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The causes and consequences of trained immunity in myeloid cells.

• Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, United States

Conventionally, immunity in humans has been classified as innate and adaptive, with the concept that only the latter type has an immunological memory/recall response against specific antigens or pathogens. Recently, a new concept of trained immunity (a.k.a. innate memory response) has emerged. According to this concept, innate immune cells can exhibit enhanced responsiveness to subsequent challenges, after initial stimulation with antigen/pathogen. Thus, trained immunity enables the innate immune cells to respond robustly and non-specifically through exposure or re-exposure to antigens/infections or vaccines, providing enhanced resistance to unrelated pathogens or reduced infection severity. For example, individuals vaccinated with BCG to protect against tuberculosis were also protected from malaria and SARS-CoV-2 infections. Epigenetic modifications such as histone acetylation and metabolic reprogramming (e.g. shift towards glycolysis) and their inter-linked regulations are the key factors underpinning the immune activation of trained cells. The integrated metabolic and epigenetic rewiring generates sufficient metabolic intermediates, which is crucial to meet the energy demand required to produce proinflammatory and antimicrobial responses by the trained cells. These factors also determine the efficacy and durability of trained immunity. Importantly, the signaling pathways and regulatory molecules of trained immunity can be harnessed as potential targets for developing novel intervention strategies, such as better vaccines and immunotherapies against infectious (e.g., sepsis) and non-infectious (e.g., cancer) diseases. However, aberrant inflammation caused by inappropriate onset of trained immunity can lead to severe autoimmune pathological consequences, (e.g., systemic sclerosis and granulomatosis). In this review, we provide an overview of conventional innate and adaptive immunity and summarize various mechanistic factors associated with the onset and regulation of trained immunity, focusing on immunologic, metabolic, and epigenetic changes in myeloid cells. This review underscores the transformative potential of trained immunity in immunology, paving the way for developing novel therapeutic strategies for various infectious and non-infectious diseases that leverage innate immune memory.

1 Background

Immunity conferred by the cells of the host immune system represents the main line of defense that recognizes, responds, and protects against invading pathogens such as bacteria, viruses, fungi, and other foreign particles ( 1 ). Conventionally, immunity in humans is classified as innate and adaptive responses. The innate immunity prevails from the neonatal stage, which protects the host non-specifically against a wide range of pathogens. Components of the innate immune response include natural barriers of the body, such as skin, and mucous as well as cellular chemical barriers, such as enzymes and antimicrobial molecules. Innate immune cells include phagocytes, such as macrophages, dendritic cells (DC), neutrophils, and non-phagocytic cells, including natural killer (NK) cells and gamma-delta T-lymphocytes ( 2 ). In contrast to innate immunity, adaptive immunity develops over time and is specific to pathogens and/or their components, with additional active and passive immunity features ( 3 ). The primary cells of adaptive immunity are the T and B lymphocytes, with several subtypes within these two classes of cells. While active immunity develops after exposure to a pathogen or vaccination, allowing the body to produce its own antibodies and memory cells for long-term protection, passive immunity is mediated by existing antibodies providing temporary protection against infection ( 4 , 5 ).

An important feature that distinguishes adaptive immunity from innate immunity is that the former type of immunity has a memory or re-call response against specific antigens or pathogens, while the latter immunity does not have a memory response. However, a new concept in host immune response has recently been proposed, namely the trained immunity (a.k.a. innate memory response) ( 6 ). The main tenant of the trained immunity concept is that innate immune cells such as macrophages as well as non-immune cells, including epithelial and endothelial cells can develop a memory response upon stimulation with antigen/pathogen ( Figure 1A ). Thus, trained immunity enables the innate immune cells to remember and respond robustly to previous pathogens encounters ( 7 , 8 ). Trained immunity is enhanced through exposure or re-exposure to infections, vaccines, or other immune-stimulating agents ( 9 ). However, these memory-like responses associated with innate immune cells are not as specific or durable as the conventional immune memory established in the cells of adaptive immunity ( 10 ).

Figure 1 Overview of classical innate, adaptive and trained immunity. (A) In classical innate immunity, innate immune cells such as macrophages recognize microbes, pathogen-associated molecular patterns (PAMPs), host-derived danger-associated molecular patterns (DAMP) or cytokines through their cell surface pattern recognition receptors (PRRs) and elicit a primary response, which mostly results in a non-specific early proinflammatory response. In contrast, during adaptive immunity, T cells are activated by specific signaling from antigen-presenting cells (APCs). Upon restimulation with PAMPs and DAMPs, the memory T cells exert a robust immune response in an antigen-specific manner. This memory (recall) response is the classical hallmark of adaptive immune response. However, the memory-like response of innate immune cells, secondary to the innate immunity, upon restimulation of PAMPs and DAMPs is the hallmark of trained immunity, which includes the characteristics of both classical innate and adaptive immunities. (B) The concept of trained immunity. In unchallenged/non-trained innate immune cells (left; blue color), engagement of PRRs by PAMPs (e.g. LPS, β-glucan), DAMPs (e.g. OxLDL) or cytokines such as IL-1β, results in a limited immune response with homeostatic levels of metabolic, proinflammatory responses and effector gene expression, which is due to closed chromatin confirmation. However, upon restimulation, these innate immune cells elicit a robust proinflammatory response upon engagement of their PPRs by microbes, PAMPs, and DAMPs, in an antigen-independent manner. Activation of trained immunity manifests in gene activation changes through epigenetic modifications (a) increased metabolic activity (b) and elevated production of proinflammatory effector molecules (c) in the innate immune cells. These interrelated processes result in (1) epigenetic modifications, and (2) metabolic remodeling that culminates in (3) immune activation of trained cells. The image was created in BioRender .

Prior to the proposal of the trained immunity concept, few studies have reported the non-specific protective efficacy of some of the bacteria against secondary infection by a different pathogen. For example, in 1968 Mackaness et al. reported that mice inoculated with Bacillus Calmette-Guerin (BCG), a live attenuated vaccine strain of Mycobacterium bovis , protected the animals against a secondary infection by a pathogenic strain of Mycobacterium tuberculosis (Mtb) ( 11 ). In another study, the same group reported that BCG vaccination protected mice against infection by several unrelated pathogens, including Salmonella typhimurium and Listeria monocytogenes ( 12 ). Furthermore, in the early 1900s, BCG vaccination was demonstrated to reduce the general morbidity in children due to infectious diseases ( 13 ). Based on these observational studies, scientists such as Calmette and Naslund suggested that BCG vaccination can confer non-specific protection against infectious diseases other than tuberculosis (TB), which is the intended target disease of the BCG vaccine ( 14 , 15 ). Built upon these observations, the concept of trained immunity was proposed by Mihai Netea in 2011 ( 16 ). Subsequently, many researchers have provided experimental evidence to explain the molecular and cellular mechanisms underpinning trained immunity, including the epigenetic and metabolic modifications in the trained innate immune cells ( 17 , 18 ). In this review, we provide an overview of conventional innate and adaptive immunity and summarize various mechanistic factors associated with the onset and regulation of trained immunity, focusing on immunologic, metabolic, and epigenetic changes in innate immune cells. We also discuss the potential applications and limitations in translating trained immunity for clinical applications.

2 Main text

2.1 the innate and adaptive immune system in higher vertebrates.

In response to the entry of any microbes, the innate and adaptive immune systems of the body recognize, destroy, and eliminate the invading organism through phagocytosis, without significant injury to the host ( 19 ). In general, innate immunity is the first line of defense that recognizes the pathogen through phagocytic pathogen recognition receptors (PRRs) on its surface, which bind with the surface molecules of pathogens, namely the pathogen-associated molecular patterns (PAMPs), or the host-derived danger-associated molecular patterns (DAMPs). The innate immune cells include monocytes, neutrophils, DCs and macrophages, mast cells, basophils, eosinophils, and NK cells. Among these cells, phagocytes, such as neutrophils and macrophages are mainly seen in infected tissues and are involved in engulfing/phagocytosing, degrading, and clearing the microbial- and host-cell-derived debris ( 20 ). Innate immune cells, such as macrophages express PRRs, such as Toll-like receptors (TLRs) C-type lectin receptors (CLRs), and NOD-like receptors (NLRs), which recognize PAMPs derived from pathogens, including, bacterial lipopolysaccharides (LPS), viral nucleic acids, and fungal cell wall components as well as DAMPs, which are endogenous molecules released from damaged or stressed host cells. Although PRRs exhibit broad specificity and recognize a range of both PAMPs and DAMPs, the response of phagocytes to various PAMPs and DAMPs is context-dependent ( 20 – 23 ). For example, TLRs, located on cell surfaces can recognize various PAMPs and initiate immune responses, while NLRs, found in the cytoplasm, form inflammasomes upon detecting PAMPs or DAMPs, leading to cytokine production ( 21 – 23 ). Furthermore, while PAMPs are mostly invariant antigens within a class of microbial agents, they are distinguishable from DAMPs, which are “danger” signaling molecules produced by the host because of inflammation, infection, and/or cell/tissue damage ( 21 ). Importantly, the interaction of PRRs with PAMP or DAMP activates phagocytosis and antigen processing within the phagocytes. Activated phagocytes can also migrate to regional draining lymph nodes to “present” the antigen to T cells of the adaptive immune response, through cognitive major histocompatibility complex (MHC) molecules ( 21 ). Hence, phagocytes are also termed as antigen-presenting cells (APCs). Upon activation, APCs produce pro-inflammatory cytokines and chemokines, leading to the recruitment and activation of other immune cells, including T and B lymphocytes, which results in the production of antibodies and cytotoxic T-cell activation ( 21 – 23 ). Thus, PRRs serve as a vital signaling link between the innate and adaptive immune cells.

Innate immunity also encompasses a complement system as an efficient defense mechanism against pathogens, contributing to pathogen clearance, inflammation, and modulation of adaptive immune responses through a complex network of soluble proteins like C3b. These proteins aid in the opsonization of microbes by binding to their surface and enhancing the phagocytosis leading to the secretion of inflammatory mediators, formation of the membrane attack complex for pathogen lysis as well as direct microbial killing, and clearing the apoptotic cells and immune complexes, thus regulating the host immune responses ( 24 ). Additionally, complement activation enhances adaptive immunity by promoting antigen presentation and antibody production. Overall, the complement system provides a rapid and effective defense mechanism against invading pathogens while regulating immune responses to maintain tissue homeostasis.

In addition to phagocytes, the innate immune system also contains non-phagocytic cells, such as mast cells, basophils, eosinophils, and NK cells. Among these, mast cells, basophils, and eosinophils are granular immune cells, that generate inflammatory responses via histamine release. It should be noted that these responses are part of the body’s defense mechanism that can sometimes contribute to disease pathology, such as in allergic reactions ( 25 ). The NK cells are a subset of cytotoxic cells of the innate immune system that mainly recognize infected cells via specific receptors, lysing and enabling them to be eliminated effectively, as recently shown for lethal CMV viremia or transcriptional control of HIV-1 ( 26 – 28 ). Thus, innate immunity has a significant role in protecting the host system from external agents in a non-specific manner; however, innate immune cells, in general, do not effectively function as classical memory cells to actively mount an immune response upon re-encountering the same PAMPs. While innate immunity lacks conventional cellular memory, phenomena such as trained immunity suggest a form of non-specific memory response in innate immune cells.

The adaptive immune response is also effective in eliminating pathogens, similar to innate immunity but in a different perspective ( 19 , 29 ). Activation of adaptive immunity eliminates pathogens along with their toxic molecules, mostly by generating a memory response to specific PAMPs of the pathogens ( 30 ). Adaptive immunity is further categorized into humoral, and cell-mediated immunity. Humoral immunity is mediated by B lymphocytes (B cells), with a unique antigen receptor on its surface, known as the B cell receptor (BCR). The BCR is a membrane-bound immunoglobulin molecule, that recognizes specific epitopes or antigens. Affinity maturation is a process that refines the specificity and affinity of antibodies resulting in the production of antibodies that neutralize the pathogen and its secreted toxins leading to their elimination ( 31 ). The antigen-specific response is the hallmark of adaptive immunity, which refers to the ability of lymphocytes to recognize and respond to specific antigens, ensuring targeted immune responses against pathogens. On the other hand, affinity maturation, primarily occurring in B cell responses, involves the refinement of antibody binding affinity to antigens through somatic hypermutation. This process leads to the generation of antibodies with progressively higher affinity, enhancing the effectiveness of the immune response over time. Together, antigen specificity and affinity maturation are fundamental aspects of the adaptive immune system’s ability to mount precise and potent responses tailored to encountered antigens.

In addition to mounting an immediate, antigen-specific effector function, the adaptive immune system develops long-term memory responses through the formation of memory T and B cells during the primary immune response. These memory cells remain quiescent but quickly respond upon re-exposure to the same pathogen, leading to a faster and more potent secondary immune response. This memory provides long-lasting immunity against previously encountered pathogens ( 31 ). Thus, adaptive immunity advances to target pathogens more effectively through processes such as antigen presentation, clonal selection, affinity maturation, and memory cell formation. While APCs initiate adaptive immune responses by presenting antigens to T and B cells, which then undergo clonal selection, leading to the expansion of cells targeting the pathogen, the affinity maturation fine-tunes those responses by generating antibodies with higher affinity for the antigen ( 32 ). Further differentiation of adaptive immune cells into effector cells aids in coordinating and executing immune responses, and finally, the memory cell formation ensures rapid and potent responses upon re-exposure to the same pathogen. Thus, adaptive immunity continuously evolves to mount faster, more specific, and more effective responses against pathogens.

2.2 Trained immunity

Trained immunity (a.k.a. Innate memory response) is an emerging branch of host immune response that defines the ability of innate immune cells to generate non-specific immunological memory responses, which can confer long-term protection against infections ( Figure 1A ). Trained immunity and innate immunity provide broad, non-specific protection against a range of pathogens. Innate immunity encompasses the broader array of non-specific defense mechanisms present from birth, whereas trained immunity specifically refers to the enhanced responsiveness of innate immune cells following exposure to certain stimuli. Similarly, while adaptive immunity is marked by a precise, antigen-specific recall response, the hallmark of trained immunity is its non-specific and antigen-independent immune response. Trained immunity is induced by innate immune cells, such as macrophages, monocytes, and NK cells, or in case of reentry of pathogen or vaccination. The development of trained immunity occurs at a preliminary level (central training) with the involvement of the hematopoietic stem (HSCs) and progenitor cells (HSPCs) ( 33 ). These cells, residing in the bone marrow have a longer life span and respond rapidly to protect the host against chronic infections ( 34 , 35 ). In addition to the bone marrow-derived HSCs and HSPCs, monocytes and granulocytes with respectively, Ly6C + and Ly6G + /Gr-1 + phenotypes, are associated with the trained immunity-mediated effector functions, including degranulation and release of proinflammatory molecules upon infection by various pathogens ( 36 – 38 ). These immune responses can retain immunological memory between the self and non-self which leads to the establishment of a long-term trained immunity ( 7 , 39 ).

Epidemiological observation studies in humans vaccinated with BCG, intended to protect against TB, indicated a non-specific cross-protection against sepsis and respiratory tract infections in vaccinated individuals ( 40 ). Similar observations of cross-protection were noted for the measles vaccine and smallpox vaccine against general mortality in children and leprosy or whooping cough, respectively ( 41 , 42 ). Further, the existence of trained immunity has been reported in various in vivo studies, including treating mice with different antigens and stimulants, which would protect against infection. For instance, administration of β-glucan, a fungal cell-surface ligand protected normal and leukemic mice against systemic sepsis caused by Staphylococcus aureus infection ( 43 , 44 ). Similarly, intraperitoneal administration of CpG, an oligo deoxy nucleotide, protects against meningitis caused by E. coli infection in neutropenic mice models ( 45 ). A study on SCID mice that lack functional T cells and B cells showed that BCG vaccination protected the mice against Candidiasis ( 46 , 47 ). Further, BCG vaccination in Rag -/- knockout and athymic mice lacking T cells were protected against re-infection with C. albicans ( 48 , 49 ). Similarly, β-glucan induces trained immunity in splenectomized mice, and removal of the spleen did not modulate the expression of pro-inflammatory cytokines or circulating monocytes or NK cells ( 50 ). In another study, activation of trained immunity through intraperitoneal injection of LPS and BCG was shown to be associated with the induction of inflammation and fibrosis in Balb/c mice, marked by increased expression of cytokines (IL-6, IL-1β, IL-6 and IL-10), chemokines (CCR2, CCR4, TLR-2, and TLR-4), inflammatory (Ly6C and CD43) and co-stimulatory receptors (CD80 and iCOS) by the stimulated splenocytes ( 51 ). It should be noted that in standard mice models, the contributions of innate and adaptive immunity are intertwined, making it challenging to dissect the specific roles of each component. However, the SCID and Rag-/- mice lack functional T and B lymphocytes, rendering them incapable of mounting adaptive immune responses ( 52 – 54 ). Therefore, SCID and Rag-/- mice allow researchers to study the role of innate immunity, including trained immunity, in host defense innate immune response, without the confounding effects of adaptive immunity. Furthermore, due to their reliance on innate immune responses, SCID and Rag-/- mice may exhibit heightened sensitivity to stimuli that induce trained immunity ( 52 – 54 ). This increased sensitivity can facilitate the detection of subtle changes in innate immune function and provide insights into the mechanisms underlying trained immunity. Thus, data from studies obtained in transgenic mice models that are defective in adaptive immunity, such as lack of secondary lymphoid organ (i.e. spleen) or T and B cells, highlights the functional contribution of trained immunity in mounting an effective immune response and/or protecting the infected host. However, findings from studies in SCID and Rag-/- mice should be interpreted in the context of their immunodeficient status and may not fully recapitulate immune responses in normal physiological conditions ( 52 – 54 ).

The immunostimulants of trained immunity, including β-glucan, oxidized low-density lipoprotein (oxLDL), and BCG induce epigenetic reprogramming through histone modifications and metabolic shifts in innate immune cells, such as macrophages. These stimuli trigger changes in proinflammatory gene expression profiles and immunometabolic pathways, enhancing immune activation and host defense mechanisms ( 18 ). For example, β-glucan activates Dectin-1 signaling, promoting glycolysis and pentose phosphate pathway while suppressing oxidative phosphorylation (OXPHOS). Similarly, oxLDL engages scavenger receptors, inducing epigenetic alterations and pro-inflammatory responses, while BCG induces epigenetic reprogramming and shifts macrophage metabolism toward glycolysis, contributing to trained immunity and improved host defense ( 18 ). An in vitro study on human monocytes using β-glucan, oxLDL, and BCG as stimulants, induced trained immunity with increased reactive oxygen species (ROS) production and metabolic shift towards glycolysis, which activated a proinflammatory response of these APCs ( 55 ). It should be noted that activation of trained immunity by different stimuli may lead to varied trained responses that have potential implications for disease resistance and homeostasis. For example, in patients with systemic lupus erythematosus (SLE), excessive inflammation triggered by necrotic debris of neutrophils and macrophages, including nucleic acids and proteins, impairs the innate immune functions, leading to poor phagocytosis, formation of immune activation complex (IAC) and auto-antigens ( 18 ). In addition, histone modifications were also impaired in SLE patients, which alters the epigenetic and immunometabolic reprogramming of APCs. Since these processes and pathways are directly associated with key immunological responses of trained immunity, activation of these biological functions through trained immune activation of APCs would further worsen the disease pathology, as seen in SLE patients ( 18 ). Overall, various antigenic stimuli, such as PAMPs, DAMPs, and vaccines can modulate the trained immunity of innate immune cells to different extents through the interplay between epigenetic remodeling, cellular metabolism, and immune function.

Apart from infection, physical exercise can also impact the ability of innate immune cells to develop trained immunity. Regular exercise has been shown to enhance trained immunity through various mechanisms, including improved immune cell function, anti-inflammatory effects (switching from proinflammatory M1 to anti-inflammatory M2 phenotype), metabolic adaptations (elevating mitochondrial quality and function), stress reduction, and epigenetic modifications ( 56 , 57 ). These effects have significant implications for human health and disease prevention strategies, as exercise may reduce the risk of infections, autoimmune diseases, and chronic inflammatory conditions. To demonstrate the impact of trained immunity on APCs’ function in the context of exercise, C57BL/6 mice were subjected to daily exercise at 1 hour per day on a treadmill for 8 weeks, and BMDMs were isolated from these mice and stimulated with LPS to elicit trained immunity. Interestingly, BMDM from exercised mice induced a higher NF-κB activation and associated proinflammatory gene expression, compared to the control mice. This suggests that moderate exercise reprograms the metabolic pathways related to trained immunity in BMDMs ( 58 ). Thus, activation of trained immunity during regular physical activity could promote immune health and protect the host against the burden of infectious and inflammatory diseases.

Trained immunity may contribute to the pathogenesis of chronic inflammatory conditions, such as atherosclerosis, rheumatoid arthritis, and inflammatory bowel disease ( 7 , 18 ). In these conditions, dysregulated trained immunity can lead to excessive inflammation and tissue damage, exacerbating disease pathology ( 7 , 18 ). Conversely, modulating trained immunity through targeted interventions may offer therapeutic opportunities for managing chronic inflammation. For example, dampening trained immune responses could help mitigate inflammation in conditions like atherosclerosis, while enhancing trained immunity may promote immune surveillance and tissue repair ( 7 , 18 ). In a study by Bhattarai et al, a dystrophin-deficient mice BMDM, which lacks a chemokine receptor, CCR2, was used to study the effect of trained immunity on Duchenne muscular dystrophy (DMD). In this study, stimulation of BMDMs isolated from DMD mice with β-glucan resulted in TLR-4-dependent functional and epigenetic changes, inducing a memory response with the release of pro-inflammatory markers of trained immunity ( 59 , 60 ).

Similarly, trained immunity has been implicated in the pathogenesis of autoimmune diseases, in which the immune system mistakenly targets self-antigens and mounts an inflammatory response. Dysregulated trained immune responses may contribute to the breakdown of immune tolerance and the perpetuation of autoimmunity as described above for SLE ( 7 , 18 ). Modulating trained immunity could be explored as a potential strategy for managing autoimmune diseases. For instance, dampening trained immune responses might help mitigate autoinflammatory processes, while enhancing regulatory mechanisms could promote immune tolerance and reduce autoimmunity ( 7 , 18 , 60 ).

Trained immunity also plays a critical role in host defense against microbial infections by providing a faster and more robust immune response. Vaccines, such as BCG, measles, and monkeypox can induce trained immunity and enhance protection against unrelated infections ( 7 , 18 ). Leveraging trained immunity in vaccine development and immunization strategies may improve vaccine efficacy, particularly in populations with impaired adaptive immune responses, such as the elderly or immunocompromised individuals. Understanding the mechanisms underlying trained immunity in specific infectious diseases could inform the development of novel therapeutic approaches and adjuvants for enhancing immune responses and combating infections.

Trained immunity is durable and can exhibit long-lasting effects, persisting for weeks to months after the initial stimulus ( 7 , 18 , 61 ). This durability allows for sustained protection against infections and may contribute to vaccine efficacy. However, the duration of trained immunity responses can vary depending on factors such as the nature of the stimulus and the strength of the elicited immune response ( 7 , 18 , 62 ). In addition, there is considerable variability exists in the magnitude and duration of trained immunity responses among individuals. Some of the factors contributing to this variability include genetic background, age, sex, environmental exposures, and pre-existing health conditions. While trained immunity is beneficial for enhancing host defense, overactivation of innate immune responses can lead to chronic inflammation and tissue damage. Dysregulated trained immunity has been implicated in the pathogenesis of chronic inflammatory conditions, autoimmune diseases, and metabolic disorders. Therefore, understanding the factors that influence individual responses and balancing the activation of trained immunity with regulatory mechanisms is important for optimizing therapeutic interventions and vaccine strategies to prevent excessive inflammation and maintain immune homeostasis.

2.3 Mechanisms underlying enhanced immune responses in trained immunity

Since the inception of the trained immunity concept, researchers have provided experimental evidence for various mechanistic aspects of the ability of innate immune cells to develop memory responses ( Figure 1B ). Modulations in the immunological, epigenetic, and metabolic aspects of innate immune cells upon stimulation with antigens, have been implicated as the primary mechanisms in establishing trained immunity. These three mechanisms are interdependent and regulated by multiple crisscrossing cellular signaling pathways and networks that ultimately culminate in the effector response changes observed in trained innate immune cells ( Figure 2 ).

Figure 2 Cellular events in the orchestration of trained immunity. Trained immunity is exerted at the level of myeloid progenitor cells in the bone marrow as “central training” that further manifests as “peripheral training” in various tissues/organs. Microbes, PAMPs and DAMPs can enter the circulation and stimulate the progenitor hematopoietic stem cells (HSC) in the bone marrow. This centrally trained immunity is characterized by the differentiation of HSCs into multipotent progenitor (MPP) and granulocyte/monocyte progenitors (GMP), which ultimately produce the monocytes/macrophages and neutrophils. These centrally trained innate immune cells reach various organs through circulation, where they exert robust and rapid proinflammatory and antimicrobial responses upon stimulation of their PRRs by PAMPs or DAMP (Peripheral trained immunity). The central training of innate immune cells in the bone marrow involves an interrelated epigenetic rewiring and metabolic remodeling that ultimately leads to immune activation. The image was created in BioRender .

2.3.1 Immunological changes of trained immunity in monocytes/macrophages

Findings from in vitro and in vivo experiments suggest that trained immune cells exhibit increased phagocytosis with effective antimicrobial defense producing pro-inflammatory cytokines and chemokines that mount a rapid response to infections ( 63 ). Specifically, trained innate immune cells produce higher levels of pro-inflammatory cytokines, such as interleukin-1 β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) as well as IL-15 and type I interferons (IFN), and chemokines including IL-8 and or monocyte chemoattractant protein (MCP-1) that regulates not only the migration and activation of cells at the site of infection or inflammation but can also have profound bystander effects on NK cell activation ( 27 , 28 , 64 , 65 ). Trained phagocytes with reinforced microbicidal activity are better equipped to kill pathogens by generating ROS and reactive nitrogen species (RNS), that are toxic to bacteria, viruses, and other pathogens ( 66 ). In addition, trained APCs, such as dendritic cells can process and represent antigens quickly and efficiently to T cells through the MHC class 1 and 2 pathways. This leads to the activation of adaptative immune responses, which generate the antigen-specific memory T cells and can confer cross-protection against a wide range of infections ( 67 ). Thus, although trained immunity is primarily associated with innate immune cells, it can also influence the development of adaptive immune memory ( 68 ). Since trained immunity is non-specific, cells of the trained immunity can cross protection meaning exposure to one pathogen can enhance the immune response to unrelated pathogens. For example, the BCG vaccine, mainly used to prevent TB, can protect against other bacterial and viral infections ( 69 ). Both the microbial PAMPs, including LPS, β-glucan, muramyl dipeptide (MDP), and ligands derived from bacterial, fungal, and viral pathogens, as well as DAMPs such as lipoproteins, uric acid, and heme, can stimulate the training of innate immune cells through respective PRRs, including TLRs. NLRs and CLRs ( 70 ). PRR engagement triggers downstream signaling cascades, including activation of NF-κB, MAPK, and IRF transcription factors, which induce the expression of pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6 ( 6 – 10 , 18 ). The outcome of trained immunity following the interaction of a stimulant with innate immune cells is determined by the nature of the stimuli, the amount of the stimulus, and the duration of the interaction ( 71 , 72 ). For example, at lower concentrations, stimulation of monocytes with LPS or flagellin has been shown to induce trained immunity, while at higher doses, these stimulants induce an opposite effect, namely immune tolerance ( 8 , 71 , 73 , 74 ). Thus, different doses of the same stimulants can differentially activate the immunologic signaling pathways, leading to diverse outcomes in the trained macrophage response. A study reported that priming of PBMCs isolated from healthy donors with β- glucan elevated the production of proinflammatory cytokines, including TNF-α and IL-6 ( 75 ). However, these monocytes when pre-stimulated with LPS before β- glucan stimulation induced tolerance without induction of proinflammatory marker expression. In this study, the monocyte training was associated with the P38 and JNK-mediated MAPK signaling pathway, which directed the differential functional fate of the trained monocytes. Interestingly these monocyte’s long-term effector functions were associated with epigenetic modifications, such as histone methylation and acetylation ( 76 , 77 ).

