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The Characterization of Novel Tissue Microbiota Using an Optimized 16S Metagenomic Sequencing Pipeline.

ABSTRACT: BACKGROUND:Substantial progress in high-throughput metagenomic sequencing methodologies has enabled the characterisation of bacteria from various origins (for example gut and skin). However, the recently-discovered bacterial microbiota present within animal internal tissues has remained unexplored due to technical difficulties associated with these challenging samples. RESULTS:We have optimized a specific 16S rDNA-targeted metagenomics sequencing (16S metabarcoding) pipeline based on the Illumina MiSeq technology for the analysis of bacterial DNA in human and animal tissues. This was successfully achieved in various mouse tissues despite the high abundance of eukaryotic DNA and PCR inhibitors in these samples. We extensively tested this pipeline on mock communities, negative controls, positive controls and tissues and demonstrated the presence of novel tissue specific bacterial DNA profiles in a variety of organs (including brain, muscle, adipose tissue, liver and heart). CONCLUSION:The high throughput and excellent reproducibility of the method ensured exhaustive and precise coverage of the 16S rDNA bacterial variants present in mouse tissues. This optimized 16S metagenomic sequencing pipeline will allow the scientific community to catalogue the bacterial DNA profiles of different tissues and will provide a database to analyse host/bacterial interactions in relation to homeostasis and disease.

SUBMITTER: Lluch J

PROVIDER: S-EPMC4636327 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The Characterization of Novel Tissue Microbiota Using an Optimized 16S Metagenomic Sequencing Pipeline.

Lluch Jérôme J Servant Florence F Païssé Sandrine S Valle Carine C Valière Sophie S Kuchly Claire C Vilchez Gaëlle G Donnadieu Cécile C Courtney Michael M Burcelin Rémy R Amar Jacques J Bouchez Olivier O Lelouvier Benjamin B

PloS one 20151106 11

<h4>Background</h4>Substantial progress in high-throughput metagenomic sequencing methodologies has enabled the characterisation of bacteria from various origins (for example gut and skin). However, the recently-discovered bacterial microbiota present within animal internal tissues has remained unexplored due to technical difficulties associated with these challenging samples.<h4>Results</h4>We have optimized a specific 16S rDNA-targeted metagenomics sequencing (16S metabarcoding) pipeline based ...[more]

PMID: 26544955

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Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Project description:Due to their potential impact on the host's phenotype, organ-specific microbiotas are receiving increasing attention in several animal species, including cattle. Specifically, the vaginal microbiota of ruminants is attracting growing interest, due to its predicted critical role on cows' reproductive functions in livestock contexts. Notably, fertility disorders represent a leading cause for culling, and additional research would help to fill relevant knowledge gaps. In the present study, we aimed to characterize the vaginal microbiota of a large cohort of 1171 female dairy cattle from 19 commercial herds in Northern France. Vaginal samples were collected using a swab and the composition of the microbiota was determined through 16S rRNA sequencing targeting the V3-V4 hypervariable regions. Initial analyses allowed us to define the core bacterial vaginal microbiota, comprising all the taxa observed in more than 90% of the animals. Consequently, four phyla, 16 families, 14 genera and a single amplicon sequence variant (ASV) met the criteria, suggesting a high diversity of bacterial vaginal microbiota within the studied population. This variability was partially attributed to various environmental factors such as the herd, sampling season, parity, and lactation stage. Next, we identified numerous significant associations between the diversity and composition of the vaginal microbiota and several traits related to host's production and reproduction performance, as well as reproductive tract health. Specifically, 169 genera were associated with at least one trait, with 69% of them significantly associated with multiple traits. Among these, the abundances of Negativibacillus and Ruminobacter were positively correlated with the cows' performances (i.e., longevity, production performances). Other genera showed mixed relationships with the phenotypes, such as Leptotrichia being overabundant in cows with improved fertility records and reproductive tract health, but also in cows with lower production levels. Overall, the numerous associations underscored the complex interactions between the vaginal microbiota and its host. Given the large number of samples collected from commercial farms and the diversity of the phenotypes considered, this study marks an initial step towards a better understanding of the intimate relationship between the vaginal microbiota and the dairy cow's phenotypes.

Project description:BackgroundAccurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.ResultsBased on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.ConclusionsThese findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Dataset Information

The Characterization of Novel Tissue Microbiota Using an Optimized 16S Metagenomic Sequencing Pipeline.

Publications

The Characterization of Novel Tissue Microbiota Using an Optimized 16S Metagenomic Sequencing Pipeline.

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