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Yuan Xu

Publications and source records attributed to Yuan Xu.

6 recordsLinked to original sources
PubMed2026

Whole-Genome Sequencing Reveals Population Structure, Genetic Diversity, and Selection Signatures in Kazakh Dromedary and Bactrian Camels.

Understanding the genomic basis of environmental adaptation is essential for the conservation and genetic improvement of domestic camels. In this study, we investigated the population structure, genetic diversity, and genomic variation potentially associated with environmental adaptation of Kazakh dromedary and Bactrian camels using whole-genome sequencing. Whole-genome sequencing data were generated for Kazakh camels (15 dromedaries and 16 Bactrian camels) and integrated with 131 publicly available genomes representing camel populations from the Arabian Peninsula, Iran, Xinjiang, Inner Mongolia, and Mongolian wild camels. Population structure, genetic diversity, and genome-wide selection were evaluated using principal component analysis, ADMIXTURE, nucleotide diversity, linkage disequilibrium, runs of homozygosity, genomic inbreeding (FROH), and selection scans based on FST, θπ ratio, and XP-EHH. Population genomic analyses revealed clear differentiation between dromedary and Bactrian camels, whereas Kazakh camel populations exhibited higher nucleotide diversity (θπ = 1.307-1.551 × 10-3), and lower genomic inbreeding (median FROH: 0.037-0.056) than Arabian populations. Genome-wide selection analyses identified MC4R as the prominent candidate gene in Kazakh dromedaries and RYR1 as a prominent candidate gene in Kazakh Bactrian camels. Functional enrichment analyses highlighted pathways related to energy metabolism, thermogenesis, calcium signaling, skeletal muscle function, mitochondrial activity, and oxidative stress response. These findings provide new insights into genomic variation potentially associated with environmental adaptation in Kazakh camels and offer valuable genomic resources for future conservation, breeding, and evolutionary studies.

MC4R↗
PubMed2026

The Evolutionary Significance of Leaf Nodulation: Evidence from Ardisia and Its Relatives (Primulaceae: Myrsinoideae).

Interactions between plants and microorganisms have long been a central topic in biological research. Bacterial symbiosis on leaf surfaces represents a distinctive and mutually beneficial system within the phyllosphere microbiome. Leaf nodules are the visible manifestation of the symbiosis and confer ecological advantages to host plants by enhancing host resistance against pathogens and herbivores. It has been hypothesized that these advantages promote higher diversification rates in host lineages, but this remains uncertain. Ardisia subg. Crispardisia and its close relatives (Amblyanthopsis and Amblyanthus) within Primulaceae are typical plant groups with leaf nodule symbiosis, making them an ideal system for testing this hypothesis. In this study, we conducted extensive sampling of "Ardisioids" (Ardisia and its allies) and reconstructed their phylogenetic relationships and evolutionary history using plastid genomes and nuclear datasets (i.e., nuclear ribosomal DNA (nrDNA) and genome-wide single nucleotide polymorphisms (SNPs)). We clarified the phylogenetic positions of several "Ardisioids" genera (e.g., Sadiria, Tapeinosperma, Amblyanthus, and Amblyanthopsis) and multiple subgenera within Ardisia. We further detected a rapid radiation during the middle Miocene in Ardisia and its allies. Notably, we found that the leaf-nodulated clade appears to have originated during this period, approximately 11-8 Ma. BAMM (Bayesian Analysis of Macroevolutionary Mixtures) analyses revealed elevated diversification rates in leaf-nodulated lineages, while HiSSE (Hidden State Speciation and Extinction) analyses indicated that leaf nodule symbiosis might have increased speciation rates without significantly affecting extinction rates. These results provide strong evidence that leaf nodule symbiosis, together with other abiotic and biotic factors, represents a key evolutionary innovation that has promoted diversification in Ardisia and its close relatives.

diversification rate↗
PubMed2026

Neocentromeres fail to maintain DNA methylation boundaries, driving CENP-A drift, instability, and chromosome missegregation.

Centromere identity is specified by CENP-A, a histone H3 variant that epigenetically defines centromere position. How CENP-A is maintained at one location in rapidly evolving centromeric DNA is unknown. Using single-cell-derived clones of human cell lines, we demonstrate heterogeneity in CENP-A position within cell populations at neocentromeres and a native centromere. CENP-A heterogeneity is accompanied by heterogeneous DNA methylation patterns, with DNA methylation shifting according to CENP-A position. We demonstrate centromere epigenetic plasticity over extended proliferation, with native centromeres maintaining stable DNA methylation boundaries, but neocentromeres exhibiting DNA methylation instability, boundary loss, and increased missegregation. Finally, we show that neocentromeres are more sensitive to DNA methylation inhibition than native centromeres, and that this inhibition is accompanied by expanded CENP-A-enriched domains and increased missegregation. This study supports a role for DNA methylation boundaries in maintaining centromere position, stability, and function and highlights the intrinsic instability of DNA methylation at neocentromeres.

CENP-A↗
PubMed2026

Oncogenic PIK3CA reprograms glutamine metabolism to drive bladder cancer progression.

BACKGROUND: Genomic analysis has revealed that approximately 40% of bladder cancer (BLCA) tumors harbor alterations in the PI3K/AKT pathway, with PIK3CA mutations occurring in 15-25% of cases. PIK3CA, which encodes the catalytic p110α subunit of PI3K, plays a critical role in regulating cell survival, proliferation, and metabolism. However, the metabolic and functional consequences of PIK3CA mutations in BLCA remain poorly defined. METHODS: To investigate the role of PIK3CA mutations in BLCA, we performed targeted sequencing on tumors from patients, identifying recurrent alterations. Using CRISPR/Cas9 knock-in models in SCaBER and UM-UC-3 cell lines, we introduced the PIK3CA E545K mutation to study its effects. We conducted transcriptomic profiling, targeted metabolomics, and stable isotope tracing to assess metabolic reprogramming. Functional assays measured proliferation, mitochondrial complex I activity, and glutaminolysis. Orthotopic xenografts in mice were used to evaluate in vivo tumor growth and metabolism. RESULTS: PIK3CA mutations were present in 20% of cases, consistent with TCGA data. The E545K and E545Q hotspots accounted for 70% of these mutations. PIK3CA E545K strongly activated PI3K/AKT signaling. Transcriptomic analysis revealed enrichment of OXPHOS, fatty acid metabolism, and mTORC1 signaling. Metabolomics indicated changes in TCA cycle metabolites and enhanced reductive carboxylation of glutamine to citrate, driving fatty acid synthesis. Mutant cells showed increased expression of GLS1 and FASN, higher proliferation rates, and elevated mitochondrial complex I activity. In vivo, PIK3CA-mutant xenografts displayed significantly increased tumor growth. CONCLUSION: PIK3CA mutations are frequent drivers of metabolic reprogramming in BLCA, leading to increased glutamine flux, elevated OXPHOS activity, and enhanced fatty acid synthesis, all of which contribute to tumor progression. These findings provide the first comprehensive evidence that PIK3CA-driven metabolic alterations are both biomarkers of aggressive disease and actionable therapeutic targets. The efficacy of PI3Kα inhibition in combination with metabolic targets may support its potential in precision medicine for PIK3CA-mutant BLCA and highlights the value of integrating metabolic biomarkers into treatment strategies for advanced BLCA.

Journal Article↗
PubMed2025

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals↗
PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