Refinement of 16S rRNA gene analysis for low biomass biospecimens

Abstract 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 amplifi...

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Autores principales:	Remy Villette, Gaelle Autaa, Sophie Hind, Johanna B. Holm, Alicia Moreno-Sabater, Martin Larsen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1818d4b5788f4d449b131b0ed8fa2f39
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spelling	oai:doaj.org-article:1818d4b5788f4d449b131b0ed8fa2f392021-12-02T16:53:19ZRefinement of 16S rRNA gene analysis for low biomass biospecimens10.1038/s41598-021-90226-22045-2322https://doaj.org/article/1818d4b5788f4d449b131b0ed8fa2f392021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90226-2https://doaj.org/toc/2045-2322Abstract 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 (108, 107, 106, 105 and 104 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 106 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 106 bacteria per sample for robust and reproducible microbiota analysis.Remy VilletteGaelle AutaaSophie HindJohanna B. HolmAlicia Moreno-SabaterMartin LarsenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Remy Villette Gaelle Autaa Sophie Hind Johanna B. Holm Alicia Moreno-Sabater Martin Larsen Refinement of 16S rRNA gene analysis for low biomass biospecimens
description	Abstract 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 (108, 107, 106, 105 and 104 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 106 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 106 bacteria per sample for robust and reproducible microbiota analysis.
format	article
author	Remy Villette Gaelle Autaa Sophie Hind Johanna B. Holm Alicia Moreno-Sabater Martin Larsen
author_facet	Remy Villette Gaelle Autaa Sophie Hind Johanna B. Holm Alicia Moreno-Sabater Martin Larsen
author_sort	Remy Villette
title	Refinement of 16S rRNA gene analysis for low biomass biospecimens
title_short	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_sort	refinement of 16s rrna gene analysis for low biomass biospecimens
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1818d4b5788f4d449b131b0ed8fa2f39
work_keys_str_mv	AT remyvillette refinementof16srrnageneanalysisforlowbiomassbiospecimens AT gaelleautaa refinementof16srrnageneanalysisforlowbiomassbiospecimens AT sophiehind refinementof16srrnageneanalysisforlowbiomassbiospecimens AT johannabholm refinementof16srrnageneanalysisforlowbiomassbiospecimens AT aliciamorenosabater refinementof16srrnageneanalysisforlowbiomassbiospecimens AT martinlarsen refinementof16srrnageneanalysisforlowbiomassbiospecimens
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Refinement of 16S rRNA gene analysis for low biomass biospecimens

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