Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Xenograft supervision and joint methylation-fragment-end encoding define a reusable molecular scoring layer for cell-free tumor DNA

Circulating tumor DNA is often scarce in plasma, and regional methylation summaries can obscure informative differences between individual molecules. Here we present ctDNA-BERT, a Transformer that scores individual bisulfite reads without predefined differentially methylated regions by jointly encoding CpG methylation state, nucleotide context and the fragment 3' end. Xenografts provide molecule-level tumor supervision. The resulting frozen scorer supported tumor-fraction estimation in synthetic and patient-derived xenograft mixtures and enriched tumor-concordant methylation and chromatin-accessibility signals in independent hepatocellular carcinoma and stage III pan-cancer plasma without cohort-specific retraining. Features derived from selected reads discriminated stage I cancers from healthy controls in a retrospective cohort of 1,477 specimens, achieving an out-of-fold area under the receiver operating characteristic curve of 0.884. By separating molecular scoring from sample-level inference, ctDNA-BERT provides a reusable read-level scoring layer for cell-free DNA methylation analysis.

bioinformatics↗

bioRxiv2026

LiverHomo: An Integrated and Interactive Human Liver Single-Cell Atlas with Protein Interaction and Cell-Cell Communication Networks

LiverHomo is an integrated cell atlas developed from publicly available human liver single-cell RNA sequencing (scRNA-seq) datasets spanning healthy and diverse liver disease contexts. While several single-cell resources for the human liver are available, LiverHomo provides broad disease coverage and extends transcriptomic exploration through the integration of protein-protein interaction (PPI) networks and cell-cell interactions. LiverHomo provides an interactive web portal (https://liverhomo.org) for exploring cellular composition, gene expression, differentially expressed genes, Gene Ontology annotations, cell-cell interactions, and context-specific PPI networks across diseases and cell types. To facilitate intuitive access to these multidimensional data, the portal incorporates Ask LiverHomo, a natural-language assistant that interprets user queries and directs users to relevant analyses and visualizations. Interactive histograms, violin plots, network visualizations, and metadata exploration enable users to examine the atlas at cellular, transcriptomic, and protein-interaction levels, while the underlying data are readily accessible for further analysis. Together, LiverHomo provides an integrated and accessible resource for investigating cellular and molecular alterations across human liver health and disease.

bioinformatics↗

bioRxiv2026

Daytime Napping and Associative-Memory Retrieval Speed: An Exploratory Behavioral and EEG Study

Purpose: To test whether a daytime nap was associated with improved associative-recognition performance relative to monitored wakefulness and to determine where any response-time (RT) difference occurred in the RT distribution. Methods: Fifteen healthy students completed nap and wake sessions in a within-participant design. Each session included word-pair learning, immediate recognition, a 60-min interval, and delayed recognition without additional study. Trial-level log RT was the focal outcome; accuracy and electroencephalography (EEG) were complementary outcomes. Results: The RT Condition-by-Time interaction was significant (b = -0.063, 95% CI [-0.117, -0.009], p = .027). Responses were 6.7% faster after the nap (p = .010) but did not change after wakefulness (p = .704), with the difference concentrated among slower responses. Accuracy showed no condition-specific change (p = .401). Nap EEG showed aperiodic spectral slowing and stronger periodic sigma activity, but no EEG measure explained the RT effect. Conclusions: Daytime napping was associated with faster delayed associative recognition, primarily through attenuation of the slowest responses. The exploratory findings warrant larger confirmatory studies.

neuroscience↗

bioRxiv2026

DeepD enables efficient genome modeling and controllable long-context sequence generation across domains of life

Genome foundation models hold great potential for advancing biological research, but modeling diverse complex genomes and generating controllable genome-scale sequences remain challenging. We present DeepD, a phylogeny-informed sparse mixture-of-experts model operating at single-nucleotide resolution over a 1-million-token context while reducing active computation. Pretrained on unselected whole-genome sequences across all domains of life, DeepD learns genic and intergenic sequence features and supports broad sequence-to-function prediction across species. DeepD performs well in human protein fitness and disease variants effect prediction, such as BRCA1 and BRCA2, with particular strength in regulatory regions, such as promoter and transcription-factor binding sites. Expert routing captures exon-intron boundaries, transposable elements, and distal cis-regulatory elements. DeepD also generates experimentally validated core promoters with specified transcriptional strengths. Finally, the Context-Anchor-Body strategy enables generation of complete yeast chromosome sequences and megabase-scale Arabidopsis segments, preserving gene structure and order while introducing variations within natural ranges. Together, DeepD is an efficient genome foundation model that unifies accurate sequence-to-function prediction with controllable genome-scale generation, advancing predictive and generative design of complex genomes.

