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      Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer

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          Abstract

          Enterobacteria, especially Escherichia coli, are abundant in patients with inflammatory bowel disease or colorectal cancer (CRC). However, it is unclear whether cancer is promoted by inflammation-induced expansion of E. coli and/or changes in expression of specific microbial genes. Here we use longitudinal (2, 12 and 20 weeks) 16S rRNA sequencing of luminal microbiota from ex-germ free mice to show that inflamed Il10 −/− mice maintain a higher abundance of Enterobacteriaceae than healthy wild-type mice. Experiments with mono-colonized Il10 −/− mice reveal that host inflammation is necessary for E. coli cancer-promoting activity. RNA-sequence analysis indicates significant changes in E. coli gene catalogue in Il10 −/− mice, with changes mostly driven by adaptation to the intestinal environment. Expression of specific genes present in the tumor-promoting E. coli pks island are modulated by inflammation/CRC development. Thus, progression of inflammation in Il10 −/− mice supports Enterobacteriaceae and alters a small subset of microbial genes important for tumor development.

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          Most cited references37

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            FLASH: fast length adjustment of short reads to improve genome assemblies.

            Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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              Is Open Access

              Pathview: an R/Bioconductor package for pathway-based data integration and visualization

              Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                101528555
                37539
                Nat Commun
                Nat Commun
                Nature communications
                2041-1723
                19 July 2014
                03 September 2014
                2014
                03 March 2015
                : 5
                : 4724
                Affiliations
                [1 ] Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27713, USA
                [2 ] Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
                [3 ] Bioinformatics Services Division, Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA
                [4 ] Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27713, USA
                [5 ] Department of Medicine, University of Florida, Gainesville, FL 32611, USA
                [6 ] Department of Infectious Diseases and Pathology, University of Florida, Gainesville, FL 32611, USA
                Author notes
                [§]

                These authors contributed equally to this work

                [* ]These authors are corresponding authors
                [#]

                Current address: Department of Medicine Division of Medical Oncology Duke University Box 3382 2141 CIEMAS Bldg. Duke University Durham, NC 27708

                Authors' contributions

                Study concept and design (JCA, JMU, MM, AAF, CJ); Acquisition of data (JCA, JMU, RZG, MM, EPC); Analysis and interpretation of data (JCA, RZG, MM, AAF, CJ); Drafting of Manuscript (JCA, RZG); Revision of manuscript for intellectual content (AAF, CJ); Statistical analysis (RZG, JM, AAF); Obtained funding (CJ); Study supervision (AAF, CJ).

                Article
                NIHMS614695
                10.1038/ncomms5724
                4155410
                25182170
                efef72a4-4dc7-4c8f-ab0c-0e1eb7b72a9d
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