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Genome-wide analysis of primary microRNA expression using H3K36me3 ChIP-seq data.

ABSTRACT: MicroRNAs are key players in gene regulatory networks controlling cell homeostasis. Their altered expression has been previously linked to disease outcomes and microRNAs thus serve as biomarkers for disease diagnostics. However, their synthesis and its transcriptional regulation have been challenging to investigate. In this study, we validated the use of H3K36me3 histone modification for the quantification of microRNA transcription levels using data from the ENCODE Consortium and then applied this approach to provide new insight into the cell-type-specific regulation in tissues, cell line models and cardiac disease. In cardiomyocytes derived from patients suffering from septal defects, carrying a G296S mutation in the transcription factor GATA4, we show that microRNA gene transcription is altered in cardiomyocytes carrying this mutation and coincides with novel super-enhancers formed within regulatory domains defined using chromatin interaction profiles. The most prominently elevated primary transcript encodes for let-7a and miR-100 that may target genes in the Hippo signaling pathway. Collectively, our work presents a methodology to quantify microRNA gene expression using histone marker data and paves the way for functional studies of cell-type-specific transcriptional regulation occurring in disease pathology.

SUBMITTER: Turunen T

PROVIDER: S-EPMC8082160 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Genome-wide analysis of primary microRNA expression using H3K36me3 ChIP-seq data.

Turunen Tanja T Hernández de Sande Ana A Pölönen Petri P Heinäniemi Merja M

Computational and structural biotechnology journal 20210405

MicroRNAs are key players in gene regulatory networks controlling cell homeostasis. Their altered expression has been previously linked to disease outcomes and microRNAs thus serve as biomarkers for disease diagnostics. However, their synthesis and its transcriptional regulation have been challenging to investigate. In this study, we validated the use of H3K36me3 histone modification for the quantification of microRNA transcription levels using data from the ENCODE Consortium and then applied th ...[more]

PMID: 33995896

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Project description:BackgroundChIPx (i.e., ChIP-seq and ChIP-chip) is increasingly used to map genome-wide transcription factor (TF) binding sites. A single ChIPx experiment can identify thousands of TF bound genes, but typically only a fraction of these genes are functional targets that respond transcriptionally to perturbations of TF expression. To identify promising functional target genes for follow-up studies, researchers usually collect gene expression data from TF perturbation experiments to determine which of the TF targets respond transcriptionally to binding. Unfortunately, approximately 40% of ChIPx studies do not have accompanying gene expression data from TF perturbation experiments. For these studies, genes are often prioritized solely based on the binding strengths of ChIPx signals in order to choose follow-up candidates. ChIPXpress is a novel method that improves upon this ChIPx-only ranking approach by integrating ChIPx data with large amounts of Publicly available gene Expression Data (PED).ResultsWe demonstrate that PED does contain useful information to identify functional TF target genes despite its inherent heterogeneity. A truncated absolute correlation measure is developed to better capture the regulatory relationships between TFs and their target genes in PED. By integrating the information from ChIPx and PED, ChIPXpress can significantly increase the chance of finding functional target genes responsive to TF perturbation among the top ranked genes. ChIPXpress is implemented as an easy-to-use R/Bioconductor package. We evaluate ChIPXpress using 10 different ChIPx datasets in mouse and human and find that ChIPXpress rankings are more accurate than rankings based solely on ChIPx data and may result in substantial improvement in prediction accuracy, irrespective of which peak calling algorithm is used to analyze the ChIPx data.ConclusionsChIPXpress provides a new tool to better prioritize TF bound genes from ChIPx experiments for follow-up studies when investigators do not have their own gene expression data. It demonstrates that the regulatory information from PED can be used to boost ChIPx data analyses. It also represents an important step towards more fully utilizing the valuable, but highly heterogeneous data contained in public gene expression databases.

Project description:ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing, is increasingly being used for mapping protein-DNA interactions in-vivo on a genome scale. Typically, short sequence reads from ChIP-Seq are mapped to a reference genome for further analysis. Although genomic regions enriched with mapped reads could be inferred as approximate binding regions, short read lengths (approximately 25-50 nt) pose challenges for determining the exact binding sites within these regions. Here, we present SISSRs (Site Identification from Short Sequence Reads), a novel algorithm for precise identification of binding sites from short reads generated from ChIP-Seq experiments. The sensitivity and specificity of SISSRs are demonstrated by applying it on ChIP-Seq data for three widely studied and well-characterized human transcription factors: CTCF (CCCTC-binding factor), NRSF (neuron-restrictive silencer factor) and STAT1 (signal transducer and activator of transcription protein 1). We identified 26 814, 5813 and 73 956 binding sites for CTCF, NRSF and STAT1 proteins, respectively, which is 32, 299 and 78% more than that inferred previously for the respective proteins. Motif analysis revealed that an overwhelming majority of the identified binding sites contained the previously established consensus binding sequence for the respective proteins, thus attesting for SISSRs' accuracy. SISSRs' sensitivity and precision facilitated further analyses of ChIP-Seq data revealing interesting insights, which we believe will serve as guidance for designing ChIP-Seq experiments to map in vivo protein-DNA interactions. We also show that tag densities at the binding sites are a good indicator of protein-DNA binding affinity, which could be used to distinguish and characterize strong and weak binding sites. Using tag density as an indicator of DNA-binding affinity, we have identified core residues within the NRSF and CTCF binding sites that are critical for a stronger DNA binding.

Dataset Information

Genome-wide analysis of primary microRNA expression using H3K36me3 ChIP-seq data.

Publications

Genome-wide analysis of primary microRNA expression using H3K36me3 ChIP-seq data.

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