Pollen foraging preferences in honey bees and the nutrient profiles of the pollen.

Honey bees are important insect pollinators that provide critical pollination services to fruit and nut crops in the US. They face challenges likely due to pressures associated with agricultural intensification related habitat loss. To better understand this, pollen preferences of foraging bees and the nutritional profile of pollen brought into hives by foraging bees in crop fields and nut orchards can provide valuable information. We trained bees to forage on bee-collected pollen from hives placed for pollination services in almond orchards, sunflower fields, or mixed species from inter-row plantings. Using bees trained to a certain kind of hive pollen, we applied a binary scoring system, to test preferences of these preconditioned foragers. We also performed metabolomic analyses of the hive pollen used for training and testing to elucidate their nutritional content. Irrespective of preconditioning, bees collected all the available choice pollen types, predominantly choosing hive-collected mixed species pollen (MSP), followed by almond orchard pollen. The hive-collected MSP was chemically diverse, richest in cholesterol, vitamins, and phytochemicals quercetin, kaempferol, coumarin, and quinine, but was not consistently high for essential amino acids and polyunsaturated fatty acids. Although diversity in chemical profiles may not directly relate to plant species diversity, our results suggest that foragers collect a variety of pollen types when available reiterating the importance of diverse floral resources.

Follicular metabolic alterations are associated with obesity in mares and can be mitigated by dietary supplementation.

Obesity is a growing concern in human and equine populations, predisposing to metabolic pathologies and reproductive disturbances. Cellular lipid accumulation and mitochondrial dysfunction play an important role in the pathologic consequences of obesity, which may be mitigated by dietary interventions targeting these processes. We hypothesized that obesity in the mare promotes follicular lipid accumulation and altered mitochondrial function of oocytes and granulosa cells, potentially contributing to impaired fertility in this population. We also predicted that these effects could be mitigated by dietary supplementation with a combination of targeted nutrients to improve follicular cell metabolism. Twenty mares were grouped as: Normal Weight [NW, n = 6, body condition score (BCS) 5.7 ± 0.3], Obese (OB, n = 7, BCS 7.7 ± 0.2), and Obese Diet Supplemented (OBD, n = 7, BCS 7.7 ± 0.2), and fed specific feed regimens for ≥ 6 weeks before sampling. Granulosa cells, follicular fluid, and cumulus-oocyte complexes were collected from follicles ≥ 35 mm during estrus and after induction of maturation. Obesity promoted several mitochondrial metabolic disturbances in granulosa cells, reduced L-carnitine availability in the follicle, promoted lipid accumulation in cumulus cells and oocytes, and increased basal oocyte metabolism. Diet supplementation of a complex nutrient mixture mitigated most of the metabolic changes in the follicles of obese mares, resulting in parameters similar to NW mares. In conclusion, obesity disturbs the equine ovarian follicle by promoting lipid accumulation and altering mitochondrial function. These effects may be partially mitigated with targeted nutritional intervention, thereby potentially improving fertility outcomes in the obese female.

Fire Impacts on the Soil Metabolome and Organic Matter Biodegradability.

Global wildfire activity has increased since the 1970s and is projected to intensify throughout the 21st century. Wildfires change the composition and biodegradability of soil organic matter (SOM) which contains nutrients that fuel microbial metabolism. Though persistent forms of SOM often increase postfire, the response of more biodegradable SOM remains unclear. Here we simulated severe wildfires through a controlled "pyrocosm" approach to identify biodegradable sources of SOM and characterize the soil metabolome immediately postfire. Using microbial amplicon (16S/ITS) sequencing and gas chromatography-mass spectrometry, heterotrophic microbes (Actinobacteria, Firmicutes, and Protobacteria) and specific metabolites (glycine, protocatechuate, citric cycle intermediates) were enriched in burned soils, indicating that burned soils contain a variety of substrates that support microbial metabolism. Molecular formulas assigned by 21 T Fourier transform ion cyclotron resonance mass spectrometry showed that SOM in burned soil was lower in molecular weight and featured 20 to 43% more nitrogen-containing molecular formulas than unburned soil. We also measured higher water extractable organic carbon concentrations and higher CO2 efflux in burned soils. The observed enrichment of biodegradable SOM and microbial heterotrophs demonstrates the resilience of these soils to severe burning, providing important implications for postfire soil microbial and plant recolonization and ecosystem recovery.

