New research reveals gut microbial markers common to multiple diseases, paving the way for more accurate disease prediction and personalized treatment.
Study: Population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases. Image credit: Kateryna Kon / Shutterstock
A study published in the Nature Portfolio journal Biofilms and Microbiome found significant differences in gut microbial composition across common human diseases in Chinese people.
background
The gut microbiota plays an important role in human health and disease. Due to its remarkable plasticity, the overall composition of the gut microbiota often remains stable even after rapid changes in physiological systems. However, chronic, long-term exposure to stressors can lead to gut microbiome dysbiosis, an imbalance in the gut microbial composition that promotes the selection of harmful pathogenic microorganisms over beneficial microorganisms. There is.
Abnormalities in the gut microbiota have been observed in several diseases, including autoimmune diseases, cardiometabolic diseases, infectious diseases, psychiatric disorders, and cancer. However, the exact causes of microbial dysbiosis in various disease states remain unclear due to the lack of a unified reference database, low accuracy of bacterial species annotation and quantification in high-throughput sequencing datasets, and variability. It is not completely understood due to several challenges such as high experimental data. Analytical methods used in the study.
In this study, scientists reanalyzed publicly available fecal metagenomes from 36 case-control studies to investigate differences in gut microbial composition and diversity between cases and controls in each study. did.
research design
In this study, we analyzed raw sequence data obtained from publicly available metagenomic datasets. This included 6,314 human fecal samples from 36 case-control studies in Chinese subjects. These datasets consisted of 3,728 patients with 28 diseases or unhealthy conditions and 2,586 healthy individuals.
A comparative analysis was performed to investigate differences in the overall microbial structure between patients (cases) and healthy individuals (controls). In this study, an integrative meta-analysis was conducted to identify universal microbial signatures across different diseases. Based on the abundance of microbial signatures, an advanced machine learning classifier was established to explore the potential of common gut microbial signatures in predicting disease states.
important findings
Analysis of the gut microbial composition of all samples identified Bacteroidetes and Firmicutes as the most abundant phyla, followed by Proteobacteria and Actinobacteria. Very diverse proportions of these phyla were observed in the analyzed studies, which may be due to differences in geographical location or experimental methods.
At the genus level, Phocaeicola, Bacteroides, Prevotella, Faecalibacterium, Alistipes, and Roseburia were identified as the dominant genera and showed similar distribution patterns in all studies.
Phocaeicola, Bacteroides, Prevotella copri, and several members of Faecalibacterium prausnitzii were the most predominant species in all samples analyzed.
Comparative analysis of case-control studies has shown that different gut microbial structures are associated with most diseases, many of which show decreased microbial richness and diversity, while others show decreased microbial richness and diversity compared to controls. It became clear that some showed an increase in sex.
Further analysis revealed that medical conditions have a significant impact on the overall gut microbial composition. Diseases such as Crohn’s disease, polycystic ovary syndrome, atrial fibrillation, Graves’ disease, systemic lupus erythematosus, liver cirrhosis, pulmonary tuberculosis, or coronavirus disease 2019 (COVID-19) show the greatest changes in microbial composition. Ta.
Characteristics of intestinal microorganisms
An integrated meta-analysis identified 277 microorganisms that differed in relative abundance between cases and controls. A total of 194 species were shown to be more abundant in healthy individuals compared to patients. Similarly, 83 species were more abundant in patients compared to healthy individuals.
Significant differences in taxonomic distribution at the phylum and genus level were observed between disease-rich and control-rich microbial species. Notably, patients showed a significant increase in opportunistic pathogens and a decrease in beneficial microorganisms.
The microbial species that showed decreased abundance in patients are the most important short-chain fatty acid (SCFA) producers in the human intestine. This indicates that reduced SCFA biosynthetic capacity is a common feature of many human diseases.
At the genus level, the analysis identified 107 genera that differed in relative abundance between cases and controls. Of these genera, 73 were more abundant in controls and 34 were more abundant in patients. Similar to the species-level analysis, a marked decrease in beneficial SCFA-producing genera and an increase in opportunistic genera was observed in patients.
Characteristics of gut microbes to predict disease
Analysis based on machine learning algorithms showed that these universal gut microbial signatures can accurately distinguish between patients and healthy individuals with high accuracy. This model can also differentiate between high-risk patients and healthy individuals.
To further validate the predictive model, this study analyzed fecal metagenomes from three independent public cohorts including bipolar depression, colorectal cancer, and end-stage renal disease. The researchers found that although the predictive model had a relatively low ability to detect mental illness, it was particularly effective at differentiating between high-risk conditions.
Significance of research
This study identifies universal gut microbial signatures for human diseases common in the Chinese population. Overall gut microbial structure was strongly associated with common human diseases. These findings suggest that common signatures of disease-associated gut microbes can accurately classify multiple disease states from healthy states, providing potential for future diagnostic tools and personalized interventions. is open.
Reference magazines:
Sun, W., Zhang, Y., Guo, R., Sha, S., Chen, C., Ullah, H., Zhang, Y., Ma, J., You, W., Meng, J., Lv, Q., Cheng, L., Fan, S., Li, R., Mu, X., Li, S., Yan, Q. (2024). Population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases. Npj Biofilms and Microbiome, 10(1), 1-10. DOI: 10.1038/s41522-024-00567-9, https://www.nature.com/articles/s41522-024-00567-9
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