Biology subjects

Peng, Q.

Publications and source records attributed to Peng, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

A brain signature of vaping cue-elicited craving

E-cigarette use among young adults has grown rapidly, raising concerns about nicotine dependence and motivating a better understanding of the neurobiology of vaping. We used a cue-induced craving fMRI paradigm to develop and evaluate a brain signature of vaping cue-elicited craving, and to compare it with smoking- and food-craving signatures developed using the same analytic pipeline. Using LASSO-PCR with leave-one-subject-out cross-validation, we identified a distributed pattern of brain activity that reliably predicted normative vaping craving from an independent sample (hereafter, normative craving). The vaping-craving signature was also substantially distinct from smoking- and food-craving signatures, both spatially and functionally, and showed specificity relative to previously established craving signatures. Moreover, greater signature expression was associated with greater e-cigarette dependence severity and showed a positive, though modest, prospective association with daily puff counts over four weeks, demonstrating the signature's behavioral and clinical relevance. Together, these findings establish a cross-validated brain signature of vaping cue-elicited craving, demonstrate its differentiation from other craving-related neural patterns, and link its expression to e-cigarette dependence and real-world use. More broadly, they support the potential of this framework for developing reproducible neural markers of craving.

neuroscience↗

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

Venetoclax Resistance Creates Canonical Pyroptosis Vulnerability in AML

Venetoclax has transformed acute myeloid leukemia (AML) therapy, yet resistance is almost inevitable. Its diverse, often difficult-to-target drivers frequently converge on monocytic differentiation, a state that survives on MCL1 rather than BCL2. Here, we show that the very cells that escape BCL2 inhibition retain the canonical pyroptosis machinery of normal monocytes. Across 15 bulk and 4 single-cell cohorts, the canonical pyroptosis program was enriched in monocytic AML and monocytic leukemia stem cells, correlated with venetoclax resistance, and associated with inferior survival specifically after venetoclax-based therapy. In 24 cell lines and 142 patient specimens, both DPP8/9 inhibition by Val-boroPro and NLRP3 activation by lipopolysaccharide (LPS) plus nigericin preferentially killed monocytic AML cells through GSDMD-mediated pyroptosis, largely sparing non-monocytic AML and lymphoid leukemias. Venetoclax preferentially depleted primitive AML blasts but enriched a monocytic residuum vulnerable to canonical pyroptosis; combining venetoclax with Val-boroPro achieved deeper ex vivo clearance than either agent alone. In venetoclax-resistant xenografts, Val-boroPro suppressed leukemia progression, and adding venetoclax further reduced leukemia burden and prolonged survival beyond either monotherapy. Thus, venetoclax resistance itself exposes an alternative, druggable cell-death program that can be therapeutically exploited to overcome this resistance.

cancer biology↗

bioRxiv2026

In vivo intercellular CRISPR screens using viral proximity barcoding reveal regulators of tumor-immune interactions

Cell-cell interactions shape tumor growth, immune evasion, and therapeutic response, but systematically dissecting their genetic regulators in vivo remains challenging. Existing cell-cell interaction tracing technologies rely largely on protein labels or enzymatic reactions, limiting their information content and scalability. To overcome these limitations, we developed match-seq, an imaging-free, sequencing-based cell proximity tracing system that uses virus-like particles to transmit barcodes between neighboring cells. Barcoded mRNAs are transmitted from sender cells to nearby receiver cells, thereby establishing a spatial linkage that can be computationally reconstructed by barcode sequencing after tissue dissociation. We apply this system to an in vivo syngeneic murine tumor model, where we observe robust labeling of all immune cell lineages, and leverage barcode labeling to reconstruct cell type niches that recapitulate known tumor spatial biology. Furthermore, we couple this system with CRISPR perturbations and single-cell RNA sequencing to perform genetic screens in vivo on tumor-immune interactions at single-cell resolution. We use this method to infer cell-cell spatial relationships and uncover genetic dependencies in cancer cells that change the composition and cell state of their local microenvironments. By calculating a local immune activation signature, we are able to prioritize targets whose deletion enhances anti-tumor immunity through distinct effector cell types: loss of Tgfb1 engages CD8 T cells and macrophages, Traf7 loss elicits a CD4 T cell response, and Nectin3 loss promotes NK cell-mediated immunity, relationships that we validate by in vivo immune cell depletion. Notably, Tgfb1 and Nectin3 deletion had little effect on cancer cell fitness in the pooled screen yet suppressed tumor growth when deleted throughout the tumor, demonstrating the potential of match-seq to reveal functionally important tumor-immune interactions that would otherwise remain hidden. Together, these results establish a general framework for pooled genetic dissection of cell-cell interactions in vivo, extending CRISPR screening from cell-intrinsic phenotypes to the mechanisms by which cells shape and respond to their local microenvironments.