Apart from the dose of the stimulant, the biological sex also appears to differentially impact the onset of trained immunity in macrophages. In a study by Sun et al, treatment with 17β estradiol has been shown to promote trained immunity mainly in female mice against sepsis, and the mechanism underpinning this trained immunity has been postulated to be the macrophage polarization through nucleus translocation of RelB, a transcriptional regulator and member of the NFκB signaling pathway ( 78 ). Therefore, the nature of the trained immune response differs depending on the stimulants, and trained immunity regulates either tolerance or immune effector functions ( 8 , 78 – 80 ).

2.3.2 Epigenetic changes in trained monocytes and macrophages

One of the crucial processes that reprogram innate immune cells such as macrophages and monocytes during trained immunity is epigenetic remodeling, which includes histone modifications by histone acetyltransferases (HATs), histone deacetylases (HDACs), histone methyltransferases (HMTs) and histone demethylases (HDMs), as well as DNA methylation, caused by DNA methyltransferases (DNMT) ( 81 ). Histone acetylation is one of the key histone modifications, in which the expression of specific genes of the innate immune cells are regulated via histone acetylation, leading to a more open chromatin structure following stimulation, marked by H3K4m1 and H3K27ac signatures ( 82 ). This facilitates the binding of transcription factors and RNA polymerase to promotors of genes and initiates their expression (e.g., increased histone acetylation in pro-inflammatory genes induces the inflammatory molecules that are involved in controlling an infection) ( 82 , 83 ). Histone methylation is another epigenetic modulation involved in trained immunity. Certain histone methylations might either activate or repress the expression of genes perturbed during trained immunity in innate immune cells. For example, increased trimethylation of histone H3 at lysine 4 (H3K4me3) gene associated with trained immunity, contributes to a stronger proinflammatory immune response against Mtb ( 84 ). The changes in histone modifications associated with trained immunity generate epigenetic memory that persists over time and allows the innate immune cells to recall previous encounters related to specific pathogens or stimuli. Epigenetic memory enables the cells to respond rapidly by promoting the expression of crucial genes that are necessary for an effective immune response ( 82 , 85 ). The reprogramming of innate immune cells by various stimulants such as BCG and β-glucan via regulating epigenetic/histone modifications is initiated by downstream host signaling upon engagement of cellular receptors with PAMPs and DAMPs ( 18 , 86 ). Recently, cytokines and chemokines including IL-1β, GM-CSF and M-CSF have emerged as inducers of epigenetic remodeling and are implicated in the training of innate immune cells ( 28 , 87 , 88 ). The mechanism of trained immunity activation includes immunometabolic reprogramming of the innate immune cells, which is also associated with changes in epigenetic regulation ( 18 , 86 ). Some of the histone modifications associated with trained immunity are discussed below:

A). Histone acetylation catalyzed by HATs, leads to relaxation of chromatin structure, allowing for increased accessibility of transcription factors to gene promoters. Enhanced histone acetylation at the promoters of cytokine and chemokine genes, such as TNF-α, IL-6, and CXCL8, facilitates their transcription. Increased acetylation of histone H3 and H4 at specific gene loci promotes the assembly of transcriptional activators and coactivators, such as NF-κB and AP-1, leading to robust and sustained expression of inflammatory mediators ( 6 – 10 , 18 ).

B). Histone methylation can occur on lysine (Lys) and arginine (Arg) residues, leading to either activation (e.g., H3K4me3) or repression (e.g., H3K9me3, H3K27me3) of gene expression depending on the site and degree of methylation. Increased trimethylation of histone H3 at lysine 4 (H3K4me3) is associated with transcriptional activation and is enriched at the promoters of actively transcribed genes, including pro-inflammatory cytokines and chemokines. Increased levels of H3K4me3 at specific gene loci, such as those encoding TNF-α, IL-6, and CXCL8, promote their transcriptional activation and contribute to the heightened inflammatory response in trained immune cells. Conversely, histone methylation at repressive marks, such as H3K9me3 and H3K27me3, is reduced at pro-inflammatory gene loci, allowing for their transcriptional activation and sustained expression.

C). DNA methylation involves the addition of a methyl group to cytosine residues within CpG dinucleotides, typically resulting in transcriptional repression when located in gene promoters. Trained immunity is associated with dynamic changes in DNA methylation patterns, including both hypomethylation and hypermethylation events. Hypomethylation of immune-related gene promoters, such as those encoding cytokines (e.g., TNF-α, IL-6) and pattern recognition receptors (e.g., TLRs), facilitates their transcriptional activation in trained immune cells. Conversely, hypermethylation of genes involved in negative regulators of inflammation, such as SOCS1 and SOCS3, may contribute to the sustained pro-inflammatory phenotype in trained cells.

These epigenetic modifications support a heightened state of readiness by promoting transcriptional activation of genes and priming innate immune cells to rapidly produce pro-inflammatory cytokines and chemokines, while dampening the expression of negative regulators, thereby enhancing immune responses upon re-stimulation and/or infection ( 6 – 10 , 18 ).

2.3.3 Aging and trained immunity regulation

Age-related changes in the epigenetic landscape, including changes in DNA methylation patterns, histone modifications, and chromatin structure. can affect the expression of genes involved in the induction and maintenance of trained immunity ( 89 ). Alterations in histone modifications and chromatin accessibility may impact the ability of immune cells to undergo epigenetic reprogramming in response to training stimuli, leading to differences in trained immunity outcomes between younger and older individuals ( 90 ). Similarly, aging influences the metabolic reprogramming of immune cells during trained immunity ( 89 ). For example, alterations in nutrient availability or mitochondrial dysfunction may impair the ability of immune cells to switch metabolic pathways and support enhanced responsiveness. Dysregulation of metabolic pathways, such as glycolysis, OXPHOS, and fatty acid metabolism, in aged immune cells can affect their energy production, biosynthetic capacity, and functional responses, potentially impacting trained immunity. Furthermore, the age-associated changes in epigenetic and metabolic regulation of immune cells can contribute to immune dysfunction, referred to as immunosenescence, characterized by reduced immune cell function, affecting the induction and maintenance of trained immunity and impaired responses to vaccination, and increased susceptibility to infections and inflammatory diseases ( 91 ). Thus, understanding the impact of age on epigenetic and metabolic pathways involved in trained immunity is essential for developing age-specific strategies to enhance immune responses in older individuals, potentially improving vaccine efficacy, host defense, and immune health in aging populations ( 91 ).

2.3.4 Metabolic changes associated with trained monocytes and macrophages

Trained immunity causes not only epigenetic reprogramming but also rewires metabolic pathways such as glycolysis, tricarboxylic acid (TCA) cycle, and lipid and amino acid metabolism of the trained innate immune cells ( Figure 3 ). The combined effect of epigenetic and transcriptional modulations of genes in trained cells also underpins the altered metabolic state, such as activation of glycolysis shift and an elevated release of pyruvate, which is the end product of glycolysis ( 92 ). As per the energy requirement of the trained macrophages, the pyruvate might enter either into OXPHOS or the TCA cycle, although the former pathway relies on the latter.

2.3.4.1 Glycolysis, OXPHOS and TCA cycle

Since activated host cells consume more glucose than resting cells, glycolysis is activated during training/stimulation of the innate immune cells to meet the energy demand to serve a proinflammatory role ( 93 ). Although glycolysis is not an efficient way for the cell to generate ATP, this process can rapidly be induced upon stimulation of the innate immune cells ( 94 ). The primary function of the glycolysis cycle is to break glucose molecules to produce ATP and release pyruvate, which is further transformed into acetyl CoA and participates either in the TCA or fatty acid oxidation (FAO) cycle. Importantly, glycolysis is activated in trained innate immune cells independent of the stimulus; thus, BCG, β-glucan, and lipoproteins can induce the glycolytic pathway ( 93 , 95 ). Studies report that stimulants like β-glucan, BCG, and lipoproteins can induce glycolysis in innate immune cells ( 96 ). Two independent studies on stimulation of mice with β-glucan reported the induction of immune mediators such as IL-1β and GM-CSF with increased glycolysis, and mainly in trained monocytes it is reported that pyruvate is converted to lactate ( 97 ). The persistent activation of glycolysis is regulated by a key transcriptional regulator, namely the hypoxia-inducible factor-1 alpha (HIF1α), which is stabilized by succinate, an intermediate metabolite of glycolysis. Furthermore, succinate and fumarate can act as epigenetic modulators for antagonizing histone or DNA methylation and facilitating long-lasting expression of genes involved in the glycolysis pathway ( 94 , 95 ). The increased glycolysis impacts the mammalian target of rapamycin (mTOR) and HIF-1α pathway representing the metabolic basis of trained immunity ( 96 ). Recent studies on HIF-1α knockout mice reported that the absence of these specific pathways, impacted the trained immunity at the epigenetic level and abrogated proinflammatory cytokine production ( 96 ).

OXPHOS is a more efficient but slow process to produce cellular energy, which involves the mitochondrial electron transport chain complexes that convert succinate or fumarate to release ATP. In general, glycolysis and OXPHOS operate in opposite directions, such that activation of the former dampens the latter and vice versa ( 98 – 100 ). It has been reported that β-glucan can induce OXPHOS shift, resulting in a higher intracellular ratio of NAD + to its reduced form NADH, thus producing more ATP ( 97 , 101 , 102 ). However, recent findings suggest that trained innate immune cells can activate both glycolysis and OXPHOS pathways at the same time ( 94 ) to serve as cellular energy sources to meet the demands of activated/trained cells ( 103 ). In a study by Arts et al., the stimulation of macrophages with β-glucan and BCG upregulated the expression of glycolytic enzymes with an increase in NAD + and NADH ratio ( 104 ).

TCA is a crucial metabolic cycle of the cell as it oxidizes the glycolysis substrates. Trained macrophages exhibit a higher level of oxygen consumption with a decrease in the use of OXPHOS ( 104 ). Although OXPHOS is decreased, the TCA cycle is not completely inactive; rather, the metabolites of the TCA cycle such as succinate, fumarate, and citrate exist at a higher level in trained cells compared to non-trained immune cells ( 104 ). Additionally, increased/accumulated TCA metabolites can serve as a key factor for fatty acid (FA) synthesis. Citrate induces gluconeogenesis and lipid metabolism pathways by inhibiting the glycolysis and TCA cycle ( 105 ). Stimulation with β-glucan increased the levels of TCA cycle metabolic intermediates, succinate, and fumarate, in trained monocytes and macrophages, with increased glycolysis and IL-1β production through HIF-1α pathway ( 106 ). Furthermore, fumarate accumulation blocked the function of HDMs and induced epigenetic modifications in βglucan-trained monocytes, which activated the proinflammatory response of these cells ( 107 ). Together, these studies demonstrate that carbohydrate metabolism, including glycolysis, OXPHOS, and TCA cycle are critical primary metabolic components of trained immunity, which is linked with the immune activation of innate immune cells upon stimulation with PAMPs and DAMPs, through alterations in epigenetic reprogramming.

2.3.4.2 Lipid metabolism

Lipid metabolism is interconnected with carbohydrate and amino acid metabolism. For example, citrate, which is a common metabolite produced from the citric acid cycle, is converted into acetyl CoA, which enters lipid metabolism or cholesterol synthesis pathway and accumulates as stored fatty acids ( 108 ). However, the organelles where the fatty acids are stored generate proinflammatory responses related to trained immunity by degrading the stored lipids to restore acetyl CoA through β-oxidation. In trained innate immune cells, the homeostasis of cholesterol and FA metabolism is mediated by the liver X receptor (LXR) ( 109 ). In vitro studies using human monocytes trained with BCG or Tri-palmitoyl-S-glyceryl-cysteine (Pam3cys), a TLR-2/6 pathway agonist, and LXR agonist were reported to induce intracellular acetyl-Coa levels, accompanied by histone modification and increased pro-inflammatory responses through activation of IL-1β signaling ( 109 ). In another study, mevalonate, a metabolite of the cholesterol pathway, was shown to induce trained immunity by activating mTOR and IGF1-R pathways and related histone modification involved in inflammation; inhibition of mevalonate pathway using statins prevented trained immunity in these myeloid cells ( 110 ). Importantly, the trained immunity phenotype is constitutively activated in patients with hyperimmunoglobulin D syndrome, who accumulate mevalonate due to a defective mevalonate degradation pathway ( 110 ). In mice treated with β-glucan, induction of cholesterol and lipid metabolism was noted in myeloid progenitor myeloid cells ( 96 , 111 ). Furthermore, modulation of lipid metabolism in the myeloid progenitor cells during trained immunity was associated with the expansion of these cells and change in their phenotype skewed towards proinflammatory responses ( 96 , 111 ). Training of mice HSC with LPS was shown to upregulate the expression of genes involved in FAO and OXPHOS ( 31 , 61 , 86 , 112 ). Furthermore, cholesterol metabolism was reported to be indispensable for trained immunity activation in monocytes upon β-glucan induction and inhibition of cholesterol metabolism reduced the β-glucan-induced trained immunity in mice ( 107 , 113 ). Additionally, inhibition of hydroxy-3-methylglutaryl CoA reductase, a key enzyme in the cholesterol synthesis pathway, negatively impacted the trained immunity established with β-glucan stimulation in vitro ( 113 ). Some of the lipid metabolism signaling pathways that impact trained immunity are as follows:

A). Peroxisome proliferator-activated receptors (PPARs). PPARs are nuclear receptors that regulate lipid metabolism and inflammation ( 114 ). Activation of PPARγ promotes lipid uptake, storage, and adipogenesis, which can modulate the metabolic state of trained cells and impact their inflammatory responses. Pharmacological activation of PPARγ has been shown to enhance trained immunity, leading to increased cytokine production and antimicrobial activity in macrophages ( 115 ).

B). Sterol regulatory element-binding proteins (SREBPs). The SREBPs are transcription factors that regulate lipid metabolism and cholesterol biosynthesis signaling pathways, which are essential for membrane integrity and cellular functions ( 116 ). SREBPs are activated by TLR4-mediated innate signaling, which upregulates caspase-1-mediated IL-1b production by macrophages ( 117 ). Since SREBP signaling integrates hypoxia, autophagy, phagocytosis and antimicrobial response in innate immune cells, dysregulation of this pathway can alter lipid metabolism and inflammatory responses in trained cells ( 116 , 117 ).

C). The mechanistic target of rapamycin (mTOR) is a central regulator of cellular metabolism, growth, and immune responses. mTOR signaling integrates signals from nutrient availability, energy status, and growth factors to modulate cellular responses ( 118 ). During training, activation of mTOR signaling promotes glycolysis, lipid biosynthesis, and protein synthesis, supporting the metabolic demands of activated immune cells. Thus, modulation of mTOR activity/signaling can influence trained immune cell metabolism and functions ( 119 ).

D). Signaling pathways activated by fatty acids , such as TLR signaling and inflammasome activation, can influence immune responses and trained immunity ( 120 ). Lipid mediators derived from fatty acids, such as prostaglandins, leukotrienes, and resolvins, regulate inflammation and immune cell activation, potentially modulating trained immunity outcomes ( 91 , 120 ). Similarly, cholesterol metabolism influences both NLRP3-dependent and independent inflammasome activation and proinflammatory cytokine (e.g., IL-1β) production in innate immune cells ( 121 ).

2.3.4.3 Amino acid metabolism

Amino acids, such as methionine, glutamine (Gln), proline (Pro), and aspartate (Asp) play a key role in stimulating and activating immune cells. Upon stimulation with agents such as BCG or β-glucan, innate immune cells undergo metabolic reprogramming to meet the increased energy demands associated with enhanced effector functions. Acquisition of amino acids, driven by specific cellular transporters, is crucial for cell function; for example, transportation of methionine stimulates T-cell activation ( 122 ). Similarly, Gln, which is present in the mitochondrial outer matrix serves as a source of succinate, fumarate, and citrate involved in the TCA cycle, and serves as a vital energy source for immune cells ( 123 ). In trained immune cells, Gln metabolism influences the activation of immune cells, while activation of HIF-1α by various stimulants, including BCG and β-glucan, increases Gln metabolism, leading to an elevated α-ketoglutarate (α-KG) level, which is channeled into the TCA cycle for energy production ( 113 ). Consistently, pharmacological inhibition of Gln metabolism attenuated the trained immunity phenotype in β-glucan-stimulated macrophages ( 113 ). Furthermore, H3K4me3, a chromosome modification that impacts gene transcription has been reported to be reduced when Gln uptake was blocked in breast cancer cells ( 124 ).

Neonatal mice infected with Pneumonia virus of mice (PVM, which is pathogenically similar to the respiratory syncytial virus [RSV] that affects humans) and stimulated with ovalbumin, have established trained immunity with increased Pro biosynthesis in alveolar macrophages ( 125 ). In addition, Asp was shown to be highly induced in trained macrophages compared to non-trained cells. Asp metabolism is also crucial for other metabolic cycles such as the urea cycle, gluconeogenesis, and purine synthesis, all of which contribute to activated cellular processes during trained immunity ( 97 , 126 ). In macrophages stimulated with LPS and IFNγ, the level of Asp metabolites, including asparagine was shown to be dampened ( 127 ). In these proinflammatory macrophages, Asp metabolism elevated the secretion of IL-1β through the activation of HIF-1α pathway. Furthermore, supplementation of Asp elevated the inflammatory response of macrophages in treated mice and piglets ( 127 ). The consumption of methionine, which is the precursor of S-adenosylmethionine (SAM) involved in histone methylation, is elevated in monocytes trained with β-glucan ( 113 ). Thus, methionine can impact the regulation of gene expression through SAM during the training of innate immune cells.

Some of the amino acids influence the trained immunity through multiple, integrated mechanisms ( 92 , 128 , 129 ). For example, Gln and Arg serve as a precursor for nucleotide synthesis, and polyamine synthesis, respectively, supporting rapid DNA replication and cell proliferation of trained cells. Gln also fuels the TCA cycle and OXPHOS, providing energy for activated trained cells. Finally, Gln and cysteine (Cys) are, respectively, a substrate and precursor for glutathione synthesis, which contributes to the antioxidant defenses, redox balance, and protection of trained cells from stress, while Arg is a precursor for nitric oxide (NO) production by inducible nitric oxide synthase (iNOS or NOS2) in innate immune cells; NO plays a crucial role in immune signaling and antimicrobial response of trained cells. Similarly, tryptophan (Trp) is the precursor to serotonin and kynurenine synthesis, which have immunomodulatory effects, including immune activation and immune tolerance. Finally, leucine (Leu) is an essential amino acid that activates the mTOR signaling pathway in trained cells, which promotes protein synthesis, cell growth, and proliferation, supporting cell activation and effector functions ( 92 , 128 , 129 ). Thus, altering the availability of specific amino acids through supplementation can have an immunomodulatory effect on trained cells, which impacts the inflammatory response and antimicrobial response of these cells ( 92 , 128 , 129 ).

2.3.5 Interdependence of immunologic, epigenetic and metabolic mechanisms of trained immunity

The immunologic mechanism of the trained immunity involves the reprogramming of innate immune cells, marked by changes in gene expression and functional alterations in immune cells, leading to increased cytokine production, antigen presentation, and antimicrobial activity of trained cells to exhibit enhanced responsiveness to subsequent antigen/pathogen challenges ( Figure 3 ). For example, data from in vitro and in vivo studies have shown that training of APCs by BCG, β-glucan and/or LPS leads to increased production of proinflammatory cytokines such as TNF-α, IL-1β and IL-6 ( 6 – 10 , 18 ). Trained proinflammatory APCs also display enhanced aerobic glycolysis and OXPHOS to meet the increased energy needs ( 74 – 78 ). Importantly, stimuli that induce trained immunity also impact epigenetic reprogramming, which alters the expression of genes involved in pro-inflammatory and metabolic responses of innate immune cells. For example, trained immunity modulates specific histone modifications such as H3K4me and H3K27ac, which can open-up the chromatin and facilitate the induced expression of pro-inflammatory cytokines (TNF-α, IL-1β and IL-6). Similarly, changes in chromatin structure facilitate the expression of antimicrobial genes in trained immune cells ( 68 – 72 ). Similarly, metabolic reprogramming is closely intertwined with immunological and epigenetic changes during trained immunity. Activation of immune cells by trained immunity requires substantial energy and metabolic resources, and different metabolic pathways, such as glycolysis, OXPHOS, and fatty acid metabolism, can impact immune cell function ( 6 – 10 , 18 ). Various stimuli that induce trained immunity can promote metabolic shifts, such as increased glycolysis, glutaminolysis, cholesterol and fatty acid metabolism in innate immune cells. These metabolic pathways generate intermediary molecules, which regulate epigenetic remodeling and proinflammatory cytokine production in trained cells ( 6 – 10 , 18 ). For instance, metabolites such as acetyl-CoA, α-KG, and S-adenosylmethionine (SAM) act as cofactors for histone acetylation, histone methylation, and DNA methylation, respectively ( 6 – 10 , 18 ). On the other hand, epigenetic modifications can influence metabolic pathways by regulating the expression of metabolic enzymes and transporters. For example, histone acetylation can activate the mTOR-HIF-1α-pathway-mediated upregulation of aerobic glycolysis, OXPHOS, and fatty acid metabolism in trained cells ( 6 – 10 , 18 ). Thus, the pathways of proinflammatory response, and epigenetic modifications, including changes in DNA methylation, histone modifications, and chromatin accessibility, as well as metabolic rewiring, are intertwined and play a central role in the regulation of trained immunity ( 6 – 10 , 18 ). This cross-talk between metabolic shifts and epigenetic changes ensures a coordinated and integrated immune response in trained cells. While metabolic reprogramming provides the energy and substrates necessary for immune cell activation, epigenetic modifications fine-tune gene expression to enhance cytokine production, antigen presentation, and antimicrobial activity ( 6 – 10 , 18 ). This synergy between immune response, metabolism, and epigenetics allows trained cells to mount a more robust and sustained immunological response upon re-exposure to pathogens, contributing to the enhanced protective effects of trained immunity ( 6 – 10 , 18 ).

Figure 3 Integrated molecular metabolic and epigenetic network adaptations and potential interventional targets of trained immunity. Activation of trained immunity in innate immune cells involves significant changes in glycolysis, TCA cycle, fatty acid, and amino acid metabolism. 1. The metabolic remodeling in trained innate immune cells involves increased glycolysis and the production of several metabolic intermediates of glycolysis, which participate in the TCA cycle, and mevalonate pathway. Thus, inhibition of glycolysis pathway enzymes (e.g., HK2 inhibition by 2-DG) can dampen the overall metabolic and epigenetic modulations needed for immune activation of trained immunity. OxLDL is involved in cholesterol formation and subsequently in the activation of the NLRP3 inflammasome pathway, thus, activating trained immunity. The latter pathway also impacts mTOR signaling. Therefore, inhibition of the oxLDL-CD36 pathway using CYTOD or inhibition of cholesterol synthesis by MβCD2 can impair NLRP3 activation. Additionally, Z-VAD-FMK can directly inhibit NLRP3 and mTOR activation pathways and reduce trained immunity. Metabolites of the integrated carbohydrate (e.g, glycolysis) and amino acid (glutamine-glutamate) network also play crucial roles in regulating epigenetic modifications such as histone acetylation (acetyl-CoA), histone demethylation (αKG) and H3K4Me3/H3K27Ac expression (fumarate). Inhibition of HMG-CoA (HMG-CoAi) blocks the channeling of acetyl-CoA into the mevalonate pathway, which can also be blocked by statins and LXR antagonists. Since the mevalonate pathway upregulates trained immunity through epigenetic remodeling, treatment with HMG-CoAi and statins can reduce trained immunity activation. Dectin-1 and NOD2 are key receptors that activate trained immunity upon binding with β-glucan and MDP, respectively. Since activation of these pathways leads to both metabolic and epigenetic reprogramming, inhibition of the Dectin-1 pathway with laminarin or wortmannin, as well as inhibition of the NOD2 signaling with GSK669 or GSK719 or butyrate downregulates trained immunity activation. Similarly, the blockade of mTOR and HIF1α with rapamycin, metformin, or ascorbate, respectively, negatively affects trained immunity activation. In addition to PAMPs, host-derived molecules such as IL-1β and GM-CSF can induce metabolic rewiring and histone modifications through IL-1R or MEK/ERK-mediated signaling, respectively, during trained immunity. Therefore, antibodies that target these cytokines/chemokines can dysregulate immune activation through interference with metabolic, epigenetic, and immune activation pathways in trained cells. Similarly, blocking of specific molecules involved in histone acetylation (e.g., H3K18ac and H3K27) and histone methylation (H3K4me) pathways can alleviate the epigenetic changes needed to activate proinflammatory gene expression in trained cells. 3. Activation of integrated metabolic, epigenetic, and immune function pathways in trained cells ensures both the energy and chromatin accessibility that is needed for an elevated proinflammatory and antimicrobial molecule production by the innate immune cells. 2DG, 2-deoxy-d-glucose; HK2, hexose kinase-2; oxLDL, oxidized low-density lipoprotein, CYTOD, cytochalasin D; MβCD, methyl-β-cyclodextrin; HMG-CoAi, hydroxy-methyl-glutaryl-coenzyme A reductase; Dectin-1, C-type lectin receptor dectin-1; NOD2, nucleotide-binding oligomerization domain-containing protein 2; MDP, muramyl dipeptide; mTOR, mechanistic target of rapamycin; HIF1α, hypoxia-inducible factor 1α; AKT, Ak strain transforming; MEK, mitogen activated protein kinase kinase; ERK, extracellular signal regulated kinase; NFκB, nuclear factor kappa-B; GM-CSF, granulocyte–macrophage colony-stimulating factor; H3K18ac, H3K18 acetylation; H3K27ac, H3K27 acetylation; H3K4me3, H3K4 trimethylation; HATi, histone acetyl transferase inhibitor; EGCG, epigallocatechin-3-gallate; HDACi, histone deacetylase inhibitor; AGK2, a sirtuin-2 inhibitor; HMTi, histone methyltransferase inhibitor; MTA, methylthioadenosine; IL1β, interleukin 1 beta; TCA cycle, tricarboxylic acid cycle; αKG, alpha-ketoglutarate. The image was created in BioRender .

