Mon travail de Thèse a porté sur la description de l’immunité antibactérienne de la planaire, et plus particulièrement la mémoire immunitaire innée.La mémoire immunitaire innée constitue une ligne de défense de l’hôte à la réinfection qui ne fait intervenir que des composants de l’immunité innée. Présente chez les vertébrés et les invertébrés, ces derniers constituent un modèle de choix car dépourvus d’immunité acquise. La planaire dispose d’une mémoire immunitaire innée envers S. aureus, qui, suite à une réinfection, se traduit par une élimination exacerbée. La déplétion des planaires en cellules souches et la greffe tissulaire ont permis de mettre en avant les cellules souches comme acteurs principaux de cette réponse immunitaire. Un criblage RNAi associé à un profilage transcriptomique ont fait ressortir des gènes en les hiérarchisant au sein d’une voie de signalisation impliquant un récepteur au peptidoglycane (pgrp-2), une histone méthyltransférase (setd8.1), et un mécanisme effecteur dans l’élimination bactérienne (p38 et morn2). Setd8.1, histone méthyltransférase, se placerait au cœur du processus en déposant des marques épi-génétiques sur des loci de l’ADN, garantissant l’expression accrue des gènes effecteurs suite à la réinfection. Ce mécanisme, décrit chez l’Homme, n’avait jusqu’alors jamais impliqué des cellules souches, ni ce type d’histone méthyltransférase comme acteurs dans la mémoire immunitaire innée.Collectivement, l’investigation du système immunitaire de la planaire a permis la découverte de mécanismes de défense antibactérienne inédits, dont le transfert à l’Homme pourrait compléter l’approche actuelle du traitement des maladies infectieuses. / My Thesis work has focused on the description of the planarian antibacterial immunity, and more precisely the innate immune memory.The innate immune memory forms a host defense line to the reinfection which only involves components from innate immunity. Present in vertebrates and invertebrates, invertebrates are a model of choice because devoid of acquired immunity. The planarian has an innate immune memory against S. aureus, which, after a reinfection, displays an exacerbated elimination. The depletion of stem cells from planarians and tissue graft highlighted stem cells as the main actors of this immune response. An RNAi screening combined with a transcriptomic profiling brought out genes and classified them within a signaling pathway involving a peptido-glycan receptor (pgrp-2), a histone methyltransferase (setd8.1), and an effector mechanism of the bacterial elimination (p38 and morn2). Setd8.1, histone methyltransferase, would be the core of the process putting epigenetic marks on DNA loci, ensuring the increased expression of effector genes after reinfection. This mechanism, described in humans, has neither involved stem cells, nor this type of histone methyltransferase as actors in the innate immune memory.Collectively, the investigation of the planarian immune system allowed the discovery of new antibacterial defense mechanisms, and transferring it to humans could complete the actual approach of the infectious disease treatment.

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.