PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
Browse
15 results
Search Results
Now showing 1 - 10 of 15
Book Part Citation - Scopus: 86The Role of Mirna in Cancer: Pathogenesis, Diagnosis, and Treatment(Humana Press, 2022) Uzuner, Erez; Ulu, Gizem Tuğçe; Gürler, Sevim Beyza; Baran, YusufCancer is also determined by the alterations of oncogenes and tumor suppressor genes. These gene expressions can be regulated by microRNAs (miRNA). At this point, researchers focus on addressing two main questions: “How are oncogenes and/or tumor suppressor genes regulated by miRNAs?” and “Which other mechanisms in cancer cells are regulated by miRNAs?” In this work we focus on gathering the publications answering these questions. The expression of miRNAs is affected by amplification, deletion or mutation. These processes are controlled by oncogenes and tumor suppressor genes, which regulate different mechanisms of cancer initiation and progression including cell proliferation, cell growth, apoptosis, DNA repair, invasion, angiogenesis, metastasis, drug resistance, metabolic regulation, and immune response regulation in cancer cells. In addition, profiling of miRNA is an important step in developing a new therapeutic approach for cancer. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.Book Part Citation - Scopus: 20Experimental MicroRNA Detection Methods(Humana Press, 2022) Yaylak, Bilge; Akgül, BünyaminMicroRNAs (miRNAs) are considerably small yet highly important riboregulators involved in nearly all cellular processes. Due to their critical roles in posttranscriptional regulation of gene expression, they have the potential to be used as biomarkers in addition to their use as drug targets. Although computational approaches speed up the initial genomewide identification of putative miRNAs, experimental approaches are essential for further validation and functional analyses of differentially expressed miRNAs. Therefore, sensitive, specific, and cost-effective microRNA detection methods are imperative for both individual and multiplex analysis of miRNA expression in different tissues and during different developmental stages. There are a number of well-established miRNA detection methods that can be exploited depending on the comprehensiveness of the study (individual miRNA versus multiplex analysis), the availability of the sample and the location and intracellular concentration of miRNAs. This review aims to highlight not only traditional but also novel strategies that are widely used in experimental identification and quantification of microRNAs. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.Book Part Citation - Scopus: 444 Current Challenges in Mirnomics(Humana Press, 2022) Akgül, Bünyamin; Stadler, Peter F.; Hawkins, Liam J.; Hadj-Moussa, Hanane; Storey, Kenneth B.; Ergin, Kemal; Allmer, JensMature microRNAs (miRNAs) are short RNA sequences about 18–24 nucleotide long, which provide the recognition key within RISC for the posttranscriptional regulation of target RNAs. Considering the canonical pathway, mature miRNAs are produced via a multistep process. Their transcription (pri-miRNAs) and first processing step via the microprocessor complex (pre-miRNAs) occur in the nucleus. Then they are exported into the cytosol, processed again by Dicer (dsRNA) and finally a single strand (mature miRNA) is incorporated into RISC (miRISC). The sequence of the incorporated miRNA provides the function of RNA target recognition via hybridization. Following binding of the target, the mRNA is either degraded or translation is inhibited, which ultimately leads to less protein production. Conversely, it has been shown that binding within the 5? UTR of the mRNA can lead to an increase in protein product. Regulation of homeostasis is very important for a cell; therefore, all steps in the miRNA-based regulation pathway, from transcription to the incorporation of the mature miRNA into RISC, are under tight control. While much research effort has been exerted in this area, the knowledgebase is not sufficient for accurately modelling miRNA regulation computationally. The computational prediction of miRNAs is, however, necessary because it is not feasible to investigate all possible pairs of a miRNA and its target, let alone miRNAs and their targets. We here point out open challenges important for computational modelling or for our general understanding of miRNA-based regulation and show how their investigation is beneficial. It is our hope that this collection of challenges will lead to their resolution in the near future. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.Book Part Citation - Scopus: 94Endogenous miRNA Sponges(Humana Press, 2022) Alkan, Ayşe Hale; Akgül, BünyaminMicroRNAs (miRNAs) are a class of noncoding RNAs of 17–22 nucleotides in length with a critical function in posttranscriptional gene regulation. These master regulators are themselves subject to regulation both transcriptionally and posttranscriptionally. Recently, miRNA function has been shown to be modulated by exogenous RNA molecules that function as miRNA sponges. Interestingly, endogenous transcripts such as transcribed pseudogenes, long noncoding RNAs (lncRNAs), circular RNAs (circRNAs) and mRNAs may serve as natural miRNA sponges. These transcripts, which bind to miRNAs and competitively sequester them away from their targets, are naturally existing endogenous miRNA sponges, called competing endogenous RNAs (ceRNAs). Here we present a historical background of miRNAs, exogenous and endogenous miRNA sponges as well as some examples of endogenous miRNA sponges involved in regulatory mechanisms associated with various diseases, developmental stages, and other cellular processes. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.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: 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 - Scopus: 18Organogenesis From Transformed Tomato Explants(Humana Press, 2005) Frary, Anne; Van Eck, JoyceTomato was one of the first crops for which a genetic transformation system was reported involving regeneration by organogenesis from Agrobacterium-transformed explants. Since the initial reports, various factors have been studied that affect the efficiency of tomato transformation and the technique has been useful for the isolation and identification of many genes involved in plant disease resistance, morphology and development. In this method, cotyledon explants from in vitro-grown seedlings are precultured overnight on a tobacco suspension feeder layer. The explants are then inoculated with Agrobacterium and returned to the feeder layer for a 2-d period of cocultivation. After cocultivation, the explants are transferred to an MS-based selective regeneration medium containing zeatin. Regenerated shoots are then rooted on a separate selective medium. This protocol has been used with several tomato cultivars and routinely yields transformation efficiencies of 10-15%.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.