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Vegetative top dry weight (DW), leaf DW, stem DW, and stem diameter of mature eggplant plants as affected by irrigation rate. Irrigation rate was applied as percentage of crop evapotranspiration. Curve was fit by linear regression. Fall of 2010, Tifton, GA.

Seasonal volumetric soil water content (measured at 12- and 30-cm depth) as influenced by irrigation rate. Irrigation rate was applied as percentage of crop evapotranspiration. Line was fit by linear regression. Fall of 2010, Tifton, GA.

Effect of irrigation rate and soil depth on the concentration of nitrate-nitrogen in the soil (0 to 60 cm) in drip-irrigated eggplant grown on raised beds and plastic film mulch. Irrigation rate was applied as percentage of crop evapotranspiration. Line was fit by linear regression. Fall of 2010, Tifton, GA.

Cumulative number of fruit and fruit yields as affected by irrigation rate in drip-irrigated eggplant grown on raised beds and plastic film mulch. Irrigation rate was applied as percentage of crop evapotranspiration. Line was fit by linear regression. Fall of 2010, Tifton, GA.

Individual fruit weight as influenced by irrigation rate in drip-irrigated eggplant grown on raised beds and plastic film mulch. Irrigation rate was applied as percentage of crop evapotranspiration. Line was fit by linear regression. Fall of 2010, Tifton, GA.

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Eggplant ( Solanum melongena L.) Plant Growth and Fruit Yield as Affected by Drip Irrigation Rate

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Eggplant ( Solanum melongena L.) is an increasingly popular crop in the United States. In the southeastern United States, eggplant is often produced with high levels of irrigation water [above the rate of crop evapotranspiration (ETc)], resulting in water waste and nitrogen (N) leaching. The objective of this research was to assess the effects of irrigation rate on plant growth and fruit yield in eggplant. The study was conducted in Tifton, GA, in the fall of 2010 and 2011. Eggplant plants cv. Santana were grown on raised beds (1.8 m centers) covered with white plastic film mulch. There was a single drip tape along the center of the bed. The design was a randomized complete block with five treatments and four replications. Treatments consisted of irrigation rates based on ETc (33%, 67%, 100%, 133%, and 167% ETc). Plant growth, chlorophyll index (CI), and volumetric soil water content (SWC) were monitored over the season. In 2010, SWC (0–30 cm deep) increased and soil nitrate levels decreased with increasing irrigation rates. Foliar N and potassium (K), and CI decreased with increasing irrigation rate, probably due to a dilution effect. Stem diameter, leaf dry weight (DW), and vegetative top DW increased with increasing irrigation rate. Net photosynthesis and stomatal conductance ( g S ) were lowest at 33% ETc. Fruit number and fruit yields (marketable and total) were also lowest at 33% ETc and there were little yield differences among irrigation rates higher than 33% ETc. In 2011, irrigation rate had minor or no effect on SWC, plant growth of mature plants, leaf gas exchange, and fruit number and yield. The no treatment effect observed for eggplant in 2011 was likely because study was conducted in a low field that remained moist most of the time, nullifying the treatment effects. Results suggested that eggplant may tolerate mild water stress, since plants irrigated at 67% ETc produced fruit yields similar to those of plants irrigated at 100% ETc or higher rates. Thus, there is a potential to save water by reducing current irrigation rates without negatively impacting fruit yields.

Eggplant, also known as aubergine and brinjal, is widely grown and consumed in southern and southeast Asia and has increased in popularity in the United States as a specialty vegetable. In 2001, U.S. eggplant production was valued at $42.5 million, and Georgia, Florida, California, New Jersey, and New York were the top five producers. The U.S. Department of Agriculture has not collected complete domestic production statistics for eggplants since 2001. In 2012, farm gate value in the state of Georgia was $17 million ( CAED, 2013 ). Average eggplant yield in Florida is ≈30 t·ha −1 ( Ozores-Hampton, 2014 ).

Eggplant is in the Solanaceae family, as are tomato ( Solanum lycopersicon ) and pepper ( Capsicum annum ) and shares similar environmental and cultural requirements as those crops. However, in contrast to tomato and pepper, eggplant crop can tolerate greater levels of drought stress ( Behboudian, 1977 ). There are several studies on eggplant irrigation carried out in Asia, Africa, and Europe ( Aujla et al., 2007 ; Behboudian, 1977 ; Chartzoulakis and Drosos, 1995 ; Gaveh et al., 2011 ; Karam et al., 2011 ) showing that eggplant can be produced at moderate levels of drought stress without major impact on fruit yield.

In southeastern United States, eggplant is often produced with high levels of irrigation water (above the rate of ETc) and N fertilizer, resulting in water waste and N leaching. Excessive irrigation rate not only wastes water, but may also result in reduced yields in bell pepper ( Díaz-Pérez et al., 2004 ; Sezen et al., 2006 ) and tomato ( Locascio et al., 1989 ; Ngouajio et al., 2007 ). To our knowledge, there are no published studies in the United States on the effect of irrigation rate on the yield and plant growth of drip-irrigated eggplants. Irrigation studies, intended to optimize use of irrigation water, are necessary to enable the protection of water resources in the United States. Therefore, the objective of this research was to assess the effects of irrigation rate on plant growth and fruit yield in eggplant.

Study site.

The study was carried out at the Horticulture Farm, University of Georgia, Tifton, GA, during the fall of 2010 and 2011. The farm is located at an altitude of 108 m above mean sea level, 31°28′ N latitude and 83°31′ W longitude. The soil of the farm is a Tifton sandy loam (a fine loamy-siliceous, thermic Plinthic Kandiudults) with pH 6.5. Available water capacity is 18 to 36 mm in the top 30 cm of soil profile ( Calhoun, 1983 ). In 2010, field had a gentle sloping (slope ≈3%); in 2011, field had a nearly level slope. The distance between the 2010 and 2011 fields was ≈70 m.

Land preparation and planting.

Eggplant plants were grown on plastic film mulch on raised beds (6 × 0.76 m, formed on 1.8-m centers). Before laying mulch, the soil was fertilized with N, phosphorous (P), and K at 60, 26, and 50 kg·ha −1 , respectively, using 10–10–10 granular fertilizer. At the same time, plastic film mulch [white on black, low-density polyethylene with a slick surface texture, 1.52 m wide and 25 µm thick (RepelGro, ReflecTek Foils, Inc., Lake Zurich, IL)] was laid with a mulch-laying machine, drip irrigation tape [20.3 cm emitter spacing and a 8.3 mL·min −1 emitter flow (Ro-Drip, Roberts Irrigation Products, Inc., San Marcos, CA)] was placed 5 cm deep in the center of the bed.

Eggplant transplants were produced in a greenhouse using peat-based medium (Pro-Mix, Quakertown, PA) and polystyrene 200-cell (2.5 × 2.5 cm cell) trays. Six-week-old eggplant transplants were planted with a mechanical transplanter on 6 Aug. 2010 and 5 Aug. 2011 in one row per bed, with a 60 cm separation between plants. About 250 mL of starter fertilizer solution (555 mg·L −1 N; 821·mg·L −1 P; 0 mg·L −1 K) was applied directly to the base of each transplant. The length of the experimental plot was 6.1 m. Starting 3 weeks after transplanting, plants were fertilized weekly through the drip system with N and K. Fertilization rates of N and K after transplanting were 0.7, 1.0, 1.5, and 2 kg·ha −1 ·d −1 in week 5, week 6; week 7; and weeks 13–15, respectively. Total N–P–K applied in the season was 218 kg·ha −1 N, 30 kg·ha −1 P, and 181 kg·ha −1 K.

Experimental design and treatments.

The design was a randomized complete block with five treatments and four replications. Treatments consisted of irrigation rates based on ETc (33%, 67%, 100%, 133%, and 167% the rate of ETc). ETc was calculated by multiplying the reference evapotranspiration (ETo) by a crop coefficient (Kc), which is dependent on the crop stage of development. Available Kc values for eggplant were developed for bare soil (unmulched) production. These Kc values, however, are not recommended for crops under plasticulture systems since plastic mulches reduce soil evaporation and ETc ( Allen et al., 1998 ; Pereira et al., 2015 ; Simonne et al., 2006 ). The Kc values used in this study were modified relative to those proposed for bell pepper in Florida ( Simonne et al., 2006 ). The Kc values used were 0.25 (week 1 after transplanting), 0.40 (week 2), 0.55 (week 3), 0.70 (week 4), 0.85 (week 5), 1.0 (week 6–11), and 0.8 (week 12–14).

All treatments received equal volumes of irrigation water (88 and 49 mm in 2010 and 2011, respectively) during the crop establishment period (first 4 weeks after transplanting). Irrigation treatments were initiated on week 5. Water was applied when cumulative ETc was ≈12 mm, which corresponded to about every 2 to 3 d in mature plants (mean ETo was 5 to 6 mm·d −1 ). Thus, amounts of water per irrigation event were ≈4 mm (33% ETc), 8 mm (67% ETc), 12 mm (100% ETc), 16 mm (133% ETc), and 20 mm (167% ETc).

Soil water content.

Soil water content (volumetric) in the 0–12 cm of soil profile over the season was measured manually once every 2–3 d (three readings per experimental plot) with a portable time-domain reflectometry (TDR) sensor (CS-620; Campbell Scientific, Logan, UT). The two metallic 12-cm rods of the TDR sensor were inserted vertically within the row between two plants. Soil water content (volumetric) in the 0–30 cm of soil profile was periodically (every 10 min) monitored with TDR sensors (CS-610; Campbell Scientific) connected to a datalogger (CR-10X; Campbell Scientific). The moisture sensors had three metallic 30-cm rods and were inserted vertically within the row between two plants.

Soil nitrate.

Soil samples were taken from each plot at 0- to 20-cm, 20- to 40-cm, and 40- to 60-cm depths on 8 Nov. 2010. Samples were taken at least 0.5 m away from the borders of the plots and from the previous sampling holes. Samples were air-dried and analyzed for nitrate-nitrogen using standard QuickChem Methods (Lachat Quick-Chem 8000 FIA; Zellweger Analytics, Milwaukee, WI).

Plant growth.

Eggplant plant height and stem diameter were measured weekly in three mature plants per plot. Plant samples obtained at the end of the season were dried at 70 °C for several days until constant weight was obtained. Leaf, stem, and vegetative top (leaf + stem) DW of individual plants were determined.

Chlorophyll indices were determined twice a week over the season on six mature, well-exposed, and healthy leaves per plot using a chlorophyll meter (Chlorophyll Meter SPAD-502; Minolta Co., Ltd., Ramsey, NJ).

Leaf gas exchange and PSII efficiency.

Simultaneous measurements of leaf gas exchange (net photosynthesis, g S , transpiration, and internal CO 2 concentration), and fluorescence were determined as PSII efficiency were made with an infrared gas analyzer (LI-COR 6400 IRGA with an integrated 6400-40 leaf chamber fluorometer; LI-COR, Inc., Lincoln, NE). PSII efficiency is the fraction of absorbed PSII photons used in photochemistry and is measured with a light-adapted leaf. Water use efficiency (WUE) was calculated as the ratio between leaf net photosynthesis and leaf transpiration. Air flow rate was set at 300 µmol·m −2 ·s −1 on the reference side. The CO 2 concentration was set at 400 µmol·mol −1 with a CO 2 mixer and a CO 2 tank. Measurements were conducted in developed plants on clear days (photosynthetically active radiation ≈2000 µmol·m −2 ·s −1 ) at 1200–1500 hr Eastern Standard Time in 2010 (6 and 20 Oct. and 9 Nov.) and 2011 (5 Oct.), using two developed and fully exposed leaves per experimental plot.

Leaf mineral nutrients.

Leaf samples (20 fully developed leaves from new growth) from developed plants were dried at 70 °C for 2 d and analyzed for mineral nutrient concentration at the University of Georgia, Agricultural & Environmental Services Laboratories, Athens, GA.

Weather data (air temperature, ETo, and rainfall) were obtained from a nearby University of Georgia weather station (within 300 m).

The harvest lasted from 28 Sept. to 23 Nov. in 2010 and from 23 Sept. to 4 Nov. in 2011. Eggplant fruit were harvested twice per week at commercial stage. Harvested section consisted of 10 plants per plot. Fruit were graded according to U.S. Department of Agriculture standards ( USDA, 2013 ) as marketable or cull and number and weight of marketable and cull fruit were determined. Average fruit weight was derived mathematically from the total weight and the total number of fruits.

Irrigation water use efficiency.

Irrigation water use efficiency (IWUE) was calculated by dividing fruit weight (kg·ha −1 ) by irrigation water received by the crop (in mm) for each irrigation treatment.

Agronomic efficiency of nitrogen.

Agronomic efficiency of nitrogen was calculated by dividing total eggplant fresh fruit weight (kg·ha −1 ) by the amount of N (kg·ha −1 ) applied to the crop.

Fruit DW content and harvest index (HI).

Statistical analysis.

Data were analyzed using the General Linear Model and Regression Procedures from SAS (SAS version 9.3, SAS Institute Inc., Cary, NC). Data means were separated by Fisher’s protected least significant difference test at 95% confidence and response curves determined by orthogonal contrasts. Percentages were transformed to arcsin values before analysis. For clarity, nontransformed percentage means were used for presentation in tables and figures. Data from all years were pooled if no year × treatment interactions were found.

In 2010, average maximal, mean, and average minimum air temperature for the season were 28.8, 22.6, and 16.4 °C, respectively. Cumulative ETo and rainfall for the season were 370 and 184 mm, respectively. In 2011, average maximal, mean, and average minimum air temperature were 28.6, 22.5, and 16.4 °C, respectively. Cumulative ETo and rainfall for the season were 344 and 256 mm, respectively.

In 2010, vegetative top DW, leaf DW, stem DW, and stem diameter increased with increasing irrigation rate ( Fig. 1 ). Leaf weight ratio (LWR) [leaf biomass as a fraction of vegetative aboveground biomass (mean = 0.529)] decreased with increasing irrigation rate ( r 2 = 0.92; P ≤ 0.05) from LWR of 0.543 at 33% ETc to LWR of 0.493 at 167% ETc, which indicates that plants allocated less biomass to leaves as irrigation rate increased. Bell pepper leaves have reduced leaf thickness at low light and low water stress conditions ( Díaz-Pérez, 2013 ). In 2011, over the season, mean stem diameter was lowest at 33% ETc ( P < 0.05), although final stem diameter was unaffected by irrigation rate ( Table 1 ). Mean seasonal plant height increased with irrigation rate, ranging from 66 cm (33% ETc) to 93 cm (167% ETc); final plant height (4 Nov.) was unaffected by irrigation rate. Mature plant DW (mean = 1.70 kg) was also unaffected by irrigation rate. Growth differences during midseason but not at the end of the season were probably because of high evaporative demand conditions that impacted plant growth at low irrigation rates during midseason. Late in the season, when evaporative demand was reduced, the effect of irrigation rate on plant growth was less detectable. The no treatment effect observed for eggplant in 2011 was likely because study was conducted in a low field that remained moist most of the time, nullifying the treatment effects.

Citation: HortScience horts 50, 11; 10.21273/HORTSCI.50.11.1709

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Plant growth, leaf chlorophyll index (CI), and soil water content (SWC) as affected by irrigation rate in eggplant. Fall of 2011, Tifton, GA.

Reduced eggplant plant growth at irrigation rates below 100% ETo has been previously reported. Eggplant irrigated at 80% pan evaporation, every 8 d, and 70% pan evaporation, every 12 d, had reduction of 18% and 27% in plant height, and 13% and 21% in stem diameter, respectively ( Kirnak et al., 2002 ). In bell pepper exposed to different soil water levels by varying drip emitter spacing, plant height and canopy diameter increased with decreasing emitter spacing (i.e., with increased soil water levels) ( Madramootoo and Rigby, 1991 ).

In 2010, CIs decreased with increased irrigation rate ( P = 0.006), from 60.8 at 33% ETc to 59.0 at 167% ETc. In 2011, CI decreased from 55.8 at 33% ETc to 53.7 at 167% ETc ( Table 1 ). Decreased CI values with increased irrigation rates were likely due to dilution effect of nutrients, since plant growth was enhanced with increased irrigation rates. Decreased CI with increased irrigation rates may also be associated with increased nitrate leaching under high irrigation rates.

In 2010, the effect of irrigation rate on SWC varied with soil depth. At 0- to 30-cm depth, SWC increased with increasing irrigation rates ( Fig. 2 ), whereas at 0- to 12-cm depth SWC was unaffected by irrigation rate. Differences in soil moisture in the different soil depths indicate a higher soil water uptake by plants, because of greater presence of roots at 0–12 cm than at 0- to 30-cm depth; they also indicate that high rates of irrigation (>100% ETc) result in wasted water because much water at 0- to 30-cm depth was not taken up by the crop; and they suggest that soil moisture measurement at 0- to 30-cm depth was more sensitive to detect changes in soil moisture than measurement at 0- to 12-cm depth.

As in 2010, seasonal SWC at 0- to 12-cm depth was also similar among irrigation rates (mean = 13.4%) in 2011. In addition to the high presence of roots at 0- to 12-cm depth, SWC values were similar among treatments in 2011 probably because the study was conducted in a low field, with a nearly level slope, where soil was commonly moist throughout the season, likely due to lateral water movement from upper sections of the field. There was an impermeable clay layer 30- to 40-cm deep in the soil profile that probably allowed water to flow from upper to lower areas within the farm.

Leaf gas exchange.

In 2010, the effect of irrigation rate on leaf gas exchange varied by date ( Table 2 ). Net photosynthesis, g S , and photosynthetic WUE were unaffected by irrigation rate on 6 Oct. 2010. Lack of treatment differences on 6 Oct. was probably attributable to relatively low temperatures on day of measurement (mean temperature = 16.4 °C), resulting in low crop evaporative demand and low crop water stress. Net photosynthesis and g S were lowest at 33% ETc on 20 Oct. and 9 Nov. Water use efficiency was highest and PSII efficiency was lowest at 33% ETc on 20 Oct. The fact that gas exchange variables were not reduced at 67% ETc compared with higher irrigation rates suggests that plants at 67% ETc were likely unaffected by water stress. However, since gas exchange measurements were conducted only in mature plants, late in the season, when evaporative demand was reduced, it is possible that earlier in the season plants may have had experienced increased water stress at reduced irrigation rates, as suggested by the reduced plant growth at reduced irrigation rates. In 2011, leaf net photosynthesis (mean = 28.3 µmol·m −2 ·s −1 ), g S (mean = 0.248 mol·m −2 ·s −1 ), WUE (mean = 4.24 µmol·mmol −1 ), and PSII (mean = 0.189 µmol·mmol −1 ) were unaffected by irrigation rate. Air maximal and minimal temperature on the day of measurement were 27.5 and 11.0 °C, respectively. Lack of differences in gas exchange are consistent with the lack of differences in plant growth among irrigation rates observed in 2011.

Leaf gas exchange and fluorescence as affected by irrigation rate and date in eggplant. Fall of 2010, Tifton, GA.

Irrigation at 33% ETc was probably insufficient to satisfy eggplant water requirements, as suggested by the reduced leaf gas exchange values ( Table 2 ). Reduced irrigation rates can result in decreased gas exchange in solanaceous crops. Transpiration, leaf g S , and leaf net photosynthesis in eggplant were reduced with water stress and effects varied depending on stress severity and duration ( Sarker et al., 2005 ). In habanero pepper ( Capsicum chinense Jacq.), there was reduced g S and net photosynthesis with increased time between irrigations ( Jaimez et al., 1999 ).

Soil nitrate concentration decreased with increasing irrigation rate ( P = 0.002) and soil depth ( P = 0.003), indicating that nitrate leaching to the deepest parts of the soil was enhanced with increased irrigation rates ( Fig. 3 ). Decreased soil nitrate concentration may also be due to high N uptake by the crop, as suggested by augmented vegetative growth with increasing irrigation rate. Nitrate present at 40–60 depth is usually lost as it is not recovered by plants’ roots. Decreased nitrate in 40- to 60-cm zone is thus solely due to leaching.

Foliar mineral nutrient concentrations and CI.

In 2010, foliar N and K concentrations decreased and P increased with increasing irrigation rate ( Table 3 ). Other foliar nutrients concentrations were unaffected by irrigation rate. Nitrogen, K, and CI decreased with irrigation rate, possibly as a result of a dilution effect associated with increased aboveground plant growth. In addition, at high irrigation rates plants likely had reduced access to soil N due to increased nitrate leaching. Plant water stress in eggplant can reduce foliar N, P, and K concentrations compared with well-irrigated plants ( Kirnak et al., 2002 ). In the present study, however, only foliar P was reduced at low irrigation rate.

Foliar mineral nutrient concentrations in eggplant as affected by several irrigation rates. Fall of 2010, Tifton, GA. z

Chlorophyll indices have been used as indirect estimators of chlorophyll and leaf N concentrations ( Liu et al., 2006 ). Crop drought stress may influence leaf morphology (e.g., increased specific leaf weight) in plants ( Larcher, 1995 ); these variations in leaf morphology may also influence CI, making difficult to use CI to estimate leaf N ( Díaz-Pérez, 2013 ). In our study, CI values increased with increasing leaf N ( R 2 = 0.921; P = 0.001), supporting the use of chlorophyll meter to estimate leaf N.

