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Zeng, Z.

Publications and source records attributed to Zeng, Z..

At least 37 records · Page 2Linked to original sources
bioRxiv2026

Managing the phyllosphere: Agronomic practices influence the ecology and evolution of Pseudomonas syringae in cherry orchards

Bacterial canker, caused the Pseudomonas syringae species complex, is a major constraint on sweet cherry production worldwide. However, the influence of agronomic practices on pathogen ecology, dispersal and evolution under field conditions remains poorly understood. Here, we combined a factorial-design field experiment with whole-genome sequencing to investigate the effects of polytunnel covering and nitrogen fertigation on phyllosphere populations and the dynamics of a key pathogen, P. syringae pathovar syringae 9644 (Pss9644) in young cherry trees. Epiphytic P. syringae populations initially resembled those in surrounding woodland environments. Over time, pathogenic phylogroup 2d lineages became dominant, particularly on uncovered trees. Diversity of P. syringae populations was higher in uncovered treatments. Polytunnel covering markedly altered community composition and limited rain-splash dispersal of Pss9644 from stem cankers to leaves, thereby interrupting a key stage of the disease cycle. By contrast, nitrogen fertigation had no detectable effect on phyllosphere community structure, but enhanced plant growth and reduced lesion expansion following inoculation. Whole-genome sequencing of re-isolated Pss9644 strains revealed limited short-term genomic diversification, with single-nucleotide polymorphisms detected in 22 re-isolates. In total, 36 mutations were identified across the chromosome although no mutation affected virulence or motility. Taken together, our results show that agronomic practices influence both pathogen ecology and disease outcomes through distinct mechanisms: polytunnel covering primarily limits pathogen dispersal and reshapes phyllosphere communities, while nitrogen fertigation enhances plant growth and reduces disease severity. These findings highlight the potential to integrate canopy management and nutrient strategies to mitigate bacterial canker risk in commercial cherry production.

microbiology↗
bioRxiv2026

Benchmarking and Experimental Validation of Machine Learning Strategies for Enzyme Engineering

Enzyme-directed evolution increasingly relies on computational tools to prioritize mutations, yet their practical value is difficult to assess because kinetic data are often aggregated across heterogeneous assay conditions, inflating apparent generalization. Here we introduce EnzyArena, a curated benchmark in which kinetic parameters (kcat, Km and kcat/Km) are grouped by experimental campaign and measured under matched conditions, thereby minimizing confounders such as pH, temperature, reaction, and batch. Using EnzyArena, we systematically evaluate 20 models spanning four widely used strategies for guiding enzyme evolution: protein-ligand binding affinity prediction, protein stability prediction, zero-shot fitness prediction and enzyme kinetic parameter prediction. Across subsets derived from public databases and 25 independent mutagenesis datasets, most models exhibit weak, fragile or inconsistent correlation with catalytic activity. Kinetic-parameter predictors perform strongly on database-derived subsets but lose their advantage on independent datasets, whereas zero-shot predictors show more consistent generalization. A simple consensus of multiple zero-shot models further improves the precision of identifying beneficial mutants. We prospectively validated these findings in a wet-lab campaign (150 mutants) comparing random mutants, UniKP-prioritized mutants and ESM-1v-prioritized mutants (representing supervised kinetic-parameter prediction and zero-shot fitness prediction, respectively), where ESM-1v achieved the highest utility and UniKP underperformed the random baseline. Together, this study establishes realistic baselines for computational mutant prioritization and highlights consensus zero-shot strategies as a practical starting point for enzyme engineering. Zero-shot fitness predictors are the most competitive and generalizable class, but still achieve only modest correlations on independent datasets. Notably, a simple consensus of multiple zero-shot models substantially improves the precision of identifying superior mutants and currently offers the most practical computational benefit for enzyme engineering. Our work establishes EnzyArena as a fair benchmark for enzyme activity prediction, defines realistic performance baselines for existing tools, and highlights consensus zero-shot strategies as the most useful computational tool in practice for enzyme-directed evolution.

bioengineering↗
bioRxiv2026

OmicClaw: executable and reproducible natural-language multi-omics analysis over the unified OmicVerse ecosystem.

