Browsing by Author "Orhan, Mehmet Emin"

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Citation - WoS: 9
Citation - Scopus: 10
Ciliaminer: An Integrated Database for Ciliopathy Genes and Ciliopathies
(Oxford Univ Press, 2023) Turan, Merve Guel; Orhan, Mehmet Emin; Cevik, Sebiha; Kaplan, Oktay, I; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.02. Moleküler Biyoloji ve Genetik; 04.01. Biyomühendislik
Cilia are found in eukaryotic species ranging from single-celled organisms, such as Chlamydomonas reinhardtii, to humans, but not in plants. The ability to respond to repellents and/or attractants, regulate cell proliferation and differentiation and provide cellular mobility are just a few examples of how crucial cilia are to cells and organisms. Over 30 distinct rare disorders generally known as ciliopathy are caused by abnormalities or functional impairments in cilia and cilia-related compartments. Because of the complexity of ciliopathies and the rising number of ciliopathies and ciliopathy genes, a ciliopathy-oriented and up-to-date database is required. Here, we present CiliaMiner, a manually curated ciliopathy database that includes ciliopathy lists collected from articles and databases. Analysis reveals that there are 55 distinct disorders likely related to ciliopathy, with over 4000 clinical manifestations. Based on comparative symptom analysis and subcellular localization data, diseases are classified as primary, secondary or atypical ciliopathies. CiliaMiner provides easy access to all of these diseases and disease genes, as well as clinical features and gene-specific clinical features, as well as subcellular localization of each protein. Additionally, the orthologs of disease genes are also provided for mice, zebrafish, Xenopus, Drosophila, Caenorhabditis elegans and Chlamydomonas reinhardtii. CiliaMiner (https://kaplanlab.shinyapps.io/ciliaminer) aims to serve the cilia community with its comprehensive content and highly enriched interactive heatmaps, and will be continually updated.
A Comprehensive MicroRNA-seq Transcriptomic Analysis of Tay-Sachs Disease Mice Revealed Distinct miRNA Profiles in Neuroglial Cells
(Springernature, 2025) Kaya, Beyza; Orhan, Mehmet Emin; Yanbul, Selman; Demirci, Muserref Duygu Sacar; Demir, Secil Akyildiz; Seyrantepe, Volkan; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
Tay-Sachs disease (TSD) is a rare lysosomal storage disorder marked by the progressive buildup of GM2 in the central nervous system (CNS). This condition arises from mutations in the HEXA gene, which encodes the alpha subunit of the enzyme beta-hexosaminidase A. A newly developed mouse model for early-onset TSD (Hexa-/-Neu3-/-) exhibited signs of neurodegeneration and neuroinflammation, evidenced by elevated levels of pro-inflammatory cytokines and chemokines, as well as significant astrogliosis and microgliosis. Identifying disease-specific MicroRNAs (miRNAs) may aid the development of targeted therapies. Although previous small-scale studies have investigated miRNA expression in some regions of GM2 gangliosidosis mouse models, thorough profiling of miRNAs in this innovative TSD model remains to be done. In this study, we employed next-generation sequencing to analyze the complete miRNA profile of neuroglial cells from Hexa-/-Neu3-/- mice. By comparing KEGG and Reactome pathways associated with neurodegeneration, neuroinflammation, and sphingolipid metabolism in Hexa-/-Neu3-/- neuroglial cells, we discovered new MicroRNAs and their targets related to the pathophysiology of GM2 gangliosidosis. For the first time, our findings showed that miR-708-5p, miR-672-5p, miR-204-5p, miR-335-5p, and miR-296-3p were upregulated, while miR-10 b-5p, miR-615-3p, miR-196a-5p, miR-214-5p, and miR-199a-5p were downregulated in Hexa-/-Neu3-/- neuroglial cells in comparison to age-matched wild-type (WT). These specific changes in miRNA expression deepen our understanding of the disease's neuropathological characteristics in Hexa-/-Neu3-/- mice. Our study suggests that miRNA-based therapeutic strategies may improve clinical outcomes for TSD patients.