In 2010, fruit number and fruit yields (marketable and total) were lowest at 33% ETc and there were little yield differences among irrigation rates higher than 33% ETc ( Fig. 4 ). Individual fruit weight was also reduced at 33% ETc ( Fig. 5 ). There was a higher correlation between fruit number and fruit yield ( R 2 = 0.94; P < 0.0001) than between individual fruit weight and fruit yield ( R 2 = 0.15; P = 0.027), suggesting that marketable yield was determined more by fruit number than individual fruit weight. In greenhouse-grown eggplant, soil water deficit decreased fruit number but not fruit size ( Chartzoulakis and Drosos, 1995 ). In a study with different levels of irrigation and N fertilizer, eggplant fruit yield was more related with fruit number than with fruit size ( Aujla et al., 2007 ). In another study, soil water deficits also reduced eggplant fruit size, but the effect of drought stress on fruit number was not evaluated ( Kirnak et al., 2002 ). In 2011, irrigation rate had no effect on the number or yields of marketable, cull, and total fruit, or on individual fruit weight ( Table 4 ). There were no significant interactions between harvest dates and irrigation rates. There was also a higher correlation between fruit number and fruit yield ( R 2 = 0.92; P < 0.0001) than between individual fruit weight and fruit yield ( R 2 = 0.185; P = 0.001). Results suggest that eggplant may tolerate moderate water stress, since plants irrigated at 67% ETc produced fruit yields similar to those of plants irrigated at 100% ETc or higher rates. Thus, there is a potential to reduce irrigation rates below 100% ETc without negatively impacting fruit yields.

Fruit yield of eggplant as affected by irrigation rate. Fall of 2011, Tifton, GA.

Irrigation water use efficiency and agronomic efficiency of nitrogen.

Plants received more irrigation water in 2010 than in 2011 as a result of reduced rainfall in 2010 ( Table 5 ). In both years, IWUE decreased with increasing irrigation rate. IWUE was greatly reduced and there were significant effects of irrigation rates on several variables in 2010, but not in 2011. Increased IWUE and increased SWC in 2011 (mean = 13.4% at 0- to 12-cm depth) relative to SWC in 2010 (mean = 7.5% at 0- to 12-cm depth) are probably associated with increased contribution of soil water from rainfall and drainage water from upper areas of the field; in 2011, field used was low and nearly flat.

Irrigation, cumulative rainfall, IWUE, and AEN of eggplant crop grown on plastic film mulch. Fall of 2010 and 2011, Tifton, GA.

Although there were differences in leaf N among irrigation treatments, fruit yield was likely more related to irrigation rate than to leaf N. Total yield showed a quadratic relationship with leaf N ( R 2 = 0.185; P = 0.013); total yield was unaffected by leaf N below 5.1% and was lowest at the highest leaf N (5.3%) occurred at the lowest irrigation rate (33% ETc).

Agronomic efficiency of N increased with irrigation rate in 2010 likely as a result of increased fruit yield associated with improved plant water status; AEN was unaffected by irrigation rate in 2011. AEN values in this study (range 92 to 187 kg·kg −1 N) were lower compared with values of other studies on eggplant (range = 324 to 859 kg·kg −1 N) ( Aujla et al., 2007 ), probably because the harvest period in this study was reduced. Low AEN values may also mean that eggplant crop in this study made inefficient use of N fertilizer, probably in part due to overfertilization. Aujla et al. (2007) reported that irrigation rate and N fertilization rate interacted in drip-irrigated eggplants; they also found that irrigation at 75% pan evaporation and 120 kg·ha −1 N produced the greatest yields, and that AEN increased with increased N fertilization rate.

Fruit DW content and HI.

In year 2010, fruit DW content (mean = 6.2%) was unaffected by irrigation rate. In a study under semiarid conditions, soluble DW or soluble solids in eggplant decreased with increased irrigation rates ( Kirnak et al., 2002 ). In greenhouse-grown eggplant, increased irrigation rates also decreased fruit DW content ( Chartzoulakis and Drosos, 1995 ).

Harvest index was unaffected by irrigation rate (mean HI = 0.32). These data suggest that eggplant is more tolerant to drought than other solanaceous crops ( Behboudian, 1977 ). Our measurements of HI did not include root biomass. However, under water stress, eggplants possibly allocated increased amounts of assimilates for root growth as occurs in other plants ( Larcher, 1995 ). In habanero pepper, an irrigation rate of 20% of available water produced reduced values of HI ( Quintal Ortiz et al., 2012 ). In tomato, there was no difference in total dry biomass and HI between the control and a partial irrigation treatment, but total dry biomass and HI significantly decreased under regulated deficit irrigation ( Lei et al., 2009 ); moderate water stress–induced osmotic regulation under partial root drying conditions, leading to normal water status and the same level of biomass. Eggplant in our study was also able to maintain high fruit yields at moderate levels of water stress, suggesting that, as tomato, eggplant is also able to develop mechanisms to deal with water stress such as osmoregulation.

In conclusion, the results from this research indicate that eggplant may tolerate moderate water stress, since plants irrigated at 67% ETc had no detrimental effects on plant growth and leaf gas exchange and produced fruit yields similar to those of plants irrigated at 100% ETc. Thus, there is a potential to reduce current irrigation rates without negatively impacting fruit yields or quality.

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Contributor Notes

Financial support was provided by the Georgia Agricultural Experiment Stations.

We thank John Silvoy, Jesús Bautista, and Nélida Bautista for their invaluable technical support. We also thank Peter Germishuizen from Lewis Taylor Farms, Ty Ty, GA, for donation of eggplant transplants. We appreciate the thorough review of the manuscript by Pat Conner, Tim Coolong, Erick Smith, and the anonymous reviewers.

Mention of trade names in this publication does not imply endorsement by the University of Georgia of products named, nor criticism of similar ones not mentioned. The cost of publishing this paper was defrayed in part by payment of page charges. Under postal regulations, this paper therefore must hereby be marked advertisement solely to indicate this fact.

1 Corresponding author. E-mail: [email protected] .

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Introduction: The Importance of Eggplant

• First Online: 31 May 2019

Cite this chapter

• Mark A. Chapman 3  

Part of the book series: Compendium of Plant Genomes ((CPG))

In this chapter, I highlight how the eggplant, whilst being globally dwarfed by other members of the Solanaceae, notably potato and tomato, offers a number of important ecological, evolutionary and agronomic features making it unique and interesting, warranting further study. It also highlights the parallels and differences between Solanaceous crops. The eggplant genome is in the process of being finalised, and once this is available to researchers, it is likely we will see a surge of papers utilising this resource for understanding the genetic basis of these important traits.

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Chapman, M.A. (2019). Introduction: The Importance of Eggplant. In: Chapman, M. (eds) The Eggplant Genome. Compendium of Plant Genomes. Springer, Cham. https://doi.org/10.1007/978-3-319-99208-2_1

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A high-quality chromosome-level genome assembly reveals genetics for important traits in eggplant

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• Plant evolution
• Structural variation

Eggplant ( Solanum melongena L.) is an economically important vegetable crop in the Solanaceae family, with extensive diversity among landraces and close relatives. Here, we report a high-quality reference genome for the eggplant inbred line HQ-1315 ( S. melongena -HQ) using a combination of Illumina, Nanopore and 10X genomics sequencing technologies and Hi-C technology for genome assembly. The assembled genome has a total size of ~1.17 Gb and 12 chromosomes, with a contig N50 of 5.26 Mb, consisting of 36,582 protein-coding genes. Repetitive sequences comprise 70.09% (811.14 Mb) of the eggplant genome, most of which are long terminal repeat (LTR) retrotransposons (65.80%), followed by long interspersed nuclear elements (LINEs, 1.54%) and DNA transposons (0.85%). The S. melongena -HQ eggplant genome carries a total of 563 accession-specific gene families containing 1009 genes. In total, 73 expanded gene families (892 genes) and 34 contraction gene families (114 genes) were functionally annotated. Comparative analysis of different eggplant genomes identified three types of variations, including single-nucleotide polymorphisms (SNPs), insertions/deletions (indels) and structural variants (SVs). Asymmetric SV accumulation was found in potential regulatory regions of protein-coding genes among the different eggplant genomes. Furthermore, we performed QTL-seq for eggplant fruit length using the S. melongena -HQ reference genome and detected a QTL interval of 71.29–78.26 Mb on chromosome E03. The gene Smechr0301963 , which belongs to the SUN gene family, is predicted to be a key candidate gene for eggplant fruit length regulation. Moreover, we anchored a total of 210 linkage markers associated with 71 traits to the eggplant chromosomes and finally obtained 26 QTL hotspots. The eggplant HQ-1315 genome assembly can be accessed at http://eggplant-hq.cn . In conclusion, the eggplant genome presented herein provides a global view of genomic divergence at the whole-genome level and powerful tools for the identification of candidate genes for important traits in eggplant.

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Introduction

The large family Solanaceae contains over 3000 plant species that are adapted to a wide range of geographic conditions, including eggplant ( Solanum melongena ), tomato ( S. lycopersicum ), potato ( S. tuberosum ), tobacco ( Nicotiana tabacum ) and petunia ( Petunia inflata ). Asian eggplant ( S. melongena L.), also known as brinjal or aubergine, is a vegetable crop widely grown across Southeast Asian, African, and Mediterranean countries 1 . Eggplant is the third most widely grown solanaceous vegetable after potatoes and tomatoes, with a global total production of ~54.08 million tons in 2018 (FAOSTAT; http://faostat3.fao.org ). Approximately 90% of eggplants are produced in Asia, mainly in China and India, with Indonesia, Turkey, Egypt, the Philippines and Iran growing ~1% of the world’s total production 1 (Fig. 1 ).

a Distribution of worldwide eggplant production according to FAOSTAT in 2018. b Diversity in fruit morphology among different eggplants

Unlike tomato and potato, which are both New World representatives of the genus Solanum 2 , eggplant is an Old World crop belonging to subgenus Leptostemonum 3 (the “spiny solanums”). Two other Solanum species, Ethiopian/scarlet eggplant ( S. aethiopicum L.) and African/Gboma eggplant ( S. macrocarpon L.), are also called eggplants, and their fruits and leaves are used for food and medicine. There are obvious local preferences for eggplant fruits, which may be either elongated or round, with colors from dark purple to light green. The domestication history of eggplant has been under debate and presumably started in Africa, with radiation to Asia; however, relationships among the African species and their Asian relatives are not well resolved 4 . The two most commonly hypothesized regions of origin are India and southern China/Southeast (SE) Asia, which have equally old written records of eggplant use for ~2000 years 4 . Both regions have vastly diverse landraces, close wild relatives and candidate progenitors of eggplant. A recent study proposed that S. insanum is the wild progenitor, which split into an Eastern and Western group, with domesticates derived from the Eastern group 5 . Eggplants exhibit highly diverse variations in growth habits, biotic and abiotic resistance, and fruit and leaf morphology among local landraces and wild relatives. Identification of candidate genes/gene families controlling these differences will provide insight into the genetic mechanisms of agronomically important traits, as well as resources for eggplant breeding.

Genome sequencing is a powerful tool in plant genetics and genomics research. The genome of Arabidopsis thaliana was sequenced and published in 2000, representing the first plant genome. Since then, the development of genome sequencing technologies has resulted in multitude of plant genomes in recent years, including those of many horticultural crops 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 . Traditionally, the majority of research in Solanum crops has focused on potato and tomato, for which genomes have been published 9 , 10 . The first genome sequence of S. melongena was published in 2014, with 85,446 predicted genes and an N50 of 64 kb 13 . However, this draft assembly is not at the chromosome level and is highly fragmented, containing 33,873 scaffolds and covering only 74% of the eggplant genome. An improved S. melongena genome of the inbred line 67/3 using Illumina sequencing and single-molecule optical mapping was then published 16 . In addition, the genome of the African eggplant S. aethiopicum , a close relative of S. melongena , has also been published 17 . However, these eggplant genomes were all sequenced with next-generation sequencing (NGS) technologies using short reads, whereas genome sequence data derived from third-generation sequencing with long reads are still not publicly available. Here, we report a high-quality chromosome-level eggplant genome using next-generation Illumina sequencing and third-generation Nanopore sequencing combined with 10X genomic and Hi-C technologies, with a contig N50 of 5.26 Mb and a scaffold N50 of 89.64 Mb.

Genome sequencing, assembly, and assessment

The genome size of the eggplant inbred line HQ-1315 is ~1205.25 Mb, with a heterozygosity rate of 0.15%, as assessed by k-mer analysis based on 93.33 Gb Illumina HiSeq data. The estimated proportion of repeat sequences was ~69.60%.

A high-quality eggplant genome (hereafter S. melongena -HQ) was assembled with a genome size of ~1.1 Gb and contig N50 of 5.26 Mb. We used a combination of Illumina HiSeq, Nanopore sequencing, and 10X Genomics sequencing technologies to sequence and assemble the eggplant genome; with the assistance of the Hi-C technique, a chromosome-level genome assembly was generated. A total of 114.45 Gb reads were obtained from Illumina HiSeq, including 93.33 Gb data for k-mer analysis and 21.12 Gb of additional read data, the average coverage of which was 94.96×; Nanopore sequencing generated 129 Gb data with 107.03× coverage. These data were used for preliminary assembly, producing a total contig length of 1159.53 Mb and a contig N50 of 5.71 Mb; the total scaffold length is 1159.53 Mb, with a scaffold N50 of 5.71 Mb. Then, we added ~113.46 Gb 10X Genomics data (~94.14×) for further assembly, resulting in a modified eggplant genome version with a contig length of 1,152.97 Mb and contig N50 of 5.75 Mb. The scaffold length is 1,157.36 Mb, with a scaffold N50 of 9.79 Mb, which is a 1.71-fold increase compared to the genome version by Hirakawa et al. 13 . Finally, with the assistance of 131.73 Gb Hi-C reads, the assembled scaffold N50 reached 89.64 Mb, with a final contig N50 of 5.26 Mb. Twelve pseudochromosomes with a total length of 1,173.14 Mb were obtained, accounting for 92.72% of the estimated eggplant genome (Fig. 2 ; Table 1 ). Detailed information on the stepwise assembly of the genome is shown in Table S1 . The GC content in the eggplant genome is 35.94%, similar to that of Arabidopsis 18 (36.06%), tomato 10 (34.05%) and celery 15 (35.35%) but lower than that of rice 19 (43.57%) and tea plant 20 (42.31%).

a Assembled chromosomes (Mb). b Gene density. c GC content. d Transposon density. e Tandem repeat density. f Syntenic blocks

The quality of the eggplant genome assembly was further assessed (Supplementary Fig. S1 ). The alignment rate of all short reads to the genome was ~99.48%, covering 91.24% of the genome. The heterozygous and homozygous SNP ratios were calculated to be 0.0253% and 0.0014%, respectively, indicating a high single-base accuracy rate for the genome assembly. The integrity of the assembled genome was assessed by the Core Eukaryotic Genes Mapping Approach (CEGMA); 237 genes were assembled from 248 core eukaryotic genes (CEGs), accounting for 95.56% of the total and reflecting that the sequence assembly was relatively complete. The statistical results of BUSCO evaluation of the eggplant genome showed that 2,190 homologous single-copy genes were assembled and that 94.2% of all single-copy genes were assembled.

Genome annotation

For the annotation of the eggplant genome, we used a combination of gene prediction strategies, including de novo, homology, and transcriptome-based predictions. RNA from five different tissues, including root, stem, leaf, flower and fruit, was extracted for next-generation transcriptome sequencing and full-length transcriptome sequencing. A total of 36,582 coding genes were predicted, with an average of 4.31 exons per gene and an average transcript length of 4095.69 bp. Repetitive sequence annotation results showed that 70.09% of the eggplant genome is repeat sequences, with a size of 811.14 Mb. Most of the repeat sequences are long terminal repeat (LTR)-type retrotransposons, which account for 65.80%; 1.54% is the long interspersed nuclear element (LINE) type, and DNA transposons account for only 0.85%. In addition, 5929 noncoding RNAs were detected in the eggplant genome, including 268 miRNAs with an average length of 127.81 bp as well as 2549 tRNAs, and 554 snRNAs (Supplementary Table S 2 ).

Evolution of the S. melongena genome

A total of 9 sequenced Solanaceae genomes were analyzed to reveal the evolution of the eggplant genome, including Nicotiana tabacum , Capsicum annuum , Petunia inflata , S. tuberosum , S. lycopersicum , S. aethiopicum , S. melongena -HQ, and two other S. melongena genomes, S. melongena -NS 13 and S. melongena -67/3 16 . Phylogenetic analysis indicated that eggplant is closer to potato and tomato than pepper (Fig. 3a ), diverging from the common ancestor ~14.4 Mya (Fig. S 2 ). The group of three Solanum species (eggplant, potato and tomato) is sister to pepper, diverging ~18.5 Mya. Among the different eggplants, S. melongena -HQ and its close relative S. aethiopicum diverged from a common ancestor ~2.4 Mya (Fig. S 2 ). Moreover, S. melongena -HQ is more closely related to the European eggplant variety S. melongena -67/3 than the Japanese eggplant cultivar S. melongena -NS, all of which are distant from S. aethiopicum (Fig. 3a ).

a Phylogenetic tree of 9 sequenced Solanaceae genomes. b Distribution of genes in different species. The horizontal axis indicates the analyzed genome, and the vertical axis indicates the number of corresponding genes. Pink represents the number of single-copy orthologs. Orange represents multiple-copy orthologs, olive green the unique genes of the corresponding genome, and green the number of other orthologs. c Venn diagram of the common and unique gene families among different eggplant genomes. d Gene family expansion and contraction in the 9 Solanaceae genomes. The green numbers indicate expanded gene families, and the red numbers represent contracted gene families

There were 32,529 gene families in total according to clustering results. Among the nine genomes, 6087 gene families are common, of which 463 single-copy gene families are common to each genome (Fig. 3b ). The corresponding clustering results for S. melongena -NS, S. melongena -67/3, S. aethiopicum, and S. melongena -HQ were extracted to draw a Venn diagram, which showed that the four eggplant genomes have 11,123 genes (Fig. 3c ). Compared with other eggplants, S. melongena -NS has the most unique genes (1,256 genes), followed by S. aethiopicum with 1226 unique genes; S. melongena -67/3 has only 295 unique genes. In addition, S. melongena -HQ has a total of 563 accession-specific gene families containing 1009 genes (Fig. 3c , Supplementary Table S 3 ). We performed GO and KEGG enrichment analyses on accession-specific gene families of S. melongena -HQ (Supplementary Table S 3 ) and found them to be mainly involved in the processes of metabolism, biosynthesis and modification of proteins/nucleic acids.

Whole-genome duplication (WGD) events in the S. melongena -HQ genome were detected based on the rate of fourfold degenerative third-codon transversion (4DTv) of paralogous gene pairs among S. melongena -HQ, A. thaliana and four other Solanaceae species. As illustrated in Fig. 4 , A. thaliana and S. melongena -HQ had one peak value at ~0.72, indicating an ancient WGD before the divergence of asterids and rosids. S. melongena -HQ had only one WGD event common to Solanaceae species at ~0.30, whereas there was no recent WGD after species differentiation. Among Solanaceae crops, S. melongena -HQ first diverged from pepper at ~0.1, followed by tomato at ~0.08, and then S. tuberosum at ~0.06. The two eggplants S. aethiopicum and S. melongena -HQ diverged from each other quite recently compared with other species.

The x -axis indicates the 4DTv distance. The y -axis indicates the percentage of gene pairs

Expansion and contraction of gene families

The 9 sequenced Solanaceae genomes were analyzed to reveal the dynamics of gene family evolution in the eggplant genome. A total of 32,522 most recent common ancestor (MRCA) gene families were found (Fig. 3d ). Compared with their ancestors six gene families expanded and 23 gene families contracted in S. melongena and S. aethiopicum . Among the different eggplant genomes, S. melongena -NS has 539 gene families that significantly expanded and 38 gene families that contracted, whereas S. melongena -67/3 has 80 expanded gene families and 76 contracted gene families. S. melongena -HQ has 73 expanded gene families, including 892 genes, and 34 contracted gene families, including 114 genes (Fig. 3d , Supplementary Table S 4 ). The expanded and contracted genes were also annotated by GO and KEGG analyses (Supplementary Table S 4 ). The KEGG pathway plant-pathogen interaction showed the most contracted genes (25 genes), which may be related to reduced resistance in cultivated eggplant.

Comparative genomic analysis

Synteny analysis showed that the S. melongena -HQ genome exhibits high collinearity with that of S. melongena -67/3, with a total of 19,620 gene pairs and 178 syntenic blocks. Chromosome E01 in these two eggplant genomes is in the same direction but inverted compared with tomato chromosome 1. There is one missing block in S. melongena -67/3 chromosome E02, which exists between S. melongena -HQ and tomato and between tomato and pepper. Similar missing segments were also found for corresponding chromosomes 5 and 9. Chromosomes 4, 5, 10, 11, and 12 have undergone more complex chromosome rearrangements, such as translocations and inversions, during evolution among eggplant, tomato and pepper, as reflected by an increased number of syntenic blocks. We identified a total of 18,337 gene pairs and 151 syntenic blocks between S. melongena -HQ and tomato. S. melongena -HQ chromosome E04 was partly aligned to tomato chromosomes 4, 10 and 11; some of the genes on S. melongena -HQ chromosome E05 were aligned to tomato chromosome 12. Genes from S. melongena -HQ chromosome E10 were aligned to S. lycopersicum chromosomes 3, 5 and 12. Similar collinearity was also detected among the genes from corresponding chromosomes 11 and 12 between S. melongena -HQ and S. lycopersicum (Fig. 5 ). Pairwise comparisons are presented in Supplementary Figs. S 3 –S 5 .

The numbers indicate the corresponding chromosomes in each species

Although the overall genome lengths of S. melongena -HQ and S. melongena -67/3 are not significantly different, the length of each chromosome differ significantly (Table 2 ). The total sizes of the two eggplant genomes are 1073.14 and 1142.80 Mb, respectively, with a total size difference of 69.66 Mb. The largest difference is with regard to chromosome E09; the length of E09 in S. melongena -HQ is 89.64 Mb, whereas that of S. melongena -67/3 is only 36.10 Mb, with a difference of 53.54 Mb. The smallest difference was found for E03, with a difference of only 0.30 Mb, followed by E02, with a difference of only 7.92 Mb. The length of E05 in S. melongena -HQ is 37.74 Mb longer than that in S. melongena -67/3, and the length of S. melongena -HQ E07 is 35.59 Mb shorter than that of S. melongena -67/3. The differences in the lengths of other chromosomes, E04, E08, E06, E10, E11, and E12, are between 19.30 and 28.93 Mb. Despite the minor differences in total genome size between the two assembled eggplant genome versions themselves, the differences in chromosome length between the two assembled versions are significant. This result may have been caused by different sequencing technologies (second vs third generation) and assembly strategies (linkage map vs Hi-C).

