High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts.
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ABSTRACT:
SUBMITTER: Kapustina Z
PROVIDER: S-EPMC8715429 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The aim of this work was to analyze and compare the bacterial communities of 663 samples from a Brazilian hospital by using high-throughput sequencing of the 16S rRNA gene. To increase taxonomic profiling and specificity of 16S-based identification, a strict sequence quality filtering process was applied for the accurate identification of clinically relevant bacterial taxa. Our results indicate that the hospital environment is predominantly inhabited by closely related species. A massive dominance of a few taxa in all taxonomic levels down to the genera was observed, where the ten most abundant genera in each facility represented 64.4% of all observed taxa, with a major predominance of Acinetobacter and Pseudomonas. The presence of several nosocomial pathogens was revealed. Co-occurrence analysis indicated that the present hospital microbial network had low connectedness, forming a clustered topology, but not structured among groups of nodes (i.e., modules). Furthermore, we were able to detect ecologically relevant relationships between specific microbial taxa, in particular, potential competition between pathogens and non-pathogens. Overall, these results provide new insight into different aspects of a hospital microbiome and indicate that 16S rRNA sequencing may serve as a robust one-step tool for microbiological identification and characterization of a wide range of clinically relevant bacterial taxa in hospital settings with a high resolution.
| S-EPMC6696720 | biostudies-literature
Project description:BackgroundMicrobial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings.MethodsTo address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection.ResultsWe validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome.ConclusionsBy lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries.
| S-EPMC3426808 | biostudies-literature
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.
| S-EPMC10964587 | biostudies-literature
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.
| S-EPMC10269663 | biostudies-literature
Project description:The objective of the present study was to identify the nutrient utilization and the SCFA production potential of gut microbes during the first year of life. The 16S sequencing data represents 100 mother-child pairs, longitudinally for the infants (0, 3mo, 6mo and 12mo) and mothers 18 weeks pregnancy. We wanted to identify the SCFA composition in pregnant woman and their infants through the first year of life, and their correlation to gut bacteria and other influencal factors. Metaproteomics on selected infants were analyzed to look for nutrient sources used by potential SCFA producers.
2021-09-09 | PXD017844 | Pride
Project description:Gender-specific antibiotic-resistance patterns and characterization of bacterial flora of undergraduate students’ mobile phones
| PRJEB78691 | ENA
Project description:Faecal microbiota and cytokine profiles of rural Cambodian infants linked to diet and diarrhoeal episodes (16S rRNA amplicon sequencing)
| PRJNA958098 | ENA
Project description:The human oral microbiome is known to play a significant role in human health and disease. While less well studied, the feline oral microbiome is thought to play a similarly important role. To determine roles oral bacteria play in health and disease, one first has to be able to accurately identify bacterial species present. 16S rRNA gene sequence information is widely used for molecular identification of bacteria and is also useful for establishing the taxonomy of novel species. The objective of this research was to obtain full 16S rRNA gene reference sequences for feline oral bacteria, place the sequences in species-level phylotypes, and create a curated 16S rRNA based taxonomy for common feline oral bacteria. Clone libraries were produced using "universal" and phylum-selective PCR primers and DNA from pooled subgingival plaque from healthy and periodontally diseased cats. Bacteria in subgingival samples were also cultivated to obtain isolates. Full-length 16S rDNA sequences were determined for clones and isolates that represent 171 feline oral taxa. A provisional curated taxonomy was developed based on the position of each taxon in 16S rRNA phylogenetic trees. The feline oral microbiome curated taxonomy and 16S rRNA gene reference set will allow investigators to refer to precisely defined bacterial taxa. A provisional name such as "Propionibacterium sp. feline oral taxon FOT-327" is an anchor to which clone, strain or GenBank names or accession numbers can point. Future next-generation-sequencing studies of feline oral bacteria will be able to map reads to taxonomically curated full-length 16S rRNA gene sequences.
| S-EPMC4297541 | biostudies-literature
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.
| S-EPMC9769501 | biostudies-literature
Project description:BackgroundMetagenomics is a rapidly growing field of research aimed at studying assemblages of uncultured organisms using various sequencing technologies, with the hope of understanding the true diversity of microbes, their functions, cooperation and evolution. There are two main approaches to metagenomics: amplicon sequencing, which involves PCR-targeted sequencing of a specific locus, often 16S rRNA, and random shotgun sequencing. Several tools or packages have been developed for analyzing communities using 16S rRNA sequences. Similarly, a number of tools exist for analyzing randomly sequenced DNA reads.ResultsWe describe an extension of the metagenome analysis tool MEGAN, which allows one to analyze 16S sequences. For the analysis all 16S sequences are blasted against the SILVA database. The result output is imported into MEGAN, using a synonym file that maps the SILVA accession numbers onto the NCBI taxonomy.ConclusionsEnvironmental samples are often studied using both targeted 16S rRNA sequencing and random shotgun sequencing. Hence tools are needed that allow one to analyze both types of data together, and one such tool is MEGAN. The ideas presented in this paper are implemented in MEGAN 4, which is available from: http://www-ab.informatik.uni-tuebingen.de/software/megan.
| S-EPMC3333176 | biostudies-literature