CELF1 contributes to aberrant alternative splicing patterns in the type 1 diabetic heart

Belanger, KarryAnne; Nutter, Curtis A.; Li, Jin; Tasnim, Sadia; Liu, Peiru; Yu, Peng; Kuyumcu‐Martinez, Muge N.

doi:10.1016/j.bbrc.2018.08.126

Cited by 15 publications

(18 citation statements)

References 40 publications

(54 reference statements)

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“…To identify the DAS events, we performed DAS analysis [57][58][59][60][61] . Briefly, the raw RNA-Seq reads first were aligned to mouse genome (mm9) using STAR 62 with default settings, and those uniquely mapped reads were retained to calculate the counts of the reads for each exon and each exon-exon junction annotated in the UCSC knownGene (mm9) table 63 using the Python package HTSeq 64 .…”

Section: Das Analysis Using Rna-seq Datamentioning

confidence: 99%

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

Jin

Deng

et al. 2020

Sci Data

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A vast amount of public RNA-sequencing datasets have been generated and used widely to study transcriptome mechanisms. These data offer precious opportunity for advancing biological research in transcriptome studies such as alternative splicing. We report the first large-scale integrated analysis of RNA-Seq data of splicing factors for systematically identifying key factors in diseases and biological processes. We analyzed 1,321 RNA-Seq libraries of various mouse tissues and cell lines, comprising more than 6.6 TB sequences from 75 independent studies that experimentally manipulated 56 splicing factors. Using these data, RNA splicing signatures and gene expression signatures were computed, and signature comparison analysis identified a list of key splicing factors in Rett syndrome and cold-induced thermogenesis. We show that cold-induced RNA-binding proteins rescue the neurite outgrowth defects in Rett syndrome using neuronal morphology analysis, and we also reveal that SRSF1 and PTBP1 are required for energy expenditure in adipocytes using metabolic flux analysis. Our study provides an integrated analysis for identifying key factors in diseases and biological processes and highlights the importance of public data resources for identifying hypotheses for experimental testing.

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Section: Das Analysis Using Rna-seq Datamentioning

confidence: 99%

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

Jin

Deng

et al. 2020

Sci Data

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“…To determine how PTBP1 splicing is modulated in T1D mouse hearts and during mouse heart development, we tested whether CELF1 and RBFOX2 are involved. We have previously shown that both CELF1 and RBFOX2 protein levels are modulated in the diabetic heart [19][20][21]. These proteins are involved in developmental regulation of AS in the heart [16,26,27].…”

Section: Regulation Of Diabetes-induced and Developmentally Regulatedmentioning

confidence: 97%

“…Streptozotocin (STZ) induced T1D and non-obese diabetic (NOD) mice were used for our experiments as described previously [19][20][21]. Animal experiments approved by the University of Texas Medical Branch Institutional Animal Care and Use Committee (Protocol # 1101001) were conducted in compliance with the NIH Guidelines.…”

Section: Animal Experimentsmentioning

confidence: 99%

“…Paired end (2 × 15 cycles) RNA sequencing was done at the UTMB Next-Generation Sequencing Core facility with an Illumina HiSeq 1000 system and yielded about 200 million reads per sample [20,21]. Changes in AS were determined by DAS analysis as we previously described [19,22]. To determine PTBP1-binding sites on transcripts mis-spliced in diabetic hearts, we overlapped the differential cassette exon events identified in our diabetic heart RNA-seq data with the binding sites of PTBP1 that were defined by merging two sets of PTBP1 CLIP-Seq peaks from GSM2259090 and GSM2259091 [23].…”

Section: Differential Alternative Splicing Analysismentioning

confidence: 99%

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A developmentally regulated spliced variant of PTBP1 is upregulated in type 1 diabetic hearts

Belanger

Nutter

et al. 2019

Biochemical and Biophysical Research Communications

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Alternative splicing (AS) is dysregulated in Type 1 diabetic (T1D) hearts but mechanisms responsible are unclear. Here, we provide evidence that the RNA binding protein (RBP) PTBP1 is modulated in adult T1D hearts contributing to AS changes. We show that a spliced variant of PTBP1 that is highly expressed in normal newborn mouse hearts is aberrantly expressed in adult T1D mouse hearts. Comparing known PTBP1-target datasets to our T1D mouse transcriptome datasets, we discovered a group of genes with PTBP1 binding sites in their premRNAs that are differentially spliced in T1D mouse hearts. We demonstrated that inducible expression of diabetesinduced PTBP1 spliced variant has less repressive splicing function. Notably, PTBP1 regulates AS of some of its targets antagonistically to RBFOX2. In sum, our results indicate that diabetic conditions disrupt developmental regulation of PTBP1 leading to differential AS of PTBP1 target genes. Identification of PTBP1 and PTBP1-regulated RNA networks can provide RNA-based therapies for the treatment of diabetes cardiac complications.

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“…To identify the DAS events, we performed DAS analysis 57,58,59,60,61 . Briefly, the raw RNA-Seq reads first were aligned to mouse genome (mm9) using STAR 62 with default settings, and those uniquely mapped reads were retained to calculate the counts of the reads for each exon and each exon-exon junction annotated in the UCSC knownGene (mm9) table 63 using the Python package HTSeq 64 .…”

Section: Das Analysis Using Rna-seq Datamentioning

confidence: 99%

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

Jin

Deng

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

A vast amount of public RNA-sequencing datasets have been generated and used widely to study transcriptome mechanisms. These data offer precious opportunity for advancing biological research in transcriptome studies such as alternative splicing. We report the first large-scale integrated analysis of RNA-Seq data of splicing factors for systematically identifying key factors in diseases and biological processes. We analyzed 1,321 RNA-Seq libraries of various mouse tissues and cell lines, comprising more than 6.6 TB sequences from 75 independent studies that experimentally manipulated 56 splicing factors. Using these data, RNA splicing signatures and gene expression signatures were computed, and signature comparison analysis identified a list of key splicing factors in Rett syndrome and cold-induced thermogenesis. We show that coldinduced RNA-binding proteins rescue the neurite outgrowth defects in Rett syndrome using neuronal morphology analysis, and we also reveal that SRSF1 and PTBP1 are required for energy expenditure in adipocytes using metabolic flux analysis. Our study provides an integrated analysis for identifying key factors in diseases and biological processes and highlights the importance of public data resources for identifying hypotheses for experimental testing.

show abstract

CELF1 contributes to aberrant alternative splicing patterns in the type 1 diabetic heart

Cited by 15 publications

References 40 publications

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

A developmentally regulated spliced variant of PTBP1 is upregulated in type 1 diabetic hearts

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

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