Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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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.Letter Citation - Scopus: 9A Call for Benchmark Data in Mass Spectrometry-Based Proteomics(Proteomass Scientific Society, 2012) Allmer, JensProteomics is a quickly developing field. New and better mass spectrometers, the platform of choice in proteomics, are being introduced frequently. New algorithms for the analysis of mass spectrometric data and assignment of amino acid sequence to tandem mass spectra are also presented on a frequent basis. Unfortunately, the best application area for these algorithms cannot be established at the moment. Furthermore, even the accuracy of the algorithms and their relative performance cannot be established. This is due to the lack of proper benchmark data. This letter first introduces the field of mass spectrometry-based proteomics and then defines the expectations of a well-designed benchmark dataset. Thereafter, the current situation is compared to this ideal. A call for the creation of a proper benchmark dataset is then placed and it is explained how measurement should be performed. Finally, the benefits for the research community are highlighted. © 2012, Proteomass Scientific Society. All rights reserved.Article Citation - WoS: 19Citation - Scopus: 20Use of Micrornas in Personalized Medicine(Humana Press Inc., 2014) Avci, C.B.; Baran, Y.Personalized medicine comprises the genetic information together with the phenotypic and environmental factors to yield healthcare tailored to an individual and removes the limitations of the "one-size-fits-all" therapy approach. This provides the opportunity to translate therapies from bench to clinic, to diagnose and predict disease, and to improve patient-tailored treatments based on the unique signatures of a patient's disease and further to identify novel treatment schedules. Nowadays, tiny noncoding RNAs, called microRNAs, have captured the spotlight in molecular biology with highlights like their involvement in DNA translational control, their impression on mRNA and protein expression levels, and their ability to reprogram molecular signaling pathways in cancer. Realizing their pivotal roles in drug resistance, they emerged as diagnostic targets orchestrating drug response in individualized therapy examples. It is not premature to think that researchers could have the US Food and Drug Administration (FDA)-approved kit-based assays for miRNA analysis in the near future. We think that miRNAs are ready for prime time. © Springer Science+Business Media New York 2014.Article Citation - Scopus: 1Determining the C-Terminal Amino Acid of a Peptide From Ms/Ms Data(Proteomass Scientific Society, 2013) Allmer, JensProteomics is currently chiefly based on mass spectrometry (MS) which is the tool of choice to investigate proteins. Two computational approaches to derive the tandem mass spectrum precursor’s sequence are widely employed. Database search essentially retrieves the sequence by matching the spectrum to all entries in a database whereas de novo sequencing does not depend on a sequence database. Both approaches benefit from knowledge about the enzyme used to generate the peptides. Most algorithms default to trypsin for its abundant usage. Trypsin cuts after arginine and lysine and thus the c-terminal amino acid is not known precisely and usually either of the two. Furthermore, 90% of protein terminal peptides may not end with either arginine or lysine and may thus contain any of the other amino acids. Here an algorithm is presented which predicts the c-terminal amino acid to be arginine, lysine or any other. Here an algorithm, named RKDecider, to sort the c-terminal amino acid into one of three groups (arginine, lysine, and other) is presented. Although around 90% accuracy was achieved during data mining spectra for rules that determine the c-terminal amino acid, the implementation’s (RKDecider) accuracy is a little less and achieves about 80%. This is due to the fact that the decision trees were implemented as a rulebased system for speed considerations. The implementation is freely available at: http://bioinformatics.iyte.edu.tr/RKDecider.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%.