Olfactory ensheathing cells (OECs), the glia of the olfactory nerve, are promising candidates for patient-specific cell-mediated repair of both peripheral nerves and the spinal cord. The recent discovery that OECs originate from the neural crest, rather than the olfactory epithelium as previously thought, potentially means that homogeneous populations of OECs for repair could be expanded in culture from neural crest stem cells persisting in the patient's own skin and hair follicles. The first step towards this long-term goal is to understand the molecular mechanisms underlying neural crest differentiation into OECs, as opposed to Schwann cells (the glia of all other peripheral nerves), which are less effective in spinal cord repair. To identify transcription factors and signalling pathways that might be involved in OEC versus Schwann cell differentiation, I took an unbiased transcriptome profiling approach. Taking advantage of Sox10 expression throughout both OEC and Schwann cell development, I used laser-capture microdissection on cryosections of mouse embryos carrying a Sox10:H2BVenus transgene, to isolate OEC subpopulations (olfactory mucosal OECs, from the olfactory nerve, and olfactory nerve layer OECs, from the olfactory nerve layer surrounding the olfactory bulb) at different stages of development, and Schwann cells from trigeminal nerve branches on the same sections, for RNA-seq and cross-wise comparison of transcriptomes. Validation of candidate genes by in situ hybridisation revealed some contamination with adjacent cells from mesenchyme, olfactory epithelium or olfactory bulb, but also identified the expression in developing OECs of various genes previously reported to be expressed in adult OECs, and of over 20 genes previously unknown in OECs. Some of these genes are expressed by OECs but not Schwann cells; some are expressed by olfactory nerve layer OECs but not olfactory mucosal OECs, while some are expressed by olfactory mucosal OECs and Schwann cells but not olfactory nerve layer OECs. For a subset of the genes, I was also able to analyse OEC differentiation in mouse mutants. I also collected transcriptome data from neural crest-derived cells that persist on the olfactory nerve in Sox10-null embryos (in which neural crest-derived cells colonise the olfactory nerve, but normal OEC differentiation is disrupted). Comparison with wild-type OEC transcriptome data from the same embryonic stage identified genes whose expression is likely either downregulated or up-regulated in the absence of Sox10, supporting a role in normal OEC differentiation. Overall, these various transcriptomic comparisons (between OECs at different developmental stages, different OEC subpopulations, OECs versus Schwann cells, and OECs versus Sox10-null neural crest-derived cells on the olfactory nerve) have identified multiple transcription factor and signalling pathway genes, amongst others, that are expressed during OEC development in vivo (including some specific to different OEC subpopulations) and that may be important for OEC differentiation. Furthermore, some of these genes are not expressed by embryonic Schwann cells. This work provides a foundation for understanding how to promote OEC rather than Schwann cell differentiation from neural crest stem cells in culture, with the potential for clinical application in the future.

Antibiotic resistance in bacteria is acquired through spontaneous chromosomal mutations or horizontal gene transfer. In the absence of antibiotics, resistant mutants generally show reduced fitness due to compromised growth rate, competitive ability and virulence compared to their antibiotic-sensitive ancestors. The focus of my research is to dissect the molecular underpinnings of the variations in the fitness cost of chromosomal antibiotic resistance using a systems-level approach. From an evolutionary perspective, my research aims are to understand how the fitness cost influences adaptation in resistant populations in an antibiotic-free environment. Using rifampicin resistance in Pseudomonas aeruginosa as a model, my work shows that most of the variation in the fitness cost of rifampicin resistance can be attributed to the direct effect of rifampicin resistance mutations on transcriptional efficiency. Through RNA-Seq transcriptome profiling, I demonstrate that global changes in gene expression levels associated with resistance mutations are surprisingly subtle, suggesting that the transcriptional regulatory network of P. aeruginosa is robust against compromised transcriptional efficiency. Using experimental evolution and whole-genome sequencing, my work reveals a systematic difference in the genetic basis of adaptation in mutants that were propagated in the absence of antibiotics. During compensatory adaptation, resistant mutants can recover the fitness cost of resistance by fixing second-site mutations that directly offset the deleterious effects of resistance mutations. Amongst resistant mutant populations with low fitness costs, general adaptation limits compensatory adaptation, which is most likely to be due to the rarity of compensatory mutations and clonal interference. Far from being the most ubiquitous mechanism in the evolution of resistance, compensatory adaptation is the exception that is more likely to be observed in resistant mutants with high fitness costs. In addition, I applied key elements of the integrative experimental approach developed in this work to dissect the molecular basis of the fitness cost associated with carriage of the pNUK73 small plasmid in P. aeruginosa, which carries the rep gene encoding a plasmid replication protein. My results confirmed that rep expression generates a significant fitness cost in P. aeruginosa and demonstrate how the molecular origins of the fitness cost of resistance can be dissected in a different biological context.

