Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Rootstock vigor dictates the canopy light environment that regulates metabolite profile and internal fruit quality development in peach

Five rootstock cultivars of differing vigor: vigorous ('Atlas™' and 'Bright's Hybrid® 5'), standard ('Krymsk® 86' and 'Lovell') and dwarfing ('Krymsk® 1') grafted with 'Redhaven' as the scion were studied for their impact on productivity, mid-canopy photosynthetic active radiation transmission (i.e., light availability) and internal fruit quality. Αverage yield (kg per tree) and fruit count increased significantly with increasing vigor (trunk cross sectional area, TCSA). Α detailed peach fruit quality analysis on fruit of equal maturity (based on the index of absorbance difference, IAD) coming from trees with equal crop load (no. of fruit cm-2 of TCSA) characterized the direct impact of rootstock vigor on peach internal quality [dry matter content (DMC) and soluble solids concentration (SSC)]. DMC and SSC increased significantly with decreasing vigor and increasing light availability, potentially due to reduced intra-tree shading and better light distribution within the canopy. Physiologically characterized peach fruit mesocarp was further analyzed by non-targeted metabolite profiling using gas chromatography mass spectrometry (GC-MS). Metabolite distribution was associated with rootstock vigor class, mid-canopy light availability and fruit quality characteristics. Fructose, glucose, sorbose, neochlorogenic and quinic acids, catechin and sorbitol were associated with high light environments and enhanced quality traits, while sucrose, butanoic and malic acids related to low light conditions and inferior fruit quality. These outcomes show that while rootstock genotype and vigor are influencing peach tree productivity and yield, their effect on manipulating the light environment within the canopy also plays a significant role in fruit quality development.

Root-associated bacterial communities and root metabolite composition are linked to nitrogen use efficiency in sorghum

The development of cereal crops with high nitrogen use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improving crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse Sorghum bicolor lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography-mass spectrometry were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly impacted the root-associated bacterial communities and root metabolite composition of sorghum. We found a positive correlation between sorghum NUE and bacterial richness and diversity in the rhizosphere. The greater alpha diversity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxon, Pseudomonas. Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to low N stress. This indicates that the shift in the sorghum microbiome due to low N is associated with the root metabolites of the host plant. Taken together, our findings suggest that host genetic regulation of root metabolites plays a role in defining the root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low N stress.IMPORTANCEThe development of crops that are more nitrogen use-efficient (NUE) is critical for the future of the enhanced sustainability of agriculture worldwide. This objective has been pursued mainly through plant breeding and plant molecular engineering, but these approaches have had only limited success. Therefore, a different strategy that leverages soil microbes needs to be fully explored because it is known that soil microbes improve plant growth through multiple mechanisms. To design approaches that use the soil microbiome to increase NUE, it will first be essential to understand the relationship among soil microbes, root metabolites, and crop productivity. Using this approach, we demonstrated that certain key metabolites and specific microbes are associated with high and low sorghum NUE in a field study. This important information provides a new path forward for developing crop genotypes that have increased NUE through the positive contribution of soil microbes.

Maturation and culture affect the metabolomic profile of oocytes and follicular cells in young and old mares

Introduction: Oocytes and follicular somatic cells within the ovarian follicle are altered during maturation and after exposure to culture in vitro. In the present study, we used a nontargeted metabolomics approach to assess changes in oocytes, cumulus cells, and granulosa cells from dominant, follicular-phase follicles in young and old mares. Methods: Samples were collected at three stages associated with oocyte maturation: (1) GV, germinal vesicle stage, prior to the induction of follicle/oocyte maturation in vivo; (2) MI, metaphase I, maturing, collected 24 h after induction of maturation in vivo; and (3) MIIC, metaphase II, mature with collection 24 h after induction of maturation in vivo plus 18 h of culture in vitro. Samples were analyzed using gas and liquid chromatography coupled to mass spectrometry only when all three stages of a specific cell type were obtained from the same mare. Results and Discussion: Significant differences in metabolite abundance were most often associated with MIIC, with some of the differences appearing to be linked to the final stage of maturation and others to exposure to culture medium. While differences occurred for many metabolite groups, some of the most notable were detected for energy and lipid metabolism and amino acid abundance. The study demonstrated that metabolomics has potential to aid in optimizing culture methods and evaluating cell culture additives to support differences in COCs associated with maternal factors.

Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network

Graph neural networks (GNNs) have shown remarkable performance in predicting the retention time (RT) for small molecules. However, the training data set for a particular target chromatographic system tends to exhibit scarcity, which poses a challenge because the experimental process for measuring RT is costly. To address this challenge, transfer learning has been used to leverage an abundant training data set from a related source task. In this study, we present an improved transfer learning method to better predict the RT of molecules for a target chromatographic system by learning from a small training data set with a pretrained GNN. We use a graph isomorphism network as the architecture of the GNN. The GNN is pretrained on the METLIN-SMRT data set and is then fine-tuned on the target training data set for a fixed number of training iterations using the limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer with a learning rate decay. We demonstrate that the proposed method achieves superior predictive performance on various chromatographic systems compared with that of the existing transfer learning methods, especially when only a small training data set is available for use. A potential avenue for future research is to leverage multiple small training data sets from different chromatographic systems to further enhance the generalization performance.

Equine maternal aging affects the metabolomic profile of oocytes and follicular cells during different maturation time points

Introduction: Oocyte quality and fertility decline with advanced maternal age. During maturation within the ovarian follicle, the oocyte relies on the associated somatic cells, specifically cumulus and granulosa cells, to acquire essential components for developmental capacity. Methods: A nontargeted metabolomics approach was used to investigate the effects of mare age on different cell types within the dominant, follicular-phase follicle at three time points during maturation. Metabolomic analyses from single oocytes and associated cumulus and granulosa cells allowed correlations of metabolite abundance among cell types. Results and Discussion: Overall, many of the age-related changes in metabolite abundance point to Impaired mitochondrial metabolic function and oxidative stress in oocytes and follicular cells. Supporting findings include a higher abundance of glutamic acid and triglycerides and lower abundance of ceramides in oocytes and somatic follicular cells from old than young mares. Lower abundance of alanine in all follicular cell types from old mares, suggests limited anaerobic energy metabolism. The results also indicate impaired transfer of carbohydrate and free fatty acid substrates from cumulus cells to the oocytes of old mares, potentially related to disruption of transzonal projections between the cell types. The identification of age-associated alterations in the abundance of specific metabolites and their correlations among cells contribute to our understanding of follicular dysfunction with maternal aging.

Improving Quantitative Accuracy in Nontargeted Lipidomics by Evaluating Adduct Formation

For large-scale lipidomic analyses, accurate and reproducible quantification of endogenous lipids is crucial for comparing results within and across studies. Many lipids present in liquid chromatography-electrospray ionization-mass spectrometry form various adducts with buffer components. The mechanisms and conditions that dictate adduct formation are still poorly understood. In a positive mode, neutral lipids like mono-, di-, and triacylglycerides and cholesteryl esters typically generate [M + NH4]+ adduct ions, although [M + Na]+, [M + K]+, and other (more complex) species can also be significantly abundant in MS1 precursor ion spectra. Variations in the ratios of these adducts (within and between matrices) can lead to dramatic inaccuracies during quantification. Here, we examine 48 unique diacylglycerol (DAG) species across 2366 mouse samples for eight matrix-specific data sets of plasma, liver, kidney, brain, heart muscle, gastrocnemius muscle, gonadal, and inguinal fat. Typically, no single adduct ion species accounted for more than 60% of the total observed abundance across each data set. Even within a single matrix, DAGs showed a high variability of adduct ratios. The ratio of [M + NH4]+ adduct ions was increased for longer-chain DAGs and for polyunsaturated DAGs, at the expense of reduced ratios of [M + Na]+ adducts. When using three deuterated internal DAG standards, we found that absolute concentrations were estimated with up to 70% error when only one adduct ion was used instead of all adducts combined. Importantly, when combining [M + NH4]+ and [M + Na]+ adduct ions, quantification results were within 5% accuracy compared to all adduct ions combined. Additional variance can be caused by other factors, such as instrument conditions or matrix effects.

Predicting Preferences for Adduct Formation in Electrospray Ionization: The Case Study of Succinic Acid

A simple theoretical approach is developed that can be used to predict the preference of ion adduct formation (with alkali Li⁺, Na⁺, K⁺ and alkaline earth Ca²⁺, Mg²⁺ metals) in electrospray ionization mass spectrometry (ESI-MS) of succinic acid, associated with several protonation/deprotonation equilibria. The applied strategy consists of using a vacuum environment as well as both implicit and explicit solvation of reactive sites and density functional theory as the method of choice. These distinct levels of theory mimic the smooth transition between the condensed environment and free ion in the gas phase. Good correlation between the Gibbs free energies for protonation/adduct formation processes with peak observation in the obtained mass spectra provide insight into the physical basis behind adduct preference and selectivity. This signifies the relationship between microscopic interactions, ionization efficiency, and types of ions that reach the detector.

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.

