Biology subjects

Li, W.

Publications and source records attributed to Li, W..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Organism-Environment Topological Interfaces Drive the Origination of Organismal Form

The origin of organismal forms is one of the most enduring unresolved challenges in evolutionary biology. While Darwinian theory and the Modern Synthesis explain adaptive evolution, they do not adequately account for the initial generation of core morphological architectures and rapid diversification events such as the Cambrian explosion. Here we establish that topological interfaces between organisms and their environment are the primary drivers of form origination. Starting from a spherical interface, topological transformations into closed disk or closed cylinder interfaces, governed by resource transport constraints and topological selection, determine the foundational forms of an organism, including shape, size, and complexity. This is supported by our simulations of homeostatic regulation under environmental stoichiometric fluctuations and geometrically allowed morphospace. Furthermore, closed cylinder interfaces generate a directional stoichiometric gradient, which provides a driving force for motion relative to the environment. Validated by empirical data cross kingdom and phylum, our theory yields qualitative and quantitative predictions for key morphological traits, including the species abundance distributions, origination of motility, body size scaling, and explosive diversification, which overcomes long-standing limitations of classical frameworks. This topological paradigm thus provides a unifying mechanism for the origin of biological form.

evolutionary biology↗

bioRxiv2026

Ancient IL-6-STAT5 signaling orchestrates inflammation in jawless vertebrates

Interleukin-6 (IL-6) is a central regulator of vertebrate immunity, yet its existence in jawless vertebrates has remained obscure because of extreme sequence divergence. The extant jawless vertebrates (lampreys and hagfish), which deploy variable lymphocyte receptors (VLRs) instead of immunoglobulins and T cell receptors, provide a unique window into the earliest interface between cytokines and adaptive immunity. Here, we identify IL-6-like genes in jawless vertebrates by combining computational structural comparisons, phylogenetic reconstruction, and conserved local and long-range synteny analyses. The encoded proteins adopt the canonical four-helix bundle characteristic of immune-related IL-6 family cytokines and align topologically with mammalian IL-6. In sea lamprey, three IL-6 paralogs show distinct leukocyte and tissue expression patterns and are differentially induced by pathogen-associated molecular patterns and skin injury, indicating early functional diversification of IL-6-mediated inflammatory responses. Stimulation of myeloid peritoneal leukocytes with recombinant lamprey IL-6 induces STAT5 phosphorylation and rapid upregulation of SOCS1/3 genes, consistent with an IL-6/STAT5/SOCS regulatory axis. These findings extend the repertoire of immune-related four-helix bundle cytokines to jawless vertebrates and indicate that IL-6-dependent inflammatory programs were already in place before the divergence of VLR-based and Ig/TCR-based adaptive immune systems in vertebrates.

immunology↗

bioRxiv2026

A rugged binding landscape unifies static and dynamic paradigms in protein-protein interactions

Predicting protein-protein interaction (PPI) affinities from structural data remains a challenge. Although binding funnel theory describes the formation of native complexes, the topography of the funnel bottom and its influence on affinity are often overlooked. Using two well-controlled nanobody-antigen datasets as model systems, we demonstrate that PPI can exist in both static and dynamic binding paradigms. These two nanobody series adopt nearly identical binding poses toward their respective antigens yet exhibit diverse affinities, each representing a distinct paradigm: a static paradigm in which affinity can be ranked by Rosetta scoring of co-crystal structures alone, and a dynamic paradigm in which affinity can only be ranked using molecular dynamics-sampled ensembles. The two paradigms differ in interfacial dynamics. In the dynamic series, the relative motion between binding partners ({Delta}RMSF) is temperature-sensitive, with optimal affinity correlation at 298 K. In the static series, {Delta}RMSF is minimal and insensitive to temperature. Local frustration analysis establishes a mechanistic bridge between interfacial dynamics and landscape topology. Dynamic interfaces exhibit increased frustration upon thermal sampling, facilitating sampling of functionally relevant microstates, whereas static interfaces show minimal frustration changes across temperatures. The temperature-dependent frustration difference mirrors {Delta}RMSF sensitivity, confirming local frustration as a determinant of interfacial dynamics. Furthermore, static interfaces are characterized by a higher density of canonical hotspot residues, while dynamic interfaces utilize interfacial ruggedness to modulate affinity. Together, these results demonstrate that conserved binding modes can encode different energy landscapes and provide a practical framework for determining when ensemble-based sampling is required for accurate affinity prediction.

