conditions as previously described. The group emergence test was subsequently performed for each group according to the protocol described above. Data analysis was performed using a Χ 2 test ( N  = 6, significance accepted at p  < 0.05).

Mirror-image stimulation

For the mirror-image stimulation (15), three rows of eight Plexiglas tanks (33 × 13 × 13 cm, capacity 3 l) were placed on a white table surface (1.0 × 1.0 m). A HD video camera (Panasonic, HDC-SD90, Panasonic Inc., Japan) was mounted 1.7 m above the table. Fish previously tested in the group emergence test (ca. 2 hCT) in sets of ten (with random elimination of two) were placed individually in a circular holding chamber, consisting of a grey tube (10 cm diameter, 15 cm height) placed vertically in the middle of the tanks. A vertical mirror (13 × 15 cm) was placed at one of the short ends of the tank. Each row contained fish from the same group emergence test, i.e. 8 fish in three rows, resulting in 24 fish per experimental run, with three experimental runs. Fish were acclimated 30 min prior to recording, after which the holding chambers were removed simultaneously from all 24 tanks in a swift vertical movement, exposing them to the open field and the mirror, and their behaviour was recorded for 10 min (ca. 4 hCT). Measured variables were the duration of freezing bouts (FRZ; complete lack of movement only for eyes and gills), the latency to realise the first approach to the mirror until a distance of 1 body length, i.e. ca. 4 cm, i.e. 1 body length (LFA), and the number of aggressive behaviours (AGR; bites to the image, parallel swimming, circles and strikes). The variables were determined by the observation from the video footage. During subsequent analysis, AGR were counted and divided by the measuring period minus the FRZ and LFA, therefore regarding only the period dedicated to potentially aggressive interactions with the mirror image. Non-responsive fish, i.e. fish with FRZ of 10 min or longer, were excluded from the analysis. The correlation between the resulting AGR frequency (s −1 ) and the emergence time of the group emergence test was evaluated using Spearman rank correlation ( n  = 59, N  = 9, significance accepted at p  < 0.05).

Transcriptional analysis

• RNA sequencing

In order to establish a first estimation of the range of RNA expression levels over emergence rank variation, four individuals of emergence rank 1 (EE) and four individuals of emergence rank 10 (LE) were sacrificed in ice water, at 7 ± 0.5 hCT. Their brains were dissected out and preserved in RNALater (Ambion, Austin, TX). Brains were homogenised in 500 μl of Trizol reagent (Qiagen). Total RNA was extracted and column-purified using the RNeasy MinElute Cleanup Kit (Qiagen), according to the manufacturer’s instructions. RNA samples were treated with DNaseI (Life Technologies) to remove residual genomic DNA. RNA integrity was analysed by Lab-on-a-chip analysis (Agilent, Amstelveen, The Netherlands). A total of 2 μg of RNA was used to make RNAseq libraries using the Illumina TruSeq RNA Sample Preparation Kit v2 (Illumina, Inc., San Diego, CA, USA) according to the manufacturer’s instructions (with two minor modifications: in the adapter ligation step, 1 μl, instead of 2.5 μl, adaptor was used, and in the library size selection step, the library fragments were isolated with a double Ampure XP purification with a 0.7× beads to library ratio (Beckman Coulter, Woerden, The Netherlands)). The resulting mRNAseq library was sequenced using an Illumina HiSeq2500 Instrument (Illumina, Inc.) with a read length of 2 × 50 nucleotides. Image analysis and base-calling were done using the Illumina HCS version 2.0.12. The raw data has been submitted to the GEO database (accession number GSE64570). The data was analysed using the GeneTiles software (http://www.genetiles.com) with a cutoff p value of 0.05. In brief, Genetiles used fastq files as input for the program Bowtie2 ( http://bowtie-bio.sourceforge.net ) to align the reads to the zebrafish genome (obtained from Ensembl version Zv9). Subsequently, the programs SAMtools ( http://samtools.sourceforge.net ), DESeq2 and DESeq ( http://bioconductor.org ) were used for data processing. Gene ontology of the significantly ( p  < 0.05) different regulated 1478 genes was analysed using the online functional classification tool DAVID ( http://david.abcc.ncifcrf.gov/summary.jsp ). In addition, for genes not classified by DAVID, information was gathered on their function, using the websites GeneCards ( http://www.genecards.org /), NCBI ( http://www.ncbi.nlm.nih.gov/gene ) and Genetics Home Reference ( http://www.ncbi.nlm.nih.gov/gene ). Using this information, all genes involved in the biological clock were identified. In the “ Results ” section, we mention the enrichment scores of the three most enriched functional annotation clusters for the top hundred most differentially expressed genes, assigned by DAVID.

Quantitative PCR

For the estimation of transcriptomic regulation, one member from each core clock gene family was selected ( bmal1a , clock1a , per1a , cry1a ), and one gene involved in the stabilising loop that showed diurnal rhythmicity in its expression ( cipca ). The transcriptional regulation of these genes was measured in individuals from all emergence ranks (1–10). After the group emergence test, random elimination of two fish per group and overnight group housing separated by emergence order, triplicates of 3 × 8 fish ( n  = 72) were housed individually in separate containers (10 × 10 × 10 cm). Sampling occurred at six time points, i.e. in the middle of the light phase (7:00 hCT), 1 h before the dark phase (13:00 hCT), 1 h into the dark phase (15:00 hCT), in the middle of the dark phase (19:00 hCT), 1 h before the light phase (23:00 hCT) and 1 h into the light phase (1:00 hCT). Fish were sacrificed in ice water, and brains were dissected and preserved in RNALater (Ambion, Austin, TX). The bodies were preserved in liquid nitrogen for further analysis of hormones (see below). Triplicate samples were taken. The tissue was homogenised in 1 ml TRIzol reagent (Invitrogen), and total RNA was extracted according to the manufacturer’s instructions. cDNA synthesis was performed using the iScript kit (Bio-Rad Laboratories) according to the manufacturer’s instructions with forward and reverse primers for the aforementioned target genes and ippA (inducing PCN production A) as a reference (housekeeping) gene. Real-time quantitative PCR was performed using the Chromo4 Real-time PCR detection system (Bio-Rad Laboratories). All reactions were performed as technical triplicates. PCR analysis was performed using the following protocol: 95 °C 3 min, 40 cycles of 95 °C 15 s and 60 °C 45 s, and final melting curve of 81 cycles from 95 °C 1 min to 55 °C 10 s. Results were analysed using the ΔΔCt method. In short, ΔCt was calculated for each gene by subtracting the Ct values determined for the housekeeping gene from Ct values determined for the target genes. Then, the ΔCt values for EE were subtracted from the ΔCt values for LE fish. The values of the resulting ΔΔCt were then taken as the potentiator of 2 (2 (ΔΔCt) ), resulting in average fold change of emergence rank per time point.

Data analysis was performed using standard procedures [ 54 ]: A LOWESS curve (5-point window) was fitted over the data plotted over time and a sine wave was fitted subsequently over the resulting curve. The area under the curve (AUC) and the amplitude of the sine wave were further plotted against emergence rank (GraphPad Prism 6.0), and the correlation was analysed by a Spearman rank test ( N  = 72, significance accepted at p  < 0.05).

Determination of cortisol and melatonin levels

Whole body cortisol and melatonin concentrations were measured in the bodies of the fish used for the qPCR analysis of the brains. After weighing and pulverising them in liquid nitrogen, 500 μl ice-cold 1 × PBS buffer was added and the samples were vortexed for 1 min. Subsequently, 500 μl chloroform was added and vortexed for 1 min and then centrifuged at 1500 g for 5 min. Following centrifugation, the organic layer of each sample containing melatonin and cortisol was transferred to a separate test tube. Extraction was repeated three times. Samples were kept overnight in the fume hood for evaporation of chloroform. The next day, 200 μl ice-cold 1 × PBS buffer was added, and 100 μl was used for the determination of melatonin, and 50 μl for cortisol, using ELISA kits (melatonin: Abelissa, Alachua, FL, 32615 USA; cortisol: Demetic Diagnostics GmbH, Kiel, Germany). Two-way ANOVA, with concentrations and time points as variables, indicated overall effects, and Sidaks’s multiple comparison test was used for the post hoc determination of differences between values per time point. Significance was accepted at p  < 0.05.

A LOWESS curve (5-point window) was fitted over the data plotted over time, and a sine wave was fitted subsequently over the resulting curve. The AUC and the amplitude of the sine wave were further plotted against emergence rank (GrapPad Prism 6.0), and the correlation was analysed by a Spearman rank test ( N  = 72, significance accepted at p  < 0.05).

Activity patterns over diurnal/circadian cycle

In order to determine activity levels over diurnal and circadian cycles, individuals were automatically tracked over a period of 3 days under the usual light-dark regime (14:10 LD). Three rows of eight Plexiglas tanks (33 × 13 × 13 cm, capacity 3 l) were placed on a table surface (1.0 × 1.0 m). A flow through system (47.6 l h −1 ) provided each tank with fresh system water. Underneath, a retro-reflecting tape surface (1.2 × 1.2 m, Scotchlite 3 M, St. Paul, USA) reflected infrared light from three spots (U48R Univivi, Shenzhen, China), placed around an infrared firewire camera (Dragonfly, Point Grey Research Inc., Richmond, Canada) in a distance of 7 cm. The camera light unit was placed 1.7 m above the table.

Fish previously tested in the group emergence test in sets of ten, with random elimination of two, were placed individually in the tanks. Each row contained fish from the same group emergence test, i.e. 8 fish in three rows, resulting in 24 fish per experimental run, with three experimental runs ( N  = 3), resulting in n  = 72. Food was provided ad libitum in the form of negatively buoyant pallets (Weekend, Tetra, USA). Fish were acclimated 30–48 h prior to recording. During the exposure to diurnal cycles (L:D), filming started at the middle of the light phase (7:00 hCT) at day 0 and lasted until 7:00 hCT of day 3 (L:D). The light was then kept on for another 3 days (72 h) in order to create constant light conditions (L:L), and filming continued until 7:00 hCT of day 3 L:L. Locomotion patterns were quantified as average velocity ( V , mm s −1 ) over periods of 30 min, using EthoVision XT 6 (Noldus Information Technology b.v., Wageningen, The Netherlands).

For the analysis of the locomotion activity, a LOWESS curve (5-point window) was fitted over the activity data plotted over time and subsequently fitted sine wave yielded area under the curve (AUC, dimensionless) as an indicator for general locomotion activity and amplitude (mm s −1 ) as an indicator for activity fluctuation.

The rhythm strength of the 24-h component in activity data was gained as follows: after testing for multicollinearity in the explanatory variables by using variance inflation factors, a multivariate model predicting emergence rank was made, based on the remaining variables. Cook’s distance was calculated for all data points, but no outliers were found. Using likelihood ratio tests and Akaike’s information criterion, the optimal model was determined. The only explanatory variable of this optimal model was rhythm strength of the 24-h component. Visual inspection of the residuals revealed no violation by this model of statistical assumptions.

Finally, the maximum velocity ( V max , mm s −1 ), i.e. the average of the maximum values for V during the first three 24-h periods (L:D), gave an indication for absolute swimming capacity. AUC, amplitude, rhythm strength and V max were plotted against emergence rank (GraphPad Prism 6.0), and the correlation was analysed by a Spearman rank test ( N  = 72, significance accepted at p  < 0.05).

McAdams DP, Olson BD. Personality development: continuity and change over the life course. Ann Rev Psychol. 2010;61:517–42.

Article   Google Scholar  

Bardo MT, Donohew RL, Harrington NG. Psychobiology of novelty seeking and drug seeking behavior. Behav Brain Res. 1996;77:23–43.

Article   CAS   Google Scholar  

Caci H, Robert P, Boyer P. Novelty seekers and impulsive subjects are low in morningness. Europ Psych. 2004;19:79–84.

Maestripieri D. Night owl women are similar to men in their relationship orientation, risk-taking propensities, and cortisol levels: implications for the adaptive significance and evolution of eveningness. Evol Psychol. 2014;12:130–47.

Maestripieri DM, Baran N, Sapienza P, Zingales L. Between- and within-sex variation in hormonal responses to psychological stress in a large sample of college students. Stress. 2010;13:413–24.

Ursin H. The psychology in psychoneuroendocrinology. Psychoneuroendocrinol. 1998;23;6:555–70.

Ebstein RP. The molecular genetic architecture of human personality: beyond self-report questionnaires. Mol Psychiatry. 2006;11:427–45.

Benjamin J, Li L, Patterson C, Greenberg BD, Murphy DL, Hamer DH. Population and familial association between the D4 dopamine receptor gene and measures of novelty seeking. Nature Genet. 1996;12:81–4.

Rey S, Boltana S, Vargas R, Roher N, MacKenzie S. Combining animal personalities with transcriptomics resolves individual variation within a wild-type zebrafish population and identifies underpinning molecular differences in brain function. Mol Ecol. 2013;22:6100–15.

Huntingford FA, Andrew G, MacKenzie S, Morera D, Coyle SM, Pilarczyk M, et al. Coping strategies in a strongly schooling fish, the common carp Cyprinus carpio. J Fish Biol. 2010;76:1576–91.

Tudorache C, Schaaf M, Slabbekoorn H. Covariation between behaviour and physiology indicators of coping style in zebrafish (Danio rerio). J Endocrinol. 2013;219:251–8.

Koolhaas JM, Korte SM, de Boer SF, Van Der Vegt BJ, Van Reenen CG, Hopster H, et al. Coping styles in animals: current status in behavior and stress-physiology. Neurosci Biobeh Rev. 1999;23:925–35.

Wolf M, van Doorn GS, Leimar O, Weissing FJ. Life-history trade-offs favour the evolution of animal personalities. Nature. 2007;447:581–4.

Conrad JL, Weinersmith KL, Brodin T, Saltz JB, Sih A. Behavioural syndromes in fishes: a review with implications for ecology and fisheries management. J Fish Biol. 2011;78:395–435.

Øverli Ø, Sørensen C, Pulman KGT, Pottinger TG, Korzan WJ, Summers CH, et al. Evolutionary background for stress-coping styles: relationships between physiological, behavioral, and cognitive traits in non-mammalian vertebrates. Neurosci BiobehavRev. 2007;31:396–412.

Weiss A. Personality traits: a view from the animal kingdom. J Pers. 2018;86:12–22.

Friedman HS. The multiple linkages of personality and disease. Brain Beh Imm. 2008;22:668–75.

van der Greef J, Hankemeier T, McBurney RN. Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials? Pharmacogenomics. 2006;7:1087–94.

Schork NJ. Personalized medicine: time for one-person trials. Nature. 2015;520:609–11.

Kumar D. The personalised medicine. A paradigm of evidence-based medicine. Ann Ist Super Sanita. 2011;47:31–40.

CAS   PubMed   Google Scholar  

Kaushik A, Vasudev A, Arya SK, Pasha SK, Bhansali S. Recent advances in cortisol sensing technologies for point-of-care application. Biosens Bioelectron. 2014;53:499–512.

Tudorache C, Braake ter A, Tromp M, Slabbekoorn H, Schaaf M. Behavioural and physiological indicators of stress coping styles in larval zebrafish. Stress. 2014;18:121–8.

Roenneberg T, Merrow M. The circadian clock and human health. Curr Biol. 2016;26:R432–43.

McKenna HT, Reiss IK, Martin DS. The significance of circadian rhythms and dysrhythmias in critical illness. J Intens Care Soc. 2017;18:121–9.

Bollinger T, Schibler U. Circadian rhythms – from genes to physiology and disease. Swiss Med Wkly. 2014;144:1–11.

CAS   Google Scholar  

Hardin PE. From biological clock to biological rhythms. Genome Biol. 2000;1(4):1023.1–5.

Foulkes NS, Whitmore D, Vallone D, Bertolucci C. Studying the evolution of the vertebrate circadian clock: the power of fish as comparative models. Adv Genet. 2016;95:1–30.

Adan A, Archer SN, Hidalgo MP, Di Milia L, Natale V, Randler C. Circadian typology: a comprehensive review. Chronobiol Int. 2012;29:1153–75.

Gorissen M, Manuel R, Pelgrim TNM, Mes W, de Wolf MJS, Zethof J, et al. Differences in inhibitory avoidance, cortisol and brain gene expression in TL and AB zebrafish. Genes Brain Behav. 2015;14:428–38.

Guryev V. Genetic variation in the zebrafish. Genome Res. 2006;16:491–7.

Brown KH, Dobrinski KP, Lee AS, Gokcumen O, Mills RE, Shi X, et al. Extensive genetic diversity and substructuring among zebrafish strains revealed through copy number variant analysis. Proc Natl Acad Sci. 2012;109:529–34.

Stickney HL. Rapid mapping of zebrafish mutations with SNPs and oligonucleotide microarrays. Genome Res. 2002;12:1929–34.

Wong RY, Oxendine SE, Godwin J. Behavioral and neurogenomic transcriptome changes in wild-derived zebrafish with fluoxetine treatment. BMC Genomics. 2013;14:348.

Drew RE, Settles ML, Churchill EJ, Williams SM, Balli S, Robison BD. Brain transcriptome variation among behaviorally distinct strains of zebrafish (Danio rerio). BMC Genomics. 2012;13:323.

Weger BD, Sahinbas M, Otto GW, Mracek P, Armant O, Dolle D, et al. The light responsive transcriptome of the zebrafish: function and regulation. Mueller F, editor. PLoS One 2011; 6: e17080–e17015.

Bell AM, Sih A. Exposure to predation generates personality in threespined sticklebacks (Gasterosteus aculeatus). Ecol Lett. 2007;10:828–34.

Sih A, Bell A, Johnson JC. Behavioral syndromes: an ecological and evolutionary overview. Trends Ecol Evol. 2004;19:372–8.

Bell AM. Evolutionary biology: animal personalities. Nature. 2007;447:539–40.

Weinert D, Schottner K. An inbred lineage of Djungarian hamsters with a strongly attenuated ability to synchronize. Chronobiol Int. 2007;24:1065–79.

Smith JM. Evolution and the theory of games. Cambridge: Cambridge University Press; 2009.

Google Scholar  

Griefahn B, Bröde P, Remer T, Blaszkewicz M. Excretion of 6-hydroxymelatonin sulfate (6-OHMS) in siblings during childhood and adolescence. Neuroendocrinology. 2003;78:241–3.

Waldhauser F, Weiszenbacher G, Tatzer E, Gisinger B, Waldhauser M, Schemper M, et al. Alterations in nocturnal serum melatonin levels in humans with growth and aging. J Clin Endocrin Metabol. 1988;66:648–52.

Zeitzer JM, Daniels JE, Duffy JF, Klerman EB, Shanahan TL, Dijk D-J, et al. Do plasma melatonin concentrations decline with age? Am J Med. 1999;107:432–6.

Srinivasan V, Pandi-Perumal SR, Trakht I, Spence DW, Hardeland R, Poeggeler B, et al. Pathophysiology of depression: role of sleep and the melatonergic system. PsychiatrRes. 2009;165:201–14.

Michel S, Block GD, Meijer JH. The aging clock. In: Colwell CS, editor. Circadian medicine. Hoboken: Wiley, Blackwell; 2015. p. 321–35.

Roff DA. The evolution of genetic correlations: an analysis of patterns. Evolution. 1996;50:1392.

Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nature Rev Gen. 2013;14:483–95.

Ben-Moshe Livne Z, Alon S, Vallone D, Bayleyen Y, Tovin A, Shainer I, et al. Genetically blocking the zebrafish pineal clock affects circadian behavior. PLoS Gen. 2016;12:e1006445–26.

Dickmeis T, Weger BD, Weger M. The circadian clock and glucocorticoids--interactions across many time scales. Mol Cell Endocrinol. 2013;380:2–15.

de Kloet RE, Joëls M, Holsboer F. Stress and the brain: from adaptation to disease. Nat Rev Neurosci. 2005;6:463–75.

Gosling SD, John O. Personality dimensions in nonhuman animals: a cross-species review. Curr Dir Psychol Sci. 2016;8:69–75.

van der Vinne V, Riede SJ, Gorter JA, Eijer WG, Sellix MT, Menaker M, et al. Cold and hunger induce diurnality in a nocturnal mammal. Proc Natl Acad Sci. 2014;111:15256–60.

van Oosterhout F, Lucassen EA, Houben T, HT vL, Antle MC, Meijer JH. Amplitude of the SCN clock enhanced by the behavioral activity rhythm. PLoS One. 2012;7:e39693.

Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM. Mixed effects models and extensions in ecology with R. New York: Springer New York; 2009.

Book   Google Scholar  

Download references

Acknowledgements

We would like to thank Peter Snelderwaard for his invaluable help with the setup and animal care.

No funding source to report

Availability of data and materials

All data generated during this study are included in this published article and its additional information files, and all materials generated during this study are available upon request.

Author information

Authors and affiliations.

Institute of Biology, Leiden University, Leiden, The Netherlands

Christian Tudorache, Hans Slabbekoorn, Eline Hin, Herman P. Spaink & Marcel J. M. Schaaf

Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands

Yuri Robbers & Johanna H. Meijer

You can also search for this author in PubMed   Google Scholar

Contributions

CT designed the study, conducted the experiments, analysed the results and wrote the paper. HS designed the study and wrote the paper. YR analysed the results and wrote the paper. EH conducted the experiments. JM wrote the paper. HS analysed the results and wrote the paper. MS designed the study and wrote the paper. All authors read and approved the final manuscript

Corresponding author

Correspondence to Christian Tudorache .

Ethics declarations

Ethics approval and consent to participate.

Zebrafish were maintained and handled according to the guidelines from the Zebrafish Model Organism Database (ZFIN, http://zfin.org ) and in compliance with the directives of the local animal welfare committee of Leiden University (DEC number 14058).

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:.

Figure S1. Evaluation of group emergence test by repeated single emergence. A) Dependence of single emergence time on the diurnal time point. Each individual fish was tested in a single emergence test at 1:00, 7:00 or 13:00 hCT on two subsequent days. This experiment was performed in a 3 × 3 design, so all possible combinations of two time points on consecutive days were tested. For each combination of times, 3 sets of 8 individuals were tested. The test was terminated after one hour. Emergence times of day 2 were plotted against emergence times of day 1. The results show a significant correlation (Spearman rank, p  < 0.05) in all cases, indicating that the emergence time is not dependent on the time of day (log-log transformed scale). B) Single emergence times and group emergence ranks for light preference testing. Single emergence times of emergence from a darkened holding compartment to a darkened novel environment (log-transformed) were plotted over standard group emergence rank. The results show a significant correlation, suggesting that emergence, as a proxy for risk-taking, is independent of differences in light intensity between the two compartments (Spearman rank, p  < 0.05). C) Single and group emergence times of all experiments. The results show a significant correlation, suggesting that emergence, as a proxy for risk-taking, is independent of social and environmental settings and consistent over time and across context (Spearman rank, significance accepted at p  < 0.05, log-log transformed scale). (PDF 184 kb)

Additional file 2:

Figure S2. No difference between performance of AB and TL fish in group emergence test. It was tested whether the observed behavioural variation in the performance in the group emergence test originated from differences between the original AB and TL lines, which had been used to generate the AB/TL line used in the present study. For this purpose, the emergence test was repeated with 6 groups of each 10 fish consisting of 5 AB and 5 TL fish in a ratio of 1:1. The results show no significant difference between the number of AB (black bars) and TL fish (grey bars) per emergence rank, indicating no difference between the two lines. Data analysis was performed using a Χ 2 test ( N  = 6, significance accepted at p  < 0.05). (PDF 34 kb)

Additional file 3:

Table S1. Differently expressed genes in early emerging (EE) and late emerging (LE) fish. Analysis of the EE and LE transcriptome data show that 1478 genes out of 31,398 genes analysed were expressed significantly different ( p  < 0.05). Out of these genes, 43 are involved in the regulation of the biological clock (yellow). Genes upregulated in EE fish are marked red and genes upregulated in LE fish are marked blue. The table gives chromosome number, gene name, Ensembl gene-id, Entrez gene-id, ZFIN gene-id, GO accessions, Human homologues, fold change and p -value. (XLSX 317 kb)

Additional file 4:

Figure S3. Correlation between expression patterns of clock-related genes and emergence rank. Area under the curve (AUC) and amplitude for relative expression patterns of bmal1a (A), clock1a (B), per1a (C), cry1a (D) and cipca (E) mRNA determined with quantitative real-time PCR (qPCR) in the brain tissue of fish from the entire range of emergence ranks (1–10). AUC values show a significant negative correlation with emergence rank for bmal1a and cipca , indicating decreasing expression activity with reduced risk-taking behaviour. Similarly, amplitude values (F–J) show a significant negative correlation with emergence rank for all genes, indicating increasingly dampened rhythmicity of the expression with reduced risk-taking behaviour. Solid lines indicate significant correlations (Spearman rank test, significance accepted at p  < 0.05). (PDF 96 kb)

Additional file 5:

Figure S4. Correlation between the concentration of cortisol and melatonin and emergence rank. The areas under the curve (AUC) and the amplitude were calculated for whole body cortisol and melatonin concentrations over time and plotted against emergence rank 1–10. The results show significant positive correlations of the AUC (A, B) and negative correlations for the amplitude (C, D) values for both, cortisol (A, B) and melatonin (C, D) (Spearman rank, significance accepted at p  < 0.05), indicating increasingly dampened rhythmicity for the concentration of these hormones and reduced hormone production with reduced risk-taking behaviour. (PDF 63 kb)

Additional file 6:

Figure S5. Correlation between diurnal pattern of locomotor activity and emergence rank under normal light/dark (LD) regime and under constant (LL) light regime. A) The strength of the diurnal rhythm (dimensionless) under LD plotted against emergence rank 1–10, showing a significant negative correlation. B) Amplitude of locomotion activity in units of swimming velocity ( V , mm s −1 ) under LD plotted against emergence rank 1–10 showing a significant negative correlation. C) Area under the curve of locomotion activity (AUC, dimensionless) under LD plotted against emergence rank 1–10, showing a significant negative correlation. D) Maximum swimming velocity ( V max , mm s −1 ) as average over three days under LD plotted against emergence rank 1–10 showing no significant correlation. E) Rhythm strength (dimensionless) under LL plotted against emergence rank 1–10, showing no significant correlation. F) Amplitude of locomotion activity in units of swimming velocity ( V , mm s −1 ) under LL plotted against emergence rank 1–10 showing no significant correlation. G) Area under the curve of locomotion activity (AUC, dimensionless) under LL plotted against emergence rank 1–10 with no significant correlation (Spearman rank test, significance accepted at p  < 0.05). The results indicate an increasingly dampened rhythmicity for locomotor activity under LD but less so under LL. A lack of this correlation in V max indicates no difference in physiological capacity for locomotion. (PDF 130 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Tudorache, C., Slabbekoorn, H., Robbers, Y. et al. Biological clock function is linked to proactive and reactive personality types. BMC Biol 16 , 148 (2018). https://doi.org/10.1186/s12915-018-0618-0

Download citation

Received : 16 October 2018

Accepted : 04 December 2018

Published : 21 December 2018

DOI : https://doi.org/10.1186/s12915-018-0618-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Circadian rhythm
• Clock genes
• Coping style
• Endocrine regulation

BMC Biology

ISSN: 1741-7007

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Advertisement

The Biological Clock: Age, Risk, and the Biopolitics of Reproductive Time

• Original Article
• Open access
• Published: 31 October 2020
• Volume 84 , pages 765–778, ( 2021 )

Cite this article

You have full access to this open access article

• Martina Yopo Díaz   ORCID: orcid.org/0000-0002-5886-8211 1 , 2  

7146 Accesses

12 Citations

21 Altmetric

Explore all metrics

The present article explores the social and subjective dimensions of the biological clock and its implications for reproductive time through a qualitative study based on 40 life story interviews of women from Santiago de Chile. Although the narrative of the biological clock has become a prevalent frame for addressing reproductive time in the context of late childbearing, age-related infertility, and the use of assisted reproductive technologies, few studies engage in an in-depth analysis of the biological clock—its boundaries, dynamics, and the particular ways in which it shapes women’s views and experiences of reproductive time. The present article aims to advance current knowledge on the intersection of time, reproduction, and biopolitics by arguing that the biological clock regulates reproductive time by shaping the boundaries and dynamics of female fertility through the clock. By determining reproductive time as quantitative, standardised, linear, and irreversible and by outlining the passing of time through pressure, risk, and burden, the biological clock determines when it is possible and desirable to have children and regulates reproduction, gender, and the female life course. These findings highlight the importance of critically addressing the narrative of the biological clock and its implications for women’s views and experiences of reproductive time.

