Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

Permanent URI for this collectionhttps://hdl.handle.net/11147/9

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Subject: search.filters.subject.MicroRNAWoS Q: search.filters.wosQuartile.N/A

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  • Research Project
    Naif CD4+T hücrelerinden Th17 fenotipinde efektör T hücrelerinin oluşturulması ve Th17 farklılaşmasında rol oynayan mikroRNA'ların belirlenmesi
    (TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2014) Nalbant Aldanmaz, Ayten
    Th17 T hücreleri IL-17 sitokini ekspres eden CD4+T hücrelerinin bir alt sınıfıdır. İnsan Th17 farklılaşması için gereken kültür koşulları, regülatör moleküller ve bu hücrelerin sürdürülebilirliği üzerindeki çalışmalar devam etmektedir. T hücre farklılaşmasında sitokinler, transkripsiyon faktörleri dışında mikroRNA gibi düzenleyici moleküllerin de etkili olabileceği önerilmektedir. Bu yüzden de, Th17 farklılaşmasında mikroRNA’ların rollerinin ortaya konması gerekmektedir. mikroRNA’lar (miRNAs) 17-23-nt uzunluğundaki RNA molekülleridir ve protein kodlayan genleri regüle ederler. Bu sebeple projenin amacı, insan periferal naif CD4+T hücrelerinden Th17 fenotipindeki T hücre alt grubunu oluşturmak ve Th17 fenotip farklılaşmasında rol oynayan mikroRNA’ları derin sekanslama metodu ile profillerini çıkarmaktır
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 8
    Distinguishing Between Microrna Targets From Diverse Species Using Sequence Motifs and K-Mers
    (SCITEPRESS, 2017) Yousef, Malik; Khalifa, Waleed; Acar, İlhan Erkin; Allmer, Jens
    A disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies. MicroRNAs provide a complementary sequence to their target messenger RNA (mRNA) as part of a complex molecular machinery. Known miRNAs and targets are listed in miRTarBase for a variety of organisms. The experimental detection of such pairs is convoluted and, therefore, their computational detection is desired which is complicated by missing negative data. For machine learning, many features for parameterization of the miRNA targets are available and k-mers and sequence motifs have previously been used. Unrelated organisms like intracellular pathogens and their hosts may communicate via miRNAs and, therefore, we investigated whether miRNA targets from one species can be differentiated from miRNA targets of another. To achieve this end, we employed target information of one species as positive and the other as negative training and testing data. Models of species with higher evolutionary distance generally achieved better results of up to 97% average accuracy (mouse versus Caenorhabditis elegans) while more closely related species did not lead to successful models (human versus mouse; 60%). In the future, when more targeting data becomes available, models can be established which will be able to more precisely determine miRNA targets in hostpathogen systems using this approach.