biophysics↗

bioRxiv2026

Accurate detection of mosaic mutations at short tandem repeats from bulk sequencing data

Short tandem repeats (STRs) are among the most mutable regions of the human genome, yet their somatic mosaicism remains poorly characterized due to the technical challenges of distinguishing genuine mutations from high intrinsic polymorphism and sequencing noise. Here, we introduce BulkMonSTR, a computational framework that combines STR-specific error modelling with machine-learning classification to enable accurate detection of mosaic STR mutations from bulk next-generation sequencing data. BulkMonSTR identifies nucleotide-resolution mutations--including insertions, deletions, and single-nucleotide variants (SNVs)--and supports both control-independent and case-control study designs. Leveraging a comprehensive training dataset derived from pedigree-based validation and in silico spike-in simulations, our random forest classifier effectively discriminates true mosaic events from germline variants and technical artifacts. Benchmarking on simulated and real datasets demonstrates that BulkMonSTR achieves substantially improved precision and F1 scores across diverse coverages and variant allele frequencies. In normal samples, cancer samples and controlled in silico mixing experiments, BulkMonSTR consistently outperforms existing methods, capturing a broader spectrum of STR mutations--including those arising on non-reference alleles--while achieving high validation rates. By enabling systematic, genome-wide interrogation of STR mosaicism, BulkMonSTR provides a scalable foundation for investigating the contributions of somatic STR mutations to aging and disease.

bioinformatics↗

bioRxiv2026

Complete genome-derived metabolic interactions reveal impact of gut ecology on human health

Metabolic interactions govern gut microbiome assembly, yet functional rules remain obscured by genomic incompleteness and fragmentation. Here, we leverage 1,150 complete genomes to construct genome-scale metabolic models, demonstrating that draft genomes introduce systemic artifacts missing critical transport functions. We observe that metabolic interactions are intrinsically driven by genomic traits and niche specialization rather than random association. We stratifies strains into four ecological groups, including active players, resource predators, utilizers, and contributors, each of which exhibits distinct metabolite exchange/competition signatures. This functional architecture is strongly coupled to secondary metabolism. Applying this framework to inflammatory bowel disease revealed distinct temporal dynamics of ecological groups across disease subtypes, where group-specific dysbiosis predicted clinical phenotypes better than whole community profiles. Furthermore, integrating metabolic and co-occurrence networks to identify keystone for classifiers as features significantly improved disease diagnosis. Our study provides novel insights into gut metabolic interaction landscape, bridging the gap between genomic potential and clinical utility for precision microbiome therapeutics.

microbiology↗

bioRxiv2026

CombinGym: a benchmark platform for machine learning-assisted design of combinatorial protein variants

Combinatorial mutagenesis is essential for exploring protein sequence-function landscapes in engineering applications. However, while large-scale machine learning benchmarks exist for protein function prediction, they are primarily limited to single-mutant libraries, leaving a critical gap for combinatorial mutagenesis. Here we introduce CombinGym, a benchmarking platform featuring 14 curated combinatorial mutagenesis datasets spanning 9 proteins with diverse functional properties including binding affinity, fluorescence, and enzymatic activities. We evaluated nine machine learning algorithms from five methodological categories (alignment-based, protein language, structure-based, sequence-label, and substitution-based) across multiple prediction tasks, assessing both zero-shot and supervised learning performance using Spearmans {rho} and Normalized Discounted Cumulative Gain metrics. Our analysis reveals the substantial impact of measurement noise and data processing strategies on model performance. By implementing hierarchical dataset splits (0-vs-rest, 1-vs-rest, 2-vs-rest, and 3-vs-rest scenarios), we demonstrate the value of lower-order mutation data for empowering machine learning models to predict higher-order mutant properties. We validated this capacity through both in silico simulation (improving fluorescence brightness of an oxygen-independent fluorescent protein) and experimental validation (engineering enzyme substrate specificity), achieving a substantial increase in specific activity. All datasets, benchmarks, and metrics are available through an interactive website (https://www.combingym.org), facilitating collaborative dataset expansion and model development through integration with automated biofoundry platforms.

synthetic biology↗

bioRxiv2026

Complete Genomes of Cultivated Gut Bacteria Reveal Mobile Genetic Element-Driven Functional Diversity with Therapeutic Implications

