Biology subjects

Liu, Q.

Publications and source records attributed to Liu, Q..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Multisensory integration of a host metabolite coordinates bacterial predation of macrophages

The crisis of antimicrobial resistance demands new strategies that circumvent conventional killing. Here, we show that Yersinia pseudotuberculosis intercepts host mesifurane, a mimic of quorum-sensing signal AI-2. Rather than acting through a single receptor, mesifurane is directly and independently sensed by three receptor classes, YcrA, NarX, and CusR, that converge on a "Sense, Arm, Recruit" program. YcrA drives chemotaxis toward host cells; NarX-NarP licenses T6SS-mediated killing; and CusR amplifies AI-2 production to recruit more bacteria. YcrA is conserved across Gram-negative pathogens, creating a therapeutic vulnerability. Suramin sodium, a repurposed drug blocking YcrA, disarms this circuit without inhibiting growth. In murine sepsis, suramin sodium conferred 80-90% survival against WHO critical-priority pathogens, including carbapenem-resistant A. baumannii, E. coli, and P. aeruginosa, even when delayed 8 hours post-infection, without selecting for resistance. These findings establish that disrupting sensory perception, rather than viability, provides an anti-infective strategy bypassing resistance while imposing minimal selective pressure.

microbiology↗

bioRxiv2026

Biologic therapy is associated with selective changes in airway eosinophil subpopulations in severe asthma

RationaleEosinophilic airway inflammation is common in severe asthma and strongly associated with symptoms, exacerbations, and impaired lung function. Although type 2 (T2)-targeted biologics improve outcomes and reduce eosinophils, many patients experience residual symptoms and exacerbations. Emerging evidence suggests that these biologics may differentially affect specific airway eosinophil subpopulations, representing a potential mechanism of suboptimal treatment response. ObjectiveDetermine the effect of biologic treatment on eosinophil subpopulations in adults with severe asthma using in-depth immune profiling with mass cytometry (CyTOF). MethodsFifty adults with severe asthma (28 biologic-naive, 22 on stable-dose biologic therapy for [≥]6 months) underwent clinical phenotyping, spirometry, blood sampling, and sputum induction. Twenty-nine sputum samples passed quality control thresholds and were profiled by CyTOF. Manually gated sputum eosinophils were clustered using FlowSOM to identify eosinophil subpopulations, and cluster abundances and marker expression were compared across treatment groups. Measurements and Main ResultsCyTOF revealed treatment-associated shifts in circulating immune cells (lower CD4+ T cells and B cells, higher monocytes) and lower sputum CD8+ T cells. Unsupervised clustering of sputum eosinophils identified eight distinct subpopulations, and selective depletion of Cluster 6 was noted in biologic-treated participants (biologic-naive vs anti-TSLP logFC -4.98, p=0.003; biologic-naive vs anti-IL5 logFC -6.89, p=0.01). Higher Cluster 6 proportion correlated with worse ACT scores (rho = -0.44, p = 0.02) and lung function (FEV1 % predicted: rho = -0.47, p < 0.01; FEV1/FVC: rho = -0.40, p = 0.03). Functionally, Cluster 6 displayed enriched trafficking/activation markers (CCR3/Eotaxin-1, CD69, CD80, CRTH2) and non-T2 inflammatory mediators (TNF, IL-8, TLR7). ConclusionBiologic therapy in severe asthma was associated with selective depletion of a highly activated sputum eosinophil subpopulation with capability to drive both T2 and non-T2 inflammatory pathways. This cluster correlated with worse asthma control and lung function, indicating it may be a biologically important driver of persistent disease and potential biomarker to more accurately predict treatment response.

immunology↗

bioRxiv2026

Whole-proteome structure/function prediction in Uropathogenic E. coli reveals previously missed host-microbe and microbe-phage interaction pathways

