Method: In this study, Rad50 mutations were retrieved from SNPeffect 4.0 database and literature. Each of the mutations was analyzed using various bioinformatic analyses such as PredictSNP, MutPred, SNPeffect 4.0, I-Mutant and MuPro to identify its impact on molecular mechanism, biological function and protein stability, respectively.
Results: We identified 103 mostly occurred mutations in the Rad50 protein domains and motifs, which only 42 mutations were classified as most deleterious. These mutations are mainly situated at the specific motifs such as Walker A, Q-loop, Walker B, D-loop and signature motif of the Rad50 protein. Some of these mutations were predicted to negatively affect several important functional sites that play important roles in DNA repair mechanism and cell cycle signaling pathway, highlighting Rad50 crucial role in this process. Interestingly, mutations located at non-conserved regions were predicted to have neutral/non-damaging effects, in contrast with previous experimental studies that showed deleterious effects. This suggests that software used in this study may have limitations in predicting mutations in non-conserved regions, implying further improvement in their algorithm is needed. In conclusion, this study reveals the priority of acid substitution associated with the genetic disorders. This finding highlights the vital roles of certain residues such as K42E, C681A/S, CC684R/S, S1202R, E1232Q and D1238N/A located in Rad50 conserved regions, which can be considered for a more targeted future studies.
METHODS: Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.
RESULTS: The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.
CONCLUSIONS: These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.
Methods: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.
Results: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.