Biology subjects

Zhao, H. N.

Publications and source records attributed to Zhao, H. N..

12 recordsLinked to original sources

bioRxiv2026

Widespread detection of Polyethylene glycol reveals chronic human exposure through pharmaceutical drugs

Polyethylene glycol (PEG) is a synthetic polymer ubiquitous in pharmaceuticals, personal care products, food additives, and industrial manufacturing. Despite its widespread use and potential importance as an exposure chemical, the prevalence of PEG exposure and its excretion in human populations remain largely uncharacterized. Moreover, PEG detected in human biofluids is frequently assumed to arise from analytical contamination during sample preparation, potentially obscuring its contribution to the human exposome and confounding metabolic phenotyping studies. Here, we show that PEG is as component of the human xenobiotic exposome and is further metabolized into PEG hydoxy acid and diacid metabolites in humans. We further find that PEG exposure is associated with alterations in microbiome composition and short-chain fatty acid metabolism, suggesting its biological impact of its exposure. We identify PEG exposure in approximately 2.3% of publicly available metabolomics data files and provide a reusable 85,484 candidate PEG and PEGylated MS/MS spectral library for future use for the metabolomic community. Together, these findings establish PEG signal in human biofluids can reflect genuine exposure, and that PEG exposure is neither metabolically inert nor biologically silent.

biochemistry↗

bioRxiv2026

The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics

Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12C/13C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.

bioinformatics↗

bioRxiv2026

Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data

The majority of chemical signals detected in public metabolomics repositories remain structurally undefined. Large language models (LLMs) are probabilistic systems whose capacity to generate outputs beyond their training data, which can cause hallucinations, makes them also potentially suited to hypothesize structures for molecules that have never been described. We aimed to build a system that could harness this LLM generative capacity combined with domain specific tools/framework to constrain hallucination and produce validated discoveries. We developed a GNPS2 agentic AI system that interprets LC-MS/MS data by integrating spectral alignment, molecular formula inference, rule-based structural enumeration, machine learning-based spectrum prediction, and translates natural language hypotheses from domain experts into dynamically generated analytical workflows. We demonstrate the annotation of unknown drug metabolites from public data guided by chemical hypotheses. The agent predicted, and we experimentally confirmed, a phosphorylated hydroxyzine, an acetaminophen-p-coumaric acid ester, and identified two new oxidative ibuprofen-carnitine conjugates from public repositories. These results demonstrate that LLM-driven agentic reasoning, when combined with domain expertise, can indeed generate experimentally testable structural hypotheses for previously uncharacterized metabolites leveraging pan repository data.

bioinformatics↗

bioRxiv2026

Alkamines reveal a hidden layer of steroid and drug metabolism

Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.

biochemistry↗

bioRxiv2026

Pan-repository analysis reveals a drug-activating function of microbial bile acid conjugation

Microbially modified bile acids shape host physiology by regulating nutrient absorption, glucose homeostasis, circadian rhythms and thermoregulation. Here we identify a previously unrecognized drug-activating function of microbial bile acid conjugation. By systematically mining human LC-MS/MS datasets across public repositories and linking uncharacterized bile acid spectra to health-associated metadata, we discovered conjugates of the >75-year-old anti-inflammatory drug 5-aminosalicylic acid (5-ASA) with primary and secondary bile acids, including cholic, deoxycholic and lithocholic acids. These bile acid-drug conjugates were detected specifically in individuals treated with 5-ASA or its prodrugs. Multiple gut bacteria, including members of the Bacteroidota and Bacillota, generated cholyl-5-ASA in vitro, and bile salt hydrolase-associated transaminase activity was required for conjugate formation. In a mouse model of colitis, cholyl-5-ASA was associated with reduced intestinal inflammatory pathology and showed markedly enhanced activation of PPAR-{gamma} in cell-based reporter assays compared with 5-ASA alone. Consistent with this activity, cholyl-5-ASA elicited selective immunophenotypic changes in CD4 T cells in vitro, including increased Foxp3+ regulatory T cells. Together with prior evidence that 5-ASA efficacy depends on the microbiome, these findings support a model in which microbial bile acid conjugation represents a key activation step for 5-ASA therapy. More broadly, this work demonstrates how pan-repository metabolomics can uncover previously unrecognized microbiome-dependent chemical functions with direct therapeutic relevance.

biochemistry↗

bioRxiv2026

Navigating the conjugated metabolome

Lifes chemical diversity far exceeds current biochemical maps. While metabolomics has catalogued tens of thousands of small molecules, conjugated metabolites, formed when two or more molecular entities are covalently fused through amidation, esterification, or related chemistries, remain underexplored. These molecules can act as microbial signals, detoxification intermediates, or endogenous regulators. Here, we mined 1.32 billion MS/MS spectra across public metabolomics repositories using reverse spectral searching coupled with delta-mass inference to map conjugation events. We generated structural hypotheses for 24,227,439 MS/MS clusters. From these, we inferred 217,291 substructure pairs with dual spectral support and 3,412,720 candidate conjugates with single-match support. Predictions span host-microbe co-metabolites, diet-derived conjugates, and drug-derived species, including drug-ethanolamine and creatinine conjugates with altered bioactivities. We also uncover a family of steroid-phosphoethanolamine conjugates. Fifty-five conjugates were matched by MS/MS of synthetic standards for this work, with 27 additionally supported by retention time matching in biological samples. Guidance on how to leverage this resource is also provided. Together, these results deliver a pan-repository map of potential conjugation chemistry, establish a resource for structural discovery and MS/MS annotation, and offer a scalable framework to explore the scope and diversity of the conjugated metabolome.

