Biology subjects

Sun, H.

Publications and source records attributed to Sun, H..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon

Pangenomes are increasingly critical for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here, we report a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated near-gapless genomes representing all seven watermelon species. The super-pangenome captures approximately one million structural variants (SVs), enabling accurate variant genotyping across ~900 watermelon accessions and substantially expanding variant discovery both across and within species. Broader sampling within the pangenome provides insights into genome evolution among watermelon species and sheds light on the origin of cultivated watermelon. SV-inclusive genome-wide association studies enhance trait mapping resolution and identify a copy number variation upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, our findings and genomic resources establish a foundational framework for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crop species.

plant biology↗

bioRxiv2025

Big1 is a cell cycle regulator linking cell size to basal body number

Cell size control in dividing cells coordinates cell growth with cell division. In the ciliated protozoan, Tetrahymena, there is a tight link between cell size and the cytoskeletal assemblies at the cell cortex organized around basal bodies (BBs). BBs dictate the distribution of ciliary units governing cell motility and are organized into 18-22 ciliary rows. The number of BBs per cell remains remarkably consistent even when the number and lengths of ciliary rows vary. big1-1 mutant cells are large and have elevated numbers of BBs, providing a system to investigate links between BB number and cell size control. We discovered BIG1 encodes a protein with an RRM3 RNA-binding domain similar to the fission yeast meiotic entry gene, mei2. The big1-1 mutation is a predicted null allele. By extending the duration of specific cell cycle stages conducive to new BB assembly, big1-1 promotes cell size increases through BB amplification. In contrast, excess Big1 protein localizes to BBs and drives cells into premature cell division, resulting in small cells with fewer BBs. Thus, Tetrahymena Big1 localizes to BBs and controls cell cycle progression, indicating BBs and Big1 link cell growth to the cell division cycle.

cell biology↗

bioRxiv2025

AlphaGEM Enables Precise Genome-Scale Metabolic Modelling by Integrating Protein Structure Alignment with deep-learning-based Dark Metabolism Mining

Constructing high-quality genome-scale metabolic models (GEMs) for less-studied species remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. Our findings show that the structural alignment or protein-language-model-based prediction (i.e., PLMSearch), could identify more homologous protein relationships than sequence-blast-based alignment, contributing to the accurate profiling of metabolism from target organisms. Additionally, AlphaGEM encompasses an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine the dark metabolic functions encoded by nonhomologous proteins, significantly expanding species-specific metabolic networks. We demonstrate AlphaGEMs accuracy by building GEMs for eukaryotes (e.g., S. pombe, C. albicans) and prokaryotes (e.g., K. pneumoniae, B. subtilis), achieving predictions comparable to manually curated models while outperforming existing tools. AlphaGEM also successfully reconstructs GEMs for M. musculus and C. griseus, showcasing its great potential for uncovering dark metabolism in complex mammals. Lastly, we demonstrate that AlphaGEM could facilitate the automatic GEMs reconstruction for 332 distinct yeast species with high prediction fidelity. In conclusion, AlphaGEM provides unprecedented opportunities for the precise, rapid construction of GEMs across diverse domains, which sets a solid foundation for universal functional analysis of non-model organisms having genome sequences available.

bioinformatics↗

bioRxiv2025

Comprehensive profiling of antibiotic resistance, virulence genes, and mobile genetic elements in the gut microbiome of Tibetan antelopes

Tibetan antelopes, native to high-altitude plateau regions, play a vital role in the local ecosystem. However, their gut microbiota harbors diverse antibiotic resistance genes (ARGs) and virulence genes (VFGs), raising concerns about the potential spread of antibiotic resistance in wildlife. In this study, in addition to collecting 26,608 metagenome-assembled genomes (MAGs) from public databases, we performed gut metagenomic sequencing on 68 Tibetan antelopes and obtained 7,318 MAGs through binning. A total of 2,968 ARGs were identified, conferring resistance to 23 antibiotic classes, with elfamycin resistance being the most prevalent. Comparative analysis revealed 7 ARGs unique to Tibetan antelopes, suggesting species-specific adaptations. Additionally, during the analysis of shared antibiotic resistance profiles between Tibetan antelopes and humans, two bacterial strains were identified within the Tibetan antelope gut microbiota: Enterococcus gallinarum exhibiting resistance to extended-spectrum class A beta-lactamases and Klebsiella grimontii demonstrating vancomycin resistance. Mobile genetic elements played a key role in ARG dissemination. ARGs were significantly correlated with VFGs, particularly those linked to adherence and effector delivery systems. These findings underscore for the first time the potential ecological and health implications of ARG dissemination in Tibetan antelopes, highlighting the need for further surveillance to assess its impact on wildlife and environmental resistomes.

