Impact of deleterious missense PRKCI variants on structural and functional dynamics of protein

Unravelling the role of PRKCI and key-cancer related genes in breast cancer development and metastasis

BACKGROUND

Breast cancer is one of the most common causes of fatalities in females globally. Rising cases of drug resistance against existing chemotherapeutics are great problem. To address this issue, there is a need to find appropriate biomarker that could be used to detect cancer at early stages, so drug resistance development can be avoided. Protein Kinase C iota (PKCɩ), an AGC kinase, has an oncogenic role in cancers and its expression and Single nucleotide polymorphisms (SNPs) have been reported to be associated with the cancer development. So, the study aims were to examine the expression of PKCɩ, Protein Kinase B (AKT), Suppressor of cytokine signaling 3 (SOCS3), Vascular endothelial growth factor (VEGF), Krupple like factor 3 (KLF3), Tumor protein D52 (TPD52), Hypoxia inducible factor (HIF1α) and microRNA-124 (miR-124) in breast cancer and association of PKCɩ variants (G34W & F66Y) with breast cancer.

METHODS

Genetic expression assay was performed through real time Polymerase Chain reaction (PCR), whereas the genotypic association of PKCɩ SNPs with breast cancer was accomplished through Tetra-ARMS PCR.

RESULTS

The expression levels of PKCɩ, AKT, SOC3, VEGF, HIF1α and TPD52 were elevated in patients as compared to control whereas the expression levels of miR-124 and KLF3 were lowered in patients. Positive association of variant G34W (TT) of PKCɩ with breast cancer has been explored through ARM's PCR, while no association of variant F66Y with breast cancer was found.

CONCLUSION

Hence, the results suggest that PKCɩ and related genes can have a role in breast cancer and after further verification can serve as the potential biomarkers for the early-diagnosis and prognosis of breast cancer.

GLP1R (glucagon-like-peptide-1 incretin receptor), diabetes and obesity phenotypes: An in silico approach revealed new pathogenic variants

OBJECTIVE

Glucagon-like peptide-1 receptor belongs to the B family of G protein-coupled receptors, serving as a binding protein in membranes and is widely expressed in human tissues. Upon stimulation by its agonist, the glucagon-like peptide-1, the receptor plays a role in glucose metabolism, enhancing insulin secretion, and regulating appetite in the hypothalamus. Mutations in the glucagon-like peptide-1 receptor gene can lead to physiological changes that may explain phenotypic variations in individuals with obesity and diabetes. Therefore, this study aimed to evaluate missense variants of the glucagon-like peptide-1 receptor gene.

METHODS

Data mining was performed on the single nucleotide polymorphism database, retrieving a total of 16,399 variants. Among them, 356 were missense. These 356 variants were analyzed using the PolyPhen-2 and filtered based on allele frequency, resulting in 6 pathogenic variants.

RESULTS

D344E, A239T, R310Q, R227H, R421P, and R176G were analyzed using four different prediction tools. The D344E and A239T resulted in larger amino acid residues compared to their wild-type counterparts. The D344E showed a slightly destabilized structure, while A239T affected the transmembrane helices. Conversely, the R310Q, R227H, R421P, and R176G resulted in smaller amino acid residues than the wild-type, leading to a loss of positive charge and increased hydrophobicity. Particularly, the R421P, due to the presence of proline, significantly destabilized the α-helix structure and caused severe damage to the receptor.

CONCLUSION

Elucidating the glucagon-like peptide-1 receptor variants and their potentially detrimental effects on receptor functionality can contribute to an understanding of metabolic diseases and the response to available pharmacological treatments.

Possible prognostic impact of PKCι genetic variants in prostate cancer

BACKGROUND

Single nucleotide polymorphisms (SNPs) have been linked with prostate cancer (PCa) and have shown potential as prognostic markers for advanced stages. Loss of function mutations in PKCι have been linked with increased risk of malignancy by enhancing tumor cell motility and invasion. We have evaluated the impact of two coding region SNPs on the PKCι gene (PRKCI) and their prognostic potential.

