Biology subjects

Shen, X.

Publications and source records attributed to Shen, X..

At least 19 recordsLinked to original sources

bioRxiv2026

ESX-1 substrates ESAT-6 and CFP-10 limit cGAS-STING activation via DNA and Zn2+ restriction

ESX-1-mediated phagosomal permeabilization exposes mycobacterial DNA to cytosolic cGAS-STING surveillance, yet the response is not overwhelming, suggesting additional layers of control exist. Here we show that the ESX-1 substrates ESAT-6 and CFP-10 constrain cGAS activation through complementary biochemical mechanisms. Purified Mycobacterium tuberculosis CFP-10 is a metal-dependent, 5'-3' exonuclease-like enzyme that preferentially degrades linear DNA, limiting the availability of stimulatory ligand. By contrast, purified M. tuberculosis ESAT-6 binds Zn2+ and limits the Zn2+-dependent conditions required for cGAS activation. During M. marinum infection, loss of these substrates augmented interferon-associated responses. Wild-type alleles restored immune suppression, whereas DNA-processing-defective CFP-10 or Zn2+-binding-defective ESAT-6 variants did not. These genotype-dependent differences were largely abolished in cGas-/- and Sting-/- macrophages. The double mutant was attenuated in zebrafish and mice, and its enhanced immune activation and attenuation were markedly diminished in cGas-/- mice. Prior exposure to the mutant improved zebrafish survival following wild-type challenge. Thus, beyond enabling mycobacterial DNA access to the cytosol, ESX-1 deploys its substrates to constrain cGAS-STING signaling by controlling activating-ligand availability and the metal-dependent environment required for sensor activation.

microbiology↗

bioRxiv2026

Autophagosome-tethering compounds selectively lower mutant ataxin-3 in preclinical models of spinocerebellar ataxia type 3

Spinocerebellar ataxia type 3 (SCA3) is most common subtype of autosomal dominant spinocerebellar ataxia worldwide and the most prevalent CAG expansion disorder in the east Asian population, but disease-modifying therapies remain unavailable. SCA3 is caused by the accumulation of polyglutamine- (polyQ-) expanded mutant ataxin-3 (mAtxn3), while the wild-type ataxin-3 (wtAtxn3) plays an important cellular role. Thus, allele-selective clearance of mAtxn3 without affecting wtAtxn3 is highly desired for SCA3 treatment. To achieve this goal, we tested previously reported compounds that have been shown to selectively degrade the Huntingtons disease causing mutant HTT protein by tethering the expanded polyQ to the autophagosome protein LC3. Here, we demonstrated that two of these compounds, AN2 and GW5074, selectively reduced both soluble and aggregated mAtxn3 with expanded polyQ (84Q), but not wtAtxn3, in both cellular and mouse models of SCA3. Mechanistically, both compounds lowered mAtxn3 by enhancing its autophagic degradation without obvious activation of global autophagy. At the molecular level, both compounds increased mAtxn3-LC3 interaction and colocalization to enhance the allele-selectively autophagic degradation of mAtxn3. Phenotypically, both compounds rescued cell viability and ATP content of SCA3 cellular models. In SCA3 patient pluripotent stem cells (iPSCs) derived cortical neurons, both compounds allele-selectively reduced mAtxn3 and partially rescued neurite shortening. Importantly, in a well-established SCA3 transgenic mouse model (MJD84.2), intracerebroventricular, intraperitoneal, and oral administration allele-selectively reduced mAtxn3 in multiple brain regions and rescued motor function deficits. Oral dosing achieved nanomolar cerebellar exposure and showed no overt liver, kidney, or glial toxicity over 28 days. Analyses of 1303 Chinese patients with SCA3 and ATXN3 constructs of different polyglutamine lengths further supported the compounds polyQ length-dependent activity with relative sparing of normal-length ataxin-3. Together, these findings provide preclinical proof of concept that utilizing orally available small molecule compounds to allele-selectively enhance mAtxn3 autophagic degradation for SCA3 treatment. One-sentence summarySmall-molecule autophagosome-tethering compounds selectively lower mutant ataxin-3 through an LC3- and autophagy-dependent mechanism and improve disease-related phenotypes in preclinical models of SCA3.

