Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Three-Dimensional Culture Enhances the Antimicrobial Activity of Mesenchymal Stem Cells against Shiga Toxin-Producing Escherichia Coli O157:H7 in Vitro(Oxford Univ Press, 2026) Fidan, Özkan; Türkyılmaz, Süheyla; Bicer, Mesude; Öztürk, Esengül; Sener, FatmaAims This study examines the in vitro antibacterial activity of palatal adipose tissue-derived mesenchymal stem cells (PAT-MSCs) and the expression of antimicrobial peptide LL-37, with a particular focus on the effect of three-dimensional (3D) nanofibrillar cellulose-based hydrogel against Shiga toxin-producing Escherichia coli (STEC) harboring stx1 and/or stx2 genes isolated from mastitis milk in Turkey.Methods and results The antibacterial activity of conventionally cultured PAT-MSCs and 3D-cultured PAT-MSCs (PAT-MSCs-3D) was evaluated against STEC isolates and Escherichia coli ATCC 35150 using quantitative colony-forming unit (CFU) assay. The expression levels of antimicrobial peptide (AMP)-encoding genes were evaluated by quantitative real-time PCR, and AMP production was further validated by immunocytochemical staining. The results indicated that PAT-MSCs-3D exhibited significantly enhanced antibacterial efficacy, resulting in marked bacterial inhibition of all tested STEC strains, with bacterial reductions reaching up to 6-7 log under specific experimental conditions. Molecular and immunocytochemical analyses demonstrated increased expression of the antimicrobial peptide LL-37 in PAT-MSCs-3D compared to 2D cultures.Conclusions Our results show that culturing PAT-MSCs in 3D conditions leads to a significant enhancement in their antimicrobial properties, which could be linked to the upregulation of LL-37.Article Citation - WoS: 53Citation - Scopus: 66Node Similarity-Based Graph Convolution for Link Prediction in Biological Networks(Oxford Univ Press, 2021-06-21) Coskun, Mustafa; Koyuturk, MehmetBackground: Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention in link prediction. Motivation: An important component of GCN-based network embedding is the convolution matrix, which is used to propagate features across the network. Existing algorithms use the degree-normalized adjacency matrix for this purpose, as this matrix is closely related to the graph Laplacian, capturing the spectral properties of the network. In parallel, it has been shown that GCNs with a single layer can generate more robust embeddings by reducing the number of parameters. Laplacian-based convolution is not well suited to single-layered GCNs, as it limits the propagation of information to immediate neighbors of a node. Results: Capitalizing on the rich literature on unsupervised link prediction, we propose using node similarity-based convolution matrices in GCNs to compute node embeddings for link prediction. We consider eight representative node-similarity measures (Common Neighbors, Jaccard Index, Adamic-Adar, Resource Allocation, Hub- Depressed Index, Hub-Promoted Index, Sorenson Index and Salton Index) for this purpose. We systematically compare the performance of the resulting algorithms against GCNs that use the degree-normalized adjacency matrix for convolution, as well as other link prediction algorithms. In our experiments, we use three-link prediction tasks involving biomedical networks: drug-disease association prediction, drug-drug interaction prediction and protein-protein interaction prediction. Our results show that node similarity-based convolution matrices significantly improve the link prediction performance of GCN-based embeddings. Conclusion: As sophisticated machine-learning frameworks are increasingly employed in biological applications, historically well-established methods can be useful in making a head-start.Article Citation - WoS: 3Citation - Scopus: 3MSABrowser: Dynamic and Fast Visualization of Sequence Alignments, Variations and Annotations(Oxford Univ Press, 2021-01-01) Torun, Furkan M.; Bilgin, Halil, I; Kaplan, Oktay, ISequence alignment is an excellent way to visualize the similarities and differences between DNA, RNA or protein sequences, yet it is currently difficult to jointly view sequence alignment data with genetic variations, modifications such as post-translational modifications and annotations (i.e. protein domains). Here, we present the MSABrowser tool that makes it easy to co-visualize genetic variations, modifications and annotations on the respective positions of amino acids or nucleotides in pairwise or multiple sequence alignments. MSABrowser is developed entirely in JavaScript and works on any modern web browser at any platform, including Linux, Mac OS X and Windows systems without any installation. MSABrowser is also freely available for the benefit of the scientific community.Article Citation - WoS: 5Citation - Scopus: 5Integrated Querying and Version Control of Context-Specific Biological Networks(Oxford Univ Press, 2020) Cowman, Tyler; Coskun, Mustafa; Grama, Ananth; Koyuturk, MehmetMotivation: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results: We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion: Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. Availability and Implementation: VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion Contact: tyler.cowman@case.eduArticle Citation - WoS: 18Citation - Scopus: 17ConVarT: A Search Engine for Matching Human Genetic Variants With Variants From Non-Human Species(Oxford Univ Press, 2021-10-28) Pir, Mustafa S.; Bilgin, Halil, I; Sayici, Ahmet; Torun, Furkan M.; Zhao, Pei; Kang, Yahong; Kaplan, Oktay, I; Coşkun, FatihThe availability of genetic variants, togetherwith phenotypic annotations from model organisms, facilitates comparing these variants with equivalent variants in humans. However, existing databases and search tools do not make it easy to scan for equivalent variants, namely 'matching variants' (MatchVars) between humans and other organisms. Therefore, we developed an integrated search engine called ConVarT (http://www.convart.org/) for matching variants between humans, mice, and Caenorhabditis elegans. ConVarT incorporates annotations (including phenotypic and pathogenic) into variants, and these previously unexploited phenotypic MatchVars from mice and C. elegans can give clues about the functional consequence of human genetic variants. Our analysis shows that many phenotypic variants in different genes from mice and C. elegans, so far, have no counterparts in humans, and thus, can be useful resources when evaluating a relationship between a new human mutation and a disease.Article Citation - WoS: 15Citation - Scopus: 18Ciliaminer: An Integrated Database for Ciliopathy Genes and Ciliopathies(Oxford Univ Press, 2023-01-01) Turan, Merve Guel; Orhan, Mehmet Emin; Cevik, Sebiha; Kaplan, Oktay, ICilia 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.Article Citation - WoS: 18Citation - Scopus: 16Ciliogenics: An Integrated Method and Database for Predicting Novel Ciliary Genes(Oxford Univ Press, 2024-07-11) Pir, Mustafa S.; Begar, Efe; Yenisert, Ferhan; Demirci, Hasan C.; Korkmaz, Mustafa E.; Karaman, Asli; Kaplan, Oktay, IUncovering the full list of human ciliary genes holds enormous promise for the diagnosis of cilia-related human diseases, collectively known as ciliopathies. Currently, genetic diagnoses of many ciliopathies remain incomplete (). While various independent approaches theoretically have the potential to reveal the entire list of ciliary genes, approximately 30% of the genes on the ciliary gene list still stand as ciliary candidates (,). These methods, however, have mainly relied on a single strategy to uncover ciliary candidate genes, making the categorization challenging due to variations in quality and distinct capabilities demonstrated by different methodologies. Here, we develop a method called CilioGenics that combines several methodologies (single-cell RNA sequencing, protein-protein interactions (PPIs), comparative genomics, transcription factor (TF) network analysis, and text mining) to predict the ciliary capacity of each human gene. Our combined approach provides a CilioGenics score for every human gene that represents the probability that it will become a ciliary gene. Compared to methods that rely on a single method, CilioGenics performs better in its capacity to predict ciliary genes. Our top 500 gene list includes 258 new ciliary candidates, with 31 validated experimentally by us and others. Users may explore the whole list of human genes and CilioGenics scores on the CilioGenics database (https://ciliogenics.com/). Graphical Abstract