Biology subjects

Zhang, W.

Publications and source records attributed to Zhang, W..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Test-Retest Reproducibility of Single- and Cross-Population White Matter Atlases in Diffusion MRI Tractography

Diffusion MRI tractography enables noninvasive mapping of white matter fiber tracts. Atlas-based white matter parcellation supports automated tract identification by assigning individual streamlines to atlas-defined clusters and anatomical tract labels. Because clustering-based white matter atlases are constructed from cohort-specific tractography data, they capture common white matter organization represented in the atlas-construction population. Although major white matter anatomy is shared across populations, subtle population-related anatomical variability may influence atlas representation and test-retest correspondence. Therefore, the reproducibility and cross-population generalizability of tractography-based white matter atlases are important considerations for quantitative neuroimaging studies. In this study, we evaluated whether incorporating cross-population anatomical variability during atlas construction improves test-retest reproducibility. To do so, we compared a single-population ORG atlas constructed from a Western cohort with the cross-population East-West White Matter Atlas constructed from both Eastern and Western cohorts. Test-retest diffusion MRI scans from the Human Connectome Project Young Adult (HCP-YA) dataset and the Connectivity-based Brain Imaging Research Database (C-BIRD) were analyzed as independent Western and Eastern test-retest cohorts, respectively. Whole-brain tractography was reconstructed for each dMRI scan and parcellated using both atlases. Reproducibility was assessed using tract detection rate at both cluster level and anatomical tract level, weighted Dice coefficient, and the relative difference of mean fractional anisotropy (FA). Both atlases showed stable tract detection across test-retest scans in both cohorts. Compared with the ORG atlas, the East-West White Matter Atlas achieved higher overall spatial overlap and lower test-retest variability in mean FA, although atlas performance varied across individual tracts. These findings suggest that integrating cross-population information during atlas construction improves the reproducibility and generalizability of white matter atlas mapping across independent populations and imaging protocols.

neuroscience↗

bioRxiv2026

A Single-cell Spatiotemporal Atlas Encoding Age-dependent Evolution of the Ischemic Penumbra

Aging is the predominant risk factor for stroke and a major driver of poor neurological recovery. Although the ischemic penumbra represents a key therapeutic target, its age-dependent molecular landscape remains poorly understood. Here, we generated a spatiotemporal transcriptomic atlas of the post-stroke penumbra in young (3-4 months) and aged (19-20 months) mice at acute (day 3) and chronic (day 14) stages following transient focal cerebral ischemia. Integrating spatial transcriptomics with dissociated single-cell RNA sequencing revealed a persistent transcriptomic penumbra (TP) that remains detectable for at least 14 days after stroke in both age groups, far beyond the window suggested by conventional imaging modalities. Aging profoundly altered TP organization, resulting in dysregulated energy metabolism, impaired neurovascular repair, diminished pro-reparative cell-cell communication, and extensive remodeling of transcriptional regulatory networks. Using this atlas, we further identified regulatory hub genes whose predicted perturbation could simultaneously counteract multiple age-associated pathological pathways. These data define the molecular architecture of the aging post-stroke penumbra and provide a publicly accessible framework for mechanistic discovery and therapeutic target prioritization aimed at improving recovery after stroke.

neuroscience↗

bioRxiv2026

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the posterior, while largely overlooking how the circuits compute the model evidence (normalization constant in Bayes' theorem), a key quantity that measures how well a model explains the observed data.Thus, it remains largely unknown about how the recurrent circuits compute the model evidence. The present study performs rigorous theoretical analyses of the continuous attractor networks, a canonical recurrent circuit model, and reveals that the nonlinear circuit dynamics can simultaneously compute the posterior and model evidence in first two dominant subspaces within the circuit dynamics. Specifically, the circuit dynamics in the stimulus feature subspace implements the Langevin posterior sampling, and the circuit dynamics in the subspace of total neuronal activity computes the model evidence in a way analogous to the evidence lower bound in stochastic variational inference. We further extend the circuit model to compute the model evidence of multiple inputs, and simulations validate the computation in the network. Our work for the first time reveals the concurrent model evidence and posterior sampling in subspaces in continuous attractor networks, significantly deepen our understanding of the computational algorithms adopted by the neural circuits.

neuroscience↗

bioRxiv2026

Stroke and Alzheimer's disease have distinct consequences on neurovascular function but synergize to increase amyloid deposition

