Dataset Information

A comparative analysis of drinking water employing metagenomics.

ABSTRACT: The microbiological content of drinking water traditionally is determined by employing culture-dependent methods that are unable to detect all microorganisms, especially those that are not culturable. High-throughput sequencing now makes it possible to determine the microbiome of drinking water. Thus, the natural microbiota of water and water distribution systems can now be determined more accurately and analyzed in significantly greater detail, providing comprehensive understanding of the microbial community of drinking water applicable to public health. In this study, shotgun metagenomic analysis was performed to determine the microbiological content of drinking water and to provide a preliminary assessment of tap, drinking fountain, sparkling natural mineral, and non-mineral bottled water. Predominant bacterial species detected were members of the phyla Actinobacteria and Proteobacteria, notably the genera Alishewanella, Salmonella, and Propionibacterium in non-carbonated non-mineral bottled water, Methyloversatilis and Methylibium in sparkling natural mineral water, and Mycobacterium and Afipia in tap and drinking fountain water. Fecal indicator bacteria, i.e., Escherichia coli or enterococci, were not detected in any samples examined in this study. Bacteriophages and DNA encoding a few virulence-associated factors were detected but determined to be present only at low abundance. Antibiotic resistance markers were detected only at abundance values below our threshold of confidence. DNA of opportunistic plant and animal pathogens was identified in some samples and these included bacteria (Mycobacterium spp.), protozoa (Acanthamoeba mauritaniensis and Acanthamoeba palestinensis), and fungi (Melampsora pinitorqua and Chryosporium queenslandicum). Archaeal DNA (Candidatus Nitrosoarchaeum) was detected only in sparkling natural mineral water. This preliminary study reports the complete microbiome (bacteria, viruses, fungi, and protists) of selected types of drinking water employing whole-genome high-throughput sequencing and bioinformatics. Investigation into activity and function of the organisms detected is in progress.

SUBMITTER: Brumfield KD

PROVIDER: S-EPMC7145143 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comparative analysis of drinking water employing metagenomics.

Brumfield Kyle D KD Hasan Nur A NA Leddy Menu B MB Cotruvo Joseph A JA Rashed Shah M SM Colwell Rita R RR Huq Anwar A

PloS one 20200409 4

The microbiological content of drinking water traditionally is determined by employing culture-dependent methods that are unable to detect all microorganisms, especially those that are not culturable. High-throughput sequencing now makes it possible to determine the microbiome of drinking water. Thus, the natural microbiota of water and water distribution systems can now be determined more accurately and analyzed in significantly greater detail, providing comprehensive understanding of the micro ...[more]

PMID: 32271799

Similar Datasets

Project description:BackgroundDuring the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community.ResultsWe developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function-regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples.ConclusionMetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: http://cqb.pku.edu.cn/ZhuLab/MetaComp/ and https://github.com/pzhaipku/MetaComp/ .

Project description:Metagenomic sequencing is becoming widespread in biomedical and environmental research, and the pace is increasing even more thanks to nanopore sequencing. With a rising number of samples and data per sample, the challenge of efficiently comparing results within a specimen and between specimens arises. Reagents, laboratory, and host related contaminants complicate such analysis. Contamination is particularly critical in low microbial biomass body sites and environments, where it can comprise most of a sample if not all. Recentrifuge implements a robust method for the removal of negative-control and crossover taxa from the rest of samples. With Recentrifuge, researchers can analyze results from taxonomic classifiers using interactive charts with emphasis on the confidence level of the classifications. In addition to contamination-subtracted samples, Recentrifuge provides shared and exclusive taxa per sample, thus enabling robust contamination removal and comparative analysis in environmental and clinical metagenomics. Regarding the first area, Recentrifuge's novel approach has already demonstrated its benefits showing that microbiomes of Arctic and Antarctic solar panels display similar taxonomic profiles. In the clinical field, to confirm Recentrifuge's ability to analyze complex metagenomes, we challenged it with data coming from a metagenomic investigation of RNA in plasma that suffered from critical contamination to the point of preventing any positive conclusion. Recentrifuge provided results that yielded new biological insight into the problem, supporting the growing evidence of a blood microbiota even in healthy individuals, mostly translocated from the gut, the oral cavity, and the genitourinary tract. We also developed a synthetic dataset carefully designed to rate the robust contamination removal algorithm, which demonstrated a significant improvement in specificity while retaining a high sensitivity even in the presence of cross-contaminants. Recentrifuge's official website is www.recentrifuge.org. The data and source code are anonymously and freely available on GitHub and PyPI. The computing code is licensed under the AGPLv3. The Recentrifuge Wiki is the most extensive and continually-updated source of documentation for Recentrifuge, covering installation, use cases, testing, and other useful topics.

Project description:Concentration- and time-dependent genomic changes in the mouse urinary bladder following exposure to arsenate in drinking water for up to twelve weeks. Inorganic arsenic (Asi) is a known human bladder carcinogen. The objective of this study was to examine the concentration dependence of the genomic response to Asi in the urinary bladders of mice. C57BL/6J mice were exposed for 1 or 12 weeks to arsenate in drinking water at concentrations of 0.5, 2, 10, and 50 mg As/L. Urinary bladders were analyzed using gene expression microarrays. A consistent reversal was observed in the direction of gene expression change: from predominantly decreased expression at 1 week to predominantly increased expression at 12 weeks. These results are consistent with evidence from in vitro studies of an acute adaptive response that is suppressed on longer exposure due to down-regulation of Fos. Pathways with the highest enrichment in gene expression changes were associated with epithelial-to-mesenchymal transition, inflammation, and proliferation. Benchmark dose (BMD) analysis determined that the lowest median BMD values for pathways were above 5 mg As/L, despite the fact that pathway enrichment was observed at the 0.5 mg As/L exposure concentration. This disparity may result from the non-monotonic nature of the concentration-responses for the expression changes of a number of genes, as evidenced by the much fewer gene expression changes at 2 mg As/L compared to lower or higher concentrations. Pathway categories with concentration-related gene expression changes included cellular morphogenesis, inflammation, apoptosis/survival, cell cycle control, and DNA damage response. The results of this study provide evidence of a concentration-dependent transition in the mode of action for the subchronic effects of Asi in mouse bladder cells in the vicinity of 2 mg Asi/L. Female C57Bl/J mice will be exposed to COLD Arsenate in drinking water. One week interium sac on 7/18/06. 100 samples including liver, lung, kidney and bladder. Bladder will be analyzed with microarrays, 24 samples. Twelve week terminal sac on 10/05/06. 75 total samples including lung, kidney, and bladder. Bladdler will be analyzed by microarray, 25 samples. Drinking water containing As will be prepared weekly with monitoring to determine amount used by mice. Following tissues will be available for genomic study: Bladder, liver, lung, and kidney.

Dataset Information

A comparative analysis of drinking water employing metagenomics.

Publications

A comparative analysis of drinking water employing metagenomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets