High-resolution microbial community reconstruction by integrating short reads from multiple 16S rRNA regions.
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ABSTRACT:
SUBMITTER: Amir A
PROVIDER: S-EPMC3905898 | biostudies-literature | 2013 Dec
REPOSITORIES: biostudies-literature
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Project description:Two consortia (Consortium A and Consortium B) that can use 1,4-dioxane (a groundwater contaminant of emerging concern) as the sole carbon source were enriched from Rice University (Houston, TX, USA) campus soil. Phylogenetic analysis by 16S rRNA sequencing revealed the dominant genus in both of the consortia is Mycobacterium (56% in Consortium A and 49% in Consortium B). The predominance of Mycobacterium spp, in these consortia support the notion that this is an important and commonly encountered genus of dioxane degraders. Among other genera present that make at least 2% of these consortia, only Afipia encompasses a strain (i.e., Afipia sp. D1) that was reported to degrade dioxane as sole carbon and energy source. A nested PCR analysis using two degenerate primers to target the hydroxylase alpha subunit of groups 3 to 6 SDIMOs was performed to gain insights into which enzymes were responsible for dioxane degradation by these consortia. The purified products obtained from the second PCR run were sequenced and compared to genes databases (NCBI) encompassing all of the currently reported SDIMOs. The dominant SDIMO genes in Consortium A corresponded to a group-6 putative propane monooxygenase-like SDIMO (98.8%); while in Consortium B, SDIMO genes from both groups 5 (47.3%) and 6 (51.9%) were observed. In both consortia, the relative abundance of thmA/dxmA gene was negligible (0.03%), which is consistent with the negative amplification of these genes as verified in qPCR. Overall, the high relative abundance of group-6 putative propane monooxygenases in our two consortia suggests the novel finding that group 6-SDIMOs could also play an important role in dioxane degradation. This underscores the need for further research on genes and enzymes involved in dioxane biodegradation to develop novel biomarkers that can be useful for forensic analysis and performance assessment of bioremediation and natural attenuation at dioxane-impacted sites. DNA was extracted from bacteria biomass harvested in exponential growth phase, when half or more of the added dioxane (100 mg/L) was consumed. Total DNA extractions were performed using the UltraClean® Microbial DNA Isolation Kit (MO BIO, Carlsbad, CA, USA) according to the manufacturer’s protocol. The V4 region of the 16S rRNA gene was amplified by PCR using the forward 515F and reverse 806R primers. Sequencing was performed at MR DNA (www.mrdnalab.com, Shallowater, TX, USA) by Illumina MiSeq paired-end sequencing (approximately 2×300 bp as the read length). Sequence data were processed using MR DNA analysis pipeline. Operational taxonomic units (OTUs) were defined by clustering at 3% divergence (97% similarity). Final OTUs were taxonomically classified using BLASTn against the RDPII (http://rdp.cme.msu.edu) and NCBI (www.ncbi.nlm.nih.gov) databases.Previously designed degenerate primers NVC57, NVC58, NVC65 and NVC66 to target conserved regions in the soluble di-iron monooxygenases (SDIMO) alpha subunit gene (groups 3 to 6) were used to examine the presence and diversity of SDIMO genes in these two consortia. A nested PCR strategy was used to increase the PCR product yield. In the first run, the PCR mixture contained 1 µL of NVC65 and NVC58 primer mixture (10 µM), 20 ng of the extracted genomic DNA, 12.5 µL of KAPA HiFi HotStart ReadyMix (2X) (KAPA Biosystems, Wilmington, MA, USA), and nuclease-free water to yield a total volume of 25 µL. PCR was performed in a Bio-Rad Thermal Cycler (Bio-Rad, Hercules, CA, USA) with the following temperature profile: initial denaturation (94°C, 5 min), then 29 amplification cycles (94°C for 30 s, 55°C for 30 s, 72°C for 1 min per kb) and a final extension (72°C for 5 min). The length of the PCR products in the first run was checked by 1% agarose gel and DNA bands of the correct size (1100 bp) were excised and purified. 20 ng of the purified PCR product was used as the DNA template in the second run, with the second set of primers (NVC57 and NVC66). The purified product (420 bp) from the second PCR was sent to MR DNA (www.mrdnalab.com, Shallowater, TX, USA) for Illumina MiSeq paired-end sequencing (approximately 2×300 bp as the read length). Sequence data were processed using MR DNA analysis pipeline. Operational taxonomic units (OTUs) were defined by clustering at 3% divergence (97% similarity). A database including all of the currently reported SDIMO genes on NCBI was created and used to taxonomically classify the final OTUs.
