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      Differential regulation of mouse and human nephron progenitors by the Six family of transcriptional regulators

      , , , , , , ,
      Development
      The Company of Biologists

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          Abstract

          Nephron endowment is determined by the self-renewal and induction of a nephron progenitor pool established at the onset of kidney development. In the mouse, the related transcriptional regulators Six1 and Six2 play non-overlapping roles in nephron progenitors. Transient Six1 activity prefigures, and is essential for, active nephrogenesis. By contrast, Six2 maintains later progenitor self-renewal from the onset of nephrogenesis. We compared the regulatory actions of Six2 in mouse and human nephron progenitors by chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq). Surprisingly, SIX1 was identified as a SIX2 target unique to the human nephron progenitors. Furthermore, RNA-seq and immunostaining revealed overlapping SIX1 and SIX2 activity in 16 week human fetal nephron progenitors. Comparative bioinformatic analysis of human SIX1 and SIX2 ChIP-seq showed each factor targeted a similar set of cis-regulatory modules binding an identical target recognition motif. In contrast to the mouse where Six2 binds its own enhancers but does not interact with DNA around Six1, both human SIX1 and SIX2 bind homologous SIX2 enhancers and putative enhancers positioned around SIX1. Transgenic analysis of a putative human SIX1 enhancer in the mouse revealed a transient, mouse-like, pre-nephrogenic, Six1 regulatory pattern. Together, these data demonstrate a divergence in SIX-factor regulation between mouse and human nephron progenitors. In the human, an auto/cross-regulatory loop drives continued SIX1 and SIX2 expression during active nephrogenesis. By contrast, the mouse establishes only an auto-regulatory Six2 loop. These data suggest differential SIX-factor regulation might have contributed to species differences in nephron progenitor programs such as the duration of nephrogenesis and the final nephron count.

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          Is Open Access

          MEME Suite: tools for motif discovery and searching

          The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.
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            GREAT improves functional interpretation of cis-regulatory regions.

            We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.
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              Is Open Access

              DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists

              All tools in the DAVID Bioinformatics Resources aim to provide functional interpretation of large lists of genes derived from genomic studies. The newly updated DAVID Bioinformatics Resources consists of the DAVID Knowledgebase and five integrated, web-based functional annotation tool suites: the DAVID Gene Functional Classification Tool, the DAVID Functional Annotation Tool, the DAVID Gene ID Conversion Tool, the DAVID Gene Name Viewer and the DAVID NIAID Pathogen Genome Browser. The expanded DAVID Knowledgebase now integrates almost all major and well-known public bioinformatics resources centralized by the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of diverse gene/protein identifiers and annotation terms from a variety of public bioinformatics databases. For any uploaded gene list, the DAVID Resources now provides not only the typical gene-term enrichment analysis, but also new tools and functions that allow users to condense large gene lists into gene functional groups, convert between gene/protein identifiers, visualize many-genes-to-many-terms relationships, cluster redundant and heterogeneous terms into groups, search for interesting and related genes or terms, dynamically view genes from their lists on bio-pathways and more. With DAVID (http://david.niaid.nih.gov), investigators gain more power to interpret the biological mechanisms associated with large gene lists.
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                Author and article information

                Journal
                Development
                Development
                The Company of Biologists
                0950-1991
                1477-9129
                February 16 2016
                February 15 2016
                February 16 2016
                February 15 2016
                : 143
                : 4
                : 595-608
                Article
                10.1242/dev.127175
                4760318
                26884396
                b753275c-a373-4e62-bac6-ebc3eede09db
                © 2016

                http://www.biologists.com/user-licence-1-1

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