Biology subjects

Li, G. X.

Publications and source records attributed to Li, G. X..

8 recordsLinked to original sources

bioRxiv2025

Acute Pericentral Liver Injury Induces a Novel Transient Hepatocyte Population

The liver exhibits robust regenerative capacity in response to injury, a property shaped by its essential metabolic and detoxification roles. While the mechanisms of regeneration following chronic liver injury have been extensively studied, the response during the early phases of acute injury remains poorly understood. Here, we employed a pericentral model of acute liver injury using carbon tetrachloride (CCl4) to investigate the initial hepatocyte response to damage. Through integration of single-nucleus multiome-sequencing, lineage tracing, spatial transcriptomics, and immunostaining, we identified a novel, transient population of damage-responsive SOX9+ pericentral hepatocytes. This population does not exhibit progenitor-like behavior but instead potentially mediates key aspects of early tissue repair. Specifically, SOX9+ pericentral hepatocytes engage in interactions with hepatic stellate cells and display migratory features associated with wound closure. Moreover, these cells exhibit an immune-related transcriptional signature and colocalize with liver macrophages at injury sites, suggesting a role in modulating immune responses. Our findings highlight a previously underappreciated function of SOX9 as a stress-responsive regulator of hepatocyte behavior and intercellular crosstalk during the early stages of acute liver injury.

cell biology↗

bioRxiv2024

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

bioinformatics↗

bioRxiv2023

Unbiased phosphoproteomics analysis unveils modulation of insulin signaling by extramitotic CDK1 kinase activity in human myotubes

Sensitivity and plasticity of insulin signaling and glucose uptake in skeletal muscle depends on determinants such as genetic variation and obesity. We collected muscle biopsies and isolated myoblasts from a multi-ethnic cohort of lean South Asians (N=10), lean Chinese (N=10), and obese Chinese (N=10), and analysed the proteome and phosphoproteome dynamics in terminally differentiated myotubes after a low-dose insulin stimulation (10nM at 0, 5, 30 min). The myotubes initially responded with increased abundance and phosphorylation level changes along the PI3K/AKT/mTOR axis, decreased abundance of translation initiation factors, and increased phosphorylation levels on proteins involved in mRNA processing at 5 min. After the acute response, protein abundance returned to baseline at 30 min, while phosphorylation changes persisted in proteins including AKT, RPS6 and AS160 (TBC1D4). A joint kinase-substrate statistical analysis revealed that protein abundance changes of AKT, PAK1 and CDK1 showed concordant phosphorylation changes in their respective substrates upon insulin stimulation. We also observed increased phosphorylation of some substrates uniquely in each group, particularly the substrates of CDKs showing stronger changes in South Asians than in Chinese. Pharmacological inhibition and siRNA knockdown of CDK1, a non-myogenic kinase, in terminally differentiated myotubes reduced glucose uptake and desensitized several phosphorylation-mediated signaling on protein translation initiation factors, IRS1, and AS160. Our data suggest that basal extramitotic activity of CDK1 is required for PI3K/AKT/mTORC1 signaling cascade and glucose uptake in insulin-stimulated myotubes. The data also provide a rich resource for studying the role of other kinases in the mechanism of insulin resistance in human myotubes.

physiology↗

bioRxiv2023

HLAProphet: Personalized allele-level quantification of the HLA proteins

Loss of HLA expression in tumor cells is a commonly observed phenotype that is known to be associated with T-cell evasion. Proteogenomic characterizations of the molecular mechanisms underpinning this loss of HLA expression are hindered by the polymorphic nature of the HLA proteins, with most individuals having germline HLA sequences that are highly divergent from the sequences found in standard reference databases. To address this issue, we have developed HLAProphet, an algorithm that utilizes HLA types from paired DNA sequencing data to provide personalized allele-level quantification of the HLA proteins from TMT mass spectrometry data. We show that HLAProphet triples the number of tryptic peptide identifications made by standard reference based approaches, and produces protein expression values that have high concordance with RNA expression and known loss of heterozygosity events.

