Biology subjects

Zhang, G.

Publications and source records attributed to Zhang, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Reinforcement learning with evolutionary priors unlocks multi-site enzyme optimization from sparse experimental feedback

Industrial enzymes underpin sustainable manufacturing, yet natural proteins rarely combine the activity, robustness and application compatibility required for practical use. Engineering such properties requires identifying beneficial combinations of interacting mutations within vast sequence spaces that are too large to search exhaustively and too sparsely sampled to model reliably. Here we introduce AlphaVariant, a reinforcement-learning (RL) framework that couples a generative evolutionary prior with iteratively updated low-N fitness models to enable feedback-driven search of large combinatorial protein sequence spaces. Across six experimentally measured protein fitness landscapes, AlphaVariant ranked first on the three large, non-enumerable multi-site landscapes. In two successive rounds of savinase activity optimization, AlphaVariant-designed libraries yielded 75.4% and 83.8% activity-improved variants among experimentally characterized candidates even as the accessible mutation space expanded from 25 to 67 positions. The search identified beneficial mutation combinations involving up to 17 structurally dispersed sites and produced a variant with 7.1-fold higher activity than wild type after only two design-test cycles. We then extended AlphaVariant to jointly optimize activity, thermostability and a patent-informed constraint for intellectual-property freedom. This multi-objective search yielded variants that outperformed the commercial protease Uno 100 L in both biochemical assays and application-relevant detergent washing tests. These results demonstrate that generative RL can efficiently navigate vast protein fitness landscapes, progressively incorporate multiple engineering objectives and convert sparse experimental feedback into enzyme variants with industrially relevant performance, effectively resolving the limits of biocatalyst engineering.

bioinformatics↗

bioRxiv2026

NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded by noise, non-rigid motion, and heterogeneous recording conditions. Here we introduce NeuroPilot, an agentic framework that transforms neural imaging analysis into a closed-loop, context-aware process, integrating a multimodal large language model with callable computational imaging modules to adaptively coordinate workflows from raw recordings to population-level readouts. Across heterogeneous imaging and experimental regimes, NeuroPilot restores motion-degraded recordings and supports consistent neural signal recovery, detection, and tracking in challenging deep-brain, freely moving, and longitudinal settings. These enable neuronal population tracking across spinal stimulation paradigms, deep-brain imaging, and continuous 24-hour mPFC recordings aligned with spontaneous sleep-wake behavior. By converting heterogeneous intravital recordings into stable population activity maps, NeuroPilot supports behavioral decoding and reveals structured neural dynamics across behavioral states and transitions. NeuroPilot establishes a scalable agentic paradigm for neural imaging analysis, expanding experimental access to the measurement, visualization, and interpretation of neural population activity.

neuroscience↗

bioRxiv2026

Brainstem cholinergic cell loss causes slowly-developing motor impairments

Pedunculopontine nucleus (PPN) cholinergic degeneration is a feature of Parkinson's disease (PD), but it is difficult to isolate the symptoms and circuit alterations caused by cholinergic cell loss in PD, where multiple nuclei are simultaneously degenerating. Here we develop a novel mouse model of brainstem cholinergic cell loss to define the timeline of symptom development and map physiological adaptations within the brainstem. Mild motor symptoms, including balance and motor learning deficits reminiscent of early-stage PD, developed after cholinergic lesion with an unexpected long delay. Ex vivo whole-cell recordings showed that cholinergic cell loss increased excitatory synaptic drive onto glutamatergic PPN neurons and increased their intrinsic ability to generate sustained action potential output. In parallel, fiber photometry experiments revealed a slight increase in glutamatergic sensory responsiveness that closely tracked the delayed onset of behavioral symptoms. Together, these results reveal that brainstem cholinergic cell loss triggers the reorganization of motor-related brainstem circuits, providing critical insight into the consequences of non-dopaminergic degeneration in PD.