Carbon sufficiency boosts phenylpropanoid biosynthesis early in peach fruit development priming superior fruit quality

Manipulating the crop load in peach trees determines carbon supply and optimum balance between fruit yield and quality potentials. The impact of carbon supply on peach fruit quality was assessed in three development stages (S2, S3, S4) on fruit of equal maturity from trees that were carbon (C) starved (unthinned) and sufficient (thinned). Previous studies determined that primary metabolites of peach fruit mesocarp are mainly linked with developmental processes, thus, the secondary metabolite profile was assessed using non-targeted liquid chromatography mass-spectrometry (LC-MS). Carbon sufficient (C-sufficient) fruit demonstrated superior quality attributes as compared to C-starved fruit. Early metabolic shifts in the secondary metabolome appear to prime quality at harvest. Enhanced C-availability facilitated the increased and consistent synthesis of flavonoids, like catechin, epicatechin and eriodyctiol, via the phenylpropanoid pathway, providing a link between the metabolome and fruit quality, and serving as signatures of C-sufficiency during peach fruit development.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome, and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.

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

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Cowpea varieties represent diverse staple foods in Sub-Saharan Africa.

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Cowpea metabolite composition is different from pigeon pea and common bean.

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Cowpea metabolites included tonkinelin, pheophytin A, and linoleoyl ethanolamide.

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Pheophytin A was only detected in the cowpea variety Sangyi.

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Pipecolic acid identification was confirmed for all three legumes.

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.

The Origin and Implications of Artifact Ions in Bioanalytical LC–MS

Liquid chromatography–mass spectrometry (LC–MS) with electrospray ionization (ESI) is a widely used bioanalytical technique with both qualitative and quantitative applications. Ions are created by electrically charging a stream of droplets from the LC system, which evaporate and leave ions that are transferred to the mass spectrometer. Ideally, these are only from the analyte, but background ions, such as metals, impurities and coeluted species, can react with analytes producing adducts, such as [M + Na]+, [M + K]+, and multimers (2M + H+, 3M + H+, and so forth). Although well known, the extent of adduct ion formation and the implications for quantitative analysis and analyte characterization by tandem MS (MS/MS) are not fully appreciated. We summarize the problem and identify areas that should be considered when developing or using electrospray LC–MS.

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Variation in Root Exudate Composition Influences Soil Microbiome Membership and Function

Root exudation is one of the primary processes that mediate interactions between plant roots, microorganisms, and the soil matrix, yet the mechanisms by which exudation alters microbial metabolism in soils have been challenging to unravel. Here, utilizing distinct sorghum genotypes, we characterized the chemical heterogeneity between root exudates and the effects of that variability on soil microbial membership and metabolism. Distinct exudate chemical profiles were quantified and used to formulate synthetic root exudate treatments: a high-organic-acid treatment (HOT) and a high-sugar treatment (HST). To parse the response of the soil microbiome to different exudate regimens, laboratory soil reactors were amended with these root exudate treatments as well as a nonexudate control. Amplicon sequencing of the 16S rRNA gene illustrated distinct microbial diversity patterns and membership in response to HST, HOT, or control amendments. Exometabolite changes reflected these microbial community changes, and we observed enrichment of organic and amino acids, as well as possible phytohormones in the HST relative to the HOT and control. Linking the metabolic capacity of metagenome-assembled genomes in the HST to the exometabolite patterns, we identified microorganisms that could produce these phytohormones. Our findings emphasize the tractability of high-resolution multiomics tools to investigate soil microbiomes, opening the possibility of manipulating native microbial communities to improve specific soil microbial functions and enhance crop production. IMPORTANCE Decrypting the chemical interactions between plant roots and the soil microbiome is a gateway for future manipulation and management of the rhizosphere, a soil compartment critical to promoting plant fitness and yields. Our experimental results demonstrate how soil microbial community and genomic diversity is influenced by root exudates of differing chemical compositions and how changes in this microbiome result in altered production of plant-relevant metabolites. Together, these findings demonstrate the tractability of high-resolution multiomics tools to investigate soil microbiomes and provide new information on plant-soil environments useful for the development of efficient and precise microbiota management strategies in agricultural systems.

Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS

Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibility to facilitate identification of isomers relies on different ionic species formed in electrospray. In positive ESI mode, both protonated molecules and adducts can be formed; however, not all isomeric structures form the same ionic species. The complicated mechanism of adduct formation has hindered the use of this molecular characteristic in the structural elucidation in non-targeted screening. Here, we have studied the adduct formation for 94 small molecules with ion mobility spectra and compared collision cross-sections of the respective ions. Based on the results we developed a fast support vector machine classifier with polynomial kernels for accurately predicting the sodium adduct formation in ESI/HRMS. The model is trained on five independent data sets from different laboratories and uses the graph-based connectivity of functional groups and PubChem fingerprints to predict the sodium adduct formation in ESI/HRMS. The validation of the model showed an accuracy of 74.7% (balanced accuracy 70.0%) on a dataset from an independent laboratory, which was not used in the training of the model. Lastly, we applied the classification algorithm to the SusDat database by NORMAN network to evaluate the proportion of isomeric compounds that could be distinguished based on predicted sodium adduct formation. It was observed that sodium adduct formation probability can provide additional selectivity for about one quarter of the exact masses and, therefore, shows practical utility for structural assignment in non-targeted screening.

