Early microbial seed priming is conceived to improve crop resilience, yet it remains unclear whether plants can discriminate among closely related beneficial strains and integrate dose-dependent microbial cues. We primed melon (Cucumis melo) seeds with two phylogenetically similar Bacillus strains (Bacillus subtilis NCIB3610 and B. velezensis FZB42) and combined transcriptomic, metabolomic, and physiological analyses across development. Despite comparable colonization, the strains provoked contrasting host programs and distinct dose responses. B. subtilis promoted radicle elongation, chloroplastic starch storage, and drought tolerance regardless of inoculum level, together with l-tryptophan and palatinose accumulation. By contrast, B. velezensis displayed a clear dose effect: low inoculum sustained normal radicle growth, whereas high inoculum transiently repressed it, coinciding with suppression of allene oxide synthase, genes related to proteasome complex, and enrichment of flavonoids and glutathione in leaves. Chemical assays showed that radicle inhibition depends on the synergistic action of surfactin, produced by both strains, and bacillomycin D, an iturin-type lipopeptide specific to FZB42. This synergy explains the strain-specific lipopeptide repertoire to the dose-dependent growth response. Although their early trajectories diverged, both primings converged on enhanced aboveground stress resilience. 3610-primed plants restricted Botrytis cinerea via caffeic and rosmarinic acid accumulation, whereas FZB42-primed plants curtailed jasmonate-sensitive Tetranychus urticae mites through jasmonic acid pathway modulation. Our results demonstrate that melon perceives inoculum dose and microbial identity, translating them into distinct metabolic and defense programs that converge on stress resilience. These mechanistic insights (linking lipopeptide fingerprints, sentinel metabolites, and defense transcripts) provide a framework for precision seed treatments in horticultural crops.

Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Freshwater ecosystems receive an influx of terrestrial litter. Microbial decomposition of this litter makes these ecosystems global hotspots for carbon emissions. The Home-Field Advantage (HFA) is a global phenomenon across freshwater and terrestrial ecosystems in which litter from local plants decomposes faster than non-local plants. Disruption of the HFA due to anthropogenic activities could impact global carbon cycling, yet few studies have moved beyond documenting the existence of the pattern. We demonstrate that the HFA in rivers corresponds with accelerated microbial respiratory activity in response to local plant secondary metabolites (PSMs). By pairing successional trajectories of bacterial and fungal decomposer communities with measurements of microbial respiratory activity, we link bacteria, but not fungi, to this accelerated utilization of local PSMs. This contrasts with the prevailing view of fungi as primarily responsible for litter decomposition on land. Linking the HFA to accelerated bacterial respiratory activity in response to local PSMs explains a key process regulating freshwater carbon cycling.

The etiology of Alzheimer's disease (AD) remains unclear but is likely driven by gene-environment interactions. We present a multi-organ untargeted metabolomics atlas (n = 2,271) paired with metagenomics data (n = 666) from two AD transgenic mouse models (3xTg and 5xFAD) under colonized and germ-free conditions. Systems-level analyses revealed clusters of dysregulated molecules across tissues, including carnitines, bile acids, B vitamins, neurotransmitters, and N-acyl lipids. Metabolic shifts were associated with the depletion of Akkermansia muciniphila and enrichment of Mucispirillum schaedleri in the 3xTg model. We identify previously unexplored carnitines linked to microbial metabolism of phenylalanine. Using tissueMASST-a mass spectrometry search tool we developed to translate animal-model findings into a human clinical context-we trace phenylacetyl-carnitine in human plasma and serum samples (n = 1,470) from independent cohorts, revealing associations with aging, cognitive impairment, and diminished memory performance. This public resource and associated tools will aid future research in AD etiology.

Untargeted metabolomics is a powerful approach for exploring the chemical diversity and dynamics of biological systems. However, the types of questions that can be addressed depend not only on experimental design but also on the data processing and analysis workflows employed, many of which require advanced computational expertise. GNPS1, now transitioning to its second major implementation (GNPS2), has evolved into an expandable platform that supports the integration of modular web applications designed to simplify and enhance downstream analysis. These apps, named MetaboApps, facilitate the post-processing of outputs of several GNPS workflows and help make repository-scale metabolomics knowledge and other areas of metabolomics more accessible to a broader community.

