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5. Stand der Wissenschaft und Technik, bisherige eigene Arbeiten, Patentlage,
Wirtschaftliche Bedeutung
Signalling networks and regulatory RNAs
Important features of many regulatory networks are feedback loops, where a downstream member of a
cascade affects an upstream member. This is also known for pathways which depend on RNA:RNA
interactions. Interestingly, many of these RNA-dependent feedback loops target directly or involve
transcription factors, providing a link to the project part on transcriptional networks.
Examples for feedback loops involving regulatory RNAs, such as microRNAs (miRNAs) exist in
bacteria, plants and various animal systems, including humans. For instance, in C. elegans a doublenegative feedback loop of two miRNAs and their transcription factor targets controls the bistable cell
fate decision between the two C. elegans taste receptor neurons "ASE left" (ASEL) and "ASE right"
(ASER) [1] (Figure 1). The stability and irreversibility of the terminal differentiated state is ensured by
the interactions of two miRNAs and their transcription factor targets in a double-negative feedback
loop.
Figure 1: Double-negative feedback loop controlling a neuronal cell fate decision - summary of
the ASE bistable system. The miRNAs lsy-6 and mir-273 together with their targets/regulators die-1
and cog-1 control cell fate decision (from [1]).
In both animals and plants, AGO (a protein required for miRNA maturation) binds to small RNA
molecules in RNAi Silencing Complexes (RISCs), resulting in degradation or translational inhibition
of the complementary mRNA, or in transcriptional silencing via chromatin changes. In a plant
example, it was shown that targeting of AGO1 by miR168 is needed for proper plant development [2],
illustrating the importance of feedback control by this miRNA as the concentrations of both RNAs are
directly determined by each other. Transgenic plants expressing a mutant AGO1 mRNA with
decreased complementarity to miR168 overaccumulate AGO1 mRNA and exhibit developmental
defects. Developmental defects induced by a miR168-resistant AGO1 mRNA can be rescued by a
compensatory miRNA that is complementary to the mutant AGO1 mRNA, proving the regulatory
relationship between miR168 and its target and opening the way for engineering artificial miRNAs in
plants. Another member of the AGO gene family, ZWILLE (ZLL) gene is required for establishing the
primary shoot meristem.
Another regulatory feedback loop exists in case of the R2R3 MYB domain TF MYB33 and it’s
cognate regulatory RNA miRNA159. Expression of the MYB33 TF is stimulated by the
phytohormone gibberelline. This factor is crucial for proper activation of organogenesis-specific genes
which mediate the formation of flowers and inflorescences. At the same time this factor induces
expression of miRNA159 (Achard et al., 2004; Millar and Gubler, 2005), a direct target of which is
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the MYB33 TF mRNA (Fig. 2). As a result, a precise timing of expression of this TF is achieved
which is relevant for proper organogenesis.
Figure 2: Regulatory feedback loop consisting of a
regulatory RNA (miRNA159) and the TF MYB33 in
higher plants, controlling hormone-induced flower
formation and organogenesis of anthers (Achard et
al., 2004; Millar and Gubler, 2005).
Several of the Freiburg groups have accumulated
extensive expertise in the field of computational RNA analysis and experimental analysis of RNAfeedback loops (Hess, Reski, Backofen, Timmer). Furthermore, studying regulatory RNAs at the
qualitative level has become a major research activity of several more groups in Freiburg (Palme,
Laux, Driever, Rennenberg, Baumeister). Collectively, quantitative aspects of the involved RNA:RNA
interactions are increasingly being recognized as truly important in these feedback loops.
Results obtained in the Laux group have shown that ZLL is part of RISC as is AGO1. The data
indicate that the different developmental functions of ZLL and AGO1 are due to a combination of
gene dosage and protein sequence, at the quantitative level modulated by the activity of miRNAs.
Based on these results it was concluded that (1) quantitative level of RISC function affect qualitative
changes of the "post-transcriptome" and thus eventually the developmental state of a cell, and (2) that
intracellular localization and short RNA selection of RISC affects which target mRNA is selected.
In the group of Wolfgang Hess a quantitative RNA:RNA interaction was discovered: In the
cyanobacterium Synechocystis, the antisense RNA IsrR controls the gene expression of isiA, coding
for the chlorophyll-binding protein IsiA. The coupled degradation of IsrR/isiA mRNA constitutes a
perfectly timed reversible switch to respond to environmental changes in iron concentrations, redox
conditions and possibly other stresses [3] (Figure 3).
IsrR RNA
395
571
TF: Active Fur
isiA
1
1026
0h
-200
6h
12 h 17 h 24 h 40 h 48 h
isiAB
isiA target
IsrRNA
isiA mRNA
Targeted degradation
by RNase III
Iron depletion
Time series
IsrR
asRNA
170 nt
Figure 3. The coupled
degradation
of
IsrR
antisense RNA/isiA-mRNA
duplexes appears as a
paradigm for RNA-based
reversible
switches
to
respond
to
complex
environmental
and
developmental clues (for
details see [3]). The
amount of IsrR determines
the extent of targeted
degradation.
