Proteomics and integrative omic approaches for understanding host–pathogen interactions and infectious diseases

Pierre M. Jean Beltran, Joel D. Federspiel, Xinlei Sheng, Ileana M. Cristea

Research output: Contribution to journalReview articlepeer-review

69 Scopus citations

Abstract

Organisms are constantly exposed to microbial pathogens in their environments. When a pathogen meets its host, a series of intricate intracellular interactions shape the outcome of the infection. The understanding of these host–pathogen interactions is crucial for the development of treatments and preventive measures against infectious diseases. Over the past decade, proteomic approaches have become prime contributors to the discovery and understanding of host–pathogen interactions that represent anti- and pro-pathogenic cellular responses. Here, we review these proteomic methods and their application to studying viral and bacterial intracellular pathogens. We examine approaches for defining spatial and temporal host–pathogen protein interactions upon infection of a host cell. Further expanding the understanding of proteome organization during an infection, we discuss methods that characterize the regulation of host and pathogen proteomes through alterations in protein abundance, localization, and post-translational modifications. Finally, we highlight bioinformatic tools available for analyzing such proteomic datasets, as well as novel strategies for integrating proteomics with other omic tools, such as genomics, transcriptomics, and metabolomics, to obtain a systems-level understanding of infectious diseases.

Original languageEnglish (US)
Article number922
JournalMolecular Systems Biology
Volume13
Issue number3
DOIs
StatePublished - Mar 1 2017

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Keywords

  • networks
  • omics
  • organelle
  • systems biology
  • virus infection

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