Biology subjects

Song, J.

Publications and source records attributed to Song, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Exercise engages a mechanically activated astrocyte state linking muscle activity to hippocampal plasticity

Physical exercise promotes brain health in part through muscle-derived factors that enter the brain, but how peripheral signals are translated into neural responses remain unclear. Here, we identify astrocyte contraction as a previously unrecognized physiological response to exercise signals that may contribute to adult hippocampal neurogenesis. In vivo, voluntary running rapidly induced nuclear localization of the mechanically sensitive transcriptional regulator Yes-associated protein (YAP), and increased non-muscle myosin II phosphorylation in hilar astrocytes in mice, consistent with acute contraction. Using an in vitro platform with ultrasensitive force sensors, we found that factors released by contracting skeletal muscles activated astrocytes which in turn increased contraction that was necessary and sufficient for their proliferation and expansion. Activated astrocytes subsequently released soluble factors that modulated neuronal network tension and promoted immature neuron abundance. These findings identify astrocyte contractility as a physiological transducer of exercise-derived muscle signals and establish cellular force generation as a potential mechanism regulating neuroplasticity.

neuroscience↗

bioRxiv2026

A Synthetic Microbial Therapy Rewires Antitumor Immunity Across Multiple Cancer Types

Microbial immunotherapies show promise against cancer, yet broad efficacy and mechanistic insight remain elusive. Here, we introduce SPIKE 1.0 (S1.0), a metabolically engineered bacterium that converts tryptophan into immunomodulatory hydroxyindoles via tryptophan monooxygenase to remodel the tumor microenvironment (TME). A single systemic dose of S1.0 elicited potent, durable antitumor responses across multiple murine models, including humanized mice, with minimal toxicity. S1.0 enhanced inflammatory signaling, activated innate and adaptive immunity, and promoted T cell persistence, memory, and resistance to exhaustion. It outperformed checkpoint inhibitors and synergized with chemotherapy. Multi-omics profiling revealed that S1.0 rewired amino acid metabolism in tumor-infiltrating immune cells and disrupted immunosuppressive networks. These results establish S1.0 as a scalable, cost-effective microbial immunotherapy with broad translational potential for solid tumors.

immunology↗

bioRxiv2026

The novel HP1a interactor Clump/CG30403 safeguards transposon silencing and reproductive resilience in the Drosophila ovary

Heterochromatin and its core effectors from the Heterochromatin Protein 1 (HP1) family are essential for epigenetic silencing of repetitive regions and genome integrity from yeast to humans. However, how HP1s and associated factors regulate heterochromatin properties in vivo to sustain silencing across development, aging, and environmental factors, remains incompletely understood. Here, we identify Clump/CG30403 -- a previously uncharacterized MADF-BESS domain protein -- as a novel heterochromatin factor required for robust transposon silencing during Drosophila oogenesis and sustained fertility with age and temperature stress. Clump/CG30403 interacts with the main HP1-family silencing effector Su(var)205/HP1a through a noncanonical binding motif within its large intrinsically disordered region. Notably, in the absence of Clump/CG30403, HP1a mobility and silencing capacity are compromised despite largely unperturbed genomic distribution, showing that HP1a presence alone is insufficient for repression. We also show that Clump/CG30403 uniquely accumulates at its own promoter to self-repress and prevent ectopic aggregation, revealing a feedback mechanism to constrain protein dosage and phase behavior. Overall, we propose that Clump/CG30403 is a HP1 corepressor that acts as a tightly calibrated safeguard of the heterochromatin environment properties to ensure stable silencing, genome integrity, and persistent reproductive function.

molecular biology↗

bioRxiv2026

Structures of the human sodium-citrate cotransporter NaCT with and without substrates

The human sodium-citrate cotransporter NaCT imports various tri- and dicarboxylates into the cell as TCA cycle intermediates. This substrate uptake process is driven by an inward sodium gradient. The protein is a member of the Divalent Anion-Sodium Symporter (DASS) family. Whereas extensive biochemical and structural studies have been carried out for NaCT, how the substrate binding and translocation is coupled to the sodium gradient remains unclear. Here using single particle cryo-electron microscopy, we determined the structures of the human NaCT protein in three states: sodium-free, in the presence of sodium, and sodium- and substrate-bound. These structures suggest a simultaneous binding mechanism for sodium-substrate coupling, distinct from the sequential binding, conformational selection mechanism previously observed for the bacterial DASS protein VcINDY.

biophysics↗

bioRxiv2026

Unearthing a fungal giant: Dianjunaceae fam. nov., a novel Paleocene lineage of Xylariales harbouring Dianjunus rex gen. et sp. nov.

