Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

Login and Edit functionaility are currrently unavailable.

AVATAR: highly parallel analysis of variation in transcription factors and their DNA binding sites


Abstract The interactions between transcription factors (TFs) and their DNA binding sites are central to gene regulatory networks. Genetic variation in TFs or their DNA binding sites can contribute to differences in traits among individuals. However, the role of interactions among such genetic variants remains poorly understood. Existing high-throughput technologies for assaying the DNA binding activities of TFs (or TF variants) are ?1-by-many? approaches, in which a given protein is assayed for its binding to a large library of different DNA sequences, or alternatively assay a large library of protein variants for activity from a given DNA sequence. A major hurdle in characterization of TF coding variants and DNA noncoding variants is the lack of a high-throughput ?many-by-many? technology that would enable testing of a large collection of TF coding variants for binding to a library of different DNA binding site sequences; such DNA binding site sequences could represent either a large collection of substitutions in a TF's DNA binding site motif, or alternatively putative cis-regulatory variants. The primary goal of this project is to develop novel technology, termed All-Variant Analysis of Transcription factor Affinity and Recognition (AVATAR), for highly parallel analysis of a library of TF coding variants for interaction with a library of variants in their DNA binding sites. We will prioritize human TFs that are associated with diseases and for which disease-associated mutations or naturally occurring polymorphisms predicted to have damaged DNA binding activity have been identified. Such technology would permit: more extensive experimental testing of putatively damaging TF coding variants whose precise effects on DNA binding activity are not currently predictable (unpublished results); an improved understanding of specificity- determining residues and TF-DNA `recognition rules' for various TF classes; and identification of potential genetic interactions between TF coding variants and noncoding variants in TF binding sites (TFBSs). !

Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.