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Record W4386460355 · doi:10.1101/2023.09.04.556169

Examining the molecular clock hypothesis for the contemporary evolution of the rabies virus

2023· preprint· en· W4386460355 on OpenAlexaff
Rowan Durrant, Christina A. Cobbold, Kirstyn Brunker, Kathryn S. Campbell, Jonathan Dushoff, Elaine A. Ferguson, Gurdeep Jaswant, Ahmed Lugelo, Kennedy Lushasi, Lwitiko Sikana, Katie Hampson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsMcMaster University
FundersEngineering and Physical Sciences Research CouncilUniversity of GlasgowMedical Research CouncilNational Institute for Health and Care Research
KeywordsMolecular clockBiologyRabies virusEvolutionary biologyDivergence (linguistics)Substitution (logic)Molecular evolutionRabiesRange (aeronautics)Mutation rateGeneticsGeneration timeLineage (genetic)MutationGenomeVirusVirologyPhylogeneticsGeneDemography

Abstract

fetched live from OpenAlex

Abstract The molecular clock hypothesis assumes that mutations accumulate on an organism’s genome at a constant rate over time, but this assumption does not always hold true. While modelling approaches exist to accommodate deviations from a strict molecular clock, assumptions about rate variation may not fully represent the underlying evolutionary processes. There is considerable variability in rabies virus (RABV) incubation periods, ranging from days to over a year, during which viral replication may be reduced. This prompts the question of whether modelling RABV on a per infection generation basis might be more appropriate. We investigate how variable incubation periods affect root-to-tip divergence under per-unit time and per-generation models of mutation. Additionally, we assess how well these models represent root-to-tip divergence in time-stamped RABV sequences. We find that at low substitution rates (<1 substitution per genome per generation) divergence patterns between these models are difficult to distinguish, while above this threshold differences become apparent across a range of sampling rates. Using a Tanzanian RABV dataset, we calculate the mean substitution rate to be 0.17 substitutions per genome per generation. At RABV’s substitution rate, the per-generation substitution model is unlikely to represent rabies evolution substantially differently than the molecular clock model when examining contemporary outbreaks; over enough generations for any divergence to accumulate, extreme incubation periods average out. However, measuring substitution rates per-generation holds potential in applications such as inferring transmission trees and predicting lineage emergence. Author Summary Rabies is a neglected disease that kills around 60,000 people each year. After entering the body, the incubation period of the virus is usually less than one month, but can sometimes span months to years. While we normally assume a virus accumulates mutations at a constant rate, it is possible that rabies’ occasional long incubation periods mean that mutations accumulate at varying rates if the virus replicates (and thus mutates) more slowly during the incubation period. We compared how the rabies virus evolves over time using two simulation models where mutations either occur per unit time or per infection generation. We also calculated the mean substitution rate per infection generation, which can be useful for inferring linkage between related rabies cases. We found that at realistic substitution rates for the rabies virus, we could not distinguish between the two models. Our calculations show that in most generations no mutations are expected to occur. Thus, over a time period long enough to observe genetic divergence, occasional long incubation periods would be “cancelled out” by shorter than average incubation periods, meaning that the two models are almost equivalent. However our work suggests that modelling substitution rates per generation may be useful for epidemiological inference.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.227
Teacher spread0.184 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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