We then compared S. melongena -HQ with two previously sequenced eggplant genomes, those of European eggplant S. melongena -67/3 and African eggplant S. aethiopicum , to investigate genomic divergence among them (Fig. 6a ). Three types of variations were analysed, including single-nucleotide polymorphisms (SNPs), insertions/deletions (indels) and structural variants (SVs). We detected 2,189,112 SNPs, 512,849 indels, and 741 large SVs between S. melongena -HQ and S. melongena -67/3. In contrast, 22,092,994 SNPs, 1,988,560 indels, and 7,362 large SVs were identified between S. melongena -HQ and S. aethiopicum . Between S. melongena -HQ and S. melongena -67/3, the 512,849 indel mutations involve 14,756 genes, which were annotated using GO and KEGG (Supplementary Table S 5 ). The 741 SVs correspond to 211 genes, among which 60 were functionally enriched by GO analyses (Supplementary Table S 5 ). For S. melongena -HQ and S. aethiopicum , 3,066 genes are associated with large SVs, among which 1,370 and 350 genes were functionally enriched according to GO and KEGG analysis, respectively (Supplementary Table S 6 ). There are 90 genes involved in antibiotic biosynthesis networks according to the KEGG enrichment results, and 16 genes related to the citrate cycle (TCA cycle). It has been proposed that the African eggplant S. aethiopicum has better disease resistance and drought tolerance than cultivated S. melongena -HQ 17 . Therefore, these genes will provide valuable resources for resistance improvement in eggplant breeding.

a Asymmetric SV accumulation among different eggplants. The tracks (from outside to inside) indicate chromosomes, SNP density, indel density, and percentage of SV length. b SV variation percentages in potential regulatory regions of protein-coding genes. The horizontal axis indicates up- and downstream gene regions, and the vertical axis indicates the variation percentage. Pink represents the number of single-copy orthologs. Purple and green lines indicate SV deletions and insertions between S. melongena -HQ and S. aethiopicum , respectively. Blue and yellow lines indicate SV deletions and insertions between S. melongena -HQ and S. melongena -67/3, respectively

We further investigated SV abundance in potential regulatory regions of protein-coding genes; different types of indel variation suggest different patterns of SV accumulation (Fig. 6b ). There were more deletions than insertions between S. melongena -HQ and S. aethiopicum . However, insertions and deletions between the two S. melongena genomes were similar in both coding and noncoding areas, with the two lines basically coinciding. Higher insertion-deletion variations were observed in transcription start site (TSS) and transcription terminal site (TTS) regions of S. melongena -HQ and S. aethiopicum , and the variation in the gene coding regions was found to be slightly higher than that in noncoding regions. In contrast, variations in coding regions were lower than those in noncoding region between cultivated eggplants.

NBS gene family and transcription factor analysis

Nucleotide-binding site-leucine-rich repeat (NBS-LRR) proteins constitute the largest family of resistance (R) proteins and play significant roles in defense against pathogens. The NBS protein family was systematically analysed in five plants of the Solanaceae family. In S. melongena -HQ, 301 NBS genes were identified as involved in seven types (Table 3 ; Supplementary Table S 7 ), whereas only 250 genes were identified in S. melongena -67/3 as involved in eight types. S. aethiopicum has outstanding resistance to various pathogens, including Fusarium , Ralstonia and Verticillium 21 , 22 , with 436 NBS genes involved in ten types. Accordingly, S. aethiopicum has been routinely used to improve disease resistance in S. melongena . S. lycopersicum was found to possess 223 NBS genes.

In terms of transcription factors, for S. melongena- HQ, a total of 1970 transcription factors divided into 64 categories, the top three of which were APETALA2/ethylene responsive factor (AP2/ERF, 150), cysteine 2-histidine 2 type zinc finger gene (C2H2, 137) and basic helix-loop-helix (bHLH, 135) were identified. The v-myb avian myeloblastosis viral oncogene homolog superfamily (MYB) has 127 transcription factors. Detailed information on the number and gene sequences of each transcription factor, including S. melongena -67/3, S. aethiopicum and S. lycopersicum , is shown in Supplementary Table S 8 .

Candidate gene identification for fruit length and QTL hotspots in eggplant

Eggplants display extensive variations in fruit morphology among landraces and wild relatives. There are obvious local market preferences for fruit shape (i.e., oval, round or linear) according to different consuming habits; thus, the fruit length, diameter and shape index of eggplants show significant differences (Fig. 1 ). The immature fruits of HQ-1315 are generally ~35 cm in length and ~3 cm in diameter, and it is a long (elongated type) eggplant. An F 2 population containing 129 individuals was obtained from a cross between HQ-1315 (P 1 ) and the short round eggplant 1815 (P 2 ; Fig. 7 ). Bulked segregant analysis (BSA) and quantitative trait locus (QTL) analysis on eggplant fruit length were then conducted using the S. melongena -HQ genome (Fig. 7 ). F 2 plants with extremely long and short fruits were selected and pooled for genome sequencing. Resequencing P 2 generated 23.41 Gb of data, and sequencing of the two extreme pools yielded 41.52 Gb for the extreme long pool and 40.05 Gb for the extreme short pool. The average length (L), diameter (D), and fruit shape index (L/D) of three fruits from each F 2 individual were measured to determine the value for the individual plant (Supplementary Table S 9 ). Based on genotyping results, a total of 1,019,131 SNPs and 116,676 indel markers showed homozygous differences between the two parents, and the index of the markers in the two progeny pools compared to those of the parents were analyzed and calculated. According to the Δ(All-Index) value, a QTL interval for fruit length was determined within 71.29–78.26 Mb on eggplant chromosome E03 (99% confidence interval) (Fig. 7 ). Combined with the genetic mapping results of our previous study, Marker2384739 and Marker2387171 are linked to QTL FS3.1 , the physical locations of which are 77.62 and 79.77 Mb respectively. As suggested by the eggplant-tomato synteny relationship, genes controlling fruit size in tomato are likely to have similar functions in determining eggplant fruit size. We obtained a total of 11 genes homologous to regulators of fruit size on eggplant chromosome E03 via homology comparison. Among them, three genes are within or adjacent to the QTL region on E03: Smechr0301760 (72.91 Mb), Smechr0301963 (78.39 Mb) and Smechr0302217 (82.30 Mb). Smechr0301760 is a potentially orthologous gene of the cell number regulator ( CNR ) gene family, whereas Smechr0301963 and Smechr0302217 are potentially orthologous genes of the SUN gene family. According to the results of QTL-seq and genetic mapping, we predict that Smechr0301963 is a key candidate gene for regulating eggplant fruit length. Moreover, 7 homologs of fruit size-related genes are distributed within 89.89–95.48 Mb region, and they may also play potential roles in controlling fruit size.

a Fruits of P 1 , P 2 , and F 1 . b F 2 individuals with extreme fruit lengths. c Distribution of Δ (All-index) on chromosomes. The light blue line indicates the results of a replacement test with 1000 replicates; the 95% confidence level was selected as the screening threshold

Based on the QTL results of previous studies and the available marker sequence information, we anchored these markers to our latest reference genome to investigate QTL hotspots in eggplant 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 . A total of 210 linkage markers related to 71 traits, including fruit-related traits (i.e., fruit size and color), leaf morphology, and nutrient components, were anchored (Fig. 8 , Supplementary Table S 10 ). Except for the linkage markers for Fusarium resistance in Miyatake et al. 29 , most of the markers were mapped to physical locations on corresponding chromosomes. We summarized the regions with clustered linkage markers or traits and finally obtained 26 QTL hotspots, with two to three on each chromosome.

The physical locations and marker names are on the right of each chromosome; names of associated traits are on the left of each chromosome. Red rectangles represent QTL hotspots. The full names of the traits involved and corresponding markers are shown in Supplementary Table S 10

Eggplant Genome Database

We constructed an advanced, intuitive, and user-friendly Eggplant Genome Database using genome assembly and annotation data (Fig. 9 ). Eggplant Genome Database consists of three main modules. The browse module has links to information for 36,582 genes, including start/end locations and chromosome information. KEGG, Pfam, GO, NR, and Swiss-Prot database annotation information can be easily accessed by clicking the gene ID, as can the coding sequence (CDS) and protein sequence information corresponding to each gene. The BLAST module aligns sequences to the genome, gene, and protein databases to obtain the required information for users. The eggplant genome assembly, as well as genome gff, CDS, protein, and other data files, can be downloaded using the download module. Eggplant Genome Database provides access to various types of data, allowing researchers and breeders to browse, search, and download information for genomics studies and breeding. The online database can be accessed at http://eggplant-hq.cn/ .

a Homepage. b Browse module. c Blast module. d Download module

Genome sequencing technologies have undergone tremendous improvement during the past decades, resulting in substantial advances in the availability of plant genomes. Since the publication of the first plant genome, Arabidopsis thaliana , using whole-genome shotgun sequencing, over 200 plant genomes have been published 31 ( www.plabipd.de ). However, genome sequencing of plant species with large genome sizes and high repetitive sequence contents remains difficult 32 . Compared with the short reads produced by NGS technologies, long reads with kilobase-length DNA fragments are extremely efficient in resolving repetitive regions and facilitating genome assembly. Several new technologies have been developed based on long reads, such as synthetic long reads, long PacBio reads, and optical mapping, and these methods have been applied to Arabidopsis 33 , tomato (3.0 genome release; www.solgenomics.net ) and maize 34 . Nevertheless, long-read sequencing technologies are still costly and rely on the previous extraction of high-quality DNA. Oxford Nanopore is a recently developed long-read sequencing technology that can greatly reduce the sequencing costs and generate gigabases of sequence data from a single flow cell 35 . Hi-C proximity ligation is another driving technology that may help in the assembly of fragmented plant genomes at the chromosome level 36 . In the present study, we combined 114.45 Gb Illumina short reads with 129 Gb long reads from Nanopore sequencing and ~113.46 Gb 10X Genomics data to generate a high-quality eggplant genome, with a contig N50 of 5.26 Mb and a scaffold N50 of 89.64 Mb. With the assistance of 131.73 Gb Hi-C data, 12 eggplant pseudochromosomes were obtained, with a total size of ~1.07 Gb, covering 92.72% of the eggplant genome. Both contig N50 and scaffold N50 were significantly improved compared with those of previously published S. melongena genomes 13 , 16 . The number of scaffolds obtained was 10,383 for S. melongena -67/3 and 33,873 for S. melongena -NS; we assembled 2,263 scaffolds. A total of 36,582 protein-coding genes were detected in the present study, similar to the ~35,000 genes annotated in other sequenced diploid Solanaceae genomes.

Eggplant belongs to the genus Solanum and the family Solanaceae, which comprises over 3000 species adapted to a wide range of environments, including nine with sequenced genomes, i.e., potato 9 , tomato 10 , pepper 11 , 12 , tobacco 37 , petunia 38 , and four eggplants 13 , 16 , 17 ( S. melongena -HQ, S. melongena -NS, S. melongena -67/3, and S. aethiopicum ). The Old World subgenus Leptostemonum comprises ~500 species and 30 sections, including half of the economically important crops 1 . The brinjal eggplant S. melongena belongs to section Melongena, whereas the closely related species, the scarlet eggplant S. aethiopicum , belongs to section Oliganthes. We found 6,087 gene families in common in the nine genomes, among which we identified 463 single-copy gene families (Fig. 3 ). S. melongena and S. aethiopicum diverged from each other ~2.4 Mya (Fig. S 2 ). In addition, comparative genomics were performed among three sequenced eggplant genomes, S. melongena -HQ, S. melongena -67/3 and S. aethiopicum , and three types of variations (SNPs, indels and SVs) were characterized. As expected, S. melongena -HQ has significantly higher numbers of SNPs (22,092,994), indels (1,988,560) and SVs (7362) when compared with S. aethiopicum than compared with S. melongena -67/3 (Fig. 5 ). SVs consist of deletions and insertions that may result in divergent gene expression and phenotypes 39 , 40 , 41 , 42 . Interestingly, asymmetric SV accumulation was found in potential regulatory regions of protein-coding genes among the different eggplants, with more deletions than insertions between S. melongena -HQ and S. aethiopicum . In contrast similar insertion and deletion levels were observed between the two S. melongena genomes. This phenomenon has also been detected between two subgenomes of the allotetraploid peanut 42 . Overall the genome sequence of the linear eggplant HQ-1315 and comparative genomic information of S. melongena with that of related species allowed for the identification of genomic divergence at the whole-genome level, and the findings provide genomic tools for the improvement of agronomic traits in eggplant.

Stress resistance and fruit morphology (i.e., shape and color) are important traits during eggplant domestication that are vastly different among cultivated S. melongena varieties and closely related species. S. aethiopicum is mostly grown in tropical Africa, with outstanding disease resistance to various pathogens, such as Fusarium and Verticillium and is cross-compatible with S. melongena 43 , 44 . We identified 301 NBS-LRR genes in S. melongena -HQ and 250 NBS-LRR genes in S. melongena -67/3. As expected, S. aethiopicum has a higher number of disease resistance genes, with 436 genes involved in ten types. S. melongena -NS (Japanese eggplant) and S. melongena -67/3 (European eggplant) both have dark-purple fruits, with elliptical, oval or round shapes, whereas S. melongena -HQ has unusually linear-shaped fruits with a bright-purple color (Fig. 1 ). We constructed an F 2 segregating population and performed QTL mapping analysis on eggplant fruit length using the S. melongena -HQ genome (Fig. 7 ). A QTL interval for fruit length was identified within a 71.29–78.26-Mb region on chromosome E03, with a 99% confidence interval. Gene prediction was conducted by homology comparison based on the syntenic relationship between eggplant and tomato, which yielded 11 homologous genes for fruit size on eggplant chromosome E03. Combining these results with the identification of the QTL region FS3.1 in our previous study 30 , we propose that Smechr0301963 (the ortholog from S. melongena -67/3 is SMEL_003g182360 ), a gene potentially orthologous to SUN gene family members, is a key candidate gene for regulating eggplant fruit length.

Eggplant research is far behind that of other Solanaceae crops (i.e., tomatoes, peppers, and potatoes) and important crops such as cucumber. For QTL mapping research, previous studies have often used tomato genomes for collinear comparisons because of the lack of high-quality eggplant reference genomes 25 , 26 , 27 , 45 , 46 . Our study provides a high-quality eggplant genome that has wide applications in eggplant genetics and genomics studies, such as marker development, gene detection and chromosome evolution. In the present study, we detected QTL hotspots based on published QTL mapping results and marker information 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , with 210 markers associated with 71 traits anchored to the S. melongena -HQ reference genome (Fig. 8 ; Supplementary Table S 10 ). We identified and summarized 26 QTL hotspots, providing a valuable reference and basis for further exploration of regulatory genes controlling important traits in eggplant.

Materials and methods

Plant materials, dna extraction, and genome sequencing.

The eggplant cultivar HQ-1315 was selected for whole-genome sequencing; it is a high-generation self-crossbred inbred line with elongated purple fruits. HQ-1315 is an important parental material derived from the Vegetable Institute of Zhejiang Academy of Agricultural Sciences. The HQ-1315 plants were grown in a greenhouse at Qiaosi of Zhejiang Academy of Agricultural Sciences (Hangzhou, China) under standard conditions. DNA was extracted from the young leaves of HQ-1315 for genome sequencing using DNA Secure Plant Kit (TIANGEN, China) and broken into random fragments. Four kinds of DNA sequencing libraries were constructed, including a 350-bp insert size library, Nanopore library, 10× Genomics library, and Hi-C library, according to the manufacturers’ instructions. The genome was sequenced using Illumina NovaSeq PE150 and Nanopore PromethION according to standard Illumina (Illumina, CA, USA) and Nanopore (Oxford Nanopore Technologies) protocols at Novogene.

To estimate the eggplant genome size, k-mer distribution analysis was used, and 17-nt k-mers were employed to determine abundance with 93.33 Gb of paired-end reads. SOAPdenovo software was used to splice and assemble the reads into scaffolds with 41-nt k-mers.

Genome assembly and evaluation

We used wtdbg2 software 47 to assemble the noncleaned raw reads from Nanopore sequencing according to the Fuzzy Bruijn Graph (FBG) algorithm. To derive each point, a 1024-bp sequence was selected from the reads, and the points were connected to construct the FBG figure using gapped sequence alignments. Finally, a consensus sequence was obtained. We polished the consensus sequence three times with Nanopore reads using Racon software 48 . The split size was 50, and the other parameters were set to defaults. Paired-end clean reads obtained from the Illumina platform were aligned to the eggplant assembly using BWA software 49 (v0.7.17). Postprocessing error correction and conflict resolution of the assembly were performed using the Pilon tool with default parameters. The fragScaff software 50 was applied for 10X Genomics scaffold extension. Linked reads generated from the 10X Genomics library were aligned to the consensus sequence of the Nanopore assembly to obtain long scaffolds. The consensus sequences were filtered, and only those with linked-read support were used for subsequent assembly. Then, clean Hi-C data were aligned to the primary draft assembly using BWA software v0.7.17 49 . SAMtools 51 was utilized to remove duplicates and nonaligned reads, and only read pairs with both reads in the pair aligned to contigs were considered for scaffolding. Ultimately, 12 superscaffolds (pseudochromosomes) were assembled from corrected contigs using LACHESIS software 52 .

To evaluate the accuracy of the assembly, short reads were blast searched against the genome using BWA software 49 . CEGMA ( http://korflab.ucdavis.edu/datasets/cegma/ ) was used to assess the completeness of the eggplant genome assembly, and BUSCO v4 53 analysis was performed to further evaluate the assembled genome.

Transcriptome sequencing and gene annotation

HQ-1315 plants were grown in a greenhouse at Qiaosi of Zhejiang Academy of Agricultural Sciences (Hangzhou, China) under standard conditions. RNA from five different tissues (root, stem, leaf, flower, and fruit) was extracted for next-generation transcriptome sequencing and full-length transcriptome sequencing using Illumina NovaSeq PE150 as an auxiliary annotation. Transcriptome read assemblies were generated with Trinity 54 (v2.1.1) for gene annotation.

To optimize the gene annotation, RNA-seq reads from different tissues were aligned to genome fasta sequences using TopHat 55 (v2.0.11) with the default parameters to identify exon regions and splice positions. The alignment results were then applied as input for Cufflinks 56 (v2.2.1) with default parameters for genome-based transcript assembly. A nonredundant reference gene set was generated by merging genes predicted by three methods with EvidenceModeler 57 (EVM, v1.1.1) using PASA 58 (Program to Assemble Spliced Alignment) terminal exon support and including masked transposable elements as gene prediction input.

For ab initio gene annotation, Augustus 59 (v3.2.3), GeneID 60 (v1.4), GeneScan 61 (v1.0), GlimmerHMM 62 (v3.04), and SNAP 63 were used in the automated gene prediction pipeline. Individual families of interest were selected for further manual curation by relevant experts. For structural annotation, ab initio prediction, homology-based prediction, and RNA-seq assisted prediction were used to annotate gene models.

Repeat annotation

A combined strategy based on homology alignment and a de novo search was used in the repeat annotation pipeline to identify repetitive elements in the eggplant genome. Tandem repeats were extracted using TRF ( http://tandem.bu.edu/trf/trf.html ) by ab initio prediction. For homolog-based prediction, the Repbase TE library and TE protein database were employed to search against the eggplant genome using RepeatMasker 64 (version 4.0) and RepeatProteinMask, respectively, with the default parameters. For de novo-based approach prediction, a de novo repetitive element database was built with LTR_FINDER 65 , RepeatScout 66 , and RepeatModeler 67 , also with default parameters.

Homolog prediction

A total of five species were included in homolog prediction: S. tuberosum , S. melongena , S. lycopersicum , C. annuum , and N. tabacum . Sequences of homologous proteins were downloaded from NCBI and aligned to the genome using tBlastn 68 (v2.2.26; E -value ≤ 1e − 5). The matching proteins were then aligned to the homologous genome sequences using GeneWise 69 (v2.4.1) software to produce accurate spliced alignments, which were applied to predict the gene structure contained in each protein region.

Functional annotation

The functions of protein-coding genes were assigned according to the best match by aligning the protein sequences against the Swiss-Prot database using Blastp 70 , with a threshold of E -value ≤ 1e −5 . Protein motifs and domains were annotated by searching against the ProDom 71 , Pfam 72 (V27.0), SMRT 73 , PANTHER 74 , and PROSITE 75 databases using InterProScan 76 (v5.31). GO IDs 77 for each gene were assigned according to the corresponding InterPro entry. Protein functions were predicted by transferring annotations from the closest BLAST hit ( E -value < 10 −5 ) in the Swiss-Prot and NR databases. We also assigned a gene set to the KEGG pathway database 78 (release 53) and identified the best matched pathway for each gene.

Noncoding RNA annotation

tRNAs were predicted using tRNAscan-SE software 79 ( http://lowelab.ucsc.edu/tRNAscan-SE/ ). rRNAs were identified by alignment to the rRNA sequences of related species using BLASTN. Other noncoding RNAs, including miRNAs and snRNAs, were identified by searching against the Rfam database 80 (release 9.1) using INFERNAL software 81 ( http://infernal.janelia.org/ ).

Gene family construction and expansion/contraction analysis

Protein sequences predicted from the S. melongena -HQ eggplant genome and eight other sequenced Solanaceae genomes, S. tuberosum , S. lycopersicum , S. melongena -NS, S. melongena -67/3, S. aethiopicum , C. annuum , P. inflata, and N. tabacum , were used for gene family clustering. The gene set from each species was filtered according to the three steps described by Sun et al. 13 , with slight changes. The genes encoding proteins of fewer than 50 amino acids were filtered out. The gene families of the four eggplant genomes ( S. melongena -HQ S. melongena -NS, S. melongena -67/3, and S. aethiopicum ) were extracted for Venn diagram analysis to identify species-specific gene families in S. melongena -HQ. GO and KEGG annotation was performed to investigate the functions of those species-specific genes.

