Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 163 records · Page 9Linked to original sources

bioRxiv2026

SLAB: A Sweep Line Algorithm in PBWT for Finding Haplotype Block Cores

With the increasing availability of high-quality phased haplotype data, researchers can more effectively identify detailed patterns of haplotype sharing and investigate the population genetic processes that shape them. In this work, we define block cores as genomic segments where multiple haplotype blocks overlap. We develop an efficient algorithm to analyze haplotype blocks, focusing on width-maximal matches and the identification of haplotype block cores. We apply our algorithm to UK Biobank haplotypes to quantify block cores and demonstrate the biological and population-level insights that can be inferred from their patterns. Specifically, identified block cores can serve as a basis for detecting signals of selection. Although our results are largely consistent with those from methods such as IBD rate analysis, they also reveal complementary information not captured by IBD rates or other conventional approaches. Source code is available at https://github.com/ZhiGroup/SLAB.

bioinformatics↗

bioRxiv2026

OmniBind: Proteome-Wide Promiscuity Predictions for Early-Stage Drug Screening

Off-target binding remains a leading cause of drug attrition, yet no method exists for rapidly quantifying small-molecule promiscuity across the human proteome. Here, we define promiscuity as the mean predicted binding affinity over 15,405 human proteins and derive a specificity score combining target affinity with this proteome-wide distribution. To make this assessment tractable at scale, we introduce OmniBind, a message-passing neural network that predicts promiscuity directly from a SMILES string at about a thousand compounds per second, several orders of magnitude faster than proteome-wide profiling. OmniBind promiscuity scores correlate with experimental binding data near assay reproducibility limits. Ranking candidates by specificity rather than affinity alone improves enrichment of approved drugs across all thresholds tested, an advantage robust to the choice of affinity predictor. OmniBind fills an unoccupied niche in the early-stage screening landscape as a fast, proteome-scale complement to traditional safety panels, with accuracy that will scale as the underlying affinity predictors continue to improve.

molecular biology↗

bioRxiv2026

Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics

Spatial transcriptomics enables in situ characterization of tissue organization by jointly profiling gene expression profiles and spatial coordinates, with histological images as complementary contextual information. However, effectively integrating these heterogeneous modalities remains challenging due to differences in statistical properties and structural patterns. We propose st-Xprop, a cross-propagative graph network with dual-graph embedding coupling for spatial domain identification. st-Xprop constructs modality-specific graphs for gene expression and histological features, and performs alternating cross-modal propagation to explicitly model inter-modal heterogeneity while enabling complementary information exchange. Through dual-graph embedding coupling, the framework progressively learns a unified low-dimensional representation that integrates multimodal signals and preserves spatial coherence. Evaluations on multiple real spatial transcriptomics datasets demonstrate that st-Xprop consistently improves clustering accuracy and robustness, particularly in weak-signal or structurally complex regions, yielding spatial domains that are more stable and biologically meaningful.

bioinformatics↗

bioRxiv2026

SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification

Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck in neuroscience. While advances in fluorescent labeling and imaging can generate vast datasets, analysis is often the limiting factor. Several deep learning-based tools have been proposed to ameliorate these issues. However, existing applications primarily focus on dendritic spines and lack robust solutions for segmenting synaptic puncta in dense tissue preparations. To address this, we introduce SynAPSeg, which encompasses an open-source framework for deep learning-based analysis and, to the best of our knowledge, the first large-scale, publicly available instance segmentation dataset specifically curated for synaptic puncta. We use this dataset to train deep learning models that reach the performance of human experts across a unique benchmark dataset. SynAPSeg integrates these models into an interactive interface, with support for multi-dimensional data, enabling fully automated segmentation and quantification pipelines alongside an annotation module for refinement and validation. We demonstrate the frameworks scalability by performing the first comprehensive mapping of nearly 4 million excitatory postsynaptic PSD95 puncta within inhibitory interneurons across the dorsal hippocampus, revealing regional differences in synapse properties. Finally, we show SynAPSegs utility for 3D quantification by applying these models to study aging-associated synaptic changes in CA1 parvalbumin (PV)-positive inhibitory neurons. Through this approach, we uncover a reduction in PSD95 density along PV dendrites in the aged CA1, indicating reduced glutamatergic recruitment of PV neurons which could contribute to age-related cognitive decline. Collectively, these results demonstrate that SynAPSeg provides a scalable solution for comprehensively studying synaptic architecture in health and disease.

