Biology subjects

Zhou, P.

Publications and source records attributed to Zhou, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Building virtual cell dynamics that evolve with biological measurements

Virtual cell models built from single-cell measurements need to predict how cells change over time, but new biological measurements often reveal dynamical relationships that existing models cannot express. Translating such measurements into dynamics therefore requires deciding what biological relation should be modeled, where it should enter the formulation and how its recovery should be validated. Here we present CellCompass, an agentic AI system that couples a scientific agent with an extensible dynamical engine for modeling state transitions and population change, along with fate and molecular readout. CellCompass reuses existing formulations when sufficient and designs, implements and refines new ones when experimental measurements introduce additional constraints. To assess this capability, we developed DynBench100, a benchmark of 100 simulated single-cell time-course scenarios with hidden dynamical ground truth. CellCompass achieved the highest aggregate performance among the tested general-purpose and biomedical agents across six dynamical tasks. Across four real single-cell studies, it retained an existing velocity-growth model to reconstruct context-dependent epithelial-mesenchymal transition and developed three new dynamical formulations for distinct experimental measurements. In drug-response data, incorporating metabolic RNA labeling reduced pulse-control identifiability error by 31.1%, revealing staged response programs and generating testable predictions for perturbing a candidate adaptive program. In hematopoietic lineage tracing, incorporating clonal barcodes improved clone-fate agreement by 54% over the strongest dynamical baseline, identifying early lineage priming among transcriptionally similar progenitors. Finally, in time-resolved CITE-seq data, CellCompass reduced future surface-protein distribution error by 13.2% for RNA-initialized trajectories, uncovering lineage-specific temporal offsets between transcriptional and protein programs. Together, CellCompass provides a framework in which virtual-cell dynamics can expand with the biological measurements used to constrain them.

bioinformatics↗

bioRxiv2026

MOFI reveals dynamic multi-omics coordination during cell fate progression

Cell fate decisions are shaped by dynamic coordination across transcriptomic, chromatin, and protein layers. Time-series single-cell multi-omics assays now enable these molecular layers to be profiled across developmental stages, yet each cell is still observed only once, and the continuous multi-omics trajectories connecting discrete snapshots remain unresolved. A central challenge is therefore to jointly integrate paired omics measurements while reconstructing their shared developmental progression. Here we present Multi-Omics Fate Inference (MOFI), a dynamically constrained multi-omics integration framework designed to reconstruct continuous cell fate transitions from paired multi-omics snapshots sampled across multiple time points. MOFI jointly models omics-specific state dynamics, cross-omics correspondence, and shared population growth within a unified framework, enabling cross-omics perturbation response analysis and identification of candidate cross-omics transformation programs. Across simulated benchmarks and paired RNA-ATAC/RNA-protein time-series datasets, MOFI reconstructed continuous fate transitions, population dynamics and cross-omics changes consistent with observed measurements. In kidney organoids, MOFI captured coordinated RNA-ATAC progression of nephron lineage specification. In hematopoietic progenitors, MOFI predicted perturbation-responsive lineage redistribution and nominated multi-omics transformation programs linking transcriptional modules with protein-level responses. In neural developmental systems, MOFI resolved gradual brain organoid maturation and interneuron lineage allocation during human cortical development. Together, MOFI provides a dynamics-grounded method for converting discrete paired multi-omics snapshots into interpretable models of cell fate progression, perturbation response, and cross-omics transformation analysis.

bioinformatics↗

bioRxiv2026

The conserved function of bHLH121 and overexpression of N-terminal bHLH121 fragment suppresses iron deficiency response in Arabidopsis

Iron (Fe) homeostasis is fundamental to plant growth and development and is centrally regulated by a well-conserved network of basic helix-loop-helix (bHLH) transcription factors. In Arabidopsis thaliana, bHLH121 regulates the expression of FER-like iron deficiency-induced transcription factor (FIT) and clade Ib bHLH38/39/100/101 genes, among others. However, its functional conservation remains unexplored, and some contradictory results have been reported regarding the effects of bHLH121 overexpression. Here, we investigated the evolutionary conservation of bHLH121 through complementary tests in the Arabidopsis bhlh121 mutant created by CRISPR/Cas9 gene editing. We also investigated the effects of ectopically expressing full-length, N-terminal, and C-terminal fragments of bHLH121 on plant responses to Fe deficiency in Arabidopsis. Transgenic plants overexpressing the full-length bHLH121 driven by the cauliflower mosaic virus 35S promoter (bHLH121 ox) resulted in shorter roots and more severe leaf chlorosis compared to wild-type plants under Fe limiting conditions. Moreover, overexpression of the N-terminal fragment (N121 ox) rendered plants highly sensitive to Fe deficiency, whereas overexpression of the C terminal fragment (C121 ox) did not. Consistent with the phenotypic observations, N121 ox plants accumulated lower Fe content, and the ferric chelate reductase activity was significantly reduced. In N121 ox plants, the induction of key Fe regulatory genes, especially FIT, was significantly inhibited under Fe deficiency. Confocal imaging showed that both N121-GFP and C121-GFP fusion proteins could localize to the nucleus. Electrophoretic mobility shift assay showed that both full-length bHLH121 and N121 could effectively bind the probes derived from the promoter of FIT. The present work reveals a conserved function of bHLH121 in Fe homeostasis across several dicot species and demonstrates that the N121 may possess a dominant nature in Fe deficiency responses.

plant biology↗

bioRxiv2026

An orally administered peptide hydrogel disentangles immune-microbiota crosstalk for long-term ulcerative colitis therapy

Ulcerative colitis (UC) is a global health challenge driven by immune dysregulation and gut microbiota imbalance.1 Current treatments, limited by insufficient efficacy and systemic toxicity during prolonged use, fail to resolve the intertwined immune-microbial pathology.2 Here, we report an orally administered self-assembled hydrogel C2-(IIRR)2I-NH2 (CIR), engineered from host defense peptides, which disrupts the immune-microbiota entanglement. The CIR hydrogel exhibits structural transformation at the inflamed sites rich in liposaccharide (LPS), a pro-inflammatory molecule derived from pathogenic bacteria. Stable {beta}-sheet nanofibers can transfer to bioactive -helix conformations, enabling localized therapeutic action with minimal off-target toxicity. In murine colitis models, CIR restores mucosal integrity and suppresses disease severity, outperforming the first-line drug 5-aminosalicylic acid (5-ASA). Microbiome profiling reveals its capacity to rebalance gut microbiota, depleting LPS produced pathogenic bacteria like Prevotellaceae. Transcriptomic analyses further indicate that CIR silences TLR4-mediated signaling pathway. By synergistically targeting immune dysregulation and microbial dysbiosis, this self-assembled peptide hydrogel establishes a paradigm-shifting strategy for UC, offering clinically translatable potential for multifactorial gastrointestinal disorders.

bioengineering↗

bioRxiv2026

Protective pan-betacoronavirus neutralizing antibodies by vaccination

The continued emergence of betacoronaviruses underscores the urgent need for vaccines that provide broadly protective immunity. Here, we present an epitope-focused vaccine strategy targeting the conserved S2 stem-helix region of the spike fusion machinery, a broadly neutralizing antibody-(bnAb) epitope shared across betacoronaviruses yet partially occluded on the native spike. Immunization of non-human primates with engineered S2 stem-helix nanoparticle immunogens, alone or followed by a SARS-CoV-2 BA.1 spike mRNA boost, elicited broadly cross-reactive antibody responses against sarbecoviruses, merbecoviruses, and embecoviruses and neutralized SARS-CoV-2, multiple variants, other sarbecoviruses, and MERS-CoV. Vaccine-elicited monoclonal antibodies displayed broad in-vitro neutralizing activity and protected against both SARS-CoV-2 and MERS-CoV in-vivo. Structural analyses revealed conserved features between rhesus and human stem-helix bnAbs, supporting the translational potential. Overall, our findings provide proof-of-concept that epitope-focused nanoparticle immunogens can target partially occluded, immunoquiescent bnAb epitopes, laying the groundwork for pan-betacoronavirus vaccines that provide broad protection and strengthen pandemic preparedness. ONE SENTENCE SUMMARYEpitope-focused S2 stem-helix nanoparticle immunogens elicit protective broadly neutralizing antibodies (bnAbs) against diverse betacoronaviruses in non-human primates, establishing a framework for development of pan-betacoronavirus vaccines.

