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Record W4404798925 · doi:10.1111/pcmr.13215

A Ball Python Colour Morph Implicates <i>MC1R</i> in Melanophore–Xanthophore Distribution and Pattern Formation

2024· article· en· W4404798925 on OpenAlexafffund
Alan García‐Elfring, Heather L. Roffey, Jaren M. Abergas, Jurgen Wuyts, Andrew P. Hendry, Athanasia C. Tzika, Rowan D. H. Barrett

Bibliographic record

VenuePigment Cell & Melanoma Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsVanier CollegeMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsBiologyPython (programming language)Melanocortin 1 receptorEvolutionary biologyPhenotypeZoologyGeneticsGene

Abstract

fetched live from OpenAlex

Reptiles showcase an extensive array of skin colours and patterns, yet little is known about the genetics of reptile colouration. Here, we investigate the genetic basis of the Clown colour morph found in captive-bred ball pythons (Python regius) to study skin pigmentation and patterning in snakes. We obtained samples by crowdsourcing shed skin from commercial breeders and hobbyists. We applied a case-control design, whole-genome pool sequencing, variant annotation, histological analyses, and electron microscopy imaging. We identified a missense mutation in a transmembrane region of the melanocortin-1 receptor (MC1R) associated with the Clown phenotype. In classic avian and mammalian model species, MC1R is known for controlling the type and amount of melanin produced. In contrast, our results suggest that MC1R signalling might play a key role in pattern formation in ball pythons, affecting xanthophore-melanophore distribution. This work highlights the varied functions of MC1R across different vertebrate lineages and promotes a novel model system to study reptile colouration.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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 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

Citations5
Published2024
Admission routes2
Has abstractyes

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