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. 2023;21(11):2362-2373.
doi: 10.2174/1570159X21666230725142338.

Epigenetics of Autism Spectrum Disorders: A Multi-level Analysis Combining Epi-signature, Age Acceleration, Epigenetic Drift and Rare Epivariations Using Public Datasets

Davide Gentilini  1   2 , Rebecca Cavagnola  1 , Irene Possenti  3 , Luciano Calzari  2 , Francesco Ranucci  1 , Marta Nola  1 , Miriam Olivola  1 , Natascia Brondino  1 , Pierluigi Politi  1
Affiliations

Epigenetics of Autism Spectrum Disorders: A Multi-level Analysis Combining Epi-signature, Age Acceleration, Epigenetic Drift and Rare Epivariations Using Public Datasets

Davide Gentilini et al. Curr Neuropharmacol. 2023.

Abstract

Background: Epigenetics of Autism Spectrum Disorders (ASD) is still an understudied field. The majority of the studies on the topic used an approach based on mere classification of cases and controls.

Objective: The present study aimed at providing a multi-level approach in which different types of epigenetic analysis (epigenetic drift, age acceleration) are combined.

Methods: We used publicly available datasets from blood (n = 3) and brain tissues (n = 3), separately. Firstly, we evaluated for each dataset and meta-analyzed the differential methylation profile between cases and controls. Secondly, we analyzed age acceleration, epigenetic drift and rare epigenetic variations.

Results: We observed a significant epi-signature of ASD in blood but not in brain specimens. We did not observe significant age acceleration in ASD, while epigenetic drift was significantly higher compared to controls. We reported the presence of significant rare epigenetic variations in 41 genes, 35 of which were never associated with ASD. Almost all genes were involved in pathways linked to ASD etiopathogenesis (i.e., neuronal development, mitochondrial metabolism, lipid biosynthesis and antigen presentation).

Conclusion: Our data support the hypothesis of the use of blood epi-signature as a potential tool for diagnosis and prognosis of ASD. The presence of an enhanced epigenetic drift, especially in brain, which is linked to cellular replication, may suggest that alteration in epigenetics may occur at a very early developmental stage (i.e., fetal) when neuronal replication is still high.

Keywords: Autism spectrum disorders; age acceleration; blood; brain; epigenetic drift; epigenetics; epivariations.

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Conflict of interest statement

The authors declare no conflict of interest, financial or otherwise.

Figures

Fig. (1)
Fig. (1)
Volcano plot of differences in DNA methylation between the ASD and Controls. Each panel describes results obtained from a single study. Each point represents a CpG site with mean differences in DNA methylation between groups on the x-axis and−log10 of the uncorrected P value from empirical bayes moderated t-test on the y-axis. Negative methylation differences indicate hypomethylation and positive differences indicate hypermethylation.
Fig. (2)
Fig. (2)
(A) Forest Plot for Age Acceleration Diff; (B) Forest Plot for age Acceleration Grim Forest plot of the effect size or standardized mean difference and 95% confidence interval (CI) of the effect of case control status on Acceleration Diff (panel A) and Acceleration Grim (panel B). The dashed vertical line represents a mean difference of 0 or no effect. Squares to the left of the line represent a decrease in Age Acceleration Diff or Age Acceleration Grim while squares to the right of the line indicate an increase. Each square represents the mean effect size for that study and reflects the relative weighting of the study to the overall effect size estimate. The larger the box, the greater the study contribution to the overall estimate.
Fig. (3)
Fig. (3)
Forest Plot for Epigenetic drift. Forest plot describes the effect size expressed as Odds Ratio and 95% confidence interval (CI) obtained from a multiple logistic regression model evaluating the effect of Epigenetic drift on case control status. The dashed vertical line represents an OR of 1 or no effect. Squares to the left of the line represent a decrease in OR while squares to the right of the line indicate an increase. Each square represents the mean effect size for that study and reflects the relative weighting of the study to the overall effect size estimate. The larger the box, the greater the study contribution to the overall estimate.
Fig. (4)
Fig. (4)
(A) Upset plot of all gene overlaps among studies, (B) Genes found in more than three cases annotated with their average level of gene expression in 12 different brain regions generated by the GTEx project.
Fig. (5)
Fig. (5)
(A) Venn diagram represents the intersection between our epivariation associated gene list, Sfari Genes and autism Database Genes; (B) Annotation of top 50 highest prioritization scores genes obtained from the prioritization analysis with phenolyzer using the HPO term “Behavior”. The image shows their average level of gene expression in 12 different brain regions generated by the GTEx project; (C) Venn diagram represents the intersection between our epivariation associated gene list and a list of genes obtained from Autism Sequencing Consortium exome analysis characterized by presence of rare missense or stop codon variants found only in ASD subject and never in controls. (D) Genes obtained from the intersection are annotated with their average level of gene expression in 12 different brain regions generated by the GTEx project.
See this image and copyright information in PMC

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