Biology subjects

Chen, Y.-m.

Publications and source records attributed to Chen, Y.-m..

3 recordsLinked to original sources

bioRxiv2023

metaExpertPro: a computational workflow for metaproteomics spectral library construction and data-independent acquisition mass spectrometry data analysis

BackgroundAnalysis of mass spectrometry-based metaproteomic data, in particular large-scale data-independent acquisition MS (DIA-MS) data, remains a computational challenge. Here, we aim to develop a software tool for efficiently constructing spectral libraries and analyzing extensive datasets of DIA-based metaproteomics. ResultsWe present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS (DDA-MS), protein identification and quantification using DIA-MS, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. To enhance accessibility and ease of use, all modules and dependencies are encapsulated within a Docker container. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap-based and PASEF-based DDA and DIA data. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples demonstrated that metaExpertPro quantified an average of 45,000 peptides in a 60-minute diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual False Discovery Rate (FDR) of less than 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and demonstrated a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host. ConclusionsmetaExpertPro presents a robust one-stop computational solution for constructing metaproteomics spectral libraries, analyzing DIA-MS data, and annotating taxonomic as well as functional data.

microbiology↗

bioRxiv2020

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Gut microbiome targets for type 2 diabetes (T2D) prevention among human cohorts have been controversial. Using an interpretable machine learning-based analytic framework, we identified robust human gut microbiome features, with their optimal threshold, in predicting T2D. Based on the results, we constructed a microbiome risk score (MRS), which was consistently associated with T2D across 3 independent Chinese cohorts involving 9111 participants (926 T2D cases). The MRS could also predict future glucose increment, and was correlated with a variety of gut microbiota-derived blood metabolites. Faecal microbiota transplantation from humans to germ-free mice demonstrated a causal role of the identified combination of microbes in the T2D development. We further identified adiposity and dietary factors which could prospectively modulate the MRS, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiota may serve as potential drug targets for T2D in future.

microbiology↗

bioRxiv2019

The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for human complex diseases

There is increasing interest about the interplay between host genetics and gut microbiome on human complex diseases, with prior evidence mainly derived from animal models. In addition, the shared and distinct microbiome features among human complex diseases remain largely unclear. We performed a microbiome genome-wide association study to identify host genetic variants associated with gut microbiome in a Chinese population with 1475 participants. We then conducted bi-directional Mendelian randomization analyses to examine the potential causal associations between gut microbiome and human complex diseases. We found that Saccharibacteria (also known as TM7 phylum) could potentially improve renal function by affecting renal function biomarkers (i.e., creatinine and estimated glomerular filtration rate). In contrast, atrial fibrillation, chronic kidney disease and prostate cancer, as predicted by the host genetics, had potential causal effect on gut microbiome. Further disease-microbiome feature analysis suggested that gut microbiome features revealed novel relationship among human complex diseases. These results suggest that different human complex diseases share common and distinct gut microbiome features, which may help re-shape our understanding about the disease etiology in humans.

microbiology↗