Similar content being viewed by others

Narrative Research

The New Roles of Men and Women and Implications for Families and Societies

Gender as a Social Structure

Avoid common mistakes on your manuscript.

The biological clock has become a prevalent framework for addressing reproductive time in the context of late childbearing, age-related infertility, and the use of assisted reproductive technologies. In many countries, women are postponing the transition to motherhood. Several studies show that over the past decades the percentage of women who are delaying childbearing and having their first child at 35 or later has increased (Cooke et al. 2010 ; Lavender et al. 2015 ; Wagner et al. 2019 ; Wyndham et al. 2012 ). Among others, Billari et al. ( 2011 , p. 616) argue that this has been “one of the most important changes in reproductive behaviour in recent decades.” This transformation is prevalent in high-income Western developed countries and is also emerging in middle-income developing countries (Beets et al. 2011 ).

Postponing the transition to motherhood imposes challenges to reproductive time because the time required to achieve personal and social milestones conflicts with the available time for childbearing determined by female fertility. As Martin ( 2017 , p. 97) argues: “women’s capacity to conceive may decline before they feel socially or emotionally competent to have a child” and “when women feel socially and emotionally prepared to have a child, they may no longer be in the optimal biological range to have a child.” Various scholars have noted this conflicting character between biological and social “clocks” (Billari et al. 2011 ; Daly and Bewley 2013 ; Martin 2017 ; Perrier 2013 ), as well as the problematic character of the biological clock in women’s reproductive experiences (Daly and Bewley 2013 ; Friese et al. 2006 ; Lavender et al. 2015 ; Martin 2017 ).

Challenging the biological clock by delaying childbearing involves managing multiple risks regarding fertility, the development and outcome of pregnancy, the health of mother and child, and women’s ability to take care of their children and be “good” mothers. Several studies assert that challenging the biological clock involves risks such as infertility, miscarriage, preterm delivery, placenta previa, caesarean section, prolonged labour, preeclampsia, hypertension, gestational diabetes, maternal mortality, low birth weights, increased perinatal morbidity, chromosomal abnormalities, and congenital malformations, among others (Billari et al. 2011 ; Cooke et al. 2010 ; Daly and Bewley 2013 ; Macintosh 2015 ; Wyndham et al. 2012 ). Challenging the biological clock by delaying childbearing is also associated with risks to women’s ability to nurture and raise children. Scholarly work on late fertility has documented the ways in which “old” age is often associated with a higher likelihood of experiencing illness, medical problems, and lack of energy (Martin 2017 ; Settersten and Hägestad 1996 ; Settersten Jr and Mayer 1997 ) and is perceived as a liability for good mothering (Chen and Landau 2015 ; Settersten and Hägestad 1996 ).

Analysing the narrative of the biological clock is important because it affects not only women’s views and experiences regarding fertility and childbearing, but also their dispositions and practices regarding sexuality, family formation, educational and professional careers, and the use of assisted reproductive technologies. Previous scholarly work has shown that women’s awareness of the biological clock affects their behaviour regarding sexual intercourse (Easton et al. 2010 ), long-term partners and union formation (Moss and Maner 2014 ; Wagner et al. 2019 ), postgraduate education and the labour market (McAlister 2008 ), and the use of donor oocytes (Friese et al. 2006 ) and egg freezing (Baldwin et al. 2019 ; Brown and Patrick 2018 ; Martin 2010 ). From this perspective, accounting for the narrative of the biological clock provides valuable insights to understand how women engage with reproduction and structure different life course trajectories.

The present article examines the narrative of the biological clock and the particular ways in which it shapes women’s views and experiences of reproductive time through the analysis of 40 life story interviews conducted with women from Santiago de Chile. The aim of my article is not to account for the time constraints of female fertility and risks of delaying childbearing, but rather to explore how they are understood and lived by women through the narrative of the biological clock. Although there is a growing number of studies referring to the biological clock, little is known about what it is, how it works, and the particular ways in which it shapes reproductive time. Drawing on theoretical insights on social time (Adam 1995 , 2006 ; Zerubavel 1985 ) and biopolitics (Foucault 1978 , 2003 ; Rose 2007 ), the present article makes a unique contribution to this field of research by addressing time as a fundamental dimension of contemporary biopolitics of reproduction and engaging in an in-depth empirical analysis of the biological clock itself—its boundaries, dynamics, and the particular ways in which it shapes women’s views and experiences of reproductive time.

Deconstructing the Biological Clock

In social sciences, scholarly work on the biological clock has increased significantly in the past two decades as a way of addressing the problem of fertility decline and the risks of ageing in the context of late motherhood and the use of assisted reproductive technologies (Beaujouan and Solaz 2013 ; Easton et al. 2010 ; Keeney and Vernik 2007 ; Lavender et al. 2015 ; Macintosh 2015 ; Martin 2010 ; Mohapatra 2014 ). Despite its prevalence, the analysis of the biological clock in most studies is constrained to its use in expressions to characterise experiences of reproductive time, such as the ticking of the biological clock (McAlister 2008 , p. 218), the problem of the biological clock (Keeney and Vernik 2007 , p. 114), the pressure of the biological clock (Mohapatra 2014 , p. 390), the struggle against the biological clock (Beaujouan and Solaz 2013 , p. 63), and snoozing the biological clock (Cooke et al. 2010 , p. 1325). Some studies also describe the biological clock as the limited period of time between puberty and menopause during which women are able to conceive children (Moss and Maner 2014 ; Wagner et al. 2019 ) and the feelings of pressure and anxiety that arise from acknowledging the finite character of biological reproduction (Brown and Patrick 2018 ; Lahad 2012 ; Martin 2017 ). Only few studies go further and characterise the biological clock as a social construction that regulates reproductive time and gender roles (Amir 2006 ; Friese et al. 2006 ). Overall, this background reveals that there is little knowledge on the biological clock itself—its boundaries, dynamics, and the particular ways in which it shapes women’s views and experiences of reproductive time—and that the social and subjective implications of making sense of the boundaries and dynamics of female fertility through clock time remain largely unexplored.

The fact that female fertility is subject to time constraints has long been acknowledged, but it has been only a couple of decades since this understanding started being interpreted through the biological clock. The narrative of the biological clock emerged in the late 1970s and early 1980s to address the tensions between the social and biological dimensions of reproductive time in the context of women’s participation in the labour force, demographic shifts toward delayed childbearing, and the emergence of assisted reproductive technologies (Friese et al. 2006 ). Around this time, the narrative of the biological clock as a metaphor of female fertility started making its way into mainstream press and publications. Representative of this trend is the book Up Against the Clock: Career Women Speak Out on the New Choice of Motherhood (Fabe and Wikler 1979 ), which addresses how professional women in their late thirties negotiate whether and when to have children as they attempt to reconcile the conflicting demands of work and family life. From its emergence, the narrative of the biological clock has been structured upon normative conceptions of gender, reproductive time, and the female life course. As Amir ( 2006 ) argues, the biological clock is a mechanism that control forms of living regarding reproduction, heteronormativity, and family formation through the normative regulation of time.

Clock Time as Biopolitics

I draw on Foucauldian and post-Foucauldian approaches to biopolitics as a theoretical framework to address the ways in which the narrative of the biological clock shapes women’s views and experiences of reproductive time. Foucault ( 2003 , p. 247) outlines biopolitics as a new technology of power that emerges to control “men insofar as they are living beings.” This politics of life means that biological processes like health, birth-rates, and life expectancy become the focus of governmental practice. “It is, in a word, a matter of taking control of life and the biological processes of man-as-species” (Foucault 2003 , pp. 246–7). The regularisation of reproduction is at the core of the governing of life. Foucault ( 2003 , p. 243) argues that the rate of reproduction and the fertility of the population “become biopolitics’ first objects of knowledge and the targets it seeks to control.” It is through techniques like the socialisation of procreative behaviour (Foucault 1978 ) and the prescription of compulsory birth-control (Foucault 2003 ), that governments seek to regularise social reproduction.

Post-Foucauldian approaches reveal that contemporary biopolitics are being reshaped by notions of choice and risk. Rose ( 2001 , p. 1) argues that new configurations of power and control have taken shape, structuring contemporary biopolitics as “risk politics.” In an age of choice and individual responsibility, the body, its capacities, the minimisation of health risks, and the optimisation of future vitality become the focus of governmental power (Rose 2007 ). Several feminist scholars have drawn on Foucauldian and post-Foucauldian approaches to address contemporary biopolitics regarding reproduction. For Whittaker ( 2015 ), the advent of contraceptives, synthetic hormones, assisted reproductive technologies, and genomic technologies enables the control of human fertility and thus the emergence of neoliberal governmentalities over women’s bodies and life politics. Similarly, Waldby and Cooper ( 2008 ) assert that women’s reproductive biology becomes the focus of extensive biomedical research, global commercial innovation, and state policy interventions. For them, policies to reverse the decline of birth rates and delay of first childbearing, such as improved childcare, better maternity leave, baby bonuses, and the exhortation of women to have more children, exemplify the ways in which states enact contemporary biopolitics regarding reproduction and female fertility.

Foucault ( 1978 , p. 136) characterises technologies of power as “essentially a right of seizure: of things, time, bodies, and ultimately life itself.” Social theory has extensively outlined time as a symbolic tool of social control (Adam 1990 , 2006 ; Elias 1989 ; Zerubavel 1985 ). Time enables regulation of the population by setting social calendars and strict boundaries to the timing of life course events (Elder Jr 1975 ; Settersten Jr 2003 ). Clock time is not time itself but rather a social construction that emerged in Europe during the late medieval period and became hegemonic through capitalism (Martineau 2015 ). Since then, it has become “a social and economic reality that structures, controls, disciplines, and provides norms for our social life” (Adam 1990 , p. 120). Through the clock, time became standardised, neutral, and disembedded from the rhythms of the body and nature (Adam 1990 , 2006 ). As a mechanism of social regularisation, “clock time is used to regulate and rationalise the pace and seasonality of organisms and beings” (Adam 2006 , p. 115).

Gender and feminist scholars have rightfully criticised the prevalence of the clock to account for women’s experience of time. Bryson ( 2007 ), Hughes ( 2002 ), and Leccardi ( 1996 ) argue that the linear, objective, divisible, and abstract time in patriarchal capitalist societies neglects the multiple, cyclical, relational, and fragmented nature of reproductive, domestic, and care time. These critiques also extend to prevalent ideas of time regarding childbirth and mothering. Adam ( 1995 , p. 49) describes time during delivery as “oscillating between two times—the archetypal and endogenous temporality of the birthing process and the rational time of obstetrics.” Similarly, Bartlett ( 2012 , p. 127) asserts that in negotiating breastfeeding within the demands of paid work, it seems that “breastfeeding time runs counter to institutional time, business time, corporate time.” These critiques suggest that clock time is not only different but also competing and in tension with women’s experiences of reproductive time.

The Chilean Context

The great majority of studies on the biological clock have been conducted in North America and Europe, but the delaying of childbearing and the tensions between the social and biological dimensions of reproductive time are not exclusive to these countries. Chile represents an interesting case to analyse the narrative of the biological clock beyond Western developed countries and contribute to bridge the gap of knowledge of contemporary reproductive time at a global level. In contemporary Chile, the prevalence of cultural norms that conflate womanhood and motherhood and naturalise childbearing in the female life course (Montecino 2018 ; Valdés 2007 ; Yopo Díaz 2020 ) coexist with an increasing participation of women in tertiary education and the labour market (Larrañaga 2007 ; The World Bank 2019 ). As elsewhere, these changes have restructured fertility and reproductive patterns. Studies conducted in the last decade reveal that an increasing number of women are delaying first childbearing and becoming mothers at an older age (Cerda 2010 ; Fuentes et al. 2010 ; Yopo Díaz 2018a , 2018b ). Data from Instituto Nacional de Estadísticas ( 2017 ) reveals that women’s average age at first childbearing has increased almost 3 years in the last decades from 22.47 years in 1972 to 25.04 years in 2016. The use of assisted reproductive technologies in Chile is rapidly increasing, from 90 cycles per million women in fertile age in 1990 to 634 in 2013, but remains constrained by its financial costs (Velarde 2016 ) and prevalent religious beliefs regarding the nature of reproduction (Herrera et al. 2013 ). Although adoption is available, it is not always considered an alternative due to the symbolic value of blood, biology, and genetics in cultural ideas of parenting in Chile (Herrera 2011 ).

The narrative of the biological clock has become prevalent to address the problem of fertility decline and the risks of ageing in the context of late childbearing in contemporary Chile. In 2015, the Chilean newspaper El Mercurio published an article in which Dr. Pommer, former president of the Chilean Society of Reproductive Medicine, explained age related infertility and the risks of delaying childbearing. In this article, Dr. Pommer ( 2015 , para. 1) argued that “the woman has a biological clock of the ovules that determines her possibility of being mother.” He further commented that “this biological clock acts as a sword of Damocles, because although the quality of the egg starts decreasing from the age of 20, it is from the age of 35 that this decrease is radically accentuated” (Pommer 2015 , para. 2). This article exemplifies the prevalence of the biological clock in mainstream press (Aburto Prieto 2016 ; El Mercurio 2017 ; El Mostrador 2017 ; Hirane 2017 ) as well as in the narrative of doctors, health professionals, and medical institutions (Clínica Alemana 2012 ; Clínica Las Condes 2017 ; Manzur 2014 ; Meier Furst 2018 ). Often depicted through the image of a woman with a clock in her hands or a pregnant woman with her hands on her stomach, in contemporary Chile the biological clock is commonly used to refer to the time constraints of female fertility, age related infertility, the risks of delaying childbearing, and the advantages of assisted reproductive technologies.

The Present Study

In the present article, I draw on the intersection of time, reproduction, and biopolitics to examine the particular ways in which the narrative of the biological clock shapes women’s views and experiences of reproductive time. Using accounts from life story interviews conducted with women from Santiago de Chile, I examine how the narrative of the biological clock outlines the boundaries and dynamics of reproductive time, the meanings of age and ageing, the role of risk and agency in timing childbearing, and reproductive inequalities between men and women. In doing so, I stress time as an essential dimension of contemporary biopolitics of reproduction and address the particular ways in which the narrative of the biological clock regulates gender and reproduction by determining when women should have children.

Research Design

In the present article I draw on the findings of a qualitative study on the timing of the transition to motherhood in contemporary Chile. My study was reviewed and approved by the Ethics Committee of the Department of Sociology, University of Cambridge. I used a qualitative research design because my intention was to “study things in their natural setting, attempting to make sense of, or interpret, phenomena in terms of the meaning people bring to them” (Denzin and Lincoln 2005 , p. 3). Much has been said about reproductive time, but only few approaches have taken women’s own voices and experiences into account to understand it. I follow Miller ( 2007 , pp. 337–338) in introducing an epistemological shift by focusing not on “what is being said about women” but on “what women themselves are saying.” Through this approach, I aimed to gain an in-depth understanding of the particular ways in which the narrative of the biological clock shapes women’s views and experiences of reproductive time.

A research strategy based on a “thick description” (Geertz 1973 ) of a small number of case studies is preferable for understanding complex social phenomena such as the intersection between female fertility and clock time. This approach also advances current knowledge on the biological clock given that most studies address it either from a quantitative perspective (Beaujouan and Solaz 2013 ; Easton et al. 2010 ; Keeney and Vernik 2007 ; Moss and Maner 2014 ; Wagner et al. 2019 ) or a literature review (Cooke et al. 2010 ; Macintosh 2015 ; Mohapatra 2014 ). The few studies that take on a qualitative approach do not focus on the biological clock itself but rather on egg freezing (Brown and Patrick 2018 ; Martin 2010 ), singlehood (Lahad 2012 ), the timing of childbearing (Lavender et al. 2015 ; Martin 2017 ), and women’s narratives of age-related fertility decline (Friese et al. 2006 ).

Participants

The findings presented in the present article are based on the analysis of narratives of transition to motherhood of 40 women born and raised in Chile who reside in the capital city of the country, Santiago de Chile. I used stratified purposeful sampling (Flick 2009 ) to select women from different ages and socioeconomic status. Socioeconomic status was determined by jointly considering variables related to educational attainment, occupation, income, and place of residence as well as women’s self-positioning within the class structure of Chilean society. The participants are lower ( n  = 12), middle ( n  = 16), and upper ( n  = 12) class and aged between 18 and 30 ( n  = 10), 31–45 ( n  = 10), 46–60 ( n  = 11), and 61–75 ( n  = 9). Most of them were mothers ( n  = 28) but some were not ( n  = 12). In comparison to women in the subsample of mothers, women in the subsample of non-mothers were younger, mostly in their 20s and 30s, and single; some had partners but only one was married.

In the present study I selected women who had children and women who did not because reproduction is firmly grounded within femininity so that all women are constrained into negotiating fertility and childbearing (Sevón 2005 ). Furthermore, by including participants who do not have children, I address the experiences of those women who enact reproductive time by delaying childbearing or remining childless. The participants were contacted through key informants using a snowball sampling method. All agreed to participate in the research and signed informed consents. In the present article, the names of the participants are replaced by pseudonyms to support confidentiality.

Data Production

Two semi-structured life story interviews were conducted with each of the participants. Life story interviewing is a qualitative research method for gathering information on the subjective essence of the life of an individual through biographical narratives (Atkinson 2002 ). The first interview addressed the women’s life stories and the second interview focused on their experiences with the transition to motherhood. In the second interview, all participants were asked general questions regarding reproductive time (e.g., “At what age did you have or would like to have your first child?”; “Why is the age at first childbearing important?”; “At what age should women have their first child?”; “What are the age limits for first childbearing?”; “What are your thoughts on the delay of childbearing?”) as well as specific questions regarding the biological clock (e.g., “Some people say that women have a biological clock. Have you heard that before?”; “If yes, where and from whom?”; “What does it mean?”; “What do you think about it?”). (For the full list of questions, refer to the online supplement.) In some of the interviews, the biological clock emerged spontaneously as a means of addressing reproductive time. All women who mentioned the clock spontaneously were middle- and upper-class, had at least a college degree, and either became mothers after 30 or didn’t have children. When it did not, I asked women if they had heard of the biological clock and encouraged them to describe what they knew and thought about it. Some of participants, mainly older and lower-class women, claimed not having heard about the biological clock or not knowing what it was. The interviews were conducted in Spanish between September 2016 and May 2017 and took place mostly in the house or workplace of the interviewees.

Data Analysis

I recorded the interviews using digital recorders and transcribed them using a verbatim method. The audio files range from 39 to 130 min and have a mean duration of 72 min. The interviews were analysed vertically and horizontally through qualitative content analysis (Schreier 2014 ) and coded using ATLAS.ti 8. Using this software saved time in processing the information, simplified the administration of texts and codes, and provided rigour to the data analysis by facilitating consistency in the organisation, selection, and presentation of the empirical material. The coding process was dynamic and creative, moving “quickly back and forth between types of coding, using analytic techniques and procedures freely and in response to the analytic task” (Strauss and Corbin 1998 , p. 58). I used a flexible version of open, axial, and selective coding (Flick 2009 ) to categorise and organise the empirical data from the interviews. In analysing the interviews, I went through the transcriptions and coded words, sentences, and paragraphs through constructed and in vivo codes (Flick 2009 ).

Sampling decisions in the research process take place not only when selecting participants but also when analysing the empirical material and presenting the findings (Flick 2009 ). All the interviews were analysed for the present article, but the findings are based mainly on the narratives of younger middle- and upper-class women because the biological clock was more significant in their views and experiences of reproductive time. This focus is consistent with the findings of Friese et al. ( 2006 ) and Martin ( 2017 ), who suggest that the narrative of the biological clock is prevalent in the reproductive experiences of middle- and upper-class, educated, and professional women because they have a higher likelihood of delaying first childbearing and becoming mothers later in life.

The findings of the narrative of the biological clock and the particular ways in which it shapes women’s views and experiences of reproductive time are presented in this section. First, I examine how the narrative of the biological clock shapes the boundaries of reproductive time, the meanings of age and ageing, and when it is possible and desirable to have children. Second, I discuss how this narrative outlines time as linear, progressive, and irreversible, determining the passing of time and the future through risk and uncertainty. Third, I analyse how the narrative of the biological clock shapes a sense of agency in which women are expected to keep track of time, rationally assess the risks of ageing, and enact responsibility in timing childbearing. Finally, I explore how this narrative reinforces traditional gender norms and reproductive inequalities between men and women. To contextualise these findings, a detailed sociodemographic characterisation of the participants is presented in Table 1 and a detailed description of emergent themes regarding the narrative of the biological clock is presented in Table 2 . Where relevant, I report the number of children a quoted woman has in parentheses.

Age and the Boundaries of Reproductive Time

In the interviews I conducted, the women often make sense of the biological clock as a limited period of time that circumscribes their capacity to have children. For Adela (0), the biological clock refers to “that constrained capacity to have children only until a certain age.” For her, reproductive time is perceived and lived as constrained; it is finite and has an ending. As Friese et al. ( 2006 , p. 1551) have pointed out, for women, the biological clock is a “kind of deadline as they made decisions about childbearing.” This is the case of Paula (1). She believes that the biological clock determines when women can become mothers:

I think that it is linked, it is linked to motherhood. It is until you get the menopause and then you no longer have your period. Then you no longer ovulate, you have no more eggs, you no longer have the possibility of being a mother. (Paula)

The narratives of the biological clock that I analysed reveal the cultural belief that the limits of reproductive time are rooted in the fertility of the female body. For Laura (3), “the biological clock is the time of life in which women are fertile.” This narrative embodies reproductive time in female fertility and its limits are within the time period between menstruation and menopause, a notion that is reflected in other studies (Friese et al. 2006 ; Keeney and Vernik 2007 ; Moss and Maner 2014 ; Wagner et al. 2019 ). This embodiment of the limits of reproductive time in female fertility through the biological clock is also reflected in the narrative of Olivia (0):

From the time you get your period at 13 until you get the menopause at 42, 43, that is the time when you can have children. That would be the biological clock, the time in which you are fertile. (Olivia)

These embodied perceptions reveal that the narrative of the biological clock shapes reproductive time as constrained; having children is not possible “at any time.” From this perspective, reproductive time is a limited quantity; it “runs out” and “less” of it is left as time passes by. This constraint is reflected in the reproductive experience of Beatriz (2). In discussing her transition to motherhood, she argues: “I felt that I was running out of time. It was like ‘ten, nine, eight, seven, six,’ like that.” Similar studies have also noted the way in which the narrative of the biological clock time shapes reproductive experiences through the feeling of “running out of time” (Baldwin et al. 2019 ; Easton et al. 2010 ; Martin 2017 ; Wagner et al. 2019 ).

The narrative of the biological clock also standardises the duration and constraints of reproductive time. The clock provides this base for standardisation by imprinting a uniform, empty, neutral, and de-contextualised character to time (Adam 2006 ). With very few exceptions, my interviewees refer to 35 as the age in which female fertility declines and 40 as the age in which it approaches its end. Other studies have also suggested that these ages act as “magic numbers” that shape women’s understanding of the time limits to experience childbearing (Brown and Patrick 2018 ; Martin 2017 ).

These standardised age “deadlines” are revealed in the narrative of Amalia (2). In discussing the limits of reproductive time, she asserts, “because all the gynaecologists say that it has to be before 35: ‘If you don’t want to have problems to get pregnant, it has to be before 35.’ They all say the same.” These standardised age “deadlines” are also relevant in Victoria’s (2) understanding of the time limits of childbearing. As she argues: “it’s said that after 40 women reach menopause and can no longer have children.”

The narrative of Amalia (2) also reveals that the knowledge of many women about the dynamics and boundaries of reproductive time comes from medical experts. Medical knowledge and practice are known to constitute technologies of power (Foucault 2003 ; Rose 2007 ). As Foucault ( 2003 , p. 252) has argued: “medicine is a power-knowledge that can be applied to both the body and the population, both the organism and biological processes, and it will therefore have both disciplinary effects and regulatory effects.” By prescribing the limits of reproductive time through the standardised age “deadlines” of the biological clock, medical experts contribute to regulating when it is possible and desirable to have children.

