Characterization of human breast tissue microbiota from core needle biopsies through the analysis of multi hypervariable 16S-rRNA gene regions.
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ABSTRACT:
SUBMITTER: Costantini L
PROVIDER: S-EPMC6237987 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82].ResultsAt both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%).ConclusionsRamifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.
| S-EPMC9264627 | biostudies-literature
Project description:The characterization of bacterial communities using DNA sequencing has revolutionized our ability to study microbes in nature and discover the ways in which microbial communities affect ecosystem functioning and human health. Here we describe Serial Illumina Sequencing (SI-Seq): a method for deep sequencing of the bacterial 16S rRNA gene using next-generation sequencing technology. SI-Seq serially sequences portions of the V5, V6 and V7 hypervariable regions from barcoded 16S rRNA amplicons using an Illumina short-read genome analyzer. SI-Seq obtains taxonomic resolution similar to 454 pyrosequencing for a fraction of the cost, and can produce hundreds of thousands of reads per sample even with very high multiplexing. We validated SI-Seq using single species and mock community controls, and via a comparison to cystic fibrosis lung microbiota sequenced using 454 FLX Titanium. Our control runs show that SI-Seq has a dynamic range of at least five orders of magnitude, can classify >96% of sequences to the genus level, and performs just as well as 454 and paired-end Illumina methods in estimation of standard microbial ecology diversity measurements. We illustrate the utility of SI-Seq in a pilot sample of central airway secretion samples from cystic fibrosis patients.
| S-EPMC3462755 | biostudies-literature
Project description:Development of an analysis pipeline characterizing multiple hypervariable regions of 16S rRNA using mock samples
| PRJEB10546 | ENA
Project description:Proper preservation of stool samples to minimize microbial community shifts and inactivate infectious agents is important for self-collected specimens requiring shipment to laboratories when cold chain transport is not feasible. In this study, we evaluated the performance of six preservation solutions (Norgen, OMNI, RNAlater, CURNA, HEMA, and Shield) for these aspects. Following storage of human stool samples with these preservatives at room temperature for 7 days, three hypervariable regions of the bacterial 16S rRNA gene (V1-V2, V3-V4, and V4) were amplicon sequenced. We found that samples collected in two preservatives, Norgen and OMNI, showed the least shift in community composition relative to -80°C standards compared with other storage conditions, and both efficiently inhibited the growth of aerobic and anaerobic bacteria. RNAlater did not prevent bacterial activity and exhibited relatively larger community shift. Although the effect of preservation solution was small compared to intersubject variation, notable changes in microbiota composition were observed, which could create biases in downstream data analysis. When community profiles inferred from different 16S rRNA gene hypervariable regions were compared, we found differential sensitivity of primer sets in identifying overall microbial community and certain bacterial taxa. For example, reads generated by the V4 primer pair showed a higher alpha diversity of the gut microbial community. The degenerate 27f-YM primer failed to detect the majority of Bifidobacteriales. Our data indicate that choice of preservation solution and 16S rRNA gene primer pair are critical determinants affecting gut microbiota profiling. IMPORTANCE Large-scale human microbiota studies require specimens collected from multiple sites and/or time points to maximize detection of the small effects in microbe-host interactions. However, batch biases caused by experimental protocols, such as sample collection, massively parallel sequencing, and bioinformatics analyses, remain critical and should be minimized. This work evaluated the effects of preservation solutions and bacterial 16S rRNA gene primer pairs in revealing human gut microbiota composition. Since notable changes in detecting bacterial composition and abundance were observed among choice of preservatives and primer pairs, a consistent methodology is essential in minimizing their effects to facilitate comparisons between data sets.
| S-EPMC6392095 | biostudies-literature
Project description:Molecular detection and classification of the bacterial groups in a sample are relevant in several areas, including medical research and forensics. Sanger sequencing of the 16S rRNA gene is considered the gold standard for microbial phylogenetic analysis. However, the development of massively parallel sequencing (MPS) offers enhanced sensitivity and specificity for microbiological analyses. In addition, 16S rRNA target amplification followed by MPS facilitates the combined use of multiple markers/regions, better discrimination of sample background, and higher sample throughput. We designed a novel set of 16S rRNA gene primers for detection of bacterial species associated with clinical, bioweapon, and biohazards microorganisms via alignment of 364 sequences representing 19 bacterial species and strains relevant to medical and forensics applications. In silico results indicated that the hypervariable regions (V1V2), (V4V5), and (V6V7V8) support the resolution of a selected group of bacteria. Interspecies and intraspecies comparisons showed 74.23%-85.51% and 94.48%-99.98% sequencing variation among species and strains, respectively. Sequence reads from a simulated scenario of bacterial species mapped to each of the three hypervariable regions of the respective species with different affinities. The minimum limit of detection was achieved using two different MPS platforms. This protocol can be used to detect or monitor as low as 2,000 genome equivalents of bacterial species associated with clinical, bioweapon, and biohazard microorganisms and potentially can distinguish natural outbreaks of pathogenic microorganisms from those occurring by intentional release.