The expansion and contraction of gene families were analyzed by comparing family sizes between the MRCA and each of the nine sequenced Solanaceae genomes using CAFE 82 . The corresponding p -value for each lineage was calculated using conditional likelihoods, and families with a p -value of 0.05 were considered significantly expanded or contracted. The expanded and contracted genes were also analysed by GO and KEGG annotation.

Phylogenetic analysis

MUSCLE 83 ( http://www.drive5.com/muscle/ ) was used to align single-copy genes from representative Solanaceae genomes, and the results were combined to generate a superalignment matrix. Using RAxML 84 ( http://sco.h-its.org/exelixis/web/software/raxml/index.html ), a phylogenetic tree of the nine sequenced Solanaceae genomes was constructed with the maximum likelihood (ML) algorithm and 1000 bootstrap replicates. P. inflata was designated as the outgroup. To determine divergence times based on the phylogenetic tree, the MCMCTree program implemented in PAML5 software 85 was used. Divergence time calibration information was obtained from the TimeTree database ( http://www.time.org/ ).

Detection of WGD events

Protein sequences from S. melongena-HQ , S. aethiopicum , S. lycopersicum , S. tuberosum , C. annuum , and A. thaliana were used for BLASTP ( E -value < 1e−05) searches within or between genomes to identify syntenic blocks, after which syntenic blocks were searched using MCScanX ( http://chibba.pgml.uga.edu/mcscan2/ ) software according to the locations of the genes and the blast results. Muscle multiple sequence alignment was performed on the paralogous genes in the syntenic blocks, and the results of the protein alignment were used as templates to generate CDS alignment results. Finally, 4DTv values were calculated according to the comparison results, and a frequency distribution diagram of the 4DTv values and gene pairs was drawn.

Chromosome collinearity analysis

The CDSs of two species in the comparison group were compared with BLAST software ( http://last.cbrc.jp/ ), and JCVI was employed to locate syntenic blocks and map them with the following parameters: —cScore=0.9, —minspan=30, ( https://github.com/ tanghaibao/jcvi/wiki/MCscan- Python - version).

Identification of SNPs, indels, and SVs

The genome sequence of S. melongena -HQ was aligned to that of S. melongena -67/3 and S. aethiopicum using BWA v0.7.17 49 using default parameters. Picard tools v1.9.4 ( https://broadinstitute.github.io/picard/ ) was applied to sort the alignment result sequence alignment map (SAM) files. SNPs and indels were called using Genome Analysis Toolkit 86 , and related genes were called according to genome position using an in-house Perl script.

Clean reads of S. melongena -HQ were aligned to those of S. melongena -67/3 and S. aethiopicum using BWA v0.7.17 49 with default parameters. BreakDancerMax-0.0.1r61 was used for genome-wide detection of SVs with default parameters 87 . Deletion and insertion structure variations <10 bp or >10 kb in length were discarded. For the identification of SV genes, any gene with SVs in the main body or upstream/downstream regions was defined as an SV gene; otherwise, it was defined as a non-SV gene.

Identification of the NBS gene family and transcription factors

Most NBS-encoding genes in eggplant were identified based on NB-ARC (NBS) conserved domains that are shared within the gene family and have relatively conserved NBS domains. The latest Markov model for the NBS transcription factor PF00931 was downloaded from the Pfam database ( http://pfam.xfam.org/ ). The HMMER program was used to search for proteins containing this domain against the annotated protein database using the PF00931 domain as a query, with a cutoff E -value of 1e−4. To annotate the maximum number of NBS genes in the genomes, we also used the obtained NBS protein sequences for homologous annotation of genome sequences. tBlastn was applied for homology comparison, and the upper and lower segments of the comparison region were expanded by 5 kb each. Genewise software was then used for gene structure prediction, and homologous protein sequences were screened with PF00931. For the identification of transcription factors, iTAK-1.5-alpha software was utilized to predict transcription factors among the longest transcribed translated protein sequences of each species.

An F 2 population with 129 individuals was generated from a cross between HQ-1315 (linear-long fruits) and 1815 (round fruits), and phenotypic data on eggplant fruit length (L), diameter (D) and fruit shape index (L/D) were collected. Three mature fruits of each individual plant were selected for measurement; plants with extremely long/short fruits were selected and pooled according to the fruit length statistics. Equal amounts of DNA from the young leaves of 20 extreme individuals in each pool were mixed and sequenced. GATK 3.8 software was used to improve multiple-sample SNP and indel detection, and VariantFiltration was applied for filtering 86 . The SNP index was calculated with QTL-seq 88 methods. Indel markers that were exactly the same as those of the parent were assigned an indel-index of 0, with those completely different from the parent assigned an indel-index of 1. To intuitively reflect the distribution of all indices on the chromosome, the SNP index and indel index were combined to obtain Δ(all-index). Any interval with an aΔ(all-index) value higher than the threshold at the 95% confidence level was selected as a candidate interval. SNPs and indels were annotated using ANNOVAR 89 .

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The present study was supported by the Natural Science Foundation of Zhejiang Province (grant number LQ18C150004) and Major Science and Technology Projects of Zhejiang (grant number 2016C02051-2-1).

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Qingzhen Wei, Jinglei Wang, Wuhong Wang, Tianhua Hu, Haijiao Hu & Chonglai Bao

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Q.Z.W. conceived and designed the experiments, performed the experiments, analyzed the data, prepared the figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. W.H.W. analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. T.H.H., H.J.H., and J.L.W. analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, and approved the final draft. C.L.B. conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

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Wei, Q., Wang, J., Wang, W. et al. A high-quality chromosome-level genome assembly reveals genetics for important traits in eggplant. Hortic Res 7 , 153 (2020). https://doi.org/10.1038/s41438-020-00391-0

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Health benefits and bioactive compounds of eggplant

Affiliations.

• 1 Izmir Institute of Technology, Department of Molecular Biology and Genetics, 35430 Urla Izmir, Turkey.
• 2 Mehmet Akif Ersoy University, Burdur Food Agriculture and Livestock Vocational School, 15030 Burdur, Turkey.
• 3 Izmir Institute of Technology, Department of Molecular Biology and Genetics, 35430 Urla Izmir, Turkey. Electronic address: [email protected].
• 4 Mount Holyoke College, Department of Biological Sciences, The Biochemistry Program, 50 College St, South Hadley, MA 01075, USA. Electronic address: [email protected].
• 5 Izmir Institute of Technology, Department of Molecular Biology and Genetics, 35430 Urla Izmir, Turkey. Electronic address: [email protected].
• PMID: 30064803
• DOI: 10.1016/j.foodchem.2018.06.093

Eggplant is a vegetable crop that is grown around the world and can provide significant nutritive benefits thanks to its abundance of vitamins, phenolics and antioxidants. In addition, eggplant has potential pharmaceutical uses that are just now becoming recognized. As compared to other crops in the Solanaceae, few studies have investigated eggplant's metabolic profile. Metabolomics and metabolic profiling are important platforms for assessing the chemical composition of plants and breeders are increasingly concerned about the nutritional and health benefits of crops. In this review, the historical background and classification of eggplant are shortly explained; then the beneficial phytochemicals, antioxidant activity and health effects of eggplant are discussed in detail.

Keywords: Bioactive compounds; Chemical composition; Eggplant; Metabolic profiling; Solanum melongena.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Publication types

• Antioxidants / isolation & purification
• Antioxidants / metabolism*
• Crops, Agricultural
• Phenols / isolation & purification
• Phenols / metabolism*
• Solanum melongena / chemistry*
• Antioxidants

REVIEW article

World vegetable center eggplant collection: origin, composition, seed dissemination and utilization in breeding.

• 1 World Vegetable Center, Tainan, Taiwan
• 2 Vegetable Crops Research Department, Agriculture Research Center, Horticulture Research Institute, Giza, Egypt
• 3 Faculty of Applied Ecology and Agricultural Sciences, Inland Norway University of Applied Sciences, Elverum, Norway
• 4 Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
• 5 Horticulture Department, Faculty of Agriculture, University of Kafrelsheikh, Kafr El-Sheikh, Egypt

Eggplant is the fifth most economically important solanaceous crop after potato, tomato, pepper, and tobacco. Apart from the well-known brinjal eggplant ( Solanum melongena L.), two other under-utilized eggplant species, the scarlet eggplant ( S. aethiopicum L.) and the gboma eggplant ( S. macrocarpon L.) are also cultivated. The taxonomy and identification of eggplant wild relatives is challenging for breeders due to the large number of related species, but recent phenotypic and genetic data and classification in primary, secondary, and tertiary genepools, as well as information on the domestication process and wild progenitors, facilitates their utilization in breeding. The World Vegetable Center (WorldVeg) holds a large public germplasm collection of eggplant, which includes the three cultivated species and more than 30 eggplant wild relatives, with more than 3,200 accessions collected from 90 countries. Over the last 15 years, more than 10,000 seed samples from the Center's eggplant collection have been shared with public and private sector entities, including other genebanks. An analysis of the global occurrences and genebank holdings of cultivated eggplants and their wild relatives reveals that the WorldVeg genebank holds the world's largest public collection of the three cultivated eggplant species. The composition, seed dissemination and utilization of germplasm from the Center's collection are highlighted. In recent years more than 1,300 accessions of eggplant have been characterized for yield and fruit quality parameters. Further screening for biotic and abiotic stresses in eggplant wild relatives is a priority, as is the need to amass more comprehensive knowledge regarding wild relatives' potential for use in breeding. However, as is the case for many other crops, wild relatives are highly under-represented in the global conservation system of eggplant genetic resources.

Introduction

Brinjal eggplant ( Solanum melongena L.) is a warm-weather crop mostly cultivated in tropical and subtropical regions of the world. Two other cultivated eggplant species, the scarlet eggplant ( S. aethiopicum L.) and the gboma eggplants ( S. macrocarpon L.), are less known but have local importance in sub-Saharan Africa ( Schippers, 2000 ; Daunay and Hazra, 2012 ). Based on data from 2014, the global production of eggplant is around 50 million tons annually, with a net value of more than US$10 billion a year, which makes it the fifth most economically important solanaceous crop after potato, tomato, pepper, and tobacco ( FAO, 2014 ). The top five producing countries are China (28.4 million tons; 57% of world's total), India (13.4 million tons; 27% of world's total), Egypt (1.2 million tons), Turkey (0.82 million tons), and Iran (0.75 million tons). In Asia and the Mediterranean, eggplant ranks among the top five most important vegetable crops ( Frary et al., 2007 ).

Regarding nutritional value, eggplant has a very low caloric value and is considered among the healthiest vegetables for its high content of vitamins, minerals and bioactive compounds for human health ( Raigón et al., 2008 ; Plazas et al., 2014b ; Docimo et al., 2016 ). In this respect, eggplant is ranked among the top 10 vegetables in terms of oxygen radical absorbance capacity ( Cao et al., 1996 ). The bioactive properties of eggplant are mostly associated with high content in phenolic compounds ( Plazas et al., 2013 ), which are mostly phenolic acids, particularly chlorogenic acid in the fruit flesh ( Stommel et al., 2015 ) and anthocyanins in the fruit skin ( Mennella et al., 2012 ). Both phenolic acids and anthocyanins have multiple properties beneficial for human health ( Plazas et al., 2013 ; Braga et al., 2016 ).

Farmers need improved eggplant varieties for sustainable production and adaptation to climate change challenges. Because eggplant has a relatively long growth period, it is more exposed than other vegetable crops to a broad range of plant diseases, pests, nematodes, and weeds. The most common diseases include bacterial wilt, verticillium wilt, fusarium wilt, anthracnose fruit rot, alternaria rot, damping off, Phytophthora blight, phomopsis blight and fruit rot, leaf spot, little leaf of brinjal, and mosaic ( Rotino et al., 1997 ). Eggplant is also subject to attack by numerous insect pests including mites, whiteflies, aphids, eggplant fruit, and shoot borer, leafhopper, thrips, spotted beetles, leaf roller, stem borer, and blister beetle ( Rotino et al., 1997 ; Medakker and Vijayaraghavan, 2007 ). Unpredictable weather with extreme temperatures, drought or flooding can reduce yield and fruit quality. In general, eggplant breeding programs aim to develop high-yielding varieties, mostly F 1 hybrids, with high fruit quality, shelf-life and resistance to major disease and insect pests, and with broad adaptation to environmental stress ( Daunay and Hazra, 2012 ).

Access to genetic diversity is fundamental for any breeding program. In this paper, we review the diversity and genetic resources of eggplant. As a point of departure, we examine the taxonomy and relationships of the crop and its wild relatives, as well as the domestication of cultivated eggplant. The relationships among wild, semi-domesticated, and cultivated eggplant are intricate, and the origin, evolution, and migration are incompletely understood ( Levin et al., 2006 ; Meyer et al., 2012 ). Here, we limit ourselves to identify global occurrences and regions of diversity. A key section is the overview of global genebank holdings of cultivated eggplant and their wild relatives. As we shall demonstrate, for such plants the collection at the WorldVeg is of paramount importance. Composition, seed dissemination and utilization of germplasm from this collection are presented and discussed. The importance of safeguarding and evaluating wild relatives is highlighted, as crop wild relatives are highly under-represented in the global conservation system of plant genetic resources and may harbor important genes for resistance or tolerance to biotic and abiotic stresses.

Taxonomy, Wild Relatives, and Domestication of Eggplant

Eggplants are berry-producing vegetables belonging to the large Solanaceae family (nightshade family), which contains ~3,000 species distributed in some 90 genera ( Vorontsova and Knapp, 2012 ). Out of these Solanum L. is the largest one, with around 1,500 species ( Frodin, 2004 ) including globally important crops such as potato ( Solanum tuberosum L.) and tomato ( Solanum lycopersicum L.), as well as many other minor crops. Most taxa of Solanum genus have a basic chromosome number of n = 12 ( Chiarini et al., 2010 ).

The Solanum genus is mega-diverse and can be divided into 13 clades, where eggplant is the member of the large and taxonomically challenging Leptostemonum clade (subgenus Leptostemonum Bitter; Knapp et al., 2013 ), which is commonly known as the “spiny Solanum ” group due to the presence of sharp epidermal prickles on stems and leaves ( Vorontsova et al., 2013 ). The subgenus Leptostemonum contains around 450 currently recognized species distributed worldwide ( Knapp et al., 2013 ), many of which originated in the New World ( Vorontsova and Knapp, 2012 ). All three cultivated eggplant species have the Old World in origin (Figure 1 ). The Old World (Africa and Eurasia) and Australia, are home to more than 300 Solanum species ( Levin et al., 2006 ; Vorontsova and Knapp, 2016 ). Solanum melongena and S. macrocarpon are usually included in section Melongena Dunal ( Lester and Daunay, 2003 ; Lester et al., 2011 ), whereas S. aethiopicum is assigned to section Oliganthes (Dunal) Bitter ( Lester, 1986 ).

Figure 1 . Schematic representation of taxonomic relationships between the cultivated brinjal eggplant ( Solanum melongena ) and other cultivated (scarlet eggplant, S. aethiopicum ; and gboma eggplant, S. macrocarpon ) and wild relatives from the genus Solanum based on Nee (1999) , Levin et al. (2006) , Weese and Bohs (2010) , Stern et al. (2011) , Knapp et al. (2013) , Syfert et al. (2016) , and Vorontsova and Knapp (2016) . For each of the species and groups it is indicated if they are part of the primary (GP1), secondary (GP2), or tertiary (GP3) brinjal eggplant genepools. The three cultivated species are indicated with an asterisk.

Solanum melongena is characterized by large morphological diversity, and frequently it has been considered as the same taxonomic species than its wild ancestor S. insanum L. ( Ranil et al., 2017 ). Four taxonomically informal groups, labeled E–H, were considered by Lester and Hasan (1991) to describe the different types of wild and weedy eggplant as well as their distribution (Table 1 ). However, these four groups are presently considered as representing two different species: the cultivated eggplant S. melongena and its wild ancestor S. insanum ( Knapp et al., 2013 ). In this way, groups E and F corresponding to extremely prickly and plants that grow wild or weedy in India and Southeast Asia are now included within S. insanum ( Ranil et al., 2017 ). The plants of group G correspond to primitive eggplant cultivars, with small fruits, while the plants of group H are less prickly than other groups and consist of large-fruited landraces and modern cultivars ( Daunay et al., 2001 ; Weese and Bohs, 2010 ; Table 1 ). Both groups, G and H, constitute S. melongena ( Knapp et al., 2013 ). Some studies ( Hurtado et al., 2012 ; Vilanova et al., 2012 ; Cericola et al., 2013 ) have also pointed to a genetic and morphological differentiation between Occidental (eggplants from the Mediterranean area, North of Africa, and Middle East) and Oriental (from southeast and eastern Asia).

Table 1 . Cultivated eggplants (brinjal eggplant, S. melongena L.; scarlet eggplant, S. anguivi L.; gboma eggplant, S. macrocarpon L.) and their wild relatives from the primary genepool, which correspond to their wild ancestors ( S. insanum L. for brinjal eggplant, S. anguivi for scarlet eggplant, and S. dasyphyllum for gboma eggplant) ( Lester, 1986 ; Lester and Niakan, 1986 ; Bukenya and Carasco, 1994 ; Schippers, 2000 ; Daunay et al., 2001 ; Weese and Bohs, 2010 ; Meyer et al., 2012 ; Knapp et al., 2013 ; Vorontsova and Knapp, 2016 ).

Solanum aethiopicum is also hyper-variable and is classified into four cultivar groups (Gilo, Shum, Kumba, and Aculeatum; Table 1 ) based on morphological characteristics and use ( Lester, 1986 ). The Gilo group has edible fruits with different shapes, color, and size, and hairy, inedible leaves; the Shum group has glabrous and small leaves that are eaten as a green vegetable but the fruits are inedible; the Kumba group has glabrous leaves and flattened large fruits, which are edible; the Aculeatum group, on the other hand, has more prickliness than other groups with flat-shaped fruit, and are used as ornamentals ( Lester, 1986 ; Prohens et al., 2012 ; Plazas et al., 2014a ).

Solanum macrocarpon is cultivated both for its leaves and fruits ( Schippers, 2000 ; Maundu et al., 2009 ; Table 1 ). The species is less morphologically diverse than S. melongena and S. aethiopicum ( Plazas et al., 2014a ).

Although, recent information exists on domestication of eggplants, there are still many unanswered questions about this process. Vavilov (1951) considered S. melongena as being native to the “Indo-Chinese center of origin.” However, recent evidence suggests that brinjal eggplant had a multiple independent domestication from S. insanum , which is naturally distributed in tropical Asia from Madagascar to the Philippines ( Knapp et al., 2013 ) in several centers of domestication ( Meyer et al., 2012 ). Although, the evidence of cultivation of eggplant in both India and China is equally old, archeological evidence suggests that utilization of wild eggplants may have started earlier in India than China, with a subsequent additional and independent center of domestication in the Philippines ( Meyer et al., 2012 ). Around the eighth century, eggplant spread eastward to Japan and then westward along the Silk Road into Western Asia, Europe, and Africa by Arab traders during the fourteenth century, then it was introduced into America soon after Europeans arrived there ( Prohens et al., 2005 ) and later expanded into other parts of world. Much less is known on the domestication of the scarlet and gboma eggplants. Both species were domesticated in Africa, from its respective wild ancestors, which are S. anguivi Lam. in the case of S. aethiopicum ( Lester and Niakan, 1986 ) and S. dasyphyllum Schumach. and Thonn. in the case of S. macrocarpon ( Bukenya and Carasco, 1994 ). Hybrids between cultivated eggplants and their respective wild ancestors are fully fertile ( Lester and Thitai, 1989 ; Bukenya and Carasco, 1994 ; Plazas et al., 2016 ).

Solanum melongena and the two other cultivated eggplants are related to a large number of wild species ( Vorontsova et al., 2013 ; Syfert et al., 2016 ) that may serve as sources of variation for breeding programs, in particular for traits related to adaptation to climate change but also pest and disease resistance ( Rotino et al., 2014 ). Some of these species are listed in Table 2 . Although, the brinjal eggplant is considered to be a vegetable of Asian origin, most eggplant wild relatives are from Africa ( Weese and Bohs, 2010 ). Wild eggplants produce small, bitter, multi-seeded fruits, almost always inedible, and the plant is generally very spiny. Some of them possess high levels of chlorogenic acid and other bioactive compounds, which may have potential interest for human health ( Meyer et al., 2015 ). The wild relatives of eggplant are one of the most variable and intricate groups, in regards to their taxonomic and phylogenetic relationships ( Vorontsova et al., 2013 ). Based on crossing and biosystematics data, nine wild species, together with S. melongena , form the “eggplant complex,” which includes the cultivated brinjal eggplant and its closest eggplant wild relatives ( Knapp et al., 2013 ). Wild relatives can be classified based on their crossability with cultivated species (genepool concept) into primary, secondary, and tertiary genepools ( Harlan and de Wet, 1971 ). The primary genepool (GP1) of brinjal eggplant consists of cultivated eggplant and its wild ancestor S. insanum ( Ranil et al., 2017 ) which can be crossed easily and produce normal fertile hybrids ( Plazas et al., 2016 ). The secondary genepool (GP2) includes a large number (over 40) wild relatives that can be crossed or are phylogenetically close to brinjal eggplant, but the success of the crosses and the viability or fertility of the hybrids with the brinjal eggplant may be reduced. For example, some interspecific hybrids derived from GP2 are partly sterile or weak due to reproductive barriers such as S. dasyphyllum, S. linnaeanum Hepper & P.-M. L. Jaeger or S. tomentosum L. ( Rotino et al., 2014 ; Kouassi et al., 2016 ). The tertiary genepool (GP3) includes more distantly related species, including New World species, which are used in breeding programs for their resistance features, but crossing needs specific breeding techniques to succeed (e.g., S. torvum Sw., S. elaeagnifolium Cav., and S. sisymbriifolium Lam.; Kouassi et al., 2016 ; Plazas et al., 2016 ; Syfert et al., 2016 ).