bioinformatics↗

bioRxiv2022

One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. We have integrated MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data dependent acquisition (DDA), or both data types combined. We compared MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, and in silico library-based DIA-NN and MaxDIA. We demonstrated the fast and sensitive performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

bioinformatics↗

bioRxiv2021

Computationally scalable regression modeling for large-scale clinical omics data with ParProx

Statistical analysis of ultrahigh-dimensional omics scale data has long depended on univariate hypothesis testing. With growing data features and samples, the obvious next step is to establish multivariable association analysis as a routine method for understanding genotype-phenotype associations. Here we present ParProx, a state-of-the-art implementation to optimize overlapping group lasso regression models for time-to-event and classification analysis, guided by biological priors through coordinated variable selection. ParProx not only enables model fitting for ultrahigh-dimensional data within the architecture for parallel or distributed computing, but also allows users to obtain interpretable regression models consistent with known biological relationships among the independent variables, a feature long neglected in statistical modeling of omics data. We demonstrate ParProx using three different omics data sets of moderate to large numbers of variables, where we use genomic regions and pathways to arrive at sparse regression models comprised of biologically related independent variables. ParProx is naturally applicable to a wide range of studies using ultrahigh-dimensional omics data, ranging from genome-wide association analysis to single cell sequencing studies where multivariable modeling is computationally intractable.

bioinformatics↗

bioRxiv2019

Gene-to-Protein-to-Disease: a segmentation-based approach to identify new cancer prognostic signatures of exome sequence variants

Somatic mutations are early drivers of tumorigenesis and tumor progression. However, the mutations typically occur at variable positions across different individuals, resulting in the data being too sparse to test meaningful associations between variants and phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which accumulates variants into new sequence units as the degree of genetic assault on structural or functional units of each protein. The variant frequencies in the sequence units were highly reproducible between two large cancer cohorts. Survival analysis identified 247 sequence units in which somatic mutations had deleterious effects on overall survival, including consensus driver mutations obtained from multiple calling algorithms. By contrast, around 75% of the survival predictive units had been undetected by conventional gene-level analysis. We demonstrate the ability of these signatures to separate patient groups according to overall survival, therefore providing novel prognostic tools for various cancers. GPD also identified sequence units with somatic mutations whose impact on survival was modified by the occupancy of germline variants in the surrounding regions. The findings indicate that a patients genetic predisposition interacts with the effect of somatic mutations on survival outcome in some cancers.

genomics↗

bioRxiv2018

PTMscape: an open source tool to predict generic post-translational modifications and map hotspots of modification crosstalk

While tandem mass spectrometry can now detect post-translational modifications (PTM) at the proteome scale, reported modification sites are often incomplete and include false positives. Computational approaches can complement these datasets by additional predictions, but most available tools are tailored for single modifications and each tool uses different features for prediction. We developed an R package called PTMscape which predicts modifications sites across the proteome based on a unified and comprehensive set of descriptors of the physico-chemical microenvironment of modified sites, with additional downstream analysis modules to test enrichment of individual or pairs of modifications in functional protein regions. PTMscape is generic in the ability to process any major modifications, such as phosphorylation and ubiquitination, while achieving the sensitivity and specificity comparable to single-PTM methods and outperforming other multi-PTM tools. Maintaining generalizability of the framework, we expanded proteome-wide coverage of five major modifications affecting different residues by prediction and performed combinatorial analysis for spatial co-occurrence of pairs of those modifications. This analysis revealed potential modification hotspots and crosstalk among multiple PTMs in key protein domains such as histone, protein kinase, and RNA recognition motifs, spanning various biological processes such as RNA processing, DNA damage response, signal transduction, and regulation of cell cycle. These results provide a proteome-scale analysis of crosstalk among major PTMs and can be easily extended to other modifications.\n\nContactall correspondence should be addressed to hwchoi@nus.edu.sg.

bioinformatics↗