Cultivated bacterial isolates are essential for elucidating gut microbiota functions, yet reliance on fragmented draft genomes and limited mobile genetic element (MGE) annotation has constrained high-resolution analyses. Here, we present the Cultivated Complete Genome Reference (CCGR), a comprehensive compendium of 1,150 fully circularized human gut bacterial genomes. Overcoming draft limitations, CCGR achieves chromosome-scale resolution and reveals distinct evolutionary strategies shaping gene topological organization in response to bacterial growth. Furthermore, we decode the dynamic landscape of MGEs, demonstrating that broad-host-range phages follow strict positional patterns by integrating at conserved chromosomal hotspots flanked by nutrient transporter loci. Importantly, we provide mechanistic in vivo evidence that accessory genomic elements drive strain-level functional heterogeneity. Specifically, the plasmid-encoded scrK gene in Levilactobacillus brevis involved in fructose catabolism is required to mitigate fructose-diet-exacerbated colitis. Collectively, CCGR establishes a framework linking chromosomal architecture and MGEs to bacterial evolution and host health, enabling precise, strain-resolved functional investigations.

microbiology↗

bioRxiv2026

Multimodal analysis of cell-free DNA identifies epigenetic biomarkers for amyotrophic lateral sclerosis diagnosis and progression

The role of the epigenome in age-related neurodegenerative disorders remains understudied. Here, we analyzed circulating cell-free DNA (cfDNA) from blood to detect methylation changes as a liquid-biopsy for Amyotrophic Lateral Sclerosis (ALS). Our study included 20 patients with sporadic ALS, 10 patients with C9orf72-associated ALS, 10 asymptomatic carriers of the C9orf72 repeat expansion mutation, and 21 non-disease controls. Following targeted enzymatic methyl-sequencing (EM-seq) of [~]4 million CpG sites, we detected numerous differentially methylated genes, including several implicated in ALS disease risk and pathogenesis. By integrating multiple epigenetic features, we delineated a distinct epigenetic signature, which achieved an average area under the curve (AUC) of 0.91 {+/-} 0.10 upon receiver operator characteristic (ROC) analysis, which enabled detection of [~]70% of ALS patients with close to 100% specificity. Furthermore, we also identified a set of genes whose methylation status significantly correlated with clinical disease progression and cerebrospinal fluid (CSF) neurofilament levels. Our results reveal the potential of cfDNA-based biomarkers to accurately diagnose ALS and potentially predict disease progression.

genetics↗

bioRxiv2026

Exploration of Orally Disintegrating Tablet for Sublingual Vaccination against Mucosal Bacterial Infection

Oral mucosal bacterial infections impose a substantial global disease burden, yet current clinical management typically reduces microbial load only transiently and rarely establishes durable protection at the oral surface. Analysis of 200 patients with periodontitis revealed that elevated levels of pathogen-specific salivary secretory Immunoglobulin A (sIgA) were strongly associated with reduced bacterial burden and improved clinical periodontal outcomes, identifying sIgA as a key determinant of effective oral protection. Guided by this observation, we developed a sublingual, orally disintegrating tablet vaccine (Capot) that incorporates bacterial extracellular vesicles providing a comprehensive repertoire of native antigens and multiple pathogen-associated molecular patterns, encapsulated within a calcium phosphate nanoshell to enable safe transmucosal delivery to submandibular lymph nodes. The rapidly disintegrating tablet format minimizes inadvertent swallowing and enhances local mucosal bioavailability. In mice and non-human primates, Capot induced robust and long-lasting salivary sIgA responses without overt oral mucosal or gastrointestinal inflammation and conferred strong protection against primary, recurrent, and antibiotic-resistant periodontitis. Together, these findings establish sublingual tablet vaccination as a practical strategy for selectively engaging oral mucosal immunity and preventing chronic bacterial diseases at oral mucosal surfaces.

