PSIX-3 Comparison of 16S rRNA gene profiles of rumen microbiome from Raramuri Criollo, European and Criollo x European lineages

Abstract Raramuri Criollo cattle from the Chihuahuan desert in northern Mexico have been described as an ecological ecotype due to their enormous advantage in land grass utilization and their capacity to diversify their diet with cacti, forbs and woody plants. This diversification in diet utilization, could reflect upon their microbiome composition. The aim of this study was to characterize the rumen microbiome of Raramuri criollo cattle and to compare it to other lineages that graze in the same area. A total of 28 cows representing three linages [Criollo (n = 13), European (n = 9) and Criollo x European Crossbred (n = 6)] were grazed without supplementation for 45 days. DNA was extracted from ruminal samples and the V4 region of the 16S rRNA gene was sequenced on an Illumina platform. Data were analyzed with the QIIME2 software package and DADA2 plugin and the amplicon sequence variants were taxonomically classified with naïve Bayesian using the SILVA 16S rRNA gene reference database (version 132). Statistical analysis was performed by ANOVA and PERMANOVA for alpha and beta diversity indexes, respectively, and the non-strict version of linear discriminant analysis effect size (LEfSe) was used to determine significantly different taxa among lineages. Differences in beta diversity indexes (P < 0.05) were found in ruminal microbiome composition between Criollo and European groups, whereas the Crossbred showed intermediate values when compared to the pure breeds (Table 1). LEfSe analysis identified a total of 20 bacterial groups that explained differences between lineages, including one for Crossbreed, ten for European and nine for Criollo. These results show ruminal microbiome differences between Raramuri criollo cattle and the mainstream European breeds used in the northern Mexico Chihuahuan desert and reflect that those differences could be a consequence of dissimilar grazing behavior.

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PhytoREF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy

Molecular Ecology Resources ◽

10.1111/1755-0998.12401 ◽

2015 ◽

Vol 15 (6) ◽

pp. 1435-1445 ◽

Cited By ~ 74

Author(s):

Johan Decelle ◽

Sarah Romac ◽

Rowena F. Stern ◽

El Mahdi Bendif ◽

Adriana Zingone ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Reference Database ◽

Rrna Gene ◽

Photosynthetic Eukaryotes

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Comparison of paperpoint and curette sampling of subgingival microbiome composition as analyzed by 16S rRNA gene amplicon sequencing

Journal of Oral Microbiology ◽

10.1080/20002297.2017.1325260 ◽

2017 ◽

Vol 9 (sup1) ◽

pp. 1325260

Author(s):

K. Beyer ◽

B.W. Brandt ◽

M.J. Buijs ◽

J.G. Brun ◽

W. Crielaard ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Microbiome Composition

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Accounting for 16S rRNA copy number prediction uncertainty and its implications in microbial diversity analyses

10.1101/2021.08.31.458422 ◽

2021 ◽

Author(s):

Yingnan Gao ◽

Martin Wu

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Microbial Diversity ◽

Bacterial Communities ◽

Beta Diversity ◽

Copy Number ◽

Rrna Gene ◽

Prediction Uncertainty ◽

Functional Profiles ◽

Diversity Studies

Background: 16S rRNA gene has been widely used in microbial diversity studies to determine the community composition and structure. 16S rRNA gene copy number (16S GCN) varies among microbial species and this variation introduces biases to the relative cell abundance estimated using 16S rRNA read counts. To correct the biases, methods (e.g., PICRUST2) have been developed to predict 16S GCN. 16S GCN predictions come with inherent uncertainty, which is often ignored in the downstream analyses. However, a recent study suggests that the uncertainty can be so great that copy number correction is not justified in practice. Despite the significant implications in 16S rRNA based microbial diversity studies, the uncertainty associated with 16S GCN predictions has not been well characterized and its impact on microbial diversity studies needs to be investigated. Results: Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S rRNA GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms PICRUST2 in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, PERMANOVA and random forest test. Conclusion: We have developed a method to accurately account for uncertainty in 16S rRNA GCN predictions and the downstream analyses. For almost all 16S rRNA surveyed bacterial communities, correction of 16S GCN should improve the results when estimating their compositional and functional profiles. However, such correction is not necessary for beta-diversity analyses.

