METLIN: A Technology Platform for Identifying Knowns and Unknowns

Small Molecule Signatures of Mice Lacking T-cell p38 Alternate Activation, a Model for Immunosuppression Conditions, after Total-Body Irradiation

Several diagnostic biodosimetry tools have been in development that may aid in radiological/nuclear emergency responses. Of these, correlating changes in non-invasive biofluid small-molecule signatures to tissue damage from ionizing radiation exposure show promise for inclusion in predictive biodosimetry models. Integral to dose reconstruction has been determining how genotypic variation in the general population will affect model performance. Here, we used a mouse model that lacks the T-cell receptor specific alternative p38 pathway [p38αβY323F, double knock-in (DKI) mice] to determine how attenuated autoimmune and inflammatory responses may affect dose reconstruction. We exposed adult male DKI mice (8-10 weeks old) to 2 and 7 Gy in parallel with wild-type mice and assessed perturbations in urine (days 1, 3, 7) and serum (day 1) using a global metabolomics approach. A multidimensional scaling plot showed excellent separation of radiation-exposed groups in wild-type mice with slightly dampened responses in DKI mice. Validated metabolite panels were developed for urine [N6,N6,N6-trimethyllysine (TML), N1-acetylspermidine, spermidine, carnitine, acylcarnitine C21H35NO5, 4-aminohippuric acid] and serum [phenylalanine, glutamine, propionylcarnitine, lysophosphatidylcholine (LysoPC 14:0), LysoPC (22:5)] to determine the area under the receiver operating characteristic curve (AUROC). For both urine and serum, excellent sensitivity and specificity (AUROC > 0.90) was observed for 0 Gy vs. 7 Gy groups irrespective of genotype using identical metabolite panels. Similarly, excellent to fair classification (AUROC > 0.75) was observed for ≤2 Gy vs. 7 Gy mice for both genotypes, however, model performance declined (AUROC < 0.75) between genotypes after irradiation. Overall, these results suggest immunosuppression should not compromise small molecule multiplex panels used in dose reconstruction for biodosimetry.

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).

Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN

Neutral loss (NL) spectral data presents a mirror of MS² data and is a valuable yet largely untapped resource for molecular discovery and similarity analysis. Tandem mass spectrometry (MS²) data is effective for the identification of known molecules and the putative identification of novel, previously uncharacterized molecules (unknowns). Yet, MS² data alone is limited in characterizing structurally related molecules. To facilitate unknown identification and complement the METLIN-MS² fragment ion database for characterizing structurally related molecules, we have created a MS² to NL converter as a part of the METLIN platform. The converter has been used to transform METLIN's MS² data into a neutral loss database (METLIN-NL) on over 860 000 individual molecular standards. The platform includes both the MS² to NL converter and a graphical user interface enabling comparative analyses between MS² and NL data. Examples of NL spectral data are shown with oxylipin analogues and two structurally related statin molecules to demonstrate NL spectra and their ability to help characterize structural similarity. Mirroring MS² data to generate NL spectral data offers a unique dimension for chemical and metabolite structure characterization.

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

Untargeted plasma metabolomics identifies broad metabolic perturbations in glycogen storage disease type I

BACKGROUND

The metabolic defect in glycogen storage disease type I (GSDI) results in fasting hypoglycemia and typical secondary metabolic abnormalities (eg, hypertriglyceridemia, hyperlactatemia, hyperuricemia). The aim of this study was to assess further perturbations of the metabolic network in GSDI patients under ongoing treatment.

METHODS

In this prospective observational study, plasma samples of 14 adult patients (11 GSDIa, 3 GSDIb. Mean age 26.4 years, range 16-46 years) on standard treatment were compared to a cohort of 31 healthy controls utilizing ultra-high performance liquid chromatography (UHPLC) in combination with high resolution tandem mass spectrometry (HR-MS/MS) and subsequent statistical multivariate analysis. In addition, plasma fatty acid profiling was performed by GC/EI-MS.

RESULTS

The metabolomic profile showed alterations of metabolites in different areas of the metabolic network in both GSD subtypes, including pathways of fuel metabolism and energy generation, lipids and fatty acids, amino acid and methyl-group metabolism, the urea cycle, and purine/pyrimidine metabolism. These alterations were present despite adequate dietary treatment, did not correlate with plasma triglycerides or lactate, both parameters typically used to assess the quality of metabolic control in clinical practice, and were not related to the presence or absence of complications (ie, nephropathy or liver adenomas).

CONCLUSION

The metabolic defect of GSDI has profound effects on a variety of metabolic pathways in addition to the known typical abnormalities. These alterations are present despite optimized dietary treatment, which may contribute to the risk of developing long-term complications, an inherent problem of GSDI which appears to be only partly modified by current therapy.

In vivo mechanotransduction: Effect of acute exercise on the metabolomic profiles of mouse synovial fluid

Objective

Exercise is known to induce beneficial effects in synovial joints. However, the mechanisms underlying these are unclear. Synovial joints experience repeated mechanical loading during exercise. These mechanical stimuli are transduced into biological responses through cellular mechanotransduction. Mechanotransduction in synovial joints is typically studied in tissues. However, synovial fluid directly contacts all components of the joint, and thus may produce a whole-joint picture of the mechanotransduction response to loading. The objective of this study was to determine if metabolic phenotypes are present in the synovial fluid after acute exercise as a first step to understanding the beneficial effects of exercise on the joint.

Material and methods

Mice underwent a single night of voluntary wheel running or standard housing and synovial fluid was harvested for global metabolomic profiling by LC-MS. Hierarchical unsupervised clustering, partial least squares discriminant, and pathway analysis provided insight into exercise-induced mechanotransduction.

Results

Acute exercise produced a distinct metabolic phenotype in synovial fluid. Mechanosensitive metabolites included coenzyme A derivatives, prostaglandin derivatives, phospholipid species, tryptophan, methionine, vitamin D3, fatty acids, and thiocholesterol. Enrichment analysis identified several pathways previously linked to exercise including amino acid metabolism, inflammatory pathways, citrulline-nitric oxide cycle, catecholamine biosynthesis, ubiquinol biosynthesis, and phospholipid metabolism.