cancer biology↗

bioRxiv2026

Estimand-aware and donor-aware triangulation of predefined gene-set signals across human tendon transcriptomic datasets

We analysed five predefined programmes across three human tendon transcriptomic contexts: acute exercise-associated fibroblast pseudobulks (E-MTAB-15400; 4 control and 4 exercise samples), mechanically stretched donor-derived cells (GSE150482; 3 normal and 3 tendinopathy donors), and paired lesional versus grossly normal-appearing tendon (GSE26051; 23 donor pairs). Inference was conducted at the sample, donor or donor-pair level. Non-stimulated GSE150482 single-cell RNA sequencing comprised only N1 and D1, whereas stretched single-cell RNA sequencing comprised N1-N3 and D1-D3; the conditions also used Drop-seq and 10X Chromium, respectively. A donor-replicated disease-by-stimulation interaction is therefore not estimable. Within-context Benjamini-Hochberg adjustment supported acute mechanical-response, extracellular-matrix (ECM) and protein-folding differences and paired-lesion collagen, integrin and ECM differences, but no stretched donor-background difference. Exact label permutations, a conservative global 15-test adjustment, leave-one-unit analyses, a Fib1-Fib4 pooled sensitivity and a paired exact Wilcoxon sensitivity qualified these findings. Integrin was fully nested in ECM, while collagen overlapped 52/79 genes with ECM. Collagen fibril organization showed the most directionally consistent matrix-associated pattern across the three non-equivalent contrasts, without establishing a disease-specific loading response or causal mechanism. The analysis demonstrates why public tendon datasets must be integrated by estimand and biological inference unit rather than pooled as interchangeable replications.

bioinformatics↗

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

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI Era

Biology has accumulated a vast ecosystem of omics methods, but much of this ecosystem remains built for expert humans rather than scientific agents. Methods are scattered across Python packages, R/Bioconductor and CRAN workflows, command-line tools, incompatible data containers and implicit object states, making even routine analyses difficult for an AI system to choose, execute and verify reliably. Here we introduce OmicOS, a comprehensive omics ecosystem infrastructure and agent system that turns OmicVerse V2, an open-source omics community, into an executable foundation for agentic biology. OmicVerse V2 provides the community substrate: scalable AnnDataOOM-compatible rust backends, agent-friendly Python algorithms for single-cell, spatial, bulk and multi-omics analysis, interfaces to single-cell foundation models, and Python-native reconstructions of historically R-centred Bioconductor/CRAN-style workflows. OmicOS makes this substrate actionable by registering analytical functions as state-aware capability contracts, allowing agents to inspect live data objects, select valid methods, execute controlled workflows and record provenance. The result is not a fixed pipeline, but a programmable omics environment in which agents compose real analyses from verified community methods rather than inventing tools. Across external and purpose-built benchmarks, OmicOS ranked first among the evaluated systems, reaching 81.2% on BiomniBench. Adding OmicVerse to a minimal agent improved task completion by up to 34.2 percentage points with qwen-3.6-35b, and controlled ablations showed that the gains came from registry-grounded execution rather than from larger models, documentation retrieval or unrestricted tool exposure. The same infrastructure scaled to atlas-sized data, reproduced R-centred workflows in Python and converted external pathology software into agent-usable skills. In a discovery task starting from a whole-body spatial map and the term "Alzheimers disease", OmicOS composed a non-canonical workflow that integrated spatial expression, genetic association, eQTL and colocalization evidence to nominate a colon epithelial risk axis centred on PICALM, CD2AP and CR1. Together, OmicVerse and OmicOS define an open foundation for AI-era omics, showing how a community of biological methods can be transformed into a reliable, extensible and agent-operable system for discovery. HighlightO_LIOmicVerse 2.0 consolidates 694 methods spanning 11 omics domains into agent-callable high-level APIs. C_LIO_LIRebuildR automatically reconstructs and evolves R/Bioconductor methods as Python-native implementations under output-equivalence gates. C_LIO_LIOmicOS establishes a state-of-the-art omics agent harness, ranking first on general omics benchmarks across models and substantially improving the analytical capability of local open-source models. C_LIO_LICompositional use of ecosystem modules nominates a colon epithelial axis associated with Alzheimers disease risk. C_LIO_LIExternal algorithm packages supporting automatic iterative evolution can be integrated into the OmicOS ecosystem. C_LI

