Journal of Clinical Investigation · 2014 · 69 citations · 29 references
Life SciencesGeneticsMolecular BiologyGene ExpressionGenomicsTranscriptional RegulationHypertrophic Cardiomyopathy GeneticsTranscription Start SitesTranscriptomics Of Stress ResponsesCardiomyopathy GenomicsGenetic Modifier EffectsNext-generation Sequencing In CardiomyopathyPathogenic EffectsProtein Isoform AnalysisStress-induced Transcriptional ProfilingTranscriptome Sequencing
The transcriptome is subject to multiple changes during pathogenesis, including the use of alternate 5' start-sites that can affect transcription levels and output. Current RNA sequencing techniques can assess mRNA levels, but do not robustly detect changes in 5' start-site use. Here, we developed a transcriptome sequencing strategy that detects genome-wide changes in start-site usage (5'RNA-Seq) and applied this methodology to identify regulatory events that occur in hypertrophic cardiomyopathy (HCM). Compared with transcripts from WT mice, 92 genes had altered start-site usage in a mouse model of HCM, including four-and-a-half LIM domains protein 1 (Fhl1). HCM-induced altered transcriptional regulation of Fhl1 resulted in robust myocyte expression of a distinct protein isoform, a response that was conserved in humans with genetic or acquired cardiomyopathies. Genetic ablation of Fhl1 in HCM mice was deleterious, which suggests that Fhl1 transcriptional changes provide salutary effects on stressed myocytes in this disease. Because Fhl1 is a chromosome X-encoded gene, stress-induced changes in its transcription may contribute to gender differences in the clinical severity of HCM. Our findings indicate that 5'RNA-Seq has the potential to identify genome-wide changes in 5' start-site usage that are associated with pathogenic phenotypes.
29
Mapping and quantifying mammalian transcriptomes by RNA-Seq
A Mortazavi, Brian A. Williams, Kenneth McCue et al. · Nature Methods · 2008 · 14.3K citations
TopHat: discovering splice junctions with RNA-Seq
Cole Trapnell, Lior Pachter, Steven L. Salzberg · Bioinformatics · 2009 · 12.1K citations · Full text
The Significance of Digital Gene Expression Profiles
Stéphane Audic, Jean‐Michel Claverie · Genome Research · 1997 · 2.9K citations · Full text