neuroscience↗

bioRxiv2026

A spatial topology atlas of type I myofibers in porcine skeletal muscle reveals local slow-oxidative niches associated with meat quality traits

Skeletal muscle metabolism and meat quality are usually interpreted through myofiber type composition, fiber size, and histochemical state. Whether the spatial topology of myofibers constitutes an independent phenotype layer remains poorly quantified. Here, using succinate dehydrogenase (SDH)-stained cross-sectional images from multiple pig breeds and more than 40 skeletal muscle sites, we established a multiscale spatial phenotyping framework for type I myofibers. To enable scalable instance-level quantification, we developed an improved Mask R-CNN segmentation workflow, trained with SAM-assisted and manually corrected annotations, and optimized for dense, small, irregular, and variably stained muscle fibers. This framework captures single-fiber morphology and staining, local contact, connected-cluster architecture, diffusion-ring neighborhoods, and global tissue configuration. Type I myofiber abundance was separable from spatial organization capacity: some muscles formed stronger-than-expected local connectivity and clustering despite modest type I fiber abundance. Spatial transcriptomic integration further showed that slow-oxidative genes and functional modules were non-randomly organized and formed local high-expression domains in type I fiber-enriched, high-contact neighborhoods. Across muscle sites and breeds, skeletal muscles displayed distinguishable type I fiber spatial topology identities, which were remodeled by breed background in a muscle-specific manner. In samples with matched meat-quality measurements, spatial topology features improved the explanation of pH, meat color, conductivity, and intramuscular fat. These results suggest that skeletal muscle should be modeled as a spatially organized tissue system rather than a simple mixture of fiber types.

bioinformatics↗

bioRxiv2026

BCKDK Inhibitors Directly Antagonize Angiotensin II Type 1 Receptor and Induce Vasodilation Independently of BCAA Catabolism or BCKDK

Branched-chain amino acids (BCAA) have been implicated in diabetes and heart failure, leading to ongoing clinical trials with inhibitors of BCKDK, a key regulator of BCAA catabolism. We have previously shown that these inhibitors promote vasodilation and lower blood pressure, well-known cardioprotective effects, but how this occurs remained unclear. We show here, using explanted vessels, in vivo hemodynamics and telemetry, and genetically modified mice, that the vasodilatory effect of BCKDK inhibitors is direct, rapid, independent of nitric oxide, independent of BCAA catabolism, and most strikingly, independent of BCKDK itself. Instead, we demonstrate, using structural and biochemical studies, that BCKDK inhibitors directly antagonize the angiotensin II type I receptor (AT1R) and consequent angiotensin-mediated vasoconstriction. These data demonstrate a strong off-target effect of BCKDK inhibitors, independent of BCAA or BCKDK, which may explain their cardioprotective effects but also questions if this protection would be additive to existing AT1R blockers.

physiology↗

bioRxiv2026

Scalable methylome-transcriptome profiling resolves clonal organization in human hematopoiesis

Understanding human tissue maintenance and remodeling requires linking clonal ancestry to molecular state, yet scalable joint measurement remains challenging. Here, we introduce Droplet-MT and an integrated computational workflow for joint transcriptome and methylome profiling at ~10,000 cells per library. Methylation epimutations enable mutation-independent clonal reconstruction from high-coverage methylomes, while reference-based label transfer assigns cells to established clones at ~0.2% CpG coverage, reducing sequencing cost. Clone-aggregated somatic variants provide genetic annotation without additional single-cell genotyping. Across ~21,000 paired profiles from healthy blood and longitudinal acute myeloid leukemia, we resolved small and expanded clones with heterogeneous lineage output. In leukemia, the framework distinguished changes in malignant clonal abundance, state remodeling within persistent clones, and recovery of healthy polyclonal hematopoiesis. Malignant monocytes remained transcriptionally and epigenetically distinct from healthy and recovery-associated cells despite treatment-associated remodeling. Droplet-MT enables scalable analysis of human tissues by linking clonal organization with molecular state and retrospective genetic annotation.