INTRODUCTION: We examined whether stroke induces chronic cerebrovascular dysfunction and thereby exacerbates A{beta} pathology. METHODS: We exposed Tg2576 mice to a transient mild subcortical ischemia and examined cerebrovascular function at chronic timepoints as well as reactive astrocytes and A{beta} deposition post-mortem. RESULTS: Baseline cerebral blood flow and cerebrovascular reactivity (CVR) are both influenced by anesthesia regimen. A{beta} strongly impairs CVR at earlier ages, while mild ischemia impairs CVR at later ages. Mild ischemia reduces neurovascular coupling more strongly than A{beta} and synergistically increases A{beta} deposition in Tg2576 mice. DISCUSSION: Stroke-induced cerebrovascular dysfunction and astrocyte reactivity persist for long periods after the injury. Although there is variability in the spatiotemporal progression of ischemia- and A{beta}-induced vascular impairments, they generally follow the pattern of Thal staging. Surprisingly, the ischemia+A{beta} group showed the least cerebrovascular dysfunction yet an exacerbation of A{beta} deposition, suggesting that these pathologies are connected but not tightly coupled.

neuroscience↗

bioRxiv2026

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

genetics↗

bioRxiv2026

The devil in citizen science data: observation processes invalidate the causal inference that photovoltaic policy reduces bird diversity

Citizen science data are increasingly used to infer biodiversity change, but causal claims based on such data are credible only if sampling effort and its temporal shifts are explicitly modeled. Zhang et al. (1) used citizen science data to conclude that greater photovoltaic policy stringency, measured using the photovoltaic policy stringency index (PSI), reduced county-level bird diversity in China. We reproduced their fixed effects and instrumental variable estimates. However, the observed Shannon diversity derived from pooled citizen science records reflects both bird communities and sampling effort, which the authors' controls do not adequately capture. Accounting for observer count changed the reported statistically significant 2.10% decline in Shannon index to a nonsignificant 0.58% increase (P = 0.288) per one-standard-deviation increase in PSI, and rendered the instrumental variable estimate statistically indistinguishable from zero (P = 0.912). Yet observer count is only one of many sources of sampling bias. PSI was also associated with multiple dimensions of sampling effort, consistent with sampling effort acting as a potential mediator in the PSI-diversity chain. The sampling domain also shifted markedly from 2014 to 2023: recorded county-months increased almost 24-fold, median observer count rose from one to three, and zero-duration records declined from 57.2% to 0.17%. Without adequate adjustment, these shifts confound estimates of temporal change in observed bird diversity. Beyond its inadequate treatment of sampling effort, the original study also misinterpreted its statistical results. Although the reported R{superscript 2} values are high, they are dominated by county and year-month fixed effects, with PSI contributing a partial R{superscript 2} of only 0.048% on observed Shannon index. The PSI-photovoltaic-area correlation is also weak (r = 0.0414) and vanishes after accounting for fixed effects (P = 0.977). Furthermore, the released bird observation data contain many erroneous outliers, raising significant concerns about insufficiently rigorous data preprocessing and quality control. These results show that the released data cannot properly distinguish ecological change from sampling effort change. Robust inference from citizen science data requires checklist-level effort metadata, explicit correction for spatiotemporal sampling shifts, and close collaboration among researchers with complementary methodological and ecological expertise.

ecology↗

bioRxiv2026

AB-Free Kava and its Kavalactones Suppress Cigarette Smoke- and Lipopolysaccharide-Induced Lung Inflammation: Efficacy and Mechanisms

