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Viruses in the fecal microbiota of monozygotic twins and their mothers

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      Abstract

      Viral diversity and lifecycles are poorly understood in the human gut and other body habitats. Therefore, we sequenced the viromes (metagenomes) of virus-like particles isolated from fecal samples collected from adult female monozygotic twins and their mothers at three time points over a one-year period. These datasets were compared to datasets of sequenced bacterial 16S rRNA genes and total fecal community DNA. Co-twins and their mothers share a significantly greater degree of similarity in their fecal bacterial communities than do unrelated individuals. In contrast, viromes are unique to individuals regardless of their degree of genetic relatedness. Despite remarkable interpersonal variations in viromes and their encoded functions, intrapersonal diversity is very low, with >95% of virotypes retained over the period surveyed, and with viromes dominated by a few temperate phage that exhibit remarkable genetic stability. These results indicate that a predatory viral-microbial dynamic, manifest in a number of other characterized environmental ecosystems, is notably absent in the very distal intestine.

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      Most cited references 53

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      MUSCLE: multiple sequence alignment with high accuracy and high throughput.

       Robert Edgar (2004)
      We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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        QIIME allows analysis of high-throughput community sequencing data.

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          Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

          The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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            Author and article information

            Affiliations
            [1 ]Center for Genome Sciences, Washington University School of Medicine, St. Louis, MO 63108
            [2 ]Department of Biology, San Diego State University, San Diego, CA 92182
            [3 ]Advanced Water Management Centre, The University of Queensland, QLD, Australia
            [4 ]Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63108
            Author notes

            Author contributions A.R. and J.I.G designed the experiments, A.H. recruited the patients, A.R, M.H, and N.H. generated the data, A.R., F.A., F.R, and J.I.G. interpreted the results, A.R., F.R., and J.I.G. wrote the paper.

            Author information Virome datasets are accessible in the NCBI Short Read Archive under accession number SRA012183. 16S rRNA and fecal microbiome datasets are available in GenBank under genome project ID 32089 and SRA002775. RNA-Seq data are deposited in Gene Expression Omnibus (GSE21906; see Methods for further details).

            Correspondence to: jgordon@ 123456wustl.edu
            Journal
            0410462
            6011
            Nature
            Nature
            0028-0836
            1476-4687
            2 June 2010
            15 July 2010
            1 January 2011
            : 466
            : 7304
            : 334-338
            2919852
            20631792
            10.1038/nature09199
            nihpa208953

            Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

            Funding
            Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
            Award ID: P01 DK078669-03S1 ||DK
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