| S-EPMC7533751 | biostudies-literature
Project description:The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.
| S-EPMC4315456 | biostudies-literature
Project description:ObjectivesThere is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology.MethodsThis paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline.ResultsResults are presented at family and genus level. The Kullback-Leibler divergence (DKL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average DKL while the V4 gave the lowest (best) average DKL. In addition to having a high DKL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria.ConclusionsThe results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.
| S-EPMC4734828 | biostudies-literature
Project description:Short read 16 S rRNA amplicon sequencing is a common technique used in microbiome research. However, inaccuracies in estimated bacterial community composition can occur due to amplification bias of the targeted hypervariable region. A potential solution is to sequence and assess multiple hypervariable regions in tandem, yet there is currently no consensus as to the appropriate method for analyzing this data. Additionally, there are many sequence analysis resources for data produced from the Illumina platform, but fewer open-source options available for data from the Ion Torrent platform. Herein, we present an analysis pipeline using open-source analysis platforms that integrates data from multiple hypervariable regions and is compatible with data produced from the Ion Torrent platform. We used the ThermoFisher Ion 16 S Metagenomics Kit and a mock community of twenty bacterial strains to assess taxonomic classification of six amplicons from separate hypervariable regions (V2, V3, V4, V6-7, V8, V9) using our analysis pipeline. We report that different amplicons have different specificities for taxonomic classification, which also has implications for global level analyses such as alpha and beta diversity. Finally, we utilize a generalized linear modeling approach to statistically integrate the results from multiple hypervariable regions and apply this methodology to data from a representative clinical cohort. We conclude that examining sequencing results across multiple hypervariable regions provides more taxonomic information than sequencing across a single region. The data across multiple hypervariable regions can be combined using generalized linear models to enhance the statistical evaluation of overall differences in community structure and relatedness among sample groups.
| S-EPMC9009396 | biostudies-literature
Project description:Amplification and sequencing of 16S amplicons are widely used for profiling the structure of oral microbiota. However, it remains not clear whether and to what degree DNA extraction and targeted 16S rRNA hypervariable regions influence the analysis. Based on a mock community consisting of five oral bacterial species in equal abundance, we compared the 16S amplicon sequencing results on the Illumina MiSeq platform from six frequently employed DNA extraction procedures and three pairs of widely used 16S rRNA hypervariable primers targeting different 16S rRNA regions. Technical reproducibility of selected 16S regions was also assessed. DNA extraction method exerted considerable influence on the observed bacterial diversity while hypervariable regions had a relatively minor effect. Protocols with beads added to the enzyme-mediated DNA extraction reaction produced more accurate bacterial community structure than those without either beads or enzymes. Hypervariable regions targeting V3-V4 and V4-V5 seemed to produce more reproducible results than V1-V3. Neither sequencing batch nor change of operator affected the reproducibility of bacterial diversity profiles. Therefore, DNA extraction strategy and 16S rDNA hypervariable regions both influenced the results of oral microbiota biodiversity profiling, thus should be carefully considered in study design and data interpretation.
| S-EPMC6218491 | biostudies-literature
Project description:Next-generation sequencing-based methods are extensively applied in studies of the human microbiota using partial 16?S rRNA gene amplicons. However, they carry drawbacks that are critical to consider when interpreting results, including differences in outcome based on the hypervariable region(s) used. Here, we show that primers spanning the V3/V4 region identify a greater number of taxa in the vaginal microbiota than those spanning the V1/V2 region. In particular, taxa such as Gardnerella vaginalis, Bifidobacterium bifidum and Chlamydia trachomatis, all species that influence vaginal health and disease, are not represented in V1/V2-based community profiles. Accordingly, missing or underestimating the frequency of these species overestimates the abundance of other taxa and fails to correctly assess the bacterial diversity in the urogenital tract. We elaborate that covering these taxa using the V3/V4 region leads to profound changes in the assignment of community state types. Altogether, we show that the choice of primers used for studying the vaginal microbiota has deep implications on the biological evaluation of the results.
| S-EPMC6018735 | biostudies-literature