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Öğe A comparison between the performance of Weibull and Log-logistic Aging Models on Saccharomyces cerevisiae lifespan data(Bilecik Şeyh Edebali Üniversitesi, 2020) Güven, EmineEmpirical lifespan datasets are often studied with the best-fitted mathematical model for aging. In this study, we focus our attention to the budding yeast S. cerevisiae lifespan and the determination of the best-fitted model of aging. We investigate the influence of model selection in yeast lifespan datasets and the fitting outcomes of the two-parameter Weibull (WE) and Log-logistic (LL) models of aging. Both of these models are commonly studied and implemented in aging research. They show similar tendency as a survival function that they correspond to mortality rates that increase, and then decrease, with time. Studies so far has been usually done with medflies, Drosophila, house flies, flour beetles, and humans with these models. Different than previous research, we focus our attention on the influence of fitting results and calibrations on empirical lifespan data samples. As expected both of the models could be used as a substitute of each other. However, we also find WE model fits the yeast lifespan data significantly better than LL model with an R2 = 0.86. This finding is especially important in yeast aging study because of typically survival models are applied and therefore one can see which model fits the yeast data best. In this article, comparisons are done and developed and the potential of the approach is demonstrated with a model comparison of yeast replicative lifespan datasets of the laboratory BY4741 and BY4742 wildtype reference strains. Our study highlights that interpreting model fitting results of experimental lifespans should take model selection and resulted variation into account.Öğe Bioinformatics analysis of molecular pathways and key candidate biomarkers associated with human bone marrow hematopoietic stem cells (HSCs) micro-array gene expression data(Elsevier, 2022) Güven, Emine; Akçay, SevinçAn identification of the molecular properties of glioma stem cells (GSCs) has led studies in clinical use, stem cell identification, and highly-effective usage. The GSE32719 contains a total expression of 54,676 genes of healthy human bone marrow HSCs in 14 young (20-31 years), 5 middle (42-61 years), and a lot of old (65-85 years) age groups. The researchers of this study described age-related changes in the human HSC population, using the gene expression profile of significance analysis to discover differentially expressed genes (DEGs) between each age group. The DEGs were subjected to significantly enriched biological processing that decoded the increase and functional decline in the HSC population. The GSE dataset analysis was conducted by the GEOquery package in Bioconductor. Using the Biobase and gplots packages, 453 DEGs were screened. DEGs analyses were conducted by gene ontology (GO) pathway enrichment and Kyoto Encylopedia of Genes and Genomes (KEGG) enrichment analysis. The Hippo signaling pathway was observed to be significant using the GO pathway enrichment analysis, which was previously reported as an effective pathway in cancers, including AML. A protein-protein interaction (PPI) network was constructed; then based on that, a subnetwork module analysis for the Hippo signaling pathway was made. Additionally, the GO pathway enrichment analysis revealed 'cellular process', 'cellular metabolic process', 'metabolic process', 'biogenesis', and 'vasculogenesis biological processes', which are involved in a wide of biological activities such as metabolic regulation, cell growth, and proliferation. Our findings offer silico evidence for candidate genes, such as the UBC, PTK2, and TCF7L2, that may be promising biomarkers for the translation approach associated with HSC population age-related diseases.Öğe Characterization of the Experimentally Observed Clustering of VEGF Receptors(Springer-Verlag Berlin, 2018) Güven, Emine; Wester, Michael J.; Wilson, Bridget S.; Edwards, Jeremy S.; Halasz, Adam M.Cell membrane-bound receptors control signal initiation in many important cellular signaling pathways. In many such systems, receptor dimerization or cross-linking is a necessary step for activation, making signaling pathways sensitive to the distribution of receptors in the membrane. Microscopic imaging and modern labeling techniques reveal that certain receptor types tend to co-localize in clusters, ranging from a few to tens, and sometimes hundreds of members. The origin of these clusters is not well understood but they are likely not the result of