Biology subjects

Hu, J.

Publications and source records attributed to Hu, J..

At least 19 recordsLinked to original sources

bioRxiv2026

FiberBrowser: a unified framework for single-molecule genomics

Single-molecule footprinting methods are rapidly transforming chromatin biology. They resolve nucleosome positioning, regulatory-factor occupancy, and accessibility on individual DNA fibers, revealing combinatorial heterogeneity that bulk measurements average away. Emerging footprinting methods leverage diverse enzyme systems and sequencing platforms, necessitating tools that can accommodate these systems. We present FiberBrowser, a unified platform that converts these diverse datasets into a common per-molecule representation of nucleosomes, accessible DNA, and subnucleosomal footprints for interactive and scripted analyses. Its inference engine, FiberHMM, weighs each observation against the assay's informative sites and sequence-specific modification probabilities, separating missing information from protection in four assays. FiberBrowser clusters, sorts and compares these calls across thousands of molecules in seconds, linking chromatin architecture to haplotype, CpG methylation and non-reference structural variants on the same molecules. Because the representation is independent of chemistry, models also transfer between assays. A classifier trained on Fiber-seq footprints identifies 12 scDAF-seq cells as lymphoblastoid without retraining, whereas models trained on raw modification patterns fail to transfer. FiberBrowser thus provides a unified, expandable analytical framework for single-molecule chromatin footprinting.

genomics↗

bioRxiv2026

Cancer cells hijack mitochondria from Bhlhe40+ T cells for immune evasion

Cancer cells hijack mitochondria from T cells via tunneling nanotubes (TNTs) to promote immune evasion, yet the underlying mechanisms of mitochondrial-loss remain poorly understood. Here, through targeted screening of relevant genes, we demonstrated that CD38 is a potent regulator of TNT formation through interaction with its cognate ligand, CD31. Conditional knockdown of Cd38 in murine models leads to CD8+ T cell-dependent tumor shrinkage. Single-cell RNA sequencing of tumor-infiltrating T cells revealed that cancer cells preferentially target the Bhlhe40+ effector subset. Bhlhe40 is a transcription factor that upregulates the expression of S100a4, a reported chemoattractant of nanotubes. Bhlhe40 upregulation creates mitochondrial-loss vulnerability for tumor neoantigen-specific T cells. Dual mitochondria transfer blockade with Cd38 knockdown and S100a4 inhibitor trifluoperazine markedly suppressed tumor growth. Collectively, our findings identified the Bhlhe40-S100a4 cascade as a critical regulator of cancer/T cell mitochondrial transfer, providing insights for the development of new cancer immunotherapies.

immunology↗

bioRxiv2026

Identification of candidate ciliary microtubule-associated proteins using CEP41 proximity labeling

Primary cilia are microtubule-based signaling organelles whose dysfunction results in a wide spectrum of ciliopathies. Extensive studies have established the functional and clinical importance of ciliary microtubule associated proteins (ciliary MAPs), but the repertoire of specialized ciliary MAPs remains incompletely defined. Here, we used the ciliopathy-associated MAP CEP41 as a BioID bait to probe proteins proximal to ciliary microtubules. Quantitative proteomics and cilia-focused prioritization identified a ciliary MAP-enriched protein set, revealed a poorly characterized Septin-9 isoform, and supported C7orf57/FCAP33, CCDC34, and PSRC1 as novel ciliary MAPs. CCDC34 and PSRC1 did not affect cilia architecture or ciliation, indicating roles in structure-independent ciliary functions, whereas C7orf57 overexpression increased cilium length. Microtubule association analysis reveals C7orf57 as a paclitaxel sensitive MAP that associates with microtubule through its N-terminal regions. Together, these findings expand the ciliary MAP repertoire and provide a resource for prioritizing candidates for future studies of cilia biology and ciliopathies.

cell biology↗

bioRxiv2026

VASP association stabilizes the actin nucleus revealed by molecular dynamics simulations