genomics↗

bioRxiv2026

PPARγ brakes cytosolic DNA-induced macrophage inflammation by super-enhancer metabolic reprogramming

Cytoplasmic DNA sensing triggers intense innate immune responses that require tight regulation. PPAR{gamma} is a master metabolic regulator in macrophages, but its role in cytoplasmic DNA immunity remains unclear. Using poly(dA:dT) to mimic cytoplasmic DNA stimulation, we combined transcriptomics, epigenomics (PPAR{gamma}/H3K27ac ChIP-seq), and functional assays. Poly(dA:dT) induced a cytokine storm with MAPK activation and apoptosis/necrosis. PPAR{gamma} activation by rosiglitazone (RGZ) antagonized this response, reducing cell death and transcriptionally inhibiting MAPK genes (e.g., DUSP4/10). Mechanistically, PPAR{gamma} bound promoters/enhancers via PPRE motifs and distal intergenic regions, remodeling H3K27ac (reduced at inflammatory loci, increased at metabolic genes). RGZ induced de novo super-enhancers coupling PPAR metabolic programs (e.g., CPT2) with immune suppression by targeting TLR4, CEBPB, and PIM3. Thus, PPAR{gamma} acts as a brake in cytoplasmic DNA immunity, integrating metabolic reprogramming and inflammation suppression through a multi-level genomic/epigenetic program. This study highlights PPAR{gamma} as a key immune-metabolic integrator and therapeutic target for diseases driven by excessive cytoplasmic DNA sensing.

immunology↗

bioRxiv2026

Simulating Bead-Tethered Biomolecules: Force-Probe Coupled Steered Molecular Dynamics (fp-SMD) in Optical Tweezers Force Spectroscopy

Interpreting single-molecule force spectroscopy (SMFS) data from optical tweezers with steered molecular dynamics (SMD) is hindered by a severe timescale mismatch: SMD trajectories span nanoseconds to microseconds, while SMFS experiments unfold over milliseconds to seconds, forcing SMD to pull six to nine orders of magnitude faster than experiment and systematically distorting rupture forces, molecular pathways, and kinetic intermediates. We introduce force-probe coupled steered molecular dynamics (fp-SMD), which explicitly propagates the force probe (the optically trapped bead) alongside the biomolecule within a single classical Hamiltonian description of the coupled molecule-linker-bead system. A Predict-Correct Trajectory Propagation (PCTP) scheme resolves the resulting computational bottleneck by decoupling the fast molecular relaxation from the slow, macroscopic bead motion, using metadynamics and transition-state theory to identify the kinetically resolved molecular intermediate reached at each macroscopic time step. On the cholesterol-{beta}-cyclodextrin ({beta}-CD) host-guest complex, fp-SMD reproduces the experimental rupture-force histogram directly at matched conditions, without extrapolation from standard SMD. fp-SMD further resolves how applied force reshapes the molecular escape pathway itself--cholesterol dissociates via a high-angle route at low force and a direct, small-angle route at high force--providing, to our knowledge, the first direct structural evidence for force-induced pathway switching within small host-guest systems believed to underlie catch-slip bond behavior. fp-SMD/PCTP thus offers a general route to simulating force-probe-coupled biomolecular mechanics at experimentally relevant timescales.

biophysics↗

bioRxiv2026

Ultrafast and Accurate Selection of High-Quality Protein Complex Models from Large-Scale Prediction

