https://hdl.handle.net/20.500.12573/39 Faculty of Life and Natural Sciences 2026-05-08T09:28:23Z https://hdl.handle.net/20.500.12573/2541 Matching variants for functional characterization of genetic variants Cevik,Sabiha; Zhao,Pei; Zorluer,Atiyye; Pir, Mustafa S.; Bian, Wenyin; Kaplan, Oktay I. Rapid and low-cost sequencing, as well as computer analysis, have facilitated the diagnosis of many genetic diseases, resulting in a substantial rise in the number of disease-associated genes. However, genetic diagnosis of many disorders remains problematic due to the lack of interpretation for many genetic variants, especially missenses, the infeasibility of high-throughput experiments on mammals, and the shortcomings of computational prediction technologies. Additionally, the available mutant databases are not well-utilized. Toward this end, we used Caenorhabditis elegans mutant resources to delineate the functions of eight missense variants (V444I, V517D, E610K, L732F, E817K, H873P, R1105K, and G1205E) and two stop codons (W937stop and Q1434stop), including several matching variants (MatchVar) with human in ciliopathy associated IFT-140 (also called CHE-11)//IFT140 (intraflagellar transport protein 140). Moreover, MatchVars carrying C. elegans mutants, including IFT-140(G680S) and IFT-140(P702A) for the human (G704S) (dbSNP: rs150745099) and P726A (dbSNP: rs1057518064 and a conflicting variation) were created using CRISPR/Cas9. IFT140 is a key component of IFT complex A (IFT-A), which is involved in the retrograde transport of IFT along cilia and the entrance of G protein-coupled receptors into cilia. Functional analysis of all 10 variants revealed that P702A and W937stop, but not others phenocopied the ciliary phenotypes (short cilia, IFT accumulations, mislocalization of membrane proteins, and cilia entry of nonciliary proteins) of the IFT-140 null mutant, indicating that both P702A and W937stop are phenotypic in C. elegans. Our functional data offered experimental support for interpreting human variants, by using ready-to-use mutants carrying MatchVars and generating MatchVars with CRISPR/Cas9. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2540 Integrating Biological Domain Knowledge with Machine Learning for Identifying Colorectal-Cancer-Associated Microbial Enzymes in Metagenomic Data Bakir-Gungor, Burcu; Ersoz, Nur Sebnem; Yousef, Malik Advances in metagenomics have revolutionized our ability to elucidate links between the microbiome and human diseases. Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, has been associated with dysbiosis of the gut microbiome. This study aims to develop a method for identifying CRC-associated microbial enzymes by incorporating biological domain knowledge into the feature selection process. Conventional feature selection techniques often evaluate features individually and fail to leverage biological knowledge during metagenomic data analysis. To address this gap, we propose the enzyme commission (EC)-nomenclature-based Grouping-Scoring-Modeling (G-S-M) method, which integrates biological domain knowledge into feature grouping and selection. The proposed method was tested on a CRC-associated metagenomic dataset collected from eight different countries. Community-level relative abundance values of enzymes were considered as features and grouped based on their EC categories to provide biologically informed groupings. Our findings in randomized 10-fold cross-validation experiments imply that glycosidases, CoA-transferases, hydro-lyases, oligo-1,6-glucosidase, crotonobetainyl-CoA hydratase, and citrate CoA-transferase enzymes can be associated with CRC development as part of different molecular pathways. These enzymes are mostly synthesized by Eschericia coli, Salmonella enterica, Klebsiella pneumoniae, Staphylococcus aureus, Streptococcus pneumoniae, and Clostridioides dificile. Comparative evaluation experiments showed that the proposed model consistently outperforms traditional feature selection methods paired with various classifiers. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2536 Discovery of a C-S lyase inhibitor for the prevention of human body malodor formation: tannic acid inhibits the thioalcohol production in Staphylococcus hominis Fidan, Ozkan; Karipcin, Ayse Doga; Kose, Ayse Hamide; Anaz, Ayse; Demirsoy, Beyza Nur; Arslansoy, Nuriye; Sun, Lei; Mujwar, Somdutt Human body odor is a result of the bacterial biotransformation of odorless precursor molecules secreted by the underarm sweat glands. In the human axilla, Staphylococcus hominis is the predominant bacterial species responsible for the biotransformation process of the odorless precursor molecule into the malodorous 3M3SH by two enzymes, a dipeptidase and a specific C-S lyase. The current solutions for malodor, such as deodorants and antiperspirants are known to block the apocrine glands or disrupt the skin microbiota. Additionally, these chemicals endanger both the environment and human health, and their long-term use can influence the function of sweat glands. Therefore, there is a need for the development of alternative, environmentally friendly, and natural solutions for the prevention of human body malodor. In this study, a library of secondary metabolites from various plants was screened to inhibit the C-S lyase, which metabolizes the odorless precursor sweat molecules, through molecular docking and molecular dynamics (MD) simulation. In silico studies revealed that tannic acid had the strongest affinity towards C-S lyase and was stably maintained in the binding pocket of the enzyme during 100-ns MD simulation. We found in the in vitro biotransformation assays that 1 mM tannic acid not only exhibited a significant reduction in malodor formation but also had quite low growth inhibition in S. hominis, indicating the minimum inhibitory effect of tannic acid on the skin microflora. This study paved the way for the development of a promising natural C-S lyase inhibitor to eliminate human body odor and can be used as a natural deodorizing molecule after further in vivo analysis. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2535 TextNetTopics-SFTS-SBTS: TextNetTopics Scoring Approaches Based Sequential Forward and Backward Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik TextNetTopics is a text classification-based topic modeling approach that performs topic selection rather than word selection to train a machine learning algorithm. However, one main limitation of TextNetTopics is that its scoring component (the S component) assesses each topic independently and ranks them accordingly, neglecting the potential relationship between topics. In order to address this limitation and improve the classification performance, this study introduces an enhancement to TextNetTopics. TextNetTopics-SFTS-SBTS integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. This integration aims to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained across three datasets offer valuable insights into the context-dependent effectiveness of the new scoring mechanisms across diverse datasets and varying numbers of topics involved in the analysis. 2024-01-01T00:00:00Z