INTRODUCTION:Mounting evidence support exposome influences on brain function and health, complementing genome influences. Understanding the molecular imprint of exposome on brain metabolism and the biochemical communication between the body and brain can impact our fundamental understanding and treatment of neuropsychiatric diseases. METHODS:Leveraging two complementary metabolomics platforms, we classified 1400 features in 514 brains from the ROSMAP collection. We evaluated the origin of these compounds using literature and databases. We correlated those metabolites with cognitive function using linear models. RESULTS:We identified over 230 non-endogenous compounds in the brain, including 103 drugs and metabolites, 120 dietary and microbial products and possibly 15 compounds from environmental exposures. Over 20 dietary and gut microbial compounds showed associations with cognition. DISCUSSION:Comprehensive profiling of chemicals in the brain and the link to cognitive function provides foundational work to connect body and brain in the study of AD and related dementias.

Molecules in living systems are not random but are shaped by biological necessity. Mass spectrometry (MS) is a powerful tool for exploring these complex molecular landscapes. Molecular networking links metabolites by spectral similarity, but conventional methods leave many nodes disconnected. We introduce molecular community networking (MCN), which identifies natural molecular clusters and prunes them to keep the strongest links. The approach increases connectivity to about 95% of molecules and better captures structurally related compounds, including distinct ion forms and in-source fragmentation ions. MCN also improves the mapping of molecular space, helping distinguish true novel molecules from artifacts. Using MCN, we discovered dipeptide-conjugated bile acids associated with Bifidobacterium breve and proposed structures for previously unexplored N-acyl amides that interact with G protein-coupled receptors. We also built a global metabolome map from public GNPS/MassIVE data, covering about 8.4 million molecular features, creating a "roadmap" for molecular diversity.

ABSTRACT Despite the prevalence and severity of enterococcal bacteremia (EcB), the mechanisms underlying systemic host responses to the disease remain unclear. Here, we present an extensive study that profiles molecular differences in plasma from EcB patients using an unbiased multi-omics approach. We performed shotgun proteomics and metabolomics on 105 plasma samples, including those from EcB patients and healthy volunteers. Comparison between healthy volunteer and EcB-infected patient samples revealed significant disparities in proteins and metabolites involved in the acute phase response, inflammatory processes, and cholestasis. Several features distinguish these two groups with remarkable accuracy. Cross-referencing EcB signatures with those of Staphylococcus aureus bacteremia revealed shared reductions in cholesterol metabolism proteins and differing responses in platelet alpha granule and neutrophil-associated proteins. Characterization of Enterococcus isolates derived from patients facilitated a nuanced comparison between EcB caused by Enterococcus faecalis and Enterococcus faecium, uncovering reduced immunoglobulin abundances in E. faecium cases and features capable of distinguishing the underlying microbe. Leveraging extensive patient metadata, we now have identified features associated with mortality or survival, revealing significant multi-omic differences and pinpointing histidine-rich glycoprotein and fetuin-B as features capable of distinguishing survival status with excellent accuracy. Altogether, this study aims to culminate in the creation of objective risk stratification algorithms—a pivotal step toward enhancing patient management and care. To facilitate the exploration of this rich data source, we provide a user-friendly interface at https://gonzalezlab.shinyapps.io/EcB_multiomics/ . IMPORTANCE Enterococcus infections have emerged as the second most common nosocomial infection, with enterococcal bacteremia (EcB) contributing to thousands of patient deaths annually. To address a lack of detailed understanding regarding the specific systemic response to EcB, we conducted a comprehensive multi-omic evaluation of the systemic host response observed in patient plasma. Our findings reveal significant features in the metabolome and proteome associated with the presence of infection, species differences, and survival outcome. We identified features capable of discriminating EcB infection from healthy states and survival from mortality with excellent accuracy, suggesting potential practical clinical utility. However, our study also established that systemic features to distinguish Enterococcus faecalis from Enterococcus faecium EcB show only a moderate degree of discriminatory accuracy, unlikely to significantly improve upon current diagnostic methods. Comparisons of differences in the plasma proteome relative to healthy samples between bacteremia caused by Enterococcus and Staphylococcus aureus suggest the presence of bacteria-specific responses alongside conserved inflammatory reactions.

