Most of the life time of an animal consist in exploring and sensing its environment without any specific goal. During this process, the animals encode the statistics of the environmental stimuli that surround them and adapt progressively their neuron responses to these stimuli. This unsupervised learning of the environment is thought to happen in the cerebellar cortex: sensory cortices encode the stimuli using Hebbian learning rule, i.e. assuming that the coactivation of a pattern in a network produces a change in the synaptic strengths between the involved neurons. Even if it exists several signatures of Hebbian learning, the validity of these leaning rules is still a matter of debate and several other learning rules have been proposed. How do the sensory cortices encode memories, patterns or stimulus associations is still an open question. With the recent development of new optical tools to record (multiphoton microscopy) and manipulate (optogenetics) the brain activity, it is now possible to imprint patterns of neural activity in the cortex of mice eliciting behavior. In parallel during the last decade, unsupervised machine learning tools have been developed in order to infer the effective connectivity matrix of a network from its activity, or even, to fit on neural activity, Hopfield models based on Hebbian learning using the restricted Boltzmann machine inference. In this project, we propose to use our ultra-fast two-photon microscopy, optogenetics perturbation and modelling techniques to understand the unsupervised learning rules and dynamics in cortical networks of mice. A series of neuronal ensembles are activated optogenetically until their fixation in the network. How fast does the cortex imprint a series of pattern and build attractor states? What is the exact underlying Hebbian learning rule? What is the validity of effective connectivity models?

Our cells’ DNA is constantly being damaged, and our cells work continuously to repair the damage, preserve genomic integrity, and ensure offspring inherit no mutations. Single-strand breaks along the genome are frequent and appear easy to repair with a simple ligation reaction, however their repair is of such urgency that the cell has developed a much more complicated repair pathway involving multiple components in addition to ligase. The complexity of the ensuing system explains why it remains rather poorly understood from a kinetic and dynamic standpoint, even as it turns out to be the target of one of the most successful recent anticancer therapies targeting “BRCA-negative” cancers. This project brings together physicists, chemists and biologists to understand in detail the succession of steps allowing single-strand breaks to be reapaired in an efficient and timely manner in humans. In humans the signaling protein PARP1 first binds the single-strand break and begins hydrolyzing NAD+ in an automodification reaction which leads to formation of a poly(ADP-ribose), or PAR, “cloud” around PARP1. This cloud recruits to the site of the lesion the XRCC1-Ligase3 complex responsible for the repair reaction. The mechanism whereby XRCC1-Ligase3 first interacts with the PAR cloud surrounding the lesion, before finding the lesion itself, remains a mystery, as do the ways in which PARP1 “hands-off” the lesion to XRCC1-Ligase3. We will deploy ultra-high-resolution single-molecule techniques to reconstitute, step by step and component by component, the full repair reaction. These experiments will provide quantitative and mechanistic insight into how repair takes place. By first laying down this quantitative foundation this work will pave the way for the future development of novel medicinal modulators of this DNA repair pathway.

Understanding the tempo and mode in which species diversify is fundamental in explaining the origin and maintenance of biodiversity. The complex interplay of species' evolving ecologies, biotic interactions, geographic distributions and fluctuating environment determine to different extents their evolutionary history. However, because of mathematical and computational limitations, available inferences unrealistically assume that these interconnected evolutionary processes happen independently and that lineages evolve in isolation, and do not integrate all sources of paleontological and neontological evidence. Consequently, our knowledge about how biotic and abiotic factors regulate species evolution and diversification is still deficient. This project aims to significantly advance our mechanistic understanding on the drivers of past biodiversity dynamics across taxa, space and time, through the development of phylogenetic probabilistic models that unify stochastic processes of diversification, trait and biogeographic evolution, integrating present-day, fossil and paleoenvironmental evidence. We will overcome current model limitations posed by the lack of analytical solutions or prohibiting computational costs in evaluating likelihoods by exploiting new unexplored capabilities offered by Bayesian data augmentation techniques to develop full efficient probabilistic inference. This will be enhanced by building a foundational, modular and extensible open-source software package written in Julia, a modern programming language at the forefront of efficient numerical computing. Applying our new models to empirical data we will be first to directly assess a suite of foundational evolutionary hypotheses about how species diversification, trait and biogeographic evolution influence each other and are determined by biotic (e.g., diversity-dependence, competition) and abiotic factors (e.g., geography, environmental fluctuations) in a robust probabilistic framework.