immunology↗

bioRxiv2026

MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics

Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states and prioritizing virtual perturbations toward defined biological endpoints. MEGA-ODE integrates molecular-network priors, graph neural ordinary differential equations and context-adaptive mixture-of-experts routing. In L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE improved held-out-feature and unseen-perturbation prediction over baseline methods, and in SARS-CoV-2 infection time-series data it remained competitive for future-time-point forecasting. In a COVID-19 patient cohort, predicted intermediate profiles improved retrospective disease-stage stratification relative to observed profiles alone, while expert programs highlighted immune and inflammatory signals associated with severity. Across the MAPK drug-response and stem-cell differentiation case studies, graph- and expert-level attributions prioritized perturbation-associated MAPK edges, developmental regulators and TF-target relationships supported by independent promoter-proximal ChIP-seq overlap. In hESC-to-definitive-endoderm differentiation, MEGA-ODE prioritized candidate transcription-factor perturbations predicted to shift 12-36 h profiles toward 96 h definitive-endoderm marker signatures, framing trajectory navigation as a concrete hypothesis-generation task. Together, these results support biologically structured continuous-time modeling for prediction, interpretation and virtual-perturbation prioritization from sparse temporal omics data.

bioinformatics↗

bioRxiv2026

Reconstructing lineage-constrained gene-program dynamics with PhyloFM

Cell-fate dynamics are defined not only by where cells end, but by when fate programs emerge, diverge, and reshape population structure. Much recent effort has been devoted to reconstructing these dynamics from temporally resolved single-cell RNA-seq and spatial transcriptomics. However, it is difficult because these measurements only provide destructive snapshots instead of continuous molecular histories. On the other hand, a diverse set of lineage-tracing methods are now available which could serve as temporal constraints to connect the dots. The challenge is how to utilize and unify these diverse ancestry records in the reconstruction of a continuous cell state dynamics. Here we introduce PhyloFM, a lineage-constrained flow-matching framework that maps heterogeneous lineage information onto a cell-fate-anchor topology and learns a continuous velocity field together with relative cell-abundance changes on a topology-regularized latent geometry. From the same integrated trajectories, PhyloFM predicts population transport, future fate probabilities, commitment timing and branch-resolved projected gene- and module-level dynamics. We tested PhyloFM across five lineage-tracing settings spanning C. elegans embryogenesis, LARRY hematopoiesis, pandaCREST ventral-midbrain development, zebrafish heart regeneration and spatial eTracer tumours. In C. elegans, our method improved population transport by 17.3% and lineage-grounded dynamic error by 16.3% relative to state-of-the-art dynamical baselines, with transport error reduced by 26.6% relative to additional lineage-aware control. Independent EPIC GFP reporter traces support the inferred timing of proneural, ciliated-neuron and late-selector programs, with 67% of matched reporter genes showing Pearson r > 0.6. On the spatial eTracer tumour benchmark, PhyloFM increased clone-grounded fate accuracy by 70.4% relative to the best spatial baseline. Together, these analyses show that diverse lineage records can serve as biological constraints for reconstructing continuous, interpretable molecular fate dynamics from single-cell snapshots.

bioinformatics↗

bioRxiv2026

Primitive GLMY Homology: An Algebraic Topology Approach for the Quantitative Characterization of Graph Pangenomes toward Population Genetic Analysis

A central task in population genetics is to identify genetic diversity in a population containing a number of individuals. In recent years, with the development of the third generation sequencing (TGS) technology, pan-genome research has become a hot topic. Although graphical representation has been a popular way to represent the pangenome, few works have attempted to describe it in a more mathematical way. In this paper, we used 79 high-quality assembly data of third-generation sequencing in yeast (includingSaccharomyces cerevisiae and Saccharomyces paradoxus) to construct the graph pangenome, and introduced the Primitive GLMY (Grigoryan-Lin-Muranov-Yau) Homology in algebraic topology to quantitatively represent the pan-genome. We further made an intriguing attempt to conduct a population genetic analysis of this resulting dataset from the topological features of the graph pangenome. We found that there was good agreement between the obtained results and the biological context. We believe this study has developed a method for population genetic analysis of the genetic diversity of genome structural variation.