Actin nucleation is a rate-limiting step in F-actin assembly. However, early actin trimers are intrinsically unstable, with limited inter-subunit contacts rendering them prone to dissociation. VASP is an important regulator of actin polymerization and assembly, but how it stabilizes unstable trimeric actin nucleus remains poorly understood at the molecular level. Here, we use coarse-grained and all-atom molecular dynamics simulations to characterize the interfacial stability and conformational features of VASP-bound actin trimers. Energetic and interfacial analyses show that VASP preferentially anchors the laterally associated subunit through multivalent interactions and reinforces its interfaces with the longitudinal actin dimer. Within the VASP-bound trimer, the three actin subunits retain distinct local conformational features, as reflected by differences in W-loop gating and SD1-to-SD4 dihedral angle. Across replicate simulations, VASP stabilizes the actin nucleus while preserving subunit-specific conformational heterogeneity, rather than uniformly rigidifying the trimer. These findings provide a molecular basis for VASP-mediated stabilization of early actin nuclei and offer broader mechanistic insights into the regulation of protein assembly.

biophysics↗

bioRxiv2026

Antigen-aware TCR embeddings reveal conserved immune patterns for personalized immune monitoring

T cell receptor (TCR) repertoires are highly individualized, while technical variation further complicates comparisons across individuals and cohorts. Here, we present TCRmeta, an antigen-aware framework that integrates population-scale repertoire relationships with experimentally defined antigen specificity to establish a shared space for repertoire analysis. TCRmeta predicted mutation-induced changes and captured antigen-related relationships in TCR specificity. Despite extensive sequence diversity, TCRmeta revealed a conserved repertoire landscape, enabling construction of a healthy reference for quantifying repertoire dispersal. Across cohorts, aging was associated with conserved V gene changes and progressive dispersal, with greater dispersal in CMV-positive individuals. Longitudinal analysis showed that repertoires progressively shifted toward the healthy reference following bone marrow transplantation or antiretroviral therapy. To directly compare repertoires in TCRmeta space, we developed Energy Shift, a pairwise measure robust to technical variation and HLA background while remaining sensitive to biological differences. Together, TCRmeta enables quantitative cross-individual and longitudinal immune monitoring.

bioinformatics↗

bioRxiv2026

A Novel Computational Framework to Investigate the Biomechanics of Burrowing

Head-first burrowing likely imposes strong mechanical demands on the skull of fossorial vertebrates, but how cranial tissues respond to these loads remains obscure. Here, we developed a computational framework integrating the discrete element method (DEM) with finite element analysis (FEA) to investigate cranial biomechanics during burrowing. We used Blanus cinereus, a round-headed amphisbaenian, as a model to establish DEM predictions that were validated and calibrated against in vitro experiments across three substrates and compared with in vivo push forces. DEM-calibrated particle contact forces were then transferred to an anatomically detailed finite element model to quantify cranial loading. The analyses suggested that soft tissues accommodated substantial deformation, while the skull transmitted burrowing loads from the premaxilla towards the occipital region. Cranial sutures further redistributed stresses and strains within the skull. This validated DEM-FEA framework links particle-scale substrate interactions with internal cranial biomechanics and provides a quantitative approach for investigating the mechanics of head-first burrowing across fossorial vertebrates.

bioengineering↗

bioRxiv2026

Cause-excluded Effect Evaluation: a framework for the faithful assessment of genetic perturbation target identification

Identifying which gene drives a desired cell-state transition is a foundational inverse problem in target discovery and cell-state engineering, yet far less developed than forward prediction. We show that prevailing benchmarks inflate performance through two artefacts: training leakage, where test-perturbation cells are seen during fitting, and evaluation leakage, where the perturbed gene's own expression change exposes the answer. Here, we introduce Cause-excluded Effect Evaluation (CEE), a framework that separates train and test perturbation targets, removes target-gene self-effects from expression profiles and scores candidates in downstream response space. With CEE, we find that simple structure-based methods consistently outperform complex deep learning models across 16 single-cell perturbation datasets, revealing that the recoverable signal is governed by low-rank and additive structure in perturbation responses rather than by model complexity. Our work disentangles evaluation artefacts from genuine predictive capacity and offers a design principle for developing perturbation target identification methods.

