Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naive individuals at up to seven time-points before, during and after three rounds of drug administration. Linear modelling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modelling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the bone marrow with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance following each round of drug exposure is observed. New insights into the mode of action of the drug are presented in the context of pyrimethamine’s predicted effect on suppression of cell division and metabolism in the immune system.