Table 2 . Cultivated eggplant and wild relatives, number of occurrences, their regions and number of conserved accessions globally and at the World Vegetable Center (WorldVeg).

Global Occurrences and Genebank Conservation of Eggplant and Wild Relatives

In the following section we review the current status of eggplant genetic resources including the cultivated species and their most recognized wild relatives using information collected from biodiversity, herbarium, and genebank databases. The Global Biodiversity Information Facility (GBIF) was applied to review the number of recorded occurrences, which can be natural populations, herbarium samples, or other biodiversity records ( GBIF, 2017 ). Scientific names were used as a filter in the search function. The total numbers of records per species were noted, as were clusters of occurrences that were identified visually by applying the database map function. The main cluster of S. melongena was in India, with more than 5,000 of the total number of around 18,000 occurrences. Other clusters were in Turkey, Southeast Asia, and Spain, while the main cluster of occurrences of S. aethiopicum and S. macrocarpon was in West Africa, with a total of 1,288 and 443 occurrences, respectively. Based on the literature of previous studies and characterization data available at the WorldVeg, a list of 35 crop wild relatives was included in this review, which had ~100 ( S. repandum G. Forst.) to more than 7,000 occurrences ( S. torvum ) on a global scale recorded by GBIF (Table 2 ). Important regions for wild relatives vary depending on the species, but include all continents; Latin America, Asia, and Africa are the most common areas for wild relatives.

The Global Gateway to Genetic Resources ( GENESYS, 2017 ) was applied to review the number of conserved genebank accessions. The database includes more than 3 million accessions, which is less than half of the estimated number of more than 7 million accessions that are conserved globally ( FAO, 2010 ). Although, not all national genebanks report to Genesys, we still used the information for reviewing global holdings. Scientific names were used as a filter in the search function of the database, and the most important holding institutions were identified from the summary function of the database. Additional sources were reviewed to try to capture important collections outside Genesys, including national genebank databases and the database for Svalbard Global Seed Vault ( SGSV, 2017 ). The WorldVeg plays a major role in the conservation and distribution of vegetable germplasm, holding 60,387 accessions comprising 173 genera and 440 species from 151 countries of origin ( AVGRIS, 2017 ).

In total, 5,665 accessions of S. melongena , 798 accessions of S. aethiopicum and 169 accessions of S. macrocarpon were reported by GENESYS (2017) . Important national eggplant collections not reporting to GENESYS are at the National Bureau of Plant Genetic Resources in India and the Institute of Vegetables and Flowers in China. Data from such collections were not included in our study. The largest collections of these three cultivated species were those of the WorldVeg [2,212 accessions of S. melongena (39%), 481 accessions of S. aethiopicum (60%), and 63 accessions of S. macrocarpon (37%)], followed by the Plant Genetic Resources Conservation Unit at the University of Georgia, USDA-ARS (close to 800 accessions of S. melongena ) and the Centre for Genetic Resources at the Netherlands Plant Research International (373 accessions of S. melongena ; GENESYS, 2017 ). The N. I. Vavilov Research Institute of Plant Genetic Resource in Russia has a significant eggplant collection with more than 500 S. melongena accessions. The conservation of wild species ranged from a few accessions (e.g., S. rigescentoides Hutch.) to 167 accessions ( S. incanum L.). None of the wild species had large collections. Interestingly, the WorldVeg has the largest collections for S. aculeatissimum Jacq. (46 accessions, 71%), S. anguivi (28 accessions, 23%), S. capense L. (3 accessions, 38%), S. ferox L. (11 accessions, 38%), S. indicum L. (12 accessions, 92%), S. insanum (11 accessions, 100%), S. lasiocarpum Dunal (31 accessions, 74%), S. stramoniifolium Jacq. (10 accessions, 63%), S. torvum (112 accessions, 85%), S. trilobatum L. (10 accessions, 71%), S. viarum Dunal (16 accessions, 27%), S. violaceum Ortega (49 accessions, 77%), and S. xanthocarpum Schrad. & J. C. Wendl. (18 accessions, 90%) ( GENESYS, 2017 ). The low number of accessions identified as S. insanum in the collections is surprising, taking into account that it is quite abundant and the progenitor of eggplant ( Knapp et al., 2013 ; Ranil et al., 2017 ). This is probably caused by the fact that many S. insanum accessions are probably conserved as S. melongena , as both species have often been considered as being a single species ( S. melongena ; e.g., Lester and Hasan, 1991 ). Also, the correct classification of accessions under “ S. indicum L.” should be determined, as this name was rejected in 1978 as it was used to refer to two clearly distinct species, the African S. anguivi and the Asian S. violaceum ( Vorontsova and Knapp, 2016 ).

According to our analysis, wild eggplants are greatly under-represented in ex situ repositories. Such findings are also reported by Castañeda-Álvarez et al. (2016) , where eggplants were among the crops whose wild genepools are highly under-represented. Indeed, there is a need for conducting collection missions and conservation actions for eggplant wild relatives (Conservation gaps, http://www.cwrdiversity.org/conservation-gaps/ , Accessed February 30, 2017).

Eggplant Germplasm Dissemination from the World Vegetable Center

As demonstrated in the previous section, the collection at the WorldVeg is the most significant eggplant collection worldwide. Eggplant is the Center's third most widely distributed vegetable crop after pepper and tomato. A total of 11,383 germplasm samples were distributed from WorldVeg headquarters to 90 countries from the period 2000 to 2017. Most of these were of S. melongena (10,519 samples; 92.4%), followed by S. aethiopicum (738 samples; 6.4%) and S. macrocarpon (126 samples; 2.2%; Table 3 ). These accessions correspond to landraces and traditional cultivars with significant diversity in plant morphology, fruit types and colors, and resistance to biotic and abiotic stresses. The largest share of germplasm samples went to other genebanks (7,042 samples; 61.8%), followed by National Agricultural Research & Extension System/Government (NARES) (2,154 samples; 18.9%), internal distribution to WorldVeg scientists (703 samples; 6.1%), and seed companies (503 samples; 4.4%).

Table 3 . The World Vegetable Center seed distribution of cultivated eggplant by recipient category during the period 2000–2017.

The large morphological diversity of the WorldVeg collection is matched by the identification of traits of significant agronomic interest. WorldVeg has compiled and maintained the world's largest germplasm collection of eggplant, and national genebanks and institutions from around the globe have requested and received many samples. A significant number of accessions are internal distributions to WorldVeg regional offices, and in collaboration with partner institutions, the material has been used in breeding programs. New open-pollinated varieties have been released in Uzbekistan, Tanzania, and Mali through selection based on local trait preferences (Table 4 ).

Table 4 . List of eggplant and African eggplant varieties released in Uzbekistan, Tanzania, and Mali based on WorldVeg germplasm.

Utilization of Eggplant Germplasm in Breeding

Screening of available accessions for targeted traits (evaluation) and morphological description of the accessions (characterization) are key issues for the breeding process. At the WorldVeg a large number of commercial cultivars, landraces, and germplasm have thus been examined to identify desired genotypes for use in eggplant breeding programs or for recommending to private sector seed companies and other partner institutions. Standardized descriptors included characters both for germination, the vegetative phase, inflorescence descriptors, and fruit and seed traits, respectively (Table 5 ).

Table 5 . A complete list of standard descriptors for eggplants used at the World Vegetable Center ( AVGRIS, 2017 ).

Large variation in yield parameters and in fruit quality parameters have been documented in the collection (Figures 2 , 3 ). Such data have been compiled over years and can be retrieved from AVGRIS, the World Vegetable Center genebank database system (2017). Among the 1,308 accessions of S. melongena that have been characterized, green and purple fruits were predominant, and could be found in 47 and 38% of the total number of accessions, respectively. Slightly longer than broad, and as long as broad, were the prevalent shapes of the accessions, with 31.1 and 18.7%, respectively. Similarly, huge diversity was found among 98 accessions belong to S. melongena, S. aethiopicum , and S. macrocarpon for 16 morpho-agronomic and fruit traits including plant height, flowering time, flower/inflorescence, fruit length and fruit acidity, but weak association was found between among morpho-agronomic and fruit quality descriptors ( Polignano et al., 2010 ). In terms of fruit taste, 26.8% of accessions had a sweet taste, 53.2% had an intermediate taste and some accessions had bitter taste (6.1%). Large variations in fruit dry matter content, total sugar content, and fiber content of the fruit have been determined in a study of 90 selected eggplant genotypes ( AVRDC, 1996 ). The distribution of dry matter, total sugar, and fiber contents ranged from 5.5 to 10.1, 7.0 to 40.1, and 4.7 to 18.1%, respectively. In another study conducted at the WorldVeg, 33 S. melongena accessions and two S. aethiopicum accessions were evaluated for superoxide scavenging and content of total phenolics and ascorbic acid ( Hanson et al., 2006 ). Solanum melongena accessions S00062, S00022, and S. aethiopicum accession S00197 exhibited high antioxidant activity ( Hanson et al., 2006 ).

Figure 2 . Horticultural characteristics of more than 1,300 accessions of Solanum melongena summarized and based on information available in AVGRIS (2017) : (A) Fruit color, (B) Fruit length, (C) Fruit yield per plant, and (D) Fruit taste.

Figure 3 . Different fruit shapes, colors, and sizes of Solanum melongena accessions in the World Vegetable Center germplasm collection.

Accessions with important traits such as early maturity, high yielding, and resistance to biotic stresses have been identified in the WorldVeg germplasm collection (Table 6 ). Based on data from Chen (1998) and the examination of 40 accessions from the WorldVeg collection, among long fruit genotypes, VI045551, VI047333, VI046110, and VI037736 were identified as stable and high yielding (>40 tons per hectare) over spring, summer, and autumn seasons. Accession VI046110 had the highest average yield and the earliest maturing genotype across the three seasons ( AVRDC, 1999 ). In round fruit type, VI046097, VI047332, VI44067, EG233, and EG235 produced the high yields in all three seasons.

Table 6 . Identified eggplant germplasm from the World Vegetable Center collection with useful traits for breeding.

Based on data from AVGRIS (2017) compiled over the years and including 1,300 accessions, only 90 accessions (6.8%) had more than 5,000 g of fruit yield per plant (Figure 2 ). Marketable yields were highly associated with fruit weight and number of fruits per plant. Large diversity in the WorldVeg germplasm collections enabled us to develop several improved eggplant and African eggplant cultivars (Table 3 ). A total of three eggplant varieties have been commercialized in Uzbekistan and three African eggplant varieties have been released in Tanzania and Mali.

More than 200 accessions have been evaluated for resistance to bacterial wilt ( Ralstonia solanacearum ) at the WorldVeg under both greenhouse and field conditions ( AVRDC, 1999 ). Among these, 38 accessions were identified with high levels of resistance. These accessions were retested using root wounding and soil drenching inoculation methods in the greenhouse. Data were summarized from the screening and retest studies, and the most resistant accessions were TS3, VI034885, and TS47 from Malaysia; and TS69, TS87, and TS90 from Indonesia with disease indices <10% under both greenhouse and field conditions.

Resistance to eggplant fruit and shoot borer ( Leucinodes orbonalis Guenee), leafhopper ( Amrasca devastans Distant), and aphids ( Aphis gossypii Glover) have been identified at WorldVeg in separate trials ( AVRDC, 1999 ). Leafhoppers and aphids have piercing mouthparts and suck the sap, especially from the leaves, which leads to yellow spots on the leaves, followed by crinkling, curling, bronzing, and drying (or “hopper burn” from leafhopper), but severe aphid infestations cause young leaves to curl and become deformed ( AVRDC, 1999 ; Ramasamy, 2009 ). Like whiteflies, aphids also produce honeydew, which leads to the development of sooty mold ( Ramasamy, 2009 ). Accessions VI034971, VI035822, and VI035835 were found promising accessions against leafhopper and aphids. Eggplant fruit and shoot borer is an extremely destructive pest, especially in South and Southeast Asia ( Ramasamy, 2009 ). It lays eggs on the foliage and neonate larvae feeds on the tender shoots, boring into the shoots and fruits, resulting in wilting of young shoots, followed by drying; the fruit becomes unfit for marketing and consumption. Total resistance was not found and moderate resistance was found only in one accession, VI047451 ( AVRDC, 1999 ). This was based on typical damage symptoms, wilting of shoots and feeding holes in a wilted shoot, as well as damaged fruit. Overall, these results show that very promising materials for breeding pest tolerant or resistant varieties can be found in the WorldVeg eggplant collection. However, additional race specific screening is needed to find resistant sources for pests where no resistance or limited resistance has been found.

The Way Forward

The food security of many countries relies on crops bred from genetic resources outside their region ( Khoury et al., 2016 ). Therefore, plant genetic resources are a global concern where access and benefit sharing is of paramount importance. Eggplant is an important vegetable crop with a global cultivation area. From the current study we have confirmed that there are critical gaps in global eggplant collections, especially related to crop wild relatives ( Syfert et al., 2016 ). We have listed more than 35 wild species conserved in germplasm collections, but for many other eggplant wild relatives no accessions are conserved in genebanks; in addition, there still might be undiscovered crop wild relatives. Genetic diversity in wild relatives is much higher than in cultivated eggplant ( Vorontsova et al., 2013 ) and could be valuable sources for resistance to biotic and abiotic stresses ( Daunay and Hazra, 2012 ). To date, a limited number of wild relatives have used in eggplant breeding ( Rotino et al., 2014 ) and commercial varieties containing wild relative introgressions are not yet available. To move forward, screening for abiotic and biotic stresses in wild relatives should be intensified and broadened for identification of valuable germplasm accessions for breeding improved eggplant varieties. This information, combined with genomics studies for the detection of genes and QTLs of agronomic importance and their associated markers, will be of great utility in eggplant breeding, as has been demonstrated in some association mapping studies ( Cericola et al., 2014 ; Portis et al., 2015 ). Recent reviews of the development in eggplant is provided by Frary and Doganlar (2013) and Gramazio et al. (in press) .

From a utilization point of view, core collections could be established and stakeholders should work together for the development of the next generation of eggplant varieties that can meet the challenges of the present and the future.

Author Contributions

DT compiled the major parts of the text; SS contributed with text on genetic resources; JP contributed with text on eggplant wild relatives; YC contributed with reviewing databases; MR and TW contributed with inputs on eggplant taxonomy and breeding.

Conflict of Interest Statement

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.

Acknowledgments

Funding for the World Vegetable Center's general research activities is provided by core donors: Republic of China (Taiwan), UK aid, United States Agency for International Development (USAID), Australian Centre for International Agricultural Research (ACIAR), Germany, Thailand, Philippines, Korea, and Japan. In addition we like to thank Global Crop Diversity Trust for contribution to meetings and to this open-access publication.

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Keywords: conservation, crop wild relatives, diversity, plant genetic resources, Solanum melongena , Solanum aethiopicum , Solanum macrocarpon

Citation: Taher D, Solberg SØ, Prohens J, Chou Y, Rakha M and Wu T (2017) World Vegetable Center Eggplant Collection: Origin, Composition, Seed Dissemination and Utilization in Breeding. Front. Plant Sci . 8:1484. doi: 10.3389/fpls.2017.01484

Received: 10 May 2017; Accepted: 10 August 2017; Published: 25 August 2017.

Reviewed by:

Copyright © 2017 Taher, Solberg, Prohens, Chou, Rakha and Wu. 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) or licensor 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: Svein Ø. Solberg, [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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Research Article

Bt eggplant ( Solanum melongena L.) in Bangladesh: Fruit production and control of eggplant fruit and shoot borer ( Leucinodes orbonalis Guenee), effects on non-target arthropods and economic returns

Roles Formal analysis, Investigation, Writing – original draft

Affiliation Tuber Crops Research Sub Centre, BARI, Bogra, Bangladesh

Roles Data curation, Investigation

Affiliation On Farm Research Division, BARI, Bogra, Bangladesh

Roles Investigation

Roles Project administration

Affiliation BARI, Joydebpur, Gazipur, Bangladesh

Affiliation Country Coordinator for Bangladesh, USAID Feed the Future South Asia Eggplant Improvement Partnership, Dhaka, Bangladesh

Roles Formal analysis, Methodology, Writing – review & editing

Affiliation USDA-ARS, Arid-Land Agricultural Research Center, Maricopa, Arizona, United States of America

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Entomology, Cornell University/New York State Agricultural Experiment Station (NYSAES), Geneva, New York, United States of America

• M. Z. H. Prodhan, 
• M. T. Hasan, 
• M. M. I. Chowdhury, 
• M. S. Alam, 
• M. L. Rahman, 
• A. K. Azad, 
• M. J. Hossain, 
• Steven E. Naranjo, 
• Anthony M. Shelton

• Published: November 21, 2018
• https://doi.org/10.1371/journal.pone.0205713
• Reader Comments

Eggplant or brinjal ( Solanum melongena ) is a popular vegetable grown throughout Asia where it is attacked by brinjal fruit and shoot borer (BFSB) ( Leucinodes orbonalis ). Yield losses in Bangladesh have been reported up to 86% and farmers rely primarily on frequent insecticide applications to reduce injury. Bangladesh has developed and released four brinjal varieties producing Cry1Ac (Bt brinjal) and is the first country to do so. We report on the first replicated field trials comparing four Bt brinjal varieties to their non-Bt isolines, with and without standard insecticide spray regimes. Results of the two-year study (2016–17) indicated Bt varieties had increased fruit production and minimal BFSB fruit infestation compared with their respective non-Bt isolines. Fruit infestation for Bt varieties varied from 0–2.27% in 2016, 0% in 2017, and was not significantly affected by the spray regime in either year. In contrast, fruit infestation in non-Bt lines reached 36.70% in 2016 and 45.51% in 2017, even with weekly spraying. An economic analysis revealed that all Bt lines had higher gross returns than their non-Bt isolines. The non-sprayed non-Bt isolines resulted in negative returns in most cases. Maximum fruit yield was obtained from sprayed plots compared to non-sprayed plots, indicating that other insects such as whiteflies, thrips and mites can reduce plant vigor and subsequent fruit weight. Statistically similar densities of non-target arthropods, including beneficial arthropods, were generally observed in both Bt and non-Bt varieties. An additional trial that focused on a single Bt variety and its isoline provided similar results on infestation levels, with and without sprays, and similarly demonstrated higher gross returns and no significant effects on non-target arthropods. Together, these studies indicate that the four Bt brinjal lines are extremely effective at controlling BFSB in Bangladesh without affecting other arthropods, and provide greater economic returns than their non-Bt isolines.

Citation: Prodhan MZH, Hasan MT, Chowdhury MMI, Alam MS, Rahman ML, Azad AK, et al. (2018) Bt eggplant ( Solanum melongena L.) in Bangladesh: Fruit production and control of eggplant fruit and shoot borer ( Leucinodes orbonalis Guenee), effects on non-target arthropods and economic returns. PLoS ONE 13(11): e0205713. https://doi.org/10.1371/journal.pone.0205713

Editor: Juan Luis Jurat-Fuentes, University of Tennessee, UNITED STATES

Received: May 8, 2018; Accepted: October 1, 2018; Published: November 21, 2018

Copyright: © 2018 Prodhan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors from the Bangladesh Agricultural Research Institute (BARI) collected and analyzed the data. Advice on the experimental design and data analysis were provided by Drs. Shelton and Naranjo who also helped write the paper. The processed data can be obtained at doi: 10.5061/dryad.q4b9r2k .

Funding: The authors gratefully acknowledge the support provided by the United States Agency for International Development (USAID) for their Feed the Future South Asia Eggplant Improvement Partnership (AID-OAA-A-15-00052). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Genetically engineered (GE) crops continue to expand and transform agriculture on a global scale. In 2017, nearly 190M hectares of GE crops were grown by about 18M farmers in 24 countries, including 101M hectares of crops with high levels of host plant resistance to caterpillar and beetle pests [ 1 ]. Between 1996 and 2015, this adoption has been associated with increases in farm income > $50,274M and $45,958M, in Bt cotton and maize, respectively, and reductions of > 268M and 87M kg of insecticide active ingredient in Bt cotton and maize, respectively [ 2 ]. However, the potential benefits provided by Bt crops have largely gone unrealized in fruits and vegetables where insect management continues to rely primarily on the use of synthetic insecticides [ 3 ]. This situation is changing in Bangladesh with the introduction of Bt eggplant.

Eggplant ( Solanum melongena L.), or brinjal as it is called in Bangladesh and India, is one of the most important and popular vegetables in South and Southeast Asia. The crop is damaged severely by the brinjal fruit and shoot borer (BFSB)( Leucinodes orbonalis Guenee) (Lepidoptera: Crambidae). The caterpillar damages brinjal by boring into the petiole and midrib of leaves and tender shoots, resulting in wilting and desiccation of stems. Larvae also feed on flowers, resulting in flower drop or misshapen fruits. The most serious economic damage caused by BFSB is to the fruit, because the holes, feeding tunnels, and larval excrement may make the fruit unmarketable and unfit for human consumption.