bioinformatics↗

bioRxiv2026

Promoter-targeted duplex RNAs activate TCF4 expression

Pitt-Hopkins syndrome (PTHS) is a severe neurodevelopmental disorder caused by haploinsufficient expression of transcription factor 4 (TCF4). Increasing expression from the remaining functional TCF4 allele could provide a mutation-independent strategy for patients with diverse pathogenic variants. We integrated genomic datasets with strand-specific RNA analysis to identify transcribed regions of the TCF4 promoter suitable for targeting by activating double-stranded RNAs (dsRNAs). Screening of overlapping promoter-targeted dsRNAs identified a hotspot for increased TCF4 expression. Activation was retained by a lead dsRNA containing extensive 2'-fluoro, 2'-O-methyl, and phosphorothioate modifications used in therapeutic oligonucleotides. Anti-AGO2 immunoprecipitation showed association with RNA transcribed from the targeted promoter region. dsRNA-mediated activation was associated with increased RNA polymerase II occupancy. These findings establish promoter-targeted dsRNAs as chemically tractable starting points for increasing TCF4 expression and support their investigation as a mutation-independent strategy for haploinsufficient disease.

molecular biology↗

bioRxiv2026

Pair-complete structural gating enables Pareto optimization of de novo cyclic peptides with preserved binding geometry and improved developability

Cyclic peptides can engage extended protein interfaces, but sequence changes that improve developability often disrupt the selected target-bound geometry. Existing property-guided design methods lack a principled way to preserve this geometry during local multiproperty optimization. Here we present RF-PepTune, which separates structural eligibility from property prioritization. Before any property score is queried, RF-PepTune evaluates single substitutions and all position-distinct pairs induced by admitted substitutions to build a parent-specific, pair-complete admission graph. Pareto Monte Carlo tree search then optimizes predicted permeability, solubility, non-fouling, and low-haemolysis scores exclusively within this graph, and selected sequences undergo full structural re-evaluation. In a matched three-arm comparison of 334 parents across MDM2, GABARAP, and MCL1, joint property-structure success rose from 10.2% without admission to 51.5% with single-substitution admission and 77.5% with pair-complete admission; pair-complete gating improved success by 26.0 percentage points over single-substitution gating (95% CI, 20.7-31.4). Property-positive but structure-failing endpoints fell from 88.3% to 9.3%. In a separate target-balanced set, all 90 prioritized outputs improved all four model-native scores while satisfying prespecified structural criteria. This work establishes pairwise structural gating as a general strategy for constrained multi-objective molecular optimization.

bioinformatics↗

bioRxiv2026

Failure to release from the ER translocon induces 60S ribosome degradation

Ribosome malfunction has toxic consequences, but how mammalian cells select ribosomal subunits for degradation remains poorly understood. Here, we develop a pulse-chase CRISPR screening strategy to identify regulators of ribosome turnover and uncover that loss of protein UFMylation promotes ribosome degradation. Normal UFMylation releases the 60S subunit from the endoplasmic reticulum (ER) translocon. Without UFMylation, single-molecule tracking and optogenetic proximity labeling reveal accumulation of ER-associated 60S subunits and their specific degradation. This translocon-associated large subunit degradation (TLSD) is independent of the lysosome and, in our human cell model, is only active in nondividing, contact-inhibited cells. Degradation requires the poorly characterized protein R3HDM4, which we term TLSD1. Upon UFMylation loss, TLSD1 redistributes to the ER and promotes 60S degradation. These findings identify a cell-state-dependent pathway for clearing ER-retained 60S subunits. We propose translocon release acts as a checkpoint for ribosome function, explaining TLSD1's genetic links to neuronal health and erythropoiesis.

cell biology↗

bioRxiv2026

A closed-loop language-model agent for target-specific multi-objective hit-to-lead optimization