2.3.6 Durability of trained immunity

The durability of the immune response in trained cells is regulated at the level of hematopoietic progenitor stem cells (HSPCs) in the bone marrow ( 18 ). For example, exposure of BCG to the bone marrow resulted in epigenetic and transcriptional changes of HSPCs, leading to elevated myelopoiesis and conferring better protection of mice against TB ( 130 ). Trained immunity involves the establishment of positive feedback loops that sustain the enhanced expression of proinflammatory cytokines and chemokines over time. Epigenetic modifications, such as H3K4me3 and H3K27ac marks, at enhancer regions of cytokine genes, facilitate the binding of transcription factors and RNA polymerase II, ensuring persistent transcriptional activation ( 6 – 10 , 18 ). Similarly, metabolic memory, characterized by the accumulation of metabolic intermediates of glycolysis and OXPHOS and mitochondrial ROS, maintains the trained immunity phenotype and facilitates rapid recall responses of innate immune cells upon re-stimulation and/or microbial infection ( 6 – 10 , 18 ). Some of the mechanisms underlying the dose-dependent response of trained immunity include the following:

A). Intensity of stimulation , such as the concentration of microbial components or cytokines, determines the extent of immune cell activation and subsequent epigenetic and metabolic reprogramming ( 6 – 10 , 18 ). Low to moderate doses of the stimulus typically induce trained immunity, characterized by enhanced responsiveness and prolonged memory-like effects in innate immune cells. High doses of the stimulus, on the other hand, may lead to immune tolerance, where immune cells become desensitized and exhibit reduced responsiveness to subsequent challenges.

B) . Epigenetic modifications , such as histone acetylation, methylation, and DNA methylation are influenced by the intensity of the stimulus in innate immune cells. Moderate stimulation induces specific epigenetic changes that promote the expression of pro-inflammatory genes and enhance immune responses, leading to trained immunity. High-intensity stimulation may result in global changes in chromatin structure or DNA methylation patterns that suppress immune gene expression and induce immune tolerance ( 6 – 10 , 18 ).

C). Metabolic Reprogramming is also impacted by the intensity of the stimulus, with moderate stimulation promoting metabolic pathways that support immune cell activation and effector functions. Low to moderate doses of the stimulus typically induce metabolic shifts towards glycolysis, pentose phosphate pathway, and glutaminolysis, providing energy and substrates for cytokine production and antimicrobial activity ( 6 – 10 , 18 ). High doses of the stimulus may overwhelm metabolic capacity or lead to metabolic exhaustion, impairing immune cell function and promoting immune tolerance.

While specific thresholds for stimulation that distinguish between training and tolerance may vary depending on the context and experimental model, there are general trends observed. Moderate doses of the stimulus, falling within an optimal range, typically induce trained immunity, whereas low doses may not provide sufficient activation, and high doses may induce tolerance ( 6 – 10 , 18 ). The threshold for stimulation may also depend on the sensitivity and responsiveness of innate immune cells, as well as the presence of regulatory mechanisms that modulate immune responses.

2.4 Trained immunity in neutrophils

Neutrophils (polymorphonuclear cells) are produced in the bone marrow and are the first responders to any injury or infection of the host. Although neutrophils have a shorter lifetime, they are the most abundant innate immune cells that provide significant and non-specific broad protection against related or unrelated pathogens ( 20 , 131 , 132 ). Although neutrophils are traditionally thought to lack long-term memory, recent studies suggest that they can retain epigenetic memory. Neutrophils are among the key effector cells of trained immunity with antigen-presenting functions, secreting cytokines that induce inflammatory factors, degranulate, and eliminate microbes through phagocytosis ( 133 ). A study reported that BCG vaccination in healthy individuals reprogrammed the neutrophils with increased expression of surface markers CD11b and CD66b, with concomitant dampening of CD62L and PDL1 expression, which persisted for at least 3 months, through epigenetic modifications ( 76 ). In this study, in vitro stimulation of blood-derived neutrophils, obtained after 3 months of BCG vaccination, with LPS or Staphylococcus aureus was shown to induce the expression of neutrophil activation markers CD11b and IL-8 ( 76 ). These trained neutrophils also showed increased degranulation and phagocytosis in vitro when stimulated with Candida albicans , Mtb, or LPS ( 76 ). Intranasal BCG vaccination of mice showed increased accumulation of neutrophils in the lungs, which helped to control subsequent Mtb infection by promoting antimicrobial responses ( 134 ).

In a zebrafish larvae model of Shigella infection, stimulation with BCG or β-glucan before infection elicited trained immunity in neutrophils, which showed epigenetic alterations, elevated ROS production, and antimicrobial responses ( 135 ). Importantly, induction of trained immunity in neutrophils has been shown to occur in a paracrine manner ( 136 ). In this study, treatment of neutrophils with soluble factors, secreted by mesenchymal stromal cells upon stimulation with CpG-ODN, a TLR-9 ligand, induced trained immunity marked by characteristic histone modifications and elevated granulopoiesis ( 136 ). Similarly, neutrophils isolated from mice treated with zymosan, which contains β-glucan, had elevated IL-6 levels upon ex vivo re-stimulation with LPS ( 35 , 137 ). These trained neutrophils also showed elevated myeloperoxidase levels and improved killing of intracellular L. monocytogenes ( 35 ). Thus, these studies suggest that induction of trained immunity in neutrophils by various stimuli can promote neutrophil activation, which is useful for effective bacterial clearance ( 137 ). In contrast to these findings, subcutaneous vaccination of C57BL/6 mice with BCG was reported to control Mtb at 7 days post-infection through a non-trained immunity mechanism, involving neutrophils and macrophages ( 138 ).

Neutrophils, despite their short lifespan, inherit epigenetic modifications from the myeloid progenitor cells during hematopoiesis ( 101 , 139 ). Neutrophils can undergo activation-induced epigenetic changes in response to antigenic stimulation or microbial challenge. These activation-induced epigenetic modifications can persist even after the stimulus is removed, allowing neutrophils to retain a primed/activated state and mount rapid and robust response upon re-stimulation ( 101 , 139 ). For example, elevated H3K4me3 at the promoters of JAK/STAT signaling (e.g., STAT4), PI3K/AKT pathway as well as proinflammatory (IL-8, IL-1B) and metabolic (mTOR, HK1 and PFKB) network genes noted in the peripheral neutrophils of BCG-vaccinated individuals, compared to non-vaccinated controls ( 101 ). Induction of these pathways is the hallmark response of trained immunity (i.e., proinflammatory and antimicrobial responses), suggesting that histone methylation underpins the long-term trained response of neutrophils ( 101 ). Other epigenetic reprogramming markers, such as DNA methylation and histone modifications can be established during neutrophil development (granulopoiesis) and maintained throughout their lifespan in both circulation and at specific tissues ( 101 , 139 ). Emerging evidence suggests that epigenetic modifications acquired by neutrophils in response to environmental cues or inflammatory signals may be transmitted to their progeny ( 139 ). For example, neutrophils can release extracellular vesicles containing epigenetic regulators such as microRNA and histones which may influence the landscape of neighboring cells or circulating progenitors ( 139 ). A recent study revealed an association between the epigenetic reprogramming of granulopoiesis as well as neutrophils and trained immunity induced by β-glucan in a murine model of cancer ( 140 ). In this study, mice treated with β-glucan displayed an anti-tumor phenotype mediated by trained neutrophils through a type I IFN signaling, and independent of the host adaptive immune response. Importantly, the trained immunity-mediated anti-tumor effect can be established in the naïve mouse upon adoptive transfer of neutrophils or transplantation of bone marrow from the β-glucan treated mice ( 140 ). These transgenerational epigenetic inheritance mechanisms could contribute to the persistence of epigenetic changes in neutrophils and their progeny, potentially contributing to immune memory across generations of immune cells.

2.5 Applications of trained immunity

Since trained immunity involves reprogramming of innate myeloid cells to induce robust and long-term efficient immune response upon pathogen encounter, several potential avenues of this pathway can be used as targets to develop intervention strategies ( Figure 4 ). For example, trained immunity is regulated by epigenetic and metabolic changes in the innate immune cells relying on specific pathways and networks ( 18 ). Among the epigenetic modifications, histone methylation, acetylation, and DNA methylation are the key targets to tweak trained immunity. Drugs that target histone modification enzymes, such as HATs, HDACs, HMTs and HDMs can modulate the epigenetic landscape of trained cells and their effector functions ( 18 ). For example, HATs and H3K4Me3 activating drugs can promote histone acetylation and promoter access to enhance the production of proinflammatory (e.g., TNF-α, IL-6) and effector molecule (e.g., cathelicidins) production and the response of trained cells. Similarly, HDAC, HMT and DNMT inhibitors can be used, respectively, to influence histone methylation dynamics and enhance the stability and maintenance of DNA methylation marks to facilitate long-term memory-like responses associated with trained immunity. Metabolic pathways such as glucose metabolism and lipid metabolism, the shift in pentose phosphate pathway, aerobic glycolysis, and alterations in FAO might also be ideal targets to activate trained immunity ( 18 ). Increased glycolytic flux, fueled by glucose uptake and utilization, provides energy and biosynthetic precursors for enhanced effort functions and cytokine/chemokine production in trained cells. Furthermore, metabolic intermediates, such as α-KG, succinate and acetyl-CoA derived from metabolic pathways serve as substrates for epigenetic modifications and regulate the gene expression profile of trained cells. Thus, inhibitors and modulators of key enzymes of glycolysis or OXPHOS, as well as amino acid and fatty acid metabolism can be used to elevate the energy metabolism of trained cells for rapid and robust effector functions against pathogenic challenge. Furthermore, PRRs such as TLRs and nucleotide binding receptors are critical in recognizing the antigens and pathogens and interacting with trained immune cells. Transcription factors such as signal transducer and activator proteins (STAT) influence immune cell functions and proinflammatory signaling pathways such as IL-1β and IFN also might act as potential targets to enhance trained immunity.

Figure 4 Potential of trained immunity for disease management. Trained immunity can be harnessed to devise novel intervention strategies to improve treatment for infectious and non-infectious diseases. For example, systemic administration of antigens (immunotherapy) that modulate trained immunity can mount a long-lasting central memory response among myeloid progenitor cells present in the bone marrow. These cells can reach various organs and tissues, where they can exert the features of trained immunity through epigenetic rewiring, metabolic remodeling, and immune activation upon encountering PAMPs or microbes. This peripheral training of innate immune cells results in increased proinflammatory and antimicrobial responses that can confer host protection in conditions such as cancer and infections, such as sepsis. Alternatively, the reprogramming of innate immune cells and their precursors can be suppressed or abolished by inhibiting specific pathways of epigenetic rewiring, metabolic remodeling, and immune activation. In this way, the exacerbated immune activation and inflammatory response due to trained immunity can be dampened. This approach is particularly useful to treat autoimmune disorders, allergies, systemic lupus erythematosus, systemic sclerosis, as well as chronic inflammatory diseases such as inflammatory bowel disease and rheumatoid arthritis. The image was created in BioRender .

Similarly, since trained immunity induces a broader but effective immune response, particularly against a range of pathogenic microbes, understanding the pathways and molecules involved in the activation of trained immunity can be useful in developing potential vaccine candidates against infectious diseases. Targeting pathways of trained immunity, such as histone modifications, metabolic reprogramming, and innate immune signaling, with specific agonists or antagonists cold enhance innate effector responses. This can be achieved by incorporating molecules, such as epigenetic or metabolic modulators that can induce trained immunity, into vaccine formulation. For example, incorporating small molecule agonists or adjuvants that promote trained immunity, such as β-glucan, BCG, or specific cytokines (e.g. IL-1β, IL-6) could be used to boost immune memory and improve vaccine efficacy. Similarly, adjuvants and live attenuated vaccines that can interact with PPRs, such as TLRs, NLRs and CLRs can augment vaccine-induced lasting immunity. These molecules act as strong adjuvants or immune stimulants during vaccine formulation and help in priming the immune system at a stronger level to encounter primary or secondary infection by divergent pathogens or in combating different types of cancer.

Since chemotherapy for cancer treatment is often highly cytotoxic, alternates such as immunotherapy have been sought as better treatment options. Importantly, the efficacy of immunotherapy can be improved by combining with agonists such as β-glucan, which can train and activate myeloid cells for better anti-tumor effector functions. This concept has recently gained attention in the treatment of neuroblastoma (NB) and metastatic pancreatic ductal adenocarcinoma (PDAC) treatment. For example, in a recent Phase II randomized clinical trial of patients with NB, adjunctive oral administration of β-glucan during bivalent, GD2 lactone/GD3 lactone-keyhole limpet hemocyanin conjugate vaccination was shown to increase the anti-GD2 IgG1 antibody titer without elevating toxicity, which was associated with better survival of vaccinated patients ( 141 , 142 ). Moreover, an ongoing phase II clinical trial (Clinical trial NCT04936529) is evaluating the protective efficacy of this bivalent vaccine (OBT-821) combined with β-glucan as a dietary supplement and granulocyte-macrophage colony-stimulating factor (GM-CSF), against NB. The idea of including GM-CSF along with β-glucan is to increase the number of granulocytes, such as neutrophils by GM-CSF, while empowering those cells through β-glucan-mediated trained immunity to effectively control NB cells. Similarly, in a Phase II study (Clinical trial NCT00874848), BTH1677, a β-glucan immune modulator was shown to improve the efficacy of cetuximab, carboplatin, and paclitaxel as first-line treatment for non-small cell lung cancer ( 143 ). In addition, the tolerability and efficacy of β-glucan combined with a CD40 agonistic monoclonal antibody (CDX-1140) is being tested in a Phase 1b study on patients with PDAC (Clinical trial NCT04834778). The logic of this approach is that both CDX-1140 and β-glucan can promote the activation and maturation of APCs through non-redundant myeloid signaling pathways that shift the immune milieu of the tumor microenvironment (TME) and facilitate better clearance and control of cancer cells. Although the results of the PDAC trial are pending, these clinical studies indicate the potential application of trained immunity-based concepts to devise novel and improved treatment modalities for various diseases. It is worth noting that the cytokine/chemokine-induced trained immunity (e.g., IL-1b, GM-CSF, M-CSF) can potentially be useful as combination therapy in preventing/alleviating treatment-associated (e.g., chemo-/radio-/immune-therapy) or disease-associated complications. In a clinical study, treatment of GM-CSF in combination with rituximab and cyclophosphamide/doxorubicin/prednisone/vincristine improved the survival of patients with de novo diffuse large B-cell lymphoma ( 144 ). Similarly, G-CSF (e.g., pegfilgrastim) is already in clinical use for treating neutropenia occurring during the myelosuppressive chemotherapeutic regimen for cancer treatment ( 145 ). Thus, there is a higher potential for chemokines such as M-CSF to be used as an immune trainer (e.g. post-chemotherapy or after stem cell transplantation), which can induce epigenetic rewiring in HSCs in vivo ( 28 ), and thus may be helpful for disease management.

Trained immunity might be useful in designing vaccines against pathogens that can mutate or develop resistance over time; an activated innate memory response can effectively recognize and respond to the evolving strains. In addition, stimulators of trained immunity can be combined with traditional vaccines or immunotherapies to enhance persistent, longer host-protective immune responses. For example, epigenetic modulators, such as HDAC or HMT inhibitors, as well as metabolic modulators targeting glycolysis, OXPHOS can be combined with vaccines to improve the durability of trained immune response. Nutritional interventions, including dietary supplements such as vitamins, amino acids, or fatty acids, as well as mitochondrial-targeting antioxidants, can enhance the effectiveness of vaccines and boost immune memory. Thus, understanding individual variations in trained immunity responses, influenced by genetics, age, and environmental factors could be useful for developing personalized vaccines tailored to elicit a specific immune response. Moreover, biomarkers of trained immunity, such as epigenetic and metabolic signatures, and cytokine profiles, can be used to assess vaccine responsiveness and guide to improvise personalized vaccination strategies, particularly in vulnerable populations. Large-scale clinical studies are needed to identify genetic determinants, biomarkers, and other factors of variations in trained immunity. This approach would aid in developing personalized precision medicine based on trained immunity for the effective treatment of infectious and chronic diseases.

2.6 Limitations of trained immunity

Although the concept of trained immunity emerged recently, it has gained significant momentum in explaining the host response to microbial infections and chronic diseases, with the potential for clinical applications. However, trained immunity generates a non-specific, immune response towards unrelated pathogens, compared to the antigen-specific, targeted response of adaptive immunity. This lack of specificity might result in an overstimulated immune system causing unnecessary inflammation and tissue damage. Unlike adaptive immunity, trained cells do not discriminate between pathogen-specific antigens, this lack of antigen specificity could lead to non-specific or inappropriate immune responses. In addition, the duration and persistence of trained immunity are yet to be fully unraveled as it is unclear how long the enhanced protective immunity would last and whether it would have any long-term side effects. The non-specific nature of trained immune cells also raises the possible risk of the immune system targeting self-antigens, mistakenly leading to immunopathology, autoimmunity, or chronic inflammatory conditions. In this scenario, the identification of targetable negative regulators or checkpoints that modulate trained immunity pathways and maintain immune homeostasis would reduce the risk of autoimmune reactions. Furthermore, designing newer vaccines based on trained immunity might be challenging, due to non-specificity, as traditional vaccines are designed to induce antigen-specific adaptive immune responses, and attempting to replicate this specificity with a trained immunity concept might be complex and complicated. Therefore, understanding the interconnectedness of pathways/networks involved in the molecular and cellular processes of trained immunity, including epigenetic modifications, metabolic reprogramming, and immune response, is vital for identifying specific, context-dependent intervention targets. Moreover, the effector and regulatory functions of trained immune responses can vary significantly between individuals and populations based on factors such as age, sex, prior exposure to microbes, and genetic variations, which makes it difficult to predict and control the trained immune responses as it leads to hyperinflammation. Therefore, accounting for this heterogeneity and developing personalized approaches, tailored to individual immune profiles may be necessary for optimizing clinical outcomes. The duration and persistence of trained immune responses may vary, and the longevity of memory-like responses is currently not well understood. Therefore, studies on the molecular pathways and epigenetic mechanisms governing the durability of trained memory and enhancing memory persistence are urgently needed to devise improved interventions for long-lasting immune protection.

2.7 Ethical and regulatory considerations

Ethical considerations pose an additional challenge in implementing interventions based on trained immunity, as inducing a non-specific immune response, without understanding the long-term effects is unlikely to be accepted by the community. Due to the potential off-target effects of interventions targeting trained immunity, ensuring the safety of such interventions and minimizing adverse effects is critical for clinical translation, particularly in vulnerable populations. Thus, designing clinical trials to evaluate the safety and efficacy of interventions targeting trained immunity poses unique challenges. Identifying appropriate biomarkers of trained immunity, defining clinically relevant endpoints, and conducting long-term follow-up studies are essential for patient stratification, treatment selection, assessing therapeutic efficacy, and establishing clinical utility. In this regard, collaboration between basic scientists, clinicians, immunologists, pharmacologists, and other stakeholders is essential for advancing translational research efforts. Integrating expertise from this multidisciplinary group can accelerate the translational research to clinical applications. In addition, compliance with regulatory approval, including patient consent, privacy, data sharing, and protection of personal data, per ethical guidelines is essential for advancing clinical translation efforts. Patient-centered approaches that prioritize patient needs, preferences, and values through the engagement of patients, caregivers, and advocacy groups that balance scientific rigor and patient welfare, can enhance awareness and support for trained immunity-based therapies.

3 Summary and conclusion

This review underscores the transformative potential of trained immunity in immunology, paving the way for novel therapeutic strategies that leverage innate immune memory. In summary, trained immunity encompasses the immunologic determinants of both classical innate and adaptive responses, elicited in innate immune cells. Several clinical observations involving vaccine-induced immune-boosting of the host that conferred broad protection against a range of pathogens were attributed and/or explained, at least in part, by trained immunity. Recently, the molecular mechanistic aspects of trained immunity regulation upon stimulation with various homogeneous or heterogeneous stimulants have been actively investigated. Different stimuli trigger varied trained immune responses, influencing disease resistance and immune health. These stimuli, including microbial components and vaccines, induce distinct changes in innate immune cells, enhancing their responsiveness upon re-exposure. Thus, although trained immunity confers protection against infections, it may also contribute to chronic inflammatory conditions. Therefore, understanding and modulating trained immunity offer therapeutic potential for improving vaccine efficacy and treating diseases. However, individual variability necessitates personalized approaches to optimize immune responses and health outcomes. Understanding the mechanisms of epigenetic persistence in myeloid cells and their implications for immune memory could provide insight into novel strategies for enhancing host defense and immune responses in health and disease. More research is needed to explore mechanisms regulating trained immunity to prevent excessive or aberrant immune responses. This would help in devising approaches to selectively activate pathways or immune cell subsets of trained immunity, minimizing non-specific off-target effects. Future research on the understanding of these mechanisms would aid in devising strategies to prolong the host-protective immunity elicited by trained immunity. Further, specific components of the trained immunity mechanisms, such as epigenetic and metabolic checkpoints may be harnessed for developing targeted interventions for infectious and non-infectious diseases in the future.

Author contributions

GB: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. SS: Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This review was supported by funding from the National Institute for Allergy and Infectious Diseases (NIAID) of the US National Institute of Health (NIH) (R01AI161822 to SS). The funder has no role in the conceptualization, design, analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

The authors acknowledge the contribution of researchers in the trained immunity field, although we couldn’t include all of their work in this review.

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.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: inflammation, macrophage, neutrophil, epigenetics, metabolism, cell signaling, innate immunity, animal models

Citation: Bhargavi G and Subbian S (2024) The causes and consequences of trained immunity in myeloid cells. Front. Immunol. 15:1365127. doi: 10.3389/fimmu.2024.1365127

Received: 17 January 2024; Accepted: 28 March 2024; Published: 11 April 2024.

Reviewed by:

Copyright © 2024 Bhargavi and Subbian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Selvakumar Subbian, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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An introduction to immunology and immunopathology

Jean s. marshall.

1 Department of Microbiology and Immunology, Dalhousie University, Halifax, NS Canada

Richard Warrington

2 Section of Allergy & Clinical Immunology, Department of Internal Medicine, University of Manitoba, Winnipeg, MB Canada

Wade Watson

3 Division of Allergy, Department of Pediatrics, IWK Health Centre, Dalhousie University, Halifax, NS Canada

Harold L. Kim

4 Western University, London, ON Canada

5 McMaster University, Hamilton, ON Canada

Associated Data

Data sharing not applicable to this article as no datasets were generated or analyzed during the development of this review.

Beyond structural and chemical barriers to pathogens, the immune system has two fundamental lines of defense: innate immunity and adaptive immunity. Innate immunity is the first immunological mechanism for fighting against an intruding pathogen. It is a rapid immune response, initiated within minutes or hours after aggression, that has no immunologic memory. Adaptive immunity, on the other hand, is antigen-dependent and antigen-specific; it has the capacity for memory, which enables the host to mount a more rapid and efficient immune response upon subsequent exposure to the antigen. There is a great deal of synergy between the adaptive immune system and its innate counterpart, and defects in either system can provoke illness or disease, such as inappropriate inflammation, autoimmune diseases, immunodeficiency disorders and hypersensitivity reactions. This article provides a practical overview of innate and adaptive immunity, and describes how these host defense mechanisms are involved in both heath and illness.

There are continuous advances in our current understanding of the immune system and how it functions to protect the body from infection. Given the complex nature of this subject, it is beyond the scope of this article to provide an in-depth review of all aspects of immunology. Rather, the purpose of this article is to provide medical students, medical residents, primary-care practitioners and other healthcare professionals with a basic introduction to the main components and function of the immune system and its role in both health and disease. This article will also serve as a backgrounder to the immunopathological disorders discussed in the remainder of this supplement.

The immune system: innate and adaptive immunity

The immune system refers to a collection of cells, chemicals and processes that function to protect the skin, respiratory passages, intestinal tract and other areas from foreign antigens, such as microbes (organisms such as bacteria, fungi, and parasites), viruses, cancer cells, and toxins. Beyond, the structural and chemical barriers which protect us from infection, the immune system can be simplistically viewed as having two “lines of defense”: innate immunity and adaptive immunity. Innate immunity represents the first line of defense to an intruding pathogen. It is an antigen-independent (non-specific) defense mechanism that is used by the host immediately or within hours of encountering an antigen. The innate immune response has no immunologic memory and, therefore, it is unable to recognize or “memorize” the same pathogen should the body be exposed to it in the future. Adaptive immunity, on the other hand, is antigen-dependent and antigen-specific and, therefore, involves a lag time between exposure to the antigen and maximal response. The hallmark of adaptive immunity is the capacity for memory which enables the host to mount a more rapid and efficient immune response upon subsequent exposure to the antigen. Innate and adaptive immunity are not mutually exclusive mechanisms of host defense, but rather are complementary, with defects in either system resulting in host vulnerability or inappropriate responses [ 1 – 3 ].

Innate immunity

Innate immunity can be viewed as comprising four types of defensive barriers: anatomic (skin and mucous membrane), physiologic (temperature, low pH and chemical mediators), endocytic and phagocytic, and inflammatory. Table  1 summarizes the non-specific host-defense mechanisms for each of these barriers. Cells and processes that are critical for effective innate immunity to pathogens that evade the anatomic barriers have been widely studied. Innate immunity to pathogens relies on pattern recognition receptors (PRRs) which allow a limited range of immune cells to detect and respond rapidly to a wide range of pathogens that share common structures, known as pathogen associated molecular patterns (PAMPs). Examples of these include bacterial cell wall components such as lipopolysaccharides (LPS) and double-stranded ribonucleic acid (RNA) produced during viral infection.

Table 1

Summary of non-specific host-defense mechanisms for barriers of innate immunity [ 1 ]

An important function of innate immunity is the rapid recruitment of immune cells to sites of infection and inflammation through the production of cytokines and chemokines (small proteins involved in cell–cell communication and recruitment). Cytokine production during innate immunity mobilizes many defense mechanisms throughout the body while also activating local cellular responses to infection or injury. Key inflammatory cytokines released during the early response to bacterial infection are: tumour necrosis factor (TNF), interleukin 1 (IL-1) and interleukin 6 (IL-6). These cytokines are critical for initiating cell recruitment and the local inflammation which is essential for clearance of many pathogens. They also contribute to the development of fever. Dysregulated production of such inflammatory cytokines is often associated with inflammatory or autoimmune disease, making them important therapeutic targets.

The complement system is a biochemical cascade that functions to identify and opsonize (coat) bacteria and other pathogens. It renders pathogens susceptible to phagocytosis, a process by which immune cells engulf microbes and remove cell debris, and also kills some pathogens and infected cells directly. The phagocytic action of the innate immune response promotes clearance of dead cells or antibody complexes and removes foreign substances present in organs, tissues, blood and lymph. It can also activate the adaptive immune response through the mobilization and activation of antigen-presenting cells (APCs) (discussed later) [ 1 , 3 ].

