Biology subjects

Zhang, P.

Publications and source records attributed to Zhang, P..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

Integrating Interpretable Machine Learning and Multi-omics Systems Biology for Personalized Biomarker Discovery and Drug Repurposing in Alzheimer's Disease

Alzheimers disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

systems biology↗

bioRxiv2025

Chimera: Ultrafast and Memory-efficient Database Construction for High-Accuracy Taxonomic Classification in the Age of Expanding Genomic Data

The rapid growth of genomic data expands species diversity but also causes taxonomic imbalance, with certain species heavily overrepresented. Both data volume and imbalance challenge the accuracy and efficiency of metagenomic tools. Here, we present Chimera, a transformative tool harnessing the Interleaved Merged Cuckoo Filter (IMCF) and FairMin-Cap (FMC) strategy for next-level performance. It achieves the highest classification accuracy while providing an astonishing 162-fold faster database assembly than Kraken2, constructing the complete RefSeq genome database within mere minutes using under 32 GB of RAM, enabling rapid and cost-effective database updates. Furthermore, Chimeras universal memory scalability supports at least 300,000 species and potentially over 800,000 species in practical 1 TB systems, overwhelming traditional software solutions. Our results establish Chimera as a foundational tool for the next era of metagenomic research, laying a crucial cornerstone for the future of ultramassive genome datasets.

bioinformatics↗

bioRxiv2025

DeepSeMS: a large language model reveals hidden biosynthetic potential of the global ocean microbiome

Microbial biosynthetic diversity holds immense potential for discovering natural products with therapeutic applications, yet a substantial quantity of natural products derived from uncultivated microorganisms remains uncharacterized. The intricate nature of biosynthetic enzymes poses a major challenge in accurately predicting the chemical structures of secondary metabolites solely based on genome sequences using current rule-based methods. Here, we present DeepSeMS, a large language model designed to predict the chemical structures of secondary metabolites from various microbial biosynthetic gene clusters. Built on the Transformer architecture, DeepSeMS innovatively identifies sequence features using functional domains of biosynthetic enzymes, and incorporates feature-aligned chemical structure enumeration for training data augmentation. External evaluation results show that DeepSeMS predicts more accurate chemical structures of secondary metabolites with a Tanimoto coefficient up to 0.6 compared with the ground truth, significantly outperforming antiSMASH and PRISM with coefficients of only 0.14 and 0.45 respectively. Moreover, DeepSeMS successfully predicted secondary metabolites for 96.60% of cryptic biosynthetic gene clusters, surpassing existing methods with success rates less than 50%. Leveraging DeepSeMS, we characterized over 65,000 novel secondary metabolites from the global ocean microbiome with previously undocumented structural types, ecological distribution, and biomedical applications especially antibiotics. A login-free and user-friendly web server for DeepSeMS (https://biochemai.cstspace.cn/deepsems/) has been launched, featuring an integrated global ocean microbial secondary metabolites repository to expedite the discovery of novel natural products. Collectively, this study underscores the great capacity of a large language model-driven method in revealing hidden biosynthetic potential of the global ocean microbiome.

synthetic biology↗

bioRxiv2025

PRRSV evades innate immune cGAS-STING antiviral function via its Nsp5 to deter STING translocation and activation

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is an important pathogen that seriously endangers pig breeding, causing significant economic losses to the global swine industry. Our previous study found that the DNA sensing innate cGAS-STING signaling pathway plays an important role in inducing interferon (IFN) upon PRRSV infection and inhibition of PRRSV replication. However, the immune evasion and its mechanism by PRRSV is still unclear. In current study, we found that PRRSV Non-structural protein 5 (Nsp5) strongly inhibits the cGAS-STING-IFN antiviral response. Further, we found that Nsp5 interacts with STING, blocking STING transport from ER to Golgi apparatus and interfering with STING recruitment of TBK1/IKK{varepsilon}/IRF3. Finally, we demonstrated that the Nsp5 36-47 and 58-67 amino acid regions are critical regions for inhibiting STING activity and PRRSV replication. The study describes a novel mechanism by PRRSV for suppression of the host innate antiviral response and have implications for our understanding of PRRSV pathogenesis. Author summaryThe innate immune system is the first line of host defense against infections. PRRSV is an immunosuppressive pathogen that has developed different strategies to evade the host innate immune responses. Here, we demonstrate for the first time that PRRSV utilizes its non-structural protein Nsp5 to inhibit the innate immune cGAS-STING-IFN signaling pathway, promoting the viral replication. Nsp5 can inhibit the cGAS-STING pathway by interacting with STING, blocking STING transport from ER to Golgi apparatus and interfering with STING recruitment of TBK1/IKK{varepsilon}/IRF3. In particular, the 36-47 and 58-67 amino acid regions of the Nsp5 is indispensable. This information will help us understand PRRSV-host interactions and provides new insights into the prevention and control of PRRSV.

