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Automatic reconstruction of the mouse segmentation network from an experimental evidence database
Aitor González1, Ryoichiro Kageyama
1Institute for Virus Research, Kyoto University, Shogoin-Kawahara, Sakyo-ku, Kyoto 606-8507, Japan. agonzale@virus.kyoto-u.ac.jp
Abstract:
Mammalian vertebrae, ribs, body wall musculature and back skin develop from repetitive embryonic tissues called somites. The development of somites depends on the molecular oscillations of the products of so-called cyclic genes. The underlying network involves the Wnt, Fgf/Mapk, Notch signaling pathways and the T-box genes. The discovery of this network is based on genetic interactions. Because of regulatory feedbacks and cross-regulation between pathways, it is often difficult to intuitively identify direct molecular interactions underlying genetic interactions. To address this problem, we developed a method based on a database and graph theory algorithms. We first encoded genetic and non-genetic experiments in a relational database. Next, we built a reference network with the data from non-genetic experiments and the KEGG pathway database. Then, we computed the shortest path between the nodes for each genetic interaction in the reference network to propose direct molecular interactions. The resulting network is the largest computational representation of the mammalian segmentation network to date with 36 nodes and 57 interactions. In some instances, a number of genetic interactions could be explained by adding a single link to the reference network, which leads to experimentally testable hypotheses. Two examples of such predictions are the direct transcriptional regulation of Dll3 and Fgf8 genes by the Rbpj and Ctnnb1 products, respectively. Furthermore, the computed shortest paths suggest that cross-talks from the Wnt to the Fgf/Mapk and Notch pathways might be mediated by the Dvl genes. This method can be applied in any system where gene expression changes are observed as a response to some gene perturbation, for instance in cancer cells.
Insights
This study introduces a novel computational method using graph theory to map the complex genetic interactions governing mammalian embryonic development. The approach reveals direct molecular interactions and testable hypotheses for segmentation gene networks.
Area of Science:
- Developmental Biology
- Systems Biology
- Computational Biology
Background:
- Mammalian embryonic development, including vertebrae and ribs, relies on somite formation driven by cyclic gene oscillations.
- Key molecular pathways like Wnt, Fgf/Mapk, and Notch signaling, along with T-box genes, orchestrate this process.
- Identifying direct molecular interactions within these complex, cross-regulated pathways is challenging due to feedback loops.
Purpose of the Study:
- To develop a computational method for elucidating direct molecular interactions within the mammalian segmentation gene network.
- To create the most extensive computational representation of this network to date.
- To generate experimentally testable hypotheses regarding gene regulation and pathway cross-talk.
Main Methods:
- A relational database was used to encode genetic and non-genetic experimental data.
- A reference network was constructed using experimental data and the KEGG pathway database.
- Graph theory algorithms, specifically shortest path computation, were applied to predict direct molecular interactions from genetic interactions.
Main Results:
- The study generated the largest computational model of the mammalian segmentation network, comprising 36 nodes and 57 interactions.
- The method successfully proposed direct molecular interactions, explaining numerous genetic interactions with single-link additions.
- Specific predictions include the direct regulation of Dll3 by Rbpj and Fgf8 by Ctnnb1, and potential Wnt pathway cross-talk mediation by Dvl genes.
Conclusions:
- The developed database and graph theory approach effectively identifies direct molecular interactions in complex gene regulatory networks.
- This method provides a powerful tool for generating testable hypotheses in developmental biology and other fields.
- The findings offer new insights into the intricate cross-talk between major signaling pathways regulating embryonic segmentation.
