SynthStripe · Synthetic gene regulatory networks for single-stripe gene expression

Synthetic gene regulatory networks for single-stripe gene expression

Pattern formation is essential in the development of higher eukaryotes. The central problem of pattern formation is how genetic information can be translated in a reliable manner to give specific spatial patterns of cellular differentiation. The French-flag model of stripe formation is a classic paradigm in developmental biology. Cell differentiation, represented by the different colours of the French flag, is caused by a gradient of a signalling molecule (morphogen); i.e. at high, middle or low concentrations of the morphogen a “blue”, “white” or “red” gene stripe is activated, respectively. How cellular gene regulatory networks (GRNs) respond to the morphogen, in a concentration-dependent manner, is a pivotal question in developmental biology. Synthetic biology is a promising tool to study the function and properties of gene regulatory networks (GRNs). The de novo construction and study of such synthetic networks can improve our quantitative understanding of naturally-occurring information processing modules such as GRNs controlling developmental processes. Building a network with non-native components performing a function of interest is a therefore a strong evidence for the particular design rule. Gene circuits with predefined behaviors have been successfully built and modeled, but largely on a case-by-case basis. In this project we went beyond individual networks and explored both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. For this purpose we used a 3-step approach for the successful creation of synthetic circuits: first, we performed a theoretical screen for finding all design classes that produce the desired behavior (stripe formation in a morphogen gradient). During this step we discovered four fundamentally-different mechanisms for forming a stripe. We identified the minimal network for each mechanism and found that they correspond to the four known types of incoherent feed-forward loops (I1-I4 FFL). Next, we successfully built the four I-FFL networks synthetically. To this end we developed a flexible network scaffold where the same components could be consistently used to build the different network topologies. The final step was to verify the distinct mechanisms by fitting all the experimental data to a mathematical model. To achieve this goal we characterized the synthetic networks in unprecedented detail – by measuring the profiles of mRNA concentration for each gene and engineered derived variants for each design class and fitted all these data simultaneously to the mathematical model. The modelling confirmed that we have a good match between theory and experiments across an entire design space. The fundamental understanding of the mechanisms of the four stripe-forming design classes (I1-I4), led us to implement the archetype of I-FFLs stripe-forming networks that we termed I0. This network is capable of reproducing the fundamental mechanism of stripe formation: staggered activator and repressor functions only allow net output gene expression at intermediate morphogen concentrations, resulting in a stripe. Shifting the dose-response curve of the repressor even allowed us to engineer an anti-stripe from the same minimal network, again demonstrating the close match between theory and experiments. We demonstrated a framework for exploring and engineering within a unified network design space and showed that this can be more powerful than building networks one-by-one. An exhaustive analysis of the multiple ways of achieving the same phenotype allows more flexibility in a given synthetic biology project. Furthermore, understanding the mechanisms of the distinct design classes facilitates identifying the fundamental principles of a regulatory task. We have focused on stripe formation from reading positional information in a morphogen concentration gradient. In the future, comprehensive computational and experimental exploration of genotype-phenotype maps has the potential to address other spatial and temporal patterns (e.g. oscillations), as well as network properties. Our approach thus provides a new and efficient recipe for synthetic biology. Press releases: http://www.crg.eu/en/news/new-study-how-engineer-synthetic-gene-networks-recreates-stripe-patterns -found-animals-using-bacteria http://youtu.be/f605IvVLJ6A http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/newssummary/news_23-9-2014-9-56-51 Publications Schaerli, Y., Isalan, M.; Building synthetic gene circuits from combinatorial libraries: screening and selection strategies, Mol. BioSyst. 2013, 9, 1559-67 Schaerli, Y., Munteanu, A., Gili M., Cotterell, J., Sharpe, J., Isalan, M.; A unified design space of synthetic stripe-forming networks, Nat. Commun., 2014, 5:4905 doi: 10.1038/ncomms5905 Schaerli, Y., Gili M., Isalan, M.; A split intein T7 RNA polymerase for transcriptional AND-logic, Nucleic Acids Res., 2014, doi: 10.1093/nar/gku884

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