Biology subjects

Li, Y.

Publications and source records attributed to Li, Y..

At least 379 records · Page 21Linked to original sources

bioRxiv2026

Single-cell transcriptomic atlas of mouse oocyte development from growth to ovulation

Oocyte growth and maturation depend on tightly coordinated programs within oocytes and their surrounding granulosa cells, yet defining the transcriptional continuum of growing oocytes has been challenging due to their large size and the limitations of current droplet-based and spatial transcriptomic platforms. Here, we optimized a high-density array-based platform called Stereo-cell for high-throughput dual-modality profiling of large mouse oocytes, allowing for both the preservation of morphology and transcript capture. By integrating unsupervised transcriptomic clustering with cell morphological features, we delineated successive temporal windows from growing oocytes to metaphase II and uncovered stage-linked shifts from early programs toward later programs. We also profiled the ovarian somatic fraction, reconstructed granulosa-cell subtype relationships, and placed ovarian cell states in tissue context using single-cell-resolution spatial data.

developmental biology↗

bioRxiv2026

Development of a genetically encoded fluorescent indicator for facilitating deorphanization of GPR52

GPR52 is an orphan G protein-coupled receptor implicated in psychiatric and neurodegenerative disorders, but its endogenous ligand remains unidentified, limiting the exploration of its physiological functions and therapeutic potential. We pioneered a novel methodology for orphan GPCR ligand discovery utilizing the GPCR-activation-based (GRAB) strategy by developing GPR52-1.0, a genetically encoded fluorescent sensor. GPR52-1.0 exhibits excellent membrane trafficking and high sensitivity in HEK293T cells, cultured neurons, and acute mouse brain slices. Notably, it detects neuronal activity-dependent endogenous ligand release in the striatum, with responses abolished by a specific antagonist. This sensor provides a powerful tool for identifying GPR52s endogenous ligand(s) and enables real-time monitoring of its activation. Our work lays the foundation for uncovering GPR52s physiological roles and supports future efforts to develop GPR52-targeted therapeutics.

neuroscience↗

bioRxiv2026

Benchmarking zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction

Reconstructing cellular trajectories from time-resolved single-cell transcriptomics is fundamental to understanding processes from embryonic development to cancer progression. While single-cell foundation models (scFMs) promise universal biological representations through large-scale pretraining, their capacity to capture the non-linear dynamics governing cell-fate decisions remains uncharacterized. Here we systematically benchmark multiple scFMs across challenging biomedical scenarios involving branching lineages and continuous state transitions. By coupling zero-shot scFM embeddings with dynamic optimal transport, we evaluated their performance against a traditional highly variable gene (HVG) baseline in backtracking progenitor states, interpolating transition intermediates, and extrapolating future fates. We find that zero-shot scFM embeddings underperform the HVG baseline across diverse biological systems, particularly in recovering the distributional complexity of unobserved cells. Mechanistic analysis reveals that current scFM architectures tend to over-compress subtle temporal signals, causing an artificial "linearization" of branched biological structures that may obscure critical divergence points in disease progression. Our findings suggest that while scFMs provide unified cell-state views, the HVG baseline remains more robust for trajectory inference, identifying a fundamental "temporal-compression" bottleneck that must be addressed to develop next-generation, dynamics-aware foundation models.

bioinformatics↗

bioRxiv2026

Recessive POPDC1 Truncation Causes Lethal Short-QT Pattern Arrhythmogenic Cardiomyopathy with Multi-Ion Channel Remodeling and Ankyrin-G Scaffold Disruption

