Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 451 records · Page 25Linked to original sources

bioRxiv2025

Cardiac-sympathetic state predicts action restraint, gated by demonstrated agency

Withholding action until the appropriate moment is a core challenge of motivated behavior. Using beat-to-beat cardiac contractility during an incentivized reaching task, we show that cardiac-sympathetic outflow predicts action restraint. Under high-reward conditions that induce a speed-accuracy tradeoff, reduced contractility at the time of instruction preceded premature responses (false starts). Under high-loss-avoidance conditions, elevated pre-movement contractility predicted slower, more controlled initiation, but only among participants with above-median task success. These findings suggest cardiac-sympathetic engagement does not simply serve mobilization but flexibly supports context-appropriate action regulation, with recruitment for restraint gated by demonstrated agency.

physiology↗

bioRxiv2025

OmniCell: Unified Foundation Modeling of Single-Cell and Spatial Transcriptomics for Cellular and Molecular Insights

A cells transcriptional programme is not fully defined by gene expression alone, but by the tissue context in which that programme is enacted. Single-cell RNA sequencing resolves molecular identity after dissociation, whereas spatial transcriptomics preserves tissue architecture but remains constrained by assay-specific sparsity and gene coverage. Here we present OmniCell, a tissue-contextual transcriptomic foundation model pretrained on 67 million dissociated and spatially resolved profiles. By integrating gene identity, expression magnitude and tissue context, OmniCell links transcriptional programmes to the cellular neighbourhoods and anatomical contexts in which they operate. OmniCell organised transcriptomes across molecular, cellular and tissue scales. It recovered cell-type-specific programmes and tissue-aligned gene modules, preserved robust cell-state structure across batches, species and rare populations, and improved the reconstruction of spatial cell identity, anatomical domains and cell-type composition. In human liver cancer Stereo-seq data, OmniCell resolved a tumour-margin transition zone characterised by immune infiltration, acute-phase inflammation, coagulation/complement activity and metallothionein-linked metal-ion detoxification. Contextual gene-embedding similarity analysis showed that gene relationships differed across tumour core, transition-zone and paratumour/adjacent non-malignant niches, indicating that OmniCell captures tissue-dependent gene function rather than expression similarity alone. In mouse brain development and macaque cortex, spatial virtual perturbations mapped regulatory genes onto stage- and region-specific anatomical programmes. Together, these results establish tissue context as a primary axis of transcriptomic representation and provide a framework for studying how cellular programmes acquire context-dependent biological meaning in intact tissues.

bioinformatics↗

bioRxiv2025

Primordial cardiomyocytes orchestrate myocardial morphogenesis and vascularization but are dispensable for regeneration

The vertebrate heart is composed of heterogeneous cardiomyocyte (CM) populations, however, the roles of distinct CM subpopulations in heart development and repair remain poorly defined. Here, using single-cell RNA sequencing analysis of adult zebrafish heart, we identified a unique CM subpopulation marked by the expression of phlda2, which is associated with anaerobic metabolism and different from mature CMs which are enriched for oxidative phosphorylation genes. We demonstrated that phlda2+ cells constituted a primordial CM compartment localized between compact and trabecular muscles. Genetic ablation of phlda2+ CMs during development severely disrupted heart morphogenesis, leading to defective myocardial trabeculation and compaction, and impaired coronary vascularization. Surprisingly, despite their essential roles in development, the depletion of phlda2+ CMs didnt impair myocardial restoration and revascularization following ventricular resection. We found that this was probably due to the limited regenerative capacity of the primordial CMs themselves, as they failed to regenerate after either surgical amputation or genetic ablation. Our findings identify primordial CMs as an organizer for heart morphogenesis but not essential for regeneration, revealing a fundamental difference between developmental and regenerative programs in the vertebrate heart.

developmental biology↗

bioRxiv2025

MetaPaCS: A novel meta-learning framework for pancreatic cancer subtype identification