Urine and Dried Blood Spots From Children and Pregnant Women Reveal Phytochemicals, Amino Acids, and Carnitine Metabolites as Cowpea Consumption Biomarkers.

SCOPE: Legumes consumption has been proven to promote health across the lifespan; cowpeas have demonstrated efficacy in combating childhood malnutrition and growth faltering, with an estimated malnutrition prevalence of 35.6% of children in Ghana. This cowpea feeding study aimed to identify a suite of metabolic consumption biomarkers in children and adults. METHODS AND RESULTS: Urine and dried blood spots (DBS) from 24 children (9-21 months) and 21 pregnant women (>18 years) in Northern Ghana are collected before and after dose-escalated consumption of four cowpea varieties for 15 days. Untargeted metabolomics identified significant increases in amino acids, phytochemicals, and lipids. The carnitine metabolism pathway is represented by 137 urine and 43 DBS metabolites, with significant changes to tiglylcarnitine and acetylcarnitine. Additional noteworthy candidate biomarkers are mansouramycin C, N-acetylalliin, proline betaine, N2, N5-diacetylornithine, S-methylcysteine, S-methylcysteine sulfoxide, and cis-urocanate. S-methylcysteine and S-methylcysteine sulfoxide are targeted and quantified in urine. CONCLUSION: This feeding study for cowpea biomarkers supports the utility of a suite of key metabolites classified as amino acids, lipids, and phytochemicals for dietary legume and cowpea-specific food exposures of global health importance.

Development of a single mobile phase for LC-IM-MS-based discovery lipidomics and metabolic phenotyping: Application to methapyrilene hepatotoxicity in the rat.

The untargeted global profiling of endogenous metabolites and lipids has the potential to increase knowledge and understanding in many areas of biology. LC-MS/MS is a key technology for such analyses however, several different LC methodologies, using different mobile phase compositions, are required to cover the diversity in polarity and analyte structure encountered in biological samples. Most notably many lipid screening methods make use of isopropanol (IPA) as a major component of mobile phases employed for comprehensive lipidomic profiling. In order to increase laboratory efficiency, and minimize opportunities for errors, a suite of methods, based on a single acetonitrile (ACN)-aqueous buffer mobile phase combination, has been developed. This mobile phase can be used for hydrophobic interaction liquid chromatography on an amide stationary phase (for polar analytes), reversed-phase (RP) LC analysis on a C8 stationary phase (for moderately polar-non-polar compounds) and RPLC using a CSH phenyl-hexyl bonded column (for lipids). All of these sub 10 minute separations had good throughput and reproducibility with CV's of analyte response <25 % whilst eliminating the need for complex mobile phase preparation and the use of IPA as an organic modifier for lipidomics. Advantages of removing IPA and replacing it with the ACN-based method were a 58 % increase in peak capacity for lipids, with improved resolution for the di- and triglycerides and cholesterol esters compared to current methods. Compared to the IPA-containing solvent system the ACN-based mobile phase also resulted in a 61 % increase in lipid feature detection. The utility of this "universal" mobile phase approach was demonstrated by its application to a rat toxicology study investigating the consequences of methapyrilene administration through on the endogenous metabolite profiles of plasma and urine. Methapyrilene and its metabolites were also profiled in these samples.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Assessing the adaptive role of cannabidiol (CBD) in Cannabis sativa defense against cannabis aphids.