Advances in bulk, single-cell and spatial omics have transformed biological discovery, yet analysis remains fragmented across packages with incompatible interfaces, heterogeneous dependencies and limited workflow reproducibility. Here, we present OmicClaw, an executable natural-language framework for multi-omics analysis built on the unified OmicVerse ecosystem and the J.A.R.V.I.S. runtime. OmicVerse organizes upstream processing, preprocessing, single-cell, spatial, bulk-transcriptomic and foundation-model workflows into a shared AnnData-centered interface spanning more than 100 methods. J.A.R.V.I.S. converts this ecosystem into a bounded analytical action space through a registry-grounded, state-aware and recoverable execution layer that validates prerequisites, preserves provenance and supports iterative repair, while enabling conversational, notebook and visual interfaces to operate over the same live analytical state. Rather than relying on unconstrained code generation, OmicClaw translates user requests into traceable workflows over live omics objects. Across a benchmark of 15 tasks spanning scRNA-seq, spatial transcriptomics, RNA velocity, scATAC-seq, CITE-seq and multiome analysis, ov.Agent improved rubric-based performance over bare one-shot large language model baselines, particularly for long-horizon multi-step workflows. OmicClaw further supports external agent access through an MCP-compatible server and a beginner-friendly web platform for interactive analysis, code execution and million-scale visualization. Together, OmicClaw provides a practical foundation for reproducible human-AI collaboration in modern multi-omics research

bioinformatics↗
bioRxiv2026

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery

The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu), an evolvable, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically, PantheonOS enables agentic code evolution, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1-Nodal inhibition in proximal-distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond. Websitehttps://pantheonos.stanford.edu Ecosystemhttps://github.com/aristoteleo SummaryLarge language model-powered agent systems are driving a paradigm shift in scientific discovery by automating, scaling, and accelerating data analysis. This transformation is particularly profound in biology, where the rapid expansion of single-cell and spatial genomics has effectively reshaped the field into a data-intensive science. However, existing biological agent systems are typically constrained to single-agent designs, are narrowly specialized, or are overly general without sufficient domain expertise, limiting their applicability to routine or shallow analyses. Here, we introduce PantheonOS (https://pantheonos.stanford.edu), an evolvable, privacy-preserving, and general-purpose multi-agent framework designed to reconcile generality with deep domain specificity. PantheonOS provides an abstract, extensible architecture that enables customized agent composition and supports end-to-end single-cell and multi-omics analysis, spanning reinforcement-learning-augmented gene panel design, raw FASTQ processing, multimodal data integration, and three-dimensional spatial genomics reconstruction. Central to this framework, Pantheon-Evolve enables agentic code evolution, allowing the system to autonomously improve state-of-the-art batch-correction algorithms and new reinforcement-learning based gene panel design algorithms, achieving performance beyond manually designed baselines. We demonstrate the power of PantheonOS across multiple biological domains. In early mouse embryogenesis, PantheonOS automatically reconstructs three-dimensional spatial gene expression landscapes and resolves asymmetric Cer1 expression and paracrine Cer1-Nodal inhibition, revealing a robust proximal-distal axis at embryonic day six (E6.0). In human development, PantheonOS integrates fetal heart single-cell multi-omics with whole-heart 3D MERFISH+ data at post-conception week 12, uncovering spatially resolved molecular programs underlying heart disease ontogeny. Finally, an intelligent model-routing mechanism enables PantheonOS to adaptively select optimal virtual cell models across heterogeneous tasks, revealing minimal regulatory networks of cardiogenesis and predicting spatially resolved perturbation effects in the developing heart. Together, PantheonOS establishes a foundation for fully automated, evolvable, and domain-aware agentic analysis in genomics, and points toward a future in which scientific discovery is increasingly driven by self-improving AI systems across biology and beyond.

bioinformatics↗
bioRxiv2025

Integrative spatial multi-omics reveals prognostic tumor niches in female genital tumors

Female genital tumors (FGTs), including ovarian, endometrial, and cervical cancers, pose a major global health challenge, yet their spatial and molecular determinants of progression and therapy resistance remain poorly understood. Here we present a large-scale integrative spatial and multi-omics atlas of FGTs, combining exome, transcriptome, methylome, and miRNA sequencing with high-resolution Visium HD spatial transcriptomics, profiling over 62 million cells from 100 tumors. We show that HPV-driven suppression of TGF{beta} signaling in cervical cancer disrupts myofibroblast (myCAF) formation and reshapes immune infiltration, while serous tumors exhibit extensive oncogene amplifications that activate MYC and PI3K pathways, linking chromosomal instability (CIN) to proliferative, immune-cold phenotypes. In high-grade serous ovarian cancer, we identify a previously unrecognized malignant domain, the Proliferating Core Desert (PCD) niche, defined by CIN-high epithelial enrichment, immune exclusion, stromal protection, and chemotherapy resistance, maintained through crosstalk with macrophages and fibroblasts. Finally, we develop POWER, a pathology foundation model that predicts PCD niche abundance from routine H&E slides, enabling robust stratification of prognosis and anti-angiogenic therapy response.

