Biology subjects

Cao, Z.

Publications and source records attributed to Cao, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Median Preoptic Brs3-Expressing Neurons Regulate Heat-Evoked Defensive Escalation

Animals confronted with increasing heat must switch from exploratory behavior to vigorous actions that limit further exposure, but the forebrain circuits controlling this transition are poorly understood. We exposed male mice to progressively stronger floor heating while providing continuous access to a climbing mesh separated from the heating plate. Higher plate settings shifted behavior from rearing toward wall-directed digging and climbing, with jumping at the highest setting, and increased c-Fos labeling in the median preoptic nucleus. Neurons expressing bombesin receptor subtype 3 showed greater c-Fos recruitment under strong heating, and their population calcium signals increased when mice entered the plate-controlled zone and decreased when they left. Optogenetic activation without heating produced place avoidance, vigorous actions, and increased locomotion. Inhibition reduced locomotion and, during strong heating, shifted behavior from climbing and jumping toward rearing and weakened avoidance of a localized heat source. These findings identify median preoptic Brs3-expressing neurons as regulators of behavioral escalation and heat avoidance.

neuroscience↗

bioRxiv2026

A single-nucleus multi-omic atlas of gene regulation across 21 adult human tissues

Diverse human cell types establish specialized functions through lineage- and context-specific regulatory programs. Interpreting non-coding genetic risk requires integrated multi-omic reference maps that directly connect regulatory DNA to cellular expression across human tissues. Here we present a single-nucleus multi-omic atlas comprising 459,856 transcriptomic and chromatin accessibility profiles from 21 adult human tissues and four donors, including paired measurements from 160,688 nuclei. The atlas resolves nine cell lineages, 61 broad cell types and 313 subclusters, and identifies 1,085,062 candidate cis-regulatory elements (cCREs), including 161,270 novel elements absent from ENCODE. Regulatory activity was dominated by cell identity but refined by tissue context. Joint profiling enabled 871,177 cCRE-gene associations and revealed lineage-specific regulatory architectures. Cross-tissue accessibility further identified lineage-restricted and constitutively inaccessible chromatin domains, the latter showing preferential hypomethylation across human cancers. Furthermore, we leverage this dataset to train sequence-to-function models to predict chromatin-accessibility effects for 548,656 fine-mapped variants, identifying 18,133 high-effect variants, including 1,120 broadly active variants. Models trained for eight endothelial subtypes further resolve predicted variant effects across vascular beds. Together, this atlas provides a comprehensive cellular and computational framework for interpreting regulatory sequence, context-dependent gene control, and complex trait genetics across the human body.

genomics↗

bioRxiv2026

Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective

Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.

neuroscience↗

bioRxiv2026

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

De novo protein design has advanced rapidly, yet designing binders to polar, solvent-exposed epitopes and small, flexible ligands remains challenging. Such hydrated surfaces and flexible molecules, including carbohydrates, provide few of the hydrophobic contacts favoured by current methods and have largely resisted de novo binders. To address this challenge, here we introduce latent generative search for binder design, a novel framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model. The model codesigns sequence and structure - generating them together in a continuous latent space - and thereby removes the inverse-folding step on which current methods rely. In a screen of more than one million designs by multiplexed phage display, latent generative search produced more validated binders than every other method tested, its codesigned sequences surpassing post hoc redesign. It delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets. Our approach also accessed previously untapped biology, generating the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens - a polar, flexible target class beyond the reach of current design methods.