neuroscience↗

bioRxiv2026

Multifunctional nanozyme therapy accelerates hematoma clearance and attenuates genome damage and senescence after intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is a devastating form of stroke characterized by rapid hematoma formation in the brain, resulting in multiple pathological events due to mass effect and toxicity of extravasated blood and its blood products. ICH leads to poor long-term outcomes despite advances in hematoma management, largely due to secondary injury mechanisms. Hemin and iron released in the peri-hematomal environment trigger genome damage, transient senescence, and inflammatory signaling that may initially limit ferroptosis but ultimately contribute to persistent neurodegeneration. Given the multiple pathological events initiated following ICH, it is not surprising that no single neuroprotective strategy has been effective. In this study, we investigated these interconnected pathways in a rodent model of ICH and evaluated the therapeutic potential of DEF-OAC-PEG, a pleiotropic synthetic oxidized carbon nano-enzyme that has catalytic mitochondrial and cellular protective actions, covalently bonded to the iron chelator deferoxamine and shown in our previous work to have strong in vitro protective effects against hemin and iron toxicity and in vivo evidence of reduction in genome damage. Here, we examined mechanisms of action in an in vivo ICH mouse model. Autologous whole blood injection into the mouse brain striatum induced robust astroglial and microglial activation, increased neuronal Heme Ooxygenase-1 expression, and DNA damage and senescence in neurons and oligodendrocytes. Systemic intraperitoneal administration of DEF-OAC-PEG, initiated 3 hours after ICH, resulted in robust brain penetration in wild-type mice, with preferential accumulation in peri-hematomal regions of ICH mice. Surprisingly, nanozyme treatment produced a rapid, significant acceleration of hematoma clearance compared with untreated ICH animals. This effect was associated with enhanced detection of CD68-positive microglia/macrophages, which also showed internalized nanozymes, suggesting that nanozyme promotes immune-mediated hematoma resolution. Importantly, DEF-OAC-PEG also markedly attenuated ICH-induced DNA damage and senescence in neurons and oligodendrocytes. Together, these findings identify genome instability and senescence as key consequences of hemorrhagic brain injury and demonstrate that multifunctional nanozyme therapy can simultaneously promote hematoma resolution and mitigate secondary neurodegenerative injury following ICH.

neuroscience↗

bioRxiv2026

Multi-component functionalized Bifidobacterium longum hydrogel for multi-target integrated therapy of colitis-associated anxiety and depression

Inflammatory bowel diseases (IBDs) are frequently accompanied by anxiety and depression, largely driven by perturbed gut-brain axis signaling. However, current oral therapies remain constrained by the spatial and functional separation between intestinal inflammation and central nervous system dysfunction. Here, we present a comprehensive gut-brain dual region integrated therapeutic strategy based on functionalized Bifidobacterium longum hydrogel (INPs@BL@Gel), in which baicalin and tyrosine are coordinated with Fe(III) to form infinite coordination polymers (ICPs), coated with inulin, assembled onto Bifidobacterium longum (BL), and subsequently encapsulated within a pH- and matrix metalloproteinase-responsive silk fibroin-gelatin hydrogel. INPs@BL@Gel exhibits high drug-loading, effective gastric protection, inflammation-triggered release, and long-term intestinal colonization. Within the inflamed intestine, BL and components synergistically suppress inflammatory responses, restore gut microbiota homeostasis, and promote intestinal barrier repair through multi-target integrated therapy. Importantly, BL combined with components markedly enhances the production of beneficial neuroactive metabolites such as homovanillic acid and short-chain fatty acids, which integrated regulate neuroinflammation, preserve synaptic function, and facilitate blood-brain barrier repair via the gut-brain axis. In vivo studies demonstrate that INPs@BL@Gel not only exert potent therapeutic efficacy against colitis and effectively alleviate associated depression, but also reshape the gut microbiota and restore barrier integrity, achieving an remarkable comprehensive therapeutic effect. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/710940v1_fig1a.gif" ALT="Figure 11"> View larger version (59K): org.highwire.dtl.DTLVardef@1a831f3org.highwire.dtl.DTLVardef@1676617org.highwire.dtl.DTLVardef@15f99c1org.highwire.dtl.DTLVardef@1677cc4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOScheme 1.C_FLOATNO (a) Schematic diagram of the design and preparation of functionalized Bifidobacterium longum hydrogel. (b) Exploration of the mechanism of INPs@BL@Gel in treating colitis-associated anxiety and depression through a dual-site multi-target synergistic strategy. C_FIG