bioinformatics↗

bioRxiv2026

A searchable metadata network graph for microbiome metabolomics

Establishing the biological context of microbial metabolites remains a major challenge. We present microbiomeMASST, a metadata-driven network graph that maps metabolites across 467 available datasets with 144,424 mass spectrometry files from humans, animals, and microbial culture systems. MicrobiomeMASST integrates monocultures, synthetic communities, and host-associated samples across multiple body sites and plants. MS/MS spectra can be queried to trace occurrence across hosts, experimental conditions, and interventions, enabling cross-study integration. We demonstrate this framework by contextualizing microbial-conjugated bile acids and interrogating microbiome-mediated drug metabolism. Screening gut bacteria revealed deprolylation of the angiotensin-converting enzyme (ACE) inhibitor prodrug enalapril. Using microbiomeMASST, we traced this metabolite across human cohorts, microbial isolates, environmental samples, and in Gorilla gorilla. Structural modeling and enzymatic assays showed that microbial deprolylation abolishes ACE inhibition, thereby inactivating its therapeutic effect. Together, microbiomeMASST links MS/MS spectra to biological context, converting isolated observations into an interpretable microbiome map for cross-study analysis.

biochemistry↗

bioRxiv2025

Charting the Undiscovered Metabolome with Synthetic Multiplexing

Most molecular features detected in untargeted metabolomics remain uncharacterized due to the limited scope of existing spectral reference libraries. We synthesized >100,000 biologically inspired compounds using multiplexed reactions, of which 91% were absent from existing structural databases, and searched the resulting MS/MS library across >1.7 billion public spectra, increasing annotation rates by 17.4%. This approach revealed previously undescribed exposure-derived metabolites, including ibuprofen-carnitine. Because ibuprofen has been linked to rhabdomyolysis, reduced mitochondrial function, and impaired muscle recovery in carnitine-limited contexts, we investigated the functional relevance of this conjugate. Ibuprofen-carnitine reduced carnitine transport via the OCTN2 transporter, and in a postpartum mouse muscle injury model, ibuprofen delayed muscle repair that could be rescued by carnitine supplementation, with urinary ibuprofen-carnitine:carnitine ratios tracking this effect. These findings support a hypothesis whereby NSAID-carnitine conjugates compete for carnitine transport, impairing energy metabolism and muscle recovery in susceptible individuals. Synthetic multiplexing thus provides a scalable route to annotate the dark metabolome and generate experimentally testable biological hypotheses.

biochemistry↗

bioRxiv2024

The microbiome diversifies N-acyl lipid pools - including short-chain fatty acid-derived compounds

N-acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N-acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N-acyl lipid patterns from 2,700 public datasets, identifying 851 N-acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N-acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

bioinformatics↗

bioRxiv2024

Empirically establishing drug exposure records directly from untargeted metabolomics data

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

bioinformatics↗

bioRxiv2024

plantMASST - Community-driven chemotaxonomic digitization of plants

Understanding the distribution of hundreds of thousands of plant metabolites across the plant kingdom presents a challenge. To address this, we curated publicly available LC-MS/MS data from 19,075 plant extracts and developed the plantMASST reference database encompassing 246 botanical families, 1,469 genera, and 2,793 species. This taxonomically focused database facilitates the exploration of plant-derived molecules using tandem mass spectrometry (MS/MS) spectra. This tool will aid in drug discovery, biosynthesis, (chemo)taxonomy, and the evolutionary ecology of herbivore interactions.

plant biology↗

bioRxiv2023

Co-Occurrence Network Analysis Reveals The Alterations Of The Skin Microbiome And Metabolome In Atopic Dermatitis Patients

Skin microbiome can be altered in patients with Atopic Dermatitis (AD). An understanding of the changes from healthy to atopic skin can help develop new targets for better treatments and identify specific microbial or molecular biomarkers. This study investigates the skin microbiome and metabolome of healthy subjects and lesion (ADL) and non-lesion (ADNL) of AD patients by 16S rRNA gene sequencing and mass spectrometry, respectively. Samples from AD patients showed alterations in the diversity and composition of the skin microbiome. Staphylococcus species, especially S. aureus, were significantly increased in the ADL group. Metabolomic profiles were also different between the groups. Dipeptide-derived are more abundant in ADL, which may be related to skin inflammation. Co-occurrence network analysis was applied to integrate the microbiome and metabolomics data and revealed higher co-occurrence of metabolites and bacteria in healthy and ADNL compared to ADL. S. aureus co-occurred with dipeptide-derived in ADL, while phytosphingosine-derived compounds showed co-occurrences with commensal bacteria, e.g. Paracoccus sp., Pseudomonas sp., Prevotella bivia, Lactobacillus iners, Anaerococcus sp., Micrococcus sp., Corynebacterium ureicelerivorans, Corynebacterium massiliense, Streptococcus thermophilus, and Roseomonas mucosa, in healthy and ADNL groups. Therefore, these findings provide valuable insights into how AD affects the human skin metabolome and microbiome. ImportanceThis study provides valuable insight into changes in the skin microbiome and associated metabolomic profiles. It also identifies new therapeutic targets that may be useful for developing personalized treatments for individuals with atopic dermatitis based on their unique skin microbiome and metabolic profiles.

microbiology↗