AimsBiallelic variants in Popeye domain containing 1 (POPDC1) classically cause limb-girdle muscular dystrophy, but their impact on cardiac system remains unclear. We investigated the functional consequence of a POPDC1 frameshift variant (c.448delT), first identified in a consanguineous family with arrhythmogenic cardiomyopathy (ACM) and sudden death. Methods and resultsComprehensive clinical and genetic evaluation was followed by mechanistic studies in an orthologous Popdc1 knock-in rat model. Functional characterization included biotelemetry and programed electrical stimulation, optical mapping, patch clamp, intracellular Ca2+ imaging, proteomics, and oxidative stress assays. Homozygous mutants (Popdc1fs/fs) exhibited a shortened QTc interval and high incidence of ventricular tachycardia compared to wild-type (Popdc1+/+). Urethane anesthesia provoked second-degree AV block in all 10 Popdc1fs/fs rats but in only 1 of 9 Popdc1+/+ littermates. Optical mapping demonstrated abbreviated action potentials, slowed conduction velocity, and inducible polymorphic VT. Patch clamp confirmed accelerated repolarization, with upregulated transient outward potassium currents (Ito) and L-type calcium currents (ICa, L), but downregulated peak sodium currents (INa). Multi-omics and ultrastructural analyses revealed a post-translational collapse of intercalated disc: POPDC1 loss destabilized Ankyrin-G and its membrane-anchored cargo, Nav1.5, coinciding with enhanced Kv4.3 and Cav1.2 protein levels. These disruptions create a convergence of delayed conduction and shortened refractoriness, forming substrate for malignant re-entry. ConclusionWe defined a recessive short-QT ACM leading to potentially fatal arrhythmias caused by biallelic POPDC1 truncation driving Ankyrin-G disruption, manifesting as a triad: (1) bradycardia/AV block, (2) accelerated repolarization as short-QT pattern/diffuse T-wave flattening or inversion, and (3) progressive cardiomyopathy and sudden death. Whats new?O_LIWe define a novel diagnostic triad for recessive short-QT arrhythmogenic cardiomyopathy (rSQT-ACM) in a loss-of-function POPDC1 truncation: (1) early-onset bradyarrhythmia (sinus bradycardia or AV block); (2) a paradoxical short-QT interval with maladaptive rate response/diffuse T-wave flattening or inversion; and (3) progressive cardiomyopathy accompanied accompanied by with SCD. C_LIO_LIMechanistically, biallelic POPDC1 truncation induces collapse of the POPDC1-AnkG hub, which triggers a unique "opposing remodeling" phenotype (severe INa reduction coupled with paradoxical upregulation of Kv4.3-mediated Ito), and established a unified "slow-conduction, short-wavelength" substrate that underpins malignant re-entry. C_LIO_LIImplications for safety and device therapy: Nav1.5 deficiency indicates that sodium channel blockers may be contraindicated, and early ICD implantation may be preferable to bradycardia pacing. C_LIO_LIMechanism-directed treatments: POPDC1s recessive inheritance and small gene size make it a strong candidate for AAV-mediated gene supplementation aimed at restoring the intercalated disc architecture and ion-channel anchoring. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/710328v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1012d79org.highwire.dtl.DTLVardef@5073dcorg.highwire.dtl.DTLVardef@1e69bc2org.highwire.dtl.DTLVardef@fc15ec_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

genetics↗

bioRxiv2026

Identification of Dynamic Genetic Influences on DNA Methylation from Birth to Adulthood

Whether and how genetic regulation of DNA methylation (DNAm) change across the lifespan remains unclear. Here, we map age-dependent methylation quantitative trait loci (longitudinal mQTLs) using linear mixed models applied to repeated blood DNAm measures from birth, childhood, adolescence and adulthood in the Avon Longitudinal Study of Parents and Children. We identify 2,210 longitudinal mQTLs (2,393 SNP-CpG pairs; 7.3% trans) and observe consistent genotype-by-age effects in two independent cohorts of diverse ancestries (Pearsons r = 0.85 in the Generation R Study; r = 0.56 in the Drakenstein Child Health Study). Longitudinal mQTLs show increasing effects with age at half of loci and associations with multiple phenotypes. CpGs with longitudinal mQTLs are more heritable and enriched in regulatory elements and pathways related to multicellular organism development and cell adhesion. These results chart dynamic genetic influences on the human methylome and provide a novel perspective on epigenetic regulation.

genomics↗

bioRxiv2026

Establishment of a Long-Term Germ-Free Medaka Model Reveals Microbiota-Dependent Regulation of Growth, Immunity, and Metabolism

