Biology subjects

Mei, Q.

Publications and source records attributed to Mei, Q..

9 recordsLinked to original sources

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗

bioRxiv2026

PEC: a robust algorithm to reconcile pedigree and SNP-chip data on the basis of LD block, haplotype information, and Mendelian conflicts

MotivationPedigree errors frequently occur in livestock populations due to long-term manual record-keeping, which reduces the efficiency of breeding programs. Although several pedigree correction methods exist, their practical application is often limited by complicated procedures, high computational cost, and insufficient accuracy. Therefore, an effective and efficient solution for pedigree error correction is needed. ResultsWe developed a new algorithm and software, PEC, to accurately and efficiently correct pedigree errors. The method matches haplotype fragments between candidate parents and offspring using estimated linkage disequilibrium patterns and subsequently checks for Mendelian conflicts to adjust the pedigree. Using simulated pig datasets, we compared PEC against SeekParentF90 and AlphaAssign in terms of accuracy, memory usage, and computation time. PEC demonstrated superior performance across all metrics. Furthermore, application of single-step genomic best linear unbiased prediction (ssGBLUP) in a real pig population showed that PEC corrected pedigrees significantly improved the accuracy and unbiasedness of genomic evaluations, highlighting the importance of pedigree error correction. AvailabilityThe PEC software is freely available at https://github.com/TXiang-lab/JPEC.

genetics↗

bioRxiv2024

PFKP regulates AXL-MET oncogenic and metabolic pathways in lung cancer

ObjectivePFKP (Phosphofructokinase, Platelet Type isoform), as an essential metabolic enzyme, contributes to the high glycolysis rates seen in cancers, while its role in oncogenic pathways, especially from a non-metabolic aspect, is not fully understood. MethodsHere we performed a comprehensive analysis of published RNA-seq, microarray data, and immunohistochemistry of tissue microarray to evaluate the significance of PFKP expression in non-small cell lung cancer (NSCLC). Functionally, we tested the cell proliferation, colony formation, invasion, and migration upon PFKP knockdown in lung cancer cells. Mechanistically, we performed RNA-seq, DIA-mass spectrum, western blot, and qPCR to probe the change of cell signaling pathways upon PFKP silencing. Co-immunoprecipitation and mass spectrum were used to uncover potential PFKP interacting proteins. ResultsWe found that PFKP was highly expressed in NSCLC and was related to poor patient survival. Knockdown of PFKP significantly inhibited cell proliferation, colony formation, invasion, and migration of NSCLC cells. Mechanistically, we found that PFKP can directly bind with AXL and promote its phosphorylation at Y779, thus activating the AXL signaling pathway and promoting MET phosphorylation. In addition, several glycolysis, glutaminolysis, and TCA cycle proteins were downregulated following PFKP silencing. ConclusionsThese data demonstrate that PFKP, beyond its known role in glycolysis, also has a distinct non-metabolic function in affecting lung cancer progression by directly interacting with the AXL-MET axis, thus indicating a potential therapeutic target for lung cancer.

cancer biology↗

bioRxiv2023

Clustering scRNA-seq data via qualitative and quantitative analysis

Single-cell RNA sequencing (scRNA-seq) technologies have been driving the development of algorithms of clustering heterogeneous cells. We introduce a novel clustering algorithm scQA, which can effectively and efficiently recognize different cell types via qualitative and quantitative analysis. It iteratively extracts quasi-trend-preserved genes to conform a consensus by representing expression patterns with dropouts qualitatively and quantitatively, and, then automatically clusters cells using a new label propagation strategy without specifying the number of cell types in advance. Validated on 20 public scRNA-seq datasets, scQA consistently outperformed 9 salient tools in both accuracy and efficiency across 16 out of 20 datasets tested, and ranked top 2 or 3 across the other 4 datasets. Furthermore, we demonstrate scQA can extract informative genes in both perspectives of biology and data wise by performing consensus, allowing genes used for landmark construction multiple characteristics, which is essential for clustering cells accurately. Overall, scQA could be a useful tool for discovery of cell types that can be integrated into general scRNA-seq analyses.