pathology↗

bioRxiv2026

Volume regulation of cancer cells during osmotic pressure variation

Osmotic pressure is a fundamental physical determinant of cellular homeostasis, and its perturbation is recognized as a critical factor in cancer progression and therapy. However, whether cancer cells and normal cells respond to osmotic pressure changes differently remains unclear. Here, we subjected cancer and normal cells to osmotic shocks and compared their volume and traction force changes. Under hypotonic conditions, cancer cells recovered their volume and forces much more slowly than normal cells, although both cell types responded similarly to hypertonic shock. We found that actin-based cortical tension controlled the recovery speed; whereas myosin-mediated contractility tuned the volume change extent. Substrate stiffness also influenced the recovery process by altering cytoskeletal constraint. A theoretical model integrating adhesion energy, ion and water transport, and surface tension was developed. The results matched the experimental observations. Our work uncovered the distinct mechanobiological behaviors of cancer cells upon hypotonic shock, providing a mechanical framework for understanding cancer mechanisms that may inform the development of cancer therapeutics.

biophysics↗

bioRxiv2026

SpatioCAD: Context-aware graph diffusion model for pinpointing spatially variable genes in heterogeneous tissues

Spatial transcriptomics enables comprehensive characterization of tissue architecture, and the identification of spatially variable genes (SVGs) is a critical step for defining region-specific molecular markers and uncovering spatially regulated mechanisms across diverse biological contexts. However, most existing methods for SVG detection overlook cell density variations, a major confounding factor in complex tissues such as tumors, where heterogeneous cellularity frequently introduces false-positive calls. Here we present SpatioCAD, a computational framework that explicitly decouples genuine spatial expression patterns from confounding effects driven by cellularity. SpatioCAD leverages and extends a graph diffusion model to simulate expression propagation under cell-density-aware conditions, thereby ensuring unbiased detection of SVGs across all expression levels. Systematic evaluations on simulated datasets demonstrate its superior statistical power and specificity. Applied to breast cancer, lung cancer, and glioma datasets, SpatioCAD identifies functionally diverse SVGs, including low-abundance transcripts with established roles in tumor progression, while also recapitulates biologically meaningful tissue architecture features.

bioinformatics↗

bioRxiv2026

Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics

In imaging-based spatial transcriptomics, transcript-to-cell assignment shapes downstream biological interpretation including cell typing, ligand-receptor inference, and niche characterization. However, two-dimensional segmentation of volumetric tissue often yields mixed cellular profiles, while cells without detected nuclei are missed entirely, distorting the aforementioned downstream analyses. We present TRACER, which refines cellular representations in imaging-based transcriptomics by leveraging gene-gene coherence and spatial co-localization of transcripts observed directly in the data, without requiring external annotations or reference atlases. TRACER resolves mixed cellular profiles and reconstructs partial cells whose nuclei are not detected, enabling more complete representation of cells within the tissue section. We also introduce coherence-based metrics that quantify transcriptional purity and conflict, enabling platform-agnostic benchmarking of segmentation quality. Across diverse platforms, tissues, and segmentation methodologies, TRACER consistently and reproducibly improves the coherence of cellular profiles and the quality of downstream analyses.

