2. Academic Validation
  3. Supergenomic network compression and the discovery of EXP1 as a glutathione transferase inhibited by artesunate

Supergenomic network compression and the discovery of EXP1 as a glutathione transferase inhibited by artesunate

  • Cell. 2014 Aug 14;158(4):916-928. doi: 10.1016/j.cell.2014.07.011.
Andreas Martin Lisewski 1,  Joel P Quiros 2,  Caroline L Ng 3,  Anbu Karani Adikesavan 4,  Kazutoyo Miura 5,  Nagireddy Putluri 6,  Richard T Eastman 7,  Daniel Scanfeld 3,  Sam J Regenbogen 8,  Lindsey Altenhofen 9,  Manuel Llinás 9,  Arun Sreekumar 10,  Carole Long 5,  David A Fidock 11,  Olivier Lichtarge 12
Affiliations

Affiliations

  • 1 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: lisewski@bcm.edu.
  • 2 Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA.
  • 3 Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA.
  • 4 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA.
  • 5 Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA.
  • 6 Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX 77030, USA.
  • 7 Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA; Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA.
  • 8 Department of Pharmacology, Baylor College of Medicine, Houston, TX 77030, USA.
  • 9 Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Biochemistry and Molecular Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA 16802, USA.
  • 10 Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX 77030, USA; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
  • 11 Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA; Division of Infectious Diseases, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA.
  • 12 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pharmacology, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: lichtarge@bcm.edu.
PMID: 25126794 DOI: 10.1016/j.cell.2014.07.011
Abstract

A central problem in biology is to identify gene function. One approach is to infer function in large supergenomic networks of interactions and ancestral relationships among genes; however, their analysis can be computationally prohibitive. We show here that these biological networks are compressible. They can be shrunk dramatically by eliminating redundant evolutionary relationships, and this process is efficient because in these networks the number of compressible elements rises linearly rather than exponentially as in other complex networks. Compression enables global network analysis to computationally harness hundreds of interconnected genomes and to produce functional predictions. As a demonstration, we show that the essential, but functionally uncharacterized Plasmodium falciparum antigen EXP1 is a membrane Glutathione S-transferase. EXP1 efficiently degrades cytotoxic hematin, is potently inhibited by artesunate, and is associated with artesunate metabolism and susceptibility in drug-pressured malaria parasites. These data implicate EXP1 in the mode of action of a frontline antimalarial drug.

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