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Analyses and modelling of regulatory processes on the molecular level
Large scale transcriptome studies in higher organisms reveal that mammalian transcriptomes are of an unforeseen complexity. Moreover, the transcriptome structure is highly dynamic, which is reflected by largely incongruent transcriptome structures of different cell states or types. Understanding a particular cellular process at the transcriptome and interactome level is, therefore, not possible when looking at single interactions and/or transcripts. Rather, the dynamics of the transcriptome and interactome has to be studied at the systems level.
DNA-protein interactions and epigenetics: The study of DNA-protein interactions allows a more detailed understanding of the molecular regulatory interactions involved in gene transcription as well as in chromatin structure. For the detection of interactions in vivo, a technique termed chromatin immunoprecipitation (ChIP) was developed. Usually, the co-immunoprecipitated DNA is analysed by PCR; however, by the use of arrays, a rapid and precise mapping of the binding sites of specific DNA-binding proteins is possible. Using the ChIP-on-chip technique, it is now possible to study epigenetic phenomena, such as differences in DNA methylation, as well as differences in chromatin structure, which are otherwise not amenable to high-throughput measurements. In parallel, potential protein binding sites on genomic DNA can be identified using comparative genomic methods. The group of P. Stadler has demonstrated experience developing and applying such methods, in particular phylogenetic footprinting.
RNA-protein interactions: RNA-protein interactions have been studied intensively at the single interaction level. We have contributed on the interaction thermodynamics of mRNAs with the stability regulator HuR using computational and single-molecule spectroscopic methods. Moreover, based on NMR and X-ray crystallography, members of the RNomics group were able to model the dependence of RNA-protein interaction on structural constraints in the RNA, the affinity of analogous interactions with high precision, and develop small RNAs which manipulate the interactions by remodelling the RNA secondary structure.
RNA-RNA interactions (Mörl, Stadler): Recently, a universe of small non-coding (nc)RNAs was discovered that regulates stability and translatability of many mRNA transcripts in the mammalian cell. While for some of theses small RNAs, individual target mRNAs are identified, it is not yet known whether these are the only target molecules or whether individual micro(mi)RNAs regulate a whole series of different transcripts. Moreover, for many miRNAs, the mRNA targets are not identified at all. Computational methods for the analysis of RNA-RNA interactions are developed in the group of P. Stadler in collaboration with the Theoretical Biochemistry group at the University Vienna. A method recently published by the two groups together with members of the RNomics group focuses on the prediction of interactions between a short ncRNA and a longer (m)RNA (Mückstein et al., 2006).
Binder, H., Preibisch, St. GeneChip microarrays-signal intensities, RNA concentrations and probe sequences. J. Phys.: Condens. Matter 2006. 18: 537-566.
Hackermuller J, Meisner NC, Auer M, Jaritz M, Stadler PF. The effect of RNA secondary structures on RNA-ligand binding and the modifier RNA mechanism: a quantitative model. Gene. 2005; 345(1):3-12.
Meisner NC, Hackermuller J, Uhl V, Aszodi A, Jaritz M, Auer M. mRNA openers and closers: modulating AU-rich element-controlled mRNA stability by a molecular switch in mRNA secondary structure. Chembiochem. 2004; 5(10):1432-47.
Missal K, Cross MA, Drasdo D. Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment. Bioinformatics. 2006; 22(6):731-8.
Muckstein U, Tafer H, Hackermuller J, Bernhart SH, Stadler PF, Hofacker IL. Thermodynamics of RNA-RNA binding. Bioinformatics. 2006; 22(10):1177-82.
Prohaska SJ, Fried C, Flamm C, Wagner GP, Stadler PF. Surveying phylogenetic footprints in large gene clusters: applications to Hox cluster duplications. Mol Phylogenet Evol. 2004;31(2):581-604.