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YILMAZ, BETÜL

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YILMAZ

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BETÜL

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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen 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.