genetics↗

bioRxiv2026

Unbalanced Perturbation Dynamics For Cell Fate Design

Large-scale single-cell perturbation sequencing provides an unprecedented opportunity to construct virtual cells for the in silico simulation of cellular responses and the inverse design of optimal interventions. However, most perturbation-response models treat cellular responses primarily as mass-preserving shifts in transcriptomic state, whereas single-cell perturbation measurements are inherently unbalanced: the recovered endpoint population is shaped by technical sampling as well as biological perturbation-induced proliferation, apoptosis and selection. Here we introduce U-Pert, an unbalanced generative framework that learns condition- and context-dependent perturbation dynamics from unpaired single-cell snapshots. U-Pert jointly models transcriptomic state transitions and cell-number dynamics, enabling scalable and robust forward prediction of unseen perturbations and contexts, as well as inverse design to screen for desired genetic or pharmacological interventions that achieve user-defined transcriptomic or population-level outcomes. Across controlled simulations, genetic perturbation benchmarks, sciPlex3 drug responses and PBMC cytokine perturbations, U-Pert predicts unseen responses, captures both molecular and abundance changes, and performs inverse design for target gene-expression programs and cell-type compositions. These results show that cell abundance is an integral component of the perturbation phenotype, providing a mass-aware framework for virtual-cell modeling and perturbation cell fate design.

bioinformatics↗

bioRxiv2026

Benchmarking the translational potential of AI-based drug-resistance prediction from Mycobacterium tuberculosis whole-genome sequencing data

BackgroundTuberculosis, especially drug-resistant tuberculosis (DR-TB) including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, remains a leading cause of infectious death worldwide. The rapid accumulation of whole-genome sequencing (WGS) data had spurred numerous computational methods for predicting antimicrobial resistance in Mycobacterium tuberculosis. However, heterogeneous datasets, preprocessing pipelines, and evaluation protocols have made fair comparisons impossible and have hindered clinical translation. A critical yet missing resource is a large-scale, unified benchmark to systematically assess and compare existing methods. MethodsWe curated an integrated MTB WGS-phenotypic drug susceptibility testing (pDST) dataset from three sources: the CRyPTIC dataset (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium), a published multi-study compilation, and newly curated literature-derived datasets. The final benchmark contains 54,364 paired WGS-pDST records with broad geographic, lineage, and drug coverage. After harmonizing phenotypes and generating standardized variant features, we evaluated seven models (including classical machine learning and deep learning architectures) across 18 drug-level and six clinical resistance category prediction tasks. ResultsXGBoost achieved the highest mean drug-level AUPRC (0.674) and F1-score (0.620) and ranked first in AUPRC for 11 of 18 drugs, whereas WDNN achieved the highest mean AUROC. Random forest yielded the highest mean specificity (0.956) and accuracy (0.933), whereas logistic regression achieved the highest mean recall (0.774), highlighting distinct clinical trade-offs. Drug-level difficulty was highly heterogeneous: rifampicin and isoniazid were predicted robustly, whereas bedaquiline, delamanid, linezolid, and clofazimine remained persistently difficult. In clinical resistance category evaluation, RR-TB, MDR-TB, and pan-susceptibility were well predicted, but XDR-TB and other resistance categories constituted major bottlenecks. ConclusionsUnder the largest unified benchmark to date, classical machine-learning methods, particularly XGBoost, provided the strongest precision-recall and F1 performance overall, while neural models remained competitive by AUROC. Emerging drugs (bedaquiline, delamanid, linezolid, clofazimine) and XDR cases remain persistently difficult to predict, identifying key bottlenecks for future method development. This benchmark can serve as a community standard for evaluating MTB resistance prediction and the provided evaluation pipeline offers an actionable baseline for regulatory qualification and clinical decision support system validation, accelerating the translation of WGS-based resistance prediction into practice.

bioinformatics↗

bioRxiv2026

Combined Checkpoint Inhibition Amplifies Post-Infarction Injury via T Cell-Mediated Macrophage Activation

