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Dive into the research topics where Osman Ugur Sezerman is active.

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Featured researches published by Osman Ugur Sezerman.


PLOS ONE | 2011

A New Methodology to Associate SNPs with Human Diseases According to Their Pathway Related Context

Burcu Bakir-Gungor; Osman Ugur Sezerman

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.


Current Genetics | 2015

Molecular diversity of LysM carbohydrate-binding motifs in fungi

Gunseli Bayram Akcapinar; Lisa Kappel; Osman Ugur Sezerman

LysM motifs are carbohydrate-binding modules found in prokaryotes and eukaryotes. They bind to N-acetylglucosamine-containing carbohydrates, such as chitin, chitio-oligosaccharides and peptidoglycan. In this review, we summarize the features of the protein architecture of LysM-containing proteins in fungi and discuss their so far known biochemical properties, transcriptional profiles and biological functions. Further, based on data from evolutionary analyses and consensus pattern profiling of fungal LysM motifs, we show that they can be classified into a fungal-specific group and a fungal/bacterial group. This facilitates the classification and selection of further LysM proteins for detailed analyses and will contribute to widening our understanding of the functional spectrum of this protein family in fungi. Fungal LysM motifs are predominantly found in subgroup C chitinases and in LysM effector proteins, which are secreted proteins with LysM motifs but no catalytic domains. In enzymes, LysM motifs mediate the attachment to insoluble carbon sources. In plants, receptors containing LysM motifs are responsible for the perception of chitin-oligosaccharides and are involved in beneficial symbiotic interactions between plants and bacteria or fungi, as well as plant defence responses. In plant pathogenic fungi, LysM effector proteins have already been shown to have important functions in the dampening of host defence responses as well as protective functions of fungal hyphae against chitinases. However, the large number and diversity of proteins with LysM motifs that are being unravelled in fungal genome sequencing projects suggest that the functional repertoire of LysM effector proteins in fungi is only partially discovered so far.


PLOS ONE | 2013

MIR376A Is a Regulator of Starvation-Induced Autophagy

Gozde Korkmaz; Kumsal Ayse Tekirdag; Deniz Gulfem Ozturk; Ali Koşar; Osman Ugur Sezerman; Devrim Gozuacik

Background Autophagy is a vesicular trafficking process responsible for the degradation of long-lived, misfolded or abnormal proteins, as well as damaged or surplus organelles. Abnormalities of the autophagic activity may result in the accumulation of protein aggregates, organelle dysfunction, and autophagy disorders were associated with various diseases. Hence, mechanisms of autophagy regulation are under exploration. Methods Over-expression of hsa-miR-376a1 (shortly MIR376A) was performed to evaluate its effects on autophagy. Autophagy-related targets of the miRNA were predicted using Microcosm Targets and MIRanda bioinformatics tools and experimentally validated. Endogenous miRNA was blocked using antagomirs and the effects on target expression and autophagy were analyzed. Luciferase tests were performed to confirm that 3′ UTR sequences in target genes were functional. Differential expression of MIR376A and the related MIR376B was compared using TaqMan quantitative PCR. Results Here, we demonstrated that, a microRNA (miRNA) from the DLK1/GTL2 gene cluster, MIR376A, played an important role in autophagy regulation. We showed that, amino acid and serum starvation-induced autophagy was blocked by MIR376A overexpression in MCF-7 and Huh7 cells. MIR376A shared the same seed sequence and had overlapping targets with MIR376B, and similarly blocked the expression of key autophagy proteins ATG4C and BECN1 (Beclin 1). Indeed, 3′ UTR sequences in the mRNA of these autophagy proteins were responsive to MIR376A in luciferase assays. Antagomir tests showed that, endogenous MIR376A was participating to the control of ATG4C and BECN1 transcript and protein levels. Moreover, blockage of endogenous MIR376A accelerated starvation-induced autophagic activity. Interestingly, MIR376A and MIR376B levels were increased with different kinetics in response to starvation stress and tissue-specific level differences were also observed, pointing out to an overlapping but miRNA-specific biological role. Conclusions Our findings underline the importance of miRNAs encoded by the DLK1/GTL2 gene cluster in stress-response control mechanisms, and introduce MIR376A as a new regulator of autophagy.


BMC Bioinformatics | 2013

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

Cem Meydan; Hasan H. Otu; Osman Ugur Sezerman

BackgroundMHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently.ResultsThe prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II.ConclusionsTemporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.


BMC Genomics | 2014

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder

Ahmet Sinan Yavuz; Osman Ugur Sezerman

BackgroundSumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism.ResultsIn this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthews correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner.ConclusionsBy using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.


Toxicology and Applied Pharmacology | 2015

Assessment of global and gene-specific DNA methylation in rat liver and kidney in response to non-genotoxic carcinogen exposure.

Sibel Ozden; Neslihan Turgut Kara; Osman Ugur Sezerman; İlknur Melis Durası; Tao Chen; Goksun Demirel; Buket Alpertunga; J. Kevin Chipman; Angela Mally

Altered expression of tumor suppressor genes and oncogenes, which is regulated in part at the level of DNA methylation, is an important event involved in non-genotoxic carcinogenesis. This may serve as a marker for early detection of non-genotoxic carcinogens. Therefore, we evaluated the effects of non-genotoxic hepatocarcinogens, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), hexachlorobenzene (HCB), methapyrilene (MPY) and male rat kidney carcinogens, d-limonene, p-dichlorobenzene (DCB), chloroform and ochratoxin A (OTA) on global and CpG island promoter methylation in their respective target tissues in rats. No significant dose-related effects on global DNA hypomethylation were observed in tissues of rats compared to vehicle controls using LC-MS/MS in response to short-term non-genotoxic carcinogen exposure. Initial experiments investigating gene-specific methylation using methylation-specific PCR and bisulfite sequencing, revealed partial methylation of p16 in the liver of rats treated with HCB and TCDD. However, no treatment related effects on the methylation status of Cx32, e-cadherin, VHL, c-myc, Igfbp2, and p15 were observed. We therefore applied genome-wide DNA methylation analysis using methylated DNA immunoprecipitation combined with microarrays to identify alterations in gene-specific methylation. Under the conditions of our study, some genes were differentially methylated in response to MPY and TCDD, whereas d-limonene, DCB and chloroform did not induce any methylation changes. 90-day OTA treatment revealed enrichment of several categories of genes important in protein kinase activity and mTOR cell signaling process which are related to OTA nephrocarcinogenicity.