BFSB poses a serious problem because of its high reproductive potential, rapid turnover of generations and intensive damage during the wet and dry seasons. Infestation levels may exceed 90% and the yield loss has been estimated up to 86% in Bangladesh [ 4 ]. It has been reported that 98% of Bangladeshi farmers rely solely on insecticide sprays to control BFSB [ 5 ] and farmers may apply as many as 84 insecticide sprays during the cropping season [ 6 ]. This practice is unhealthy for consumers, farmers, and the environment, and is expensive to farmers. As an alternative to intensive use of insecticides, the India-based Maharashtra Hybrid Seed Company (Mahyco) inserted the Cry1Ac gene, under the control of the constitutive 35S CaMV promoter, into eggplant (termed ‘event’ EE-1) to control feeding damage by EFSB. Bt eggplant demonstrated control of EFSB in contained greenhouse trials in India [ 7 ]. In late 2003, a partnership was formed between Mahyco, Cornell University, United States Agency for International Development (USAID), and public sector partners in India, Bangladesh, and the Philippines under the Agricultural Biotechnology Support Project II [ 7 ]. Mahyco donated the EE-1 event to the Bangladesh Agricultural Research Institute (BARI), where it was incorporated into BARI-developed local varieties. Subsequently, BARI applied to the National Technical Committee on Crop Biotechnology (NTCCB) to release Bt eggplant. Following the recommendation from the NTCCB, the application for release was forwarded to the National Technical Committee on Crop Biotechnology (NTCCB) Core Committee followed by the National Committee on BioSafety (NCB). The Bangladesh government granted approval for release of four varieties on 30 October 2013. On 22 January 2014, Bt seedlings were distributed to 20 farmers in four districts making Bangladesh a pioneer in the world to allow the commercial cultivation of a genetically engineered vegetable crop.

The effective use of this technology requires important knowledge of the agronomic nature of the four varieties and their ability to control BFSB. Furthermore, information is needed on how Bt brinjal affects non-target pest arthropods that likely can affect the yield and quality of brinjal. Performance of the four Bt brinjal varieties and their isolines in spray and no-spray conditions should provide information about the potential damage by these other pests. Likewise, it is important to assess the effect of Bt brinjal on beneficial arthropods, especially biological control agents that can help suppress pest populations. Here we report on the first replicated field trials in Bangladesh to assess the ability of the four Bt brinjal varieties to control BFSB, with and without a standard insecticide regime, compared to their non-Bt isolines. In addition, we assessed plant growth characteristics, economic returns, and potential effects on non-target arthropod pests and on beneficial arthropods that might supply important biological control services.

Materials and methods

Two sets of complementary experiments were conducted over a two-year period (2016–7) in Bangladesh. In the first experiment, the four commercialized Bt lines were compared to their non-Bt isolines, with and without insecticide sprays to: a) assess their ability to protect the plant from EFSB, b) assess their agronomic characteristics, c) document effects on other arthropods, and d) assess their economic return. In the second experiment, a single Bt line was compared to its isoline, with and without insecticide sprays. This experiment placed more emphasis on assessing the effects of the lines and spray treatments on non-target arthropods, while also assessing the ability of the treatments to control EFSB and provide favorable economic returns.

Comparisons of four Bt Brinjal varieties and their isolines

Plants, sprays and experimental design..

Experiments were conducted at the On-Farm Research Division (OFRD) of BARI, in the Bogra District (089 0 22.922 l E; 24 0 51.056 l N) of Bangladesh. In both years, the experimental field was laid out in a randomized complete block split-plot design with four replications that included insecticide spray regimes as main plots and varieties as sub-plots.

The trials utilized the four Bt brinjal varieties released by BARI to farmers in 2014 [ 7 ]: BARI Bt begun-1, BARI Bt begun-2, BARI Bt begun-3, BARI Bt begun-4) and their respective non-Bt isolines (BARI begun-1, BARI begun-2, BARI begun-3, BARI begun-4). In each year, the experimental area was ca. 0.1 ha. Main plots receiving insecticides treatments were 6.0 m x 12.0 m with 3.0 m × 3.0 m sub-plots receiving the brinjal varieties. The distances between sub-plots, main-plots and blocks were 30.0 cm, 1.0 m and 1.5 m, respectively. Row-to-row and plant-to-plant spacing was 100 cm and 75 cm, respectively. Seedlings 35-days old) were transplanted on 12 January 2016 and 10 February 2017.

Sprayed plots were treated weekly with both Admire 20SL (imidacloprid) at 0.5 ml/L of water (active ingredient 50 ml/ha) for sucking arthropods (whiteflies, mites, jassids and aphids) and Proclaim 5SG (emamectin benzoate) at 1 g/L of water (active ingredient 25 g/ha) for BFSB. These two insecticides are commonly used in brinjal production in Bangladesh and weekly, or more frequent, spray schedules are the norm. Sprays were applied using a Knapsack sprayer. Spraying started from crop establishment and continued at weekly intervals to the last harvest, 30 May in 2016 and 25 June in 2017. Before spraying, the non-sprayed plots were covered with a non-porous cloth to prevent spray drift. Non-sprayed plots were sprayed with water only. Fertilizers were used at 138-40-100-18-1.7–3.6 kg/ha (NPKSBZn) and cowdung at10 t/ha. Irrigation, weeding, pruning of side shoots and other cultural operations were done when necessary following standard practices for brinjal production in Bangladesh [ 8 ].

Measurements and data analysis.

Data were collected weekly on plant growth patterns (plant height and width (or bushiness)), number of flowers per plant, percent damaged shoots and fruits by BFSB, and marketable and non-marketable fruits on each of four plants per plot. Arthropod populations (pests and beneficials) were sampled weekly on the five newest leaves on each of four randomly selected plants per plot. The upper and lower surfaces of the leaves were thoroughly examined for the presence of arthropods. All weekly counts were taken from 1 February to 30 May, 2016 and from 1 April to 25 June, 2017. Arthropod counts were made before 9 am. A mixed-model, split-plot ANOVA was used for analyses with block as the random effect and plant type and insecticide as fixed effects. Each year was analyzed separately. The response variable was the seasonal mean for each variable examined over time. Arcsine square-root transformations were applied to percentage data but untransformed means are presented. Mean differences were contrasted using Tukey’s HSD test and analyses were done using the statistical package ‘R’. The total seasonal pesticide load (each insecticide applied × number of applications × dose) was used to estimate the Environmental Impact Quotient (EIQ) [ 9 ].

Comparisons of one Bt Brinjal variety and its isoline

The experiment was conducted at OFRD, BARI, Bogra (089 0 22.858 ′ E; 24 0 51.088 ′ N), Bangladesh. In both years, the experiment was laid out in a ca. 0.1 ha field using a randomized complete block design with four replications. Plot size was 4.5m × 9.0 m and the distances between plots and blocks were 30.0 cm and 1.5 m, respectively. Treatments consisted of two varieties of brinjal, BARI Btbegun-1 and BARI begun-1 (non-Bt isoline), each sprayed or unsprayed with insecticides for a total of four treatments. Seedlings were transplanted on January 13, 2016 and on 11 February 2017. Plant spacing, fertilizer use, cultural practices and insecticide sprays were as described above.

Data were taken weekly on percent damaged shoots and fruits by BFSB, marketable and non-marketable fruits, economic returns and densities of arthropods (pest and beneficial arthropods). Arthropod populations were assessed using three methods: 1) Counts on plants were taken from 10 randomly selected plants from the interior of the plot. For each plant, all arthropods were counted on the upper and lower surface of the top 5 leaves and counts were made before 9 am; 2) yellow sticky traps (45×18 cm) were used to measure aerial populations of insects with three traps placed in each plot at crop canopy level; 3) pitfall traps were used to measure ground dwelling arthropods. Three plastic cups (10 cm diameter and 8 cm deep) were placed in the soil in each of the plots with the mouth of the cup at ground level. Each cup was half-filled with water and a few drops of detergent as a trapping fluid. Each week the trapped arthropods were placed into plastic bottles filled with 70% alcohol. Samples were labeled and stored until identified. All counts were done weekly from 2 February to 31 May, 2016 and from 1 April to 25 June, 2017. A mixed-model ANOVA was used with block as the random effect and plant type and insecticides as fixed effects. Each year was analyzed separately. The response variable was the seasonal mean for each variable examined over time. Arcsine square-root transformations were applied to percentage data but untransformed means are presented. Mean differences were contrasted using Tukey’s HSD test and analysis were done using the statistical package ‘R’. The total seasonal pesticide load (each insecticide applied × number of applications × dose) was used to estimate the Environmental Impact Quotient (EIQ) [ 9 ]. As before, gross returns and gross margins were estimating with a partial budgeting analysis.

Infestation by BFSB.

In both years, significant differences were observed among the varieties for BFSB infestation ( Table 1 ). Regardless of spray regime, there was no shoot infestation by EFSB in any of the four Bt brinjal varieties in either year, but shoot infestation occurred in all non-Bt brinjal varieties regardless of spray regime. Infested fruit for Bt varieties varied from 0 to 2.27% and was not significantly affected by the spray regime in either year. In contrast, the percent infested fruit in the non-Bt brinjal varieties reached 36.70% in 2016 and 45.51% in 2017 ( Table 1 ). Another measure of fruit infestation was the percent fruit infested by weight, which was more reflective of income because brinjal is sold by weight and infested fruit bring a lower value. By weight, the highest percent of infested Bt brinjal fruit was only 2.27% (2016, BARI Bt begun 2), compared to the highest rate for non-Bt brinjal of 44.30% (2017, non-Bt isoline 1). In some cases, spraying significantly reduced infestation in non-Bt varieties [e.g. 2016, sprayed non-Bt isoline 4 reduced damage by 25.38% (34.49–9.11%) while in other cases spraying did not (e.g. 2016, non-Bt isoline 2 (21.89–19.17%)].

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https://doi.org/10.1371/journal.pone.0205713.t001

Yields and gross margins.

In both years, significant differences were evident in the economic returns due to BFSB infestation and costs for spraying ( Table 2 ). In both years all Bt lines has higher gross margins than their isolines, regardless of whether they were sprayed or not. In 2016, all four Bt brinjal varieties showed a positive gross margin, even when no sprays were applied. In contrast only two of the non-Bt isolines that were sprayed showed a positive gross margin when sprayed and only one of the unsprayed non-Bt isolines showed a positive gross margin. In 2017, all of the non-sprayed, non- Bt isolines had negative gross margins, as did one of the non-sprayed Bt brinjal varieties (BARI Bt begun-4). In 2016, spraying Bt brinjal varieties always improved the gross margin, despite the higher production costs. Spraying a non-Bt isoline could also improve its gross margin, but not to the level of the sprayed Bt variety (e.g., in 2016 the gross margin for sprayed Bt begun-2 was $7,634.74 compared to its sprayed non-Bt isoline of $2,458.00).

https://doi.org/10.1371/journal.pone.0205713.t002

Effects on non-target pest arthropods.

Eleven different non-target pest arthropods were observed. Five sucking pests, including whitefly ( Bemisia tabaci Gennadius), thrips ( Thrips palmi Karny), aphid ( Aphis gossypii Glover), jassid ( Amrasca biguttula biguttula Ishida) and mites ( Tetranychus urticae Koch) had populations > 0.2 per leaf per and were analyzed ( Table 3 ). Populations of flea beetle ( Phyllotreta striolata ), armyworms ( Spodoptera litura ), Mirid bug ( Helopeltis sp.), Epilachna beetle ( Epilachna sp.) and stink bug ( Nezara sp.) were too low (< 0.01) for meaningful analysis. In 2016, spraying generally significantly increased populations of whiteflies and mites but decreased populations of thrips, aphids and jassids. While there were significant differences in other non-target pest populations, they were always at low densities and effects were likely of little consequence. In 2017, generally there were statistically higher populations of whiteflies, but statistically lower populations of aphids and jassids in sprayed plots. Mites were not present in 2017 probably because of the wetter weather. Variety did not have a consistent effect on densities of non-target pest arthropods.

https://doi.org/10.1371/journal.pone.0205713.t003

Effects on non-target beneficial arthropods.

Ladybird beetles and spiders were the most abundant beneficial arthropods but still only reached 0.067 beetles/leaf and 0.030 spiders/leaf in 2016 ( Table 4 ). Populations of red ant ( Solenopsis sp.), rove beetle ( Homaeotarsus sp.), assassin bug ( Zelus sp.), ground beetle ( Ophionia nigrofasciata ,), syrphid fly ( Syrphus sp.) and small black ant ( Camponotus sp.) were too low (< 0.01) for meaningful analysis. In 2016 spraying sometimes, but not consistently, significantly reduced the populations of Coccinella sp. but not spiders. In 2017, there were no significant differences between populations of Coccinella and spiders on a variety in sprayed and non-sprayed plots. Variety did not have a consistent effect on non-target beneficial arthropods.

https://doi.org/10.1371/journal.pone.0205713.t004

Variety characteristics.

There were some significant differences in plant height, bushiness (width) and number of shoots and flowers per plant between Bt varieties and their non-Bt isolines ( Table 5 ). However, there were no clear trends that Bt plants differed from their isolines in height or bushiness, although it has been suggested that BFSB-infested shoots may affect plant architecture by killing stems. Likewise, spraying did not appear to have a consistent effect on plant characteristics. The most dramatic and consistent differences were the number of fruit per plant. Generally, Bt plants had significantly more fruit per plant than their respective non-Bt isolines in both years.

https://doi.org/10.1371/journal.pone.0205713.t005

Environmental impact quotient.

In both years, the same number of sprays (19) was applied to sprayed plots. The seasonal insecticide load/ha for imidacloprid and emamectin benzoate was 36.7 and 26.3 mg active ingredient per ha, respectively, and the seasonal calculated EIQ values for imidacloprid were 8.4 (consumer), 5.6 (field worker) and 75.5 (ecological) and for emamectin benzoate 1.7 (consumer), 3.8 (farmer worker) and 27.9 (ecological).

Comparisons of one Bt Brinjal varieties and its isoline

In both years, in all but one case (i.e., sprayed plots in 2016) there were significant differences in infested shoots and fruit for Bt begun-1 compared to its isoline ( Table 6 ). In 2016, percent infested fruit for Bt begun-1varied between 0–0.16% depending on whether it was sprayed or not, while its non-Bt isoline had infestation rates between 39.33 and 50.85% when sprayed or not, respectively. In 2017, similar lack of infestation of Bt begun-1 fruit was observed whether it was sprayed or not, while its isoline had 41.52% infested fruit when sprayed and 52.43% when not sprayed ( Table 6 ).

https://doi.org/10.1371/journal.pone.0205713.t006

In 2016 Bt begun-1 had a higher gross margin than its isoline. Spraying Bt begun-1 improved the gross margin to $2,962.94 /ha compared to not spraying Bt begun-1 ($939.42/ha), despite the increased cost of production ( Table 7 ). Likewise, in 2017 spraying Bt begun-1 improved the gross margin from $1,289.78 to $3,654.59. In both years, not spraying the non-Bt line resulted in a negative gross margin.

https://doi.org/10.1371/journal.pone.0205713.t007

Eleven non-target pest arthropods species were observed on leaves in 2016, with whiteflies, aphids, thrips, jassids, flea beetles and mites having sufficient populations for meaningful analysis ( Table 8 ). Only with jassids and mites did variety have a significant effect, but it was not consistent. Spraying increased whiteflies abundance in both years, but this was not the case with most other species.

https://doi.org/10.1371/journal.pone.0205713.t008

Populations of mirid bug ( Helopeltis sp.), monolepta beetles ( Monolepta sp), shield bug ( Scutiphora sp.), leaf miner ( Liriomyza sp.) and green leafhopper ( Nephotettix sp.) were too low (< 0.01) for meaningful analysis. In 2017, populations of flea beetle ( Phyllotreta striolata ), Mirid bug ( Helopeltis sp.), Epilachna ( Epilachna sp.), Bombardier beetle ( Pheropsophus sp.), Hooded hopper ( Oxyrachis terandus Fab.), Semiloper ( Trichoplusia sp.), mites ( Tetranychus urticae Koch) and green leaf hopper ( Nephotettix sp.) were too low (< 0.01) for meaningful analysis.

In 2016, there were only six non-target species of pest arthropods caught in pitfall traps: flea beetles, grasshoppers, monolepta beetles, termites, June beetles and stink bugs, and their populations were all too low for meaningful analysis. Sticky traps captured aphids, whiteflies, flea beetles and jassids but populations were too low for meaningful analysis. Similar results were observed in 2017 with all different sampling methods.

As in the first experiment, lady beetles and spiders were the most abundant beneficial arthropods in 2016 on plant samples, but only reached a peak of 0.04 beetles/leaf and 0.029 spiders/leaf ( Table 9 ). Spraying did not have a consistent effect on reducing the densities of either beneficial in either year. Populations of red ant ( Solenopsis sp.), small black ant ( Camponotus sp.), rove beetle ( Homaeotarsus sp.), ground beetle ( Ophionia nigrofasciata sp.) and syrphid fly ( Syrphus sp.), honeybee ( Apis sp.) and bombardier beetle ( Pheropsophus sp.) were too low (<0.01) for meaningful analysis. For pitfall traps, the numbers of beneficial arthropods captured were too low (<0.1/trap) for meaningful analysis.

https://doi.org/10.1371/journal.pone.0205713.t009

Yellow sticky traps caught flying insects in the crop canopy but the numbers were low, with the highest counts being ladybird beetles at 0.446 per trap/wk. ( Table 10 ). In neither year were populations affected by insecticide sprays or brinjal variety. Populations of rove beetle ( Homaeotarsus sp.), damsel fly ( Agriocnemis sp.) and ground beetle ( Ophionia nigrofasciata ) were too low (<0.01) for meaningful analysis.

https://doi.org/10.1371/journal.pone.0205713.t010

Environmental impact quotient (EIQ).

The same number of sprays (19) was applied to sprayed plots in both years. The seasonal insecticide load/ha for imidacloprid and emamectin benzoate was 36.7 and 26.3 mg active ingredient per ha, respectively, and the seasonal calculated EIQ values for imidacloprid were 8.4 (consumer), 5.6 (field worker) and 75.5 (ecological) and for emamectin benzoate 1.7 (consumer), 3.8 (farmer worker) and 27.9 (ecological). This was the same as in the first experiment with four varieties.

These studies present the first replicated field trials assessing the four Bt brinjal varieties that were first introduced to Bangladesh farmers in 2014. Collectively, the results demonstrate that the four Bt varieties provided more fruit and nearly complete protection from infestation by BFSB, compared to their non-Bt isolines, even when no insecticide sprays were applied. Most importantly, these studies revealed that all Bt lines had higher gross returns than their non-Bt isolines.

The insecticide spray regime of using imidacloprid and emamectin benzoate on non-Bt brinjal was unable to decrease the level of BFSB infestation to the level achieved by using its Bt brinjal isoline. Furthermore, it is worth noting that spraying these insecticides tended to increase the infestation in non-Bt fruit. For example, in Table 1 for 2016 the average percent of infested fruit by weight of all four non-Bt lines was 28.2% when they were sprayed and only 16.5% when not sprayed. In 2017 the same phenomenon occurred with an average infestation of 38.4% when sprayed and only 18.9% when not sprayed. One hypothesis for this phenomenon is that spraying reduced the natural enemy population of BFSB and thus increased damage to the brinjal. Further work is needed to confirm this hypothesis.

When comparing the arthropod communities in Bt and non-Bt brinjal, we were not able to detect any differences in numbers of either non-target pest species or beneficial species, suggesting that the four Bt brinjal varieties control the most important insect pest of brinjal in Bangladesh, BFSB, without disrupting arthropod biodiversity. However insecticide sprays did have a disruptive effect on some species of beneficial arthropods and this could support the hypothesis proposed above.

It is important to note that the yield of Bt brinjal, based on weight of the fruit, was improved with the insecticide spray regime. It appears that arthropods such as whiteflies, mites, jassids and aphids, none of which are susceptible to Cry1Ac, still need to be managed. Scheduled applications of the two insecticides, without regard to any threshold, resulted in a relatively high EIQ. The next challenge will be to develop thresholds for the common sucking arthropods encountered in Bangladesh using selective insecticides that will not disrupt biological control agents of BFSB. These experiments are currently underway.

These results from Bangladesh are similar to those from studies conducted in the Philippines in which event EE-1, the same event used to create the four Bt brinjal varieties used in these studies, was incorporated into open pollinated lines and provided almost complete control of BFSB in different locations over three cropping periods [ 10 ]. Furthermore, additional ecological studies in the Philippines [ 11 ] documented that many arthropod taxa are associated with Bt eggplants and their non-Bt comparators, but found few significant differences in seasonal mean densities of arthropod taxa between Bt and non-Bt eggplants when no insecticides were used. Principal Response Curve analyses showed no statistically significant impact of Bt eggplants on overall arthropod communities through time in any season. Furthermore, the Philippine studies found no significant adverse impacts of Bt eggplants on species abundance, diversity and community dynamics, particularly for beneficial NTOs. Similarly, in the present study we did not find any differences in the arthropod communities in any Bt brinjal variety compared to its non-Bt isoline. This is not surprising because the ecological effect of Cry1Ac has been extensively studied and shown to have little to no effect on non-target organisms outside of the Lepidoptera [ 12 – 24 ].

In most cases, statistically higher numbers of non-target pest arthropods were observed in no-spray plots irrespective of varieties, except for whitefly and mites. Furthermore, populations were similar in Bt and non-Bt isolines, irrespective of spray regime in most cases. Similar patterns have been observed before. In India, it has been reported that populations of major non-target insect pests (leafhoppers, whiteflies, ash weevils, aphids, dusky and red cotton bug, and green bug) and generalist predators (ladybirds, chrysopids, and spiders) did not differ significantly between Bt and non-Bt cotton lines, while their numbers were lower in insecticide protected than under unprotected conditions, except for aphids and whiteflies [ 25 ]. Ladybird beetles were more abundant in no-spray plots but similar abundances of non-target beneficial and other arthropods were observed in Bt and non-Bt isolines irrespective of spray regime in most cases. Other larger scale studies and meta-analyses have documented that Bt crops were much safer to non-target organisms than the alternative use of traditional insecticides for control of the pests targeted by the Bt proteins [ 17 , 19 , 21 , 26 ].

Plot sizes in this study were relatively small and the effect of this on study outcomes would depend somewhat on the general mobility of the species examined. While specific guidance on plot size for any given study is not available, there is general agreement that plots should be as large as possible to avoid inter-plot exchanges of arthropods [ 27 , 28 ]. In reality, plot size is typically dictated by experimental design issues, and space resources as they were here. Nonetheless, despite small plots sizes, this study clearly delineated the effects of Bt eggplant on target pest abundance and its associated impact on yield. Furthermore, other experiments and research syntheses that have examined a wide range of plots sizes have shown that plot size has a relatively small impact on the assessment of non-target effects [ 17 , 29 ]. Finally, the very small scale of individual farms growing eggplant in Bangladesh suggests that our studies are reflective of commercial practices, and thus of potential non-target effects.

