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Yang, X.

Publications and source records attributed to Yang, X..

6 recordsLinked to original sources

bioRxiv2026

β4-integrins safeguard nuclear mechanics to suppress prostate cancer progression

Prostate cancer (PCa) progression is accompanied by profound alterations in cell-extracellular matrix (ECM) adhesion, nuclear architecture and mechanical adaptability, yet the molecular mechanisms linking these processes remain poorly understood. Hemidesmosomes (HDs), formed by 6{beta}4-integrins, anchor epithelial cells to the basement membrane and couple extracellular forces to the intermediate filament (IF) cytoskeleton. Here, we identify a previously unrecognized tumor-suppressive function of {beta}4-integrins in preserving nuclear integrity in prostate epithelial cells. Loss of {beta}4-integrins disrupted the cytokeratin-5 network and its coupling to the nucleus, leading to nuclear softening, lamin remodeling, reduced heterochromatin content and enhanced confined migration. Unexpectedly, proximity-labeling proteomics revealed that {beta}4-integrins engage nuclear pore complex (NPC) components in an 6-independent manner, particularly upon HD disassembly. Selected interactions were validated using proximity ligation and co-immunoprecipitation assays. {beta}4-integrin loss was associated with enlarged nuclear pores and aberrant nucleocytoplasmic transport, including nuclear accumulation of YAP1. Consistent with these findings, reduced {beta}4-integrin expression in a large PCa tissue cohort correlated with altered nuclear morphology, adverse clinicopathological features, metastatic progression, and poor patient survival. Collectively, our study establishes {beta}4-integrins as a critical molecular link between cell-ECM adhesion, nuclear mechanics and genome integrity.

cancer biology↗

bioRxiv2026

AmPair: automating housekeeping-gene primer design for species-level metataxonomics

Amplicon sequencing of the 16S rRNA gene is the most widely used approach for profiling bacterial communities, but its taxonomic resolution is typically limited to the genus level. Many species carry multiple divergent 16S rRNA alleles that overlap across species boundaries, an ambiguity that even full-length, long-read sequencing cannot fully resolve. Shotgun metagenomics achieves species-level resolution but remains costly, particularly when only a single genus is of interest. Amplicon sequencing of rapidly evolving, protein-coding housekeeping genes offers a cost-effective alternative, yet no tool exists to identify suitable primer sets for a given target taxon. Here we present AmPair, a Snakemake pipeline that, given a target genus and one or more candidate housekeeping genes, designs and ranks primer pairs binding conserved regions while flanking a variable region capable of species-level discrimination, and validates them in silico across all available genomes. Using the genus Bacillus and the housekeeping gene tuf as a case study, the primer set recommended by AmPair amplified 99% of 2,392 genomes; only 0.04% carried multiple alleles and none showed inter-species allele overlap, compared with 91.41% and 69.49%, respectively, for the standard 16S rRNA V1-V9 region. Applied to a Bacillus community profiled by Nanopore sequencing, the same primers resolved closely related species. AmPair thus offers a generalizable and accessible route to species-level community profiling.

bioinformatics↗

bioRxiv2026

RECON infers regions of interest from H&E images and reconstructs whole-slide molecular profiles at single-cell resolution

Spatial omics technologies resolve molecular expression and spatial architecture at single-cell resolution, but profiling whole slides remains costly. In practice, only a few regions of interest (ROIs) are profiled, leaving the rest of the tissue unmeasured. S2-omics was the first framework to unify ROI selection with out-of-ROI prediction, but it operates on superpixels rather than individual cells and predicts discrete cell types rather than continuous molecular profiles. Superpixel-based representations do not explicitly preserve cell boundaries, while categorical cell-type labels cannot quantify molecular expression within cells. Here we present RECON, a two-stage framework that performs ROI inference and whole-slide molecular reconstruction at single-cell resolution, predicting both continuous molecular profiles and discrete cell-type labels. In the first stage, RECON extracts morphological and microenvironmental features from individual cells to identify a representative ROI for spatially resolved single-cell molecular profiling. In the second stage, RECON trains deep learning models on molecular measurements acquired within the selected ROI and reconstructs transcriptomic or proteomic profiles for all remaining cells on the slide. Benchmarked against pathologist annotations, RECONs ROI selection outperforms the superpixel-based S2-omics approaches (IoU: 0.75 versus 0.64). For transcriptomics, refining the modeling unit from superpixels to single cells improves per-gene Pearson correlation by 22%. For proteomics, RECON surpasses the current state-of-the-art method, ROSIE, across all 16 markers, with a median per-cell Pearson correlation of 0.91 versus 0.84. Moreover, RECON delineates tumour boundaries and regions with distinct immune-cell densities, and highlights candidate tertiary lymphoid structures. Together, these results demonstrate that RECON enables informative ROI selection and whole-slide molecular reconstruction at single-cell resolution for both spatial transcriptomics and spatial proteomics.