Conclusion

To our knowledge, this is the first study to investigate metabolomic profiles of synovial fluid during in vivo mechanotransduction. These profiles indicate that exercise induced stress-response processes including both pro- and anti-inflammatory pathways. Further research will expand these results and define the relationship between the synovial fluid and the serum.

Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR

Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Investigation and dynamic profiling of oligopeptides, free amino acids and derivatives during Pu-erh tea fermentation by ultra-high performance liquid chromatography tandem mass spectrometry

Microbial fermentation is the critical step of Pu-erh tea manufacture, which will induce dramatic changes in the chemical composition and content of tea. In this research, we applied multi-methods based on UHPLC-Q-TOF/MS to profile the dynamic changes of oligopeptides, free amino acids, and derivatives (OPADs) during Pu-erh fermentation and predicted the potential bioactivities in silico. A total of 60 oligopeptides, 18 free amino acids, and 42 amino acid derivatives were identified, and the contents of most of them decreased after fermentation. But several N-acetyl amino acids increased 7-36 times after fermentation, and they might be the potential inhibitors of neurokinin-1 receptor. Moreover, the results of metamicrobiology showed Aspergillus niger and Aspergillus luchuensis were more prominent to metabolize protein, oligopeptides, and amino acids. Overall, these findings provide valuable insights about dynamic variations of OPADs during Pu-erh tea fermentation and are beneficial for guiding practical fermentation and quality control of Pu-erh tea.

Mass spectrometry-based metabolomics in microbiome investigations

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

SDHB knockout and succinate accumulation are insufficient for tumorigenesis but dual SDHB/NF1 loss yields SDHx-like pheochromocytomas

Inherited pathogenic succinate dehydrogenase (SDHx) gene mutations cause the hereditary pheochromocytoma and paraganglioma tumor syndrome. Syndromic tumors exhibit elevated succinate, an oncometabolite that is proposed to drive tumorigenesis via DNA and histone hypermethylation, mitochondrial expansion, and pseudohypoxia-related gene expression. To interrogate this prevailing model, we disrupt mouse adrenal medulla SDHB expression, which recapitulates several key molecular features of human SDHx tumors, including succinate accumulation but not 5hmC loss, HIF accumulation, or tumorigenesis. By contrast, concomitant SDHB and the neurofibromin 1 tumor suppressor disruption yields SDHx-like pheochromocytomas. Unexpectedly, in vivo depletion of the 2-oxoglutarate (2-OG) dioxygenase cofactor ascorbate reduces SDHB-deficient cell survival, indicating that SDHx loss may be better tolerated by tissues with high antioxidant capacity. Contrary to the prevailing oncometabolite model, succinate accumulation and 2-OG-dependent dioxygenase inhibition are insufficient for mouse pheochromocytoma tumorigenesis, which requires additional growth-regulatory pathway activation.

Untargeted LC-MS/MS Metabolomics Study on the MCF-7 Cell Line in the Presence of Valproic Acid

To target breast cancer (BC), epigenetic modulation could be a promising therapy strategy due to its role in the genesis, growth, and metastases of BC. Valproic acid (VPA) is a well-known histone deacetylase inhibitor (HDACi), which due to its epigenetic focus needs to be studied in depth to understand the effects it might elicit in BC cells. The aim of this work is to contribute to exploring the complete pharmacological mechanism of VPA in killing cancer cells using MCF-7. LC-MS/MS metabolomics studies were applied to MCF-7 treated with VPA. The results show that VPA promote cell death by altering metabolic pathways principally pentose phosphate pathway (PPP) and 2′deoxy-α-D-ribose-1-phosphate degradation related with metabolites that decrease cell proliferation and cell growth, interfere with energy sources and enhance reactive oxygen species (ROS) levels. We even suggest that mechanisms such as ferropoptosis could be involved due to deregulation of L-cysteine. These results suggest that VPA has different pharmacological mechanisms in killing cancer cells including apoptotic and nonapoptotic mechanisms, and due to the broad impact that HDACis have in cells, metabolomic approaches are a great source of information to generate new insights for this type of molecule.

Fibroblast activation protein alpha: Comprehensive detection methods for drug target and tumor marker

Fibroblast activation protein alpha (FAP-α, EC3.4.2. B28), a type II transmembrane proteolytic enzyme for the serine protease peptidase family. It is underexpressed in normal tissues but increased significantly in disease states, especially in neoplasm, which is a potential biomarker to turmor diagnosis. The inhibition of FAP-α activity will retard tumor formation, which is expected to be a promising tumor therapeutic target. At present, although the FAP-α expression detection methods has diversification, a superlative detection means is necessary for the clinical diagnosis. This review covers the discovery and the latest advances in FAP-α, as well as the future research prospects. The tissue distribution, structural characteristics, small-molecule ligands and structure-activity relationship of major inhibitors of FAP-α were summarized in this review. Furthermore, a variety of detection methods including traditional detection methods and emerging probes detection were classified and compared, and the design strategy and kinetic parameters of these FAP-α probe substrates were summarized. In addition, these comprehensive information provides a series of practical and reliable assays for the optimal design principles of FAP-α probes, promoting the application of FAP-α as a disease marker in diagnosis, and a drug target in drug design.

Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners

Background

Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO₂max) and longer-term outcomes, including the development of diabetes and its complications.

Methods

Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO₂max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome.

Results

NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO₂max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO₂max.

Conclusion

These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO₂max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance.

Trial registration

ClinicalTrials.gov, NCT02150889.

Funding

NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.