bioinformatics↗

bioRxiv2026

CucurbitAgent: An AI agent for exploring cucurbit multi-omics resources

Functional genomics in crops increasingly depends on integrating information from an extensive literature and heterogeneous multi-omics datasets. Large language models (LLMs) offer a powerful approach to synthesizing these resources and revealing hidden connections across datasets; however, they frequently hallucinate, producing plausible but unsupported claims, when responding to gene-specific queries. Here, we present CucurbitAgent, an AI research agent for functional genomics in the Cucurbitaceae, a major family of horticultural crops. CucurbitAgent integrates multi-omics resources from nine cultivated cucurbit species and more than 18,000 publications, encompassing approximately 256,000 genes, tissue-resolved expression profiles, over 16 million natural genetic variants, and roughly 242,000 predicted protein structures with more than 14 million predicted binding-site residue annotations. Responses link retrieved evidence to source records, including gene entries, expression measurements, structural predictions, and literature passages. On a curated benchmark of 50 tasks assessed by domain experts under blinded conditions, CucurbitAgent achieved an overall pairwise win rate of 96.0% against nine general-purpose LLMs, based on weighted scores for factual accuracy, completeness, relevance, and traceability. The platform is freely available at https://cucurbitagent.com and supports queries in 37 languages collectively spoken by an estimated 70% or more of the world's population. CucurbitAgent not only unifies evidence retrieval and interpretation but also reveals latent associations across datasets, helping researchers formulate testable hypotheses and substantially accelerating candidate-gene research. Its framework is readily transferable to other crops and, more broadly, provides a blueprint for building specialized agents in any domain.

bioinformatics↗

bioRxiv2026

Successor representation supports structural learning of syllable sequences

Humans efficiently learn the temporal structure of speech, yet the underlying cognitive mechanisms remain unclear. Recent research in visuospatial sequential memory has proposed the successor representation (SR), important in reinforcement learning, which encodes multi-step transitional relationships. To test whether SR also supports the structural learning of rapidly changing speech syllable sequences, we adapted a pyramid-structured sequential learning paradigm using syllable streams presented at a natural speech rate (~ 4 Hz). Participants exhibited robust structural learning, evidenced by asymmetric memory performance modulated by adjacent transitional probabilities. Notably, the SR model captured recognition performance for non-adjacent syllable pairs. Moreover, SR-based learning effects generalized across all syllable sequences, regardless of adjacency. Furthermore, the effectiveness of SR learning was associated with cognitive flexibility and working memory capacity. Together, these findings suggest that humans rely on effortful SR-like mechanisms for the structural learning of syllable sequences, highlighting a modality-general computational principle for sequential learning across various temporal modulation scales.

neuroscience↗

bioRxiv2026

An AI-powered cloud biofoundry for autonomous biological research

Autonomous experimentation, where an automated system designs, performs, analyzes, and iteratively improves experiments with minimal human intervention represents a transformative scientific research paradigm. However, it remains hampered by the disconnect between computational design and experimental execution. Here we present iCloudBiofoundry, a cloud-accessible, scalable, and self-evolving platform that bridges this divide. A scientific multi-agent system, iBioGenie, plans research workflows by converting natural language research objectives into testable hypotheses and experimentally grounded protocols using literature, databases, and computational tools. A physical AI layer, BiofoundryAI, translates them into executable workflows that run autonomously on distributed robotic biofoundries, and collects the data for iterative design. We demonstrate the end-to-end capabilities of iCloudBiofoundry across three representative case studies spanning enzyme retrieval, protein engineering, and metabolic engineering. By integrating agentic AI with physical AI, iCloudBiofoundry provides a cloud accessible platform that unifies computational design and robotic execution, enabling autonomous biological research.

synthetic biology↗

bioRxiv2026

GraPPI: A Self-Supervised Graph Encoder for Transferable Protein--Protein Interaction Modeling