Cigarette smoke-induced lung inflammation is a central driver of pulmonary diseases. The limited efficacy of current anti-inflammatory agents underscores the need for structurally novel therapeutics with distinct mechanisms. We recently demonstrated that AB-free kava, a flavokavains A/B-depleted formulation from Piper methysticum containing six major kavalactones, effectively suppresses cigarette smoke-induced lung inflammation in mice. This study aims to identify the bioactive constituent(s) and elucidate underlying mechanisms. These kavalactones revealed a clear structure-activity relationship in suppressing lipopolysaccharide (LPS)-stimulated prostaglandin E2 (PGE2) production in macrophages with desmethoxyyangonin (DMY) as the most potent kavalactone whereas dihydrokavain (DHK, a structurally similar analog) with minimal activity. DMY also effectively reduced LPS-induced interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-) production while DHK was ineffective. Mechanistically, DMY, but not DHK, attenuated COX-2 induction and reduced phosphorylation of cAMP response element-binding protein (CREB). Pharmacological inhibition of protein kinase A (PKA) similarly reduced p-CREB, COX-2 and PGE2, supporting a PKA-dependent CREB/COX-2 signaling in mediating PGE2 suppression while these effects were independent of nuclear factor kappa B (NF-{kappa}B) and activator protein 1 (AP-1) signaling. Similar results were observed for DMY and DHK in attenuating cigarette smoke condensate-induced proinflammatory pathways and PGE2 production. Consistently, DMY demonstrated significant in vivo efficacy in suppressing cigarette smoke-induced lung inflammation while DHK was not effective. Interestingly, dihydromethysticin (DHM) demonstrated the greatest in vivo anti-inflammatory efficacy, although it only exhibited moderate in vitro potency, likely due to its superior bioavailability over DMY. Concordantly, cigarette smoke exposure elevated p-CREB and COX-2 expressions in mouse lungs, which were attenuated by AB-free kava and its bioactive kavalactones with the extent of suppression correlating with their in vivo anti-inflammatory efficacy. DHM effectively suppressed LPS-induced neutrophil accumulation in mouse lungs as well. Collectively, these studies identify bioactive kavalactones in AB-free kava that suppress cigarette smoke- and LPS-induced lung inflammation through the modulation of the PKA/CREB/COX-2 signaling axis, providing a foundation for developing structurally distinct anti-inflammatory agents, particularly targeting smoke-induced inflammation and associated pulmonary diseases.

immunology↗

bioRxiv2026

Paralemmin 2 is a shared, alternatively-spliced regulator of axon initial segments and paranodal junctions in oligodendrocytes

Nervous system function depends on highly specialized axonal membrane domains. For example, axon initial segments (AIS), nodes of Ranvier, and paranodal junctions are essential for action potential initia-tion and saltatory conduction. These domains use shared molecular machinery converging on the master scaffolding protein AnkyrinG (AnkG). Using endogenous AnkG-TurboID proximity proteomics, we identify Paralemmin 2 (Palm2), a previously uncharacterized CNS protein, as a shared component of the AIS and nodes of Ranvier in neurons, and paranodes in oligodendrocytes. Palm2 localizes transiently to the AIS and nodes during development, but is found at paranodes throughout life, reflecting cell type-specific splicing of a lipid-modified domain both necessary and sufficient for AnkG interaction. Mice lacking oli-godendroglial Palm2 assemble normal paranodes during development, but aged mice lose paranode in-tegrity due to loss of paranodal AnkG and Neurofascin-155. Our findings identify Palm2 as a lipid raft-associated regulator of axonal and oligodendroglial membrane domains and reveal cell type-specific pro-tein variants as a recurring principle for specializing axonal and glial membrane architectures.

neuroscience↗

bioRxiv2026

Human panepigenome represents epigenomic diversity

The human pangenome captures genetic diversity beyond a single linear reference, yet conventional DNA methylome maps largely assume that the underlying CpG substrate is fixed. Here we present a first draft of the human panepigenome on DNA methylation, generated from 440 haplotype-resolved long-read methylomes spanning 26 globally distributed populations. This map jointly captures CpG presence, absence and methylation across diverse human haplotypes, revealing 12.8 million CpGs absent from GRCh38 and an average of 1.2 million variant-associated CpGs (var-CpGs) per methylome. Var-CpGs captured population-associated epigenomic variation beyond that represented by shared reference CpGs, and structural variants could create or remove entire CpG islands, frequently through mobile element insertions. Promoter var-CpG methylation was associated with transcript expression, with most associations persisting after adjustment for the underlying CpG-altering variant, whereas a subset showed evidence of mediation or variant-by-methylation interactions. CpG-altering variants were also enriched among molecular QTLs in HPRC2 and across GTEx tissues, and among clinically and pharmacogenomically annotated loci. Together, these results establish a haplotype-resolved framework in which human epigenomic diversity reflects not only variation in methylation level but also genetic variation in presence or absence of CpG substrates, providing a foundation for panepigenomic studies across tissues, populations and disease contexts.