Scholarly work on female fertility provides a counterpoint to the standardisation of time outlined by the narrative of the biological clock. Leader ( 2006 ) suggests that the relationship between fertility decline and age is variable because the decreased rate of the number and quality of eggs is uncertain and subject to individual variations. For Billari et al. ( 2011 ), the fact that fertility age limits are expressed through multiples of 5 and 10 is influenced by the research standards used in the field of medicine, human reproduction, and fertility. For them, the prevalence of these age limits is interesting given that “there is evidence that for obstetric outcomes, increasing age is a continuum rather than a threshold effect” (Billari et al. 2011 , p. 617). Drawing on these approaches, I argue that the narrative of the biological clock embodies reproductive time within female fertility and shapes its boundaries through standardised thresholds that are socially constructed but are nonetheless perceived as natural and experienced as universal and inherent to women.

Risk and the Dynamics of Reproductive Time

The narrative of the biological clock imprints a particular rhythm to the passing of time. In the interviews, the women perceive and experience it as something linear, progressive, and irreversible. For them, time is a force that cannot be stopped or reversed. As it moves forward, it reduces women’s capacity to experience childbearing. This understanding imprints a sense of scarcity and urgency to reproductive time. This linear character of female fertility is not “natural” but rather imposed upon individual experience through the narrative of the biological clock. As Adam ( 1990 ) has argued, the linearity and unidirectionality of time derives from the prevalence of the clock as a tool for social coordination.

When discussing when it is possible to have children, the women I interviewed often resorted to the metaphor of “missing the train.” Lahad ( 2012 ) also notes that this expression is used to refer to the passing of time in the context of reproduction. In discussing the time limits of childbearing, Adela (0) says: “it comes an age when everyone says ‘no, she missed the train, she can’t have children anymore.’” Similarly, Susana (2) recalls that people used to tell her that “she was going to miss the train” because she had her first child later than most women her age:

I had my first daughter when I was 29 years-old and it was not normal…Of the 29 [women in my class], I think that only two had [children] after me, and all the rest had [children] before. It was strange. It was like “you are going to miss the train.” (Susana)

The metaphor of “missing the train” reveals the understanding of reproductive time underlying the narrative of the biological clock. Time, symbolised by the train, is a force in motion, moving forward at a certain speed. If you are “late,” the train departs without you, and once it has departed, it is no longer possible to get on board. As Paloma (3) asserts, when it comes to the biological clock, “there is no turning back.”

The narrative of the biological clock outlines the passing of time as a pressing problem. Given that fertility decreases with age and that the uterus, eggs, and female body “grow old,” the passing of time is shaped as an urgency in which the future is determined by risk and uncertainty. Among others, Rose ( 2007 , p. 70) has outlined risk as central to biopolitics: “risk here denotes a family of ways of thinking and acting that involve calculations about probable futures in the present followed by interventions into the present in order to control that potential future.” In the case of reproductive time, biopolitics is enacted through the management of the risks of age and ageing.

Through the narrative of the biological clock, ageing is framed as a threat for reproduction. In discussing what the biological clock is and how it works, Loreto (4) argues, “it is that eggs age. Eggs age and that’s why eggs start having more problems to endure, because they are older, their cells are older. And that’s the biological clock; it’s a time of pregnancy.” Among others, Friese et al. ( 2006 ) noted that this narrative of “old eggs” works as a marker of ageing and the risk of infertility. For my interviewees, experiencing ageing through the narrative of the biological clock is often intertwined with feelings of fear and anxiety. Referring to her reproductive experience, Dominga (1) recalls feeling afraid of ageing and burden of age:

The years start burdening you. You have a biological clock and you want to become a mother, [and it’s difficult] if you are not together with a partner or with someone that you can see as the father of your children. I think that issue for women that are maybe my age must be a very heavy burden. (Dominga)

These views reveal the particular ways in which the narrative of the biological clock shapes women’s lived experience of reproductive time. Similar subjective experiences of the biological clock have been documented elsewhere. Brown and Patrick ( 2018 , p. 967) describe how their interviewees “panicked” when they became aware of their biological clock and the infertility risks it posed. Martin ( 2017 , p. 95) also describes the way in which her interviewees felt “pressured” and “anxious” by the biological clock. Overall, these views demonstrate the extent to which the narrative of the biological clock produces and reproduces “cultural anxieties about aging, illness, reproduction, and risk” (Martin 2010 , p. 527).

As women approach what they understand to be the end of their childbearing capacity, time seems to go by faster. Among others, Brown and Patrick ( 2018 ) and Friese et al. ( 2006 ) also suggest that passing of time seems to “speed up” for women approaching the “limit” of their fertility. Among my interviewees, this acceleration is intertwined with the “ticking” function of the biological clock, which operates as a constant reminder of the fast-approaching finitude of reproductive time. This was the experience of Beatriz (2). As she argues, “I knew that I was running out of [time]. It is like a clock against you “ten, nine, eight.” The pressure of the years.” These findings are consistent with those of a study conducted by Hoffnung and Williams ( 2013 , p. 332), who argue that the biological clock starts “ticking loudly” when women get beyond their late thirties. I follow Amir ( 2006 ) in arguing that the biological clock functions as a mechanism of social regulation by sensitising women to the passing of time and its implications for their childbearing capacity.

Agency and the Making of Reproductive Time

The narrative of the biological clock shapes a particular sense of the female self. Women are expected to keep track of time, rationally assess the risks of ageing, and take action to allocate childbearing at the “right time.” Within this framework, fertility and infertility are outlined as a matter of choice and individual responsibility. This emphasis on freedom, choice, and responsibility is consistent with neoliberal governmentality (Foucault 2008 ). In exploring the intersection between neoliberal governmentality and biopolitics in the field of health, Rose ( 2007 , p. 154) argues that individuals today are required “to undergo perpetual assessment” that involves monitoring health and managing risks. Similarly, Clarke et al. ( 2003 ) assert that optimising one’s health is becoming an individual’s moral responsibility to be fulfilled through access to knowledge, self-surveillance, prevention, and risk assessment.

In the interviews that I conducted, enacting reproduction through the narrative of the biological clock often involves being aware of time and its passing as well as of the risks and constraints that different ages pose to female fertility. As Flora (5) argues: “the woman that wants to be a mother, that’s considering motherhood, has to take a look at the clock.” This awareness often involves engaging in a mathematical calculation through which women assess how much time they have left to have children. For Susana (2), this calculation works in the following way:

At 35 you start calculating, ok, and if I have been [taking the pill] for all these years? And if I don’t get pregnant right away? And if I’ve been taking the pill for many years then I wouldn’t have [a child] immediately? And then if I want at 35, but it doesn’t really work until I am 36, 37, 38? Ok, at 38 I have one, and if I want to have another? Then, ok, at 38, at 40. Will I be able to at 40? (Susana)

“Tracking” and “calculating” are strategies that women often use to manage their biological clock. This expected agency over reproductive time also involves planning and anticipating the future. Other studies have also documented the ways in which contemporary reproductive time is shaped by the expectation of predicting and anticipating the future (Martin 2010 ; Myers 2014 ). Among my interviewees, this calculation often involved knowing and shaping your future fertility in the present. This practice of knowing oneself acts as a technology of power that regularises individual behaviour (Foucault 1986 ). My interviewees narrate that deciding if and when to have children in the years to come often involves knowing and anticipating your future self. As Matilde (0) argues:

It’s a decision that you make at a certain moment, and, as with everything, you don’t know if in 10 more years you are going to regret it. And probably in 10 more years you are not going to be able to [have children]. If you are, I don’t know, 36, and you say “No, I didn’t have, I’m not going to continue, I’m not going to try.” And then, at 46, 50, you say “Oh, I should’ve done it.” (Matilde)

Through the narrative of the biological clock, women are constrained to think about and decide when to have children and to be responsible for the timing of childbearing. Consequently, “waiting” and “letting time pass” are shaped as irresponsible behaviour. For Olivia (0), childbearing “over 40 is irresponsible because it’s something dangerous for you, and it’s something dangerous for a baby. That’s why I think it’s irresponsible.” As Lahad ( 2012 ) has pointed out, in the narrative of the biological clock, waiting takes on a negative meaning because it is associated with fears and anxieties about the future. From a biopolitical perspective, letting time pass represents a subversion of the regulation of reproductive time and a neglect of the mandate of caring for oneself (Foucault 1986 ).

Gender and the Inequalities of Reproductive Time

In the interviews I conducted, it is often mentioned that the biological clock functions as a mandate that “dictates” when women should have children and become mothers. For Consuelo (0), “your biological clock marks the time to have babies,” and for Blanca (0), “the biological clock dictates the ‘now’ to become a mother.” Like Consuelo and Blanca, many of the women in my study believed that beyond the boundaries of the biological clock it is not possible for women to become mothers. This is the view of Elisa (0): “Us women have a certain amount of eggs and it comes a certain age in which you get the menopause and you can no longer become a mother, obviously, because you are no longer ovulating.”

The interviews I conducted suggest that the narrative of the biological clock regulates reproduction by reinforcing prevalent norms regarding womanhood and motherhood. By stressing that the biological clock is inherent to female nature and dictates when women should have children, womanhood and motherhood become conflated by naturalising childbearing in the female life course. Furthermore, by stressing that the female reproductive system determines women’s capacity to have children, motherhood and biological reproduction also become conflated by excluding the possibility of enacting childbearing through adoption and assisted reproductive technologies. Overall, this analysis suggests that the narrative of the biological regulates reproduction in a way that reinforces traditional gender roles and structures women’s lives according to them.

At the same time, the narrative of the biological clock creates a hierarchy between biological and social fertility (Martin 2017 ) by subsuming the role of motherhood to the childbearing capacity of the female body. In doing so, it outlines a disjuncture between the boundaries of female fertility and the fact that women may want or feel constrained to achieve milestones like having a partner, a good job, or financial security before having children (Brown and Patrick 2018 ; Lavender et al. 2015 ; Yopo Díaz 2020 ). This conclusion is consistent with the findings of Friese et al. ( 2006 , p. 1551) who argue that for women, the public domain and paid labour are outlined as an “interference” to reproduction. In my interviews, women often mention that postponing motherhood is problematic because then there is not enough time left to have children. This is the view of Rafaela (3):

So “no, not yet, not yet,” “I want to do this first,” “I want to finish my degree first.” And time passes by because when they finish their degree they want to work. Then they find a good job and “if I get pregnant, I might lose my job and it’s going to be difficult to work.” And so, they postpone it. “Ok, no, but next year.” And it turns out that years go by, and then it’s much more difficult…What is problematic? Well, then you want to have a child and it’s a lot more difficult to get pregnant because you postponed it so much. (Rafaela)

The narrative of the biological clock also creates a “natural” distinction between men and women regarding reproductive time. Despite evidence which demonstrates that male fertility also decreases with age (Thacker 2004 ), the narrative of the biological clock outlines that whereas women’s capacity to become mothers is time-constrained, men’s capacity to become fathers is free from those constraints. In discussing the limits of reproductive time, the women I interviewed often refer to this gender inequality. For Loreto (4), “the man can become a father for many more years, but the woman is limited. I mean, we have a different nature.” Similarly, Elena (3) argues:

Then it comes the issue that you are going to run out of time to have children. And then there is a moment in which you say “oh, I’m running out of time.” That doesn’t happen to men, they never run out of time. (Elena)

This perceived difference between men and women regarding reproductive time not only reproduces the belief that there is a “natural” difference between women and men, but also outlines gender inequalities that shape women’s reproductive experiences. As Amir ( 2006 , p. 52) has noted: “the biological clock constructs a crude gender differentiation between the female bodies, to which it applies, and the male bodies, which are beyond its reach.” Other studies also reveal that this gendered construction of reproductive time shapes women’s understanding of childbearing (Beaujouan and Solaz 2013 ; Billari et al. 2011 ; Brown and Patrick 2018 ) and outlines inequalities between men and women, for example, regarding freedom and choice in couple formation (Pickens and Braun 2018 ). The narrative of the biological clock produces and reproduces gender inequalities regarding reproductive time that are not only perceived as natural but that also shape asymmetries regarding men and women’s positions to negotiate the timing of reproduction against other family, education, and work milestones.

In the present article I have aimed to advance current knowledge on the intersection of time, reproduction, and biopolitics by focusing on the narrative of the biological clock and the particular ways in which it shapes women’s views and experiences of reproductive time. The findings presented in my article reveal that the biological clock regulates reproductive time by shaping the boundaries and dynamics of female fertility through the clock. By determining reproductive time as quantitative, standardised, linear, and irreversible and by outlining the passing of time through pressure, risk, and burden, the narrative of the biological clock determines when it is possible and desirable to have children and regulates reproduction, gender, and the female life course.

Although the narrative of the biological clock has become prevalent to address the problem of fertility decline, the risks of ageing in the context of late motherhood, and the use of assisted reproductive technologies, too few studies engage in an analysis of the biological clock itself. This oversight means that the social and subjective implications of making sense of the boundaries and dynamics of female fertility through clock time remain largely unexplored. By drawing on theoretical insights on social time (Adam 1995 , 2006 ; Zerubavel 1985 ) and biopolitics (Foucault 1978 , 2003 ; Rose 2007 ), I have stressed time as an essential dimension of contemporary biopolitics of reproduction and shed light on the particular ways in which the narrative of the biological clock regulates reproduction, gender, and the female life course.

Clock time is a social construction but it has come to be understood as a natural and inevitable feature of social life (Zerubavel 1985 ). This understanding becomes evident in the case of the biological clock. Although the female body, fertility, and reproductive cycle have rhythms and temporal dynamics of their own (Adam 1990 , 2006 ), it has become common to address them through the abstract, standardised, and neutral character of clock time. The narrative of the biological clock shapes female fertility according to the clock by embodying it in the nature of the female body. In doing so, this narrative reifies particular ways of understanding and experiencing reproductive time that are socially constructed but are often perceived as given and unalterable. Lived accounts of reproductive time analysed in the present article reveal the extended belief that women have a biological clock that determines when they are capable to have children according to chronological age as well as the fact that their reproductive time is limited, decreases with ageing, and ultimately runs out.

Time is known to be a symbolic means of social control (Adam 1990 , 2006 ; Elias 1989 ; Zerubavel 1985 ) and normative structuration of life (Elder Jr 1975 ; Settersten Jr 2003 ). By outlining norms regarding when it is possible and desirable for women to have children, the narrative of the biological clock regulates not only reproductive time but also gender roles and the female life course. It is by conflating womanhood, motherhood, and biological childbearing that the biological clock regulates female fertility and controls women insofar as they are living beings. This perspective demonstrates the particular ways in which heteronormativity, gender, and family formation are regulated through ideological narrations of time (Amir 2006 ).

By providing an empirical and interpretive analysis of the narrative of the biological clock and the ways in which it shapes women’s reproductive experiences, my article contributes to advance the understanding of contemporary reproductive time and unravel the social and subjective implications of understanding it through the biological clock. In doing so, my article provides a novel perspective to address prevalent reproductive and fertility trends as well as to make sense of the lived experiences of women who delay childbearing and become mothers at an older age.

Limitations and Future Research Directions

There are some limitations to the findings presented in the present article. I used a qualitative research design based on a thick description of a small number of cases that enabled gaining an in-depth understanding of the particular ways in which the narrative of the biological clock shapes women’s views and experiences of reproductive time. However, the breadth and scope of my study means that attention must be paid when generalising these findings to make sense of views and experiences of reproductive time of Chilean women and women elsewhere. Furthermore, the narrative of the biological clock outlined in my article seems to have extensive similarities with that documented by studies in North America and Western Europe. Future research should aim to continue exploring the universal character of the narrative of the biological clock as well as the particular ways in which the relationship between clock time and female fertility is enacted in specific cultural and social settings.

By focusing on the narrative of the biological clock, the present article has made a unique contribution to understanding prevalent social constructions of reproductive time and how they shape women’s choices and practices regarding when to have children. However, this focus meant that the complexities of the relationship between agency and reproductive time were only partially addressed. Future studies should further analyse the relationship between agency and the biological clock as well as describe the strategies through which women manage and negotiate it in timing childbearing, for example, through assisted reproductive technologies (Baldwin et al. 2019 ; Brown and Patrick 2018 ; Martin 2010 ). This focus on the narrative of the biological clock also meant emphasising clock time over other ontologies of time involved in women’s childbearing experiences. Future studies should also identify alternative time ontologies that shape reproductive time, like God, nature, and technology (Roberts 2012 ; Yopo Díaz 2020 ), and outline the ways in which they conflict and coincide with the normative construction of the timing of childbearing as outlined by the narrative of the biological clock.

Practice Implications

Social constructions of reproductive time are important because they shape the particular ways in which women engage with childbearing and structure life course trajectories related to partnership, family formation, education, and labour. In recent decades, the biological clock has become a prevalent narrative to address the rhythms and boundaries of female fertility. Increasingly, healthcare professionals, policymakers, and the media are using the notion of the biological clock to refer to reproductive time in the context of late childbearing, age-related infertility, and the use of assisted reproductive technologies. The present article stresses the importance of critically addressing the narrative of the biological clock and its implications for women’s views and experiences of reproductive time. Scholars and professionals working on fertility and reproduction should examine their preconceptions of reproductive time and become aware that naturalising clock time and reproducing it as a given feature of female fertility contributes to reinforce normative regulations of reproduction, gender, and the female life course. Critically reflecting upon the narrative of the biological clock is an invitation to construct comprehensive approaches to reproductive time that encompass both biological and social fertility; disentangle prevalent conflations among womanhood, motherhood, and biological childbearing; and address prevalent gender inequalities regarding reproductive time. This critical reflection is also an invitation to shift the focus of reproductive time from individual choice to the social conditions that enable and constrain those choices. Such an approach represents an opportunity to craft public initiatives that provide a comprehensive socialisation of reproductive agency and address the social determinants that shape women’s choices toward delaying childbearing.

In the present article I have argued that the biological clock regulates reproductive time by shaping the boundaries and dynamics of female fertility through clock time. Drawing on the analysis of 40 life story interviews of women from Santiago de Chile, it demonstrates that the narrative of the biological clock shapes reproductive time as quantitative, standardised, linear, and irreversible, as well as outlines the passing of time through pressure, risk, and burden. Given that fertility and infertility are considered as a matter of choice and individual responsibility, women are expected to keep track of time, assess reproductive risks, calculate how much time is left for childbearing, and anticipate the future. By outlining a reproductive imperative that conflates womanhood and motherhood, as well as hierarchises biological fertility over social fertility, the narrative of the biological clock naturalises childbearing, reinforces traditional gender norms, and structures the female life course.

Aburto Prieto, F. (2016). Congelando el reloj biológico [freezing the biological clock]. La Segunda . https://digital.lasegunda.com/2016/07/09/A/V02VD0CL#zoom=page-width .

Adam, B. (1990). Time and social theory . Cambridge and Oxford: Polity Press.

Google Scholar  

Adam, B. (1995). Timewatch. The social analysis of time . Cambridge: Polity Press.

Adam, B. (2006). Time . Cambridge and Malden: Polity Press.

Amir, M. (2006). Bio-temporality and social regulation: The emergence of the biological block. Polygraph: An International Journal of Culture and Politics, 18 , 47–72 https://pure.qub.ac.uk/en/publications/bio-temporality-and-social-regulation-the-emergence-of-the-biolog .

Atkinson, R. (2002). The life story interview. In J. F. Gubrium & J. A. Holstein (Eds.), Handbook of interview research. Context and method (pp. 121–140). Thousand Oaks: Sage Publications.

Baldwin, K., Culley, L., Hudson, N., & Mitchell, H. (2019). Running out of time: Exploring women’s motivations for social egg freezing. Journal of Psychosomatic Obstetrics and Gynecology, 40 (2), 166–173. https://doi.org/10.1080/0167482X.2018.1460352 .

Article   PubMed   Google Scholar  

Bartlett, A. (2012). Babydaze: Maternal time. Time & Society, 19 (1), 120–132. https://doi.org/10.1177/0961463X09354419 .

Article   Google Scholar  

Beaujouan, E., & Solaz, A. (2013). Racing against the biological clock? Childbearing and sterility among men and women in second unions in France. European Journal of Population, 29 , 39–67. https://doi.org/10.1007/s10680-012-9271-4 .

Beets, G., Schippers, J., & te Velde, E. R. (2011). Introduction. In G. Beets, J. Schippers, & E. R. te Velde (Eds.), The future of motherhood in western societies. Late fertility and its consequences (pp. 1–4). New York: Springer.

Chapter   Google Scholar  

Billari, F. C., Goisis, A., Liefbroer, A. C., Settersten, R. A., Aassve, A., Hagestad, G., … Spéder, Z. (2011). Social age deadlines for the childbearing of women and men. Human Reproduction, 26 (3), 616–622. https://doi.org/10.1093/humrep/deq360 .

Brown, E., & Patrick, M. (2018). Time, anticipation and the life course: Egg freezing as temporality disentangling romance and reproduction. American Sociological Review, 83 (5), 959–982. https://doi.org/10.1177/0003122418796807 .

Bryson, V. (2007). Gender and the politics of time. Feminist theory and contemporary debates . Bristol: The Policy Press.

Book   Google Scholar  

Cerda, R. (2010). Familia y fecundidad [Family and fecundity]. In Encuesta Nacional Bicentenario Universidad Católica - Adimark 2009 . Una mirada al alma de Chile . Santiago de Chile: PUC.

Chen, W., & Landau, R. (2015). First childbirth and motherhood at post natural fertile age: A persistent and intergenerational experience of personal and social anomaly? Social Work in Health Care, 54 (1), 16–32. https://doi.org/10.1080/00981389.2014.966880 .

Clarke, A. E., Shim, J. K., Mamo, L., Fosket, J. R., & Fishman, J. R. (2003). Biomedicalization: Technoscientific transformations of health, illness, and U.S. biomedicine. American Sociological Review, 68 (2), 161–194. https://doi.org/10.2307/1519765 .

Clínica Alemana. (2012). Solteras después de los 30: ¿cuál es la razón? [Singles after 30: what is the reason?] Clínica Alemana . https://www.clinicaalemana.cl/articulos/detalle/2012/solteras-despues-de-los-30-cual-es-la-razon .

Clínica Las Condes. (2017). Fertilidad: Aún falta conciencia sobre el reloj biológico de la mujer [fertility: There is still lack of awareness about the biological clock of women]. Clínica Las Condes . https://www.clinicalascondes.cl/Comunidad-Mujer-Y-Mama/blog-cmm/blog-listado/planificacion/Fertilidad-falta-conciencia-sobre-reloj-biologico .

Cooke, A., Mills, T. A., & Lavender, T. (2010). “Informed and uninformed decision making” – Women’s reasoning, experiences and perceptions with regard to advanced maternal age and delaying childbearing: A meta-synthesis. International Journal of Nursing Studies, 47 , 1371–1329. https://doi.org/10.1016/j.ijnurstu.2011.07.013 .

Daly, I., & Bewley, S. (2013). Reproductive ageing and conflicting clocks: King Midas’ touch. Reproductive Biomedicine Online, 27 , 722–732. https://doi.org/10.1016/j.rbmo.2013.09.012 .

Denzin, N. K., & Lincoln, Y. S. (2005). The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), The sage handbook of qualitative research (pp. 1–32). Thousand Oaks: Sage Publications.

Easton, J. A., Confer, J. C., Goetz, C. D., & Buss, D. M. (2010). Reproduction expediting: Sexual motivations, fantasies, and the ticking biological clock. Personality and Individual Preferences, 49 , 516–520. https://doi.org/10.1016/j.paid.2010.05.018 .

El Mercurio. (2017). Reloj biológico y maternidad: El aumento en los problemas de fertilidad según la edad de la mujer [Biological clock and motherhood: Increase in fertility problems according to women’s age]. El Mercurio . https://www.emol.com/noticias/Tendencias/2017/11/14/883168/Reloj-biologico-y-maternidad-El-aumento-en-los-problemas-de-fertilidad-segun-la-edad-de-la-mujer.html .

El Mostrador. (2017). Rejuvenecimiento ovárico: la ciencia que retardaría el reloj biológico [ovarian rejuvenation: The science that would delay the biological clock]. El Mostrador . https://www.elmostrador.cl/braga/2017/05/30/rejuvenecimiento-ovarico-la-ciencia-que-retardaria-el-reloj-biologico/ .

Elder Jr., G. H. (1975). Age differentiation and the life course. Annual Review of Sociology, 1 , 165–190.

Elias, N. (1989). Sobre el tiempo [Time: An essay] . México, Madrid and Buenos Aires: Fondo de Cultura Económica.

Fabe, M., & Wikler, N. J. (1979). Up against the clock: Career women speak out on the new choice of motherhood . New York: Random House.

Flick, U. (2009). An introduction to qualitative research (4th ed.). Thousand Oaks: Sage Publications.

Foucault, M. (1986). The history of sexuality (Vol. 3). New York: Pantheon Books.

Foucault, M. (1978). The history of sexuality (Vol. 1). New York: Pantheon Books.

Foucault, M. (2003). Society must be defended. Lectures at the Collège de France, 1975–76 . New York: Picador.

Foucault, M. (2008). The birth of biopolitics. Lectures at the Collège de France, 1978–79 . New York: Palgrave Macmillan.

Friese, C., Becker, G., & Nachtigall, R. D. (2006). Rethinking the biological clock: Eleventh-hour moms, miracle moms and meaning of age-related infertility. Social Science and Medicine, 63 , 1550–1560. https://doi.org/10.1016/j.socscimed.2006.03.034 .

Fuentes, A., Jesam, C., Devoto, L., Angarita, B., Galleguillos, A., Torres, A., … Mackenna, A. (2010). Postergación de la maternidad en Chile: Una realidad oculta [postponement of motherhood in Chile: A hidden reality]. Revista Médica de Chile, 138 , 1240–1245. https://doi.org/10.4067/S0034-98872010001100005 .

Geertz, C. (1973). The interpretation of culture . London: Hutchinson.

Herrera, F. (2011). The building of parental bonds: Adoption and assisted reproduction in Chile. Revista de cercetare si interventie sociala, 32 , 25–43.

Herrera, F., Teitelbom, B., Russo, M., Salas, S. P., & Zegers Hochschild, F. (2013). Encuesta de opinión pública sobre reproducción humana y usos de tecnología de reproducción asistida en habitantes de Santiago, Chile [Public opinion survey on human reproduction and the uses of assisted reproductive technology among inhabitants of Santiago, Chile]. Revista Médica de Chile, 141 , 853–860. https://doi.org/10.4067/S0034-98872013000700004 .