Protein--Protein Interactions (PPIs) are fundamental to cellular processes and represent major targets for therapeutic development. Recent advances in machine learning for protein modeling, particularly protein language models, have enabled powerful representations of protein sequences and monomeric structures. However, compared with the increasingly generalizable representations available for individual proteins, transferable representations explicitly designed to capture protein-complex and interface contexts remain less developed. Here, we developed GraPPI, a unified graph-based deep learning framework for learning transferable representations of protein--protein complexes. At its core, PPIencoder represents protein complexes as heterogeneous residual graphs and uses self-supervised masked-edge prediction to learn interaction-aware representations that integrate sequence, three-dimensional geometry, and physicochemical properties. We then evaluated the transferability of these pretrained representations across complementary PPI tasks spanning binding-mode classification, binding-affinity prediction, and mutation-associated affinity prediction. GraPPI accurately distinguished plausible binding interfaces from perturbed or decoy binding interfaces, achieving AUROC values above 0.92 on an independent benchmark. For protein--protein binding-affinity prediction, GraPPI achieved Pearson correlation coefficients of 0.690 and 0.684 on the S90 and S79 benchmarks, respectively, with performance competitive with or exceeding that of recently reported affinity-prediction methods. GraPPI further transferred effectively to mutation-associated binding-affinity prediction, achieving a Pearson correlation of 0.84 and a mean absolute error of 1.20 kcal/mol on an independent SKEMPI benchmark. Together, these results establish GraPPI as a generalizable framework for protein-complex representation learning, extending pretrained protein representations from individual proteins toward the interaction context in which protein function is realized.

biochemistry↗

bioRxiv2026

Parental Genome Assemblies of Suyunuo1 Reveal Structural Variation Underlying Edible Waxy Maize Evolution, Superior Hybrid Performance and Yield - Flavor Balance

Edible waxy maize is a unique domesticated cereal valued for its superior sensory and nutritional properties, yet high quality gap free genomes remain lacking, hindering the exploration of structural variations (SVs) and domestication related divergence. Here, we assembled two chromosome level, gap free genomes of the elite waxy maize inbred lines Tongxi 5 and Hengbai 522, the parents of the widely cultivated hybrid Suyunuo 1. Comparative genomic analysis revealed substantial genome size variation, prominent megabase scale SVs, and extensive knob repeat expansion in Tongxi 5. We identified widespread gene presence absence variation and hyperdivergent regions enriched in transposable elements, which largely underpin genomic differentiation between waxy and field maize. Population genomic analyses further demonstrated asymmetric introgression from field maize into the two parents, whereas the conserved waxy haplotype supports a shared ancestral origin. Integrative GWAS based on SNPs, InDels and SVs revealed that SVs substantially contribute to phenotypic diversity and mediate the co-regulation of yield traits and flavor-related metabolites. Our results uncover key genomic events underlying post-domestication divergence and highlight the essential roles of SVs in heterosis and flavor-yield balance, providing valuable genomic resources for waxy maize improvement.

plant biology↗

bioRxiv2026

ProteoScopeR: A Shiny Workflow for Method Comparison and Reproducible Quantitative Proteomics Analysis

Quantitative proteomics requires decisions about normalization, missing-data handling, and statistical modeling that can change the reported results. ProteoScopeR is an R package and Shiny application that connects these decisions in a traceable workflow and complements downstream exploration in xOmicsShiny. Side-by-side comparisons expose changes in distributions, feature retention, effect estimates, and selected protein sets. An optional external artificial-intelligence assistant reviews exported evidence and proposes settings for researcher approval; statistical calculations remain in R. A group-only case study used DIA-NN-derived aqueous humor data from 69 samples and 3,667 imported protein groups. Six normalization methods and seven missing-data strategies were screened, and the approved cyclic-loess analysis compared five differential workflows. For nAMD versus pmCNV, limma and proDA selected 54 and 37 protein groups, respectively, with 31 shared selections at adjusted P < 0.05 and absolute log2 fold change [≥] 0.5. Input routing, scientist decisions, locked settings, and an execution receipt preserve the analysis record. These comparisons describe sensitivity to analytical choices rather than identify a universally superior method.

bioinformatics↗

bioRxiv2026

Reconstruction of FACS-partitioned Adaptive Immune Receptor Repertoires from FACS-partitioned B and T Cell Subsets

