ALaSCA: a computational platform for quantifying the effect of proteins using Pearlian causal inference, with an example application in Alzheimer′s disease

Introduction An urgent need to delay the onset of aging-associated diseases has arisen due to increasing human lifespan. A dramatic surge in the number of identified potential molecular targets that could promote successful aging, has led to the challenge of prioritizing these targets for further research and drug development. In our previous work, we prioritized genes associated with aging processes based on their similarity to known aging-related genes and dysfunction marker genes in C. elegans. The goal of this study was to demonstrate the ability of our computational platform to identify molecular drivers of neuronal aging using specialized causal inference techniques. S6K was highly ranked in the previous study and here the nearby neighbors in its protein interaction network were selected to explore ALaSCA′s (Adaptable Large-Scale Causal Analysis) ability to identify possible drivers of Alzheimer′s disease. Methods Utilizing head and brain proteome data, two of ALaSCA′s capabilities were used to understand how protein changes over the lifespan of Drosophila melanogaster affect a feature of neuronal aging, namely climbing ability: ● Pearson correlation analysis was used to assess the relationship between the changes in abundance of specific proteins associated (through protein-protein interactions) with S6K and climbing ability. ● Pearlian causal inference, required to achieve formal causal analysis, was used to determine which pathway, associated with proteins linked to S6K, has the largest effect on climbing ability and therefore to what degree these specific proteins are driving neuronal aging. Results and discussion Based on the correlation results, the proteins associated with fz, a gene encoding for the fz family of receptors that are involved in Wnt signaling, display an increase in abundance as climbing ability declines over time. When viewed together with the fz proteins′ strong negative causal value, it seems that their increased abundance over the lifespan of Drosophila is an important driver of the observed decrease in climbing ability. Additionally, expression of the genes FZD1 and FZD7 (fz orthologs) is altered in the hippocampus early on in Alzheimer′s disease human samples and in an amyloid precursor protein mouse model. Conclusion We have demonstrated the potential of the ALaSCA platform to identify and provide evidence behind molecular mechanisms. This capability enables identification of possible drivers of Alzheimer′s disease - as the human orthologs of the proteins identified here, through its Pearlian causal inference capability, have been linked to Alzheimer′s disease progression. ### Competing Interest Statement NT, ZJVR, RS, CL are employed by incubate.bio, a commercial company applying computational solutions involving Pearlian causal analysis to the life sciences and drug discovery fields.