Positional weight matrices have sufficient prediction power for analysis of noncoding variants

The commonly accepted model to quantify the specificity of transcription factor binding to DNA is the position weight matrix, also called the position-specific scoring matrix. Position weight matrices are used in thousands of projects and computational tools in regulatory genomics, including prediction of the regulatory potential of single-nucleotide variants. Yet, recently Yan et al. presented new experimental method for analysis of regulatory variants and, based on its results, reported that "the position weight matrices of most transcription factors lack sufficient predictive power". Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can successfully quantify transcription factor binding to alternative alleles.

The promise of microRNA-based therapies in Alzheimer’s disease: challenges and perspectives

Multi-pathway approaches for the treatment of complex polygenic disorders are emerging as alternatives to classical monotarget therapies and microRNAs are of particular interest in that regard. MicroRNA research has come a long way from their initial discovery to the cumulative appreciation of their regulatory potential in healthy and diseased brain. However, systematic interrogation of putative therapeutic or toxic effects of microRNAs in (models of) Alzheimer’s disease is currently missing and fundamental research findings are yet to be translated into clinical applications. Here, we review the literature to summarize the knowledge on microRNA regulation in Alzheimer’s pathophysiology and to critically discuss whether and to what extent these increasing insights can be exploited for the development of microRNA-based therapeutics in the clinic.

Transposable elements that have recently been mobile in the human genome

Background Transposable elements (TE) comprise nearly half of the human genome and their insertions have profound effects to human genetic diversification and as well as disease. Despite their abovementioned significance, there is no consensus on the TE subfamilies that remain active in the human genome. In this study, we therefore developed a novel statistical test for recently mobile subfamilies (RMSs), based on patterns of overlap with > 100,000 polymorphic indels. Results Our analysis produced a catalogue of 20 high-confidence RMSs, which excludes many false positives in public databases. Intriguingly though, it includes HERV-K, an LTR subfamily previously thought to be extinct. The RMS catalogue is strongly enriched for contributions to germline genetic disorders (P = 1.1e-10), and thus constitutes a valuable resource for diagnosing disorders of unknown aetiology using targeted TE-insertion screens. Remarkably, RMSs are also highly enriched for somatic insertions in diverse cancers (P = 2.8e-17), thus indicating strong correlations between germline and somatic TE mobility. Using CRISPR/Cas9 deletion, we show that an RMS-derived polymorphic TE insertion increased the expression of RPL17, a gene associated with lower survival in liver cancer. More broadly, polymorphic TE insertions from RMSs were enriched near genes with allele-specific expression, suggesting widespread effects on gene regulation. Conclusions By using a novel statistical test we have defined a catalogue of 20 recently mobile transposable element subfamilies. We illustrate the gene regulatory potential of RMS-derived polymorphic TE insertions, using CRISPR/Cas9 deletion in vitro on a specific candidate, as well as by genome wide analysis of allele-specific expression. Our study presents novel insights into TE mobility and regulatory potential and provides a key resource for human disease genetics and population history studies.

Proteome-wide quantitative RNA interactome capture (qRIC) identifies phosphorylation sites with regulatory potential in RBM20

RNA-binding proteins (RBPs) are major regulators of gene expression at the post- transcriptional level. While many posttranslational modification sites in RBPs have been identified, little is known about how these modifications regulate RBP function. Here, we developed quantitative RNA-interactome capture (qRIC) to quantify the fraction of cellular RBPs pulled down with polyadenylated mRNAs. Applying qRIC to HEK293T cells quantified pull-down efficiencies of over 300 RBPs. Combining qRIC with phosphoproteomics allowed us to systematically compare pull-down efficiencies of phosphorylated and non-phosphorylated forms of RBPs. Over hundred phosphorylation events increased or decreased pull-down efficiency compared to the unmodified RBPs and thus have regulatory potential. Our data captures known regulatory phosphorylation sites in ELAVL1, SF3B1 and UPF1 and identifies new potentially regulatory sites. Follow-up experiments on the cardiac splicing regulator RBM20 revealed that multiple phosphorylation sites in the C-terminal disordered region affect nucleo-cytoplasmic localization, association with cytosolic RNA granules and alternative splicing. Together, we show that qRIC is a scalable method to identify functional posttranslational modification sites in RBPs.

Proximity to the Promoter and Terminator Regions Regulates the Transcription Enhancement Potential of an Intron

An evolutionarily conserved feature of introns is their ability to enhance expression of genes that harbor them. Introns have been shown to regulate gene expression at the transcription and post-transcription level. The general perception is that a promoter-proximal intron is most efficient in enhancing gene expression and the effect diminishes with the increase in distance from the promoter. Here we show that the intron regains its positive influence on gene expression when in proximity to the terminator. We inserted ACT1 intron into different positions within IMD4 and INO1 genes. Transcription Run-On (TRO) analysis revealed that the transcription of both IMD4 and INO1 was maximal in constructs with a promoter-proximal intron and decreased with the increase in distance of the intron from the promoter. However, activation was partially restored when the intron was placed close to the terminator. We previously demonstrated that the promoter-proximal intron stimulates transcription by affecting promoter directionality through gene looping-mediated recruitment of termination factors in the vicinity of the promoter region. Here we show that the terminator-proximal intron also enhances promoter directionality and results in compact gene architecture with the promoter and terminator regions in close physical proximity. Furthermore, we show that both the promoter and terminator-proximal introns facilitate assembly or stabilization of the preinitiation complex (PIC) on the promoter. On the basis of these findings, we propose that proximity to both the promoter and the terminator regions affects the transcription regulatory potential of an intron, and the terminator-proximal intron enhances transcription by affecting both the assembly of preinitiation complex and promoter directionality.

CHIPS: A Snakemake pipeline for quality control and reproducible processing of chromatin profiling data

Motivation: The chromatin profile measured by ATAC-seq, ChIP-seq, or DNase-seq experiments can identify genomic regions critical in regulating gene expression and provide insights on biological processes such as diseases and development. However, quality control and processing chromatin profiling data involves many steps, and different bioinformatics tools are used at each step. It can be challenging to manage the analysis. Results: We developed a Snakemake pipeline called CHIPS (CHromatin enrIchment ProcesSor) to streamline the processing of ChIP-seq, ATAC-seq, and DNase-seq data. The pipeline supports single- and paired-end data and is flexible to start with FASTQ or BAM files. It includes basic steps such as read trimming, mapping, and peak calling. In addition, it calculates quality control metrics such as contamination profiles, polymerase chain reaction bottleneck coefficient, the fraction of reads in peaks, percentage of peaks overlapping with the union of public DNaseI hypersensitivity sites, and conservation profile of the peaks. For downstream analysis, it carries out peak annotations, motif finding, and regulatory potential calculation for all genes. The pipeline ensures that the processing is robust and reproducible. Availability: CHIPS is available at https://github.com/liulab-dfci/CHIPS.