The rapid advancement of high-throughput sequencing technologies has vastly increased the number of known protein sequences, but the experimental characterization of their structures and functions lags behind. This gap in knowledge impedes our understanding of biological mechanisms of these proteins, hinders the interpretation of high-throughput experiments, and exposes a significant challenge in modern biology: deducing the structural and functional information of proteins based on their sequences. Most computational approaches rely on homology with well-annotated proteins, yet many proteins lack identifiable homologues, reducing the power of this approach. Here, we integrated cutting-edge protein structure and function prediction methods to develop a complete sequence-structure-function pipeline that predicts structures and functions based on primary sequences. We applied this pipeline to predict the structure and function of all proteins in Escherichia coli UTI89, a model strain of uropathogenic E. coli. Based on the predicted functions, we performed enrichment analysis on the whole genome and revealed the possible roles and related biological mechanisms of poorly annotated proteins in this organism. Moreover, the performance of our pipeline was further validated through detailed case studies of the UTI89_C0931 and ybtS genes. Finally, we compiled the UTI89 structure and function database (https://seq2fun.dcmb.med.umich.edu/UTI89), offering it as a community resource to aid researchers in elucidating the roles of unannotated proteins in uropathogenic E. coli. This database aims to bridge critical knowledge gaps in microbial pathogenicity and resistance, enhancing our capacity to tackle emerging health threats.

microbiology↗

bioRxiv2026

Spatiotemporal unfolding of prefrontal neural response divergence in adolescent depression during naturalistic experience

Depression alters how emotional experience is interpreted and regulated, yet how depression-related neural divergence unfolds during ongoing experience remains poorly understood, leaving unresolved whether such divergence reflects stable trait-like abnormalities or state-dependent responses that emerge only at particular moments. This question is especially consequential in adolescence, when depression often emerges amid heightened social sensitivity and continuing maturation of prefrontal regulatory systems. Here, we used naturalistic film watching fMRI data of 36 adolescents with depression and 36 matched controls to map where, how, and when neural responses diverge between diagnostic groups. Neural polarization analysis localized group-level divergence to two prefrontal hubs, the dmPFC-rACC and left dlPFC, where individual similarity to the depression-group response pattern was associated with depressive symptom severity across the cohort. These local divergence sites were not spatially isolated. Seed-based inter-subject functional connectivity showed that they were embedded within distributed stimulus-locked coupling patterns spanning prefrontal control, salience, and subcortical reward circuits. Time-resolved phase synchrony further revealed that this divergence was not continuous, but arose at specific narrative moments, with the dmPFC-rACC diverging during socio-emotional events and the left dlPFC during cognitively demanding ones. Together, these findings move beyond static localization to reveal depression-related neural divergence as a spatially organized, network-embedded, and temporally gated response pattern shaped by the unfolding content of naturalistic experience.

neuroscience↗

bioRxiv2026

Extracellular NAD(P) activates systemic acquired resistance through LecRK-VI.2-mediated phosphorylation of NPR1

Systemic acquired resistance (SAR) is a long-lasting, broad-spectrum immune response induced in distal tissues by signals generated at primary infection sites. Although numerous mobile immune signals have been implicated in SAR, how these signals are perceived and mechanistically coupled to transcriptional reprogramming in systemic tissues remains poorly understood. Extracellular NAD(P) [eNAD(P)] functions as a key integrative SAR signal that activates immunity through the plasma membrane-localized lectin receptor kinase LecRK-VI.2 and the master immune coactivator NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1 (NPR1). However, the mechanism linking eNAD(P) perception to activation of NPR1 has remained unknown. Here, we show that LecRK-VI.2 constitutively associates with NPR1 and directly phosphorylates NPR1 at T359 and likely S356 upon eNAD(P) perception. NADP+-induced phosphorylation of NPR1 occurs rapidly in vivo and requires LecRK-VI.2. Nonphosphorylatable NPR1 variants abolish eNADP+-induced local and systemic immunity as well as biologically induced SAR, whereas phosphomimetic variants retain NPR1 function. Mechanistically, LecRK-VI.2-mediated phosphorylation promotes NPR1 interaction with TGACG-binding transcription factors (TGAs) and the Mediator subunit MED15, thereby enhancing assembly of a transcriptional activation complex required for defense gene expression. We further demonstrate that NPR1 facilitates TGA-MED15 association in a phosphorylation-dependent manner. Together, these findings establish a receptor-to-coactivator signaling mechanism that directly links extracellular immune signal perception to transcriptional activation. This work closes a major mechanistic gap in the SAR signaling pathway and reveals receptor-mediated coactivator activation as a mechanism for rapid conversion of extracellular immune cues into coordinated transcriptional outputs during systemic immunity.

plant biology↗

bioRxiv2026

scMTG reconstructs single-cell temporal dynamics with Markov transition generators

