There has been a strong push to test new gene therapies in autism, including use of new antisense oligonnucleotide therapy, which targets working copies of genes to increase production of its associated protein. This highly innovative approach could remove a major bottleneck in the development of gene therapies for autism by developing a new way to test genetic therapy targets genome-wide.
Developing a High Throughput Screening Platform for Gene Therapy and Drug Discovery in ASD
Many lines of evidence have shown that brain regions do not communicate well in people with autism, leading to symptoms of ASD. This can include too much or too little connectivity between brain regions, causing decreased or misdirected connections. Applying a technology new to autism, individual neurons will be labeled with bar codes and then tracked to determine where and how brain cells connect. This novel approach will allow scientists to better understand the nature of connectivity problems in autism, and potentially provide clues to new druggable targets.
Even in cases of autism with a known genetic mutation, there can be differences in the presentation of symptoms, which is also known as “phenotypic heterogeneity.” One way to measure this variability across individuals with autism is by examining brainwave patterns. Earlier research in people with Fragile X Syndrome has shown that individuals have different patterns of brainwave activity, which may predict their response to treatments. Building on this research, the fellow will collect cells from individuals with Fragile X Syndrome and turn them into neurons. These cells will then be tested for their own electrical activity, validating the brainwave data collected earlier. This study will then take the research a step further by examining if and how different therapeutics affect these neurons in different ways, leading to more targeted therapeutics.
Genetic testing is recommended for all children with autism. However, many children receive test results that reveal mutations in genes that have not yet been associated with autism. Unfortunately, these variants of uncertain significance can cause confusion and problems for parents seeking clinical diagnoses and support. This study will utilize machine learning to integrate genetic findings with the child’s attainment of key developmental milestones, because often milestone delays are associated with rare genetic disorders. Eventually, this research could lead to a brief, low-cost clinical prediction tool that increases the diagnostic certainty of genetic testing in autism.