METHODS

Genotypic association of non-synonymous PKCι SNPs rs1197750201 and rs1199520604 with PCa was determined through tetra-ARMS PCR. PKCι was docked with interacting partner Par-6 to determine the effect of these variants on PKCι binding capabilities. Molecular dynamic simulations of PKCι docked with Par-6 were performed to determine variant effects on PKCι protein interactions. The possible impact of changes in PKCι protein interactions on epithelial cell polarity was hypothesized.

RESULTS

PKCι rs1199520604 mutant genotype TT showed association with PCa (p = 0.0055), while rs1197750201 mutant genotype AA also showed significant association with PCa (P = 0.0006). The binding interaction of PKCι with Par-6 was altered for both variants, with changes in Van der Waals energy and electrostatic energy of docked structures.

CONCLUSION

Genotypic analysis of two non-synonymous PKCι variants in association with PCa prognosis was performed. Both variants in the PB1 domain showed potential as a prognostic marker for PCa. In silico analysis of the effect of the variants on PKCι protein interactions indicated they may be involved in PCa progression through aberration of epithelial cell polarity pathways.

Multiplex Cas9-based excision of CLCuV betasatellite and DNA-A revealed reduction of viral load with asymptomatic cotton plants

MAIN CONCLUSION

Multiplexed Cas9-based genome editing of cotton resulted in reduction of viral load with asymptomatic cotton plants. In depth imaging of proteomic dynamics of resulting CLCuV betasatellite and DNA-A protein was also performed. The notorious  cotton leaf curl virus (CLCuV), which is transmitted by the sap-sucking insect whitefly, continuously damages cotton crops. Although the application of various toxins and RNAi has shown some promise, sustained control has not been achieved. Consequently, CRISPR_Cas9 was applied by designing multiplex targets against DNA-A (AC2 and AC3) and betasatellite (βC1) of CLCuV using CRISPR direct and ligating into the destination vector of the plant using gateway ligation method. The successful ligation of targets into the destination vector was confirmed by the amplification of 1049 bp using a primer created from the promoter and target, while restriction digestion using the AflII and Asc1 enzymes determined how compact the plasmid developed and the nucleotide specificity of the plasmid was achieved through Sanger sequencing. PCR confirmed the successful introduction of plasmid into CKC-1 cotton variety. Through Sanger sequencing and correlation with the mRNA expression of DNA-A and betasatellite in genome-edited cotton plants subjected to agroinfiltration of CLCuV infectious clone, the effectiveness of knockout was established. The genome-edited cotton plants demonstrated edited efficacy of 72% for AC2 and AC3 and 90% for the (βC1) through amplicon sequencing, Molecular dynamics (MD) simulations were used to further validate the results. Higher RMSD values for the edited βC1 and AC3 proteins indicated functional loss caused by denaturation. Thus, CRISPR_Cas9 constructs can be rationally designed using high-throughput MD simulation technique. The confidence in using this technology to control plant virus and its vector was determined by the knockout efficiency and the virus inoculation assay.

Exploring the prognostic significance of PKCε variants in cervical cancer

BACKGROUND

Protein Kinase C-epsilon (PKCε) is a member of the novel subfamily of PKCs (nPKCs) that plays a role in cancer development. Studies have revealed that its elevated expression levels are associated with cervical cancer. Previously, we identified pathogenic variations in its different domains through various bioinformatics tools and molecular dynamic simulation. In the present study, the aim was to find the association of its variants rs1553369874 and rs1345511001 with cervical cancer and to determine the influence of these variants on the protein-protein interactions of PKCε, which can lead towards cancer development and poor survival rates.

METHODS

The association of the variants with cervical cancer and its clinicopathological features was determined through genotyping analysis. Odds ratio and relative risk along with Fisher exact test were calculated to evaluate variants significance and disease risk. Protein-protein docking was performed and docked complexes were subjected to molecular dynamics simulation to gauge the variants impact on PKCε's molecular interactions.