Single-cell time-series data offer a unique opportunity to study how cell states emerge, evolve, and diversify over time. However, cells are destructively measured across time points and individual cells cannot be directly tracked, making it challenging to reconstruct cell-state transitions and uncover the dynamic regulatory programs. Existing methods are predominantly based on optimal transport and typically require predefined low-dimensional representations, which can limit scalability, flexibility, and mechanistic interpretability. Here we present scMTG, a single-cell Markov transition generative framework that jointly learns cell representations and temporal cell-state transitions from unpaired single-cell time-series data. In a series of experiments, scMTG demonstrates superior performance in interpolating held-out missing-time-point data and inferring cross-time-point transition matrices, compared with state-of-the-art methods. More importantly, the learned Markov transition generator provides an interpretable framework for identifying the molecular programs that drive cell-state changes, enabling characterization of cell fate decisions and construction of time-resolved regulatory networks. Together, scMTG provides a unified generative framework for reconstructing cellular dynamics and uncovering regulatory programs that shape development, differentiation, and disease progression. scMTG is available as open-source software at https://github.com/liuq-lab/scMTG.

bioinformatics↗

bioRxiv2026

Dual-channel whole-brain imaging reveals distinct dopamine and calcium dynamics in walking Drosophila

Simultaneous recording of intra- and extracellular neuronal signals across the brain during behavior is crucial for unraveling brain information processing. In Drosophila, large-scale recordings have been explored, yet simultaneous dual-channel whole-brain imaging remains a significant challenge. We developed a system combining a Fourier light-field microscope with dual-focal microlens arrays optimized for adult walking flies, extending imaging volume while maintaining resolution requirements. This optical system is further combined with a cross-modal 3D registration and segmentation pipeline to integrate functional and structural data. Using this system, we simultaneously measured brain-wide intracellular calcium activity and extracellular dopamine release during locomotion. We found that functional maps and neural dynamics for dopamine and calcium are distinct across the brain and within specific compartments, particularly in the mushroom body and central complex, aligning with their anatomical bases. Both calcium and dopamine representations of locomotion are distributed, yet they exhibit different patterns. Mushroom body compartments can be functionally categorized into two types based on their responses to specific locomotive actions. Forward walking or acceleration boosts activity in compartments innervated by PAM dopaminergic neurons, while dampening activity in those targeted by PPL1 dopaminergic neurons. Conversely, backward walking or deceleration heightens PPL1 activity while reducing PAM activity, consistent with the functions of these neurons in encoding approach and avoidance choices. Notably, single-compartment activity can reliably decode behavior choices. Our findings, spanning whole-brain, brain-region, and single-compartment, demonstrate the capability of our system to uncover neural dynamics across multiple scales and channels in behaving animals.

neuroscience↗

bioRxiv2026

Hypothalamic MCH neurons links tau pathology to sleep disruption

Sleep disruption is an early and pervasive feature of Alzheimers disease (AD), yet the circuit mechanisms linking tau pathology to sleep-wake dysfunction remain unresolved. Here, we identify melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH) as a critical node disrupted in tauopathy. Longitudinal EEG/EMG recordings in PS19 mice reveal progressive impairments in sleep architecture and homeostasis. Histological analyses revealed significant degeneration of both MCH neurons and the neighboring hypocretin (Hcrt) neuronal population in the LH at late stages of tauopathy. In vivo fiber photometry recordings demonstrated a selective functional impairment of MCH neurons, characterized by reduced activity during REM sleep, whereas Hcrt neuronal activity remained largely preserved. In addition, cell-autonomous expression of mutant tau in MCH neurons recapitulates sleep disruption, establishing a causal role. Despite tauopathy-induced neuronal loss and reduced endogenous activity, optogenetic and chemogenetic activation show that MCH neurons retain functional capacity, and their activation restores sleep in aged PS19 mice. Together, these findings define a circuit mechanism linking tau pathology to sleep and identify MCH neurons as a tractable therapeutic target.

neuroscience↗

bioRxiv2026

Studying Effects of PDA Media Strengths on the Growth of Colletotrichum sublineola Using MPLEx-Based Integrative Proteomics and Metabolomics Analyses

