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Refinement of 16S rRNA gene analysis for low biomass biospecimens
High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, h...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144411/ https://www.ncbi.nlm.nih.gov/pubmed/34031485 http://dx.doi.org/10.1038/s41598-021-90226-2 |
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author | Villette, Remy Autaa, Gaelle Hind, Sophie Holm, Johanna B. Moreno-Sabater, Alicia Larsen, Martin |
author_facet | Villette, Remy Autaa, Gaelle Hind, Sophie Holm, Johanna B. Moreno-Sabater, Alicia Larsen, Martin |
author_sort | Villette, Remy |
collection | PubMed |
description | High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (10(8), 10(7), 10(6), 10(5) and 10(4) microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 10(6) cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 10(6) bacteria per sample for robust and reproducible microbiota analysis. |
format | Online Article Text |
id | pubmed-8144411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81444112021-05-25 Refinement of 16S rRNA gene analysis for low biomass biospecimens Villette, Remy Autaa, Gaelle Hind, Sophie Holm, Johanna B. Moreno-Sabater, Alicia Larsen, Martin Sci Rep Article High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (10(8), 10(7), 10(6), 10(5) and 10(4) microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 10(6) cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 10(6) bacteria per sample for robust and reproducible microbiota analysis. Nature Publishing Group UK 2021-05-24 /pmc/articles/PMC8144411/ /pubmed/34031485 http://dx.doi.org/10.1038/s41598-021-90226-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Villette, Remy Autaa, Gaelle Hind, Sophie Holm, Johanna B. Moreno-Sabater, Alicia Larsen, Martin Refinement of 16S rRNA gene analysis for low biomass biospecimens |
title | Refinement of 16S rRNA gene analysis for low biomass biospecimens |
title_full | Refinement of 16S rRNA gene analysis for low biomass biospecimens |
title_fullStr | Refinement of 16S rRNA gene analysis for low biomass biospecimens |
title_full_unstemmed | Refinement of 16S rRNA gene analysis for low biomass biospecimens |
title_short | Refinement of 16S rRNA gene analysis for low biomass biospecimens |
title_sort | refinement of 16s rrna gene analysis for low biomass biospecimens |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144411/ https://www.ncbi.nlm.nih.gov/pubmed/34031485 http://dx.doi.org/10.1038/s41598-021-90226-2 |
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