bioinformatics↗

bioRxiv2026

STITCH: Spatial Transcriptomics Imputation via Flow Matching with Internal Learning

AO_SCPLOWBSTRACTC_SCPLOWSpatial transcriptomics datasets frequently suffer from spatial gaps and missing regions due to sectioning artifacts, tissue damage, and the high cost of sequencing that limits tissue coverage. We present STITCH, a scalable and robust generative framework for multidimensional virtual spatial transcriptomics reconstruction. STITCH models intrinsic spatial-transcriptomic patterns directly from individual tissue samples, enabling reconstruction without requiring external reference atlases or matched histological image priors. The framework adopts a decoupled architecture that separates spatial morphology restoration from transcriptomic generation. STITCH first compresses high dimensional transcriptomic profiles into a low-dimensional latent representation through a spatial-aware graph autoencoder. For 3D cross-slice gaps, STITCH employs optimal transport-conditioned flow matching for spatial reconstruction, whereas 2D in-slice damage is repaired through an internal learning strategy. To generate the corresponding transcriptomic profiles, STITCH further establishes a point-wise conditional flow matching model in the latent space. This module achieves linear computational complexity, enabling continuous 3D atlas reconstruction of over 11 million cells within 5 hours on a single commodity GPU. Extensive evaluations across diverse spatial transcriptomics platforms, spanning both single-cell and spot-level technologies, demonstrate that STITCH consistently preserves transcriptomic identities, spatial topologies, and anatomical continuity. Overall, STITCH provides a scalable and platform-compatible computational framework for reconstructing high-resolution continuous spatial transcriptomic atlases.

bioinformatics↗

bioRxiv2026

DynoSys 2.0: Graph-Based Modeling of Dynamic Risk States and System Transitions in Human Behaviours Development

Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks often model these components jointly, but many treat variables independently or use static representations. This limits their ability to capture system-level dynamics and changes over time. To address this, we developed DynoSys, a unified framework that integrates these signals using three layers: predictive models, relationship exploration models, and mechanism-oriented explanation models. Building on this framework, we introduce DynoSys 2.0, a graph-based temporal modeling approach inspired by the free-energy principle by Karl Friston. In this framework, each individual is represented as a dynamic graph that evolves over time. We hypothesize that healthy development and adverse mental health outcomes correspond to different system states and trajectories. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study, we construct time-indexed graphs that integrate polygenic risk scores (PRS), multi-domain environmental features, and neuroimaging-derived representations. We study six phenotypes: externalizing behavior, internalizing behavior, and sub-stance use initiation (alcohol, nicotine, cannabis, and any substance). In these graphs, nodes represent domain-level features, and edges capture relationships derived from data-driven feature selection and temporal dependencies. We model graph evolution using recurrent neural networks and graph-temporal learning methods. We also define system-level measures, including graph energy and state transitions, to quantify dynamic patterns. Our results show that DynoSys 2.0 can model behavioral development using longitudinal multi-domain data. The framework achieved meaningful prediction for both continuous behavioral symptoms and substance-use initiation outcomes, but performance differed by outcome type. Externalizing behavior was predicted more accurately than internalizing behavior, and alcohol and any substance initiation showed stronger prediction than cannabis and nicotine initiation. Graph-derived energy measures showed clearer separation for high-versus low-symptom externalizing and internalizing groups, suggesting that continuous behavioral symptoms may be linked to different latent system states over time. Overall, DynoSys 2.0 provides a flexible framework for studying behavioral risk as a dynamic developmental process, while rare-event prediction and detailed graph-level interpretation require further work.