BackgroundThis study aimed to investigate whether combined PD-1/CTLA-4 immune checkpoint inhibition predisposes the heart to a hyperinflammatory state, thereby exacerbating cardiac injury following acute myocardial infarction (MI), a critical unresolved question in cardio-oncology. MethodsMyocardial infarction was induced in Pd1-/-Ctla4+/- mice, a genetic model mimicking combined checkpoint inhibition. Key mechanistic insights were gained through in vivodepletion of CD8+ T cells (using anti-CD8a antibody) and pharmacological inhibition of the JAK-STAT1 pathway (using Tofacitinib). Cardiac function, structural injury, and immune responses were comprehensively assessed via echocardiography, flow cytometry, immunofluorescence, and molecular analyses. ResultsCompared to wild-type controls, Pd1-/-Ctla4+/- mice exhibited significantly increased post-MI mortality, worse cardiac function, and larger infarct size. Mechanistically, the aggravated injury was driven by an amplified infiltration of activated, IFN-{gamma}-producing CD8+ T cells, which activated the JAK-STAT1 pathway in macrophages, polarizing them towards a pro-inflammatory state. Depleting CD8+ T cells or inhibiting the JAK-STAT1 pathway effectively attenuated macrophage-driven inflammation and improved all aspects of post-MI injury. ConclusionsCombined PD-1/CTLA-4 blockade exacerbates post-infarction cardiac injury by promoting CD8+ T cell-mediated activation of macrophages via the JAK-STAT1 axis. This work elucidates MI as a context-dependent immune-related adverse event in ICI therapy and identifies CD8+ T cells and the JAK-STAT1 pathway as promising therapeutic targets for cardioprotection in these patients. RESEARCH PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABSO_LIThis study identifies acute myocardial infarction (MI) as a potential, context-dependent immune-related adverse event in the setting of combined PD-1/CTLA-4 checkpoint inhibition, shifting the paradigm beyond the classic focus on myocarditis. C_LIO_LIIt elucidates a novel pathogenic axis where combined checkpoint deficiency exacerbates post-MI injury specifically through CD8+ T cell-derived IFN-{gamma}, which activates macrophages via the JAK-STAT1 pathway. C_LI What Question Should Be Addressed Next?O_LIFuture studies should employ anti-PD-1/CTLA-4 monoclonal antibodies in wild-type or humanized mouse models to validate findings and better recapitulate the pharmacokinetics of clinical ICI therapy, strengthening translational relevance. C_LIO_LIThe long-term consequences of this primed inflammatory state on chronic cardiac remodeling, heart failure development, and the potential interplay with atherosclerosis warrant further investigation. C_LI

immunology↗

bioRxiv2026

Deciphering interaction syntax via decoupling intrinsic lineages and niche pressure

Spatial transcriptomics enables the mapping of gene expression within intact tissues, yet a fundamental gap remains between knowing where cells are and understanding how they interact. A cells measured transcriptome reflects both its intrinsic lineage identity and niche pressure. Here we introduce TRINUS, a self-supervised model that deciphers interaction syntax by generative decoupling of a cells intrinsic lineage identity from the extrinsic niche pressure. TRINUS maintains a library of context-free cell prototypes to isolate lineage identity while modeling cooperative interaction dependencies among neighbors. We validated TRINUS on synthetic datasets with known interaction logic and benchmarked it against existing methods with superior performance in cell clustering and spatial domain detection. Applied across diverse platforms and biological systems, TRINUS resolves multi-level interaction syntax and maps tissue-wide interaction patterns in colorectal cancer, and identifies stage-specific signaling dependencies and time-dependent receptor windows during mouse organogenesis. We also show TRINUSs bidirectional in silico engineering capability in the ovarian tumor microenvironment, where forward perturbation revealed subtype-specific macrophage immunosuppressive programs via virtual transplantation and inverse design identified molecular modifications in macrophages predicted to rescue adjacent T-cell function. Collectively, TRINUS provides a practical tool for interaction syntax discovery and predictive tissue engineering on spatial transcriptomics data.

bioinformatics↗

bioRxiv2026

Generating and navigating single cell dynamics via a geodesic bridge between nonlinear transcriptional and linear latent manifolds