Using mass spectrometry to investigate fluorescent compounds in squirrel fur

While an array of taxa are capable of producing fluorescent pigments, fluorescence in mammals is a novel and poorly understood phenomenon. A first step towards understanding the potential adaptive functions of fluorescence in mammals is to develop an understanding of fluorescent compounds, or fluorophores, that are present in fluorescent tissue. Here we use Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) of flying squirrel fur known to fluoresce under ultraviolet (UV) light to identify potentially fluorescent compounds in squirrel fur. All of the potentially fluorescent compounds we identified were either present in non-fluorescent fur or were not present in all species of fluorescent flying squirrel. Therefore, we suggest that the compounds responsible for fluorescence in flying squirrels may also be present in non-fluorescent mammal fur. Some currently unexplained factor likely leads to excitation of fluorophores in flying squirrel fur. A recently suggested hypothesis that fluorescence in mammals is widely caused by porphyrins is consistent with our findings.

Predictable Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry Fragmentation of Ozone-Reactive N-Nitrosodimethylamine Precursors Coupled with In Silico Fragmentation and Ion Mobility-Quadrupole Time-of-Flight Facilitates Their Identification in Sewage

This study investigated the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) fragmentation of 10 potent model ozone (O₃)-reactive N-nitrosodimethylamine (NDMA) precursors bearing (CH₃)₂N-N or (CH₃)₂N-(SO₂)-N. Fragments (m/z 61.0766, 60.0688 Da loss, and 72.0688 Da loss) were discovered as pertinent diagnostic fragments for precursors bearing (CH₃)₂N-N, whereas a loss of 108.0119 Da was consistent for precursors bearing (CH₃)₂N-S(O₂)-N. Using the fragments as structural hints on a sewage fraction with a high concentration of O₃-reactive precursors, peaks of precursors sharing m/z 61.0766, a 60.0688 Da loss, or both were flagged. Then, using in silico fragmenters and (CH₃)₂N-N as a substructure filter on online-chemical structure databases, we identified PubChem's compound identifier (PCCID) 141210417 and 1,1,1',1'-tetramethyl-4,4'-(methylene-di-p-phenylene)disemicarbazide (TMDS). TMDS was confirmed using an authentic standard, and ion mobility (IM)-QTOF/MS confirmed its rider peak as PCCID 141210417. PCCID 141210417 is an isomer of TMDS, and its environmental occurrence is associated with technical-grade TMDS and industrial effluents. The estimated contribution of TMDS to the total NDMA formation potential of the sewage fraction was 20-24%, which was suggestive of the significance of PCCID 141210417 and other precursors.

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-¹³C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their ¹²C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

Targeting intestinal flora and its metabolism to explore the laxative effects of rhubarb

Rhubarb, a traditional herb, has been used in clinical practice for hundreds of years to cure constipation, but its mechanism is still not clear enough. Currently, growing evidence suggests that intestinal flora might be a potential target for the treatment of constipation. Thus, the aim of this study was to clarify the laxative effect of rhubarb via systematically analyzing the metagenome and metabolome of the gut microbiota. In this study, the laxative effects of rhubarb were investigated by loperamide-induced constipation in rats. The gut microbiota was determined by high-throughput sequencing of 16S rRNA gene. Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used for fecal metabolomics analysis. The data showed that rhubarb could significantly shorten gastrointestinal transit time, increase fecal water content and defecation frequency, improve gastrointestinal hormone disruption, and protect the colon mucus layer. Analysis of 16S rRNA gene sequencing indicated that rhubarb could improve the disorder of intestinal microbiota in constipated rats. For example, beneficial bacteria such as Ligilactobacillus, Limosilalactobacillus, and Prevotellaceae UCG-001 were remarkably increased, and pathogens such as Escherichia-Shigella were significantly decreased after rhubarb treatment. Additionally, the fecal metabolic profiles of constipated rats were improved by rhubarb. After rhubarb treatment, metabolites such as chenodeoxycholic acid, cholic acid, prostaglandin F2α, and α-linolenic acid were markedly increased in constipation rats; in contrast, the metabolites such as lithocholic acid, calcidiol, and 10-hydroxystearic acid were notably reduced in constipation rats. Moreover, correlation analysis indicated a close relationship between intestinal flora, fecal metabolites, and biochemical indices associated with constipation. In conclusion, the amelioration of rhubarb in constipation might modulate the intestinal microflora and its metabolism. Moreover, the application of fecal metabolomics could provide a new strategy to uncover the mechanism of herbal medicines.Key points• Rhubarb could significantly improve gut microbiota disorder in constipation rats.• Rhubarb could markedly modulate the fecal metabolite profile of constipated rats.

Plant Responses Underlying Timely Specialized Metabolites Induction of Brassica Crops

A large subset of plant stress-signaling pathways, including those related with chemical defense production, exhibit diurnal or circadian oscillations. However the extent to which diurnal or circadian time influences the stress mediated accumulation of plant specialized metabolites remains largely unknown. Because plant responses to physical stress (e.g., wounding) is considered a common component of mounting a response against a broad range of environmental stresses, including herbivory, we have utilized mechanical wounding as the stress stimulus to determine the direct contribution of time of day on the induced defenses of Brassica crops. We analyzed glucosinolates (GSLs) from leaves of broccoli (Brassica oleracea) and turnip greens (Brassica rapa) following exposure to mechanical wounding at dawn (ZT0), mid-day (ZT4), and dusk (ZT8). Several GSLs differentially accumulated and their changes depended upon the time of day at wounding was performed. This response varied considerably between species. In a parallel experiment, we investigated whether diurnal activation of Brassica phytochemicals in response to wounding might prime plants against herbivore attack. Results showed that maximal response of plant chemical defense against larvae of the generalist pest Mamestra brassicae occurred at ZT0 in broccoli and ZT8 in turnip greens. Metabolome analysis for global trends of time dependent compounds showed that sulfur-containing phytochemicals, GSL hydrolysis products, auxin-signaling components, and other metabolites activators of plant disease resistance (nicotinamide and pipecolate) had important contributions to the responses of M. brassicae feeding behavior in broccoli at morning. Overall, the findings in this study highlight a significant role for time of day in the wound stress responsive metabolome, which can in turn affect plant-herbivore interactions.

