An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens
An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens
It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.
Climate Change, Evolution, Genomic analysis, Machine Learning, Pathogens
Campbell, Amy M.
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Hauton, Chris
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Baker-Austin, Craig
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van Aerle, Ronny
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Martinez-Urtaza, Jaime
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1 April 2023
Campbell, Amy M.
b623d9a6-2917-4715-9333-c8b459002100
Hauton, Chris
7706f6ba-4497-42b2-8c6d-00df81676331
Baker-Austin, Craig
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van Aerle, Ronny
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Martinez-Urtaza, Jaime
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Campbell, Amy M., Hauton, Chris, Baker-Austin, Craig, van Aerle, Ronny and Martinez-Urtaza, Jaime
(2023)
An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens.
Current Opinion in Biotechnology, 80, [102898].
(doi:10.1016/j.copbio.2023.102898).
Abstract
It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.
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e-pub ahead of print date: 3 February 2023
Published date: 1 April 2023
Additional Information:
Funding Information:
This work was supported by the Natural Environmental Research Council [Grant number NE/S007210/1 ] and Centre for Environment, Fisheries and Aquaculture Science (Cefas) internal Seedcorn funding. J. Martinez-Urtaza is funded by grant PID2021-127107NB-I00 from Ministerio de Ciencia e Innovación (Spain) .
Publisher Copyright:
© 2023 The Author(s)
Keywords:
Climate Change, Evolution, Genomic analysis, Machine Learning, Pathogens
Identifiers
Local EPrints ID: 476580
URI: http://eprints.soton.ac.uk/id/eprint/476580
ISSN: 0958-1669
PURE UUID: dcf90d62-030d-42bc-bcb3-ff8ef8c37fed
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Date deposited: 09 May 2023 16:45
Last modified: 17 Mar 2024 04:04
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Contributors
Author:
Craig Baker-Austin
Author:
Ronny van Aerle
Author:
Jaime Martinez-Urtaza
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