Time-series single-cell RNA sequencing (scRNA-seq) captures cellular processes as sparse and unpaired snapshots, limiting our ability not only to reconstruct continuous cell state transitions, but also to navigate between states in a controlled and interpretable manner. Here we present GeoBridge, a framework modeling cellular dynamics as geodesic trajectories on the transcriptional manifold based on isometric geodesic theory. By learning the geodesic bridge mapping, the method theoretically and computationally transforms time-varying nonlinear transcriptional geodesics (original nonlinear manifold) into constant-velocity straight-line geodesics (latent linear manifold). In the learned geodesic space, continuous interpolation becomes biologically meaningful, enabling reconstruction of unobserved intermediate states and efficient navigation between distinct cellular phenotypes at a single-cell resolution. By mapping interpolated trajectories back to the original gene expression space, GeoBridge recovers smooth transcriptional programs that are robust to noise and snapshot sparsity. Leveraging the derived geodesic potentials, GeoBridge further infers pseudo-temporal trajectories with superior fidelity compared to mainstream approaches from single-snapshot scRNA-seq data without temporal annotation, and directly identifies genes that drive progression along transition paths. We demonstrate the utility of GeoBridge across diverse systems, where GeoBridge resolves EMT-MET progression in cancer stem cells and identifies stage-specific modules as well as the branching cell-fate dynamics in human pluripotent stem cells with higher reconstruction accuracy than state-of-art methods. More importantly, GeoBridge supports single-cell fate navigation in multi-target hematopoietic lineages, allowing neutrophil-biased cells to be virtually guided toward mast-cell fates along biologically plausible paths. Together, GeoBridge establishes a principled method that transforms sparse single-cell measurements into a continuous, controllable landscape for the reconstruction, navigation and manipulation of cellular state transitions.

bioinformatics↗

bioRxiv2026

Genetically Engineered Biomimetic Nanozymes Reprogram Immune Niches to Intercept Colitis-Carcinoma Transition

Colitis-associated colorectal cancer (CAC) arises from chronic inflammatory niches characterized by persistent oxidative stress and dysregulated immune cell recruitment. Current anti-inflammatory therapies provide only transient symptom relief and fail to prevent malignant progression due to their inability to simultaneously mitigate oxidative injury and immune chemotaxis. Our analysis of clinical samples revealed markedly elevated C-X-C motif chemokine ligand 2 (CXCL2) in intestinal tissues from patients with inflammatory bowel disease (IBD) and CAC, implicating the CXCL2-CXCR2 axis in driving excessive neutrophil and macrophage infiltration and fostering tumorigenesis. Herein, we report a biomimetic nanozymes (PB@ECM) that reprogram inflammatory immune niches to intercept CAC progression. PB@ECM integrates a Prussian blue nanozyme core with genetically engineered macrophage membranes, enabling concurrent scavenging of reactive oxygen species through superoxide dismutase- and catalase-like activities and sequestration of CXCL2 via membrane-displayed CXCR2 receptors. In murine models of colitis and CAC, PB@ECM significantly alleviated intestinal inflammation, suppressed neutrophil and macrophage infiltration, and effectively inhibited colitis-carcinoma transition. By disrupting the pathological crosstalk between oxidative stress and immune chemotaxis, this work establishes a biomimetic nanozyme strategy for preventing inflammation-driven carcinogenesis.

bioengineering↗

bioRxiv2026

Benchmarking zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction

Reconstructing cellular trajectories from time-resolved single-cell transcriptomics is fundamental to understanding processes from embryonic development to cancer progression. While single-cell foundation models (scFMs) promise universal biological representations through large-scale pretraining, their capacity to capture the non-linear dynamics governing cell-fate decisions remains uncharacterized. Here we systematically benchmark multiple scFMs across challenging biomedical scenarios involving branching lineages and continuous state transitions. By coupling zero-shot scFM embeddings with dynamic optimal transport, we evaluated their performance against a traditional highly variable gene (HVG) baseline in backtracking progenitor states, interpolating transition intermediates, and extrapolating future fates. We find that zero-shot scFM embeddings underperform the HVG baseline across diverse biological systems, particularly in recovering the distributional complexity of unobserved cells. Mechanistic analysis reveals that current scFM architectures tend to over-compress subtle temporal signals, causing an artificial "linearization" of branched biological structures that may obscure critical divergence points in disease progression. Our findings suggest that while scFMs provide unified cell-state views, the HVG baseline remains more robust for trajectory inference, identifying a fundamental "temporal-compression" bottleneck that must be addressed to develop next-generation, dynamics-aware foundation models.

bioinformatics↗

bioRxiv2026

Identification of 4,5,6,7-Tetrabromo-1H-benzotriazole (TBB) as a Small Molecule MESH1 Inhibitor that Suppresses Ferroptosis