Hirane, J. (2017). ¿Debo congelar óvulos? Revista Paula [Should I freeze eggs?] . Retrieved from https://www.paula.cl/reportajes-y-entrevistas/debo-congelar-ovulos/ .

Hoffnung, M., & Williams, M. A. (2013). Balancing act: Career and family during college-educated women’s 30s. Sex Roles, 68 , 321–334. https://doi.org/10.1007/s11199-012-0248-x .

Hughes, C. (2002). Key concepts in feminist theory and research . London, Thousand Oaks: Sage Publications.

Instituto Nacional de Estadísticas. (2017). Anuario de estadísticas vitales 2017 [yearbook of vital statistics 2017]. Santiago: INE.

Keeney, R. L., & Vernik, D. A. (2007). Analysis of the biological clock decision. Decision Analysis, 4 (3), 114–135. https://doi.org/10.1287/deca.1070.0094 .

Lahad, K. (2012). Singlehood, waiting, and the sociology of time. Sociological Forum, 27 (1), 163–186. https://doi.org/10.1111/j.1573-7861.2011.01306.x .

Larrañaga, O. (2007). Participación laboral de la mujer en Chile: 1958–2003[Labor participation of women in Chile: 1958–2003] . Santiago de Chile: Departmento de Economía, Universidad de Chile.

Lavender, T., Logan, J., Cooke, A., Lavender, R., & Mills, T. A. (2015). “Nature makes you blind to the risks”: An exploration of women’s’ views surrounding decisions on the timing of childbearing in contemporary society. Sexual & Reproductive Health, 6 , 157–163. https://doi.org/10.1016/j.srhc.2015.04.006 .

Leader, A. (2006). Pregnancy and motherhood: The biological clock. Sexuality, Reproduction & Menopause, 4 (1), 3–6. https://doi.org/10.1016/j.sram.2006.03.003 .

Leccardi, C. (1996). Rethinking social time: Feminist perspectives. Time & Society, 5 (2), 169–186.

Macintosh, K. L. (2015). Teaching about the biological clock: Age-related fertility decline and sex education. UCLA Women’s Law Journal, 22 (1), 1–37 https://escholarship.org/uc/item/5b1147x9 .

Manzur, A. (2014). Fácil, de baja complejidad y con buenos resultados [Easy, low complexity and with good results]. Red de Salud UC CHRISTUS . https://www.ucchristus.cl/blog-salud-uc/articulos/2014/facil-de-baja-complejidad-y-con-buenos-resultados .

Martin, L. J. (2010). Anticipating infertility. Egg freezing, genetic preservation, and risk. Gender & Society, 24 (4), 526–545. https://doi.org/10.1177/0891243210377172 .

Martin, L. J. (2017). Pushing for the perfect time: Social and biological fertility. Women’s Studies International Forum, 62 , 91–98. https://doi.org/10.1016/j.wsif.2017.04.004 .

Martineau, J. (2015). Time, capitalism and alienation . A socio-historical inquiry into the making of modern time . Boston: Brill.

McAlister, J. F. (2008). Lives of the mind/body: Alarming notes on the tenure and biological clocks. Women’s Studies in Communication, 31 (2), 218–225. https://doi.org/10.1080/07491409.2008.10162536 .

Meier Furst, R. (2018). Vitrificación de óvulos, la nueva revolución femenina [Egg vitrification, the new female revolution]. Clínica IVI Centro de Salud Reproductiva . https://ivinet.cl/notas/la-nueva-revolucion-femenina/ .

Miller, T. (2007). Is this what motherhood is all about? Weaving experiences and discourses through transition to first-time motherhood. Gender & Society, 21 (3), 337–358. https://doi.org/10.1177/0891243207300561 .

Mohapatra, S. (2014). Using egg freezing to extend the biological clock: Fertility insurance or false hope? Harvard Law and Policy Review, 8 (2), 381–411.

Montecino, S. (2018). Madres y huachos. Alegorías del mestizaje chileno [mothers and huachos. Allegories of Chilean mestizaje] (8th ed.). Santiago de Chile: Catalonia.

Moss, J. H., & Maner, J. K. (2014). The clock is ticking. The sound of a ticking clock speeds up women’s attitudes on reproductive timing. Human Nature, 25 , 328–341. https://doi.org/10.1007/s12110-014-9210-7 .

Myers, C. E. C. (2014). Colonizing the (reproductive) future: The discursive construction of ARTS as technologies of self. Frontiers: A Journal of Women Studies, 35 (1), 73–106. https://doi.org/10.5250/fronjwomestud.35.1.0073 .

Perrier, M. (2013). No right time: The significance of reproductive timing for younger and older mothers’ moralities. The Sociological Review, 61 , 69–87. https://doi.org/10.1111/1467-954X.12005 .

Pickens, C., & Braun, V. (2018). “Stroppy bitches who just need to learn how to settle”? Young single women and norms of femininity and heterosexuality. Sex Roles, 79 , 431–448. https://doi.org/10.1007/s11199-017-0881-5 .

Pommer, R. (2015). ¿Cuánto saben nuestras mujeres sobre su fertilidad? [how much do our women know about their fertility?]. El Mercurio. https://www.emol.com/noticias/Tendencias/2015/05/17/741923/Cuanto-saben-nuestras-mujeres-sobre-su-fertilidad.html .

Roberts, E. F. S. (2012). God’s laboratory. Assisted reproduction in the Andes . Los Angeles: University of California Press.

Rose, N. (2001). The politics of life itself. Theory, Culture & Society , 18 (6), 1-30. 10.1177%2F02632760122052020.

Rose, N. (2007). The politics of life itself . Biomedicine, power and subjectivity in the twenty-first century. Oxford: Princeton University Press.

Schreier, M. (2014). Qualitative content analysis. In U. Flick (Ed.), The sage handbook of qualitative data analysis (pp. 170–183). London: Sage Publications.

Settersten Jr., R. A. (2003). Age structuring and the rhythm of the life course. In J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the life course (pp. 81–98). New York: Kluwer Academic and Plenum Publishers.

Settersten, R. A., & Hägestad, G. O. (1996). What’s the latest? Cultural age deadlines for family transitions. The Gerontologist, 36 (2), 178–188. https://doi.org/10.1093/geront/36.2.178 .

Settersten Jr., R. A., & Mayer, K. U. (1997). The measurement of age, age structuring and the life course. Annual Review of Sociology, 23 , 233–261. https://doi.org/10.1146/annurev.soc.23.1.233 .

Sevón, E. (2005). Timing motherhood: Experiencing and narrating the choice to become a mother. Feminism & Psychology , 15 (4), 461–482. 10.1177%2F0959-353505057619.

Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory . Thousand Oaks: Sage Publications.

Thacker, P. D. (2004). Biological clock ticks for men, too. Journal of the American Medical Association, 291 (14), 1683–1685. https://doi.org/10.1001/jama.291.14.1683 .

The World Bank. (2019). The little data book on gender 2019. Washington, D.C: The World Bank.

Valdés, X. (2007). Notas sobre la metamorfosis de la familia en Chile [notes on the metamorphosis of the family in Chile] . Santiago de Chile: CEPAL and UNFPA.

Velarde, M. (2016). Reproducción asistida [assisted reproduction]. In C. Dides & C. Fernández (Eds.), Primer informe salud sexual, salud reproductiva y derechos humanos en Chile (pp. 99–110). Santiago de Chile: Miles Chile.

Waldby, C., & Cooper, M. (2008). The biopolitics of reproduction. Australian Feminist Studies, 23 (55), 57–73. https://doi.org/10.1080/08164640701816223 .

Wagner, M., Huinink, J., & Liefbroer, A. C. (2019). Running out of time? Understanding the consequences of the biological clock for the dynamics of fertility intentions and union formation. Demographic Research, 40 , 1–26. https://doi.org/10.4054/DemRes.2019.40.1 .

Whittaker, A. (2015). Technology, biopolitics, rationalities and choices: Recent studies of reproduction. Medical Anthropology, 34 (3), 259–273. https://doi.org/10.1080/01459740.2015.1019066 .

Wyndham, N., Marin Figueria, P. G., & Patrizio, P. (2012). A persistent misperception: Assisted reproductive technology can reverse the “aged biological clock”. Fertility and Sterility, 97 (5), 1044–1047. https://doi.org/10.1016/j.fertnstert.2012.02.015 .

Yopo Díaz, M. (2018a). Enacting motherhood: Time and social change in Chile. Journal of Gender Studies, 27 (4), 411–427. https://doi.org/10.1080/09589236.2016.1223619 .

Yopo Díaz, M. (2018b). Revisiting individualization: The transition to marriage and motherhood in Chile. Current Sociology, 66 (5), 748–768. https://doi.org/10.1177/0011392117737819 .

Yopo Díaz, M. (2020). Making the right time: The transition to motherhood in contemporary Chile (doctoral thesis, University of Cambridge, Cambridge, United Kingdom). https://doi.org/10.17863/CAM.50351

Zerubavel, E. (1985). Hidden rhythms. Schedules and calendars in social life . Los Angeles: University of California Press.

Download references

Acknowledgements

I would like to thank Hande Güzel for her insightful comments on an earlier version of this article. Some of the workshop ‘Pensando la maternidad: experiencias y desafíos en Chile, España y Reino Unido’ (April 2017) organised by ICSO-UDP and the Institutskolloquium (SS/2019) of the Max-Weber-Institut für Soziologie, University of Heidelberg.

This study received financial support from the National Commission for Scientific and Technological Research (Chile) and the Cambridge Commonwealth, European and International Trust, University of Cambridge (UK). Support for editing this article comes from the Millenium Nucleus for the Study of the Life Course and Vulnerability (MLIV) in Chile.

Author information

Authors and affiliations.

Department of Sociology, University of Cambridge, Cambridge, UK

Martina Yopo Díaz

Max-Weber-Institut für Soziologie, University of Heidelberg, Bergheimer Str. 58, 69115, Heidelberg, Germany

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Martina Yopo Díaz .

Ethics declarations

Conflict of interest.

The author declares no conflicts of interest.

Ethical Approval

This study received clearance from the Ethics Committee of the Department of Sociology, University of Cambridge.

Informed Consent

All the participants voluntarily agreed to participate in this study and signed informed consents. No animals were involved in this study.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

(DOCX 58 kb)

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Yopo Díaz, M. The Biological Clock: Age, Risk, and the Biopolitics of Reproductive Time. Sex Roles 84 , 765–778 (2021). https://doi.org/10.1007/s11199-020-01198-y

Download citation

Accepted : 07 October 2020

Published : 31 October 2020

Issue Date : June 2021

DOI : https://doi.org/10.1007/s11199-020-01198-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Biological clock
• Reproduction
• Biopolitics
• Find a journal
• Publish with us
• Track your research

Frontiers for Young Minds

• Download PDF

Daily Rhythms of the Body and the Biological Clock

Earth’s rotation creates a cycle of day and night, which is observed as changes in light levels and temperature. During evolution, plants and animals adapted to these cycles, developing daily cycles of physical and behavioral processes that are driven by a central biological clock, also known as the circadian clock. Even in the absence of changes in light between day and night, the biological clock creates cycles called circadian rhythms. The nervous system transfers information about the external light level to the biological clock in the brain, which matches the clock’s cycle to the external environment. The biological clock prepares the body for environmental changes. The modern world has created disruptions in the circadian clock’s timing, because of electrical lighting, flights to other time zones, and work during the night. The study of chronobiology studies the mechanisms of the biological clock and the clock’s influence on human health.

Introduction

Repeating processes, such as waves moving up and down, create a rhythm characterized by a consistent cycle. The earth’s rotation creates the phenomenon of day and night in a 24-h cycle. This cycle results in environmental changes during the day, such as higher light levels and warmer temperatures. So, it is not surprising that, during evolution, the daily cycle was a significant factor to which animals and plants adapted. Many processes in our bodies show daily fluctuations, including our body temperature, blood pressure, and hormone levels. For example, the secretion of a hormone called melatonin (the sleep hormone) reaches its peak late at night and decreases in the morning, whereas, in the morning, the hormone cortisol reaches its peak. The concentrations of many other proteins in our bodies also show daily fluctuations. We are also all aware of daily cycles in our emotional and behavioral processes, such as our alertness and our ability to concentrate or learn, and the cycle of wakefulness and activity during the day and sleep during the night.

The Biological Clock Creates Daily Rhythms

What drives the daily rhythms in our bodies? One possibility is that the body responds to cyclical changes in the environment. An increase in light levels with sunrise makes us wake up, and the darkness at night results in an increase in melatonin, which promotes sleep. An additional explanation is that there is an internal mechanism in the body, creating the daily rhythm independently of environmental changes. How can we differentiate between these two mechanisms? In 1962, a French scientist named Michel Siffre conducted an experiment on himself. He lived for 2 months in a cave in the Alps, without exposure to daylight, with constant temperature, and without knowing if it was day or night. He lived in a tent with an artificial lighting, connected to machines that tracked his body’s activity. The results of the experiment ( Figure 1 ) showed a daily, cyclical, organized activity pattern, where the significant difference was that each day he woke up about half an hour later. In fact, the length of the “day” he lived by in the cave was 24.5 h. The main conclusion was that the body has an internal clock, which is independent of the environment’s fluctuations. This clock creates daily rhythms, with a cycle slightly longer than 24 h, even in the absence of environmental cues. In normal conditions, when we are exposed to sunlight, our clock cycle shortens to exactly 24 h.

• Figure 1 - Siffre’s cave experiment.
• The daily activity from the beginning of the experiment (top) to the end (bottom). The thick lines symbolize the time Siffre spent asleep. The first 10 days (1–10) and last 10 days (35–45) were spent in natural conditions outside the cave, and therefore his activity was synchronized to a 24-h clock. While in the cave, his activity reflects the cycle of the circadian clock, which is about 24.5 h, which is why his sleeping time can be seen to shift to the right.

Sifrre’s experiment earned him the nickname “the caveman,” and received much publicity. But scientists who conducted similar experiments in animals and plants already knew about the existence of a biological clock and called it the circadian clock . In Greek, “circa” means “about” and “diem” means “day.” The circadian clock exists in almost all animals on earth—in mammals, insects, plants, fungi, and even bacteria. The cycle of the clock is different in different creatures, and even among individuals from the same species, and can be shorter or longer than 24 h.

The ability to create daily rhythms exists in different cells, tissues, and organs in the body. These clocks, called peripheral clocks , are obedient to the central clock, which is located at the base of the brain in an area called the suprachiasmatic nucleus (SCN) . This small region is the size of a rice grain and includes about 20,000 nerve cells. In experiments with mice in which the SCN is surgically removed, the behavior of the mice is quite normal, except that they lose the ability to keep their daily rhythms under constant conditions.

Why Do We Need the Circadian Clock?

We assume that the circadian clock exists in so many creatures because it provides some advantage to the survival of those species. To understand what this evolutionary advantage is, experiments were conducted on animals with faulty circadian clocks, both in the lab and the natural environment. In laboratory light-darkness conditions, animals without circadian clocks will still exhibit daily rhythms. For example, in lab conditions in which there is no danger of death from predators, fruit-flies with defective biological clocks have similar lifespan as flies with intact clocks.

The advantage of the circadian clock becomes clear when examining animals in natural conditions, in competition with their own kind. For example, an experiment done on chipmunks demonstrated that, if the SCN was surgically removed, these rodents showed decreased survival in the forest, since their activity patterns were irregular and predators found them more easily [ 1 ]. Another experiment was conducted with a mix of two different species of bacteria, one with a short daily cycle (23 h) and the other with a long cycle (30 h) exposed to short and long days [ 2 ]. The results showed that one species pushed the other one aside. The “winning” species was the one whose circadian rhythm better suited the day’s duration, so the species with the long cycle took over the culture in long-day conditions, while the species with the short cycle took over the culture that was grown in short-day conditions. These studies and others show that the circadian clock allows the body to act in harmony with the external environment.

The Clock’s Genetics

The first gene of the circadian clock was discovered in 1971, through research on a tiny fly called Drosophila , which is commonly used by genetic researchers [ 3 ]. The researchers gave the flies a chemical that damaged the genes in their DNA. Then they observed the daily activity of the flies and searched for those whose cyclical activity was disrupted. They soon found a fly in which the sleep and wakefulness times changed each day. This fly had lost its circadian rhythm because of one damaged gene, which got the name period ( per for short). Per is a gene found in all animals, including humans. Later, the Clock gene was discovered ( Clk for short) in mice. Over time, additional genes involved with the circadian clock were discovered.

The genes in animals are different from those in plants or fungi, but the principle of operation is similar, and is an example of a negative feedback loop . In this loop ( Figure 2 ), the CLK protein stimulates the production of the PER protein. When the PER protein reaches a high level, it delays the production of the CLK protein. This delay results in a gradual reduction in the PER protein. The relationship between these two proteins leads to their daily fluctuations: when PER is high, CLK is low, and vice versa. These findings won three American scientists the Nobel Prize in physiology or medicine in 2017, for their studies using Drosophila .

• Figure 2 - The molecular circuit of the clock.
• Activator proteins (such as CLK) in the cell nucleus increase the production of clock proteins, which are inhibitory (such as PER). The inhibitory proteins move back to the nucleus where they inhibit the activator proteins, slowing down their own production. When the level of the inhibitory proteins goes down, the inhibition stops and a new cycle resumes. This is an example of a negative feedback loop.

The Influence of Light on the Biological Clock

As Siffre discovered in his cave experiment, the circadian clock operates even without exposure to cycles of light and darkness, although the daily cycles are longer than 24 h. In a natural environment, natural light is a cue that synchronizes us to a 24-h clock. Additional time clues include temperature and social time cues, such as mealtimes, work times, and study times. But light is considered the strongest time cue influencing the circadian clock.

How does this happen? In the retina of the eye, there are three types of molecules that transform light energy to the electrical activity that travels to the brain. Two types of receptors, the rods and cones, are used for seeing. A third, lesser-known group of receptors is called intrinsically photosensitive retinal ganglion cells (ipRGCs). These receptors supply information to the nerve cells that connect the retina to the SCN. Two decades ago, researchers found that the ipRGCs contain a light-sensitive pigment called melanopsin, and they figured out how melanopsin helps to synchronize the circadian rhythms with the external environment [ 4 ].

The Influence of Modern Life on the Biological Clock

Shortly before the identification of melanopsin, researchers discovered that the light-detecting molecules that influence the circadian clock are sensitive to blue light, which is light with a wavelength of 460 nanometers. This finding has great significance for the modern world, because today we are exposed not only to sunlight, but to electronic light sources that we use long after sunset. LED bulbs and devices, such as televisions, computers, and smartphones all produce blue light, therefore influencing our circadian rhythms.

There are other ways that modern life makes it harder for our circadian clocks to synchronize with the environment. A common example is flying to far-away countries, in which case the circadian clock must be delayed or brought forward by a few hours in order to synchronize. This process can take a few days and it is experienced as unpleasant sensations called jet lag. Jet lag can be caused by shifting the light cycle by just a few hours ( Figure 3 ). Studies show that people frequently experiencing jet lag are at higher risk of cancer and other chronic illnesses. Higher levels of certain diseases/disorders are also seen in people who work at night, who are effectively experiencing prolonged social jet lag. To reduce the health damage that can result from these disruptions to the circadian rhythm, it is recommended that we are exposed to daylight as much as possible, and that we avoid the light from our screens at night.

• Figure 3 - Jet lag experiment.
• An example of the activity of a hamster (nocturnal animal). The gray background symbolizes hours of darkness, and the yellow background symbolizes hours of light. After 8 days (top) the light is switched off 6 h earlier (indicated by the arrow). The hamster needs 1 week to synchronize to the new rhythm. The 6-h shift in the light-darkness cycle is equivalent to the jet lag experienced after flying east, for example from Israel to Japan, where bedtime comes earlier.

The circadian clock is built from genes and nerve cells that allow us to be in complete coordination with the daily fluctuations of the environment. Exposure to artificial light makes it harder for the circadian clock to synchronize with the environment, which could lead to health issues. The science of chronobiology (biological clock research) focuses on understanding the circadian clock’s mechanisms and the differences in clocks between people. This knowledge will help scientists to develop personalized medicine in the future, which will consider the unique body rhythms of each and every one of us.

Circadian Clock : ↑ A biological system that generate rhythmic changes in physiological and behavioral functions that repeat themselves every 24-h. The system is based on a network of proteins interacting with each other within a cell, as well as interaction between different cells. The central clock resides in the Suprachiasmatic nucleus in the brain. Examples of circadian rhythms that are regulated by the clock include the sleep/wake cycle, core body temperature, and melatonin secretion. The circadian clock is entrained to the environment by the 24-h changes in light exposure.

Peripheral Clocks : ↑ These clocks are in cells, tissues, and organs across the body. They receive information from the central clock in the brain and from other internal and external sources. For example, mealtimes are cues for peripheral clocks in the liver, kidney, and pancreas. The relationship between central and peripheral clocks is like the relationship between a conductor and musicians in an orchestra.

Suprachiasmatic Nucleus (SCN) : ↑ A small brain structure that consists of around 20,000 nerve cells and function as the central clock. The SCN is located in the hypothalamus, above the area where the optic nerves from the eyes cross.

Negative Feedback Loop : ↑ A process that paces itself. The thermostat is an example of negative feedback; when the temperature rises to a certain value, the thermostat turns off the heat. When the temperature goes down, the heating starts again. This process creates upward and downward temperature swings.

Synchronize : ↑ Adjusting two waves to each other, so peaks and troughs coincide or present at a fixed time difference (synchronization is possible only between waves that have the same cycle length).

Intrinsically Photosensitive Retinal Ganglion Cells (ipRGC) : ↑ A type of nerve cells in the retinas of mammals, which contain light receptors that do not participate in the process of seeing, but rather in the synchronizing of the circadian clock to light from the environment.

Chronobiology : ↑ The scientific discipline that studies biological timing systems and their effects on health and functioning.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

[1] ↑ DeCoursey, P. J., Walker, J. K., and Smith, S. A. 2000. A circadian pacemaker in free-living chipmunks: essential for survival? J. Comp. Physiol. A 186:169–80. doi: 10.1007/s003590050017

[2] ↑ Ouyang, Y., Andersson, C. R., Kondo, T., Golden, S. S., and Johnson, C. H. 1998. Resonating circadian clocks enhance fitness in cyanobacteria. Proc. Natl. Acad. Sci. U.S.A. 95:8660–4. doi: 10.1073/pnas.95.15.8660

[3] ↑ Konopka, R., and Benzer, S. 1971. Clock mutants of Drosophila melanogaster . Proc. Natl. Acad. Sci. U.S.A. 68:2112–6. doi: 10.1073/pnas.68.9.2112

[4] ↑ Gooley, J. J., Lu, J., Chou, T. C., Scammell, T. E., and Saper, C. B. 2001. Melanopsin in cells of origin of the retinohypothalamic tract. Nat. Neurosci. 4:1165. doi: 10.1038/nn768

Subscribe or renew today

Every print subscription comes with full digital access

Science News

The origin of biological clocks.

The evolutionary story of circadian rhythms is under scrutiny

LIFE'S CYCLES  Throughout the human body, circadian clocks keep activities running on a daily schedule of day and night. New work is shedding light on the origin and evolution of biological clocks.

Todd Churin

Share this:

By Tina Hesman Saey

July 14, 2015 at 1:00 pm

The Earth has rhythm. Every 24 hours, the planet pirouettes on its axis, bathing its surface alternately in sunlight and darkness.

Organisms from algae to people have evolved to keep time with the planet’s light/dark beat. They do so using the world’s most important timekeepers: daily, or circadian, clocks that allow organisms to schedule their days so as not to be caught off guard by sunrise and sunset.

A master clock in the human brain appears to synchronize sleep and wake with light. But there are more. Circadian clocks tick in nearly every cell in the body. “There’s a clock in the liver. There’s a clock in the adipose [fat] tissue. There’s a clock in the spleen,” says Barbara Helm, a chronobiologist at the University of Glasgow in Scotland. Those clocks set sleep patterns and meal times. They govern the flow of hormones and regulate the body’s response to sugar and many other important biological processes ( SN: 4/10/10, p. 22 ).

Having timekeepers offers such an evolutionary advantage that species have developed them again and again throughout history, many scientists say. But as common and important as circadian clocks have become, exactly why such timepieces arose in the first place has been a deep and abiding mystery.

Many scientists favor the view that multiple organisms independently evolved their own circadian clocks, each reinventing its own wheel. Creatures probably did this to protect their fragile DNA from the sun’s damaging ultraviolet rays. But a small group of researchers think otherwise. They say there had to be one mother clock from which all others came. That clock evolved to shield the cell from oxygen damage or perhaps provide other, unknown advantages.

Circadian clocks don’t have gears and hands. They’re composed of RNA molecules and proteins that oscillate in abundance. At particular times of day, certain clock proteins switch on production of messenger RNA, used by the cell to bake fresh batches of other clock proteins. Eventually levels of those proteins reach a certain threshold; they then shut off creation of the messenger RNA that produces them. The self-suppressing proteins disintegrate or get nibbled away by other proteins until their levels fall below a threshold, signaling the need for another batch, and the cycle starts again.

Just as Rolex, Timex, Swatch and Seiko make their own versions of a wristwatch, organisms including cyanobacteria, fungi, plants and insects have all invented their own varieties of circadian clocks. The cycling proteins are as different among these organisms as digital watches are from precision quartz clockworks. But all of them mark days with the predictable ebb and flow of messenger RNA and protein production.

There’s no doubt that today’s circadian clocks are must-have accessories for most organisms living on Earth’s surface. But does the run-away-from-the-light origin story make sense?

A main piece of evidence in favor of the “flight from light” idea is that cells tend to replicate their DNA at night safely under cover of darkness and repair it during the day as damage from UV light accumulates. Some of the same protein cogs that drive the circadian clocks are also involved in DNA repair, further solidifying the connection.

Related story

Some animals’ internal clocks follow a different drummer.

“That’s a nice idea,” says circadian cell biologist John O’Neill of the MRC Laboratory of Molecular Biology in Cambridge, England, “but it doesn’t fit with modern data.”

Going way back

Several lines of evidence argue against flight from light as the common force propelling the evolution of circadian clocks, says O’Neill, one of the scientists rewriting the circadian clock origin story.

