Establishing and validating regulatory regions for variant annotation and expression analysis.

BACKGROUND: The regulatory effect of inherited or de novo genetic variants occurring in promoters as well as in transcribed or even coding gene regions is gaining greater recognition as a contributing factor to disease processes in addition to mutations affecting protein functionality. Thousands of such regulatory mutations are already recorded in HGMD, OMIM, ClinVar and other databases containing published disease causing and associated mutations. It is therefore important to properly annotate genetic variants occurring in experimentally verified and predicted transcription factor binding sites (TFBS) that could thus influence the factor binding event. Selection of the promoter sequence used is an important factor in the analysis as it directly influences the composition of the sequence available for transcription factor binding analysis. RESULTS: In this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual transcription start sites (vTSS) calculation to define the best supported promoter for a gene. We have performed a comparison of the virtually calculated promoters between the best supported and secondary promoters in hg19 and hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax and TRANSFAC databases. A total of 841 and 736 experimentally verified TFBSs within best supported promoters were mapped over HGMD and ClinVar mutation sites respectively. Tens of thousands of predicted ChIP-Seq derived TFBSs were mapped over mutations as well. We have further analyzed some of these mutations for potential gain or loss in transcription factor binding. CONCLUSIONS: We have confirmed the validity of TRANSFAC's approach to define the best supported promoters and established a workflow of their use in annotation of regulatory genetic variants.

Genome wide prediction of HNF4alpha functional binding sites by the use of local and global sequence context.

We report an application of machine learning algorithms that enables prediction of the functional context of transcription factor binding sites in the human genome. We demonstrate that our method allowed de novo identification of hepatic nuclear factor (HNF)4alpha binding sites and significantly improved an overall recognition of faithful HNF4alpha targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.

A novel computational approach for the prediction of networked transcription factors of aryl hydrocarbon-receptor-regulated genes.

A novel computational method based on a genetic algorithm was developed to study composite structure of promoters of coexpressed genes. Our method enabled an identification of combinations of multiple transcription factor binding sites regulating the concerted expression of genes. In this article, we study genes whose expression is regulated by a ligand-activated transcription factor, aryl hydrocarbon receptor (AhR), that mediates responses to a variety of toxins. AhR-mediated change in expression of AhR target genes was measured by oligonucleotide microarrays and by reverse transcription-polymerase chain reaction in human and rat hepatocytes. Promoters and long-distance regulatory regions (>10 kb) of AhR-responsive genes were analyzed by the genetic algorithm and a variety of other computational methods. Rules were established on the local oligonucleotide context in the flanks of the AhR binding sites, on the occurrence of clusters of AhR recognition elements, and on the presence in the promoters of specific combinations of multiple binding sites for the transcription factors cooperating in the AhR regulatory network. Our rules were applied to search for yet unknown Ah-receptor target genes. Experimental evidence is presented to demonstrate high fidelity of this novel in silico approach.

In vivo effects of the Epstein-Barr virus small RNA EBER-1 on protein synthesis and cell growth regulation.

Recent studies have suggested a role for the Epstein-Barr virus-encoded RNA EBER-1 in malignant transformation. EBER-1 inhibits the activity of the protein kinase PKR, an inhibitor of protein synthesis with tumour suppressor properties. In human 293 cells and murine embryonic fibroblasts, transient expression of EBER-1 promoted total protein synthesis and enhanced the expression of cotransfected reporter genes. However reporter gene expression was stimulated equally well in cells from control and PKR knockout mice. NIH 3T3 cells stably expressing EBER-1 exhibited a greatly increased frequency of colony formation in soft agar, and protein synthesis in these cells was relatively resistant to inhibition by the calcium ionophore A23187. Nevertheless clones containing a high concentration of EBER-1 were not invariably tumourigenic. We conclude that EBER-1 can enhance protein synthesis by a PKR-independent mechanism and that, although this RNA may contribute to the oncogenic potential of Epstein-Barr virus, its expression is not always sufficient for malignant transformation.