PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article Citation - WoS: 4Citation - Scopus: 4Integrative Biological Network Analysis To Identify Shared Genes in Metabolic Disorders(Institute of Electrical and Electronics Engineers, 2022) Tenekeci, Samet; Işık, ZerrinIdentification of common molecular mechanisms in interrelated diseases is essential for better prognoses and targeted therapies. However, complexity of metabolic pathways makes it difficult to discover common disease genes underlying metabolic disorders; and it requires more sophisticated bioinformatics models that combine different types of biological data and computational methods. Accordingly, we built an integrative network analysis model to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks by combining gene expression, protein-protein interaction, and gene ontology data from multiple sources. For 90 different configurations of disease networks, we detected the significant modules by using MCL, SPICi, and Linkcomm graph clustering algorithms. We also performed a comparative evaluation on disease modules to determine the best method providing the highest biological validity. By overlapping the disease modules, we identified 22 shared genes for MS-CAD and T2D-CAD. Moreover, 19 out of these genes were directly or indirectly associated with relevant diseases in the previous medical studies. This study does not only demonstrate the performance of different biological data sources and computational methods in disease-gene discovery, but also offers potential insights into common genetic mechanisms of the metabolic disorders.Article Citation - WoS: 4Citation - Scopus: 4Sensitive and Specific Detection of Ligands Using Engineered Riboswitches(Elsevier Ltd., 2018) Morse, Daniel P.; Nevins, Colin E.; Aggrey-Fynn, Joana Efua; Bravo, Rick J.; Pfaeffle, Herman O.I.; Laney, Jess E.Riboswitches are RNA elements found in non-coding regions of messenger RNAs that regulate gene expression through a ligand-triggered conformational change. Riboswitches typically bind tightly and specifically to their ligands, so they have the potential to serve as highly effective sensors in vitro. In B. subtilis and other gram-positive bacteria, purine nucleotide synthesis is regulated by riboswitches that bind to guanine. We modified the xpt-pbuX guanine riboswitch for use in a fluorescence quenching assay that allowed us to specifically detect and quantify guanine in vitro. Using this assay, we reproducibly detected as little as 5 nM guanine. We then produced sensors for 2′-deoxyguanosine and cyclic diguanylate (c-diGMP) by appending the P1 stem of the guanine riboswitch to the ligand-binding domains of a 2′-deoxyguanosine riboswitch and a c-diGMP riboswitch. These hybrid sensors could detect 15 nM 2′-deoxyguanosine and 3 nM c-diGMP, respectively. Each sensor retained the ligand specificity of its corresponding natural riboswitch. In order to extend the utility of our approach, we developed a strategy for the in vitro selection of sensors with novel ligand specificity. Here we report a proof-of-principle experiment that demonstrated the feasibility of our selection strategy.Article Citation - WoS: 560Citation - Scopus: 607A Community Effort To Assess and Improve Drug Sensitivity Prediction Algorithms(Nature Publishing Group, 2014) Costello, James C.; Heiser, Laura M.; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P.; Wang, Nicholas J.; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A.; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; NCI-DREAM Community; Karaçalı, Bilge; Collins, James J.; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W.; Stolovitzky, GustavoPredicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.Article Citation - WoS: 240Citation - Scopus: 264A Community Computational Challenge To Predict the Activity of Pairs of Compounds(Nature Publishing Group, 2014) Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P.; Costello, James C.; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M.; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J.; Shen, Yao; NCI-DREAM Community; Karaçalı, Bilge; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, AndreaRecent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.Article Citation - WoS: 36Citation - Scopus: 42Resveratrol