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Relationship of Phenotypic Variation with Mechanisms of Craniofacial Development in Two Connexin‐43 Mutant Mouse Models

2019· article· en· W3176076263 on OpenAlexafffund
Elizabeth Jewlal, Kevin Barr, Andrew Nelson, Dale W. Laird, Katherine E. Willmore

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyCraniofacialConnexinPhenotypeSkullMutantGeneticsFunction (biology)Evolutionary biologyVariation (astronomy)MutationGeneAnatomyGap junction

Abstract

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Background The skull is complex in its development and function. This complexity makes it vulnerable to disruptive forces, and upwards of one third of congenital abnormalities in humans involve the craniofacial complex. Abnormalities in the skull can be viewed as extreme examples of variation, and it is generally assumed that both normal and extreme variation has a developmental basis. If this assumption is correct, then we expect that patterns of variation will be similarly structured if the developmental inputs are similar, and that phenotypic variation can then be used to uncover potential developmental disruptions in cases of aberrant development. The focus of this study is to test the hypothesis that phenotypic variation is structured through developmental processes using mouse models of normal and abnormal skull phenotypes. Methods In humans, reduced connexin‐43 (Cx43) function caused by mutations results in distinct anomalies of the facial skeleton, and mouse models of reduced Cx43 function display a similar phenotype. We use two mouse models with mutations to the Gja1 gene encoding for the gap‐junctional protein connexin‐43 (Cx43 I130T/+ : 50% channel function, Cx43 G60S/+ : 15–20% channel function) to test the relationship between connexin‐43 function, and variation. Geometric morphometric analyses were done on 3D landmark data from μCT scans of mutant skulls (20μm) and their wild type littermates at post‐natal day zero and three months of age (N=30) to assess and compare mean shape, phenotypic variation and covariation among genotypes. Results As expected, both mutant models exhibit significantly altered skull morphology and greater phenotypic variation at both P0 and three months. While morphological changes and variation are more severe in the Cx43 G60S/+ mice than the Cx43 I130T/+ mice, the mean phenotypic changes and patterns of variation are similar in both mutants. Unexpectedly, the greatest phenotypic changes are within the cranial base in both mutants and at both time points, whereas changes to facial morphology are only evident in the three month mice. Significance Our findings indicate that reduced Cx43 function causes distinct skull anomalies in a dosage‐dependent manner. Given that the same skull structures are disrupted in both of our mutants, that variation is increased in both mutants with dosage effect and that this variation is structured the same in each model, our results support the hypothesis that phenotypic variation is structured through developmental processes. Additionally, our results uncovered the previously undocumented involvement of the cranial base, and the relatively late‐timing of alterations to the facial skeleton of adult mice in response to reduced Cx43 function. These findings potentially point to two skeletal disruptions caused by reduced Cx43: 1. An early disruption of skeletal morphogenesis of the cranial base and 2. Altered bone remodelling in the facial skeleton. This study bolsters the idea that study of phenotypic variation is a useful tool to help us uncover and pinpoint developmental disruptions. Support or Funding Information Research was funding through a grant from NSERC This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.233
Teacher spread0.218 · 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 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

Citations0
Published2019
Admission routes2
Has abstractyes

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