genomics↗
bioRxiv2025

CDH3 as a Novel Therapeutic Target in Basal-like Double-Negative Prostate Cancer

PurposeBasal-like (also known as double-negative) prostate cancers are aggressive tumors that lack effective targeted therapies. We aimed to delineate the role of CDH3 (P-cadherin) in basal-like prostate cancer and evaluate CDH3-directed therapeutic strategies. MethodsWe integrated genetically engineered mouse models (GEMMs) of prostate cancer, bulk and single-cell transcriptomic analyses, and a suite of in vitro and in vivo experiments. CDH3 expression and associated signaling pathways were examined in Pten/Apc double-knockout mouse prostates and human datasets. Functional studies included antibody-drug conjugate (ADC) cytotoxicity assays and the development of chimeric antigen receptor (CAR) T cells targeting CDH3, tested in prostate cancer cell lines and xenograft models. ResultsPten/Apc double deletion in prostate GEMMs led to highly aggressive tumors with markedly elevated CDH3 expression and enrichment of non-canonical WNT signaling components. Transcriptomic analyses of patient-derived prostate tumors confirmed that CDH3 is significantly upregulated in basal-like prostate cancer subtypes relative to luminal subtypes. Single-cell RNA sequencing revealed CDH3 expression predominantly in basal epithelial cells. Mechanistically, we found that active YAP1 signaling and a WNT5A-ROR2 non-canonical WNT axis drive CDH3 expression. Targeting CDH3 with a CDH3-specific ADC induced potent, antigen-dependent killing of CDH3 prostate cancer cells in vitro and significantly suppressed tumor growth in in vivo metastatic prostate cancer models. Likewise, CDH3-targeted CAR T cells specifically recognized and lysed CDH3-expressing prostate tumor cells while sparing CDH3-negative cells, leading to tumor regression and improved survival in mouse models, especially when combined with PD-1 checkpoint blockade. ConclusionsCDH3 is a key marker and functional driver of basal-like prostate cancer. Therapeutic strategies leveraging CDH3, including ADCs and CAR T cells, demonstrate strong preclinical efficacy, supporting the development of CDH3-targeted treatments to overcome resistance in aggressive prostate cancer.

cancer biology↗
bioRxiv2025

Emergence of Individualized Functional Topography in the Neonatal Brain

The early postnatal period represents a critical phase for establishing the brains fundamental architecture. However, how individualized functional topography emerges and shapes long-term cognition remains unknown. Using multimodal neuroimaging data from 419 neonates, we map personalized functional topographies in the first weeks of life and reveal their association with 18-month neurocognitive outcomes. Unique and stable functional topographies are already present at birth, with interindividual variability organized along a conserved sensorimotor-to-association hierarchy. These individualized maps encode early brain maturity and robustly predict cognitive, language, and motor outcomes at 18-month of age, with predictive features concentrated at network boundaries. This functional refinement is structurally anchored by concurrent maturation in cortical myelination and sulcal depth. Preterm birth results in distinctive topographic alterations characterized by accelerated maturation of association networks. Our work establishes a foundation for precision models of early brain development and elucidates its significance for long-term neurocognitive outcomes.

neuroscience↗
bioRxiv2025

MERFISH+, a large-scale, multi-omics spatial technology resolves the molecular holograms of the 3D human developing heart