Oxygen Isotope Exchange Reaction for Untargeted LC-MS Analysis

LC-MS is a key technique for the identification of small molecules in complex samples. Accurate mass, retention time, and fragmentation spectra from LC-MS experiments are compared to reference values for pure chemical standards. However, this information is often unavailable or insufficient, leading to an assignment to a list of candidates instead of a single hit; therefore, additional features are desired to filter candidates. One such promising feature is the number of specific functional groups of a molecule that can be counted via derivatization or isotope-exchange techniques. Hydrogen/deuterium exchange (HDX) is the most widespread implementation of isotope exchange for mass spectrometry, while oxygen ¹⁶O/¹⁸O exchange is not applied as frequently as HDX. Nevertheless, it is known that some functional groups may be selectively exchanged in ¹⁸O enriched media. Here, we propose an implementation of ¹⁶O/¹⁸O isotope exchange to highlight various functional groups. We evaluated the possibility of using the number of exchanged oxygen atoms as a descriptor to filter database candidates in untargeted LC-MS-based workflows. It was shown that ¹⁶O/¹⁸O exchange provides 62% (median, n = 45) search space reduction for a panel of drug molecules. Additionally, it was demonstrated that studying the fragmentation spectra after ¹⁶O/¹⁸O can aid in eliminating false positives and, in some cases, help to annotate fragments formed with water traces in the collisional cell.

Metataxonomics, metagenomics and metabolomics analysis of the influence of temperature modification in full-scale anaerobic digesters

Full-scale anaerobic digesters' performance is regulated by modifying their operational conditions, but little is known about how these modifications affect their microbiome. In this work, we monitored two originally mesophilic (35 °C) full-scale anaerobic digesters during 476 days. One digester was submitted to sub-mesophilic (25 °C) conditions between days 123 and 373. We characterized the effect of temperature modification using a multi-omics (metataxonomics, metagenomics, and metabolomics) approach. The metataxonomics and metagenomics results revealed that the lower temperature allowed a substantial increase of the sub-dominant bacterial population, destabilizing the microbial community equilibrium and reducing the biogas production. After restoring the initial mesophilic temperature, the bacterial community manifested resilience in terms of microbial structure and functional activity. The metabolomic signature of the sub-mesophilic acclimation was characterized by a rise of amino acids and short peptides, suggesting a protein degradation activity not directed towards biogas production.

A multi-omics study to boost continuous bolaform sophorolipid production

Biodegradable and biobased surface active agents are renewable and environmentally friendly alternatives to petroleum derived or oleochemical surfactants. However, they are accompanied by relatively high production costs. In this study, the aim was to reduce the production costs for an innovative type of microbial biosurfactant: bolaform sophorolipids, produced by the yeast Starmerella bombicola ΔsbleΔat. A novel continuous retentostat set-up was performed whereby continuous broth microfiltration retained the biomass in the bioreactor while performing an in situ product separation of bolaform sophorolipids. Although a mean volumetric productivity of 0.56 g L^-1 h^-1 was achieved, it was not possible to maintain this productivity, which collapsed to almost 0 g L^-1 h^-1. Therefore, two process adaptations were evaluated, a sequential batch strategy and a phosphate limitation alleviation strategy. The sequential batch set-up restored the mean volumetric productivity to 0.66 g L^-1 h^-1 for an additional 132 h but was again followed by a productivity decline. A similar result was obtained with the phosphate limitation alleviation strategy where a mean volumetric productivity of 0.54 g L^-1 h^-1 was reached, but a productivity decline was also observed. Whole genome variant analysis uncovered no evidence for genomic variations for up to 1306 h of retentostat cultivation. Untargeted metabolomics analysis identified 8-hydroxyguanosine, a biomarker for oxidative RNA damage, as a key metabolite correlating with high bolaform sophorolipid productivity. This study showcases the application of a retentostat to increase bolaform sophorolipid productivity and lays the basis of a multi-omics platform for in depth investigation of microbial biosurfactant production with S. bombicola.

Optimization of the extraction conditions of Nypa fruticans Wurmb. using response surface methodology and artificial neural network

In this study, we conducted response surface methodology (RSM) and artificial neural network (ANN) to predict and estimate the optimized extraction condition of Nypa fruticans Wurmb. (NF). The effect of ethanol concentration (X₁; 0-100%), extraction time (X₂; 6-24 h), and extraction temperature (X₃; 40-60 °C) on the antioxidant potential was confirmed. The optimal conditions (57.6% ethanol, 19.0 h extraction time, and 51.3 °C extraction temperature) of 2,2-diphenyl-1-1picrylhydrazyl (DPPH) scavenging activity, cupric reducing antioxidant capacity (CUPRAC) and ferric reducing antioxidant power (FRAP), total phenolic content (TPC), and total flavonoid contents (TFC) resulted in a maximum value of 62.5%, 41.95 and 48.39 µM, 143.6 mg GAE/g, and 166.8 CAE/g, respectively. High-resolution mass spectroscopic technique was performed to profile phenolic and flavonoid compounds. Upon analyzing, total 48 compounds were identified in NF. Altogether, our findings can provide a practical approach for utilizing NF in various bioindustries.

Effects of Cage and Floor Rearing Systems on the Metabolic Components of the Uropygial Gland in Ducks

Simple Summary

With the development of the modern poultry industry, people have gradually transformed the floor rearing system into a cage rearing system. However, due to some factors, including the environment and management, the feather condition of caged ducks is generally poor, which impairs the healthy growth of ducks and the economic efficiency of breeders. It is believed that birds usually collect secretions from their uropygial gland and smear them on their feathers and cuticle scales during preening to improve their waterproofing and resistance to pathogens, thus protecting their health and growth. Therefore, we studied the uropygial glands of ducks in different rearing systems. The results showed that the cage rearing system affected the weight and metabolic components in the uropygial gland of ducks. Caged ducks have a lower relative weight of their uropygial gland and lower levels of certain amino acids and fatty acids that contribute to their development. This allows us to better understand the causes of the poor appearance of feathers in caged ducks.