In the post-AlphaFold era, advances in protein complex structure prediction have enabled the generation of thousands of candidate models per target, making the accurate and efficient selection of high-quality models from large candidate pools a critical challenge. Existing model-selection approaches commonly employ estimated model accuracy (EMA) methods, which include single-model and consensus-based methods. The former lack cross-model comparative information, whereas the latter often incur high computational costs from pairwise structural alignments and are influenced by candidate-pool quality. Here, we present DeepUMQA-Selection, a two-stage framework combining single-model accuracy estimators for preliminary selection with Ultrafast Shape Recognition (USR)-based structural alignment for consensus selection. Preliminary selection enriches the candidate pool with high-quality models and reduces subsequent structural comparisons, while USR-based structural alignment enables efficient consensus selection without explicit structural superposition. On the retrospective CASP16 QMODE3 benchmark dataset, DeepUMQA-Selection outperformed all participating methods and predictor-derived confidence metrics, as measured by mean Top-5 weighted penalty. The advantage was particularly pronounced for heteromeric targets, with a 13.9% improvement in model-selection performance relative to the best-performing CASP16 participating method. Furthermore, a confidence-assisted strategy enabled the framework to accommodate larger protein complexes while maintaining superior model-selection performance over competing methods, supporting its scalability. Runtime benchmarking showed that the proposed USR-based structural alignment achieved a substantial 317-fold speedup over US-align for candidate pools containing 1,000 models, demonstrating its computational efficiency for large-scale model selection. Our results demonstrate that DeepUMQA-Selection enables accurate and computationally efficient selection of high-quality protein complex models from large-scale candidate pools.

bioinformatics↗

bioRxiv2026

TopoFlow: Evolutionarily Conditioned Flow Matching for Protein Conformational Ensemble Generation

Proteins function through dynamic conformational ensembles rather than a single static structure, yet accurately modeling these ensembles remains challenging. Here, we introduce TopoFlow, an evolutionarily conditioned flow-matching framework for protein conformational ensemble generation. TopoFlow uses evolutionary representations from multiple sequence alignments (MSAs) to encode global conformational heterogeneity. It combines this with structural variables from a variational autoencoder (VAE) to capture local flexibility. These complementary representations are then adaptively fused through a conditional modulation module to construct unified conditioning features that guide protein conformational ensemble generation. On the ATLAS molecular dynamics (MD) benchmark, TopoFlow improved the Jensen Shannon metrics for pairwise distances (JS-PwD), radius of gyration (JS-Rg), and time-lagged independent components (JS-TIC) by 10.73%, 5.78% and 7.75%, respectively, relative to BioEmu. Notably, this performance generalizes to experiment-based evaluation, where TopoFlow exhibits strong agreement with experimental observables, including chemical shifts (CS) and small-angle X-ray scattering (SAXS). Ablation analyses further indicated that the evolutionary representations and structural latent variables contributed complementary information to ensemble generation. Our results suggest that TopoFlow, through its integration of evolutionary and structural latent information, offers a promising strategy for generating structurally plausible and conformationally informative protein ensembles.

bioinformatics↗

bioRxiv2026

MorphCell learns reconstructable shape representations with explicit scale control for 3D cell morphology

Three-dimensional (3D) cell morphology provides a measurable phenotype of cellular state and function, but representing it across biological systems and imaging modalities remains difficult. Existing approaches do not jointly provide transferable representations, complete surface reconstruction, geometric interpretation and explicit control of physical scale. Here we introduce MorphCell, a self-supervised framework that uses cell-surface point clouds to learn shape-driven representations independently of physical scale. MorphCell combines cross-view reconstruction pretraining with spherical self-reconstruction. The former captures geometric relationships between surface regions, whereas the latter recovers complete 3D morphology from individual representations. Pretrained on non-biological object surfaces, MorphCell transfers to cellular datasets without biological task-specific training and outperforms existing point-cloud representations in morphological classification. The learned representations capture both global contour and local surface variation, enabling geometric interpretation through reconstruction, biophysical descriptors and saliency analysis. By retaining physical scale as a separate variable for controlled fusion, MorphCell further reveals that shape and scale contribute differently across biological distinctions. This framework provides a general approach for representing, reconstructing and interpreting 3D cellular morphology across imaging modalities and biological contexts.

cell biology↗

bioRxiv2026

ForceFlowAb: physics-aware mixture-of-experts flow matching model for antibody CDRs design

