Food safety is an important societal challenge in Europe and worldwide. Among the microbial hazards, the presence of Listeria monocytogenes in food is a major concern given the high mortality rate in listeriosis cases, from 15 to 30%. For several years, EFSA has reported an upsurge of listeriosis cases. This phenomenon is worrying, especially since populations at high risk of listeriosis are increasing, particularly the elderly. Indeed, the aging of the European population is leading to an increase of the at-risk populations. Therefore, the objective of this application is to constitute a training network for young researchers. For the past four years, a first H2020 MSCA-ITN-ETN network has allowed us to initiate and consolidate scientific collaborations on the mapping of transcriptional modifications according to the environmental conditions to which bacteria are subjected. The presence of certain compounds in food promotes the expression of virulence factors, which increases the level of virulence of L. monocytogenes after ingestion of the contaminated food. A better risk assessment for the elderly population requires first to focus the research work on the composition of food matrices and the expression of the virulence of L. monocytogenes, and, in the other hand, to take into account the diversity of strains in relation to their habitat. Through the integration of new disciplines into the existing network, the objective of this MRSEI application is to establish the effective structure to integrate the health dimension into the research of food intake and the nutritional status of the elderly population. The establishment of a transdisciplinary network will provide Europe with a training structure adapted to the emergence of a new generation of researchers who can successfully participate in the control of health risks and the nutritional well-being of the elderly.

Non-coding pervasive transcription initiating from cryptic signals or resulting from terminator read-through is widespread in all organisms. Its biological role is well-established in eukaryotes, but poorly understood in bacteria. Two major mechanisms control bacterial pervasive transcription: transcription termination by Rho and RNA degradation by RNases. Our recent data suggest a connection between these two pathways. The multidisciplinary project CoNoCo aims to define the mutual contributions of Rho and RNase III in the control of pervasive transcription in the Gram-positive model bacteria Bacillus subtilis and Staphyloccoccus aureus. It will also establish the roles of the non-coding transcriptome in bacterial cell biology highlighted by recent discoveries of Rho-mediated regulation of B. subtilis cell differentiation and the involvement of the double-strand specific RNase III in gene regulation by small non-coding RNAs.

Synthetic microbiology is among the most promising approaches for getting more at lower cost and in the respect of the environment. Directed evolution is recognized as a key approach to obtain biobricks for synthetic biology. In this context there is a considerable interest in the development of continuous systems for directed evolution of biomolecules based on “orthogonal” evolution vector on which accumulation of mutations can be uncoupled from accumulation of mutations on the host genome. This project aims at developing such a system for the gram-positive bacterium Bacillus subtilis. An important step towards biotechnological applications will also be made by using the proposed system for: the evolution of new transcription factors for genetic circuit engineering in B. subtilis; and the evolution of new proteins binding inorganic ions such as heavy metals that might serve as biosensors and in bioextraction systems. The work program decomposes into three work-packages : development of a system for directed evolution in B. subtilis ; in silico analyses for the optimization of the system ; application to biobrick production. B. subtilis is a totally harmless bacterium of considerable biotechnological interest: it stands as the second model bacterium after Escherichia coli and is as such a natural chassis for synthetic biology; it is also a soil dweller (and probably a normal gut commensal in humans) with highly diverse physiological capabilities, and an ability to survive extreme conditions in the form of spores. B. subtilis and several of its close relatives of the Bacillus genus (notably B. licheniformis and B. amyloliquefaciens) exhibit a remarkable capacity of biological compound production that can be scaled-up to industrial levels are widely used in the industry for enzyme production.

Agriculture has currently to tackle new challenges, largely due to the need to increase global food supply under the declining availability of soil and water resources and increasing threats from climate change. It has to face main changes and to adapt to new conditions, in particular environmental ones. To better handle this adaptation, it is necessary to better understand several key notions such as genetic variability and interactions between the plant and its environment. In this context, predictive approaches relying on ecophysiology and genetic knowledge, as well as mathematical modeling are very promising. The Stat4Plant project aims at developing new statistical methodologies and new algorithmic tools for modeling and analyzing genotypic variability and interaction between plant and its environment in a context of climate change. The project consortium gathers scientists in modeling and applied statistics with large experience in interdisciplinary collaborations in plant sciences and biologists with strong expertise in phenotype-genotype relations. The project is structured in four main research axes, supported by strong collaborations between statisticians and biologists and motivated by practical questions linked with biological dataset. The first axis aims at developing new methods for identifying key biological processes driving plant development lying behind the observed genotypic variability. These works combine mechanistic ecophysiologic modeling highly nonlinear of plant development, statistical mixed effects modeling for genotypic variability and statistical testing procedures, in particular adapted to small data samples, to identify genotype-dependent parameters. These approaches will allow to better understand genotype by environment interactions and to identify new tools for varietal selection. The second axis is dedicated to joint modeling of a time of interest such as flowering time or harvest time and of a phenotypic dynamical trait depending on time such as biomass or pest presence. The considered joint models combine survival models with random effects and covariates of high dimension and nonlinear mixed effects models for the dynamical trait. The objective is to identify the relevant covariates, to estimate the parameters and to predict the time of interest. These methods will allow for example to better predict flowering time or optimal harvest time. The third axis aims at developing new methods for identifying among a high number of covariates those who are the most influent for a phenotypic trait of interest, solely or jointly with a time of interest. Nonlinear mixed effects models combining mechanistic models of plant development and genetic models integrating a high number of genetic covariates will be used to model genotypic variability of the trait of interest. New covariates selection methods adapted to the nonlinear context will be developed. These methods will allow to identify the main genetic factors influencing the trait. Finally, the last axis aims at building new criteria for varietal selection, integrating randomness of environmental conditions and targeting simultaneously several objectives, such as maximizing yield and minimizing yield variability. New methodologies for optimizing these criteria will be developed. Such criteria will be new tools for decision support system in agriculture.