bioengineering↗

bioRxiv2026

Substrate recognition and ion coupling mechanism of the human VIAAT

Inhibitory neurotransmission, essential for neural circuit homeostasis and proper neurological function, depends on the efficient sequestration of {gamma}-aminobutyric acid (GABA) and glycine into synaptic vesicles. This critical process is mediated exclusively by the vesicular inhibitory amino acid transporter (VIAAT). Despite extensive biochemical and physiological investigation, the molecular mechanism governing VIAAT-mediated transport has remained incompletely understood. Here, we determined structures of human VIAAT in multiple functional states, including apo, GABA-bound, and glycine-bound states, as well as apo state in chloride-free condition. VIAAT adopts a classical LeuT-fold and we elucidated how its large, electronegative binding pocket accommodates both GABA and glycine. Moreover, we resolved two previously unidentified chloride-binding sites. Through integrative molecular dynamics simulations and functional mutagenesis, our results support roles for chloride and the conserved residue E213 in substrate binding and proton-coupled transport. Together, our findings establish a structural framework for VIAAT-mediated inhibitory neurotransmitter transport that illuminates the molecular basis of inhibitory synaptic transmission and provides a foundation for understanding VIAAT dysfunction in epilepsy and related neurodevelopmental disorders.

neuroscience↗

bioRxiv2026

Uncertainty-Aware Model Selection with a Calibrated Probability-Generating-Function-Based Bayesian Information Criterion

Selecting stochastic gene-expression models from single-cell counts requires balancing goodness of fit against unnecessary mechanistic complexity. The probability-generating-function-based Bayesian information criterion (PGF-BIC) combines covariance-weighted fitting in generating-function space with a complexity penalty, allowing candidate models to be compared without reconstructing their full count distributions. However, its conventional zero-threshold rule does not account for sampling uncertainty in the fitted score difference and may therefore favor overly complex models in finite samples. To address this limitation, we develop an uncertainty-aware PGF-BIC rule that selects the more complex model only when its score advantage exceeds a data-driven threshold. We use Cantelli's one-sided inequality to motivate a selection margin expressed in terms of a standard deviation. To determine this scale, we use influence functions to quantify sensitivity to small perturbations in the data distribution and obtain a first-order description of sampling fluctuations. The resulting variance estimate accounts for variability in both the empirical probability generating function and the estimated covariance weights, yielding a data-driven threshold for assessing the complex model's score advantage. A Poisson versus Bursty benchmark shows that the calibrated rule reduces incorrect selection of the more complex model. The calibration requires neither resampling nor additional optimization, incorporating sampling uncertainty into model selection while retaining the computational efficiency of PGF-BIC.

bioinformatics↗

bioRxiv2026

An Integrated Single-Nucleus Atlas Resolves Cell-Type-Specific Programs and Molecular Subtypes in Alzheimer's Disease

Interindividual heterogeneity in Alzheimer's disease (AD) remains poorly understood, as disparate single-cell studies leave it unclear whether findings reflect shared architecture or dataset-specific idiosyncrasies. Here, we present panAD, a transcriptomic atlas of >3 million nuclei from 791 individuals across 13 studies, spanning AD, mild cognitive impairment, and cognitively normal aging. AD converges on a reproducible, cell-type-specific molecular architecture: co-expression modules track neuropathology and cognitive decline; GWAS risk genes act predominantly as downstream targets of transcription factor hubs such as microglial SPI1; intercellular communication is remodeled with disease stage; and sex differences concentrate in microglial immune-activation programs. To model patient-level transcriptomic heterogeneity, we developed the Multi-seed Optimization of Neural Embeddings for subTyping (MONET) framework, in which a masked variational autoencoder applied to covariate-adjusted, multi-cell-type profiles resolves four subtypes (Metal-Ion Stress, Neuroinflammatory, Synaptic Integrity, and Tissue Remodeling) that dissociate neuropathological burden from cognitive impairment and nominate predominantly non-overlapping candidate therapeutics. Finally, Stellar Atlas provides an AI-native conversational interface to the atlas.

neuroscience↗

bioRxiv2026

Likelihood-Based Inference and Model Selection for Stochastic Gene Expression in Probability-Generating-Function Space

Selecting stochastic gene-expression models from single-cell counts requires accurate parameter inference and efficient model selection. Likelihood methods in count space can be costly when full stationary count distributions are unavailable, whereas approximate methods may lose accuracy. Probability generating functions (PGFs) offer a compact analytical alternative, but existing PGF workflows are generally not likelihood based and therefore rely on computationally intensive cross-validation. We develop a likelihood-based PGF framework for both tasks. Correlated empirical PGF values are used to construct a Gaussian quasi-likelihood for parameter inference and PGF-based Bayesian information criterion (BIC) for model selection. We show that the empirical PGF is exactly unbiased and that the parameter estimator is consistent, converges at the inverse-square-root sample-size rate, and is first-order asymptotically unbiased. For large samples and a uniquely preferred model, PGF-BIC selects the same model as leave-one-out cross-validation in PGF space.

systems biology↗

bioRxiv2026

Screening of Isolation and Culture Conditions of Lactic Acid Bacteria in Mongolian Horse Feces

ObjectiveThis study aimed to establish an efficient isolation, culture, and preservation system for lactic acid bacteria from Mongolian horse feces by systematically evaluating the effects of sampling methods, culture conditions, media types, and cryopreservation protocols. MethodsFecal samples were collected using rectal and natural defecation methods, and bacteria were isolated by the spread plate technique, with isolates subcultured and identified via 16S rRNA gene sequencing. The performance of five commercial media (A-E), aerobic versus anaerobic conditions, and different glycerol concentrations (10% and 30%), temperatures (-20 {degrees}C and -80 {degrees}C), and storage durations (7, 15, and 30 days) were compared. ResultsRectal sampling showed significantly higher lactic acid bacterial survival and greater species diversity (12 vs. 8). Anaerobic culture yielded substantially higher counts and diversity (707 strains, 12 species) compared to aerobic conditions (36 strains, 5 species). Medium C (M.R.S. AGAR) exhibited the best isolation performance, producing the highest number of strains (311) and species (11), with selective enrichment for both bacilli and cocci. Glycerol effectively improved strain survival during sample cryopreservation, with the optimal short-term storage ([≤]15 days) achieved at -20 {degrees}C with 30% glycerol. Functional screening of randomly selected strains from optimized conditions demonstrated promising growth, acid and bile salt tolerance, and antimicrobial activity. ConclusionRectal sampling combined with anaerobic incubation using Medium C is recommended for optimal isolation. For samples that cannot be processed immediately, short-term preservation at -20 {degrees}C with 30% glycerol prior to isolation is effective. This study provides practical technical references for the acquisition and preservation of equine intestinal lactic acid bacteria resources.

microbiology↗

bioRxiv2026

Effect of CSFV on Differential Genes of Histone Lactylation at H3K18 in the PI3K-AKT Signaling Pathway

Classical swine fever (CSF) is a highly contagious disease caused by Classical swine fever virus (CSFV), posing a serious threat to the global swine industry. This study aimed to investigate the effect of CSFV on differential genes of histone lactylation at the H3K18 site in the PI3K-AKT signaling pathway. The site with the most significant change in histone lactylation antibody level was screened by Western blot. Omics analysis was performed using CUT&Tag technology to identify differential genes in the PI3K-AKT pathway between the CSFV-infected group and the mock group, followed by validation using RT-qPCR. Functional analysis of significantly differential proteins was conducted, and the protein expression level of THBS4 was detected by Western blot. The results showed that after CSFV infection of 3D4/21 cells, the H3K18la site exhibited the most significant difference in antibody level. A total of 8,859 differential genes at the H3K18la site were identified by CUT&Tag analysis, including 6,349 up-regulated genes and 2,510 down-regulated genes. Further focusing on the PI3K-AKT signaling pathway, 10 differential genes were identified, comprising 6 up-regulated genes and 4 down-regulated genes. Compared with the control group, the mRNA expression levels of CD19, LAMA1, PDGFRA, BDNF, ANGPT4, and THBS4 were up-regulated in the CSFV-infected group, while FOXO3 and NRTN were down-regulated. Western blot results showed that the protein expression level of THBS4 increased after CSFV infection. These findings lay an important foundation for understanding the molecular mechanisms regulating viral replication and immune evasion, and have significant scientific implications and potential application value.