Since their first introduction in 1996, biotech crops have been large scale field crops except for the relatively small-scale production of virus resistant papaya and squash and insect-resistant sweet corn, and the brief commercialization of Bt potatoes [ 3 ]. Bt brinjal is the first Bt vegetable crop introduced into a developing country and the results reported here indicate that it can be highly successful. An ex-ante study in 2005 estimated that the introduction of Bt eggplant into Bangladesh would result in a decrease of insecticides by 70–90%, increase yield by 15–30% and increase the gross return by 37–64% [ 30 ]. The data from this study supports these general predictions.

To realize these benefits of Bt brinjal for the long term, it is critical that EFSB does not rapidly evolve resistance to the Cry1Ac protein it produces. A government condition for the release of Bt brinjal in Bangladesh requires planting a refuge of non-Bt brinjal and training materials provided to farmers emphasizing that non-Bt brinjal should be planted around Bt fields as a structured refuge. In addition, to this refuge, there are many non-Bt varieties commonly grown in Bangladesh that also may serve as a natural refuge for resistance management. Nonetheless, it will be important to develop lines that express multiple Bt proteins because this is another key factor in delaying the evolution of resistance [ 31 ].

In conclusion, the four varieties of Bt eggplant examined here provide high levels of BFSB control, demonstrate higher gross returns than their non-Bt isolines and have the potential to greatly reduce insecticide inputs and their associated costs for management of this devastating pest. Additional controls of other pests in the system appear to be important and further development of management strategies for these will likely lead to further favorable economic returns in crop production for farmers growing brinjal in Bangladesh. In addition to providing excellent pest suppression, cultivation of Bt brinjal demonstrated no undesirable non-target effects on other arthropods in the system, especially those beneficial organisms that contribute important ecosystem services like biological control. Overall, careful stewardship will be critical to preserving this valuable pest control technology as adoption continues to increase from the more than 27,000 farmers who grew Bt brinjal in 2018 [ 32 ].

Acknowledgments

We gratefully acknowledge the support provided by the United States Agency for International Development (USAID) for their Feed the Future South Asia Eggplant Improvement Partnership (AID-OAA-A-15-00052). We are grateful for the help provided by the Bangladesh Agricultural Research Institute.

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A Feasibility on the Establishment of Fusion of Eggplant Nuggets in Quirino Highway Novaliches Quezon City

• Michaella Dela Cruz
• Erdie Aguinaldo
• Glaiza Torres
• Eduardo Sereno
• Paul Vincent SD. Quinto, LPT

Many Filipinos love to eat vegetable, so the researchers created a simple eggplant nuggets for people especially for those people who don't eat vegetables. We created this dish to help those people who don't like to eat vegetables. The primary concept of the business is to produce quality eggplant nuggets that is healthy, tasty, affordable and within the budget. The product we produce has a unique quality, these product are for any people. This type of nuggets is most likely for kids, teens and also for adults. This is not just an ordinary tasting nuggets that is healthy but one that is also available at a very affordable price. The researchers chose survey because this method is easy to do. The researchers will just give free taste of their product to the respondents and allow them to answer the questions about the researchers' product or business. The survey result of our product (veggie nuggets) is 100% successful, because most of our respondents/customers like the taste of the product (Eggplant Nuggets), because of its new flavor, affordable price and nutritious benefits. Veggie Nuggets is much healthier compares to other foods because we add a very nutritious vegetable which is eggplant and carrots. The main focus of our business is to give the healthier version of veggie nuggets. Veggie Nuggets introduces new version of Eggplant nuggets made by the researchers which is to aim offer a new food not only delicious but also nutritious. According to Sally Kuzenchak, these veggie nuggets are healthy and simple to make and is perfect for lunch boxes and snacks. In the Philippines, we are able to have our own combination which fits our dishes. Researchers discover Veggie Nuggets and Eggplant Nuggets for those people who don't eat vegetables. Researchers created this dish to help those people who don't like to eat vegetables. The primary concept of the business is to produce quality Eggplant Nuggets that is healthy, tasty and affordable.

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Francis Collins: Why I’m going public with my prostate cancer diagnosis

I served medical research. now it’s serving me. and i don’t want to waste time..

Over my 40 years as a physician-scientist, I’ve had the privilege of advising many patients facing serious medical diagnoses. I’ve seen them go through the excruciating experience of waiting for the results of a critical blood test, biopsy or scan that could dramatically affect their future hopes and dreams.

But this time, I was the one lying in the PET scanner as it searched for possible evidence of spread of my aggressive prostate cancer . I spent those 30 minutes in quiet prayer. If that cancer had already spread to my lymph nodes, bones, lungs or brain, it could still be treated — but it would no longer be curable.

Why am I going public about this cancer that many men are uncomfortable talking about? Because I want to lift the veil and share lifesaving information, and I want all men to benefit from the medical research to which I’ve devoted my career and that is now guiding my care.

Five years before that fateful PET scan, my doctor had noted a slow rise in my PSA, the blood test for prostate-specific antigen. To contribute to knowledge and receive expert care, I enrolled in a clinical trial at the National Institutes of Health, the agency I led from 2009 through late 2021.

At first, there wasn’t much to worry about — targeted biopsies identified a slow-growing grade of prostate cancer that doesn’t require treatment and can be tracked via regular checkups, referred to as “active surveillance.” This initial diagnosis was not particularly surprising. Prostate cancer is the most commonly diagnosed cancer in men in the United States, and about 40 percent of men over age 65 — I’m 73 — have low-grade prostate cancer . Many of them never know it, and very few of them develop advanced disease.

Why am I going public about this cancer that many men are uncomfortable talking about? Because I want to lift the veil and share lifesaving information.

But in my case, things took a turn about a month ago when my PSA rose sharply to 22 — normal at my age is less than 5. An MRI scan showed that the tumor had significantly enlarged and might have even breached the capsule that surrounds the prostate, posing a significant risk that the cancer cells might have spread to other parts of the body.

New biopsies taken from the mass showed transformation into a much more aggressive cancer. When I heard the diagnosis was now a 9 on a cancer-grading scale that goes only to 10, I knew that everything had changed.

Thus, that PET scan, which was ordered to determine if the cancer had spread beyond the prostate, carried high significance. Would a cure still be possible, or would it be time to get my affairs in order? A few hours later, when my doctors showed me the scan results, I felt a rush of profound relief and gratitude. There was no detectable evidence of cancer outside of the primary tumor.

Later this month, I will undergo a radical prostatectomy — a procedure that will remove my entire prostate gland. This will be part of the same NIH research protocol — I want as much information as possible to be learned from my case, to help others in the future.

While there are no guarantees, my doctors believe I have a high likelihood of being cured by the surgery.

My situation is far better than my father’s when he was diagnosed with prostate cancer four decades ago. He was about the same age that I am now, but it wasn’t possible back then to assess how advanced the cancer might be. He was treated with a hormonal therapy that might not have been necessary and had a significant negative impact on his quality of life.

Because of research supported by NIH, along with highly effective collaborations with the private sector, prostate cancer can now be treated with individualized precision and improved outcomes.

As in my case, high-resolution MRI scans can now be used to delineate the precise location of a tumor. When combined with real-time ultrasound, this allows pinpoint targeting of the prostate biopsies. My surgeon will be assisted by a sophisticated robot named for Leonardo da Vinci that employs a less invasive surgical approach than previous techniques, requiring just a few small incisions.

Advances in clinical treatments have been informed by large-scale, rigorously designed trials that have assessed the risks and benefits and were possible because of the willingness of cancer patients to enroll in such trials.

I feel compelled to tell this story openly. I hope it helps someone. I don’t want to waste time.

If my cancer recurs, the DNA analysis that has been carried out on my tumor will guide the precise choice of therapies. As a researcher who had the privilege of leading the Human Genome Project , it is truly gratifying to see how these advances in genomics have transformed the diagnosis and treatment of cancer.

I want all men to have the same opportunity that I did. Prostate cancer is still the No. 2 cancer killer among men. I want the goals of the Cancer Moonshot to be met — to end cancer as we know it. Early detection really matters, and when combined with active surveillance can identify the risky cancers like mine, and leave the rest alone. The five-year relative survival rate for prostate cancer is 97 percent, according to the American Cancer Society , but it’s only 34 percent if the cancer has spread to distant areas of the body.

But lack of information and confusion about the best approach to prostate cancer screening have impeded progress. Currently, the U.S. Preventive Services Task Force recommends that all men age 55 to 69 discuss PSA screening with their primary-care physician, but it recommends against starting PSA screening after age 70.

Other groups, like the American Urological Association , suggest that screening should start earlier, especially for men with a family history — like me — and for African American men, who have a higher risk of prostate cancer. But these recommendations are not consistently being followed.

Our health-care system is afflicted with health inequities. For example, the image-guided biopsies are not available everywhere and to everyone. Finally, many men are fearful of the surgical approach to prostate cancer because of the risk of incontinence and impotence, but advances in surgical techniques have made those outcomes considerably less troublesome than in the past. Similarly, the alternative therapeutic approaches of radiation and hormonal therapy have seen significant advances.

A little over a year ago, while I was praying for a dying friend, I had the experience of receiving a clear and unmistakable message. This has almost never happened to me. It was just this: “Don’t waste your time, you may not have much left.” Gulp.

Having now received a diagnosis of aggressive prostate cancer and feeling grateful for all the ways I have benefited from research advances, I feel compelled to tell this story openly. I hope it helps someone. I don’t want to waste time.

Francis S. Collins served as director of the National Institutes of Health from 2009 to 2021 and as director of the National Human Genome Research Institute at NIH from 1993 to 2008. He is a physician-geneticist and leads a White House initiative to eliminate hepatitis C in the United States, while also continuing to pursue his research interests as a distinguished NIH investigator.

An earlier version of this article said prostate cancer is the No. 2 killer of men. It is the No. 2 cause of cancer death among men. The article has been updated.

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CYBERSECEVAL 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models

April 18, 2024

Large language models (LLMs) introduce new security risks, but there are few comprehensive evaluation suites to measure and reduce these risks. We present CYBERSECEVAL 2, a novel benchmark to quantify LLM security risks and capabilities. We introduce two new areas for testing: prompt injection and code interpreter abuse. We evaluated multiple state of the art (SOTA) LLMs, including GPT-4, Mistral, Meta Llama 3 70B-Instruct, and Code Llama. Our results show conditioning away risk of attack remains an unsolved problem; for example, all tested models showed between 25% and 50% successful prompt injection tests. Our code is open source and can be used to evaluate other LLMs. We further introduce the safety-utility tradeoff : conditioning an LLM to reject unsafe prompts can cause the LLM to falsely reject answering benign prompts, which lowers utility. We propose quantifying this tradeoff using False Refusal Rate (FRR). As an illustration, we introduce a novel test set to quantify FRR for cyberattack helpfulness risk. We find many LLMs able to successfully comply with “borderline” benign requests while still rejecting most unsafe requests. Finally, we quantify the utility of LLMs for automating a core cybersecurity task, that of exploiting software vulnerabilities. This is important because the offensive capabilities of LLMs are of intense interest; we quantify this by creating novel test sets for four representative problems. We find that models with coding capabilities perform better than those without, but that further work is needed for LLMs to become proficient at exploit generation. Our code is open source and can be used to evaluate other LLMs.

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Physicochemical, Functional, and Nutraceutical Properties of Eggplant Flours Obtained by Different Drying Methods

Jenny r. rodriguez-jimenez.

1 Facultad de Ciencias Biologicas, Universidad Autonoma de Nuevo Leon, Ave. Universidad S/N, Cd. Universitaria, 66450 San Nicolas de los Garza, Mexico; [email protected] (J.R.R.-J.); xm.moc.oohay@naujzeab (J.G.B.-G.); [email protected] (C.A.-G.)

Carlos A. Amaya-Guerra

Juan g. baez-gonzalez, carlos aguilera-gonzalez, vania urias-orona.

2 Laboratorio de Quimica y de Alimentos, Facultad de Salud Publica y Nutricion, Universidad Autonoma de Nuevo Leon, Col. Mitras Centro, C.P. 64460 Monterrey, Nuevo Leon, Mexico; [email protected]

Guillermo Nino-Medina

3 Laboratorio de Quimica y Bioquimica, Facultad de Agronomia, Universidad Autonoma de Nuevo Leon, Francisco Villa S/N, Col. Ex-Hacienda El Canada, C.P. 66050 General Escobedo, Nuevo Leon, Mexico

The importance of consuming functional foods has led the food industry to look for alternative sources of ingredients of natural origin. Eggplants are a type of vegetable that is valued for its content in phytochemical compounds and it is due to the fact that this research is conducted towards the development of eggplant flour as a proposal to be used as a functional ingredient in the food industry. In this study, the eggplant fruits were divided into four groups, based on the drying method and the equipment used: Minced, drying oven (T1); sliced, drying oven (T2); sliced and frozen, drying tunnel (T3); and sliced, drying tunnel (T4). All the eggplant flours showed the same trend regarding their antioxidant capacity and phenolic content in the order T2 > T4 > T1 > T3. The freezing of eggplant was found to have a negative effect on functional and antioxidant properties. With respect to their nutritional composition, the flours did not change in their crude fiber, protein, and fat contents. In general terms, the T2 flour is a potential ingredient for the preparation of foods with functional properties since it is rich in phenolic compounds and antioxidants.

1. Introduction

In recent years, the food industry has focused its efforts in the development of new products with properties that not only provide the necessary nutrients for human food, but also help prevent diseases related to nutrition such as diabetes, obesity, hypertension, and cardiovascular complications. It has been found that there is a significant correlation between the regular intake of phytochemicals and the prevention of these lifestyle-related diseases [ 1 ]. Antioxidants have attracted great attention as possible agents to prevent and treat diseases related to oxidative stress [ 2 ]. The antioxidants used by the food industry can be either from natural sources or from a synthetic origin (such as butylated hydroxytoluene and butylated hydroxyanisole). The latter has been found to be potentially carcinogenic and toxic [ 3 ]. Consequently, a niche in the food industry is opened to replace the existing synthetic antioxidants with those of natural origin found in fruits and vegetables, which are mainly vitamins and polyphenols [ 2 ].

Eggplant is an economically important vegetable crop from the tropical and subtropical zones of the world [ 4 ]. This crop produces fruit of different colors, sizes, and shapes [ 5 ]. Eggplant is a valued vegetable for its composition in phytochemicals considered as nutraceuticals [ 6 ], in particular, polyphenols and dietary fiber [ 4 ].

In Mexico, eggplant production was 172,112 tons in 2016. It is mostly exported to the United States as this vegetable is not commonly consumed domestically [ 7 ] due to a lack of information regarding its preparation and characteristics. Eggplant has a non-climacteric pattern of respiration, which leads to a short shelf life despite being harvested in immature stages of development [ 8 ]. Therefore, the use of eggplant is suggested as a flour with high nutritional value, which can also be used as an antioxidant of natural origin. Therefore, the objective of this work is to evaluate the physicochemical, functional, and nutraceutical properties of eggplant flour as a proposed functional ingredient.

2. Results and Discussion

2.1. eggplant flour samples.

The eggplant flour produced was labelled as T1 (eggplant minced and dried at 45 °C–50 °C in a drying oven), T2 (sliced eggplant dried at 45 °C–50 °C in a drying oven), T3 (sliced eggplant was frozen and dried at 40 °C–45 °C in a tunnel dryer), and T4 (sliced eggplant dried at 40 °C–45 °C in a tunnel dryer). Eggplant is a vegetable with a high percentage of water (approximately 90%), which allows microorganisms and biochemical reactions to deteriorate, thus reducing its shelf life. In general, eggplant is a difficult vegetable to dehydrate due to its high percentage of water, which implies long drying times. With the use of the drying tunnel, the drying time was reduced from 48 h (drying oven) to 16 h (70% reduction in efficiency), showing that dehydration is faster when the air speed increases [ 9 ] and the speed of drying at high temperature decreases due to the hardening phenomenon [ 10 , 11 , 12 ].

2.2. Proximal Chemical Analysis

The results of the nutritional composition of the eggplant flours are shown in Table 1 . Eggplant flour has low values for moisture content (1.5% to 8.5%), below the Mexican standard (NOM-247-SSA1-2008) of 15% [ 13 ]. The T2 sample had the highest moisture content, while the T4 sample had the lowest moisture content. The moisture content obtained in this study was lower than other results (7.7% to 9.45%) reported from different types of Solanum melongena , dried in the same range (45 °C to 50 °C) of temperature [ 4 , 14 ].

Nutritional components of different obtained flours.

Eggplant minced and dried in a drying oven (T1), sliced eggplant dried in a drying oven (T2), sliced eggplant frozen and dried in a tunnel dryer (T3), and sliced eggplant dried in a tunnel dryer (T4). Average values with three replicates ± standard deviations, of three different lots. Mean values labeled with a different letter in the same file are significantly different ( p < 0.05). + Carbohydrates (%) = 100 − (% moisture + % ash + % fat + % protein + % crude fiber).

Flour having a moisture content of 9% to 10% is suitable for extended shelf life [ 15 ] since a lower moisture content in flour shows a better storage stability. The range of the average ash content determined among the four eggplant flours was 6.47%–7.31%, and it was similar to the eggplant ash content of other investigations treated under the same drying temperature conditions [ 4 , 14 ], compared to the ash content obtained from different types of eggplants (0.48%–1%), and 4.93%–13.7% (dry base) [ 16 ]; the drying treatment allows the concentration of the eggplant nutrients. Regarding the determination of proteins, the results obtained fell in a small range of 12.55%–12.77%. The results of this study are in accordance with the USDA database [ 17 ]. They have reported that protein content for fresh eggplant was 0.98% (12.73% in dry basic). Various types (Indian, Thai, Chinese, and white) of eggplants dried at the same temperature produced similar protein contents (12%–15%) [ 4 ] to the results obtained in this study.

The average fat content (1.75%) of the flour in this study was higher than that reported by Nino-Medina et al. [ 16 ] in fresh eggplant (Chinese, Philippine, Thai, Hindu and American types), with obtained values between 0.3% and 0.4% (dry base). Uthumporn et al. [ 4 ] found levels of 0.88% to 5.18% in different types (Indian, Thai, Chinese, and white) of eggplant flour; the lowest values were for flour samples made at 50 °C. Carbohydrates contents for the samples were between 57% and 65%. The result of the present investigation is similar in the amount of carbohydrates contained in the eggplant flour mentioned before, which were in the range of 62%–68%. The main soluble sugars were glucose and fructose [ 18 ]. They reported starch content between 1.43% and 2.38% in fresh eggplant. Eggplant flour contained a lower amount of carbohydrates and moisture compared with wheat flour, yet it had more fiber.

2.3. Physicochemical Parameters

The pH and titratable acidity are analytically determined in separate ways, and each has its own particular impact on food quality [ 19 ]. The pH is a good predictor of the ability of a microorganism to grow in a specific food, while the titratable acidity is a good predictor of the impact of acid content on the flavor of food [ 20 ]. On the other hand, color is the first notable characteristic of a food and often predetermines our expectations. Natural and synthetic colors play several roles in foods and consumers use the color as a way to identify a food and also as a way to judge the quality of a food [ 21 ].

With the exception of the titratable acidity and b* chromatic property, in which statistical differences were not observed ( p > 0.05), other physicochemical parameters showed statistical differences ( p < 0.05) between eggplant flour samples ( Table 2 ). The values of pH of eggplant flours were slightly acidic and ranged from 3.89 to 4.14, while titratable acidity values were low ranging from 0.46% to 0.47%. In addition, chromatic values were from 52.50 to 64.60, 4.55 to 9.65, 20.15 to 21.65, 21.09 to 23.60, and 65.98 to 77.54 in L* , a* , b* , C* , and h , respectively. All the eggplant flours had a “mostly desaturated dark orange” color. However, the color of the treatments 1 and 2 can be classified as a “pale brown”, while the treatments the color of the treatments 3 and 4 can be classified as “clear brown”; the main difference among these two colors tonalities is due mainly to the L* value.

Physicochemical parameters of different eggplant flours.

Potential of hydrogen (pH), titratable acidity (TA) and chromatic properties ( L* , a* , b* , C* , h ) of eggplant flours. Values are the average of three replicates ± standard deviations, of three different lots. Mean values labeled with a different letter in the same column are significantly different ( p < 0.05).

The cause of this color difference is attributed to the enzymatic browning of vegetable tissue, which is one of the main causes of loss of quality in food drying. The color values corresponding to the T1 and T2 samples show the effect caused by the Maillard reaction in eggplant during the drying process due to the formation of brown complex polymers (melanins) [ 22 ]. The T1 and T2 samples are more affected by this phenomenon due to the long drying times in the drying oven. The sample T3 shows a color similar to the aforementioned samples due to the damage by the low temperatures to which it was subjected before drying.

There is no literature available for comparison with the current report as there are no studies on the evaluation of chromatic properties of eggplant flour; however, flours obtained from other vegetables through similar methods to the ones used in this study have been previously reported. In this regard, Noor and Komathi [ 23 ] obtained flour from peeled pumpkin pulp and unpeeled pumpkin pulp. Their process for production of flour consisted in soaking the pumpkin pulps in a 0.1% sodium methabisulphite for 30 min; after that, the pulps were washed, sliced, and dried overnight at 60 °C. The chromatic properties of the obtained flours were 63.45, 15.68, 53.83, 56.07, and 73.76 for peeled pumpkin pulp flour and 64.93, 13.53, 49.45, 51.27, and 74.70 for unpeeled pumpkin pulp flour in L* , a* , b* , C* and h chromatic parameters. On the other hand, Que et al. [ 24 ] (2007) also obtained flour from pumpkin through hot air-drying procedures. In this study, the pumpkin flesh was cut into slices and hot air-dried at 70 °C for 54 h. Both products were ground and sieved using a 60 mesh screen (250 μm). The chromatic properties of the obtained flours were 80.15, 13.43, 48.63, 50.45, and 74.56 for freeze-dried flour, and 61.83, 11.12, 41.87, 43.32, and 75.13 for hot air-dried flour in L* , a* , b* , C* and h chromatic parameters.

