Section: New Results

Inference of bacterial regulatory networks from reporter gene data

The use of fluorescent and luminescent reporter genes allows real-time monitoring of gene expression, both at the level of individual cells and cell populations (Section  3.2 ). In order to fully exploit this technology, we need methods to rapidly construct reporter genes, both on plasmids and on the chromosome, mathematical models to infer biologically relevant quantities from the primary data, and computer tools to achieve this in an efficient and user-friendly manner. For instance, in a typical microplate experiment, 96 cultures are followed in parallel, over several hours, resulting in 10,000-100,000 measurements of absorbance and fluorescence and luminescence intensities. Over the past few years, we put into place an experimental platform and data analysis software, notably the WellReader program (Section  5.2 ), to allow biologists to make the most out of the information contained in reporter gene expression data. A web-based version of WellReader is currently in preparation. Valentin Zulkower has analyzed the measurement models underlying WellReader , work was presented at the Journées Ouvertes Biologie, Informatique et Mathématiques (JOBIM'13) [13] and submitted for publication.

The above tools have been used in a series of studies directed at the experimental mapping of gene regulatory networks in E. coli. A first example, which was carried out in the framework of the PhD thesis of former IBIS member Guillaume Baptist, concerns the development of a new screening methodology for identifying all genes that control the expression of a target gene through genetic or metabolic interactions. The screen combines mutant libraries with luciferase reporter constructs. Instead of a static picture of gene expression, this method allows dynamical monitoring in different environmental conditions. Mutants with interesting phenotypes can thus be selected based on multiple criteria, and the expression dynamics of the target gene can be extensively characterized. The method has been applied to the identification of the direct and indirect regulators of the gene acs in Escherichia coli. We confirmed known genetic regulators of the gene and identified new regulatory influences, many of which involve metabolic intermediates or metabolic sensing. An analysis of mutants involved in glycolysis and glucose transport demonstrates that the classical model of catabolite repression in E. coli needs to be amended. A paper describing the above work was published in Nucleic Acids Research this year [3] .

A second example is a study, led by Stéphan Lacour in collaboration with Akira Ishihama and Hiroshi Ogasawara in Japan, on the lifestyle adaptation of E. coli. The study concerns the switch between swimming motility and biofilm formation in response to changes in environmental growth conditions. The stationary phase sigma factor RpoS is an important regulator of this switch since it stimulates adhesion and represses flagellar biosynthesis. By measuring the dynamics of gene expression, we show that RpoS inhibits the transcription of the flagellar sigma factor, FliA, in exponential growth phase. RpoS also partially controls the expression of CsgD and CpxR, two transcription factors important for bacterial adhesion. We have demonstrated that these two regulators repress the transcription of fliA, flgM and tar, and that this regulation is dependent on the growth medium. CsgD binds to the flgM and fliA promoters around their -10 promoter element, strongly suggesting direct repression. The results show that CsgD and CpxR also affect the expression of other known modulators of cell motility. An updated structure of the regulatory network controlling the choice between adhesion and motility was proposed in the paper based on this work, published in the Journal of Bacteriology [7] .

A third study, published in Research in Microbiology [8] , also focuses on the alternative sigma factor RpoS. The small protein Crl increases the interaction between RpoS and RNA polymerase and thereby activates certain RpoS-dependent promoters. However, the growth-phase dependence of the interaction of Crl with different forms of polymerase remains unknown. We have used 41 GFP transcriptional fusions to study the dynamics of gene regulation by RpoS and Crl during growth transition from exponential to stationary phase in Escherichia coli. This has confirmed that RpoS can regulate gene expression in exponential phase, both positively and negatively. Crl slightly stimulates transcription by RpoS in exponential phase and controls a subset of RpoS-dependent genes in stationary phase. Growth temperature strongly affects induction of specific promoters by RpoS, whereas its impact on gene regulation by Crl is much less significant. In addition, we have identified five new genes regulated by Crl (ada, cbpA, glgS, sodC and flgM), and demonstrated that Crl improves promoter binding and opening by RpoS-containing RNA polymerase at the hdeA promoter. The study also shows that Crl is a cognate enhancer of RpoS activity under different growth conditions, since its deletion has no effect on genes transcribed by other sigma factors.

In the framework of the PhD thesis of Diana Stefan, a network inference method developed by Eugenio Cinquemani and colleagues, first published in Bioinformatics in 2010, has been applied to reporter gene data from the network regulating motility of E. coli, described above. The results are currently being prepared for publication.