Researcher: Gönen, Mehmet
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Gönen, Mehmet
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Publication Metadata only Cytokine response in crimean-congo hemorrhagic fever virus infection(Wiley, 2017) Eren, Şebnem; Çelikbaş, Aysel; Baykam, Nurcan; Dokuzoğuz, Başak; N/A; N/A; Department of Industrial Engineering; N/A; Ergönül, Önder; Şeref, Ceren; Gönen, Mehmet; Can, Füsun; Faculty Member; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; Graduate School of Health Sciences; College of Engineering; School of Medicine; Koç University Hospital; 110398; N/A; 237468; 103165We described the predictive role of cytokines in fatality of Crimean Congo Hemorrhagic Fever Virus (CCHFV) infection by using daily clinical sera samples. Consequent serum samples of the selected patients in different severity groups and healthy controls were examined by using human cytokine 17-plex assay. We included 12 (23%) mild, 30 (58%) moderate, 10 (19%) severe patients, and 10 healthy volunteers. The mean age of the patients was 52 (sd 15), 52% were female. Forty-six patients (88%) received ribavirin. During disease course, the median levels of IL-6, IL-8, IL-10, IL-10/12, IFN-gamma, MCP-1, and MIP-1b were found to be significantly higher among CCHF patients than the healthy controls. Within the first 5 days after onset of disease, among the fatal cases, the median levels of IL-6 and IL-8 were found to be significantly higher than the survived ones (Fig. 3), and MCP-1 was elevated among fatal cases, but statistical significance was not detected. In receiver operating characteristic (ROC) analysis, IL-8 (92%), IL-6 (92%), MCP-1 (79%) were found to be the most significant cytokines in predicting the fatality rates in the early period of the disease (5 days). IL-6 and IL-8 can predict the poor outcome, within the first 5 days of disease course. Elevated IL-6 and IL-8 levels within first 5 days could be used as prognostic markers.Publication Metadata only Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies(Oxford Univ Press, 2017) Nikolova, Olga; Moser, Russell; Kemp, Christopher; Margolin, Adam A.; Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Motivation: In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. Results: We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.Publication Metadata only A multicenter international study to evaluate different aspects of relationship between MS and pregnancy(Sage, 2019) Zakaria, M.; Alroughani, R.; Moghadasi, A. N.; Terzi, M.; Sen, S.; Koseoglu, M.; Efendi, H.; Soysal, A.; Gozubatik-Celik, G.; Ozturk, M.; Sahraian, M.; Akinci, Y.; Kaya, Z. E.; Saip, S.; Siva, A.; N/A; Department of Industrial Engineering; Altıntaş, Ayşe; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 11611; 237468N/APublication Metadata only Discriminating early- and late-stage cancers using multiple kernel learning on gene sets(Oxford Univ Press, 2018) 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: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early-and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets. Results: In this study, we addressed the problem of separating early- and late-stage cancers from each other using their gene expression profiles. We proposed to use a multiple kernel learning (MKL) formulation that makes use of pathways/gene sets (i) to obtain satisfactory/improved predictive performance and (ii) to identify biological mechanisms that might have an effect in cancer progression. We extensively compared our proposed MKL on gene sets algorithm against two standard machine learning algorithms, namely, random forests and support vector machines, on 20 diseases from the Cancer Genome Atlas cohorts for two different sets of experiments. Our method obtained statistically significantly better or comparable predictive performance on most of the datasets using significantly fewer gene expression features. We also showed that our algorithm was able to extract meaningful and disease-specific information that gives clues about the progression mechanism.Publication Metadata only Promoters of colistin resistance in acinetobacter baumannii infections(2019) Bilman, Fulya Bayındır; Menekşe, Şirin; Azap, Özlem Kurt; N/A; Department of Industrial Engineering; N/A; N/A; Nurtop, Elif; Gönen, Mehmet; Ergönül, Önder; Can, Füsun; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Health Sciences; College of Engineering; School of Medicine; School of Medicine; N/A; 237468; 110398; 103165Objectives: We aimed to describe the mechanisms of colistin resistance in Acinetobacter baumannii. Materials and Methods: Twenty-nine patients diagnosed with colistin-resistant A. baumannii infection were included to the study. The mutations in pmrCAB, lpxA, lpxC, and lpxD genes, expression of pmrCAB, carbapenemases, and mcr-1 positivity were studied. Results: Twenty-seven (93%) of the patients received IV colistin therapy during their stay, and the case fatality rate was 45%. All mutations in pmrC and pmrB were found to be accompanied with a mutation in lpxD. The most common mutations were I42V and L150F in pmrC (65%), E117K in lpxD (65%), and A138T in pmrB (58.6%). The colistin minimum inhibitory concentrations (MICs) of the isolates having any of these four mutations were higher than the isolates with no mutations (p < 0.001). The two most common mutations in pmrC (I42V and L150F) were found to be associated with higher expressions of pmrA and pmrC and higher colistin MIC values (p = 0.010 and 0.031). All isolates were bla(OXA-23) positive. Conclusion: Coexistence of the lpxD mutation along with mutations in pmrCAB indicates synergistic function of these genes in development of colistin resistance