PIQ-ing into chromatin architecture

Modulating transcription factor activity: Interfering with protein-protein interaction networks

Biophysical parameters that govern transcription factors activity are binding locations across the genome, dwelling time at these regulatory elements and specific protein-protein interactions. Most molecular strategies used to develop small compounds that block transcription factors activity have been based on biochemistry and cell biology methods that that do not take into consideration these key biophysical features. Here, we review the advance in the field of transcription factor biology and describe how their interactome and transcriptional regulation on a genome wide scale have been deciphered. We suggest that this new knowledge has the potential to be used to implement innovative research drug discovery program.

Correcting nucleotide-specific biases in high-throughput sequencing data

Background

High-throughput sequence (HTS) data exhibit position-specific nucleotide biases that obscure the intended signal and reduce the effectiveness of these data for downstream analyses. These biases are particularly evident in HTS assays for identifying regulatory regions in DNA (DNase-seq, ChIP-seq, FAIRE-seq, ATAC-seq). Biases may result from many experiment-specific factors, including selectivity of DNA restriction enzymes and fragmentation method, as well as sequencing technology-specific factors, such as choice of adapters/primers and sample amplification methods.

Results

We present a novel method to detect and correct position-specific nucleotide biases in HTS short read data. Our method calculates read-specific weights based on aligned reads to correct the over- or underrepresentation of position-specific nucleotide subsequences, both within and adjacent to the aligned read, relative to a baseline calculated in assay-specific enriched regions. Using HTS data from a variety of ChIP-seq, DNase-seq, FAIRE-seq, and ATAC-seq experiments, we show that our weight-adjusted reads reduce the position-specific nucleotide imbalance across reads and improve the utility of these data for downstream analyses, including identification and characterization of open chromatin peaks and transcription-factor binding sites.

Conclusions

A general-purpose method to characterize and correct position-specific nucleotide sequence biases fills the need to recognize and deal with, in a systematic manner, binding-site preference for the growing number of HTS-based epigenetic assays. As the breadth and impact of these biases are better understood, the availability of a standard toolkit to correct them will be important.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1766-x) contains supplementary material, which is available to authorized users.

Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network

We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation.

Pharmacological manipulation of transcription factor protein-protein interactions: opportunities and obstacles

Much research on transcription factor biology and their genetic pathways has been undertaken over the last 30 years, especially in the field of developmental biology and cancer. Yet, very little is known about the molecular modalities of highly dynamic interactions between transcription factors, genomic DNA, and protein partners. Methodological breakthroughs such as RNA-seq (RNA-sequencing), ChIP-seq (chromatin immunoprecipitation sequencing), RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins), and single-molecule imaging will dramatically accelerate the discovery rate of their molecular mode of action in the next few years. From a pharmacological viewpoint, conventional methods used to target transcription factor activity with molecules mimicking endogenous ligands fail to achieve high specificity and are limited by a lack of identification of new molecular targets. Protein-protein interactions are likely to represent one of the next major classes of therapeutic targets. Transcription factors, known to act mostly via protein-protein interaction, may well be at the forefront of this type of drug development. One hurdle in this field remains the difficulty to collate structural data into meaningful information for rational drug design. Another hurdle is the lack of chemical libraries meeting the structural requirements of protein-protein interaction disruption. As more attempts at modulating transcription factor activity are undertaken, valuable knowledge will be accumulated on the modality of action required to modulate transcription and how these findings can be applied to developing transcription factor drugs. Key discoveries will spawn into new therapeutic approaches not only as anticancer targets but also for other indications, such as those with an inflammatory component including neurodegenerative disorders, diabetes, and chronic liver and kidney diseases.