neuroscience↗

bioRxiv2026

Biological rationales from language models enable leakage-resistant forecasts of target-indication success

Identifying which target-indication (T-I) hypotheses can translate into clinical success remains a central challenge in drug discovery. We present PRIORITI (Prospective Rationale-Informed Outcome Reasoning for Integrated Target-indication Intelligence), a leakage-resistant framework that separates upstream biological evidence synthesis from outcome prediction. Given only a target gene and disease indication, a domain-instructed large language model (LLM) synthesizes drug-agnostic human genetics across prespecified channels. These evidence were embedded and combined with static entity context derived from gene summaries and disease definitions. The resulting representation is used to fit a supervised model trained on 6,784 temporally curated historical T-I outcomes, which is one of the largest cohorts assembled for this task. On a held-out historical test set of 1,696 T-I pairs, PRIORITI achieved a ROC-AUC of 0.883 and an expected calibration error (ECE) of 0.023, outperforming both a static target and indication context baseline and a human-curated genetic-evidence reference. On a strictly post-cutoff out-of-time benchmark of 765 T-I pairs, the largest such evaluation cohort reported to date, performance remained robust, with ROC-AUC 0.817, PR-AUC 0.357 and ECE 0.044, exceeding static target and indication context baseline and direct zero-shot LLM forecasting. A label-blinded LLM-based explainer further converted locked predictions into auditable prioritization rationales. Together, PRIORITI provides a calibrated, biology-first probability together with decision-oriented rationales for T-I prioritization before downstream translational investment.

bioinformatics↗

bioRxiv2026

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗

bioRxiv2026

The rhomboid-like pseudoprotease TMEM115 defines a two-factor mechanism for Rab6A effector recruitment

Protein trafficking and organelle identity depends on precise localisation of membrane proteins, anchored by small GTPases that bind to specific cellular compartments. Here we show that the rhomboid-like pseudoprotease TMEM115 defines an unexpected two-factor system with Rab6A to target the golgin TMF1. In the absence of TMEM115, TMF1 is delocalised from the rim of the Golgi apparatus; instead, it is retained in Golgi-targeting vesicles. TMEM115, TMF1, and Rab6A assemble into a ternary complex which, in striking contrast to canonical rhomboid-like mechanisms, is independent of transmembrane domain interactions. Disruption of the TMEM115-Rab6A-TMF1 complex affects the efficiency of trafficking of proteins from the Golgi; moreover, human disease-associated mutations target the interfaces of this ternary complex, thereby delocalising TMF1 from the Golgi. Finally, loss of TMEM115 in Drosophila causes abnormal Golgi morphology, and mouse knockouts undergo perinatal death. We propose that two-factor effector recruitment defines a mechanism for precise Rab GTPase-mediated membrane targeting.

cell biology↗

bioRxiv2026

Compressed representations underpin knowledge awareness in sequence learning

Humans can acquire sequence structure before its presence enters awareness, and in some learners this implicit knowledge later becomes accessible and reportable. Yet the neural reformatting that enables this transition remains unclear. Here we used electro/magnetoencephalography (EEG/MEG) to resolve how category-based triplet rules were organized before and after awareness emerged. We found that implicit sequence knowledge was already represented in an abstract, compressed format. Within this format, rule information generalized across triplet elements, and regular-sequence elements became more similar while perceptual-category structure remained separable. Compression rate quantified how efficiently local rule information entered this shared format, and its pre-awareness level predicted later awareness specifically in the event-related potential P3 window. MEG source imaging localised pre-transition compression to left middle temporal cortex and showed post-awareness expansion into prefrontal cortex. Pre-awareness directional connectivity further showed that temporal-to-prefrontal theta-band transfer was rule-specific and predicted later awareness. Overall, these findings identify a mechanism supporting conscious access to abstract sequence-rule knowledge, involving cross-element representational compression and theta-band routing from the compression source to prefrontal cortex.