Abstract Motivation: Antibodies are a major class of therapeutic molecules, and their recognition of target antigens is largely mediated by complementarity-determining regions (CDRs), making antigen-conditioned CDR design a central problem in antibody engineering. Recent generative methods have enabled antigen-conditioned co-design of CDR sequences and structures, but their limited capacity to capture local interface heterogeneity and lack explicit energy-based guidance during sampling, which may result in unfavorable antibody-antigen interaction energies. Overcoming these limitations requires methods that better represent diverse interface environments via adaptive routing and incorporate physical guidance to steer sampling toward energetically favorable conformations. Results: We present ForceFlowAb, a physics-aware mixture-of-experts flow-matching framework for antigen-conditioned CDR sequence-structure co-design. The framework models heterogeneous interface environments through specialized expert routing and applies differentiable force-field guidance during sampling to guide CDR generation toward energetically favorable conformations. For CDR-H3 design, ForceFlowAb achieved more favorable antibody-antigen interaction energies than FlowDesign and Diffab, with improvement rates (IMP) of 46.5% versus 35.0% and 35.5%, respectively. For simultaneous six-CDR design, ForceFlowAb also outperformed Diffab, with IMP values of 16% versus 9%. These results suggest complementary roles for interface-adaptive modeling and energy-based guidance, with the former capturing binding-mode diversity and the latter leveraging physical constraints to ensure biophysical feasibility. Availability and implementation: The web server is freely available at http://zhanglab-bioinf.com/ForceFlowAb. The source code and implementation are available at https://github.com/iobio-zjut/ForceFlowAb. Contact: zgj@zjut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

SVD-SN2N: Self-Inspired Noise2Noise Learning for Denoising Log-Compressed SVD-Filtered Ultrasound Imaging

Singular value decomposition (SVD) is the reference clutter rejection strategy for medical ultrafast ultrasound imaging, yet SVD filtered images retain a residual noise floor that obscures microvascular signals at depth. Supervised denoising cannot address this gap because clean references do not exist, and most physics-based alternatives require radio-frequency or in-phase/quadrature data that clinical scanners do not expose. We introduce SVD SN2N, a self supervised Noise2Noise framework that operates entirely on the post SVD image and requires neither clean targets nor channel level access. Our central contribution is to show that the residual fluctuations of SVD filtered images are dominated by multiplicative speckle, which violates the zero mean additive assumption of Noise2Noise theory, and that a single log compression step converts it into an additive perturbation whose variance no longer depends on the signal, restoring the conditions the framework requires. The log compressed image is split into two statistically independent half size copies by diagonal 2 times 2 resampling, rescaled by Fourier zero padding interpolation, and used to train a U Net under a self constrained twin-prediction loss. We assess the underlying assumptions directly by measuring the residual noise distribution, its spatial correlation and the signal similarity within diagonal pixel pairs, and we validate the framework on four heterogeneous datasets spanning preclinical and clinical, contrast enhanced and contrast-free, and 2D to 3D regimes. Relative to conventional SVD, SVD SN2N raises image derived SNR by 4.2 to10.6 dB and narrows the apparent vessel full width at half maximum by 34% to 59%, providing a post SVD denoising front end compatible with the image only data available on clinical scanners.

bioengineering↗

bioRxiv2026

Contrast-Free Microvascular and Functional Brain Imaging by Sparse Deconvolution of Ultrafast Power Doppler