If the cycle arose to protect DNA, one would expect cycling to happen only if there was DNA to protect. But circadian rhythms can happen in a test tube without DNA.

A type of cyanobacteria, or blue-green algae, known as Synechococcus elongatus has one of the simplest known circadian clocks. It consists of three proteins called KaiA, KaiB and KaiC. Those three gears, along with two accessory proteins, help the algae prepare for sunrise by stockpiling proteins needed for photosynthesis and other important daily activities.

Plop the three clock proteins into a test tube. Add energy from adenosine triphosphate (better known as ATP), and the clock will rhythmically add and subtract phosphate molecules from KaiC, Takao Kondo of Nagoya University in Japan and colleagues reported in Science in 2005. The finding shook up circadian researchers because it showed that clocks can operate without DNA. It also revealed that they don’t need to switch messenger RNA and protein production on and off to keep time.

Those blue-green algae and the mysterious, unnamed ancestor of insects and animals formed different branches on the evolutionary tree more than 1 billion years ago. Clock proteins of S. elongatus are nothing like the central timekeeping proteins of mammals. So some researchers doubted that DNA-free clocks existed in organisms more complex than algae.

Roller coaster

In fruit flies, amounts of circadian clock “gears,” proteins (bold lines) and messenger RNAs (dotted lines), rise and fall at certain times of day. Three of the important gears, called Clock (purple), Timeless (gray) and Period (light blue), each peak and plummet once every 24 hours, as seen in this computer simulation. If nothing disturbs it, the clock will go on producing the waves of activity day after day.

Source: Chen Li et al/BMC Systems Biology 2010

O’Neill and his collaborator Akhilesh Reddy of the University of Cambridge thought that they could find DNA-free clocks elsewhere. They decided to look for circadian clocks in human red blood cells, which lack a nucleus where DNA is stored. Without DNA, there is also no messenger RNA production, which is essential for the classic circadian clocks to work. Nevertheless, the cells still have circadian rhythms, O’Neill and Reddy reported in Nature in 2011. 

The red blood cell clock is entirely different from the protein and messenger RNA cycle that synchronizes nucleus-containing cells with the sun. In the red blood cells, antioxidant proteins called peroxiredoxins accept or give up oxygen molecules in a persistent circadian rhythm. Their action helps mop up hydrogen peroxide, a by-product of a cell’s normal energy-manufacturing activities. Hydrogen peroxide and other oxidants can damage many components of a cell, so keeping them in check is essential for survival.

Peroxiredoxins are found in a wide variety of organisms, including marine algae called Ostreococcus tauri . Working with other collaborators, O’Neill and Reddy examined the peroxiredoxins in the algae. “Just like the red blood cells, there was a rhythm,” O’Neill says. The amount of oxygen molecules clinging to the peroxiredoxins rose and fell in a persistent 24-hour cycle. The team reported that finding in the same 2011 issue of Nature .

A year later, the researchers reported in Nature that they had found peroxiredoxin cycles in fruit flies, the plant Arabidopsis thaliana , a fungus called Neurospora crassa , S. elongatus cyanobacteria and an archaean called Halobacterium salinarum . Together, the organisms represent all of the major domains of life. If every domain has peroxiredoxin clocks, the antioxidants are most likely ancient, probably dating back billions of years.

The oxygen menace

No one knows for sure how far back those antioxidant clocks go, but O’Neill has a time frame in mind: 2.5 billion years. That’s when cyanobacteria, which had recently begun using photosynthesis to fuel their activities, started releasing vast amounts of oxygen in the Great Oxidation Event. While photosynthesis and an oxygen-rich atmosphere are now considered necessities, oxygen was poison for Precambrian life-forms. Organisms that could not tolerate free oxygen either died or ended up in the anaerobic deep sea. “If they didn’t die off, they had to cope,” O’Neill says.

Oxygen would have been a problem mainly during the day, when photosynthesis was taking place. Organisms that geared up their antioxidant defenses — stripping peroxiredoxin of oxygen molecules so that it could sponge up hydrogen peroxide when the sun peeked over the horizon — would get a jump on survival. A timing mechanism that could anticipate oxygen’s arrival instead of just reacting to it would be “such an enormous advantage,” says O’Neill, “that it just became hardwired.”

Story continues below graphic

Peroxiredoxins themselves are not clock gears. They are more like a clock’s hands; the amount of oxygen bound to them is an indicator of time being kept by an as yet unknown and even more ancient central timekeeper. That mysterious clock was such an advantage that organisms have maintained it across evolutionary history, tinkering with it as needed. Like watches that can tell time in several time zones and display a.m. and p.m. plus calendar information, circadian clocks have added components to keep track of different environmental challenges, O’Neill speculates.

Other researchers have proposed that because the circadian clock proteins of cyanobacteria, animals and plants are so different, ancestors of those organisms must have evolved clocks independently. But even though the core cogs are different, says O’Neill, “you always find the same handful of kinases that set the speed of the clock.”

Kinases are proteins that tack phosphate molecules onto other proteins, slating them for destruction or altering their function. Two of the most important of those kinases — casein kinase 1 (CK1) and glycogen synthase kinase 3 (GSK3) — are also important in pacing peroxiredoxin clocks, O’Neill has found. They may be the ancestral clocks he and others have been seeking.

Even organisms that don’t have circadian rhythms have kinase-driven peroxiredoxin cycles, O’Neill, Helen Causton of Columbia University and colleagues  reported  in the April 20  Current Biology . Baker’s yeast,  Saccharomyces cerevisae,  has neither proteins recognizable as clock proteins nor a 24-hour cycle. That doesn’t mean yeast can’t keep time. They have about eight three-hour-long respiratory oscillations in which their rate of oxygen consumption rises and falls. Chemically blocking yeast’s version of CK1 slows down the yeast oscillation. Messing with CK1 also alters the circadian rhythm in mouse cells, the researchers reported.

The problem with all these evolutionary arguments is you can’t test them without a time machine.

—  John O’Neill, MRC Laboratory of Molecular Biology 

Those findings suggest that kinases are important for establishing the rhythm of circadian timers. The researchers think the kinases may have formed a simple timer, similar to cyanobacteria’s KaiA, B, C system. With those simple gears in place, organisms would have added more gears to form the clocks we see today, O’Neill says. There is still no evidence, however, that  the kinases are the ancestral molecules that spawned today’s clocks.

O’Neill admits that there is another possibility. There may be no mother clock. Cell biology may simply be driven by biochemical reactions that naturally fall into rhythmic patterns. “I don’t like that possibility because it’s quite difficult to disprove” or test, he says. The only real way to demonstrate that’s not the answer is to go back and find the master clock. But, O’Neill laments, “the problem with all these evolutionary arguments is you can’t test them without a time machine.”

Independent evolution

Not everyone is enamored with the peroxiredoxin hypothesis. “They have a very grandiose scheme,” says Joseph Takahashi, a circadian geneticist and neuroscientist at the University of Texas Southwestern Medical Center in Dallas. “There’s just no evidence.”

True enough, acknowledges O’Neill. “We don’t have a mechanism.” All they have are observations that are incompatible with classic models that describe clocks as machines of oscillating proteins and messenger RNA that evolved as light-avoidance mechanisms.

Central to O’Neill’s argument is the idea that there must have been an ancestral clock upon which all clock-carrying organisms built their daily timers. Other researchers aren’t so quick to dismiss independent evolution.

“I don’t think we should assume it’s hard to build a clock,” says Susan Golden, a microbiologist at the University of California, San Diego. The timing mechanisms seen in nature today are just the ones that have stuck around. Organisms may have tried and rejected other timers or other rhythms. Recently, independent research groups found that a marine worm has a lunar clock, and a sea louse has a tidal timer ( SN: 11/2/13, p. 6 ). Golden’s lab group is tinkering with a cyanobacterial circadian clock to see if it can keep time on a different scale — weeks or hours instead of days, for instance.

Real-world advantages

Although no one has dug up the original clock, some scientists are philosophizing about why such a mechanism might have been useful in the first place. Avoiding oxygen toxicity and fleeing damaging light aren’t the only reasons a circadian clock is a good idea. Some researchers say the advantage of having a clock may be in keeping contradictory chemical reactions separated or making cells run more smoothly by creating a production schedule for molecules needed for each step of a biochemical chain reaction.

“We wonder why is the clock turning on and off metabolism each day rather than just letting everything run with the taps on,” Takahashi says. He and colleagues are testing the idea that producing things in big bursts the way the clock dictates is more energy-efficient than making small amounts over a longer period. One 2010 computer simulation estimates that circadian clocks may save organisms enough energy to grow 15 percent faster. Measuring that possible advantage in the real world, however, has been difficult.

Moran put the surface and cave fish in swimming tubes and flowed water over them so that they swam at “a slow walk” for several days. He measured how much oxygen the fish used. As expected, the surface fish used more oxygen during the day than at night. But the cave fish used the same amount of oxygen day and night. “Maybe it’s just that one fish,” he recalls thinking. “So then we put the next fish in.” That fish’s oxygen consumption stayed flat, too.

By keeping their metabolism at a steady rate all day, rather than a rhythmic boost that follows light cycles, the cave fish saved 27 percent of their energy, the team reported last September in PLOS ONE . When both surface and cave fish were tested in darkness, cave fish did even better, expending 38 percent less energy than surface fish did.

The finding doesn’t mean Takahashi is wrong about circadian clocks saving energy in a rhythmic world. It’s just that cave fish live in a fairly constant — dark — environment. In that case, wonders Moran, “what are you rousing your metabolism for?” If fish gear up in anticipation of an event, “and it doesn’t happen, what a waste,” he says. But in a world where sunrise is the gold standard of predictability, circadian clocks may indeed be thrifty options.

Just because some animals in extreme environments have radically different clocks doesn’t mean that living without rhythm is a good idea for everybody. “I’m rather skeptical that — except in some freaky situations — life is better without a clock,” says Helm, the chronobiologist from Glasgow. Cave fish also lack eyes, but nobody would argue that means eyes are unimportant, she says.

Maybe, says Golden, clocks didn’t evolve for just one reason. Clocks, she says, may generally be necessary for “not being jerked around by the environment.” 

This story appears in the July 25, 2015 issue with the headline, “Life’s cycles: Poking holes in classic models of circadian clock evolution.” 

Editor’s note: This story was updated on July 16, 2015, to amend the caption of the “Mother of all clocks” infographic. It previously incorrectly stated that the last universal common ancestor of all living things may have had a primitive circadian clock. 

More Stories from Science News on Life

Flowers may be big antennas for bees’ electrical signals

Eavesdropping on fish could help us keep better tabs on underwater worlds

During a total solar eclipse, some colors really pop. Here’s why

Here’s why some pigeons do backflips.

Chickadees use memory ‘bar codes’ to find their hidden food stashes

Here’s how magnetic fields shape desert ants’ brains

An extinct sofa-sized turtle may have lived alongside humans

By fluttering its wings, this bird uses body language to tell its mate ‘after you’

Subscribers, enter your e-mail address for full access to the Science News archives and digital editions.

Not a subscriber? Become one now .

• Research Article
• Cell Biology
• Genetics and Genomics

An integrative study of five biological clocks in somatic and mental health

• Laura KM Han
• Josine E Verhoeven
• Karolina A Aberg
• Edwin CGJ van den Oord
• Yuri Milaneschi
• Brenda WJH Penninx
• Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Netherlands ;
• Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, United States ;
• Open access
• Copyright information

Share this article

Cite this article.

• Rick Jansen
• Copy to clipboard
• Download BibTeX
• Download .RIS

Introduction

Materials and methods, data availability, article and author information.

Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one’s biological age is best reflected by combining aging measures from multiple cellular levels.

Aging can be conceptualized in different ways. While chronological age is measured by date of birth, biological age reflects the relative aging of an individual’s physiological condition. Biological aging can be estimated by various cellular indices ( López-Otín et al., 2013 ). Commonly used indices are based on telomere length, DNA methylation patterns (epigenetic age), variation in transcription (transcriptomic age) as well as alterations in the metabolome (metabolomic age) and in the proteome (proteomic age) (see Han et al., 2019 ;  Xia et al., 2017 and Jylhävä et al., 2017 for recent reviews). Biological aging is defined as the residuals of regressing predicted biological age on chronological age: a positive value indicates that the biological age is larger than the chronological age. Advanced biological aging (i.e. an increased biological clock) has been associated to poor somatic health, including the onset of aging-related somatic diseases such as cardiovascular disease, diabetes, and cognitive decline ( Xia et al., 2017 ). Advanced biological aging has also been correlated to mental health: childhood trauma ( Li et al., 2017 ), psychological stress, and psychiatric disorders ( Darrow et al., 2016 ; Han et al., 2018 ). Specifically, telomere length has been most extensively researched and was found to be shorter in various somatic conditions ( Jin et al., 2018 ), all-cause mortality ( Mons et al., 2017 ; Wang et al., 2018 ) and a range of psychiatric disorders ( Lindqvist et al., 2015 ). Advanced epigenetic aging has also been linked to worse somatic health, mortality ( Marioni et al., 2015 ), depressive disorder ( Han et al., 2018 ; Whalley, 2017 ), and post-traumatic stress disorder ( Wolf et al., 2018 ), although some studies have found associations with the opposite direction of effect ( Verhoeven et al., 2018 ; Boks et al., 2015 ). Advanced transcriptomic aging was found in those with higher blood pressure, cholesterol levels, fasting glucose, and body mass index (BMI) ( Peters et al., 2015 ). Advanced metabolomic aging increases risk on future cardiovascular disease, mortality, and functionality ( Akker et al., 2019 ).

While all biological clocks aim to measure the biological aging process, there is limited evidence for cross-correlations among different clocks. Belsky and colleagues ( Belsky et al., 2017 ) recently showed low agreement between eleven quantifications of biological aging including telomere length, epigenetic aging, and biomarker-composites. In contrast, Hastings et al., 2019 showed relatively strong correlations (r > 0.50) between three physiological composite biological clocks (i.e. homeostatic dysregulation, Klemer and Doubal’s method and Levine’s method), but not with telomere length. Other studies showed that telomere length was not correlated with epigenetic aging ( Han et al., 2018 ; Marioni et al., 2018 ), although cell type composition adjustments revealed a modest association ( Chen et al., 2017 ). Further, both Hannum and Horvath epigenetic clocks ( Hannum et al., 2013 ; Horvath et al., 2012 ) showed modest correlations to a transcriptomic clock.

Most previous studies, however, have separately considered the relation between a single biological clock and different somatic and mental health conditions. To date, extensive integrated analyses across multiple cellular and molecular aging markers in one study are lacking and it remains unknown to what extent different biological clocks are similarly associated to different health determinants. In addition, most studies did not examine health in its full range and, consequently, whether both somatic and mental health are associated with biological aging remains elusive. As it is unlikely that a single biological clock can fully capture the complexity of the aging process ( Cole et al., 2019 ), a composite index, that integrates the different biological clocks and thereby aging at several molecular levels, may reveal the strongest health impact. Therefore, there is an additional need to integrate different biological clocks and test whether such a ‘composite clock’ outperforms single biological blocks in its association with health determinants.

To develop a better understanding of the mechanisms underlying biological aging, this study aimed to examine (1) the intercorrelations between biological aging indicators based on different molecular levels ranging from DNA to metabolites, namely telomere length, epigenetic, transcriptomic, proteomic and metabolomic clocks; (2) the relationships between different biological aging indicators with both somatic and mental health determinants; and (3) whether a composite biological clock outperforms single biological aging indicators in its association with health. For the five biological aging indicators and the composite clock, associations were computed with a wide panel of lifestyle (e.g. alcohol use, physical activity, smoking), somatic health (functional indicators, BMI, metabolic syndrome, chronic diseases) and mental health (childhood trauma, depression status) determinants.

Sample characteristics

To create indicators for biological aging we used whole blood derived measurements from the Netherlands Study of Depression and Anxiety (NESDA) baseline assessment: telomere length ( N  = 2936), epigenetics (DNA methylation, N  = 1130, MBD-seq, 28M CpGs), gene expression ( N  = 1990, Affymetrix U219 micro arrays, >20K genes), proteomics ( N  = 1837, Myriad RBM DiscoveryMAP 250+, 171 proteins) and metabolites ( N  = 2910, Nightingale Health platform, 231 metabolites), with 653 overlapping samples (see Table 1 for sample characteristics). Each subsample included around 66% female, with mean age of around 42 years.

Sample description.

Computing biological clocks.

The methods for creating the biological clocks are described in detail in the Materials and methods section. In brief, for each of the four omics measures (epigenetic, transcriptomic, metabolomic and proteomic) we estimated biological age using ridge regression and cross validation (see Figure 1 for study design). As telomere length values usually decline with increasing chronological age, this indicator was multiplied by −1 to be able to compare directions of effects consistent with the other biological clocks. Correlations between chronological age and predicted biological age were 0.30 for telomere length, 0.95 for epigenetic age, 0.72 for transcriptomic age, 0.85 for proteomic age, and 0.70 for metabolomic age ( Figure 1 ). For each omics-based biological clock, biological aging is defined as the residuals of regressing predicted biological age on chronological age: a positive value means that the biological age is larger than the chronological age. The individual clocks residualized for chronological age are also referred to as biological aging indicators. Correlations between biological aging indicators, corrected for sex, are presented in Figure 2 . Correlations were significant for 3 out of 10 pairs; proteomic vs metabolomic aging ( r  = 0.19, p=2e-16), transcriptomic vs epigenetic aging ( r  = 0.15, p=3e-06) and transcriptomic vs proteomic aging ( r  = 0.08, p=2e-06).

Study design.

The upper part of the figure shows the five biological layers. From each of the four omics layers (epigenetic, transcriptomic, proteomic, and metabolomic data), biological age was estimated, and biological age was regressed on age to obtain measures of biological aging. Only telomere length was not age-regressed. The five biological aging indicators were associated with multiple demographic, lifestyle, somatic health and mental health determinants.

Correlations between the biological aging indicators.

The heatmap represents Spearman rank correlations between the five biological aging indicators, all corrected for sex. Out of 10 pairs, three are significant: transcriptomic vs epigenetic aging, metabolomic vs proteomic aging and proteomic vs transcriptomic aging. All biological aging indicators were age-regressed, only telomere length was not.

Associations between individual biological aging indicators and health determinants

For each of the five biological aging indicators, we computed associations with several demographic (sex, education), lifestyle (physical activity, smoking, alcohol use), somatic health (BMI, hand grip strength, lung function, physical disability, chronic diseases), and mental health (current depression, depression severity, childhood trauma) determinants. Except for proteomic aging, sex was associated with all biological aging indicators: women were biologically younger than men (p=3e-4 for telomere length, p=5e-4 for epigenetic aging, p=4e-11 for transcriptomic aging, p=1e-5 for metabolomic aging). Education was not associated with any biological aging indicator. We controlled for sex by using it as a covariate in all following models (except for in the model where sex was the outcome). Table 2 and Figure 3 give an overview of all associations. Correction for multiple testing was done using permutation-based FDR (Materials and methods), resulting in a p-value threshold of 2e-2 for an FDR of 5% for all tests.

Forest plot of associations between biological aging and health determinants.

For each of the associations between biological aging indicators and health determinants, the standardized beta and standard deviation derived from linear models were plotted. The significant associations (p<2e-2, FDR < 5%) are shown with red stars. The composite index, which is the scaled sum of the five biological aging indicators, clearly shows most associations and often largest effect sizes. Biological aging was used as outcome in the linear models. Beta for telomere length was multiplied by −1 to compare with other biological clocks. Red stars indicate FDR < 5%. All biological aging indicators were age-regressed, only telomere length was not.

Associations between five biological aging indicators and multiple health determinants.

For each biological aging indicator, linear models were fit with the health determinant as predictor, while controlling for sex. Beta’s and p-values from these models are presented here. In the 653 samples with all five data layers available, a composite index was constructed which was significantly associated with more variables than any of the five individual biological aging indicators. All biological aging indicators were age-regressed, only telomere length was not. Telomere length models were corrected for age instead. * Beta for telomere length was multiplied by −1 to compare with other biological aging indicators. All measures are coded such that higher values indicate advanced biological aging. Bold indicates FDR < 5%.

* Beta for telomere length was multiplied by -1 to compare with other biological clocks.

Bold indicates FDR<5%.

Among the lifestyle determinants, alcohol use was associated with advanced proteomic aging (p=3e-3) and smoking (packs per year) was associated with shorter telomere length (p=3e-3), and advanced transcriptomic (p=2e-2), proteomic (p=1e-5) and metabolomic aging (p=5e-3). Physical activity was not associated with any biological aging indicator.

From the somatic health determinants, high BMI was strongly associated with advanced biological aging of all indicators (p=2e-2 for telomere length, p=4e-3 for epigenetic aging, p=6e-10 for transcriptomic aging, p=1e-7 for proteomic aging, and p=2e-35 for metabolomic aging). Physical disability was associated with advanced epigenetic aging (p=1e-4). Within the domain of chronic diseases, the presence of digestive diseases and endocrine diseases were associated with advanced proteomic aging (p=2e-2 and p=1e-2, respectively). Subjects with cardiometabolic disease showed advanced metabolomic aging (p=4e-3) and subjects with digestive disease exhibited advanced transcriptomic aging (p=1e-2). Those with metabolic syndrome showed advanced biological aging across four indicators (p=6e-4 for telomere length, p=1e-8 for transcriptomic aging, p=5e-9 for proteomic aging, p=5e-29 for metabolomic aging).

The presence of current depression and depression severity were associated advanced epigenetic (p = 2e-3 and p = 9e-5) and proteomic aging (p = 8e-3 and p= 6 e-3, respectively). Current depression was also associated with advanced transcriptomic aging (p=2e-2) and those with childhood trauma showed advanced epigenetic aging (p=8e-5). To verify if the results were confounded by medication use, we computed associations between antidepressant medication (SSRIs, TCAs, or other antidepressants), metabolic-syndrome-related medication (‘metabolic medication’: anti-diabetic, fibrates, or anti-hypertensives) and biological aging ( Supplementary file 1 ). After FDR correction, we found that metabolomic aging was associated with the increased use of metabolic medication ( Beta = 0.153, p=2.35 e-3), and antidepressant use with proteomic ( Beta = 0.208, p= 7.16e-5) and transcriptomic aging ( Beta = 0.129, p=8.1e-3 ). The design of the current observational study cannot conclusively prove whether this is a direct medication effect or confounding by indication.

Association between biological aging indicators and mortality in longitudinal analysis

We conducted post-hoc analyses on the relationship between the biological aging indicators and subsequent outcomes after 6 years of follow-up duration. Mortality data and self-reported somatic disease onset (in the categories cardiometabolic, respiratory, musculoskeletal, digestive, neurological, and endocrine diseases, and cancer) was gathered at each measurement wave. There were no significant associations between chronic disease onset or mortality and baseline biological aging, likely due to the low numbers of mortality and disease onset ( Supplementary file 3 ).

Associations between the biological aging composite index and all health determinants

The composite index was computed as the sum of the five scaled biological aging indicators in the 653 samples with data of all five biological levels. Correlations between the five biological aging indicators and the composite index were between 0.43 and 0.51. We found more and stronger associations for the composite index than for any of the individual biological aging indicators: including sex (p=2e-6), BMI (p=2e-10), smoking (p=2e-2), metabolic syndrome (p=9e-13), current MDD (p=6e-3), depression severity (p=7e-4), and childhood trauma (p=2e-2). As an alternative approach, Principal Component Analysis (PCA) was used to compute an alternative composite index. We used the first principle component (PC) of this analysis, which was a weighted sum of the biological aging indicators (for telomere length the weight (w) = 0.042, epigenetic aging w = 0.094, transcriptomic aging w = 0.220, proteomic aging w = 0.707, metabolomic aging w = 0.664), reflecting the highest correlations between the biological aging indicators, which is between metabolomic and proteomic aging. Compared to the composite index that was based on the sum and thus gives equal weight to all five biological aging indicators, the PC-based index had less significant associations with sex, smoking, BMI, and metabolic syndrome. The PC-based index was not significantly associated with physical disability, or mental health outcomes, as opposed to the summed index. The five PC’s each explain more than 15% of variance (the first 2 PC’s more than 25% each), indicating the multidimensionality and non-redundancy of the five biological aging indicators.

To allow for direct effect size comparisons between the composite (summed) index and the individual aging indicators, we compared the findings for the composite index to those of each individual biological aging indicator with the same subsample. In this analysis, p - values and effect sizes were often more pronounced for the composite index ( Figure 4 , Supplementary file 2 ). For example, sex, BMI, metabolic syndrome and current MDD, were significantly associated with the composite index, but the betas for the composite index were larger than the betas from any individual indicator. For the other five variables significantly associated with the composite index (smoking, physical disability, cardiometabolic disease, depression severity, and childhood trauma) the betas for the composite index were larger than four out of five betas from the individual biological aging indicators.

Barplots of betas from associations between biological aging and health determinants.

For each of the associations between biological aging and health determinants, the standardized beta and standard deviation derived from linear models were plotted. Only samples that had data for all five biological clocks ( N  = 653) were used. All biological aging indicators were age-regressed, only telomere length was not.

In this study, we examined five biological clocks based on telomere length and four omics levels from a large, clinically well-characterized cohort. We demonstrated significant intercorrelations between three pairs of biological aging indicators, illustrating the complex and multifactorial processes of biological aging. Furthermore, we observed both overlapping and unique associations between the individual biological aging indicators and different lifestyle, somatic and mental health determinants. Separate linear regressions showed that male sex, high BMI, smoking, and metabolic syndrome were consistently associated with more advanced levels of biological aging across at least four of the biological clocks. Strikingly, depression was associated to more advanced levels of epigenetic, transcriptomic and proteomic aging, signifying that both somatic and mental health are associated with advanced biological aging. Finally, by integrating a composite index of all biological aging indicators we were able to obtain larger effect sizes with for example physical disability and childhood trauma exposure, underscoring the broad impact of determinants on cumulative multi-system biological aging.