2018-05-01 | GSE97756 | GEO
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.
| S-EPMC7455996 | biostudies-literature
Project description:Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ∼8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.
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Project description:Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.
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Project description:With the aid of next-generation sequencing technology, researchers can now obtain millions of microbial signature sequences for diverse applications ranging from human epidemiological studies to global ocean surveys. The development of advanced computational strategies to maximally extract pertinent information from massive nucleotide data has become a major focus of the bioinformatics community. Here, we describe a novel analytical strategy including discriminant and topology analyses that enables researchers to deeply investigate the hidden world of microbial communities, far beyond basic microbial diversity estimation. We demonstrate the utility of our approach through a computational study performed on a previously published massive human gut 16S rRNA data set. The application of discriminant and topology analyses enabled us to derive quantitative disease-associated microbial signatures and describe microbial community structure in far more detail than previously achievable. Our approach provides rigorous statistical tools for sequence-based studies aimed at elucidating associations between known or unknown organisms and a variety of physiological or environmental conditions.
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Project description:Restoration of degraded aquatic habitats is critical to preserve and maintain ecosystem processes and economic viability. Effective restoration requires contaminant sources identification. Microbial communities are increasingly used to characterize fecal contamination sources. The objective was to determine whether nearshore and adjacent beach bacterial contamination originated from the Grand Calumet River, a highly urbanized aquatic ecosystem, and to determine if there were correlations between pathogens/feces associated bacteria in any of the samples to counts of the pathogen indicator species Escherichia coli. Water samples were collected from the river, river mouth, nearshore, and offshore sites along southern Lake Michigan. Comparisons among communities were made using beta diversity distances (weighted and unweighted Unifrac, and Bray Curtis) and Principal Coordinate Analysis of 16S rRNA gene Illumina sequence data that indicated river bacterial communities differed significantly from the river mouth, nearshore lake, and offshore lake samples. These differences were further supported using Source Tracker software that indicated nearshore lake communities differed significantly from river and offshore samples. Among locations, there was separation by sampling date that was associated with environmental factors (e.g., water and air temperature, water turbidity). Although about half the genera (48.1%) were common to all sampling sites, linear discriminant analysis effect size indicated there were several taxa that differed significantly among sites; there were significant positive correlations of feces-associated genera with E. coli most probable numbers. Results collectively highlight that understanding microbial communities, rather than relying solely on select fecal indicators with uncertain origin, are more useful for developing strategies to restore degraded aquatic habitats.
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Project description:Dairy farms generate a considerable amount of manure, which is applied in cropland as fertilizer. While the use of manure as fertilizer reduces the application of chemical fertilizers, the main concern with regards to manure application is microbial pollution. Manure is a reservoir of a broad range of microbial populations, including pathogens, which have potential to cause contamination and pose risks to public and animal health. Despite the widespread use of manure fertilizer, the change in microbial diversity of manure under various treatment processes is still not well-understood. We hypothesize that the microbial population of animal waste changes with manure handling used in a farm environment. Consequential microbial risk caused by animal manure may depend on manure handling. In this study, a reconnaissance effort for sampling dairy manure in California Central Valley followed by 16S rRNA analysis of content and diversity was undertaken to understand the microbiome of manure after various handling processes. The microbial community analysis of manure revealed that the population in liquid manure differs from that in solid manure. For instance, the bacteria of genus Sulfuriomonas were unique in liquid samples, while the bacteria of genus Thermos were observed only in solid samples. Bacteria of genus Clostridium were present in both solid and liquid samples. The population among liquid samples was comparable, as was the population among solid samples. These findings suggest that the mode of manure application (i.e., liquid versus solid) could have a potential impact on the microbiome of cropland receiving manure as fertilizers.
