Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians

Abstract

Advances in technical capabilities for reading complex human microbiomes are leading to an explosion of microbiome research, leading in turn to intense interest among clinicians in applying these techniques to their patients. In this review, we discuss the content of the human microbiome, including intersubject and intrasubject variability, considerations of study design including important confounding factors, and different methods in the laboratory and on the computer to read the microbiome and its resulting gene products and metabolites. We highlight several common pitfalls for clinicians, including the expectation that an individual's microbiome will be stable, that diet can induce rapid changes that are large compared with the differences among subjects, that everyone has essentially the same core stool microbiome, and that different laboratory and computational methods will yield essentially the same results. We also highlight the current limitations and future promise of these techniques, with the expectation that an understanding of these considerations will help accelerate the path toward routine clinical application of these techniques developed in research settings.

Cognition and hospitalizations are linked with salivary and faecal microbiota in cirrhosis cohorts from the USA and Mexico

“…Gut and salivary microbiota are affected by diet, ethnicity, socio‐economic status and disease conditions 1,2 . These can potentially alter the progression of cirrhosis and its complications such as hepatic encephalopathy among different populations 3‐6 .…”

Soluble Fibers Improve Metabolic Syndrome but May Cause Liver Disease and Hepatocellular Carcinoma

“…(6) Also, the total microbial genome is significantly greater than the human genome, and the function of most bacterial genes is not known. (7)(8)(9) This certainly provides the microbiome with the potential to generate metabolic intermediates which may have pathological effects, and changes in the metabolome after manipulation of the microbiome are recognized. (2) Detracting from the hypothesis is the fact that analysis of large data sets inevitably results in some positive associations, and the findings in this case are certainly intriguing and will need to be robustly verified experimentally as well as in diverse and independent cohorts of patients with biopsy-proven NAFLD.…”

Cost‐effectiveness of integrating gut microbiota analysis into hospitalisation prediction in cirrhosis

“…Therefore, microbial composition analysis could be potentially used as biosensors to identify a group of patients at high risk of hospitalisations over 90 days . Microbial analysis can be performed using 16srRNA (ribosomal RNA, till genus level) or high‐ and low‐depth metagenomics (till species level) and needs bioinformatics expertise . However, these can be expensive considering the effort of collecting, storing, sequencing and bio‐informatics expertise that is required.…”

“…23 Microbial analysis can be performed using 16srRNA (ribosomal RNA, till genus level) or high-and low-depth metagenomics (till species level) and needs bioinformatics expertise. 10,24 However, these can be expensive considering the effort of collecting, storing, sequencing and bio-informatics expertise that is required. While the additive impact of these microbial changes could improve prognostication, their cost-effectiveness remains unclear.…”