microbiology↗

bioRxiv2026

Regulatory co-option of a homeobox gene drives parasitoid venom evolution

How gene regulatory networks are rewired to generate phenotypic and functional innovation remains a central question in evolutionary biology. Parasitoid wasp venoms provide a powerful system for addressing this question, as their repertoires evolve rapidly through extensive lineage-specific turnover, yet the regulatory principles underlying such flexibility are largely unknown. Here we integrate tissue-resolved transcriptomic, chromatin-accessibility and histone-modification profiling to reconstruct the venom regulatory network of the parasitoid wasp Pteromalus puparum. We show that venom expression is embedded in distinct chromatin states and shaped by regulatory elements associated with venom-gland transcription. Comparative and functional analyses support a general model in which regulators related to the endoplasmic reticulum stress and unfolded protein response pathways have been repeatedly recruited to venom regulation across venomous lineages. Unexpectedly, we identify the recently co-opted homeobox gene Lbx as a lineage-specific hub that regulates more than half of venom genes and is linked to enhancer evolution. These results reveal a nested model of venom regulatory evolution, in which an ancestral secretory programme provides a reusable regulatory backbone, while newly co-opted homeobox gene specify a lineage-specific venom expression. Our study highlights regulatory co-option as a mechanism by which conserved developmental genes can acquire new physiological functions during adaptive evolution.

evolutionary biology↗

bioRxiv2026

PRIME: scalable, robust inference of mechanistic cell states from multimodal single-cell counts via probability generating functions

Single-cell multiomic technologies can now quantify complementary RNA species within the same cell, creating an opportunity to move beyond descriptive clustering toward mechanistically interpretable cell states. Yet most current methods depend on heuristic integration steps and become computationally burdensome at scale, limiting their ability to robustly detect subtle kinetic differences across heterogeneous populations. Here we introduce PRIME, a scalable framework for mechanistic cell-state discovery from multimodal single-cell count data. PRIME embeds multimodal measurements in a probability generating function (PGF) space, where transcriptional dynamics are encoded compactly and compared efficiently. This representation enables robust inference of latent kinetic structure and supports rapid cell grouping with a power K-means backbone that remains stable under noise, sparsity, and multimodality. Across synthetic benchmarks and experimental multimodal datasets, PRIME consistently recovers cell populations distinguished by transcriptional kinetics, outperforms conventional integration-and-clustering pipelines in robustness, and yields interpretable parameters that link observed variability to underlying regulatory mechanisms. By providing a mathematically principled yet practical route from multimodal counts to kinetic cell states, PRIME empowers biologists to uncover dynamic transcriptional regimes, dissect regulatory heterogeneity, and connect cell identity to mechanism rather than markers.

systems biology↗

bioRxiv2026

The molecular asynchrony of single cells

Integrating single-cell transcriptomic and epigenomic data provides a robust framework for investigating gene regulation mechanisms. Existing analyses typically treat these modalities as synchronized features that can be translated in a static manner; however, the temporal delays that underpin cellular kinetics are intrinsic to dynamic biological systems. To address this limitation, we propose utilizing the "molecular asynchrony" within regulatory hierarchies to determine the thermodynamic properties of individual cells. Here, we present SeqTag, a single-cell multiomics sequencing method that simultaneously profiles the transcriptome, chromatin accessibility, and histone modifications, supported by an analytical framework to identify asynchronous states across regulatory layers for characterization of single-cell kinetics. By measuring the epigenetic priming potential and remodeling rates during adult mouse oligodendrogenesis, we delineated a sequential program for bivalency resolution as maturing cells traverse Waddingtons landscape. This process becomes increasingly decoupled with age, a change linked to a drift in progenitor cell-fate probabilities. By identifying entropy-driving regulatory elements, we characterized the aging-related decline in cell identity across various cell types and proposed a dynamic model linking static genetic variants to the risk of late-onset diseases. In summary, our integrated approach established a unified framework for employing multimodal single-cell genomics to model the kinetics of complex cellular processes.