Adaptive immune-receptor repertoire sequencing (AIRRseq) is crucial for understanding immune system diversity and its relationship to disease dynamics. Partitioning of total B and T cells into their major subsets with distinct immunological functions - IgM+ vs. class-switched B cells (IgG+ > IgA+) and CD4+ vs. CD8+ T cells, respectively - allows for AIRRseq-based analysis of the unique contributions of each compartment to the overall immune response, a major advantage over traditional bulk sequencing workflows. However, data from these subsets is not directly comparable with the vast majority of publicly available AIRRseq data, which comes from unfractionated B and T cells, an important incompatibility. Here we investigate computational methods for reconstructing complete AIRRseq repertoires from partitioned B and T cell subsets in diverse individuals. Peripheral blood mononuclear cells (PBMCs) were partitioned via positive selection of IgM+ B-cell subsets and CD4+ T cells using immunomagnetic beads; genomic DNA was then extracted and B- and T-cell receptors were sequenced. Four reconstruction methods are introduced and evaluated for concordance with matching unpartitioned repertoires to assess preservation of key repertoire characteristics. Results show that these methods enable accurate estimates of overall immune-repertoire diversity from B- and T-cell subsets in a way that simply pooling the sequence data from sub-repertoires cannot.

immunology↗

bioRxiv2026

Characterization of Resolved, Localized, and Progressing Infections at the Maternal-Fetal Interface in a Nonhuman Primate Model

Background: Inflammation and infection are responsible for the majority of early preterm births. Host innate immune responses at the maternal-fetal interface that contribute to sterile intra-amniotic inflammation (SIAI), bacterial clearance, or invasive infection remain poorly understood. Group B Streptococcus (Streptococcus agalactiae) is a leading cause of preterm birth, stillbirth, and invasive neonatal disease. Objective: To determine the host innate immune factors and transcriptional profiles that characterize the persistence versus resolution of a Group B Streptococcus infection at the maternal-fetal interface in a nonhuman primate (pigtail macaque, Macaca nemestrina) model. We hypothesized that a highly inflammatory profile, associated with interleukin-1 signaling, is linked to progression, whereas a mild or non-inflammatory signature is associated with infection resolution. Study Design: In a chronically catheterized pregnant pigtail macaque model, Group B Streptococcus was inoculated into the choriodecidual space. Infections were classified as progressing (invasive into the amniotic fluid), localized (bacteria persisting at the inoculation site), or resolved (minimal to no bacteria detected). Temporal and spatial RNA and protein profiles in the chorioamniotic membranes and decidua and in the myometrium were assessed by histopathology, multiplex protein assays, total RNA-sequencing, Nanostring nCounter, and spatial transcriptomics (digital spatial profiling). Differential gene expression was analyzed with limma using moderated t-tests and by gene set enrichment analysis. Results: A GBS infection at the maternal-fetal interface produced three infection outcomes: progressing infections that disseminated into the amniotic fluid (AF, N=5), low-titer localized infections remaining at the inoculation site (N=6), and resolved infections (N=5), compared with saline controls (N=3). Preterm labor occurred frequently regardless of infection outcome (40-80%). In resolved infections, SIAI occurred in the AF and lymphocytes infiltrated the myometrium. Total RNA-Seq of the chorioamniotic membranes demonstrated that 99 genes separated progressing from resolved infections, 98 of them were upregulated and dominated by IL-1 signaling and granulocyte recruitment (i.e., IL1B, CXCL8, TLR2, CLEC4A). Histone deacetylase 10 (HDAC10) was the only significantly downregulated gene in the progressing versus resolved contrast (p<0.05). An exploratory spatial transcriptomics experiment localized the progressing inflammatory response to the chorion. In the myometrium, zinc and RING finger 3 (ZNRF3) was significantly downregulated in localized versus control and resolved versus control and was also the only differentially expressed gene. Downregulated WNT signaling and upregulated lymphocyte-mediated immunity gene sets in the myometrium were a signature of resolved infections. Conclusion: A GBS infection at the maternal-fetal interface can result in a spectrum of progressing, localized and resolved outcomes. An early IL-1 and granulocyte response in the chorion layer of the chorioamniotic membranes marks bacterial dissemination, with loss of HDAC10 suggesting epigenetic regulation within the membranes with progressing infections. In the myometrium, ZNRF3 and downregulation of WNT signaling persisted even after infection resolution, reflecting an infectious exposure. Unexpectedly, resolved infections were associated with SIAI and a myometrial inflammatory response, offering a possible explanation for preterm labor associated with SIAI.

