WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Book Part Mirnomics: Microrna Biology and Computational Analysis Preface(Humana Press, 2014) Yousef, Malik; Allmer, Jens[No abstract available]Article Citation - WoS: 13Citation - Scopus: 15Cytotoxic Tolerance of Healthy and Cancerous Bone Cells To Anti-Microbial Phenolic Compounds Depend on Culture Conditions(Humana Press, 2019) Karadaş, Özge; Meşe, Gülistan; Özçivici, EnginCarnosol and carnosic acid are polyphenolic compounds found in rosemary and sage with known anti-oxidant, anti-inflammatory, and anti-microbial properties. Here, we addressed the potential use of carnosol and carnosic acid for in vitro bone tissue engineering applications, specifically depending on their cytotoxic effects on bone marrow stromal and stem cells, and osteosarcoma cells in monolayer and 3D cultures. Carnosol and carnosic acid displayed a bacteriostatic effect on Gram-positive bacteria, especially on S. aureus. The viability results indicated that bone marrow stromal cells and bone marrow stem cells were more tolerant to the presence of carnosol compared to osteosarcoma cells. 3D culture conditions increased this tolerance further for healthy cells, while not affecting the cytotoxic potential of carnosol for osteosarcoma cells. Carnosic acid was found to be more cytotoxic for all cell types used in the study. Results suggest that phenolic compounds might have potential use as anti-microbial and anti-carcinogenic agents for bone tissue engineering with further optimization for controlled release.Article Citation - WoS: 19Citation - Scopus: 21Physicochemical and Antimicrobial Properties of Oleoresin Capsicum Nanoemulsions Formulated With Lecithin and Sucrose Monopalmitate(Humana Press, 2019) Akbaş, Elif; Söyler, U. Betül; Öztop, Mecit HalilOleoresin capsicum (OC) is an extract of chili pepper containing the active agent capsaicin. In this study, OC-loaded nanoemulsions were prepared by microfluidization and stabilized with sucrose monopalmitate (SMP) and lecithin. The difference in size and distribution of droplets determined the nanoemulsion behavior mainly due to the interaction of emulsifiers between oil and aqueous phase. The hydrophilic interaction between SMP and aqueous phase and the hydrophobic interaction between lecithin and oil phase were monitored with NMR relaxometry. OC nanoemulsion fabricated with SMP showed the best transparency with smallest droplet size (around 34nm) and stable with glycerol after 28days at ambient storage. Lecithin containing nanoemulsions showed improved bioactivity as showing antioxidant (0.82mg DPPH/L) and antimicrobial (3.40 log for Escherichia coli and 4.37 log for Staphylococcus aureus) activity. Finally, results have important implications to determine the appropriate formulation conditions for OC with food-grade surfactants to be used in pharmaceuticals and food industry.Book Part Citation - WoS: 13Citation - Scopus: 15Single Cell Densitometry and Weightlessness Culture of Mesenchymal Stem Cells Using Magnetic Levitation(Humana Press, 2020) Anıl İnevi, Müge; Yılmaz, Esra; Sarıgil, Öykü; Tekin, Hüseyin Cumhur; Özçivici, EnginMagnetic levitation methodology enables density-based separation of microparticles/cells and sustains cell culture in different media. Levitation process can be accomplished via negative magnetophoresis (diamagnetophoresis), where the applied magnetic force compensates gravitational acceleration and the density of the diamagnetic object (e.g., cell) determines its levitation height. Here we describe a portable, sensitive, and cost-effective technology that uses the principles of magnetic levitation to measure single cell density and cell culture under desired conditions. © 2019, Springer Science+Business Media New York.Article Citation - WoS: 16Citation - Scopus: 18Computational and Bioinformatics Methods for Microrna Gene Prediction(Humana Press, 2014) Allmer, JensMicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.Article Citation - WoS: 37Citation - Scopus: 46Computational Methods for Microrna Target Prediction(Humana Press, 2014) Hamzeiy, Hamid; Yousef, Malik; Allmer, JensMicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.Article Citation - WoS: 6Citation - Scopus: 7A Biodesign Approach To Obtain High Yields of Biosimilars by Anti-Apoptotic Cell Engineering: A Case Study To Increase the Production Yield of Anti-Tnf Alpha Producing Recombinant Cho Cells(Humana Press, 2018) Gülce İz, Sultan; Anıl İnevi, Müge; Sağlam Metiner, Pelin; Ayyıldız Tamiş, Duygu; Kisbet, NazlıRecent developments in medical biotechnology have facilitated to enhance the production of monoclonal antibodies (mAbs) and