genomics↗

bioRxiv2026

Nature-inspired Nanoparticle Adiposomes Enable Targeted Delivery of Hydrophobic Drug for Anti-Cancer Treatment

The predominance of hydrophobic molecules in drug pipelines poses a fundamental delivery challenge in the aqueous physiological environment. Inspired by the endogenous lipid-transport machinery of lipid droplets and lipoproteins, adiposome was developed as a biocompatible platform for hydrophobic drug delivery. Here, docetaxel (DTX) was selected as a model hydrophobic chemotherapeutic agent to evaluate the therapeutic versatility of adiposome. Non-targeted DTX-loaded adiposomes (DTX-Ad) reduced systemic toxicity by avoiding the allergenic excipients Tween 80 and ethanol used in commercial DTX formulations. The cytotoxic mechanism and intracellular responses of DTX-Ad were comprehensively characterized. To boost antitumor efficacy, three tumor-targeting DTX-Ad formulations were subsequently engineered. Lung-targeting DTX-adiposomes (Lung@DTX-Ad), mediated by AAM-B1-28-CRGDK fusion peptide/NRP1 axis, enhanced antitumor efficacy in a lung metastasis model. BCMA-targeting DTX-adiposomes (BCMA@DTX-Ad), conferred by biotin-avidin-mediated antibody conjugation, achieved sustained suppression in a multiple myeloma model. Liver-targeting DTX-adiposomes (Liver@DTX-Ad), enabled by the ApoE/LDLR axis, outperformed both commercial DTX injection and sorafenib in hepatocellular carcinoma models. Taken together, these results demonstrate adiposome as a promising and broadly applicable drug delivery platform, with considerable potential for clinical translation across diverse malignancies.

cancer biology↗

bioRxiv2026

A Novel Hydrogen Polysulfide Donor Alleviates Neuropathic Pain by Enhancing A-Type Potassium Currents via LIMK1-Cofilin Mediated Interaction between Filamin A and Kv4.2

Hydrogen persulfide (H2S2) is an important endogenous signaling molecule, holding significant therapeutic potential across diverse disease models due to its potent antioxidant and redox-regulating properties. Herein, we report the synthesis, characterization, and in vivo evaluation of an esterase responsive H2S2 donor, HPD1. It reduced mechanical and cold allodynia at 14 mg/kg (i.p.) in chronic constriction injury and paclitaxel-induced neuropathic pain models. Moreover, HPD1 exhibited negligible systemic toxicity and behavioral side effects even at 28 mg/kg. Electrophysiological tests showed that HPD1 suppressed PTX-induced hyperexcitability in dorsal root ganglion (DRG) neurons by specifically potentiating A-type potassium currents (IA). Mechanistically, we demonstrate that HPD1 activates LIMK1, which inactivates cofilin and stabilizes F-actin, thereby promoting the interaction between the actin-binding protein Filamin A and Kv4.2. Furthermore, both the HPD1-induced increase in Filamin A-Kv4.2 co-localization and the subsequent restoration of IA density in DRG neurons, as well as the analgesic effect of HPD1 were dampened by pharmacological inhibition of LIMK1 with BMS-5. This work has developed a new generation of H2S2 donors, demonstrated the analgesic efficacy of HPD1, and uncovered the novel LIMK1-cofilin-Filamin A-Kv4.2 dependent mechanism that restores IA, thus providing a reliable therapeutic strategy for neuropathic pain based on H2S2 donors.

pharmacology and toxicology↗

bioRxiv2026

Functional divergence of the repressors of photomorphogenesis SPA2 and SPA3 during Arabidopsis seedling deetiolation

