1.1. MicroRNA-target interaction network analysis<\/strong><\/p>\n Project leader: Bettina Benczik, MSc<\/u><\/p>\n We aim to develop software based on network theoretic approaches that are capable of identifying mediators and pathways involved in the pathomechanism of various cardiovascular diseases by the analysis of datasets assessed with high throughput molecular biological techniques (e.g. microarray, RNA-seq) of samples gathered from small and large animal models of the studied cardiovascular disorders. Our main focus is the analysis of the transcriptomics datasets, including microRNA fingerprints.<\/p>\n MicroRNAs (miRNAs) are short (18-24 nt) RNA sequences that could decrease the expression of various target genes both on the messenger RNA (mRNA) and the protein level. As miRNAs could have multiple hundreds of targets, and also one mRNA could be regulated by dozens of miRNAs the post-transcriptional regulatory network of miRNAs are extremely complex (Figure 1). Therefore, to understand and predict the effect of changes in the miRNA expression profiles measured by the so called omics (global, high throughput) methodologies, sophisticated bioinformatics and network theoretic approaches are necessary.<\/p>\n In this project we aim to develop, implement and utilize such network theoretic models to efficiently search for drug target candidates in various cardiovascular diseases.<\/p>\n We also aim to make it possible for researchers without previous bioinformatics experiences to analyze miRNA expression datasets with user friendly tools. For this purpose our team in collaboration with the Pharmahungary Group<\/a> has developed the miRNAtarget.com<\/a> web application, which could be used to predict common targets of differentially expressed miRNAs.<\/p>\n Figure 1. An example of miRNA-target interaction network. Downregulated, upregulated miRNAs and mRNAs are indicated in green, red and blue, respectively. Dark blue represents mRNAs with at least 4 target interactions. (\u00c1gg et al, Sci Rep 2018)<\/p>\n 1.2. Bioinformatics analysis of RNA sequencing datasets<\/strong><\/p>\n Project leader: Dr. Barnab\u00e1s V\u00e1radi, MD<\/u><\/p>\n The starting point for an unbiased, hypothesis-free analysis of various diseases is the assessment of global transcriptomics profiles. Nowadays the most well-established and most cost-effective approach for transcriptomics measurements is RNA sequencing. RNA sequencing produces huge raw datasets, which need to be analyzed by various bioinformatics methods.<\/p>\n Our group is continuously developing, adapting and utilizing bioinformatics and statistical methodologies to be able to better understand RNA sequencing results.<\/p>\n Our aims are to develop tools to be able:<\/p>\n Figure 2. Radar plot with the dissimilarity of the Gene Ontology term-based functional expression vectors of cardiac cell lines and primary cultures compared to the corresponding reference tissue in human, rat and mouse species (On\u00f3di et al, J Mol Cell Cardiol 2022)<\/p>\n Publications in the field:<\/p>\n To be able to discover novel drug targets we aim to develop software based on network theoretic approaches that are capable to identify mediators and pathways involved in the pathomechanism of various cardiovascular diseases by the analysis of datasets assessed with high throughput molecular biological techniques (e.g. microarray, RNA-seq) of samples gathered from small and …<\/p>\n","protected":false},"author":101485,"featured_media":0,"parent":8300,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-11591","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/pages\/11591","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/users\/101485"}],"replies":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/comments?post=11591"}],"version-history":[{"count":9,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/pages\/11591\/revisions"}],"predecessor-version":[{"id":12425,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/pages\/11591\/revisions\/12425"}],"up":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/pages\/8300"}],"wp:attachment":[{"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/media?parent=11591"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/categories?post=11591"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/semmelweis.hu\/pharmacology\/wp-json\/wp\/v2\/tags?post=11591"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/semmelweis.hu\/pharmacology\/files\/2022\/09\/41598_2018_27740_Fig2_HTML.jpg\")
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