microbiology↗

bioRxiv2025

Graph pangenome reveals structural variation dynamics during cucumber breeding

Structural variants (SVs) represent a significant yet underexplored component of plant genome diversity. Analyzing SV dynamics is important for understanding their contributions to phenotypic variation and their influence on genome evolution. Here, we present a graph-based pangenome for cucumber, constructed from 39 reference-level genome assemblies. The pangenome captures 171,892 high-confidence SVs and enables genotyping of these SVs across 443 cucumber accessions, representing diverse geographic origins and spanning wild progenitors, landraces, and modern cultivars. Our analyses reveal that during cucumber domestication, a substantial portion of mildly deleterious SNPs are retained, whereas SVs are consistently purged, highlighting their potentially highly deleterious nature. During the geographic expansion of cucumber, a reduction in SV burden and a younger age of SVs than SNPs were observed, suggesting stronger purifying selection acting on SVs. Additionally, notable gene flow from wild population to African and European populations was detected, resulting in an increased SV burden, potentially due to hitchhiking effects. Importantly, incorporating SV burden into genomic prediction models improved the prediction accuracy for several agronomically important traits. This study illuminates the dynamics of SVs during cucumber domestication and improvement, highlights the complex interplay between SVs and selection pressures, and underscores the extensive implications of SVs for future cucumber breeding.

plant biology↗

bioRxiv2025

Thermogenic Adipose ADH5 Counteracts Age-related Metabolic Decline

Aging-associated decline in brown adipose tissue (BAT) function and mass contributes to energy and metabolic homeostasis disruption. Alcohol dehydrogenase 5 (ADH5) is a major denitrosylase that prevents cellular nitro-thiol redox imbalance, an essential feature of aging. However, the functional significance of BAT ADH5 in the context of aging is largely unknown. Here, we aimed to investigate the role of BAT ADH5 in protecting against age-related metabolic dysfunction. We show that aging elevates BAT protein S-nitrosylation modification and downregulates ADH5 in mice. Furthermore, BAT ADH5-deletion accelerates BAT senescence and aging-associated declines in metabolic homeostasis and cognition. Mechanistically, we found that aging inactivates BAT Adh5 by suppressing heat shock factor 1 (HSF1), a well-recognized proteostasis regulator. Moreover, pharmacologically enhancing HSF1 improved age-related BAT senescence, metabolic decline, and cognitive dysfunction. Together, these findings suggest that the BAT HSF1-ADH5 signaling cascade plays a key role in protecting against age-related systemic functional decline. Ultimately, unraveling the role of thermogenic adipose nitrosative signaling will provide novel insights into the interplay between BAT nitric oxide bioactivity and metabolism in the context of aging. HighlightsO_LIAging elevates general protein S-nitrosylation while downregulating ADH5 expression in BAT. C_LIO_LILoss of thermogenic adipose tissue ADH5 accelerates BAT senescence and exacerbates aging-associated metabolic and cognitive dysfunctions. C_LIO_LIDisruption of HSF1-activated Adh5 in BAT contributes to aging-associated metabolic and cognitive impairments. C_LIO_LIPharmacological targeting of BAT HSF1 alleviates aging-related BAT senescent and systemic declines. C_LI

