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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
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
0958-1669
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
02af9b3a-aa27-4b15-a7cd-9bc28794767f
Campbell, Amy M.
b623d9a6-2917-4715-9333-c8b459002100
Hauton, Chris
7706f6ba-4497-42b2-8c6d-00df81676331
Baker-Austin, Craig
09166c86-e073-43c6-87f8-4e0493c2ad73
van Aerle, Ronny
fadfd4e5-3ede-4b42-b728-fe3c49447bb6
Martinez-Urtaza, Jaime
02af9b3a-aa27-4b15-a7cd-9bc28794767f

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).

Record type: Article

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
ORCID for Amy M. Campbell: ORCID iD orcid.org/0000-0003-4111-8286
ORCID for Chris Hauton: ORCID iD orcid.org/0000-0002-2313-4226

Catalogue record

Date deposited: 09 May 2023 16:45
Last modified: 17 Mar 2024 04:04

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Contributors

Author: Amy M. Campbell ORCID iD
Author: Chris Hauton ORCID iD
Author: Craig Baker-Austin
Author: Ronny van Aerle
Author: Jaime Martinez-Urtaza

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