As the third leading cause of cancer related deaths in the United States, pancreatic cancer (PaC) is a highly heterogenous malignancy that can be divided into a multitude of potential subtypes, with the main 4 consisting of aberrantly differentiated endocrine exocrine (ADEX), immunogenic, progenitor, and squamous. Each PaC subtype is characterized by unique molecular pathways and therapeutic characteristics. Identifying PaC molecular subtypes is essential for downstream patient risk stratification and tailored treatment design. Conventional wet-lab approaches for PaC subtyping like microdissection, histopathological studies or molecular profiling are often laborious, costly, and time-consuming. To address these concerns, we present MetaPaCS, a novel meta-learning framework to accurately identify PaC subtypes based on transcriptomics data only. Specifically, after preprocessing, the transcriptome-based feature vectors were classified by 10 base machine learning (ML) classifiers, whose prediction outputs were then combined with the initial preprocessed feature vectors to constitute a new set of ensemble feature vectors for a meta-learning model. Our meta-learning model could learn and leverage the diversity of different base classifiers to boost the prediction performance beyond any single ML model. Results based on 100 times ten-fold cross validation tests on benchmarking datasets demonstrated that MetaPaCS performed significantly better than existing state-of-the-art methods for PaC subtyping. In addition, our meta-learning model remarkably outperformed each individual base classifier, demonstrating that MetaPaCS could combine diverse results from multiple base classifiers to boost the ensemble performance. We believe that MetaPaCS is a promising tool for characterizing PaC subtypes and will have positive impacts on downstream risk stratification and personalized treatment design for PaC patients.

bioinformatics↗

bioRxiv2025

A gastric microbial chromatin remodeler drives gastric cancer progression and immune evasion by reprogramming the host epigenome

Emerging evidence highlights the crucial role of microbial communities in modulating cancer development and progression. In gastric cancer (GC), we identify Streptococcus anginosus (SA) as a tumor-resident oncobacterium that drives both tumor progression and immune evasion. We define a novel mechanism in which SA secretes extracellular vesicles (saEVs) that translocate the bacterial chromatin remodeler saSNF2 into host cell. There, saSNF2 partners with host transcription factor TEAD1--through its ATPase activity and participation in BAF complex assembly--to coordinately activate oncogenic transcription. This transkingdom interaction upregulates the palmitoyltransferase ZDHHC11, which in turn stabilizes PD-L1 via palmitoylation to establish an immunosuppressive niche, while also amplifying other TEAD1 target genes to fuel tumor progression. Functionally, saEVs promote tumor growth and limit CD8+ T-cell infiltration in vivo. Strikingly, pharmacological inhibition of ZDHHC11 reverses immune evasion and synergizes with anti-PD-1 checkpoint blockade. Our results establish SA as a multifaceted driver of GC and reveal the saSNF2-ZDHHC11 axis as a promising target to potentiate immunotherapy.

cancer biology↗

bioRxiv2025

STEAP4+ neutrophils: a promising circulating biomarker for lung metastasis

As the leading contributor to cancer-related mortality, metastasis often evades detection until advanced stages, underscoring the critical need for early predictive biomarkers. Neutrophils are naturally circulating sentinels that have been implicated in pioneering pre-metastatic niche formation in distant organs. Nevertheless, their heterogeneity within evolving metastatic microenvironments remains poorly characterized. Here, through dynamically delineating neutrophil subsets via single-cell transcriptomics, we identified a conserved feature of lung metastasis in breast cancer and other cancer models. Among the core genes, a cell surface marker, STEAP4, was selected and validated to indicate lung metastases at both RNA and protein levels. In both humans and mice, these STEAP4+ neutrophils were detectable not only in lung metastatases but also in the circulation. Clinically, the abundance of circulating STEAP4+ neutrophils robustly discriminates patients with lung metastasis from those with localized primary tumor. Our findings uncover STEAP4+ neutrophils as a promising biomarker and lay the groundwork for a non-invasive, blood-based diagnostic strategy for metastatic disease.

cancer biology↗

bioRxiv2025

Neuropathologically validated MRI to tau PET synthesis via Covariate-modulated attention networks

