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

  1. Preethi Balasundaram
  2. Kirti Dubli
  3. Rinku Chaudhari
  4. Sarvesh Vettrivelan
  5. Amrita Kaur
  6. Raman Kapoor
  7. Raja Singh
  8. Anmol Kapoor
  9. Minal Borkar Tripathi

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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.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/20448795.

    Does the introduction explain the objective of the research presented in the preprint? Partly This is what they mention: "we aim to provide insights into the complex interplay between diet, gut microbiome, and human health" I like the aim framing in the sense that it is position in the write context given what has been said in the introduction. But I feel like the "SMART" Framework could help improve the phrasing of the aim to understand how this is going to be measured (which populations are going to be included, how many people, is it only at the taxonomy level or only at the gene/functional level).
    Are the methods well-suited for this research? Somewhat inappropriate 73 participants with different age groups ranging from 10-80 years: maybe a description of the demographics of the participants will establish a framework and also justify why comparing to HMP data is relevant. Were analyses were read-based or contig-based? This is not totally clear. There is no mention of how the cohort was separated into plant and meat consumers. Did you have the metadata for this ? were they food consumption surveys? How was the comparison at the species level between MD and PD calculated ? was this based on presence accounted by read-mapping ? what were the mapping quality and prevalence thresholds to decide those species were only present in one of the groups? BMI calculations and health status could also be included in the metadata description in methods. Maybe also including a table of the sample IDs, age, BMI of participants to also see the number of participants in each category (diet,BMI, health) Why did you use Bray-curtis dissimilarity metric if you had taxonomic information ? Why do IBS markers just show a few datapoints ?
    Are the conclusions supported by the data? Somewhat supported I think that due to some things that weren't clear in the methods, the conclusions might be drawn with unclear evidence.
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear Yes but the way they generate some of them is unclear (see methods feedback)
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly I didn't notice a thorough discussion of the results and next steps are not clearly mentioned.
    Is the preprint likely to advance academic knowledge? Moderately likely
    Would it benefit from language editing? Yes There are some typos and instructions from the journal that are included in the pre-print version.
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? No, it needs a major revision

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.

  2. Version published to 10.3390/microbiolres16090197
  3. Version published to 10.20944/preprints202507.1551.v1