Untargeted metabolomics analysis identifies creatine, myo-inositol, and lipid pathway modulation in a murine model of tendinopathy

Tendinopathy has been broadly characterized as alterations in cell proliferation, extracellular matrix turnover/synthesis, and inflammatory alterations. However, the underlying glucose metabolism pathways which contribute to these responses have not been well explored. The potential link between glucose metabolism and tendon pathology is interesting from a global standpoint since the development of spontaneous tendinopathy is associated with systemic metabolic disorders including diabetes mellitus. Therefore, the overarching goal of this study was to understand the potential pathogenic role of glucose metabolism-driven mechanisms in the development of tendinopathy. To test this, we have utilized an untargeted metabolomics approach to discover pathways which may be altered following tendinopathic injury and treadmill running in an established murine model of TGF-β1 induced tendinopathy. While specific tendon glucose alterations were not observed via metabolomics or 18 F-fluoroeoxyglucose (FDG) positron emission tomography/microcomputed tomography imaging (18 F-FDG PET/CT), metabolites including creatinine, D-chiro-inositol, and lipids were dysregulated following tendon injury. As novel pathways for manipulation, the creatine pathway, myo-inositol pathway, and lipid signaling may lead to the development of enhanced preventative strategies and therapeutic options for all patients who suffer from tendon-related injuries.

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.

Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification

A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R² > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.

The impact of extraction protocol on the chemical profile of cannabis extracts from a single cultivar

The last two decades have seen a dramatic shift in cannabis legislation around the world. Cannabis products are now widely available and commercial production and use of phytocannabinoid products is rapidly growing. However, this growth is outpacing the research needed to elucidate the therapeutic efficacy of the myriad of chemical compounds found primarily in the flower of the female cannabis plant. This lack of research and corresponding regulation has resulted in processing methods, products, and terminology that are variable and confusing for consumers. Importantly, the impact of processing methods on the resulting chemical profile of full spectrum cannabis extracts is not well understood. As a first step in addressing this knowledge gap we have utilized a combination of analytical approaches to characterize the broad chemical composition of a single cannabis cultivar that was processed using previously optimized and commonly used commercial extraction protocols including alcoholic solvents and super critical carbon dioxide. Significant variation in the bioactive chemical profile was observed in the extracts resulting from the different protocols demonstrating the need for further research regarding the influence of processing on therapeutic efficacy as well as the importance of labeling in the marketing of multi-component cannabis products.

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.

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSⁿ spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSⁿ spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

Characterization of advanced wastewater treatment with ozone and activated carbon using LC-HRMS based non-target screening with automated trend assignment

Advanced treatment is increasingly being applied to improve abatement of micropollutants in wastewater effluent and reduce their load to surface waters. In this study, non-target screening of high-resolution mass spectrometry (HRMS) data, collected at three Swiss wastewater treatment plants (WWTPs), was used to evaluate different advanced wastewater treatment setups, including (1) granular activated carbon (GAC) filtration alone, (2) pre-ozonation followed by GAC filtration, and (3) pre-ozonation followed by powdered activated carbon (PAC) dosed onto a sand filter. Samples were collected at each treatment step of the WWTP and analyzed with reverse-phase liquid chromatography coupled to HRMS. Each WWTP received a portion of industrial wastewater and a prioritization method was applied to select non-target features potentially resulting from industrial activities. Approximately 37,000 non-target features were found in the influents of the WWTPs. A number of non-target features (1207) were prioritized as likely of industrial origin and 54 were identified through database spectral matching. The fates of all detected non-target features were assessed through a novel automated trend assignment method. A trend was assigned to each non-target feature based on the normalized intensity profile for each sampling date. Results showed that 73±4% of influent non-target features and the majority of industrial features (89%) were well-removed (i.e., >80% intensity reduction) during biological treatment in all three WWTPs. Advanced treatment removed, on average, an additional 11% of influent non-target features, with no significant differences observed among the different advanced treatment settings. In contrast, when considering a subset of 66 known micropollutants, advanced treatment was necessary to adequately abate these compounds and higher abatement was observed in fresh GAC (7,000-8,000 bed volumes (BVs)) compared to older GAC (18,000-48,000 BVs) (80% vs 56% of micropollutants were well-removed, respectively). Approximately half of the features detected in the WWTP effluents were features newly formed during the various treatment steps. In ozonation, between 1108-3579 features were classified as potential non-target ozonation transformation products (OTPs). No difference could be observed for their removal in GAC filters at the BVs investigated (70% of OTPs were well-removed on average). Similar amounts (67%) was observed with PAC (7.7-13.6 mg/L) dosed onto a sand filter, demonstrating that a post-treatment with activated carbon is efficient for the removal of OTPs.

Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS

Computational QSAR models have gradually been preferred for retention time prediction in data mining of emerging environmental contaminants using liquid chromatography coupled with mass spectrometry. Generally, the model performance relies on the components such as machine learning algorithms, chemical features, and example data. In this study, we evaluated the performances of four algorithms on three feature sets, using 321 and 77 pesticides as the training and validation sets, respectively. The results were varied with different combinations of algorithms on distinct feature sets. Two strategies including enhancing the complexity of chemical features and enlarging the size of the training set were proved to improve the results. XGBoost, Random Forest, and lightGBM algorithms exhibited the best results when built on a large-scale chemical descriptors, while the Keras algorithm preferred fingerprints. These four models have comparable prediction accuracies that at least 90% of pesticides in validation set can be successfully predicted with ΔRT <1.0 min. Meanwhile, a blended prediction strategy using average results from four models presented a better result than any single model. This strategy was used for assisting identification of pesticides and pesticide transformation products in 120 strawberry samples from a national survey of food contamination. Twenty pesticides and twelve pesticide transformation products were tentatively identified, where all pesticides and two pesticide transformation products (bifenazate diazene and spirotetramat-enol) were confirmed by standard materials. The outcome of this study suggested that retention time prediction is a valuable approach in compound identification when integrated with in silico MS² spectra and other MS identification strategies.

Equine maternal aging affects oocyte lipid content, metabolic function and developmental potential

Advanced maternal age is associated with a decline in fertility and oocyte quality. We used novel metabolic microsensors to assess effects of mare age on single oocyte and embryo metabolic function, which has not yet been similarly investigated in mammalian species. We hypothesized that equine maternal aging affects the metabolic function of oocytes and in vitro-produced early embryos, oocyte mitochondrial DNA (mtDNA) copy number, and relative abundance of metabolites involved in energy metabolism in oocytes and cumulus cells. Samples were collected from preovulatory follicles from young (≤14 years) and old (≥20 years) mares. Relative abundance of metabolites in metaphase II oocytes (MII) and their respective cumulus cells, detected by liquid and gas chromatography coupled to mass spectrometry, revealed that free fatty acids were less abundant in oocytes and more abundant in cumulus cells from old vs young mares. Quantification of aerobic and anaerobic metabolism, respectively measured as oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in a microchamber containing oxygen and pH microsensors, demonstrated reduced metabolic function and capacity in oocytes and day-2 embryos originating from oocytes of old when compared to young mares. In mature oocytes, mtDNA was quantified by real-time PCR and was not different between the age groups and not indicative of mitochondrial function. Significantly more sperm-injected oocytes from young than old mares resulted in blastocysts. Our results demonstrate a decline in oocyte and embryo metabolic activity that potentially contributes to the impaired developmental competence and fertility in aged females.

Comprehensive Evaluation of Metabolites and Minerals in 6 Microgreen Species and the Influence of Maturity

BACKGROUND

Microgreens are the young leafy greens of many vegetables, herbs, grains, and flowers with potential to promote human health and sustainably diversify the global food system. For successful further integration into the global food system and evaluation of their health impacts, it is critical to elucidate and optimize their nutritional quality.

OBJECTIVES

We aimed to comprehensively evaluate the metabolite and mineral contents of 6 microgreen species, and the influence of maturity on their contents.

METHODS

Plant species evaluated were from the Brassicaceae (arugula, broccoli, and red cabbage), Amaranthaceae (red beet and red amaranth), and Fabaceae (pea) plant families. Nontargeted metabolomics and ionomics analyses were performed to examine the metabolites and minerals, respectively, in each microgreen species and its mature counterpart.

RESULTS

Nontargeted metabolomics analysis detected 3321 compounds, 1263 of which were annotated and included nutrients and bioactive compounds. Ionomics analysis detected and quantified 26 minerals including macrominerals, trace minerals, ultratrace minerals, and other metals. Principal component analysis indicated that microgreens have distinct metabolite and mineral profiles compared with one another and with their mature counterparts. Several compounds were higher (P  < 0.05; fold change ≥2) in microgreens compared with their mature counterparts, whereas some were not different or lower. In many cases, compounds that were higher in microgreens compared with the mature counterpart were also unique to that microgreen species.

CONCLUSIONS

These data provide evidence for the nutritional quality of microgreens, and can inform future research and development aimed at characterizing and optimizing microgreen nutritional quality and health impacts.