Abstract

Background: As a unique skin derivative of birds, the uropygial gland has a potential role in maintaining feather health and appearance. Cage-reared ducks usually have a worse feather condition than floor-reared ducks. We suspected that the metabolic components in the uropygial gland might play a vital role in their feather conditions. Methods: Herein, the uropygial glands of floor- and cage-reared ducks were weighed, and a nontargeted metabolic analysis was performed. Results: At 20 weeks of age, the relative weight of floor-reared duck uropygial glands was significantly higher than that of cage-reared ducks, indicating that the floor rearing system is better for inducing the development of uropygial glands. The nontargeted metabolic data revealed 1190 and 1149 differential metabolites under positive and negative ion modes, respectively. Among them, 49 differential metabolites were annotated between the two rearing systems. Three sulfur-containing amino acids, namely, 2-ketobutyric acid, L-aspartate-semialdehyde, and N-formyl-L-methionine, and some lipids, including inositol and sphingosine, might be responsible for the changes in plumage appearance among the various rearing conditions. Conclusions: The results of our study revealed the differences in the metabolic components of the uropygial gland in ducks reared under different rearing systems and found metabolic components to be possibly responsible for the poor feather condition of caged ducks.

Multi-Omics-Based Discovery of Plant Signaling Molecules

Plants produce numerous structurally and functionally diverse signaling metabolites, yet only relatively small fractions of which have been discovered. Multi-omics has greatly expedited the discovery as evidenced by increasing recent works reporting new plant signaling molecules and relevant functions via integrated multi-omics techniques. The effective application of multi-omics tools is the key to uncovering unknown plant signaling molecules. This review covers the features of multi-omics in the context of plant signaling metabolite discovery, highlighting how multi-omics addresses relevant aspects of the challenges as follows: (a) unknown functions of known metabolites; (b) unknown metabolites with known functions; (c) unknown metabolites and unknown functions. Based on the problem-oriented overview of the theoretical and application aspects of multi-omics, current limitations and future development of multi-omics in discovering plant signaling metabolites are also discussed.

Metabolite discovery: Biochemistry's scientific driver

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21^st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.

A non-targeted metabolomics analysis identifies wound-induced oxylipins in Physcomitrium patens

Plant oxylipins are a class of lipid-derived signaling molecules being involved in the regulation of various biotic and abiotic stress responses. A major class of oxylipins are the circular derivatives to which 12-oxo-phytodienoic acid (OPDA) and its metabolite jasmonic acid (JA) belong. While OPDA and its shorter chain homologue dinor-OPDA (dnOPDA) seem to be ubiquitously found in land plants ranging from bryophytes to angiosperms, the occurrence of JA and its derivatives is still under discussion. The bryophyte Physcomitrium patens has received increased scientific interest as a non-vascular plant model organism over the last decade. Therefore, we followed the metabolism upon wounding by metabolite fingerprinting with the aim to identify jasmonates as well as novel oxylipins in P. patens. A non-targeted metabolomics approach was used to reconstruct the metabolic pathways for the synthesis of oxylipins, derived from roughanic, linoleic, α-linolenic, and arachidonic acid in wild type, the oxylipin-deficient mutants of Ppaos1 and Ppaos2, the mutants of Ppdes being deficient in all fatty acids harboring a Δ⁶-double bond and the C20-fatty acid-deficient mutants of Ppelo. Beside of OPDA, iso-OPDA, dnOPDA, and iso-dnOPDA, three additional C18-compounds and a metabolite being isobaric to JA were identified to accumulate after wounding. These findings can now serve as foundation for future research in determining, which compound(s) will serve as native ligand(s) for the oxylipin-receptor COI1 in P. patens.

Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics

Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.

Utilizing imaging mass spectrometry to analyze microbial biofilm chemical responses to exogenous compounds

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an appealing label-free method for imaging biological samples which focuses on the spatial distribution of chemical signals. This approach has been used to study the chemical ecology of microbes and can be applied to study the chemical responses of microbes to treatment with exogenous compounds. Specific conjugated cholic acids such as taurocholic acid (TCA), have been shown to inhibit biofilm formation in the enteric pathogen Vibrio cholerae and MALDI-IMS can be used to directly observe the chemical responses of V. cholerae biofilm colonies to treatment with TCA. A major challenge of MALDI-IMS is optimizing the sample preparation and drying for a particular growth condition and microbial strain. Here we demonstrate how V. cholerae is cultured and prepared for MALDI-IMS analysis and highlight critical steps to ensure proper sample adherence to a MALDI target plate and maintain spatial distributions when applying this technique to any microbial strain. We additionally show how to use both manual interrogation and statistical analyses of MALDI-IMS data to establish the adequacy of the sample preparation protocol. This protocol can serve as a guideline for the development of sample preparation techniques and the acquisition of high quality MALDI-IMS data.

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures. CKD-associated dyslipidaemia occurs as a consequence of complex interactions between genetic, environmental and kidney-specific factors, which to understand, requires an appreciation of perturbations in the underlying network of genes, proteins and lipids. Modern lipidomic technologies attempt to systematically identify and quantify lipid species from biological systems. The rapid development of a variety of analytical platforms based on mass spectrometry has enabled the identification of complex lipids at great precision and depth. Insights from lipidomics studies to date suggest that the overall architecture of free fatty acid partitioning between fatty acid oxidation and complex lipid fatty acid composition is an important driver of CKD progression. Available evidence suggests that CKD progression is associated with metabolic inflexibility, reflecting a diminished capacity to utilize free fatty acids through β-oxidation, and resulting in the diversion of accumulating fatty acids to complex lipids such as triglycerides. This effect is reversed with interventions that improve kidney health, suggesting that targeting of lipid abnormalities could be beneficial in preventing CKD progression.