bioengineering↗

bioRxiv2026

pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction

Predicting genetic perturbation responses at a single-cell level is central to building models for cell state and disease. However, existing approaches are limited on predicting phenotypic outcomes beyond expression changes and generalizing predictions across genome-scale perturbations in biologically relevant contexts. Here we introduce pertTF, a transformer-based single-cell genetic perturbation model. pertTF was trained from a unique dataset capturing single cell expressions profiles of 30 full gene knockouts across 14 relevant cell types during human pancreatic development and beta-cell differentiation. pertTF outperforms current methods in predicting expression changes of perturbing unseen genes in unseen cellular contexts. In addition, pertTF infers perturbation-induced shifts in cell identity and population composition, an important phenotypic outcome of perturbation in many physiology and disease settings. Through transfer learning, pertTF operates in physiologically relevant systems, including primary human islets, where large-scale perturbation experiments are challenging. The generalizability of pertTF is further demonstrated by in silico pooled and single-cell CRISPR screens, capturing critical regulators of stem cells and early pancreatic cell development. These results establish pertTF as a framework for integrating large-scale single-cell perturbation data with AI models to predict genetic perturbation effects across cellular systems and disease contexts.

bioinformatics↗

bioRxiv2026

AetherCell: A generative engine for virtual cell perturbation and in vivo drug discovery

Virtual cell modeling is currently hindered by a "data-utility paradox": biological information is fragmented between context-rich clinical RNA-seq and perturbation-dense experimental assays, leading to poor predictive generalization in human contexts. Here, we introduce AetherCell, a generative foundation model that unifies these disparate domains into a shared, platform-aligned transcriptomic manifold. By implementing a specificity-driven learning framework, AetherCell successfully recovers low-frequency, mechanism-specific signals often obscured by systematic noise. Across extensive benchmarks, AetherCell demonstrates robust generalization, accurately predicting responses to unseen compounds and genetic perturbations. We show that the model effectively translates signals from simple cell lines to complex 3D organoids, achieving high-fidelity, whole-transcriptome prediction. Building on this foundational manifold, we demonstrate precise drug response prediction across biological scales--including patient-derived organoids and clinical cohorts. We also implement a phenotype-knowledge mixture-of-experts strategy for precision drug repurposing. This approach is validated by the in vivo discovery of teriflunomide for dry eye disease and dabigatran for ulcerative colitis. Together, AetherCell establishes a scalable, human-centric virtual cell framework for translational biology and accelerated drug discovery.

bioinformatics↗

bioRxiv2026

Programming Biomolecular Interactions with All-Atom Generative Model

Biomolecular interactions lie at the core of cellular life, spanning diverse molecular modalities from small molecules to nucleic acids and proteins. Nevertheless, design strategies remain separated despite shared physicochemical principles of molecular recognition. Here we present AnewOmni, a unified generative framework trained on more than 5 million biomolecular complexes, that enables transferable molecular design across molecular scales by assembling chemically meaningful building blocks at atomic resolution. We further introduce programmable graph prompts to support user-defined chemical, topological, and geometric steering during generation, exploring hybrid and unconventional chemistries beyond canonical structures. We demonstrate that transferable learning of interaction patterns and physical constraints across molecular modalities is possible, via an atom-to-block latent space capturing both atomic details and structural priors. The framework successfully designed small molecules, peptides, and nanobodies targeting the challenging KRAS G12D switch II pocket, as well as orthosteric peptides and allosteric small-molecule inhibitors for PCSK9 in the absence of known binding site, achieving 23%-75% success with only low-throughput validation, bypassing modality-specific high-throughput screening. AnewOmni is the first to succeed in functional molecular design across all scales, from small chemical entities to large biologics, and represents a stepstone towards general molecular reasoning engines, advocating a generative foundation model for biomolecular interactions to enter regimes where data and human intuition remain limited.

bioengineering↗

bioRxiv2026

Learning the All-Atom Equilibrium Distribution of Biomolecular Interactions at Scale

Biomolecular functions are governed by dynamic conformational ensembles rather than static structures. While models like AlphaFold have revolutionized static structure prediction, accurately capturing the equilibrium distribution of all-atom biomolecular interactions remains a significant challenge due to the high computational cost of molecular dynamics (MD). We present AnewSampling, a transferable generative foundation framework designed for the high-fidelity sampling of all-atom equilibrium distributions, which is the first model to faithfully reproduce MD at the all-atom level. It uses a quotient-space generative framework to ensure mathematical consistency and leverages the largest self-curated database of protein-ligand trajectories to date, with over 15 million conformations. Statistically, AnewSampling consistently outperforms all prior generative methods on the ATLAS monomer benchmark, and the all-atom capabilities of AnewSampling enable close statistical alignment with ground-truth MD for evaluating atomic biomolecular interactions in protein-ligand dynamics. Furthermore, AnewSampling successfully recovers coupled ligand and side-chain motions in CDK2 systems, overcoming a major sampling hurdle inherent to conventional MD. AnewSampling enables rapid exploration of conformational landscapes prior to intensive simulations, elucidating fundamental biophysical mechanisms and accelerating the broader design of functional biomolecules.