Tau PET is a powerful tool to assess tau pathology in vivo; however, in comparison to MRI, its development is more recent, is rarely available at scale, and substantially more difficult to acquire. Here, we present Covariate-Modulated Attention UNet (CoMA-UNet) to synthesize subject-specific 3D tau PET from T1 MRI while incorporating in the synthesis procedure readily available covariates. Across six external validation datasets, CoMA-UNet reproduced regional patterns of tau PET uptake showing strong agreement with true PET that was generalizable across tracers. Next, we submitted the synthetic tau PET to a series of downstream clinically relevant tasks. First, MMSE associations between the synthetic tau PET were statistically indistinguishable from true PET. Second, the synthetic tau PET achieved out-of-sample diagnostic classification of dementia with an AUROC=0.99. Third, in two independent autopsy cohorts, voxel wise synthetic tau PET images closely followed neuropathologically defined Braak-stages. Fourth, when fine-tuned on a small subset of atypical presentations of AD, CoMA-UNet preserved phenotype-relevant tau topographies and recovered clinically meaningful structure across atypical clinical phenotypes. These findings demonstrate that the novel CoMA-UNet MRI-based synthesis augmented with covariate information can approximate tau PET with sufficient accuracy for downstream scientific and clinical applications.

neuroscience↗

bioRxiv2025

Biophysical Modeling Elucidates Mechanistic Principles for Rational Molecular Glue Design

Molecular glues are small molecules that offer a powerful strategy to target previously "undruggable" proteins of interest (POI) by enhancing their interactions with other proteins (effector). Depending on the nature of the effectors, molecular glues can induce stabilizing sequestration or degradation of POI. However, their rational design has been hindered by a poor understanding of how kinetic parameters impact their performance. To address this, we developed a unified mathematical framework that accurately analyzes the dynamics of both glue degraders and stabilizers. Our model reveals that the strong binding affinity of the ternary complex is the key determinant of performance across both modalities, provided that the initial component concentrations and degradation rate constants are fixed. Furthermore, we demonstrate that degrader performance is ultimately limited by its catalytic efficiency and the target proteins natural half-life. We also identify distinct roles for effector abundance, showing that the relative concentrations of the effector and POI are critical for stabilizers but less so for degraders. This quantitative framework provides mechanistic principles for the rationale design and optimization of molecular glues.

molecular biology↗

bioRxiv2025

Combined antagonism of Oncostatin M (OSM) and Interleukin 6 (IL-6) provides both anti-fibrotic and anti-inflammatory benefit in pulmonary fibrosis

Interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-associated ILD (SSc-ILD), are irreversible fibrosing diseases with a mean survival time of less than 5 years for IPF. Poorly understood etiology and complex pathogenesis of these diseases have hampered the identification and development of effective therapeutics. Existing treatments can slow progressive fibrosis and lung function decline but do not stop it entirely, resulting in a minimal impact on patient survival. Thus, novel therapeutic interventions are needed. Tocilizumab, an anti-IL-6 receptor antibody, was recently approved by FDA for the treatment of SSc-ILD based on evidence demonstrating a reduction in the rate of lung function decline. In this study, we have characterized an IL-6-driven feed-forward myeloid axis contributing to lung inflammation providing a mechanistic hypothesis for tocilizumab. Concomitantly we have identified an oncostatin M (OSM) orchestrated lung injury response contributing to epithelial and endothelial cell disruption, myofibroblast activation and fibrosis. Despite dysregulated expression of IL11 in IPF, SSc-ILD, and murine models of fibrosis, we found no evidence for a pro-fibrotic role for IL-11 in vitro or in vivo. Instead, in pre-clinical models of IPF we demonstrate that antagonism of OSM alone, or to a greater degree in combination with IL-6, reduced lung fibrosis and inflammation. Translating these observations, we validated gp130:OSMR, rather than gp130:LIFR, as the dominant human receptor complex used by OSM, identifying OSMR as a potential therapeutic target to stall fibrosis.