It was the experimental perturbation of IsrR RNA levels, which had tremendous physiological
consequences on the photosynthetic machinery in this organism rather then the knock-out of this
RNA. Up to a critical concentration IsrR blocks the expression of isiA virtually 100%. Because both
RNas are degraded, however, once the isiA mRNA reaches a critical threshold, it is IsrR which falls
prey to the degradation machinery and isiA may even become the single most highly expressed
protein-coding gene in this organism [3]. However, an imminent question here is what the RNA:RNA
regulation contributes in quantitative terms versus the regulation mediated by the TF FUR which is
fundamentally controlling the expression of this gene (Fig. 3).
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The Hess group has expertise in the computational and experimental identification of various types of
regulatory RNA and in their systematic analysis based on total genome sequences [4-7].
Functional analyses of RNAi pathways in the model plant Physcomitrella patens are the focus of the
Reski group. The targeted knockout of a Dicer-like gene led to a developmental arrest at the
protonema stage, caused by a block in the miRNA-mediated post-transcriptional cleavage of TF
mRNAs. The data suggest a feedback control at the transcriptional level involving miRNA genes as
well as this set of genes targeted at the post-transcriptional level. The Reski group has developed a
microarray harbouring all Physcomitrella TF genes. This array will be used to perform an expression
profiling of these genes in the Dicer moss knockout lines. By this, TF networks which are controlled
by miRNAs can be identified. The group has also cloned small RNAs from Physcomitrella. Hitherto,
154 new small RNAs were identified by RNomics and 20 conserved miRNA families were predicted
computationally. For this set 31 hairpin-like precursors were identified from EST as well as genomic
sequences and the expression of fourteen miRNAs was verified experimentally. Furthermore,
computational analysis of ESTs led to the prediction of 21 putative miRNA target genes providing a
solid data basis for identifying additional control loops which are regulated by small RNAs in
Physcomitrella. Within the intended cooperations, the group will benefit from the expertise of the
groups of Hess, Backofen and Timmer including RNA motif and structure analysis and model testing.
The group of Rolf Backofen has a focus on the development and application of methods to analyse
RNA motifs [6-10]. Different pairwise and multiple sequence structure alignment algorithms have
been developed for the detection of functional RNA motifs. Sequence-structure alignment extends the
problem of finding a minimal energy structures for a given RNA to the problem of finding common
folds among several RNAs. The Backofen group developed the first local sequence/structure
alignment method [6] and applied it to the problem of clustering putative non-coding RNAs in Ciona
intestinalis. In this group, the multiple sequence/structure alignment tool MARNA [7] was developed,
which is currently one of the best available systems. Methods for analysing RNA are usually very time
consuming, and the group has experience to apply different techniques to improve the efficiency of the
methods [8-10], Without these improvements, the application of the methods to real data is often
unfeasible.
The group of Jens Timmer has extensive experience with the data-based derivation of models for the
dynamics of cellular processes. This includes methods of experimental design [11], estimation of
parameters [12], model testing [13], sensitivity analysis [14], and inference of system properties [15].
Several of the groups mentioned above already collaborate with each other on some of these aspects.
Backofen-Hess collaborate in the frame of the DFG-funded SPP “Sensory and small RNAs in
Prokaryotes”. Hess-Reski-Timmer in a project funded by the Landesstiftung Baden-Wuerttemberg on
RNAi and the identification of additional new miRNAs and antisense RNAs. Now the time is ripe to
fully model the quantitative principles underlying these RNA-dependent feedback loops.
In order to setup mathematical models of such networks, qualitative effects have to be supplemented
with kinetic parameters for the individual interactions.
RNA:RNA interactions can be seen as two-component reactions and a means of modelling such
interactions has been presented in [16]. This method assumes a two-step reaction requiring three
reaction constants (association, dissociation and hybridisation) in order to model it. The constant for
hybridisation can be derived via a function of the free energy of the duplex, i.e. the lower the free
energy of the duplex the better the hybridisation. The free energy of an RNA duplex can be computed
using experimentally derived thermodynamic parameters and this is a common task in RNA
bioinformatics,
To derive models based on in vivo experimental data, time resolved data of the concentrations of the
involved RNA molecules and their targets will be used to estimate parameters in different equations
describing the dynamics. In an iterative cycle between modeling and experiment, these models will be
used to suggest new experiments, and the results of these experiments will be applied to refine and
validate the models. Based on validated models the systems property of robustness and bistability will
be investigated.
Another approach was applied in [17], where a co-folding algorithm was used. Co-folding is an
extension of secondary structure prediction methods to the problem of finding a fold for an RNA
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duplex. Under certain conditions on the possible structure for the interaction (namely that the common
structure is nested when drawing the two RNAs linearly, which is a usual condition applied by most of
the co-fold algorithms), [17] has shown that one can determine ensemble probabilities using similar
techniques as for RNA secondary structure prediction. In addition, they have shown that one can
determine kinetic parameters out of the ensemble probabilities. As shown in [17,18], one can directly
use the partition functions calculated by the co-folding algorithm to build a quantitative model of
RNA-RNA interactions. The major remaining problem to reduce the restrictions imposed on the
common structure, since many of the known RNA-RNA-interactions do not follow these restrictions.