Numerous cells are involved in the innate immune response such as phagocytes (macrophages and neutrophils), dendritic cells, mast cells, basophils, eosinophils, natural killer (NK) cells and innate lymphoid cells. Phagocytes are sub-divided into two main cell types: neutrophils and macrophages. Both of these cells share a similar function: to engulf (phagocytose) microbes and kill them through multiple bactericidal pathways. In addition to their phagocytic properties, neutrophils contain granules and enzyme pathways that assist in the elimination of pathogenic microbes. Unlike neutrophils (which are short-lived cells), macrophages are long-lived cells that not only play a role in phagocytosis, but are also involved in antigen presentation to T cells (see Fig.  1 ) [ 1 ].

Characteristics and function of cells involved in innate immunity [ 1 , 3 , 4 ]. *Dust cells (within pulmonary alveolus), histiocytes (connective tissue), Kupffer cells (liver), microglial cells (neural tissue), epithelioid cells (granulomas), osteoclasts (bone), mesangial cells (kidney)

Dendritic cells also phagocytose and function as APCs, initiating the acquired immune response and acting as important messengers between innate and adaptive immunity. Mast cells and basophils share many salient features with each other, and both are instrumental in the initiation of acute inflammatory responses, such as those seen in allergy and asthma. Mast cells also have important functions as immune “sentinel cells” and are early producers of cytokines in response to infection or injury. Unlike mast cells, which generally reside in the connective tissue surrounding blood vessels and are particularly common at mucosal surfaces, basophils reside in the circulation. Eosinophils are granulocytes that possess phagocytic properties and play an important role in the destruction of parasites that are often too large to be phagocytosed. Along with mast cells and basophils, they also control mechanisms associated with allergy and asthma. Natural killer (NK) cells play a major role in the rejection of tumours and the destruction of cells infected by viruses. Destruction of infected cells is achieved through the release of perforins and granzymes (proteins that cause lysis of target cells) from NK-cell granules which induce apoptosis (programmed cell death) [ 4 ]. NK cells are also an important source of another cytokine, interferon-gamma (IFN-γ), which helps to mobilize APCs and promote the development of effective anti-viral immunity. Innate lymphoid cells (ILCs) play a more regulatory role. Depending on their type (i.e., ILC-1, ILC-2, ILC-3), they selectively produce cytokines such as IL-4, IFN-γ and IL-17 that help to direct the appropriate immune response to specific pathogens and contribute to immune regulation in that tissue.

The main characteristics and functions of the cells involved in the innate immune response are summarized in Fig.  1 .

Adaptive immunity

The development of adaptive immunity is aided by the actions of the innate immune system, and is critical when innate immunity is ineffective in eliminating infectious agents. The primary functions of the adaptive immune response are: the recognition of specific “non-self” antigens, distinguishing them from “self” antigens; the generation of pathogen-specific immunologic effector pathways that eliminate specific pathogens or pathogen-infected cells; and the development of an immunologic memory that can quickly eliminate a specific pathogen should subsequent infections occur [ 2 ]. Adaptive immune responses are the basis for effective immunization against infectious diseases. The cells of the adaptive immune system include: antigen-specific T cells, which are activated to proliferate through the action of APCs, and B cells which differentiate into plasma cells to produce antibodies.

T cells and APCs

T cells derive from hematopoietic stem cells in bone marrow and, following migration, mature in the thymus. These cells express a series of unique antigen-binding receptors on their membrane, known as the T-cell receptor (TCR). Each T cell expresses a single type of TCR and has the capacity to rapidly proliferate and differentiate if it receives the appropriate signals. As previously mentioned, T cells require the action of APCs (usually dendritic cells, but also macrophages, B cells, fibroblasts and epithelial cells) to recognize a specific antigen.

The surfaces of APCs express a group of proteins known as the major histocompatibility complex (MHC). MHC are classified as either class I (also termed human leukocyte antigen [HLA] A, B and C) which are found on all nucleated cells, or class II (also termed HLA DP, DQ and DR) which are found only on certain cells of the immune system, including macrophages, dendritic cells and B cells. Class I MHC molecules present endogenous (intracellular) peptides, while class II molecules on APCs present exogenous (extracellular) peptides to T cells. The MHC protein displays fragments of antigens (peptides) when a cell is infected with an intracellular pathogen, such as a virus, or has phagocytosed foreign proteins or organisms [ 2 , 3 ].

T cells have a wide range of unique TCRs which can bind to specific foreign peptides. During the development of the immune system, T cells that would react to antigens normally found in our body are largely eliminated. T cells are activated when they encounter an APC that has digested an antigen and is displaying the correct antigen fragments (peptides) bound to its MHC molecules. The opportunities for the right T cells to be in contact with an APC carrying the appropriate peptide MHC complex are increased by the circulation of T cells throughout the body (via the lymphatic system and blood stream) and their accumulation (together with APCs) in lymph nodes. The MHC-antigen complex activates the TCR and the T cell secretes cytokines which further control the immune response. This antigen presentation process stimulates T cells to differentiate primarily into either cytotoxic T cells (CD8+ cells) or T-helper (Th) cells (CD4+ cells) (see Fig.  2 ). CD8+ cytotoxic T cells are primarily involved in the destruction of cells infected by foreign agents, such as viruses, and the killing of tumour cells expressing appropriate antigens. They are activated by the interaction of their TCR with peptide bound to MHC class I molecules. Clonal expansion of cytotoxic T cells produces effector cells which release substances that induce apoptosis of target cells. Upon resolution of the infection, most effector cells die and are cleared by phagocytes. However, a few of these cells are retained as memory cells that can quickly differentiate into effector cells upon subsequent encounters with the same antigen [ 2 , 3 ].

Adaptive immunity: T-cell and B-cell activation and function. APC antigen-presenting cell, TCR T-cell receptor, MHC major histocompatibility complex

(figure adapted from images available at: http://en.wikipedia.org/wiki/Image:B_cell_activation.png and http://commons.wikimedia.org/wiki/Image:Antigen_presentation.svg )

CD4+ Th cells play an important role in establishing and maximizing the immune response. These cells have no cytotoxic or phagocytic activity, and cannot directly kill infected cells or clear pathogens. However, they “mediate” the immune response by directing other cells to perform these tasks and regulate the type of immune response that develops. Th cells are activated through TCR recognition of antigen bound to class II MHC molecules. Once activated, Th cells release cytokines that influence the activity of many cell types, including the APCs that activate them.

Several types of Th cell responses can be induced by an APC, with Th1, Th2 and Th17 being the most frequent. The Th1 response is characterized by the production of IFN-γ which activates the bactericidal activities of macrophages and enhances anti-viral immunity as well as immunity to other intracellular pathogens. Th1-derived cytokines also contribute to the differentiation of B cells to make opsonizing antibodies that enhance the efficiency of phagocytes. An inappropriate Th1 response is associated with certain autoimmune diseases.

The Th2 response is characterized by the release of cytokines (IL-4, 5 and 13) which are involved in the development of immunoglobulin E (IgE) antibody-producing B cells, as well as the development and recruitment of mast cells and eosinophils that are essential for effective responses against many parasites. In addition, they enhance the production of certain forms of IgG that aid in combatting bacterial infection. As mentioned earlier, mast cells and eosinophils are instrumental in the initiation of acute inflammatory responses, such as those seen in allergy and asthma. IgE antibodies are also associated with allergic reactions (see Table  2 ). Therefore, an imbalance of Th2 cytokine production is associated with the development of atopic (allergic) conditions. Th17 cells have been more recently described. They are characterized by the production of cytokines of the IL-17 family, and are associated with ongoing inflammatory responses, particularly in chronic infection and disease. Like cytotoxic T cells, most Th cells will die upon resolution of infection, with a few remaining as Th memory cells [ 2 , 3 ].

Table 2

Major functions of human Ig antibodies [ 5 ]

A subset of the CD4+ T cell, known as the regulatory T cell (T reg), also plays a role in the immune response. T reg cells limit and suppress immune responses and, thereby, may function to control aberrant responses to self-antigens and the development of autoimmune disease. T reg cells may also help in the resolution of normal immune responses, as pathogens or antigens are eliminated. These cells also play a critical role in the development of “immune tolerance” to certain foreign antigens, such as those found in food.

B cells arise from hematopoietic stem cells in the bone marrow and, following maturation, leave the marrow expressing a unique antigen-binding receptor on their membrane. Unlike T cells, B cells can recognize antigens directly, without the need for APCs, through unique antibodies expressed on their cell surface. The principal function of B cells is the production of antibodies against foreign antigens which requires their further differentiation [ 2 , 3 ]. Under certain circumstances, B cells can also act as APCs.

When activated by foreign antigens to which they have an appropriate antigen specific receptor, B cells undergo proliferation and differentiate into antibody-secreting plasma cells or memory B cells (see Fig.  2 ). Memory B cells are “long-lived” survivors of past infection and continue to express antigen-binding receptors. These cells can be called upon to respond quickly by producing antibodies and eliminating an antigen upon re-exposure. Plasma cells, on the other hand, are relatively short-lived cells that often undergo apoptosis when the inciting agent that induced the immune response is eliminated. However, these cells produce large amounts of antibody that enter the circulation and tissues providing effective protection against pathogens.

Given their function in antibody production, B cells play a major role in the humoral or antibody-mediated immune response (as opposed to the cell-mediated immune response, which is governed primarily by T cells) [ 2 , 3 ].

Antibody-mediated vs. cell-mediated immunity

Antibody-mediated immunity is the branch of the acquired immune system that is mediated by B-cell-antibody production. The antibody-production pathway begins when the B cell’s antigen-binding receptor recognizes and binds to antigen in its native form. Local Th cells secrete cytokines that help the B cell multiply and direct the type of antibody that will be subsequently produced. Some cytokines, such as IL-6, help B-cells to mature into antibody-secreting plasma cells. The secreted antibodies bind to antigens on the surface of pathogens, flagging them for destruction through complement activation, opsonin promotion of phagocytosis and pathogen elimination by immune effector cells. Upon elimination of the pathogen, the antigen–antibody complexes are cleared by the complement cascade (see Fig.  2 ) [ 2 ].

Five major types of antibodies are produced by B cells: IgA, IgD, IgE, IgG and IgM. IgG antibodies can be further subdivided into structurally distinct subclasses with differing abilities to fix complement, act as opsonins, etc. The major classes of antibodies have substantially different biological functions and recognize and neutralize specific pathogens. Table  2 summarizes the various functions of the five Ig antibodies [ 5 ].

Antibodies play an important role in containing virus proliferation during the acute phase of infection. However, they are not generally capable of eliminating a virus once infection has occurred. Once an infection is established, cell-mediated immune mechanisms are most important in host defense against most intracellular pathogens.

Cell-mediated immunity does not involve antibodies, but rather protects an organism through [ 2 ]:

• The activation of antigen-specific cytotoxic T cells that induce apoptosis of cells displaying foreign antigens or derived peptides on their surface, such as virus-infected cells, cells with intracellular bacteria, and cancer cells displaying tumour antigens;
• The activation of macrophages and NK cells, enabling them to destroy intracellular pathogens; and
• The stimulation of cytokine (such as IFNγ) production that further mediates the effective immune response.

Cell-mediated immunity is directed primarily at microbes that survive in phagocytes as well as those that infect non-phagocytic cells. This type of immunity is most effective in eliminating virus-infected cells and cancer cells, but can also participate in defending against fungi, protozoa, cancers, and intracellular bacteria. Cell-mediated immunity also plays a major role in transplant rejection.

Passive vs. active immunization

Acquired immunity is attained through either passive or active immunization. Passive immunization refers to the transfer of active humoral immunity, in the form of “ready-made” antibodies, from one individual to another. It can occur naturally by transplacental transfer of maternal antibodies to the developing fetus, or it can be induced artificially by injecting a recipient with exogenous antibodies that are usually manufactured for this purpose and that are targeted to a specific pathogen or toxin. The latter is used when there is a high risk of infection and insufficient time for the body to develop its own immune response, or to reduce the symptoms of chronic or immunosuppressive diseases.

Active immunization refers to the production of antibodies against a specific antigen or pathogen after exposure to the antigen. It can be acquired through either natural infection with a microbe or through administration of a vaccine that can consist of attenuated (weakened) pathogens, inactivated organisms or specific proteins or carbohydrates known to induce immunity. Effective active immunization often requires the use of “adjuvants” which improve the ability of the immune system to respond to antigen injection.

Immunopathology

As mentioned earlier, defects or malfunctions in either the innate or adaptive immune response can provoke illness or disease. Such disorders are generally caused by an overactive immune response (known as hypersensitivity reactions), an inappropriate reaction to self (known as autoimmunity) or ineffective immune responses (known as immunodeficiency).

Hypersensitivity reactions

Hypersensitivity reactions refer to undesirable responses produced by the normal immune system. There are four types of hypersensitivity reactions [ 6 , 7 ]:

• Type I: immediate hypersensitivity.
• Type II: cytotoxic or antibody-dependent hypersensitivity.
• Type III: immune complex disease.
• Type IV: delayed-type hypersensitivity.

Type I hypersensitivity is the most common type of hypersensitivity reaction. It is an allergic reaction provoked by re-exposure to a specific type of antigen, referred to as an allergen. Unlike the normal immune response, the type I hypersensitivity response is characterized by the secretion of IgE by plasma cells. IgE antibodies bind to receptors on the surface of tissue mast cells and blood basophils, causing them to be “sensitized”. Later exposure to the same allergen cross-links the bound IgE on sensitized cells resulting in degranulation and the secretion of active mediators such as histamine, leukotrienes, and prostaglandins that cause vasodilation and smooth-muscle contraction of the surrounding tissue. Common environmental allergens inducing IgE-mediated allergies include pet (e.g., cat, dog, horse) epithelium, pollen, house dust mites, and molds. Food allergens are also a common cause of type I hypersensitivity reactions, however, these types of reactions are more frequently seen in children than adults. Treatment of type I reactions generally involves trigger avoidance, and in the case of inhaled allergens, pharmacological intervention with bronchodilators, antihistamines and anti-inflammatory agents. Some types of allergic disease can be treated with immunotherapy (see Allergen-specific Immunotherapy article in this supplement). Severe cases of type 1 hypersensitivity (anaphylaxis) may require immediate treatment with epinephrine.

Type II hypersensitivity reactions are rare and take anywhere from 2 to 24 h to develop. These types of reactions occur when IgG and IgM antibodies bind to the patient’s own cell-surface molecules, forming complexes that activate the complement system. This, in turn, leads to opsonization, red blood cell agglutination (process of agglutinating or “clumping together”), cell lysis and death. Some examples of type II hypersensitivity reactions include: erythroblastosis fetalis, Goodpasture syndrome, and autoimmune anemias.

Type III hypersensitivity reactions occur when IgG and IgM antibodies bind to soluble proteins (rather than cell surface molecules as in type II hypersensitivity reactions) forming immune complexes that can deposit in tissues, leading to complement activation, inflammation, neutrophil influx and mast cell degranulation. This type of reaction can take days, or even weeks, to develop and treatment generally involves anti-inflammatory agents and corticosteroids. Examples of type III hypersensitivity reactions include systemic lupus erythematosus (SLE), serum sickness and reactive arthritis.

Unlike the other types of hypersensitivity reactions, type IV reactions are cell-mediated and antibody-independent. They are the second most common type of hypersensitivity reaction and usually take 2 or more days to develop. These types of reactions are caused by the overstimulation of T cells and monocytes/macrophages which leads to the release of cytokines that cause inflammation, cell death and tissue damage. In general, these reactions are easily resolvable through trigger avoidance and the use of topical corticosteroids. An example of this is the skin response to poison ivy.

A brief summary of the four types of hypersensitivity reactions is provided in Table  3 .

Table 3

Types of hypersensitivity reactions [ 6 , 7 ]

Autoimmunity

Autoimmunity involves the loss of normal immune homeostasis such that the organism produces an abnormal response to its own tissue. The hallmark of autoimmunity is the presence of self-reactive T cells, auto-antibodies, and inflammation. Prominent examples of autoimmune diseases include: Celiac disease, type 1 diabetes mellitus, Addison’s disease and Graves’ disease [ 8 ].

Inflammation

Poorly regulated inflammatory responses and tissue damage as a result of inflammation are often immunopathological features. Defects in immune regulation are associated with many chronic inflammatory diseases, including: rheumatoid arthritis, psoriasis, inflammatory bowel disease and asthma. Classical features of inflammation are heat, redness, swelling and pain. Inflammation can be part of the normal host response to infection and a required process to rid the body of pathogens, or it may become uncontrolled and lead to chronic inflammatory disease. The overproduction of inflammatory cytokines (such as TNF, IL-1 and IL-6) as well as the recruitment of inflammatory cells (such as neutrophils and monocytes) through the function of chemokines are important drivers of the inflammatory process. Additional mediators produced by recruited and activated immune cells induce changes in vascular permeability and pain sensitivity.

Immunodeficiency

Immunodeficiency refers to a state in which the immune system’s ability to fight infectious disease is compromised or entirely absent. Immunodeficiency disorders may result from a primary genetic defect (primary immunodeficiency—see Primary Immunodeficiency article in this supplement) which can effect either innate or acquired immune function through inhibition of selected immune cells or pathways, or it may be acquired from a secondary cause (secondary immunodeficiency), such as viral or bacterial infections, malnutrition, autoimmunity or treatment with drugs that induce immunosuppression. Certain diseases can also directly or indirectly impair the immune system such as leukemia and multiple myeloma. Immunodeficiency is also the hallmark of acquired immunodeficiency syndrome (AIDS), caused by the human immunodeficiency virus (HIV). HIV directly infects Th cells and also impairs other immune system responses indirectly [ 9 , 10 ].

Innate immunity is the first immunological, non-specific mechanism for fighting against infections. This immune response is rapid, occurring minutes or hours after aggression and is mediated by numerous cells including phagocytes, mast cells, basophils and eosinophils, as well as the complement system. Adaptive immunity develops in conjunction with innate immunity to eliminate infectious agents; it relies on the tightly regulated interplay between T cells, APCs and B cells. A critical feature of adaptive immunity is the development of immunologic memory or the ability of the system to learn or record its experiences with various pathogens, leading to effective and rapid immune responses upon subsequent exposure to the same or similar pathogens. A brief overview of the defining features of innate and adaptive immunity are presented in Table  4 .

Table 4

Overview of the defining features of innate and adaptive immunity [ 1 ]

There is a great deal of synergy between the adaptive immune system and its innate counterpart, and defects in either system can lead to immunopathological disorders, including autoimmune diseases, immunodeficiencies and hypersensitivity reactions. The remainder of this supplement will focus on the appropriate diagnosis, treatment and management of some of these more prominent disorders, particularly those associated with hypersensitivity reactions.

Declarations

Authors’ contributions All authors wrote and/or edited sections of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to extend special thanks to Dr. Francesca Antonetti whose accredited online course entitled “An Introduction to Immunology” provided the foundation and framework for this article. This informative, entry-level course can be accessed through the Excellence in Medical Education (EXCEMED) website at: https://www.excemed.org .

This article is an update to the article entitled, An Introduction to Immunology and Immunopathology, that originally appeared in the supplement, Practical Guide to Allergy and Immunology in Canada, which was published in Allergy, Asthma & Clinical Immunology in 2011 (available at: https://aacijournal.biomedcentral.com/articles/supplements/volume-7-supplement-1 ).

The authors would like to thank Julie Tasso for her editorial services and assistance in the preparation of this manuscript.

Competing interests

Dr. Jean S. Marshall has no competing interests to disclose. Dr. Richard Warrington is the past president of the Canadian Society of Allergy & Clinical Immunology and Editor-in-Chief of Allergy, Asthma & Clinical Immunology. He has received consulting fees and honoraria from Nycomed, CSL Behring, Talecris, Grifols, Novartis and Shire. Dr. Wade Watson is an associate editor of Allergy, Asthma & Clinical Immunology. Dr. Harold Kim is Vice President of the Canadian Society of Allergy and Clinical Immunology, Past President of the Canadian Network for Respiratory Care, and Co-chief Editor of Allergy, Asthma and Clinical Immunology. He has received consulting fees and honoraria for continuing medical education from AstraZeneca, Aralez, Boehringer Ingelheim, CSL Behring, Kaleo, Merck, Novartis, Pediapharm, Sanofi, Shire and Teva.

Availability of data and materials

Consent for publication.

Not applicable.

Ethics approval and consent to participate

Ethics approval and consent to participate are not applicable to this review article.

Publication of this supplement has been supported by AstraZeneca, Boehringer Ingelheim, CSL Behring Canada Inc., MEDA Pharmaceuticals Ltd., Merck Canada Inc., Pfizer Canada Inc., Shire Pharma Canada ULC, Stallergenes Greer Canada, Takeda Canada, Teva Canada Innovation, Aralez Tribute and Pediapharm.

About this supplement

This article has been published as part of Allergy, Asthma & Clinical Immunology Volume 14 Supplement 2, 2018: Practical guide for allergy and immunology in Canada 2018. The full contents of the supplement are available online at https://aacijournal.biomedcentral.com/articles/supplements/volume-14-supplement-2 .

Publisher’s Note

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

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Study finds potential new treatment path for lasting Lyme disease symptoms

by Tulane University

Tulane University researchers have identified a promising new approach to treating persistent neurological symptoms associated with Lyme disease, offering hope to patients who suffer from long-term effects of the bacterial infection, even after antibiotic treatment. Their results were published in Frontiers in Immunology .

Lyme disease, caused by the bacterium Borrelia burgdorferi and transmitted through tick bites, can lead to a range of symptoms, including those affecting the central and peripheral nervous systems. While antibiotics can effectively clear the infection in most cases, a subset of patients continues to experience symptoms such as memory loss , fatigue, and pain—a condition often referred to as post-treatment Lyme disease syndrome.

Principal investigator Geetha Parthasarathy, Ph.D., an assistant professor of microbiology and immunology at the Tulane National Primate Research Center, has discovered that fibroblast growth factor receptor inhibitors, a type of drug previously studied in the context of cancer, can significantly reduce inflammation and cell death in brain and nerve tissue samples infected with Borrelia burgdorferi.

This discovery suggests that targeting FGFR pathways may offer an exciting new therapeutic approach to addressing persistent neuroinflammation in patients with post-treatment Lyme disease syndrome.

"Our findings open the door to new research approaches that can help us support patients suffering from the lasting effects of Lyme disease," Parthasarathy said. "By focusing on the underlying inflammation that contributes to these symptoms, we hope to develop treatments that can improve the quality of life for those affected by this debilitating condition."

Researchers treated nerve tissue with live or inactivated Borrelia burgdorferi, followed by an application of FGFR inhibitors. Study results revealed a significant reduction in both inflammatory markers and cell death.

While further research is needed to translate these findings into clinical treatments, the study represents an important step forward in understanding and potentially managing the complex aftermath of Lyme disease.

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Small Extracellular Vesicle piR-hsa-30937 Derived from Pancreatic Neuroendocrine Neoplasms Upregulates CD276 in Macrophages to Promote Immune Evasion

Y. Zhong, Y. Tian, and Y. Wang contributed equally to this article.

Cancer Immunol Res 2024;XX:XX–XX

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Yuan Zhong , Ye Tian , Yan Wang , Jian'an Bai , Qin Long , Lijun Yan , Zhihui Gong , Wei Gao , Qiyun Tang; Small Extracellular Vesicle piR-hsa-30937 Derived from Pancreatic Neuroendocrine Neoplasms Upregulates CD276 in Macrophages to Promote Immune Evasion. Cancer Immunol Res 2024; https://doi.org/10.1158/2326-6066.CIR-23-0825

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The role of PIWI-interacting RNAs (piRNA) in small extracellular vesicles (sEV) derived from pancreatic neuroendocrine neoplasms (PNEN) in the tumor microenvironment (TME) remains unexplored. We used multiplex IHC to analyze the expression of CD68, CD276 (B7H3), and CD3 on PNEN. CD276 + tumor-associated macrophages (TAM) were more abundant in tumor tissues than nontumor tissues and negatively correlated with T-cell infiltration. Serum sEV piRNA sequencing was performed to identify piRNAs enriched in patients with PNEN. We then investigated the function and mechanism of sEV piR-hsa-30937 in the cross-talk between tumor cells and macrophages in the PNEN TME. PNEN-derived sEV piR-hsa-30937 targeted PTEN to activate the AKT pathway and drive CD276 expression. CD276 + macrophages inhibited T-cell proliferation and IFNγ production. piR-hsa-30937 knockdown and anti-CD276 treatment suppressed progression and metastasis in a preclinical model of PNEN by enhancing T-cell immunity. Thus, our data show that PNEN-derived sEV piR-hsa-30937 promotes CD276 expression in macrophages through the PTEN/AKT pathway and that CD276 + TAMs suppress T-cell antitumor immunity. sEV piR-hsa-30937 and CD276 are potential therapeutic targets for immunotherapy of PNEN.

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Supplementary data.

Supplementary Fig. S1 PNEN cell-derived sEVs drive CD276 expression in human monocyte-derived macrophages (hMDMs) to inhibit T-cell immunity.

Supplementary Fig. S2 Representative mIHC images of CD3 in nontumor and tumor tissues (scale bar: 20 μm). The number of CD3+ T cells in tumor tissues was significantly higher than that in nontumor tissues. ***p < 0.001.

Supplementary Fig. S3 The association of CD3+ and CD8+ T cells with PNEN progression.

Supplementary Fig. S4 CD276 expression was detected in PNEN cells and PNEN cell-derived sEVs by western blot.

The RIP assay was conducted in THP-1 macrophages incubated with PNEN cell-derived sEVs

Supplementary Fig. S6 The distribution of piRNA length (A), piRNA expression (B) and piRNA expression density (C) in each sample.

Supplementary Fig. S7 The RIP assay was conducted in THP-1 macrophages incubated with 10 µg/ml PNEN cell-derived sEVs for 48 h. Results showed no significant relative enrichment of piR-hsa-30937 by anti-PTEN.

Supplementary Fig. S8 THP-1 macrophages were incubated with 10 µg/ml PNEN cell-derived sEVs for 6 h, 12 h, 24 h and 48 h to show the kinetic.

Supplementary Fig. S9 PNEN cell-derived sEV piR-hsa-30937 is transferred to macrophages and drives CD276 expression by targeting PTEN to activate the AKT signaling pathway. CD276+ macrophages suppress T-cell immunity and promote PNEN immune evasion and progression.

