Biology subjects

yang, g.

Publications and source records attributed to yang, g..

3 recordsLinked to original sources

bioRxiv2022

Programmable acetylation modification of bacterial proteins by a Cas12a-guided acetyltransferase

Protein lysine acetylation (PLA) is a crucial post-translational modification in organisms that regulates a variety of metabolic and physiological activities. Many advances have been made in PLA-related research; however, the quick and accurate identification of causal relationships between specific protein acetylation events and phenotypic outcomes at the proteome level remains unattainable due to the lack of in situ targeted modification techniques. In this study, based on the characteristics of transcription-translation coupling in bacteria, we designed and constructed an in situ targeted protein acetylation (TPA) system fusing the dCas12a protein, guiding element crRNA, and bacterial acetylase At2. Rapid identification of multiple independent protein acetylation and cell phenotypic analyses in gram-negative E. coli and gram-positive C. ljungdahlii demonstrated that TPA is a specific and efficient targeting tool for protein modification studies and engineering.

microbiology↗

bioRxiv2021

Single-cell and single-variant resolution analysis of clonal evolution in human liver cancer

Genetic heterogeneity of tumor is closely related to clonal evolution, phenotypic diversity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.

genomics↗

bioRxiv2020

De novo reconstruction of microbial haplotypes by integrating statistical and physical linkage

DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or haplotypes. However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.

bioinformatics↗