All the chromatic parameters obtained in the studies mentioned above were higher than the chromatic properties of our eggplant flours; this could be mainly attributed to the fact that pumpkin has different chemical and physical characteristics from eggplant. Another important fact that produces a lower L* value in eggplant in contrast to pumpkin is the high concentration of phenolics in the eggplant skin (anthocyanins) and pulp (phenolic acids), which are oxidized by an enzymatic mechanism once they are sliced, and also to the non-enzymatic browning due to the heat treatment used in the production of the flour.

2.4. Functional Properties

The water holding capacity (WHC) of the samples was between 1.2 to 2 g water/g flour ( Table 3 ). Sample T4 (2.08 g water/g flour) had the highest amount of WHC and T1 (1.28 g water/g flour) had the lowest values. Similar values were found in frozen-dried flour from soy beans (1.8 g water/g flour) and pumpkin flour (1.5–2.5 g water/g flour) dried at 60 °C [ 23 , 25 ]. The capacity to absorb water is considered a functional property of proteins, fundamental in viscous foods such as sauces, soups, baked goods, and doughs, products where a good protein-water interaction is required [ 26 ]. Different protein structure and different hydrophilic carbohydrates contribute to the variation in WHC of flours [ 27 , 28 ]. This agrees with the result of Chen and et al. [ 29 ], study which reported that high WHC of fruit fibers is linked to the high pectin content of the fruits. The WHC aids modification of texture and viscosity in formulated food.

Functional properties of eggplant flours.

Water holding capacity (WHC), oil holding capacity (OHC) and emulsion capacity (EC) of eggplant flours. DW = dried weight. Values are the average of three replicates ± standard deviations, of three different lots. Mean values labeled with a different letter in the same column are significantly different ( p < 0.05).

The oil holding capacity (OHC) differed significantly ( p ≤ 0.05) among T1, T2, T3, and T4 ( Table 3 ). Treculia africana seed flour, prepared at 100 °C, parboiled and dried (55 °C, 24 h) had an OHC in the range of 1.14–1.3 g oil/g for flour [ 30 , 31 ] and the flour from soy beans (1.93 g oil/g flour) [ 25 ] had lower values than the eggplant samples. However, the Canavalia ensiformis flour (3.15 g oil/g flour) [ 32 ] had similar values to these results. This high oil holding capacity can be attributed to the high levels of nonpolar residues protein molecules [ 32 ]. On the other hand, the heat treatment increases the absorption of oil [ 31 ]. This is an increase attributed to the dissociation and denaturation of proteins by heat. The T4 and T2 treatments have a greater water/oil retention capacity than the T1 and T3 samples; these changes in the retention capacity can be attributed to the modification of the physical structure of the food. Methods of food processing such as freezing and mincing can affect protein conformation and hydrophobicity [ 33 , 34 ].

For the emulsification capacity (EC), T1 (25%) had the lowest value with respect to the T2, T3, and T4 ( Table 3 ), as Yu et al. [ 34 ] suggests, food processing methods affect protein conformation and hydrophobicity. The mincing process was the most probable reason for the lower EC of the T1 sample. Emulsification capacity is considered as an index of the ability of proteins or peptides to adsorb on the new created surface, delaying coalescence [ 35 ]. According to Kinsella et al. and Sathe et al. [ 36 , 37 ], the emulsifying capacity of proteins tend to decrease as protein concentration is increased; nevertheless, it was the opposite in this study.

In short, these functional properties verify the application of this flour as an ingredient in the formulation of a food, as the physical-chemical characteristics define the behavior of proteins, carbohydrates, and fibers in the processed food.

2.5. Total Phenols Content (TPC)

Polyphenols are a large group of phytochemicals that are considered responsible for the health benefits associated with fruits and vegetables [ 38 ]. Plant polyphenols can scavenge free radicals due to their chemical structure. The total phenols content (TPC) was markedly higher in samples T2 and T4 ( Table 4 ), while it was lower for samples T3 and T1 (4183 and 8211 mg chlorogenic acid/kg flour, respectively). Similar data were reported [ 39 ] in the juice from 31 eggplant varieties (commercial varieties, landraces, and hybrids between the landraces) that were in the range of 5450 to 10,480 (mg chlorogenic acid/kg of sample). It was found that eggplant displays an important intraspecific variation for the composition traits studied, and in some cases, there are considerable differences among the varietal types.

Phenolic compounds of eggplant flours.

Total phenols content (TPC), total flavonoid content (TFC) and condensed tannin content (CTC). mgCAE = milligrams of chlorogenic acid equivalents, mgCatE = milligrams of catechin equivalents, mgC3G = milligrams of cyanidin-3-glucoside, DW = dried weight. Values are the average of three replicates ± standard deviations, of three different lots. Mean values labeled with a different letter in the same column are significantly different ( p < 0.05).

Nino-Medina et al. [ 16 ] report similar results in their report based on a study in frozen, dried eggplant from different varieties (Chinese, Philippine, American, Hindu, and Thai); the total phenols content ranged from 15,120 to 20,490 (mg chlorogenic acid/kg of sample). The results obtained in this study were higher than the results of fresh eggplant by Nisha et al. [ 38 ] on different eggplant varieties that reported to contain between 490 to 1070 (mg gallic acid equivalents/kg of sample) and 570 to 650 (mg chlorogenic acid/kg of sample) for Black Beauty and Violetta Lunga varieties [ 40 ]. The low value of the T3 sample is due to the freezing before the dehydration; freezing reduces the original value of the food up to 80% due to the increase in water activity [ 41 ]. This has a greater effect than the mincing the sample, as it was in the case of the T1 sample.

2.6. Total Flavonoids Content (TFC)

The total flavonoids content of different eggplant flours is shown in Table 4 . TFC content follows the order T2 > T4 > T1 > T3. According to these results, a significant difference was found between the pre-drying treatments (mincing, slicing, and slicing/freezing), where the T3 sample was the most affected by the freezing treatment of eggplant slices before drying, showing the same behavior as the TPC. The decrease in TFC level in the flour subjected to pre-drying treatments (mincing and slicing/freezing) could occur because part of the anthocyanin was degraded during these treatments. Ninfali et al. [ 40 ] report a total flavonoid content between 257 and 284 mg caffeic acid equivalents/kg of the sample in fresh Black Beauty and Violetta Lunga eggplant varieties. Uthumporn et al. [ 4 ] found a range between 9090 and 29,180 mg catechin equivalent/kg, but their report was based on a study of eggplant flour dried at same temperature range as the one used in this study.

2.7. Total Catechins Content (TCC)

The content of total catechins was maintained in the range of 1240 to 3022 (mg catechins/kg flour), which reveals a significant difference between tannin contents of the four eggplant flour extracts ( p < 0.05). Indeed, the T1 extract presented the highest level among the four flour samples. Alkurd et al. [ 42 ] obtained 4137 mg tannic acid equivalents/kg from eggplant extract whole fruit, while Boulekbache et al. [ 43 ] obtained 42.6 mg tannic acid equivalents/kg from eggplant peels extract. The mincing of the eggplants before the drying process had a significant effect on the TCC in comparison with the other treatments after drying the eggplant.

2.8. Total Anthocyanins

The total anthocyanins content of different eggplant flours is shown in Table 4 . The results were in the range of 230 a 1612 (mgC3GE/kg Flour DW) and follows the order T1 > T2 > T4 > T3. Anthocyanins results obtained in this study were similar to those reported by Nino et al. [ 16 ] in different eggplant types of Chinese (1287 mgC3GE/kg of eggplant), Philippine (1610 mgC3GE/kg of eggplant), American (1234 mgC3GE/kg of eggplant), Hindu 828 mgC3GE/kg of eggplant), Thai (39 mgC3GE/kg of eggplant), and higher than those reported in the Black Bell eggplant type [ 6 ], Tunisina, Buia, and L305 [ 44 ] raw, grill and boiled (50 to 90, 15 to 41, 31 to 155, and 17 to 96 mg D3R/ 100 g of dry matter, respectively).

2.9. Antioxidant Capacity

Currently, there are numerous methods to measure the antioxidant capacity of a food. In this study, the antioxidant capacity of the flours was measured by using three methods (DPPH, ABTS, and FRAP), using vitamin E analogue as reference (Trolox).

Determination of scavenging stable DPPH free radical is a quick way to evaluate the antioxidant activity of the extracts [ 45 ]. Table 5 shows the DPPH activity results of all four different samples. The range was between 9111 to 54,815 (μM Trolox equivalents/kg flour). Nino-Medina et al. [ 16 ] found higher results than the results of this study, which were 78,500 μM Trolox equivalents/kg on frozen, dried American eggplant type.

Antioxidant activity of eggplant flours.

μMTE = micromoles Trolox equivalents. DW = dried weight. Values are the average of three replicates ± standard deviations of three different lots. Mean values labeled with a different letter in the same column are significantly different ( p < 0.05).

The ABTS (2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) assay is generated by the oxidation of the ABTS with potassium persulfate [ 46 ]. The results for ABTS assay ranged from 14,272 to 63,583 (μM Trolox equivalents/g flour). These results can be seen in Table 5 . The results of this study were higher than those reported by Okmen et al. [ 47 ]. Their report was based on a study of total water soluble antioxidant activity of 26 eggplant ( Solanum melongena L.) cultivars from Turkish with an antioxidant activity range from 2664 μM Trolox equivalents/kg to 8247 μM Trolox equivalents/kg.

The ferric reducing antioxidant power (FRAP) assay measures the ability of eggplant flour to reduce Fe 3+ /tripyridyltriazine complex to its ferrous form [ 48 ]. The results shown in Table 5 reveal a significant difference between μM Trolox equivalents/kg flour; the results ranged from 17,820 to 105,617 μM Trolox equivalents/kg flour. Results reported for eggplant extract [ 43 ] with different solvents (acetone, methanolic, and ethanolic) were in the range of 21,000 to 27,000 mg of quercetin equivalent/kg of extract.

In general terms, the results of antioxidant activity, such as the content of total phenols content and total flavonoids content, follow the following order T1 > T4 > T1 > T3. A highly significant difference was found between the samples; the sample treated with a pre-treatment of slicing/freezing before drying was the most affected sample, followed by the sample crushed before drying, as explained above in Section 2.5 and Section 2.6 . Concellon et al. Reference [ 49 ] found that eggplant (American type) stored at 0 °C had a rapid degradation of antioxidant compounds. This behavior was described by other authors [ 49 , 50 ] as related to the antioxidant and phenolic content with the degree of browning of the eggplant. Eggplants generate a cellular disruption when being cut, with a loss of compartmentalization that allows contact between enzymes responsible for browning, such as polyphenoloxidase (PPO) and phenolic substrates [ 49 , 51 , 52 , 53 ]. Treatments such as mincing and freezing, and the time of exposure to air and light contribute to the generation of the browning of the eggplant, thus affecting both its content and antioxidant capacity.

3. Materials and Methods

3.1. flour preparation.

The eggplants fruits used in this study did not had the quality requirements for exportation market (American type) and were purchased from local market in San Nicolas de los Garza County (Nuevo Leon, Mexico). The chemical composition of the eggplant was: Moisture 90%, ash 0.55%, protein 1.07%, fat 0.15%, 1.03% crude fiber, and carbohydrate 5.69%. The fruits (60 units, 25 kg) were washed and separated into four treatments. The fruits of treatment one (T1) were minced (Cyclone Sample mill-model 3010-030, UDY Corporation, Fort Collins, CO, USA) and dried in the drying oven (Model 630, Napco, Oregon, OR, USA) at 45 °C to 50 °C for 2 days. The fruits of treatment two (T2) were sliced and dried using the same condition of the first group. The fruits of treatment 3 (T3) were sliced, frozen, and dried in a tunnel dryer (Procmex Model LQ001, Procomm, Mexico) at 40 °C to 45 °C for 16 h. In the treatment four (T4) the fruits were sliced and dried with the same condition of the third group. The drying temperature range were based on the experience of Uthumporn et al. and Vega-Galvez et al. [ 4 , 9 ] in order to keep the content of phenolic compounds. To determine the drying time, the humidity content was measured as a preliminary result until a percentage below 15% (Mexican Standard NOM-247-SSA1-2008) [ 13 ] was obtained. Two drying methods were used, a drying oven (no air circulation and in total darkness, 15.5% humidity,) and a drying tunnel (air circulation, which passes through a set of resistance becomes dry air, 2.5% humidity). The tunnel design allows for the entrance of light, yet it was controlled. The flour was stored in a refrigerator at 4 °C prior to use. Table 6 describes the conditions of the treatments.

Pre-treatments and drying method for obtaining of eggplant flours.

3.2. Proximate Composition

Analyses were performed according to the Association of Official Analytical Chemistry [ 54 ]. Ash, moisture, and crude fiber content were evaluated gravimetrically (method AOAC 14.006, AOAC 925.15, and AOAC 962.09, respectively). The Goldfisch method (AOAC 920.36C) was used to determine the fat content. The protein content was measured using the Kjeldahl method (AOAC 930.29), and total carbohydrates were determined by difference.

3.3. Physicochemical Properties

In order to measure the potential of hydrogen (pH) and titratable acidity (TA), 10 mL of sample were diluted with 40 mL of distilled water; then, the pH was read. After that, samples were titrated with 0.1 M NaOH to a pH 8.2 (citric acid as predominant) using a Corning, 440 pH meter (Woburn, MA, USA) according to the Association of Official Analytical Chemist methods [ 55 ].

For color determination, a 1.5 mL spectrophotometric cuvette was filled with sample and color and was measured using a CR-20 Konica Minolta Color Reader (Tokyo, Japan). Chromatic parameters were obtained using CIELAB ( L* , a* , b* ) and CIELCH ( L* , C* , h ) color systems according to Commission Internationale De L′ecleirage [ 56 ]. L* defines Lightness (0 = black, 100 = white), a* indicates red (positive a* ) or green value (negative a* ) and b* indicates yellow (positive b* ) or blue value (negative b* ), C* (Chroma; saturation level of h ), and h (hue angle: 0° = red, 90° = yellow, 180° = green, 270° = blue). Color view was obtained by using the online software ColorHexa, color converter using L* , a* , and b* values [ 57 ].

3.4. Functional Properties

Water and oil holding capacity were determined according to the method described by Beuchat [ 58 ] with some modifications; 0.5 g of the sample were taken in 5 mL of distilled water (pH was adjusted to 7) or vegetable oil and mixed by vortexing (model V2H, Boeco, Hamburg, Germany) for 1 min. Then, it was centrifuged at 3000 rpm/30 min. The results were expressed in grams of water-oil retained per gram of sample. The measurements were carried out at room temperature.

For the emulsifying activity, the methods described by Yasumatsu et al. [ 59 ] and Zhao et al. [ 60 ] were used; 0.5 g of sample with 20 mL of distilled water were mixed in a vortex for 15 min and the pH was adjusted to 7. Vegetable oil was mixed in a relation 1:1 (20 mL) and homogenized (OMNI GLH model glh-01, OMNI International, Georgia, GE, USA) for 3 min at medium speed, and it was then centrifuged at 1300 rpm. The results were expressed as a percentage of the height of the emulsification layer with respect to the total liquid.

3.5. Preparation of the Eggplant Flour Extracts (EFE)

Dried powder (90 mg) was extracted with 5 mL of 80% methanol. The extraction was carried out at room temperature, using a magnetic stirrer. After 40 min, the solution was centrifuged for 5 min at 9500× g (10 °C). The supernatant was collected and stored under refrigerated conditions until it was used.

3.6. Total Phenols Content (TPC)

The total phenols content was determined by using the Folin-Ciocalteu method [ 61 ]. This was carried out by mixing 200 μL of the samples extract with 2.6 mL of distilled water, 200 μL of Folin-Ciocalteu reagent, and 2 mL of sodium carbonate solution (7%). After 120 min in the dark (incubation was at room temperature, 23 °C–25 °C), absorbance was measured at 730 nm. The total phenolic content was expressed as mg of chlorogenic acid equivalent (CAE) per 100 g of eggplant flour.

3.7. Total Flavonoids Content (TFC)

The total flavonoids content was measured using the Xiong et al. [ 62 ] method, with slight modifications. Briefly, 200 μL of the samples extract were mixed with 3.5 mL of distilled water and 150 μL of 5% NaNO 2 solution. After 5 min, 150 μL of 10% AlCl 3 solution were dissolved in distilled water, which was added. The mixture was incubated at room temperature for 5 min; then, 1 mL of 1 M NaOH was added and vortexed well for 5 s and left for 15 min. TFC was expressed as mg of catechins equivalent (CAE) per 100 g of eggplant flour.

3.8. Total Catechins Content (TCC)

The total catechins content of the extract was determined by using the vanillin method [ 63 ] with some modifications. The sample extract (250 μL) was mixed with a solution of 1% vanillin (650 μL) and a solution of 25% H 2 SO 4 (650 μL). After 15 min, the solution was incubated at 30 °C; absorbance was measured at 500 nm. TCC was expressed as mg of catechins equivalent (CAE) per gram of eggplant flour.

3.9. Total Anthocyanins (TAC)

The total anthocyanins content was evaluated according to Abdel-Aal and Hucl [ 64 ]. For the extraction of anthocyanins, 200 mg of maize flour was mixed with 10 mL of ethanol-HCl 1N (85:15 v / v , pH 1, 4 °C), purged for 30 s with argon and stirred for 30 min at 200 rpm. Afterwards, the sample was centrifuged at 7759× g (4 °C, 15 min) and finally, 3.5 mL of sample was measured at 535 nm. The content of was reported as milligrams of cyanidin-3-glucoside (C3G) per kilogram of flour (mgC3GE/kg) as follows: C = (A/ε) × (V/1000) × MW × (1/weight of sample) × 10 6 , where: C = concentration in mgC3GE/L, A = absorbance of sample, ε = molar absortivity (mgC3GE = 26,965 cm −1 mol −1 ), V = volume of sample, and MW = molecular weight of C3G (449.2 g/mol).

3.10. Determination of Antioxidant Capacity

The electron-hydrogen donation ability of eggplant flour extract was measured by using DPPH, ABTS, and FRAP methods. The DPPH method was performed according to the method described by Tai et al. [ 65 ] with slight modifications. For this, 1.5 mL of 2 mg/L DPPH solution in methanol 80% and 50 μL of sample were mixed, and incubated at room temperature (23 °C–25 °C) in darkness for 30 min. The absorbance was measured at 517 nm against a blank. Results were expressed in micromoles of Trolox equivalents (μMTE)/g eggplant flour.

For the ABTS assay, the procedure followed the method used in previous assays [ 66 , 67 ] with a few modifications. The stock solutions included 2.6 mM potassium persulfate solution and 7.7 mM ABTS·+ solution; these solutions were mixed in equal quantities. After 12 h at room temperature in the darkness, the ABTS·+ solution was diluted with methanol 80% to obtain an absorbance of 1.000 units at 734 nm using the spectrophotometer. The eggplant flour extract (50 μL) was allowed to react with 1500 μL of ABTS·+ solution for 30 min in the dark; then, the absorbance was measured at 734 nm. Results are expressed in micromoles of Trolox equivalents (μMTE)/g eggplant flour.

The ferric reducing-antioxidant capacity was measured according to the method described by Suárez et al. [ 68 ] with slight modifications. The working FRAP reagent was prepared with 5 mL of TPTZ (10 mM), 5 ml of FeCl 3 (20 mM), and 50 mL of sodium acetate buffer (300 mM, pH = 3.6). Then, 50 μL of the sample extract were mixed with 1.5 mL of freshly working FRAP reagent. The FRAP assay was carried out at 37 °C in an incubator. The absorbance was measured at 595 nm and the results were expressed in micromoles of Trolox equivalents (μMTE)/g eggplant flour.

3.11. Statistical Analysis

Data from the three replicated experiments were analyzed to determine whether the variances were statistically homogeneous, and the results were expressed as means ± SD. Statistical comparisons were made by one-way analysis of variance (ANOVA) followed by a Tukey’s test using SPSS 17 Software. Difference between means were considered significant at p < 0.05.

4. Conclusions

All the eggplant flours showed the same trend regarding their antioxidant capacity and phenolic content in the order T2 > T4 > T1 > T3. The freezing of eggplant was found to have a negative effect on functional and antioxidant properties. With respect to their nutritional composition, the flours did not change in their crude fiber, protein, and fat contents. In general terms, the T2 flour is a potential ingredient for the preparation of foods with functional properties since it is rich in phenolic compounds and antioxidants.

Acknowledgments

We would like to thank Consejo Nacional de Ciencia y Tecnologia (CONACyT) for financially supporting J.R.R.-J. to obtain her Ph.D. (scholarship 331700).

Author Contributions

J.R.R.-J., C.A.A.-G. and G.N.-M. conceived and designed the experiments; J.R.R.-J. performed the experiments; J.R.R.-J, C.A.A.-G and G.N.-M. analyzed data; C.A.A.-G., G.N.-M., J.G.B.-G., V.U.-O. and C.A.-G. contributed with reagents, materials, and analysis tools; J.R.R.-J., C.A.A.-G. and G.N.-M. wrote and edited the original draft. Carlos A. Amaya-Guerra and Guillermo Niño-Medina contributed equally to this work.

This research received no external funding.

Conflicts of Interest

The authors reported no potential conflict of interest.

Sample Availability: Samples of the compounds are not available from the authors.

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

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About 1 in 4 U.S. teachers say their school went into a gun-related lockdown in the last school year

About half of americans say public k-12 education is going in the wrong direction, what public k-12 teachers want americans to know about teaching, what’s it like to be a teacher in america today, race and lgbtq issues in k-12 schools, most popular.

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