TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets

Differential Regulation of Mouse Hippocampal Gene Expression Sex Differences by Chromosomal Content and Gonadal Sex

Common neurological disorders, like Alzheimer's disease (AD), multiple sclerosis (MS), and autism, display profound sex differences in prevalence and clinical presentation. However, sex differences in the brain with health and disease are often overlooked in experimental models. Sex effects originate, directly or indirectly, from hormonal or sex chromosomal mechanisms. To delineate the contributions of genetic sex (XX v. XY) versus gonadal sex (ovaries v. testes) to the epigenomic regulation of hippocampal sex differences, we used the Four Core Genotypes (FCG) mouse model which uncouples chromosomal and gonadal sex. Transcriptomic and epigenomic analyses of ~ 12-month-old FCG mouse hippocampus, revealed genomic context-specific regulatory effects of genotypic and gonadal sex on X- and autosome-encoded gene expression and DNA modification patterns. X-chromosomal epigenomic patterns, classically associated with X-inactivation, were established almost entirely by genotypic sex, independent of gonadal sex. Differences in X-chromosome methylation were primarily localized to gene regulatory regions including promoters, CpG islands, CTCF binding sites, and active/poised chromatin, with an inverse relationship between methylation and gene expression. Autosomal gene expression demonstrated regulation by both genotypic and gonadal sex, particularly in immune processes. These data demonstrate an important regulatory role of sex chromosomes, independent of gonadal sex, on sex-biased hippocampal transcriptomic and epigenomic profiles. Future studies will need to further interrogate specific CNS cell types, identify the mechanisms by which sex chromosomes regulate autosomes, and differentiate organizational from activational hormonal effects.

Deciphering Pro-angiogenic Transcription Factor Profiles in Hypoxic Human Endothelial Cells by Combined Bioinformatics and in vitro Modeling

Background

Constant supply of oxygen is crucial for multicellular tissue homeostasis and energy metabolism in cardiac tissue. As a first response to acute hypoxia, endothelial cells (ECs) promote recruitment and adherence of immune cells to the dysbalanced EC barrier by releasing inflammatory mediators and growth factors, whereas chronic hypoxia leads to the activation of a transcription factor (TF) battery, that potently induces expression of growth factors and cytokines including platelet-derived growth factor (PDGF) and vascular endothelial growth factor (VEGF). We report a hypoxia-minded, targeted bioinformatics approach aiming to identify and validate TFs that regulate angiogenic signaling.

Results

A comprehensive RNA-Seq dataset derived from human ECs subjected to normoxic or hypoxic conditions was selected to identify significantly regulated genes based on (i) fold change (normoxia vs. hypoxia) and (ii) relative abundancy. Transcriptional regulation of this gene set was confirmed via qPCR in validation experiments where HUVECs were subjected to hypoxic conditions for 24 h. Screening the promoter and upstream regulatory elements of these genes identified two TFs, KLF5 and SP1, both with a potential binding site within these regions of selected target genes. In vitro, siRNA experiments confirmed SP1- and KLF5-mediated regulation of identified hypoxia-sensitive endothelial genes. Next to angiogenic signaling, we also validated the impact of TFs on inflammatory signaling, both key events in hypoxic sensing. Both TFs impacted on inflammatory signaling since endogenous repression led to increased NF-κB signaling. Additionally, SP1 silencing eventuated decreased angiogenic properties in terms of proliferation and tube formation.

Conclusion

By detailed in silico analysis of promoter region and upstream regulatory elements for a list of hypoxia-sensitive genes, our bioinformatics approach identified putative binding sites for TFs of SP or KLF family in vitro. This strategy helped to identify TFs functionally involved in human angiogenic signaling and therefore serves as a base for identifying novel RNA-based drug entities in a therapeutic setting of vascularization.

Detection and identification of cis-regulatory elements using change-point and classification algorithms

BACKGROUND

Transcriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Identification of these binding regions enhances understanding of tissue formation and potentially facilitates the development of gene therapies. However, successful identification of binding regions is made difficult by the lack of a universal biological code for their characterisation.

RESULTS

We extend an alignment-based method, changept, and identify clusters of biological significance, through ontology and de novo motif analysis. Further, we apply a Bayesian method to estimate and combine binary classifiers on the clusters we identify to produce a better performing composite.

CONCLUSIONS

The analysis we describe provides a computational method for identification of conserved binding sites in the human genome and facilitates an alternative interrogation of combinations of existing data sets with alignment data.

A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease

BACKGROUND

Congenital heart diseases are the major cause of death in newborns, but the genetic etiology of this developmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses on screening genes that display heart-specific expression during development. However, this approach would have discounted genes that are expressed widely in other tissues but may play critical roles in heart development.

RESULTS

We report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development and congenital heart disease. With this pipeline, we retrieve 76% of the known cardiac developmental genes and predict 35 novel genes that previously had no known connectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of the conserved orthologs in Drosophila cardiac tissue reveals that disrupting the activity of 71% of these genes leads to adult mortality. Among these genes, RpL14, RpS24, and Rpn8 are associated with heart phenotypes.

CONCLUSIONS

Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental and disease genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.