The range of correlations among the biological aging indicators considered in this study indicates that the correlates of chronological age in different molecular layers were not strongly correlated, suggesting that biological aging may be differently manifested at certain cellular levels. Consistent with prior studies, we showed weak correlations between different biological aging indicators ( Li et al., 2020 ) and we confirm the absent relationship between telomere length and epigenetic aging ( Marioni et al., 2018 ; Belsky et al., 2017 ;  Breitling et al., 2016 ), but also show lack of associations with transcriptomic, proteomic or metabolomic aging. However, we do confirm an earlier finding showing a significant but modest correlation between epigenetic and transcriptomic aging ( Peters et al., 2015 ). The correlation between metabolomic and proteomic aging may partly be explained by the fact that both data were obtained from platforms that were aimed at probing central inflammation lipid processes, rather than the full proteome or metabolome. Nevertheless, we can infer that only some biological aging indicators show overlap, while most of them seem to be tracking distinctive parts of the aging process, even if they are associated with the same somatic or health determinants.

Our study showed that several of the determinants considered exhibited consistent associations with different biological aging indicators. First, male sex was associated with shorter telomere length and advanced epigenetic, transcriptomic, and metabolomic aging, in line with a large body of literature that shows advanced biological aging and earlier mortality in males compared to females ( Austad and Fischer, 2016 ). Second, high BMI was consistently related to all biological aging indicators, showing that the more overweight or obese, the higher the biological age ( Gielen et al., 2018 ), also after controlling for sex. Earlier studies showed similar associations between high BMI and shorter telomere length ( Gielen et al., 2018 ), and older epigenetic ( Horvath and Raj, 2018 ) and transcriptomic aging signatures ( Peters et al., 2015 ). Third, our analyses showed similarly consistent associations between the prevalence of metabolic syndrome and advanced levels of aging. Further, all but epigenetic aging was advanced with respect to cigarette smoking.

Major depressive disorder (MDD) status was consistently related to advanced aging in three (epigenetic, transcriptomic, proteomic) out of the five biological aging indicators. In contrast, a recent study ( N  > 1000) in young adults (20–39 years) did not show associations between mental health (as measured by the CIDI) and biological aging (indicated by telomere length, homeostatic dysregulation, Klemer and Doubal’s method and Levine’s method) ( Hastings et al., 2019 ), but it seems possible that this sample was too young to fully develop aging-related manifestations of mental health problems, or lacked age variation. It is likely that our data (obtained from participants 18–64 years) may have been more sensitive in picking up associations with mental health due to increased variation in both chronological age (i.e. inclusion of older persons), as well as symptom severity. To further examine whether the results were consistent across participants with and without depressive psychopathology, we repeated all models in post-hoc analyses and added an interaction term between current depression status and health determinants. There was an overall consistent pattern of non-significant interaction terms for most health determinants and biological aging, although only higher BMI was significantly associated to advanced epigenetic aging in the psychopathology group. However, taken together, the results suggest that findings are not different in persons with and without mental disorders. We observed some significant associations between biological aging and medication use. The design of the current observational study cannot conclusively prove whether this is a direct medication effect or confounding by indication: the patient group using antidepressant medication is also the group that is more chronically and severely depressed. This is similar for the metabolic syndrome related medication. Future studies using randomized clinical trial designs are needed to investigate the mechanism of action of direct pharmacological effects of medication on biological aging.

Furthermore, we computed a composite index by summing up the five biological aging indicators studied here. In other words, this integrative metric contains cumulative independent signal from the individual markers and dependent shared signal – with possible reduced noise due to the summation – between them. Given that this composite index demonstrates larger effect sizes for BMI, sex, smoking, depression severity, and metabolic syndrome than the individual aging indicators, it is suggested that being biologically old at multiple cellular levels has a cumulative multi-systemic effect. When integrated, the composite index reveals stronger (i.e. greater cumulative betas for the composite index than individual clocks) converging associations with sex, BMI, metabolic syndrome and current MDD. This provides further support for the hypothesis that not one biological clock sufficiently captures the biological aging process and that not all clocks are under the control of one unitary aging process. There is abundant room for further progress in determining whether biological aging can be modified by intervening on these determinants.

Nonetheless, the question remains which biological mechanism could plausibly link the current quantification of biological aging and its lifestyle, somatic, and mental health determinants. Part of this answer requires discussion on the features used to build the different clocks: the proteomic and metabolomic clocks mostly measure inflammatory or metabolic factors, two highly integrated processes in aging and aging-related diseases ( Frasca et al., 2017 ). Previous studies suggest immune-mediated mechanisms (specifically inflammatory signaling) connecting metabolic syndrome ( Révész et al., 2015 ), mental health disorders ( Wohleb et al., 2016 ), and aging ( Révész et al., 2018 ). Moreover, MDD is a condition in which inflammation, obesity, and premature or advanced aging co-occur and converge. It might therefore be speculated that immunity and 'inflammaging' ( Franceschi et al., 2018 ) may tie together the currently observed associations.

This study did not include existing biological clocks. While the application of established algorithms would increase generalizability of our findings, there are several reasons why it was not optimal to implement previously published algorithms in the NESDA data. First and foremost, generated omics data are platform-dependent and the existing epigenetic ( Horvath, 2013 ) and gene expression ( Peters et al., 2015 ) clocks rely on arrays with different coverage of probes as was used in NESDA, that also target different parts of genes. Second, a subsample of NESDA was part of the previously published metabolomic clock ( Akker et al., 2019 ), thus application of this model to the current dataset would result in overfitting. The current proteomic platform has not been used before to train a biological clock. Moreover, there is currently no validated gold standard for calculating transcriptomic, proteomic, or metabolomic clocks. Importantly, in spite of these limitations, we have followed an alternative but consistent methodological approach for training our omics-based biological clocks, leveraging the advantage to compare, combine, and integrate these clocks within the same population. However, we emphasize the need for epidemiological replication of these determinants in other datasets (e.g. those including different ethnicities) and we recognize that data harmonization and pooling are important strategies on the scientific research agenda that may overcome this limitation in the future.

Since no previously published algorithms were used, we trained our own clocks using ridge regression with cross-validation. This approach relies on the assumption that the determined cross-sectional correlation between the omics patterns and chronological age arise mainly as a consequence of biological aging, and is independent from potential secular trends ( Nelson et al., 2020 ; Belsky, 2015 ; Belsky et al., 2020 ). As common to cross-sectional studies, it is, however, impossible to completely rule out potential cohort effects or uncontrolled individual differences and results should be interpreted in light of this limitation. Future longitudinal studies are needed to identify patterns of biological changes that go beyond their ability to predict age at the time of sampling. While the current study only used chronological age as criterion endpoint, it is important to mention that other epigenetic clocks exist that are trained to predict other potential criteria such as phenotypic markers of age (DNAm PhenoAge) ( Levine et al., 2018 ) or a composite biomarker that was derived from DNAm surrogates and smoking in pack-years (GrimAge) ( Hillary et al., 2019 ). Such clocks were developed to lead to improved predictions of risk of mortality.

More research is needed to elucidate whether: (1) physiological disturbances, such as loss of inflammatory control associated with somatic and psychopathology, accelerate biological aging over time, (2) advanced biological aging precedes and constitutes a vulnerability factor that causes somatic and psychopathology, or (3) somatic and psychopathology and biological aging processes are not causally linked, but share underlying etiological roots (e.g. shared genetic risks or environmental factors) ( Han et al., 2019 ). Yet, it could conceivably be hypothesized that dysregulation of immunoinflammatory control may be related to metabolic outcomes, aging, and depression ( Diniz and Vieira, 2018 ), providing scope as to why some of these determinants converge across different platforms and multiple biological levels.

Here, we used a large cohort that was well-characterized in terms of demographics, lifestyle, and both somatic and mental health assessments, to study and integrate five biological clocks across multiple levels of analysis. This is particularly important as we show that the determinants of biological aging encompass several different domains. Moreover, our sample was adequately powered to detect statistically significant associations, limiting the possibility for chance findings and increasing probability for identifying robust biological age determinants. On the other hand, an obvious limitation is the cross-sectional nature of this study that prevents us from drawing any conclusions on whether the determinants accelerate the aging trajectory over time, the other way around, or whether ‘third’ variables effect this association.

Another aspect that limits the interpretability of our findings in the context of increased risk of developing aging-related diseases and mortality was the relatively young age of the current sample. To illustrate, we were unable to predict future incidence of chronic disease or mortality from baseline biological aging, likely due to the low numbers of mortality and disease onset ( Supplementary file 3 ), for example the number of deceased cases ranged from 64 (TL) to 27 (proteomic clock). Previous studies that have associated biological aging with mortality risk commonly include aging cohorts (Danish longitudinal twin study with mean age of 86.1 years; Framingham Offspring Study with mean age 61.0 years; Swedish population cohort SATSA with mean age 63.6 years; German population cohort ESTHER with mean age 62.5 years; Lothian Birth Cohorts with mean age >69.5 years; Normative Aging Study with mean age 71.7 years) ( Marioni et al., 2018 ; Li et al., 2020 ; Christiansen et al., 2016 ; Perna et al., 2016 ; Murabito et al., 2018 ; Chen et al., 2016 ). Before definitively interpreting a ‘clock’ as a measure of biological aging, further independent studies are needed to establish that the clock changes with advancing age and forecasts disease, disability and mortality.

Conclusions

In conclusion, this study examined the overlap between five biological aging indicators and their shared and unique associations with somatic and mental health. Our findings indicate that they largely track distinct, but also partially overlapping aspects of this aging process. Further, we demonstrated that male sex, smoking, higher BMI and metabolic syndrome were consistently related to advanced aging at multiple biological levels. Remarkably, our study also converges evidence of depression and childhood trauma associations across multiple platforms, cellular levels, and sample sizes, highlighting the important link between mental health and biological aging. Taken together, our findings contribute to the understanding and identification of biological age determinants, important to the development of end points for clinical and epidemiological research.

Study design and participants

Data used were from the Netherlands Study of Depression and Anxiety (NESDA), an ongoing longitudinal cohort study examining course and consequences of depressive and anxiety disorders. The NESDA sample consists of 2981 persons between 18 and 65 years including persons with a current or remitted diagnosis of a depressive and/or anxiety disorder (74%) and healthy controls (26%). Individuals were recruited from mental health care settings, general practitioners, and the general population in the period from September 2004 to February 2007. Persons with insufficient command of the Dutch language or a primary clinical diagnosis of other severe mental disorders, such as severe substance use disorder or a psychotic disorder were excluded. Participants were assessed during a 4 hr clinical visit, consisting of the collection of all somatic and mental health determinants in the current study, as well as a fasting blood draw. All omics data was obtained from the same blood sample, drawn at the same time point as the health determinant examination during the face-to-face visit. The study was approved by the Ethical Review Boards of participating centers, and all participants signed informed consent. More than 94% of the NESDA participants were from North European origin. The population and methods of the NESDA study have been described in more detail elsewhere ( NESDA Research Consortium et al., 2008 ).

Data to derive different biological clocks was available for different subsamples and all based on the same fasting blood draw from participants in the morning between 8:30 and 9:30 after which samples were stored in a −80°C freezer or – for RNA - transferred into PAXgene tubes (Qiagen, Valencia, California, USA) and stored at −20°C. To create biological clocks, we used telomere length ( N  = 2936), DNA methylation ( N  = 1130, MBD-seq, 28M CpGs), gene expression ( N  = 1990, Affymetrix U219 micro arrays, >20K genes), proteins ( N  = 1837, Myriad RBM DiscoveryMAP 250+, 171 proteins) and metabolites ( N  = 2910, Nigthingale platform, 231 metabolites), see Table 1 and details in the following sections.

Biological clock assessments

Telomere length.

Leukocyte telomere length was determined at the laboratory of Telomere Diagnostics, Inc (Menlo Park, CA, USA), using quantitative polymerase chain reaction (qPCR), adapted from the published original method by Cawthon, 2002 . Telomere sequence copy number in each patient’s sample (T) was compared to a single-copy gene copy number (S), relative to a reference sample. The resulting T/S ratio is proportional to mean leukocyte telomere length. The detailed method is described elsewhere ( Verhoeven et al., 2014 ). The reliability of the assay was adequate: eight included quality control DNA samples on each PCR run illustrated a small intra-assay coefficient of variation (CV = 5.1%), and inter-assay CV was also sufficiently low (CV = 4.6%).

DNA methylation (epigenetic clock)

To assay the methylation levels of the approximately 28 million common CpG sites in the human genome, we used an optimized protocol for MBD-seq ( Han et al., 2018 ; Aberg et al., 2020 ). With this method, genomic DNA is first fragmented and the methylated fragments are then bound to the MBD2 protein that has high affinity for methylated DNA. The non-methylated fraction is washed away and only the methylation-enriched fraction is sequenced. This optimized protocol assesses about 94% of the CpGs in the methylome. The sequenced reads were aligned to the reference genome (build hg19/GRCh37) with Bowtie2 ( Langmead and Salzberg, 2012 ) using local and gapped alignment. Aligned reads were further processed using the RaMWAS Bioconductor package ( Shabalin et al., 2018 ) to perform quality control and calculate methylation scores for each CpG.

Gene expression (transcriptomic clock)

RNA processing and assaying -done at Rutgers University Cell and DNA repository- have been described previously ( Jansen et al., 2014 ; Jansen et al., 2017 ; Wright et al., 2014 ). Samples were hybridized to Affymetrix U219 arrays (Affymetrix, Santa Clara, CA). Array hybridization, washing, staining, and scanning were carried out in an Affymetrix GeneTitan System per the manufacturer’s protocol. Gene expression data were required to pass standard Affymetrix QC metrics (Affymetrix expression console) before further analysis. We excluded from further analysis probes that did not map uniquely to the hg19 (Genome Reference Consortium Human Build 37) reference genome sequence, as well as probes targeting a messenger RNA (mRNA) molecule resulting from transcription of a DNA sequence containing a single nucleotide polymorphism (based on the dbSNP137 common database). After this filtering step, data for analysis remained for 423,201 probes, which was summarized into 44,241 probe sets targeting 18,238 genes. Normalized probe set expression values were obtained using Robust Multi-array Average (RMA) normalization as implemented in the Affymetrix Power Tools software (APT, version 1.12.0, Affymetrix). Data for samples that displayed a low average Pearson correlation with the probe set expression values of other samples, and samples with incorrect sex-chromosome expression were removed.

Proteins (proteomic clock)

As described previously ( Lamers et al., 2016 ), a panel of 243 analytes (Myriad RBM DiscoveryMAP 250+) involved in various hormonal, immunological, and metabolic pathways was assessed in serum using multiplexed immunoassays in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory (Myriad RBM; Austin, TX, USA). After excluding analytes with more than 30% missing data (mostly due to values outside the ranges of detection), 171 of the 243 analytes remained for analysis (with values below and above detection limits imputed with the detection limit values).

Metabolites (metabolomic clock)

Metabolite measurements have been described in detail previously ( Akker et al., 2019 ; BBMRI-NL Metabolomics Consortium et al., 2020 ). In short, a total of 232 metabolites or metabolite ratios were reliably quantified from Ethylenediaminetetraacetic acid plasma samples using targeted high-throughput proton Nuclear Magnetic Resonance ( 1 H-NMR) metabolomics (Nightingale Health Ltd, Helsinki, Finland) ( Soininen et al., 2015 ). Metabolites measures provided by the platform include (1) lipids, fatty acids and low-molecular-weight metabolites ( N  = 51); (2) lipid composition and particle concentration measures of lipoprotein subclasses ( N  = 98); (3) metabolite ratios ( N  = 81). This metabolomics platform has been extensively used in large-scaled epidemiological studies in the field of diabetes, cardiovascular disease, mortality and alcohol intake ( Akker et al., 2019 ; Würtz et al., 2016 ; Wurtz et al., 2012 ; Würtz et al., 2015 ; Fischer et al., 2014 ). The data contained missing values due to detection limits. Samples with more than 25 missings were removed ( N  = 71), metabolites with more than 250 missings were removed ( N  = 1). Other missing values were replaced with the median value per metabolite. In total 231 metabolites in 2910 samples remained for analysis.

Building biological clocks for multiple omics domains

Telomere length was multiplied by −1 to be able to compare directions of effects consistent with that of other biological clocks. For each of the other four omics domains (epigenetic, transcriptomic, metabolomic, and proteomic data) the same approach was used to compute biological clocks. First, the omics data were residualized with respect to technical covariates (batch, lab). Second, data per omics marker were normalized using a quantile-normal transformation. Finally, biological age was computed using cross-validation by splitting the sample in 10 equal parts. For each of the 10 groups, nine parts were used as training set and the 10th as test set. In the training set the biological age estimator was computed using ridge regression (R library glmnet), with chronological age as the outcome, and the omics data as predictors. Only for methylation and gene expression a selection of predictors (CpGs for methylation-based models and genes for gene-expression-based models) was made for each cross validation step: we increased the number of sites included in the elastic net in steps (steps for CpGs: 0, 100, 1000, 10,000, 80,000, 100,000, steps for gene expression 100, 500, 1000, 1200, 1400). CpGs/genes were selected in the order of their ranks derived from the association with age in the training sample. We selected the number of CpGs/genes where the cumulative association signal reached a stable plateau. This approach is based on the rationale that adding more markers should theoretically never decrease predictive power. We previously performed tests where the number of CpGs/genes was included in the loop over the k-folds. However, as it produced very similar results but is much more computer intensive ( Clark et al., 2020 ), this latter approach was not used. This approach resulted in 80,000 CpGs (mapping to 2976 genes) for the epigenetic clock, and 1200 probes (mapping to 767 genes) for the transcriptomic clock. For the proteomic and metabolomic data, all markers were used to predict age, because leaving markers out decreased the prediction accuracy. The predictor was then used in the test set to create an unbiased omics-based biological age. For each omics domain, biological aging was defined as the residuals of regressing biological age on chronological age ( Han et al., 2018 ; Peters et al., 2015 ). Thus, in the terminology we use here, the biological aging indicators represent the biological age acceleration: a positive value means that the biological age is larger than the chronological age. A composite index of biological aging was made by scaling each of the five biological indicators and taking the sum, in the 653 samples that had data for all five omics levels.

Health determinants

Alcohol consumption was assessed as units per week by using the AUDIT ( Babor et al., 1989 ). Smoking status was assessed by pack years (smoking duration * cigarettes per day/20). Physical activity ( Gerrits et al., 2013 ) was assessed using the International Physical Activity Questionnaire (IPAQ) ( Craig et al., 2003 ) and expressed as overall energy expenditure in Metabolic Equivalent Total (MET) minutes per week (MET level * minutes of activity * events per week).

Somatic health

BMI was calculated as measured weight divided by height-squared. Functional status is one of the most potent health status indicators in predicting adverse outcomes in aging populations ( Guralnik et al., 1996 ), including depression ( Milaneschi and Penninx, 2014 ). Assessment of functional status includes measures of physical impairments and disability, reflecting how individuals’ limitations interact with the demands of the environment. Two measures of physical impairments were available: Lung capacity was determined by measuring the peak expiratory flow (PEF in liter/minute) using a mini Wright peak flow meter. Hand grip strength was measured with a Jamar hand held dynamometer in kilograms of force and was assessed for the dominant hand. Furthermore, physical disability was measured with the World Health Organization Disability Assessment Schedule II (WHODAS-II)s the sum of scale 2 (mobility) and scale 3 (self-care). The number of self-reported current somatic diseases for which participants received medical treatment was counted. We used somatic disease categories as categorized previously ( Gerrits et al., 2013 ; Gaspersz et al., 2018 ): cardiometabolic, respiratory, musculoskeletal, digestive, neurological and endocrine diseases, and cancer. Metabolic syndrome components included waist circumference, systolic blood pressure, HDL cholesterol, triglycerides, and glucose levels, which measurement methods are described elsewhere ( Révész et al., 2014 ).

Mental health

Presence of current (6 month recency) major depressive disorder was assessed by the DSM-IV Composite International Diagnostic Interview (CIDI) version 2.1. Depressive severity levels in the week prior to assessment were measured with the 28-item Inventory of Depressive Symptomatology (IDS) self-report ( Rush et al., 1996 ). Childhood trauma was assessed with the Childhood Trauma Interview (CTI) ( de Graaf et al., 2002 ). In this interview, participants were asked whether they were emotionally neglected, psychologically abused, physically abused or sexually abused before the age of 16. The CTI reports the sum of the categories that were scored from 0 to 2 (0: never happened; 1: sometimes; 2: happened regularly), which was categorized into five categories.

Statistical analyses

For each of the five biological aging indicators we computed associations with demographic (sex, education), lifestyle (physical activity, smoking, alcohol use), somatic health (BMI, hand grip strength, lung function, physical disability, chronic diseases), and mental health (current depression, depression severity, childhood trauma) determinants using linear models with health determinants as predictors and biological aging as outcome (for each health determinant separately). All models included a covariate for sex, except for when sex was the outcome. For telomere length, chronological age was used as covariate in the models, for the other biological aging indicators age was not used as covariate because they are independent of chronological age by design. Standardized betas from these models are reported (by scaling predictor and outcome). Correction for multiple testing was done using permutation based FDR ( Fehrmann et al., 2011 ). Subject labels were permuted 1000 times and associations were computed using the permuted data (all biological aging indicators vs all health determinants). For each of the observed p-values ( p) the FDR was computed as the average number of permuted p-values smaller than p , divided by the amount of real p-values smaller than p, resulting in a p-value threshold of 2e-2 for a FDR of 5% for all tests. In the 653 overlapping samples with data in each biological clock domain, we scaled (mean 0, standard deviation 1) and summed up the five biological aging indicators in order to create a composite index of biological aging.

Longitudinal analysis of mortality and chronic disease onset

As NESDA is a longitudinal study, with several follow-up measurement waves, we conducted post-hoc analyses on the relationship between the biological aging indicators and subsequent outcomes after six years of follow-up duration. The average chronological age of our cohort (mean = 41 years, sd = 13, range = 18–65 years) is rather young, so high rates of mortality and morbidity were not expected. Mortality data was gathered at each measurement wave. Also, at each wave self-reported somatic diseases for which participants received medical treatment were assessed. Based on this, we created somatic disease categories as categorized previously ( Gerrits et al., 2013 ; Gaspersz et al., 2018 ): cardiometabolic, respiratory, musculoskeletal, digestive, neurological and endocrine diseases, and cancer. For these categories, we computed chronic disease onset defined as the disease not being present at baseline (time of biological aging assessment) and present at the latest wave (6 years after baseline). For each biological clock, we computed longitudinal analyses, using a linear model with mortality or chronic disease onset as outcome, and the biological clock residualized for chronological age as predictor, while correcting for sex.

According to European law (GDPR) data containing potentially identifying or sensitive patient information are restricted; our data involving clinical participants are not freely available in a public repository. However, we highly value scientific collaboration, therefore, in principle, NESDA data are available to all scientific researchers working at non-commercial research organizations worldwide. Researchers can request either existing data for data analyses or bioanalysis. Please visit the online data overview for an extensive overview of the available data and NESDA's current output ( http://www.nesda.nl ). Data are available upon request via the NESDA Data Access Committee ([email protected]). Gene expression data used for this study are available at dbGaP, accession number phs000486.v1.p1 ( https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000486.v1.p1 ). As agreed with NIH and approved by the local IRB, the data was scheduled to be deposited in the NIH controlled access repository dbGAP. However, dbGAP is currently full and as soon as the new NIH controlled access repository comes online data will be deposited.

• Sullivan PF
• Tischfield J
• D'Ambrosia D
• van Grootheest G
• Willemsen G
• Hottenga J-J
• Shabalin AA
• Milaneschi Y
• van den Oord E
• Google Scholar
• Suchiman HED
• Kranzler HR
• Lauerman RJ
• BBMRI-NL Metabolomics Consortium
• Al-Shehri T
• Onderwater GLJ
• Vijfhuizen LS
• Vogelzangs N
• van Duijn C
• van Greevenbroek M
• van der Kallen CJH
• van den Maagdenberg A
• Mook-Kanamori DO
• de Mutsert R
• Stehouwer CDA
• Terwindt GM
• Willems van Dijk K
• Zhernakova A
• Slagboom PE
• Corcoran DL
• Arseneault L
• Baccarelli A
• Harrington HL
• Rasmussen LJH
• Williams BS
• Radstake TRDJ
• Schalkwyk LC
• Vermetten E
• Breitling LP
• Schöttker B
• Holleczek B
• Ward-Caviness CK
• Demerath EW
• Studenski S
• Vandiver AR
• Murabito JM
• Bandinelli S
• Hernandez DG
• Singleton AB
• Kretschmer A
• Kronenberg F
• Linseisen J
• Meisinger C
• Waldenberger M
• Visscher PM
• Uitterlinden AG
• Feinberg AP
• van Meurs J
• Kooperberg C
• Christiansen L
• Christensen K
• Marshall AL
• Ainsworth BE
• Verhoeven JE
• Lindqvist D
• Penninx BWJH
• Delucchi KL
• Wolkowitz OM
• Vollebergh WAM
• Fehrmann RSN
• Smolonska A
• Hofstra RMW
• van den Berg LH
• van Heel DA
• te Meerman GJ
• Havulinna AS
• Tammesoo M-L
• Ala-Korpela M
• Franceschi C
• Garagnani P
• Blomberg BB
• Paganelli R
• Beekman ATF
• van Hemert AM
• Schoevers RA
• van Oppen P
• van Marwijk HW
• van der Horst H
• Antoniou EE
• Nordfjall K
• Balasubramanyam M
• Hendricks AE
• Nettleton JA
• Farzaneh-Far R
• Pavanello S
• Ramsey-Goldman R
• Hjelmborg JBH
• Mirabello L
• van der Harst P
• Kollerits B
• Strandberg T
• Eisenberg DTA
• dos Reis RMR
• Charchar FJ
• Tomaszewski M
• Krasnienkov D
• Kasielski M
• McDonnell BJ
• Sundquist K
• TELOMAAS group
• Guralnik JM
• Månsson KNT
• Rajapakse I
• Hastings WJ
• Stevenson AJ
• McCartney DL
• Hernández M
• Muñoz-Maniega S
• Spires-Jones TL
• McIntosh AM
• Langfelder P
• Tischfield JA
• de Geus EJC
• Abdellaoui A
• Pedersen NL
• Salzberg SL
• Baccarelli AA
• Magnusson PK
• Blackburn EH
• López-Otín C
• Partridge L
• Montgomery GW
• Multhaup ML
• von Zglinicki T
• Martin-Ruiz C
• Müezzinler A
• Dieffenbach AK
• Butterbach K
• Trichopoulou A
• Benjamin EJ
• Promislow DEL
• NESDA Research Consortium
• Spinhoven P
• Van Marwijk HW
• Assendelft WJ
• Van Der Meer K
• Hoogendijk WJ
• Schurmann C
• Conneely KN
• Tukiainen T
• Stranger BE
• De Jager PL
• Rivadeneira F
• van Rooij J
• Verbiest MMPJ
• Zeilinger S
• Yaghootkar H
• Williams-Blangero S
• Suchy-Dicey AM
• Enquobahrie DA
• Houwing-Duistermaat J
• Kloppenburg M
• den Hollander W
• Meulenbelt I
• Korstanje R
• van Meurs JBJ
• Abu-Amara D
• Makotkine I
• Hammamieh R
• Maihofer AX
• Armstrong D
• Ratanatharathorn A
• Ashley-Koch AE
• Risbrough V
• Nievergelt CM
• D'Ambrosio D
• Hottenga JJ
• Kattenberg M
• Middeldorp CM
• Makinen V-P
• Savolainen MJ
• Lehtimaki T
• Raitakari OT
• Jarvelin M-R
• Tynkkynen T
• Prieto-Merino D
• Kaikkonen J
• Lehtimäki T
• Gerszten RE
• Chaturvedi N
• Blankenberg S
• Järvelin M-R
• McDermott J

Author details

Contribution, contributed equally with, for correspondence, competing interests.