Biomaterials | 2014

Direct in vitro selection of titanium-binding epidermal growth factor

Seiichi Tada; Emel Timucin; Takashi Kitajima; Osman Ugur Sezerman; Yoshihiro Ito

Epidermal growth factor (EGF) with affinity to TiO2 surfaces was obtained by direct in vitro selection. A random peptide library was generated for fusion to the N-terminal of EGF, and polypeptides exhibiting affinity were selected in vitro by ribosome display. The best-performing polypeptide sequence was selected for synthesis using a solid-phase method and showed high affinity to TiO2 after refolding. Molecular dynamic simulations indicated that the interaction of the selected peptide segment with the TiO2 surface was comparable to that of a previously reported titanium-binding peptide, TBP-1. The hydroxyl groups in the selected peptide segment were found to be critical for the binding interaction. NIH3T3 cell culture for two days in the presence of the TiO2-binding EGF showed that it was able to enhance cell proliferation as much as unmodified EGF in solution. As a result, the selected EGF construct was able to induce cell proliferation on titanium surfaces. This direct in vitro selection technique should extend the possibilities for the design of other surface-binding growth factors.


BMC Genomics | 2015

A novel analysis strategy for integrating methylation and expression data reveals core pathways for thyroid cancer aetiology.

Bugra Ozer; Osman Ugur Sezerman

BackgroundRecently, a wide range of diseases have been associated with changes in DNA methylation levels, which play a vital role in gene expression regulation. With ongoing developments in technology, attempts to understand disease mechanism have benefited greatly from epigenetics and transcriptomics studies. In this work, we have used expression and methylation data of thyroid carcinoma as a case study and explored how to optimally incorporate expression and methylation information into the disease study when both data are available. Moreover, we have also investigated whether there are important post-translational modifiers which could drive critical insights on thyroid cancer genetics.ResultsIn this study, we have conducted a threshold analysis for varying methylation levels to identify whether setting a methylation level threshold increases the performance of functional enrichment. Moreover, in order to decide on best-performing analysis strategy, we have performed data integration analysis including comparison of 10 different analysis strategies. As a result, combining methylation with expression and using genes with more than 15% methylation change led to optimal detection rate of thyroid-cancer associated pathways in top 20 functional enrichment results. Furthermore, pooling the data from different experiments increased analysis confidence by improving the data range. Consequently, we have identified 207 transcription factors and 245 post-translational modifiers with more than 15% methylation change which may be important in understanding underlying mechanisms of thyroid cancer.ConclusionWhile only expression or only methylation information would not reveal both primary and secondary mechanisms involved in disease state, combining expression and methylation led to a better detection of thyroid cancer-related genes and pathways that are found in the recent literature. Moreover, focusing on genes that have certain level of methylation change improved the functional enrichment results, revealing the core pathways involved in disease development such as; endocytosis, apoptosis, glutamatergic synapse, MAPK, ErbB, TGF-beta and Toll-like receptor pathways. Overall, in addition to novel analysis framework, our study reveals important thyroid-cancer related mechanisms, secondary molecular alterations and contributes to better knowledge of thyroid cancer aetiology.


PLOS ONE | 2013

The Identification of Pathway Markers in Intracranial Aneurysm Using Genome-Wide Association Data from Two Different Populations

Burcu Bakir-Gungor; Osman Ugur Sezerman

The identification of significant individual factors causing complex diseases is challenging in genome-wide association studies (GWAS) since each factor has only a modest effect on the disease development mechanism. In this study, we hypothesize that the biological pathways that are targeted by these individual factors show higher conservation within and across populations. To test this hypothesis, we searched for the disease related pathways on two intracranial aneurysm GWAS in European and Japanese case–control cohorts. Even though there were a few significantly conserved SNPs within and between populations, seven of the top ten affected pathways were found significant in both populations. The probability of random occurrence of such an event is 2.44E−36. We therefore claim that even though each individual has a unique combination of factors involved in the mechanism of disease development, most targeted pathways that need to be altered by these factors are, for the most part, the same. These pathways can serve as disease markers. Individuals, for example, can be scanned for factors affecting the genes in marker pathways. Hence, individual factors of disease development can be determined; and this knowledge can be exploited for drug development and personalized therapeutic applications. Here, we discuss the potential avenues of pathway markers in medicine and their translation to preventive and individualized health care.


Lecture Notes in Computer Science | 2006

Functional classification of g-protein coupled receptors, based on their specific ligand coupling patterns

Burcu Bakir; Osman Ugur Sezerman

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them remain as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-length feature vectors, based on the existence of activating ligand specific patterns, has been developed and utilized for a Support Vector Machine (SVM)-based classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization.

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Burcu Bakir-Gungor

Scientific and Technological Research Council of Turkey

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Ozan Ozisik

Yıldız Technical University

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Banu Diri

Yıldız Technical University

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Akira Meguro

Yokohama City University

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