Colletotrichum sublineola (Cs), the hemibiotrophic fungus that causes sorghum anthracnose, impacts sorghum grain and biomass crop production worldwide. Although nutrient availability is known to influence development in filamentous fungi, including Colletotrichum species, how in vitro nutrient limitation reprograms the Cs cellular state remains unclear. We cultured Cs on full-strength, half-strength, and one-tenth-strength potato dextrose agar (PDA) to define responses across a nutrient gradient. Nutrient limitation induced a pronounced high-sporulation phenotype, with one-tenth-strength PDA producing the strongest conidiation response, followed by half-strength PDA. To study the underlying molecular programs in each condition, we employed a multiplexed metabolite, protein, and lipid extraction (MPLEx) protocol for global proteomics and metabolomics. Global proteomics resulted in 4,590 protein identifications, including 204 unique to one-tenth-strength PDA. Among them are proteins linked to sporulation, vesicular transport, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and common in fungal extracellular membrane (CFEM)-domain proteins. Differential abundance and pathway analyses revealed a broad reduction of central carbon and energy metabolism, including glycolysis/gluconeogenesis, pentose phosphate, pyruvate metabolism, and glyoxylate pathways, together with increased ribosome-related processes, cAMP signaling, and cell-surface remodeling in one-tenth-strength PDA conditions. In addition, correlative metabolomics supported selective metabolic depletion and resource reallocation toward stress adaptation, membrane remodeling, and conidiation, supporting proteomics findings. Together, these data support a starvation-adapted Cs developmental state associated with enhanced sporulation, cellular pathway reprogramming, and potential virulence linked preparedness under nutrient-limited growth conditions in vitro. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/724728v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f9039borg.highwire.dtl.DTLVardef@5bef0eorg.highwire.dtl.DTLVardef@1abb7b6org.highwire.dtl.DTLVardef@aa11a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene-protein-reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity.

systems biology↗

bioRxiv2026

Encoded metabolic remodeling amplifies drug resistance in Mycobacterium tuberculosis

Antibiotic pressure causes pathogens to evolve many forms of altered drug susceptibility. In addition to target or activator mutations conferring canonical drug resistance, mutations can serve as steppingstones to or enhancers of resistance. In clinical strains of Mycobacterium tuberculosis (Mtb), we find that idsA2, which encodes an isoprenyl pyrophosphate synthase involved in the synthesis of precursors for essential components of the cell wall and electron transport chain, is undergoing diversifying selection and that these mutations are associated with the acquisition of first-line antibiotic resistance. By engineering isogenic Mtb strains that express clinically prevalent variants of idsA2, we show that clinical variants increase the minimum inhibitory concentration of ethambutol and, to a lesser extent, of isoniazid. Targeted lipid analyses reveal that disrupting IdsA2 function redirects limited resources in the isoprenoid synthesis pathway, leading to increased production of decaprenylphosporyl pentose which can compete with ethambutol for binding to arabinosyltransferases. IdsA2 mutations most often occur after embB mutation and lead to a multiplicative increase in ethambutol resistance. Thus, identification of idsA2 mutations can be utilized to improve the specificity of genotypic ethambutol susceptibility testing. Together, this work defines idsA2 as an ethambutol resistance gene and demonstrates how metabolic remodeling can augment drug resistance. Author SummaryTuberculosis is the deadliest infectious disease in the world and becomes more difficult to treat with the acquisition of drug resistance. Defining the mechanisms and genetic basis of drug resistance will allow for improved screening and drug regimen optimization for treatment success. To identify previously unrecognized mechanisms of altered drug susceptibility, we have combined population genomics and experimental genetics approaches, focusing on Mycobacterium tuberculosis genes evolving in clinical strains. We identified idsA2 as a target of frequent mutations that are associated with drug resistance. Experimental data indicate that idsA2 variants decrease the susceptibility of the bacteria to multiple antibiotics through isoprenoid synthesis remodeling, with the strongest effect on ethambutol resistance. IdsA2 mutations often occur after embB mutations to multiplicatively increase ethambutol resistance. These data suggest that inclusion of idsA2 variants in drug resistance testing could improve the specificity of genotypic detection of ethambutol resistance.

microbiology↗

bioRxiv2026

AM-DMF-SCP-pro: An Ultrafast and Streamlined Nanolitre-Scale Lab-on-a-Chip Platform for Single-Cell Proteomics and Tumor Microenvironment Profiling