Indirect exposure to antibiotics during early life, via maternal intrapartum antibiotic prophylaxis (IAP) or postpartum maternal antibiotic usage, is increasingly common and has been epidemiologically linked to altered growth and immune developmental trajectories in offspring. Nevertheless, the underlying mechanisms remain poorly understood. Here, we explored the effects of antepartum and postpartum maternal ampicillin administration on the dams' fecal microbiome and metabolic profiles in vivo. Ampicillin caused a reproducible depletion of beneficial bacterial species belonging to the Muribaculaceae family, including Muribaculum intestinale and Duncaniella dubosii, and led to cohort-dependent enrichments of Enterococcus and Prevotella species. These microbial alterations were accompanied by substantial metabolic remodeling, characterized by elevated fecal acylcarnitines and dysregulation of the bile acids profile. Intriguingly, we identified two previously uncharacterized trihydroxylated bile acids conjugated to a hexose moiety, which appeared to be associated with antibiotic exposure across public metabolomics repositories. These alterations in the fecal maternal microbiome and metabolome coincided with increased weight gain in offspring, suggesting a possible role for maternal antibiotic exposure in shaping early developmental trajectories. Further studies are warranted to elucidate the long-term implications of these changes in infant health.

Early life is a critical period for immune and metabolic development, but these patterns remain underexplored in populations from low- and middle-income countries. Here, we profile the microbiome and metabolome of 55 Bangladeshi mother-infant dyads over the first 6 months of life. Importantly, we observe an increase in microbially derived bile amidates and N-acyl lipids with age in conjunction with reads matching the bile salt hydrolase/transferase (bsh) gene. Although microbial source tracking confirms maternal fecal seeding, a substantial environmental contribution is also highlighted. Differences in infant fecal metabolic profiles are associated with delivery mode, maternal milk composition, household assets, and household-level water treatment. Cesarean section (C-section) delivery and untreated drinking water are linked to transient metabolic differences, including increases in bile amidates, N-acyl lipids, and other host-microbe co-metabolic products, including acylcarnitines. Multi-omics analysis reveals specific microbial-metabolite relationships, highlighting how early environmental and maternal living circumstances influence gut metabolic development through the microbiome.

Tropical cyclones regularly form above the ocean's largest subsurface oxygen minimum zone (OMZ) in the eastern tropical North Pacific Ocean (ETNP), yet how these powerful storms affect this biogeochemically important region remains unknown. We captured multiple direct and potentially interactive oceanographic effects of a Category 4 hurricane (Bud) during a 2018 research cruise in the ETNP. Profiles and samples collected directly beneath Bud's wake revealed rapid OMZ shoaling of 29 to 50 meters, reaching depths as shallow as 41 meters. Untargeted mass spectrometry-based characterization of organic matter, along with elevated particulate organic carbon and chlorophyll concentrations, demonstrated production and accumulation of distinct organic compounds-including phytoplankton biomarkers-within a hurricane-generated phytoplankton bloom. 16S rRNA transcripts from active microbes were dominated by degraders of phytoplankton-derived organic matter near the surface and by anaerobic bacteria (including sulfate-reducing bacteria) within the shoaled OMZ-indicating rapid microbial responses. Tropical cyclones therefore severely disrupt OMZ biogeochemistry through vertical OMZ expansion and altered carbon cycling.

