SFARI | Award Type: Director

Assessing mRNA translation dysregulation in Gigyf1/2 mouse models of autism

The SFARI gene database lists both GIGYF1 and GIGYF2 as high confidence risk factors, as both have a probability of loss of function intolerance (pLI) score of 1. It is our hypothesis that GIGYF1/2 mutations disrupt the function of the GIGYF1/2-4EHP translation repression, thus resulting in dysregulation of protein synthesis which causes impaired synaptic function and susceptibility to behavioral impairments. We will use cell-specific GIGYF1/2 conditional knockout mice, which will be subjected to a battery of behavioral tests, ribosome profiling and proteomics to investigate the mice for behavioral impairments and changes in translational efficiency. Our goal is to provide novel insight into the molecular mechanisms mediating ASD-like behaviors via GIGYF1/2 and establish a preclinical basis for therapeutic intervention in ASD patients.

Annotation of the synaptic interactome and synaptic pathways relevant for autism

Although some ASD risk genes have hardly been studied, for many others, a wealth of basic research data is already available. In the current project, August Smit and Matthijs Verhage aim to better disclose this available information, especially for synaptic genes, to support the translation of genetic findings into specific disease concepts.

Prenatal COVID-19 impact on autism risk in genetically susceptible families

MARBLES recruits mothers who have a child with clinically confirmed ASD and are pregnant with another child in order to understand what influences the outcomes of the younger siblings, through what pathways and to identify early markers of ASD. In the current project, Rebecca J. Schmidt plans to leverage the existing infrastructure of the MARBLES cohort to collect data and biologic specimens during the COVID-19 pandemic that will inform biologic responses and their associations with neurodevelopmental outcomes in offspring.

Long-term ethological monitoring of motor and social behaviors in mouse models of autism

There is a critical need for better ASD preclinical phenotyping approaches for interventional studies. With this in mind, Vivek Kumar and colleagues at the Jackson Laboratory aim to develop new, high-throughput, reproducible and objective assays that quantify social and motor behaviors ethologically in mice over long periods using machine learning.

The COUNT project: Using multiomic profiling to identify predictors of autism traits

ASD, a chronic and heterogeneous condition with many traits and co-occurring conditions, is poorly understood. In the current project, Michael Snyder and colleagues plan to initiate a study known as COUNT, whose precision approach will leverage longitudinal deep multiomic profiling of individuals with ASD to better understand the biology, characterize its heterogeneity and identify predictors of ASD traits.

A registry for clinical and functional data for variants in the GRIN gene family

GRIN disorders are rare neurodevelopmental genetic conditions that alter genes that encode NMDA receptor subunits. Stephen Traynelis and colleagues plan to understand the natural history, incidence and full spectrum of clinical and functional consequences of variation that lead to GRIN disorders. This multi-dimensional dataset will be compiled and shared in a fashion that will help to catalyze the development of new therapeutic treatment strategies.

Development and validation of an online webcam-based performance measurement battery for autism spectrum disorders

Online measures have the potential to provide greater sensitivity to change in longitudinal studies and clinical trials. In the current study, Thomas Frazier and colleagues plan to develop and validate an online evaluation tool that includes: (1) a survey completed by caregivers to better understand behavior and functioning and (2) patient-completed measures that use a webcam to collect gaze and facial expression responses to evaluate thinking skills. If successful, the measures developed could greatly enhance research in autism and related neurodevelopmental genetic syndromes and might one day enhance clinical practice.

Copy number variation and pathogenic variant analyses of SPARK exomes

Genetic variants contribute significantly to the etiology of autism, and unlike single-nucleotide variants (SNVs), copy number variants (CNVs) are more difficult to detect from genetic sequencing datasets. The goal of this proposal is to significantly increase the yield of high-impact autism mutations by focusing on the discovery of both CNVs and SNVs in autism families from SPARK. Using established and novel computational pipelines, Evan Eichler and colleagues propose to work with the SPARK Consortium to generate a high-confidence set of potential pathogenic variants and then integrate these data into larger genetic variant databases to pinpoint pathogenic variants and novel genes associated with autism.

Statistical analysis of autism phenotypic and genotypic data

Abba Krieger and colleagues serve as statistical consultants to the Simons Foundation. In this role, they analyze autism data generated by SFARI. The current funding will be used to support statistical analyses on phenotypic and genotypic data from SPARK.

Assessing how the autism risk gene ASXL3 regulates cortical neuronal fate

Stephanie Bielas plans to study an Asxl3 null mouse model to better understand the role that Asxl3-dependent deubiquitination activity plays in specifying neural progenitor cell transcriptional programs that regulate cortical neuronal fate.