Hybridization-based spatial transcriptomics technologies have advanced our ability to map cellular and subcellular organization in complex tissues. However, existing methods remain constrained in gene coverage, multimodal compatibility, and scalability. Here, we present MERFISH+, an enhanced version of Multiplexed Error-Robust Fluorescence in Situ Hybridization (MERFISH), which integrates chemical probe anchoring in protective hydrogels with high-throughput microfluidics and microscopy. This optimized design supports robust and repeated hybridization cycles across an entire centimeter-scale tissue sample. MERFISH+ allowed to simultaneously quantify over 1,800 genes and resolve the 3D organization of chromatin loci and their associated epigenomic marks in developing human hearts. Using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omic atlas of the developing human heart at subcellular resolution capturing 3.1 million cells across 34 distinct populations. This 3D atlas provides a holistic view of an entire organ enabling the characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Thus, MERFISH+ offers a robust, large-format platform for spatial multi-omics that enables high resolution mapping of gene expression at subcellular resolution and the characterization of cellular organization within 3D organs. One Sentence SummaryMERFISH+ is an spatial multi-omics platform that integrates hydrogel-based probe anchoring, automated high-throughput microfluidics, and large-format multimodal data production to enable comprehensive, subcellular resolution mapping of gene expression and chromatin organization across millions of cells within complex developing human organs. HighlightsO_LIMERFISH+ expands MERFISH capabilities to measure >1,800 genes and at whole-organ 3D imaging scale C_LIO_LICombines chemical probe anchoring with high-throughput volumetric microscopy and microfluidics C_LIO_LIGenerates a 3D molecular atlas of a developing human heart with > 3.1 million cells at subcellular resolution C_LIO_LIIntroduces Spateo-VI, a novel generative framework integrating 3D multimodal datasets C_LI

genomics↗
bioRxiv2025

PET-Microplastics Trigger Endothelial Glycocalyx Loss via ER Stress and ROS Unleashing IL-1β-Driven SMC Switching and Early Aortic Structural Impairment

Polyethylene terephthalate microplastics (PET-MPs), a major microplastics component identified in human vasculature, pose emerging environmental health risks. This study systemically profiled MPs in human aortic tissues and investigated the mechanisms underlying PET-MPs-induced aortic injury in vivo and in vitro. Chronic oral exposure of Sprague-Dawley rats to PET-MPs (1.0-100 mg/L) resulted in endothelial glycocalyx loss and structural impairment of aortic elastic fibers, with MPs accumulating within aortic endothelial cells. Transcriptomic and biochemical analyses revealed that PET-MPs triggered endoplasmic reticulum stress (ERS) and reactive oxygen species (ROS) generation in human aortic endothelial cells (HAECs), driving glycocalyx degradation and NF-{kappa}B-mediated inflammation. Proteomic profiling identified endothelial-derived IL-1{beta} as a key mediator, which subsequently induced phenotypic switching in human aortic smooth muscle cells (HASMCs) in vitro. Pharmacological inhibition of ERS (TUDCA), ROS (NAC), or IL-1{beta} (Canakinumab) attenuated this pathogenic cascade. Crucially, restoration of the glycocalyx using Sulodexide mitigated endothelial dysfunction and downstream HASMC phenotypic switching. These findings establish endothelial glycocalyx degradation via ERS-ROS as a novel mechanism for PET-MPs-induced vascular injury and highlight glycocalyx protection as a potential strategy against environmental microplastic hazards.

pharmacology and toxicology↗
bioRxiv2025

Dual checkpoint blockade of glioblastoma with Anti-PD-1 and Anti-LAG-3 promotes expansion of tumor-reactive T cell clones along a unique pathway of differentiation

IDH-wildtype grade IV glioblastoma is the most aggressive adult primary brain tumor and remains refractory to anti-PD-1 monotherapy despite evidence of limited tumor-specific T cell induction. To determine the impact of immune checkpoint inhibitors (ICIs) on glioblastoma T cell transcriptional landscape and repertoire, we conducted paired single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) of tumor-infiltrating lymphocytes (TILs) from patients with untreated, newly diagnosed glioblastoma and from recurrent glioblastoma treated with dual checkpoint blockade targeting PD-1 and LAG-3. Using a validated transcriptional signature, we found that predicted tumor-reactive T cells (TRC) in untreated glioblastomas reside almost exclusively in a clonally expanded GZMKhi population with developmental plasticity, affording them the potential to differentiate into both tissue-resident and terminal effector T cells. Dual ICI therapy induced substantial clonal remodeling, characterized by the recruitment of new TRC from the periphery into the tumor microenvironment (TME) and differentiation into transitional effectors and ultimately terminal effectors along a gradient characterized by simultaneous acquisition of cytotoxic and exhaustion genes, regulated by specific transcriptional, metabolic, and epigenetic programs. Longitudinal clonal tracking in peripheral blood confirmed that with ICI treatment, most TRC expand transiently in circulation prior to tumor infiltration, with peripherally derived clones becoming the major contributor to the GZMKhi TRC that further expand in the tumor. Our study provides the first comprehensive map of T cell clonal dynamics and differentiation in glioblastoma following dual ICIs and highlights a potential mechanism of immune activation and peripheral recruitment of TRC in glioblastoma not previously described. Our results suggest that therapeutic strategies to sustain these GZMKhi early effector and transitional effector T cells may further enhance ICI therapeutic efficacy in glioblastoma.