Germ-free (GF) animal models are indispensable for dissecting host-microbiota interactions and their roles in health and disease. The small teleost fish medaka (Oryzias latipes) provides unique advantages for establishing GF models across developmental stages, yet the functions of its intestinal microbiota and metabolites remain poorly characterized. Here, we developed both early-life and chronic GF medaka models to systematically characterize host biology in the absence of microbiota and evaluate the contribution of gut-derived metabolites to growth and immune development. Using a refined sterile feeding and verification protocol, we successfully maintained GF medaka for up to 57 days post-fertilization (dpf). As anticipated, GF fish displayed developmental delays, impaired organogenesis, reduced immune competence, and metabolic dysregulation. Supplementation with sterile gut-derived metabolites partially alleviated these deficits, as evidenced by enhanced locomotor activity and immune responses. Mechanistically, recovery was associated with improved ribosome biogenesis, tricarboxylic acid cycle activity, and histidine and pyruvate metabolism, suggesting enhanced protein synthesis and immune maturation. However, metabolite supplementation also elevated oxidative stress and inflammatory responses and failed to fully restore long-term survival or organ development. Our findings support the use of GF medaka as a versatile platform for investigating microbiota-host interactions across life stages. By integrating metabolite interventions, this model provides critical insights into the functional roles of gut microbiota and offers a valuable tool for advancing microbiome research in health and disease.

microbiology↗

bioRxiv2026

Platelets promote acute liver injury via extracellular vesicles-mediated Aldolase A

Platelets have emerged as active regulators of acute liver injury (ALI), yet the molecular mechanisms underlying their pathogenic functions remain poorly defined. Here, we demonstrate that platelets contribute to liver injury, in part, by metabolically reprogramming Kupffer cells (KCs). We show that platelets are actively recruited to the injured liver and communicate with KCs through extracellular vesicles (EVs), which deliver the glycolytic enzyme aldolase A (ALDOA) into recipient cells. This EV mediated transfer promotes glycolytic reprogramming in KCs, contributing to their proinflammatory activation and amplification of hepatic injury. Genetic ablation of Aldoa in platelets attenuated liver injury, demonstrating a pathogenic role for platelet derived ALDOA in vivo. Pharmacological inhibition of ALDOA with Aldometanib markedly attenuated liver injury. Circulating ALDOA levels were elevated in patients with ALI and correlated with disease severity. These findings uncover a platelet macrophage metabolic axis that drives ALI and nominate ALDOA as a therapeutic target and biomarker.

pathology↗

bioRxiv2026

MRPL47 as a Novel Mitochondrial Biomarker for Early Detection and Therapeutic Response in Ovarian Cancer

MRPL47 (Mitochondrial Ribosomal Protein Large Subunit 47) gene in chromosome 3q26 encodes a protein that is part of the large subunit of the mitochondrial ribosome. We observed that MRPL47 is frequently amplified and overexpressed in ovarian cancer samples. Importantly, increased expression of MRPL47 mRNA is associated with high levels of MRPL47 protein in ovarian cancer patients. High expression of MRPL47 is also associated with poor overall and recurrence free survival of ovarian cancer patients. Notably, MRPL47 improved metabolic fitness by enhancing cellular respiration, and glycolysis in cancer cells. Gene set enrichment analysis and target specific knockdown assays revealed that MYC transcription factor regulates MRPL47 expression. Furthermore, MRPL47 was identified very high in the plasma samples of ovarian cancer patients compared to those of healthy volunteers. MRPL47 was also associated with cisplatin resistance, whereas its expression predicted sensitivity to cisplatin therapy. Taken together, we demonstrated that MRPL47 can be used as a diagnostic biomarker for ovarian cancer and other cancers with 3q26 chromosomal amplification.

cancer biology↗

bioRxiv2026

SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data

Spatial transcriptomics data are largely available with RNA expression alone, limiting the detection of cell states defined by surface protein abundance. The lack of multi-omics spatial data limits the ability to identify immune cells and their signaling in the tumor microenvironment, as most solid tumors are immunologically poor and exhibit protein-RNA abundance discordance in critical immune cell surface markers. Although emerging technologies enable spatial multi-omics profiling, technical and cost constraints remain a hurdle. We introduce SR2P, a stacking-based machine-learning framework for predicting spatial protein abundance from RNA expression. SR2P integrates 11 complementary predictive models and consistently outperforms existing methods across multiple spatial multi-omics benchmark. We showcased an application of SR2P recovered macrophage-enriched regions and identified potential immune markers associated with therapeutic response from head-and-neck squamous cell carcinoma patients. SR2P enables protein-abundance inference from RNA-only spatial data, extending the analytical capabilities of current spatial platforms for studies of tumor immunology.

bioinformatics↗

bioRxiv2026

Telomere-to-telomere assembly and haplotype analysis of tetraploid Dendrobium officinale illuminate Orchidaceae polyploid evolution and mycorrhizal symbiosis genes

