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

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.

scholarly journals Beyond Taxonomic Identification: Integration of Ecological Responses to a Soil Bacterial 16S rRNA Gene Database

2021 ◽  
Vol 12 ◽  
Author(s):  
Briony Jones ◽  
Tim Goodall ◽  
Paul B. L. George ◽  
Hyun S. Gweon ◽  
Jeremy Puissant ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Soil Ph ◽  
Soil Survey ◽  
Taxonomic Resolution ◽  
Reference Database ◽  
Rrna Gene ◽  
Ecological Attributes ◽  
Hof Models ◽  
Analytical Approaches

High-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 formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize 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 survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pH-abundance responses, and pH optima (pH at which OTU abundance is maximal). We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing 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 modeled 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.

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

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 441-442
Author(s):  
Adrian Maynez-Perez ◽  
Francisco Jahuey-Martinez ◽  
Jose A Martinez-Quintana ◽  
Michael E Hume ◽  
Robin C Anderson ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Beta Diversity ◽  
Chihuahuan Desert ◽  
Reference Database ◽  
Rrna Gene ◽  
Linear Discriminant ◽  
Microbiome Composition ◽  
Northern Mexico ◽  
Rumen Microbiome

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.

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

2015 ◽  
Vol 15 (6) ◽  
pp. 1435-1445 ◽  
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

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

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
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

scholarly journals Species level resolution of female bladder microbiota from marker gene surveys

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.

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

2014 ◽  
Vol 16 (8) ◽  
pp. 2389-2407 ◽  
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

scholarly journals DAIRYdb: A manually curated gold standard reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products

2018 ◽  
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 ◽  
Close Phylogenetic Relationship ◽  
Gold Standard Reference ◽  
Phylogenetic Resolution

Reads assignment to taxonomic units is a key step in microbiome analysis pipelines. To date, accurate taxonomy annotation, particularly at species rank, is still challenging due to the short size of read sequences and differently curated classification databases. However, the close phylogenetic relationship between species encountered in dairy products requires accurate species annotation to achieve sufficient phylogenetic resolution for further downstream ecological studies or for food diagnostics. Taxonomy annotation in universal 16S databases with environmental sequences like Silva, RDP or Greengenes is based on predictions rather than on studies of type strains or isolates. We provide a manually curated database composed of 10’290 full-length 16S rRNA gene sequences from prokaryotes tailored for dairy products analysis (https://github.com/marcomeola/DAIRYdb). The performance of the DAIRYdb was compared with the universal databases Silva, LTP, RDP and Greengenes. The DAIRYdb significantly outperformed all other databases independently of the classification algorithm by enabling higher accurate taxonomy annotation down to the species rank. The DAIRYdb accurately annotates over 90% of the sequences of either single or paired hypervariable regions automatically.The manually curated DAIRYdb strongly improves taxonomic classification accuracy for microbiome studies in dairy environments. The DAIRYdb is a practical solution that enables automatization of this key step, thus facilitating the routine application of NGS microbiome analyses for microbial ecology studies and diagnostics in dairy products.

scholarly journals Application of a Database-Independent Approach To Assess the Quality of Operational Taxonomic Unit Picking Methods

mSystems ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Patrick D. Schloss
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Clustering Algorithms ◽  
Reference Database ◽  
Rrna Gene ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences ◽  
Bacterial Taxonomy ◽  
Data Set ◽  
Operational Taxonomic Units

ABSTRACT Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. I have applied the Matthews correlation coefficient to assess the ability of 15 reference-independent and -dependent clustering algorithms to assign sequences to OTUs. This metric quantifies the ability of an algorithm to reflect the relationships between sequences without the use of a reference and can be applied to any data set or method. The most consistently robust method was the average neighbor algorithm; however, for some data sets, other algorithms matched its performance.

scholarly journals Examining the Evidence for an Adult Healthy Middle Ear Microbiome

mSphere ◽  
2019 ◽  
Vol 4 (5) ◽  
Author(s):  
Jake Jervis-Bardy ◽  
Lex E. X. Leong ◽  
Lito E. Papanicolas ◽  
Kerry L. Ivey ◽  
Sharad Chawla ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Otitis Media ◽  
Middle Ear ◽  
Bacterial Colonization ◽  
Amplicon Sequencing ◽  
Molecular Techniques ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Analytical Approaches

