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Accurate identification of fastidious Gram-negative rods: integration of both conventional phenotypic methods and 16S rRNA gene analysis.

ABSTRACT: BACKGROUND: Accurate identification of fastidious Gram-negative rods (GNR) by conventional phenotypic characteristics is a challenge for diagnostic microbiology. The aim of this study was to evaluate the use of molecular methods, e.g., 16S rRNA gene sequence analysis for identification of fastidious GNR in the clinical microbiology laboratory. RESULTS: A total of 158 clinical isolates covering 20 genera and 50 species isolated from 1993 to 2010 were analyzed by comparing biochemical and 16S rRNA gene sequence analysis based identification. 16S rRNA gene homology analysis identified 148/158 (94%) of the isolates to species level, 9/158 (5%) to genus and 1/158 (1%) to family level. Compared to 16S rRNA gene sequencing as reference method, phenotypic identification correctly identified 64/158 (40%) isolates to species level, mainly Aggregatibacter aphrophilus, Cardiobacterium hominis, Eikenella corrodens, Pasteurella multocida, and 21/158 (13%) isolates correctly to genus level, notably Capnocytophaga sp.; 73/158 (47%) of the isolates were not identified or misidentified. CONCLUSIONS: We herein propose an efficient strategy for accurate identification of fastidious GNR in the clinical microbiology laboratory by integrating both conventional phenotypic methods and 16S rRNA gene sequence analysis. We conclude that 16S rRNA gene sequencing is an effective means for identification of fastidious GNR, which are not readily identified by conventional phenotypic methods.

SUBMITTER: de Melo Oliveira MG

PROVIDER: S-EPMC3717280 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Accurate identification of fastidious Gram-negative rods: integration of both conventional phenotypic methods and 16S rRNA gene analysis.

de Melo Oliveira Maria G MG Abels Susanne S Zbinden Reinhard R Bloemberg Guido V GV Zbinden Andrea A

BMC microbiology 20130716

<h4>Background</h4>Accurate identification of fastidious Gram-negative rods (GNR) by conventional phenotypic characteristics is a challenge for diagnostic microbiology. The aim of this study was to evaluate the use of molecular methods, e.g., 16S rRNA gene sequence analysis for identification of fastidious GNR in the clinical microbiology laboratory.<h4>Results</h4>A total of 158 clinical isolates covering 20 genera and 50 species isolated from 1993 to 2010 were analyzed by comparing biochemical ...[more]

PMID: 23855986

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Project description:BackgroundFor decades, 16S ribosomal RNA sequencing has been the primary means for identifying the bacterial species present in a sample with unknown composition. One of the most widely used tools for this purpose today is the QIIME (Quantitative Insights Into Microbial Ecology) package. Recent results have shown that the newest release, QIIME 2, has higher accuracy than QIIME, MAPseq, and mothur when classifying bacterial genera from simulated human gut, ocean, and soil metagenomes, although QIIME 2 also proved to be the most computationally expensive. Kraken, first released in 2014, has been shown to provide exceptionally fast and accurate classification for shotgun metagenomics sequencing projects. Bracken, released in 2016, then provided users with the ability to accurately estimate species or genus relative abundances using Kraken classification results. Kraken 2, which matches the accuracy and speed of Kraken 1, now supports 16S rRNA databases, allowing for direct comparisons to QIIME and similar systems.MethodsFor a comprehensive assessment of each tool, we compare the computational resources and speed of QIIME 2's q2-feature-classifier, Kraken 2, and Bracken in generating the three main 16S rRNA databases: Greengenes, SILVA, and RDP. For an evaluation of accuracy, we evaluated each tool using the same simulated 16S rRNA reads from human gut, ocean, and soil metagenomes that were previously used to compare QIIME, MAPseq, mothur, and QIIME 2. We evaluated accuracy based on the accuracy of the final genera read counts assigned by each tool. Finally, as Kraken 2 is the only tool providing per-read taxonomic assignments, we evaluate the sensitivity and precision of Kraken 2's per-read classifications.ResultsFor both the Greengenes and SILVA database, Kraken 2 and Bracken are up to 100 times faster at database generation. For classification, using the same data as previous studies, Kraken 2 and Bracken are up to 300 times faster, use 100x less RAM, and generate results that more accurate at 16S rRNA profiling than QIIME 2's q2-feature-classifier.ConclusionKraken 2 and Bracken provide a very fast, efficient, and accurate solution for 16S rRNA metataxonomic data analysis. Video Abstract.

Project description: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.

Dataset Information

Accurate identification of fastidious Gram-negative rods: integration of both conventional phenotypic methods and 16S rRNA gene analysis.

Publications

Accurate identification of fastidious Gram-negative rods: integration of both conventional phenotypic methods and 16S rRNA gene analysis.

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