This is currently more a computational than a conceptional problem, since the co-fold algorithm could
be extend to incorporate more possibilities for the struture, but under the cost of drastically enlarged
time complexity, which would make the method infeasible for most RNAs.
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Johnston, R.J., et al. (2005) MicroRNAs acting in a double-negative feedback loop to control a neuronal
cell fate decision. Proc Natl Acad Sci USA 102, 12449-54.
Vaucheret, H., et al. (2004) The action of ARGONAUTE1 in the miRNA pathway and its regulation by the
miRNA pathway are crucial for plant development. Genes Dev 18, 1187-97.
Dühring, U., Axmann, I. M., Hess, W. R.,Wilde, A. (2006) An internal antisense RNA regulates
expression of the photosynthesis gene isiA. Proc Natl Acad Sci USA 103, 7054–7058.
Axmann I.M., Kensche P., Kohl S., Vogel J., Herzel H., Hess W.R. (2005) Identification of
cyanobacterial non-coding RNAs by comparative genome analysis. Genome Biol. 6, R73: 1-16.
Hess W.R. (2004) Genome analysis of marine photosynthetic microbes and their global role. Curr. Opin.
Biotechnol. 15, 191-198.
Rocap et al. (2003) Genome divergence in two Prochlorococcus ecotypes reflects Oceanic niche
differentiation. Nature 424, 1042-1047.
Dufresne et al. (2003) Genome sequence of the cyanobacterium Prochlorococcus marinus SS120, a
nearly minimal oxyphototrophic genome. Proc. Natl. Acad. Sci. USA 100, 10020-10025.
Busch A., Will S., Backofen R. (2005) SECISDesign: a server to design SECIS-elements within the
coding sequence. Bioinformatics 21, 3312-3.
Siebert S., Backofen R. (2005). MARNA: multiple alignment and consensus structure prediction of RNAs
based on sequence structure comparisons. Bioinformatics 21, 3352-9.
Backofen R., Will S. (2004) Local sequence-structure motifs in RNA. J. Bioinf. Comp. Biol. 2, 681-698.
Backofen R., Siebert S. (2006) Fast detection of common sequence structure patterns in RNAs. J. Discrete
Algorithms, in print.
Backofen R., Hermelin D., Landau G.M., Weimann O. (2005) Normalized similarity of RNA sequences.
In Proc. 12th Symposium on String Processing and Information Retrieval (SPIRE 2005), vol 3772, 360369. Springer-Verlag.
Faller D., Klingmüller U., Timmer J. (2003) Simulation methods for optimal experimental design in
systems biology. Simulation: Trans. Soc. Modeling Computer Simulation 79, 2717-725.
Timmer J., Müller T.G., Swameye I., Sandra O., Klingmüller U. (20049 Modelling the nonlinear
dynamics of cellular signal transduction. Int. J. Bif. Chaos 14, 2069-2079:
Müller T.G., Faller D., Timmer J., Swameye I., Sandra O., Klingmüller U. (2004) Tests for cycling in a
signalling pathway. J. Roy. Stat. Soc. C: Applied Stat. 53, 557 – 568.
Swameye I., Müller T.G., Timmer J., Sandra O., Klingmüller U. (2003) Identification of
nucleocytoplasmic cycling as a remote sensor in cellular signaling by data-based modeling. Proc. Natl.
Acad. Sci. 100, 1028-1033.
Kollmann M., Bartholome K., Lovdok L., Timmer J., Sourjik V. (2005) Design principles of a bacterial
signalling network. Nature 438, 504-507.
Persson C., Wagner E.G., Nordstrom K. (1990) Control of replication of plasmid R1: formation of an
initial transient complex is rate-limiting for antisense RNA--target RNA pairing. EMBO J. 9, 3777-85.
Stephan H, Bernhart, S. H. et al. (2006) Partition function and base pairing probabilities of RNA
heterodimers. Algorith. Mol. Biol. 1, in press.
Hackermüller, J., et al. (2005) The Effect of RNA Secondary Structures on RNA-Ligand Binding and the
Modifier RNA Mechanism: A Quantitative Model. Gene 345, 3-12.
6. Struktur der Forschungseinheit, Projektmanagement
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7. Beteiligte Partner aus Wissenschaft und Industrie und deren Kompetenzen
Combimatrix Europe GmbH, 79108-Freiburg, Engesser Str. 4a (letter of intent 25. April 2006
to w. R. Hess); Expertise: generation of scalable microarrays for dedicated systems biology
applications; Collaboration: Experimental Bioinformatics, Prof. Hess; Plant Biotechnology,
Prof. Reski.
8. Beschreibung des wissenschaftlichen Konzeptes der geplanten
Forschungseinheit; Personalplanung, Infrastruktur etc.