Cannabis sativa is known for having unique specialized or secondary metabolites, cannabinoids that are derived from an extension of the terpene pathway in the Cannabis lineage and includes more than 100 other similar metabolites. Despite the assumption that cannabinoids evolved as novel herbivory defense adaptations, there is limited research addressing the role of cannabinoids in C. sativa responses to insect herbivores. Here we investigated the role of cannabidiol (CBD), the predominant cannabinoid in hemp, in plant defense against cannabis aphid (Phorodon cannabis), one of the most damaging pests of hemp. We hypothesize that insect feeding may induce changes in cannabinoids as an adaptive strategy for defense. We found that mean fecundity, net reproductive rate (R0) and adult longevity of cannabis aphids was reduced on the high cannabinoid cultivar compared to the low- cannabinoid cultivar in whole plant assays. In contrast, supplementation of CBD in artificial feeding assays increased aphid fecundity from day 1 to day 3. Additionally, aphid feeding did not impact cannabinoid levels in leaf tissues with the exception of Δ9-tetrahydrocannabinol (THC). This suggests that other cannabinoids and/or metabolites such as terpenes are causing the observed decrease in aphid performance in the whole plant assays. In addition to cannabinoids, C. sativa also possesses a range of defense mechanisms via phytohormone signaling pathways that are well described in other plant species. Indeed, cannabis aphid feeding significantly increased levels of the major phytohormones, salicylic acid, jasmonic acid, and abscisic acid, which are known to be involved in plant defense responses against aphid species. These results highlight the interplay between cannabinoid synthesis and phytohormone pathways and necessitate further investigation into this complex interaction.

Quantitative proteomic analysis of super soft kernel texture in soft white spring wheat.

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar 'Alpowa', a soft white spring wheat, was crossed to a closely related super soft spring wheat line 'BC2SS163' to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

Applying network and genetic analysis to the potato metabolome.

Compositional traits in potato [Solanum tuberosum L.] are economically important but genetically complex, often controlled by many loci of small effect; new methods need to be developed to accelerate analysis and improvement of such traits, like chip quality. In this study, we used network analysis to organize hundreds of metabolic features detected by mass spectrometry into groups, as a precursor to genetic analysis. 981 features were condensed into 44 modules; module eigenvalues were used for genetic mapping and correlation analysis with phenotype data collected by the Solanaceae Coordinated Agricultural Project. Half of the modules were associated with at least one SNP according to GWAS; 11 of those modules were also significantly correlated with chip color. Within those modules features associated with chipping provide potential targets for selection in addition to selection for reduced glucose. Loci associated with module eigenvalues were not evenly distributed throughout the genome but were instead clustered on chromosomes 3, 7, and 8. Comparison of GWAS on single features and modules of clustered features often identified the same SNPs. However, features with related chemistries (for example, glycoalkaloids with precursor/product relationships) were not found to be near neighbors in the network analysis and did not share common SNPs from GWAS. Instead, the features within modules were often structurally disparate, suggesting that linkage disequilibrium complicates network analyses in potato. This result is consistent with recent genomic studies of potato showing that chromosomal rearrangements that create barriers to recombination are common in cultivated germplasm.

Mass spectrometric-based assessment of the serum kappa to lambda immunoglobulin light chain ratio (κ:λ) in dogs with immunoglobulin secretory diseases.

The ratio of κ light chains to λ light chains (κ:λ) in serum is used as a biomarker of immunoglobulin secreting neoplasia in humans but has not been evaluated in dogs. A mass-spectrometry based method for determining the canine serum κ:λ was developed and used to evaluate samples from control dogs, dogs with an infectious aetiology, dogs with secretory plasma cell tumours (sPCT) and dogs with non-secretory B cell neoplasia. A human-targeted immunoturbidometric κ:λ assay and immunofixation using antisera targeting human κ light chain or λ light chain was also performed on all samples. Using whole serum samples, the MS-based κ:λ method identified 5 sPCT as κ-predominant (mean κ:λ = 3.307) and 5 sPCT as λ-predominant (mean κ:λ = 0.023) and documented differences between these groups and all other groups (p < 0.05 for all). The infectious aetiology group had a lower mean κ:λ ratio (mean κ:λ = 0.069) than control samples (mean κ:λ = 0.103, p = 0.035). Similar results were obtained when samples were enriched for proteins between 10 and 50 kDa using size exclusion chromatography, except for the statistical difference between the control and infectious aetiology group. All λ-predominant cases had only anti-human λ light chain labelling by immunofixation. Three κ-predominant cases had only anti-human κ-light chain labelling and the remaining two cases did not label with either antisera by immunofixation. The immunoturbidometric method had high analytical CV% (λ light chain CV = 13%, κ light chain CV = 50%), was unable to measure light chains in 20.5% of samples and did not distinguish groups. The data suggests that the human-targeted immunoturbidometric method would not be diagnostically useful and that the MS-derived serum κ:λ may be a useful biomarker of canine immunoglobulin secretory neoplasia which may have the ability to distinguish neoplasia from infectious causes of immunoglobulin secretion.

