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Your search keyword '"Arga, Kazim Yalcin"' showing total 13 results
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13 results on '"Arga, Kazim Yalcin"'

2. Correction to: Lower COVID-19 Mortality in Patients with Type 2 Diabetes Mellitus Taking Dipeptidyl Peptidase-4 Inhibitors: Results from a Turkish Nationwide Study

Author

Emral, Rifat, primary, Haymana, Cem, additional, Demirci, Ibrahim, additional, Tasci, Ilker, additional, Sahin, Mustafa, additional, Cakal, Erman, additional, Ata, Naim, additional, Unluturk, Ugur, additional, Demir, Tevfik, additional, Ertugrul, Derun, additional, Sahin, Ibrahim, additional, Atmaca, Aysegül, additional, Celik, Osman, additional, Caglayan, Murat, additional, Arga, Kazim Yalcin, additional, Dagdelen, Selcuk, additional, Salman, Serpil, additional, Satman, Ilhan, additional, and Sonmez, Alper, additional

Published
2021
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3. Lower COVID-19 Mortality in Patients with Type 2 Diabetes Mellitus Taking Dipeptidyl Peptidase-4 Inhibitors: Results from a Turkish Nationwide Study

Author

Emral, Rifat, primary, Haymana, Cem, additional, Demirci, Ibrahim, additional, Tasci, Ilker, additional, Sahin, Mustafa, additional, Cakal, Erman, additional, Ata, Naim, additional, Unluturk, Ugur, additional, Demir, Tevfik, additional, Ertugrul, Derun, additional, Sahin, Ibrahim, additional, Atmaca, Aysegül, additional, Celik, Osman, additional, Caglayan, Murat, additional, Arga, Kazim Yalcin, additional, Dagdelen, Selcuk, additional, Salman, Serpil, additional, Satman, Ilhan, additional, and Sonmez, Alper, additional

Published
2021
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8. Transcriptomic analysis displays the effect of (-)-roemerine on the motility and nutrient uptake in Escherichia coli

Author

Ayyildiz, Dilara, primary, Arga, Kazim Yalcin, additional, Avci, Fatma Gizem, additional, Altinisik, Fatma Ece, additional, Gurer, Caglayan, additional, Gulsoy Toplan, Gizem, additional, Kazan, Dilek, additional, Wozny, Katharina, additional, Brügger, Britta, additional, Mertoglu, Bulent, additional, and Sariyar Akbulut, Berna, additional

Published
2016
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13. A system based network approach to ethanol tolerance in Saccharomyces cerevisiae

Author

Kazim Yalcin Arga, Ebru Toksoy Oner, Serpil Eraslan, Betul Kirdar, Ceyda Kasavi, Kasavi, Ceyda, Eraslan, Serpil, Arga, Kazim Yalcin, Oner, Ebru Toksoy, and Kirdar, Betul

Subjects
Candidate gene, STRESS, Transcription, Genetic, Systems biology, Genes, Fungal, Saccharomyces cerevisiae, Gene regulatory network, PROTEIN, Candidate genes, Transcriptome, chemistry.chemical_compound, Structural Biology, Modelling and Simulation, Gene Regulatory Networks, YEAST, Protein Interaction Maps, Molecular Biology, Gene, 2. Zero hunger, Ethanol tolerance network, FERMENTATION, Ethanol, IDENTIFICATION, biology, Protein-protein interaction network, business.industry, Systems Biology, Applied Mathematics, LOCALIZATION, biology.organism_classification, GENE, FAMILY, Computer Science Applications, Biotechnology, MODULARITY, Gene Ontology, Biochemistry, chemistry, Modeling and Simulation, CELL-GROWTH, Fermentation, business, Research Article
Abstract

Background: Saccharomyces cerevisiae has been widely used for bio-ethanol production and development of rational genetic engineering strategies leading both to the improvement of productivity and ethanol tolerance is very important for cost-effective bio-ethanol production. Studies on the identification of the genes that are up-or down-regulated in the presence of ethanol indicated that the genes may be involved to protect the cells against ethanol stress, but not necessarily required for ethanol tolerance. Results: In the present study, a novel network based approach was developed to identify candidate genes involved in ethanol tolerance. Protein-protein interaction (PPI) network associated with ethanol tolerance (tETN) was reconstructed by integrating PPI data with Gene Ontology (GO) terms. Modular analysis of the constructed networks revealed genes with no previously reported experimental evidence related to ethanol tolerance and resulted in the identification of 17 genes with previously unknown biological functions. We have randomly selected four of these genes and deletion strains of two genes (YDR307W and YHL042W) were found to exhibit improved tolerance to ethanol when compared to wild type strain. The genome-wide transcriptomic response of yeast cells to the deletions of YDR307W and YHL042W in the absence of ethanol revealed that the deletion of YDR307W and YHL042W genes resulted in the transcriptional re-programming of the metabolism resulting from a mis-perception of the nutritional environment. Yeast cells perceived an excess amount of glucose and a deficiency of methionine or sulfur in the absence of YDR307W and YHL042W, respectively, possibly resulting from a defect in the nutritional sensing and signaling or transport mechanisms. Mutations leading to an increase in ribosome biogenesis were found to be important for the improvement of ethanol tolerance. Modulations of chronological life span were also identified to contribute to ethanol tolerance in yeast. Conclusions: The system based network approach developed allows the identification of novel gene targets for improved ethanol tolerance and supports the highly complex nature of ethanol tolerance in yeast.

Published
2014

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