Dendrobium officinale is a typical epiphytic orchid. We report the telomere-to-telomere (T2T) genome assembly for D. officinale, representing the first T2T reference genome within the Orchidaceae family. The assembly is anchored to 19 chromosomes and contains 38 complete telomeres and 15 characterized centromeres. We further generated haplotype-resolved assemblies of the autotetraploid genome, identifying 12,761 sets of tetra-allelic genes. Based on synonymous substitution analysis, we inferred that the autotetraploidization event occurred approximately 0.86 million years ago. A systematic analysis of the SWEET gene family across the genus Dendrobium revealed that the gene family size is shaped primarily by epiphytic types and environmental factors. In D. officinale from Langshan, eight SWEET genes were specifically expressed in roots, suggesting they may play specialized roles in the root mycorrhizal system, potentially contributing to the D. officinales ability to recruit and maintain fungal partners. Together, these resources provide valuable foundations for studies of orchid evolution, functional genomics, and molecular breeding.

bioinformatics↗

bioRxiv2026

Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network

Network analysis of human brain connectivity provides a fundamental framework for identifying the neurobiological mechanisms that cause cognitive variations and neurological disorders. However, existing diagnostic models often treat structural connectivity (SC) as a fixed or optimal topological scaffold for functional connectivity (FC). This consequently overlooks the higher-order dependencies between brain regions that are critical for characterizing pathological alterations. Moreover, the distinct spatial organizations of SC and FC complicate their direct integration, as naive alignment methods may distort the inherent nonlinear patterns of brain connectivity. To address these limitations, we propose the Graph Diffusion Optimal Transport Network (GDOT-Net), which models disease-related topological evolution and achieves precise alignment between SC and FC. Unlike existing diffusion studies, the proposed model introduces an evolvable brain connectome modeling approach to infer the complex topological structure of brain networks, unveiling higher-order connectivity patterns linked to specific neuropsychiatric disorders. Furthermore, GDOT-Net incorporates a Pattern-Specific Alignment mechanism, leveraging optimal transport to align structural and functional topological representations in a geometry-aware manner. To capture nonlinear topological relationships between brain regions, a Neural Graph Aggregator Module was developed, which adaptively learns complex node interaction patterns in brain networks. By leveraging this module, GDOT-Net generates highly discriminative representations that form a robust basis for the precision diagnosis of brain disorders. Experiments on REST-meta-MDD and ADNI demonstrate that GDOT-Net surpasses SOTA methods in uncovering structural-functional misalignments and disorder-specific subnetworks. The source code is publicly available at this Link.

neuroscience↗

bioRxiv2026

Diffusion-ACP39: A Decoder-Adaptive Latent Diffusion Framework for Generative Anticancer Peptide Discovery

Cancer remains a major global health threat, with its incidence and mortality rates consistently rising in recent years. Anticancer peptides (ACPs) are short amino acid chains that can inhibit the growth or spread of cancer cells. Compared to traditional treatments, ACPs are a promising class of potential cancer therapies due to their multiple mechanisms, potential for combination cancer therapy, enhanced immune function, lower toxicity to normal tissues, fewer side effects, and less drug resistance. Although it is necessary to explore novel ACPs, traditional wet-lab methods for selecting them are labor-intensive, time-consuming, and expensive. To accelerate the discovery of novel ACPs, we proposed Diffusion-ACP39, a latent diffusion-based generative model with synchronized seed autoencoder for anticancer peptide design, capable of generating novel peptides with lengths ranging from 5 to 39 amino acids. Furthermore, we developed RF-ACP39, a random forest classifier model to assess the generative power of Diffusion-ACP39. Finally, Diffusion-ACP39 achieved an accuracy of 94.5% when generating 10,000 peptides with RF-ACP39. We also qualitatively analyzed the differences among true ACPs, random sequences, random peptides, and generated ACPs, demonstrating that the generated ACPs are most similar to true ACPs.

bioinformatics↗

bioRxiv2026

Compartmentalized cytokine networks and systemic immune remodeling in bovine mammary H5N1 infection.