Ferroptosis is a regulated form of cell death driven by iron-dependent lipid peroxidation and contributes to diverse pathologies including ischemia-reperfusion injury and neurodegenerative disorders. Current ferroptosis inhibitors largely function as nonspecific radical-trapping antioxidants, limiting their clinical utility. We previously identified MESH1 as a key regulator of ferroptosis through its NADPH phosphatase activity. Here, we identify 4,5,6,7-tetrabromo-1H-benzotriazole (TBB) as a small molecule inhibitor of MESH1 with an IC50 value of 4.7 {+/-} 0.3 {micro}M. X-ray crystallography revealed the molecular determinants of TBB recognition which are corroborated through structure-activity relationships of TBB analogs. TBB protected multiple cell lines against ferroptosis in vitro, and this effect was mitigated by MESH1 knockdown, consistent with on-target activity. Furthermore, TBB reduced neuronal death in an ex vivo brain slice model of Alzheimers disease. Collectively, these findings establish TBB as a bona fide small-molecule MESH1 inhibitor that suppresses ferroptosis and establishes MESH1 as a promising therapeutic target. Graphical AbstractDepicting mechanism of TBB suppressing ferroptosis through the inhibition of MESH1. Figure Created with Biorender.com O_FIG O_LINKSMALLFIG WIDTH=131 HEIGHT=200 SRC="FIGDIR/small/706832v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1fd60e9org.highwire.dtl.DTLVardef@1e56518org.highwire.dtl.DTLVardef@15010c2org.highwire.dtl.DTLVardef@17c313a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Disentangled multimodal evolutionary representations for cross-virus predictive modeling of antigenic change

Antigenic evolution drives immune escape and complicates early assessment of emerging pathogens, especially when genomic surveillance is sparse and functional measurements lag behind real-world spread. Early prediction and generalization to unknown viruses require faithful and information-rich representations of viral evolution, a need that is not fully addressed by existing protein language model-based approaches. Here we introduce DERIVE, a flow-based generative framework that learns a disentangled latent representation of multimodal evolutionary history by integrating sequence homology with physicochemical and structural features. Using only pre-pandemic SARS-CoV-2 sequences, DERIVE prioritizes high-risk mutations, reconstructs strain-level evolutionary trajectories, forecasts immune escape and prevalence trends, and produces mutation-level effect maps. The learned representation transfers robustly across viral families, with strong concordance to functional data for influenza virus, HIV, rabies lyssavirus and chikungunya virus. Together, these results position DERIVE as a generalizable and interpretable framework for anticipating antigenic evolution and supporting early, actionable assessment of future "Disease X" threats.

bioinformatics↗

bioRxiv2026

Wetness modulates the effects of grazing on net ecosystem productivity in global grasslands

Overgrazing has induced widespread grassland degradation globally. However, how grazing intensity affects ecosystem carbon dioxide (CO2) fluxes with wetness fluctuations in grasslands at a global scale remains poorly understood. Here we measured ecosystem CO2 fluxes covering the 7th to 11th years of a continuous grazing experiment in a typical steppe, and conducted a meta-analysis of 585 observations by collecting relevant data in global grasslands. Our experimental results showed that mean gross primary productivity (GPP) and ecosystem respiration (ER) were decreased under heavy grazing (HG) compared with control without grazing (CK), whereas net ecosystem productivity (NEP) was not different between HG and CK in the typical steppe. The NEP was higher under light grazing in the wetter years, but it was not different between LG and CK under drier years in the typical steppe. The structural equation model showed that grazing altered NEP by increasing soil temperature and relative growth rate, while decreasing AGB. In contrast, grazing decreased GPP, ER and NEP, respectively, in global grasslands. Both our field experiment and the meta-analysis revealed that the response of NEP to light grazing, rather than heavy grazing, was lineally correlated with the wetness index. Higher wetness and aboveground biomass (AGB) increased the response of NEP to grazing in global grasslands. However, heavy grazing reduced NEP and AGB even under higher wetness indices in global grasslands, resulting from a loss of their resilience in long-term heavy grazing. These findings indicate that light grazing appears to be a promising management to promote plant relative growth rate and carbon sequestration. Overall, the meta-analysis and field experiment jointly provide global perspectives on the response of ecosystem CO2 fluxes to grazing intensity and improve our understanding of the factors influencing the response of ecosystem CO2 fluxes to grazing intensity.

ecology↗