Oscillibacter spp. are strongly associated with stool cholesterol and its derivatives

(A) Cholesterol-to-coprostanol biotransformation pathway.

(B) Spearman correlation coefficients for MSPs in most represented genera, IsmA encoders, and Other (“+”, mean).

(C) Scaled Oscillibacter MSP abundances in FHS samples grouped based on presence/absence of IsmA-encoding MSPs. Samples annotated by corresponding cholesterol, cholestenone, and coprostanol abundances (log10[peak intensity]) and Oscillibacter total abundance.

(D) Stool cholesterol abundance in samples stratified by presence of selected Oscillibacter MSPs (STAR Methods) and IsmA encoders.

(E) Candidate cholesterol derivative metabolic peaks. Seven seed peaks with predicted formula C27H46O, including authentic standard and MS/MS matches, are extended using mass shifts and absolute retention time difference < 0.5.

(F) Targeted MS/MS of predicted peaks yielded 36 formula predictions, 19 MS/MS curated identities, and 6 peaks with standards.

(G) Spearman correlation coefficients between Oscillibacter genus abundance and predicted peaks in ismA+ or ismA− samples shown with fitted regression line (intercept = 0). Black points, cholestenone peaks.

(H) Relationship between Oscillibacter genus abundance and stool cholesterol in ismA+ and ismA− samples with fitted linear regression line.

(I) Mass shift network between LC-MS peaks and Oscillibacter associations for ismA+ and ismA− samples. Each node represents a metabolic peak, and edges represent assumed biotransformations. Colors denote effect size of association with Oscillibacter genus.

(J) Oscillibacter and IsmA encoders were associated with decreased plasma cholesterol in a combinatorial manner.

Credit:DOI:10.1016/j.cell.2024.03.014

Cholesterol is an important lipid in the human body, but an excess of cholesterol can lead to cardiovascular diseases (CVD) such as atherosclerosis. Current research indicates that gut microbiota may play a crucial role in this process. However, the underlying mechanisms remain unclear due to the lack of matched multi-omic data and diagnostic biomarkers. To comprehensively analyze the impact of the gut microbiome on cholesterol metabolism and cardiovascular health, Li et al. conducted metagenomic and metabolomic analyses on fecal samples from 1,429 participants of the Framingham Heart Study. Their goal was to identify microbes and metabolites associated with cardiovascular health markers and to explore relevant microbial pathways, thus providing new insights into the pathogenesis of cardiovascular diseases.

First, the authors conducted metagenomic sequencing on the fecal samples of 1,429 participants to obtain high-throughput microbial gene data. Through comprehensive analysis, they found a significant association between Oscillibacter species and reduced levels of cholesterol in both feces and plasma. Secondly, the researchers used liquid chromatography-mass spectrometry (LC-MS) to perform untargeted metabolomic analysis on 899 matched fecal samples, generating 130,877 LC-MS peaks, which were matched with standard compounds, identifying 568 known metabolites. Using the PROSE model, the study identified and validated key cholesterol-metabolizing enzymes in Oscillibacter species, including IsmA and CgT. Finally, the researchers selected three isolates of Oscillibacter (RJX3711, J115, and RJX3347) for in vitro culture experiments in a cholesterol-supplemented medium. Using a fluorescently labeled cholesterol assay, they observed intracellular fluorescent signals in all Oscillibacter isolates, whereas the control group E. coli did not show any fluorescent signals, indicating that these Oscillibacter strains could actively uptake cholesterol. LC-MS analysis of their metabolic products showed a significant increase in cholestenone, 5-cholesten-3-one, 7α-hydroxycholesterol, and glycosylated cholesterol (e.g., cholesterol α-D-glucoside) after cholesterol supplementation in all three Oscillibacter isolates. Additionally, the researchers added 13C-labeled cholesterol to the culture medium and detected the metabolites using LC-MS. This confirmed that Oscillibacter strains could metabolize cholesterol into labeled cholestenone and glycosylated cholesterol, further validating their cholesterol metabolism capability. Lastly, using a generalized linear model to control for host factors (age, gender, BMI, etc.), the study identified a significant association between Oscillibacter and cholesterol levels. Through elastic net regularization, they further validated the negative correlation between Oscillibacter species and fecal cholesterol concentration. In summary, through multi-omic analysis and experimental validation, the study systematically revealed the critical role of Oscillibacter species in cholesterol metabolism. These findings not only provide new perspectives on the relationship between gut microbiota and cardiovascular health but also offer a theoretical foundation and practical guidance for future clinical applications and related industry development.

Although this study has made significant progress in revealing the role of gut microbiota in cholesterol metabolism, there are still many issues and challenges that need to be addressed. Despite the relatively large sample size, the samples were primarily drawn from the Framingham Heart Study, which may limit their representativeness and diversity. The gut microbiota varies significantly among individuals from different regions, ethnicities, and lifestyles, necessitating validation in more diverse populations. Additionally, the interaction between gut microbiota and the host is bidirectional; while microbes influence the host's metabolism, the host's health status and dietary habits also impact the composition and function of the microbial community. Further research is needed to elucidate the mechanisms of this bidirectional interaction. Therefore, future studies should focus on more diverse samples, stricter causal validation, reproducibility of in vivo experiments, long-term effects, safety assessments, and the exploration of personalized treatment strategies. These efforts will help to further consolidate and expand the findings of this study, providing a more robust scientific foundation for clinical applications and health management.

By learning from this paper about multi-omic analysis methods, the role of gut microbiota in cholesterol metabolism, methods for functional prediction and validation, and experimental design and validation, many innovative ideas and inspirations can be gained. For example, in future research, integrating multi-omic data and developing comprehensive analytical tools and methods to reveal key factors in complex biological systems could be considered. Alternatively, developing products based on probiotics or microbial metabolites for cholesterol management and cardiovascular health improvement could also be explored.

reference：

Li C, Stražar M, Mohamed A M T, et al. Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria[J]. Cell, 2024, 187(8): 1834-1852. e19.