During mycological surveys in Yunnan Province, China, specimens of a fungus producing massive, upright stromata up to 50 cm high and individually 2.2 Kg in weight were sampled. Through an integrative taxonomic approach combining detailed morphology, multilocus phylogeny (ITS, LSU, RPB2, TUB2), and phylogenomic analyses, this fungus is proposed as the new species Dianjunus rex gen. et sp. nov., the type of the new family Dianjunaceae (Xylariales). Phylogenetic analyses robustly place Dianjunaceae as a distinct sister clade to Graphostromataceae. Divergence time estimation dates the origin of this family to the early Paleocene ([~]65 Mya), coinciding with the post-K-Pg extinction period, when an estimated 75% of all plant and animal species went extinct, and a significant ecological reorganization of life on earth happened. The stromata of D. rex represent the largest fructifications documented within the Ascomycota, significantly expanding the known morphological range of the Xylariales. The study provides a comprehensive description, including a nodulisporium-like anamorph with periconiella-like branching patterns, and discusses the taxons phylogenetic placement, and distinctive morphology. This discovery highlights the unexplored fungal diversity in East Asian forests.

microbiology↗

bioRxiv2026

Cryo-EM structures of Arabidopsis PRC2 histone methyltransferase isoforms reveal a differential regulatory mechanism

Polycomb Repressive Complex 2 (PRC2) is a histone H3K27 methyltransferase that represses gene expression. Arabidopsis thaliana (A. thaliana) has several different PRC2 isoforms that are functionally distinct during the life cycle of the plants. However, their biochemical and structural characteristics have not been investigated. Here, we biochemically characterized PRC2 isoforms having different catalytic subunits: SWNINGER (SWN; PRC2SWN) and CURLY LEAF (CLF; PRC2CLF). Interestingly, PRC2SWN showed much lower activity than PRC2CLF. In addition, PRC2SWN methylates histone H3K27 in mono and di-methylation, while PRC2CLF shows robust tri-methylase activity. We also determined the cryo-electron microscopy (cryo-EM) structures of PRC2SWN and PRC2CLF, revealing that the substrate binding pocket of the SWN SET domain is blocked by a loop in the pre-SET domain, functioning as an auto-inhibitory loop, while that of the CLF SET domain is freely accessible. Introduction of CLF-like mutations in the auto-inhibitory loop in SWN enhances PRC2SWN activity. Furthermore, structure-guided in planta analysis shows that a CLF-mimetic SWN mutant rescues the CLF knockout phenotype. Our work provides structural and molecular insights into the isoform-specific regulatory mechanism of plant PRC2.

Plant Biology↗

bioRxiv2026

Generative design of antigen-specific T-cell receptor sequences with a conditional diffusion model

T cell receptor (TCR)-based immunotherapy holds immense potential for treating cancers, autoimmunity, and infectious diseases, where antigen-specific TCR recognition is crucial for adaptive immune responses. Engineering or de novo generation of the complementarity-determining region 3 (CDR3) loops of TCRs using artificial intelligence offers a powerful alternative to designing antigen-specific TCRs rather than laborious experimental screening. However, current in silico approaches are constrained by weak conditional guidance, limited flexibility, and a lack of rigorous functional validation. To address these limitations, we introduce TCRDiff, a generative diffusion framework for designing antigen-specific TCRs conditioned on peptide-MHC (pMHC) targets and germline-encoded TCR variable genes. By leveraging pre-trained knowledge from massive T-cell repertoires and TCR-pMHC recognition data, TCRDiff generates CDR3{beta} sequences that closely resemble native-binding TCRs via a denoising diffusion process. Furthermore, incorporating interface geometry features generated TCR-pMHC complexes with superior structural plausibility than models relying solely on sequence-based diffusion or structure-based modeling. As a proof of concept, we deployed TCRDiff in a systematic pipeline to design candidate TCRs against a clinically validated cancer antigen. In vitro activation assays validated that TCRDiff-generated TCRs efficiently recognize the MAGE-A3 epitope with minimal off-target reactivity. Thus, TCRDiff establishes a powerful, validated computational paradigm to accelerate the development of TCR-based immunotherapies.

bioinformatics↗

bioRxiv2026

SRPK3 regulates back-splicing of pre-MYOCD through Phosphorylating SRSF10 to modulate cardiac hypertrophy