Details of PNEN patients and healthy volunteers

Constructed sequences for plasmids

Antibodies used in assays

Primers used in assays

Differentially expressed piRNAs in serum sEVs from PNEN patients compared to healthy volunteers

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• Published: 08 April 2024

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

• Felicity Liew 1   na1 ,
• Claudia Efstathiou   ORCID: orcid.org/0000-0001-6125-8126 1   na1 ,
• Sara Fontanella 1 ,
• Matthew Richardson 2 ,
• Ruth Saunders 2 ,
• Dawid Swieboda 1 ,
• Jasmin K. Sidhu 1 ,
• Stephanie Ascough 1 ,
• Shona C. Moore   ORCID: orcid.org/0000-0001-8610-2806 3 ,
• Noura Mohamed 4 ,
• Jose Nunag   ORCID: orcid.org/0000-0002-4218-0500 5 ,
• Clara King 5 ,
• Olivia C. Leavy 2 , 6 ,
• Omer Elneima 2 ,
• Hamish J. C. McAuley 2 ,
• Aarti Shikotra 7 ,
• Amisha Singapuri   ORCID: orcid.org/0009-0002-4711-7516 2 ,
• Marco Sereno   ORCID: orcid.org/0000-0003-4573-9303 2 ,
• Victoria C. Harris 2 ,
• Linzy Houchen-Wolloff   ORCID: orcid.org/0000-0003-4940-8835 8 ,
• Neil J. Greening   ORCID: orcid.org/0000-0003-0453-7529 2 ,
• Nazir I. Lone   ORCID: orcid.org/0000-0003-2707-2779 9 ,
• Matthew Thorpe 10 ,
• A. A. Roger Thompson   ORCID: orcid.org/0000-0002-0717-4551 11 ,
• Sarah L. Rowland-Jones 11 ,
• Annemarie B. Docherty   ORCID: orcid.org/0000-0001-8277-420X 10 ,
• James D. Chalmers 12 ,
• Ling-Pei Ho   ORCID: orcid.org/0000-0001-8319-301X 13 ,
• Alexander Horsley   ORCID: orcid.org/0000-0003-1828-0058 14 ,
• Betty Raman 15 ,
• Krisnah Poinasamy 16 ,
• Michael Marks 17 , 18 , 19 ,
• Onn Min Kon 1 ,
• Luke S. Howard   ORCID: orcid.org/0000-0003-2822-210X 1 ,
• Daniel G. Wootton 3 ,
• Jennifer K. Quint 1 ,
• Thushan I. de Silva   ORCID: orcid.org/0000-0002-6498-9212 11 ,
• Antonia Ho 20 ,
• Christopher Chiu   ORCID: orcid.org/0000-0003-0914-920X 1 ,
• Ewen M. Harrison   ORCID: orcid.org/0000-0002-5018-3066 10 ,
• William Greenhalf 21 ,
• J. Kenneth Baillie   ORCID: orcid.org/0000-0001-5258-793X 10 , 22 , 23 ,
• Malcolm G. Semple   ORCID: orcid.org/0000-0001-9700-0418 3 , 24 ,
• Lance Turtle 3 , 24 ,
• Rachael A. Evans   ORCID: orcid.org/0000-0002-1667-868X 2 ,
• Louise V. Wain 2 , 6 ,
• Christopher Brightling 2 ,
• Ryan S. Thwaites   ORCID: orcid.org/0000-0003-3052-2793 1   na1 ,
• Peter J. M. Openshaw   ORCID: orcid.org/0000-0002-7220-2555 1   na1 ,
• PHOSP-COVID collaborative group &

ISARIC investigators

Nature Immunology volume  25 ,  pages 607–621 ( 2024 ) Cite this article

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• Inflammasome
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One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood 1 . Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials.

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Keith Sacco, Riccardo Castagnoli, … Luigi D. Notarangelo

In COVID-19, NLRP3 inflammasome genetic variants are associated with critical disease and these effects are partly mediated by the sickness symptom complex: a nomothetic network approach

Michael Maes, Walton Luiz Del Tedesco Junior, … Andréa Name Colado Simão

Epidemiology, clinical presentation, pathophysiology, and management of long COVID: an update

Sizhen Su, Yimiao Zhao, … Lin Lu

One in ten severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections results in post-acute sequelae of coronavirus disease 2019 (PASC) or long coronavirus disease (COVID), which affects 65 million people worldwide 1 . Long COVID (LC) remains common, even after mild acute infection with recent variants 2 , and it is likely LC will continue to cause substantial long-term ill health, requiring targeted management based on an understanding of how disease phenotypes relate to underlying mechanisms. Persistent inflammation has been reported in adults with LC 1 , 3 , but studies have been limited in size, timing of samples or breadth of immune mediators measured, leading to inconsistent or absent associations with symptoms. Markers of oxidative stress, metabolic disturbance, vasculoproliferative processes and IFN-, NF-κB- or monocyte-related inflammation have been suggested 3 , 4 , 5 , 6 .

The PHOSP-COVID study, a multicenter United Kingdom study of patients previously hospitalized with COVID-19, has reported inflammatory profiles in 626 adults with health impairment after COVID-19, identified through clustering. Elevated IL-6 and markers of mucosal inflammation were observed in those with severe impairment compared with individuals with milder impairment 7 . However, LC is a heterogeneous condition that may be a distinct form of health impairment after COVID-19, and it remains unclear whether there are inflammatory changes specific to LC symptom subtypes. Determining whether activated inflammatory pathways underlie all cases of LC or if mechanisms differ according to clinical presentation is essential for developing effective therapies and has been highlighted as a top research priority by patients and clinicians 8 .

In this Letter, in a prospective multicenter study, we measured 368 plasma proteins in 657 adults previously hospitalized for COVID-19 (Fig. 1a and Table 1 ). Individuals in our cohort experienced a range of acute COVID-19 severities based on World Health Organization (WHO) progression scores 9 ; WHO 3–4 (no oxygen support, n  = 133 and median age of 55 years), WHO 5–6 (oxygen support, n  = 353 and median age of 59 years) and WHO 7–9 (critical care, n  = 171 and median age of 57 years). Participants were hospitalized for COVID-19 ≥3 months before sample collection (median 6.1 months, interquartile range (IQR) 5.1–6.8 months and range 3.0–8.3 months) and confirmed clinically ( n  = 36/657) or by PCR ( n  = 621/657). Symptom data indicated 233/657 (35%) felt fully recovered at 6 months (hereafter ‘recovered’) and the remaining 424 (65%) reported symptoms consistent with the WHO definition for LC (symptoms ≥3 months post infection 10 ). Given the diversity of LC presentations, patients were grouped according to symptom type (Fig. 1b ). Groups were defined using symptoms and health deficits that have been commonly reported in the literature 1 ( Methods ). A multivariate penalized logistic regression model (PLR) was used to explore associations of clinical covariates and immune mediators at 6 months between recovered patients ( n  = 233) and each LC group (cardiorespiratory symptoms, cardioresp, n  = 398, Fig. 1c ; fatigue, n  = 384, Fig. 1d ; affective symptoms, anxiety/depression, n  = 202, Fig. 1e ; gastrointestinal symptoms, GI, n  = 132, Fig. 1f ; and cognitive impairment, cognitive, n  = 61, Fig. 1g ). Women ( n  = 239) were more likely to experience CardioResp (odds ratio (OR 1.14), Fatigue (OR 1.22), GI (OR 1.13) and Cognitive (OR 1.03) outcomes (Fig. 1c,d,f,g ). Repeated cross-validation was used to optimize and assess model performance ( Methods and Extended Data Fig. 1 ). Pre-existing conditions, such as chronic lung disease, neurological disease and cardiovascular disease (Supplementary Table 1 ), were associated with all LC groups (Fig. 1c–g ). Age, C-reactive protein (CRP) and acute disease severity were not associated with any LC group (Table 1 ).

a , Distribution of time from COVID-19 hospitalization at sample collection. All samples were cross-sectional. The vertical red line indicates the 3 month cutoff used to define our final cohort and samples collected before 3 months were excluded. b , An UpSet plot describing pooled LC groups. The horizontal colored bars represent the number of patients in each symptom group: cardiorespiratory (Cardio_Resp), fatigue, cognitive, GI and anxiety/depression (Anx_Dep). Vertical black bars represent the number of patients in each symptom combination group. To prevent patient identification, where less than five patients belong to a combination group, this has been represented as ‘<5’. The recovered group ( n  = 233) were used as controls. c – g , Forest plots of Olink protein concentrations (NPX) associated with Cardio_Resp ( n  = 365) ( c ), fatigue (n = 314) ( d ), Anx_Dep ( n  = 202) ( e ), GI ( n  = 124) ( f ) and cognitive ( n  = 60) ( g ). Neuro_Psych, neuropsychiatric. The error bars represent the median accuracy of the model. h , i , Distribution of Olink values (NPX) for IL-1R2 ( h ) and MATN2, neurofascin and sCD58 ( i ) measured between symptomatic and recovered individuals in recovered ( n  = 233), Cardio_Resp ( n  = 365), fatigue ( n  = 314) and Anx_Dep ( n  = 202) groups ( h ) and MATN2 in GI ( n  = 124), neurofascin in cognitive ( n  = 60) and sCD58 in Cardio_Resp and recovered groups ( i ). The box plot center line represents the median, the boundaries represent IQR and the whisker length represents 1.5× IQR. The median values were compared between groups using two-sided Wilcoxon signed-rank test, * P  < 0.05, ** P  < 0.01, *** P  < 0.001 and **** P  < 0.0001.

To study the association of peripheral inflammation with symptoms, we analyzed cross-sectional data collected approximately 6 months after hospitalizations. We measured 368 immune mediators from plasma collected contemporaneously with symptom data. Mediators suggestive of myeloid inflammation were associated with all symptoms (Fig. 1c–h ). Elevated IL-1R2, an IL-1 receptor expressed by monocytes and macrophages modulating inflammation 11 and MATN2, an extracellular matrix protein that modulates tissue inflammation through recruitment of innate immune cells 12 , were associated with cardioresp (IL-1R2 OR 1.14, Fig. 1c,h ), fatigue (IL-1R2 OR 1.45, Fig. 1d,h ), anxiety/depression (IL-1R2 OR 1.34. Fig. 1e,h ) and GI (MATN2 OR 1.08, Fig. 1f ). IL-3RA, an IL-3 receptor, was associated with cardioresp (OR 1.07, Fig. 1c ), fatigue (OR 1.21, Fig. 1d ), anxiety/depression (OR 1.12, Fig. 1e ) and GI (OR 1.06, Fig. 1f ) groups, while CSF3, a cytokine promoting neutrophilic inflammation 13 , was elevated in cardioresp (OR 1.06, Fig. 1c ), fatigue (OR 1.12, Fig. 1d ) and GI (OR 1.08, Fig. 1f ).

Elevated COLEC12, which initiates inflammation in tissues by activating the alternative complement pathway 14 , associated with cardioresp (OR 1.09, Fig. 1c ), fatigue (OR 1.19, Fig. 1d ) and anxiety/depression (OR 1.11, Fig. 1e ), but not with GI (Fig. 1f ) and only weakly with cognitive (OR 1.02, Fig. 1g ). C1QA, a degradation product released by complement activation 15 was associated with GI (OR 1.08, Fig. 1f ) and cognitive (OR 1.03, Fig. 1g ). C1QA, which is known to mediate dementia-related neuroinflammation 16 , had the third strongest association with cognitive (Fig. 1g ). These observations indicated that myeloid inflammation and complement activation were associated with LC.

Increased expression of DPP10 and SCG3 was observed in the GI group compared with recovered (DPP10 OR 1.07 and SCG3 OR 1.08, Fig. 1f ). DPP10 is a membrane protein that modulates tissue inflammation, and increased DPP10 expression is associated with inflammatory bowel disease 17 , 18 , suggesting that GI symptoms may result from enteric inflammation. Elevated SCG3, a multifunctional protein that has been associated with irritable bowel syndrome 19 , suggested that noninflammatory disturbance of the brain–gut axis or dysbiosis, may occur in the GI group. The cognitive group was associated with elevated CTSO (OR 1.04), NFASC (OR 1.03) and SPON-1 (OR 1.02, Fig. 1g,i ). NFASC and SPON-1 regulate neural growth 20 , 21 , while CTSO is a cysteine proteinase supporting tissue turnover 22 . The increased expression of these three proteins as well as C1QA and DPP10 in the cognitive group (Fig. 1g ) suggested neuroinflammation and alterations in nerve tissue repair, possibly resulting in neurodegeneration. Together, our findings indicated that complement activation and myeloid inflammation were common to all LC groups, but subtle differences were observed in the GI and cognitive groups, which may have mechanistic importance. Acutely elevated fibrinogen during hospitalization has been reported to be predictive of LC cognitive deficits 23 . We found elevated fibrinogen in LC relative to recovered (Extended Data Fig. 2a ; P  = 0.0077), although this was not significant when restricted to the cognitive group ( P  = 0.074), supporting our observation of complement pathway activation in LC and in keeping with reports that complement dysregulation and thrombosis drive severe COVID-19 (ref. 24 ).

Elevated sCD58 was associated with lower odds of all LC symptoms and was most pronounced in cardioresp (OR 0.85, Fig. 1c,i ), fatigue (OR 0.80, Fig. 1d ) and anxiety/depression (OR 0.83, Fig. 1e ). IL-2 was negatively associated with the cardioresp (Fig. 1c , OR 0.87), fatigue (Fig. 1d , OR 0.80), anxiety/depression (Fig. 1e , OR 0.84) and cognitive (Fig. 1g , OR 0.96) groups. Both IL-2 and sCD58 have immunoregulatory functions 25 , 26 . Specifically, sCD58 suppresses IL-1- or IL-6-dependent interactions between CD2 + monocytes and CD58 + T or natural killer cells 26 . The association of sCD58 with recovered suggests a central role of dysregulated myeloid inflammation in LC. Elevated markers of tissue repair, IDS and DNER 27 , 28 , were also associated with recovered relative to all LC groups (Fig. 1c–g ). Taken together, our data suggest that suppression of myeloid inflammation and enhanced tissue repair were associated with recovered, supporting the use of immunomodulatory agents in therapeutic trials 29 (Supplementary Table 2 ).

We next sought to validate the experimental and analytical approaches used. Although Olink has been validated against other immunoassay platforms, showing superior sensitivity and specificity 30 , 31 , we confirmed the performance of Olink against chemiluminescent immunoassays within our cohort. We performed chemiluminescent immunoassays on plasma from a subgroup of 58 participants (recovered n  = 13 and LC n  = 45). There were good correlations between results from Olink (normalized protein expression (NPX)) and chemiluminescent immunoassays (pg ml −1 ) for CSF3, IL-1R2, IL-3RA, TNF and TFF2 (Extended Data Fig. 3 ). Most samples did not have concentrations of IL-2 detectable using a mesoscale discovery chemiluminescent assay, limiting this analysis to 14 samples (recovered n  = 4, LC n  = 10, R  = 0.55 and P  = 0.053, Extended Data Fig. 3 ). We next repeated our analysis using alternative definitions of LC. The Centers for Disease Control and Prevention and National Institute for Health and Care Excellence definitions for LC include symptoms occurring 1 month post infection 32 , 33 . Using the 1 month post-infection definition included 62 additional participants to our analysis (recovered n  = 21, 3 females and median age 61 years and LC n  = 41, 15 females and median age 60 years, Extended Data Fig. 2c ) and found that inflammatory associations with each LC group were consistent with our analysis based on the WHO definition (Extended Data Fig. 2d–h ). Finally, to validate the analytical approach (PLR) we examined the distribution of data, prioritizing proteins that were most strongly associated with each LC/recovered group (IL-1R2, MATN2, NFASC and sCD58). Each protein was significantly elevated in the LC group compared with recovered (Fig. 1h,i and Extended Data Fig. 4 ), consistent with the PLR. Alternative regression approaches (unadjusted regression models and partial least squares, PLS) reported results consistent with the original analysis of protein associations and LC outcome in the WHO-defined cohort (Fig. 1c–g , Supplementary Table 3 and Extended Data Figs. 5 and 6 ). The standard errors of PLS estimates were wide (Extended Data Fig. 6 ), consistent with previous demonstrations that PLR is the optimal method to analyze high-dimensional data where variables may have combined effects 34 . As inflammatory proteins are often colinear, working in-tandem to mediate effects, we prioritized PLR results to draw conclusions.

To explore the relationship between inflammatory mediators associated with different LC symptoms, we performed a network analysis of Olink mediators highlighted by PLR within each LC group. COLEC12 and markers of endothelial and mucosal inflammation (MATN2, PCDH1, ROBO1, ISM1, ANGPTL2, TGF-α and TFF2) were highly correlated within the cardioresp, fatigue and anxiety/depression groups (Fig. 2 and Extended Data Fig. 7 ). Elevated PCDH1, an adhesion protein modulating airway inflammation 35 , was highly correlated with other inflammatory proteins associated with the cardioresp group (Fig. 2 ), suggesting that systemic inflammation may arise from the lung in these individuals. This was supported by increased expression of IL-3RA, which regulates innate immune responses in the lung through interactions with circulating IL-3 (ref. 36 ), in fatigue (Figs. 1d and 2 ), which correlated with markers of tissue inflammation, including PCDH1 (Fig. 2 ). MATN2 and ISM1, mucosal proteins that enhance inflammation 37 , 38 , were highly correlated in the GI group (Fig. 2 ), highlighting the role of tissue-specific inflammation in different LC groups. SCG3 correlated less closely with mediators in the GI group (Fig. 2 ), suggesting that the brain–gut axis may contribute separately to some GI symptoms. SPON-1, which regulates neural growth 21 , was the most highly correlated mediator in the cognitive group (Fig. 2 and Extended Data Fig. 7 ), highlighting that processes within nerve tissue may underlie this group. These observations suggested that inflammation might arise from mucosal tissues and that additional mechanisms may contribute to pathophysiology underlying the GI and cognitive groups.

Network analysis of Olink mediators associated with cardioresp ( n  = 365), fatigue ( n  = 314), anxiety/depression ( n  = 202), GI ( n  = 124) and cognitive groups ( n  = 60). Each node corresponds to a protein mediator identified by PLR. The edges (blue lines) were weighted according to the size of Spearman’s rank correlation coefficient between proteins. All edges represent positive and significant correlations ( P  < 0.05) after FDR adjustment.

Women were more likely to experience LC (Table 1 ), as found in previous studies 1 . As estrogen can influence immunological responses 39 , we investigated whether hormonal differences between men and women with LC in our cohort explained this trend. We grouped men and women with LC symptoms into two age groups (those younger than 50 years and those 50 years and older, using age as a proxy for menopause status in women) and compared mediator levels between men and women in each age group, prioritizing those identified by PLR to be higher in LC compared with recovered. As we aimed to understand whether women with LC had stronger inflammatory responses than men with LC, we did not assess differences in men and women in the recovered group. IL-1R2 and MATN2 were significantly higher in women ≥50 years than men ≥50 years in the cardioresp group (Fig. 3a , IL-1R2 and MATN2) and the fatigue group (Fig. 3b ). In the GI group, CSF3 was higher in women ≥50 years compared with men ≥50 years (Fig. 3c ), indicating that the inflammatory markers observed in women were not likely to be estrogen-dependent. Women have been reported to have stronger innate immune responses to infection and to be at greater risk of autoimmunity 39 , possibly explaining why some women in the ≥50 years group had higher inflammatory proteins than men the same group. Proteins associated with the anxiety/depression (IL-1R2 P  = 0.11 and MATN2 P  = 0.61, Extended Data Fig. 8a ) and cognitive groups (CTSO P  = 0.64 and NFASC P  = 0.41, Extended Data Fig. 8b ) were not different between men and women in either age group, consistent with the absent/weak association between sex and these outcomes identified by PLR (Fig. 1e,g ). Though our findings suggested that nonhormonal differences in inflammatory responses may explain why some women are more likely to have LC, they require confirmation in adequately powered studies.

a – c , Olink-measured plasma protein levels (NPX) of IL-1R2 and MATN2 ( a and b ) and CSF3 ( c ) between LC men and LC women divided by age (<50 or ≥50 years) in the cardiorespiratory group (<50 years n  = 8 and ≥50 years n  = 270) ( a ), fatigue group (<50 years n  = 81 and ≥50 years n  = 227) ( b ) and GI group (<50 years n  = 34 and ≥50 years n  = 82) ( c ). the median values were compared between men and women using two-sided Wilcoxon signed-rank test, * P  < 0.05, ** P  < 0.01, *** P  < 0.001 and **** P  < 0.0001. The box plot center line represents the median, the boundaries represent IQR and the whisker length represents 1.5× IQR.

To test whether local respiratory tract inflammation persisted after COVID-19, we compared nasosorption samples from 89 participants (recovered, n  = 31; LC, n  = 33; and healthy SARS-CoV-2 naive controls, n  = 25, Supplementary Tables 4 and 5 ). Several inflammatory markers were elevated in the upper respiratory tract post COVID (including IL-1α, CXCL10, CXCL11, TNF, VEGF and TFF2) when compared with naive controls, but similar between recovered and LC (Fig. 4a ). In the cardioresp group ( n  = 29), inflammatory mediators elevated in plasma (for example, IL-6, APO-2, TGF-α and TFF2) were not elevated in the upper respiratory tract (Extended Data Fig. 9a ) and there was no correlation between plasma and nasal mediator levels (Extended Data Fig. 9b ). This exploratory analysis suggested upper respiratory tract inflammation post COVID was not specifically associated with cardiorespiratory symptoms.

a , Nasal cytokines measured by immunoassay in post-COVID participants ( n  = 64) compared with healthy SARS-CoV-2 naive controls ( n  = 25), and between the the cardioresp group ( n  = 29) and the recovered group ( n  = 31). The red values indicate significantly increased cytokine levels after FDR adjustment ( P  < 0.05) using two-tailed Wilcoxon signed-rank test. b , SARS-CoV-2 N antigen measured in sputum by electrochemiluminescence from recovered ( n  = 17) and pooled LC ( n  = 23) groups, compared with BALF from SARS-CoV-2 naive controls ( n  = 9). The horizontal dashed line indicates the lower limit of detection of the assay. c , Plasma S- and N-specific IgG responses measured by electrochemiluminescence in the LC ( n  = 35) and recovered ( n  = 19) groups. The median values were compared using two-sided Wilcoxon signed-rank tests, NS P  > 0.05, * P  < 0.05, ** P  < 0.01, *** P  < 0.001 and **** P  < 0.0001. The box plot center lines represent the median, the boundaries represent IQR and the whisker length represents 1.5× IQR.

To explore whether SARS-CoV-2 persistence might explain the inflammatory profiles observed in the cardioresp group, we measured SARS-CoV-2 nucleocapsid (N) antigen in sputum from 40 participants (recovered n  = 17 and LC n  = 23) collected approximately 6 months post hospitalization (Supplementary Table 6 ). All samples were compared with prepandemic bronchoalveolar lavage fluid ( n  = 9, Supplementary Table 4 ). Only four samples (recovered n  = 2 and LC n  = 2) had N antigen above the assay’s lower limit of detection, and there was no difference in N antigen concentrations between LC and recovered (Fig. 4b , P  = 0.78). These observations did not exclude viral persistence, which might require tissues samples for detection 40 , 41 . On the basis of the hypothesis that persistent viral antigen might prevent a decline in antibody levels over time, we examined the titers of SARS-CoV-2-specific antibodies in unvaccinated individuals (recovered n  = 19 and LC n  = 35). SARS-CoV-2 N-specific ( P  = 0.023) and spike (S)-specific ( P  = 0.0040) immunoglobulin G (IgG) levels were elevated in LC compared with recovered (Fig. 4c ).

Overall, we identified myeloid inflammation and complement activation in the cardioresp, fatigue, anxiety/depression, cognitive and GI groups 6 months after hospitalization (Extended Data Fig. 10 ). Our findings build on results of smaller studies 5 , 6 , 42 and are consistent with a genome-wide association study that identified an independent association between LC and FOXP4 , which modulates neutrophilic inflammation and immune cell function 43 , 44 . In addition, we identified tissue-specific inflammatory elements, indicating that myeloid disturbance in different tissues may result in distinct symptoms. Multiple mechanisms for LC have been suggested, including autoimmunity, thrombosis, vascular dysfunction, SARS-CoV-2 persistence and latent virus reactivation 1 . All these processes involve myeloid inflammation and complement activation 45 . Complement activation in LC has been suggested in a proteomic study in 97 mostly nonhospitalized COVID-19 cases 42 and a study of 48 LC patients, of which one-third experienced severe acute disease 46 . As components of the complement system are known to have a short half-life 47 , ongoing complement activation suggests active inflammation rather than past tissue damage from acute infection.

Despite the heterogeneity of LC and the likelihood of coexisting or multiple etiologies, our work suggests some common pathways that might be targeted therapeutically and supports the rationale for several drugs currently under trial. Our finding of increased sCD58 levels (associated with suppression of monocyte–lymphocyte interactions 26 ) in the recovered group, strengthens our conclusion that myeloid inflammation is central to the biology of LC and that trials of steroids, IL-1 antagonists, JAK inhibitors, naltrexone and colchicine are justified. Although anticoagulants such as apixaban might prevent thrombosis downstream of complement dysregulation, they can also increase the risk of serious bleeding when given after COVID-19 hospitalization 48 . Thus, clinical trials, already underway, need to carefully assess the risks and benefits of anticoagulants (Supplementary Table 2 ).

Our finding of elevated S- and N-specific IgG in LC could suggest viral persistence, as found in other studies 6 , 42 , 49 . Our network analysis indicated that inflammatory proteins in the cardioresp group interacted strongly with ISM1 and ROBO1, which are expressed during respiratory tract infection and regulate lung inflammation 50 , 51 . Although we were unable to find SARS-CoV-2 antigen in sputum from our LC cases, we did not test for viral persistence in GI tract and lung tissue 40 , 41 or in plasma 52 . Evidence of SARS-CoV-2 persistence would justify trials of antiviral drugs (singly or in combination) in LC. It is also possible that autoimmune processes could result in an innate inflammatory profile in LC. Autoreactive B cells have been identified in LC patients with higher SARS-CoV-2-specific antibody titers in a study of mostly mild acute COVID cases (59% WHO 2–3) 42 , a different population from our study of hospitalized cases.

Our observations of distinct protein profiles in GI and cognitive groups support previous reports on distinct associations between Epstein–Barr virus reactivation and neurological symptoms, or autoantibodies and GI symptoms relative to other forms of LC 49 , 53 . We did not assess autoantibody induction but found evidence of brain–gut axis disturbance (SCG3) in the GI group, which occurs in many autoimmune diseases 54 . We found signatures suggestive of neuroinflammation (C1QA) in the cognitive group, consistent with findings of brain abnormalities on magnetic resonance imaging after COVID-19 hospitalization 55 , as well as findings of microglial activation in mice after COVID-19 (ref. 56 ). Proinflammatory signatures dominated in the cardioresp, fatigue and anxiety/depression groups and were consistent with those seen in non-COVID depression, suggesting shared mechanisms 57 . The association between markers of myeloid inflammation, including IL-3RA, and symptoms was greatest for fatigue. Whilst membrane-bound IL-3RA facilitates IL-3 signaling upstream of myelopoesis 36 its soluble form (measured in plasma) can bind IL-3 and can act as a decoy receptor, preventing monocyte maturation and enhancing immunopathology 58 . Monocytes from individuals with post-COVID fatigue are reported to have abnormal expression profiles (including reduced CXCR2), suggestive of altered maturation and migration 5 , 59 . Lung-specific inflammation was suggested by the association between PCDH1 (an airway epithelial adhesion molecule 35 ) and cardioresp symptoms.

