Dataset Information

High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts.

ABSTRACT: Sequence-based characterization of bacterial communities has long been a hostage of limitations of both 16S rRNA gene and whole metagenome sequencing. Neither approach is universally applicable, and the main efforts to resolve constraints have been devoted to improvement of computational prediction tools. Here, we present semi-targeted 16S rRNA sequencing (st16S-seq), a method designed for sequencing V1-V2 regions of the 16S rRNA gene along with the genomic locus upstream of the gene. By in silico analysis of 13 570 bacterial genome assemblies, we show that genome-linked 16S rRNA sequencing is superior to individual hypervariable regions or full-length gene sequences in terms of classification accuracy and identification of gene copy numbers. Using mock communities and soil samples we experimentally validate st16S-seq and benchmark it against the established microbial classification techniques. We show that st16S-seq delivers accurate estimation of 16S rRNA gene copy numbers, enables taxonomic resolution at the species level and closely approximates community structures obtainable by whole metagenome sequencing.

SUBMITTER: Kapustina Z

PROVIDER: S-EPMC8715429 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts.

Kapustina Žana Ž Medžiūnė Justina J Alzbutas Gediminas G Rokaitis Irmantas I Matjošaitis Karolis K Mackevičius Gytis G Žeimytė Simona S Karpus Laurynas L Lubys Arvydas A

Microbial genomics 20210901 9

Sequence-based characterization of bacterial communities has long been a hostage of limitations of both 16S rRNA gene and whole metagenome sequencing. Neither approach is universally applicable, and the main efforts to resolve constraints have been devoted to improvement of computational prediction tools. Here, we present semi-targeted 16S rRNA sequencing (st16S-seq), a method designed for sequencing V1-V2 regions of the 16S rRNA gene along with the genomic locus upstream of the gene. By <i>in s ...[more]

PMID: 34473015

Similar Datasets

Project description:16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain variable regions that can help distinguish different genera. However, intra-genus distinction using variable region homology is often impossible due to the high overall sequence identities among closely related species, even though some residues may be conserved within respective species. Using a computational method that included the allelic diversity within individual genomes, we discovered that certain Escherichia and Shigella species can be distinguished by a multi-allelic 16S rRNA variable region single nucleotide polymorphism (SNP). To evaluate the performance of 16S rRNAs with altered variable regions, we developed an in vivo system that measures the acceptance and distribution of variant 16S rRNAs into a large pool of natural versions supporting normal translation and growth. We found that 16S rRNAs containing evolutionarily disparate variable regions were underpopulated both in ribosomes and in active translation pools, even for an SNP. Overall, this study revealed that variable region sequences can substantially influence the performance of 16S rRNAs and that this biological constraint can be leveraged to justify refining taxonomic assignments of variable region sequence data. IMPORTANCE This study reevaluates the notion that 16S rRNA gene variable region sequences are uninformative for intra-genus classification and that single nucleotide variations within them have no consequence to strains that bear them. We demonstrated that the performance of 16S rRNAs in Escherichia coli can be negatively impacted by sequence changes in variable regions, even for single nucleotide changes that are native to closely related Escherichia and Shigella species; thus, biological performance is likely constraining the evolution of variable regions in bacteria. Further, the native nucleotide variations we tested occur in all strains of their respective species and across their multiple 16S rRNA gene copies, suggesting that these species evolved beyond what would be discerned from a consensus sequence comparison. Therefore, this work also reveals that the multiple 16S rRNA gene alleles found in most bacteria can provide more informative phylogenetic and taxonomic detail than a single reference allele.

Project description:BackgroundSequencing variable regions of the 16S rRNA gene (≃300 bp) with Illumina technology is commonly used to study the composition of human microbiota. Unfortunately, short reads are unable to differentiate between highly similar species. Considering that species from the same genus can be associated with health or disease it is important to identify them at the lowest possible taxonomic rank. Third-generation sequencing platforms such as PacBio SMRT, increase read lengths allowing to sequence the whole gene with the maximum taxonomic resolution. Despite its potential, full length 16S rRNA gene sequencing is not widely used yet. The aim of the current study was to compare the sequencing output and taxonomic annotation performance of the two approaches (Illumina short read sequencing and PacBio long read sequencing of 16S rRNA gene) in different human microbiome samples. DNA from saliva, oral biofilms (subgingival plaque) and faeces of 9 volunteers was isolated. Regions V3-V4 and V1-V9 were amplified and sequenced by Illumina Miseq and by PacBio Sequel II sequencers, respectively.ResultsWith both platforms, a similar percentage of reads was assigned to the genus level (94.79% and 95.06% respectively) but with PacBio a higher proportion of reads were further assigned to the species level (55.23% vs 74.14%). Regarding overall bacterial composition, samples clustered by niche and not by sequencing platform. In addition, all genera with > 0.1% abundance were detected in both platforms for all types of samples. Although some genera such as Streptococcus tended to be observed at higher frequency in PacBio than in Illumina (20.14% vs 14.12% in saliva, 10.63% vs 6.59% in subgingival plaque biofilm samples) none of the differences were statistically significant when correcting for multiple testing.ConclusionsThe results presented in the current manuscript suggest that samples sequenced using Illumina and PacBio are mostly comparable. Considering that PacBio reads were assigned at the species level with higher accuracy than Illumina, our data support the use of PacBio technology for future microbiome studies, although a higher cost is currently required to obtain an equivalent number of reads per sample.

Project description:The major aquatic interface between host and environment in teleost finfish species is the gill. The diversity of this infraclass, high complexity of the organ, and its direct exposure to the surrounding environment make it an ideal candidate for furthering our understanding of the intertwined relationships between host and microbiome. Capturing the structure and diversity of bacterial communities from this low-biomass, inhibitor-rich tissue can, however, prove challenging. Lessons learned in doing so are directly applicable to similar sample types in other areas of microbiology. Through the development of a quantitative PCR assay for both host material and 16S rRNA genes, we tested and developed a robust method for low-biomass sample collection which minimized host DNA contamination. Quantification of 16S rRNA facilitated not only the screening of samples prior to costly library construction and sequencing but also the production of equicopy libraries based on 16S rRNA gene copies. A significant increase in diversity of bacteria captured was achieved, providing greater information on the true structure of the microbial community. Such findings offer important information for determining functional processes. Results were confirmed across fresh, brackish, and marine environs with four different fish species, with results showing broad homology between samples, demonstrating the robustness of the approach. Evidence presented is widely applicable to samples similar in composition, such as sputum or mucus, or those that are challenging due to the inherent inclusion of inhibitors. IMPORTANCE The interaction between the fish gill and surrounding bacteria-rich water provides an intriguing model for examining the interaction between the fish, free-floating bacteria, and the bacterial microbiome on the gill surface. Samples that are inherently low in bacteria, or that have components that inhibit the ability to produce libraries that identify the components of microbial communities, present significant challenges. Gill samples present both of these types of challenges. We developed methods for quantifying both the bacterial and host DNA material and established a sampling method which both reduced inhibitor content and maximized bacterial diversity. By quantifying and normalizing bacteria prior to library construction, we showed significant improvements with regards to the fidelity of the final data. Our results support wide-ranging applications for analyzing samples of similar composition, such as mucus and sputum, in other microbiological spheres.

Dataset Information

High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts.

Publications

High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets