The list of risk genes for ASD with associated mutations continues to grow at a substantial pace, but for many of these mutations, a basic understanding of the functional impact of the mutations on the encoded proteins is missing. In this project, Marc Vidal and Lilia Iakoucheva plan to functionally characterize a collection of ASD gene variants by assessing the effects of mutations on protein stability and protein-protein interactions. This integrative approach will enable the identification of causative variants and characterization of the functional impact of these variants in the context of brain-expressed isoforms.

For many proteins encoded by autism risk genes, having too much or too little of them in each neuron are both problematic. To treat such disorders, it is important to increase the amount of gene expression in the “too little” scenario without overshooting into the “too much” scenario. Wei-Hsiang Huang, in collaboration with Xiaojing Gao, plan to work on a feedback control loop, consisting of engineered biomolecules, to achieve such quantitatively consistent regulation of gene levels.

Although thousands of de novo missense mutations have been detected in people with ASD, it is challenging to identify which mutations induce risk. To address this gap, Haiyuan Yu and Kathryn Roeder aim to continue their efforts to develop an experimentally and computationally integrated interactome perturbation screening pipeline to study these mutations. Additionally, they plan to continue to develop analytical methods to identify ASD risk genes and understand the interrelationships among these genes.