physiology↗

bioRxiv2025

Fluctuation structure predicts genome-wide perturbation outcomes

Pooled single-cell perturbation screens represent powerful experimental platforms for functional genomics, yet interpreting these rich datasets for meaningful biological conclusions remains challenging. Most current methods fall at one of two extremes: either opaque deep learning models that obscure biological meaning, or simplified frameworks that treat genes as isolated units. As such, these approaches overlook a crucial insight: gene co-fluctuations in unperturbed cellular states can be harnessed to model perturbation responses. Here we present CIPHER (Covariance Inference for Perturbation and High-dimensional Expression Response), a conceptual framework leveraging ideas from linear response in statistical physics to model transcriptome-wide perturbation outcomes using gene co-fluctuations in unperturbed cells. We validated our approach on synthetic regulatory networks before applying it to 29 large-scale single-cell genetic perturbation datasets covering 19,003 perturbations and over 6.26M cells. Our work robustly recapitulated genome-wide responses to single and double perturbations by exploiting baseline gene covariance structure. Importantly, eliminating gene-gene covariances, while retaining gene-intrinsic variances, i.e. mean-field conditions, dramatically reduced model performance by several folds across multiple metrics, demonstrating the rich information stored within baseline fluctuation structures. Benchmarked against recent deep learning and linear baselines, CIPHER matched or exceeded the best-performing approaches with fitting a single parameter. Moreover, gene-gene correlations transferred successfully across independent studies of the same cell type, revealing stereotypic fluctuation structures. We further extended CIPHER to the inverse problem of identifying true driver perturbations, where it achieved high performance across both genetic and chemical perturbation screens through uncertainty-aware Bayesian inference. We further used CIPHER to nominate drivers of therapy resistance in melanoma and pancreatic cancer, validating its top predictions experimentally. Finally, most genome-wide responses propagated through the covariance matrix along approximately 1-3 independent and global gene modules, consistent with a low-rank structure of the underlying gene regulatory network, which we show can enable the framework's success. We have also created a package called cipher-perturb, available on PyPI, to apply the framework to any dataset, accompanied by a detailed website (https://goyallab.github.io/CIPHERWebsite/). Our study underscores the importance of theoretically-grounded models in capturing complex biological responses, highlighting fundamental design principles encoded in cellular fluctuation patterns.

systems biology↗

bioRxiv2025

An atlas of cell type specific regulatory effects in cattle

Understanding the genetic and molecular architecture of complex traits and artificial selection is crucial for advancing sustainable precision breeding in cattle and other livestock. Yet, how genetic variation affects cellular gene expression remains elusive in cattle. Here, by integrating 8,866 bulk RNA-seq samples and 999,192 single cells of 81 cell types in 22 bovine tissues, we presented a comprehensive atlas of regulatory variants at the cell type resolution in cattle. By colocalizing with bulk-tissue expression quantitative trait loci (beQTL), we detected 57,043 novel cell-type stratified eQTL and cell-type/state interaction eQTL in 18,153 genes, which also exhibited a stronger tissue/cell-type specificity than beQTL. By examining genome-wide associations (GWAS) of 44 complex traits, these cell-resolved eQTL were colocalized with 505 (24%) additional GWAS loci compared to beQTL. Through integrating this resource with selection signatures between dairy and beef cattle, we provided tissue/cell-specific regulatory insights into cattle breeding. Overall, the current atlas of cell-type-specific regulatory variants will serve as an invaluable resource for cattle genomics and selective breeding.

genetics↗

bioRxiv2025

Human myelocyte and metamyelocyte-stage neutrophils suppress tumor immunity and promote cancer progression

Tumor-infiltrating neutrophils (TINs) are highly heterogeneous and mostly immunosuppressive in the tumor immune microenvironment (TIME). Current biomarkers of TINs and treatment strategies targeting TINs have not yielded optimal responses in patients across cancer types. Here, we separated human and mouse neutrophils into three developmental stages, including promyelocyte (PM), myelocyte & metamyelocyte (MC & MM), and banded & segmented (BD & SC) neutrophils. Based on this separation, we observed the predominance of human but not mouse MC & MM-stage neutrophils in bone marrow (BM), which exhibit potent immunosuppressive and tumor-promoting properties. MCs & MMs also occupy the majority of TINs among patients with 17 cancer types. Moreover, through the creation of a NCG-Gfi1-/- human immune system (HIS) mouse model, which supports efficient reconstitution of human TIN, we found a significant increase of BM MCs & MMs in tumor-bearing mice. By comparing the scRNA-seq analysis results of human neutrophils from both BM and tumors, we found CD63 and Galectin-3 distinguish MC & MM from neutrophil populations in cancer patients. Furthermore, we proposed a strategy with Flt3L to specifically induce the trans-differentiation of MCs & MMs into monocytic cells, and trigger tumor control in NCG-Gfi1-/- HIS mice. Thus, our findings establish an essential role of human MC & MM-stage neutrophils in promoting cancer progression, and suggest their potential as targets for developing potential biomarkers and immunotherapies for cancer.

