Biology subjects

Zheng, J.-S.

Publications and source records attributed to Zheng, J.-S..

8 recordsLinked to original sources

bioRxiv2026

Gut Roseburia hominis-derived 10-hydroxystearic acid alleviates metabolic disorder by restoring hepatic fatty acid metabolism

Metabolic disorders and health-promoting dietary interventions represent complementary contexts of metabolic impairment and improvement, providing an opportunity to identify gut microbial taxa associated with both disease status and dietary response. Here, we integrated five independent human cohorts spanning metabolic disorders and dietary interventions and identified Roseburia hominis as a gut microbial taxon that consistently decreased in metabolic disorders and increased after dietary interventions. Beyond species abundance, strain-level genetic variation in R. hominis provided additional information for distinguishing metabolic disorder status and stratifying cardiometabolic severity. Integration of phenotype-associated variants with circulating metabolomics prioritized ohyA, encoding an oleate hydratase, as a candidate functional determinant linking R. hominis genetic variation to host metabolic heterogeneity. An ohyA-based genetic burden score was associated with adverse cardiometabolic traits and changed longitudinally during dietary intervention, while circulating metabolites statistically accounted for part of the associations between ohyA variants and host phenotypes. Functional experiments demonstrated that R. hominis OhyA protein converts oleic acid to 10-hydroxystearic acid (10-HSA), whereas an OhyA carrying five prioritized missense substitutions showed markedly impaired enzymatic activity. In high-fat diet-fed mice, 10-HSA attenuated body-weight gain, improved glucose homeostasis and insulin sensitivity, and reduced dyslipidemia and hepatic steatosis. Multi-omics profiling revealed limited remodeling of the gut microbiota but pronounced hepatic transcriptional and metabolic responses, characterized by activation of PPAR-associated lipid-metabolic pathways, altered fatty-acid oxidation and desaturation, and remodeling of hepatic lipid composition. Together, these findings establish a diet-responsive R. hominis-OhyA-10-HSA axis linking microbial variation to host metabolic dysfunction and show that 10-HSA can improve metabolic health in mice.

microbiology↗

bioRxiv2025

Revisiting the Fungal ITS2 Sequencing Data from the Human Microbiome Project

While the Human Microbiome Project (HMP) significantly advanced our understanding of the human microbiome, our revisiting of its fecal ITS2 sequencing data reveals two significant issues. First, technical variations in sequencing or data processing introduce batch effects in downstream analyses. Second, we discovered a mismatch between the reported and actual amplification primers, which requires careful consideration when interpreting, reusing, or referencing HMP ITS2 data and methodology.

bioinformatics↗

bioRxiv2025

Food pairing independently associated with cardiometabolic traits beyond dietary patterns across western and eastern populations

Cardiometabolic health is closely linked to diet. Both specific food choices and dietary indices are commonly used to guide cardiometabolic protection strategies. However, the balance between foods may also significantly impact cardiometabolic health. Here, we explore the balance and imbalance in long-term dietary food intake to quantify food pairing patterns (FPs), providing insights beyond conventional dietary indices and single-food intake frequencies. Using data from our GGMP (n = 6,994) and the NHANES (n = 7,350) cohorts, we observed that long-term food pairing patterns are independent of single food intake frequency and dietary indices. We identified 1,759 and 306 cardiometabolic-related long-term food pairing patterns from two cohorts (FDR < 0.05), respectively. Notably, around 80% of these pairing foods not individually associated with cardiometabolic traits, and food pairing pattern associations with cardiometabolic traits at hyper food group level were consistent across Eastern and Western populations. Besides, mediation analysis revealed that 72.7% of long-term food pairing patterns affected cardiometabolic traits through 31 microbial genera, with Clostridiumsensustricto1 playing a predominant role. Moreover, multi-step mediation analysis found that microbes mediated the impact of long-term food pairing patterns on cardiometabolic traits primarily through their metabolic pathways, such as pyruvate fermentation to propanoate and ergothioneine biosynthesis pathways. Our data suggest that balance between dietary foods is broadly associated with cardiometabolic traits by modulating gut microbial functionalities, in contrast to the lower occurrence when considering individual foods in isolation. These results offer a novel perspective for designing personalized dietary strategies beyond present dietary indices to enhance cardiometabolic health.

microbiology↗

bioRxiv2024

Population-based metaproteomics reveals functional associations between gut microbiota and phenotypes

The association between gut microbiota and physiological disorders underscores the crucial need to understand its largely unexplored biological functions. In this study, we analyzed 86,876 microbial proteins and 1,277 human proteins from 1,967 Chinese stool samples. Our results indicate that metaproteomic data captures the microbial biodiversity of a population more effectively than metagenomic data, displaying greater variability in microbial functions and providing reliable assessments of microbiota stability. We identified crucial functions of core microbes and discovered 10,714 associations between metaproteomic taxon, microbial function, or human proteins with 39 phenotypes, including 1,381 features related to Type 2 Diabetes (T2D). Importantly, Megasphaera elsdeniis conversion of lactate to butyric acid appears to lower blood glucose levels, suggesting a protective mechanism against T2D. Our study underscores the power of population-based metaproteomics to offer unique functional insights into the gut microbiota. Highlights- The first large-scale population-based metaproteomics landscape. - Metaproteomics efficiently maps microbial functions, exposing individual functional variation. - 10,714 associations between metaproteomic taxa, microbial functions, or human proteins with 39 phenotypes. - Megasphaera elsdenii protects against elevated blood glucose levels in the host by degrading lactate to produce butyric acid.

microbiology↗

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↗

bioRxiv2023

Microbiome-based correction of nutrient profiles derived from self-reported dietary assessments

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

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↗