No external funding was received for this work.

Acknowledgements

The infrastructure for the NESDA study ( http://www.nesda.nl ) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (ZonMw, grant number 10-000-1002) and financial contributions by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Leiden University Medical Center, Leiden University, GGZ Rivierduinen, University Medical Center Groningen, University of Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Rob Giel Onderzoekscentrum). Telomere length assaying was supported through a NWO-VICI grant (number 91811602). Methylation sequencing was supported by NIMH grant R01MH099110. Metabolomics data were generated within the framework of the BBMRI Metabolomics Consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO, grant nr 184.021.007 and 184033111). Gene expression data were funded by the US National Institute of Mental Health (RC2MH089951).

Human subjects: The NESDA study was approved by the Ethical Review Boards of participating centers, and all participants signed informed consent. The population and methods of the NESDA study have been described in more detail elsewhere (Hillary et al., 2019).

Version history

• Received: May 29, 2020
• Accepted: December 14, 2020
• Version of Record published: February 9, 2021 (version 1)

© 2021, Jansen et al.

This article is distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use and redistribution provided that the original author and source are credited.

• 4,473 Page views
• 544 Downloads
• 43 Citations

Article citation count generated by polling the highest count across the following sources: Crossref , Scopus , PubMed Central .

Download links

Downloads (link to download the article as pdf).

• Article PDF

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools), categories and tags.

• biological aging
• epigenetics
• metabolomics
• transcriptomics

Research organism

Further reading, aging: unite to predict.

Integrating the analysis of molecular data from different sources may improve our understanding of the effects of biological aging.

Listen to Laura Han talk about the different biological clocks that influence ageing.

Biological ageing is linked to both physical and mental health

• Cancer Biology

N-cadherin directs the collective Schwann cell migration required for nerve regeneration through Slit2/3 mediated contact inhibition of locomotion

Collective cell migration is fundamental for the development of organisms and in the adult, for tissue regeneration and in pathological conditions such as cancer. Migration as a coherent group requires the maintenance of cell-cell interactions, while contact inhibition of locomotion (CIL), a local repulsive force, can propel the group forward. Here we show that the cell-cell interaction molecule, N-cadherin, regulates both adhesion and repulsion processes during rat Schwann cell (SC) collective migration, which is required for peripheral nerve regeneration. However, distinct from its role in cell-cell adhesion, the repulsion process is independent of N-cadherin trans-homodimerisation and the associated adherens junction complex. Rather, the extracellular domain of N-cadherin is required to present the repulsive Slit2/Slit3 signal at the cell-surface. Inhibiting Slit2/Slit3 signalling inhibits CIL and subsequently collective Schwann cell migration, resulting in adherent, nonmigratory cell clusters. Moreover, analysis of ex vivo explants from mice following sciatic nerve injury showed that inhibition of Slit2 decreased Schwann cell collective migration and increased clustering of Schwann cells within the nerve bridge. These findings provide insight into how opposing signals can mediate collective cell migration and how CIL pathways are promising targets for inhibiting pathological cell migration.

Be the first to read new articles from eLife

Scientists Have Reached a Key Milestone in Learning How to Reverse Aging

I t’s been 13 years in the making, but Dr. David Sinclair and his colleagues have finally answered the question of what drives aging. In a study published Jan. 12 in Cell , Sinclair, a professor of genetics and co-director of the Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, describes a groundbreaking aging clock that can speed up or reverse the aging of cells.

Scientists studying aging have debated what drives the process of senescence in cells—and primarily focused on mutations in DNA that can, over time, mess up a cell’s normal operations and trigger the process of cell death. But that theory wasn’t supported by the fact that older people’s cells often were not riddled with mutations, and that animals or people harboring a higher burden of mutated cells don’t seem to age prematurely .

Sinclair therefore focused on another part of the genome, called the epigenome. Since all cells have the same DNA blueprint, the epigenome is what makes skin cells turn into skin cells and brain cells into brain cells. It does this by providing different instructions to different cells for which genes to turn on, and which to keep silent. Epigenetics is similar to the instructions dressmakers rely on from patterns to create shirts, pants, or jackets. The starting fabric is the same, but the pattern determines what shape and function the final article of clothing takes. With cells, the epigenetic instructions lead to cells with different physical structures and functions in a process called differentiation.

More from TIME

In the Cell paper, Sinclair and his team report that not only can they age mice on an accelerated timeline, but they can also reverse the effects of that aging and restore some of the biological signs of youthfulness to the animals. That reversibility makes a strong case for the fact that the main drivers of aging aren’t mutations to the DNA, but miscues in the epigenetic instructions that somehow go awry. Sinclair has long proposed that aging is the result of losing critical instructions that cells need to continue functioning, in what he calls the Information Theory of Aging. “Underlying aging is information that is lost in cells, not just the accumulation of damage,” he says. “That’s a paradigm shift in how to think about aging. “

His latest results seem to support that theory. It’s similar to the way software programs operate off hardware, but sometimes become corrupt and need a reboot, says Sinclair. “If the cause of aging was because a cell became full of mutations, then age reversal would not be possible,” he says. “But by showing that we can reverse the aging process, that shows that the system is intact, that there is a backup copy and the software needs to be rebooted.”

In the mice, he and his team developed a way to reboot cells to restart the backup copy of epigenetic instructions, essentially erasing the corrupted signals that put the cells on the path toward aging. They mimicked the effects of aging on the epigenome by introducing breaks in the DNA of young mice. (Outside of the lab, epigenetic changes can be driven by a number of things, including smoking, exposure to pollution and chemicals.) Once “aged” in this way, within a matter of weeks Sinclair saw that the mice began to show signs of older age—including grey fur, lower body weight despite unaltered diet, reduced activity, and increased frailty.

Stay up-to-date on the latest health news, and get expert advice on living well in TIME’s Health Matters newsletter. Subscribe here.

The rebooting came in the form of a gene therapy involving three genes that instruct cells to reprogram themselves—in the case of the mice, the instructions guided the cells to restart the epigenetic changes that defined their identity as, for example, kidney and skin cells, two cell types that are prone to the effects of aging. These genes came from the suite of so-called Yamanaka stem cells factors—a set of four genes that Nobel scientist Shinya Yamanaka in 2006 discovered can turn back the clock on adult cells to their embryonic, stem cell state so they can start their development, or differentiation process, all over again. Sinclair didn’t want to completely erase the cells’ epigenetic history, just reboot it enough to reset the epigenetic instructions. Using three of the four factors turned back the clock about 57%, enough to make the mice youthful again.

“We’re not making stem cells, but turning back the clock so they can regain their identity,” says Sinclair. “I’ve been really surprised by how universally it works. We haven’t found a cell type yet that we can’t age forward and backward.”

Rejuvenating cells in mice is one thing, but will the process work in humans? That’s Sinclair’s next step, and his team is already testing the system in non-human primates. The researchers are attaching a biological switch that would allow them to turn the clock on and off by tying the activation of the reprogramming genes to an antibiotic, doxycycline. Giving the animals doxycycline would start reversing the clock, and stopping the drug would halt the process. Sinclair is currently lab-testing the system with human neurons, skin, and fibroblast cells, which contribute to connective tissue.

In 2020, Sinclair reported that in mice, the process restored vision in older animals; the current results show that the system can apply to not just one tissue or organ, but the entire animal. He anticipates eye diseases will be the first condition used to test this aging reversal in people, since the gene therapy can be injected directly into the eye area.

“We think of the processes behind aging, and diseases related to aging, as irreversible,” says Sinclair. “In the case of the eye, there is the misconception that you need to regrow new nerves. But in some cases the existing cells are just not functioning, so if you reboot them, they are fine. It’s a new way to think about medicine.”

That could mean that a host of diseases—including chronic conditions such as heart disease and even neurodegenerative disorders like Alzheimer’s —could be treated in large part by reversing the aging process that leads to them. Even before that happens, the process could be an important new tool for researchers studying these diseases. In most cases, scientists rely on young animals or tissues to model diseases of aging, which doesn’t always faithfully reproduce the condition of aging. The new system “makes the mice very old rapidly, so we can, for example, make human brain tissue the equivalent of what you would find in a 70 year old and use those in the mouse model to study Alzheimer’s disease that way,” Sinclair says.

Beyond that, the implications of being able to age and rejuvenate tissues, organs, or even entire animals or people are mind-bending. Sinclair has rejuvenated the eye nerves multiple times, which raises the more existential question for bioethicists and society of considering what it would mean to continually rewind the clock on aging.

This study is just the first step in redefining what it means to age, and Sinclair is the first to acknowledge that it raises more questions than answers. “We don’t understand how rejuvenation really works, but we know it works,” he says. “We can use it to rejuvenate parts of the body and hopefully make medicines that will be revolutionary. Now, when I see an older person, I don’t look at them as old, I just look at them as someone whose system needs to be rebooted. It’s no longer a question of if rejuvenation is possible, but a question of when.”

More Must-Reads From TIME

• Exclusive: Google Workers Revolt Over $1.2 Billion Contract With Israel
• Stop Looking for Your Forever Home
• Jane Fonda Champions Climate Action for Every Generation
• Hormonal Birth Control Doesn’t Deserve Its Bad Reputation
• The Sympathizer Counters 50 Years of Hollywood Vietnam War Narratives
• Essay: The Relentless Cost of Chronic Diseases
• The Best TV Shows to Watch on Peacock
• Want Weekly Recs on What to Watch, Read, and More? Sign Up for Worth Your Time

Write to Video by Andrew. D Johnson at [email protected]

You May Also Like

How pregnancy may speed up the aging process

The fatigue and pangs of pregnancy have made many women feel older than their years. Now there’s new research that suggests pregnancy may, in fact, accelerate the aging process.

Two new studies of genetic markers in the blood cells of pregnant women suggest that their cells seem to age at an exaggerated clip, adding extra months or even years to a woman’s so-called biological age as her pregnancy progresses.

But one of the studies also suggests this process may reverse itself once a woman gives birth, rewinding time so that some mothers’ cells seemingly end up biologically younger afterward than they’d been during gestation, especially if a mother breastfeeds her baby.

Together, the studies underscore how physically demanding pregnancy is. But they also raise important questions about aging itself and whether it really can be sped up, slowed or reversed by pregnancy.

Aging in pregnancy

The newest of the studies , published today in Proceedings of the National Academy of Sciences, found pregnancy “has a big impact on a woman’s body” and biological age, said Calen P. Ryan, an associate research scientist at the Columbia Aging Center at Columbia University in New York, who led the new research.

In it, scientists used several different biological-age “clocks” and other measures to analyze DNA markers in blood samples. These clocks aren’t timepieces but, instead, algorithms, developed using artificial intelligence programs, that examine the patterns of specialized chemical markers found on the outside of some genes. These markers accumulate and change in response to our age, health and lifestyles, a process known as epigenetics.

The algorithms can use these epigenetic markers to estimate the relative age of cells. This measure, often referred to as biological age, can differ from someone’s chronological age, which just means how long he or she’s been alive.

In the new study, the researchers checked blood samples from 825 young women in the Philippines, all born in the same year. Some had been or were pregnant, and others hadn’t conceived. Analyzing these samples, the epigenetic clocks broadly agreed that the biological age of the young women who’d been or were pregnant tended to be higher than that of the others, by at least several months, even after the researchers controlled for economic disparities and other social and health factors.

Pregnancy as a stress test

Similarly, the other new study, published in March in Cell Metabolism , used several different epigenetic clocks to estimate the changing internal age of pregnant women at several points during their pregnancies.

“We were very interested in looking at the impacts of pregnancy as a naturally occurring stress test,” said Kieran J. O’Donnell, an assistant professor at the Yale Child Study Center and Yale School of Medicine, who oversaw one of the new studies.

Get Well+Being tips straight to your inbox

With blood samples from 119 pregnant American women and five different clocks, the researchers tracked the epigenetic changes related to the women’s biological age, starting early in gestation and ending three months after they’d given birth.

The clocks again agreed that pregnancy seemed to be aging the incipient moms as they approached full term, making their blood cells’ DNA appear to be as much as two years older than it had been earlier in the pregnancy.

More encouraging, though, O’Donnell said, is that this aging seemed to reverse for most of the women within three months after birth. In general, their patterns of DNA markers soon reverted to an earlier, more-youthful state, and for some new moms who’d breastfed exclusively in the first three months postpartum, overshot the mark, leaving them apparently “younger” biologically than before, by as much as eight years, the study’s authors wrote, “indicating a pronounced reversal of biological aging.”

Disagreement about aging

But some researchers who study aging, longevity and epigenetics are skeptical of the studies’ findings and conclusions. “It seems unlikely to me that pregnancy induces a whole-body acceleration of biological aging which is then reversed soon after pregnancy,” said Matt Kaeberlein, a longtime longevity researcher who serves as CEO of Optispan, a company that promotes longevity and produces the “Optispan” podcast.

Charles Brenner, who studies metabolism, cancer and aging at the Beckman Research Institute of the City of Hope National Medical Center in California was blunter in an email. “100%, it’s a misuse of aging biomarkers,” he wrote.

Both scientists, as well as others who’ve discussed the studies online, speculate that the epigenetic shifts seen during pregnancy probably reflect the profound physiological demands of carrying a child. They’re “a transient response to the stress of pregnancy, particularly in the immune system,” Kaeberlein said.

What they aren’t is evidence that pregnant women suddenly get older and then younger, these researchers say, or experience lasting effects that could directly shorten or lengthen their life spans.

But the cellular changes being picked up and analyzed by the epigenetic clocks might someday be useful health indicators. If a pregnant woman’s epigenetic markers don’t soon bounce back once she’s no longer pregnant, she and her doctor might want to closely monitor her blood pressure, blood sugar and other standard measures of health, not because she suddenly seems older after becoming pregnant, but because, Ryan said, “pregnancy is such a big deal, physically.”

Do you have a fitness question? Email [email protected] and we may answer your question in a future column.

Read more from Well+Being

Well+Being shares news and advice for living well every day. Sign up for our newsletter to get tips directly in your inbox.

Are you taking your meds wrong ? Many patients make these common mistakes.

Centenarians give their advice about everything.

The wall sit is a simple exercise that can lower your blood pressure.

Tart cherries — more specifically, tart cherry juice — may help with inflammation and pain.

Do you self-sabotage ? Here’s how to stop.

Premium Content

Just one pregnancy can add months to your biological age

A landmark new study confirms that growing a human being in nine months takes a toll—and multiple pregnancies can have a cumulative effect.

Surprising no one who has ever been pregnant, scientists have found that growing a human being from scratch makes your body “older."

New research suggests that a single pregnancy can add between two to 14 months to your biological age.  

“Pregnancy has a cost that appears to be detectable even" as early as your 20s, says study leader Calen Ryan , a human biologist at Columbia University’s school of public health in New York City.

It’s a “landmark study” that reaffirms what women already know—pregnancy takes a tremendous toll on the body, says Yousin Suh , a Columbia University professor who researches how pregnancy affects aging and wasn’t involved in the study, published April 8 in Proceedings of the National Academy of Sciences .  

Your chronological age—or the number of trips you’ve made around the sun—may be different than your biological age, which is how old your cells and organs seem based on their biochemistry.  

Ryan studies the reasons why our bodies may age faster or slower than we expect them too, and a lot of that comes down to epigenetics, or how and when our bodies decide to turn genes on and off. (Read how scientists are finally studying women's bodies—and what they're learning.)

Certain life events—including major illnesses, trauma, or periods of intense stress —seem to cause “jumps” in epigenetic age as the body redirects energy and resources toward coping with these challenges.

And since there are few biological functions more arduous than growing an entire person in just nine months, the recent study confirms the scientists’ suspicion that pregnancy—particularly multiple pregnancies—come at a cost to biological age.

Your epigenetic clock

If our genome is an instruction manual, the epigenome is a complex system of bookmarks, highlights, and underlines that tells our cells which genes to read and when. This often happens through methylation, a process by which tiny chemical tags called methyl groups attach to a section of DNA .

Which genes need to be active changes constantly in response to our environment and experiences, so those methyl groups need frequent moving and replacing. Yet as we age, this maintenance machinery appears to start making errors, causing methylations to accumulate in some places and disappear in others. (Read how influencing your genes could help you live longer.)

By taking a blood sample and tallying methyl bookmarks in key locations along the genetic code, scientists can calculate a person’s epigenetic age via a suite of algorithms called “clocks.” These clocks predict your risk of death and health complications, but less known is how fertility impacts your biological age .

To learn more, Ryan and his colleagues turned to a long-running study on intergenerational health in the Philippines . In 2005, they analyzed blood samples from 825 women participants between the ages of 20 and 22. (Learn about simple innovations that could help millions of pregnant women.)

The scientists identified a striking difference—the number of epigenetic changes in their DNA revealed that women who had been pregnant were between four and 14 months were biologically older than their peers who hadn’t, even after controlling for factors such as income level and smoking habits.

You May Also Like

Scientists are finally studying women’s bodies. This is what we’re learning.

Can aging be cured? Scientists are giving it a try

It's not your life span you need to worry about. It's your health span.

A cumulative effect.

Despite being close in age, the women in the study were already on very different fertility trajectories—some had never been pregnant, some reported one or more previous pregnancies, and some were pregnant at the time the samples were collected.

That raised a crucial question: Did multiple pregnancies create a cumulative effect of aging, with each additional pregnancy further raising the mother’s epigenetic age?

Using the first blood samples as a baseline, the researchers collected new samples from 331 of the same women while they were pregnant between four and nine years later.   (Learn how babies develop in the womb.)

By comparing the two snapshots of each woman’s epigenetic age, Ryan and his team calculated the impact of each additional pregnancy during the intervening years.

“Women who had more pregnancies during that time had more change in epigenetic aging,” Ryan says, with each pregnancy tacking on two to three months to the parent’s biological age.

Suh, who studies the cost of reproduction on the human body, says Ryan’s findings represent an important advancement in our understanding of how multiple pregnancies affect biological age, as the bulk of existing research has examined just one pregnancy.

The new research, she says, squares with what we know about high birthrates—that experiencing many pregnancies can lead to a shorter life span and higher risk of cardiovascular disease.

Reason for optimism  

But would-be parents shouldn’t despair, Suh and Ryan agree—it’s not certain that a slightly higher epigenetic age during your childbearing years will lead to complications decades down the road.

In fact, some research suggests there may be a “sweet spot” for fertility, Suh says. For instance, one or two pregnancies may be better than none in some cases, as pregnancy is linked to lower risks of certain cancers and having at least one child is associated with a slightly longer life expectancy .

As scientists learn more about aging and fertility, “we can work towards identifying people who might be at higher risk,” Ryan adds, and come up with strategies to lessen the negative impacts of pregnancy.

Recent studies indicate the epigenetic cost of pregnancy may differ by country and culture, suggesting that parental support and access to healthcare may play a significant role—improving these could soften pregnancy’s blow to epigenetic age.

Suh adds more research will be needed to untangle the impact of child- rearing   from childbirth on epigenetic age, as well as investigate whether the burden of pregnancy is greater when parents are older than those in the study.

While it may feel like common knowledge that pregnancy ages you, it’s a relatively new concept in the scientific literature—and Suh says that research like Ryan’s is long overdue.

“I’m so encouraged that this kind of study is now being done,” she adds.

Related Topics

• FAMILY LIFE

What can your DNA say about your risk of opioid addiction?

IVF revolutionized fertility. Will these new methods do the same?

What is our fear of aging doing to our kids’ mental health?

Twins can become ‘unidentical’—and more fascinating twin facts

This is one of the leading causes of maternal mortality. A new test could change that.

• History & Culture
• Environment
• Paid Content

History & Culture

• History Magazine
• Terms of Use
• Privacy Policy
• Your US State Privacy Rights
• Children's Online Privacy Policy
• Interest-Based Ads
• About Nielsen Measurement
• Do Not Sell or Share My Personal Information
• Nat Geo Home
• Attend a Live Event
• Book a Trip
• Inspire Your Kids
• Shop Nat Geo
• Visit the D.C. Museum
• Learn About Our Impact
• Support Our Mission
• Advertise With Us
• Customer Service
• Renew Subscription
• Manage Your Subscription
• Work at Nat Geo
• Sign Up for Our Newsletters
• Contribute to Protect the Planet

Copyright © 1996-2015 National Geographic Society Copyright © 2015-2024 National Geographic Partners, LLC. All rights reserved

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 13 April 2023

Application and limitation of a biological clock-based method for estimating time of death in forensic practices

• Akihiko Kimura 1 ,
• Yuko Ishida 1 ,
• Mizuho Nosaka 1 ,
• Akiko Ishigami 1 ,
• Hiroki Yamamoto 1 ,
• Yumi Kuninaka 1 ,
• Satoshi Hata 2 ,
• Mitsunori Ozaki 3 &
• Toshikazu Kondo 1  

Scientific Reports volume  13 , Article number:  6093 ( 2023 ) Cite this article

1493 Accesses

1 Altmetric

Metrics details

• Medical research

Estimating time of death is one of the most important problems in forensics. Here, we evaluated the applicability, limitations and reliability of the developed biological clock-based method. We analyzed the expression of the clock genes, BMAL1 and NR1D1, in 318 dead hearts with defined time of death by real-time RT-PCR. For estimating the time of death, we chose two parameters, the NR1D1/BMAL1 ratio and BMAL1/NR1D1 ratio for morning and evening deaths, respectively. The NR1D1/BMAL1 ratio was significantly higher in morning deaths and the BMAL1/NR1D1 ratio was significantly higher in evening deaths. Sex, age, postmortem interval, and most causes of death had no significant effect on the two parameters, except for infants and the elderly, and severe brain injury. Although our method may not work in all cases, our method is useful for forensic practice in that it complements classical methods that are strongly influenced by the environment in which the corpse is placed. However, this method should be applied with caution in infants, the elderly, and patients with severe brain injury.

Similar content being viewed by others

Expression of HO1 and PER2 can predict the incidence of delirium in trauma patients with concomitant brain injury

Matti Steimer, Sandra Kaiser, … Nils Schallner

Gene expression profile in cases of infectious death in infancy

Linda Ferrante, Siri H. Opdal, … Torleiv O. Rognum

Cell survival and DNA damage repair are promoted in the human blood thanatotranscriptome shortly after death

Laura G. Antiga, Lode Sibbens, … Bram Bekaert

Introduction

Estimating the time of death, which is often extremely difficult, is one of the most important tasks in forensic practice. To date, numerous methods for estimating the time of death have been developed 1 , 2 . Over the last decade, various innovative techniques, such as tissue nano mechanics 3 , mass spectrometry-based quantitative proteomics 4 , analysis of oral microbiota community 5 and micro-RNA analysis 6 , have been introduced to estimate the postmortem interval, bringing substantial progress into this field. However, most of these methods estimate the time since death, but not estimate the time of death. The current method for estimating the time of death remains unsatisfactory.

Advances in chronobiology have brought about great impacts and progress in various medical fields, such as chronopharmacology, chronotherapy and sleep disorder therapy 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 . Chronobiology can contribute to forensic medicine, especially in the estimation of the time of death. However, the forensic application of chronobiology is quite limited. To our knowledge, there is currently only one report of the application of chronobiology to forensic investigation, in which the time of death was estimated based on the melatonin concentration in pineal body, serum and urine 15 . Therefore, we tried to apply the biological clock to the estimation of the time of death. In 2011, we reported the first forensic application of chronobiology in the estimation of the time of death using a mouse model and applied the method to a few autopsy cases 16 . In our previous report, we used two main oscillator genes, brain and muscle aryl hydrocarbon receptor nuclear translocator-like 1 ( BMAL1 or ARNTL ) and nuclear receptor subfamily 1 group D member 1 (Rev-Erbα, NR1D1 ), in the circadian clock system to read the biological clock in the kidneys, livers and hearts. Since these two clock genes oscillate in opposite phases 17 , 18 , the NR1D1 / BMAL1 ratio amplifies the circadian oscillation of each gene expression 16 . We demonstrated the applicability of our method in forensic practice, but we could not clarify the reliability and limitations of the method, because only a limited number of autopsy cases were examined.

Since its development, we have applied the method to our routine practice of estimating the time of death in autopsy cases. In this study, we evaluated our method based on the results of its application to 318 autopsy cases with known times of death in our department. We show the practical applicability and limitations of our method, which estimates the time of death based on the biological clock.

The pattern of clock gene expression in the hearts of autopsy cases

The NR1D1/BMAL1 ( N/B ) and BMAL1/NR1D1 ( B/N ) ratios were plotted against the time of death, resulting in clear peaks around 6:00 and 18:00, respectively (Fig.  1 a and b), indicating that clock gene expression can be precisely detected even in dead bodies.

Temporal pattern of the N/B and B/N ratios in autopsy cases. The N/B ( a ) and B/N ( b ) ratios were plotted against the time of death. The autopsy cases were divided into four-time domains, and the N/B ( c ) and B/N ( d ) ratios in each time domain were examined by multiple comparison tests. ** p  < 0.01, 3:00–8:59 time domain versus other time domains; ## p  < 0.01, 15:00–20:59 time domain versus other time domains.

