Interactome‐guided machine learning modeling reveals information embedded in blood plasma protein‐protein interactions in Alzheimer’s Disease patients treated by the complex plasma fraction GRF6019

Abstract

AbstractBackgroundThe relevance of plasma fraction treatment to reverse age‐related conditions was underlined by our previous pre‐clinical results. In two Phase 2 clinical trials (GRF6019‐201 and GRF6019‐202), patients with Alzheimer’s Disease (AD) were treated with the complex plasma fraction GRF6019, which was determined to be safe and well‐tolerated. Interestingly, no cognitive decline and minimal functional decline were observed. While GRF6019 treatment was associated with a quick and widespread proteomics response, lasting molecular changes were more subtle. Here, we developed new machine learning models guided by protein‐protein interaction (PPI) information to deeper understand the molecular effects of GRF6019 plasma fraction treatment.MethodMore than 7k proteins were measured in 120 plasma samples from treated subjects enrolled in the GRF6019‐201 and GRF6019‐202 trials (n = 21 and 18, respectively) using the Somascan assay. Interacting protein pairs were curated from STRING and OmniPath PPI databases. For each protein‐protein interaction, linear Support Vector Machine (SVM) models were trained to separate before and after treatment samples based on measured protein levels and compared to single‐protein models. Proteins were ranked based on their individual performance and their gain of performance when combined with interaction partnersResultProteasome‐associated elements were overrepresented among proteins with top individual performance. Interaction models provided more details about related processes involving ubiquitination. Furthermore, various other immune, hormonal, and neuronal terms found to be enriched among the top interactors.ConclusionComplex biological functions can be mapped into pathways which are composed of and regulated by PPIs. Biological functionality can be encoded in concentrations of individual proteins, also in relative concentrations of interacting proteins. Our results indicate that the latter aspect may contain a significant amount of complementary information, not accessible with traditional approaches analyzing proteins as independent entities. Consequently, the development and the application of novel system biology tools on blood plasma proteomics will help to better understand patients’ responses to treatment in clinical trials.