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Duplication and diversification of insulin genes in ray-finned fish

2019· article· en· W2951089612 on OpenAlexaff
David M. Irwin

Bibliographic record

Venue动物学研究 · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyGeneInsulinGene duplicationGeneticsProglucagonGRB10IncretinEndocrinologyInsulin receptorInsulin resistanceType 2 diabetesGlucagon-like peptide-1Diabetes mellitus

Abstract

fetched live from OpenAlex

Insulin is a key hormone for the regulation of metabolism in vertebrates. Insulin is produced by pancreatic islet cells in response to elevated glucose levels and leads to the uptake of glucose by tissues such as liver and adipose tissue to store energy. Insulin also has additional functions in regulating development. Previous work has shown that the proglucagon gene, which encodes hormones counter regulating insulin, is duplicated in teleost fish, and that the peptide hormones encoded by these genes have diversified in function. I sought to determine whether similar processes have occurred to insulin genes in these species. Searches of fish genomes revealed an unexpected diversity of insulin genes. A triplication of the insulin gene occurred at the origin of teleost fish, however one of these three genes, insc, has been lost in most teleost fish lineages. The two other insulin genes, insa and insb, have been retained but show differing levels of selective constraint suggesting that they might have diversified in function. Intriguingly, a duplicate copy of the insa gene, which I named insab, is found in many fish. The coding sequence encoded by insab genes is under weak selective constraint, with its predicted protein sequences losing their potential to be processed into a two-peptide hormone. However, these sequences have retained perfectly conserved cystine residues, suggesting that they maintain insulin's three-dimensional structure and therefore might modulate the processing and secretion of insulin produced by the other genes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.204
Teacher spread0.188 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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