Computational protocol: CREB mediates the insulinotropic and anti-apoptotic effects of GLP-1 signaling in adult mouse β-cells

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Protocol publication

[…] Islet chromatin was prepared from approximately 600 mouse islets each for two biological replicates, as previously described in Ref. . Immunoprecipitations were performed using anti-CREB (Santa Cruz Biotech, sc-186) as previously described in Ref. . Multiplexed libraries were prepared using the NEBNext ChIP-Seq Library Prep Reagent Set (NEB E6200) and sequenced on an Illumina hiSeq2000. Sequence reads were mapped to the mouse genome (mm8) using ELAND. Only those reads with a unique best match containing up to 2 mismatches were used for further analysis. Multiple reads mapping to identical positions were condensed to a single read to remove amplification artifacts. Peak calling was carried out by HOMER (v3.16, 0.1% false discovery rate, default settings) . [...] Following glucose-stimulated insulin secretion assays, islets were collected from four control and for CREB-deficient mice each and total RNA was extracted as described above. Multiplexed RNA-Seq libraries were prepared from 200 ng of total RNA using the NEBNext Ultra RNA Library Prep Kit (NEB). mRNA enrichment was performed using the NEBNext Poly(A) Magnetic Isolation Module (NEB). Libraries were single-end sequenced on an Illumina hiSeq2000. Sequencing reads were analyzed and gene expression profiles were determined as previously described in Ref. . Reads from ribosomal RNA and genomic repeats were identified by aligning the 5′ 50 bp of each read to ribosomal sequences and mouse repeats in RepBase using Bowtie , allowing up to three mismatches. The remaining reads were processed with RUM and aligned to the set of known transcripts included in RefSeq, UCSC known genes, and ENSEMBL transcripts, and the mouse genome (mm9). Transcript-, exon-, and intron-level quantification was done using only the uniquely aligning reads. To analyze global gene expression profiles, the number of uniquely aligning read counts to mRNA transcripts in RefSeq were extracted from the RUM output and processed by EDGE. The p-values from EDGE were corrected for multiple testing using the Benjamini & Hochberg mode of the R function p.adjust. We then summarized these data for individual genes by selecting a “representative transcript” with the highest read counts for each gene. The fold-change computed in EDGE expressed as the ratio of mutant versus control is shown in C, with those determined to be statistically significantly different indicated by stars. […]

Pipeline specifications

Software tools Bowtie, RUM
Databases Repbase
Application RNA-seq analysis
Organisms Mus musculus
Diseases Diabetes Mellitus
Chemicals Glucose, Tamoxifen