Dataset Information

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria.

ABSTRACT: The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12-14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments. Although microorganisms play a crucial role in sustaining life on Earth and impacting human health, little is known about their diversity, ecology, and evolution in community structures. We used five Ozark region caves as test sites for exploring bacterial diversity and monitoring long-term biodiversity. Illumina MiSeq sequencing of five cave soil samples and a control sample revealed a total of 49 bacterial phyla, with seven major phyla: Proteobacteria, Acidobacteria, Actinobacteria, Firmicutes, Chloroflexi, Bacteroidetes, and Nitrospirae. Variation in bacterial composition was observed among the five caves studied. Sandtown Cave had the lowest richness and most divergent community composition. 16S rRNA gene-based metagenomic analysis of cave-dwelling microbial communities in the Ozark caves revealed that species abundance and diversity are vast and included ecologically, agriculturally, and economically relevant taxa.

SUBMITTER: Oliveira C

PROVIDER: S-EPMC5856467 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria.

Oliveira Cássia C Gunderman Lauren L Coles Cathryn A CA Lochmann Jason J Parks Megan M Ballard Ethan E Glazko Galina G Rahmatallah Yasir Y Tackett Alan J AJ Thomas David J DJ

Diversity 20170815 3

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12-14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments. Although microorganisms play a crucial role in sustaining life on Earth and impacting human health, little is known about their diversity, ecology, ...[more]

PMID: 29551950

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Project description:The gastrointestinal tract, the largest human microbial reservoir, is highly dynamic. The gut microbes play essential roles in causing colorectal diseases. In the present study, we explored potential keystone taxa during the development of colorectal diseases in central China. Fecal samples of some patients were collected and were allocated to the adenoma (Group A), colorectal cancer (Group C), and hemorrhoid (Group H) groups. The 16S rRNA amplicon and shallow metagenomic sequencing (SMS) strategies were used to recover the gut microbiota. Microbial diversities obtained from 16S rRNA amplicon and SMS data were similar. Group C had the highest diversity, although no significant difference in diversity was observed among the groups. The most dominant phyla in the gut microbiota of patients with colorectal diseases were Bacteroidetes, Firmicutes, and Proteobacteria, accounting for >95% of microbes in the samples. The most abundant genera in the samples were Bacteroides, Prevotella, and Escherichia/Shigella, and further species-level and network analyses identified certain potential keystone taxa in each group. Some of the dominant species, such as Prevotella copri, Bacteroides dorei, and Bacteroides vulgatus, could be responsible for causing colorectal diseases. The SMS data recovered diverse antibiotic resistance genes of tetracycline, macrolide, and beta-lactam, which could be a result of antibiotic overuse. This study explored the gut microbiota of patients with three different types of colorectal diseases, and the microbial diversity results obtained from 16S rRNA amplicon sequencing and SMS data were found to be similar. However, the findings of this study are based on a limited sample size, which warrants further large-scale studies. The recovery of gut microbiota profiles in patients with colorectal diseases could be beneficial for future diagnosis and treatment with modulation of the gut microbiota. Moreover, SMS data can provide accurate species- and gene-level information, and it is economical. It can therefore be widely applied in future clinical metagenomic studies.

Project description:Introduction:Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. Objectives:To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. Methods:We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. Results:A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Conclusions:Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Dataset Information

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria.

Publications

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria.

Similar Datasets

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