Karsten Zengler

3-Hydroxy N -acyl amides are bioactive lipids with reported anti-obesity and glucose-regulating effects, yet they are rarely detected in untargeted metabolomics studies because they are largely absent from existing spectral reference libraries. To address this gap, we synthesized an MS/MS spectral resource comprising 436 structurally diverse 3-hydroxy N -acyl amides, spanning 3- to 18-carbon chains with a wide range of amine headgroups, such as ornithine, valine, and dopamine. Using a synthesis-driven reverse metabolomics approach, we found 161,626 spectral matches across 54,744 publicly available files in untargeted metabolomics data sets, revealing widespread occurrences in biological samples, including human-derived specimens. Of these molecules detected through MS/MS spectral matching, 334 represent newly reported biological entities. We further confirmed their presence in human saliva, stool, and skin using retention time and ion mobility measurements. Frequent detection in microbial data sets and validation in communities of human-derived gut bacteria support microbial production. Several metabolites also showed altered abundance in individuals with diabetes mellitus, showing that this lipid class is modulated in human metabolic disease. Together, these findings establish 3-hydroxy N -acyl amides as a distinct and biologically relevant lipid class, and the accompanying MS/MS spectral resource will enable their broader recognition and study in untargeted metabolomics data. IMPORTANCE: Microbe-host interactions lead to the production of metabolites that play an important role in human health and disease, though the annotation of such metabolites remains scarce. We created a re-usable MS/MS spectral library of an underrepresented class of compounds—3-hydroxy N -acyl amides. Our study characterized the presence of over 400 3-hydroxy N -acyl amides among thousands of public untargeted metabolomics datasets. We found over 100,000 instances of these metabolites within microbial, plant, and animal data, and used orthogonal validation methods to confirm nine previously unreported molecules within human feces, saliva, and skin samples. We cultured communities of human-derived gut bacteria and found evidence of bacterial production of four 3-hydroxy N -acyl amides. Our results suggest that these metabolites play important roles in human metabolism and disease, and sharing this curated resource enables the detection of these metabolites in future studies.

Bacteroides fragilis, a key member of the human gut microbiota, contributes to host health by maintaining intestinal homeostasis. Yet, it is also the most frequently isolated anaerobe in clinical infections. These contrasting roles raise questions about the genetic and ecological factors that explain why this common symbiont is disproportionately linked to infection. We analyzed 813 Division I B. fragilis genomes, including 147 new isolates from intestinal and extra-intestinal sites. Infection-associated isolates spanned all phylogroups, indicating no pathogenic lineage. We identified 16 phylogroups, distinguished by genes associated with capsule biosynthesis and interbacterial competition. Additionally, differential metabolomic analysis identified 12 metabolites associated with isolation source, while a microbial genome-wide association study uncovered 44 genes enriched in isolates from extra-intestinal sites, providing the first population-scale markers tied to clinical recovery sites. These results do not implicate a pathogenic lineage; instead, they point to associational links between accessory modules and recovery from extra-intestinal sites under permissive host conditions. This work underscores how genomic diversity and ecological context may jointly shape the clinical impact of gut commensals. IMPORTANCE: Bacteroides fragilis, a human gut resident, is paradoxically one of the most frequent anaerobes recovered from bloodstream and abscess infections. The genetic features that enable frequent recovery from extra-intestinal sites remain poorly defined. Using comparative genomic and metabolomic analyses of strains from intestinal and extra-intestinal sources, we show that strains isolated from infections are phylogenetically dispersed rather than restricted to a single lineage. We observe lineage-linked differences in capsule loci and competition systems, which suggests constrained gene flow and lineage-specific adaptation within the gut. Additionally, a subset of genes and metabolites is enriched among extra-intestinal isolates. Together, these findings suggest that extra-intestinal survival among B. fragilis strains reflects the interplay between species-wide genomic diversity and permissive host conditions, rather than the emergence of a single pathogenic lineage.

Algae represent one of the most metabolically diverse and ecologically significant groups of photosynthetic organisms, contributing fundamentally to global biogeochemical cycles while offering major potential for biotechnology applications such as biofuels, nutraceuticals, wastewater remediation, and carbon capture. However, the complexity of algal metabolism, driven by evolutionary diversity, compartmentalized cellular organization, and strong environmental coupling, makes predictive understanding of their physiology challenging. In recent years, systems biology approaches combining omics technologies, genome-scale metabolic models, and data-driven methods have begun to transform algal research from descriptive studies toward predictive frameworks. This review summarizes the current state of algal systems biology, highlighting advances in genomics, transcriptomics, proteomics, and metabolomics that enable mechanistic insights into metabolic regulation and environmental adaptation. We discuss the development, curation, and application of algal GEMs across diverse lineages, emphasizing their role in predicting metabolic flux distributions, nutrient utilization, and lipid biosynthesis. In parallel, machine learning and artificial intelligence approaches have emerged to model algal growth and cultivation performance from large physiological datasets. Finally, we discuss emerging hybrid modeling strategies that integrate mechanistic metabolic networks with data-driven predictions, outlining how these frameworks can enable next-generation predictive algal biotechnology and guide rational design of cultivation and metabolic engineering strategies.

Metaribosome profiling (metaRibo-Seq) enables genome-wide measurement of translation across complex microbial communities by sequencing ribosome-protected mRNA fragments, but the short length of these footprints creates substantial nonspecific mapping against large reference genome collections, leading to spurious taxonomic and functional assignments. Here we present MOPP (Multi-Omics Processing Pipeline), a modular reference-based workflow that denoises metaRibo-Seq data by leveraging matched metagenomic coverage breadth to identify genomes likely to be truly present in a sample before aligning metatranslatomic and optional metatranscriptomic reads. MOPP generates taxon-by-gene count tables across genomic, transcriptional and translational layers, enabling integrated downstream analyses of microbial function. We evaluated MOPP using a defined 79-member synthetic human gut community profiled by metagenomics and metaRibo-Seq. Coverage breadth filtering markedly improved detection accuracy relative to a standard baseline workflow, with performance remaining robust across a broad intermediate threshold range and peaking at 92-95% coverage breadth. At a 92% threshold, MOPP reduced the number of distinct detected operational genomic units by 99.4% while retaining 87.8% of aligned metaRibo-Seq reads on average, and increased the F1 score from 0.02 to 0.61. Residual false positives were predominantly attributable to genomes with extremely high nucleotide similarity to true community members, whereas false negatives were enriched among low-abundance taxa, indicating that remaining errors are driven primarily by biological similarity and detection limits rather than widespread nonspecific mapping. Together, these results establish MOPP as a high-throughput workflow for robust processing of metaRibo-Seq in the context of matched metagenomics and position it as a scalable framework for integrated taxonomic and functional analysis of microbial communities across genomic, transcriptional and translational layers.

3-hydroxy N-acyl amides are bioactive lipids with reported anti-obesity and glucose-regulating effects, yet they are rarely detected in untargeted metabolomics studies because they are largely absent from existing spectral reference libraries. To address this gap, we synthesized an MS2 spectral resource comprising 436 structurally diverse 3-hydroxy N-acyl amides, spanning 3- to 18-carbon chains with a wide range of amine headgroups such as ornithine, valine, and dopamine. Using a synthesis-driven reverse metabolomics approach, we found 161,626 spectral matches across 54,744 publicly available files in untargeted metabolomics datasets revealing widespread occurrences in biological samples, including human-derived specimens. Of these molecules detected through MS2 spectral matching, 334 represent newly reported biological entities. We further confirmed their presence in human saliva, stool, and skin using retention time and ion mobility measurements. Frequent detection in microbial datasets and validation in communities of human-derived gut bacteria support microbial production. Several metabolites also showed altered abundance in individuals with diabetes mellitus, showing that this lipid class is modulated in human metabolic disease. Together, these findings establish 3-hydroxy N-acyl amides as a distinct and biologically relevant lipid class, and the accompanying MS2 spectral resource will enable their broader recognition and study in untargeted metabolomics data.

Despite extensive efforts, extracting medication exposure information from clinical records remains challenging. To complement this approach, here we show the Global Natural Product Social Molecular Networking (GNPS) Drug Library, a tandem mass spectrometry (MS/MS) based resource designed for drug screening with untargeted metabolomics. This resource integrates MS/MS references of drugs and their metabolites/analogs with standardized vocabularies on their exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data, supporting flexible summarization at multiple ontology levels to align with different research goals. We demonstrate its application by stratifying participants in a human immunodeficiency virus (HIV) cohort based on detected drug exposures. We uncover drug-associated alterations in microbiota-derived N -acyl lipids that are not captured when stratifying by self-reported medication use. Overall, GNPS Drug Library provides a scalable resource for empirical drug screening in clinical, nutritional, environmental, and other research disciplines, facilitating insights into the ecological and health consequences of drug exposures. While not intended for immediate clinical decision-making, it supports data-driven exploration of drug exposures where traditional records are limited or unreliable.