chemical binding. Our goal is to build a simple, descriptive framework which provides quantitative measures that can be compared across samples and systems, as groundwork for more ambitious modeling aimed at uncovering specific biochemical mechanisms. Here we discuss a method of defining clusters based on mutual distance, applying it to a set of transmission microscopy images of VEGF receptors. Preliminary analysis using standard measures such as the Hopkins' statistic reveals a compelling difference between the observed distributions and random placement. A key element to cluster identification is identifying an optimal length parameter L*. Distance based clustering hinges on the separation between two length scales: the typical distance between neighboring points within a cluster vs. the typical distance between clusters. This provides a guiding principle to identify L* from experimentally derived cluster scaling functions. In addition, we assign a geometric shape to each cluster, using a previously developed procedure that relates closely to distance based clustering. We applied the cluster [support] identification procedure to the entire data set. The observed particle distribution results are consistent with the random placement of receptors within the clusters and, to a lesser extent, the random placement of the clusters on the cell membrane. Deviations from uniformity are typically due to large scale gradients in receptor density and/or the emergence of "megaclusters" that are very likely the expression of a different biological function than the one behind the emergence of the quasi-ubiquitous small scale clusters.Öğe The Effect of Gaussian Noise on Maximum Likelihood Fitting of Gompertz and Weibull Mortality Models with Yeast Lifespan Data(Taylor & Francis Inc, 2019) Güven, Emine; Akçay, Sevinç; Qin, HongBackground/study context: Empirical lifespan data sets are often studied with the best-fitted mathematical model for aging. Here, we studied how experimental noises can influence the determination of the best-fitted aging model. We investigated the influence of Gaussian white noise in lifespan data sets on the fitting outcomes of two-parameter Gompertz and Weibull mortality models, commonly adopted in aging research. Methods: To un-equivocally demonstrate the effect of Gaussian white noises, we simulated lifespans based on Gompertz and Weibull models with added white noises. To gauge the influence of white noise on model fitting, we defined a single index, , for the difference between the maximal log-likelihoods of the Weibull and Gompertz model fittings. We then applied the approach using experimental replicative lifespan data sets for the laboratory BY4741 and BY4742 wildtype reference strains. Results: We systematically evaluated how Gaussian white noise can influence the maximal likelihood-based comparison of the Gompertz and Weibull models. Our comparative study showed that the Weibull model is generally more tolerant to Gaussian white noise than the Gompertz model. The effect of noise on model fitting is also sensitive to model parameters. Conclusion: Our study shows that Gaussian white noise can influence the fitting of an aging model for yeast replicative lifespans. Given that yeast replicative lifespans are hard to measure and are often pooled from different experiments, our study highlights that interpreting model fitting results should take experimental procedure variation into account, and the best fitting model may not necessarily offer more biological insights.Öğe Gene Expression Characteristics of Tumor and Adjacent Non-Tumor Tissues of Pancreatic Ductal Adenocarcinoma (PDAC) In-Silico(Natl Inst Genetic Engineering & Biotechnology, 2022) Güven, EmineBackground: One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs. Objectives: This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database. Materials and Methods: Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied. Results: Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database. Conclusions: The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as pathways in cancer (hsa05200), protein processing in the endoplasmic reticulum (hsa04141), antigen processing and presentation (hsa04612), dopaminergic synapse (hsa04728), and measles (hsa05162); in which these pathways, the protein processing in endoplasmic reticulum (hsa04141), was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.