Person: YILMAZ, BETÜL
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
YILMAZ
First Name
BETÜL
Name
8 results
Search Results
Now showing 1 - 8 of 8
Publication Open Access Molecular cardiotoxic effects of proteasome inhibitors carfilzomib and ixazomib and their combination with dexamethasone involve mitochondrial dysregulation(2023-01-01) YILMAZ, BETÜL; JANNUZZI A. T., Korkmaz N. S., GÜNAYDIN AKYILDIZ A., Arslan Eseryel S., Karademir Yilmaz B., ALPERTUNGA B.With the development and approval of new proteasome inhibitors, proteasome inhibition is increasingly recognized in cancer therapy. Besides successful anti-cancer effects in hematological cancers, side effects such as cardiotoxicity are limiting effective treatment. In this study, we used a cardiomyocyte model to investigate the molecular cardiotoxic mechanisms of carfilzomib (CFZ) and ixazomib (IXZ) alone or in combination with the immunomodulatory drug dexamethasone (DEX) which is frequently used in combination therapies in the clinic. According to our findings, CFZ showed a higher cytotoxic effect at lower concentrations than IXZ. DEX combination attenuated the cytotoxicity for both proteasome inhibitors. All drug treatments caused a marked increase in K48 ubiquitination. Both CFZ and IXZ caused an upregulation in cellular and endoplasmic reticulum stress protein (HSP90, HSP70, GRP94, and GRP78) levels and DEX combination attenuated the increased stress protein levels. Importantly, IXZ and IXZ-DEX treatments caused upregulation of mitochondria fission and fusion gene expression levels higher than caused by CFZ and CFZ-DEX combination. The IXZ-DEX combination reduced the levels of OXPHOS proteins (Complex II–V) more than the CFZ-DEX combination. Reduced mitochondrial membrane potential and ATP production were detected with all drug treatments in cardiomyocytes. Our findings suggest that the cardiotoxic effect of proteasome inhibitors may be due to their class effect and stress response and mitochondrial dysfunction may be involved in the cardiotoxicity process.Publication Open Access Next-generation grade and survival expression biomarkers of human gliomas based on algorithmically reconstructed molecular pathways(2022-07-01) YILMAZ, BETÜL; Zolotovskaia M. A., Kovalenko M. A., Tkachev V. S., Simonov A. M., Sorokin M., Kim E., Kuzmin D., Karademir-Yilmaz B., Buzdin A. A.In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.Publication Open Access Exploring the anticancer effects of brominated plastoquinone analogs with promising cytotoxic activity in MCF-7 breast cancer cells via cell cycle arrest and oxidative stress induction(2022-06-01) YILMAZ GÖLER, AYŞE MİNE; YILMAZ, BETÜL; Jannuzzı A. T., Yılmaz Göler A. M., Bayrak N., Yıldız M., Yıldırım H., Yılmaz B., Shilkar D., Jayaprakash Venkatesan R., Jayaprakash V., Tuyun A. F.Plastoquinone analogs are privileged structures among the known antiproliferative natural product-based compound families. Exploiting one of these analogs as a lead structure, we report the investigation of the brominated PQ analogs (BrPQ) in collaboration with the National Cancer Institute of Bethesda within the Developmental Therapeutics Program (DTP). These analogs exhibited growth inhibition in the micromolar range across leukemia, non-small cell lung cancer (EKVX, HOP-92, and NCI-H522), colon cancer (HCT-116, HOP-92), melanoma (LOX IMVI), and ovarian cancer (OVCAR-4) cell lines. One brominated PQ analog (BrPQ5) was selected for a full panel five-dose in vitro assay by the NCI’s Development Therapeutic Program (DTP) division to determine GI50, TGI, and LC50parameters. The brominated PQ analog (BrPQ5) displayed remarkable activity against most tested cell lines, with GI50values ranging from 1.55 to 4.41 µM. The designed molecules (BrPQ analogs) obeyed drug-likeness rules, displayed a favorable predictive Absorption, Distribution, Metabolism, and Excretion (ADME) profile, and an in silico simulation predicted a possibleBrPQ5interaction with proteasome catalytic subunits. Furthermore, the in vitro cytotoxic activity ofBrPQ5was assessed, and IC50values for U-251 glioma, MCF-7 and MDA-MB-231 breast cancers, DU145 prostate cancer, HCT-116 colon cancer, and VHF93 fibroblast cell lines were evaluated using an MTT assay. MCF-7 was the most affected cell line, and the effects ofBrPQ5on cell proliferation, cell cycle, oxidative stress, apoptosis/necrosis induction, and proteasome activity were further investigated in MCF-7 cells. The in vitro assay results showed thatBrPQ5caused cytotoxicity in MCF-7 breast cancer cells via cell cycle arrest and oxidative stress induction. However,BrPQ5did not inhibit the catalytic activity of the proteasome. These results provide valuable insights for further discovery of novel antiproliferative agents.Publication Metadata only