Our observations do not align with all published observations on LC. One proteomic study of 55 LC cases after generally mild (WHO 2–3) acute disease found that TNF and IFN signatures were elevated in LC 3 . Vasculoproliferative processes and metabolic disturbance have been reported in LC 4 , 60 , but these studies used uninfected healthy individuals for comparison and cannot distinguish between LC-specific phenomena and residual post-COVID inflammation. A study of 63 adults (LC, n  = 50 and recovered, n  = 13) reported no association between immune cell activation and LC 3 months after infection 61 , though myeloid inflammation was not directly measured, and 3 months post infection may be too early to detect subtle differences between LC and recovered cases due to residual acute inflammation.

Our study has limitations. We designed the study to identify inflammatory markers identifying pathways underlying LC subgroups rather than diagnostic biomarkers. The ORs we report are small, but associations were consistent across alternative methods of analysis and when using different LC definitions. Small effect sizes can be expected when using PLR, which shrinks correlated mediator coefficients to reflect combined effects and prevent colinear inflation 62 , and could also result from measurement of plasma mediators that may underestimate tissue inflammation. Although our LC cohort is large compared with most other published studies, some of our subgroups are small (only 60 cases were designated cognitive). Though the performance of the cognitive PLR model was adequate, our findings should be validated in larger studies. It should be noted that our cohort of hospitalized cases may not represent all types of LC, especially those occurring after mild infection. We looked for an effect of acute disease severity within our study and did not find it, and are reassured that the inflammatory profiles we observed were consistent with those seen in smaller studies including nonhospitalized cases 42 , 46 . Studies of posthospital LC may be confounded by ‘posthospital syndrome’, which encompasses general and nonspecific effects of hospitalization (particularly intensive care) 63 .

In conclusion, we found markers of myeloid inflammation and complement activation in our large prospective posthospital cohort of patients with LC, in addition to distinct inflammatory patterns in patients with cognitive impairment or gastrointestinal symptoms. These findings show the need to consider subphenotypes in managing patients with LC and support the use of antiviral or immunomodulatory agents in controlled therapeutic trials.

Study design and ethics

After hospitalization for COVID-19, adults who had no comorbidity resulting in a prognosis of less than 6 months were recruited to the PHOSP-COVID study ( n  = 719). Patients hospitalized between February 2020 and January 2021 were recruited. Both sexes were recruited and gender was self-reported (female, n  = 257 and male, n  = 462). Written informed consent was obtained from all patients. Ethical approvals for the PHOSP-COVID study were given by Leeds West Research Ethics Committee (20/YH/0225).

Symptom data and samples were prospectively collected from individuals approximately 6 months (IQR 5.1–6.8 months and range 3.0–8.3 months) post hospitalization (Fig. 1a ), via the PHOSP-COVID multicenter United Kingdom study 64 . Data relating to patient demographics and acute admission were collected via the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization Clinical Characterisation Protocol United Kingdom (ISARIC4C study; IRAS260007/IRAS126600) (ref. 65 ). Adults hospitalized during the SARS-CoV-2 pandemic were systematically recruited into ISARIC4C. Written informed consent was obtained from all patients. Ethical approval was given by the South Central–Oxford C Research Ethics Committee in England (reference 13:/SC/0149), Scotland A Research Ethics Committee (20/SS/0028) and WHO Ethics Review Committee (RPC571 and RPC572l, 25 April 2013).

Data were collected to account for variables affecting symptom outcome, via hospital records and self-reporting. Acute disease severity was classified according to the WHO clinical progression score: WHO class 3–4: no oxygen therapy; class 5: oxygen therapy; class 6: noninvasive ventilation or high-flow nasal oxygen; and class 7–9: managed in critical care 9 . Clinical data were used to place patients into six categories: ‘recovered’, ‘GI’, ‘cardiorespiratory’, ‘fatigue’, ‘cognitive impairment’ and ‘anxiety/depression’ (Supplementary Table 7 ). Patient-reported symptoms and validated clinical scores were used when feasible, including Medical Research Council (MRC) breathlessness score, dyspnea-12 score, Functional Assessment of Chronic Illness Therapy (FACIT) score, Patient Health Questionnaire (PHQ)-9 and Generalized Anxiety Disorder (GAD)-7. Cognitive impairment was defined as a Montreal Cognitive Assessment score <26. GI symptoms were defined as answering ‘Yes’ to the presence of at least two of the listed symptoms. ‘Recovered’ was defined by self-reporting. Patients were placed in multiple groups if they experienced a combination of symptoms.

Matched nasal fluid and sputum samples were prospectively collected from a subgroup of convalescent patients approximately 6 months after hospitalization via the PHOSP-COVID study. Nasal and bronchoalveolar lavage fluid (BALF) collected from healthy volunteers before the COVID-19 pandemic were used as controls (Supplementary Table 4 ). Written consent was obtained for all individuals and ethical approvals were given by London–Harrow Research Ethics Committee (13/LO/1899) for the collection of nasal samples and the Health Research Authority London–Fulham Research Ethics Committee (IRAS project ID 154109; references 14/LO/1023, 10/H0711/94 and 11/LO/1826) for BALF samples.

Ethylenediaminetetraacetic acid plasma was collected from whole blood taken by venepuncture and frozen at −80 °C as previously described 7 , 66 . Nasal fluid was collected using a NasosorptionTM FX·I device (Hunt Developments), which uses a synthetic absorptive matrix to collect concentrated nasal fluid. Samples were eluted and stored as previously described 67 . Sputum samples were collected via passive expectoration and frozen at −80 °C without the addition of buffers. Sputum samples from convalescent individuals were compared with BALF from healthy SARS-CoV-2-naive controls, collected before the pandemic. BALF samples were used to act as a comparison for lower respiratory tract samples since passively expectorated sputum from healthy SARS-CoV-2-naive individuals was not available. BALF samples were obtained by instillation and recovery of up to 240 ml of normal saline via a fiberoptic bronchoscope. BALF was filtered through 100 µM strainers into sterile 50 ml Falcon tubes, then centrifuged for 10 min at 400  g at 4 °C. The resulting supernatant was transferred into sterile 50 ml Falcon tubes and frozen at −80 °C until use. The full methods for BALF collection and processing have been described previously 68 , 69 .

Immunoassays

To determine inflammatory signatures that associated with symptom outcomes, plasma samples were analyzed on an Olink Explore 384 Inflammation panel 70 . Supplementary Table 8 (Appendix 1 ) lists all the analytes measured. To ensure the validity of results, samples were run in a single batch with the use of negative controls, plate controls in triplicate and repeated measurement of patient samples between plates in duplicate. Samples were randomized between plates according to site and sample collection date. Randomization between plates was blind to LC/recovered outcome. Data were first normalized to an internal extension control that was included in each sample well. Plates were standardized by normalizing to interplate controls, run in triplicate on each plate. Each plate contained a minimum of four patient samples, which were duplicates on another plate; these duplicate pairs allowed any plate to be linked to any other through the duplicates. Data were then intensity normalized across all cohort samples. Finally, Olink results underwent quality control processing and samples or analytes that did not reach quality control standards were excluded. Final normalized relative protein quantities were reported as log 2 NPX values.

To further validate our findings, we performed conventional electrochemiluminescence (ECL) assays and enzyme-linked immunosorbent assay for Olink mediators that were associated with symptom outcome ( Supplementary Methods ). Contemporaneously collected plasma samples were available from 58 individuals. Like most omics platforms, Olink measures relative quantities, so perfect agreement with conventional assays that measure absolute concentrations is not expected.

Sputum samples were thawed before analysis and sputum plugs were extracted with the addition of 0.1% dithiothreitol creating a one in two sample dilution, as previously described 71 . SARS-CoV-2 S and N proteins were measured by ECL S-plex assay at a fixed dilution of one in two (Mesoscale Diagnostics), as per the manufacturers protocol 72 . Control BALF samples were thawed and measured on the same plate, neat. The S-plex assay is highly sensitive in detecting viral antigen in respiratory tract samples 73 .

Nasal cytokines were measured by ECL (mesoscale discovery) and Luminex bead multiplex assays (Biotechne). The full methods and list of analytes are detailed in Supplementary Methods .

Statistics and reproducibility

Clinical data was collected via the PHOSP REDCap database, to which access is available under reasonable request as per the data sharing statement in the manuscript. All analyses were performed within the Outbreak Data Analysis Platform (ODAP). All data and code can be accessed using information in the ‘Data sharing’ and ‘Code sharing’ statements at the end of the manuscript. No statistical method was used to predetermine sample size. Data distribution was assumed to be normal but this was not formally tested. Olink assays and immunoassays were randomized and investigators were blinded to outcomes.

To determine protein signatures that associated with each symptom outcome, a ridge PLR was used. PLR shrinks coefficients to account for combined effects within high-dimensional data, preventing false discovery while managing multicollinearity 34 . Thus, PLR was chosen a priori as the most appropriate model to assess associations between a large number of explanatory variables (that may work together to mediate effects) and symptom outcome 34 , 62 , 70 , 74 . In keeping with our aim to perform an unbiased exploration of inflammatory process, the model alpha was set to zero, facilitating regularization without complete penalization of any mediator. This enabled review of all possible mediators that might associate with LC 62 .

A 50 repeats tenfold nested cross-validation was used to select the optimal lambda for each model and assess its accuracy (Extended Data Fig. 1 ). The performance of the cognitive impairment model was influenced by the imbalance in size of the symptom group ( n  = 60) relative to recovered ( n  = 250). The model was weighted to account for this imbalance resulting in a sensitivity of 0.98, indicating its validity. We have expanded on the model performance and validation approaches in Supplementary Information .

Age, sex, acute disease severity and preexisting comorbidities were included as covariates in the PLR analysis (Supplementary Tables 1 and 3 ). Covariates were selected a priori using features reported to influence the risk of LC and inflammatory responses 1 , 39 , 64 , 75 . Ethnicity was not included since it has been shown not to predict symptom outcome in this cohort 64 . Individuals with missing data were excluded from the regression analysis. Each symptom group was compared with the ‘recovered’ group. The model coefficients of each covariate were converted into ORs for each outcome and visualized in a forest plot, after removing variables associated with regularized OR between 0.98 and 1.02 or in cases where most variables fell outside of this range, using mediators associated with the highest decile of coefficients either side of this range. This enabled exclusion of mediators with effect sizes that were unlikely to have clinical or mechanistic importance since the ridge PLR shrinks and orders coefficients according to their relative importance rather than making estimates with standard error. Thus, confidence intervals cannot be appropriately derived from PLR, and forest plot error bars were calculated using the median accuracy of the model generated by the nested cross-validation. To verify observations made through PLR analysis, we also performed an unadjusted PLR, an unadjusted logistic regression and a PLS analysis. Univariate analyses using Wilcoxon signed-rank test was also performed (Supplementary Table 8 , Appendix 1 ). Analyses were performed in R version 4.2.0 using ‘data.table v1.14.2’, ‘EnvStats v2.7.0’ ‘tidyverse v1.3.2’, ‘lme4 v1.1-32’, ‘caret v6.0-93’, ‘glmnet v4.1-6’, ‘mdatools v0.14.0’, ‘ggpubbr v0.4.0’ and ‘ggplot2 v3.3.6’ packages.

To further investigate the relationship between proteins elevated in each symptom group, we performed a correlation network analysis using Spearman’s rank correlation coefficient and false discovery rate (FDR) thresholding. The mediators visualized in the PLR forest plots, which were associated with cardiorespiratory symptoms, fatigue, anxiety/depression GI symptoms and cognitive impairment were used, respectively. Analyses were performed in R version 4.2.0 using ‘bootnet v1.5.6 ’ and ‘qgraph v1.9.8 ’ packages.

To determine whether differences in protein levels between men and women related to hormonal differences, we divided each symptom group into premenopausal and postmenopausal groups using an age cutoff of 50 years old. Differences between sexes in each group were determined using the Wilcoxon signed-rank test. To understand whether antigen persistence contributed to inflammation in adults with LC, the median viral antigen concentration from sputum/BALF samples and cytokine concentrations from nasal samples were compared using the Wilcoxon signed-rank test. All tests were two-tailed and statistical significance was defined as a P value < 0.05 after adjustment for FDR ( q -value of 0.05). Analyses were performed in R version 4.2.0 using ‘bootnet v1.5.6’ and ‘qgraph v1.9.8’ packages.

Extended Data Fig. 10 was made using Biorender, accessed at www.biorender.com .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

This is an open access article under the CC BY 4.0 license.

The PHOSP-COVID protocol, consent form, definition and derivation of clinical characteristics and outcomes, training materials, regulatory documents, information about requests for data access, and other relevant study materials are available online at ref. 76 . Access to these materials can be granted by contacting [email protected] and [email protected].

The ISARIC4C protocol, data sharing and publication policy are available at https://isaric4c.net . ISARIC4C’s Independent Data and Material Access Committee welcomes applications for access to data and materials ( https://isaric4c.net ).

The datasets used in the study contain extensive clinical information at an individual level that prevent them from being deposited in an public depository due to data protection policies of the study. Study data can only be accessed via the ODAP, a protected research environment. All data used in this study are available within ODAP and accessible under reasonable request. Data access criteria and information about how to request access is available online at ref. 76 . If criteria are met and a request is made, access can be gained by signing the eDRIS user agreement.

Code availability

Code was written within the ODAP, using R v4.2.0 and publicly available packages (‘data.table v1.14.2’, ‘EnvStats v2.7.0’, ‘tidyverse v1.3.2’, ‘lme4 v1.1-32’, ‘caret v6.0-93’, ‘glmnet v4.1-6’, ‘mdatools v0.14.0’, ‘ggpubbr v0.4.0’, ‘ggplot2 v3.3.6’, ‘bootnet v1.5.6’ and ‘qgraph v1.9.8’ packages). No new algorithms or functions were created and code used in-built functions in listed packages available on CRAN. The code used to generate data and to analyze data is publicly available at https://github.com/isaric4c/wiki/wiki/ISARIC ; https://github.com/SurgicalInformatics/cocin_cc and https://github.com/ClaudiaEfstath/PHOSP_Olink_NatImm .

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Acknowledgements

This research used data assets made available by ODAP as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref. MC_PC_20058). This work is supported by the following grants: the PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research (NIHR; grant references MR/V027859/1 and COV0319). ISARIC4C is supported by grants from the National Institute for Health and Care Research (award CO-CIN-01) and the MRC (grant MC_PC_19059) Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research (grant reference C18616/A25153). Other grants that have supported this work include the UK Coronavirus Immunology Consortium (funder reference 1257927), the Imperial Biomedical Research Centre (NIHR Imperial BRC, grant IS-BRC-1215-20013), the Health Protection Research Unit in Respiratory Infections at Imperial College London and NIHR Health Protection Research Unit in Emerging and Zoonotic Infections at University of Liverpool, both in partnership with Public Health England, (NIHR award 200907), Wellcome Trust and Department for International Development (215091/Z/18/Z), Health Data Research UK (grant code 2021.0155), MRC (grant code MC_UU_12014/12) and NIHR Clinical Research Network for providing infrastructure support for this research. We also acknowledge the support of the MRC EMINENT Network (MR/R502121/1), which is cofunded by GSK, the Comprehensive Local Research Networks, the MRC HIC-Vac network (MR/R005982/1) and the RSV Consortium in Europe Horizon 2020 Framework Grant 116019. F.L. is supported by an MRC clinical training fellowship (award MR/W000970/1). C.E. is funded by NIHR (grant P91258-4). L.-P.H. is supported by Oxford NIHR Biomedical Research Centre. A.A.R.T. is supported by a British Heart Foundation (BHF) Intermediate Clinical Fellowship (FS/18/13/33281). S.L.R.-J. receives support from UK Research and Innovation (UKRI), Global Challenges Research Fund (GCRF), Rosetrees Trust, British HIV association (BHIVA), European & Developing Countries Clinical Trials Partnership (EDCTP) and Globvac. J.D.C. has grants from AstraZeneca, Boehringer Ingelheim, GSK, Gilead Sciences, Grifols, Novartis and Insmed. R.A.E. holds a NIHR Clinician Scientist Fellowship (CS-2016-16-020). A. Horsley is currently supported by UK Research and Innovation, NIHR and NIHR Manchester BRC. B.R. receives support from BHF Oxford Centre of Research Excellence, NIHR Oxford BRC and MRC. D.G.W. is supported by an NIHR Advanced Fellowship. A. Ho has received support from MRC and for the Coronavirus Immunology Consortium (MR/V028448/1). L.T. is supported by the US Food and Drug Administration Medical Countermeasures Initiative contract 75F40120C00085 and the National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections (NIHR200907) at the University of Liverpool in partnership with UK Health Security Agency (UK-HSA), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford. L.V.W. has received support from UKRI, GSK/Asthma and Lung UK and NIHR for this study. M.G.S. has received support from NIHR UK, MRC UK and Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool. J.K.B. is supported by the Wellcome Trust (223164/Z/21/Z) and UKRI (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1 and MC_PC_20029). The funders were not involved in the study design, interpretation of data or writing of this manuscript. The views expressed are those of the authors and not necessarily those of the Department of Health and Social Care (DHSC), the Department for International Development (DID), NIHR, MRC, the Wellcome Trust, UK-HSA, the National Health Service or the Department of Health. P.J.M.O. is supported by a NIHR Senior Investigator Award (award 201385). We thank all the participants and their families. We thank the many research administrators, health-care and social-care professionals who contributed to setting up and delivering the PHOSP-COVID study at all of the 65 NHS trusts/health boards and 25 research institutions across the United Kingdom, as well as those who contributed to setting up and delivering the ISARIC4C study at 305 NHS trusts/health boards. We also thank all the supporting staff at the NIHR Clinical Research Network, Health Research Authority, Research Ethics Committee, Department of Health and Social Care, Public Health Scotland and Public Health England. We thank K. Holmes at the NIHR Office for Clinical Research Infrastructure for her support in coordinating the charities group. The PHOSP-COVID industry framework was formed to provide advice and support in commercial discussions, and we thank the Association of the British Pharmaceutical Industry as well the NIHR Office for Clinical Research Infrastructure for coordinating this. We are very grateful to all the charities that have provided insight to the study: Action Pulmonary Fibrosis, Alzheimer’s Research UK, Asthma and Lung UK, British Heart Foundation, Diabetes UK, Cystic Fibrosis Trust, Kidney Research UK, MQ Mental Health, Muscular Dystrophy UK, Stroke Association Blood Cancer UK, McPin Foundations and Versus Arthritis. We thank the NIHR Leicester Biomedical Research Centre patient and public involvement group and Long Covid Support. We also thank G. Khandaker and D. C. Newcomb who provided valuable feedback on this work. Extended Data Fig. 10 was created using Biorender.

Author information

These authors contributed equally: Felicity Liew, Claudia Efstathiou, Ryan S. Thwaites, Peter J. M. Openshaw.

Authors and Affiliations

National Heart and Lung Institute, Imperial College London, London, UK

Felicity Liew, Claudia Efstathiou, Sara Fontanella, Dawid Swieboda, Jasmin K. Sidhu, Stephanie Ascough, Onn Min Kon, Luke S. Howard, Jennifer K. Quint, Christopher Chiu, Ryan S. Thwaites, Peter J. M. Openshaw, Jake Dunning & Peter J. M. Openshaw

Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK

Matthew Richardson, Ruth Saunders, Olivia C. Leavy, Omer Elneima, Hamish J. C. McAuley, Amisha Singapuri, Marco Sereno, Victoria C. Harris, Neil J. Greening, Rachael A. Evans, Louise V. Wain, Christopher Brightling & Ananga Singapuri

NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK

Shona C. Moore, Daniel G. Wootton, Malcolm G. Semple, Lance Turtle, William A. Paxton & Georgios Pollakis

The Imperial Clinical Respiratory Research Unit, Imperial College NHS Trust, London, UK

Noura Mohamed

Cardiovascular Research Team, Imperial College Healthcare NHS Trust, London, UK

Jose Nunag & Clara King

Department of Population Health Sciences, University of Leicester, Leicester, UK

Olivia C. Leavy, Louise V. Wain & Beatriz Guillen-Guio

NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK

Aarti Shikotra

Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University of Leicester, Leicester, UK

Linzy Houchen-Wolloff

Usher Institute, University of Edinburgh, Edinburgh, UK

Nazir I. Lone, Luke Daines, Annemarie B. Docherty, Nazir I. Lone, Matthew Thorpe, Annemarie B. Docherty, Thomas M. Drake, Cameron J. Fairfield, Ewen M. Harrison, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Lisa Norman, Riinu Pius & Catherine A. Shaw

Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK

Matthew Thorpe, Annemarie B. Docherty, Ewen M. Harrison, J. Kenneth Baillie, Sarah L. Rowland-Jones, A. A. Roger Thompson & Thushan de Silva

Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK

A. A. Roger Thompson, Sarah L. Rowland-Jones, Thushan I. de Silva & James D. Chalmers

University of Dundee, Ninewells Hospital and Medical School, Dundee, UK

James D. Chalmers & Ling-Pei Ho

MRC Human Immunology Unit, University of Oxford, Oxford, UK

Ling-Pei Ho & Alexander Horsley

Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK

Alexander Horsley & Betty Raman

Radcliffe Department of Medicine, University of Oxford, Oxford, UK

Betty Raman & Krisnah Poinasamy

Asthma + Lung UK, London, UK

Krisnah Poinasamy & Michael Marks

Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK

Michael Marks

Hospital for Tropical Diseases, University College London Hospital, London, UK

Division of Infection and Immunity, University College London, London, UK

Michael Marks & Mahdad Noursadeghi

MRC Centre for Virus Research, School of Infection and Immunity, University of Glasgow, Glasgow, UK

Antonia Ho & William Greenhalf

Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

William Greenhalf & J. Kenneth Baillie

The Roslin Institute, University of Edinburgh, Edinburgh, UK

J. Kenneth Baillie, J. Kenneth Baillie, Sara Clohisey, Fiona Griffiths, Ross Hendry, Andrew Law & Wilna Oosthuyzen

Pandemic Science Hub, University of Edinburgh, Edinburgh, UK

J. Kenneth Baillie

The Pandemic Institute, University of Liverpool, Liverpool, UK

Malcolm G. Semple & Lance Turtle

University of Manchester, Manchester, UK

Kathryn Abel, Perdita Barran, H. Chinoy, Bill Deakin, M. Harvie, C. A. Miller, Stefan Stanel & Drupad Trivedi

Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh, UK

Kathryn Abel & J. Kenneth Baillie

North Bristol NHS Trust and University of Bristol, Bristol, UK

H. Adamali, David Arnold, Shaney Barratt, A. Dipper, Sarah Dunn, Nick Maskell, Anna Morley, Leigh Morrison, Louise Stadon, Samuel Waterson & H. Welch

University of Edinburgh, Manchester, UK

Davies Adeloye, D. E. Newby, Riinu Pius, Igor Rudan, Manu Shankar-Hari, Catherine Sudlow, Sarah Walmsley & Bang Zheng

King’s College Hospital NHS Foundation Trust and King’s College London, London, UK

Oluwaseun Adeyemi, Rita Adrego, Hosanna Assefa-Kebede, Jonathon Breeze, S. Byrne, Pearl Dulawan, Amy Hoare, Caroline Jolley, Abigail Knighton, M. Malim, Sheetal Patale, Ida Peralta, Natassia Powell, Albert Ramos, K. Shevket, Fabio Speranza & Amelie Te

Guy’s and St Thomas’ NHS Foundation Trust, London, UK

Laura Aguilar Jimenez, Gill Arbane, Sarah Betts, Karen Bisnauthsing, A. Dewar, Nicholas Hart, G. Kaltsakas, Helen Kerslake, Murphy Magtoto, Philip Marino, L. M. Martinez, Marlies Ostermann, Jennifer Rossdale & Teresa Solano

Royal Free London NHS Foundation Trust, London, UK

Shanaz Ahmad, Simon Brill, John Hurst, Hannah Jarvis, C. Laing, Lai Lim, S. Mandal, Darwin Matila, Olaoluwa Olaosebikan & Claire Singh

University Hospital Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK

N. Ahmad Haider, Catherine Atkin, Rhiannon Baggott, Michelle Bates, A. Botkai, Anna Casey, B. Cooper, Joanne Dasgin, Camilla Dawson, Katharine Draxlbauer, N. Gautam, J. Hazeldine, T. Hiwot, Sophie Holden, Karen Isaacs, T. Jackson, Vicky Kamwa, D. Lewis, Janet Lord, S. Madathil, C. McGee, K. Mcgee, Aoife Neal, Alex Newton-Cox, Joseph Nyaboko, Dhruv Parekh, Z. Peterkin, H. Qureshi, Liz Ratcliffe, Elizabeth Sapey, J. Short, Tracy Soulsby, J. Stockley, Zehra Suleiman, Tamika Thompson, Maximina Ventura, Sinead Walder, Carly Welch, Daisy Wilson, S. Yasmin & Kay Por Yip

Stroke Association, London, UK

Rubina Ahmed & Richard Francis

University College London Hospital and University College London, London, UK

Nyarko Ahwireng, Dongchun Bang, Donna Basire, Jeremy Brown, Rachel Chambers, A. Checkley, R. Evans, M. Heightman, T. Hillman, Joseph Jacob, Roman Jastrub, M. Lipman, S. Logan, D. Lomas, Marta Merida Morillas, Hannah Plant, Joanna Porter, K. Roy & E. Wall

Oxford University Hospitals NHS Foundation Trust and University of Oxford, Oxford, UK

Mark Ainsworth, Asma Alamoudi, Angela Bloss, Penny Carter, M. Cassar, Jin Chen, Florence Conneh, T. Dong, Ranuromanana Evans, V. Ferreira, Emily Fraser, John Geddes, F. Gleeson, Paul Harrison, May Havinden-Williams, P. Jezzard, Ivan Koychev, Prathiba Kurupati, H. McShane, Clare Megson, Stefan Neubauer, Debby Nicoll, C. Nikolaidou, G. Ogg, Edmund Pacpaco, M. Pavlides, Yanchun Peng, Nayia Petousi, John Pimm, Najib Rahman, M. J. Rowland, Kathryn Saunders, Michael Sharpe, Nick Talbot, E. M. Tunnicliffe & C. Xie

St George’s University Hospitals NHS Foundation Trust, London, UK

Mariam Ali, Raminder Aul, A. Dunleavy, D. Forton, Mark Mencias, N. Msimanga, T. Samakomva, Sulman Siddique, Vera Tavoukjian & J. Teixeira

University Hospitals of Leicester NHS Trust and University of Leicester, Leicester, UK

M. Aljaroof, Natalie Armstrong, H. Arnold, Hnin Aung, Majda Bakali, M. Bakau, E. Baldry, Molly Baldwin, Charlotte Bourne, Michelle Bourne, Nigel Brunskill, P. Cairns, Liesel Carr, Amanda Charalambou, C. Christie, Melanie Davies, Enya Daynes, Sarah Diver, Rachael Dowling, Sarah Edwards, C. Edwardson, H. Evans, J. Finch, Sarah Glover, Nicola Goodman, Bibek Gooptu, Kate Hadley, Pranab Haldar, Beverley Hargadon, W. Ibrahim, L. Ingram, Kamlesh Khunti, A. Lea, D. Lee, Gerry McCann, P. McCourt, Teresa Mcnally, George Mills, Will Monteiro, Manish Pareek, S. Parker, Anne Prickett, I. N. Qureshi, A. Rowland, Richard Russell, Salman Siddiqui, Sally Singh, J. Skeemer, M. Soares, E. Stringer, T. Thornton, Martin Tobin, T. J. C. Ward, F. Woodhead, Tom Yates & A. J. Yousuf

