Biology subjects

Tang, Z.

Publications and source records attributed to Tang, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Scaling genetic discovery through automated post-GWAS interpretation

Genome-wide association studies (GWAS) have made association discovery systematic and cumulative, yet evidence for interpreting associated loci remains selective, heterogeneous and difficult to accumulate across studies. Here we present Post-GWAS Intelligence (PGI), which couples context-dependent post-GWAS analysis to a common representation of analysis-derived evidence. Its multi-agent engine, VariantAgent, executes workflows, records tool-derived results in standardized variant-centred units and generates trait-level interpretations linked to their analytical basis and evidential status. Complementary benchmarks showed that VariantAgent outperformed tested general-purpose and biomedical agents, with its largest advantage when success required both a correct answer and a valid analytical trace. Across 34 published GWAS, VariantAgent achieved a mean per-study recovery of 82.1% for directly comparable findings. The largest gains lay downstream of association mapping, where uniform analysis extended published interpretations and identified regulatory relationships at established risk loci not previously reported in the source studies. Analysis of 1,041 GWAS yielded 530,949 evidence units and corresponding reports, which PGI organized into a cross-trait catalogue. Catalogue interrogation resolved an apparent cross-trait overlap into distinct signals and trait-specific biological hypotheses. This work demonstrates how PGI can extend genetic discovery beyond association mapping by enabling biological evidence to accumulate across studies.

genetics↗

bioRxiv2026

Agency and Embodiment Provide Dissociable Benefits to Perception, Metacognition and Neural Processing During Active Sensing

Metacognition, judging how reliable our own perceptual decisions are, is central to adaptive behavior. Such judgments hinge on the reliability of the sensory evidence on which that decision is based, and active sensing through self-directed exploration is known to improve perception itself. Yet, how agency, the authorship over self-initiated actions, and the processing of the sensory consequences, which is implicit to active sensing, modulate metacognition and its calibration, remains unknown. Whether physical embodiment adds to this beyond virtual exploration is similarly unclear, yet relevant both for daily life and for self-monitoring in agentic AI. Here, we combined a naturalistic auditory search paradigm with mobile electroencephalography. Twenty participants performed a target-detection task under three conditions: active embodied exploration , active screen-based exploration, and passive listening to replays of their own trials, while we obtained steady-state auditory evoked potentials (SSAEPs) to characterize stimulus-locked neural responses. We find that both agency conditions improved accuracy, metacognitive sensitivity, and overall SSAEP amplitude relative to passive listening, indicating that agency, rather than whole-body movement, drives these enhancements. Crucially, however, embodiment uniquely grounded both the target-evoked neural response and the subjects' confidence to immediate sensory evidence. Thus, agency alone broadly enhances perceptual and neural processing, but embodiment specifically anchors confidence, neurally and behaviorally, to the present.

neuroscience↗

bioRxiv2026

When label-free morphology is sufficient for targeted cellular measurements

Label-free morphology captures cellular responses, but its ability to substitute for targeted cellular measurements depends on the intended use. We assess measurement sufficiency using nearly 10 million paired phase-reporter observations across 1,000 gene knockouts, 52 fluorescent reporters and 73 A549 screens. Ten predictors differed in recovery across held-out fields, genes and screens. Same-cell pairing controls identified reporter-state information beyond perturbation averages and measured covariates. Perturbation ranking often persisted despite reduced variation in predicted response magnitudes. Predictions of held-out knockout responses retained a median 79% of the strongest hits, whereas functional-term retention varied. Atlas-derived criteria identified 30 quantitative and 7 ranking proxies for a fixed ensemble, with reference reproducibility constraining interpretation. In independent primary-hepatocyte data, brightfield predictions prioritized 10 of the 11 compounds with the largest measured metabolic-activity losses among 217 held-out compounds. Measurement sufficiency therefore depends on target, predictor, context and scientific task, beyond predictive correlation alone.

bioinformatics↗

bioRxiv2026

The common evolution of fungicide persistence in cryptococcosis patients

Fungicide persistence, the ability of dormant fungal cells to survive lethal drug exposure, undermines treatment efficacy, yet its clinical evolution remains largely unexplored. We assembled a nationwide collection of clinical Cryptococcus neoformans isolates from China and revealed extensive inter strain variation in amphotericin B (AmB) persistence. This variation can arise from common evolutionary events in patients, generating previously undescribed high persistence cryptococcal variants refractory to AmB clearance. Genome wide fitness landscape analysis shows that persistence associated mutations generally impose minimal fitness costs, facilitating persistence evolution even in fungistatic resistant and hypervirulent genetic backgrounds. Machine learning identified deficient ACO2 expression as a key predictor of high persistence clinical isolates. ACO2 deficiency reproduces the high persistence phenotype by promoting POPC accumulation, which competes with AmB for its target. Moreover, the clinical stage antifungal T-2307 effectively eliminates high persistence strains across diverse genetic backgrounds. Collectively, our findings unveil common evolution of fungicide persistence in cryptococcosis patients, representing a previously overlooked clinical concern.