neuroscience↗

bioRxiv2026

The Prefrontal Cortex Organizes Visual Evidence by Representational Abstraction

Perceptual decisions often depend on both lower-level visual features and higher-level object representations, yet natural images entangle these levels of abstraction. We used deep neural network features to dissociate lower- and higher-level visual similarity during a naturalistic image-similarity judgment task. Participants relied on both forms of evidence, with greater weighting of higher-level similarity. Across prefrontal cortex, visual evidence was systematically organized by representational level: lower- and higher-level similarity were expressed in distinct prefrontal regions during comparison and remained spatially differentiated as they were transformed into decision signals. This organization was behaviorally relevant, with prefrontal activation predicting which level of evidence guided choice when the two conflicted. Together, these findings identify representational level as an organizing principle of prefrontal cortex during visual decisions.

neuroscience↗

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

neuroscience↗

bioRxiv2026

Endothelial ANGPT2 insufficiency impairs retinal vascularization with ROP-like neovascular tufts

ANGPT2 is widely recognized as a critical regulator of pathological neovascularization. By analyzing scRNA-seq data from neonatal retinas, we demonstrate that Angpt2 transcripts are highly enriched in tip cells relative to other endothelial subtypes, where Angpt1/4 expression is absent. However, mechanisms underlying ANGPT2 function at angiogenic fronts remain inadequately understood. Here, we show that endothelial Angpt2 deletion severely disrupted retinal vascularization, characterized by neovascular tufts and micro-hemorrhage. Similar angiogenic defects also occurred in the brain, but were less evident in other tissues examined. Mechanistically, ANGPT2 insufficiency attenuated retinal tip cell invasion with aberrant mural cell coverage, compromising sprouting into non-vascularized tissues. Retinal RNA-seq analysis revealed that transcripts associated with endothelial migration and junction assembly were reduced in Angpt2 mutants compared to littermate controls, while upregulated genes were enriched in hypoxia-responsive pathways and mural cell development. Notably, abnormal mural-tip cell associations were detected within 48 hours post-Angpt2 deletion, displaying also a hypoxia-driven transcriptomic signature. These closely resemble the vascular pathologies observed in human retinopathy of prematurity. In contrast, Angpt1 insufficiency or Angpt4 deficiency primarily affected venous morphogenesis. Collectively, our findings imply that ANGPT2 is essential for driving tip cell invasion during sprouting angiogenesis, and that its insufficiency triggers hypoxia-driven vascular anomalies.

developmental biology↗

bioRxiv2026

Overexpression of miR-424(322)/-503 induces severe dilated cardiomyopathy by regulating the fatty acid oxidation gene expression program

Fatty acid oxidation (FAO) is a major energy source in the adult heart, and disruption of cardiac metabolism is closely associated with heart failure. Here, we investigated the effects of cardiac-specific overexpression of the H19X-encoded miR-424(322)/-503 cluster using an inducible transgenic mouse model. Sustained miR-424(322)/-503 overexpression caused rapid metabolic and functional deterioration, with early impairment of fatty acid oxidation. Short-term induction reduced FAO activity and downregulated genes involved in lipid metabolism, while glycolytic enzyme activity remained largely unchanged. Continued miR-424(322)/-503 expression subsequently led to severe dilated cardiomyopathy characterized by ventricular dilation, wall thinning, fibrosis, reduced contractility, and high mortality. Importantly, disease progression was dependent on the level and duration of miR-424(322)/-503 expression, as intermittent or lower-dose induction delayed cardiac dysfunction and prolonged survival. Withdrawal of miR-424(322)/-503 expression after the onset of dysfunction promoted substantial functional recovery. Together, these findings identify miR-424(322)/-503 as a potent regulator of cardiac metabolic reprogramming that disrupts fatty acid metabolism and drives progressive heart failure.

molecular biology↗

bioRxiv2026

Microbial Metabolites Potentiate MAIT Cell Anti-Tumor Immunity Against Solid Tumors