RESULTS

This study revealed that genetic variants rs1553369874 and rs1345511001 were associated with cervical cancer. Smad3 interacts with PKCε and this interaction promotes cervical cancer angiogenesis; therefore, Smad3 was selected for protein-protein docking. The analysis revealed PKCε variants promoted aberrant interactions with Smad3 that might lead to the activation of oncogenic pathways. The data obtained from this study suggested the prognostic significance of PRKCE gene variants rs1553369874 and rs1345511001.

CONCLUSION

Through further in vitro and in vivo validation, these variants can be used at the clinical level as novel prognostic markers and therapeutic targets against cervical cancer.

Investigation of UTR Variants by Computational Approaches Reveal Their Functional Significance in PRKCI Gene Regulation

Single nucleotide polymorphisms (SNPs) are associated with many diseases including neurological disorders, heart diseases, diabetes, and different types of cancers. In the context of cancer, the variations within non-coding regions, including UTRs, have gained utmost importance. In gene expression, translational regulation is as important as transcriptional regulation for the normal functioning of cells; modification in normal functions can be associated with the pathophysiology of many diseases. UTR-localized SNPs in the PRKCI gene were evaluated using the PolymiRTS, miRNASNP, and MicroSNIper for association with miRNAs. Furthermore, the SNPs were subjected to analysis using GTEx, RNAfold, and PROMO. The genetic intolerance to functional variation was checked through GeneCards. Out of 713 SNPs, a total of thirty-one UTR SNPs (three in 3' UTR region and twenty-nine in 5' UTR region) were marked as ≤2b by RegulomeDB. The associations of 23 SNPs with miRNAs were found. Two SNPs, rs140672226 and rs2650220, were significantly linked with expression in the stomach and esophagus mucosa. The 3' UTR SNPs rs1447651774 and rs115170199 and the 5' UTR region variants rs778557075, rs968409340, and 750297755 were predicted to destabilize the mRNA structure with substantial change in free energy (∆G). Seventeen variants were predicted to have linkage disequilibrium with various diseases. The SNP rs542458816 in 5' UTR was predicted to put maximum influence on transcription factor binding sites. Gene damage index(GDI) and loss of function (o:e) ratio values for PRKCI suggested that the gene is not tolerant to loss of function variants. Our results highlight the effects of 3' and 5' UTR SNP on miRNA, transcription and translation of PRKCI. These analyses suggest that these SNPs can have substantial functional importance in the PRKCI gene. Future experimental validation could provide further basis for the diagnosis and therapeutics of various diseases.

Insilico prediction and functional analysis of nonsynonymous SNPs in human CTLA4 gene

The CTLA4 receptor is an immune checkpoint involved in the downregulation of T cells. Polymorphisms in this gene have been found to be associated with different diseases like rheumatoid arthritis, autosomal dominant immune dysregulation syndrome, juvenile idiopathic arthritis and autoimmune Addison's disease. Therefore, the identification of polymorphisms that have an effect on the structure and function of CTLA4 gene is important. Here we identified the most damaging missense or non-synonymous SNPs (nsSNPs) that might be crucial for the structure and function of CTLA4 using different bioinformatics tools. These in silico tools included SIFT, PROVEAN, PhD-SNP, PolyPhen-2 followed by MutPred2, I-Mutant 2.0 and ConSurf. The protein structures were predicted using Phyre2 and I-TASSER, while the gene-gene interactions were predicted by GeneMANIA and STRING. Our study identified three damaging missense SNPs rs1553657429, rs1559591863 and rs778534474 in coding region of CTLA4 gene. Among these SNPs the rs1553657429 showed a loss of potential phosphorylation site and was found to be highly conserved. The prediction of gene-gene interaction showed the interaction of CTlA4 with other genes and its importance in different pathways. This investigation of damaging nsSNPs can be considered in future while studying CTLA4 related diseases and can be of great importance in precision medicine.