Figure  1 c and d show the mean values of the N/B and B/N ratio in the four-time domains (morning, 3:00–8:59, noon, 9:00–14:59, evening, 15:00–20:59 and night, 21:00–2:59). The N/B and B/N ratios were significantly higher in the morning and evening than in the other time domains, respectively, which confirms that these ratios are suitable parameters for estimating the time of death. However, in some autopsy cases, the N/B and B/N ratios exhibited very low values in the morning and evening, respectively (Fig.  1 a and b), suggesting that some factors affected these parameters.

Evaluation of the factors affecting the biological clock in the deceased

We next examined the factors affecting the ratios in the deceased. First, we examined gender differences in the temporal pattern of the ratios (male, n = 224; female, n = 94). Both genders showed a similar temporal pattern of the N/B (Fig.  2 a) and B/N (Fig.  2 b) ratios. The N /B (Fig.  2 c) and B/N (Fig.  2 d) ratios in deceased males were significantly higher in the morning and evening, respectively, which was similar to the results of total cases (Fig.  1 c and d). On the other hand, the N/B ratio in deceased females (Fig.  2 c) was significantly higher in the morning, which is similar to the results in deceased males, whereas the B/N ratio was higher in the evening than in other time domains, but the difference was not statistically significant. (Fig.  2 d).

Assessment of the effect of gender on the N/B ( a ) and B/N ( b ) ratios in the hearts of the deceased. The N/B and B/N ratios in males (closed circles, n = 224) and females (open circles, n = 94) were plotted against the time of death. The N/B ( c ) and B/N ( d ) ratios in four-time domains were examined in males (solid columns) and females (open columns) by multiple comparison tests. ** p  < 0.01, 3:00–8:59 time domain versus other time domains; ## p  < 0.01, 15:00–20:59 time domain versus other time domains.

We divided the cases into three age groups (≤ 19 years, n = 13; 20–69 years, n = 200; ≥ 70 years, n = 105). All age groups showed similar temporal patterns (Fig.  3 a–d). The N/B ratio in the morning was significantly higher than those in the three other time domains in the 20–69 and ≥ 70 years groups (Fig.  3 c). The B/N ratio in the evening was higher than those in the three other time domains only in the 20–69 years group (Fig.  3 d). In contrast, the temporal pattern of the N/B ratio in the morning (3:00–8:59) and that of the B/N ratio in the evening (15:00–20:59) did not significantly differ from those in other time domains in the ≤ 19 years group (Fig.  3 c and d). The N/B ratio in the morning and the B/N ratio in the evening were plotted against age; the results showed that the N/B and B/N ratios are independent of age (Fig.  3 e and f). However, the case number in young and high-age groups was small. Therefore, more cases must be used for statistical analysis of these groups.

Assessment of the effect of age on the N/B ( a ) and B/N ( b ) ratios in the hearts of the deceased. The autopsy cases were divided into three age groups: ≤ 19 years (closed circles, n = 13), 20–69 years, (open circles, n = 200), and ≥ 70 years (gray circles, n = 105). The N/B ( c ) and B/N ( d ) ratios in four-time domains were examined in the ≤ 19 years (solid columns), 20–69 years (open columns), and ≥ 70 years (gray columns) groups by multiple comparison tests. The N/B ratios in the 3:00–8:59 time domain ( e ) and the B/N ratio in the 15:00–20:59 time domain ( f ) were plotted against age. ** p  < 0.01, 3:00–8:59 time domain versus other time domains; ## p  < 0.01, 15:00–20:59 time domain versus other time domains.

Finally, we examined the effect of post-mortem intervals on the ratios. We divided the cases into two groups, < 30 h postmortem interval (n = 250) and > 30 h postmortem interval (n = 68). The N/B and B/N ratios in both groups showed peaks in the morning and evening, respectively, indicating that the post-mortem interval had virtually no effect on them (Fig.  4 a–f). However, there was no significant difference in the B/N ratio between the evening and noon time domains in the > 30 h post-mortem interval group (Fig.  4 d). This is likely due to the small number of cases (n = 9) in the noon time domain of the > 30 h post-mortem interval group. The N/B ratio in the morning and the B/N ratio in the evening were plotted against the postmortem interval; the results indicated that the ratios are independent of the postmortem interval (Fig.  4 e and f).

Assessment of the effect of postmortem interval on the N/B ( a ) and B/N ( b ) ratios in the hearts of the deceased. The autopsy cases were divided into two groups of postmortem interval, < 30 h (closed circles, n = 250) and > 30 h (open circles, n = 68). The N/B ( c ) and B/N ( d ) ratios in four-time domains were examined in the < 30 h (closed columns) and > 30 h (open columns) postmortem interval groups by multiple comparison tests. The N/B ratios in the 3:00–8:59 time domain ( e ) and the B/N ratios in the 15:00–20:59 time domain ( f ) were plotted against postmortem interval. ** p  < 0.01, 3:00–8:59 time domain versus other time domains; ## p  < 0.01, 15:00–20:59 time domain versus other time domains.

Evaluation of the cause of death affecting the biological clock

We next examined the differences in the temporal pattern of the N/B and B/N ratios between intrinsic (n = 73) and extrinsic (n = 245) death groups. As shown in Fig.  5 a and b, there were no significant differences between the groups. In the extrinsic death cases, the N/B ratio in the morning and the B/N ratio in the evening were significantly higher than those in other time domains (Fig.  5 c and d). However, in the intrinsic death cases, the N/B ratio in the morning was significantly higher than those in other time domains, but the B/N ratio in the evening did not significantly differ from those in other time domains (Fig.  5 c and d). We also examined the effect of specific causes of death on the ratios. The most common causes of death (Table 1 ), including hemorrhagic and traumatic shock, aortic rupture, drowning, burn, asphyxia, intoxication, and ischemic heart failure, except brain injury, did not seem to have a significant effect on the ratios (data not shown).

Assessment of the effect of the cause of death on the N/B ( a ) and B/N ( b ) ratios in the hearts of the deceased. The autopsy cases were divided into two groups, intrinsic death (closed circles, n = 73) and extrinsic death (open circles, n = 245). The N/B ( c ) and B/N ( d ) ratios in four-time domains were examined in the intrinsic death (closed column) and extrinsic death (open column) groups by multiple comparison tests. ** p  < 0.01, 3:00–8:59 time domain versus other time domains; ## p  < 0.01, 15:00–20:59 time domain versus other time domains.

Of note, brain injury, especially, chronic brain injury with cerebral edema, cerebral hernia, and cerebral hypoxia seemed to strongly affect the ratios in the hearts of the deceased. As shown in Fig.  6 a and b, the morning peak of the N/B ratio and the evening peak of the B/N ratio did not take place in cases of delayed death due to chronic brain injury (n = 15), whereas the peaks of the N/B and B/N ratios were observed in acute death cases with severe brain injury (n = 35). The cases of delayed death due to chronic brain injury did not show an oscillation in the N/B and B/N ratios (Fig.  6 c and d). The N/B ratio in the morning significantly differs from that in the evening in cases of acute death with severe brain injury (Fig.  6 c). However, these findings are from a limited small number of cases, and the loss of oscillation of N/B and B/N ratios due to chronic brain injury needs to be confirmed in more cases.

Assessment of the effect of severe brain injury on the N/B ( a ) and B/N ( b ) ratios in the hearts of the deceased. The severe brain injury cases were divided into two groups, cases of immediate death by acute brain injury (closed circles, n = 35) and those of protracted death by chronic brain injury with cerebral edema, cerebral hernia, or cerebral hypoxia (open circles, n = 15). The N/B ( c ) and B/N ( d ) ratios in four-time domains were examined in the immediate death by acute brain injury (closed columns) and protracted death by chronic brain injury (open columns) groups by multiple comparison tests. ** p  < 0.01, 3:00–8:59 time domain versus 15:00–20:59 time domain. $ p  < 0.05, immediate death by acute brain injury versus protracted death by chronic brain injury.

Applicability of our method to forensic practice

Our method reads the biological clock in the deceased; however, there are only two-time domains (morning, around 6:00; evening, around 18:00) in the clock. The N/B ratio is suitable for reading at 6:00 and the B/N ratio is suitable for reading at 18:00. All cases where the N/B ratio was > 25 were deaths occurring from 1:00 to 10:00 (n = 40), and those where the ratio was > 40 were deaths occurring from 3:00 to 9:00 (n = 23) (Fig.  7 a). On the other hand, all cases where the B/N ratio was > 1.5 were deaths occurring from 14:00 to 22:00 (n = 39), and those where the ratio was > 4 were deaths occurring from 15:00 to 20:00 (n = 11) (Fig.  7 b). However, only 24.8% (79/318) of morning and evening deaths were predicted by our method, and low values of N/B and B/N ratios do not exclude morning and evening deaths. Therefore, although this method is not effective in all cases, it is still important in forensic practice because it complements conventional methods from a completely different perspective.

Criteria for applying our method to forensic practice. Temporal pattern of the N/B ratio in the heart of the deceased. ( a ) Cases with N/B ratio > 25 (red line) had a time of death between 1:00 and 10:00 (n = 40), and those with a ratio > 40 (blue line) had a time of death between 3:00 and 9:00 (n = 23). Temporal pattern of the B/N ratio in the hearts of the deceased. ( b ) Cases with B/N ratio > 1.5 (red line) had a time of death between 14:00 and 22:00 (n = 39), and those with a ratio > 4 (blue line) had a time of death between 15:00 and 20:00 (n = 11).

To date, most methods for estimating the time of death estimate the time since death and are affected by internal, external, antemortem, and postmortem conditions. We hypothesized that the biological clock stops at the time of death, and developed a method to read this stopped biological clock 16 . Therefore, our method estimates the time of death, not the time since death, and appears to be independent of environmental factors; however, it can be influenced by internal factors such as age, gender, cause of death, and lifestyle of the deceased. The reliability and limitations of the practical application of newly developed methods must be evaluated. Thus, we examined our method in increased number of cases with a defined time of death. The N/B ratio showed a peak around 6:00, indicating that the method can give a stable result. Furthermore, we examined a novel reverse parameter, the B/N ratio, which showed a peak around 18:00. The N/B ratio was high in the morning, while the B/N ratio was high in the evening; therefore, we can determine whether death occurred in the morning or evening with this method. However, low N/B and B/N values were often found in cases of death at around 6:00 and 18:00, respectively. Such irregular values were not seen in the animal experiments because mice had a uniform genetic background and were bred in a strictly controlled environment 16 . Furthermore, all mice were sacrificed quickly by cervical dislocation under deep anesthesia. On the other hand, humans have different genetic backgrounds and live in various time patterns (e.g., shift workers), which might affect the expression pattern of biological clock genes 19 , 20 .

In the present study, we demonstrated that gender, age, and postmortem interval (within 96 h after death) did not significantly affect the N/B and B/N ratios. However, the youngest (< 1 year old, n = 5), and oldest (> 90 years old, n = 14) cases as well as those with long postmortem intervals (> 48 h, n = 11) were examined in a limited number. It is known that circadian rhythms such as body temperature and nocturnal sleep onset appear within 60 days after birth 21 . Moreover, the circadian oscillation of clock gene expression in the SNC (suprachiasmatic nucleus) and some peripheral tissues has been confirmed in nonhuman primate fetuses 21 , suggesting that clock gene expression in the heart of human infants may also show circadian oscillation. Therefore, the biological clock-based estimation of the time of death seems to be applicable to infant cases. However, maternal melatonin affects clock gene expression in nonhuman primate fetuses 22 , indicating that the breastfeeding pattern might affect the circadian clock in infants. Therefore, differences in clock gene expression patterns between the infant's and adult's heart may be found in future research. On the other hand, it has been reported that aging significantly affects the circadian pattern of gene expression in the human prefrontal cortex, which might bring about changes in the circadian rhythm in old age 23 . Different circadian rhythms in older individuals, especially the feeding pattern, can affect biological clock gene expression 19 , 20 . Since the biological clock in the peripheral tissues is also under adrenergic control 24 , age-related changes in the beta-adrenergic neuroeffector system might alter the clock gene expression pattern in the heart of older adults 25 . Based on the above-mentioned facts, our method should be applied carefully to infants and older adults. Longer postmortem intervals might cause RNA deterioration 26 , which increases the uncertainty of the results. Since the number of cases in children, the elderly, and cases with a long postmortem interval is small, a study using an increased number of cases is necessary for a statistically meaningful discussion.

The cause of death seemed to affect the N/B and B/N ratios. However, there were no significant differences in the temporal patterns between intrinsic and extrinsic death cases. Moreover, most causes of death did not significantly affect the ratios. Exceptionally, the peaks of both ratios almost disappeared in the cases of death with cerebral edema, cerebral hernia, or cerebral hypoxia. We also found an alteration of the N/B ratio in the iliopsoas muscle tissue of cases with chronic brain injury (not shown), suggesting that chronic brain injury-induced SCN damage brings about a systemic alteration of peripheral clock gene expression. Disturbances in circadian rhythms due to brain trauma have been reported 27 , 28 , 29 . Recently, traumatic brain injury-induced alteration of clock gene expression in the SCN and hippocampus was reported in a rat model 30 . Our preliminary result in mouse model of water intoxication showed that cerebral edema induced alteration of biological clock in the heart ( Supplementary Data ). Therefore, biological clock-based estimation of the time of death should be applied with caution to cases of severe brain injury or intrinsic death with diseases affecting brain function such as severe hepatic encephalopathy.

We analyzed 318 cases in the present study. However, there was bias in the number of cases with regards to gender, age, cause of death, and other factors. The number of cases in some groups, such as females, was less than 100, and some of these cases did not show statistical significance in the N/B and B/N ratios between morning and evening time domains compared to other time domains. Therefore, our method should be further validated with studies using a larger number of cases. Multifacility research may be necessary to conduct an analysis with a sufficient number of cases.

Recently, an analysis of human transcriptional rhythms using a cyclic ordering algorithm called Cyclops was reported 31 . The Cyclops algorithm enables the estimation of the circadian phase of a sample from high-throughput data that lack temporal information, and is expected to be an innovative approach to estimating the time of death in forensic practice. As Cyclops is an algorithm for the temporary reconstruction of population-based human organ data, its usefulness as a method for estimating the time of death for individual autopsy samples in forensic practice is uncertain. The usefulness and problems of Cyclops will be clarified by verifying it in forensic practice. Another problem is that high-throughput analysis is currently expensive for forensics.

In the present study, our method was able to predict only 79 cases of morning or evening deaths out of a total of 318 cases (about 25%). This indicates that our method only works in limited cases. However, all classical methods for estimating time of death have uncertainties, and are based on postmortem changes that begin at death and are influenced by various environmental factors. In contrast, our method directly estimates the death time based on the circadian clock, which stops at death and is unaffected by factors that influence postmortem changes. For example, after a deceased person's body temperature reaches ambient temperature, it is difficult to estimate time since death based on body temperature. In the case of burn death, many classical estimation methods, such as body temperature, corneal opacity and rigor mortis cannot be used. Therefore, all classical estimation methods have limitations in their applicability. Our method complements conventional methods from a completely different perspective and can be used where conventional methods are not applicable.

In conclusion, our method makes it possible to estimate the morning and evening deaths by reading the N/B and B/N ratios in the heart of the deceased, regardless of gender, age, postmortem interval, and cause of death. Although the N/B and B/N ratios cannot exclude the possibility of death occurring in the morning or evening, our method is still valuable in forensic practice because it can complement the classical methods that are dependent on postmortem changes. However, since severe brain injury profoundly affects the peripheral circadian clock, our method may not apply to cases of severe brain injury. Additionally, the applicability of the method to infants and older adults needs to be evaluated in more cases.

Autopsy samples

Heart samples were obtained from 318 forensic autopsy cases with known times of death (224 men and 94 women). The age of autopsied subjects ranged from 2 months to 97 years (average: 58.7 years), and postmortem intervals in all cases were less than 96 h (average: 22.3 h). The causes of death of the subjects were shown in Table 1 . Tissue samples were taken during autopsy, immediately frozen in liquid nitrogen and stored at − 80 °C until use. Clock gene expression is routinely analyzed in all autopsy cases at our Institute as part of the process for estimating the time of death.

Extraction of total RNA and real-time RT-PCR

Total RNA was extracted from tissue samples (about 100 mg) and applied to Maxwell System with Maxwell RSC simplyRNA Tissue Kit (Promega Corporation, Madison, WI) according to the manufacturer’s instructions. Then 1 μg of total RNA was reverse-transcribed into cDNA by using a PrimeScript RT reagent Kit (TAKARA BIO INC., Otsu, Japan) with six random primers (TAKARA BIO INC.). Thereafter, generated cDNA was subjected to qPCR analysis using a SYBR ® Premix Ex Taq ™ II kit (TAKARA BIO INC.) with specific primer sets (Table 2 ). Amplification and detection of mRNA were performed using Thermal Cycler Dice ®  Real Time System (TP800, TAKARA BIO INC).

Statistical analysis

Data were expressed as the mean ± standard error of the mean. Unpaired Student t -test and Scheffe’s F test were performed to compare the values between two groups and for multiple comparisons, respectively. Statistical significance was set at p  < 0.05.

Ethical approval

Our study was approved by the Research Ethics Committee of Wakayama Medical University (No. 3177). All procedures were carried out in accordance with the principles of the Declaration of Helsinki. In addition, this study was conducted using past autopsy records and heart tissues; we were unable to obtain informed consent from the bereaved family for the use of the records and the heart tissues. In accordance with the "Ethical Guidelines for Medical Research Involving Human Subjects (enacted by the Ministry of Health, Labor and Welfare in Japan)," Sect. 12–1 (2) (a) (c), the review board of the Research Ethics Committee of Wakayama Medical University waived the need for written informed consent from relatives of the individuals studied because this was a de-identified retrospective study of archived autopsy-derived tissues.

Data availability

The authors declare that all data are available upon request. All requests should be made to Dr. Toshikazu Kondo.

Henssge, C. & Madea, B. Estimation of the time since death in the early post-mortem period. Forensic Sci. Int. 144 (2–3), 167–175. https://doi.org/10.1016/j.forsciint.2004.04.051 (2004).

Article   CAS   PubMed   Google Scholar  

Madea, B. Estimation of the time of death. 3rd Ed. (CRC Press, London, 2015). https://doi.org/10.1201/b19276

De-Giorgio, F. et al. A novel method for post-mortem interval estimation based on tissue nano-mechanics. Int. J. Leg. Med. 133 (4), 1133–1139. https://doi.org/10.1007/s00414-019-02034-z (2019).

Article   Google Scholar  

Choi, K. M. et al. Postmortem proteomics to discover biomarkers for forensic PMI estimation. Int. J. Leg. Med. 133 (3), 899–908. https://doi.org/10.1007/s00414-019-02011-6 (2019).

Dong, K. et al. Succession of oral microbiota community as a tool to estimate postmortem interval. Sci. Rep. 9 (1), 13063. https://doi.org/10.1038/s41598-019-49338-z (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Tao, L. et al. Early postmortem interval estimation based on Cdc25b mRNA in rat cardiac tissue. Leg. Med. (Tokyo) 35 , 18–24. https://doi.org/10.1016/j.legalmed.2018.09.004 (2018).

Baiardi, S. et al. Chronobiology, sleep-related risk factors and light therapy in perinatal depression: The “Life-ON” project. BMC Psychiatry 16 , 374 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Go, A. et al. Analysis of hypoxanthine and lactic acid levels in vitreous humor for the estimation of post-mortem interval (PMI) using LC–MS/MS. Forensic Sci. Int. 299 , 135–141. https://doi.org/10.1016/j.forsciint.2019.03.024 (2019).

White, W. B., Anders, R. J, MacIntyre, J. M., Black, H. R., Sica, D. A., Verapamil Study Group. Nocturnal dosing of a novel delivery system of verapamil for systemic hypertension. Am. J. Cardiol. 76:375–80 (1995).

White, W. B., Mehrotra, D. V., Black, H. R. & Fakouhi, T. D. Effects of controlled-onset extended-release verapamil on nocturnal blood pressure (dippers vs. nondippers). COER-Verapamil Study Group. Am. J. Cardiol. 80 , 469–474 (1997).

Dallmann, R., Brown, S. A. & Gachon, F. Chronopharmacology: New insights and therapeutic implications. Annu. Rev. Pharmacol. Toxicol. 54 , 339–361 (2014).

Barion, A. & Zee, P. C. A clinical approach to circadian rhythm sleep disorders. Sleep Med. 8 , 566–577 (2007).

Rosenthal, N. E. et al. Phase-shifting effects of bright morning light as treatment for delayed sleep phase syndrome. Sleep 13 , 354–361 (1990).

CAS   PubMed   Google Scholar  

Cuesta, M., Boudreau, P., Cermakian, N. & Boivin, D. B. Rapid resetting of human peripheral clocks by phototherapy during simulated night shift work. Sci. Rep. 7 (1), 16310. https://doi.org/10.1038/s41598-017-16429-8 (2017).

Mikami, H. et al. Estimation of time of death by quantification of melatonin in corpses. Int. J. Leg. Med. 107 , 42–51 (1994).

Article   CAS   Google Scholar  

Kimura, A., Ishida, Y., Hayashi, T., Nosaka, M. & Kondo, T. Estimating time of death based on the biological clock. Int. J. Legal Med. 125 (3), 385–391. https://doi.org/10.1007/s00414-010-0527-4 (2011).

Article   PubMed   Google Scholar  

Storch, K. F. et al. Extensive and divergent circadian gene expression in liver and heart. Nature 417 (6884), 78–83. https://doi.org/10.1038/nature744 (2002).

Article   ADS   CAS   PubMed   Google Scholar  

Reppert, S. M. & Weaver, D. R. Coordination of circadian timing in mammals. Nature 418 (6901), 935–941. https://doi.org/10.1038/nature00965 (2002).

Arendt, J. Shift work: Coping with the biological clock. Occup. Med. (Lond) 60 (1), 10–20. https://doi.org/10.1093/occmed/kqp162 (2010).

Kuroda, H. et al. Meal frequency patterns determine the phase of mouse peripheral circadian clocks. Sci. Rep. 2 , 711. https://doi.org/10.1038/srep00711 (2012).

Article   CAS   PubMed   PubMed Central   Google Scholar  

McGraw, K., Hoffmann, R., Harker, C. & Herman, J. H. The development of circadian rhythms in a human infant. Sleep 22 (3), 303–310. https://doi.org/10.1093/sleep/22.3.303 (1999).

Torres-Farfan, C. et al. Maternal melatonin effects on clock gene expression in a nonhuman primate fetus. Endocrinology 147 (10), 4618–4626. https://doi.org/10.1210/en.2006-0628 (2006).

Chen, C. Y. et al. Effects of aging on circadian patterns of gene expression in the human prefrontal cortex. Proc. Natl. Acad. Sci. USA 113 (1), 206–211. https://doi.org/10.1073/pnas.1508249112 (2016).

Terazono, H. et al. Adrenergic regulation of clock gene expression in mouse liver. Proc. Natl. Acad. Sci. USA 100 (11), 6795–6800. https://doi.org/10.1073/pnas.0936797100 (2003).

White, M. et al. Age-related changes in beta-adrenergic neuroeffector systems in the human heart. Circulation 90 (3), 1225–1238 (1994).

Inoue, H., Kimura, A. & Tuji, T. Degradation profile of mRNA in a dead rat body: Basic semi-quantification study. Forensic Sci. Int. 130 (2–3), 127–132. https://doi.org/10.1016/s0379-0738(02)00352-3 (2002).

Duclos, C. et al. Sleep and wake disturbances following traumatic brain injury. Pathol. Biol. (Paris) 62 (5), 252–261. https://doi.org/10.1016/j.patbio.2014.05.014 (2014).

Grima, N. A., Ponsford, J. L., St Hilaire, M. A., Mansfield, D. & Rajaratnam, S. M. Circadian melatonin rhythm following traumatic brain injury. Neurorehabilit. Neural Repair 30 (10), 972–977. https://doi.org/10.1177/1545968316650279 (2016).

Zhanfeng, N., Hechun, X., Zhijun, Z., Hongyu, X. & Zhou, F. Regulation of circadian clock genes on sleep disorders in traumatic brain injury patients. World Neurosurg. 130 , e475–e486. https://doi.org/10.1016/j.wneu.2019.06.122 (2019).

Boone, D. R. et al. Traumatic brain injury-induced dysregulation of the circadian clock. PLoS ONE 7 (10), e46204 (2012).

Anafi, R. C., Francey, L. J., Hogenesch, J. B. & Kim, J. CYCLOPS reveals human ranscriptional rhythms in health and disease. Proc. Natl. Acad. Sci. USA 114 (20), 5312–5317. https://doi.org/10.1073/pnas.1619320114 (2017).

Download references

Acknowledgements

This work was supported in part by Grants-in-Aids for Scientific Research (A) (Grant 25253055, to T. Kondo) from the Ministry of Education, Culture, Science, and Technology of Japan. We sincerely thank Ms. Mariko Kawaguchi for her administrative management of the research.

Author information

Authors and affiliations.

Department of Forensic Medicine, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan

Akihiko Kimura, Yuko Ishida, Mizuho Nosaka, Akiko Ishigami, Hiroki Yamamoto, Yumi Kuninaka & Toshikazu Kondo

Department of Cardiovascular Medicine, Kinan Hospital, Wakayama, Japan

Satoshi Hata

Department of Neurological Surgery, National Hospital Organization Minami Wakayama Medical Center, Wakayama, Japan

Mitsunori Ozaki

You can also search for this author in PubMed   Google Scholar

Contributions

A.K. contributed to the conceptualization, data analysis &amp; interpretation, and writing of the manuscript. T.K. takes responsibility for data integrity and contributed to the study design and writing of the manuscript. Y.I. conceptualized, interpreted the data. M.N., A.I., H.Y., Y.K. and S.H. contributed to analysis of the autopsy samples. M.O. contributed to animal experiment. All authors reviewed the manuscript.

Corresponding author

Correspondence to Toshikazu Kondo .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kimura, A., Ishida, Y., Nosaka, M. et al. Application and limitation of a biological clock-based method for estimating time of death in forensic practices. Sci Rep 13 , 6093 (2023). https://doi.org/10.1038/s41598-023-33328-3

Download citation

Received : 11 November 2022

Accepted : 11 April 2023

Published : 13 April 2023

DOI : https://doi.org/10.1038/s41598-023-33328-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.