systems biology↗

bioRxiv2026

Integrative pipeline to profile and target endocrine therapy-insensitive cell populations in ER+ breast cancer

Up to 40% of patients with estrogen receptor positive breast cancer will experience relapse, either while on endocrine therapy (ET) or after ET is completed. A major contributor to ET failure is the presence of ET-insensitive cell populations within tumors. Here, we developed an analytical pipeline to systematically identify and target these populations by integrating single-cell RNA sequencing of ER+ tumors from the FELINE clinical trial with functional validation in a panel of patient-derived xenograft organoid models. We found that ET-insensitive cells are detected in all tumors regardless of clinical response and exhibit higher transcriptional heterogeneity than ET-sensitive populations. Using our pipeline, we identified and validated new therapeutic options that target patient-specific and shared ET-insensitive populations. Our integrated workflow provides a robust platform for identifying and targeting ET-insensitive cells and offers a translational framework to develop precision medicine approaches to improve outcome in breast cancer patients.

cancer biology↗

bioRxiv2026

Plant-adapted OTO staining improves ultrastructural imaging by transmission electron microscopy

Transmission electron microscopy (TEM) is widely used to examine plant cellular ultrastructure, but sample preparation remains challenging because polysaccharide-rich cell walls, large vacuoles and complex membrane systems compromise staining and structural preservation. Here, we developed a plant-adapted TEM preparation method based on a modified osmium-thiocarbohydrazide-osmium (OTO) staining workflow combined with optimized washing, dehydration and resin infiltration. Using tomato roots and leaves, we show that the optimized method produces cleaner backgrounds, better-defined cellular boundaries and improved preservation of cellular morphology compared with conventional preparation. Fine membrane-associated structures, including mitochondrial cristae, chloroplast grana and stroma lamellae, nuclear membranes and other endomembrane structures, were more clearly resolved, while staining-related precipitates and diffuse background artifacts were reduced. This method provides a practical approach for high-contrast TEM imaging of plant tissues and ultrastructural analysis of plant cells and organelles.

plant biology↗

bioRxiv2026

Systematic CRISPRi perturbation of 1,408 autism risk genes maps multilevel transcriptional convergence in human cortical neurons

Genetic heterogeneity in autism spectrum disorder (ASD) complicates the identification of shared molecular pathways amenable to therapeutic intervention. Here, we perform a CRISPRi Perturb-seq screen targeting 1,408 ASD risk genes in human embryonic stem cell-derived immature cortical neurons and profile the resulting transcriptomic effects by single-cell RNA sequencing. We identify 215 ASD risk genes whose repression induces significant global transcriptomic dysregulation. Leveraging this functional atlas, we characterize candidate genes based on transcriptional similarity to established ASD hub genes. We identify gene programs recurrently dysregulated across perturbations, anchored by processes governing microtubule dynamics, neuron differentiation, cell migration, and transmembrane transport. We further identify CHAMP1 as a previously unrecognized regulator of Wnt signaling and define specific ASD risk genes that modulate the rate of cortical neuron differentiation. Analysis of differentially expressed genes reveals both convergent and perturbation-specific downstream transcriptional responses. Together, these findings provide a multilevel map of transcriptomic convergence in ASD and establish a framework for identifying both pathway-level and genotype-specific therapeutic strategies.

neuroscience↗

bioRxiv2026

Five novel ubiquitous totiviruses function as virulence-promoting symbionts in the obligate biotrophic fungus Puccinia triticina

Mycoviruses modulate fungal fitness and pathogenicity, yet their biological roles in obligate biotrophic phytopathogens remain poorly understood. Here, we report the first functional characterization of totiviruses in rust fungi, identifying five novel totiviruses, designated Puccinia triticina totivirus 1 to 5 (PtTV1?PtTV5), from the wheat leaf rust fungus Puccinia triticina (Pt). All five PtTVs possess the canonical genomic architecture of Totiviridae, including two overlapping ORFs and a conserved ?1 ribosomal frameshifting motif. Transcriptional profiling revealed that PtTVs are highly expressed during early Pt infection. PtTV-encoded proteins suppressed BAX-triggered programmed cell death in Nicotiana benthamiana, indicating immune-suppressive activity. Using BSMV-mediated host-induced gene silencing (HIGS), we showed that knockdown of PtTV transcripts significantly impaired fungal hyphal expansion and uredinial formation, concomitant with enhanced host H?O? accumulation. A survey of 90 Pt field isolates from four major wheat-growing regions of China revealed that PtTVs are ubiquitously distributed in natural rust populations. Collectively, these findings demonstrate that totiviruses function as virulence-promoting symbionts in Pt, establishing for the first time a functional link between totiviral infection and enhanced pathogenicity in cereal rust fungi and identifying candidate targets for RNAi-based disease control.