immunology↗

bioRxiv2025

Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 31 methods across independent cohorts demonstrates superior predictive performance. The frameworks interpretability uncovers linear and non-linear transcriptomic dynamics that reveal phase-specific physiological transitions, and identifies pro-youthful or pro-aging cellular contributions. Application to systemic lupus erythematosus (SLE) reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.

bioinformatics↗

bioRxiv2025

Orphan Non-Coding RNAs Drive Tumorigenesis and Enable Pre-Diagnostic Detection in HPV-Negative Head and Neck Cancer

HPV-negative head and neck squamous cell carcinoma (HNSCC) lacks reliable biomarkers for early detection and molecular stratification. We recently described orphan non-coding RNAs (oncRNAs) as a class of cancer-emergent small RNAs that arise from cryptic promoters largely silent in normal tissues. Here, we define the comprehensive landscape of oncRNAs in HNSCC. Using TCGA and CPTAC-3 data, we demonstrate that these transcripts are not only tumor-specific but provide digital fingerprints that accurately stratify tumors by anatomical site. Moving from association to function, we investigated whether these emergent transcripts actively drive tumorigenesis. We focused on oncRNA64585, a transcript highly enriched in oral cavity tumors and associated with tumor progression. Its depletion significantly impaired proliferation in vitro and suppressed tumor growth in syngeneic murine models. Mechanistically, transcriptomic and chromatin accessibility profiling revealed that oncRNA64585 regulates transcriptional programs governing cell cycle progression and apoptosis through interaction with the Polycomb Repressive Complex 2 (PRC2). Finally, we demonstrate the clinical utility of these signatures: oncRNA profiles in plasma predicted oral cavity cancer with >90% accuracy up to four years prior to clinical diagnosis. These findings establish oncRNAs as active drivers of HNSCC biology and potent tools for early detection and therapeutic targeting.

cancer biology↗

bioRxiv2025

PDK1 and YAP1/TEAD signaling drive acquired KRAS inhibitor resistance in KRAS-mutant non-small cell lung cancer.

Mutations in KRAS are responsible for driving approximately 30% of NSCLC. While historically considered undruggable, recent breakthroughs have seen the FDA approval of two potent KRASG12C inhibitors, sotorasib (AMG510) and adagrasib (MRTX849). However, the efficacy of these inhibitors in the clinics has been limited by primary and acquired means of resistance. To elucidate mechanisms of acquired resistance, we generated a panel of resistant cell lines to the allele-specific KRAS inhibitors MRTX849 and MRTX1133 and observed an increased activation of the PDK1 and YAP1/TEAD signaling pathways. Pharmacological inhibition and genetic loss-of-function studies revealed a strong dependence on these pathways for the generation and maintenance of resistance to KRAS inhibition, which was then validated in vitro and in vivo. Furthermore, overexpression studies revealed that forced expression of either PDK1 or YAP1 led to increased resistance to KRAS inhibition in the sensitive lines. Taken together, our findings suggest that co-targeting PDK1 or YAP1/TEAD might be a potential approach to overcoming resistance to KRAS inhibition in NSCLC.

cancer biology↗

bioRxiv2025

MOTLAB: A Weighted Multi-Omics Transfer Learning Approach to Mitigate Breast Cancer Racial Disparities

Breast cancer mortality remains markedly higher among Black women in the United States, while the limited representation of Black women in genomic datasets constrains the development of reliable clinical outcome prediction models for this racial group. Conventional transfer learning improves prediction in data-minority groups, but it does not simultaneously optimize omics-specific contributions in multi-omics integration and enhance learning from limited target-domain data. We developed MOTLAB to address both challenges through optimized multi-omics weighting, and data augmentation. In 1,085 female TCGA breast cancer cases, MOTLAB yielded the highest AUROC with three-omics integration across all progression-free interval time settings, reduced prediction error, and lowered calibration error in most settings compared with transfer learning alone. MOTLAB performance remained robust across molecular and clinical subgroups, while MOTLAB-derived risk scores captured breast cancer-relevant pathway, miRNA, and methylation signals. MOTLAB provides an integrated strategy that combines omics-specific weighting, transfer learning, and data augmentation to improve breast cancer clinical outcome prediction in data-minority groups.

