Genomics of host-pathogen interactions: challenges and opportunities across ecological and spatiotemporal scales
Journal article, 2019

Evolutionary genomics has recently entered a new era in the study of host-pathogen interactions. A variety of novel genomic techniques has transformed the identification, detection and classification of both hosts and pathogens, allowing a greater resolution that helps decipher their underlying dynamics and provides novel insights into their environmental context. Nevertheless, many challenges to a general understanding of host-pathogen interactions remain, in particular in the synthesis and integration of concepts and findings across a variety of systems and different spatiotemporal and ecological scales. In this perspective we aim to highlight some of the commonalities and complexities across diverse studies of host-pathogen interactions, with a focus on ecological, spatiotemporal variation, and the choice of genomic methods used. We performed a quantitative review of recent literature to investigate links, patterns and potential tradeoffs between the complexity of genomic, ecological and spatiotemporal scales undertaken in individual host-pathogen studies. We found that the majority of studies used whole genome resolution to address their research objectives across a broad range of ecological scales, especially when focusing on the pathogen side of the interaction. Nevertheless, genomic studies conducted in a complex spatiotemporal context are currently rare in the literature. Because processes of host-pathogen interactions can be understood at multiple scales, from molecular-, cellular-, and physiological-scales to the levels of populations and ecosystems, we conclude that a major obstacle for synthesis across diverse host-pathogen systems is that data are collected on widely diverging scales with different degrees of resolution. This disparity not only hampers effective infrastructural organization of the data but also data granularity and accessibility. Comprehensive metadata deposited in association with genomic data in easily accessible databases will allow greater inference across systems in the future, especially when combined with open data standards and practices. The standardization and comparability of such data will facilitate early detection of emerging infectious diseases as well as studies of the impact of anthropogenic stressors, such as climate change, on disease dynamics in humans and wildlife.

Infectious diseases

Natural selection

Immunotoxins

MHC

Co-evolution

GWAS

Plasmodium

Epidemiological surveillance

Mucus

Anthropogenic stressors

Author

Kathrin Naepflin

Harvard University

Emily A. O'Connor

Lund University

Lutz Becks

University of Konstanz

Staffan Bensch

Lund University

Vincenzo A. Ellis

Lund University

Nina Hafer-Hahmand

Eawag - Swiss Federal Institute of Aquatic Science and Technology

Max Planck Society

Karin C. Harding

Chalmers, Centre for Environment and Sustainability (GMV)

University of Gothenburg

Sara K. Linden

University of Gothenburg

Morten T. Olsen

University of Copenhagen

Jacob Roved

Lund University

Timothy B. Sackton

Harvard University

Allison J. Shultz

National History Museum Los Angeles

Vignesh Venkatakrishnans

University of Gothenburg

Elin Videvall

Lund University

Smithsonian's Conservation Biology Institute

Helena Westerdahl

Lund University

Jamie C. Winternitz

Bielefeld University

Max Planck Society

Scott V. Edwards

Chalmers, Centre for Environment and Sustainability (GMV)

Harvard University

University of Gothenburg

PeerJ

2167-8359 (ISSN)

Vol. 7 e8013

Subject Categories

Evolutionary Biology

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

DOI

10.7717/PEERJ.8013

PubMed

31720122

More information

Latest update

5/17/2022