neuroscience↗

bioRxiv2026

Externalizing Polygenic Liability, Brain Imaging Phenotypes, and Adolescent Substance Use Initiations: A Multistage Association and Mediation Analysis in ABCD

BackgroundAdolescent substance use initiation is shaped by multiple genetic and neurobiological factors. Externalizing liability, a transdiagnostic genetic dimension capturing shared predisposition to impulsivity, disinhibition, and related traits, is among the strongest polygenic predictors of early substance initiation. Yet how this genetic risk relates to brain structure and function, and whether baseline brain phenotypes statistically account for or instead act in parallel with genetic liability, remains unresolved. MethodsUsing the ABCD Study, we analyzed an analytic cohort of 10,608 participants with genotype data, baseline multimodal neuroimaging-derived phenotypes (IDPs), and longitudinal substance initiation assessments. Outcome-specific models included up to 10,599 participants after complete-case filtering for survival variables and covariates. We implemented a multistage framework linking an externalizing polygenic risk score (extPRS) to baseline IDPs and longitudinal substance initiation outcomes, including alcohol, nicotine, cannabis, and any substance. Stage 1 screened extPRS-IDP associations using covariate-adjusted linear models with false discovery rate (FDR) control. Stage 2 estimated extPRS effects on time-to-initiation using Cox proportional hazards models. Stage 3 fit joint extPRS + IDP Cox models to identify IDPs that predicted initiation beyond extPRS. Stage 4 conducted bootstrap-based mediation analyses to quantify average causal mediation effects (ACME), average direct effects (ADE), and the proportion of the extPRS-initiation association statistically accounted for by individual IDPs. ResultsHigher extPRS was robustly associated with earlier initiation across all substances: alcohol, hazard ratio (HR) = 1.13; nicotine, HR = 1.63; cannabis, HR = 1.67; and any substance, HR = 1.15. Thousands of extPRS-associated IDPs were identified at baseline, with highly concordant effect profiles across robustness specifications. In joint models, numerous IDPs independently predicted initiation timing above and beyond extPRS: 31 for alcohol, 32 for any substance, 137 for cannabis, and 459 for nicotine, with a replicated core set across specifications. Cannabis and nicotine initiation were jointly predicted by superficial white matter (SWM) microstructural integrity in sensorimotor cortex as a protective factor, and by irregular activity in a right-hemisphere region as a risk factor. Alcohol initiation was predicted by a largely distinct, strongly left-lateralized frontolimbic SWM intensity axis. Nicotine initiation additionally and uniquely involved restricted gray matter diffusion in the anterior cingulate cortex and subcallosal cortex. Despite these robust independent IDP associations, mediation analyses showed that indirect effects through individual baseline IDPs were very small in magnitude (ACME {approx} 10-4), accounting for less than 2% of the total extPRS effect, with FDR-significant mediation surviving only for alcohol and any-substance initiation. ConclusionsWithin the scope of externalizing polygenic risk and baseline neuroimaging, the predominant pattern is one of largely parallel, additive contributions to adolescent substance initiation rather than a dominant genetic [->] brain [->] behavior pathway. Baseline brain features, particularly prefrontal functional variability and frontolimbic and sensorimotor white matter integrity, predict initiation risk beyond extPRS, indicating neurobiological vulnerabilities not captured by this genetic dimension. However, these baseline IDPs explain only a small fraction of the extPRS-initiation association, suggesting that externalizing genetic liability may operate through pathways not fully represented by cross-sectional baseline imaging. Whether other genetic risk dimensions, such as substance-specific PRS, or dynamic longitudinal brain measures show stronger mediation patterns remains an important open question.

neuroscience↗

bioRxiv2025

ERAD Activity Distinguishes the Functional Heterogeneity of Hematopoietic Stem Cells