A current challenge lies in understanding the molecular determinants of neurotransmitter receptor functional diversity in the brain. NMDA receptors (NMDARs), a class of glutamate?gated channels involved in synaptic transmission and plasticity, but also in several neurological and psychiatric diseases, exist as multiple subtypes that differ by the nature of their GluN2 subunits. The three major NMDAR populations in the adult forebrain are 2A/2A and 2B/2B di-heteromers (containing two identical GluN2 subunits) and 2A/2B tri-heteromers (containing one GluN2A and one GluN2B subunit). Current genetic and pharmacological approaches, limited in specificity and spatio-temporal resolution, cannot target each NMDAR population in isolation. We have developed in the lab the first optopharmacological tool to selectively potentiate 2B/2B di-heteromers with light (Opto2B). Our choice of targeting 2B/2B NMDARs was motivated by the intimate association of the GluN2B subunit with synaptic plasticity and cognitive performance. However, the relative involvement of 2B/2B and 2A/2B NMDARs in these processes, as well as in neurotoxicity, is still highly debated. We have preliminary data showing that our Opto2B tool allows reversible photomodulation of either synaptic or extrasynaptic 2B/2B ex vivo, thus providing control of NMDAR subsets with unprecedented spatio-temporal resolution, as well as cellular and molecular specificity. In the Opto2B project, we will use this tool ex vivo to investigate the involvement of 2B/2B in synaptic plasticity and neurotoxicity, and in vivo to investigate the behavioral and pro-cognitive consequences of 2B/2B potentiation in control mice and two mouse models of NMDAR hypofunction. We thus expect to bring light to the debated physiological role of 2B/2B NMDARs in the brain and the translational potential of their potentiation.

Polyploid phases are recurrent throughout the evolution of eukaryotes, initiated by whole genome duplication (WGD) events that occur either alone (auto-polyploidy) or in association with interspecific hybridization allo-polyploidy). In plants, WGDs are particularly pervasive and are associated with an initial adaptive advantage in contexts of extreme selective pressures such as past climatic upheavals, domestications, or habitat invasions. Although the adaptive success of newly formed polyploids has been attributed to an increased genomic plasticity, the molecular mechanisms involved remain unclear. Here, I propose to investigate the role of transposable element (TE) mobilization in the rapid generation of genetic variation following WGD. Indeed, TEs are powerful generators of major-effect mutations with specific sensitivities to environmental stress, which confer them a unique potential to contribute to rapid local adaptations. Furthermore, the epigenetic mechanisms that control their mobilization, notably DNA methylation, is disrupted by WGD in several species. Thus, TEs could represent a major engine of phenotypic exploration for neo-polyploids, notably in the face of challenging environments. Using a panel of synthetic and natural autopolyploid Arabidopsis thaliana lines, where the effects of WGD are not confounded by hybridization, I will 1) determine the extent to which WGD favors transposition in response to environmental stress, 2) describe precisely the DNA methylation alterations induced by WGD over TE sequences and identify the molecular pathways involved, and 3) determine the functional consequences of increased TE mobilization for autopolyploids in the lab and in nature. Through the novel focus on stress-induced transposition and its functional impact, both in experimental settings and natural populations, POLYSTRESS aims to offer novel insights on the molecular mechanisms underlying the adaptive advantages of neo-polyploid genomes. Given the high prevalence of polyploidy, not only within plants, but also in fungi and animals, the results of this project should have broad implications for our understanding of rapid adaptation in contexts of extreme selective pressure, notably the increasingly brutal climatic disruptions faced by ecosystems worldwide.