Indian Journal of Microbiology

, Volume 59, Issue 1, pp 90–95 | Cite as

Mining the Core Gut Microbiome from a Sample Indian Population

  • Abhijit S. Kulkarni
  • Shreyas V. Kumbhare
  • Dhiraj. P. Dhotre
  • Yogesh S. ShoucheEmail author
Short Communications


Human gut microbiome studies are increasing at a rapid pace to understand contributions of the prokaryotic life to the innate workings of their eukaryotic host. Majority of studies focus on the pattern of gut microbial diversity in various diseases, however, understanding the core microbiota of healthy individuals presents a unique opportunity to study the microbial fingerprint in population specific studies. Present study was undertaken to determine the core microbiome of a healthy population and its imputed metabolic role. A total of 8990, clone library sequences (> 900 bp) of 16S rRNA gene from fecal samples of 43 individuals were used. The core gut microbiota was computed using QIIME pipeline. Our results show the distinctive predominance of genus Prevotella and the core composition of genera Prevotella, Bacteroides, Roseburia and Megasphaera in the Indian gut. PICRUSt analysis for functional imputation of the microbiome indicates a higher potential of the microbiota for carbohydrate metabolism. The presence of core microbiota may indicate key functions played by these microbes for the human host.


Core microbiome Indian gut microbiome Gut microbiota 



We acknowledge the support of faculty from Department of Microbiology, Modern College of Arts, Science and Commerce, Ganeshkhind, Pune. We extend our thanks to Dr. Shrikant Bhute, Dr. Mangesh Suryawanshi, Dr. Deepak Patil, Dr. Nachiket Marathe, Diptaraj Chaudhari and Sudarshan Shetty for their support. We also acknowledge the support extended by all lab members at the YSS lab and NCMR.

Author’s Contribution

YSS and DD conceptualized the work. AK performed the bioinformatic analysis, compiled the data and wrote the manuscript. YSS, DD and SK gave crucial inputs for analysis and manuscript revision.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

12088_2018_742_MOESM1_ESM.docx (308 kb)
Supplementary material 1 (DOCX 308 kb)


  1. 1.
    Qin J, Li R, Raes J et al (2010) A human gut microbial gene catalog established by metagenomic sequencing. Nature 464:59–65. CrossRefGoogle Scholar
  2. 2.
    Inman M (2011) How bacteria turn fiber into food. PLoS Biol 9:e1001227. CrossRefGoogle Scholar
  3. 3.
    Round JL, Mazmanian SK (2009) The gut microbiome shapes intestinal immune responses during health and disease. Nat Rev Immunol 9:313–323. CrossRefGoogle Scholar
  4. 4.
    Flint JH, Scott PK, Louis P, Duncan HS (2012) The role of the gut microbiota in nutrition and health. Nat Rev Gastroenterol Hepatol 9:577–589. CrossRefGoogle Scholar
  5. 5.
    Hooper LV, Littman DR, Macpherson AJ (2012) Interactions between the microbiota and the immune system. Science 336:1268–1273. CrossRefGoogle Scholar
  6. 6.
    Holzer P, Farzi A (2014) Neuropeptides and the microbiota-gut-brain axis. Adv Exp Med Biol 817:195–219. CrossRefGoogle Scholar
  7. 7.
    Carabotti M, Scirocco A, Maselli MA, Severi C (2015) The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann Gastroenterol 28:203–209Google Scholar
  8. 8.
    Cummings JH, MacFarlane GT (1997) Role of intestinal bacteria in nutrient metabolism. JPEN J Parenter Enter Nutr 21:357–365. CrossRefGoogle Scholar
  9. 9.
    Björkstén B et al (2001) Allergy development and the intestinal microflora during the first year of life. J Allergy Clin Immunol 108:516–520. CrossRefGoogle Scholar
  10. 10.
    Guarner F, Malagelada J (2003) Gut flora in health and disease. Lancet 361:512–519. CrossRefGoogle Scholar
  11. 11.
    Sears CL (2005) A dynamic partnership: celebrating our gut flora. Anaerobe 11:247–251. CrossRefGoogle Scholar
  12. 12.
    Steinhoff U (2005) Who controls the crowd? New findings and old questions about the intestinal microflora. Immunol Lett 99:12–16. CrossRefGoogle Scholar
  13. 13.
    Purohit HJ (2018) Gut-bioreactor and human health in future. Indian J Microbiol 58:3–7. CrossRefGoogle Scholar
  14. 14.
    Sood U, Bajaj A, Kumar R, Khurana S, Kalia VC (2018) Infection and microbiome: impact of tuberculosis on human gut microbiome of Indian cohort. Indian J Microbiol 58:123–125. CrossRefGoogle Scholar
  15. 15.
    Turnbaugh PJ, Hamady M, Yatsunenko T et al (2009) A core gut microbiome in obese and lean twins. Nature 457:480–484. CrossRefGoogle Scholar
  16. 16.
    Suryavanshi MV, Bhute SS, Jadhav SD, Bhatia MS, Gune RP, Shouche YS (2016) Hyperoxaluria leads to dysbiosis and drives selective enrichment of oxalate metabolizing bacterial species in recurrent kidney stone endures. Sci Rep 6:34712. CrossRefGoogle Scholar
  17. 17.
    Patil DP, Dhotre DP, Chavan SG, Sultan A, Jain DS, Lanjekar VB, Gangawani J, Shah PS, Todkar JS, Shah S, Ranade DR, Patole MS, Shouche YS (2012) Molecular analysis of gut microbiota in obesity among Indian individuals. J Biosci 37:647–657. CrossRefGoogle Scholar
  18. 18.
    Marathe N, Shetty S, Lanjekar V, Ranade D, Shouche Y (2012) Changes in human gut flora with age: an Indian familial study. BMC Microbiol 12:222. CrossRefGoogle Scholar
  19. 19.
    Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. CrossRefGoogle Scholar
  20. 20.
    Langille MGI, Zaneveld J, Caporaso JG et al (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814–821. CrossRefGoogle Scholar
  21. 21.
    Wattam AR, Davis JJ, Assaf R et al (2017) Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45:D535–D542. CrossRefGoogle Scholar
  22. 22.
    Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739. CrossRefGoogle Scholar
  23. 23.
    Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680CrossRefGoogle Scholar
  24. 24.
    Sinha R, Anderson DE, McDonald SS, Greenwald P (2003) Cancer risk and diet in India. J Postgrad Med 49:222–228Google Scholar
  25. 25.
    Bhute S, Pande P, Shetty SA et al (2016) Molecular characterization and meta-analysis of gut microbial communities illustrate enrichment of Prevotella and Megasphaera in Indian subjects. Front Microbiol 7:660. CrossRefGoogle Scholar

Copyright information

© Association of Microbiologists of India 2018

Authors and Affiliations

  • Abhijit S. Kulkarni
    • 1
  • Shreyas V. Kumbhare
    • 1
  • Dhiraj. P. Dhotre
    • 2
  • Yogesh S. Shouche
    • 1
    • 2
    Email author
  1. 1.National Centre for Cell Science (NCCS)University of Pune CampusGaneshkhind, PuneIndia
  2. 2.National Centre for Microbial Resource (NCMR), National Centre for Cell Science (NCCS)PuneIndia

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