plant biology↗

bioRxiv2026

Shared phylogeographic structuring and recurrent hybridization underlie the diversification of Microhyla frog complexes in the Indochinese biodiversity hotspot

Comparative phylogeography provides a powerful framework to identify the historical processes shaping biodiversity hotspots by testing whether co-distributed species exhibit shared patterns of diversification. Southeast Asia harbors exceptional biodiversity, yet the extent to which common paleogeographic and climatic drivers have structured diversification across taxa remains poorly understood. Here, we investigated the evolutionary history of five widespread Microhyla species complexes distributed across the Indochinese Peninsula and adjacent regions using dense mitochondrial sampling (1,388 individuals) combined with genome-scale ddRAD sequencing (280 individuals). Across all complexes, both approaches recovered multiple lineages and remarkably congruent phylogeographic breaks and contact/intergradation zones associated with major Indochinese regions, including Myanmar, the Tenasserim-Malay Peninsula, southern Vietnam, and northern Vietnam-southern China, supporting the hypothesis that Indochina functions as a mosaic of stable biogeographic units. However, lineage divergence varied substantially among complexes, suggesting that common biogeographic drivers interacted with species-specific demographic histories. Phylogenomic analyses and cyto-nuclear discordance further revealed historical introgression in every complex, indicating that diversification involved both long-term allopatric isolation and reticulate evolution. These findings portray the Indochinese biodiversity hotspot as a dynamic evolutionary system where cycles of fragmentation and reconnection have repeatedly reshaped lineage boundaries. Moreover, the complex phylogeographic structure recovered exemplifies the urgent need for taxonomic revisions, for which genome-scale data provide an essential framework to validate mitochondrial hypotheses and assess admixture patterns for species delimitation. Finally, regions such as the southern Annamites and Tenasserim Hills emerged as recurrent hotspots of genetic diversity across independent lineages, highlighting their importance for conserving not only species/lineage richness but also the evolutionary processes and adaptive potential that sustain biodiversity.

zoology↗

bioRxiv2026

GWAS and multi-omics uncover the SLC6A3 locus for canine stranger-directed social anxiety and implicate NRIP3 in dopaminergic network regulation

The social anxiety disorder (SAD) is one of the most common mental health disorders, often developing in adolescence. It can severely impair educational achievement, career progression, and social functioning. Certain individuals within the Chinese Kunming dog (CKD) population display spontaneous, SAD-like behaviors towards strangers, offering a valuable model for genetic investigation. Using whole-genome sequencing data, we conducted a genome-wide association study on this SAD-like behavior in 100 CKDs, identifying several genes previously linked to human psychiatric disorders. Notably, SLC6A3 reached genome-wide significance, whereas CADPS2 exceeded only the suggestive threshold and is therefore considered a network-supported candidate locus. By integrating weighted gene co-expression network analysis with public RNA-seq data from the VTA of 38 mice (after quality control), we further showed that Slc6a3 and Cadps2 are part of a co-expression network module associated with the dopamine system. These findings suggest that this specific module may play a functional role in the stranger-directed anxiety-related behaviours in CKDs. Furthermore, we identified Nrip3 as a key candidate transcriptional coregulator in this specific module. Subsequent virus-mediated overexpression and behavioural experiments showed that Nrip3 regulates gene expression within this network and induces a spectrum of behavioural alterations in mice, including social avoidance, anhedonia, and increased locomotor activity. Collectively, this study highlights the involvement of dopamine system genes in canine social anxiety and suggests Nrip3 as an upstream regulator of Slc6a3 associated with anxiety. The identification of conserved anxiety-related signatures between humans and dogs further supports the dog as a valuable translational model for psychiatric research.

genetics↗