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.
Highlights
The human body is colonized by complex microbial communities at most external body sites
Stools from two healthy donors were selected to assess sample biomass limit, below which 16S rRNA gene sequencing loses the ability to correctly represent microbiota composition. To this end we conducted serial dilutions to prepare samples of 108, 107, 106, 105 and 1 04 microbes, which were extracted with three different extraction protocols. 16S rRNA genes were amplified using two different polymerase chain reaction (PCR) protocols, a semi-nested PCR and a standard PCR
From the three different DNA extraction protocols, only the Zymobiomic’s Miniprep (MP) kit extracted genomic DNA from which the V3-V4 16S rRNA gene fragment could be amplified for all microbial dilutions
Summary
The human body is colonized by complex microbial communities at most external body sites. Storage, DNA extraction, PCR amplification, sequencing technology and bioinformatic analysis have been shown to dramatically modify microbiota analytical outcomes. These variations are most likely a result of genotypic and phenotypic variation of microbiota. Microbiota from scarcely colonized body sites gained interest, such as skin, lung or even breastmilk microbiomes as well as neonate infant gut microbiota or cell sorted microbiota Such samples contain fewer bacteria than gut microbiota from adult donors, which has far been the most studied type of biospecimen. 16S rRNA gene amplicon sequencing benefit from PCR amplification, which makes it adapted for analysis of low biomass microbiota samples. We decided not to assess the effect of bioinformatics processing in the present study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
