Biology subjects

Zhang, L.

Publications and source records attributed to Zhang, L..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Dynamic Gating Mechanism of the b0,+AT-Mediated Arg Transport: Insights from ASMD Simulations

Heteromeric amino acid transporters (HATs) mediate essential amino acid flux across membranes, but the molecular dynamics of substrate translocation remain poorly defined for many family members. Here, using conventional and adaptive steered molecular dynamics (cMD and ASMD) simulations, we identify residue W230 in the b0,+AT transport channel as a dynamic gate that regulates arginine (Arg) influx through side chain flipping. By integrating dynamic network analysis with dynamical cross-correlation of residue motions, we show that regulatory signals propagate from the Arg binding site through transmembrane helix 5 (TM5), a connecting loop, and TM6 to reach W230. We propose a dynamic gating mechanism for b0,+AT - mediated amino acid transport. Arg binding at V186 triggers signal propagation that enhances cooperative interactions between W230 and Arg, driving the side chain flipping of W230. Our findings reveal a dynamic gating mechanism underlying b0,+AT - dependent Arg transport and suggest that residue-triggered side chain reorientation may represent a conserved and efficient strategy in transporter function. Author SummaryAmino acids are the essential building blocks of life, and their transport across cell membranes is vital for nutrition and cellular signaling. Heteromeric amino acid transporters (HATs) mediate this process, yet how they physically move substrates through the protein at the atomic level remains poorly understood. In this study, we used advanced computer simulations to observe, in unprecedented detail, how b0,+AT--a key HAT member--transports the amino acid arginine. Our simulations revealed that a single residue, tryptophan 230 (W230), functions as a molecular gate: its side chain flips open to allow arginine to pass and then closes behind it, ensuring one-way traffic into the cell. We further discovered that the initial binding of arginine sends a signal through specific structural elements (helices and loops) to trigger this gate opening. This work not only uncovers a dynamic gating mechanism for b0,+AT but also suggests that similar side-chain flipping events may represent a common and efficient strategy used by other transporters to control substrate movement. Our findings provide a new framework for understanding transporter function and could inform future drug design targeting these critical membrane proteins.

biophysics↗

bioRxiv2026

A global regulatory atlas of Streptomyces reveals conserved and diversified transcriptional networks across actinomycetes

Transcriptional regulatory networks determine how bacteria integrate environmental signals with growth, metabolism and stress adaptation. Actinomycetes encode exceptionally large repertoires of transcription factors (TFs) coordinating morphological development, environmental adaptation and specialized metabolism, yet their regulatory networks remain poorly defined. Here, we apply DNA affinity purification sequencing (DAP-seq) to 789 predicted TFs of Streptomyces coelicolor, generating genome-wide binding maps for 393 regulators and expanding the experimentally supported regulome from [~]8% to [~]50%. Integration with ChIP-seq reveals pleiotropic regulators and hierarchical network architecture linking primary metabolism, development and biosynthetic gene clusters (BGCs). Multiplexed DAP-seq (multiDAP) across 16 additional actinomycetes uncovered deeply conserved regulatory circuits alongside widespread divergence in TF-target interactions. Together, these data establish a regulatory atlas for Streptomyces and related actinomycetes, enabling researchers to explore control of genes, pathways, BGCs and conserved network modules. This provides a foundation for predictive analysis and engineering of complex bacterial phenotypes in biotechnology and medicine.

microbiology↗

bioRxiv2026

Procedure-Matched Comparison of TNBS/Ethanol Enema and DSS Free-Drinking Models of Ulcerative Colitis in Rats: Immune Endotype Profiling and a Translational Model-Selection Framework