systems biology↗

bioRxiv2026

Spiral-on-a-Curve: wireless photoacoustic neuromodulation patch

Focused ultrasound neuromodulation offers a promising noninvasive strategy for precise deep-brain stimulation, yet conventional piezoelectric phased arrays rely on bulky hardware, high-voltage electronics, and complex phase control, limiting their scalability and wearable integration. Photoacoustic approaches enable wireless ultrasound generation but remain constrained by a trade-off between focusing precision, penetration depth, and robustness to optical misalignment. Here, we present a geometrically encoded passive photoacoustic patch (PPP) based on a spherical double logarithmic spiral (SDLS) array that achieves intrinsically stable and programmable acoustic focusing without electronic phase modulation. By distributing hemispherical CNT/PDMS photoacoustic emitters quasi-uniformly over an equal-path spherical surface and orienting each emitter toward a predefined focal point, the device establishes geometry-dominated wavefront convergence. Numerical simulations demonstrate that curved geometry is a prerequisite for phase-free focusing, while the nonperiodic spiral topology suppresses sidelobes and mitigates interference artifacts Compared with continuous spherical or periodic concentric arrays, the SDLS architecture exhibits substantially enhanced robustness to optical axis displacement, reducing focal tilt from > 14{degrees} to approximately 5{degrees} under 2 mm lateral misalignment. Experimental three-dimensional hydrophone mapping confirms millimeter-scale focusing at approximately 7 mm depth with a full width at half-maximum of 1.3 mm and peak pressures up to 8 MPa under safe laser exposure ([≤] 20 mJ/cm2). The focal region can be continuously tuned by adjusting illumination aperture size without altering device geometry or excitation schemes. The patch demonstrates excellent thermal and acoustic stability during prolonged operation and enables region-specific motor cortex stimulation in vivo, eliciting distinct electromyographic responses in forelimb and hindlimb muscles. By shifting ultrasound beam formation from electronic phase control to intrinsic three-dimensional geometry, this work establishes a lightweight, wire-free, and optically programmable platform for robust wearable neuromodulation and scalable bioacoustic interfaces.

bioengineering↗

bioRxiv2026

MetaSAG: A Tool for Multi-level Exploration and Taxonomic Analysis of Microbial Single-Amplified Genomes

Microbial single-amplified genome (SAG) sequencing technologies have elevated microbial research resolution to the single-cell level. However, neither upstream data processing nor downstream analysis has been fully developed, greatly limiting the research in strain level. Herein, we developed MetaSAG (Multi-level Exploration and Taxonomic Analysis of microbial Single-Amplified Genomes), which enables accurate and rapid taxonomic classification of microbial SAGs. MetaSAG outperforms existing method in species classification certainty, computational efficiency, and sensitivity of low abundance species identification. In addition, MetaSAG enables species-level functional analysis, as well as strain-level evolutionary analysis. With the help of MetaSAG, we discovered the parasitic relationship between phages and bacteria, identifying multiple susceptible bacteria and a broad spectrum of phages. Furthermore, we developed MetaK-Lytic (k-mer-based meta-learning framework to predict phage lytic ability) to achieve accurate prediction of phage lytic activity based on 31-mer short sequences, which is well adapted to the characteristics of incomplete SAG sequences. Overall, we offer a comprehensive integrated tool that can parse microbial SAG data from raw data to the strain level to decipher the functional ecology of microbial dark matter, with broad implications for microbial ecology and phage therapy (https://github.com/liangcheng-hrbmu/MetaSAG). Significance StatementThis work provides a comprehensive framework for high-resolution SAG data analysis. The developed pipeline improves taxonomic annotation sensitivity and speed. Strain-level tracking enables dynamic evolutionary and functional insights, while single-cell bacterial-virus network reconstruction reveals precise interaction patterns. The novel annotation-free short sequence-based MetaK-Lytic facilitates functional prediction of uncharacterized phage sequences. Integrated into the MetaSAG platform, these tools deliver a streamlined, multi-level solution for interpreting SAG data, advancing studies in microbial ecology, evolution, and virology.

microbiology↗

bioRxiv2026

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. Because ALT is absent from normal human cells, it is an appealing target for cancer therapy, but the lack of ability to determine ALT status at scale has hindered discovery. Here, we developed ALTitude, a machine learning method from a tabular foundation model that infers ALT from cell line whole genome sequencing data, without need for paired germline analysis. We deployed ALTitude across the DepMap, doubling the number of known ALT+ cancer models. Systematic integration of ALTitude with CRISPR-Cas9 screens yielded the selective dependency on SMARCAL1 in ALT+ cell lines, where we show it stabilizes the ALT phenotype. Acute depletion of SMARCAL1 leads to G2/M arrest, mitotic catastrophe, and cell death. These data provide a valuable resource for studying ALT-related genomic features and present SMARCAL1 as a therapeutic target for ALT+ malignancies.

cancer biology↗

bioRxiv2026

Myristoylation licenses disordered viral VP4 protein to anchor to and perforate the membrane through phase separation