microbiology↗

bioRxiv2026

Genome evolution at the extreme of angiosperm miniaturization

Eukaryotic genomes vary by several orders of magnitude, yet this vast variation bears little relation to the complexity of the organisms they encode. Thus, how genome evolution accompanies changes in organismal complexity remains unresolved. A central obstacle is that major differences in body plan usually occur among deeply divergent lineages, entangling body plan evolution with the genomic divergence accumulated over long independent histories. Duckweeds offer a rare system in which successive body plan reductions can be traced within a single plant family. Across this trajectory, body size declined by nearly an order of magnitude and roots were progressively lost, culminating in the extreme of angiosperm miniaturization. Yet genome size increased nearly sixfold. Here, using a new chromosome-scale genome of Wolffia globosa and comparative genomics across nested evolutionary scales, we show that genome size, gene number, and functional repertoire followed distinct trajectories during miniaturization. Genome expansion was driven largely by transposable element accumulation, whereas the number of protein-coding genes remained stable. Aquatic adaptation itself promotes functional simplification, but establishes only a baseline. Duckweeds pushed this streamlining much further through additional contraction of developmental, structural, and biotic defense functions, alongside selective expansion of functions associated with growth and abiotic adaptation. This remodeling accumulated across successive evolutionary transitions through continued contraction of the same gene families and, more commonly, contraction of different families affecting the same biological processes. Organismal complexity may therefore reflect not simply the size of a genome or its functional repertoire, but how that repertoire is selectively reconfigured through evolution.

evolutionary biology↗

bioRxiv2026

Depleting prion protein using splice-switching small molecules

Prion diseases are fatal neurodegenerative disorders driven by prion protein (PrP) misfolding, and lowering cellular PrP represents a promising therapeutic strategy. Here we report a small-molecule approach that reduces PrP by modulating pre-mRNA splicing of the PRNP gene. Through chemical modification of the clinically approved splicing modulator risdiplam, we generated CP3, the first class of compounds that selectively activate a cryptic exon in PRNP and routes its mRNA product for degradation, reducing PrP by [~]70% in cells. We demonstrate that CP3 activity critically depends on alternative splicing factor Luc7L, revealing a novel requirement for alternative splicing factors in small molecule splicing modulation. Strikingly, co-administration of CP3 with a Luc7L activator PTC258 significantly lowers PrP levels in the brains of transgenic mice. These results establish splicing modulation as a powerful strategy for PrP reduction and highlight the potential of small-molecule cooperativity for therapeutic RNA targeting.

biochemistry↗

bioRxiv2026

Computational reconstruction of hierarchical cis-regulatory networks reveals synergistic transcription control and disease-associated rewiring

Gene regulation emerges from coordinated interactions among dispersed cis-regulatory elements, yet how these elements integrate into functional regulatory networks and collectively regulate gene transcription remains poorly understood. Here, we present ORIGAMI, a multi-omics, gene-centric deep learning framework that reconstructs functional cis-regulatory networks constrained by transcriptional output. ORIGAMI formulates cis-regulatory modeling as a latent graph inference task, which integrates DNA sequence, epigenomic signals, and three-dimensional chromatin priors to infer denoised regulatory graphs that capture functional interactions rather than structural proximity alone. The inferred regulatory graphs exhibit distinct topological regimes, where hierarchical and modular organization encodes cell-state-specific functional demands and enables synergistic transcriptional control. Furthermore, we show that these regulatory architectures undergo measurable state-dependent rewiring across disease contexts. Finally, ORIGAMI accurately predicts the transcriptional consequences of both cis- and trans-regulatory perturbations and links the rearrangement of regulatory architecture to perturbation response. Together, ORIGAMI advances a network-based view of gene regulation and establishes a foundation for virtual cell modeling of regulatory dynamics.

