Background:
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several
challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic
mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In
order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive
genetic profiling of targeted individuals with ASD was needed.
Methods:
The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has
allowed to develop novel strategies for characterization of comprehensive genetic profiles of individuals with ASD. In
particular, this review will describe integrated approaches based on the combination of various omics technologies that
will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD.
Results:
In order to analyze the big data collected by assays such as whole genome, epigenome, transcriptome, and
proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to
identify non-linear relationships between the omics technologies and therefore establish a functional informative network
among the different data sources.
Conclusion:
The potential advantage provided by these new integrated omics-based strategies is to better characterize the
genetic background of ASD cohorts, identify novel molecular targets for drug development, and ultimately offer a more
personalized approach in the design of clinical trials for ASD.
Editorial: Multi-Omics Technologies for Optimizing Synthetic Biomanufacturing