immunology↗

bioRxiv2025

PathoEye: a deep learning framework for the whole-slide image analysis of skin tissue

ObjectiveTo provide an interpretable computational framework for the examination of whole-slide images (WSI) in skin biopsies, PathoEye focuses on the dermis-epidermis junctional (DEJ) areas, also known as the basement membrane zone (BMZ), to enrich the pathological features of various skin diseases. MethodsWe presented PathoEye for WSI analysis in dermatology, which integrates epidermis-guided sampling, deep learning and radiomics. It enables unsupervised semantic segmentation of the BMZ and extracts distinct features associated with various skin conditions. ResultsPathoEye performs comparably with existing methods in binary classification tasks, while outperforming them in multi-classification tasks involving different skin conditions. It enables the investigation of histopathological aberrations in aged skin compared with young skin. Additionally, it highlighted the texture changes in the BMZ of young skin compared with aged skin. Further experimental analyses revealed that senescence cells were enriched in the BMZ, and the turnover of basement membrane (BM) components, including COL17A1, COL4A2, and ITGA6, was increased in aged skin. ConclusionPathoEye is a comprehensive tool for characterizing the unique features of different skin conditions associated with BMZ, thereby ensuring its potential applications in skin disease diagnosis and treatment planning.

bioinformatics↗

bioRxiv2025

Prediction of soil probiotics based on foundation model representation enhancement and stacked aggregation classifier

Soil probiotics are indispensable in agro-ecosystem, which enhances crop yield through nutrient solubilization, pathogen suppression, and soil structure improvement. However, reliable prediction methods for soil probiotics remain absent. In this study, we utilize genomic foundation models to generate representations from samples sequences, and then, enhance them by deeply integrating domain-specific engineered features. The enhanced representations enable training a powerful classifier for a target task, instead of common parameter fine-tuning. Inspired by the stacking ensemble learning framework, we also design a stacked aggregation classifier. It predicts the label of a sample with only leveraging partial sequence segments from this sample, effectively addressing the challenges in processing long sequences. The proposed method is applied on prediction of soil probiotics and obtains 97.50% accuracy and 0.9807 AUC value on balanced and imbalanced test sets, respectively. Potential functional genes are revealed from the predicted probiotics, providing biologically insights for more related studies.

bioinformatics↗

bioRxiv2025

Advanced adaptive strategies in an ancestral body plan: insights from a 510-Ma-old leptomitid sponge

Sponges have thrived in diverse environmental conditions since the early Cambrian till today. However, little is known about how their adaptive capability and strategies have been shaped throughout evolutionary history. Here, we explore this question based on a new leptomitid sponge fossil from the Cambrian Stage 4. The family Leptomitidae was an abundant sponge group inhabiting Cambrian soft substrates but significantly declined thereafter. The new species exhibits a sophisticated set of morphological characteristics adaptive to a shallow siliciclastic environment, which are unprecedented among leptomitids. These include (1) a robust body wall woven by spirally twisted monaxonic spicules; (2) a thick stub-like root tuft for anchoring; (3) spicules radiating out from the sponge body to prevent clogging and sinking; and (4) the inferred capability to close the osculum against unfavourable stimuli. Nevertheless, the new fossil species maintains a leptomitid body plan which lacks modularity and morphological plasticity, the two common and critical attributes in extant sponges to enhance flexibility and resilience in changing environmental conditions. This juxtaposition of evolutionary innovation and structural conservatism offers a compelling case for further exploration of the evolutionary mechanisms that shaped early sponge lineages.

