The interferon-sensitive gene 20 (ISG20) is a broad viral inhibitor previously thought to directly degrade invading viral RNA thanks to its RNase activity. This mechanism remained however controversial. In a recently published study, we have instead determined that ISG20 inhibits viral replication by impairing translation from viral RNAs and not by degrading them. Importantly, we also show that ISG20-mediated translation inhibition is distinct from previously known translation blocks (PKR, IFITs) and that ISG20 discriminates between mRNAs derived from chromosomal genes (self) and those originated from foreign genetic elements, be them viruses with a purely cytoplasmic life cycle or plasmid DNAs transcribed in the nucleus. Using orthogonal approaches, we want to identify the mechanism/s and specificities of ISG20 inhibition. In light of the current context, the characterization of the mechanism of translation inhibition by ISG20 will first focus on two RNA viruses: VSV as a model of negative-strand RNA viruses and SARS-CoV2 as a model for positive-strand RNA virus. The former will be used to decorticate and zoom into the mechanism of viral specificity and action of ISG20 in BSL2 conditions and the latter will be used to more finely characterize the specificity or differences with respect to viruses of high relevance for human health. After, the results obtained here will be extended to other viral pathogens as Zika or Retroviruses. By studying a mechanism that broadly affects viral replication, we aim at characterizing a vulnerable step common to different viruses.

Inherited retinal degenerative diseases constitute a major cause of vision loss in humans. Retinitis pigmentosa and macular degeneration (MD) are the two main classes of retinal diseases. They are characterized by the loss of rod and cone photoreceptor (PR) cells leading to progressive blindness. Most monogenic forms of retinitis pigmentosa and MD are associated with genes expressed in PR or retinal pigment epithelial (RPE) cells where they encode proteins that are critical for PR structure, function and survival. Specific cellular processes and biochemical pathways implicated in retinopathies include: phototransduction, visual cycle, PR development, morphogenesis, cellular metabolism, protein folding, among others. Lipid metabolism is of major importance for retina integrity and its dysregulation can lead to retinopathies. Lipid disorders have been implicated in macular degeneration such as the Age-Related Macular Degeneration (AMD) and Stargardt disease. Partner 1 has found that loss of function mutation in fatp (fatty acid transport protein) gene leads to PR loss in Drosophila and partner 2 has identified FATP1 as a potential regulatory component of the visual cycle in mammals. In the following four specific tasks, we propose to 1) Coordinate the project; use mouse FATP and Drosophila fatp mutants to study in 2) the roles of Fatp genes in PR function and survival, 3) examine the roles of Fatp genes in the visual cycle, and 4) investigate the role of Fatp genes in lipid metabolism in the retina (Partners 1, 2 and 3). All together our proposal will help elucidating the mechanisms by which a lipid dysregulation leads to PR degeneration and retinal pathology.

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.

Perturbations of RNA homeostasis are implicated in the aetiology of several human conditions, for which myotonic dystrophy type 1 (DM1) is a paradigmatic example. DM1 is the most common form of inherited neuromuscular disease in adults, but it affects patients of all ages, in multiple tissues and cell types. The neurological manifestations are exceptionally debilitating, however important gaps still exist in our understanding of brain disease. DM1 molecular pathogenesis involves primarily alterations in the activity of MBNL proteins, an important family of RNA-binding proteins that play a plethora of roles in RNA processing, including the regulation of alternative splicing, polyadenylation and intracellular trafficking of transcripts. Such features, notably the RNA distribution into subcellular domains, are critical for the functional plasticity of brain cells, including astrocytes - a highly ramified and compartmentalized cell type. We have recently collected strong evidence of astrocyte abnormalities in DM1, suggesting a non-neuronal component in this disorder. In this project we will investigate how the DM1 mutation disturbs astrocyte function through altered MBNL protein activity and defective RNA processing, and how these defects ultimately lead to brain dysfunction. To this end, we will combine the multidisciplinary expertise of four complementary research partners and their relevant disease models (transgenic mice, iPSC and organoids). Transcriptomics, bioinformatics and high-resolution imaging will elucidate the mechanisms behind DM1 brain disease, as well as fundamental aspects of RNA biology in astrocytes. Importantly, our findings will corroborate the emerging role of astrocytes in neurological disease. Through the identification of the molecular pathways perturbed in DM1, and the development of new mouse and human disease models, we will establish rational grounds and powerful tools to respond to the urgent therapeutic needs for this condition.

This project aims at understanding how chromatin-remodeling machines work by using a combination of innovative experimental and theoretical approaches and building on the recent discovery of an unexpected remodeling intermediate. Chromatin remodeling is a vital process within eukaryotic cells. It is involved in controlling gene expression, in epigenetic phenomena, and also in several human pathologies. Despite many years of study, how multicomponent remodeling machines work remains unknown. A breakthrough made by the experimental teams involved in this project has shown that remodeling need not be a continuous process, as previously supposed. The RSC chromatin remodeler in fact functions via a two-step mechanism, forming, releasing, and then rebinding and mobilizing a stable intermediate (a "remosome") containing 35-40 bp more DNA than canonical nucleosomes. In combination with a team specialized in modeling biomacromolecules and their complexes, this project will characterize remosome structure and stability, determine how a two-step process conditions the overall remodeling mechanism, test its generality, and its sensitivity to compositional and environmental factors. This project relies on bringing together biophysical, biochemical, biological and modeling data. The recognized expertise of the contributing teams, carefully designed structural and functional probe experiments, and a constant feedback between experiment and theory will provide the means to decode a complex and vital process with a significant impact on fundamental biology and important implications for human health.