genomics↗

bioRxiv2026

MOLECULAR BASIS FOR PINK1 MATURATION

Phosphatase and tensin homolog (PTEN)-induced putative kinase 1 (PINK1), a key regulator of mitophagy, has been linked to the pathogenesis of Parkinson's disease (PD). PINK1 recruits Parkin, an E3 ubiquitin ligase, triggering mitophagy in response to mitochondrial damage. During mitophagy, the quantity, stability, and activity of PINK1 must be strictly regulated; however, the mechanisms governing these parameters under cellular stress are still unclear. Herein, we determined the structural basis for PINK1 maturation mediated by heat shock protein 90/cell division cycle 37/FK506-binding protein 51 (HSP90/CDC37/FKBP51) chaperone complex. We identified PINK1-associated proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and determined the structures of the complexes using Cryo-Electron Microscopy (Cryo-EM). Results showed that FKBP51 potentially interacts with a conserved leucine-proline-phenylalanine (LPF) motif on the activation loop of PINK1 and negatively regulates PINK1 functions in mitophagy. A PINK1 mutation located at the FKBP51 recognition site is linked to mitophagy deficiency, which can be partially rescued by specific inhibition of FKBP51. These findings reveal a general mechanism for PINK1 recognition by the HSP90/CDC37/FKBP51 chaperone complex and suggest a potential approach for upregulating PINK1 activity, which is impaired in PD.

biophysics↗

bioRxiv2026

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

Computational design has transformed many fields of engineering, where simulators can explore millions of candidate design configurations before experimental development and testing. Therapeutic design in biomedicine has resisted computational design approaches because disease progression and therapeutic response emerge from interactions among many cell types within human tissue, governed by biochemical parameters that are largely unknown and potentially unknowable. Here, we introduce the Cell Interaction Foundation Model (CIFM), a virtual tissue model that forward-simulates the transcriptional dynamics of cells in human tissue under arbitrary therapeutic conditions based upon a spatial transcriptomic seed. CIFM is a geometric graph neural network trained by self-supervised masked-transcriptome prediction on millions of cellular microenvironments spanning human tissue types and disease states; generative, auto-regressive, monte-carlo play-out, then, simulates transcriptional dynamics under combinatorial perturbations from a spatial transcriptomic seed. We validate CIFM by showing accuracy gains in gene expression prediction and imputation, disease classification, recapitulation of perturbation responses in prostate cancer models, and recovery of T cell-tumor signaling measured in cell-cell sequencing experiments. Beyond such conventional tasks, CIFM enables target identification and therapeutic design through generative tissue simulation play-outs. Analyzing over 106 single and combinatorial perturbations, CIFM designs immunotherapy strategies for cancer and autoimmune disease that exploit combinatorial manipulation of signaling pathways to induce or suppress immune activation. Broadly, CIFM shows how generative artificial intelligence methods can be applied to model emergent behavior in highly interacting biological systems, yielding new approaches to fundamental understanding of tissue behavior as well as large-scale therapeutic design.

bioinformatics↗

bioRxiv2026

A Five-Gene Stromal-EMT Signature Predicts Prognosis, Immunotherapy Resistance, and Therapeutic Vulnerability in Bladder Cancer

Background: Bladder cancer has entered an era in which immune checkpoint blockade (ICB) and antibody-drug conjugate (ADC)-based combinations are reshaping clinical management. However, transcriptomic scores that connect prognosis, tumor microenvironment state, and treatment response are incompletely defined. Methods: Open-access TCGA-BLCA RNA-seq, clinical, mutation, copy-number, and RPPA data were downloaded from the Genomic Data Commons (GDC). Tumor-normal differential expressions, survival screening, LASSO-Cox modeling, train-test validation, GEO validation, pathway enrichment, immune signature scoring, mutation/CNV/RPPA support, drug sensitivity prediction, single-cell/spatial localization, and ICB validation were performed using reproducible Python and R scripts. A reduced model was derived using only genes shared by TCGA, GSE13507, and GSE31684. The fixed formula was then applied without refitting to IMvigor210 and GSE176307. Results: A five-gene model composed of EMP1, AHNAK, TNFRSF14, CLEC2D, and GSDMB retained TCGA internal prognostic value (train C-index 0.693, test C-index 0.605, all-sample C-index 0.667; TCGA test log-rank p = 0.015), although GEO survival validation in GSE13507 and GSE31684 was modest. High-risk tumors were enriched for epithelial-mesenchymal transition (EMT), TNF-alpha/NF-kB signaling, inflammatory response, hypoxia, complement, CAF, macrophage, checkpoint, and cytotoxic programs. Single-cell and spatial analyses localized the score to basal tumor, endothelial, fibroblast, and perivascular compartments. In IMvigor210, risk scores were higher in ICB non-responders than responders (Wilcoxon p = 0.044; AUC for non-response = 0.580), high-risk tumors had a lower responder rate (17.6% vs. 28.0%), and high risk predicted poorer OS (log-rank p = 0.016; multivariate continuous risk HR = 3.15, p = 0.044). GSE176307 showed directionally consistent but non-significant response results (AUC = 0.576). Conclusions: The five-gene score is best interpreted not as a standalone universal prognostic classifier, but as a compact stromal-EMT and immune-suppression phenotype associated with inferior ICB response. These findings support a framework linking prognosis, microenvironment biology, immunotherapy resistance, and therapeutic hypotheses in bladder cancer.

