Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach.

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

Identifying pathogenic variants related to systemic lupus erythematosus by integrating genomic databases and a bioinformatic approach.

Systemic lupus erythematosus (SLE) is an inflammatory-autoimmune disease with a complex multi-organ pathogenesis, and it is known to be associated with significant morbidity and mortality. Various genetic, immunological, endocrine, and environmental factors contribute to SLE. Genomic variants have been identified as potential contributors to SLE susceptibility across multiple continents. However, the specific pathogenic variants that drive SLE remain largely undefined. In this study, we sought to identify these pathogenic variants across various continents using genomic and bioinformatic-based methodologies. We found that the variants rs35677470, rs34536443, rs17849502, and rs13306575 are likely damaging in SLE. Furthermore, these four variants appear to affect the gene expression of NCF2, TYK2, and DNASE1L3 in whole blood tissue. Our findings suggest that these genomic variants warrant further research for validation in functional studies and clinical trials involving SLE patients. We conclude that the integration of genomic and bioinformatic-based databases could enhance our understanding of disease susceptibility, including that of SLE.

Metformin Modulates Cyclin D1 and P53 Expression to Inhibit Cell Proliferation and to Induce Apoptosis in Cervical Cancer Cell Lines.

Background: Cervical cancer is one of the most prevalent gynecological cancers worldwide and contributes in high mortality of Indonesian women. The efficacy of chemotherapy as a standart therapy for cervical cancer decreases because it frequenly rises adverse effects. Recent studies have found that metformin has a potential anticancer effect mostly through reduction of cyclin expression and activation of Activated Adenosine Monophosphate Kinase (AMPK). This study aimed to investigate the effect of metfomin on expression of cyclin D1 and p53 and apoptosis in HeLa cancer cell line. Methods: HeLa cells were treated with various doses of metformin and doxorubicin as a positive control. Cytotoxic effect of metformin was determined using the MTT assay. Immunocytochemistry was used to assess cyclin D1 and p53 expression and apoptosis levels of treated HeLa cells were analyzed using flowcytometry. Data of cyclin D1 expression was statistically analyzed using the Kruskal-Wallis test followed by the Tamhane test, whilst ANOVA and Tukey post Hoc tests were used to analyze data of p53 and apoptosis level. The significant value was p< 0.05. Results: Metformin was able to inhibit proliferation of HeLa cells with IC50 60 mM. HeLa cells treated with 60 and 120 mM metformin had lower cyclin D1 expression than HeLa cells treated without metformin and reached a significant difference (p= 0.001). Moreover, 30 mM or higher doses of metformin increase significantly p53 expression (p< 0.001). Induction of apoptosis was observed in HeLa cells treated with all doses of metformin and reached statistically difference (p= 0.04 and p < 0.001). Conclusion: Metformin can modulate cyclin D1 and p53 expression in HeLa cancer cell line, leading to inhibition of cell proliferation and induction of apoptosis. Other cyclin family members, CDK inhibitors and AMPK signaling should be further investigated in order to know mechanism of metformin action.