Targeting the Non-Coding Genome for the Diagnosis of Disorders of Sex Development

Disorders of sex development (DSD) are a complex group of conditions with highly variable clinical phenotypes, most often caused by failure of gonadal development. DSD are estimated to occur in around 1.7% of all live births. Whilst the understanding of genes involved in gonad development has increased exponentially, approximately 50% of patients with a DSD remain without a genetic diagnosis, possibly implicating non-coding genomic regions instead. Here, we review how variants in the non-coding genome of DSD patients can be identified using techniques such as array comparative genomic hybridization (CGH) to detect copy number variants (CNVs), and more recently, whole genome sequencing (WGS). Once a CNV in a patient's non-coding genome is identified, putative regulatory elements such as enhancers need to be determined within these vast genomic regions. We will review the available online tools and databases that can be used to refine regions with potential enhancer activity based on chromosomal accessibility, histone modifications, transcription factor binding site analysis, chromatin conformation, and disease association. We will also review the current in vitro and in vivo techniques available to demonstrate the functionality of the identified enhancers. The review concludes with a clinical update on the enhancers linked to DSD.

Fast and exact quantification of motif occurrences in biological sequences

BACKGROUND

Identification of motifs and quantification of their occurrences are important for the study of genetic diseases, gene evolution, transcription sites, and other biological mechanisms. Exact formulae for estimating count distributions of motifs under Markovian assumptions have high computational complexity and are impractical to be used on large motif sets. Approximated formulae, e.g. based on compound Poisson, are faster, but reliable p value calculation remains challenging. Here, we introduce 'motif_prob', a fast implementation of an exact formula for motif count distribution through progressive approximation with arbitrary precision. Our implementation speeds up the exact calculation, usually impractical, making it feasible and posit to substitute currently employed heuristics.

RESULTS

We implement motif_prob in both Perl and C+ + languages, using an efficient error-bound iterative process for the exact formula, providing comparison with state-of-the-art tools (e.g. MoSDi) in terms of precision, run time benchmarks, along with a real-world use case on bacterial motif characterization. Our software is able to process a million of motifs (13-31 bases) over genome lengths of 5 million bases within the minute on a regular laptop, and the run times for both the Perl and C+ + code are several orders of magnitude smaller (50-1000× faster) than MoSDi, even when using their fast compound Poisson approximation (60-120× faster). In the real-world use cases, we first show the consistency of motif_prob with MoSDi, and then how the p-value quantification is crucial for enrichment quantification when bacteria have different GC content, using motifs found in antimicrobial resistance genes. The software and the code sources are available under the MIT license at https://github.com/DataIntellSystLab/motif_prob .

CONCLUSIONS

The motif_prob software is a multi-platform and efficient open source solution for calculating exact frequency distributions of motifs. It can be integrated with motif discovery/characterization tools for quantifying enrichment and deviation from expected frequency ranges with exact p values, without loss in data processing efficiency.

NoPeak: k-mer-based motif discovery in ChIP-Seq data without peak calling

MOTIVATION

The discovery of sequence motifs mediating DNA-protein binding usually implies the determination of binding sites using high-throughput sequencing and peak calling. The determination of peaks, however, depends strongly on data quality and is susceptible to noise.

RESULTS

Here, we present a novel approach to reliably identify transcription factor-binding motifs from ChIP-Seq data without peak detection. By evaluating the distributions of sequencing reads around the different k-mers in the genome, we are able to identify binding motifs in ChIP-Seq data that yield no results in traditional pipelines.

AVAILABILITY AND IMPLEMENTATION

NoPeak is published under the GNU General Public License and available as a standalone console-based Java application at https://github.com/menzel/nopeak.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers because of diagnosis at late stage and inherent/acquired chemoresistance. Recent advances in genomic profiling and biology of this disease have not yet been translated to a relevant improvement in terms of disease management and patient’s survival. However, new possibilities for treatment may emerge from studies on key epigenetic factors. Deregulation of microRNA (miRNA) dependent gene expression and mRNA splicing are epigenetic processes that modulate the protein repertoire at the transcriptional level. These processes affect all aspects of PDAC pathogenesis and have great potential to unravel new therapeutic targets and/or biomarkers. Remarkably, several studies showed that they actually interact with each other in influencing PDAC progression. Some splicing factors directly interact with specific miRNAs and either facilitate or inhibit their expression, such as Rbfox2, which cleaves the well-known oncogenic miRNA miR-21. Conversely, miR-15a-5p and miR-25-3p significantly downregulate the splicing factor hnRNPA1 which acts also as a tumour suppressor gene and is involved in processing of miR-18a, which in turn, is a negative regulator of KRAS expression. Therefore, this review describes the interaction between splicing and miRNA, as well as bioinformatic tools to explore the effect of splicing modulation towards miRNA profiles, in order to exploit this interplay for the development of innovative treatments. Targeting aberrant splicing and deregulated miRNA, alone or in combination, may hopefully provide novel therapeutic approaches to fight the complex biology and the common treatment recalcitrance of PDAC.

Navigating the non-coding genome in heart development and Congenital Heart Disease

Congenital Heart Disease (CHD) is characterised by a wide range of cardiac defects, from mild to life-threatening, which occur in babies worldwide. To date, there is no cure to CHD, however, progress in surgery has reduced its mortality allowing children affected by CHD to reach adulthood. In an effort to understand its genetic basis, several studies involving whole-genome sequencing (WGS) of patients with CHD have been undertaken and generated a great wealth of information. The majority of putative causative mutations identified in WGS studies fall into the non-coding part of the genome. Unfortunately, due to the lack of understanding of the function of these non-coding mutations, it is challenging to establish a causal link between the non-coding mutation and the disease. Thus, here we review the state-of-the-art approaches to interpret non-coding mutations in the context of CHD and address the following questions: What are the non-coding sequences important for cardiac function? Which technologies are used to identify them? Which resources are available to analyse them? What mutations are expected in these non-coding sequences? Learning from developmental process, what is their expected role in CHD?