Ultrafast power Doppler imaging combined with singular value decomposition (SVD) clutter filtering has become a standard approach for label-free microvascular ultrasound, enabling the visualization of small vessels without microbubble contrast agents. In the absence of contrast, however, SVD filtered Doppler images remain limited by the blur of the imaging system point spread function (PSF) and by a residual noise floor that reduces sensitivity at depth, which together hinder the resolution of fine microvasculature. Here we establish a sparse deconvolution framework to SVD filtered ultrafast power Doppler images. Each Doppler frame is processed in two cascaded stages: a Split-Bregman optimization that solves a regularized least-squares problem combining a sparsity prior and a Hessian continuity prior, followed by an accelerated Richardson Lucy deconvolution with an estimated system PSF. We first validated the framework on a simulation phantom with known ground truth, and then evaluated the framework on in vivo rat-brain plane-wave acquisitions obtained with a Verasonics Vantage system and a 15-MHz linear array. Compared to conventional SVD power Doppler, sparse deconvolution improved the resolution by around 4 and 8 times to lambda/2 and lambda/4, in the lateral and axial directions respectively. We further show that decomposing the Doppler signal into velocity bands before deconvolution disentangles slow and fast flow and yields a velocity-resolved microvascular map. Finally, applying the same framework to task-evoked functional ultrasound, we show that sparse deconvolution preserves the stimulus-locked cerebral-blood-volume response measured by conventional functional ultrasound while sharpening the corresponding activation map from a diffuse cortical region to discrete penetrating vessels. These results indicate that the sparsity-prior super-resolution principles established in label-free ultrafast Doppler ultrasound, and that sparse deconvolution can serve as a practical, contrast-free post-processing front-end for super-resolution microvascular and functional imaging.

bioengineering↗

bioRxiv2026

A cross-kingdom interactome predicted by AlphaFold3 reveals a DNF2-centered interface required for symbiotic accommodation

Legumes convert atmospheric nitrogen into ammonium through symbiotic bacteria housed in root nodules, yet the molecular interactions between rhizobial and host proteins inside nodules remain poorly understood. Here we employed AlphaFold3 to construct a cross-kingdom interactome between Medicago truncatula and its symbiont Sinorhizobium meliloti. Screening more than 217,000 protein pairs yielded 7,137 putative interactions, providing a valuable resource for the broader symbiosis community. Within this network, we focused on DEFECTIVE IN NITROGEN FIXATION 2 (DNF2), a host protein required for rhizobial persistence within nodules. We showed that DNF2 localizes to the peribacteroid space and associates with previously uncharacterized secreted rhizobial proteins (SRPs), suggesting it may function as a hub for host-symbiont communication. Notably, knockout of two DNF2-interacting proteins, SRP86 and SRP485, results in white, nitrogen-fixation-deficient nodules with abnormal symbiosomes and elevated expression of senescence-associated genes, closely phenocopying the dnf2 loss-of-function mutant. Together, our findings define a DNF2-SRP molecular framework underlying symbiotic accommodation, and illustrate the potential of AI-guided interactome mapping to uncover molecular mechanisms of plant-microbe interactions with relevance to sustainable agriculture.

plant biology↗

bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗

bioRxiv2026

Spatiotemporal expression of the zebrafish pax9 gene that is essential for median fin patterning

PAX9 is an evolutionarily conserved paired-box transcription factor that is critical for embryonic development and human diseases. The mouse model has been predominantly used to investigate Pax9 functions. Zebrafish has emerged as a complementary vertebrate model for various human diseases, including cancers. Until recently, the functions of the zebrafish pax9 gene in jaw and hematopoiesis have started to be uncovered. However, detailed pax9 spatiotemporal expression, molecular mechanisms, and potential functions in other zebrafish organs remain largely unknown. With the technical advances in CRISPR-Cas9, non-homologous end joining (NHEJ) has made knockin and knockout a convenient way to examine endogenous gene expression in vivo and to generate a loss-of-function allele simultaneously. Here, we first generated pax9 knockin fish lines by inserting fluorescent proteins at the start of the endogenous pax9 coding region. Then, we examined pax9 expression in real time from early embryonic stages through adulthood. Except for previously reported expression domains, we were able to identify pax9 expression in high resolution in the paired and median fins, where pax9 marks anterior fin rays. Moreover, our knockin and knockout mutants showed increased fin ray number in median fins, but no evident effect on paired fins in pax9 null mutants. Thus, PAX9 is critical for median fin patterning in zebrafish.

developmental biology↗

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