Highly accessible AU-rich regions in 3′ untranslated regions are hotspots for binding of proteins and miRNAs

Mireya Plass; Simon H Rasmussen; Anders Krogh

doi:10.1101/042986

Home/Archive/bioRxiv

bioRxiv · 10.1101/042986

Highly accessible AU-rich regions in 3′ untranslated regions are hotspots for binding of proteins and miRNAs

Mireya Plass·Simon H Rasmussen·Anders Krogh

View original source ↗Export metadata ↓

Abstract

BackgroundMicroRNAs (miRNAs) are endogenous short non-coding RNAs involved in the regulation of gene expression at the post-transcriptional level typically by promoting destabilization or translational repression of target RNAs. Sometimes this regulation is absent or different, which likely is the result of interactions with other post-transcriptional factors, particularly RNA-binding proteins (RBPs). Despite the importance of the interactions between RBPs and miRNAs, little is known about how they affect post-transcriptional regulation in a global scale.\n\nResultsIn this study, we have analyzed CLIP datasets of 49 RBPs in HEK293 cells with the aim of understanding the interplay between RBPs and miRNAs in post-transcriptional regulation. Our results show that RBPs bind preferentially in conserved regulatory hotspots that frequently contain miRNA target sites. This organization facilitates the competition and cooperation among RBPs and the regulation of miRNA target site accessibility. In some cases RBP enrichment on target sites correlates with miRNA expression, suggesting coordination between the regulatory factors. However, in most cases, competition among factors is the most plausible interpretation of our data. Upon AGO2 knockdown, transcripts that contain such hotspots that overlap target sites of expressed miRNAs in 3UTRs are significantly less up-regulated than transcripts without them, suggesting that RBP binding limits miRNA accessibility.\n\nConclusionsWe show that RBP binding is concentrated in regulatory hotspots in 3UTRs. The presence of these hotspots facilitates the interaction among post-transcriptional regulators, that interact or compete with each other under different conditions. These hotspots are enriched in genes with regulatory functions such as DNA binding and RNA binding. Taken together, our results suggest that hotspots are important regulatory regions that define an extra layer of auto-regulatory control of post-transcriptional regulation.

Explore related subjects

Bioinformatics Preprint

Keep this discovery

BibTeX RIS

Mireya Plass, Simon H Rasmussen, Anders Krogh. 2016-03-10. Highly accessible AU-rich regions in 3′ untranslated regions are hotspots for binding of proteins and miRNAs. https://doi.org/10.1101/042986

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

iCARE: An R Package to Build and Apply Absolute Risk Models

This report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individuals risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Bioinformatics↗

bioRxiv2016

deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

SummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.\n\nAvailabilitythe C++ source code of deSPI is available at https://github.com/hitbc/deSPI\n\nContactydwang@hit.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗

bioRxiv2016

De novo assembly of viral quasispecies using overlap graphs

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Bioinformatics↗