Hematopoietic stem cells (HSCs) rely on precisely controlled proteostasis to sustain lifelong self-renewal, yet whether intrinsic proteostatic characteristics can prospectively define functional diversity within the HSC pool remains unknown. In this study, we developed a dual-fluorescence reporter system to visualize endoplasmic reticulum-associated degradation (ERAD) activity in vivo and discovered that ERAD activity can prospectively predicts the long-term reconstitution potential of HSCs. Remarkably, ERAD states were maintained through self-renewal, revealing an inheritable "proteostatic memory" within the HSC pool. Transcriptomic and functional analyses identified SOCS2 as a key regulator of this process: SOCS2 inhibits JAK2 signaling to sustain ERAD activity, whereas hyperactive JAK2 interacts with the ERAD E3 ligase Hrd1 and suppresses VCP-mediated misfolded substrate. These findings define a SOCS2-JAK2-ERAD axis that connects cytokine signaling to intrinsic proteostasis remodeling, establishing ERAD activity as a predictive and heritable marker of HSC function and providing a conceptual framework for understanding hematopoietic stem cell heterogeneity.

cell biology↗

bioRxiv2025

Parental Influence on Children's Educational Achievements: Analysing Direct and Indirect Genetic Effects through Trio-GCTA

Educational achievement is a key predictor of later-life outcomes, including financial security, social mobility, health, and mortality. Knowing its familial determinants, such as genetic predispositions, is crucial for understanding intergenerational educational mobility and addressing educational inequality. Here, we disentangle direct and indirect genetic effects on educational achievement using trio genome-wide complex trait analysis (Trio-GCTA). Leveraging up to 23,200 genotyped parent-offspring trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa), we find that direct genetic effects explain 15-21% of the variance in educational achievements, including 8th-grade national assessments in Math, Reading, and English, and 10th-grade grade point average (GPA). Notably, indirect genetic effects additionally explain 7-15% of the variance. Both mothers and fathers exert notable influences, exceeding those suggested in previous studies. Moreover, positive gene-environment correlations between parents and offspring show that the intrafamilial environment and childrens genetic predisposition are dependent and mutually reinforcing.

genetics↗

bioRxiv2025

Engineering an Anaerobic Microenvironment to Empower Hydrogenase-Catalyzed Hydrogen Therapy for Diabetic Wound Healing

The inherent oxygen sensitivity of hydrogenases has long restricted their applications to anaerobic systems. Here, we present a hybrid peptide-nanocluster hydrogel that establishes a self-sustaining anaerobic microenvironment to enable hydrogenase-catalyzed hydrogen therapy under aerobic conditions. This system integrates a custom-designed Fmoc-KYF peptide hydrogel with integrated silver nanoclusters (AgNCs), which host an engineered [NiFe]-hydrogenase (3S1C-Hyd-2). Mechanistic studies combining molecular dynamics simulations and spectroscopic analysis revealed that hydrophobic pockets within the peptide network trap oxygen molecules, while photoexcited AgNCs rapidly deplete invasive O2. Our design ensures stable hydrogen evolution, effectively overcoming the oxygen sensitivity that has long restricted hydrogenase applications in biomedical contexts. In vitro, the in-situ-generated hydrogen effectively scavenges reactive oxygen species, suppresses pro-inflammatory cytokine production. In a diabetic mouse model, this light-activated system markedly accelerates wound closure (87% by day 11), reduces oxidative stress, promotes macrophage polarization toward a reparative phenotype, enhances angiogenesis, and upregulates tissue repair pathways, as confirmed by transcriptomic profiling. Our work establishes a new bioengineering paradigm for constructing anaerobic microenvironments through synergistic material-enzyme-light interactions, offering a versatile platform for hydrogenase-enabled therapeutic interventions in oxidative stress-related diseases.

biochemistry↗

bioRxiv2025

Isolation of the first cultured representative from a deep-branching phylum in serpentinite-hosted ecosystems