Öğe Modeling the Cluster Size Distribution of Vascular Endothelial Growth Factor (VEGF) Receptors(Sage Publications Ltd, 2022) Güven, Emine; Wester, Michael J.; Edwards, Jeremy S.; Halasz, Adam M.We previously developed a method of defining receptor clusters in the membrane based on mutual distance and applied it to a set of transmission microscopy images of vascular endothelial growth factor receptors. An optimal length parameter was identified, resulting in cluster identification and a procedure that assigned a geometric shape to each cluster. We showed that the observed particle distribution results were consistent with the random placement of receptors within the clusters and, to a lesser extent, the random placement of the clusters on the cell membrane. Here. we develop and validate a stochastic model of clustering, based on a hypothesis of preexisting domains that have a high affinity for receptors. The proximate objective is to clarify the mechanism behind cluster formation and to estimate the effect on signaling. Receptor-enriched domains may significantly impact signaling pathways that rely on ligand-induced dimerization of receptors. We define a simple statistical model, based on the preexisting domain hypothesis, to predict the probability distribution of cluster sizes. The process yielded sets of parameter values that can readily be used in dynamical calculations as the estimates of the quantitative characteristics of the clustering domains.Öğe Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme(Elsevier, 2022) Akçay, Sevinç; Güven, Emine; Afzal, Muhammad; Kazmi, ImranOne of the most prevailing primary brain tumors in adult human male is glioblastoma multiforme (GBM), which is categorized by rapid cellular growth. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate, on average, is 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and defiance against chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE124145 was retrieved; the non-negative matrix factorization (NMF) method was applied on GBM dataset, and differentially expressed genes analysis (DEGs) was performed. After which, overlapping genes between metagenes and DEGs were detected to examine the Gene Ontology (GO) categories in the biological process (BP) in the stemness of GBM. The common hub genes were used to construct protein-protein interaction (PPI) network and further GO, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was utilized to pinpoint the real hub genes. The analysis of hub genes particular for the same GO categories demonstrated that specific hub genes triggered distinct features of the same biological processes. After utilizing GSE124145 and The Cancer Genome Atlas (TCGA) dataset for survival analysis, we screened five real hub genes: GUCA1A, RFC2, GNG11, MMP19, and NRG1, which are strongly associated with the progression and prognosis of GBM. The DEGs analysis revealed that all real hub genes were overexpressed in GBM and TCGA datasets, which further validates our results. The constructed study of PPI, GO, and KEGG pathway on common hub genes was performed. Finally, the KEGG pathways performed on the top 15 candidate hub genes (including six real hub genes) of the PPI network in the GBM gene expression dataset study found mitogen-activated protein kinase (Mapk) signaling pathway to be the most significant pathway. The rest of the hub genes reviewed throughout the analysis might be favorable targets for diagnosing and treating GBM and lower-grade gliomas.Öğe Polycystic Ovarian Syndrome: A Complex Disease with a Genetics Approach(Mdpi, 2022) Nautiyal, Himani; Imam, Syed Sarim; Alshehri, Sultan; Ghoneim, Mohammed M.; Afzal, Muhammad; Alzarea, Sami I.; Güven, EminePolycystic ovarian syndrome (PCOS) is a complex endocrine disorder affecting females in their reproductive age. The early diagnosis of PCOS is complicated and complex due to overlapping symptoms of this disease. The most accepted diagnostic approach today is the Rotterdam Consensus (2003), which supports the positive diagnosis of PCOS when patients present two out of the following three symptoms: biochemical and clinical signs of hyperandrogenism, oligo, and anovulation, also polycystic ovarian morphology on sonography. Genetic variance, epigenetic changes, and disturbed lifestyle lead to the development of pathophysiological disturbances, which include hyperandrogenism, insulin resistance, and chronic inflammation in PCOS females. At the molecular level, different proteins and molecular and signaling pathways are involved in disease progression, which leads to the failure of a single genetic diagnostic approach. The genetic approach to elucidate the mechanism of pathogenesis of PCOS was recently developed, whereby four phenotypic variances of PCOS categorize PCOS patients into classic, ovulatory, and non-hyperandrogenic types. Genetic studies help to identify the root cause for the development of this PCOS. PCOS genetic inheritance is autosomal dominant but the latest investigations revealed it as a multigene origin disease. Different genetic loci and specific genes have been identified so far as being associated with this disease. Genome-wide association studies (GWAS) and related genetic studies have changed the scenario for the diagnosis and treatment of this reproductive and metabolic condition known as PCOS. This review article briefly discusses different genes associated directly or indirectly with disease development and progression.