Recent advances in mass spectrometry-based computational metabolomics.

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

Secreted metabolome of porcine blastocysts encapsulated within in vitro 3D alginate hydrogel culture systems undergoing morphological changes provides insights into specific mechanisms involved in the initiation of porcine conceptus elongation.

CONTEXT: The exact mechanisms regulating the initiation of porcine conceptus elongation are not known due to the complexity of the uterine environment. AIMS: To identify contributing factors for initiation of conceptus elongation in vitro , this study evaluated differential metabolite abundance within media following culture of blastocysts within unmodified alginate (ALG) or Arg-Gly-Asp (RGD)-modified alginate hydrogel culture systems. METHODS: Blastocysts were harvested from pregnant gilts, encapsulated within ALG or RGD or as non-encapsulated control blastocysts (CONT), and cultured. At the termination of 96h culture, media were separated into blastocyst media groups: non-encapsulated control blastocysts (CONT); ALG and RGD blastocysts with no morphological change (ALG- and RGD-); ALG and RGD blastocysts with morphological changes (ALG+ and RGD+) and evaluated for non-targeted metabolomic profiling by liquid chromatography (LC)-mass spectrometry (MS) techniques and gas chromatography-(GC-MS). KEY RESULTS: Analysis of variance identified 280 (LC-MS) and 1 (GC-MS) compounds that differed (P <0.05), of which 134 (LC-MS) and 1 (GC-MS) were annotated. Metabolites abundance between ALG+ vs ALG-, RGD+ vs RGD-, and RGD+ vs ALG+ were further investigated to identify potential differences in metabolic processes during the initiation of elongation. CONCLUSIONS: This study identified changes in phospholipid, glycosphingolipid, lipid signalling, and amino acid metabolic processes as potential RGD-independent mechanisms of elongation and identified changes in lysophosphatidylcholine and sphingolipid secretions during RGD-mediated elongation. IMPLICATIONS: These results illustrate changes in phospholipid and sphingolipid metabolic processes and secretions may act as mediators of the RGD-integrin adhesion that promotes porcine conceptus elongation.

Metabolomic Profiles of Multidrug-Resistant Salmonella Typhimurium from Humans, Bovine, and Porcine Hosts.

Antimicrobial resistance (AMR) is a global public health threat, yet tools for detecting resistance patterns are limited and require advanced molecular methods. Metabolomic approaches produce metabolite profiles and help provide scientific evidence of differences in metabolite expressions between Salmonella Typhimurium from various hosts. This research aimed to evaluate the metabolomic profiles of S. Typhimurium associated with AMR and it compares profiles across various hosts. Three samples, each from bovine, porcine, and humans (total n = 9), were selectively chosen from an existing library to compare these nine isolates cultured under no drug exposure to the same isolates cultured in the presence of the antimicrobial drug panel ACSSuT (ampicillin, chloramphenicol, streptomycin, sulfisoxazole, tetracycline). This was followed by metabolomic profiling using UPLC and GC-mass spectrometry. The results indicated that the metabolite regulation was affected by antibiotic exposure, irrespective of the host species. When exposed to antibiotics, 59.69% and 40.31% of metabolites had increased and decreased expressions, respectively. The most significantly regulated metabolic pathway was aminoacyl-tRNA biosynthesis, which demonstrated increased expressions of serine, aspartate, alanine, and citric acid. Metabolites that showed decreased expressions included glutamate and pyruvate. This pathway and associated metabolites have known AMR associations and could be targeted for new drug discoveries and diagnostic methods.

Non-targeted metabolomics of cooked cowpea (Vigna unguiculata) and pigeon pea (Cajanus cajan) from Ghana using two distinct and complementary analytical platforms.