bioinformatics↗

bioRxiv2026

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

Cancer progression and outcomes are driven in part by changes to molecular networks that result from genetic and/or environmental perturbations. These network changes manifest across multiple interconnected network layers and include accumulation of somatic mutations, altered proteinprotein interactions and dysregulated gene-expression. Here we describe a graph autoencoder-based framework (Graph Autoencoder-Delta (GAE-{Delta})), for characterizing phenotype-specific gene role shifts across multi-omics data. Given samples stratified into two contrasting phenotypic groups and a prior gene interaction network, GAE-{Delta} constructs group-specific gene graphs for each omics modality and trains, for each modality, a single graph autoencoder jointly on both group graphs, so that the two group-conditional embeddings share a common latent space. Contrasting these embeddings defines a multi-omics embedding-shift representation for each gene that reflects how its network role reorganizes across phenotypic contexts. These gene-level shifts are subsequently used for unsupervised gene prioritization, multi-omics late fusion and sample-level classification. Applied to five TCGA cancer types with a survival endpoint, GAE-{Delta} achieves competitive or superior predictive performance compared with classical network-based methods and multi-omics matrix-factorisation methods (MOFA+, iNMF), with statistically significant AUC gains over MOFA+ in three of five cohorts and statistical ties on the remaining two. Beyond predictive performance, the consensus shift genes are significantly enriched for known cancer drivers in three of five cohorts (hypergeometric p < 0.01; 11-17x fold-enrichment), whereas matrix-factorisation baselines reach p < 0.05 in zero of five cohorts (best per-cancer p = 0.06), indicating that GAE-{Delta} captures biological signal that linear factor methods miss. In summary, the GAE-{Delta} approach provides for both improved outcome classification as well as for biological and mechanistic discovery through deep network-based integration of disease-associated multi-omics data.

bioinformatics↗

bioRxiv2026

The Structural Code of Breast Cancer Proteoform: Alternative Splicing-driven Protein Isoform Variation and Functional Diversification

Alternative splicing generates extensive transcript diversity, but the extent to which this diversity contributes to the translated proteome and reshapes protein function remains unclear. Here, we develop 3DisoGalaxy, an atlas integrating transcript reconstruction, ribosome profiling, structural modelling and regulatory annotation in breast cancer. We find that 58,292 of 90,929 transcripts are linked to translation-supported ORFs, 71.9% of which arise from non-canonical transcript structures. These products are broadly accommodated within established protein fold space, indicating that alternative isoforms frequently preserve recognizable protein architectures despite substantial sequence variation. Functional divergence is instead concentrated in domains, intrinsically disordered regions, localization determinants and regulatory motifs. Motif remodelling is biased towards gains and is more extensive in cancer-driver genes than in other genes, linking isoform-associated regulatory variation to cancer-relevant proteins. As an example of domain remodelling, we identify an AKT1-{Delta}PH isoform that lacks the N-terminal PH domain while retaining the kinase core, with immunoblotting revealing a lower-molecular-weight AKT1 species consistent with this isoform. Together, our results show that alternative transcript diversity extensively enters translation-supported proteoform space and establish a systematic link between transcript variation and protein functional diversification.

cancer biology↗

bioRxiv2026

An Isoform-Centric, Structure-Aware Framework for Protein Function Prediction and Evaluation, Instantiated in 3DisoDeepPF

Understanding and accurately predicting function across protein isoforms has been a long-standing challenge with profound implications for both biological and translational research. However, most functional annotations and benchmarks remain tied to genes or reference protein sequences, leaving limited support for distinguishing protein isoforms from the same gene whose sequence, structure and domain composition may lead to different functions. To address this gap, we developed an isoform-centric framework for protein function and domain annotation that integrates a dense graph of sequence and structure similarity with protein features. We implemented this framework as 3DisoDeepPF, a graph-based multimodal model, and applied it to a breast cancer isoform atlas. Across canonical benchmarks and evaluations at isoform resolution, 3DisoDeepPF showed strong performance in predicting GO terms and Pfam domains and remained robust in tests with homology control. It further captured changes in Pfam domain composition among isoforms from the same gene, including reference-relative domain gain and loss. An evidence tracing module links predicted labels to supporting proteins in the graph, as illustrated by the calcium and integrin binding protein 1 (CIB1). Together, this study provides a framework informed by protein structure for function and domain annotation at isoform resolution, converting human protein isoform diversity into traceable functional evidence for cancer atlas interpretation.