immunology↗
bioRxiv2025

Using targeted therapy to promote a pro-inflammatory tumour microenvironment and anti-tumour immune response in high grade serous ovarian cancer

BACKGROUNDHigh-grade serous ovarian cancer (HGSOC) is characterized by elevated replication stress and an immunosuppressive microenvironment. A synergistic combination of checkpoint kinase 1 inhibitor (CHK1i) with low-dose hydroxyurea (LDHU) promotes a unique ATR-independent moderate replication stress response with potent anti-tumour effects. The ability of this approach to reprogram the tumour immune microenvironment (TIME) to overcome the immunosuppression and promote an anti-tumour immune response in HGSOC is the focus of this study. METHODSWe investigated the therapeutic potential of CHK1i+LDHU in established HGSOC cell cultures, fresh tumour cell explants from HGSOC patient ascites, and syngeneic mouse models, assessing tumour cell killing, immunogenic cell death, pro-inflammatory cytokine/chemokine expression, and anti-tumour immune responses. RESULTSCHK1i+LDHU effectively killed ovarian cancer cells regardless of chemotherapy resistance, BRCA2 mutation and homologous recombination repair status in vitro. In vivo, treatment significantly reduced tumour burden and ascites accumulation. CHK1i+LDHU enhanced expression of pro-inflammatory cytokines/chemokines and triggered immunogenic cell death in tumour. In syngeneic models, treatment promoted CD8+ cytotoxic T cell-dependent anti-tumour responses and reduced immunosuppressive signalling within the TIME. CONCLUSIONSCHK1i+LDHU is a promising therapy for chemotherapy-resistant HGSOC, combining direct cytotoxic effects with reprogramming the TIME to reduce immunosuppression and activate a CD8+ T cell-dependent anti-tumour response.

cancer biology↗
bioRxiv2025

A trade-off mechanism underpins the evolution of a young two-gene sex-determining system in plants

Sexual systems in animals and plants are remarkably diverse, with dioecy having evolved independently in numerous lineages. In plants, dioecy often evolved more recently than in the best-studied animal systems, making plants especially important for understanding how separate sexes evolved independently from functionally hermaphrodite ancestors. Despite long-standing theories of developmental trade-offs in sex allocation, the underlying genetic mechanisms remain elusive. Here, we show that the XY sex determination system in the dioecious plant species Eurycorymbus cavaleriei in Sapindaceae involves two Y-linked mutations that act jointly within the developmental male-female trade-off: YUN{Delta}, a truncated allele that lowers the dosage of the D-class MADS-box gene YUN, and SUNMAO, a novel sRNA locus that silences the X-linked SUN allele. In females, SUN stabilizes the HD-ZIP transcription factor KUN, which is a known sex determinant in another dioecious plant, thereby promoting femaleness by increasing YUN expression; loss of SUN expression, together with the effect of YUN{Delta}, shifts development toward males. Two interlocking regulatory loops in this "SKY" module (SUN-KUN-YUN) fine-tunes YUN dosage. This dioecious system in E. cavaleriei likely evolved by sequential mutations in genes acting in the predicted male-female trade-off system, with their close linkage reflecting a translocation, and later recombination-suppressing inversions.

plant biology↗
bioRxiv2025

The yEvo Mutation Browser: Enhancing student understanding of experimental evolution and genomics through interactive data visualization