Hit-to-lead optimization is a multi-objective molecular-design problem in which binding-related scores must be considered together with drug-likeness and synthetic accessibility. We developed a closed-loop language-model medicinal-chemistry agent that combines target and starting-hit context, PubMed retrieval, molecular-property tools, docking, and feedback from previously scored analogues. In a matched computational benchmark comprising six kinase targets, six methods, a 40-candidate budget, and six independent campaigns per method, the agent achieved the highest mean aggregate score under a pre-specified composite objective on four targets. Re-analysis of the same candidate sets with drug-likeness-gated docking and docking-drug-likeness hypervolume produced different target-level winners, showing that evaluation rules can change the comparative interpretation of an optimization campaign. Candidate distributions and component ablations indicated that the agent's advantage was associated primarily with retention of scored-candidate feedback and higher-QED regions rather than with docking alone. An EGFR case study illustrates a scaffold-preserving in-silico optimization trajectory from erlotinib. The study is a computational benchmark and evaluation framework; the prioritized molecules are hypotheses for subsequent experimental testing, not experimentally validated leads.

bioinformatics↗

bioRxiv2026

FlowMap: Geometry Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories

Single-cell RNA sequencing reveals how cells vary across states, but these measurements capture only static snapshots of dynamic biological processes. RNA velocity addresses this limitation by estimating how gene expression is changing over time, providing directional information about cell state transitions. However, current approaches treat cellular state and dynamics separately, leading to representations that violate fundamental geometric consistency and obscure biological interpretation. Here, we present FlowMap, a framework that jointly models cellular states and their dynamics within a unified geometric representation. FlowMap simultaneously reconstructs a smooth low-dimensional manifold of gene expression and constrains RNA velocity to follow its local geometry, producing coherent and denoised representations of cellular trajectories. Across simulated and real datasets, FlowMap recovers interpretable dynamical patterns, including continuous progressions, branching events, cyclic behaviors, and stable-like states. It highlights key genes and gene programs involved in development that play distinct roles in these dynamics, and extends naturally to spatial transcriptomics, where it captures spatially organized developmental processes. Together, FlowMap establishes a principled geometric framework for modeling cellular dynamics, bridging representation learning and dynamical inference in single-cell analysis.

bioinformatics↗

bioRxiv2026

From single-sequence structure prediction to protein fitness landscape through a composable, epistasis-aware mutation atlas

Mapping the multi-mutant fitness landscape is vital to protein engineering, but is challenging due to the vast combinatorial sequence space awaiting exploration. A central difficulty lies in the accurate and efficient modeling of non-additive epistatic effects among individual mutations, which partially arise from the physical inter-residue interactions prescribed by the protein structure. Existing fitness predictors usually perform well on single mutants but become less powerful for higher-order mutants, due to the lack of explicitly considering the relationship between sequence, structure and function of the target protein. Here, we present an end-to-end framework named Cerebra-Epistasis, which couples a single-sequence structure predictor that explicitly endows the structure awareness beyond the conventional sequence-fitness mapping with a downstream fitness prediction network that deliberately models the non-linear epistatic effects beyond the traditional additive terms. When evaluated across diverse assays, Cerebra-Epistasis outperforms the other state-of-the-art baselines in multi-mutant fitness prediction, with enhanced advantage over increasing mutation orders. Moreover, our special design on the epistasis modeling allows reliable extrapolation from low-order mutant data to unseen higher-order combinations, enabling one-shot inference of the overall mutation atlas from the starting sequence, a benefit that supposedly introduces three orders of magnitude acceleration in the landscape-scale prediction.

bioinformatics↗

bioRxiv2026

GPR182 promotes atherosclerosis via facilitating arterial lipid deposition

Elevated circulating low-density lipoprotein cholesterol (LDL-C) is a primary risk factor for atherosclerosis; however, the molecular mechanisms underlying lipid plaque formation within the artery wall remain poorly understood. Here, we identify GPR182, a recently characterized lipoprotein receptor of the atypical chemokine receptor family, as a key mediator of lipid deposition in the aortic endothelium during atherosclerosis. Genetic depletion of GPR182 protects against atherosclerosis across multiple mouse models without altering circulating cholesterol levels or immune cell recruitment to plaques. GPR182 is expressed by aortic endothelial cells (ECs) and is further upregulated during disease progression. GPR182 mediates LDL uptake by aortic ECs both in vitro and in vivo. Blockade of GPR182 with a monoclonal antibody reduces lipid uptake in the aorta and attenuates disease progression under hypercholesterolemic conditions. Collectively, these findings identify endothelial GPR182 as a critical regulator of aortic lipid deposition and atherosclerotic plaque formation and support GPR182 inhibition as a promising therapy for atherosclerotic cardiovascular disease.

molecular biology↗

bioRxiv2026

The PSInet Plant Water Potential Database: advancing new perspectives on plant water status, traits, and hydraulic processes

Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.

plant biology↗

bioRxiv2026

Comprehensive characterization of genomic, transcriptomic and epigenomic artifacts introduced in formalin-fixed, paraffin-embedded tissues.