The COP1/SPA ubiquitin ligase is a key repressor of photomorphogenesis that is inactivated by photoreceptors to initiate light signalling. The four SPA proteins (SPA1-SPA4) confer functional specificity to COP1 during plant growth, yet the underlying molecular mechanisms remain unclear. Here, we used a domain-swap approach in transgenic seedlings to address the functional divergence of SPA2 and SPA3. We show that the respective N-terminal kinase domain determines the contrasting protein stabilities of SPA2 and SPA3 in light-grown seedlings. The instability of SPA2 correlates with a specific ability of the SPA2 N-terminal domain to bind phytochrome A in the light, suggesting that phytochrome A promotes the CUL4DET1/COP1-dependent degradation of SPA2 but not of SPA3. We uncover that the coiled-coiled and WD-repeat domains of SPA2 and SPA3 substantially differ in their activity in repression of photomorphogenesis, with those of SPA2 being more active repressors than those of SPA3. Thus, SPA2 combines a potent repressor activity with light-induced instability. We conclude that the evolution of SPA2 instability in response to light counterbalances its inherent strong repressor activity, thereby allowing seedling etiolation in darkness followed by rapid reduction in COP1 activity through SPA2 degradation upon light-exposure as seedlings emerge from soil to initiate photosynthetic growth. HighlightThe repressor of light signaling SPA2 combines a phytochrome A-interacting instability domain with a potent repressor domain to allow greatly contrasting activities of COP1 in skoto- and photomorphogenesis.

plant biology↗

bioRxiv2026

Disease-guided functional gene mapping across species reveals translational correspondences beyond sequence orthology

Selecting the correct mouse gene to model a human disease phenotype is critical for translational research, yet sequence-based orthology fails when genes have been lost, duplicated, or functionally rewired between species. Here we present BRIDGE (Biological Rank Integration for Disease Gene Equivalence), a framework that identifies functional mouse equivalents of human disease genes without sequence input. BRIDGE integrates 3.37 million disease-gene associations, biological pathways, and Gene Ontology annotations into a unified heterogeneous graph (94,897 nodes, [~]8.3 million edges), encoded by a heterogeneous graph transformer with fused Gromov-Wasserstein alignment and multi-strategy reciprocal rank fusion. On two sequence-independent benchmarks, BRIDGE achieves Recall@5 of 61.8-66.7%, compared with 0.0-20.1% for Ensembl Compara. We validate BRIDGE through case studies including neutrophil pathway rewiring (CXCL8[->]Cxcl1/2/5), acute-phase divergence (CRP[->]Apcs), and immune checkpoint substitution (LILRB2[->]Pirb), and demonstrate complementarity with sequence methods in drug-translation analysis. Prospective validation of 30 novel predictions against three independent data modalities (tissue expression, cell-type expression, and phenotype concordance) shows that BRIDGE picks are favoured in 64 of 65 orthogonal tests (sign test P = 3.6 x 10-{superscript 1}) and significantly outperform all tested baselines including Ensembl Compara, BLAST RBH, and ESM-2. BRIDGE provides a benchmarked framework for functional cross-species gene mapping in disease-model design.

bioinformatics↗

bioRxiv2026

Sustained Relief of Chronic Pain via a Nav1.7_Targeting ASO_siRNA Conjugate

Chronic pain affects billions globally, yet safe, long-lasting, and non-addictive analgesics remain lacking. Nav1.7 is a genetically validated pain target, but traditional small molecules have repeatedly failed. Therapeutic oligonucleotides-antisense oligonucleotides (ASOs) and siRNAs-offer selective, durable silencing. We developed N02C0702, an ASO-siRNA conjugate (ASC), achieving robust Nav1.7 knockdown and sustained analgesia without additional delivery vehicles. N02C0702 outperformed individual ASO (N02A114) and siRNA (N02S154) moieties at mRNA and protein levels and in pain relief. In CFA-induced inflammatory pain, a single intrathecal dose exceeded naproxen and suzetrigine, while in SNL neuropathic pain, efficacy persisted up to 56 days, comparable to or surpassing pregabalin. Genome-wide RNA sequencing confirmed minimal off-target effects. N02C0702 highlights Nav1.7 as a key analgesic target and demonstrates the ASC platforms potential for chronic pain and other CNS-related pathologies, offering durable, selective, and safe therapeutic effects.

neuroscience↗