Experimental evolution is a powerful method for studying the relationship between genotype and phenotype by observing how populations genetically adapt to controlled selective pressures. In educational settings, this approach also offers a dynamic way for students to engage with molecular genetics. One such educational effort, known as "yEvo" (yeast evolution), introduces experimental evolution into high school classrooms, allowing students to evolve the bakers yeast Saccharomyces cerevisiae under various stressors and investigate the resulting genetic changes. While the hands-on experiments have been successful in fostering student interest and understanding of evolution, the downstream data analysis (interpreting whole-genome sequencing results of evolved yeast compared to the ancestor) remains a challenge. Students often struggle to grasp the significance of their mutated genes and lack the broader context to determine which mutations are most phenotypically relevant. To address these issues, we developed the yEvo Mutation Browser, an intuitive web tool designed to assist students and researchers alike in visualizing and contextualizing genome sequencing data. Developed using R Shiny, this tool features an interactive chromosome map displaying mutated genes, graphs categorizing mutation types, a gene viewer illustrating specific mutation sites within genes, and a protein view that maps specific mutations onto protein structures. The app also features an option for non-yEvo-affiliated users to upload their own experimental evolution or genetic screen datasets and compare them with all yEvo data collected since 2018. The yEvo Mutation Browser streamlines data interpretation, helping students understand how organisms employ diverse genetic strategies to adapt to environmental stress. In the future, this framework could be adapted for use with other model organisms, offering a valuable resource for both genetics research and education. SignificanceBringing novel research into the classroom can transform how students learn science. The yEvo project engages high school students in experimental evolution, enabling them to witness natural selection in action and connect genetic mutations to evolutionary outcomes. By providing an accessible web-based tool, the "yEvo Mutation Browser," this work lowers barriers to interpreting genome sequencing data, allowing students, teachers, and researchers alike to explore how yeast populations adapt to diverse stress conditions. This resource not only strengthens STEM education by making cutting-edge genetics more approachable, but also builds a shared dataset that enriches the broader yeast genetics community.

scientific communication and education↗
bioRxiv2025

Revalidation of Manis aurita, based on integrative morphological and genomic evidence

Pangolins face severe conservation threats globally, necessitating accurate taxonomy for effective conservation. Previous genomic research on the Chinese pangolin (Manis pentadactyla) identified two deeply divergent lineages (MPA and MPB), suggesting underestimated species diversity. The recent description of M. indoburmanica (corresponding to MPB), however, did not assess its relationship with historical names, particularly the subspecies M. p. aurita, leaving the groups taxonomic status uncertain. To resolve this issue, we employed an integrative framework, analyzing genomic and morphological data from museum specimens including the lectotype of M. p. aurita to clarify phylogenetic relationships and taxonomy within M. pentadactyla sensu lato. Our results demonstrate that MPB, which includes both M. indoburmanica and M. p. aurita, is geographically restricted to the southern Himalayas and thus distinct from other M. pentadactyla populations. Genomic analyses indicate the two clades diverged [~]1.8 Ma and have remained largely isolated, with only minimal gene flow with congeners. Furthermore, morphometric analyses of both cranial and external features reveal consistent and significant differentiation between the Himalayan lineage (MPB) and M. pentadactyla sensu stricto. Collectively, these congruent findings provide unequivocal support for the revalidation of Manis aurita Hodgson, 1836, thereby establishing it as a distinct extant species of Asian pangolin.

evolutionary biology↗
bioRxiv2025

CellOntologyMapper: Consensus mapping of cell type annotation

Single-cell RNA sequencing has revolutionized cellular biology, with atlases now encompassing over 100 million cells. However, researchers employ vastly different naming conventions when annotating cell types, creating a fragmented landscape that severely impedes data integration and comparative analysis. Here, we present CellOntologyMapper, an automated framework that standardizes cell type annotations by intelligently mapping user-defined names to established Cell Ontology and Cell Taxonomy identifiers. Our approach leverages advanced natural language processing, including sentence transformers and large language models, to interpret diverse naming conventions and resolve them to standardized ontological terms. The system handles complex challenges including abbreviated cell names, synonym resolution, and context-dependent interpretation. We built a comprehensive query system based on 19,381 cell type entries from established Cell Ontology and Cell Taxonomy databases, which organize into 24 biologically coherent clusters. Systematic validation across datasets spanning different scales-- from 17-cell-type lung studies to 91-cell-type immune atlases and complex developmental systems--consistently demonstrated robust performance and high accuracy. CellOntologyMapper successfully resolved annotation challenges across conventional tissue studies, rare cell populations, and developmentally intricate datasets rich in abbreviated nomenclature. By providing automated, scalable annotation harmonization, our framework enables researchers to leverage existing single-cell datasets while ensuring compatibility with future atlas efforts. When incorporated into publications, CellOntologyMapper enhances the credibility and reference value of cell type annotations, representing a crucial step toward an integrated single-cell genomics ecosystem.

bioinformatics↗
bioRxiv2025

ArchLips: A comprehensive in silico database for high-throughput identification of archaeal lipids