Early metabolic priming under differing carbon sufficiency conditions influences peach fruit quality development

Crop load management is an important preharvest factor to balance yield, quality, and maturation in peach. However, few studies have addressed how preharvest factors impact metabolism on fruit of equal maturity. An experiment was conducted to understand how carbon competition impacts fruit internal quality and metabolism in 'Cresthaven' peach trees by imposing distinct thinning severities. Fruit quality was evaluated at three developmental stages (S2, S3, S4), while controlling for equal maturity using non-destructive visual to near-infrared spectroscopy. Non-targeted metabolite profiling was used to characterize fruit at each developmental stage from trees that were unthinned (carbon starvation) or thinned (carbon sufficiency). Carbon sufficiency resulted in significantly higher fruit dry matter content and soluble solids concentration at harvest when compared to the carbon starved, underscoring the true impact of carbon manipulation on fruit quality. Significant differences in the fruit metabolome between treatments were observed at S2 when phenotypes were similar, while less differences were observed at S4 when the carbon sufficient fruit exhibited a superior phenotype. This suggests a potential metabolic priming effect on fruit quality when carbon is sufficiently supplied during early fruit growth and development. In particular, elevated levels of catechin may suggest a link between secondary/primary metabolism and fruit quality development.

Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification

Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]⁺ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]⁺ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]⁺ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance.

Machine learning to predict retention time of small molecules in nano-HPLC

Retention time is an important parameter for identification in untargeted LC-MS screening. Precise retention time prediction facilitates the annotation process and is well known for proteomics. However, the lack of available experimental information for a long time has limited the prediction accuracy for small molecules. Recently introduced large databases for small-molecule retention times make possible reliable machine learning-based predictions for the whole diversity of compounds. Applying simple projections may expand these predictions on various LC systems and conditions. In our work, we describe a complex approach to predict retention times for nano-HPLC that includes the consequent deployment of binary and regression gradient boosting models trained on the METLIN small-molecule dataset and simple projection of the results with a small number of easily available compounds onto nano-HPLC separations. The proposed model outperforms previous attempts to use machine learning for predictions with a 46-s mean absolute error. The overall performance after transfer to nano-LC conditions is less than 155 s (10.8%) in terms of the median absolute (relative) error. To illustrate the applicability of the described approach, we successfully managed to eliminate averagely 25 to 42% of false-positives with a filter threshold derived from ROC curves. Thus, the proposed approach should be used in addition to other well-established in silico methods and their integration may broaden the range of correctly identified molecules.

Chemotype classification and biomarker screening of male Eucommia ulmoides Oliv. flower core collections using UPLC-QTOF/MS-based non-targeted metabolomics

Background

In the Chinese health care industry, male Eucommia ulmoides Oliv. flowers are newly approved as a raw material of functional food. Core collections have been constructed from conserved germplasm resources based on phenotypic traits and molecular markers. However, little is known about these collections’ phytochemical properties. This study explored the chemical composition of male E. ulmoides flowers, in order to provide guidance in the quality control, sustainable cultivation, and directional breeding of this tree species.

Methods

We assessed the male flowers from 22 core collections using ultra-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) non-targeted metabolomics, and analyzed them using multivariate statistical methods including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA).

Results

We annotated a total of 451 and 325 metabolites in ESI+ and ESI− modes, respectively, by aligning the mass fragments of the secondary mass spectra with those in the database. Four chemotypes were well established using the ESI+ metabolomics data. Of the 29 screened biomarkers, 21, 6, 19, and 5 markers corresponded to chemotypes I, II, III, and IV, respectively. More than half of the markers belonged to flavonoid and amino acid derivative classes.

Conclusion

Non-targeted metabolomics is a suitable approach to the chemotype classification and biomarker screening of male E. ulmoides flower core collections. We first evaluated the metabolite profiles and compositional variations of male E. ulmoides flowers in representative core collections before establishing possible chemotypes and significant biomarkers denoting the variations. We used genetic variations to infer the metabolite compositional variations of male E. ulmoides flower core collections instead of using the geographical origins of the germplasm resources. The newly proposed biomarkers sufficiently classified the chemotypes to be applied for germplasm resource evaluation.

Metabolic compounds within the porcine uterine environment are unique to the type of conceptus present during the early stages of blastocyst elongation

