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    A case control study investigating the methylation levels of GHRL and GHSR genes in alcohol use disorder
    (Springer, 2024) Ozkan-Kotiloglu, Selin; Kaya-Akyuzlu, Dilek; Guven, Emine; Dogan, Ozlem; Agtas-Ertan, Ece; Ozgur-Ilhan, Inci
    Background Alcohol use disorder (AUD) is a relapsing disease described as excessive use of alcohol. Evidence of the role of DNA methylation in addiction is accumulating. Ghrelin is an important peptide known as appetite hormone and its role in addictive behavior has been identified. Here we aimed to determine the methylation levels of two crucial genes (GHRL and GHSR) in ghrelin signaling and further investigate the association between methylation ratios and plasma ghrelin levels. Methods Individuals diagnosed with (n = 71) and without (n = 82) AUD were recruited in this study. DNA methylation levels were measured through methylation-sensitive high-resolution melting (MS-HRM). Acylated ghrelin levels were detected by ELISA. The GHRL rs696217 polymorphism was analyzed by the standard PCR-RFLP method. Results GHRL was significantly hypermethylated (P < 0.0022) in AUD between 25 and 50% methylation than in control subjects but no significant changes of GHSR methylation were observed. Moreover, GHRL showed significant positive correlation of methylation ratio between 25 and 50% with age. A significant positive correlation between GHSR methylation and ghrelin levels in the AUD group was determined (P = 0.037). The level of GHRL methylation and the ghrelin levels showed a significant association in the control subjects (P = 0.042). Conclusion GHSR and GHRL methylation levels did not change significantly between control and AUD groups. However, GHRL and GHSR methylations seemed to have associations with plasma ghrelin levels in two groups. This is the first study investigating the DNA methylation of GHRL and GHSR genes in AUD.
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    Fitmix: An R Package for Mixture Modeling of the Budding Yeast S. cerevisiae Replicative Lifespan (RLS) Distributions
    (Mdpi, 2021) Guven, Emine; Qin, Hong
    Replicative lifespan (RLS) of the budding yeast is the number of mother cell divisions until senescence and is instrumental to understanding mechanisms of cellular aging. Recent research has shown that replicative aging is heterogeneous, which argues for mixture modeling. The mixture model is a statistical method to infer subpopulations of the heterogeneous population. Mixture modeling is a relatively underdeveloped area in the study of cellular aging. There is no open access software currently available that assists extensive comparison among mixture modeling methods. To address these needs, we developed an R package called fitmix that facilitates the computation of well-known distributions utilized for RLS data and other lifetime datasets. This package can generate a group of functions for the estimation of probability distributions and simulation of random observations from well-known finite mixture models including Gompertz, Log-logistic, Log-normal, and Weibull models. To estimate and compute the maximum likelihood estimates of the model parameters, the Expectation-Maximization (EM) algorithm is employed.
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    Gene expression analysis of MCF7 cell lines of breast cancer treated with herbal extract of Cissampelos pareira revealed association with viral diseases
    (Elsevier, 2021) Guven, Emine
    Background: It is necessary to assess the cellular, molecular, and pathogenetic characteristics of COVID-19 and attention is required to understand highly effective gene targets and mechanisms. In this study, we suggest understandings into the fundamental pathogenesis of COVID-19 through gene expression analyses using the microarray data set GSE156445 publicly reachable at NIH/NCBI Gene Expression Omnibus database. The data set consists of MCF7 which is a human breast cancer cell line with estrogen, progesterone and glucocorticoid receptors. The cell lines treated with different quantities of Cissampelos pareira (Cipa). Cipa is a traditional medicinal plant which would possess an antiviral potency in preventing viral diseases such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods: Utilizing Biobase, GEOquery, gplots packages in R studio, the differentially expressed genes (DEGs) were identified. The gene ontology (GO) of pathway enrichments employed by utilizing DAVID and KEGG enrichment analyses were studied. We further constructed a human protein-protein interaction (PPI) network and performed, based upon that, a subnetwork module analysis for significant signaling pathways. Results: The study identified 418 differentially expressed genes (DEGs) using bioinformatics tools. The gene ontology of pathway enrichments employed by GO and KEGG enrichment analyses of down-regulated and up-regulated DEGs were studied. Gene expression analysis utilizing gene ontology and KEGG results uncovered biological and signaling pathways such as cell adhesion molecules, plasma membrane adhesion molecules, synapse assembly, and Interleukin-3-mediated signaling which are mostly linked to COVID-19. Our results provide in silico evidence for candidate genes which are vital for the inhibition, adhesion, and encoding cytokine protein including LYN, IGFBP5, IL-1R1, and IL-13RA1 that may have strong biomarker potential for infectious diseases such as COVID-19 related therapy targets.