Rapid visualization of lipopeptides and potential bioactive groups of compounds by combining ion mobility and MALDI imaging mass spectrometry

Mass spectrometry imaging (MSI) has become a powerful method for mapping metabolite distribution in a tissue. Applied to bacterial colonies, MSI has a bright future, both for the discovery of new bioactive compounds and for a better understanding of bacterial antibiotic resistance mechanisms. Coupled with separation techniques such as ion mobility mass spectrometry (IM-MS), the identification of metabolites directly on the image is now possible and does not require additional analysis such as HPLC-MS/MS. In this article, we propose to apply a semi-targeted workflow for rapid IM-MSI data analysis focused on the search for bioactive compounds. First, chemically-related compounds showing a repetitive mass unit (i.e. lipids and lipopeptides) were targeted based on the Kendrick mass defect analysis. The detected groups of potentially bioactive compounds were then confirmed by fitting their measured ion moibilites to their measured m/z values. Using both their m/z and ion mobility values, the selected groups of compounds were identified using the available databases and finally their distribution was observed on the image. Using this workflow on a co-culture of bacteria, we were able to detect and localize bioactive compounds involved in the microbial interaction.

The Potential of Sewage Sludge to Predict and Evaluate the Human Chemical Exposome

Chemicals are part of our daily lives, and we are exposed to numerous chemicals through multiple pathways. Relevant scientific evidence contributing to the regulation of hazardous chemicals require a holistic approach to assess simultaneous exposure to multiple compounds. Biomonitoring provides an accurate estimation of exposure to chemicals through very complex and costly sampling campaigns. Finding efficient proxies to predict the risk of chemical exposure in humans is an urgent need to cover large areas and populations at a reasonable cost. We conducted an exploratory study to characterize the human chemical exposome in maternal blood and placenta samples of a population-based birth cohort in Barcelona (2018-2021). Ultimate HRMS-based approaches were applied including wide-scope target, suspect, and nontarget screening. Forty-two chemicals were identified including pesticides, personal care products, or industrial compounds, among others, in the range of ng/mL and ng/g. In parallel, sewage sludge from the wastewater treatment plants serving the residence areas of the studied population were also screened, showing correlations with the type and concentrations of chemicals found in humans. Our findings were suggestive for the potential use of sewage sludge as a proxy of the human exposure and its application in early warning systems to prevent bioaccumulation of hazardous chemicals.

Enhancing Metabolomic Coverage in Positive Ionization Mode Using Dicationic Reagents by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization

Mass spectrometry imaging is a powerful tool to analyze a large number of metabolites with their spatial coordinates collected throughout the sample. However, the significant differences in ionization efficiency pose a big challenge to metabolomic mass spectrometry imaging. To solve the challenge and obtain a complete data profile, researchers typically perform experiments in both positive and negative ionization modes, which is time-consuming. In this work, we evaluated the use of the dicationic reagent, 1,5-pentanediyl-bis(1-butylpyrrolidinium) difluoride (abbreviated to [C5(bpyr)2]F2) to detect a broad range of metabolites in the positive ionization mode by infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI MSI). [C5(bpyr)2]F2 at 10 µM was doped in 50% MeOH/H2O (v/v) electrospray solvent to form +1 charged adducted ions with anionic species (-1 charged) through post-electrospray ionization. This method was demonstrated with sectioned rat liver and hen ovary. A total of 73 deprotonated metabolites from rat liver tissue sections were successfully adducted with [C5(bpyr)2]2+ and putatively identified in the adducted positive ionization polarity, along with 164 positively charged metabolite ions commonly seen in positive ionization mode, which resulted in 44% increased molecular coverage. In addition, we were able to generate images of hen ovary sections showing their morphological features. Following-up tandem mass spectrometry (MS/MS) indicated that this dicationic reagent [C5(bpyr)2]2+ could form ionic bonds with the headgroup of glycerophospholipid ions. The addition of the dicationic reagent [C5(bpyr)2]2+ in the electrospray solvent provides a rapid and effective way to enhance the detection of metabolites in positive ionization mode.

Living with a giant, flowering parasite: metabolic differences between Tetrastigma loheri Gagnep. (Vitaceae) shoots uninfected and infected with Rafflesia (Rafflesiaceae) and potential applications for propagation

Metabolites in Rafflesia-infected and non-infected Tetrastigma were compared which may have applications in Rafflesia propagation. Benzylisoquinoline alkaloids, here reported for the first time in Vitaceae, were abundant in non-infected shoots and may be a form of defense. In Rafflesia-infected shoots, oxylipins, which mediate immune response, were elevated. Endemic to the forests of Southeast Asia, Rafflesia (Rafflesiaceae) is a genus of holoparasitic plants producing the largest flowers in the world, yet completely dependent on its host, the tropical grape vine, Tetrastigma. Rafflesia species are threatened with extinction, making them an iconic symbol of plant conservation. Thus far, propagation has proved challenging, greatly decreasing efficacy of conservation efforts. This study compared the metabolites in the shoots of Rafflesia-infected and non-infected Tetrastigma loheri to examine how Rafflesia infection affects host metabolomics and elucidate the Rafflesia infection process. Results from LC-MS-based untargeted metabolomics analysis showed benzylisoquinoline alkaloids were naturally more abundant in non-infected shoots and are here reported for the first time in the genus Tetrastigma, and in the grape family, Vitaceae. These metabolites have been implicated in plant defense mechanisms and may prevent a Rafflesia infection. In Rafflesia-infected shoots, oxygenated fatty acids, or oxylipins, and a flavonoid, previously shown involved in plant immune response, were significantly elevated. This study provides a preliminary assessment of metabolites that differ between Rafflesia-infected and non-infected Tetrastigma hosts and may have applications in Rafflesia propagation to meet conservation goals.