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DAIRYdb: a manually curated reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products

BMC Genomics ◽

10.1186/s12864-019-5914-8 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 9

Author(s):

Marco Meola ◽

Etienne Rifa ◽

Noam Shani ◽

Céline Delbès ◽

Hélène Berthoud ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Dairy Products ◽

Reference Database ◽

Rrna Gene ◽

Gene Sequences ◽

16S Rrna Gene Sequences

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Improved group-specific primers based on the full SILVA 16S rRNA gene reference database

Environmental Microbiology ◽

10.1111/1462-2920.12350 ◽

2014 ◽

Vol 16 (8) ◽

pp. 2389-2407 ◽

Cited By ~ 22

Author(s):

Stefan Pfeiffer ◽

Milica Pastar ◽

Birgit Mitter ◽

Kathrin Lippert ◽

Evelyn Hackl ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Reference Database ◽

Rrna Gene ◽

Specific Primers

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Presence of Human Pathogens of the Borrelia burgdorferi sensu lato Complex Shifts the Sequence Read Abundances of Tick Microbiomes in Two German Locations

Microorganisms ◽

10.3390/microorganisms9091814 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1814

Author(s):

Angeline Hoffmann ◽

Thomas Müller ◽

Volker Fingerle ◽

Matthias Noll

Keyword(s):

Nucleic Acid ◽

16S Rrna ◽

Borrelia Burgdorferi ◽

16S Rrna Gene ◽

Borrelia Burgdorferi Sensu Lato ◽

Rrna Gene ◽

Pathogenic Species ◽

Microbiome Composition ◽

Tick Microbiome ◽

Bacterial 16S Rrna Gene

The distribution of human Lyme borreliosis (LB) is assumed random in Germany, indicating that the human pathogenic species of the Borrelia burgdorferi sensu lato complex (Bb) are similarly distributed as part of the tick microbiome. The aim of this study was to differentiate if the presence of Bb occurs with a defined tick microbiome composition. Furthermore, the effect of location on tick microbiome composition was addressed for two German locations. Therefore, nucleic acid extracts from 82 Borrelia-positive and 118 Borrelia-negative Ixodes ricinus ticks sampled from human hosts in both districts were selected. Nucleic acid extracts were used for human pathogenic Bb species diagnostics based on qPCR and multilocus sequence typing (MLST) and bacterial 16S rRNA gene amplicon sequencing followed by network analyses. As a result, the presence of Bb shifted the sequence read abundances of Candidatus Midichloria, Rickettsia, Pseudomonas, Staphylococcus, and Candidatus Neoehrlichia and their topological roles in the tick microbiome. Moreover, the location was less important in the tick microbiome composition but shifted significantly sequence read abundances of Pseudomonas and Wolbachia as well as the topological role of microbial members. Since the presence of human pathogenic Bb species with other tick-associated pathogens varies regionally, we suggest that a bacterial 16S rRNA gene-based microbiome survey should be implemented in the routine diagnostics for both tick and host if human pathogenic species of Bb were detected. This diagnostic extension will help to optimize therapeutic approaches against Bb infection and co-occurring pathogens.

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Inconsistent Patterns of Microbial Diversity and Composition Between Highly Similar Sequencing Protocols: A Case Study With Reef-Building Corals

Frontiers in Microbiology ◽

10.3389/fmicb.2021.740932 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hannah E. Epstein ◽

Alejandra Hernandez-Agreda ◽

Samuel Starko ◽

Julia K. Baum ◽

Rebecca Vega Thurber

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Beta Diversity ◽

Sequence Data ◽

Alpha Diversity ◽

Gene Profiling ◽

Rrna Gene ◽

Sequencing Platform ◽

Alpha And Beta Diversity ◽

Diversity Metrics

16S rRNA gene profiling (amplicon sequencing) is a popular technique for understanding host-associated and environmental microbial communities. Most protocols for sequencing amplicon libraries follow a standardized pipeline that can differ slightly depending on laboratory facility and user. Given that the same variable region of the 16S gene is targeted, it is generally accepted that sequencing output from differing protocols are comparable and this assumption underlies our ability to identify universal patterns in microbial dynamics through meta-analyses. However, discrepant results from a combined 16S rRNA gene dataset prepared by two labs whose protocols differed only in DNA polymerase and sequencing platform led us to scrutinize the outputs and challenge the idea of confidently combining them for standard microbiome analysis. Using technical replicates of reef-building coral samples from two species, Montipora aequituberculata and Porites lobata, we evaluated the consistency of alpha and beta diversity metrics between data resulting from these highly similar protocols. While we found minimal variation in alpha diversity between platform, significant differences were revealed with most beta diversity metrics, dependent on host species. These inconsistencies persisted following removal of low abundance taxa and when comparing across higher taxonomic levels, suggesting that bacterial community differences associated with sequencing protocol are likely to be context dependent and difficult to correct without extensive validation work. The results of this study encourage caution in the statistical comparison and interpretation of studies that combine rRNA gene sequence data from distinct protocols and point to a need for further work identifying mechanistic causes of these observed differences.