Metabolism is a fundamental process that shapes the pharmacological and toxicological profiles of drugs, making metabolite identification and analysis critical in drug development and biological research. Global Natural Products Social Networking (GNPS) is a community-driven infrastructure for mass spectrometry data analysis, storage and knowledge dissemination. GNPS2 is an improved version of the platform offering higher processing speeds, improved data analysis tools and a more intuitive user interface. Molecular networking based on tandem mass spectrometry spectral alignments, combined with other tools in the GNPS2 analysis environment, enables the discovery of candidate drug metabolites without prior knowledge, even from complex biological matrices. This protocol represents an extension of a previously established protocol for fundamental molecular networking in GNPS, with a specific focus on metabolism studies. This article uses the example of the drug sildenafil to identify candidate metabolites obtained from liquid chromatography–quadrupole time-of-flight mass spectrometry analysis of liver microsomal fractions and mice plasma to guide the reader through a step-by-step process consisting of five GNPS2-based analytical workflows. It demonstrates how the tools in GNPS2 can be used not only to identify candidate drug metabolites from in vitro studies but also to evaluate the translational relevance of these in vitro findings to humans by using reverse metabolomics. We provide a step-by-step analytical approach based on published studies to showcase how GNPS2 can be effectively applied in drug metabolism studies. This protocol extension guides users in identifying candidate drug metabolites without prior knowledge by analyzing tandem mass spectrometry data in the web-based molecular networking tool GNPS2.

Recent studies reveal that gut microbes produce diverse bile acid conjugates, termed microbially conjugated bile acids (MCBAs). However, their regulation and health effects remain unclear. Here, we analyzed early-life MCBA patterns and their link to islet autoimmunity. We quantified 110 MCBAs in 303 stool samples collected longitudinally (3-36 months) from children who developed one or more islet autoantibodies and controls who remained autoantibody-negative. Stool MCBAs showed distinct age-dependent trajectories and correlated with gut microbiome composition. Altered levels of ursodeoxycholic and deoxycholic acid conjugates were linked to islet autoimmunity as well as modulated monocyte activation in response to immunostimulatory lipopolysaccharide and Th17/Treg cell balance. These findings suggest MCBAs influence immune development and type 1 diabetes risk.

Glucuronidation is an important detoxification pathway that operates in balance with gastrointestinal microbial β-glucuronidase (GUS) enzymes that can regenerate active metabolites from their glucuronidated forms. Although significant progress has been made in characterizing GUS enzymes, methods to comprehensively define the glucuronidome - the collection of glucuronidated metabolites - remain limited. In this study we employed pattern-filtering data science approaches alongside untargeted LC-MS/MS metabolomics to map the glucuronidome in urine, serum, and colon/fecal samples from gnotobiotic and conventional mice. Our findings reveal microbiome-driven shifts in the glucuronidome, highlighting how differential GUS activity can influence host metabolite profiles. Reverse metabolomics of known glucuronidated chemicals and glucuronidation pattern filtering searches in public metabolomics datasets exposed the diversity of glucuronidated metabolites in human and mouse ecosystems. In summary, we present a new glucuronidation fingerprint resource that provides broader access to and analysis of the glucuronidome. By systematically capturing glucuronidation patterns, this resource enhances unknown metabolite annotation efforts and provides new insights into the dynamic relationship between the host and bacterial biotransformation activities.

The metabolome is an intermediate phenotype, summarizing the profile of all small molecules (<1.5 kDa) in biospecimens. The metabolome provides a readout for the net influence of the chemical exposome, diet, gut microbiome, and genome on human health. Metabolic changes observed in exposome studies may thus provide clues about adverse outcome pathways related to cancer, diabetes, heart disease, cognitive impairment and other neurological conditions such as Alzheimer's disease (AD). Whilst the number of human cohort studies including both metabolomic and exposomic profiles is increasing, they are particularly limited in the domain of neurological conditions. Environmental exposures and chemical toxicants are known to have significant effects on the brain, gut microbiome, and gut-brain axis. Environmental chemicals of greatest interest include bisphenols, phthalates, persistent organic pollutants such as polychlorinated biphenyls (PCBs) and per- and poly-fluoroalkyl substances (PFAS), heavy metals, chemicals from household products and pesticides/herbicides; all of which may increase the risk of AD as they impact relevant biochemical mechanisms, especially with chronic exposure. In this review we describe how the chemical exposome can be assessed, including the approach our consortium is taking in the context of AD. Further, we review the current evidence about the impact of the chemical exposome on cognition as well as its influence on the risk and pathogenesis of AD. Finally, we highlight our approach to study the exposome in AD as part of large national and international collaborative efforts on the topic.