Structural variation (SV) – that encompasses sequence deletions, duplications, insertions, inversions and translocations – has long been considered as rare and meaningless for phenotypic evolution. From recent studies, it is now clear that SV is common to many species including maize, and involves a much greater proportion of the genome than previously thought. Understanding the nature and content of SV in maize will allow to elucidate the structure, evolution and variability of the maize genome. Because maize is a major crop, discovery of particular SVs such as Copy Number Variants (CNVs) or Presence Absence Variants (PAVs) is important regarding their potential contribution to phenotypes, especially as they may account for part of the genetic variation of complex traits and heterosis. The maize germplasm contains contrasted lines that are likely to show large SV. Therefore, to investigate the potential implications of SV in important agronomic traits and heterosis, it is essential to characterize SV for several representative lines. In the CNV-Maize project, we propose to address these issues using an original approach that combines next generation sequencing and array-based Comparative Genomic Hybridization (aCGH) to reveal maize SVs at the whole genome level. This project will be conducted by 3 French laboratories which are leaders in maize genetics and genomics and statistics. Their partnership gathers complementary skills in molecular biology, bioinformatics and statistics. This project will also take advantage of strong interactions with a U.S. laboratory pioneer in this domain which has been deeply involved in maize genome sequencing, as well as support of a biotechnology company that develops arrays, and of the french National Center for Sequencing (CNS). In the first part of our project will be to capture SVs among a core collection of genetically distant European and American maize lines, using aCGH array. The maize genome has been sequenced by an American consortium using the B73 U.S. line, and a CGH array for this sequence is available. To capture as many SVs as possible from our core collection, we will complement this array with sequences that are specific of the French F2 line, which is widely used in French breeding programs. Hybridization of the DNAs of our core collection on this pangenomic CGH array will allow to characterize the extent, organization and nature of SV in maize, and will generate a comprehensive dataset of maize SVs. In the second part of the project, the most relevant SVs will be selected to develop an innovative CGH array dedicated to the genotyping of a large association panel. To decipher whether the use of SVs as genotyping markers will provide information complementary to that of conventional markers such as SNPs for genetic association studies, we will characterize the nature and prevalence of each relevant SV, as well as its link with other polymorphisms. SV data will then be used to investigate the genotype-phenotype association for agronomic traits using an SV-adapted Linkage Disequilibrium mapping approach, with a particular interest in understanding how SVs can be predictive to the heterotic response observed when crossing lines from different genetic groups. We will also develop methodologies to include this new type of information within breeding programs. Finally, high-throughput molecular markers will be derived from the most associated SVs, leading to the construction of a «CNV markers toolkit » that will be useful for the whole maize community, including breeders and researchers. These CNV markers will likely be particularly powerful for further prospects on recombination, heterosis and chromosome plasticity, which are key processes to be elucidated for genetics and breeding programs. Inclusion of markers that are specific to European lines will lead to major improvement for European breeding and is likely to pave the way for future maize research in Europe.

