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      Growth and stress response mechanisms underlying post-feeding regenerative organ growth in the Burmese python

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          Abstract

          Background

          Previous studies examining post-feeding organ regeneration in the Burmese python ( Python molurus bivittatus) have identified thousands of genes that are significantly differentially regulated during this process. However, substantial gaps remain in our understanding of coherent mechanisms and specific growth pathways that underlie these rapid and extensive shifts in organ form and function. Here we addressed these gaps by comparing gene expression in the Burmese python heart, liver, kidney, and small intestine across pre- and post-feeding time points (fasted, one day post-feeding, and four days post-feeding), and by conducting detailed analyses of molecular pathways and predictions of upstream regulatory molecules across these organ systems.

          Results

          Identified enriched canonical pathways and upstream regulators indicate that while downstream transcriptional responses are fairly tissue specific, a suite of core pathways and upstream regulator molecules are shared among responsive tissues. Pathways such as mTOR signaling, PPAR/LXR/RXR signaling, and NRF2-mediated oxidative stress response are significantly differentially regulated in multiple tissues, indicative of cell growth and proliferation along with coordinated cell-protective stress responses. Upstream regulatory molecule analyses identify multiple growth factors, kinase receptors, and transmembrane receptors, both within individual organs and across separate tissues. Downstream transcription factors MYC and SREBF are induced in all tissues.

          Conclusions

          These results suggest that largely divergent patterns of post-feeding gene regulation across tissues are mediated by a core set of higher-level signaling molecules. Consistent enrichment of the NRF2-mediated oxidative stress response indicates this pathway may be particularly important in mediating cellular stress during such extreme regenerative growth.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-017-3743-1) contains supplementary material, which is available to authorized users.

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          Most cited references43

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          STEM: a tool for the analysis of short time series gene expression data

          Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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            maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments.

            Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.
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              Expanding roles for SREBP in metabolism.

              Sterol regulatory element-binding protein (SREBP) transcription factors regulate cellular lipogenesis and lipid homeostasis. Recent studies reveal expanding roles for SREBPs with the description of new regulatory mechanisms, the identification of unexpected transcriptional targets, and the discovery of functions for SREBPs in type II diabetes, cancer, immunity, neuroprotection, and autophagy. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                aandrew@uta.edu
                blair.perry@uta.edu
                dcard@uta.edu
                dschield@uta.edu
                robert.ruggiero.phd@gmail.com
                suzanne.mcgaugh@gmail.com
                achoud@broadinstitute.org
                ssecor@ua.edu
                817-272-9084 , todd.castoe@uta.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                2 May 2017
                2 May 2017
                2017
                : 18
                : 338
                Affiliations
                [1 ]ISNI 0000 0001 2181 9515, GRID grid.267315.4, Department of Biology, , The University of Texas Arlington, ; 501 S. Nedderman Dr, Arlington, TX 76019 USA
                [2 ]GRID grid.440573.1, Department of Biology, , New York University Abu Dhabi, Saadiyat Island, ; Abu Dhabi, United Arab Emirates
                [3 ]ISNI 0000000419368657, GRID grid.17635.36, Department of Ecology, Evolution, and Behavior, , University of Minnesota, ; St. Paul, MN 55108 USA
                [4 ]ISNI 000000041936754X, GRID grid.38142.3c, , Harvard Medical School, Renal Division, Brigham and Woman’s Hospital, ; Cambridge, MA 02142 USA
                [5 ]GRID grid.66859.34, , Center for the Science of Therapeutics, Broad Institute, ; Cambridge, MA 02142 USA
                [6 ]ISNI 0000 0001 0727 7545, GRID grid.411015.0, Department of Biological Sciences, , University of Alabama, ; Tuscaloosa, AL 35487 Box 870344, USA
                Article
                3743
                10.1186/s12864-017-3743-1
                5412052
                28464824
                e9d13ed2-7061-4950-a1f0-0d28156e2ea8
                © 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.

                History
                : 17 October 2016
                : 27 April 2017
                Funding
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/100000154, Division of Integrative Organismal Systems;
                Award ID: IOB-0466139
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                hyperplasia,hypertrophy,nrf2,mtor,regeneration,rnaseq
                Genetics
                hyperplasia, hypertrophy, nrf2, mtor, regeneration, rnaseq

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