ObjectiveChoosing between TNBS/ethanol enema and DSS free-drinking water protocols for establishing rat ulcerative colitis(UC) models remains a practical challenge in preclinical research. Beyond confirming that both methods induce colitis, no prior study has quantified the independent contribution of enema procedural trauma to inflammatory readouts, reported sex-stratified cytokine responses, or formally linked model-specific immune profiles to human UC endotypes. MethodsTen SPF-grade SD rats(half male and half female) were allocated to a normal control, a procedure-matched saline enema control(anesthesia and enema without chemical agent), a TNBS group(100 mg/kg TNBS + 50% ethanol enema), and a DSS group(3% DSS free-drinking, 7 days). Endpoint assessments included body weight, colon length, macroscopic and histological scoring(modified Macpherson criteria, 0-10), and serum TNF- and IL-6 by ELISA. Post-hoc exploratory analyses included sex-stratified cytokine sub-analysis and coefficient of variation(CV%) for each readout. ResultsBoth model groups successfully induced UC(P<0.05 vs. controls for all endpoints). Critically, the procedure-matched saline enema control confirmed that colonic inflammation in the TNBS group was attributable to the chemical agent rather than mechanical trauma. TNBS induced significantly higher TNF-(90.2{+/-}10.5 vs. 73.5{+/-}9.8 pg/mL, P<0.05) and IL-6(66.5{+/-}8.3 vs. 53.2{+/-}7.6 pg/mL, P<0.05) than DSS,consistent with Th1-polarized vs. innate barrier-disruption immune endotypes, respectively. Coefficient of variation analysis indicated greater intra-group variability in TNBS versus DSS, reflecting the procedural dependency of enema-based models. ConclusionA procedure-matched control design is essential for valid TNBS-vs.-DSS comparisons and has been systematically underutilized in prior studies. Based on immune endotype alignment, TNBS is recommended for screening T-cell-targeted therapies and acute anti-inflammatory agents, while DSS is recommended for epithelial barrier repair and microbiome-modulating interventions. A model-selection decision framework derived from these data is provided to guide preclinical study design.

animal behavior and cognition↗

bioRxiv2026

CytoGem-XAI:A Hypergraph Neural Network Framework for Genome-Scale Metabolic Modeling and Interpretable Analysis

Genome-scale metabolic models are essential for understanding cellular metabolism, yet existing deep learning approaches remain black boxes, and traditional flux balance analysis (FBA) cannot provide sample-specific predictions. To our knowledge, CytoGem-XAI is the first framework to combine hypergraph neural network representation with interpretable, FBA-parallel analysis and sample-specific metabolic characterization. Built upon hypergraph representations where reactions are encoded as hyperedges connecting their participating metabolites, CytoGem-XAI introduces three analysis modules: perturbation-based carbon source importance ranking, hard intervention reaction bottleneck identification, and pathway-level topological attribution. Beyond prediction, CytoGem-XAI uniquely enables condition-dependent carbon source essentiality and reaction bottlenecks that vary with genetic background--capabilities absent from both traditional FBA and existing deep learning methods. Trained on 17,400 E. coli growth conditions using 10-fold cross-validation, our framework achieves R2 = 0.862, substantially outperforming AMN (R2 = 0.81, +6.4%), FBA (R2 = 0.62, +39%), and gradient boosting baselines (R2 = 0.71, +21%). Biological validation confirms that CytoGem-XAI identifies known essential carbon sources (e.g., alanine, malate) and rate-limiting enzymes (e.g., TCA cycle), while also revealing N-acetylmuramate--a peptidoglycan precursor--as a previously underappreciated essential nutrient.

bioinformatics↗

bioRxiv2026

Molecular mechanisms underlying the prepupal-instar-forming metamorphosis in black soldier fly (Hermetia illucens)

The black soldier fly (Hermetia illucens, Diptera) undergoes an atypical metamorphic program in which a distinct non-feeding prepupal instar precedes pupation, but its developmental and molecular basis remains poorly understood. Here, we investigated this unusual metamorphic program through integrated developmental and RNAi-based analyses. Postembryonic staging confirmed that the 6th instar feeding larva molts into a non-feeding 7th instar prepupa, whose hardened cuticle subsequently serves as the puparium during intra-puparial development. Expression profiles of four key metamorphic genes revealed a stage-specific Chinmo/Kr-h1- Br-c-E93 regulatory shift corresponding to larval, prepupal, and pupal/adult development, with sustained Br-c expression defining the 7th instar prepupal stage. RNAi-mediated knockdown of Kr-h1 and/or Chinmo induced precocious larval-prepupal metamorphosis, supporting their roles in larval stage maintenance, whereas depletion of Br-c or E93 disrupted prepupal-pupal transition and adult differentiation, respectively, consistent with their functions as pupal and adult specifiers. These results support generally conserved functions of the metamorphic gene network while indicating the absence of a repressive effect of Br-c on E93 in H. illucens prepupae. Together, these findings establish the 7th instar prepupa as an independently regulated transitional stage, providing insight into how metamorphic programs are reorganized to diversify life-history strategies in holometabolous insects.

developmental biology↗

bioRxiv2026

Data-Efficient Exploration of Enzyme Function Using Family-Specific Machine Learning

