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      Defining the transcriptomic landscape of the developing enteric nervous system and its cellular environment

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          Abstract

          Background

          Motility and the coordination of moving food through the gastrointestinal tract rely on a complex network of neurons known as the enteric nervous system (ENS). Despite its critical function, many of the molecular mechanisms that direct the development of the ENS and the elaboration of neural network connections remain unknown. The goal of this study was to transcriptionally identify molecular pathways and candidate genes that drive specification, differentiation and the neural circuitry of specific neural progenitors, the phox2b expressing ENS cell lineage, during normal enteric nervous system development. Because ENS development is tightly linked to its environment, the transcriptional landscape of the cellular environment of the intestine was also analyzed.

          Results

          Thousands of zebrafish intestines were manually dissected from a transgenic line expressing green fluorescent protein under the phox2b regulatory elements [ Tg(phox2b:EGFP) w37 ]. Fluorescence-activated cell sorting was used to separate GFP-positive phox2b expressing ENS progenitor and derivatives from GFP-negative intestinal cells. RNA-seq was performed to obtain accurate, reproducible transcriptional profiles and the unbiased detection of low level transcripts. Analysis revealed genes and pathways that may function in ENS cell determination, genes that may be identifiers of different ENS subtypes, and genes that define the non-neural cellular microenvironment of the ENS. Differential expression analysis between the two cell populations revealed the expected neuronal nature of the phox2b expressing lineage including the enrichment for genes required for neurogenesis and synaptogenesis, and identified many novel genes not previously associated with ENS development. Pathway analysis pointed to a high level of G-protein coupled pathway activation, and identified novel roles for candidate pathways such as the Nogo/Reticulon axon guidance pathway in ENS development.

          Conclusion

          We report the comprehensive gene expression profiles of a lineage-specific population of enteric progenitors, their derivatives, and their microenvironment during normal enteric nervous system development. Our results confirm previously implicated genes and pathways required for ENS development, and also identify scores of novel candidate genes and pathways. Thus, our dataset suggests various potential mechanisms that drive ENS development facilitating characterization and discovery of novel therapeutic strategies to improve gastrointestinal disorders.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-017-3653-2) contains supplementary material, which is available to authorized users.

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          Most cited references 186

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

           K Livak,  T Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Trimmomatic: a flexible trimmer for Illumina sequence data

              Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Affiliations
                [1 ]GRID grid.34421.30, Department of Genetics, Development and Cell Biology, , Iowa State University, ; Ames, IA 50011 USA
                [2 ]GRID grid.94365.3d, , Present Address: National Cancer Institute, US National Institutes of Health, ; Bethesda, Maryland USA
                [3 ]Present address: North Central Regional Plant Introduction Station, 1305 State Ave, Ames, IA 50014 USA
                [4 ]Present address: Pioneer Hi-Bred International, Johnson, IA 50131 USA
                [5 ]GRID grid.34421.30, , 642 Science II, Iowa State University, ; Ames, IA 50011 USA
                Contributors
                sweta@iastate.edu
                kevin87@iastate.edu
                hseinchao.chou@nih.gov
                narinder.pal@ars.usda.gov
                cafarris@alumni.iastate.edu
                sqs@iastate.edu
                jkuhlman@iastate.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                12 April 2017
                12 April 2017
                2017
                : 18
                5389105 3653 10.1186/s12864-017-3653-2
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100001024, Roy J. Carver Charitable Trust;
                Award ID: ID0ETABG19824
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009227, Iowa State University;
                Award ID: ID0E2HBG19826
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005280, American Association of University Women;
                Award ID: ID0EPGBG19825
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

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