Mucosal-associated invariant T (MAIT) cells sense riboflavin metabolites through the monomorphic antigen-presenting molecule MR1, providing a unique opportunity to therapeutically mobilize a broadly shared T cell population without genetic engineering. Here, we show that the highly potent microbial metabolites, 5-OP-RU and 5-OE-RU, can be exploited as pharmacologic precision immune activators to drive human MAIT cell responses against solid tumors. Ligand stimulation in human co-culture systems elicited robust MAIT cell cytotoxicity, inflammatory cytokine secretion, and transcriptional states transformation. In vivo administration of riboflavin ligands 5-OP-RU significantly suppressed tumor growth in xenograft liver cancer models. Metabolite-driven MAIT activation also reprogrammed the local immune landscape, enhancing effector function and overcoming features of the immunosuppressive niche by markedly eliminating tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs) within the tumor microenvironment (TME). These findings reveal that microbial riboflavin metabolites can power and redirect MAIT cells to solid tumors, establishing MR1-metabolite signaling as a tractable therapeutic axis for liver cancer and other solid malignancies.

cancer biology↗

bioRxiv2026

A multi-agent workflow converts CAR-T patient evidence into experimentally testable hypotheses

Clinical studies of chimeric antigen receptor (CAR)-T therapy generate diverse molecular and clinical evidence that remains fragmented across publications, public repositories and patient-derived datasets, limiting systematic therapeutic discovery. Here we present BioPathfinder, an evidence-guided multi-agent workflow for closed-loop biomedical discovery. BioPathfinder constructs a provenance-aware knowledge resource linking publications with patient single-cell RNA sequencing (scRNA-seq) datasets and clinical metadata, and uses role-specialized large language model agents to generate, review and prioritize diverse, falsifiable and dataset-aware mechanistic hypotheses for computational and experimental validation. Applied to a curated corpus of CAR-T-treated patient studies and matched scRNA-seq datasets, BioPathfinder identified candidate mechanisms underlying CAR-T persistence, dysfunction and therapeutic resistance. The workflow prioritized the hypothesis that genes associated with an NK-like transition programme could be targeted to reduce CAR-T exhaustion and improve persistence. Analysis of patient scRNA-seq datasets showed enrichment of this programme in exhausted post-infusion CAR-T cells. Virtual perturbation prioritized transition-associated receptor genes, including KLRC1, KLRD1 and KLRG1, and expert review selected KLRC1, encoding NKG2A, for experimental validation. In vitro and in vivo chronic-stimulation models showed that NKG2A marked activated, exhaustion-associated CD8 CAR-T cells, whereas NKG2A blockade enhanced antitumour activity and persistence-associated functional readouts in vivo. BioPathfinder establishes a generalizable framework that transforms fragmented clinical single-cell evidence into experimentally validated therapeutic hypotheses, providing a scalable strategy for AI-guided biomedical discovery. HIGHLIGHTSO_LIBioPathfinder integrates fragmented CAR-T clinical studies, patient scRNA-seq datasets and metadata into a provenance-aware evidence resource for AI-guided hypothesis discovery. C_LIO_LIA multi-agent workflow generates, critiques and prioritizes diverse, falsifiable, dataset-aware mechanisms underlying CAR-T persistence, dysfunction and therapeutic resistance. C_LIO_LIBioPathfinder prioritizes KLRC1/NKG2A as a therapeutic target, and experimental validation demonstrates that NKG2A blockade enhances CAR-T persistence and antitumour function. C_LI

bioengineering↗

bioRxiv2026

Plaque-associated oligodendrocyte proteostatic failure underlies myelin loss in Alzheimers disease