bioinformatics↗

bioRxiv2022

Interdependent progression of bidirectional sister replisomes in E. coli

Bidirectional DNA replication complexes initiated from the same origin remain colocalized in a factory configuration for part or all of their lifetimes. However, there is little evidence that sister replisomes are functionally interdependent, and the consequence of factory replication is unknown. Here, we investigated the functional relationship between sister replisomes in E. coli, which naturally exhibits both factory and solitary configurations in the same replication cycle. Using an inducible transcription factor roadblocking system, we found that blocking one replisome caused a significant decrease in overall progression and velocity of the sister replisome. Remarkably, progression was impaired only if the block occurred while sister replisomes were still in a factory configuration - blocking one fork had no significant effect on the other replisome when sister replisomes were physically separate. Disruption of factory replication also led to increased fork stalling and requirement of fork restart mechanisms. These results suggest that physical association between sister replisomes is important for establishing an efficient and uninterrupted replication program. We discuss the implications of our findings on mechanisms of replication factory structure and function, and cellular strategies of replicating problematic DNA such as highly transcribed segments.

genetics↗

bioRxiv2021

blupADC: An R package and shiny toolkit for comprehensive genetic data analysis in animal and plant breeding

SummaryGenetic analysis is a systematic and complex procedure in animal and plant breeding. With fast development of high-throughput genotyping techniques and algorithms, animal and plant breeding has entered into a genomic era. However, there is a lack of software, which can be used to process comprehensive genetic analyses, in the routine animal and plant breeding program. To make the whole genetic analysis in animal and plant breeding straightforward, we developed a powerful, robust and fast R package that includes genomic data format conversion, genomic data quality control and genotype imputation, breed composition analysis, pedigree tracing, analysis and visualization, pedigree-based and genomic-based relationship matrix construction, and genomic evaluation. In addition, to simplify the application of this package, we also developed a shiny toolkit for users. Availability and implementationblupADC is developed primarily in R with core functions written in C++. The development version is maintained at https://github.com/TXiang-lab/blupADC. Supplementary informationSupplementary data are available online

genetics↗

bioRxiv2021

Clustering single-cell RNA-seq data by rank constrained similarity learning

MotivationRecent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved. ResultsHere, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearmans rank correlations of a cells expression vector with those of other cells to measure its global similarity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics. AvailabilityThe RCSL algorithm is implemented in R and can be freely downloaded at https://github.com/QinglinMei/RCSL. Contactguojunsdu@gmail.com, zcsu@uncc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Gamblers: an Antibiotic-induced Evolvable Cell Subpopulation Differentiated by Reactive-oxygen-induced General Stress Response

Antibiotics can induce mutations that cause antibiotic resistance. Yet, despite their importance, mechanisms of antibiotic-promoted mutagenesis remain elusive. We report that the fluoroquinolone antibiotic ciprofloxacin (cipro) induces mutations that cause drug resistance by triggering differentiation of a mutant-generating cell subpopulation, using reactive oxygen species (ROS) to signal the sigma-S ({sigma}S) general-stress response. Cipro-generated DNA breaks activate the SOS DNA-damage response and error-prone DNA polymerases in all cells. However, mutagenesis is restricted to a cell subpopulation in which electron transfer and SOS induce ROS, which activate the {sigma}S response, allowing mutagenesis during DNA-break repair. When sorted, this small {sigma}S-response-\"on\" subpopulation produces most antibiotic cross-resistant mutants. An FDA-approved drug prevents {sigma}S induction specifically inhibiting antibiotic-promoted mutagenesis. Furthermore, SOS-inhibited cell division, causing multi-chromosome cells, is required for mutagenesis. The data support a model in which within-cell chromosome cooperation together with development of a \"gambler\" cell subpopulation promote resistance evolution without risking most cells.

microbiology↗

bioRxiv2018

Bacteria-to-human protein networks reveal origins of endogenous DNA damage

DNA damage provokes mutations and cancer, and results from external carcinogens or endogenous cellular processes. Yet, the intrinsic instigators of DNA damage are poorly understood. Here we identify proteins that promote endogenous DNA damage when overproduced: the DNA-damaging proteins (DDPs). We discover a large network of DDPs in Escherichia coli and deconvolute them into six DNA-damage-causing function clusters, demonstrating DDP mechanisms in three: reactive-oxygen increase by transmembrane transporters, chromosome loss by replisome binding, and replication stalling by transcription factors. Their 284 human homologs are over-represented among known cancer drivers, and their expression in tumors predicts heavy mutagenesis and poor prognosis. Half of tested human homologs, when overproduced in human cells, promote DNA damage and mutation, with DNA-damaging mechanisms like those in E. coli. Together, our work reveals DDP networks that provoke endogenous DNA damage and may indicate functions of many human known and newly implicated cancer-promoting proteins.

cancer biology↗