The development and growth of tissues derived from cranial neural crest and primitive mesoderm is dependent on the ligation status of retinoic acid receptor γ:evidence that retinoic acid receptor γ functions to maintain stem/progenitor cells in the absence of retinoic acid

Retinoic acid (RA) signaling is important to normal development. However, the function of the different RA receptors (RARs)-RARα, RARβ, and RARγ-is as yet unclear. We have used wild-type and transgenic zebrafish to examine the role of RARγ. Treatment of zebrafish embryos with an RARγ-specific agonist reduced somite formation and axial length, which was associated with a loss of hoxb13a expression and less-clear alterations in hoxc11a or myoD expression. Treatment with the RARγ agonist also disrupted formation of tissues arising from cranial neural crest, including cranial bones and anterior neural ganglia. There was a loss of Sox 9-immunopositive neural crest stem/progenitor cells in the same anterior regions. Pectoral fin outgrowth was blocked by RARγ agonist treatment. However, there was no loss of Tbx-5-immunopositive lateral plate mesodermal stem/progenitor cells and the block was reversed by agonist washout or by cotreatment with an RARγ antagonist. Regeneration of the caudal fin was also blocked by RARγ agonist treatment, which was associated with a loss of canonical Wnt signaling. This regenerative response was restored by agonist washout or cotreatment with the RARγ antagonist. These findings suggest that RARγ plays an essential role in maintaining stem/progenitor cells during embryonic development and tissue regeneration when the receptor is in its nonligated state.

A study of the regulatory role of retinoic acid receptor gamma in Zebrafish development

Retinoic acid (RA) is thought to signal through retinoic acid receptors (RARs), i.e. RARα, β, and γ to play important roles in embryonic development and tissue regeneration. In this thesis, the zebrafish (Danio rario) was used as a vertebrate model organism to examine the role of RARγ. Treatment of zebrafish embryos with a RARγ specific agonist reduced the axial length of developing embryos, associated with reduced somite number and loss of hoxb13a expression. There were no clear alterations in hoxc11a or myoD expression. Treatment with the RARγ agonist disrupted the formation of anterior structures of the head, the cranial bones and the anterior lateral line ganglia, associated with a loss of sox9 immunopositive cells in the same regions. Pectoral fin outgrowth was blocked by treatment with the RARγ agonist; however, this was not associated with loss of tbx5a immunopositive lateral plate cells and was reversed by wash out of the RARγ agonist or co-treatment with a RARγ antagonist. Regeneration of the transected caudal fin was also blocked by RARγ agonist treatment and restored by agonist washout or antagonist co-treatment; this phenotype was associated with a localised reduction in canonical Wnt signalling. Conversely, elevated canonical Wnt signalling after RARγ treatment was seen in other tissues, including ectopically in the notochord. Furthermore, some phenotypes seen in the RARγ treated embryos were present in mutant zebrafish embryos in which canonical Wnt signalling was constitutively increased. These data suggest that RARγ plays an essential role in maintaining neural crest and mesodermal stem/progenitor cells during normal embryonic development and tissue regeneration when the receptor is in its non-ligated state. In addition, this work has provided evidence that the activation status of RARγ may regulate hoxb13a gene expression and canonical Wnt signalling. Further research is required to confirm such novel regulatory roles.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.