Biology subjects

Onal-Suzek, T.

Publications and source records attributed to Onal-Suzek, T..

5 recordsLinked to original sources

bioRxiv2026

Integrated Transcriptomic and Structural Prioritization of Sulforaphane Targets in Neurodegeneration: A Multi-Dataset In Silico Study

Sulforaphane (SFN), a natural isothiocyanate from cruciferous vegetables, exerts neuroprotective effects through mechanisms extending beyond canonical Nrf2/ARE activation. Here, we developed a multi-dataset computational framework integrating transcriptomic analysis, multi-criteria target prioritization, and structural docking evaluation. R- and S-SFN were docked against 693 prioritized targets using AutoDock Vina, followed by cross-validation, brain expression profiling, and molecular dynamics simulations. Four genes (IGSF9, KCNQ5, KCTD12, and NFIX) were dysregulated across all four datasets. Among 693 targets, 88 showed favorable predicted docking scores (deltaG less than or equal to -4.0 kcal/mol), with 65 (73.9%) exhibiting expression in at least one GTEx brain region. Independent cross-validation showed significant enrichment of prioritized targets in an SFN-responsive Alzheimer disease transcriptomic dataset (OR = 1.44, 95% CI: 1.08-1.89, p = 0.0104). Complementary disease gene set enrichment confirmed that 33% of strong hits (29/88) overlap established neurodegeneration gene sets across AD, PD, and ALS - including ACHE, target of FDA-approved anti-AD drugs - with highly significant enrichment across all three disease categories (OR range: 67-127, all p less than 0.001). R-SFN showed a modest but statistically detectable preference over S-SFN across the 88 strong hits (Wilcoxon p = 0.0081; paired t-test p = 0.0047). TMEM45A, GRIK2, and FABP3 emerged as putative neurodegeneration-relevant targets. Molecular dynamics simulations supported the structural stability of R-SFN poses for TMEM45A, GRIK2, and FABP3, while MM-GBSA estimated favorable binding energies for GRIK2 (deltaG_bind = -19.78 kcal/mol) and FABP3 (deltaG_bind = -14.49 kcal/mol). These findings generate testable hypotheses regarding previously underexplored molecular targets that may contribute to the neuroprotective actions of SFN.

bioinformatics↗

bioRxiv2023

CompCorona: A Web Portal for Comparative Analysis of the Host Transcriptome of PBMC and Lung SARS-CoV-2, SARS-CoV, and MERS-CoV

MotivationUnderstanding the host response to SARS-CoV-2 infection is crucial for deciding on the correct treatment of this epidemic disease. Although several recent studies reported the comparative transcriptome analyses of the three coronaviridae (CoV) members; namely SARS-CoV, MERS-CoV, and SARS-CoV-2, there is yet to exist a web-tool to compare increasing number of host transcriptome response datasets against the pre-processed CoV member datasets. Therefore, we developed a web application called CompCorona, which allows users to compare their own transcriptome data of infected host cells with our pre-built datasets of the three epidemic CoVs, as well as perform functional enrichment and principal component analyses (PCA). ResultsComparative analyses of the transcriptome profiles of the three CoVs revealed that numerous differentially regulated genes directly or indirectly related to several diseases (e.g., hypertension, male fertility, ALS, and epithelial dysfunction) are altered in response to CoV infections. Transcriptome similarities and differences between the host PBMC and lung tissue infected by SARS-CoV-2 are presented. Most of our findings are congruent with the clinical cases recorded in the literature. Hence, we anticipate that our results will significantly contribute to ongoing studies investigating the pre-and/or post-implications of SARS-CoV-2 infection. In addition, we implemented a user-friendly public website, CompCorona for biomedical researchers to compare users own CoV-infected host transcriptome data against the built-in CoV datasets and visualize their results via interactive PCA, UpSet and Pathway plots. AvailabilityCompCorona is freely available on the web at http://compcorona.mu.edu.tr Contacttugbasuzek@mu.edu.tr

bioinformatics↗

bioRxiv2023

TCGAnalyzeR: a web application for integrative visualization of molecular and clinical data of cancer patients for cohort discovery

MotivationThe vast size and complexity of The Cancer Genome Atlas (TCGA) database with multidimensional molecular and clinical data of ~11,000 cancer patients of 33 cancer types challenge the effective utilization of this valuable resource. Therefore, we built a web application named TCGAnalyzeR with the main idea of presenting an integrative visualization of mutations, transcriptome profile, copy number variation and clinical data allowing researchers to facilitate the identification of customized patient cohorts and gene sets for better decision-making for oncologists and cancer researchers. ResultsWe present TCGAnalyzeR for integrative visualization of pre-analyzed TCGA data with the several novel modules: (i) Simple nucleotide variations with driver prediction; (ii) Recurrent copy number alterations; (iii) Differential expression in tumor versus normal, with pathway enrichment and the survival analysis; (iii) TCGA clinical data and survival analysis; (iv) External subcohorts from literature, curatedTCGAData and BiocOncoTK R packages; (v) Internal patient clusters determined using iClusterPlus R package or signature-based expression analysis. TCGAnalyzeR provides clinical oncologists and cancer researchers interactive and integrative representations of these multi-omic, pan-cancer TCGA data with availability of subcohort analysis and visualization. TCGAnalyzeR can be used to create their own custom gene sets for pan-cancer comparisons, to create custom patient subcohorts comparing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, etc) along with our internal patient clusters, to visualize cohort-centric or gene-centric results along with pathway enrichment and survival analysis graphically on an interactive web tool. AvailabilityTCGAnalyzeR is freely available on the web at http://tcganalyzer.mu.edu.tr. Contacttugbasuzek@mu.edu.tr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinoma

Lung cancer is the second frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma and lung squamous cell carcinoma are subtypes of non-small cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed univariate cox model and then lasso regularized cox model with leave-one-out cross-validation using TCGA gene expression data in tumor samples. We generated a 35-gene signature and a 33-gene signature for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high-risk and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more down-regulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both upregulated and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. Low-risk groups of both LUAD and LUSC, tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

bioinformatics↗

bioRxiv2020

An Interactive Health Game Using Machine Learning: A Prototype

According to World Health Organization (WHO) 2016 report, there are over 650 million obese adults and more than 2 billion overweight individuals in the world and it is estimated that this number will reach 2.7 billion in 2025 [1]. A sedentary lifestyle with low physical activity is considered to be one of the most effective environmental effects leading to various chronic disease phenotypes such as obesity and metabolic syndrome. On average, every 1 out of 3 people over the age of 20 in Turkey are known to have struggled with the metabolic syndrome [2]. Our project aims to apply the concept of "serious gaming", to entertain people, play games, socialize and exercise in parallel to increase the ratio of the healthy individuals in our society. In this project, we applied machine learning techniques to integrate real-life accelerometer and gyroscope sensor data obtained from mobile phones to develop an interactive mobile based exercise game which does not require any external device such as smart watches. To our knowledge and research, our game is the first mobile-only interactive serious game that integrates machine learning techniques and an encouraging virtual environment to the individuals in need of exercise.

physiology↗