University of Exeter, Exeter, UK

Louise Allan, Clive Ballard & Andrew McGovern

University of Leicester, Leicester, UK

Richard Allen, Michelle Bingham, Terry Brugha, Selina Finney, Rob Free, Don Jones, Claire Lawson, Daniel Lozano-Rojas, Gardiner Lucy, Alistair Moss, Elizabeta Mukaetova-Ladinska, Petr Novotny, Kimon Ntotsis, Charlotte Overton, John Pearl, Tatiana Plekhanova, M. Richardson, Nilesh Samani, Jack Sargant, Ruth Saunders, M. Sharma, Mike Steiner, Chris Taylor, Sarah Terry, C. Tong, E. Turner, J. Wormleighton & Bang Zhao

Liverpool University Hospitals NHS Foundation Trust and University of Liverpool, Liverpool, UK

Lisa Allerton, Ann Marie Allt, M. Beadsworth, Anthony Berridge, Jo Brown, Shirley Cooper, Andy Cross, Sylviane Defres, S. L. Dobson, Joanne Earley, N. French, Kera Hainey, Hayley Hardwick, Jenny Hawkes, Victoria Highett, Sabina Kaprowska, Angela Key, Lara Lavelle-Langham, N. Lewis-Burke, Gladys Madzamba, Flora Malein, Sophie Marsh, Chloe Mears, Lucy Melling, Matthew Noonan, L. Poll, James Pratt, Emma Richardson, Anna Rowe, Victoria Shaw, K. A. Tripp, Lilian Wajero, S. A. Williams-Howard, Dan Wootton & J. Wyles

Sherwood Forest Hospitals NHS Foundation Trust, Nottingham, UK

Lynne Allsop, Kaytie Bennett, Phil Buckley, Margaret Flynn, Mandy Gill, Camelia Goodwin, M. Greatorex, Heidi Gregory, Cheryl Heeley, Leah Holloway, Megan Holmes, John Hutchinson, Jill Kirk, Wayne Lovegrove, Terri Ann Sewell, Sarah Shelton, D. Sissons, Katie Slack, Susan Smith, D. Sowter, Sarah Turner, V. Whitworth & Inez Wynter

Nottingham University Hospitals NHS Trust and University of Nottingham, London, UK

Paula Almeida, Akram Hosseini, Robert Needham & Karen Shaw

Manchester University NHS Foundation Trust and University of Manchester, London, UK

Bashar Al-Sheklly, Cristina Avram, John Blaikely, M. Buch, N. Choudhury, David Faluyi, T. Felton, T. Gorsuch, Neil Hanley, Tracy Hussell, Zunaira Kausar, Natasha Odell, Rebecca Osbourne, Karen Piper Hanley, K. Radhakrishnan & Sue Stockdale

Imperial College London, London, UK

Danny Altmann, Anew Frankel, Luke S. Howard, Desmond Johnston, Liz Lightstone, Anne Lingford-Hughes, William Man, Steve McAdoo, Jane Mitchell, Philip Molyneaux, Christos Nicolaou, D. P. O’Regan, L. Price, Jennifer K. Quint, David Smith, Jonathon Valabhji, Simon Walsh, Martin Wilkins & Michelle Willicombe

Hampshire Hospitals NHS Foundation Trust, Basingstoke, UK

Maria Alvarez Corral, Ava Maria Arias, Emily Bevan, Denise Griffin, Jane Martin, J. Owen, Sheila Payne, A. Prabhu, Annabel Reed, Will Storrar, Nick Williams & Caroline Wrey Brown

British Heart Foundation, Birmingham, UK

Shannon Amoils

NHS Greater Glasgow and Clyde Health Board and University of Glasgow, Glasgow, UK

David Anderson, Neil Basu, Hannah Bayes, Colin Berry, Ammani Brown, Andrew Dougherty, K. Fallon, L. Gilmour, D. Grieve, K. Mangion, I. B. McInnes, A. Morrow, Kathryn Scott & R. Sykes

University of Oxford, Oxford, UK

Charalambos Antoniades, A. Bates, M. Beggs, Kamaldeep Bhui, Katie Breeze, K. M. Channon, David Clark, X. Fu, Masud Husain, Lucy Kingham, Paul Klenerman, Hanan Lamlum, X. Li, E. Lukaschuk, Celeste McCracken, K. McGlynn, R. Menke, K. Motohashi, T. E. Nichols, Godwin Ogbole, S. Piechnik, I. Propescu, J. Propescu, A. A. Samat, Z. B. Sanders, Louise Sigfrid & M. Webster

Belfast Health and Social Care Trust and Queen’s University Belfast, Belfast, UK

Cherie Armour, Vanessa Brown, John Busby, Bronwen Connolly, Thelma Craig, Stephen Drain, Liam Heaney, Bernie King, Nick Magee, E. Major, Danny McAulay, Lorcan McGarvey, Jade McGinness, Tunde Peto & Roisin Stone

Airedale NHS Foundation Trust, Keighley, UK

Lisa Armstrong, Brigid Hairsine, Helen Henson, Claire Kurasz, Alison Shaw & Liz Shenton

Wrightington Wigan and Leigh NHS Trust, Wigan, UK

A. Ashish, Josh Cooper & Emma Robinson

Leeds Teaching Hospitals and University of Leeds, Leeds, UK

Andrew Ashworth, Paul Beirne, Jude Clarke, C. Coupland, Matthhew Dalton, Clair Favager, Jodie Glossop, John Greenwood, Lucy Hall, Tim Hardy, Amy Humphries, Jennifer Murira, Dan Peckham, S. Plein, Jade Rangeley, Gwen Saalmink, Ai Lyn Tan, Elaine Wade, Beverley Whittam, Nicola Window & Janet Woods

University of Liverpool, Liverpool, UK

M. Ashworth, D. Cuthbertson, G. Kemp, Anne McArdle, Benedict Michael, Will Reynolds, Lisa Spencer, Ben Vinson, Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L. Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Nicola Carlucci, Emily Cass, Benjamin W. A. Catterall, Jordan J. Clark, Emily A. Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Alejandra Doce Carracedo, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis W. S. Fisher, Lisa Flaherty, Terry Foster, Isabel Garcia-Dorival, Philip Gunning, Catherine Hartley, Karl Holden, Anthony Holmes, Rebecca L. Jensen, Christopher B. Jones, Trevor R. Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A. Livoti, Maria Mancini, Hannah Massey, Nicole Maziere, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S. Miah, Joanna Middleton, Joyce Mitchell, Ellen G. Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, P. Matthew Ridley, Debby Sales, Rebecca K. Shears, Benjamin Small, Krishanthi S. Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby van Tonder, Eve Wilcock & J. Eunice Zhang

University College London, London, UK

Shahab Aslani, Amita Banerjee, R. Batterham, Gabrielle Baxter, Robert Bell, Anthony David, Emma Denneny, Alun Hughes, W. Lilaonitkul, P. Mehta, Ashkan Pakzad, Bojidar Rangelov, B. Williams, James Willoughby & Moucheng Xu

Hull University Teaching Hospitals NHS Trust and University of Hull, Hull, UK

Paul Atkin, K. Brindle, Michael Crooks, Katie Drury, Nicholas Easom, Rachel Flockton, L. Holdsworth, A. Richards, D. L. Sykes, Susannah Thackray-Nocera & C. Wright

East Kent Hospitals University NHS Foundation Trust, Canterbury, UK

Liam Austin, Eva Beranova, Tracey Cosier, Joanne Deery, Tracy Hazelton, Carly Price, Hazel Ramos, Reanne Solly, Sharon Turney & Heather Weston

Baillie Gifford Pandemic Science Hub, Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Nikos Avramidis, J. Kenneth Baillie, Erola Pairo-Castineira & Konrad Rawlik

Roslin Institute, University of Edinburgh, Edinburgh, UK

Nikos Avramidis, J. Kenneth Baillie & Erola Pairo-Castineira

Newcastle upon Tyne Hospitals NHS Foundation Trust and University of Newcastle, Newcastle upon Tyne, UK

A. Ayoub, J. Brown, G. Burns, Gareth Davies, Anthony De Soyza, Carlos Echevarria, Helen Fisher, C. Francis, Alan Greenhalgh, Philip Hogarth, Joan Hughes, Kasim Jiwa, G. Jones, G. MacGowan, D. Price, Avan Sayer, John Simpson, H. Tedd, S. Thomas, Sophie West, M. Witham, S. Wright & A. Young

East Cheshire NHS Trust, Macclesfield, UK

Marta Babores, Maureen Holland, Natalie Keenan, Sharlene Shashaa & Helen Wassall

Sheffield Teaching NHS Foundation Trust and University of Sheffield, Sheffield, UK

J. Bagshaw, M. Begum, K. Birchall, Robyn Butcher, H. Carborn, Flora Chan, Kerry Chapman, Yutung Cheng, Luke Chetham, Cameron Clark, Zach Coburn, Joby Cole, Myles Dixon, Alexandra Fairman, J. Finnigan, H. Foot, David Foote, Amber Ford, Rebecca Gregory, Kate Harrington, L. Haslam, L. Hesselden, J. Hockridge, Ailsa Holbourn, B. Holroyd-Hind, L. Holt, Alice Howell, E. Hurditch, F. Ilyas, Claire Jarman, Allan Lawrie, Ju Hee Lee, Elvina Lee, Rebecca Lenagh, Alison Lye, Irene Macharia, M. Marshall, Angeline Mbuyisa, J. McNeill, Sharon Megson, J. Meiring, L. Milner, S. Misra, Helen Newell, Tom Newman, C. Norman, Lorenza Nwafor, Dibya Pattenadk, Megan Plowright, Julie Porter, Phillip Ravencroft, C. Roddis, J. Rodger, Peter Saunders, J. Sidebottom, Jacqui Smith, Laurie Smith, N. Steele, G. Stephens, R. Stimpson, B. Thamu, N. Tinker, Kim Turner, Helena Turton, Phillip Wade, S. Walker, James Watson, Imogen Wilson & Amira Zawia

University of Nottingham, Nottingham, UK

David Baguley, Chris Coleman, E. Cox, Laura Fabbri, Susan Francis, Ian Hall, E. Hufton, Simon Johnson, Fasih Khan, Paaig Kitterick, Richard Morriss, Nick Selby, Iain Stewart & Louise Wright

Wirral University Teaching Hospital, Wirral, UK

Elisabeth Bailey, Anne Reddington & Andrew Wight

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

University of Swansea, Swansea, UK

University of Southampton, London, UK

David Baldwin, P. C. Calder, Nathan Huneke & Gemma Simons

Royal Brompton and Harefield Clinical Group, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

R. E. Barker, Daniele Cristiano, N. Dormand, P. George, Mahitha Gummadi, S. Kon, Kamal Liyanage, C. M. Nolan, B. Patel, Suhani Patel, Oliver Polgar, L. Price, P. Shah, Suver Singh & J. A. Walsh

York and Scarborough NHS Foundation Trust, York, UK

Laura Barman, Claire Brookes, K. Elliott, L. Griffiths, Zoe Guy, Kate Howard, Diana Ionita, Heidi Redfearn, Carol Sarginson & Alison Turnbull

NHS Highland, Inverness, UK

Fiona Barrett, A. Donaldson & Beth Sage

Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK

Helen Baxendale, Lucie Garner, C. Johnson, J. Mackie, Alice Michael, J. Newman, Jamie Pack, K. Paques, H. Parfrey, J. Parmar & A. Reddy

University Hospitals of Derby and Burton, Derby, UK

Paul Beckett, Caroline Dickens & Uttam Nanda

NHS Lanarkshire, Hamilton, UK

Murdina Bell, Angela Brown, M. Brown, R. Hamil, Karen Leitch, L. Macliver, Manish Patel, Jackie Quigley, Andrew Smith & B. Welsh

Cambridge University Hospitals NHS Foundation Trust, NIHR Cambridge Clinical Research Facility and University of Cambridge, Cambridge, UK

Areti Bermperi, Isabel Cruz, K. Dempsey, Anne Elmer, Jonathon Fuld, H. Jones, Sherly Jose, Stefan Marciniak, M. Parkes, Carla Ribeiro, Jessica Taylor, Mark Toshner, L. Watson & J. Worsley

Loughborough University, Loughborough, UK

Lettie Bishop & David Stensel

Betsi Cadwallader University Health Board, Bangor, UK

Annette Bolger, Ffyon Davies, Ahmed Haggar, Joanne Lewis, Arwel Lloyd, R. Manley, Emma McIvor, Daniel Menzies, K. Roberts, W. Saxon, David Southern, Christian Subbe & Victoria Whitehead

Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK

Charlotte Bolton, J. Bonnington, Melanie Chrystal, Catherine Dupont, Paul Greenhaff, Ayushman Gupta, W. Jang, S. Linford, Laura Matthews, Athanasios Nikolaidis, Sabrina Prosper & Andrew Thomas

King’s College London, London, UK

Kate Bramham, M. Brown, Khalida Ismail, Tim Nicholson, Carmen Pariante, Claire Sharpe, Simon Wessely & J. Whitney

Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK

Lucy Brear, Karen Regan, Dinesh Saralaya & Kim Storton

South London and Maudsley NHS Foundation Trust and King’s College London, London, UK

G. Breen & M. Hotopf

London School of Hygiene and Tropical Medicine, London, UK

Andrew Briggs

Whittington Health NHS Trust, London, UK

E. Bright, P. Crisp, Ruvini Dharmagunawardena & M. Stern

Cardiff and Vale University Health Board, Cardiff, UK

Lauren Broad, Teriann Evans, Matthew Haynes, L. Jones, Lucy Knibbs, Alison McQueen, Catherine Oliver, Kerry Paradowski, Ramsey Sabit & Jenny Williams

Yeovil District Hospital NHS Foundation Trust, Yeovil, UK

Andrew Broadley

University of Birmingham, Birmingham, UK

Mattew Broome, Paul McArdle, Paul Moss, David Thickett, Rachel Upthegrove, Dan Wilkinson, David Wraith & Erin L. Aldera

BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

Anda Bularga

University of Cambridge, Cambridge, UK

Ed Bullmore, Jonathon Heeney, Claudia Langenberg, William Schwaeble, Charlotte Summers & J. Weir McCall

NIHR Leicester Biomedical Research Centre–Respiratory Patient and Public Involvement Group, Leicester, UK

Jenny Bunker, Rhyan Gill & Rashmita Nathu

Imperial College Healthcare NHS Trust and Imperial College London, London, UK

L. Burden, Ellen Calvelo, Bethany Card, Caitlin Carr, Edwin Chilvers, Donna Copeland, P. Cullinan, Patrick Daly, Lynsey Evison, Tamanah Fayzan, Hussain Gordon, Sulaimaan Haq, Gisli Jenkins, Clara King, Onn Min Kon, Katherine March, Myril Mariveles, Laura McLeavey, Silvia Moriera, Unber Munawar, Uchechi Nwanguma, Lorna Orriss-Dib, Alexandra Ross, Maura Roy, Emily Russell, Katherine Samuel, J. Schronce, Neil Simpson, Lawrence Tarusan, David Thomas, Chloe Wood & Najira Yasmin

Harrogate and District NHD Foundation Trust, Harrogate, UK

Tracy Burdett, James Featherstone, Cathy Lawson, Alison Layton, Clare Mills & Lorraine Stephenson

Newcastle University/Chair of NIHR Dementia TRC, Newcastle, UK

Oxford University Hospitals NHS Foundation Trust, Oxford, UK

A. Burns & N. Kanellakis

Tameside and Glossop Integrated Care NHS Foundation Trust, Ashton-under-Lyne, UK

Al-Tahoor Butt, Martina Coulding, Heather Jones, Susan Kilroy, Jacqueline McCormick, Jerome McIntosh, Heather Savill, Victoria Turner & Joanne Vere

University of Oxford, Nuffield Department of Medicine, Oxford, UK

University of Glasgow, Glasgow, UK

Jonathon Cavanagh, S. MacDonald, Kate O’Donnell, John Petrie, Naveed Sattar & Mark Spears

United Lincolnshire Hospitals NHS Trust, Grantham, UK

Manish Chablani & Lynn Osborne

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Trudie Chalder

University Hospital of South Manchester NHS Foundation Trust, Manchester, UK

N. Chaudhuri

University Hospital Southampton NHS Foundation Trust and University of Southampton, Southampton, UK

Caroline Childs, R. Djukanovic, S. Fletcher, Matt Harvey, Mark Jones, Elizabeth Marouzet, B. Marshall, Reena Samuel, T. Sass, Tim Wallis & Helen Wheeler

King’s College Hospital/Guy’s and St Thomas’ NHS FT, London, UK

A. Chiribiri & C. O’Brien

Barts Health NHS Trust, London, UK

K. Chong-James, C. David, W. Y. James, Paul Pfeffer & O. Zongo

NHS Lothian and University of Edinburgh, Edinburgh, UK

Gaunab Choudhury, S. Clohisey, Andrew Deans, J. Furniss, Ewen Harrison, S. Kelly & Aziz Sheikh

School of Cardiovascular Medicine and Sciences. King’s College London, London, UK

Phillip Chowienczyk

Lewisham and Greenwich NHS Trust, London, UK

Hywel Dda University Health Board, Haverfordwest, UK

S. Coetzee, Kim Davies, Rachel Ann Hughes, Ronda Loosley, Heather McGuinness, Abdelrahman Mohamed, Linda O’Brien, Zohra Omar, Emma Perkins, Janet Phipps, Gavin Ross, Abigail Taylor, Helen Tench & Rebecca Wolf-Roberts

NHS Tayside and University of Dundee, Dundee, UK

David Connell, C. Deas, Anne Elliott, J. George, S. Mohammed, J. Rowland, A. R. Solstice, Debbie Sutherland & Caroline Tee

Swansea Bay University Health Board, Port Talbot, UK

Lynda Connor, Amanda Cook, Gwyneth Davies, Tabitha Rees, Favas Thaivalappil & Caradog Thomas

Faculty of Medicine, Nursing and Health Sciences, School of Biomedical Sciences, Monash University, Melbourne, Victoria, Australia

Eamon Coughlan

Rotherham NHS Foundation Trust, Rotherham, UK

Alison Daniels, Anil Hormis, Julie Ingham & Lisa Zeidan

Salford Royal NHS Foundation Trust, Salford, UK

P. Dark, Nawar Diar-Bakerly, D. Evans, E. Hardy, Alice Harvey, D. Holgate, Sean Knight, N. Mairs, N. Majeed, L. McMorrow, J. Oxton, Jessica Pendlebury, C. Summersgill, R. Ugwuoke & S. Whittaker

Cwm Taf Morgannwg University Health Board, Mountain Ash, UK

Ellie Davies, Cerys Evenden, Alyson Hancock, Kia Hancock, Ceri Lynch, Meryl Rees, Lisa Roche, Natalie Stroud & T. Thomas-Woods

Borders General Hospital, NHS Borders, Melrose, UK

Joy Dawson, Hosni El-Taweel & Leanne Robinson

Aneurin Bevan University Health Board, Caerleon, UK

Amanda Dell, Sara Fairbairn, Nancy Hawkings, Jill Haworth, Michaela Hoare, Victoria Lewis, Alice Lucey, Georgia Mallison, Heeah Nassa, Chris Pennington, Andrea Price, Claire Price, Andrew Storrie, Gemma Willis & Susan Young

University of Exeter Medical School, Exeter, UK

London North West University Healthcare NHS Trust, London, UK

Shalin Diwanji, Sambasivarao Gurram, Padmasayee Papineni, Sheena Quaid, Gerlynn Tiongson & Ekaterina Watson

Alzheimer’s Research UK, Cambridge, UK

Hannah Dobson

Health and Care Research Wales, Cardiff, UK

Yvette Ellis

University of Bristol, Bristol, UK

Jonathon Evans

University of Sheffield, Sheffield, UK

L. Finnigan, Laura Saunders & James Wild

Great Western Hospital Foundation Trust, Swindon, UK

Eva Fraile & Jacinta Ugoji

Royal Devon and Exeter NHS Trust, Barnstaple, UK

Michael Gibbons

Kettering General Hospital NHS Trust, Kettering, UK

Anne-Marie Guerdette, Melanie Hewitt, R. Reddy, Katie Warwick & Sonia White

NIHR Leicester Biomedical Research Centre, Leicester, UK

Beatriz Guillen-Guio

University of Leeds, Leeds, UK

Elspeth Guthrie & Max Henderson

Royal Surrey NHS Foundation Trust, Cranleigh, UK

Mark Halling-Brown & Katherine McCullough

Chesterfield Royal Hospital NHS Trust, Calow, UK

Edward Harris & Claire Sampson

Long Covid Support, London, UK

Claire Hastie, Natalie Rogers & Nikki Smith

King’s College Hospital, NHS Foundation Trust and King’s College London, London, UK

Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK

Simon Heller

NIHR Office for Clinical Research Infrastructure, London, UK

Katie Holmes

Asthma UK and British Lung Foundation Partnership, London, UK

Ian Jarrold & Samantha Walker

North Middlesex University Hospital NHS Trust, London, UK

Bhagy Jayaraman & Tessa Light

Action for Pulmonary Fibrosis, Peterborough, UK

Cardiff University, National Centre for Mental Health, Cardiff, UK

McPin Foundation, London, UK

Thomas Kabir

Roslin Institute, The University of Edinburgh, Edinburgh, UK

Steven Kerr

The Hillingdon Hospitals NHS Foundation Trust, London, UK

Samantha Kon, G. Landers, Harpreet Lota, Mariam Nasseri & Sofiya Portukhay

Queen Mary University of London, London, UK

Ania Korszun

Swansea University, Swansea Welsh Network, Hywel Dda University Health Board, Swansea, UK

Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK

Nazir I. Lone

Barts Heart Centre, London, UK

Barts Health NHS Trust and Queen Mary University of London, London, UK

Adrian Martineau

Salisbury NHS Foundation Trust, Salisbury, UK

Wadzanai Matimba-Mupaya & Sophia Strong-Sheldrake

University of Newcastle, Newcastle, UK

Hamish McAllister-Williams, Stella-Maria Paddick, Anthony Rostron & John Paul Taylor

Gateshead NHS Trust, Gateshead, UK

W. McCormick, Lorraine Pearce, S. Pugmire, Wendy Stoker & Ann Wilson

Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester, UK

Katherine McIvor

Kidney Research UK, Peterborough, UK

Aisling McMahon

NHS Dumfries and Galloway, Dumfries, UK

Michael McMahon & Paula Neill

Swansea University, Swansea, UK

MQ Mental Health Research, London, UK

Lea Milligan

BHF Centre for Cardiovascular Science, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK

Nicholas Mills

Shropshire Community Health NHS Trust, Shropshire, UK

Sharon Painter, Johanne Tomlinson & Louise Warburton

Somerset NHS Foundation Trust, Taunton, UK

Sue Palmer, Dawn Redwood, Jo Tilley, Carinna Vickers & Tania Wainwright

Francis Crick Institute, London, UK

Markus Ralser

Manchester University NHD Foundation Trust, Manchester, UK

Pilar Rivera-Ortega

Diabetes UK, University of Glasgow, Glasgow, UK

Elizabeth Robertson

Barnsley Hospital NHS Foundation Trust, Barnsley, UK

Amy Sanderson

MRC–University of Glasgow Centre for Virus Research, Glasgow, UK

Janet Scott

Diabetes UK, London, UK

Kamini Shah

British Heart Foundation Centre, King’s College London, London, UK

King’s College Hospital NHS Foundation Trust, London, UK

University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK

Institute of Cardiovascular and Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK

University College London NHS Foundation Trust, London and Barts Health NHS Trust, London, UK

Northumbria University, Newcastle upon Tyne, UK

Ioannis Vogiatzis

Swansea University and Swansea Welsh Network, Swansea, UK

N. Williams

DUK | NHS Digital, Salford Royal Foundation Trust, Salford, UK

Queen Alexandra Hospital, Portsmouth, UK

• Kayode Adeniji

Princess Royal Hospital, Haywards Heath, UK

Daniel Agranoff & Chi Eziefula

Bassetlaw Hospital, Bassetlaw, UK

Darent Valley Hospital, Dartford, UK

Queen Elizabeth the Queen Mother Hospital, Margate, UK

Ana Alegria

School of Informatics, University of Edinburgh, Edinburgh, UK

Beatrice Alex, Benjamin Bach & James Scott-Brown

North East and North Cumbria Ingerated, Newcastle upon Tyne, UK

Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Petros Andrikopoulos, Kanta Chechi, Marc-Emmanuel Dumas, Julian Griffin, Sonia Liggi & Zoltan Takats

Section of Genomic and Environmental Medicine, Respiratory Division, National Heart and Lung Institute, Imperial College London, London, UK

Petros Andrikopoulos, Marc-Emmanuel Dumas, Michael Olanipekun & Anthonia Osagie

John Radcliffe Hospital, Oxford, UK

Brian Angus

Royal Albert Edward Infirmary, Wigan, UK

Abdul Ashish

Manchester Royal Infirmary, Manchester, UK

Dougal Atkinson

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK

Section of Molecular Virology, Imperial College London, London, UK

Wendy S. Barclay

Furness General Hospital, Barrow-in-Furness, UK

Shahedal Bari

Hull University Teaching Hospital Trust, Kingston upon Hull, UK

Gavin Barlow

Hillingdon Hospital, Hillingdon, UK

Stella Barnass

St Thomas’ Hospital, London, UK

Nicholas Barrett

Coventry and Warwickshire, Coventry, UK

Christopher Bassford

St Michael’s Hospital, Bristol, UK

Sneha Basude

Stepping Hill Hospital, Stockport, UK

David Baxter

Royal Liverpool University Hospital, Liverpool, UK

Michael Beadsworth

Bristol Royal Hospital Children’s, Bristol, UK

Jolanta Bernatoniene

Scarborough Hospital, Scarborough, UK

John Berridge

Golden Jubilee National Hospital, Clydebank, UK

Colin Berry

Liverpool Heart and Chest Hospital, Liverpool, UK

Nicola Best

Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Debby Bogaert & Clark D. Russell

James Paget University Hospital, Great Yarmouth, UK

Pieter Bothma & Darell Tupper-Carey

Aberdeen Royal Infirmary, Aberdeen, UK

Robin Brittain-Long

Adamson Hospital, Cupar, UK

Naomi Bulteel

Royal Devon and Exeter Hospital, Exeter, UK

Worcestershire Royal Hospital, Worcester, UK

Andrew Burtenshaw

ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Gail Carson, Laura Merson & Louise Sigfrid

Conquest Hospital, Hastings, UK

Vikki Caruth

The James Cook University Hospital, Middlesbrough, UK

David Chadwick

Dorset County Hospital, Dorchester, UK

Duncan Chambler

Antimicrobial Resistance and Hospital Acquired Infection Department, Public Health England, London, UK