Untargeted Microbial Exometabolomics and Metabolomics Analysis of Helicobacter pylori J99 and jhp0106 Mutant

Untargeted metabolomic profiling provides the opportunity to comprehensively explore metabolites of interest. Herein, we investigated the metabolic pathways associated with Jhp0106, a glycosyltransferase enzyme in Helicobacter pylori. Through untargeted exometabolomic and metabolomic profiling, we identified 9 and 10 features with significant differences in the culture media and pellets of the wild-type (WT) J99 and jhp0106 mutant (Δjhp0106). After tentative identification, several phosphatidylethanolamines (PEs) were identified in the culture medium, the levels of which were significantly higher in WT J99 than in Δjhp0106. Moreover, the reduced lysophosphatidic acid absorption from the culture medium and the reduced intrinsic diacylglycerol levels observed in Δjhp0106 indicate the possibility of reduced PE synthesis in Δjhp0106. The results suggest an association of the PE synthesis pathway with flagellar formation in H. pylori. Further investigations should be conducted to confirm this finding and the roles of the PE synthesis pathway in flagellar formation. This study successfully demonstrates the feasibility of the proposed extraction procedure and untargeted exometabolomic and metabolomic profiling strategies for microbial metabolomics. They may also extend our understanding of metabolic pathways associated with flagellar formation in H. pylori.

A High-Throughput HILIC-MS-Based Metabolomic Assay for the Analysis of Polar Metabolites

Mass spectrometry (MS)-based metabolomics approaches have been used for characterizing the metabolite content and composition of biological samples in drug discovery and development, as well as in metabolic engineering, and food and plant sciences applications. Here, we describe a protocol routinely used in our laboratory to conduct a metabolic profiling of small polar metabolites from biological samples. Metabolites can be extracted from each sample using a methanol-based single-phase extraction procedure. The combination of LC-based hydrophilic interaction liquid chromatography (HILIC) and a hybrid quadrupole-time of flight (Q-ToF) mass spectrometer allows the comprehensive analysis of small polar metabolites including sugars, phosphorylated compounds, purines and pyrimidines, nucleotides, nucleosides, acylcarnitines, carboxylic acids, hydrophilic vitamins and amino acids. Retention times and accurate masses of metabolites involved in key metabolic pathways are annotated for routine high-throughput screening in both untargeted and targeted metabolomics analyses.

Biofluid Metabolomics of Mice Exposed to External Low-Dose Rate Radiation in a Novel Irradiation System, the Variable Dose-Rate External 137Cs Irradiator

An important component of ionizing radiation (IR) exposure after a radiological incident may include low-dose rate (LDR) exposures either externally or internally, such as from ¹³⁷Cs deposition. In this study, a novel irradiation system, VAriable Dose-rate External ¹³⁷Cs irradiatoR (VADER), was used to expose male and female mice to a variable LDR irradiation over a 30 d time span to simulate fall-out-type exposures in addition to biofluid collection from a reference dose rate (0.8 Gy/min). Radiation markers were identified by untargeted metabolomics and random forests. Mice exposed to LDR exposures were successfully identified from control groups based on their urine and serum metabolite profiles. In addition to metabolites commonly perturbed after IR exposure, we identified and validated a novel metabolite (hexosamine-valine-isoleucine-OH) that increased up to 150-fold after LDR and 80-fold after conventional exposures in urine. A multiplex panel consisting of hexosamine-valine-isoleucine-OH with other urinary metabolites (N6,N6,N6-trimethyllysine, carnitine, 1-methylnicotinamide, and α-ketoglutaric acid) achieved robust classification performance using receiver operating characteristic curve analysis, irrespective of the dose rate or sex. These results show that in terms of biodosimetry, dysregulated energy metabolism is associated with IR exposure for both LDR and conventional IR exposures. These mass spectrometry data have been deposited to the NIH data repository via Metabolomics Workbench with study IDs ST001790, ST001791, ST001792, ST001793, and ST001806.

Potential biomarkers identified in plasma of patients with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.

Small Molecule Responses to Sequential Irradiation with Neutrons and Photons for Biodosimetry Applications: An Initial Assessment

Mass casualty exposure scenarios from an improvised nuclear device are expected to be far more complex than simple photons. Based on the proximity to the explosion and potential shielding, a mixed field of neutrons and photons comprised of up to approximately 30% neutrons of the total dose is anticipated. This presents significant challenges for biodosimetry and for short-term and long-term medical treatment of exposed populations. In this study we employed untargeted metabolomic methods to develop a biosignature in urine and serum from C57BL/6 mice to address radiation quality issues. The signature was developed in males and applied to samples from female mice to identify potential sex differences. Thirteen urinary (primarily amino acids, vitamin products, nucleotides) and 18 serum biomarkers (primarily mitochondrial and fatty acid β oxidation intermediates) were selected and evaluated in samples from day 1 and day 7 postirradiation. Sham-irradiated groups (controls) were compared to an equitoxic dose (3 Gy X-ray equivalent) from X rays (1.2 Gy/min), neutrons (∼1 Gy/h), or neutrons-photons. Results showed a time-dependent increase in the efficiency of the signatures, with serum providing the highest levels of accuracy in distinguishing not only between exposed from non-exposed populations, but also between radiation quality (photon exposures vs. exposures with a neutron component) and in between neutron-photon exposures (5, 15 or 25% of neutrons in the total dose) for evaluating the neutron contribution. A group of metabolites known as acylcarnitines was only responsive in males, indicating the potential for different mechanisms of action in baseline levels and of neutron-photon responses between the two sexes. Our findings highlight the potential of metabolomics in developing biodosimetric methods to evaluate mixed exposures with high sensitivity and specificity.

Metabolomics as a potential tool for the diagnosis of growth hormone deficiency (GHD): a review

Metabolomics uses several analytical tools to identify the chemical diversity of metabolites present in organisms. These metabolites are low molecular weight molecules (<1500 Da) classified as a final or intermediary product of metabolic processes. The application of this omics technology has become prominent in inferring physiological conditions through reporting on the phenotypic state; therefore, the introduction of metabolomics into clinical studies has been growing in recent years due to its efficiency in discriminating pathophysiological states. Regarding endocrine diseases, there is a great interest in verifying comprehensive and individualized physiological scenarios, in particular for growth hormone deficiency (GHD). The current GHD diagnostic tests are laborious and invasive and there is no exam with ideal reproducibility and sensitivity for diagnosis neither standard GH cut-off point. Therefore, this review was focussed on articles that applied metabolomics in the search for new biomarkers for GHD. The present work shows that the applications of metabolomics in GHD are still limited, since the little complementarily of analytical techniques, a low number of samples, GHD combined to other deficiencies, and idiopathic diagnosis shows a lack of progress. The results of the research are relevant and similar; however, their results do not provide an application for clinical practice due to the lack of multidisciplinary actions that would be needed to mediate the translation of the knowledge produced in the laboratory, if transferred to the medical setting.

