PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - WoS: 26Citation - Scopus: 33miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules(Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, BurcuIncreasing evidence that MicroRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis.Article Citation - Scopus: 23Synthesis and Comprehensive in Vivo Activity Profiling of Olean-12-en-28-ol, 3β-Pentacosanoate in Experimental Autoimmune Encephalomyelitis: A Natural Remyelinating and Anti-Inflammatory Agent(American Chemical Society, 2023-01-04) Şenol, Halil; Ozgun-Acar, Özden; Daǧ, Aydan; Eken, Ahmet; Guner, Hüseyin; Aykut, Zaliha Gamze; Sen, AlaattinMultiple sclerosis (MS) treatment has received much attention, yet there is still no certain cure. We herein investigate the therapeutic effect of olean-12-en-28-ol, 3β-pentacosanoate (OPCA) on a preclinical model of MS. First, OPCA was synthesized semisynthetically and characterized. Then, the mice with MOG<inf>35-55</inf>-induced experimental autoimmune/allergic encephalomyelitis (EAE) were given OPCA along with a reference drug (FTY720). Biochemical, cellular, and molecular analyses were performed in serum and brain tissues to measure anti-inflammatory and neuroprotective responses. OPCA treatment protected EAE-induced changes in mouse brains maintaining blood-brain barrier integrity and preventing inflammation. Moreover, the protein and mRNA levels of MS-related genes such as HLD-DR1, CCL5, TNF-α, IL6, and TGFB1 were significantly reduced in OPCA-treated mouse brains. Notably, the expression of genes, including PLP, MBP, and MAG, involved in the development and structure of myelin was significantly elevated in OPCA-treated EAE. Furthermore, therapeutic OPCA effects included a substantial reduction in pro-inflammatory cytokines in the serum of treated EAE animals. Lastly, following OPCA treatment, the promoter regions for most inflammatory regulators were hypermethylated. These data support that OPCA is a valuable and appealing candidate for human MS treatment since OPCA not only normalizes the pro- and anti-inflammatory immunological bias but also stimulates remyelination in EAE. © 2023 Elsevier B.V., All rights reserved.Article Citation - WoS: 15Citation - Scopus: 15PriPath: Identifying Dysregulated Pathways From Differential Gene Expression via Grouping, Scoring, and Modeling With an Embedded Feature Selection Approach(BMC, 2023-02-23) Yousef, Malik; Ozdemir, Fatma; Jaber, Amhar; Allmer, Jens; Bakir-Gungor, BurcuBackgroundCell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases.ResultsPriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research.ConclusionsPriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.Article Citation - WoS: 4Citation - Scopus: 4Evaluation of Selected Plant Phenolics Via Beta-Secretase Inhibition, Molecular Docking, and Gene Expression Related to Alzheimer's Disease(MDPI, 2024-10-28) Akyurek, Tugba Ucar; Orhan, Ilkay Erdogan; Deniz, F. Sezer Senol; Eren, Gokcen; Acar, Busra; Sen, Alaattin; Şenol Deniz, F. Sezer; Uçar Akyürek, TugbaBackground: The goal of the current study was to investigate the inhibitory activity of six phenolic compounds, i.e., rosmarinic acid, gallic acid, oleuropein, epigallocatechin gallate (EGCG), 3-hydroxytyrosol, and quercetin, against beta-site amyloid precursor protein cleaving enzyme-1 (BACE1), also known as beta-secretase or memapsin 2, which is implicated in the pathogenesis of Alzheimer's disease (AD). Methods and Results: The inhibitory potential against BACE1, molecular docking simulations, as well as neurotoxicity and the effect on the AD-related gene expression of the selected phenolics were tested. BACE1 inhibitory activity was carried out using the ELISA microplate assay via fluorescence resonance energy transfer (FRET) technology. Molecular docking experiments were performed in the human BACE1 active site (PDB code: 2WJO). Neurotoxicity of the compounds was carried out in SH-SY5Y, a human neuroblastoma cell line, by the Alamar Blue method. A gene expression analysis of the compounds on fourteen genes linked to AD was conducted using the real-time polymerase chain reaction (RT-PCR) method. Rosmarinic acid, EGCG, oleuropein, and quercetin (also used as the reference) were able to inhibit BACE1 with their respective IC50 values 4.06 +/- 0.68, 1.62 +/- 0.12, 9.87 +/- 1.01, and 3.16 +/- 0.30 mM. The inhibitory compounds were observed to occupy the non-catalytic site of the BACE1. However, hydrogen bonds were found to be present between rosmarinic acid and EGCG and aspartic amino acid D228 in the catalytic site. Oleuropein and quercetin effectively suppressed the expression of PSEN, APOE, and CLU, which are recognized to be linked to the pathogenesis of AD. Conclusions: The outcomes of the work bring quercetin, EGCG, and rosmarinic acid to the forefront as promising BACE1 inhibitors.