Single-cell proteomics (SCP) remains constrained by slow sample preparation and background interference in low-input, multistep workflows, limiting its broader practical use in complex biological samples. Here we report AM-DMF-SCP-pro, a streamlined active-matrix digital microfluidic workflow that enables rapid on-chip lysis and digestion in 6 nL droplets within a closed nanolitre-confined microenvironment. AM-DMF-SCP-pro reduces total sample-preparation time to as little as 30 min, including a 15 min digestion step, while maintaining quantitative reproducibility and digestion performance. Systematic optimization of mass spectrometry acquisition and nanolitre-scale digestion conditions enables deeper, more sensitive and lower-background proteome profiling from ultra-low peptide inputs. In murine tumor samples, AM-DMF-SCP-pro reliably resolves immune and tumor compartments, identifies treatment-associated tumor cell states and enables quantitative analysis of neutrophil-associated proteomic signatures. These results establish AM-DMF-SCP-pro as a practical workflow for rapid, low-background single-cell proteome profiling in complex biological samples.

systems biology↗

bioRxiv2026

Reconstructing True 3D Spatial Omics at Single-Cell Resolution

Capturing the three-dimensional (3D) organization of cells is essential for deciphering complex biological processes, yet comprehensive 3D spatial omics is severely hindered by the destructive nature of physical sectioning and the depth limitations of intact tissue imaging. Current computational methods rely on 2.5D stacking of discrete slices, which inherently disrupts tissue topology and fails to resolve continuous depth-dependent molecular gradients. To bridge this gap, we introduce DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW, an Optimal Transport flow matching framework that models tissue evolution as a continuous dynamic vector field. By solving the underlying probability flow ODEs, DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW enables the direct extraction of uninterrupted, infinitely resolvable tissue states at arbitrary spatial depths. Using Deep STAR/RIBOmap 3D technologies, we demonstrate that DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW achieves improved 3D reconstruction fidelity relative to 2.5D approaches, yielding structures that more closely recapitulate native tissue microenvironments in real-world datasets. Across diverse spatial omics modalities, including spatial proteomics using imaging mass cytometry in human breast cancer and spatial transcriptomics using openST in head and neck squamous cell carcinoma metastatic lymph nodes, DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW produces biologically interpretable and high-fidelity reconstructions across datasets. We evaluated the scalability and robustness of DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW on a large-scale mouse brain dataset, reconstructing a continuous 3D cellular atlas comprising 39 million cells within 41.6 hours. Systematic downstream characterization validated its ability to recapitulate consistent spatial architectures, cell-type distributions, transcriptomic patterns, and microenvironmental structures across brain regions. Collectively, these results demonstrate DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW as a generalizable and efficient solution for true 3D spatial reconstruction across scales and modalities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/721395v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1a19624org.highwire.dtl.DTLVardef@188361forg.highwire.dtl.DTLVardef@199321corg.highwire.dtl.DTLVardef@a8f411_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

NEURA: An agentic system for autonomous neuroimaging workflows

Neuroimaging research depends on heterogeneous software, multimodal data and multistage statistical workflows. Large language model (LLM)-based agents offer a route to automate these workflows, but their susceptibility to hallucination limits their credibility in scientific use. Here we introduce NEURA, a proof-carrying framework for hallucination-resistant neuroimaging automation. NEURA converts free-text research questions and neuroimaging datasets into executable analysis plans, validated outputs and structured reports. The system combines disease- and tool-aware planning with a deterministic verification layer inspired by formal proof: before any claim is retained for reporting, it must be checked against tool-derived evidence and domain axioms. On NeuroEval, an expert-curated benchmark of 110 neuroimaging tasks, NEURA achieved 89.5% planning accuracy, a 30.5% improvement over direct LLM queries. In a controlled hallucination-injection experiment, the verification layer detected all the injected error classes under the specified axiom bank and trust assumptions, with no false positives. In case studies of spinocerebellar ataxia type 3, NEURA reproduced cerebellar atrophy and abnormal diffusion patterns consistent with established pathology and independent expert analyses. Together, these findings show that coupling domain-grounded agency with proof-carrying verification can turn LLM-driven workflow automation from probabilistic self-checking into auditable scientific computation.