Triggers Apoptosis Through Regulating Ceramide Metabolizing Genes in Human K562 Chronic Myeloid Leukemia Cells(Routledge, 2011) Kartal Yandım, Melis; Saydam, Güray; Şahin, Fahri; Baran, YusufResveratrol, an important phytoalexin in many plants, has been reported to have cytotoxic effects on various types of cancer. Ceramide is a bioactive sphingolipid that regulates many signaling pathways, including cell growth and proliferation, senescence and quiescence, apoptosis, and cell cycle. Ceramides are generated by longevity assurance genes (LASS). Glucosylceramide synthase (GCS) and sphingosine kinase-1 (SK-1) enzymes can convert ceramides to antiapoptotic molecules, glucosylceramide, and sphingosine-1-phosphate, respectively. C8:ceramide, an important cell-permeable analogue of natural ceramides, increases intracellular ceramide levels significantly, while 1-phenyl-2-decanoylamino-3-morpholino-1-propanol (PDMP) and SK-1 inhibitor increase accumulation of ceramides by inhibiting GCS and SK-1, respectively. Chronic myelogenous leukemia (CML) is a hematological disorder resulting from generation of BCR/ABL oncogene. In this study, we examined the roles of ceramide metabolizing genes in resveratrol-induced apoptosis in K562 CML cells. There were synergistic cytotoxic and apoptotic effects of resveratrol with coadministration of C8:ceramide, PDMP, and SK-1 inhibitor. Interestingly, there were also significant increases in expression levels of LASS genes and decreases in expression levels of GCS and SK-1 in K562 cells in response to resveratrol. Our data, in total, showed for the first time that resveratrol might kill CML cells through increasing intracellular generation and accumulation of apoptotic ceramides. Copyright © 2011, Taylor & Francis Group, LLC.Article Citation - WoS: 18Citation - Scopus: 16Suppression of Stat5a Increases Chemotherapeutic Sensitivity in Imatinib-Resistant and Imatinib-Sensitive K562 Cells(Informa Healthcare, 2010) Kosova, Buket; Tezcanlı, Burçin; Ekiz, Hüseyin Atakan; Çakır, Zeynep; Selvi, Nur; Dalmızrak, Ayşegül; Yandım, Melis Kartal; Gündüz, Ufuk; Baran, YusufSTAT proteins are cytoplasmic transcription factors that are involved in the regulation of numerous cellular activities such as cell growth, differentiation, and survival. In this study, we aimed to identify the expression pattern of STAT genes in imatinib-sensitive and-resistant K562 cells, and further, to reveal the effects of STAT5A siRNA knockdown on cell growth and apoptosis induction. The XTT cell proliferation assay showed that both sensitive and resistant K562 cells were sensitized to imatinib upon transfection with STAT5A siRNA. Caspase-3 enzyme activity was increased significantly in both cells. These results may open up new opportunities to overcome chemotherapeutic resistance in leukemia. © 2010 Informa UK, Ltd.Article Citation - WoS: 15Citation - Scopus: 14Prolactin Receptor Gene Expression in Rat Splenocytes and Thymocytes During Oestrous Cycle, Pregnancy and Lactation(John Wiley and Sons Inc., 1997) Güneş, Hatice; Mastro, Andrea M.Much evidence suggests that prolactin (PRL) has an immunoregulatory function. Part of this evidence is that the receptors for PRL are present on lymphocytes. Probably the effects of PRL on cells of the immune system depend on the level and specific forms of PRL-R present on the target cells. Therefore, PRL-R expression at both protein and mRNA levels was examined during oestrous cycle, pregnancy and lactation using Western blotting and PCR analysis. Antibody to the long form of PRL-R detected 84 and 42 kDa protein bands in the spleen but only 84 kDa band in the thymus. The expression pattern of these two protein bands was different in the spleen, suggesting that these two isoforms of PRL-R long form are differentially regulated by the hormones of oestrous cycle. In addition, depending on the tissue, the level of mRNA for the short and long forms of PRL-R showed a significant change at different stages of oestrous cycle. Moreover, 42 and 84 kDa PRL-R bands were detected in both spleen and thymus throughout the pregnancy and lactation; however, the expression pattern of 84 kDa protein band was different between tissues. This finding suggests that each tissue exhibits differential response to hormones which affect PRL-R content.