The ability to measure genome-wide gene expression or mutations from a biological sample made of thousands or millions of cells has revolutionized biology in the late 1990’s, allowing for example to characterize subtypes of cancers from their molecular profile or to identify comprehensive lists of genes expressed or inhibited in particular conditions. Cells within a sample are however never all the same, and measuring an average over thousands of cells may mask or even misrepresent signals of interest that vary between individual cells. Fortunately, recent technological advance in massively parallel sequencing and high-throughput cell biology technologies now give us the ability to measure, at the level of individual cells, genome-wide measurements based on DNA, RNA, chromatin states or proteins. The use of these techniques, which we collectively refer to as single-cell genomics, allows us to study cell-to-cell variability within a biological sample and investigate new questions out of reach for classical bulk genomics. For example, intra-tissue heterogeneity is now clearly established in many cell types including T cells, lung cells, or myeloid progenitors. The construction of a comprehensive atlas of human cell types is now within our reach. Cell-to-cell variability is also central in many biological processes such as gene regulation or cell differentiation, as it reflects the intrinsic stochastic molecular processes and provides information on the underlying molecular networks. This variability has been shown to play an important functional role in the cell decision-making process and beyond. Consequently, the measurement of gene expression in single cells has the promise of revolutionizing our understanding of gene regulation and resolving many longstanding debates in biology. Besides technological aspects, single-cell genomics raises new mathematical and computational challenges. The nature of data produced by single-cell genomics techniques, as well as the questions we need to answer, differ indeed a lot from standard bulk genomics. For example, due to the extremely small amount of biological material present in a single cell, it is common to have 90% of missing values in a single-cell experiment, and the observed values can themselves be strongly distorted by particular experimental artifacts, calling for new statistical modelling of these data. In addition, the quantity of cells that are investigated simultaneously by the latest (and future) single-cell technologies goes easily in the millions, orders of magnitude more than the number of samples in standard bulk genomics, raising new computational challenges for scalability. Finally, new biological questions are raised, such as modelling a differentiation process or integrating genetic and epigenetic data at the single-cell level, which calls for new mathematical models and algorithms. In short, new dedicated analytical tools are crucially needed to unleash the full power of single cell genomics. The goal of this project is to attack some of these pressing challenges, by developing new mathematical models and computational tools for three biological problems: (i) investigating sample heterogeneity and cell identity, (ii) modelling the dynamics of cell differentiation and gene regulation, and (iii) exploring single cell epigenomics. For that purpose, we have gathered a consortium with a unique combined experience in high dimensional statistics, machine learning, bioinformatics, computational and systems biology, and an extended network of collaborators on single-cell genomics in France and abroad.

While the primary role of metabolism is chemical conversions, can it also serve as an information processing device? To answer this question, we propose to encode various microbial metabolic models into Artificial Metabolic Networks (AMNs), which can be trained on experimental data or model simulations. Unlike “black box” artificial neural networks, our AMNs will be sparse and will reflect faithfully the structure and dynamics of metabolic networks. Our AMNs will be benchmarked on classical machine learning problems to assess what level of computational sophistication metabolism is able to handle. In the context of biotechnology, our AMNs will be applied to the design of experiments to (i) optimize the productivity of an added-value chemical (lycopene) E. coli producing strain defining nutrient compositions and gene deletions and (ii) classify infectious disease severity by engineering an E. coli biosensing strain detecting metabolic biomarkers in COVID-19 clinical samples.

Antibiotic resistance is a dramatic health challenge and development of new antibiotics efficient against Gram-negative bacteria of the ESKAPE group is an emergency. In this project, we propose the development of effective first-in-class antibiotics to tackle bacterial resistance by acting on a novel bacterial target, the Mutation Frequency Decline protein (Mfd), and by promoting its inhibition by novel therapeutic molecules. Mfd is a non-essential transcription repair coupling factor conserved in bacteria and absent in eukaryotes. Mfd enables the bacteria to overcome the host defense responses, by conferring resistance to nitric oxide, a major toxic component of the innate immune system. We have identified selective Mfd inhibitors and demonstrated their efficacy against Gram-negative bacteria. Herein, we will optimize these molecules and test their efficacy against bacteria of the ESKAPE group. We will also tackle their pharmacologically challenging properties and develop optimal nanoparticle formulations to ensure their efficient delivery. The originality of our project from a therapeutic perspective is the optimization of compounds that, instead of killing the bacteria responsible for the infection, will block their pathogenic pathways. Targeting Mfd’s function will allow to boost the immune system efficiency and to only focus on bacteria restricted to the inflammation site, thus reducing resistance. This translational project aims to deliver a drug candidate with a solid pre-clinical proof of concept of innocuity to the host and broad range efficacy. As result, a strong therapeutic innovation able to bring an effective healthcare solution to the out-of-control rise of antibiotic resistance will be provided. The complementary and multidisciplinary consortium has already successfully worked together and obtained significant preliminary data, which allow us to be confident about the ultimate success on our challenging project.