Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy

Abstract

The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis. Methylation analysis of newborn blood DNA using an Illumina HumanMethylation450K array was performed in 23 CP cases and 21 unaffected controls. There were 230 significantly differentially-methylated CpG loci in 258 genes. Each locus had at least 2.0-fold change in methylation in CP versus controls with a FDR p-value ≤ 0.05. Methylation level for each CpG locus had an area under the receiver operating curve (AUC) ≥ 0.75 for CP detection. Using Artificial Intelligence (AI) platforms/Machine Learning (ML) analysis, CpG methylation levels in a combination of 230 significantly differentially-methylated CpG loci in 258 genes had a 95% sensitivity and 94.4% specificity for newborn prediction of CP. Using pathway analysis, multiple canonical pathways plausibly linked to neuronal function were over-represented. Altered biological processes and functions included: neuromotor damage, malformation of major brain structures, brain growth, neuroprotection, neuronal development and de-differentiation, and cranial sensory neuron development. In conclusion, blood leucocyte epigenetic changes analyzed using AI/ML techniques appeared to accurately predict CP and provided plausible mechanistic information on CP pathogenesis.

Epigenetic Marks at the Ribosomal DNA Promoter in Skeletal Muscle Are Negatively Associated With Degree of Impairment in Cerebral Palsy

“…However, speaking against a general inability to methylate DNA, as suggested by Schoendorfer et al is a series of papers published in recent years that investigate differences in global DNA methylation between CP and TD children, i.e., DNA methylation in white blood cells. Two of these studies are case (CP) vs. control (TD) studies (30,31), and two are studies on monozygotic twins discordant for CP (32,33). All of these studies indicate that distinct epigenetic imprinting is evident in CP and that this is detectable very early on, even before 1 year of age.…”

Insights From Genetic Studies of Cerebral Palsy

“…In monozygotic twins discordant for CP, pathway enrichment analysis for alterations in genome-wide DNA methylation at birth ( 54 ) shows changes in MAPK signaling, hypoxia-associated signaling, inflammation, cell adhesion, cytokine–cytokine receptor interaction, and Ras signaling. In unrelated individuals with CP, methylation differences have been identified in genes involved in axonal guidance, the actin cytoskeleton, insulin and ephrin receptors, crosstalk between dendritic cells and natural killer cells, TGF-β, Wnt, neuregulin, PI3K/AKT, and tight junction signaling ( 55 ). These findings support the notion that perinatal hypoxic-ischemia and inflammatory responses are associated with an eventual CP outcome.…”

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer’s disease

“…DNA methylation plays an important role in Alzheimer’s disease [ 14 – 16 ]. Leukocyte DNA methylation from CpG-based biomarker analyses was used for early detection of many diseases, including our recently published brain disorders cerebral palsy [ 17 ], autism [ 18 ], and concussion [ 19 ]. However, the genome-wide blood DNA methylation-based molecular mechanisms that contribute to the pathogenesis of AD remain still largely unknown.…”

“…There is an increasing interest in using DL in the analysis of biologic big-data such as genomics [ 22 , 23 ] to understand and accurately predict diseases. We have recently published using AI/ML-based technologies of epigenomic [ 17 ] and metabolomics [ 24 – 26 ] data for accurate disease prediction. In the present study, we used DL and other commonly used ML platforms combined with genome-wide DNA methylation analysis of leucocytes DNA for AD detection/prediction.…”