Öğe Screening the Significant Hub Genes by Comparing Tumor Cells, Normoxic and Hypoxic Glioblastoma Stem-like Cell Lines Using Co-Expression Analysis in Glioblastoma(Mdpi, 2022) Güven, Emine; Afzal, Muhammad; Kazmi, ImranGlioblastoma multiforme (GBM) is categorized by rapid malignant cellular growth in the central nervous system (CNS) tumors. It is one of the most prevailing primary brain tumors, particularly in human male adults. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate is on average 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and counteracting chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE45117 was retrieved and differentially expressed genes (DEGs) were spotted. The co-expression network analysis was employed on DEGs to find the significant modules. The most significant module resulting from co-expression analysis was the turquoise module. The turquoise module related to the tumor cells, hypoxia, normoxic treatments of glioblastoma tumor (GBT), and GSCs were screened. Sixty-one common genes in the turquoise module were selected generated through the co-expression analysis and protein-protein interaction (PPI) network. Moreover, the GO and KEGG pathway enrichment results were studied. Twenty common hub genes were screened by the NetworkAnalyst web instrument constructed on the PPI network through the STRING database. After survival analysis via the Kaplan-Meier (KM) plotter from The Cancer Genome Atlas (TCGA) database, we identified the five most significant hub genes strongly related to the progression of GBM. We further observed these five most significant hub genes also up-regulated in another GBM gene expression dataset. The protein-protein interaction (PPI) network of the turquoise module genes was constructed and a KEGG pathway enrichments study of the turquoise module genes was performed. The VEGF signaling pathway was emphasized because of the strong link with GBM. A gene-disease association network was further constructed to demonstrate the information of the progression of GBM and other related brain neoplasms. All hub genes assessed through this study would be potential markers for the prognosis and diagnosis of GBM.Öğe Weibull and Log-logistic yaşlanma modellerinin performansının Saccharomyces cerevisiae ömür verisi kullanılarak karşılaştırılması(2020) Güven, EmineAmpirik yaşam veri setleri genellikle yaşlanma için en uygun matematiksel modelle incelenir. Bu çalışmada, dikkatimizi tomurcuklanan maya bakterisi S. cerevisiae ömrüne ve bu bakterilerin en uygun yaşlanma modelininbelirlenmesine verdik. Model seçiminin maya bakterisi ömür veri kümelerindeki etkisini ve iki parametreli Weibull (WE) ve Log-logistic (LL) yaşlanma modellerinin uyum sonuçlarını araştırdık. Bu modellerin her ikisi de yaşlanmaaraştırmalarında yaygın olarak incelenmekte ve uygulanmaktadır. Bir sağkalım fonksiyonu olarak, zamanla artan ve sonra azalan mortalite oranlarına karşılık gelen benzer bir eğilim gösterirler. Şu ana kadar yapılan çalışmalar genellikleAkdeniz meyve sinekleri, meyve sinekleri, ev sinekleri, un böcekleri ve insanlarin ömür verisi üzerinde bu modellerle çalışmalar yapılmıştır. Önceki araştırmalardan farklı olarak, dikkatimizi sonuçların ve kalibrasyonların ampirik ömürveri örnekleri üzerindeki etkisine odakladik. Beklendiği gibi her iki model de birbirlerinin yerine kullanılabilir. Bununla birlikte, WE modelinin maya ömür verilerine R2= 0.86 ile LL modelinden önemli ölçüde daha iyi fit olduğunugördük. Bu bulgu, tipik olarak hayatta kalma modelleri uygulandığından maya yaşlanma çalışmasında özellikle önemlidir ve bu nedenle hangi modelin maya verilerine daha uygun olduğunu öngörebilir. Bu makalede,karşılaştırmalarla geliştirilen bu yaklaşımın potansiyeli, labaratuvar BY4741 ve BY4742 değişime uğramamışreferans suşlarının maya replikatif ömür veri setlerinin model karşılaştırması ile gösterilmiştir. Çalışmamız, deneysel ömürlerinmodel uyum sonuçlarının yorumlanmasının model seçimini dikkate alması ve sonuçlanan varyasyonu göz önünde bulundurulması gerektiğini vurgulamaktadır