Alzheimers disease (AD) features amyloid-{beta} plaques and tau pathology, yet the mechanism underlying early and clinically significant myelin loss remains unresolved. Here, we report human iPSC-derived forebrain organoids with doxycycline-inducible expression of SOX10, OLIG2, and NKX6-2 (SON), which generate robust, mature oligodendrocytes and compact myelin in vitro and in vivo. Introducing amyloid precursor protein (APP) pathogenic mutations produces extracellular amyloid-{beta} plaques and phosphorylated tau, accompanied by reduced myelin basic protein (MBP) expression and disrupted myelin ultrastructure. Single-cell and spatial transcriptomics combined with amyloid plaque imaging reveal that oligodendrocytes in plaque-dense regions show pathological changes in calcium signaling, immune activation, lipid remodeling, and protein catabolic pathways. Notably, MBP protein is reduced despite elevated myelin-related transcription, suggesting plaque-induced degradation of myelin proteins. Consistent with this, proteasomal inhibition restores MBP protein levels and improves compact myelin ultrastructure in AD organoids. In human AD brain tissue, protein catabolic pathways are similarly upregulated with increasing plaque density. Together, these findings identify plaque-associated oligodendrocyte proteostatic failure as a candidate mechanism of myelin loss and a potential therapeutic pathway in AD.

neuroscience↗

bioRxiv2026

BBBP_Atlas: Unified Interpretable Modeling of Blood Brain Barrier Permeability across Small Molecules and Peptides

Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system drug discovery, yet existing models are often limited by their reliance on predefined physicochemical descriptors, small-molecule-centered training sets, or conformation-dependent representations, which restricts their transferability across chemically diverse modalities especially peptides. In addition, publicly available BBBP datasets remain fragmented, inconsistently standardized, and weakly controlled for molecular redundancy, increasing the risk of data leakage and overestimated model performance. In this study, we propose BBBP-Atlas, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP. Designed to bypass descriptor and conformation dependencies, our model represents standardized molecular structures as atom-level graphs to capture local atom-bond environments and long-range topological dependencies associated with BBB transport. This design enables direct learning of structure-permeability relationships from molecular topology. For model training and evaluation, we curated a cross-modal, redundancy-filtered database OmniBBBP that seamlessly unifies small molecules and complex peptides, containing 10,218 unique compounds with 9,316 small molecules and 902 peptides. BBBP-Atlas achieved an accuracy of 0.8914 and an MCC of 0.7678 on the independent test set. On a balanced external benchmark of 200 compounds, our model reached an AUC of 0.9108, an accuracy of 0.8500, and an MCC of 0.7000, outperforming LightBBB by an absolute MCC gain of 6%. Case studies further showed that BBBP-Atlas captured clinically meaningful BBB permeability patterns, correctly identifying lorlatinib as BBB-permeable and vancomycin as BBB-impermeable with high confidence. The OmniBBBP-backed BBBP-Atlas offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug discovery. BBBP-Atlas is available at https://cadd.drugflow.com/bbbp/.

bioinformatics↗

bioRxiv2026

LOESS and DE-SWAN can induce artifactual "waves" of molecular aging

A growing body of literature has investigated the relationship between age and biomolecular changes, leading to conclusions that aging occurs in discrete molecular "waves." Data summary tools such as LOESS and sliding window analyses like DE-SWAN are common approaches that have gained acceptance in recent years. We demonstrate via simple simulations that these tools can identify non-linear patterns of aging where they do not exist. Specifically, we show that (i) clustering of molecular trajectories using LOESS can lead to artifactual characteristic patterns of molecular aging, (ii) "waves" of aging identified using the combination of LOESS and DE-SWAN in real data are not robust to changes in the underlying age distribution and are not supported by valid permutation testing, and (iii) DE-SWAN alone can generate pronounced "waves" of nonlinear molecular aging in linear data due to differences in statistical power along the age continuum. Our results specifically challenge the statistical support for discrete aging crests inferred in the literature, but do not rule out nonlinear molecular aging or age-associated transitions that may be detectable using other cohorts and statistical models.

bioinformatics↗

bioRxiv2026

Epigenetic age acceleration is associated with contaminant exposure in common dolphins (Delphinus delphis)