Enzymes are essential biocatalysts across diverse industries, driving demand for high-performing variants. Foundation models are attractive for guiding enzyme discovery, but often lack the resolution to model subtle variations driving function within homologous families. Navigating these rugged functional landscapes to identify elite variants remains challenging and experimentally costly, even when guided by such models. Here we show that coupling dense, family-specific experimental screening with targeted, sequence-based deep learning provides a data-efficient discovery strategy. We experimentally screened 1,513 natural homologues from an esterase superfamily (>7,500 assays) and used this functional landscape to train task-specific models that predict activity, thermostability, and substrate specificity from sequence alone. Prospective experimental validation of previously untested sequences demonstrated that these task-specific models significantly outperformed generalist pre-trained and physics-based models in enriching for target traits. Residue-level attribution further indicated that the models captured sequence patterns consistent with underlying structural features. Finally, retrospective simulations showed that iterative retraining compresses the search space, discovering 60% of top-tier hits using nearly half the samples required by pre-trained baseline models. Together, these results highlight that machine learning can provide mechanistic insight, and that integrating targeted data acquisition with iterative machine learning provides a more data-efficient discovery strategy than relying on generic model scale.

bioengineering↗

bioRxiv2026

Repetition Selectively Reconfigures Offset-Defined Temporal Integration in Auditory Cortex

Auditory perception requires integrating sound over time, but whether cortical integration windows are fixed or adaptively shaped by recent sensory history remains unclear. Because auditory offset responses are shaped by the temporal structure preceding sound termination, they provide a readout of offset-defined temporal integration. Here, we recorded single-unit activity in awake rat auditory cortex while presenting 512-ms click trains with inter-click intervals of 1, 2, 4, 8 or 16 ms. Each stimulus was repeated ten times, allowing repetition number to index increasing adaptation. Across 271 offset-responsive neurons, repetition strongly compressed the maximal ICI capable of evoking a significant offset response and produced a smaller population-level shift in preferred ICI. These preferred-ICI changes were heterogeneous across neurons, with bidirectional shifts at the single-neuron level. In contrast, the effective temporal window measured with irregular-regular click trains remained stable at the population level, despite neuron-level variability. AAF and A1 further showed distinct repetition-dependent tuning profiles. These findings suggest that adaptation selectively reconfigures ICI-based offset tuning while preserving a population-level local regularity window, revealing flexible and stable components of cortical temporal integration.

neuroscience↗

bioRxiv2026

Engineering Carbon Nanotube Quantum Well Defects with Recognition Tripeptides for Optical Detection of Extracellular Vesicles in Plasma

Extracellular vesicles (EVs) carry molecular signatures of their originating cells and have thus emerged as promising biomarkers. However, their clinical utility remains limited due to their low abundance and the modest sensitivity of current EV detection methods in complex biological environments. Here, we present a quantum well defect functionalized carbon nanotube sensor coupled with integrin-recognition RGD tripeptide for EV detection in human plasma. Leveraging the abundance of integrins on EV surfaces, we targeted 5{beta}1, V{beta}1, and V{beta}3 subtypes. The nanosensor exhibited robust hypsochromic shifts in defect emission upon integrin binding, achieving sub-picomolar detection limits for integrin subunits and quantifying EVs at concentrations as low as 104 EVs{middle dot}mL-1 for glioblastoma, ovarian cancer, and fibroblast cell-derived EV types. Molecular dynamics simulation indicated that integrin docking at the RGD-coupled quantum defect can substantially reshape the interfacial environments of the quantum defects, explaining the high sensitivity in EV detection in complex biological media. Finally, transmembrane protein analysis validated the expression of surface integrins across the tested EV types. The modular nanosensor construct can be targeted to detect disease-associated EV subpopulations, advancing EV-based diagnostics.

biochemistry↗

bioRxiv2026

Epigenome Alterations and 3D Chromatin Architecture Remodeling in Inflammatory Macrophage Activation under Diabetic Conditions

Aberrant monocyte/macrophage activation in diabetes drives chronic inflammation and complications. While epigenetic mechanisms are implicated, the role of 3D-chromatin reorganization remains unclear. Using integrated multi-Omics, we profiled gene expression and 3D-chromatin architecture in human CD14+ monocyte-differentiated macrophages treated with high glucose + TNF- (HT) mimicking the diabetic milieu. HT induced inflammatory programs resembling those in diabetes, and dynamically altered chromatin accessibility, enhancer-promoter loops, transcriptionally active/inactive (A/B) compartments, and topologically associated domains. Inflammatory genes exhibited increased, whereas cell-cycle and metabolic genes showed reduced chromatin accessibility and enhancer-promoter interactions. These architectural changes facilitated the convergence of lineage-specific and signal-dependent transcription factors at enhancer-promoter loops, forming network hubs orchestrating pro-inflammatory responses. Similar enhancer-promoter interactions were observed in monocytes from individuals with diabetes. Perturbing chromatin interactions via CRISPR-interference targeting HT-induced enhancers suppressed inflammatory gene expression. Results from this first comprehensive enhancer connectome maps in macrophages under diabetic conditions reveal disease-associated rewiring of the 3D epigenome, highlighting epigenetic mechanisms as potential therapeutic targets. TeaserDiabetic conditions rewire 3D chromatin organization and enhancer-promoter interactions in human macrophages, promoting chronic inflammation.

genomics↗

bioRxiv2026

Shared binding sites for the chromosomal architectural protein Su(Hw) mediate physical interactions between Drosophila TAD boundaries

The head-to-tail pairing of the nhomie and homie boundaries is responsible for the formation of a stem-loop TAD (topologically associated domain) that encompasses the even-skipped (eve) gene and its regulatory elements. TAD boundaries like homie and nhomie have partner preferences and one mechanism thought to be important in linking compatible boundaries is shared binding sites for the same architectural protein. Both eve boundaries have a single binding site for the polydactyl zinc finger protein Su(Hw). Here we have investigated the role of the shared Su(Hw) sites in the long-distance regulation of a dual reporter by the eve enhancers. Consistent with this mechanism, we find that when the transgene nhomie or homie boundary has a mutation in the Su(Hw) binding site, reporter activation by the eve enhancers is severely compromised. Using MicroC we show the mutating the single Su(Hw) substantially weakens the ability of the nhomie and homie boundaries in the transgene to physically pair with the two endogenous eve boundaries. Su(Hw) binding sites are not, however, sufficient as the gypsy su(Hw) insulator is unable to support long-distance regulation. In a less demanding transvection assay mutations in the nhomie and homie Su(hw) site have only a modest effect on regulatory interactions. The eve boundaries can mediate transvection with the gypsy insulator; however. they are not compatible with the dCTCF Fab-8 boundary even in the transvection assay. Finally, we show that the regulatory and physical interactions of the nhomie boundary in the transgene and the two endogenous eve boundaries are incompatible with the popular cohesin-loop extrusion model.

genetics↗

bioRxiv2026

COLOR-3D: a versatile tool for revealing novel 3D histological features

Hematoxylin and eosin (H&E) has been the fundamental method for visualising tissue morphology. Recent advances in tissue clearing and microscopy have enabled the observation of tissue morphology in 3D, but incomplete penetration of nucleic acid dyes has remained the bottleneck. To address this, we develop a new staining chemistry called Cyclodextrin and Organic solvent-assisted deep Labelling of ORgans in 3D (COLOR-3D), which attains the best penetration depth and homogeneity among state-of-the-art methods. We also demonstrate the scalability of COLOR-3D and its compatibility with other staining modalities. To bridge the gap between 3D histology and its wider application in biomedical research and histopathology, we develop a computational pipeline to convert 3D fluorescence images into bright-field H&E images, enabling the creation of a 3D atlas of both normal tissues and pathological specimens. Apart from qualitative observation of tissue morphology, COLOR-3D also enables quantitative analysis for studying biological phenomena. In the mouse liver, we discover rare populations of tetranuclear hepatocytes as well as m16n, t4n and t8n hepatocytes. We also propose the first structural model of the liver lobule based on 3D histology. With a more complete penetration, we reveal the following aging-related changes in tissue microarchitecture, including an increase in extreme nuclear polyploidy, and the disruption of vasculature and portal triad.

pathology↗

bioRxiv2026

Annotation-free sequence-level multimodal graph learning reveals gut microbiome signatures of atherosclerotic cardiovascular disease

