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Front Cell Dev Biol. 2014 Oct 8;2:54. doi: 10.3389/fcell.2014.00054. eCollection 2014.

Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study.

Author information

1
Center for Integrative Genomics, School of Biology, Georgia Institute of Technology Atlanta, GA, USA.
2
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology Atlanta, GA, USA.
3
Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, School of Medicine, Emory University Atlanta, GA, USA.
4
Emory Vaccine Center and Yerkes National Primate Research Center, Emory University Atlanta, GA, USA.
5
Emory Vaccine Center and Yerkes National Primate Research Center, Emory University Atlanta, GA, USA ; Division of Infectious Diseases, Department of Medicine, Emory University Atlanta, GA, USA.
6
Center for Topical and Emerging Global Diseases, University of Georgia Athens, GA, USA.
7
Institute of Bioinformatics, University of Georgia Athens, GA, USA.
8
Center for Topical and Emerging Global Diseases, University of Georgia Athens, GA, USA ; Institute of Bioinformatics, University of Georgia Athens, GA, USA.

Abstract

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.

KEYWORDS:

axes of variation; bayesian network inference; bone marrow; peripheral blood; principal component analysis (PCA); pyrimethamine

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