paleontology↗

bioRxiv2025

Separable Spatial Single-cell Transcriptome Representation Learning via Graph Transformer and Hyperspherical Prototype Clustering

Spatial transcriptomics enables the exploration of cell types, spatial domain organization, and cell-cell communication at tissue scale, serving as a powerful link between tissue morphology and molecular function. However, existing methods still struggle with limited spatial resolution, poor robustness in multi-slice alignment, and suboptimal ligand-receptor (L-R) detection in complex tissues. To address these challenges, we propose S3RL (Separable Spatial Single-cell transcriptome Representation Learning via Graph Transformer and Hyperspherical Prototype Clustering), a unified framework that integrates gene expression, spatial coordinates, and histological image features via a graph neural network and hyperspherical prototype-based separable representation learning. In spatial clustering tasks, S3RL improves the average Adjusted Rand Index (ARI) by nearly 120% on the Nanostring lung cancer dataset and over 26% on the 10X DLPFC dataset compared to state-of-the-art methods. For multi-slice spatial alignment, S3RL achieves an average ARI improvement of over 65.4% in partial brain slices and over 48.3% in complete slices relative to GraphST, highlighting its superior robustness and alignment consistency across heterogeneous spatial inputs. Moreover, S3RL reveals more biologically meaningful ligand-receptor signaling relationships, enhancing the interpretability of cell-cell communication patterns. Together, these results demonstrate S3RLs effectiveness in enhancing spatial resolution, cell type identification, and biological insight across diverse spatial transcriptomics datasets.

bioinformatics↗

bioRxiv2025

Grapevine phyllosphere pan-metagenomics reveals pan-microbiome structure, diversity, and functional roles in downy mildew resistance

Grapevines are among the most economically important fruit crops, and microbiome profoundly influence their health, yield, and quality. However, mechanistic insights into microbiome-orchestrated grapevine biology remain limited. Here, we conduct large-scale pan-metagenomic and pan-metatranscriptomic analyses of the phyllosphere microbiome from 107 grapevine accessions spanning 34 Vitis species. We show that the grapevine core microbiome is dominated by phylum Bacillota and Pseudomonadota. Leveraging PacBio sequencing, we assembled 19 high-quality metagenome-assembled genomes (MAGs) from the grapevine pan-microbiome, representing the first MAG reconstruction in plant-associated microbial communities using PacBio reads. These MAGs encode genes associated with antibiotic resistance, secondary metabolism, and carbohydrate-active enzymes (CAZymes), which could potentially influence grapevine biology. During downy mildew (DM) infection, DM-resistant grapevines exhibit significantly higher microbial network complexity than susceptible counterparts. Among the key taxa contributing to this complexity, Bacillota emerged as the dominant phylum with displaying strong abundance correlations with phylum Euglenozoa and Cyanobacteriota, and an isolated Bacillota species from the grapevine leaves, Bacillus cereus, demonstrated potent biocontrol activity against DM infection. Pan-metatranscriptomic analysis further revealed significant upregulation of eukaryotic microbial genes involved in primary and secondary metabolism. This pan-metagenomic study offers unprecedented insights into the complex structure, diversity and functional roles of grapevine phyllosphere microbiome, and presents valuable genomic and microbial resources for microbiome research and engineering to enhance viticulture productivity and quality.

microbiology↗

bioRxiv2025

Identification of a Thermogenic Target in the Dorsal Raphe Nucleus for Weight Management

Obesity emerges from a complex interplay of factors, including imbalanced interoception, genetic predisposition, and environmental cues, ultimately disrupting body weight homeostasis1. While much research has concentrated on strategies to suppress appetite for sustained weight loss, insufficient attention has been given to counterregulatory mechanisms that promote energy expenditure. Here, we show that chronic inhibition of GABAergic neurons in the Dorsal Raphe Nucleus (DRNVGAT) reduces body weight in diet-induced obese (DIO) mice. In this study, molecular profiling and in-situ hybridization in rodent and human brains revealed that the constitutively activated orphan receptor GPR6 is selectively enriched in DRNVGAT neurons. We next developed and administered a potent and highly selective GPR6 inverse agonist, which significantly reduced weight gain in DIO mice by stimulating brown adipose tissue thermogenesis without affecting appetite. Altogether, this study transitions from transcriptomic profiling, high-throughput drug screening and metabolic phenotyping to successfully identify a novel candidate to treat obesity.