The objective of this study was to identify metabolites within the porcine uterine milieu during the early stages of blastocyst elongation. At Days 9, 10, or 11 of gestation, reproductive tracts of White cross-bred gilts (n = 38) were collected immediately following harvest and flushed with Roswell Park Memorial Institute-1640 medium. Conceptus morphologies were assessed from each pregnancy and corresponding uterine flushings were assigned to one of five treatment groups based on these morphologies: (a) uniform spherical (n = 8); (b) heterogeneous spherical and ovoid (n = 8); (c) uniform ovoid (n = 8); (d) heterogeneous ovoid and tubular (n = 8); and (e) uniform tubular (n = 6). Uterine flushings from these pregnancies were submitted for nontargeted profiling by gas chromatography-mass spectrometry (GC-MS) and ultra performance liquid chromatography (UPLC)-MS techniques. Unsupervised multivariate principal component analysis (PCA) was performed using pcaMethods and univariate analysis of variance was performed in R with false discovery rate (FDR) adjustment. PCA analysis of the GC-MS and UPLC-MS data identified 153 and 104 metabolites, respectively. After FDR adjustment of the GC-MS and UPLC-MS data, 38 and 59 metabolites, respectively, differed (p < .05) in uterine flushings from pregnancies across the five conceptus stages. Some metabolites were greater (p  < .05) in abundance for uterine flushings containing earlier stage conceptuses (i.e., spherical), such as uric acid, tryptophan, and tyrosine. In contrast, some metabolites were greater (p < .05) in abundance for uterine flushings containing later stage conceptuses (i.e., tubular), such as creatinine, serine, and urea. These data illustrate several putative metabolites that change within the uterine milieu during early porcine blastocyst elongation.

The METLIN small molecule dataset for machine learning-based retention time prediction

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.

LAS: A Lipid Annotation Service Capable of Explaining the Annotations It Generates

The Lipid Annotation Service (LAS) is a representational state transfer (REST) application programming interface (API) service designed to aid researchers performing lipid annotation. It assigns certainty levels (very unlikely, unlikely, likely, and very likely) to the putative annotations received as input and explains the rationale of such assignments. Its rules, obtained from the Centre for Metabolomics and Bioanalysis (CEMBIO) and from a literature review, enable LAS to extract evidence to support or refute the annotations automatically by checking the inter-rule relationships.

LAS is the first metabolite annotation tool capable of explaining in natural language (English) the evidence that supports or refutes the annotations. This facilitates the understanding of the results by the user and, thus, increases the user's confidence in the results. Concerning its performance, in an evaluation of blood plasma samples whose compounds had previously been identified using well-established standards, LAS yielded an F-measure higher than 80%.

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification

Background:

There is a continued need for improvements in the efficiency of metabolite structure elucidation.

Objective:

We propose to take LC Retention Time (RT) into consideration during the process of structure determination.

Methods:

Herein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a pre-dictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature exam-ples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransfor-mation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding).

Results:

The CHI system for retention indexing is shown to be practical and simple to implement. A da-tabase of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an addi-tional 17 datasets from the literature. Application of this database is illustrated.

Conclusion:

In our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.

Metabolomics of sorghum roots during nitrogen stress reveals compromised metabolic capacity for salicylic acid biosynthesis

Sorghum (Sorghum bicolor [L.] Moench) is the fifth most productive cereal crop worldwide with some hybrids having high biomass yield traits making it promising for sustainable, economical biofuel production. To maximize biofuel feedstock yields, a more complete understanding of metabolic responses to low nitrogen (N) will be useful for incorporation in crop improvement efforts. In this study, 10 diverse sorghum entries (including inbreds and hybrids) were field-grown under low and full N conditions and roots were sampled at two time points for metabolomics and 16S amplicon sequencing. Roots of plants grown under low N showed altered metabolic profiles at both sampling dates including metabolites important in N storage and synthesis of aromatic amino acids. Complementary investigation of the rhizosphere microbiome revealed dominance by a single operational taxonomic unit (OTU) in an early sampling that was taxonomically assigned to the genus Pseudomonas. Abundance of this Pseudomonas OTU was significantly greater under low N in July and was decreased dramatically in September. Correlation of Pseudomonas abundance with root metabolites revealed a strong negative association with the defense hormone salicylic acid (SA) under full N but not under low N, suggesting reduced defense response. Roots from plants with N stress also contained reduced phenylalanine, a precursor for SA, providing further evidence for compromised metabolic capacity for defense response under low N conditions. Our findings suggest that interactions between biotic and abiotic stresses may affect metabolic capacity for plant defense and need to be concurrently prioritized as breeding programs become established for biofuels production on marginal soils.

Metabolomic Investigation of Tenderness and Aging Response in Beef Longissimus Steaks