immunology↗

bioRxiv2025

SLICK: A Sandwich-LIke Culturing Kit for in situ Cryo-ET Sample Preparation

In situ cryo-electron tomography (cryo-ET) has recently been widely used in observing subcellular structures and macromolecules in their native states at high resolution. One of the reasons that it has not been more widely adopted by cell biologists and structural biologists is the difficulties in sample preparation. Here we present the Sandwich-LIke Culturing Kit (SLICK), simplifying the procedure and increasing the throughput for sample preparation for in situ cryo-ET (69 words).

biophysics↗

bioRxiv2025

Bacterial pathogen deploys iminosugar galactosyrin to manipulate plant glycobiology

The extracellular space (apoplast) of plants is an important molecular battleground during infection by many pathogens. We previously found that a plant-secreted {beta}-galactosidase BGAL1 acts in immunity by facilitating the release of immunogenic peptides from bacterial flagellin and that Pseudomonas syringae suppresses this enzyme by producing a small molecule inhibitor called galactosyrin. Here, we elucidated the structure and biosynthesis of galactosyrin and uncovered its multifunctional roles during infection. Structural elucidation by cryo-EM and chemical synthesis revealed that galactosyrin is an iminosugar featuring a unique geminal diol attached to the pyrrolidine moiety that mimics galactose binding to the {beta}-galactosidase active site. Galactosyrin biosynthesis branches off from purine biosynthesis and involves three enzymes of which the first is a reductase that is unique in iminosugar biosynthesis. Besides inhibiting BGAL1 to avoid detection, galactosyrin also changes the glycoproteome and metabolome of the apoplast. The manipulation of host glycobiology may be common to plant-associated bacteria that carry putative iminosugar biosynthesis clusters.

plant biology↗

bioRxiv2025

TLR4 Modulates Simvastatin's Impact on HDL Cholesterol and Glycemic Control

Statins lower cardiovascular risk by inhibiting HMG-CoA reductase, with their efficacy and adverse effects linked to SREBP-regulated cholesterol metabolism. Nutritional states-- particularly the alternating fasting and feeding cycles, rather than intrinsic circadian rhythms-- exert tight control over hepatic lipid metabolism via SREBP-mediated regulation of cholesterol and fatty acid synthesis. We exploited the natural diurnal oscillations in food intake in mice to determine how nutritional state modulates simvastatins metabolic actions. When administered during fasting, simvastatin impaired glucose homeostasis through SREBP-2-driven autophagy that enhanced PPAR activity, whereas simvastatin given during feeding reduced HDL cholesterol and improved glucose metabolism. Mechanistically, a feeding-induced surge in lipopolysaccharide (LPS) suppressed hepatic oxysterol synthesis, thereby enabling simvastatin to further deplete oxysterols and inhibit activation of LXR and SREBP-1c--key regulators of PPAR signaling under both postprandial and fasting conditions. This cascade resulted in decreased HDL cholesterol levels and improved glycemic control. Moreover, TLR4 deficiency disrupted the LPS-simvastatin interplay on LXR/SREBP-1c/PPAR signaling, reversing the HDL-lowering and glucose-improving effects seen during feeding. Together, these results identify feeding-induced LPS-TLR4 signaling as a critical determinant of simvastatins metabolic effects and suggest strategies to optimize statin therapy based on nutritional state.

pharmacology and toxicology↗

bioRxiv2025

DAZAP2 functions as a pan-coronavirus restriction factor by inhibiting viral entry and genomic replication