bioinformatics↗

bioRxiv2025

RefLA: Efficient reference-guided genome assembly from long reads

High-quality de novo genome assembly from long reads remains computationally demanding, while fast assemblers often compromise on contiguity or accuracy. This trade-off between speed and assembly quality presents a persistent challenge in large-scale genomic studies. Fortunately, high-quality reference genomes are now available for a growing number of species. These references can be leveraged to assist the assembly of closely related genomes, enabling efficient reconstruction without sacrificing fidelity--particularly when evolutionary divergence is moderate.We present RefLA, a reference-assisted assembler tailored for thirdgeneration long-read data. By combining alignment-guided structural refinement with localized reassembly, RefLA achieves accuracy comparable to or better than state-of-the-art de novo tools such as Flye and hifiasm, while running significantly faster. It also surpasses rapid assemblers like wtdbg2 and Shasta in both contiguity and base-level correctness.

bioinformatics↗

bioRxiv2025

Highly Efficient Multiplexed Genome Engineering and Clone Selection to Enable Next Generation Induced Pluripotent Stem Cell (iPSC)-based Cell Therapies

Human induced pluripotent stem cells (iPSCs) have revolutionized regenerative medicine and cellular therapies. To improve the functionality and safety of iPSC-based therapies, genome engineering has been employed to disrupt gene expression and introduce therapeutic transgenes. However, current genome editing methods face significant challenges, including labor-intensive procedures, low efficiency, and safety concerns. Here, we report a novel and efficient approach for multiplex genome engineering in iPSCs using Integrase-mediated Programmable Genomic Integration (I-PGI) technology. I-PGI combines CRISPR-mediated genome editing and site-specific integrases, enabling precise gene insertion without DNA double-strand breaks. By optimizing I-PGI components and protocols, we achieved multiple gene knockouts and knock-ins of up to five transgenes in a single process. Additionally, we developed a streamlined workflow for enrichment, deposition, and screening of single-cell clones with the desired genome edits. This approach significantly accelerates and improves the precision of multiplex iPSC engineering, paving the way for next-generation iPSC-based therapies.

genomics↗

bioRxiv2025

Mdig shapes 3D chromatin architecture of immune checkpoint genes and limits metastasis of triple negative breast cancer cells

Despite decades of progress in metastasis research, the identity of bona fide metastasis-driving genes remains elusive. By shifting the focus from individual genes to the three-dimensional (3D) architecture of the genome, we identify the mineral dust-induced gene (mdig) as a chromatin-topology regulator that suppresses immune-checkpoint activation and metastasis in triple-negative breast cancer (TNBC). Re-expression of mdig in mdig-knockout (KO) cells markedly reduced liver metastasis, whereas mdig loss activated epithelial-mesenchymal transition (EMT), T-cell exhaustion, and antigen-presentation pathways. Across multiple cancer types, high mdig expression predicted improved responses to immune checkpoint blockade (ICB). Mechanistically, mdig depletion derepressed inhibitory checkpoint genes, including PD-L1, PD-L2, LGALS9, and PLXNB1/2, accompanied by increased H3K9me3 and H3K36me3 at these loci. Hi-C profiling revealed that mdig maintains topologically associating domains (TADs) and long-range repressive loops at checkpoint loci; loss of mdig disrupted these structures, enabling coordinated activation of immune-evasion programs. Genome-wide analyses showed extensive TAD remodeling in mdig KO cells, with pronounced alterations on chromosome X. Collectively, these findings position mdig as a key chromatin-architectural regulator of immune evasion and metastatic behavior, underscoring its potential as a biomarker for ICB responsiveness in TNBC and beyond. Significance statementMetastasis remains the leading cause of cancer mortality, yet bona fide metastasis-driving genes remain difficult to define. By examining three-dimensional genome architecture rather than individual gene function, we identify mdig as a chromatin-topology regulator that restrains immune evasion and metastasis in triple-negative breast cancer. Loss of mdig disrupts TAD structures and long-range repressive loops at immune-checkpoint loci, enabling coordinated activation of EMT, antigen-presentation, and T-cell-exhaustion programs. Restoring mdig expression markedly reduces liver metastasis, and high mdig levels across cancers predict improved responses to immune-checkpoint blockade. These findings establish mdig as a key regulator of chromatin organization and a potential biomarker for immunotherapy responsiveness.

cancer biology↗

bioRxiv2025

Real-time functional connectivity-based neurofeedback of the DLPFC-amygdala pathway during threat-exposure attenuates anxiety

BackgroundAdaptive regulation of negative emotion is vital for mental health and dysregulations in this domain contribute to the major mental disorders, including anxiety and depression. On the neural level, efficient emotion regulation has been linked to functional communication between the dorsolateral prefrontal cortex (DLPFC) and the amygdala. Gaining voluntary control over this pathway may present an effective strategy for potentiating emotion regulation. ObjectiveAgainst this background, we developed a novel connectivity-based real-time fMRI (rt-fMRI) neurofeedback (NF) training that enables individuals to gain volitional control over the DLPFC-amygdala pathway during exposure to threatening stimuli. MethodsThe study employed a pre-registered, randomized, sham-controlled design with the experimental group (n = 22) receiving real-time NF information on connectivity between the right DLPFC and the amygdala during threat exposure and the sham control group (n = 23) receiving connectivity NF from a circuit not related to emotion regulation. The ability to maintain regulatory control in the absence of feedback was assessed after four NF runs. Primary outcomes included functional connectivity of the target pathway, as well as anxiety scores. ResultsThe results demonstrate that successful acquisition of self-regulation of the rDLPFC-amygdala top-down regulatory circuit in the experimental NF group facilitated attenuation of anxiety. ConclusionIn summary, our findings suggest that real-time fMRI neurofeedback (rtfMRI-NF) allows to volitionally enhance rDLPFC-amygdala connectivity, and in turn reduces negative emotional states, rendering the training a promising neurotechnology intervention for mental disorders.

neuroscience↗

bioRxiv2025

LISA: A Comprehensive R Package for Lentiviral Integration Site Analysis in Gene Therapy Safety Assessment

BackgroundGene therapy using integrating viral vectors has shown remarkable clinical success, but the random integration of vectors into the host genome poses potential safety concerns, particularly insertional mutagenesis. Common Integration Sites (CIS) near oncogenes significantly increase the risk of clonal expansion and malignant transformation. Current tools for integration site (IS) analysis lack comprehensive functionality for safety assessment, particularly in identifying risk-associated genomic regions and tracking clonal dynamics over time. ResultsWe developed LISA (Longitudinal Integration Site Analysis), a comprehensive R package specifically designed for gene therapy safety evaluation. LISA incorporates a novel approach that trains integration site profiles using cumulative curves derived from normal cell infusion samples and normal patient samples, with subsequent statistical testing to distinguish these normal samples from those with exceptional clonal expansion, thereby enabling effective identification of dominant clone formation. For longitudinal studies, LISA monitors fluctuations in clonal dynamics via Richness and Evenness metrics, and integrates this cumulative curve-based statistical testing approach into its early warning system to detect potentially dominant clones. Additionally, LISA features an optimized CIS detection algorithm with O (n log n) time complexity, achieved through chromosome-position encoding and a seed-site strategy, which facilitates efficient analysis of large-scale clinical datasets. The package also provides systematic functional annotations, including annotations for Safe Harbor regions, enhancer/promoter elements, and proximity to three curated gene sets: adverse event-associated genes (AE genes, n=36), cancer-related genes (n=980), and immune-related genes (n=2,485). Furthermore, it encompasses six categories of interactive visualizations and has been validated using real clinical data from gene therapy trials.

bioinformatics↗