Alzheimer’s Disease mechanistic pathways were discovered through in silico experiments in causal AI based digital twins

Abstract

AbstractBackgroundBeyond a single‐gene‐based approach, pathway‐based approaches have become important in drug development paradigm. Several pathways are known for the pathogenesis of Alzheimer’s disease (AD), but these may be from animal studies or observational human subject studies. Here, we focus on identification of gene pathways with direct causal relationships to AD through Gemini Digital Twins of AD patients.MethodTwo independent and complementary AD Digital Twins were built using an A.I. platform REFSTM [aitiabio.com], based on Bayesian network models of ADNI and ANMerge consortium data which reverse‐engineered the connectivity of ∼59K and ∼35K multi‐modal variables and a wide range of AD‐related outcomes (cognitive, imaging and biomarker) profiled from 317 subjects (Control:MCI:Dementia = 97:191:29) and 199 subjects (Control:MCI:Dementia = 65:63:71), respectively [Fig1]. The average causal effect of each gene (at the blood gene expression level) on each outcome was estimated through in silico counterfactual experiments. Pathway enrichment analyses were performed on metascape.org.ResultWe identified a total of 573 and 300 AD‐driver‐genes from ADNI‐ and ANMerge‐based Digital Twins, of which 41 overlapped (significance‐of‐overlap = 6e‐15). These 41 robustly validated genes, of which 61% were identified by the network as causal mediators of APOE4 genotype, were enriched in several biological processes known for AD such as ‘regulation of RNA splicing’ and ‘macrophage activation’ [Fig2]. The AD‐driver‐genes were also enriched in other interesting pathways in both Digital Twins, such as ‘immune response’ and ‘oxidative stress’. Furthermore, the ADNI‐specific AD‐driver‐genes, representing a sample population with a higher proportion of MCI patients, were related to additional processes such as ‘mitochondrial respiration’ and ‘ribosomal sub‐complexes pathways’, previously known to be involved specifically in early stage AD.ConclusionWe prioritized several AD‐related pathways for potential drug development identified from causal driver genes discovered and validated in two independent Digital Twins. Some of these pathways are more related to proteopathic biochemical phase of AD, rather than cellular phase, suggesting an area of a potential early intervention. Investigating causal genes from two complementary consortium studies with a different proportion of early vs. late stage AD patients may be a promising approach to identify disease‐stage‐specific and disease‐stage‐common signatures in the pathway‐based drug discovery efforts.