Untangling Sources of Phenotypic Variation Characterizing the Craniofacial Disease Holoprosencephaly
Bibliographic record
Abstract
Holoprosencaphly (HPE)—a craniofacial disorder in which the forebrain fails to develop into two hemispheres and the midface does not fully form—is characterized by a broad spectrum of severity. Phenotypes range from very mild midfacial narrowing to cyclopia. Mutations in Sonic hedgehog (SHH) and in SHH signaling pathway members are known to underlie HPE; however, the source of the highly variable craniofacial morphology manifesting in the HPE patient population is not understood. Previous research in a chick model suggests that reduced SHH‐signaling in the brain correlates with a continuous distribution of HPE‐like phenotypes, and that very small modifications in ligand concentration can result in highly heterogeneous morphologies. Here, we are probing the origins of morphological variation in a genetic mouse model. Although humans heterozygous for SHH mutations exhibit the full spectrum of HPE, mice heterozygous for Shh do not exhibit even mild HPE although homozygous mutants are cyclopic. Mice homozygous for mutations in the SHH‐signaling pathway member low‐density lipoprotein receptor‐related protein (Lrp) 2 however exhibit mild to severe forms of HPE, and are therefore a better model for examining sources of variation. LRP2 is an endocytic receptor required to induce proper SHH signaling in the forebrain. We used geometric morphometric analysis to quantify the shape of embryos with sequential, genetic loss of Shh and Lrp2 alleles, and qPCR was used to assess SHH signaling pathway activation. We found that there is no statistically significant difference in shape among wild‐type, Lrp2+/− , Shh+/− , and Lrp2+/−;Shh+/− embryos, while Lrp2−/− and Lrp2−/−;Shh+/− embryos diverged from the group, exhibiting a more narrow midface. Interestingly, there was increased phenotypic variance in Lrp2−/− and Lrp2−/−;Shh+/− embryos in comparison to the other genotypes, with a greater increase in variance seen in the Lrp2−/−;Shh+/− group. Our results support the hypothesis that very slight modifications to SHH pathway activation can yield large amounts of phenotypic variation. Support or Funding Information This work is supported by NIH/NIDCR‐R01DE018234, ‐R01DE019638, ‐T32DE730619, ‐F31DE02579001. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".