Highly pathogenic avian influenza A H5N1 has recently expanded its mammalian host range; in 2024, genotype B3.13 emerged in U.S. dairy cattle with pronounced mammary tropism. In the past, Influenza A virus immunology has been characterized primarily in respiratory infection models, whereas this study delineates immune responses after intramammary infection. An intramammary H5N1 challenge in Jersey cows in the early dry-off period enabled integration of dose- and compartment-resolved (alveoli versus teat cistern) cytokine and chemokine profiles with peripheral leukocyte dynamics and H5/N1-specific antibody responses. Infection-induced quarter-restricted, monophasic inflammatory networks peaking at 3-7 days post-infection, coordinated peripheral myeloid expansion and IFN-{gamma}-competent lymphocyte activation, and rising antibody titers across quarters.

microbiology↗

bioRxiv2026

DPGT: A spark based high-performance joint variant calling tool for large cohort sequencing

BackgroundJoint variant calling is a crucial step in population-scale sequencing analysis. While population-scale sequencing is a powerful tool for genetic studies, achieving fast and accurate joint variant calling on large cohorts remains computationally challenging. FindingsTo meet this challenge, we developed Distributed Population Genetics Tool (DPGT), an efficient computing framework and a robust tool for joint variant calling on large cohorts based on Apache Spark. DPGT simplifies joint calling tasks for large cohorts with a single command on a local computer or a computing cluster, eliminating the need for users to create complex parallel workflows. We evaluated the performance of DPGT using 2,504 1000 Genomes Project (1KGP), 6 Genome in a Bottle (GIAB) and 9,158 internal whole genome sequencing (WGS) samples together with existing methods. As a result, DPGT produced results comparable in accuracy to existing methods, with less time and better scalability. ConclusionsDPGT is a fast, scalable, and accurate tool for joint variant calling. The source code is available under a GPLv3 license at https://github.com/BGI-flexlab/DPGT, implemented in Java and C++.

bioinformatics↗

bioRxiv2026

Rewiring of 3D Enhancer-Promoter Interactome Underlies Diabetic Endothelial Dysfunction

BackgroundDiabetic vascular complications are driven by endothelial dysfunction, yet the role of 3D genome organization in this process is unknown. We sought to define the alterations in chromatin architecture in diabetic endothelium and identify the key regulators involved. MethodsWe generated a high-resolution 3D epigenomic atlas of diabetic endothelial cells from mouse models and human subjects using H3K27ac HiChIP, complemented by ChIP-seq, ATAC-seq, and RNA-seq. A human cohort was used to assess protein expression in diabetic versus non-diabetic endothelial cells. To identify JUNB-interacting proteins, we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME), with protein-protein interaction validated by co-immunoprecipitation. Functional validation was performed using in vitro, ex vivo, and in vivo approaches, including endothelial-specific knockdown in a diabetic hindlimb ischemia model. ResultsMulti-omics profiling revealed extensive enhancer reprogramming in diabetic endothelium, with AP-1 binding motifs being consistently and selectively enriched in downregulated enhancers across three distinct diabetic models. Analysis of a human cohort confirmed significantly reduced JUNB protein levels in diabetic endothelial cells. We identified widespread disruption of JUNB-anchored enhancer-promoter interactions, which underlies transcriptional repression of key endothelial genes. RIME and co-immunoprecipitation established the E3 ubiquitin ligase RBBP6 as a direct JUNB interactor that promotes its polyubiquitination and proteasomal degradation in response to hyperglycemia. Human cohort analysis further showed reciprocal elevation of RBBP6 in diabetic endothelial cells. Either JUNB overexpression or RBBP6 knockdown restored enhancer-promoter connectivity, reactivated vasoprotective transcriptional programs, and rescued endothelial function. Critically, endothelial-specific knockdown of Rbbp6 in diabetic mice restored endothelium-dependent vasorelaxation and improved perfusion recovery after hindlimb ischemia, independent of systemic glucose levels. ConclusionsOur study unveils a novel mechanism whereby hyperglycemia induces enhancer reprogramming and disrupts endothelial 3D genome architecture through RBBP6-mediated degradation of JUNB. The RBBP6-JUNB axis is established as a crucial link between metabolic stress and epigenomic reprogramming in vascular disease, presenting a promising therapeutic target for diabetic vasculopathy.

genomics↗

bioRxiv2026

Metabolic compensation via gluconeogenesis explains the non-essentiality of glycogen phosphorylase as an insecticidal target in Plutella xylostella