Serpentinization drives abiotic synthesis of organics (e.g., hydrocarbon) potentially conducive to the emergence of life, making serpentinite-hosted systems and associated microbial community key windows into nature of lifes origin. Although cultivation-independent studies uncovered the Candisdatus Bipolaricaulota widely distributed in serpentinizing environments, cultivation of this phylum has been unsuccessful. Here we cultured the first pure strain, J31, of Ca. Bipolaricaulota from the Lost City hydrothermal field, a well-characterized marine serpentinite system, using hydrocarbons as the primary nutrient source. As an early-branching bacterial lineage, strain J31 exhibits an unusual morphology composed of a central rod and elongated Toga-like extensions at both ends, and divides by binary fission. Strain J31 absorbs hexadecane through Toga ends via coordinated processes resembling inhalation and swallowing, after which hexadecane is efficiently transported to the central rod in the vesicle-like structures and subsequently converted into membrane lipids to support Toga synthesis and cellular growth. Hydrocarbon-degrading capability is widespread among the globally distributed members of Ca. Bipolaricaulota. Strain J31 colonizes serpentine minerals, facilitating the utilization of hydrocarbons derived from serpentines and promoting the release of soluble iron and silicon, thereby linking microbial activity to geochemical cycling. Thus, our study presents a novel strategy for cultivating deep-branching bacteria and offers insights into the metabolic foundations of early life on Earth--and potentially on other rocky planets undergoing serpentinization.

microbiology↗

bioRxiv2025

Hydrogen sulfide increases intracellular oxygen and regulates the HIF response

O2 sensing by hypoxia-inducible factor (HIF) is a principal mechanism by which aerobic organisms adjust cellular energy metabolism and adapt to O2 limitation. In this study, we show that H2S, a product of host and microbial metabolism, profoundly influences the threshold for HIF-dependent hypoxia-sensing by increasing intracellular O2. The dose-dependent destabilization of HIF by H2S is inversely correlated with sulfide quinone oxidoreductase, which oxidizes sulfide in the mitochondrion. Hypoxia sensors provide a quantitative estimate of the magnitude of H2S-induced perturbation. The O2 concentration in cells grown in a 2% O2 atmosphere is sensed as 5 or 15 % O2 in the presence of 25 or 100 ppm H2S, respectively. Sustained exposure to H2S elicits the hallmarks of hyperoxia-associated cytotoxicity, including loss of Fe-S proteins in cellular and murine models. H2S thus emerges as a powerful regulator of O2 sensing and signaling with possible implications for dysregulation in O2 toxicity diseases. Significance StatementThe mitochondrial electron transport chain (ETC) accounts for [~]90% of whole body O2 consumption. However, our understanding of how metabolites modify ETC flux and therefore, intracellular O2 availability, are poor. In this study, we demonstrate that hydrogen sulfide (H2S), which is produced by host and gut microbes alike, increases intracellular O2 by decreasing ETC flux, and destabilizes the principal hypoxia sensor, HIF-1. The upshift in intracellular O2 levels is quantitatively significant, such that 2% O2 is sensed as 5-15% O2 at varying H2S concentrations, with concomitant destabilization of Fe-S proteins, a signature of cellular hyperoxia. Our study identifies H2S as a HIF-1 regulator with important implications for the large class of mitochondrial diseases characterized by dysregulated O2 metabolism.

cell biology↗

bioRxiv2025

Trans-kingdom coupling of redox signaling to environmental cell stress responses through multiphase partitioning.

Reactive Oxygen Species (ROS) signaling is a conserved biological process with parallel functions in all evolutionary branches of life. Here, we identify Receptor for Activated C Kinase 1 (RACK1) as a conserved redox-regulated hub that integrates ROS signals to coordinate cellular stress responses. Using cysteine reactivity profiling in intestinal epithelial cells, we demonstrate that RACK1 undergoes NOX1-dependent oxidation at multiple residues, with C286 serving as a key regulatory site. Functional studies reveal that RACK1 negatively regulates NF{kappa}B signaling through redox-dependent interactions with upstream signaling complexes. Upon stress stimulation, RACK1 dynamically redistributes into membrane-less condensates that act as redox-privileged microenvironments enriched for hydrogen peroxide. We find that oxidized RACK1 condensates are conserved through evolution with analogous stress response behavior in bacteria and yeast indicating a ubiquitous and ancient stress sensor-effector system. Functionally, redox-dependent RACK1 activity links environmental stress to translational control, with oxidation promoting inhibition of protein synthesis. Furthermore, RACK1 mediates responses to diverse pathogen-associated stimuli, including viral and bacterial infection, highlighting its role in epithelial innate immune responses. Collectively, these findings establish RACK1 as a cellular node for redox signaling, operating within condensate-based microdomains to spatially encode oxidative signals and regulate environmental stress pathways in cells. Significance StatementThis study defines a mechanism by which cells achieve specificity in redox signaling through compartmentalization within condensates. By identifying RACK1 as a redox sensor-effector, we reveal an ancient and broadly conserved system linking environmental stress to innate immune signaling and translational control. The discovery of redox-privileged condensates provides a conceptual framework for understanding how transient ROS signals are stabilized and interpreted in epithelial biology, with implications for inflammatory diseases, host-pathogen interactions, and fundamental cellular stress responses.

