Scientists from University of Massachusetts Medical School have developed a new powerful tool which could reveal the character of human disease related genetic mutations. The study may help scientists describe the genome mutations hidden behind the complex polygenetic diseases. This study was published in Cell.

Gene regulatory networks(GRN) comprising physical and functional interactions between transcription factors(TFs) and regulatory elements play a critical role in development and physiology. Consequently, inappropriate gene regulation underlies a variety of human diseases. Abroad variety of disease-associated mutations have been uncovered, including mutations in TF-encoding genes as well as mutations in non-coding sequences such as enhancers and promoters.

TF-DNA interactions can be mapped with either “TF-centered ” or “gene-centered” methods. Chromatin immunoprecipitation(ChIP) is the most widely used TF centered method to identify the DNA regions with which a TF interacts in vivo. Enhanced yeast one-hybrid(eY1H) assays provide a gene-centered method for the detection and identification of TF-DNA interactions. Briefly, eY1H assays measure TF-DNA interactions in the milieu of the yeast nucleus. DNA regions to be assayed are fused upstream of two reporter genes LacZ and HIS3, and integrated into the yeast genome, enabling their incorporation.

In this study, researchers test their human eY1H platform to identify TFs interacting with human enhancers and to determine protein-DNA interaction changes caused by mutant TFs as well as non-coding disease-associated mutations. They find that eY1H assays more effectively retrieve TFs with limited expression patterns or levels when compared to ChIP.

They provide examples of functional models of target sharing by TFs, including redundancy, which may provide robustness and opposing function, which can ascertain proper timing of enhancer activity during development. Finally, they demonstrate that eY1H assays can be effectively used to identify changes in TF binding conferred by disease-associated coding or non-coding mutations.