recombinant proteins in mammalian cells. Human mAbs for clinical applications have focused on three areas, particularly cancer, immunological disorders, and infectious diseases. Tumor necrosis factor alpha (TNF-α), which has both proinflammatory and immunoregulatory functions, is an important target in biopharmaceutical industry. In this study, a humanized anti-TNF-α mAb producing stable CHO cell line which produces a biosimilar of Humira (adalimumab) was used. Adalimumab is a fully human anti-TNF mAb among the top-selling mAb products in recent years as a biosimilar. Products from mammalian cell bioprocesses are a derivative of cell viability and metabolism, which is mainly disrupted by cell death in bioreactors. Thus, different strategies are used to increase the product yield. Suppression of apoptosis, also called anti-apoptotic cell engineering, is the most remarkable strategy to enhance lifetime of cells for a longer production period. In fact, using anti-apoptotic cell engineering as a BioDesign approach was inspired by nature; nature gives prolonged life span to some cells like stem cells, tumor cells, and memory B and T cells, and researchers have been using this strategy for different purposes. In this study, as a biomimicry approach, anti-apoptotic cell engineering was used to increase the anti-TNF-α mAb production from the humanized anti-TNF-α mAb producing stable CHO cell line by Bcl-xL anti-apoptotic protein. It was shown that transient transfection of CHO cells by the Bcl-xL anti-apoptotic protein expressing plasmid prolonged the cell survival rate and protected cells from apoptosis. The transient expression of Bcl-xL using CHO cells enhanced the anti-TNF-α production. The production of anti-TNF-α in CHO cells was increased up to 215 mg/L with an increase of 160% after cells were transfected with Bcl-xL expressing plasmid with polyethylenimine (PEI) reagent at the ratio of 1:6 (DNA:PEI). In conclusion, the anti-apoptotic efficacy of the Bcl-xL expressing plasmid in humanized anti-TNF-α MAb producing stable CHO cells is compatible with curative effect for high efficiency recombinant protein production. Thus, this model can be used for large-scale production of biosimilars through transient Bcl-xL gene expression as a cost-effective method.Book Part Citation - WoS: 299Citation - Scopus: 406Introduction To Machine Learning(Humana Press, 2014) Baştanlar, Yalın; Özuysal, MustafaThe machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.Article Citation - WoS: 30Machine Learning Methods for Microrna Gene Prediction(Humana Press, 2014) Saçar, Müşerref Duygu; Allmer, JensMicroRNAs (miRNAs) are single-stranded, small, noncoding RNAs of about 22 nucleotides in length, which control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, or destabilization of their target mRNAs. Although hundreds of miRNAs have been identified in various species, many more may still remain unknown. Therefore, discovery of new miRNA genes is an important step for understanding miRNA-mediated posttranscriptional regulation mechanisms. It seems that biological approaches to identify miRNA genes might be limited in their ability to detect rare miRNAs and are further limited to the tissues examined and the developmental stage of the organism under examination. These limitations have led to the development of sophisticated computational approaches attempting to identify possible miRNAs in silico. In this chapter, we discuss computational problems in miRNA prediction studies and review some of the many machine learning methods that have been tried to address the issues.Article Citation - WoS: 4Citation - Scopus: 7Master Regulators of Posttranscriptional Gene Expression Are Subject To Regulation(Humana Press, 2014) Hamid, Syed Muhammad; Akgül, BünyaminMicroRNAs (miRNAs) are small noncoding RNAs of 17-25 nt in length that control gene expression posttranscriptionally. As master regulators of posttranscriptional gene expression, miRNAs themselves are subject to tight regulation at multiple steps. The most common mechanisms include miRNA transcription, processing, and localization. Additionally, intricate feedback loops between miRNAs and transcription factors result in unidirectional, reciprocal, or self-directed elegant control mechanisms. In this chapter, we focus on the posttranscriptional regulatory mechanisms that generate miRNAs whose sequence might be slightly different from the miRNA-coding sequences. Hopefully, this information will be helpful in the discovery of novel miRNAs as well as in the analysis of deep-sequencing data and ab initio prediction of miRNAs. © Springer Science+Business Media New York 2014.