The SARS-CoV-2 pandemic and the emergence of novel variants underscore the need to understand host-virus interactions and identify host factors that restrict viral infection. Here, we perform a genome-wide CRISPR knockout screen to identify host restriction factors for SARS-CoV-2, revealing DAZAP2 as a potent antiviral gene. DAZAP2, previously implicated in SARS-CoV-2 restriction, is found to inhibit viral entry by blocking virion fusion with both endolysosomal and plasma membranes. Additionally, DAZAP2 suppresses genomic RNA replication without affecting the primary translation of viral replicases. We demonstrate that DAZAP2 functions as a pan-coronavirus restriction factor across four genera of coronaviruses. Importantly, knockout of DAZAP2 enhances SARS-CoV-2 infection in mouse models and in human primary airway epithelial cells, confirming its physiological relevance. Mechanistically, antiviral activity of DAZAP2 appears to be indirect, potentially through the regulation of host gene expression, as it primarily localizes to the nucleus. Our findings provide new insights into the host defense system against coronaviruses and highlight DAZAP2 as a potential target for host-directed antiviral therapies. IMPORTANCEDuring viral infection, the host defense response is mediated by a variety of host factors through distinct mechanisms that have yet to be fully elucidated. Although DAZAP2 was previously implicated in SARS-CoV-2 restriction, its mechanisms of action and in vivo relevance remain unclear. In this study, we identify the DAZAP2 as a potent pan-coronavirus restriction factor that inhibits viral infection through dual mechanisms: blocking virion fusion with both endolysosomal and plasma membranes, and suppressing genomic RNA replication. We confirm its physiological relevance in host defense using mouse models and primary cell cultures. This study advances our understanding of host-pathogen interactions. Targeting DAZAP2 or its regulatory pathways could provide a new approach to enhance host defense against current and future coronavirus threats.

microbiology↗

bioRxiv2025

Leopard Cats Occupied Human Settlements in China for 3,500 years before the Arrival of Domestic cats in 600-900 CE around the Tang Dynasty

The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.

evolutionary biology↗

bioRxiv2025

Self-Contemplating In-Context Learning Enhances T Cell Receptor Generation for Novel Epitopes

Computational design of T cell receptors (TCRs) that bind to epitopes holds the potential to revolutionize targeted immunotherapy. However, computational design of TCRs for novel epitopes is challenging due to the scarcity of training data, and the absence of known cognate TCRs for novel epitopes. In this study, we aim to generate high-quality cognate TCRs particularly for novel epitopes with no known cognate TCRs, a problem that remains under-explored in the field. We propose to incorporate in-context learning, successfully used with large language models to perform new generative tasks, to the task of TCR generation for novel epitopes. By providing cognate TCRs as additional context, we enhance the models ability to generate high-quality TCRs for novel epitopes. We first unlock the power of in-context learning by training a model to generate new TCRs based on both a target epitope and a small set of its cognate TCRs, so-called in-context training (ICT). We then self-generate its own TCR contexts based on a target epitope, as novel epitopes lack known binding TCRs, and use it as an inference prompt, referred to as self-contemplation prompting (SCP). Our experiments first demonstrate that aligning training and inference distribution by ICT is critical for effectively leveraging context TCRs. Subsequently, we show that providing context TCRs significantly improves TCR generation for novel epitopes. Furthermore, we show TCR generation using SCP-synthesized context TCRs achieves performance comparable to, and sometimes surpassing, ground-truth context TCRs, especially when combined with refined prompt selection based on binding affinity and authenticity metrics.

bioinformatics↗

bioRxiv2025

Run-and-tumble dynamics of E. coli is governed by its mechanical properties

The huge variety of microorganisms motivates fundamental studies of their behavior with a possibility to construct artificial mimics. A prominent example is the E. coli bacterium which employs several helical flagella to exhibit a motility pattern that alternates between run (directional swimming) and tumble (change in swimming direction) phases. We establish a detailed E. coli model, coupled to fluid flow described by the dissipative particle dynamics method, and investigate its run-and-tumble behavior. Different E. coli characteristics, including body geometry, flagella bending rigidity, the number of flagella and their arrangement at the body are considered. Experiments are also performed to directly compare with the model. Interestingly, in both simulations and experiments, the swimming velocity is nearly independent of the number of flagella. The rigidity of a hook (the short part of a flagellum which connects it directly to the motor), polymorphic transformation (spontaneous change in flagella helicity) of flagella, and their arrangement at the body surface strongly influence the run-and-tumble behavior. Mesoscale hydrodynamics simulations with the developed model help us better understand physical mechanisms which govern E. coli dynamics, yielding the run-and-tumble behavior that compares well with experimental observations. This model can further be used to explore the behavior of E. coli and other peritrichous bacteria in more complex realistic environments.

biophysics↗

bioRxiv2025

Behavior decoding delineates seizure microfeatures and associated sudden death risks in mice

Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral patterns that can inform seizure types, onset zones, and outcomes. However, refined analysis of behaviors in epilepsy remains challenging in both clinical and preclinical settings. Current manual video inspection approaches are subjective, time-consuming, and often focus on gross and ambiguous descriptions of seizure behaviors, overlooking much of the intricate behavioral dynamics and action kinematics. Here, we utilized two machine learning-aided tools, DeepLabCut (DLC) and Behavior Segmentation of Open Field in DLC (B-SOiD), to decode previously underexplored behavior and action domains of epilepsy. We identified 63 interpretable behavior groups during seizures in a population of 32 genetically diverse mouse strains. Analysis of these behavior groups demonstrates significant differential behavior expression and complexity that can delineate distinct seizure states, unravel intrinsic seizure progression over time, and inform mouse strain backgrounds and genotypes. We also identified seizure behavior patterns and action/subaction kinematics that determine the risks of sudden unexpected death in epilepsy (SUDEP). These findings underscore the significant potential for translation into inpatient settings for video analysis in epilepsy monitoring units and outpatient settings via home surveillance devices and smartphones. One Sentence SummaryAI-aided behavior decoding delineates intricate seizure microfeatures in mice.

neuroscience↗

bioRxiv2025

An explainable artificial intelligence framework reveals mutations associated with drug resistance in Mycobacterium tuberculosis

Understanding the mechanisms of drug resistance in Mycobacterium tuberculosis (MTB) is essential for the rapid detection of resistance and for guiding effective treatment, ultimately contributing to reducing the global burden of tuberculosis (TB). Under anti-TB drugs pressure, MTB continues to accumulate resistance loci. The current repertoire of known resistance-associated mutations requires further refinement, necessitating efficient methods for the timely identification of potential resistance sites. Here, we introduce xAI-MTBDR, an explainable artificial intelligence framework designed to identify new resistance-associated mutations and predict drug resistance in MTB. It outperforms state-of-the-art methods in predicting drug resistance for all first-line drugs, and scoring each mutations contribution to resistance. By leveraging public whole-genome sequencing data from nearly 40,000 MTB isolates, the framework identified 788 candidate resistance-related mutations and revealed 30 potential resistance markers, several of which are structurally located closer to their respective drugs compared to known resistance mutations. Furthermore, these scores enabled the framework to efficiently subgroup isolates with different resistance mechanisms and reflect varying levels of resistance. The framework serves as a valuable tool for accurate detection of drug-resistant MTB and offers new insights into its underlying mechanisms.

microbiology↗

bioRxiv2025

A Novel XACT lncRNA Transcript with Functions Transcending X-Chromosome Inactivation.

X-chromosome inactivation (XCI) is initiated during early mammalian embryogenesis by a long non-coding RNA (lncRNA) XIST, which coats one of the two X-chromosomes and facilitates epigenetic transcriptional silencing. A second, evolutionarily recent primate-specific lncRNA XACT was proposed to antagonize XISTs ability to induce XCI. XACT expression is restricted to pluripotent states and early embryonic stages and coats the active X-chromosome in both females and males. Here, we report a novel XACT transcript expressed in normal and cancerous somatic cells from both the inactive (Xi) and active (Xa) X chromosomes. It coexists with XIST on the Xi without affecting XIST expression or (re)activating X-linked genes inactivated by XCI. During hematopoietic stem cell (HSC) differentiation, XACT is primarily expressed in myeloid progenitors in both sexes. XACT expression is activated in HSCs and peaks in megakaryocyte-erythrocyte progenitor cells (MEPs) before rapidly declining as the MEPs differentiate into megakaryocytes or erythrocytes. By combining CRISPR-based XACT perturbation with epigenomic and transcriptional studies, we revealed the key role of XACT in the self-renewal and differentiation of erythroid progenitors into erythrocytes, by recruiting cis-regulatory proteins and regulating transcription through ETS and AP-1 transcription factors. Furthermore, XACT is expressed in a subset of acute myeloid leukemia (AML) patients, with high levels found in erythroid-megakaryocytic blast cells; suggesting XACTs potential as a marker for AML subtypes. Thus, we identified a novel XACT transcript with a unique expression profile and important roles in normal and cancer cells, revealing XACT lncRNA functions beyond its previously proposed role in XCI during embryogenesis.

genetics↗

bioRxiv2024

Systematic Benchmarking of High-Throughput Subcellular Spatial Transcriptomics Platforms

Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. To address this, we collected clinical samples from three cancer types - colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer - and generated serial tissue sections for systematic evaluation. Using these uniformly processed samples, we generated spatial transcriptomics data across five high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, Visium HD FF, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profiled proteins from adjacent tissue sections corresponding to all five platforms using CODEX and performed single-cell RNA sequencing on the same samples. Leveraging manual cell segmentation and detailed annotations, we systematically assessed each platforms performance across key metrics, including capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and transcript-protein alignment with adjacent CODEX. The uniformly generated, processed, and annotated multi-omics dataset is valuable for advancing computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download (http://spatch.pku-genomics.org/).