cell biology↗

bioRxiv2025

Evaluation of epistasis detection methods for quantitative phenotypes

BackgroundEpistasis, or genetic interaction, has been increasingly recognized for its ubiquity and for its role in susceptibility to common human diseases, such as Alzheimers. A wide variety of epistasis detection tools are currently available with several studies comparing the performance of methods suitable for case-control data. However, there is limited understanding of how well these tools perform with quantitative phenotypes. MethodsWe identified six epistasis detection methods suitable for quantitative phenotype data: EpiSNP, Matrix Epistasis, MIDESP, PLINK Epistasis, QMDR, and REMMA. To evaluate these tools, we generated simulated datasets using EpiGEN. The datasets modeled various pairwise interactions between disease-associated SNPs, including dominant, multiplicative, recessive, and XOR interactions. Additionally, we assessed the BOOST and MDR algorithms on discretized (case-control) version of the datasets. These tools were then tested on the Adolescent Brain Cognitive Development (ABCD) dataset for the externalizing behavior phenotype. ResultsEach tool exhibited strong performance for certain interaction types, but weaker performance for others. MDR achieved the highest overall detection rate of 60%, while EpiSNP had the lowest overall detection rate of 7%. MDR and MIDESP performed best at detecting multiplicative interactions with detection rates of 54% and 41% respectively. Both MDR and MIDESP were also effective at detecting XOR interactions with detection rates of 84% and 50% respectively. PLINK Epistasis, Matrix Epistasis, and REMMA excelled at detecting dominant interactions, all achieving a 100% detection rate. On the other hand, EpiSNP was particularly effective at detecting recessive interactions with a detection rate of 66%. When analyzing the ABCD dataset, Plink Epistasis and Plink BOOST identified SNPs within the DRD2 and DRD4 genes, which have been previously linked to externalizing behavior. ConclusionSince no single method consistently outperforms others across all types of epistasis, and given that the specific types of epistasis present in a dataset are often unknown, it may be more effective to use multiple epistasis detection algorithms in combination to obtain comprehensive results.

bioinformatics↗

bioRxiv2025

High-efficiency likelihood inference of shared proteomic architectures across 50 complex human traits

Advancements in genetic correlation estimation have elucidated genome-wide pleiotropys influence on phenotypic correlations among human complex traits and diseases. However, the role of proteomic domains in these correlations remains underexplored. Traditional genetic correlation analysis assumptions, including the minute effects of SNPs and their linkage disequilibrium, do not suit proteomic data. We present a novel method, Likelihood-based Estimation for Proteomic Correlation (LEAP), tailored to provide unbiased estimation of shared proteomic architectures between trait pairs. LEAP notably decreases computational demands by approximately 1000-fold compared to conventional bivariate linear mixed models. We applied LEAP to data from the UK Biobank Pharma Proteomics Project, identifying 585 significant proteomic correlations among 1,225 pairs of 50 biochemical, anthropometric, and behavioral traits. Furthermore, we quantified the distinct proteomic and genetic contributions to phenotypic correlations, highlighting significant gender differences. This study provides a comprehensive computational approach for proteomic correlation estimation, clarifying the specific roles of genomics and proteomics in complex trait correlations. Our findings not only advance the understanding of proteomic contributions to phenotypic traits but also suggest potential applications for evaluating shared omics architectures in other domains such as transcriptomics and metabolomics.

bioinformatics↗