bioinformatics↗

bioRxiv2026

Mapping vascular plasticity in liver fibrogenesis identifies novel fibrosis-associated endothelial cells in early-stage liver disease

Vascular plasticity is a crucial biological asset enabling our bodies to rapidly adapt to infections and acute inflammation. However, repeated insult during chronic disease can result in these vascular adaptations becoming irreversible, thereby driving disease progression and fibrosis. This study aimed to understand if phenotypic changes in endothelial cell (EC) identity could be indicative of progressive fibrosis and thereby offer new diagnostic and therapeutic opportunities for patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Previous research has documented that a significant shift in EC transcriptomic signature occurs during liver fibrosis in both pre-clinical models and patients. However, the protein expression profile, phenotype and functional role of these new EC subpopulations that are induced during fibrogenesis is unclear. In this study, we integrate high-resolution imaging, proteomic and transcriptomic analysis which collectively highlight a central role for endothelial-to-mesenchymal transition (EndMT)-induced EC plasticity in the derivation of fibrosis-associated EC (FAEC). We demonstrate that: 1) full spectrum flow cytometry can provide new opportunities to categorize and phenotype EC subpopulations, 2) two distinct EndMT-derived FAEC subpopulations expand during fibrogenesis; THY1.2+ICAM1+ and TAGLN+MCAM+ EC that display unique immunomodulatory and metabolic phenotypes, 3) TAGLN+ FAEC are a conserved, pro-fibrotic cell type arising at early stages of MASLD, and 4) increased hepatic expression of TAGLN is significantly associated with detrimental patient outcomes at all stages of liver disease. This study will pave the way for the development of FAEC-specific diagnostic and therapeutic approaches to tackle progressive fibrotic disease. One Sentence SummaryPhenotyping fibrosis-associated endothelial cells reveals pro-fibrotic and immunomodulatory subpopulations at early stages of liver disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/710459v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@dc91fforg.highwire.dtl.DTLVardef@f526d9org.highwire.dtl.DTLVardef@c9783borg.highwire.dtl.DTLVardef@f469e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2026

PRMT5-Mediated Arginine Methylation of HSP90AA1 Drives Esophageal Squamous Cell Carcinoma Progression

Dysregulated HSP90AA1 chaperone activity is a hallmark of multiple human cancers, yet its post-translational modifications in esophageal squamous cell carcinoma (ESCC) are poorly defined. Here, we identify a PRMT5-driven modification of HSP90AA1, symmetric dimethylation at arginine 182 (R182), as a pivotal switch that fuels ESCC progression. HSP90AA1 physically associates with PRMT5, and genetic or pharmacologic PRMT5 blockade diminishes HSP90AA1-R182 methylation and downstream oncogenic signaling. Functionally, HSP90AA1 loss suppresses ESCC cell proliferation, migration, and invasion; enforces cell cycle arrest and reverses epithelial-mesenchymal transition (EMT). Re-expression of wild-type HSP90AA1 rescues cancer cell growth and EMT in vitro and in vivo, whereas the methylation-deficient R182A mutant does not. Furthermore, clinically oriented analysis shows HSP90AA1 overexpression in ESCC tissues and indicates moderate prognostic utility. Moreover, dual targeting of the PRMT5/HSP90AA1 axis is therapeutically attractive, and combined PRMT5/HSP90AA1 inhibition acts synergistically to exert anti-tumor activity, markedly reducing clonogenicity, invasion, and tumor burden in both cell-derived and patient-derived xenograft models. These findings demonstrate that PRMT5/HSP90AA1 serve as mechanistic drivers and actionable biomarkers in ESCC and provide a rationale for biomarker-guided co-inhibition of PRMT5/HSP90AA1 in translational studies.

cancer biology↗

bioRxiv2026

Acute Degradation of Pumilio Proteins Uncovers a Biphasic Post-transcriptional Regulatory Hierarchy Controlling Embryonic Stem Cell Fate Decisions

