OPINION

Method for quantitative assesment of gut microbiota: a comparative analysis of 16S NGS and qPCR

About authors

1 Centre for Strategic Planning and Management of Biomedical Health Risks of the Federal Medical Biological Agency, Moscow, Russia

2 National Medical Research Center for Therapy and Preventive Medicine of the Ministry of Healthсare of the Russian Federation, Moscow, Russia

Correspondence should be addressed: Olga A. Zlobovskaya
Pogodinskaya, 10, str. 1, Moscow, 119121, Russia; ur.abmfpsc@ayaksvobolZO

About paper

Author contribution: Zlobovskaya OA — concept, literature review, manuscript writing; Kurnosov AS, Sheptulina AF, Glazunova EV — manuscript editing.

Received: 2024-10-15 Accepted: 2024-10-25 Published online: 2024-10-30
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  1. Abellan-Schneyder I, Matchado MS, Reitmeier S, Sommer A, Sewald Z, Baumbach J, et al. Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing. Tringe SG, editor. mSphere. 2021; 6 (1): e01202–20.
  2. Gonzalez JM, Portillo MC, Belda-Ferre P, Mira A. Amplification by PCR Artificially Reduces the Proportion of the Rare Biosphere in Microbial Communities. Gilbert JA, editor. PLoS ONE. 2012; 7 (1): e29973.
  3. Boers SA, Jansen R, Hays JP. Understanding and overcoming the pitfalls and biases of next-generation sequencing (NGS) methods for use in the routine clinical microbiological diagnostic laboratory. Eur J Clin Microbiol Infect Dis. 2019; 38 (6): 1059–70.
  4. Vaginal Microbiome Consortium (additional members), Brooks JP, Edwards DJ, Harwich MD, Rivera MC, Fettweis JM, et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 2015; 15 (1): 66.
  5. Barlow JT, Bogatyrev SR, Ismagilov RF. A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities. Nat Commun. 2020; 11 (1): 2590.
  6. Vandeputte D, Kathagen G, D’hoe K, Vieira-Silva S, VallesColomer M, Sabino J, et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature. 2017; 551 (7681): 507–11.
  7. Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun. 2022; 13 (1): 342.
  8. Johnson JS, Spakowicz DJ, Hong BY, Petersen LM, Demkowicz P, Chen L, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019; 10 (1): 5029.
  9. Rintala A, Pietilä S, Munukka E, Eerola E, Pursiheimo JP, Laiho A, et al. Gut Microbiota Analysis Results Are Highly Dependent on the 16S rRNA Gene Target Region, Whereas the Impact of DNA Extraction Is Minor. J Biomol Tech JBT. 2017; 28 (1): 19–30.
  10. Kameoka S, Motooka D, Watanabe S, Kubo R, Jung N, Midorikawa Y, et al. Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1–V2 and V3–V4 primer sets. BMC Genomics. 2021; 22 (1): 527.
  11. Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, et al. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol. 2015; 6.
  12. Chuang HH, Huang CG, Chou SH, Li HY, Lee CC, Lee LA. Comparative analysis of gut microbiota in children with obstructive sleep apnea: assessing the efficacy of 16S rRNA gene sequencing in metabolic function prediction based on weight status. Front Endocrinol. 2024; 15: 1344152.
  13. Ceccarani C, Severgnini M. A comparison between Greengenes, SILVA, RDP, and NCBI reference databases in four published microbiota datasets. 2023 [cited 2024 Oct 4]. Available from: http://biorxiv.org/lookup/doi/10.1101/2023.04.12.535864.