Model system identification of novel congenital heart disease gene candidates: focus on RPL13.

Genetics is a significant factor contributing to congenital heart disease (CHD), but our understanding of the genetic players and networks involved in CHD pathogenesis is limited. Here, we searched for de novo copy number variations (CNVs) in a cohort of 167 CHD patients to identify DNA segments containing potential pathogenic genes. Our search focused on new candidate disease genes within 19 deleted de novo CNVs, which did not cover known CHD genes. For this study, we developed an integrated high-throughput phenotypical platform to probe for defects in cardiogenesis and cardiac output in human induced pluripotent stem cell (iPSC)-derived multipotent cardiac progenitor (MCPs) cells and, in parallel, in the Drosophila in vivo heart model. Notably, knockdown (KD) in MCPs of RPL13, a ribosomal gene and SON, an RNA splicing cofactor, reduced proliferation and differentiation of cardiomyocytes, while increasing fibroblasts. In the fly, heart-specific RpL13 KD, predominantly at embryonic stages, resulted in a striking 'no heart' phenotype. KD of Son and Pdss2, among others, caused structural and functional defects, including reduced or abolished contractility, respectively. In summary, using a combination of human genetics and cardiac model systems, we identified new genes as candidates for causing human CHD, with particular emphasis on ribosomal genes, such as RPL13. This powerful, novel approach of combining cardiac phenotyping in human MCPs and in the in vivo Drosophila heart at high throughput will allow for testing large numbers of CHD candidates, based on patient genomic data, and for building upon existing genetic networks involved in heart development and disease.

Clinical predictors of I-131 therapy failure in differentiated thyroid cancer by machine learning: A single-center experience.

Well-differentiated thyroid carcinoma is predominantly a slow-growing malignancy, amendable to treatment, and has an excellent prognosis following thyroidectomy and radioiodine (RAI) therapy. However, patients who fail the initial RAI treatment attempt may require repeated RAI or other treatments and with this, comes an associated impact on patient quality of life. Therefore, the anticipation of patients in whom there is a higher risk of RAI failure may help in patient risk stratification and subsequent management. We conducted a retrospective review to determine the factors associated with initial RAI therapy failure in well-differentiated thyroid cancer patients. Using scikit-learn from Python, we implemented a machine-learning algorithm to determine the clinical patient factors associated with a higher likelihood of treatment resistance. We found that clinical factors such as tumor focality (P = 0.026) and lymph node invasion at surgical resection (P = 0.0135) were significantly associated with initial treatment failure following RAI. Elevated serum thyroglobulin (Tg) and Tg antibody levels following surgery but before RAI were also associated with treatment resistance (P < 0.0001 and P = 0.011 respectively). Less expected factors such as decreased time from surgery to RAI were also associated with treatment failure, however not to a statistically significant degree (P > 0.064). Clinical outcomes following RAI can be stratified by identifying factors that are associated with initial treatment failure. These findings can help restratify patients for RAI treatment and change patient management in certain cases. Such stratification will ultimately help to optimize successful treatment outcomes and improve patient quality of life.