In aquatic environments, the CO2 concentration is lower that the Michaelis constant for RuBisCO, and microalgae have evolved specific CO2 concentrating mechanisms (CCM) that increase the local CO2 concentration nearby the RuBisCO active site. Recently, we have identified a new class of carbonic anhydrase, the iota carbonic anhydrase (ι-CA) that participates to the biophysical CCM of a marine diatom, Thalassiosira pseudonana[1]. ι-CA presents unique specificities in its catalysis of CO2 ↔ HCO3– conversion: it does not require Zn2+; can work either independently of metal or with Mn2+; it oligomerises in an atypical drone-like shape[2]. Upon acquisition, the CO2 is assimilated via the Calvin-Benson-Basham cycle, that is regulated by a range of processes that are well studied in Paris, including redox post-translational modifications, metabolite availability, or the association of supramolecular complexes. I will present you our particular interest on a small disordered protein that is the master of the CBB cycle : CP12.…

S-nitrosylation is a redox post-translational modification widely recognized to play an important role in cellular signaling as it can modulate protein function and conformation. At the physiological level, nitrosoglutathione (GSNO) is considered the major physiological NO-releasing compound due to its ability to transfer the NO moiety to protein thiols. However, the enzymatic systems controlling its intracellular concentration as well as the structural determinants regulating its redox specificity are not fully elucidated. Here we provide novel insights into a new class of enzymes belonging to the aldo-keto reductase (AKR) protein superfamily which may be integrated with NADH-dependent GSNO reductase (GSNOR) activity to control NO homeostasis in plants. The molecular mechanisms underlying GSNO-dependent thiol oxidation were investigated using plant glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a model protein tool. Starting from the 3D structure, we carried out comput!ational studies to identify the protein residues involved in GSNO binding and to disclose the reaction mechanism of GSNO-dependent…

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No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. I investigate evolutionary rescue in an environment that fluctuates between a favorable state, in which the population grows, and a harsh state, in which the population declines. I quantify the probability of evolutionary rescue using a stochastic framework with numerical and analytical tools, resulting in an exact computation of the population's fate under deterministic versus stochastic environmental fluctuations. I also compare a perfectly harsh environment (i.e., fully birth-preventing) to an imperfectly harsh one (i.e., not fully birth-preventing) and identify which growth parameters promote evolutionary rescue using different population growth types. Because I find that the type of population growth is essential in assessing the fate of a population, I discuss the main current methods…

I will give an overview of my recent, ongoing and future work in machine learning for biological sequences. This will cover in particular: - Microbial GWAS: tools to identify genetic determinants of phenotypic traits such as antimicrobial resistances. The importance of accessory genes in microbes make the usual focus on SNPs inappropriate. I am developing solutions based on k-mers, i.e., the presence of short sequences in the genomes. - Prediction from biological sequences: neural networks that take a biological sequence as input and predict a property of this sequence. This applies e.g. to regulatory genomics or fold prediction. I am working on regularization and on statistical inference on the features extracted by these networks. - Machine learning for evolutionary genomics: neural networks to infer parameters of sequence evolution models. For some complex models, likelihood maximization is too hard but sampling is easy. I use a large amount of simulated data to learn…

Cancer is a heterogeneous disease, where multiple, phenotypically distinct subpopulations co-exist. Tumour evolution is a result of a complex interplay of genetic and epigenetic factors. Predicting the different cancer phenotypes requires linking the molecular state of a clone to its fate with high precision. Single-cell lineage tracing techniques are being successfully applied to the study of cancer evolution. I will show how single-cell assays endowed with genetic barcodes can be exploited to extract transcriptional stability properties of cancer clones, link molecular states to phenotypes, and study tumour heterogeneity, using a breast cancer model as a case study. Next, I will show how a new framework, called GALILEO, capturing lineage tracing, transcriptomic, and chromatin accessibility information simultaneously and at single-cell resolution, can be used to gain deep insights on aggressive cancer phenotypes. By tracing cancer cells in mice, we observe that the highly tumorigenic niche properly contains two transcriptionally distinct clonal subpopulations. Importantly,…