neuroscience↗

bioRxiv2026

Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility

Single-cell ATAC-sequencing enables high-resolution mapping of cell states and their putative regulatory elements. The enrichment of genome-wide association study (GWAS) signals within context-specific regulatory regions can reveal disease-relevant cell populations. We present Single-Cell ATAC-seq Disease Score (SCADS), a computational framework that integrates GWAS summary statistics with scATAC-seq data to prioritize disease-relevant cells. SCADS produces calibrated, cell-level scores that are comparable across datasets and traits by: (i) identifying chromatin co-regulatory regions via topic modeling, (ii) quantifying their polygenic GWAS enrichment, and (iii) calculating cell-specific disease scores from topic weights and enrichment levels. Across extensive simulations, SCADS outperforms existing methods in power while controlling false positives. Applied to multiple autoimmune traits, SCADS reveals marked heterogeneity in disease relevance within canonical immune cell types. We specifically studied the differential disease relevance within CD8+ T and colon epithelial cells for inflammatory bowel disease, and pinpointed underlying gene programs and variants. SCADS is scalable, interpretable, and modular, providing a general framework to connect noncoding regulatory variation to cellular identity and function.

genomics↗

bioRxiv2026

Spatial-neighbour encoding enables fast RNA 3D structure search

Experimental and predicted RNA three-dimensional structures are expanding rapidly, but RNA structure search still lacks a compact residue-level representation that supports database-scale comparison. Using family-held-out ablations across the currently available experimental RNA structure collection, we found that spatial-neighbour features are markedly more informative for family-level discrimination than conventional backbone and base descriptors. Building on this result, we developed RiboSeek, a search framework based on a 20-letter geometric alphabet (RS-20), an 80-letter structure-and-base composite alphabet (RS-80). Across family-level classification and retrieval benchmarks, RS-80 delivered the strongest overall performance, whereas RS-20 most closely tracked US-align TM-score, indicating better preservation of geometric similarity. RiboSeek searches the full experimental RNA structure database in 204 ms per query and can be applied to predicted RNA structure libraries to prioritize candidate structural relationships for downstream analysis.

bioinformatics↗

bioRxiv2026

PEACE: Prototype-aware Effector Analysis via Contrastive Embeddings

Pathogenic fungi and oomycetes secrete effector proteins that manipulate host defenses and physiology to facilitate infection. However, in a typical secretome, effectors represent only a small fraction of proteins, creating extreme class imbalance and making deep-learning-based effector prediction prone to false positives. Here, we introduce PEACE (Prototype-aware Effector Analysis via Contrastive Embeddings), a lightweight pipeline that integrates ProtTrans (ProtT5) sequence embeddings with prototype-aware contrastive training to enhance effector identification. We benchmark PEACE against EffectorP 3.0 on two datasets with realistic class ratios: a fungal-only dataset and a combined fungi+oomycete dataset. PEACE outperforms EffectorP 3.0 on both datasets, while maintaining highly competitive recall. Post-hoc analysis shows that PEACE forms compact effector clusters against a well-dispersed non-effector background, which improves precision in the high-recall regime. These findings demonstrate that prototype-aware objectives, combined with curated data, can improve effector discovery for high-throughput screening in plant pathology and biotechnology.

bioinformatics↗

bioRxiv2026

BioClaw: Human-Bot Research Collaboration Ecosystems in Group Chats

Day-to-day research discussions in group chats often generate hypotheses, analysis requests, and interpretation decisions, yet executing those analyses still requires researchers to leave the conversation and rely on fragmented local tools, databases, visualization software, and literature search engines. In this work, we present BioClaw, a human-bot research collaboration ecosystem that converts natural-language requests in group conversations into tool-grounded analyses executed within isolated Docker containers. Deployed across 8 messaging platforms, BioClaw turns each group chat into a persistent execution workspace. Its design combines multi-channel orchestration, per-group state and workspace management, and isolated containerized execution for reliable shared use over long-lived conversations. To support practical research workflows, BioClaw combines containerized execution with preinstalled 31 biomedical tools and 95+ skills. The application of BioClaw spans various biomedical domains (e.g., genomics, clinics, structural biology) and data modalities (e.g., sequencing data, EHR data, protein structure data). These results establish the viability of embedding executable, tool-rich agent workflows within shared digital workspaces, positioning group chats as a transformative paradigm for collaborative scientific discovery and innovation.

bioinformatics↗