in A. baumannii.Publication Metadata only Automated diagnosis of keratoconus from corneal topography(Assoc Research Vision Ophthalmology Inc, 2021) N/A; N/A; N/A; Department of Industrial Engineering; N/A; Taş, Ayşe Yıldız; Hasanreisoğlu, Murat; Balım, Haldun; Gönen, Mehmet; Şahin, Afsun; Faculty Member; Faculty Member; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; School of Medicine; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; 200905; 182001; N/A; 237468; 171267Purpose: Although visual inspection of corneal topography maps by trained experts can be powerful, this method is inherently subjective. Quantitative classification methods that can detect and classify abnormal topographic patterns would be useful. An automated system was developed to differentiate keratoconus patterns from other conditions using computer-assisted videokeratoscopy. Methods: This system combined a classification tree with a linear discriminant function derived from discriminant analysis of eight indices obtained from TMS-1 videokeratoscope data. One hundred corneas with a variety of diagnoses (keratoconus, normal, keratoplasty, epikeratophakia, excimer laser photorefractive keratectomy, radial keratotomy, contact lens-induced warpage, and others) were used for training, and a validation set of 100 additional corneas was used to evaluate the results. Results: In the training set, all 22 cases of clinically diagnosed keratoconus were detected with three false-positive cases (sensitivity 100%, specificity 96%, and accuracy 97%). With the validation set, 25 out of 28 keratoconus cases were detected with one false-positive case, which was a transplanted cornea (sensitivity 89%, specificity 99%, and accuracy 96%). Conclusions: This system can be used as a screening procedure to distinguish clinical keratoconus from other corneal topographies. This quantitative classification method may also aid in refining the clinical interpretation of topographic maps.Publication Metadata only Identification of novel molecular players of GBM cell dispersal through an in vitro profiling approach(Oxford Univ Press, 2016) Gümüş, Zeynep Hülya; N/A; N/A; N/A; N/A; Department of Industrial Engineering; N/A; Şeker-Polat, Fidan; Erkent, Mahmut Alp; Ergüder, Nazlı; Sevinç, Kenan; Gönen, Mehmet; Önder, Tuğba Bağcı; Phd Student; Undergraduate Student; Undergraduate Student; Phd Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Health Sciences; School of Medicine; School of Medicine; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; N/A; N/A; N/A; N/A; 237468; 184359Glioblastoma multiforme (GBM) is the most common and aggressive type of gliomas with a mean survival of 1 year after diagnosis. A major obstacle in treating GBMs is extensive tumor cell infiltration into the surrounding brain. Despite tumor resection and combined therapy, recurrence occurs in the vicinity of the resection margin due to individual cells that dispersed out of the primary tumor, therefore; developing novel therapies that target tumor cell dispersal is of high priority. The goal of this project is to identify genes that are differentially regulated during GBM cell dispersal and to validate their function in in vitro models of dispersal. In this project, we have used an in vitro model of cell motility whereby the dynamics of GBM cell dispersal can be monitored in real-time and quantitated. Accordingly, we isolated motile/migratory/dispersive cells from non-motile/core cells and used these cells for investigating the genes that are differentially regulated during different phases of cell movement by using RNA sequencing. Analysis of the sequencing experiments showed the presence of many differentially expressed genes in motile vs non-motile cells. Most of the genes that have the highest expression in motile cells compared to non-motile ones were linked to epithelial to mesenchymal transition and cell motility based on our pathway and gene set enrichment analyses. Our current focus is on five different candidate genes: CTGF, CYR61, SERPINE1, INHBA and PTX3. Among these, the expression of SERPINE1, a serine protease inhibitor, had predictive value for overall survival of gliomas and therefore is an interesting therapeutic candidate. Currently, we are conducting loss-of-function and gain-of function experiments targeting these genes. Together, these studies have the potential to discover novel molecular players of GBM cell dispersal and open up new avenues for designing new therapeutic strategies against the invasive phenotype of otherwise untreatable malignant GBMs.Publication Metadata only Comprehensive analysis of miRNA-mRNA regulatory modules and their association with survival in diffuse lower grade gliomas(2016) Tihan, Tarık; Keleş, Güven Evren; Department of Industrial Engineering; N/A; Gönen, Mehmet; Solaroğlu, İhsan; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; School of Medicine; Koç University Hospital; 237468; 102059N/APublication Metadata only Polymeric and collagen biomaterials enhance implantation of mouse blastocysts in three-dimensional culture models(Elsevier, 2021) Başoz, Deniz; Yücel, Deniz; N/A; N/A; N/A; N/A; N/A; Department of Industrial Engineering; N/A; Ergün, Yağmur; Şahin, Gizem Nur; Şevgin, Kübra; Kocabay, Ahmet; Taşkın, Ali Cihan; Gönen, Mehmet; Karahüseyinoğlu, Serçin; PhD Student; PhD Student; PhD Student; Other; Other; Faculty Member; Faculty Member; Department of Industrial Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Health Sciences; Graduate School of Health Sciences; Graduate School of Health Sciences; N/A; N/A; College of Engineering; School of Medicine; N/A; N/A; N/A; N/A; 291296; 237468; 110772Publication 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.