ABSTRACT Otitis media (OM) is a cluster of diseases of the middle ear that commonly result from bacterial infection. OM subtypes in which the tympanic membrane is intact (acute otitis media and otitis media with effusion) are presumed to result from pathogen translocation through the eustachian tube. Recent molecular-based studies have suggested that a diverse middle ear microbiome exists in the absence of disease. These have been largely unsupported by culture and feature species that commonly contaminate low-biomass sequencing data. Combining culture-based and molecular techniques, we undertook a detailed investigation of the evidence for bacterial colonization of the healthy middle ear. Middle ear (ME), nasopharynx (NP), and external ear canal (EC) swabs were collected from a total of 25 adult patients undergoing cochlear implant, stapedotomy, or translabyrinthine vestibular schwannoma resection. Diagnostic culture, microscopy, quantitative PCR, and 16S rRNA gene amplicon sequencing were used to assess sample bacterial content. EC and NP microbiota were consistent with previous reports. In contrast, bacterial levels in ME samples were not significantly above those in unused control swabs. Commonly detected taxa were among recognized sequencing contaminants (Methylobacterium, Pseudomonas, and Acinetobacter). Linear regression of dominant ME taxa confirmed a negative relationship between relative abundance and bacterial load, consistent with contamination. No bacteria were detected by microscopy or diagnostic culture in any middle ear sample. Our findings cast substantial doubt on previous reports identifying a healthy middle ear microbiome using 16S amplicon sequencing. IMPORTANCE Recent molecular-based studies have suggested that a diverse middle ear microbiome in adults and children can exist in the absence of disease. These studies have been largely unsupported by culture and feature species that commonly contaminate low-biomass sequencing data. While 16S rRNA gene amplicon sequencing has proven to be a highly informative technique in many clinical contexts, it is susceptible to spurious signal arising from sequencing reagent contaminants where sample biomass is low. Combining culture-based and molecular techniques, we undertook a detailed investigation of the evidence for bacterial colonization of the healthy middle ear. In finding no evidence of viable bacterial cells in middle ear samples, our study further underlines the importance of careful consideration of amplicon sequence data derived from very-low-biomass contexts and the value of analytical approaches that combine culture and molecular techniques.

scholarly journals Archaeal Abundance across a pH Gradient in an Arable Soil and Its Relationship to Bacterial and Fungal Growth Rates

2012 ◽  
Vol 78 (16) ◽  
pp. 5906-5911 ◽  
Author(s):  
Per Bengtson ◽  
Anna E. Sterngren ◽  
Johannes Rousk
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Soil Ph ◽  
Fungal Growth ◽  
Ph Gradient ◽  
Gene Copy ◽  
Rrna Gene ◽  
Copy Numbers ◽  
Ph Range ◽  
Gene Copy Numbers

ABSTRACTSoil pH is one of the most influential factors for the composition of bacterial and fungal communities, but the influence of soil pH on the distribution and composition of soil archaeal communities has yet to be systematically addressed. The primary aim of this study was to determine how total archaeal abundance (quantitative PCR [qPCR]-based estimates of 16S rRNA gene copy numbers) is related to soil pH across a pH gradient (pH 4.0 to 8.3). Secondarily, we wanted to assess how archaeal abundance related to bacterial and fungal growth rates across the same pH gradient. We identified two distinct and opposite effects of pH on the archaeal abundance. In the lowest pH range (pH 4.0 to 4.7), the abundance of archaea did not seem to correspond to pH. Above this pH range, there was a sharp, almost 4-fold decrease in archaeal abundance, reaching a minimum at pH 5.1 to 5.2. The low abundance of archaeal 16S rRNA gene copy numbers at this pH range then sharply increased almost 150-fold with pH, resulting in an increase in the ratio between archaeal and bacterial copy numbers from a minimum of 0.002 to more than 0.07 at pH 8. The nonuniform archaeal response to pH could reflect variation in the archaeal community composition along the gradient, with some archaea adapted to acidic conditions and others to neutral to slightly alkaline conditions. This suggestion is reinforced by observations of contrasting outcomes of the (competitive) interactions between archaea, bacteria, and fungi toward the lower and higher ends of the examined pH gradient.

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