bioinformatics↗

bioRxiv2026

TRACER navigates rearrangement-driven sesterterpene chemical space via multimodal enzyme-product representation learning

Skeletal rearrangement drives the immense structural complexity of terpene, yet predicting it remains a formidable challenge due to sequence-function decoupling in terpene synthases. Here, we established TRACER (terpene rearrangement annotation via co-attentive enzyme-product representation), a multimodal framework mapping the latent associations between sequence-derived enzyme representations and product chemotypes. Retrospective validation proved TRACERs exceptional precision in predicting compound classes and discriminating skeletal rearrangement (SR) from non-skeletal rearrangement (NSR) pathways. TRACER-guided genome mining characterized two bifunctional synthases, FsPS and AcPS, uncovering four unprecedented carbon skeletons. Density functional theory calculations deciphered these cyclization cascades, pinpointing a critical 5/6/11 tricyclic intermediate as the key branching node for scaffold diversification. Mutagenesis and molecular dynamics simulations suggested that E305 in FsPS enables rearrangement by maintaining active-site water exclusion, whereas its alanine mutation causes premature carbocation quenching. Collectively, this work establishes a predictive paradigm for the rational discovery and mechanistic elucidation of complex terpene architectures.

synthetic biology↗

bioRxiv2026

Probing intracellular physical environments by rotational and translational single-particle tracking

Single-particle tracking is widely used to probe nanoscale dynamics in biological systems, yet most approaches rely exclusively on translational motion, overlooking rotational dynamics that offer complementary information about the local physical environment. Here, we present a simultaneous rotational and translational single-particle tracking approach using a vortex-engineered point spread function in a single detection channel. We validate this approach with static and freely diffusing nanorods, demonstrating accurate orientation recovery and quantitative agreement with theoretical predictions of rotational diffusion. Using a biomimetic lipid bilayer system, we show that translational and rotational diffusion exhibit distinct sensitivities to environmental perturbations, confirming that these two modalities capture complementary local environment information. Applying this framework to living HeLa cells, we show that combined translational and rotational diffusion signatures define distinct biophysical fingerprints of cytoplasmic and endocytic compartments and reveal compartment-specific responses to metabolic perturbation. Finally, time-resolved analysis of individual endocytic compartments uncovers dynamic changes in the local physical environment that are inaccessible to conventional translational tracking. By coupling translational and rotational readouts, this framework opens a new dimension for probing the physical organization and dynamics of living systems at the nanoscale.

biophysics↗

bioRxiv2026

Tyrosine phosphorylation and dimerization cooperatively activate NAMPT to enable NAD+ synthesis in cancer

Nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD salvage pathway, is frequently upregulated in cancer, yet mechanisms regulating its catalytic activity remain undefined. We identify NAMPT as a direct substrate of multiple proto-oncogenic tyrosine kinases, including ALK, insulin receptor, IGF1R, and PDGFRA. Phosphoproteomics identified NAMPT Y188 as the major phosphorylation site, including the oncogenic fusion kinase NPM1::ALK. NAMPT interacted with NPM1::ALK in the cytoplasm, nucleus, and mitochondria, while Y188 phosphorylation enhanced catalytic activity, NMN/NAD biosynthesis, and downstream metabolism. Conversely, the Y188F mutant reduced enzymatic activity, proliferation, and clonogenicity, whereas disrupting dimerization similarly impaired phosphorylation and function. Interactome analyses showed phosphorylation and dimerization cooperatively remodel NAMPT-associated networks, enriching phosphorylated dimers for metabolic/redox regulators and monomeric NAMPT for ribosome biogenesis. NAMPT inhibition suppressed the growth of both ALK inhibitor-sensitive and -resistant lymphoma cells and enhanced the efficacy of ALK inhibition, revealing kinase-dependent NAMPT activation as a metabolic vulnerability in oncogene-driven cancers.