Archaeal membrane lipids are markedly distinct from those in bacteria and eukaryotes, serving as unique biomarkers for unraveling their ecological and biogeochemical roles. Recent advancements in lipidomics have facilitated detailed cellular-level characterizations of lipid compounds. However, the lack of a comprehensive and dedicated database has severely limited large-scale, high-throughput investigations of archaeal lipids. We present ArchLips, a comprehensive database containing 218,868 in silico molecular structures and tandem mass spectra of 199,248 corresponding archaeal lipids. ArchLips enables the rapid and accurate identification of characterized archaeal lipid compounds from both pure cultures and environmental samples, serving as a transformative tool for enhancing our understanding of archaeal diversity and their ecological and evolutionary significance within global ecosystems.

biochemistry↗
bioRxiv2025

Highly heterozygous Citrus changshan-huyou Y. B. Chang originated from ancient hybridization between mandarin and pummelo and displayed distinct tissue-specific allelic imbalance

The genus Citrus is characterized by a reticulate evolutionary history with frequent hybridization, making it an intriguing subject for genome evolution investigation. Citrus changshan Y. B. Chang (Huyou) is a unique landrace first discovered in Zhejiang Province, China with premium fruit quality. The evolutionary origin of Huyou has puzzled local botanists and growers. Here, we sequenced a 120-years-old "ancestral tree" of Huyou using PacBio long read and Hi-C sequencing and assembled 2 high-quality haplotype-resolved genomes HY1 and HY2. Huyou displayed a genome heterozygosity level at 3.07%, among the highest in published citrus genomes. Using a k-mer-based tracing approach, we explicitly resolved that HY1 genome contained 87.8% mandarin, 7.3% pummelo, 0.2% citron origin, whereas HY2 had 85.0% pummelo, 2.9% mandarin, 0.3% citron, implying a hybridization event between mandarin and pummelo. Phylogeny dating showed that HY1 (2.0 Mya) and HY2 (2.18 Mya) had diverged earlier than the split of Citrus clementina and Citrus reticulata, and the split of Citrus grandis and Citrus maxima, respectively. We observed clear chromosomal recombination on chr8 and chr9 in HY1, which may have occurred after the ancestral hybridization. Further transcriptome analyses in 6 tissues revealed a strong allelic dominance of HY2 over HY1 in root tissue and moderately in stem, leaf, flower, and fruits. KEGG enrichment analyses revealed that genes related to antioxidants biosynthesis and lipid metabolisms were most significantly affected by allelic imbalance. This first report of allelic imbalance in citrus species support Huyou as an interesting model to investigate genome evolution following distant hybridization.

evolutionary biology↗
bioRxiv2025

Comparative Analysis of 3D Culture Methodologies in Prostate Cancer Cells

Three-dimensional (3D) cell culture models are increasingly utilized in cancer research to better replicate in vivo tumor microenvironments. This study examines the effects of different 3D scaffolding materials, including Matrigel, GelTrex, and the plant-based GrowDex, on prostate cancer cell lines, with a particular emphasis on neuroendocrine prostate cancer (NEPC). Four cell lines (LNCaP, LASCPC-01, PC-3, and KUCaP13) were cultured in these scaffolds to evaluate spheroid formation, cell viability, and gene expression. The results revealed that while all scaffolds supported cell viability, spheroid formation varied significantly: Matrigel promoted the most robust spheroids, especially for LASCPC-01, whereas GrowDex exhibited limitations for certain cell lines. Gene expression analysis indicated a consistent reduction in androgen receptor (AR) expression in LNCaP cells across all scaffolds, suggesting a potential shift towards a neuroendocrine phenotype. However, the expression of neuroendocrine markers varied depending on the scaffold and culture method, with the mini-domes method in Matrigel leading to decreased expression of both castration-resistant prostate cancer (CRPC) and NEPC markers. These findings highlight the scaffold-dependent variability in 3D culture outcomes and emphasize the need for standardized methodologies to ensure consistency and relevance in prostate cancer research. HighlightsO_LIComparing of Matrigel, GelTrex, and GrowDex in 3D prostate cancer culture C_LIO_LIIdentifying scaffold-dependent spheroid formation and gene expression shifts C_LIO_LIObserving consistent AR reduction and variable neuroendocrine marker changes C_LIO_LIHighlighting the need for standard 3D culture methods in cancer studies C_LI

cancer biology↗