Genomic, transcriptomic and epigenomic characterization has accelerated the discovery of clinically-relevant alterations in cancer, predominantly using fresh frozen (FF) specimens. However, clinical molecular pathology laboratories prefer formalin-fixed paraffin-embedded (FFPE) methods, known to introduce artifacts at the nucleic acid level, over fresh frozen methods. Extending the multi-platform analysis to FFPE specimens for comprehensive clinical molecular diagnosis requires a thorough understanding of the consequence of formalin-fixation. We present a detailed multi-platform characterization of FFPE preservation using paired FF specimens as the 'gold standard'. DNA and RNA were obtained from 38 patients across 6 cancer types using a FFPE optimized co-isolation. The impact of FFPE on exome sequencing was dependent on filtering, where a minimum coverage or supporting read filter can mitigate FFPE-specific false positives. Copy number alterations, MSI assessment, mutational signatures, and DNA methylation were comparable between FFPE and FF. FFPE biases in RNA expression can be overcome when using biology-relevant genes and we describe a novel consequence of FFPE on miRNA species diversity. Collectively, this data provides a broad view of FFPE artifact and offers best practices for overcome these biases.

bioinformatics↗

bioRxiv2026

A virus co-opts vector's m⁶A machinery for adaptive compensation

Insect vectors harbor diverse viruses that threaten human health and agricultural production, yet the genetic basis of mutualistic virus-vector interactions remain elusive. This underscores a long-standing paradox in pest management: viruses that cause destructive diseases also facilitate vector adaptation in ways that benefit both parties. Here we show that the globally devastating tomato yellow leaf curl virus (TYLCV) acts as a Cooperative Partner, co-opting its vector's m6A epitranscriptomic machinery to offset the reproductive fitness costs of neonicotinoid resistance in the whitefly. In resistant vectors, TYLCV infection alleviates reproductive deficits via the m6A-dependent pathway, where the methyltransferase METTL14 and demethylase ALKBH4 coordinately stabilize vitellogenin (Vg) transcripts to boost female fecundity. Mechanistically, the viral C2 and CP proteins directly interact with METTL14 and ALKBH4, respectively, remodeling their RNA binding affinity to facilitate mA modification on Vg. Disrupting either of these viral-vector protein interactions restores a reproductive cost phenotype in viruliferous resistant vectors, offering novel targets for sustainable pest control. Our findings uncover a fundamental role of epigenetic marks in adaptive trait, reshaping beneficial virus-vector relationships and providing new insights for innovative public health and crop protection strategies to address global challenges.

microbiology↗

bioRxiv2026

Multiplexed Quantification of Variant Abundance in the Globin Gene Family: Integrating Saturation Mutagenesis with Cross-Paralog Prediction

Widespread genetic testing has expanded variant identification, yet functional characterization remains a bottleneck in genome guided medicine. Here, we present a modified Variant Abundance by Massively Parallel Sequencing (VAMP-seq) platform integrating experimental and computational approaches for high-resolution abundance profiling of protein variants. Utilizing a lentiviral integration system, we systematically assessed the stability effects of 2,696 amino acid substitutions in {zeta}-globin (HBZ) via saturation mutagenesis in human cells, achieving complete variant coverage with high reproducibility. Representative variants showed strong concordance with orthogonal low-throughput validation assays. We further developed a deep learning framework leveraging VAMP-seq derived HBZ data to predict variant abundance across thalassemia-associated globin paralogs (HBA, HBB, and HBG1) not experimentally tractable. Our hybrid framework demonstrates how targeted experimental profiling combined with AI-driven extrapolation can accelerate variant interpretation across protein family members.

genetics↗