Oscillatory expression of Hes family transcription factors: insights from mathematical modelling

Insm1a-mediated gene repression is essential for the formation and differentiation of Müller glia-derived progenitors in the injured retina

In zebrafish, retinal injury stimulates Müller glia (MG) reprograming; allowing them to generate multipotent progenitors that regenerate damaged cells and restore vision. Recent studies suggest transcriptional repression may underlie these events. To identify these repressors, we compared the transcriptomes of MG and MG-derived progenitors and identified insm1a, a transcriptional repressor exhibiting a biphasic pattern of expression that is essential for retina regeneration. Insm1a was found to suppress ascl1a and its own expression and link injury-dependent ascl1a induction with dickkopf (dkk) suppression, which is necessary for MG dedifferentiation. We also found that Insm1a was responsible for sculpting the zone of injury-responsive MG by suppressing hb-egf_a expression. Finally, we provide evidence that Insm1a stimulates progenitor cell cycle exit by suppressing a genetic program driving progenitor proliferation. Our studies identify Insm1a as a key regulator of retina regeneration and provide a mechanistic understanding of how it contributes to multiple phases of this process.

Automatic reconstruction of the mouse segmentation network from an experimental evidence database

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.

Dissecting the dynamics of the Hes1 genetic oscillator

Serum stimulation of a number of different mouse cell lines results in sustained oscillations of Hes1, a member of this Hes/Her family of transcription factors. Quantitative time-course expression data obtained in this system provide an excellent opportunity to explore transcriptional oscillations in a relatively simple setting. Simple models of the Hes1 regulatory circuit are capable of generating oscillations that share many features with those observed in mouse fibroblasts, and highlight the central role played by delayed negative feedback. However, taking into account constraints on model parameters imposed by experimental data, these models can only generate oscillations with quite low peak-to-trough expression ratios. To explore the origin of this limitation, we develop a more detailed model of the Hes1 circuit, incorporating nucleo-cytoplasmic transport, Hes1 dimerisation, and differential stability of Hes1 monomers and dimers. We show that differential protein stability can increase the amplitude of Hes1 oscillations, but that the resulting expression profiles do not fully match experimental data. We extend the model by incorporating periodic forcing of the Hes1 circuit by cyclic phosphorylation of the protein Stat3. We show that time delays and differential stability act synergistically in this extended model to generate large amplitude oscillatory solutions that match the experimental data well.