cancer biology↗

bioRxiv2026

MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes

Drugs induce coordinated phenotypic changes across multiple modalities, including transcriptional reprogramming and cellular morphological remodeling. Predicting these drug-induced modality changes is central to drug discovery, mechanism-of-action studies and precision therapeutics, however, prediction performance depends critically on how both drug compounds and cellular states are represented. Despite rapid advances in drug molecular and gene representation methods, a systematic evaluation of these methods remains lacking. Herein, we introduce MVCBench, a comprehensive benchmarking framework for evaluating drug molecular and gene representation methods in predicting drug-induced multimodal virtual cell (MVC) phenotypes. MVCBench leverages large-scale transcriptomic and high-content imaging data and systematically evaluates 24 representation methods (12 drug molecular and 12 gene representation methods) across nearly 1.1 million drug-induced profiles, under both in-distribution and out-of-distribution settings spanning unseen compounds, cell lines, assay plates and datasets. Our benchmarking reveals a pronounced modality-dependent asymmetry: advanced drug molecular representations substantially improve the prediction of drug-induced morphological phenotypes but provide only limited gains for gene expression prediction relative to classical fingerprints, whereas task-specific gene representations outperform general-purpose foundation models in predicting drug-induced transcriptomic responses. Predictive performance also deteriorates sharply under distribution shift, highlighting persistent challenges in cross-dataset and cross-platform generalization. We further show that integrating transcriptomic and morphological modalities consistently improves prediction accuracy, and derive practical design principles for MVC architectures, including modality-aware loss calibration and fusion strategies. Together, MVCBench provides a systematic foundation for evaluating representation methods and offers guidance for developing robust MVC models of drug-induced cellular responses.

bioinformatics↗

bioRxiv2026

Application of Engineered NK-92 Cell Extracellular Vesicles in the Treatment of Systemic Lupus Erythematosus

IntroductionSystemic Lupus Erythematosus (SLE) is a chronic autoimmune disease characterized by loss of immune tolerance, autoantibody production, and multi-organ damage. Current therapies, including glucocorticoids and CAR-T/CAR-NK cell therapies, are limited by adverse effects, high cost, and safety concerns. ObjectivesTo develop engineered NK-92 cell-derived extracellular vesicles displaying CD19 single-chain variable fragment (V-CD19-Exo) and evaluate their therapeutic efficacy in an MRL/lpr mouse model of SLE. MethodsThe CD19scFv-LAMP-2B fusion construct was stably expressed in NK-92 cells via lentiviral transduction. Extracellular vesicles were isolated by differential centrifugation and characterized by NTA, TEM, and Western Blot. In vivo efficacy was assessed in MRL/lpr mice through B cell depletion analysis, renal function monitoring, cytokine profiling, autoantibody detection, and survival observation. ResultsV-CD19-Exo significantly reduced splenic CD19{square}CD20{square} B cells from 10.53% to 1.51% (p < 0.0001). Treatment attenuated proteinuria, ameliorated lupus nephritis pathology, reversed splenomegaly, and downregulated serum IgE, IL-17A, IFN-{gamma}, anti-dsDNA, and ANA levels. Notably, V-CD19-Exo improved survival to approximately 80% compared to 25% in untreated controls. ConclusionEngineered NK-92 cell-derived extracellular vesicles represent a novel, safe, and effective cell-free therapeutic strategy for SLE, offering advantages over conventional cell therapies including lower immunogenicity, scalable production, and no requirement for lymphodepletion.

synthetic biology↗

bioRxiv2026

Transition metal-triggered immunity via an Arabidopsis NLR pair

Plants are challenged by multiple biotic (e.g. pathogens) and abiotic (e.g. heavy metals) stressors. Some transition metals can enhance plants defense against pathogens, but the mechanism remains unclear. Here, we demonstrate that an Arabidopsis head-to-head gene pair of intracellular nucleotide-binding leucine-rich repeat (NLR) receptors, both expressed in the endodermis of roots, antagonistically control transition metal-triggered immunity. One NLR, STM2 binds transition metal ions, such as Cd2+, Cu2+ and Zn2+, with its LRR domain to enhance its NAD+ hydrolytic activity and immune responses via the EDS1/PAD4/ADR1 module, triggering enhanced resistance to bacterial wilt Ralstonia solanacearum. The other NLR, STM1 suppresses STM2 to protect plants from transition metal-triggered immunity and growth inhibition in the presence of excess metals. STM1 also dampens resistance to the pathogen. Our study defines an NLR activated by transition metals and reveals a trade-off between susceptibility to pathogens and sensitivity to transition metals that are pervasive in soil.

plant biology↗

bioRxiv2026

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

bioinformatics↗

bioRxiv2026

Differential kin interactions between invasive and native plants: evidence from Alternanthera philoxeroides and its native congener