The VP4 protein of enteroviruses, such as Coxsackievirus B3, is a small, intrinsically disordered protein essential for perforating the host cell membrane during viral entry. A key feature of VP4 is its N-terminal myristoylation, which is required for infectivity in some enteroviruses but dispensable in others, suggesting a complex and context-dependent role that is not fully understood. The precise biophysical mechanisms by which this lipid anchor enables a disordered protein to breach a membrane remain unresolved. Here, using Coxsackievirus B3 VP4 as a model system and integrating multi-scale molecular dynamics simulations with confocal microscopy, we demonstrate that myristoylation is not a simple membrane tether but a multi-functional regulator that orchestrates VP4 activity through distinct, hierarchical roles. First, it provides the necessary hydrophobic anchor to recruit the disordered VP4 to the membrane interface. Second, the myristoyl group acts as a key molecular driver that promotes the liquid-liquid phase separation of VP4, leading to the formation of dynamic condensates on the membrane surface. These condensates actively remodel the membrane, generating substantial curvature that, in turn, lowers the free energy barrier for VP4 penetration. Furthermore, we find evidence that the myristoyl group plays a third role in stabilizing the final transmembrane pore. Our findings establish a novel paradigm where a single lipid modification empowers a disordered viral protein to form a functionally potent condensate that mechanically primes and physically breaches the target membrane, a mechanism that may explain the conditional myristoylation requirement across enteroviruses.

biophysics↗

bioRxiv2026

A light-labile signal molecule acts as a photoregulator of secondary metabolite biosynthesis in a heterotrophic bacterium

While light is essential for many phototrophic organisms, our knowledge of the light-dependent regulation in heterotrophic organisms is scarce. Here, we found that the heterotrophic soil bacterium Pseudomonas protegens differentially accumulated 234 secreted metabolites depending on the light conditions. These metabolites included important antimicrobials such as pyoluteorin, 2,4-diacetylphloroglucinol, pyrrolnitrin, and rhizoxin analogs. Pyoluteorin, for instance, was 140-fold more abundant in the dark, due to a strong upregulation of the transcript of the pyoluteorin biosynthesis gene pltL. We discovered that 2,4-dichlorophloroglucinol (PG-Cl2), an activator of pyoluteorin biosynthesis, acts as a photoregulator that degrades much faster in the presence of light than in the dark. The resulting higher PG-Cl2 levels activate the pltL promoter in the dark. These findings reveal a novel mode of regulation in which light instability of a signal molecule allows the producing organism to sense and transmit light information to regulate the biosynthesis of a secondary metabolite.

microbiology↗

bioRxiv2026

Female iPSC X-chromosome inactivation (XCI) erosion and its transcriptomic effects during CRISPR gene editing and neural differentiation

Human induced pluripotent stem cells (hiPSC) and iPSC-differentiated neural cells, in combination with CRISPR editing, are commonly used for studying neurodevelopmental and other brain disorders. Female iPSCs undergo random X-chromosome inactivation (XCI) via epigenetic silencing by noncoding X inactive specific transcript (XIST). It is known that female iPSCs may lose XIST expression, leading to XCI erosion that affects both X-linked and autosomal gene expression. However, the effects of CRSIPR editing and neural differentiation on XCI erosion in iPSC-derived neurons and how this may confound a real-world transcriptomic analysis of differentially expressed genes (DEGs) are poorly understood. Here, leveraging bulk RNA-seq of hundreds of CRISPR-edited female iPSC lines from four donor lines for 66 genes and single-cell RNA-seq of iPSC-derived neurons of a subset of 42 edited genes, we investigated the effects of XCI erosion during CRISPR editing and in iPSC-derived neurons. We found that XCI erosion was variable in CRISPR-edited female iPSCs and largely preserved in iPSC-derived neurons. Like in iPSCs, XIST in neurons predominately influenced the expression of X-linked genes; however, its effect on autosomal genes was more pronounced in single neurons. Mechanistically, XIST epigenetically causes allelic imbalance of both X-linked and autosomal genes, with the former showing stronger allele-specific expression (ASE) bias. Notably, XIST-induced ASE bias exhibited a conserved positional pattern at loci affecting neurodevelopmental genes across different female lines and cell types. Finally, we demonstrated a confounding effect of XCI erosion on DEG analyses in iPSC-derived neurons. These results have significant implications in hiPSC modeling of neurodevelopmental and other brain disorders.