| S-EPMC5755784 | biostudies-literature
Project description:Microbial communities host unparalleled taxonomic diversity. Adequate characterization of environmental and host-associated samples remains a challenge for microbiologists, despite the advent of 16S rRNA gene sequencing. In order to increase the depth of sampling for diverse bacterial communities, we developed a method for sequencing and assembling millions of paired-end reads from the 16S rRNA gene (spanning the V3 region; ∼200 nucleotides) by using an Illumina genome analyzer. To confirm reproducibility and to identify a suitable computational pipeline for data analysis, sequence libraries were prepared in duplicate for both a defined mixture of DNAs from known cultured bacterial isolates (>1 million postassembly sequences) and an Arctic tundra soil sample (>6 million postassembly sequences). The Illumina 16S rRNA gene libraries represent a substantial increase in number of sequences over all extant next-generation sequencing approaches (e.g., 454 pyrosequencing), while the assembly of paired-end 125-base reads offers a methodological advantage by incorporating an initial quality control step for each 16S rRNA gene sequence. This method incorporates indexed primers to enable the characterization of multiple microbial communities in a single flow cell lane, may be modified readily to target other variable regions or genes, and demonstrates unprecedented and economical access to DNAs from organisms that exist at low relative abundances.
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Project description:Contemporary microbial community analysis frequently involves PCR-amplified sequences of the 16S rRNA gene (rDNA). However, this technology carries the inherent problem of heterogeneity between copies of the 16S rDNA in many species. As an alternative to 16S rDNA sequences in community analysis, we employed the gene for the RNA polymerase beta subunit (rpoB), which appears to exist in one copy only in bacteria. In the present study, the frequency of 16S rDNA heterogeneity in bacteria isolated from the marine environment was assessed using bacterial isolates from the red alga Delisea pulchra and from the surface of a marine rock. Ten strains commonly used in our laboratory were also assessed for the degree of heterogeneity between the copies of 16S rDNA and were used to illustrate the effect of this heterogeneity on microbial community pattern analysis. The rock isolates and the laboratory strains were also used to confirm nonheterogeneity of rpoB, as well as to investigate the versatility of the primers. In addition, a comparison between 16S rDNA and rpoB PCR-DGGE (denaturing gradient gel electrophoresis)-based community analyses was performed using a DNA mixture of nine isolates from D. pulchra. Eight out of 14 isolates from D. pulchra, all rock isolates, and 6 of 10 laboratory strains displayed multiple bands for 16S rDNA when analyzed by DGGE. There was no indication of heterogeneity for either the rock isolates or the laboratory strains when rpoB was used for PCR-DGGE analysis. Microbial community pattern analysis using 16S rDNA PCR-DGGE showed an overestimation of the number of laboratory strains in the sample, while some strains were not represented. Therefore, the 16S rDNA PCR-DGGE-based community analysis was proven to be severely limited by 16S rDNA heterogeneity. The mixture of isolates from D. pulchra proved to be more accurately described using rpoB, compared to the 16S rDNA-based PCR-DGGE.
| S-EPMC92158 | biostudies-literature
Project description:As new sequencing technologies become cheaper and older ones disappear, laboratories switch vendors and platforms. Validating the new setups is a crucial part of conducting rigorous scientific research. Here we report on the reliability and biases of performing bacterial 16S rRNA gene amplicon paired-end sequencing on the MiSeq Illumina platform. We designed a protocol using 50 barcode pairs to run samples in parallel and coded a pipeline to process the data. Sequencing the same sediment sample in 248 replicates as well as 70 samples from alkaline soda lakes, we evaluated the performance of the method with regards to estimates of alpha and beta diversity. Using different purification and DNA quantification procedures we always found up to 5-fold differences in the yield of sequences between individually barcodes samples. Using either a one-step or a two-step PCR preparation resulted in significantly different estimates in both alpha and beta diversity. Comparing with a previous method based on 454 pyrosequencing, we found that our Illumina protocol performed in a similar manner - with the exception for evenness estimates where correspondence between the methods was low. We further quantified the data loss at every processing step eventually accumulating to 50% of the raw reads. When evaluating different OTU clustering methods, we observed a stark contrast between the results of QIIME with default settings and the more recent UPARSE algorithm when it comes to the number of OTUs generated. Still, overall trends in alpha and beta diversity corresponded highly using both clustering methods. Our procedure performed well considering the precisions of alpha and beta diversity estimates, with insignificant effects of individual barcodes. Comparative analyses suggest that 454 and Illumina sequence data can be combined if the same PCR protocol and bioinformatic workflows are used for describing patterns in richness, beta-diversity and taxonomic composition.
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