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Publication Metadata only A CLOCK-binding small molecule disrupts the interaction between CLOCK and BMAL1 and enhances circadian rhythm amplitude(Elsevier, 2020) Akyel, Yasemin Kübra; Yılmaz, Fatma; Öztürk, Nuri; Öztürk, Narin; Okyar, Alper; N/A; N/A; Department of Chemical and Biological Engineering; N/A; Department of Molecular Biology and Genetics; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Doruk, Yağmur Umay; Yarparvar, Darya; Gül, Şeref; Taşkın, Ali Cihan; Barış, İbrahim; Türkay, Metin; Kavaklı, İbrahim Halil; Master Student; PhD Student; Researcher; Other; Teaching Faculty; Faculty Member; Faculty Member; Department of Molecular Biology and Genetics; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; College of Sciences; College of Engineering; College of Engineering; N/A; N/A; N/A; 291296; 111629; 24956; 40319Proper function of many physiological processes requires a robust circadian clock. Disruptions of the circadian clock can result in metabolic diseases, mood disorders, and accelerated aging. Therefore, identifying small molecules that specifically modulate regulatory core clock proteins may potentially enable better management of these disorders. In this study, we applied a structure-based molecular-docking approach to find small molecules that specifically bind to the core circadian regulator, the transcription factor circadian locomotor output cycles kaput (CLOCK). We identified 100 candidate molecules by virtual screening of ?2 million small molecules for those predicted to bind closely to the interface in CLOCK that interacts with its transcriptional co-regulator, Brain and muscle Arnt-like protein-1 (BMAL1). Using a mammalian two-hybrid system, real-time monitoring of circadian rhythm in U2OS cells, and various biochemical assays, we tested these compounds experimentally and found one, named CLK8, that specifically bound to and interfered with CLOCK activity. We show that CLK8 disrupts the interaction between CLOCK and BMAL1 and interferes with nuclear translocation of CLOCK both in vivo and in vitro. Results from further experiments indicated that CLK8 enhances the amplitude of the cellular circadian rhythm by stabilizing the negative arm of the transcription/translation feedback loop without affecting period length. Our results reveal CLK8 as a tool for further studies of CLOCK's role in circadian rhythm amplitude regulation and as a potential candidate for therapeutic development to manage disorders associated with dampened circadian rhythms.Publication Metadata only A meta-analysis for the role of aminoglycosides and tigecyclines in combined regimens against colistin- and carbapenem-resistant Klebsiella pneumoniae bloodstream infections(Springer, 2022) N/A; N/A; N/A; N/A; N/A; N/A; N/A; N/A; N/A; Department of Industrial Engineering; N/A; Demirlenk, Yusuf Mert; Gücer, Lal Sude; Uçku, Duygu; Tanrıöver, Cem; Akyol, Merve; Kalay, Zeynepgül; Barçın, Erinç; Akcan, Rüştü Emre; Can, Füsun; Gönen, Mehmet; Ergönül, Önder; Undergraduate Student; Researcher; Researcher; Undergraduate Student; Undergraduate Student; Undergraduate Student; Master Student; N/A; Undergraduate Student; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; Graduate School of Health Sciences; School of Medicine; School of Medicine; School of Medicine; College of Engineering; School of Medicine; N/A; 375775; N/A; N/A; N/A; N/A; N/A; N/A; N/A 237468; 110398We aimed to describe the effect of aminoglycosides and tigecycline to reduce the mortality in colistin- and carbapenem-resistant Klebsiella pneumoniae (ColR-CR-Kp) infections. We included the studies with defined outcomes after active or non-active antibiotic treatment of ColR-CR-Kp infections. The active treatment was defined as adequate antibiotic use for at least 3 days (72 h) after the diagnosis of ColR-CR-Kp infection by culture. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and the checklist of PRISMA 2020 was applied. Crude and adjusted odds ratios (OR) with 95% confidence interval (CI) were calculated and pooled in the random effects model. Adding aminoglycosides to the existing treatment regimen reduced overall mortality significantly (OR 0.34, 95% CI 0.20-0.58). Overall mortality was 34% in patients treated with aminoglycoside-combined regimens and was 60% in patients treated with non-aminoglycoside regimens. Treatment with tigecycline is not found to reduce mortality (OR: 0.76, 95% CI: 0.47-1.23). Our results suggest that aminoglycoside addition to the existing regimen of colistin- and carbapenem-resistant Klebsiella pneumoniae infections reduces mortality significantly.Publication Metadata only A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers(Oxford University Press (OUP), 2020) N/A; N/A; Department of Industrial Engineering; Rahimi, Arezou; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction. Results: We studied the problem of discriminating early- and late-stage tumors of several cancers using genomic information while enforcing interpretability on the solutions. To this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to identify the relationships between the input tasks and kernels. We tested our algorithm on 15 cancer cohorts and observed that, in most cases, MTMKL outperforms other algorithms (including random forests, support vector machine and single-task multiple kernel learning) in terms of predictive power. Using the aggregate results from multiple replications, we also derived similarity matrices between cancer cohorts, which are, in many cases, in agreement with available relationships reported in the relevant literature.Publication Open Access A prospective prediction tool for understanding Crimean-Congo haemorrhagic fever dynamics in Turkey(Elsevier, 2020) N/A; N/A; Department of Industrial Engineering; Ak, Çiğdem; Ergönül, Önder; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; School of Medicine; College of Engineering; N/A; 110398; 237468Objectives: we aimed to develop a prospective prediction tool on Crimean-Congo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: we used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: we predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases.Publication Open Access An efficient framework to identify key miRNA-mRNA regulatory modules in cancer(Oxford University Press (OUP), 2020) N/A; Department of Industrial Engineering; Mokhtaridoost, Milad; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of MedicineMotivation: micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA-mRNA regulatory modules. Results: we presented a two-step framework to model miRNA-mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supportedmiRTarBase database.Publication Metadata only Analysis and network representation of hotspots in protein interfaces using minimum cut trees(Wiley, 2010) Department of Chemical and Biological Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Computer Engineering; Tunçbağ, Nurcan; Salman, Fatma Sibel; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; 245513; 178838; 26605; 8745We propose a novel approach to analyze and visualize residue contact networks of protein interfaces by graph-based algorithms using a minimum cut tree (mincut tree). Edges in the network are weighted according to an energy function derived from knowledge-based potentials. The mincut tree, which is constructed from the weighted residue network, simplifies and summarizes the complex structure of the contact network by an efficient and informative representation. This representation offers a comprehensible view of critical residues and facilitates the inspection of their organization. We observed, on a nonredundant data set of 38 protein complexes with experimental hotspots that the highest degree node in the mincut tree usually corresponds to an experimental hotspot. Further, hotspots are found in a few paths in the mincut tree. In addition, we examine the organization of hotspots (hot regions) using an iterative clustering algorithm on two different case studies. We find that distinct hot regions are located on specific sites of the mincut tree and some critical residues hold these clusters together. Clustering of the interface residues provides information about the relation of hot regions with each other. Our new approach is useful at the molecular level for both identification of critical paths in the protein interfaces and extraction of hot regions by clustering of the interface residues.Publication Open Access Androgen receptor-binding sites are highly mutated in prostate cancer(Nature Publishing Group (NPG), 2020) McNeill, Daniel R.; Wilson, David M., III; Lallous, Nada; Dalal, Kush; Department of Industrial Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Morova, Tunç; Lack, Nathan Alan; Gönen, Mehmet; Gürsoy, Attila; Keskin, Özlem; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of Medicine; College of Engineering; N/A; N/A; 237468; 8745; 26605Androgen receptor (AR) signalling is essential in nearly all prostate cancers. Any alterations to AR-mediated transcription can have a profound effect on carcinogenesis and tumor growth. While mutations of the AR protein have been extensively studied, little is known about those somatic mutations that occur at the non-coding regions where AR binds DNA. Using clinical whole genome sequencing, we show that AR binding sites have a dramatically increased rate of mutations that is greater than any other transcription factor and specific to only prostate cancer. Demonstrating this may be common to lineage-specific transcription factors, estrogen receptor binding sites were also found to have elevated rate of mutations in breast cancer. We provide evidence that these mutations at AR binding sites, and