bioinformatics↗

bioRxiv2026

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

genetics↗

bioRxiv2026

Structural basis for catalytic and inhibitory divergence between archaeal and bacterial ammonia monooxygenases

Ammonia oxidation initiates nitrification and is closely linked to microbial N2O production. Ammonia monooxygenase (AMO) catalyzes the first and rate-limiting step of nitrification and is widespread across evolutionarily distinct ammonia-oxidizing archaea (AOA) and bacteria (AOB). The ocean is the largest biome for AOA and AOB, which have distinct ecological niches and markedly different sensitivities to nitrification inhibitors. However, the lack of archaeal AMO structures and inhibitor-bound AMO complexes has hindered mechanistic understanding of the architectural, catalytic, and inhibitory divergence between these two enzyme systems. Here, we report high-resolution cryo-electron microscopy (cryo-EM) structures of marine archaeal AMO captured in active and inactivated states within its native membrane environment, together with inhibitor-bound structures of estuarine bacterial AMO. Archaeal AMO forms an unexpected cup-shaped homotrimer composed of eight subunits per protomer and exhibits substantial architectural divergence from bacterial AMO. Integrated structural, biochemical, kinetic, and computational analyses reveal distinct periplasmic architectures, copper-center organization, and hydrophobic channels between archaeal and bacterial AMOs for ammonium acquisition, catalysis and inhibitor response. These findings provide a structural and mechanistic framework for understanding how archaeal and bacterial AMOs have diverged to distinct ammonia-oxidizing strategies and inhibitor susceptibilities across environmentally important ammonia oxidizers.

molecular biology↗

bioRxiv2026

Single-Cell Profiling of Dynamic Epicardial Cell States During Myocardial Infarction

Background: The epicardium is reactivated after myocardial infarction (MI); however, the gene expression profiles of post-MI adult epicardial subpopulations remain incompletely defined. Methods: Single-cell RNA sequencing was performed on lineage-traced Wt1+ epicardial cells from Wt1CreERT2/+; R26tdT/+; PdgfranGFP/+ adult mice after sham surgery or at 7 and 14 days after permanent artery ligation to induce MI. Immunostaining was performed on Wt1-lineage-traced cardiac tissue to validate spatial expression after ischemic injury. Results: Unbiased clustering identified nine transcriptionally distinct epicardial populations, encompassing mesothelial, fibroblast/mesenchymal, transitional, and proliferative phenotypes. Fibroblast-like epicardial cells (Wt1+/Pdgfra+) showed time-dependent expression profiles associated with upregulation of epithelial-to-mesenchymal transition (EMT) and extracellular matrix (ECM) gene programs. At 7 days post-MI, there was notable enrichment of genes related to chemokines and Wnt components. By 14 days post-MI, the expression profile shifted toward immune regulation. In contrast, a Wt1high/Msln+ population showed minimal upregulation of EMT gene programs but enhanced paracrine signaling related to wound healing and semaphorins, suggesting reactivation of reparative and angiogenic functions akin to those of the epicardium during embryonic development. Immunostaining and in situ hybridization fluorescence analyses validated laminar epicardial cell placement after MI, comprising a surface Msln+ sheet, an overlapping Wt1-lineage band, and a subadjacent PDGFR+ and Periostin+ compartment that expands 7-14 days after MI and regresses by day 28 post-ischemia. Conclusions: Our data define epicardial gene programs in which a signaling epithelial cell surface overlays an effector mesenchymal cell stroma to coordinate angiogenesis, leukocyte recruitment, and ECM remodeling. This study presents the first integrated single-cell atlas of epicardial-derived cells across multiple post-ischemic timepoints, offering new insights into their reparative potential and dynamic signaling diversity in the injured adult heart.

molecular biology↗