Metagenomic Insights into the Impact of Nutrition on Human Gut Microbiota and Associated Disease Risk

Metagenomic investigation of gut microbiome is a comprehensive and rapid technique for the analysis and diagnosis of numerous diseases. The gut microbiome is an intricate ecosystem, coordinated by the interaction of various microbes and the metabolites produced by them, which helps in developing and sustaining immunity and homeostasis. A healthy gut microbiome is driven by different factors, such as nutrition, lifestyle, etc. The current study examines the association of diet to gut microbiome dysbiosis and its role in various disease conditions. Gut microbiome data was collected from 73 patients and tested at BioAro Inc. lab, using shotgun metagenomics through next generation sequencing. It was then analyzed and compared with data from 20 healthy subjects from HMP database. An in-house bioinformatics pipeline (PanOmiQ) and Pathogen Fast Identifier were utilized for secondary analysis, while tertiary analysis was accomplished using R software. Results showed a higher number of opportunistic pathogen microorganisms in the gut microbiome of subjects consuming a meat diet, as compared to those consuming a plant diet. These opportunistic pathogens included Ruminococcus torques (>3.34%), Ruminococcus gnavus (>2.22%), and Clostridium symbiosum (>1.87%). The study also found a higher relative abundance of these pathogens in cancer patients, as compared to healthy subjects. We also observed a highly significant (p < 0.0001) correlation of a meat diet with obesity in comparison to the subjects on a plant diet and the healthy subjects. Our findings suggest that patients following a plant diet have a lower relative abundance of pathogens that are associated with cancer and obesity. These findings provide critical insight into how we can use shotgun metagenomics to study the composition and diversity of the gut microbiome and the effects of a diet on the gut microbiome and its role in metabolic diseases. This is the first report investigating gut microbiota using shotgun metagenomics, correlating with different diseases and diet followed, which might impact the presence of opportunistic pathogens or keystones species. Additionally, it can provide valuable insights to physicians and dietetic practitioners for providing personalized treatment or customizing a diet plan.

Authors

Institutions

Publication Details

Journal
Microbiology Research
Published
2025-09-01
DOI
https://doi.org/10.3390/microbiolres16090197
Citations
1
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
0.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Metagenomic Insights into the Impact of Nutrition on Human Gut Microbiota and Associated Disease Risk

Manjul Tripathi, Raja Singh, Raman Kapoor, Amrita Kaur et al.
1 citations
Microbiology Research
Gut microbiota and health
0.81
article

Metagenomic Insights into the Impact of Nutrition on Human Gut Microbiota and Associated Disease Risk

Manjul Tripathi, Raja Singh, Raman Kapoor, Amrita Kaur, Rajesh Y. Chaudhari, Anmol Kapoor, Preethi Balasundaram, Sarvesh Vettrivelan, Kirti Dubli
article en
1 citations

Abstract

Metagenomic investigation of gut microbiome is a comprehensive and rapid technique for the analysis and diagnosis of numerous diseases. The gut microbiome is an intricate ecosystem, coordinated by the interaction of various microbes and the metabolites produced by them, which helps in developing and sustaining immunity and homeostasis. A healthy gut microbiome is driven by different factors, such as nutrition, lifestyle, etc. The current study examines the association of diet to gut microbiome dysbiosis and its role in various disease conditions. Gut microbiome data was collected from 73 patients and tested at BioAro Inc. lab, using shotgun metagenomics through next generation sequencing. It was then analyzed and compared with data from 20 healthy subjects from HMP database. An in-house bioinformatics pipeline (PanOmiQ) and Pathogen Fast Identifier were utilized for secondary analysis, while tertiary analysis was accomplished using R software. Results showed a higher number of opportunistic pathogen microorganisms in the gut microbiome of subjects consuming a meat diet, as compared to those consuming a plant diet. These opportunistic pathogens included Ruminococcus torques (>3.34%), Ruminococcus gnavus (>2.22%), and Clostridium symbiosum (>1.87%). The study also found a higher relative abundance of these pathogens in cancer patients, as compared to healthy subjects. We also observed a highly significant (p < 0.0001) correlation of a meat diet with obesity in comparison to the subjects on a plant diet and the healthy subjects. Our findings suggest that patients following a plant diet have a lower relative abundance of pathogens that are associated with cancer and obesity. These findings provide critical insight into how we can use shotgun metagenomics to study the composition and diversity of the gut microbiome and the effects of a diet on the gut microbiome and its role in metabolic diseases. This is the first report investigating gut microbiota using shotgun metagenomics, correlating with different diseases and diet followed, which might impact the presence of opportunistic pathogens or keystones species. Additionally, it can provide valuable insights to physicians and dietetic practitioners for providing personalized treatment or customizing a diet plan.

Microbiology ResearchVol. 16(9)
University of Lethbridge (CA), University of Calgary (CA), BP (Canada) (CA)
Mitacs
Openalex Percentile: Top 24%
Gut microbiota and health
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.