Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

Kevin J. Lee, Weiwei Yin, Dalia Arafat, Yan Tang, Karan Uppal, ViLinh Tran, Monica Cabrera-Mora, Stacey Lapp, Alberto Moreno, Esmeralda Meyer, Jeremy D. DeBarry, Suman Pakala, Vishal Nayak, Jessica C. Kissinger, Dean P. Jones, Mary Galinski, Mark P. Styczynski, Greg Gibson

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 (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling 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 modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM 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 was observed following each round of drug exposure. 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.