Cardiac hypertrophy represents a hallmark of pathological remodeling in heart failure, involving complex molecular networks that remain incompletely defined. While post-transcriptional regulation is increasingly recognized as a key control layer, the role of splicing kinases in this process requires further elucidation. This study identifies serine/arginine-rich protein kinase 3 (SRPK3) as a central regulator of myocardial hypertrophy through its control of circular RNA (circRNA) biogenesis. We observed marked SRPK3 upregulation in phenylephrine/angiotensin II-treated cardiomyocytes, transverse aortic constriction (TAC) rats, and human heart failure datasets. Gain- and loss-of-function studies demonstrated that SRPK3 overexpression exacerbates hypertrophic phenotypes, including increased cell surface area and upregulation of markers (ANP, BNP, Myh7). Conversely, siRNA knockdown or pharmacological inhibition with the pan-SRPK inhibitor MSC-1186 effectively attenuated these pathological features. Mechanistically, SRPK3 interacts with and phosphorylates the splicing factor SRSF10 at specific serine residues (S129, S131, and S133). This phosphorylation event inhibits the back-splicing of pre-MYOCD, resulting in reduced cardioprotective circ-MYOCD levels and a concomitant increase in linear MYOCD expression. The depletion of circ-MYOCD relieves its competitive inhibition on linear splicing, driving the transcriptional activation of pro-hypertrophic signaling pathways. Crucially, restoring circ-MYOCD expression reversed SRPK3-driven pathology, confirming its protective role. These findings establish the SRPK3-SRSF10-circ-MYOCD axis as a critical regulatory circuit in cardiac remodeling. By bridging kinase activity with splicing dynamics, this study highlights RNA processing as a vital therapeutic target. Targeting this axis via SRPK3 inhibition or circ-MYOCD delivery represents a promising dual-modality strategy for treating heart failure.

Molecular Biology↗

bioRxiv2026

The molecular asynchrony of single cells

Integrating single-cell transcriptomic and epigenomic data provides a robust framework for investigating gene regulation mechanisms. Existing analyses typically treat these modalities as synchronized features that can be translated in a static manner; however, the temporal delays that underpin cellular kinetics are intrinsic to dynamic biological systems. To address this limitation, we propose utilizing the "molecular asynchrony" within regulatory hierarchies to determine the thermodynamic properties of individual cells. Here, we present SeqTag, a single-cell multiomics sequencing method that simultaneously profiles the transcriptome, chromatin accessibility, and histone modifications, supported by an analytical framework to identify asynchronous states across regulatory layers for characterization of single-cell kinetics. By measuring the epigenetic priming potential and remodeling rates during adult mouse oligodendrogenesis, we delineated a sequential program for bivalency resolution as maturing cells traverse Waddingtons landscape. This process becomes increasingly decoupled with age, a change linked to a drift in progenitor cell-fate probabilities. By identifying entropy-driving regulatory elements, we characterized the aging-related decline in cell identity across various cell types and proposed a dynamic model linking static genetic variants to the risk of late-onset diseases. In summary, our integrated approach established a unified framework for employing multimodal single-cell genomics to model the kinetics of complex cellular processes.

genomics↗

bioRxiv2026

An AI-agent-orchestrated grey-box Transformer framework for sparse pharmacokinetic curve reconstruction and pharmacometric model initialization

Clinical pharmacokinetic (PK) modelling is constrained by sparse sampling, limited general-isability of single-drug models, and labour-intensive workflows, making it difficult to infer complete drug exposure from limited concentration observations. We present the Pharmacokinetic Foundation Model (PKFM), a grey-box Transformer framework pre-trained across 32 drugs that reconstructs concentration-time profiles from sparse concentration observations, dosing events, molecular descriptors, and physiological covariates while preserving output interpretability. In representative oral PK curves, three sparse input points recovered the principal absorption-elimination trajectory, achieving coefficient of determination (R2) = 0.992 for Midazolam oral and R2 = 0.990 for Verapamil oral. Using reconstructed curves in NONMEM (nonlinear mixed-effects modelling) improved covariance stability and individual prediction accuracy. Contrastive-learning embeddings supported Top-10 physiologically based pharmacokinetic (PBPK) candidate retrieval, with 75.6% of observations within the 2-fold range. A pharmacometrics-informed AI Agent (PM Agent) outperformed general-purpose programming tools in stability and pairwise win rate on a standardised modelling benchmark, with each run requiring human pharmaco-metrician confirmation before downstream use. These results support cross-drug pre-trained PK models as an information-completion layer for sparse PK evidence and a structured scaffold for the modelling workflow; clinical or regulatory use requires prospective validation, broader external benchmarking, and independent expert assessment.

pharmacology and toxicology↗

bioRxiv2026

Dissecting the regulatory and genomic drivers of the dichogamy determining G-locus in pecan

