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Record W4406569257 · doi:10.1101/2025.01.14.632940

Rising temperatures favour parasite virulence and parallel molecular evolution following a host jump

2025· preprint· en· W4406569257 on OpenAlexaff
Tobias E. Hector, Julia M. Kreiner, James C Forward, Kim L. Hoang, Emily J. Stevens, Serena Johnson, Jingdi Li, Kayla C. King

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of British Columbia
FundersNatural Environment Research CouncilSight Research UK
KeywordsVirulenceBiologyHost (biology)Parasite hostingTemperate climateRange (aeronautics)Adaptation (eye)OutbreakEcologyEvolutionary biologyZoologyGeneticsGeneVirology

Abstract

fetched live from OpenAlex

Climate change is facilitating the poleward emergence of parasites, increasing the risk of jumping into new animal species, including humans. Whether more virulent or transmissible variants will spread during these climate-driven outbreaks is unclear. We experimentally evolved a wild parasitic bacterium, across the thermal range (20-30°C) and extremes (35°C) of Cape Verde - the site of field collection - in a novel, temperate animal host. At the parasite's typical warm environmental temperature, we found that virulence escalated across evolutionary time. Parasites evolved at hot temperatures, towards the limit of host-parasite survival, displayed a 'cryptic' virulence boost, deadlier once infecting animals at cooler temperatures. Patterns of molecular evolution were constrained to parallel changes in fewer loci at extreme temperatures. Our findings suggest that rising temperatures will leave predictable phenotypic and genomic signatures on evolving parasites as they emerge with climate change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.226
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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