BackgroundAtherosclerotic cardiovascular disease (ACVD) remains one of the leading causes of mortality, and the gut microbiome has been implicated in ACVD-related metabolic and inflammatory processes. However, current microbiome-based disease prediction frameworks predominantly rely on annotation-dependent taxonomic or pathway-level abundance profiles. While effective for community-level association analysis, these approaches compress metagenomic information into predefined biological categories and may consequently obscure fine-grained sequence variation, uncharacterized microbial fragments, regulatory sequence signals and sequence-based structure information embedded within metagenomic DNA. Whether disease-relevant microbiome information can be directly learned from raw metagenomic sequences without prior annotation remains incompletely explored. ResultsWe developed MLMGCN-CVD, an annotation-free sequence-level multimodal graph-learning framework for ACVD prediction directly from gut metagenomic DNA fragments. Rather than relying on taxonomic aggregation, MLMGCN-CVD integrates pretrained genomic language-model embeddings, sequence-derived structural priors, and read-mapping quantitative evidence to learn disease-associated representations at the DNA fragment level. In a primary cohort comprising 218 ACVD patients and 187 healthy controls, MLMGCN-CVD achieved an area under the receiver operating characteristic curve (AUC) of 0.978 under grouped 10-fold cross-validation. External validation on an independent cohort (PRJNA615842) yielded an AUC of 0.940, outperforming relative- abundance-, pathway-, and sequence-only reference frameworks. Importantly, model-prioritized fragments independently recovered multiple microbiome-associated functions previously implicated in ACVD, including trimethylamine (TMA) metabolism, O-antigen of lipopolysaccharide (LPS) biosynthesis, phosphotransferase systems (PTS), and Enterobacteriaceae-associated modules. Beyond these established signatures, Rfam-supported analyses identified candidate regulatory RNA-associated signals, including riboswitches and Bacterial small RNAs, suggesting that regulatory sequence information may contribute previously underexplored discriminatory signals within the ACVD gut microbiome. Several representative sequence elements further showed significant correlations with host cardiometabolic indicators. ConclusionsOur findings suggest that microbiome-associated disease signals extend beyond conventional taxonomic or pathway abundance summaries and can be directly inferred from raw metagenomic sequences. By shifting microbiome modelling from annotation-dependent community aggregation toward annotation-free sequence-level representation learning, MLMGCN-CVD provides a framework for uncovering biologically informative regulatory and functional signals embedded within metagenomic data.

bioinformatics↗

bioRxiv2026

Novel Muscle-Tropic AAV Capsids with Dramatically Enhanced Transduction and Safety Profiles in Non-Human Primates

Adeno-associated virus (AAV) gene therapy holds immense promise for treating muscular dystrophies, yet its efficacy and safety are constrained by the suboptimal tissue tropism of natural serotypes. Here, we employed the REACH platform, which combines rational design and directed evolution, to engineer muscle targeting vectors. Systemic administration in non-human primates (NHPs) revealed that lead candidate M1 mediates a >10-fold increase in skeletal muscle transduction compared to the AAV9 and 2-3 fold higher than MyoAAV, while concurrently achieving a remarkable 183-fold reduction in liver distribution. Furthermore, M1 exhibited significant de-targeting from key off-target tissues, including dorsal root ganglia (11 fold), lung (27 fold), spleen (2 fold), and kidney (2 fold). These findings demonstrate that the REACH platform can generate AAV capsids with simultaneously enhanced muscle tropism and favorable safety profiles, addressing a critical bottleneck in muscle-directed gene therapy.

bioengineering↗

bioRxiv2026

Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia

Temporal dynamics of the peripheral blood transcriptome are crucial for understanding leukemia evolution and response to therapy because they can reveal how gene expression programs drive abnormal cell states, disease heterogeneity, and treatment resistance. Using a mathematical model of state-transitions, we studied the temporal dynamics of peripheral blood messenger RNA (mRNA) and microRNA (miRNA) transcriptomes in a mouse model of acute myeloid leukemia (AML). In our state-transition model, mRNA and miRNA transcriptomes are represented as a particle undergoing Brownian motion in a two-dimensional multiomic potential landscape. Following chemotherapy, we observed a temporal desynchronization between mRNA and miRNA transcriptomic responses corresponding to an asymmetric shift in the landscape. Specifically, mRNA trajectories responded almost immediately post-treatment, whereas miRNA responses were delayed by approximately two weeks. Clustering analysis identified that the temporal delay is driven by a prominent cluster of miRNAs from the imprinted Dlk1-Dio3 region. Although previously implicated in acute promyelocytic leukemia, lymphomas, and metabolic dysregulation, this provides the first evidence linking the Dlk1-Dio3 locus to AML chemotherapy response and treatment-induced transcriptomic desynchronization. This framework offers an innovative dynamics-based strategy to identify biological drivers of therapeutic response and novel therapeutic targets across hematological malignancies. Key PointsO_LITreatment can induce desynchronization between messenger RNA and microRNA transcriptome dynamics in a murine model of AML C_LIO_LIMicroRNAs from the imprinted Dlk1-Dio3 locus are identified as key contributors to desynchronization and may serve as therapeutic targets C_LI

cancer biology↗

bioRxiv2026

Wheat MYB transcription factor TaMYB83-7B regulates seed dormancy by influencing the balance between abscisic acid and gibberellin