genomics↗

bioRxiv2026

Nitro Reduction-Based RNA Control and Ultrafast Release

RNA protection and controlled release are critical for both fundamental research and therapeutic applications, yet the development of simple, efficient, and reversible post-synthetic RNA modification strategies remains a significant challenge. Here, we introduce a straight forward approach based on ribose 2'-hydroxyl acylation with a nitro-functionalized carbamate, which acts as a redox-responsive center. This modification selectively inhibits native RNA function while remaining inert to endogenous biogenic reductants and common reducing agents used in biological assays. Upon treatment with low millimolar concentrations of the THDB- BIPY reducing pair, RNA function is rapidly restored on a minute timescale. This methodology is broadly applicable across diverse RNA classes and functional contexts, including synthetic RNA oligomers, fluorogenic RNA aptamers, single-guide RNAs in CRISPR-Cas9 gene-editing systems, and mRNAs in living cells for translation control and RNAi-mediated gene silencing. These results demonstrate the general utility of this approach as a chemically controllable functional switch, providing a versatile toolkit for temporal regulation of RNA activity in both research and biotechnological applications.

biochemistry↗

bioRxiv2026

Opposing regulatory logics converge on ABA receptors to govern the trade-off between growth and drought acclimation

Plants dynamically balance growth with stress responses through abscisic acid (ABA). How ABA signaling, but how ABA sensitivity is finely tuned is unclear. Here, we uncover a regulatory network of leucine-rich repeat receptor-like kinases (LRR-RLKs) that directly controls ABA receptors stability. We identify GASSHO1 (GSO1) as a core component that phosphorylates the PYL2 and PYL4 receptors, targeting them for degradation and thus serving as a brake on ABA signaling. Under drought stress, ABA accumulates and suppresses expression of GSO1-activating CIF peptides, releasing this brake to increase PYLs and sensitivity. This mechanism opposes our previously identified CEPR2 pathway, where drought-induced CEP peptides inhibit the kinase to stabilize PYLs. The integration of these two antagonistic modules within an LRR-RLK network enables precise, dynamic control of ABA perception. This work reveals a molecular framework explaining how plants calibrate the critical balance between growth and drought acclimation.

molecular biology↗

bioRxiv2026

Stool Metabolomics Reveals Catecholamine and Tryptophan-NAD+ Pathway Alterations in Autism Spectrum Disorder

Gut-brain axis dysregulation and microbiome-linked metabolic disturbances have been implicated in autism spectrum disorder (ASD), yet gut-derived neuroactive metabolites remain incompletely characterized. We applied targeted liquid chromatography-tandem mass spectrometry in a cross-sectional case-control study to quantify 18 stool metabolites related to catecholamine synthesis, GABAergic signaling, and tryptophan (Trp) and NAD+ metabolism in 59 participants (32 ASD, 27 non-ASD controls). Group differences were evaluated using log2 fold change and covariate-adjusted linear models. Random forest classifiers with five-fold cross-validation assessed multivariate discriminative performance, and within-group Spearman correlations examined metabolic network organization. Norepinephrine exhibited the largest elevation in ASD, while dopamine and tetrahydrobiopterin showed nominal group effects consistent with altered BH4-dependent catecholamine metabolism. Among individual metabolites, tetrahydrobiopterin, GABA, and kynurenine were the most informative, and their combination achieved an area under the receiver operating characteristic curve of 0.737 (95% confidence interval, 0.603 to 0.871). Correlation analyses revealed conserved lithocholic acid-deoxycholic acid coupling in both groups. In controls, Trp correlated with kynurenine, whereas ASD showed expanded Trp associations with GABA, norepinephrine, anandamide, and nicotinic acid, consistent with reorganization of Trp-NAD+ precursor pathways. These findings identify coordinated, gut-linked metabolic differences in ASD and define a metabolite signature reflecting altered catecholaminergic and Trp-related network structure. ImportanceAutism spectrum disorder is currently diagnosed using behavioral assessments, and objective biological measures are limited. The gut microbiome produces many small molecules that can influence brain development and function, but the specific metabolites involved in ASD remain unclear. Using targeted analysis of stool samples, we identified changes in metabolites related to neurotransmitter synthesis and tryptophan metabolism and showed that their relationships form distinct metabolic networks in individuals with ASD. A small panel of metabolites provided moderate ability to distinguish ASD from controls. These results suggest that gut-derived metabolites capture biologically meaningful variation in ASD and may help guide future studies aimed at understanding gut-brain communication and developing noninvasive biomarkers.

microbiology↗