Reduced competition or facilitation between kin relative to nonkin can improve plant performance, particularly under resource-limited conditions. Understanding whether kin interactions differ between invasive and native species may provide insights into the mechanisms underlying the persistence and spread of invasive species, particularly for species that spread clonally. To explore this, we conducted a greenhouse experiment using the invasive Alternanthera philoxeroides and its native congener A. sessilis in China. For both species, we grew central plants without or with neighbors, and for the latter we had three intraspecific neighbor kinship treatments (kin only, nonkin only, and both kin and nonkin [mixed] neighbors). To test whether kinship effects are affected by resource limitation, we grew the plants under two watering conditions (well-watered and drought-stressed). Our findings revealed that at both the group (i.e., pot-level) and individual levels, invasive plants had a higher biomass production and experienced a less negative relative neighbor effect in kin groups than in nonkin groups, while these patterns were reversed in the native species. Although aboveground architecture of central plants did not differ significantly between kin and nonkin neighbors in either species, neighbor plants of the invasive species produced fewer nodes in kin groups than in nonkin groups, while the reverse was true for the native species. These patterns were not affected by the watering treatment. Together, these results indicate that while the native plants has stronger kin competition, the invasive species has reduced kin competition. Such reduced competition among kin in the invasive Alternanthera philoxeroides may enhance its population dominance and facilitate its spread.

ecology↗

bioRxiv2026

Quantal PCR: A Calibrator-Free Method for Determining the Unit for Nucleic Acid Quantification

Quantitative polymerase chain reaction (qPCR) is limited in measuring absolute nucleic acid copy numbers due to the inherent variability of calibrators. Here, we introduce the Quantal PCR (quPCR), a novel method that eliminates the need for calibrators by defining an intrinsic quantal unit derived from the thermodynamic and kinetic properties of the replication system. This approach first determines amplification efficiency at high template concentrations, which is then used as the replication probability to construct quantification cycle (Cq) distribution profiles. These profiles are compared with those from limiting dilution PCR to derive the Cq value for the minimal quantal-replication unit ("quCq"), enabling calculation of the sample copy number. Validation using a dual-target DNA template showed near-identical copy numbers using two distinct target-specific replication systems. Thus, quPCR represents a new method for absolute nucleic acid quantification at the single-molecule level, offering a calibrator-free alternative for absolute quantification.

biochemistry↗

bioRxiv2026

Decomposing multi-scale dynamic regulation from single-cell multiomics with scMagnify

Deciphering the highly coupled regulatory circuits that drive cellular dynamics remains a fundamental goal in biology. However, capturing the multi-scale time-lagged dynamics and combinatorial regulatory logic of gene regulation remains computationally challenging. Here we present scMagnify, a deep-learning-based framework that leverages multiomic single-cell assays of chromatin accessibility and gene expression via nonlinear Granger causality to reconstruct and decompose multi-scale gene regulatory networks (GRNs). Benchmarking on both simulated and real datasets demonstrates that scMagnify achieves superior performance. scMagnify employs tensor decomposition to systematically identify combinatorial TF modules and their activation profiles across different time-lags. It enables a hierarchical dissection of the regulatory landscape-- from the activity of individual regulator to the combinatorial logic of regulatory modules and intercellular communications. We applied scMagnify to human hematopoiesis and mouse pancreas development, where it successfully recovered known lineage-driving regulators and provided novel insights into the combinatorial logic that governs cell fate decisions. Furthermore, in the context of kidney injury, scMagnifys intracellular communication module mapped key signaling-to-transcription cascades linking microenvironment cues to pathological epithelial cell changes. In summary, scMagnify provides a powerful and versatile computational framework for dissecting the multi-scale regulatory logic that governs complex biological processes in development and disease.

bioinformatics↗

bioRxiv2026

SPCoral: diagonal integration of spatial multi-omics across diverse modalities and technologies

Spatial multi-omics is indispensable for decoding the comprehensive molecular landscape of biological systems. However, the integration of multi-omics remains largely unresolved due to inherent disparities in molecular features, spatial morphology, and resolution. Here we developed SPCoral for diagonal integration of spatial multiomics across adjacent slices. SPCoral extracts spatial covariation patterns via graph attention networks, followed by the use of optimal transport to identify high-confidence anchors in an unsupervised, feature-independent manner. SPCoral utilizes a crossmodality attention network to enable seamless cross-resolution feature integration alongside robust cross-omics prediction. Comprehensive benchmarking demonstrates SPCorals superior performance across different technologies, modalities and varied resolutions. The integrated multi-omics representation further improves spatial domain identification, effectively augments experimental data, enables cross-modal association analysis, and facilitates cell-cell communication. SPCoral exhibits good scalability with data size, reveals biological insights that are not attainable using a single modality. In summary, SPCoral offers a powerful framework for spatial multi-omics integration across various technologies and biological scenarios.

bioinformatics↗