Effects of long-term exercise and a high-fat diet on synovial fluid metabolomics and joint structural phenotypes in mice: an integrated network analysis

OBJECTIVE

To explore how systemic factors that modify knee osteoarthritis risk are connected to 'whole-joint' structural changes by evaluating the effects of high-fat diet and wheel running exercise on synovial fluid (SF) metabolomics.

METHODS

Male mice were fed a defined control or high-fat (60% kcal fat) diet from 6 to 52 weeks of age, and half the animals were housed with running wheels from 26 to 52 weeks of age (n = 9-13 per group). Joint tissue structure and osteoarthritis pathology were evaluated by histology and micro-computed tomography. Systemic metabolic and inflammatory changes were evaluated by body composition, glucose tolerance testing, and serum biomarkers. SF metabolites were analyzed by high performance-liquid chromatography mass spectrometry. We built correlation-based network models to evaluate the connectivity between systemic and local metabolic biomarkers and osteoarthritis structural pathology within each experimental group.

RESULTS

High-fat diet caused moderate osteoarthritis, including cartilage pathology, synovitis and increased subchondral bone density. In contrast, voluntary exercise had a negligible effect on these joint structure components. 1,412 SF metabolite features were detected, with high-fat sedentary mice being the most distinct. Diet and activity uniquely altered SF metabolites attributed to amino acids, lipids, and steroids. Notably, high-fat diet increased network connections to systemic biomarkers such as interleukin-1β and glucose intolerance. In contrast, exercise increased local joint-level network connections, especially among subchondral bone features and SF metabolites.

CONCLUSION

Network mapping showed that obesity strengthened SF metabolite links to blood glucose and inflammation, whereas exercise strengthened SF metabolite links to subchondral bone structure.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Effect of intravenous ghrelin administration, combined with alcohol, on circulating metabolome in heavy drinking individuals with alcohol use disorder

BACKGROUND

Ghrelin may influence several alcohol-related behaviors in animals and humans by modulating central and/or peripheral biological pathways. The aim of this exploratory analysis was to investigate associations between ghrelin administration and the human circulating metabolome during alcohol exposure in nontreatment seeking, heavy drinking individuals with alcohol use disorder (AUD).

METHODS

We used serum samples from a randomized, crossover, double-blind, placebo-controlled human laboratory study with intravenous (IV) ghrelin or placebo infusion in two experiments. During each session, participants received a loading dose (3 µg/kg) followed by continuous infusion (16.9 ng/kg/min) of acyl ghrelin or placebo. The first experiment included an IV alcohol self-administration (IV-ASA) session and the second experiment included an IV alcohol clamp (IV-AC) session, both with the counterbalanced infusion of ghrelin or placebo. Serum metabolite profiles were analyzed from repeated blood samples collected during each session.

RESULTS

In both experiments, ghrelin infusion was associated with an altered serum metabolite profile, including significantly increased levels of cortisol (IV-ASA q-value = 0.0003 and IV-AC q < 0.0001), corticosterone (IV-ASA q = 0.0202 and IV-AC q < 0.0001), and glycochenodeoxycholic acid (IV-ASA q = 0.0375 and IV-AC q = 0.0013). In the IV-ASA experiment, ghrelin infusion increased levels of cortisone (q = 0.0352) and fatty acids 18:1 (q = 0.0406) and 18:3 (q = 0.0320). Moreover, in the IV-AC experiment, ghrelin infusion significantly increased levels of glycocholic acid (q < 0.0001) and phenylalanine (q = 0.0458).

CONCLUSION

IV ghrelin infusion, combined with IV alcohol administration, was associated with increases in the circulating metabolite levels of corticosteroids and glycine-conjugated bile acids, among other changes. Further research is needed to understand the role that metabolomic changes play in the complex interaction between ghrelin and alcohol.

MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics

To uncover metal toxicity targets and defense mechanisms of the facultative anaerobe Pantoea sp. strain MT58 (MT58), we used a multiomic strategy combining two global techniques, random bar code transposon site sequencing (RB-TnSeq) and activity-based metabolomics. MT58 is a metal-tolerant Oak Ridge Reservation (ORR) environmental isolate that was enriched in the presence of metals at concentrations measured in contaminated groundwater at an ORR nuclear waste site. The effects of three chemically different metals found at elevated concentrations in the ORR contaminated environment were investigated: the cation Al³⁺, the oxyanion CrO₄^2-, and the oxycation UO₂²⁺. Both global techniques were applied using all three metals under both aerobic and anaerobic conditions to elucidate metal interactions mediated through the activity of metabolites and key genes/proteins. These revealed that Al³⁺ binds intracellular arginine, CrO₄^2- enters the cell through sulfate transporters and oxidizes intracellular reduced thiols, and membrane-bound lipopolysaccharides protect the cell from UO₂²⁺ toxicity. In addition, the Tol outer membrane system contributed to the protection of cellular integrity from the toxic effects of all three metals. Likewise, we found evidence of regulation of lipid content in membranes under metal stress. Individually, RB-TnSeq and metabolomics are powerful tools to explore the impact various stresses have on biological systems. Here, we show that together they can be used synergistically to identify the molecular actors and mechanisms of these pertubations to an organism, furthering our understanding of how living systems interact with their environment. IMPORTANCE Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In this study, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism, Pantoea sp. strain MT58, and metals contaminating a site where the organism can be located. A number of novel metal-microbe interactions were uncovered, including Al³⁺ toxicity targeting arginine synthesis, which could lead to a deeper understanding of the impact Al³⁺ contamination has on microbial communities as well as its impact on higher-level organisms, including plants for whom Al³⁺ contamination is an issue. Using multiomic approaches like the one described here is a way to further our understanding of microbial interactions and their impacts on the environment overall.