A study was conducted to identify molecular changes reflective of beef tenderness variation and tenderization during postmortem aging. Carcasses (U.S. Select) were selected to represent extremes in tenderness (n = 20; 10 per class). Two pairs of adjacent longissimus lumborum steaks from each strip loin were blocked by location and assigned to each aging time (2, 7, 14, or 28 d postmortem). One steak from each pair was designated for slice shear force determination and the other was used for sarcomere length, western blotting for desmin, and non-targeted LC- and GC–MS metabolite profiling. Tough steaks had higher (P &lt; 0.001) slice shear force values than tender steaks, and increasing aging time decreased (P &lt; 0.001) slice shear force values. Tender steaks had a greater (P &lt; 10–4) proportion of desmin degraded than tough steaks, and increasing aging time increased (P &lt; 10–22) desmin degradation in steaks from both classes. From 2,562 profiled metabolites, 102 metabolites were included in the final analysis after statistical screening. Twenty-eight metabolites could be annotated and loosely categorized into amino acids/peptides (n = 16), metabolism intermediates (n = 7), glycosides (n = 4), and fatty acids and phospholipids (n = 3). Amino acids were primarily associated with desmin degradation. Increased glucose levels were strongly associated to the tender classification and moderately associated to increased proteolysis, while increased glucose-6-phosphate was strongly related to the tender class but was related to decreased proteolysis. Increased malic acid was strongly associated to the tough classification, increased slice shear force, and decreased proteolysis. Increased levels of 3-phosphoglyceric acid and glycerol-3-phosphate was moderately associated with increased slice shear force and decreased proteolysis. These data indicate that accumulation of amino acids during aging is strongly related to postmortem proteolysis and may provide evidence of the fate of proteins degraded postmortem. Measures of glucose, glucose-6-phosphate, and malic acid concentrations may provide a metabolic fingerprint indicative of tenderness differences in beef longissimus.

Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics

Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu .

Non-Targeted Metabolomics Reveals Sorghum Rhizosphere-Associated Exudates are Influenced by the Belowground Interaction of Substrate and Sorghum Genotype

Root exudation is an important plant process by which roots release small molecules into the rhizosphere that serve in overall plant functioning. Yet, there is a major gap in our knowledge in translating plant root exudation in artificial systems (i.e., hydroponics, sterile media) to crops, specifically for soils expected in field conditions. Sorghum (Sorghum bicolor L. Moench) root exudation was determined using both ultra-performance liquid chromatography and gas chromatography mass spectrometry-based non-targeted metabolomics to evaluate variation in exudate composition of two sorghum genotypes among three substrates (sand, clay, and soil). Above and belowground plant traits were measured to determine the interaction between sorghum genotype and belowground substrate. Plant growth and quantitative exudate composition were found to vary largely by substrate. Two types of changes to rhizosphere metabolites were observed: rhizosphere-enhanced metabolites (REMs) and rhizosphere-abated metabolites (RAMs). More REMs and RAMs were detected in sand and clay substrates compared to the soil substrate. This study demonstrates that belowground substrate influences the root exudate profile in sorghum, and that two sorghum genotypes exuded metabolites at different magnitudes. However, metabolite identification remains a major bottleneck in non-targeted metabolite profiling of the rhizosphere.

Data Processing for GC-MS- and LC-MS-Based Untargeted Metabolomics

Gas chromatography and liquid chromatography coupled to mass spectrometry are used extensively in untargeted metabolomics, which involves the profiling of small metabolites in biological samples. The complex raw dataset produced from untargeted metabolomics requires proper processing before it can be statistically analyzed and interpreted. This chapter describes a high-throughput data processing workflow routinely used in our laboratory, including feature detection and alignment, data reduction, and spectral-matching-based annotation. This semiautomated workflow uses vendor neutral data file formats and freely available data processing tools and therefore can be readily implemented on datasets acquired from instruments of different vendors.

Pregnancy-induced changes in metabolome and proteome in ovine uterine flushings

Mass spectrometry (MS) approaches were used herein to identify metabolites and proteins in uterine flushings (UF) that may contribute to nourishing the conceptus. Ovine uteri collected on Day 12 of the estrous cycle (n = 5 ewes exposed to vasectomized ram) or Days 12 (n = 4), 14 (n = 5), or 16 (n = 5) of pregnancy (bred with fertile ram) were flushed using buffered saline. Metabolites were extracted using 80% methanol and profiled using ultraperformance liquid chromatography (LC) tandem mass spectrometry. The proteome was examined by digestion with trypsin, followed by the analysis of peptides with LC-MS/MS. Metabolite profiling detected 8510 molecular features of which 9 were detected only in UF from Day 14-16 pregnant ewes that function in fatty acid transport (carnitines), hormone synthesis (androstenedione like), and availability of nutrients (valine). Proteome analysis detected 783 proteins present by Days 14-16 of pregnancy in UF, 7 of which are as follows: annexin (ANX) A1, A2, and A5; calcium-binding protein (S100A11); profilin 1; trophoblast kunitz domain protein 1 (TKDP); and interferon tau (IFNT). These proteins function in endocytosis, exocytosis, calcium signaling, and inhibition of prostaglandins (annexins and S100A11); protecting against maternal proteases (TKDP); remodeling cytoskeleton (profilin 1); and altering uterine release of prostaglandin F2 alpha as well as inducing IFNT-stimulated genes in the endometrium and the corpus luteum (IFNT). Identifying metabolites and proteins produced by the uterus and conceptus advances our understanding of embryo/maternal signaling and provides insights into possible the causes of reproductive failure.