Autism spectrum disorder: a neuro-immunometabolic hypothesis of the developmental origins

TL;DR

This paper proposes an integrative model linking fetal neuroinflammation and prenatal stress to autism spectrum disorder through gene-environment interactions affecting glial immunometabolic phenotypes, supported by multi-scale genomic data analysis.

Contribution

It introduces a novel multi-scale model connecting neuroinflammation, stress, and glial immunometabolism to ASD development, validated by genomic and network analysis.

Findings

Confirmed 21 ASD-related genes from SFARI database.

Identified six protein interaction clusters related to immunometabolism and stress.

Supported the gene-environment interaction hypothesis for ASD etiology.

Abstract

Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia, among the brain's non-neuronal glia cell populations, play a pivotal role in neurodevelopment, predisposition to and initiation of disease throughout lifespan. One of the most common neurodevelopmental disorders manifesting between 1-4 years of age is autism spectrum disorder (ASD). A pathological glial-neuronal interplay is thought to increase the risk for clinical manifestation of ASD in at-risk children, but the mechanisms remain poorly understood and integrative, multi-scale models are needed. We propose a model that integrates the data across the scales of physiological organization, from genome to phenotype, and provides a foundation to explain the disparate findings on the genomic level. We hypothesize that via gene-environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes that impact neurodevelopment and neurobehavior. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conduct an analysis on the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirm 21 gene hits. Using unsupervised statistical network analysis, we then identify six clusters of probable protein-protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches. We conclude with delineation of the next steps to verify our model on the individual gene level in an assumption-free manner.