Glycogen phosphorylase (GP) catalyzes the rate-limiting step of glycogenolysis and occupies a central position in insect carbohydrate metabolism, supplying precursors for chitin biosynthesis. Acyl urea compounds structurally related to benzoylphenylurea (BPU) insecticides are potent inhibitors of mammalian GP, raising the question of whether insect GP could serve as an independent insecticidal target. Here, we systematically evaluate this possibility in the diamondback moth Plutella xylostella. We show that a mammalian GP inhibitor (GPI) potently inhibits recombinant PxGP (IC50 = 2.96 nM), while the BPU insecticide diflubenzuron (DFB) does not. Molecular docking and MM/GBSA analysis predict that this selectivity reflects differential side-chain engagement: GPI is predicted to occupy the allosteric site at the dimer interface via contacts with seven residues spanning both subunits ({Delta}G = -34.63 kcal/mol), whereas DFB's difluorobenzoyl moiety is predicted to remain solvent exposed without productive protein contacts ({Delta}G = -29.29 kcal/mol). Despite potent in vitro inhibition, GPI exhibits no insecticidal activity, and RNAi-mediated knockdown of PxGP (confirmed by enzyme activity measurements showing ~29% reduction in per-larva GP-a activity) does not impair development or survival. We provide evidence that insects compensate through a multi-layered metabolic response: upregulation of gluconeogenic enzymes (PEPCK, G-6-Pase), selective upregulation of glycogen branching enzyme (GBE) but not -amylase, and changes in protein content suggestive of catabolic substrate mobilization. Fitness assessment reveals transient larval weight loss (24-48 h) with complete recovery of pupal weight, adult wing morphology, and female fecundity, indicating that the metabolic compensation response is ultimately effective in sustaining development. These findings suggest that GP is functionally non-essential under these conditions, likely through a combination of gluconeogenic compensation and the availability of alternative carbon sources, and highlight the importance of considering metabolic network redundancy in target-based insecticide design.

biochemistry↗

bioRxiv2026

Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles

AO_SCPLOWBSTRACTC_SCPLOWGenerative models have recently advanced de novo protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks, where pretraining can be a solution; (2) Current pretraining methods mostly rely on local, non-rigid atomic representations for property prediction downstream tasks, limiting global geometric understanding for protein generation tasks; and (3) Existing approaches have yet to effectively model the rich dynamic and conformational information of protein structures. To overcome these issues, we introduce RigidSSL (Rigidity-Aware Self-Supervised Learning), a geometric pretraining framework that front-loads geometry learning prior to generative finetuning. Phase I (RigidSSL-Perturb) learns geometric priors from 432K structures from the AlphaFold Protein Structure Database with simulated perturbations. Phase II (RigidSSL-MD) refines these representations on 1.3K molecular dynamics trajectories to capture physically realistic transitions. Underpinning both phases is a bi-directional, rigidity-aware flow matching objective that jointly optimizes translational and rotational dynamics to maximize mutual information between conformations. Empirically, RigidSSL variants improve designability by up to 43% while enhancing novelty and diversity in unconditional generation. Furthermore, RigidSSL-Perturb improves the success rate by 5.8% in zero-shot motif scaffolding and RigidSSL-MD captures more biophysically realistic conformational ensembles in G protein-coupled receptor modeling. The code is available on this repository.

bioinformatics↗

bioRxiv2026

Dissociable frequency regimes in human temporal cortex integrate facial and acoustic cues during natural speech

Face-to-face communication relies on the seamless integration of visual and acoustic cues, yet the spatiotemporal principles governing how the human brain dynamically represents and combines these multisensory streams remain largely unresolved. To address this, we recorded high-density electrocorticography (ECoG) from eight participants perceiving matched audiovisual, audio-only, and video-only continuous natural Mandarin speech. Using time-frequency-resolved encoding models, we reveal complementary, frequency-dependent integration regimes across the temporal lobe. We show that the superior temporal gyrus (STG) implements a feature-selective, auditory-dominant strategy, utilizing visual input to selectively strengthen low-frequency representations of lip-reading kinematics. Conversely, the middle temporal gyrus (MTG) acts as a higher-order multisensory hub, employing a frequency-selective strategy to broadly integrate diverse facial and articulatory features. Crucially, we demonstrate that access to visual information during perception significantly improves the acoustic and lexical accuracy of neural speech decoding and re-synthesis, with the MTG driving the largest gains in linguistic intelligibility. These findings uncover the dissociable neural architectures supporting robust multisensory perception, providing critical mechanistic insights for the development of next-generation, multimodal brain-computer interfaces.

neuroscience↗