genomics↗

bioRxiv2024

Modest functional diversity decline and pronounced composition shifts of microbial communities in a uranium-contaminated aquifer

BackgroundMicrobial taxonomic diversity declines with increased environmental stress. Yet, few studies have explored whether phylogenetic and functional diversities track taxonomic diversity along the stress gradient. Here, we investigated bacterial communities within an aquifer in Oak Ridge, Tennessee, USA, which is characterized by a broad spectrum of stressors, including extremely high levels of nitrate, heavy metals like cadmium and chromium, radionuclides such as uranium, and extremely low pH (<3). ResultsBoth taxonomic and phylogenetic -diversities were reduced in the most impacted wells, while the decline in functional -diversity was modest and statistically insignificant, indicating a more robust buffering capacity to environmental stress. Differences in functional gene composition (i.e., functional {beta}-diversity) were pronounced in highly contaminated wells, while convergent functional gene composition was observed in uncontaminated wells. The relative abundances of most carbon degradation genes were decreased in contaminated wells, but genes associated with denitrification, adenylylsulfate reduction, and sulfite reduction were increased. Compared to taxonomic and phylogenetic compositions, environmental variables played a more significant role in shaping functional gene composition, suggesting that niche selection could be more closely related to microbial functionality than taxonomy. ConclusionsOverall, we demonstrated that despite a reduced taxonomic -diversity, microbial communities under stress maintained functionality underpinned by environmental selection.

microbiology↗

bioRxiv2024

14-3-3 binding maintains the Parkinson's associated kinase LRRK2 in an inactive state

Leucine-rich repeat kinase 2 (LRRK2) is a central player in cellular signaling and a significant contributor to Parkinsons disease (PD) pathogenesis. 14-3-3 proteins are essential regulators of LRRK2, modulating its activity. Here, we present the cryo- electron microscopy structure of the LRRK2:14-3-32 autoinhibitory complex, showing that a 14-3-3 dimer stabilizes an autoinhibited LRRK2 monomer by binding to key phosphorylation sites and the COR-A and COR-B subdomains within the Roc-COR GTPase domain of LRRK2. This interaction locks LRRK2 in an inactive conformation, restricting LRR domain mobility and preventing dimerization and oligomer formation. Our mutagenesis studies reveal that PD-associated mutations at the COR:14-3-3 interface and within the GTPase domain reduce 14-3-3 binding, diminishing its inhibitory effect on LRRK2. These findings provide a structural basis for understanding how LRRK2 likely remains dormant within cells, illuminate aspects of critical PD biomarkers, and suggest therapeutic strategies to enhance LRRK2-14-3-3 interactions to treat PD and related disorders.

biochemistry↗

bioRxiv2024

Proteome-wide reverse molecular docking reveals folic acid receptor as a mediator of PFAS-induced neurodevelopmental toxicity

Per- and polyfluoroalkyl substances (PFAS) are a class of long-lasting chemicals with widespread use and environmental persistence that have been increasingly studied for their detrimental impacts to human and animal health. Several major PFAS species are linked to neurodevelopmental toxicity. For example, epidemiological studies have associated prenatal exposure to perfluorooctanoate (PFOA) and perfluorononanoate (PFNA) with autism risk. However, the neurodevelopmental toxicities of major PFAS species have not been systematically evaluated in an animal model, and the molecular mechanisms underlying these toxicities have remained elusive. Using a high-throughput zebrafish social behavioral model, we screened six major PFAS species currently under regulation by the Environmental Protection Agency (EPA), including PFOA, PFNA, perfluorooctane sulfonate (PFOS), perfluorohexanesulfonic acid (PFHxS), perfluorobutane sulfonate (PFBS), and hexafluoropropylene oxide dimer acid ammonium salt (GenX). We found that embryonic exposure to PFNA, PFOA, and PFOS induced social deficits in zebrafish, recapitulating one of the hallmark behavioral deficits in autistic individuals. To uncover protein targets of the six EPA-regulated PFAS, we screened a virtual library containing predicted binding pockets of over 80% of the 3D human proteome through reverse molecular docking. The screen predicts that folate receptor beta (FR-{beta}, encoded by the gene FOLR2) interacts strongly with PFNA, PFOA, and PFOS but to a lesser degree with PFHxS, PFBS, and GenX, correlating positively with their in vivo toxicity. These predictions were validated through in silico molecular docking, in vitro protein binding analysis, and in vivo loss-of-function verification. Furthermore, embryonic co-exposure to folic acid effectively rescued social deficits induced by PFAS. The folate pathway has been implicated in autism, indicating a novel molecular mechanism for PFAS in autism etiology.

pharmacology and toxicology↗