Past, present, and future of therapies for pituitary neuroendocrine tumors: need for omics and drug repositioning guidance(2022-03-01) ERDOĞAN, ONUR; ARĞA, KAZIM YALÇIN; BOZKURT, SÜHEYLA; BAYRAKLI, FATİH; YILMAZ, BETÜL; TURANLI, BESTE; Aydin B., Yildirim E., ERDOĞAN O., ARĞA K. Y., Yilmaz B., BOZKURT S., BAYRAKLI F., TURANLI B.Innovation roadmaps are important, because they encourage the actors in an innovation ecosystem to creatively imagine multiple possible science future(s), while anticipating the prospects and challenges on the innovation trajectory. In this overarching context, this expert review highlights the present unmet need for therapeutic innovations for pituitary neuroendocrine tumors (PitNETs), also known as pituitary adenomas. Although there are many drugs used in practice to treat PitNETs, many of these drugs can have negative side effects and show highly variable outcomes in terms of overall recovery. Building innovation roadmaps for PitNETs\" treatments can allow incorporation of systems biology approaches to bring about insights at multiple levels of cell biology, from genes to proteins to metabolites. Using the systems biology techniques, it will then be possible to offer potential therapeutic strategies for the convergence of preventive approaches and patient-centered disease treatment. Here, we first provide a comprehensive overview of the molecular subtypes of PitNETs and therapeutics for these tumors from the past to the present. We then discuss examples of clinical trials and drug repositioning studies and how multi-omics studies can help in discovery and rational development of new therapeutics for PitNETs. Finally, this expert review offers new public health and personalized medicine approaches on cases that are refractory to conventional treatment or recur despite currently used surgical and/or drug therapy.Publication Metadata only Acute myeloid leukemia: New multiomics molecular signatures and implications for systems medicine diagnostics and therapeutics innovation(2022-07-01) ARĞA, KAZIM YALÇIN; TURANLI, BESTE; YILMAZ, BETÜL; Kelesoglu N., Kori M., TURANLI B., ARĞA K. Y., Yilmaz B., Duru O. A.Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.Publication Metadata only Combination of second-generation proteasome inhibitor carfilzomib with bortezomib in four different breast cancer cell lin(2022-01-01) YILMAZ GÖLER, AYŞE MİNE; ŞAHİN, ALİ; YILMAZ, BETÜL; Altundag E. M., Yilmaz A. M., Sahin A., Yilmaz B.Background: Proteasome inhibitors target different pathways in cells and therefore are promising drugs in cancer therapy. The use of these inhibitors is approved mainly in hematological cancers, and recently many clinical trials and preclinical studies have been conducted on efficacy in solid tumors. Carfilzomib is a second-generation inhibitor and was developed to decrease the side effects of bortezomib. Although there are many valid therapies for breast cancer, resistance and recurrence are inevitable in many cases and the proteasomal system plays an important role in related pathways. Objective: This study is a preliminary work to evaluate the combined effects of bortezomib and carfilzomib in four different breast cancer cells. Methods: MDA-MB-231, MCF-7, UACC-2087, and SKBR-3 cell lines were used. Cell viability was determined using bortezomib and carfilzomib alone and in combination. Combination effect values were determined using the Chou-Talalay method. Apoptosis, proteasome activity, cleaved PARP, and HSP70 expressions were analyzed in the determined doses. Results: The response to the combination of the two inhibitors was different in four cell lines. Apoptosis was significantly higher in combination groups compared to carfilzomib in three cell lines except for SKBR-3, and higher in the combination group compared to bortezomib only in UACC-2087. Combination decreased cleaved PARP levels in MDA-MB-231 and MCF-7 and increased SKBR-3 compared to bortezomib. HSP70 levels decreased in combination with UACC-2087 and SKBR-3 compared to carfilzomib. Conclusion: Taken together, the combination of the two inhibitors was more apoptotic compared to carfilzomib and apoptosis was higher only in UACC-2087 compared to bortezomib. This apoptosis data can not be directly correlated to the degree of proteasome inhibition, PARP cleavage, and HSP70 response.Publication Metadata only Driving precision oncology to clinical practice: The road ahead from biomarker validation to clinical decision systems(2022-06-01) ARĞA, KAZIM YALÇIN; YILMAZ, BETÜL; Yilmaz B., ARĞA K. Y.Publication Open Access Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns(2023-01-01) YILMAZ, BETÜL; Borisov N., Tkachev V., Simonov A., Sorokin M., Kim E., Kuzmin D., Karademir-Yilmaz B., Buzdin A.Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.