Comprehensive Prediction of FBN1 Targeting miRNAs: A Systems Biology Approach for Marfan Syndrome
(Galenos Publ House, 2025) Orhan, Mehmet Emin; Demirci, Yilmaz Mehmet; Demirci, Muserref Duygu Sacar; 02.01. Mühendislik Bilimleri; 01. Abdullah Gül University; 02. Mühendislik Fakültesi; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
Objective: Marfan syndrome (MFS) is a genetic connective tissue disorder primarily caused by mutations in the FBN1 gene. Emerging evidence highlights the regulatory role of microRNAs (miRNAs) in modulating gene expression in MFS, but a systematic investigation into miRNAs targeting FBN1 is lacking. This study aimed to comprehensively identify miRNAs interacting with the FBN1 transcript to reveal potential molecular regulators and therapeutic targets. Methods: Human miRNA sequences were retrieved from miRBase (Release 22.1), and the canonical FBN1 transcript (RefSeq: NM_000138.5) was used for target prediction. Computational interaction analysis was conducted using the psRNATarget server with stringent parameters to detect potential miRNA binding sites. Expression profiles and disease associations of the top candidate miRNAs were further investigated through database integration and literature review. Results: Out of 2656 human mature miRNAs analyzed, 251 were predicted to bind FBN1, with the hsa-miR-181 family exhibiting the highest number of predicted interactions. Evidence from the literature highlighted dysregulation of hsa-miR-181 expression in MFS patients, suggesting a functional role in disease pathophysiology. Conclusion: This study identifies key members of the hsa-miR-181 family as post-transcriptional regulators of FBN1, offering new insights into miRNA-driven mechanisms in MFS. These findings support the potential of RNA-based diagnostics and therapeutic strategies targeting miRNA-FBN1 interactions.
Citation - WoS: 2
Citation - Scopus: 2
NeRNA: A Negative Data Generation Framework for Machine Learning Applications of Noncoding RNAs
(Pergamon-Elsevier Science Ltd, 2023) Orhan, Mehmet Emin; Demirci, Yilmaz Mehmet; Demirci, Mueserref Duygu Sacar; 01. Abdullah Gül University; 02.01. Mühendislik Bilimleri; 02. Mühendislik Fakültesi; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
Many supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including MicroRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is per-formed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naive Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.
RNA Etkileşimlerinin İn Silico Analizi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Orhan, Mehmet Emin; Demirci, Müşerref Duygu Saçar; 01. Abdullah Gül University; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
Many supervised machine learning models have been developed for the classification and identification of non-coding RNA (ncRNA) sequences. These models play a significant role in the diagnosis and treatment of various diseases. During such analyses, positive learning datasets typically consist of known ncRNA examples, some of which may even be confirmed with strong experimental evidence. However, there is no database of validated negative sequences for ncRNA classes or standardized methodologies for generating high quality negative samples. To overcome this challenge, a new method for generating negative data called the NeRNA (Negative RNA) method has been developed in this study. NeRNA generates negative sequences using known ncRNA sequences and their octal representations, similar with frame shift mutations found in biology but without base deletions or insertions. In this thesis, the NeRNA method was tested separately with four different ncRNA datasets, including microRNA (miRNA), transfer RNA (tRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). Additionally, a species-specific case study was conducted to demonstrate and compare the performance of the study's miRNA predictions. The results of 1000-fold cross-validation on machine learning algorithms such as Decision Trees, Naive Bayes, Random Forest classifiers, and deep learning algorithms like Multilayer Perceptrons, Convolutional Neural Networks, and Simple Feedforward Neural Networks showed that models developed using datasets generated by NeRNA exhibited significantly high prediction performance. NeRNA has been published as an easy-to-use, updatable, and modifiable KNIME workflow, along with example datasets and required extensions that can be downloaded and utilized. NeRNA is designed specifically as a powerful tool for RNA sequence data analysis.