Legumes are global staple foods with multiple human health properties that merit detailed composition analysis in cooked forms. This study analyzed cowpea [Vigna unguiculata] (three varieties: Dagbantuya, Sangyi, and Tukara), pigeon pea [Cajanus cajan], and common bean [Phaseolus vulgaris] using two distinct ultra-performance liquid chromatography mass spectrometry (UPLC-MS) platforms and analytical workflows. Comparisons between cowpea and pigeon pea consumed in Ghana, and common bean (navy bean) from USA, revealed 75 metabolites that differentiated cowpeas. Metabolite fold-change comparisons resulted in 142 metabolites with significantly higher abundance in cowpea, and 154 higher in abundance from pigeon pea. 3-(all-trans-nonaprenyl)benzene-1,2-diol, N-tetracosanoylphytosphingosine, and sitoindoside II are novel identifications in cowpea, with notably higher abundance than other legumes tested. Cowpea variety specific markers were tonkinelin (Dagbantuya), pheophytin A (Sangyi), and linoleoyl ethanolamide (Tukara). This study identified novel cowpea and pigeon pea food metabolites that warrant continued investigation as bioactive food components following consumption in people.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES: This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS: The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS: The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Quantitative analysis of short-chain fatty acids in human plasma and serum by GC-MS.

Short-chain fatty acids (SCFAs) are volatile fatty acids produced by gut microbial fermentation of dietary nondigestible carbohydrates. Acetate, propionate, and butyrate SCFA measures are important to clinical and nutritional studies for their established roles in promoting healthy immune and gut function. Additionally, circulating SCFAs may influence the metabolism and allied function of additional tissues and organs. The accurate quantification of SCFAs in plasma/serum is critical to understanding the biological role of SCFAs. The low concentrations of circulating SCFAs and their volatile nature present challenges for quantitative analysis. Herein, we report a sensitive method for SCFA quantification via extraction with methyl tert-butyl ether after plasma/serum acidification. The organic extract of SCFAs is injected directly with separation and detection using a polar GC column coupled to mass spectrometry. The solvent-to-sample ratio, plasma volume, and amount of HCl needed for SCFA protonation were optimized. Method validation shows good within-day and inter-day repeatability. The limit of detection was 0.3-0.6 µg/mL for acetate and 0.03-0.12 µg/mL for propionate and butyrate. Successful application of this method on clinical plasma and serum samples was demonstrated in six datasets. By simplifying the sample preparation procedure, the present method reduces the risk of contamination, lowers the cost of analysis, increases throughput, and offers the potential for automated sample preparation.

Oocyte metabolic function, lipid composition, and developmental potential are altered by diet in older mares.

Dietary supplementation is the most feasible method to improve oocyte function and developmental potential in vivo. During three experiments, oocytes were collected from maturing, dominant follicles of older mares to determine whether short-term dietary supplements can alter oocyte metabolic function, lipid composition, and developmental potential. Over approximately 8 weeks, control mares were fed hay (CON) or hay and grain products (COB). Treated mares received supplements designed for equine wellness and gastrointestinal health, flaxseed oil, and a proprietary blend of fatty acid and antioxidant support (reproductive support supplement (RSS)) intended to increase antioxidant activity and lipid oxidation. RSS was modified for individual experiments with additional antioxidants or altered concentrations of n-3 to n-6 fatty acids. Oocytes from mares supplemented with RSS when compared to COB had higher basal oxygen consumption, indicative of higher aerobic metabolism, and proportionately more aerobic to anaerobic metabolism. In the second experiment, oocytes collected from the same mares prior to (CON) and after approximately 8 weeks of RSS supplementation had significantly reduced oocyte lipid abundance. In the final experiment, COB was compared to RSS supplementation, including RSS modified to proportionately reduce n-3 fatty acids and increase n-6 fatty acids. The ability of sperm-injected oocytes to develop into blastocysts was higher for RSS, regardless of fatty acid content, than for COB. We demonstrated that short-term diet supplementation can directly affect oocyte function in older mares, resulting in oocytes with increased metabolic activity, reduced lipid content, and increased developmental potential.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.).

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites-avenanthramides, avenacins, and avenacosides-we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance.

The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R 2 > 0.7 in ∼34% of the metabolites) and predicted (Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.