Post-transcriptional regulation is critical for mammalian embryogenesis yet has been underexplored. We previously showed that RNA-binding Pumilio proteins (Pum1/2) are essential for early mouse embryogenesis and embryonic stem cell (ESC) functions. Here, using acute protein degradation systems combined with time-resolved RNA-seq and eCLIP, we delineate a two-phase regulatory hierarchy modulated by Pum1/2 in mouse ESCs. The first phase, occurring within 10 hours of Pum1/2 depletion, is predominantly the stabilization of over 100 Pum1/2-target mRNAs, while the second phase, occurring in subsequent 66 hours, propagates to over 1,000 mRNAs mostly through indirect regulatory effects. Functionally, Pum1/2 depletion delays transition from naive to formative pluripotency, impairs neuroectoderm differentiation, and enhances germline specification. Mechanistically, Pum1/2 directly repress mRNAs encoding PRC2 subunits, including Suz12, thereby constraining H3K27me3 deposition at neuroectodermal gene loci. These findings establish Pum1/2 as biphasic post-transcriptional regulators of pluripotency and lineage balance and link RNA stability control to chromatin-mediated silencing. HIGHLIGHTSO_LIAcute Pum1/2 degradation provides temporal resolution for profiling post-transcriptional regulation. C_LIO_LIPum1/2 destabilize more than 100 direct targets and modulate a biphasic network of over 1,000 mRNAs. C_LIO_LIPum1/2 loss delays pluripotency transition, suppresses neuroectoderm, and promotes germline fate. C_LIO_LIPum1/2 directly regulate Suz12 mRNA decay to modulate PRC2-mediated repression. C_LI

cell biology↗

bioRxiv2026

Role of desolvation on biomolecular liquid-liquid phase separation

Biomolecular condensates play essential roles in cellular organization and are implicated in diverse pathological processes. Their formation is driven by liquid-liquid phase separation (LLPS), a process that requires coordinated multistep desolvation of biomolecular chains and multivalent inter-chain interactions. Although coarse-grained (CG) models with implicit solvent are widely used to probe LLPS thermodynamics and kinetics, they typically neglect water-mediated desolvation effects, limiting their accuracy and mechanistic interpretability. Here, guided by all-atom simulations and experimental measurements, we develop a desolvation-aware implicit-solvent CG model by incorporating residue-level desolvation terms directly into the pairwise energy function and apply it to investigate LLPS of intrinsically disordered proteins. Incorporating these desolvation interactions reshapes the phase diagram, alleviating dense-phase overcompaction. Notably, we observe an approximately linear correlation between the temperature gap (simulation temperature relative to the critical point) and the extent of conformational expansion accompanying the dilute-to-dense phase transition, a result further supported by theoretical analysis. We also find that desolvation barriers slow early density-fluctuation growth and shorten transient kinetic arrest, whereas solvent-separated contact interactions exert the opposite effects. Both terms further modulate chain mobility within mature condensates through competing packing and energy-landscape effects. Together, this framework enables an efficient representation of desolvation in CG simulations and reveals how desolvation energetics shape both the thermodynamic landscape and kinetic properties of biomolecular LLPS.

biophysics↗

bioRxiv2026

Positive Affect Modulates Early Valuation and Conflict Processing in Social Decision-Making

Social decision-making relies on dynamic affect-cognition interactions across distributed brain networks, yet how incidental positive affect modulates these mechanisms at a millisecond timescale remains unclear. This study investigated the impact of music-induced positive emotion on the neural dynamics of decision-making in the Ultimatum Game. Fifty-six participants were assigned to either a happy music group or an active control (rain sound) group. Fifty-six participants were assigned to either a happy music group or an active control (rain sound) group, while electroencephalography was recorded to capture rapid neural dynamics. Behaviorally, happy music accelerated reaction times (RTs) and decoupled the ERP-RT correlations observed in the control condition. Neurally, positive affect amplified event-related potential amplitudes during early conflict detection (220-280 ms) and late valuation (520-560 ms) stages. Multivariate pattern analysis further revealed that happy music enhanced the neural separability and temporal stability of decision states (accept vs. reject). Moreover, using support vector regression based on functional network features, we found that decision acceptance rates were predicted with significantly higher accuracy in the happy music group (R = 0.60) compared to controls (R = 0.41). Crucially, feature weight analysis indicated a topological shift in decision strategy: while the control group relied on frontal-central edges (implicating executive control), the happy music group was characterized by central-temporal connections (suggesting integrative processing). Collectively, these findings provide novel evidence that incidental emotion intervenes at the millisecond timescale to bias social choices, offering a dynamic network-based account of the affect-cognition interaction.

neuroscience↗