In wheat, weak seed dormancy (SD) is related to an increased tendency for pre-harvest sprouting (PHS), which reduces yield and quality. However, the molecular mechanism underlying SD remains elusive. Here, we identified a wheat R2R3-MYB transcription factor (TaMYB83-7B) related to SD. Expression analysis showed that TaMYB83-7B was highly expressed in wheat seeds, and was more highly expressed in strong-dormancy varieties than in weak-dormancy varieties. Sequence and association analysis indicated that T/C mutations at -907 bp and -1133 bp in the TaMYB83-7B promoter were significantly associated with wheat SD, with C at both sites related to strong dormancy. Dual-luciferase reporter assays demonstrated that the transcriptional activity of the TaMYB83-7B promoter was significantly higher in strong-dormancy varieties than in weak-dormancy varieties. Further analyses indicated that TaMYB83-7B functions as a transcriptional inhibitor. Germination experiments revealed that overexpression of TaMYB83-7B significantly enhanced SD, while its loss-of-function reduced SD. Finally, TaMYB83-7B was found to regulate SD by influencing the balance between abscisic acid (ABA) and gibberellin (GA) in wheat seeds. Overall, the results of this study enhance our understanding of the complex regulatory mechanism underlying SD, and provide gene targets and molecular markers for the genetic improvement of PHS resistance in wheat.

molecular biology↗

bioRxiv2026

Physiological trade-offs drive the archaeal dominance and carbon turnover in deep subsurface

Marine sediments host a vast deep biosphere, yet how microorganisms persist under severe energy limitation and govern long-term organic carbon (OC) preservation remains poorly understood. Here we show that archaea, primarily Bathyarchaeia, systematically displace bacteria with depth and form net growth zones across East China Sea shelf deep sediments. Multi-omics analyses and bioenergetic modelling reveal that this transition is driven by sustained archaeal metabolism of diverse recalcitrant OC compounds, and a physiological trade-off that prioritizes cellular maintenance over growth, minimizing mortality in deep sediments. This strategy triggers a fundamental shift in sedimentary carbon turnover: from rapid bacterial degradation of labile OC near the surface to persistent archaea-driven turnover of recalcitrant OC at depth. We estimate that Bathyarchaeia mediate [~]77% of total OC degradation after 1,000 years of burial, corresponding to [~]18% ([~]11.4 Pg C) of millennial OC degradation in global shelf sediments. These findings identify subsurface archaea as key microbial regulators of long-term OC preservation and reveal how physiological trade-offs sustain life and carbon turnover in the energy-limited deep biosphere.

microbiology↗

bioRxiv2026

A General Statistical Framework for Hardy-Weinberg Equilibrium Inference on the X Chromosome

Testing for Hardy-Weinberg equilibrium (HWE) is a fundamental component of genetic data analysis, widely used for quality control and model validation. Although HWE testing is well established for autosomal loci, inference on the X chromosome is more complex due to sex-specific genotype structures and potential sex differences in minor allele frequency (sdMAF). Existing tests differ in their assumptions about sdMAF and male sample inclusion, often leading to distinct but poorly characterized null hypotheses. We develop a general statistical framework for HWE inference using the robust allele-based regression model. By formulating HWE testing as an assessment of allele-level dependence, the framework directly parameterizes Hardy-Weinberg disequilibrium, unifies existing Pearson{chi} 2-based tests under explicit modeling assumptions, and clarifies their null hypotheses, degrees of freedom, and sensitivity to sdMAF. The framework also accommodates covariate and population-structure adjustment within a unified regression-based formulation. The proposed framework provides robust, interpretable, and flexible inference, establishing a unified statistical foundation for HWE testing across autosomal and X-chromosomal regions. Simulation studies and analysis of high-coverage 1000 Genomes Project data demonstrate that commonly used X-chromosome tests can exhibit inflated type I error or misleading inference when sdMAF is present.

genetics↗

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↗