Metals and per- and polyfluoroalkyl substances (PFAS) represent a significant environmental concern, yet their association with epigenetic age acceleration (EAA) remain largely understudied in marine mammals. Here, associations between EAA in common dolphins (Delphinus delphis) and life history (sex and sexual maturity), trace metals, and PFAS were investigated. EAA was calculated as the residual in the regression of epigenetic age vs chronological age, hence providing a direct measure of the deviation of the epigenetic age of an organism (positive or negative) by comparison with expectation, given their actual chronological age. Sixteen trace elements were quantified in hepatic and renal tissues (n = 53). In addition, 28 PFAS were quantified in hepatic tissue (n = 58). Associations between EAA and explanatory variables were assessed using regression-based and multivariate modelling approaches (linear models and canonical analysis of principal coordinates). No effect of sex was observed, although sexual maturity did significantly increase EAA. Exposure to metals was significantly associated with EAA, explaining 55.4% of the variation, with hepatic metals (Se, Zn, Cu, Al, Mn) driving this relationship. Although EAA was not significantly related to the total PFAS exposure overall, a subset of PFAS variables (PFBA, PFDA, PFHxS-B, PFNA) showed significant association with EAA after adjusting for sex and sexual maturity. Together, these subsets of metal and PFAS variables, in addition to the selenium-to-mercury (Se:Hg) molar ratio, explained 66.7% of the variation in EAA. Our results identify sexual maturity and specific contaminant mixtures as key potential drivers of EAA in common dolphins, highlighting the possible use of EAA as a biomarker of environmental and physiological stress in marine mammals.

zoology↗

bioRxiv2026

Translation sustains productive Pol II elongation through maintenance of nuclear RNA surveillance

Although transcription and translation are spatially separated in eukaryotic cells, gene expression requires coordination between the nucleus and cytoplasm. Whether translation rapidly influences nuclear transcription remains unclear. Here we show that ongoing translation sustains productive RNA polymerase II (Pol II) elongation in mouse embryonic stem cells. Translation inhibition reduces nascent transcription within 15 minutes, impairs Pol II pause release, and preferentially represses long genes before substantial loss of chromatin-associated Pol II. Unexpectedly, despite reduced transcription, nuclear RNA transiently accumulates owing impaired in RNA turnover. Mechanistically, translational arrest redistributes RNA-processing and RNA-surveillance factors from the nucleus to stalled ribosome-associated complexes in the cytoplasm, reducing their nuclear availability. Acute depletion of RNA-surveillance components phenocopies elongation defects caused by translational inhibition, particularly in long genes. Together, our findings identify ongoing translation as a regulator of nuclear RNA surveillance capacity and reveal a feedback mechanism sustaining productive Pol II elongation and nuclear RNA homeostasis.

Molecular Biology↗

bioRxiv2026

FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis

Liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics detects thousands of metabolic features, but converting these chemical signals into metabolite set-level biological knowledge remains challenging. This is because most features lack unambiguous metabolite identities. Conventional metabolite set enrichment analysis (MSEA) generally requires identified metabolites and metabolite-level ranked inputs, leaving much of the untargeted feature space unused. Here, we present FeatureMSEA, a feature rank-based framework for metabolite set enrichment directly from metabolic features with ambiguous annotations. FeatureMSEA integrates multi-evidence feature-to-metabolite annotation, feature rank-based enrichment scoring, permutation-based inference, and iterative leading-edge-guided annotation refinement, with an optional LLM-assisted module for post-enrichment interpretation. In null comparisons of randomly split healthy samples, FeatureMSEA detected no significant metabolite sets, whereas metabolite-set spike-in simulations showed recovery of implanted signals. In a cerebrospinal fluid metabolomics study of Huntingtons disease, FeatureMSEA identified dysregulated metabolite sets related to amino acid metabolism, mitochondrial energy metabolism, and neuroactive signaling. MS/MS-based annotation analysis further showed that FeatureMSEA refinement reduced annotation ambiguity and prioritized chemically consistent candidate metabolites. In summary, FeatureMSEA provides a general framework for extracting metabolite set-level biological insights from LC-MS untargeted metabolomics in which confident metabolite identification remains incomplete.

bioinformatics↗