In silico prediction, molecular modeling, and dynamics studies on the targeted next-generation sequencing identified genes underlying congenital heart disease in Down syndrome patients.

Background: Individuals with Down syndrome (DS) have a 40%-60% chance of being born with congenital heart disease (CHD). This indicates that CHD in individuals with DS is not solely caused by trisomy 21, and there may be other genetic factors contributing to the development of CHD in these children. A study has identified variants in the specific genes that contribute to the pathogenesis of CHD in children with DS, isolated DS, and the CHD group. Computational studies on these identified variants, which, together with trisomy 21, determine the risk for CHD in DS cases, were limited. Here, we aimed to identify the impact of the identified variants that contribute to the pathogenesis of CHD in children with DS through in silico prediction, molecular modeling, and dynamics studies. Methodology and Results: The target single-nucleotide polymorphisms included in the study were examined for pathogenicity, residue conservation, and protein structural changes. The structural predictions were done using I-TASSER, Robetta, SWISS-MODEL, and Phyre2 tools. Further, the predicted models were validated through the PROCHECK server and molecular dynamics simulation using GROMACS software. The conservation analysis conducted on the identified variant highlights its significance in relation to the genetic disorders. Furthermore, a dynamics simulation study revealed the impact of the variant on protein structural stability (≤3 Å), providing valuable insights into its pathogenicity. We have also observed that the structure of the centrosomal protein of 290 kDa gene is relatively unstable, which may be attributed to its exclusive inclusion of helices within its secondary structural components. Conclusions: This computational study explores, for the first time, the association between genes and CHD-DS, evaluating the identified specific frameshift variants. The observed pathogenic mutations in CHD-DS patients require further experimental validation and may contribute to the development of prospective drug design research. The insights gained from the structural and functional implications of these variants could potentially serve as a cornerstone in the development of effective treatments for this debilitating condition.

Androgen receptor CAG repeats length polymorphism and the risk of polycystic ovarian syndrome (PCOS).

OBJECTIVE: Polycystic ovarian syndrome (PCOS) refers to an inheritable androgen excess disorder characterized by multiple small follicles located at the ovarian periphery. Hyperandrogenism in PCOS, and inverse correlation between androgen receptor (AR) CAG numbers and AR function, led us to hypothesize that CAG length variations may affect PCOS risk. METHODS: CAG repeat region of 169 patients recruited following strictly defined Rotterdam (2003) inclusion criteria and that of 175 ethnically similar control samples, were analyzed. We also conducted a meta-analysis on the data taken from published studies, to generate a pooled estimate on 2194 cases and 2242 controls. RESULTS: CAG bi-allelic mean length was between 8.5 and 24.5 (mean = 17.43, SD = 2.43) repeats in the controls and between 11 and 24 (mean = 17.39, SD = 2.29) repeats in the cases, without any significant difference between the two groups. Further, comparison of bi-allelic mean and its frequency distribution in three categories (short, moderate and long alleles) did not show any significant difference between controls and various case subgroups. Frequency distribution of bi-allelic mean in two categories (extreme and moderate alleles) showed over-representation of extreme sized alleles in the cases with marginally significant value (50.3% vs. 61.5%, χ(2) = 4.41; P = 0.036), which turned insignificant upon applying Bonferroni correction for multiple comparisons. X-chromosome inactivation analysis showed no significant difference in the inactivation pattern of CAG alleles or in the comparison of weighed bi-allelic mean between cases and controls. Meta-analysis also showed no significant correlation between CAG length and PCOS risk, except a minor over-representation of short CAG alleles in the cases. CONCLUSION: CAG bi-allelic mean length did not differ between controls and cases/case sub-groups nor did the allele distribution. Over-representation of short/extreme-sized alleles in the cases may be a chance finding without any true association with PCOS risk.