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Record W1830986661 · doi:10.1186/s12862-015-0436-8

Rapid growth accelerates telomere attrition in a transgenic fish

2015· article· en· W1830986661 on OpenAlexaff
Angela Pauliny, Robert H. Devlin, Jörgen I. Johnsson, Donald Blomqvist

Bibliographic record

VenueBMC Evolutionary Biology · 2015
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsFisheries and Oceans Canada
FundersCarl Tryggers Stiftelse för Vetenskaplig ForskningSvenska Forskningsrådet FormasStiftelsen Oscar och Lili Lamms Minne
KeywordsTelomereBiologySomatic cellAgeingLongevityAttritionSenescenceTransgeneFish <Actinopterygii>GeneticsEvolutionary biologyGeneFishery

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals rarely grow as fast as their physiologies permit despite the fitness advantages of being large. One reason may be that rapid growth is costly, resulting for example in somatic damage. The chromosomal ends, the telomeres, are particularly vulnerable to such damage, and telomere attrition thus influences the rate of ageing. Here, we used a transgenic salmon model with an artificially increased growth rate to test the hypothesis that rapid growth is traded off against the ability to maintain somatic health, assessed as telomere attrition. RESULTS: We found substantial telomere attrition in transgenic fish, while maternal half-sibs growing at a lower, wild-type rate seemed better able to maintain the length of their telomeres during the same time period. CONCLUSIONS: Our results are consistent with a trade-off between rapid growth and somatic (telomere) maintenance in growth-manipulated fish. Since telomere erosion reflects cellular ageing, our findings also support theories of ageing postulating that unrepaired somatic damage is associated with senescence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

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

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 head0.097
GPT teacher head0.303
Teacher spread0.206 · 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.

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

Citations51
Published2015
Admission routes1
Has abstractyes

Explore more

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