Meera Chand

Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK

Kanta Chechi

Royal Bournemouth General Hospital, Bournemouth, UK

Harrogate Hospital, Harrogate, UK

Jenny Child

Royal Blackburn Teaching Hospital, Blackburn, UK

Srikanth Chukkambotla

Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK

Richard Clark, Audrey Coutts, Lorna Donelly, Angie Fawkes, Tammy Gilchrist, Katarzyna Hafezi, Louise MacGillivray, Alan Maclean, Sarah McCafferty, Kirstie Morrice, Lee Murphy & Nicola Wrobel

Torbay Hospital, Torquay, UK

Northern General Hospital, Sheffield, UK

Paul Collini, Cariad Evans & Gary Mills

Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK

Marie Connor, Jo Dalton, Chloe Donohue, Carrol Gamble, Michelle Girvan, Sophie Halpin, Janet Harrison, Clare Jackson, Laura Marsh, Stephanie Roberts & Egle Saviciute

Department of Infectious Disease, Imperial College London, London, UK

Graham S. Cooke & Shiranee Sriskandan

St Georges Hospital (Tooting), London, UK

Catherine Cosgrove

Blackpool Victoria Hospital, Blackpool, UK

Jason Cupitt & Joanne Howard

The Royal London Hospital, London, UK

Maria-Teresa Cutino-Moguel

MRC-University of Glasgow Centre for Virus Research, Glasgow, UK

Ana da Silva Filipe, Antonia Y. W. Ho, Sarah E. McDonald, Massimo Palmarini, David L. Robertson, Janet T. Scott & Emma C. Thomson

Salford Royal Hospital, Salford, UK

University Hospital of North Durham, Durham, UK

Chris Dawson

Norfolk and Norwich University Hospital, Norwich, UK

Samir Dervisevic

Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK

Annemarie B. Docherty & Seán Keating

Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK

Cara Donegan & Rebecca G. Spencer

Salisbury District Hospital, Salisbury, UK

Phil Donnison

National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Gonçalo dos Santos Correia, Matthew Lewis, Lynn Maslen, Caroline Sands, Zoltan Takats & Panteleimon Takis

Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Gonçalo dos Santos Correia, Matthew Lewis, Lynn Maslen, Caroline Sands & Panteleimon Takis

Guy’s and St Thomas’, NHS Foundation Trust, London, UK

Sam Douthwaite, Michael MacMahon, Marlies Ostermann & Manu Shankar-Hari

The Royal Oldham Hospital, Oldham, UK

Andrew Drummond

European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille University Hospital, University of Lille, Lille, France

Marc-Emmanuel Dumas

McGill University and Genome Quebec Innovation Centre, Montreal, Qeubec, Canada

National Infection Service, Public Health England, London, UK

Jake Dunning & Maria Zambon

Hereford Count Hospital, Hereford, UK

Ingrid DuRand

Southampton General Hospital, Southampton, UK

Ahilanadan Dushianthan

Northampton General Hospital, Northampton, UK

Tristan Dyer

University Hospital of Wales, Cardiff, UK

Chrisopher Fegan

University Hospitals Bristol NHS Foundation Trust, Bristol, UK

Liverpool School of Tropical Medicine, Liverpool, UK

Tom Fletcher

Leighton Hospital, Crewe, UK

Duncan Fullerton & Elijah Matovu

Manor Hospital, Walsall, UK

Scunthorpe Hospital, Scunthorpe, UK

Sanjeev Garg

Cambridge University Hospital, Cambridge, UK

Effrossyni Gkrania-Klotsas

West Suffolk NHS Foundation Trust, Bury St Edmunds, UK

Basingstoke and North Hampshire Hospital, Basingstoke, UK

Arthur Goldsmith

North Cumberland Infirmary, Carlisle, UK

Clive Graham

Paediatric Liver, GI and Nutrition Centre and MowatLabs, King’s College Hospital, London, UK

Tassos Grammatikopoulos

Institute of Liver Studies, King’s College London, London, UK

Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK

Christopher A. Green

Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK

William Greenhalf

Institute for Global Health, University College London, London, UK

Rishi K. Gupta

NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK

Hayley Hardwick, Malcolm G. Semple, Tom Solomon & Lance C. W. Turtle

Warwick Hospital, Warwick, UK

Elaine Hardy

Birmingham Children’s Hospital, Birmingham, UK

Stuart Hartshorn

Nottingham City Hospital, Nottingham, UK

Daniel Harvey

Glangwili Hospital Child Health Section, Carmarthen, UK

Peter Havalda

Alder Hey Children’s Hospital, Liverpool, UK

Daniel B. Hawcutt

Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, UK

Antonia Y. W. Ho

Bronglais General Hospital, Aberystwyth, UK

Maria Hobrok

Worthing Hospital, Worthing, UK

Luke Hodgson

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Peter W. Horby

Rotheram District General Hospital, Rotheram, UK

Anil Hormis

Virology Reference Department, National Infection Service, Public Health England, Colindale Avenue, London, UK

Samreen Ijaz

Royal Free Hospital, London, UK

Michael Jacobs & Padmasayee Papineni

Homerton Hospital, London, UK

Airedale Hospital, Airedale, UK

Paul Jennings

Basildon Hospital, Basildon, UK

Agilan Kaliappan

The Christie NHS Foundation Trust, Manchester, UK

Vidya Kasipandian

University Hospital Lewisham, London, UK

Stephen Kegg

The Whittington Hospital, London, UK

Michael Kelsey

Southmead Hospital, Bristol, UK

Jason Kendall

Sheffield Childrens Hospital, Sheffield, UK

Caroline Kerrison

Royal United Hospital, Bath, UK

Ian Kerslake

Department of Pharmacology, University of Liverpool, Liverpool, UK

Nuffield Department of Medicine, Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, UK

Paul Klenerman

Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Public Health Scotland, Edinburgh, UK

Susan Knight, Eva Lahnsteiner & Sarah Tait

Western General Hospital, Edinburgh, UK

Oliver Koch

Southend University Hospital NHS Foundation Trust, Southend-on-Sea, UK

Gouri Koduri

Hinchingbrooke Hospital, Huntingdon, UK

George Koshy & Tamas Leiner

Royal Preston Hospital, Fulwood, UK

Shondipon Laha

University Hospital (Coventry), Coventry, UK

Steven Laird

The Walton Centre, Liverpool, UK

Susan Larkin

ISARIC, Global Support Centre, COVID-19 Clinical Research Resources, Epidemic diseases Research Group, Oxford (ERGO), University of Oxford, Oxford, UK

James Lee & Daniel Plotkin

Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

Gary Leeming

Hull Royal Infirmary, Hull, UK

Patrick Lillie

Nottingham University Hospitals NHS Trust:, Nottingham, UK

Wei Shen Lim

Darlington Memorial Hospital, Darlington, UK

Queen Elizabeth Hospital (Gateshead), Gateshead, UK

Vanessa Linnett

Warrington Hospital, Warrington, UK

Jeff Little

Bristol Royal Hospital for Children, Bristol, UK

Mark Lyttle

St Mary’s Hospital (Isle of Wight), Isle of Wight, UK

Emily MacNaughton

The Tunbridge Wells Hospital, Royal Tunbridge Wells, UK

Ravish Mankregod

Huddersfield Royal, Huddersfield, UK

Countess of Chester Hospital, Liverpool, UK

Ruth McEwen & Lawrence Wilson

Frimley Park Hospital, Frimley, UK

Manjula Meda

Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, UK

Alexander J. Mentzer

Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

Alison M. Meynert & Murray Wham

St James University Hospital, Leeds, UK

Jane Minton

Arrowe Park Hospital, Birkenhead, UK

Kavya Mohandas

Great Ormond Street Hospital, London, UK

Royal Shrewsbury Hospital, Shrewsbury, UK

Addenbrookes Hospital, Cambridge, UK

Elinoor Moore

Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK

Shona C. Moore, William A. Paxton & Georgios Pollakis

East Surrey Hospital (Redhill), Redhill, UK

Patrick Morgan

Burton Hospital, Burton, UK

Craig Morris & Tim Reynolds

Peterborough City Hospital, Peterborough, UK

Katherine Mortimore

Kent and Canterbury Hospital, Canterbury, UK

Samuel Moses

Weston Area General Trust, Bristol, UK

Mbiye Mpenge

Bedfordshire Hospital, Bedfordshire, UK

Rohinton Mulla

Glasgow Royal Infirmary, Glasgow, UK

Michael Murphy

Macclesfield General Hospital, Macclesfield, UK

Thapas Nagarajan

Derbyshire Healthcare, Derbyshire, UK

Megan Nagel

Chelsea and Westminster Hospital, London, UK

Mark Nelson & Matthew K. O’Shea

Watford General Hospital, Watford, UK

Lillian Norris & Tom Stambach

EPCC, University of Edinburgh, Edinburgh, UK

Lucy Norris

Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, London, UK

Michael Olanipekun

Imperial College Healthcare NHS Trust: London, London, UK

Peter J. M. Openshaw

Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Anthonia Osagie

Prince Philip Hospital, Llanelli, UK

Igor Otahal & Andrew Workman

George Eliot Hospital – Acute Services, Nuneaton, UK

Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

Carlo Palmieri

Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK

Kettering General Hospital, Kettering, UK

Selva Panchatsharam

University Hospitals of North Midlands NHS Trust, North Midlands, UK

Danai Papakonstantinou

Russells Hall Hospital, Dudley, UK

Hassan Paraiso

Harefield Hospital, Harefield, UK

Lister Hospital, Lister, UK

Natalie Pattison

Musgrove Park Hospital, Taunton, UK

Justin Pepperell

Kingston Hospital, Kingston, UK

Mark Peters

Queen’s Hospital, Romford, UK

Mandeep Phull

Southport and Formby District General Hospital, Southport, UK

Stefania Pintus

St George’s University of London, London, UK

Tim Planche

King’s College Hospital (Denmark Hill), London, UK

Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, School of Immunology and Microbial Sciences, King’s College London, London, UK

Nicholas Price

Department of Infectious Diseases, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

The Clatterbridge Cancer Centre NHS Foundation, Bebington, UK

David Price

The Great Western Hospital, Swindon, UK

Rachel Prout

Ninewells Hospital, Dundee, UK

Nikolas Rae

Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK

Andrew Rambaut

Poole Hospital NHS Trust, Poole, UK

Henrik Reschreiter

William Harvey Hospital, Ashford, UK

Neil Richardson

King’s Mill Hospital, Sutton-in-Ashfield, UK

Mark Roberts

Liverpool Women’s Hospital, Liverpool, UK

Devender Roberts

Pinderfields Hospital, Wakefield, UK

Alistair Rose

North Devon District Hospital, Barnstaple, UK

Guy Rousseau

Queen Elizabeth Hospital, Birmingham, UK

Tameside General Hospital, Ashton-under-Lyne, UK

Brendan Ryan

City Hospital (Birmingham), Birmingham, UK

Taranprit Saluja

Department of Pediatrics and Virology, St Mary’s Medical School Bldg, Imperial College London, London, UK

Vanessa Sancho-Shimizu

The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK

Matthias Schmid

NHS Greater Glasgow and Clyde, Glasgow, UK

Janet T. Scott

Respiratory Medicine, Institute in The Park, University of Liverpool, Alder Hey Children’s Hospital, Liverpool, UK

Malcolm G. Semple

Broomfield Hospital, Broomfield, UK

Stoke Mandeville, UK

Prad Shanmuga

University Hospital of North Tees, Stockton-on-Tees, UK

Anil Sharma

Institute of Translational Medicine, University of, Liverpool, Merseyside, UK

Victoria E. Shaw

Royal Manchester Children’s Hospital, Manchester, UK

Anna Shawcross

New Cross Hospital, Wolverhampton, UK

Jagtur Singh Pooni

Bedford Hospital, Bedford, UK

Jeremy Sizer

Colchester General Hospital, Colchester, UK

Richard Smith

University Hospital Birmingham NHS Foundation Trust, Birmingham, UK

Catherine Snelson & Tony Whitehouse

Walton Centre NHS Foundation Trust, Liverpool, UK

Tom Solomon

Chesterfield Royal Hospital, Calow, UK

Nick Spittle

MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK

Shiranee Sriskandan

Princess Alexandra Hospital, Harlow, UK

Nikki Staines & Shico Visuvanathan

Milton Keynes Hospital, Eaglestone, UK

Richard Stewart

Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

David Stuart

Royal Bolton Hopital, Farnworth, UK

Pradeep Subudhi

Department of Medicine, University of Cambridge, Cambridge, UK

Charlotte Summers

Department of Child Life and Health, University of Edinburgh, Edinburgh, UK

Olivia V. Swann

Royal Gwent (Newport), Newport, UK

Tamas Szakmany

The Royal Marsden Hospital (London), London, UK

Kate Tatham

Blood Borne Virus Unit, Virus Reference Department, National Infection Service, Public Health England, London, UK

Richard S. Tedder

Transfusion Microbiology, National Health Service Blood and Transplant, London, UK

Department of Medicine, Imperial College London, London, UK

Queen Victoria Hospital (East Grinstead), East Grinstead, UK

Leeds Teaching Hospitals NHS Trust, Leeds, UK

Robert Thompson

Royal Stoke University Hospital, Stoke-on-Trent, UK

Chris Thompson

Whiston Hospital, Rainhill, UK

Ascanio Tridente

Tropical and Infectious Disease Unit, Royal Liverpool University Hospital, Liverpool, UK

Lance C. W. Turtle

Croydon University Hospital, Thornton Heath, UK

Mary Twagira

Gloucester Royal, Gloucester, UK

Nick Vallotton

West Hertfordshire Teaching Hospitals NHS Trust, Hertfordshire, UK

Rama Vancheeswaran

North Middlesex Hospital, London, UK

Rachel Vincent

Medway Maritime Hospital, Gillingham, UK

Lisa Vincent-Smith

Royal Papworth Hospital Everard, Cambridge, UK

Alan Vuylsteke

Derriford (Plymouth), Plymouth, UK

St Helier Hospital, Sutton, UK

Rachel Wake

Royal Berkshire Hospital, Reading, UK

Andrew Walden

Royal Liverpool Hospital, Liverpool, UK

Ingeborg Welters

Bradford Royal infirmary, Bradford, UK

Paul Whittaker

Central Middlesex, London, UK

Ashley Whittington

Royal Cornwall Hospital (Tresliske), Truro, UK

Meme Wijesinghe

North Bristol NHS Trust, Bristol, UK

Martin Williams

St. Peter’s Hospital, Runnymede, UK

Stephen Winchester

Leicester Royal Infirmary, Leicester, UK

Martin Wiselka

Grantham and District Hospital, Grantham, UK

Adam Wolverson

Aintree University Hospital, Liverpool, UK

Daniel G. Wootton

North Tyneside General Hospital, North Shields, UK

Bryan Yates

Queen Elizabeth Hospital, King’s Lynn, UK

Peter Young

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•  & J. Eunice Zhang

Contributions

F.L. recruited participants, acquired clinical samples, analyzed and interpreted data and cowrote the manuscript, including all drafting and revisions. C.E. analyzed and interpreted data and cowrote this manuscript, including all drafting and revisions. S.F. and M.R. supported the analysis and interpretation of data as well as drafting and revisions. D.S., J.K.S., S.C.M., S.A., N.M., J.N., C.K., O.C.L., O.E., H.J.C.M., A. Shikotra, A. Singapuri, M.S., V.C.H., M.T., N.J.G., N.I.L. and C.C. contributed to acquisition of data underlying this study. L.H.-W., A.A.R.T., S.L.R.-J., L.S.H., O.M.K., D.G.W., T.I.d.S. and A. Ho made substantial contributions to conception/design and implementation of this work and/or acquisition of clinical samples for this work. They have supported drafting and revisions of the manuscript. E.M.H., J.K.Q. and A.B.D. made substantial contributions to the study design as well as data access, linkage and analysis. They have supported drafting and revisions of this work. J.D.C., L.-P.H., A. Horsley, B.R., K.P., M.M. and W.G. made substantial contributions to the conception and design of this work and have supported drafting and revisions of this work. J.K.B. obtained funding for ISARIC4C, is ISARIC4C consortium co-lead, has made substantial contributions to conception and design of this work and has supported drafting and revisions of this work. M.G.S. obtained funding for ISARIC4C, is ISARIC4C consortium co-lead, sponsor/protocol chief investigator, has made substantial contributions to conception and design of this work and has supported drafting and revisions of this work. R.A.E. and L.V.W. are co-leads of PHOSP-COVID, made substantial contributions to conception and design of this work, the acquisition and analysis of data, and have supported drafting and revisions of this work. C.B. is the chief investigator of PHOSP-COVID and has made substantial contributions to conception and design of this work. R.S.T. and L.T. made substantial contributions to the acquisition, analysis and interpretation of the data underlying this study and have contributed to drafting and revisions of this work. P.J.M.O. obtained funding for ISARIC4C, is ISARIC4C consortium co-lead, sponsor/protocol chief investigator and has made substantial contributions to conception and design of this work. R.S.T. and P.J.M.O. have also made key contributions to interpretation of data and have co-written this manuscript. All authors have read and approve the final version to be published. All authors agree to accountability for all aspects of this work. All investigators within ISARIC4C and the PHOSP-COVID consortia have made substantial contributions to the conception or design of this study and/or acquisition of data for this study. The full list of authors within these groups is available in Supplementary Information .

Corresponding authors

Correspondence to Ryan S. Thwaites or Peter J. M. Openshaw .

Ethics declarations

Competing interests.

F.L., C.E., D.S., J.K.S., S.C.M., C.D., C.K., N.M., L.N., E.M.H., A.B.D., J.K.Q., L.-P.H., K.P., L.S.H., O.M.K., S.F., T.I.d.S., D.G.W., R.S.T. and J.K.B. have no conflicts of interest. A.A.R.T. receives speaker fees and support to attend meetings from Janssen Pharmaceuticals. S.L.R.-J. is on the data safety monitoring board for Bexero trial in HIV+ adults in Kenya. J.D.C. is the deputy chief editor of the European Respiratory Journal and receives consulting fees from AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Insmed, Janssen, Novartis, Pfizer and Zambon. A. Horsley is deputy chair of NIHR Translational Research Collaboration (unpaid role). B.R. receives honoraria from Axcella therapeutics. R.A.E. is co-lead of PHOSP-COVID and receives fees from AstraZenaca/Evidera for consultancy on LC and from AstraZenaca for consultancy on digital health. R.A.E. has received speaker fees from Boehringer in June 2021 and has held a role as European Respiratory Society Assembly 01.02 Pulmonary Rehabilitation secretary. R.A.E. is on the American Thoracic Society Pulmonary Rehabilitation Assembly program committee. L.V.W. also receives funding from Orion pharma and GSK and holds contracts with Genentech and AstraZenaca. L.V.W. has received consulting fees from Galapagos and Boehringer, is on the data advisory board for Galapagos and is Associate Editor for the European Respiratory Journal . A. Ho is a member of NIHR Urgent Public Health Group (June 2020–March 2021). M.M. is an applicant on the PHOSP study funded by NIHR/DHSC. M.G.S. acts as an independent external and nonremunerated member of Pfizer’s External Data Monitoring Committee for their mRNA vaccine program(s), is Chair of Infectious Disease Scientific Advisory Board of Integrum Scientific LLC, and is director of MedEx Solutions Ltd. and majority owner of MedEx Solutions Ltd. and minority owner of Integrum Scientific LLC. M.G.S.’s institution has been in receipt of gifts from Chiesi Farmaceutici S.p.A. of Clinical Trial Investigational Medicinal Product without encumbrance and distribution of same to trial sites. M.G.S. is a nonrenumerated member of HMG UK New Emerging Respiratory Virus Threats Advisory Group and has previously been a nonrenumerated member of the Scientific Advisory Group for Emergencies (SAGE). C.B. has received consulting fees and/or grants from GSK, AstraZeneca, Genentech, Roche, Novartis, Sanofi, Regeneron, Chiesi, Mologic and 4DPharma. L.T. has received consulting fees from MHRA, AstraZeneca and Synairgen and speakers’ fees from Eisai Ltd., and support for conference attendance from AstraZeneca. L.T. has a patent pending with ZikaVac. P.J.M.O. reports grants from the EU Innovative Medicines Initiative 2 Joint Undertaking during the submitted work; grants from UK Medical Research Council, GSK, Wellcome Trust, EU Innovative Medicines Initiative, UK National Institute for Health Research and UK Research and Innovation–Department for Business, Energy and Industrial Strategy; and personal fees from Pfizer, Janssen and Seqirus, outside the submitted work.

Peer review

Peer review information.

Nature Immunology thanks Ziyad Al-Aly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Ioana Staicu was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Additional information

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

Extended data

Extended data fig. 1 penalized logistic regression performance..

Graphs show classification error and Area under curve (AUC) from the 50 repeats tenfold nested cross-validation used to optimise and assess the performance of PLR testing associations with each LC outcome relative to Recovered (n = 233): Cardio_Resp (n = 398), Fatigue (n = 384), Anxiety/Depression (n = 202), GI (n = 132), ( e ) Cognitive (n = 6). The distributions of classification error and area under curve (AUC) from the nested cross-validation are shown. Box plot centre line represents the Median and boundaries of the box represent interquartile range (IQR), the whisker length represent 1.5xIQR.

Extended Data Fig. 2 Associations with long COVID symptoms in full study cohort.

( a ) Fibrinogen levels at 6 months were compared between pooled LC cases (n = 295) and Recovered (n = 233) and between the Cognitive group (n = 41) and Recovered (n = 233). Box plot centre line represent the Median and boundaries of the box represent interquartile range (IQR), the whisker length represents 1.5xIQR, any outliers beyond the whisker range are shown as individual dots. Median differences were compared using two-sided Wilcoxon signed-rank test *= p  < 0·05, **= p  < 0·01, ***= p  < 0·001, ****= p  < 0·0001. Unadjusted p-values are reported. b ) Distribution of time from COVID-19 hospitalisation at sample collection applying CDC and NICE definitions of LC (n = 719) ( c ) Upset plot of symptom groups. Horizontal coloured bars represent the number of patients in each symptom group: Cardiorespiratory (Cardio_Resp), Fatigue, Cognitive, Gastrointestinal (GI) and Anxiety/Depression (Anx_Dep). Vertical black bars represent the number of patients in each symptom combination group. To prevent patient identification, where less than 5 patients belong to a combination group, this has been represented as ‘<5’. The Recovered group (n = 250) were used as controls. Forest plots show Olink protein concentrations (NPX) associated with ( d ) Cardio_Resp (n = 398), ( e ) Fatigue (n = 342), ( f ) Anx_Dep (n = 219), ( g ) GI (n = 134), and ( h ) Cognitive (n = 65). Error bars represent the median accuracy of the model.

Extended Data Fig. 3 Validation of olink measurements using conventional assays in plasma.

Olink measured protein (NPX) were compared to chemiluminescence assays (ECL or ELISA, log2[pg/mL]) to validate our findings, where contemporaneously collected plasma samples were available (n = 58). Results from key mediators associated with LC groups were validated: CSF3, IL1R2, IL2, IL3RA, TNFa, TFF2. R = spearman rank correlation coefficient and shaded areas indicated the 95% confidence interval. Samples that fell below the lower limit of detection for a given assay were excluded and the ‘n’ value on each panel indicates the number of samples above this limit.

Extended Data Fig. 4 Univariate analysis of proteins associated with each symptom.

Olink measured plasma protein levels (NPX) compared between LC groups (Cardio_Resp, n = 398, Fatigue n = 384, Anxiety/Depression, n = 202, GI, n = 132 and Cognitive, n = 60) and Recovered (n = 233). Proteins identified by PLR were compared between groups. Median differences were compared using two-sided Wilcoxon signed-rank test. * = p < 0·05, ** = p < 0·01, *** = p < 0·001, ****= p < 0·0001 after FDR adjustment. Box plot centre line represent the Median and boundaries of the box represent interquartile range (IQR), the whisker length represents 1.5xIQR, any outliers beyond the whisker range are shown as individual dots.

Extended Data Fig. 5 Unadjusted Penalised Logistic Regression.

Olink measured proteins (NPX) and their association with Cardio_Resp (n = 398), Fatigue (n = 342), Anx_Dep (n = 219), GI (n = 134), and Cognitive (n = 65). Forest plots show odds of each LC outcome vs Recovered (n = 233), using PLR without adjusting for clinical co-variates. Error bars represent the median accuracy of the model.

Extended Data Fig. 6 Partial Least Squares analysis.

Olink measured proteins (NPX) and their association with Cardio_Resp (n = 398), Fatigue (n = 342), Anx_Dep (n = 219), GI (n = 134), and Cognitive (n = 65) groups. Forest plots show odds of LC outcome vs Recovered (n = 233), using PLS analysis. Error bars represent the standard error of the coefficient estimate.

Extended Data Fig. 7 Network analysis centrality.

Each graph shows the centrality score for each Olink measured protein (NPX) found to have significant associations with other proteins that were elevated in the Cardio_Resp (n = 398), Fatigue (n = 342), Anx_Dep (n = 219), GI (n = 134), and Cognitive (n = 65) groups relative to Recovered (n = 233).

Extended Data Fig. 8 Inflammation in men and women with long COVID.

Olink measured plasma protein levels (NPX) between men and women with symptoms, divided by age (<50 or >=50years): (a) shows IL1R2 and MATN2 in the Anxiety/Depression group (<50 n = 55, >=50 n = 133), (b) shows CTSO and NFASC in the Cognitive group (<50 n = 11, >=50 n = 50). Median values were compared between men and women using two-sided Wilcoxon signed-rank test. Box plot centre line represent the Median and boundaries represent interquartile range (IQR), the whisker length represents 1.5xIQR.

Extended Data Fig. 9 Inflammation in the upper respiratory tract.

Nasal cytokines measured by immunoassay in the CardioResp Group (n = 29) and Recovered (n = 31): ( a ) shows IL1a, IL1b, IL-6, APO-2, TGFa, TFF2. Median differences were compared using two-sided Wilcoxon signed-rank test. Box plot centre line represents the Median and boundaries of the box represent interquartile range (IQR), the whisker length represent 1.5xIQR. ( b ) Shows cytokines measured by immunoassay in paired plasma and nasal (n = 70). Correlations between IL1a, IL1b, IL-6, APO-2, TGFa and TFF2 in nasal and plasma samples were compared using Spearman’s rank correlation coefficient ( R ). Shaded areas indicated the 95% confidence interval of R.

Extended Data Fig. 10 Graphical abstract.

Summary of interpretation of key findings from Olink measured proteins and their association with CardioResp (n = 398), Fatigue (n = 342), Anx/Dep (n = 219), GI (n = 134), and Cognitive (n = 65) groups relative to Recovered (n = 233).

Supplementary information

Supplementary information.

Supplementary Methods, Statistics and reproducibility statement, Supplementary Results, Supplementary Tables 1–7, Extended data figure legends, Appendix 1 (Supplementary Table 8), Appendix 2 (PHOSP-COVID author list) and Appendix 3 (ISARIC4C author list).

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Liew, F., Efstathiou, C., Fontanella, S. et al. Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease. Nat Immunol 25 , 607–621 (2024). https://doi.org/10.1038/s41590-024-01778-0

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Received : 11 August 2023

Accepted : 06 February 2024

Published : 08 April 2024

Issue Date : April 2024

DOI : https://doi.org/10.1038/s41590-024-01778-0

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