likely other related transcription factors, are caused by faulty repair of abasic sites. Overall, this work demonstrates that non-coding AR binding sites are frequently mutated in prostate cancer and can impact enhancer activity.Publication Open Access Assessment of quarter billion primary care prescriptions from a nationwide antimicrobial stewardship program(Nature Publishing Group (NPG), 2021) Aksoy, Mesil; İşli, Fatma; Gürpınar, Umut Emre; Göbel, Pınar; Gürsöz, Hakkı; Department of Industrial Engineering; Ergönül, Önder; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 110398; 237468We described the significance of systematic monitoring nationwide antimicrobial stewardship programs (ASPs) in primary care. All the prescriptions given by family physicians were recorded in Prescription Information System established by the Turkish Medicines and Medical Devices Agency of Ministry of Health. We calculated, for each prescription, ""antibiotics amount"" as number of boxes times number of items per box for medicines that belong to antiinfectives for systemic use (i.e., J01 block in the Anatomical Therapeutic Chemical Classification System). We compared the antibiotics amount before (2015) and after (2016) the extensive training programs for the family physicians. We included 266,389,209 prescriptions from state-operated family healthcare units (FHUs) between January 1, 2015 and December 31, 2016. These prescriptions were given by 26,313 individual family physicians in 22,518 FHUs for 50,713,181 individual patients. At least one antimicrobial was given in 37,024,232 (28.31%) prescriptions in 2015 and 36,154,684 (26.66%) prescriptions in 2016. The most common diagnosis was ""acute upper respiratory infections (AURI)"" (i.e., J00-J06 block in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems) with 28.05%. The average antibiotics amount over prescriptions with AURI decreased in 79 out of 81 provinces, and overall rate of decrease in average antibiotics amount was 8.33%, where 28 and 53 provinces experienced decreases (range is between 28.63% and-3.05%) above and below this value, respectively. In the most successful province, the highest decrease in average amount of ""other beta-lactam antibacterials"" per prescription for AURI was 49.63% in January. Computational analyses on a big data set collected from a nationwide healthcare system brought a significant contribution in improving ASPs.Publication Metadata only Classification of cytochrome P450 inhibitors with respect to binding free energy and pIC50 using common molecular descriptors(Amer Chemical Soc, 2009) N/A; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Dağlıyan, Onur; Kavaklı, İbrahim Halil; Türkay, Metin; Master Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 40319; 24956Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure based drug discovery. However, the relationship between binding free energies and biological activities (pIC(50)) of drug candidates is sfill an unsolved issue that limits the efficiency and speed of drug development processes. In this study, the relationship between them is investigated based on a common molecular descriptor set for human cytochrome P450 enzymes (CYPs). CYPs play an important role in drug-drug interactions, drug metabolism, and toxicity. Therefore, in silico prediction of CYP inhibition by drug candidates is one of the major considerations in drug discovery. The combination of partial leastsquares regression (PLSR) and a variety of classification algorithms were employed by considering this relationship as a classification problem. Our results indicate that PLSR with classification is a powerful tool to predict more than one output such as binding free energy and pIC(50) simultaneously. PLSR with mixedinteger linear programming based hyperboxes predicts the binding free energy and pIC(50) with a mean accuracy of 87.18% (min: 81.67% max: 97.05%) and 88.09% (min: 79.83% max: 92.90%), respectively, for the cytochrome p450 superfamily using the common 6 molecular descriptors with a 10-fold cross- val idati on.Publication Open Access Classification of drug molecules considering their IC(50) values using mixed-integer linear programming based hyper-boxes method(BioMed Central, 2008) Department of Industrial Engineering; Department of Chemical and Biological Engineering; Armutlu, Pelin; Özdemir, Muhittin Emre; Yüksektepe, Fadime Üney; Kavaklı, İbrahim Halil; Türkay, Metin; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; N/A; N/A; 40319; 24956Background: A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC(50) values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results: We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC(50) values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naive Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Conclusion: Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.