neuroscience↗

bioRxiv2025

Imputation Disparities Driven by Recent Selectionand Their Impact on Disease Risk Estimation in East and Southeast Asian Populations

Using genotype data consisting of 8,316 individuals, we systematically evaluated imputation performance across six state-of-the-art reference panels for Chinese and Thai populations. A substantial proportion of variants identified through whole-genome sequencing, especially low-frequency variants, remained undetected by existing reference panels. In the Chinese population, the TOPMed panel required an R2 threshold of 0.60-0.70 to achieve comparable imputation accuracy of the ChinaMAP panel without R2 filtering, challenging the standard practice of applying a fixed R2 threshold for downstream analyses. Regional analysis highlighted the role of recent selection in imputation discrepancies and revealed an enrichment of immune-related genes in poorly imputed regions. In addition, we showed that the selection of reference panels and R2 thresholds could significantly influence estimation of polygenic risk score for disease prediction. These findings underscore the importance of developing ancestrally diverse reference panels and provide valuable guidelines for improving genotype imputation in East and Southeast Asian populations.

genomics↗

bioRxiv2025

Respiratory Airway Secretory Cells act as Immune Sentinels in Human Distal Airways

Pulmonary immunity in the human distal respiratory airways is essential for lung function but remains poorly explored, mainly due to limited physiologically relevant models. Here, we develop distal lung organoids containing respiratory airway secretory (RAS) cells, a recently identified epithelial population unique to the distal airways of humans and large mammals, using human pluripotent stem cells (hPSCs). Lineage tracing identified RAS cells as descendants of SOX9brightNKX2-1bright progenitors. Single-cell transcriptomics elucidated that RAS cells exhibited a distinct immune-competent phenotype with enriched genes associated with viral host entry and pattern recognition receptor signaling. Functionally, RAS cells infected by respiratory syncytial virus (RSV) exhibited enhanced antiviral activity marked by upregulation of interferon-stimulated genes (ISGs) and complement component 3 (C3). In contrast, bacterial flagellin or Pseudomonas aeruginosa (PAO1) triggered a TLR5-driven immune response in RAS cells, inducing complement system activation, a response suppressed by selective TLR5 inhibition. Notably, in chronic obstructive pulmonary disease (COPD), RAS cells displayed dysregulated immune activation, characterized by elevated expression of adaptive immune mediators. Together, our findings identify RAS cells as previously unrecognized sensors and effectors of mucosal immunity in the human distal airways and reveal their roles in viral infection, bacterial stimulation, and chronic inflammatory lung disease, providing potential therapeutic targets for respiratory diseases.

cell biology↗

bioRxiv2025

Convergent strigolactone perception via independent horizontal gene transfer of pre-adapted α/β-hydrolases

Strigolactones (SLs) are not only phytohormones that influence multiple aspects of plant growth and development, but also signaling molecules for interactions between plants and certain fungi or bacteria. In plants, the SL receptor is an /{beta}-hydrolase (ABH) encoded by the D14/KAI2 gene family, which is known to be derived from proteobacterial RsbQ through horizontal gene transfer (HGT). Here we show that the CpD14-like (CDL) gene family, which encodes another ABH exhibiting SL receptor functions in fungi, was acquired from actinobacteria via an independent HGT event. X-ray crystallographic experiments reveal that CDL and D14/KAI2/RsbQ (DKR) proteins have distinctive lid structures but maintain the same core /{beta} fold and catalytic triad. Biochemical assays further show that both actinobacterial CDL and proteobacterial RsbQ can recognize and hydrolyze SLs, suggesting that they are pre-adapted for SL perception. This work demonstrates that independent HGT of pre-adapted genes can trigger convergent evolution of key innovations and provides insights into how SLs are sensed by organisms across kingdoms.

evolutionary biology↗