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Author Notes:

Correspondence: Greg Gibson, Center for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building, 770 State Street, Atlanta, GA 30332, USA; e-mail: greg.gibson@ biology.gatech.edu

We thank the following individuals for their essential contributions:

The Yerkes veterinarian and research resources team and Trenton Hoffman assisted with the clinical follow-up of the animals.

Zachary Johnson performed the HiSeq2000 sequencing at the Yerkes Genomics Core.

We are especially grateful to Aleksey Zimin and Rob Norgren for providing revisions to the annotated M. mulatta genome sequence.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Research Funding:

This project has been funded in part with funds from the National Institute of Allergy and Infectious Diseases; National Institutes of Health, Department of Health and Human Services [Contract No. HHSN272201200031C], and in part by ORIP/OD P51OD011132 (formerly NCRR P51RR000165).

Keywords:

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

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

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Journal Title:

Frontiers in Cell and Developmental Biology

Volume:

Volume 2

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Type of Work:

Article | Final Publisher PDF

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.

Copyright information:

© 2014 Lee, Yin, Arafat, Tang, Uppal, Tran, Cabrera-Mora, Lapp, Moreno, Meyer, DeBarry, Pakala, Nayak, Kissinger, Jones, Galinski, Styczynski and Gibson.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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