Project

EMT; regulation of gene expression; modeling; miRNA

Lead
Das Zustandekommen der deutlichen phänotypischen Auswirkungen der miRNAs aufgrund ziemlich kleiner Veränderungen auf der mRNA Ebene gehört zu den offen Fragen in diesem Gebiet.
Lay summary
Um dieses Paradox zu erklären, sind bereits verschiedene Hypothesen vorgeschlagen worden. Eine davon lautet, dass microRNAs nur das "Rauschen" der Genexpression reduzieren, indem sie zusammen mit den Transkriptionsfaktoren spezielle "Netzwerkmotive" bilden. In solchen Motiven ist ein Transkriptionsfaktor (TF) involviert, welcher die Expression der microRNA und eines mRNA Zieles aktiviert, wobei die microRNA den Abbau des mRNA - Zieles veranlasst. Dieses Netzwerkmotiv wird als "incoherent feed-forward loop" (IFFL, inkohärente Feed-Forward-Schleife) bezeichnet, weil der TF und die microRNA genau umgekehrte Auswirkungen auf ihr gemeinsames Ziel haben. Eine Reihe von IFFLS konnten bereits entdeckt werden, vor allem mit Hilfe von computergestützten Auswertungen.   In den vergangenen Jahren haben wir Methoden zur Identifizierung von microRNA - Zielen entwickelt und um deren Wirkung abzuschätzen. Für dieses Projekt wollen wir ein Modell entwickeln, welches uns erlaubt, die gemeinsamen Auswirkungen von TFs und microRNAs auf Zielgene abzuschätzen. Dies würde eine umfassende Katalogisierung von Promotoren von microRNAs, die Vorhersage von Bindungsstellen von TFs an diese Promotoren, eine Verbesserung der Vorhersage von microRNA – Zielen, und die Zuordnung von mRNA Expressionsveränderungen zu den Bindungsstellen für TF – Regulatoren in Promotoren und für microRNAs in den Transkripten beinhalten. Wir würden diese Methode gerne auf ein Modell zur Metastasierung von Krebs anwenden, indem wir einen engmaschigen Zeitverlauf der microRNA und mRNAExpression während der Epithelial-zu-Mesenchymal-Transition (EMT) analysieren.

Direct link to Lay Summary

Last update: 03.05.2013

Number	Title	Start	Funding scheme

microRNAs emerged in the past decade as important regulators of geneexpression, essential for many processes including embryonicdevelopment, cell differentiation, metabolism and immunity. Theyappear able to drive, on their own, the induction of pluripotency insomatic cells. Although initially thought to induce translationinhibition without affecting mRNA levels, it has become clear miRNAsdo induce mRNA degradation, with some delay relative to translationalinhibition. Surprisingly, transfection or induction of miRNAexpression appear to have small effects on the targetmRNAs. Therefore, one of the open questions in the field is how thedramatic phenotypic effects of miRNAs are brought about by relativelysmall changes in target mRNA levels. Various hypotheses have beenproposed to explain this paradox, one being that miRNAs reduce thevariance rather than the average levels of their targets, specificallythrough regulatory network motifs that they form with transcriptionfactors. Such motifs involve a transcription factor (TF) thatactivates the expression of a miRNA and of an mRNA target, the miRNApost-transcriptionally down-regulating the same target. Thisfeed-forward loop (FFL) is called 'incoherent' (iFFL) because the TFand miRNA have opposite effects on the common target, the ultimateoutcome being a reduction in the fluctuations that the common targetexperiences. Especially through computational analysis, a number ofexamples of iFFLs have been discovered.In the past years we developed methods for identification of miRNAtargets and for estimating their effects. Specifically, we obtainedbinding sites of the Argonaute protein through crosslinking andimmunoprecipitation and used them to infer a biophysical model ofmiRNA-target interaction. We showed that this model enables us toaccurately identify targets of individual miRNAs, including those thatdo not perfectly match the 5' end of the miRNA (also callednon-canonical). We further collaborated with the group of Erik vanNimwegen on modeling mRNA levels in terms of transcriptionalregulation by transcription factors and post-transcriptionalregulation by miRNAs. The model that we developed is able to identifykey miRNA and TF regulators in individual cell types (embryonic stemcells) or processes (epithelial-to-mesenchymal transition, EMT). Wetherefore would like to develop this model further in the coming yearsby1. Comprehensively identifying promoters of miRNA genes and predictingthe associated transcription regulatory elements.2. Extending the set of curated transcription factor regulatory motifsbased on recently released chromatin immunoprecipitation data.3. Analyzing a fine-grained time course of miRNA and mRNA expressionduring EMT (that will be obtained in our lab) in the context of acomputational model, to identify and characterize the dynamics ofnetworks involving miRNAs and transcription factors that operateduring EMT.4. Extending the model to the prediction of key splicing regulatoryfactors, some of which are known to be targeted by miRNAs and/or playimportant roles in EMT.I believe that this project will result in new predictive tools thatcould be generally used to identify key regulators of gene expressionin specific cell types and at the transition between celltypes. Furthermore, by applying our computational tools to EMT we willon the one hand have the possibility to validate the model against thelarge body of available data and on the other hand to contribute newinsights to this process of high medical relevance.