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16S rRNA Gene Sequencing as a Clinical Diagnostic Aid for Gastrointestinal-related Conditions

10.1101/084657 ◽

2016 ◽

Cited By ~ 1

Author(s):

Daniel E. Almonacid ◽

Laurens Kraal ◽

Francisco J. Ossandon ◽

Yelena V. Budovskaya ◽

Juan Pablo Cardenas ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

16S Rrna Gene Sequencing ◽

Epidemiological Studies ◽

Clinical Test ◽

Rrna Gene ◽

Bioinformatics Pipeline ◽

Microbiome Composition ◽

Rrna Gene Sequencing ◽

Gut Microorganisms

AbstractAccurate detection of the microorganisms underlying gut dysbiosis in the patient is critical to initiate the appropriate treatment. However, most clinical microbiology techniques used to detect gut bacteria were developed over a century ago and rely on culture-based approaches that are often laborious, unreliable, and subjective. Further, culturing does not scale well for multiple targets and detects only a minority of the microorganisms in the human gastrointestinal tract. Here we present a clinical test for gut microorganisms based on targeted sequencing of the prokaryotic 16S rRNA gene. We tested 46 clinical prokaryotic targets in the human gut, 28 of which can be identified by a bioinformatics pipeline that includes sequence analysis and taxonomic annotation. Using microbiome samples from a cohort of 897 healthy individuals, we established a reference range defining clinically relevant relative levels for each of the 28 targets. Our assay accurately quantified all 28 targets and correctly reflected 38/38 verification samples of real and synthetic stool material containing known pathogens. Thus, we have established a new test to interrogate microbiome composition and diversity, which will improve patient diagnosis, treatment and monitoring. More broadly, our test will facilitate epidemiological studies of the microbiome as it relates to overall human health and disease.

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Species level resolution of female bladder microbiota from marker gene surveys

10.1101/2020.10.27.358408 ◽

2020 ◽

Author(s):

Carter Hoffman ◽

Nazema Y Siddiqui ◽

Ian Fields ◽

W. Thomas Gregory ◽

Holly Simon ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Gene Sequencing ◽

Variable Region ◽

16S Rrna Gene Sequencing ◽

Species Level ◽

Reference Database ◽

Rrna Gene ◽

Rrna Gene Sequencing ◽

Level Identification

AbstractThe human bladder contains bacteria in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing, but the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA gene sequencing is commonly used to identify bacteria, but is typically restricted to genus-level identification. Our primary aim was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA gene sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a 16S rRNA gene variable region, a reference database, and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible, and that the reference database chosen is the most important component, followed by the 16S variable region sequenced.ImportanceSpecies-level information may deepen our understanding of associations between bladder microbiota and bladder conditions, such as lower urinary tract symptoms and urinary tract infections. The capability to identify bacterial species depends on large databases of sequences, algorithms that leverage statistics and available computer hardware, and knowledge of bacterial genetics and classification. Taken together, this is a daunting body of knowledge to become familiar with before the simple question of bacterial identity can be answered. Our results show the choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show this improvement can be achieved through the more careful application of existing methods and use of existing resources.

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Beyond taxonomic identification: integration of ecological responses to a soil bacterial 16S rRNA gene database

10.1101/843847 ◽

2019 ◽

Author(s):

Briony A. Jones ◽

Tim Goodall ◽

Paul George ◽

Hyun Soon Gweon ◽

Jeremy Puissant ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Soil Ph ◽

Soil Survey ◽

Reference Database ◽

Rrna Gene ◽

Ph Gradients ◽

Ecological Attributes ◽

Hof Models ◽

Analytical Approaches

AbstractHigh-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalising syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterise taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from the survey metadata. Specifically, we modelled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on putative landscape scale pH-abundance responses. We identify that most of the soil OTUs examined exhibit predictable abundance responses across soil pH gradients, though with the exception of known acidophilic lineages, the pH optima of OTU relative abundance was variable and could not be generalised by broad taxonomy. This highlights the need for tools and databases to predict ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER/), and flat files are made available for use in bioinformatic pipelines. The further development of advanced informatics infrastructures incorporating modelled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

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