Many hermaphroditic species increase outcrossing rates by partitioning reproduction so that male and female organs mature at different times, a phenomenon known as dichogamy. Previous work has documented that dichogamy in pecan trees is governed by the Mendelian super-gene "G-locus"; however, its non-recombinant sex chromosome-like architecture has impeded quantitative genetic exploration and candidate gene discovery. Here, we probe the genetic drivers of the G-locus through a pangenome-integrated quantitative genomics experiment. We first provide one of the clearest examples to date of mapping bias, where a linear reference-based GWAS discovered 66 off-target peaks while mapping with a pangenome graph reference resolved the known single Mendelian locus. Across six new genome assemblies, the fully haplotype phased G-locus QTL spanned 223-491kb and included 25 candidate gene families. The strongest candidate gene encoded a MATE efflux protein and had dominant allele-specific action during male flower developmental stages. Combined, these candidates and genomic resources provide a powerful foundation for breeding and optimal dichogamy phenotype engineering for future pecan orchards.

genomics↗

bioRxiv2026

spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics Analysis

Spatial transcriptomics and proteomics map tissue architecture and cellular interactions, but analysis remains limited by programming demands and text-centered AI agents that lack viewer grounding and cross-turn context. We present spatiAlytica, a viewer-centric multimodal interactive agentic system embedded in the Napari viewer that enables non-programmer biologists to perform iterative, hypothesis-driven spatial omics analysis via natural language. spatiAlytica couples viewer-state serialization, agentic memory, biological concept-to-data-field mapping, code generation and debugging, Spatial VQA, and grounded interpretation to support an exploratory analysis and interpretive reasoning workflow. We introduce spatiAlyticaBench, a comprehensive benchmark spanning 222 single-turn spatial analytical coding questions, 178 multi-turn sequential workflow questions, and 7,350 image-grounded reasoning questions. spatiAlytica outperformed strong agentic baselines, while using less time and tokens. Case studies across Kaposis sarcoma, colorectal cancer, and ovarian cancer recapitulated known spatial patterns and uncovered progressive CD8 T-cell dysfunction during KS progression.

bioinformatics↗

bioRxiv2026

Dual Pathways of Extracellular ATP Action in Cancer Cells: Purinergic Signaling Driven Senescence and Macropinocytic ATP Internalization

BackgroundOpportunistic nutrient uptake is a hallmark of cancer metabolism. Cancer cells upregulate macropinocytosis to acquire extracellular nutrients to support growth and stress adaptation. We previously showed that extracellular ATP (eATP) is internalized by macropinocytosis and promotes multiple cancer phenotypes. Here, we tested whether eATP uptake is prevalent across cancers and whether eATP also induces senescence through purinergic receptor (PR) signaling. MethodsIntracellular ATP (iATP) levels were measured following eATP exposure across multiple cancer cell lines. eATP internalization was visualized in vitro and in vivo using a non-hydrolyzable fluorescent ATP analog together with high-molecular-weight dextran as a macropinocytosis marker. Senescence was quantified using three SA-{beta}-galactosidase assays and flow cytometry. Pharmacologic inhibitors of macropinocytosis and purinergic receptors were used to define pathway dependence. Combination treatments with the glucose transporter inhibitor DRB18 and the senolytic navitoclax were evaluated for antiproliferative effects. ResultseATP produced dose- and time-dependent increases in iATP across diverse cancer cell types. Imaging demonstrated widespread macropinocytic internalization of ATP in vitro and in tumor xenografts. eATP induced senescence in NSCLC cells, confirmed by multiple {beta}-gal assays and flow cytometry. PR inhibition significantly reduced senescence, whereas macropinocytosis inhibition had minimal effect on senescence induction. ConclusionseATP acts through dual pathways in cancer cells: macropinocytic internalization that elevates iATP and PR signaling that drives senescence. Targeting metabolic uptake together with senolytic therapy may offer a novel anticancer strategy.

cancer biology↗

bioRxiv2026

Linked origins but distinct roles for extreme length and sequence variation at a tandem repeat in CACNA1C

Length variation in tandem repeats is a well-established driver of disease risk and is commonly assumed to arise from persistent genomic instability. Here, we characterize TRACT, a 30-bp variable number tandem repeat (VNTR) intronic to the calcium channel gene CACNA1C. TRACT exhibits extreme length variation (3-30+ kb) and has been previously linked to risk for bipolar disorder and schizophrenia. By examining multiple human cohorts, we find that TRACT alleles are strikingly bimodal in both length and sequence composition. Short alleles (TRACTS, [~]6 kb) and long alleles (TRACTL, [~]24 kb) are enriched for distinct 30-bp variants and are found on separate haplotypes that arose prior to the human migration out of Africa. Our data suggest that these ancient alleles expanded via perfect repeat tracts that were disrupted by accumulated mutations to result in relative length stability in extant humans, where there is no evidence for overt germline or somatic instability. Interestingly, neuropsychiatric disease risk is associated with specific 30-bp variants within TRACTS alleles, but not with overall TRACT length or with 30-bp variants enriched in TRACTL alleles. Instead, TRACTL alleles are associated with decreased gene expression in fibroblasts and testis. Together, these findings motivate joint examination of both sequence composition and length variation to fully understand the effects of VNTRs on evolution, trait variation, and disease risk.

genetics↗

bioRxiv2026

Structure-aware geometric graph learning for modeling protease-substrate specificity at scale

Protease-substrate specificity is central to cellular regulation and disease pathogenesis, and accurately modeling its structural determinants remains challenging. Substrate recognition is governed by spatial constraints and higher-order relationships that extend beyond local sequence motifs. Most computational approaches rely predominantly on motif-centric or sequence-based representations, limiting their ability to capture the geometric and relational structure underlying enzymatic specificity. Here, we introduce OmniCleave, a structure-aware geometric graph learning framework for modeling protease-substrate specificity at scale. OmniCleave is trained on 57,278 structure-informed protease-substrate pairs derived from 9,651 substrates spanning over 100 proteases across six distinct families. The framework integrates multi-scale structural graphs with higher-order protease relational topology, explicitly encoding spatial context and inter-protease dependencies within a unified geometric representation. This formulation moves beyond local pattern recognition and enables transferable modelling across six protease families. Across large-scale benchmarks, the framework consistently outperforms existing approaches and reveals interpretable geometric determinants underlying substrate recognition. Experimental validation confirms three novel caspase-3 substrates and 21 cleavage sites predicted by OmniCleave, supporting the biological relevance of the learned representations. Together, OmniCleave provides a scalable geometric framework for modeling protease-substrate specificity, with practical utility for systematic analysis of protease biology.

bioinformatics↗

bioRxiv2026

Modification-aware AI enables terminal chemical modifications for peptide design and discovers potent antimicrobials

Antimicrobial resistance (AMR) is an escalating global health threat, intensifying the urgent need for novel antibiotics. Here, we introduce Termini, a generative machine learning (ML) framework that integrates peptide generation, classification, and regression modules to systematically identify potent antimicrobial peptides (AMPs). A key feature of this pipeline is its explicit design of both N- and C-terminal modifications, combined with predictive modeling across 15 bacterial species. We experimentally validated the framework against 11 pathogenic species, representing the broadest spectrum of testing reported to date in such a study. We synthesized and tested 120 peptides (including 60 unique amino acid sequences) with and without terminal modifications. Remarkably, 111 of these peptides (92.5%) demonstrated antimicrobial activity in vitro. Comparative analyses demonstrated that terminal modifications substantially enhanced antimicrobial potency. Furthermore, the lead candidates identified through in vitro screening exhibited robust anti-infective efficacy in subsequent in vivo experiments, confirming their therapeutic potential. Collectively, these results highlight the high hit rate, biological relevance, and translational promise of this integrative, modification-aware AI framework for next-generation antimicrobial peptide development.

microbiology↗

bioRxiv2026

Circulating Immune Cells are Associated with Non-Inflammatory Pain in Rheumatoid Arthritis

Over half of patients with rheumatoid arthritis (RA) report clinically meaningful pain, despite treatment with disease-modifying antirheumatic drugs (DMARDs). While joint inflammation is a known cause of pain in patients with rheumatic diseases, emerging data indicate that many patients also suffer from centralized or nociplastic pain. There is a critical unmet need to characterize the altered cellular state that distinguishes patients with centralized pain. In the IMPACT study, 39 RA patients with minimal joint inflammation but varying levels of pain underwent quantitative sensory testing (QST) to assess nociplastic pain, completed patient-reported outcome (PRO) surveys, and provided blood samples for immune profiling. Supervised and unsupervised analysis of the multi-parameter spectral flow cytometry data identified immune cell populations correlated with nociplastic pain and patient-reported pain intensity. Moreover, analyses of single-cell RNA-seq from a subset of 22 patients revealed differences in cell type proportions and differential expression between the high and low pain groups. These studies provide novel insights into the role of circulating immune cells in altered central nervous system (CNS) pain regulation in adults with RA.

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↗