Epigenomic footprints across 111 reference epigenomes reveal tissue-specific epigenetic regulation of lincRNAs

Amin, Viren; Harris, R. Alan; Onuchic, Vitor; Jackson, Andrew; Charnecki, Tim; Paithankar, Sameer; Subramanian, Sai Lakshmi; Riehle, Kevin; Coarfa, Cristian; Milosavljevic, Aleksandar

doi:10.1038/ncomms7370

Cited by 81 publications

(71 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using data from the Epigenetic Roadmap, we annotated the chromatin states across each polycistronic let-7 locus (Fig 3 and S2 Fig). The Roadmap database includes data from dozens of human cell types, including several of the neural lineage and pluripotent stem cells, both highly relevant to our current study[24, 25]. This analysis showed a clear distinction between the regulatory framework for the dynamically regulated let-7a3/b locus, versus that of the constitutively expressed let-7a1/let-7d/let-7f1 locus.…”

Section: Resultsmentioning

confidence: 99%

Defining Transcriptional Regulatory Mechanisms for Primary let-7 miRNAs

Gaeta

Lin

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

The let-7 family of miRNAs have been shown to control developmental timing in organisms from C. elegans to humans; their function in several essential cell processes throughout development is also well conserved. Numerous studies have defined several steps of post-transcriptional regulation of let-7 production; from pri-miRNA through pre-miRNA, to the mature miRNA that targets endogenous mRNAs for degradation or translational inhibition. Less-well defined are modes of transcriptional regulation of the pri-miRNAs for let-7. let-7 pri-miRNAs are expressed in polycistronic fashion, in long transcripts newly annotated based on chromatin-associated RNA-sequencing. Upon differentiation, we found that some let-7 pri-miRNAs are regulated at the transcriptional level, while others appear to be constitutively transcribed. Using the Epigenetic Roadmap database, we further annotated regulatory elements of each polycistron identified putative promoters and enhancers. Probing these regulatory elements for transcription factor binding sites identified factors that regulate transcription of let-7 in both promoter and enhancer regions, and identified novel regulatory mechanisms for this important class of miRNAs.

show abstract

Section: Resultsmentioning

confidence: 99%

Defining Transcriptional Regulatory Mechanisms for Primary let-7 miRNAs

Gaeta

Lin

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…We quantified histone H3 lysine 4 monomethylation (H3K4me1) and trimethylation (H3K4me3) marks in the transcription start site (TSS; +/− 3kb) of each gene to identify active enhancers and promoters, respectively(28). To identify transcriptionally active lncRNA we quantified histone 3 lysine 36 trimethlyation (H3K36me3) marks in the gene body(26).…”

Section: Methodsmentioning

confidence: 99%

“…Recent studies have shown that lncRNAs can act in cis - or trans - to regulate gene expression and promote tumorigenesis through transcriptional regulation, initiation of chromatin remodeling, modulating alternative splicing, altering protein activity or localization, and genomic imprinting(16, 22, 25–27). Similar to other regulatory elements, lncRNAs exhibit cell-type specificity with varying expression and activity across different tissue types(28). …”

Section: Introductionmentioning

confidence: 99%

Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci

Reid

Permuth

Chen

et al. 2017

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

Background Genome wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P<5×10−8). Most risk SNPs lie within non-protein-encoding regions, and we hypothesize that long non-coding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets. Methods Using imputed GWAS data from ~18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1×10−5>P>5×10−8) overlapping lncRNAs. Results Of 5,294 EOC-associated SNPs (P<1.0×10−5), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r2>0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0×10−4) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression. Conclusion lncRNAs are significantly enriched at EOC risk regions suggesting a mechanistic role for lncRNAs in driving predisposition to EOC. Impact lncRNAs represent key candidates for integrative epidemiological and functional studies. Further research on their biological role in ovarian cancer is indicated.

show abstract

“…Particular considerations for lncRNA transcript construction include sample pooling according to species and tissue type. LncRNA expression is known to demonstrate tissue speciicity [66][67][68].…”

Section: Transcript Construction and Quantiicationmentioning

confidence: 99%

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Ngoc¹,

Dudemaine²,

Fomenky³

et al. 2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

View full text Add to dashboard Cite

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a beter understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identiication of ncRNAs and functional inference of identiied ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species.

show abstract

Epigenomic footprints across 111 reference epigenomes reveal tissue-specific epigenetic regulation of lincRNAs

Cited by 81 publications

References 40 publications

Defining Transcriptional Regulatory Mechanisms for Primary let-7 miRNAs

Defining Transcriptional Regulatory Mechanisms for Primary let-7 miRNAs

Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Contact Info

Product

Resources

About