cancer biology↗

bioRxiv2026

Structural evolution of a yeast amyloid in vivo is shaped by chaperones

Cryo-electron microscopy (cryo-EM) studies of amyloid fibrils have revealed endpoint structures of disease-relevant filaments and polymorphic intermediates formed during in vitro assembly of prion-like proteins. However, how transmissible prion or prion-like amyloids evolve during de novo formation and maturation in living cells remains unknown. Here, using the yeast prion [PSI+] as a model, we isolated Sup35NM amyloid fibrils from successive stages of [PSI+] maturation in Saccharomyces cerevisiae and characterised their near-atomic structures and population-level structural diversity by combining cryo-EM and atomic force microscopy. We show that intermediate and mature states differ in predominant fibril structure and the regions of the Sup35 sequence incorporated into the core, and that structural diversity decreases during maturation. Curing of [PSI+] at the mature state by guanidine hydrochloride (GdnHCl), which selectively inhibits ATPase activity of the chaperone Hsp104, restored both the predominant intermediate amyloid structure and the broader structural diversity characteristic of the intermediate state. In addition, Hsp104, Ssa1 (Hsp70) and Sis1 (Hsp40) associate differently with fibrils from the two states. Together, these findings provide direct structural evidence for amyloid evolution in vivo and support a chaperone-mediated mechanism of conformer selection within a polymorphic amyloid population.

molecular biology↗

bioRxiv2026

Hidden genetic diversity in 320 nearly-complete East Asian genome assemblies

East Asian populations, representing over 20% of the global population, remain critically underrepresented in human genomic studies, limiting our understanding of population-stratified genetic variation and its implications for health and disease. Here we present the first phase of the Asian Pan-Genome project (APG), comprising 320 nearly complete, fully phased haploid genome assemblies from 160 East Asian individuals. These assemblies achieve unprecedented quality, with an average contig N50 of 144.3 megabase pairs and an average quality value of 64.5. Leveraging these superior assemblies, we reveal previously uncharacterized diversity in human repeatome, including population-stratified patterns in centromere satellites and rDNA arrays. Compared to existing global human genome assemblies, the newly generated genomes supplement 152 million base pairs of novel sequences, 355 gene gains, 18,300 structural variation loci and 26 large euchromatic inversions missing from current human pangenomes. We perform population stratification analyses of structural variations, and further resolve the structural haplotypes of complex genomic regions such as Major Histocompatibility Complex and Survival Motor Neuron loci across global pangenomes, exemplifying tandem-duplicate and inversion-rich complex locus architectures in the human genome, respectively. This resource provides a critical foundation for human genetic studies, especially for East Asian populations, promoting more accurate variant discovery, reducing bias, and ultimately advancing the equity and efficacy of genomic medicine.

genomics↗

bioRxiv2026

Integrative AI-Enabled Virtual Cell Modeling Reveals a Clinically Relevant Latent Effector State of Human CD8 T Cells Undetectable by Conventional Analyses

Understanding how immune checkpoint inhibitors (ICIs) reshape human T-cell responses requires models that move beyond static transcriptomic snapshots and discrete cell-state classifications. Here, we present an integrative AI-enabled virtual cell framework that represents human CD8 T-cell responses as dynamic and computable systems during ICI therapy. By integrating single-cell RNA sequencing with paired T-cell receptor sequencing within the C2S-scale foundation model, we construct a virtual representation of individual T cells, in which each cell is encoded by a unique functional identity that captures its transcriptional, signaling, and clonal characteristics. Using this framework, we identify a previously unrecognized dynamic latent effector state of CD8 T cells characterized by intermediate expression of effector genes, distinct signaling activity, and ongoing clonal expansion. Across independent patient cohorts, the virtual cell model consistently indicates that ICI therapy mainly acts by unmasking pre-existing effector potential rather than inducing de novo effector differentiation. Notably, this latent effector population remains transcriptionally restrained despite active signaling and clonal expansion, revealing a hidden reservoir of antitumor immune capacity. More broadly, our study demonstrates how AI-enabled virtual cell modeling can reconstruct latent cellular states and their dynamic transitions from multidimensional single-cell data. By incorporating functional identity into virtual cell model, this framework uncovers biologically meaningful yet non-obvious T-cell effector program during cancer immunotherapy and provides a generalizable approach for studying immune dynamics in human disease.

immunology↗