Dans ce travail nous avons élaboré une nouvelle méthode de l'analyse de réseau à définir des membres possibles des voies moléculaires qui sont important pour ce phénotype en utilisant la « hit-liste » des expériences « omics » qui travaille dans le réseau intégré (le réseau comprend des interactions protéine-protéine, de transcription, l’acide ribonucléique micro-l’acide ribonucléique messager et celles métaboliques). La méthode tire des sous-réseaux qui sont construit des voies de quatre types les plus courtes (qui ne se composent des interactions protéine-protéine, ayant au minimum une interaction de transcription, ayant au minimum une interaction l’acide ribonucléique micro-l’acide ribonucléique messager, ayant au minimum une interaction métabolique) entre des hit –gènes et des soi-disant « exécuteurs terminaux » - les composants biologiques qui participent à la réalisation du phénotype finale (s’ils sont connus) ou entre les hit-gènes (si « des exécuteurs terminaux » sont inconnus). La méthode calcule la valeur de la centralité de chaque point culminant et de chaque voie dans le sous-réseau comme la quantité des voies les plus courtes trouvées sur la route précédente et passant à travers le point culminant et la voie. L'importance statistique des valeurs de la centralité est estimée en comparaison avec des valeurs de la centralité dans les sous-réseaux construit des voies les plus courtes pour les hit-listes choisi occasionnellement. Il est supposé que les points culminant et les voies avec les valeurs de la centralité statistiquement signifiantes peuvent être examinés comme les membres possibles des voies moléculaires menant à ce phénotype. S’il y a des valeurs expérimentales et la P-valeur pour un grand nombre des points culminant dans le réseau, la méthode fait possible de calculer les valeurs expérimentales pour les voies (comme le moyen des valeurs expérimentales des points culminant sur la route) et les P-valeurs expérimentales (en utilisant la méthode de Fischer et des transpositions multiples).A l'aide de la méthode masterPATH on a analysé les données de la perte de fonction criblage de l’acide ribonucléique micro et l'analyse de transcription de la différenciation terminal musculaire et les données de la perte de fonction criblage du procès de la réparation de l'ADN. On peut trouver le code initial de la méthode si l’on suit le lien https://github.com/daggoo/masterPATH / In this work we developed a new exploratory network analysis method, that works on an integrated network (the network consists of protein-protein, transcriptional, miRNA-mRNA, metabolic interactions) and aims at uncovering potential members of molecular pathways important for a given phenotype using hit list dataset from “omics” experiments. The method extracts subnetwork built from the shortest paths of 4 different types (with only protein-protein interactions, with at least one transcription interaction, with at least one miRNA-mRNA interaction, with at least one metabolic interaction) between hit genes and so called “final implementers” – biological components that are involved in molecular events responsible for final phenotypical realization (if known) or between hit genes (if “final implementers” are not known). The method calculates centrality score for each node and each path in the subnetwork as a number of the shortest paths found in the previous step that pass through the node and the path. Then, the statistical significance of each centrality score is assessed by comparing it with centrality scores in subnetworks built from the shortest paths for randomly sampled hit lists. It is hypothesized that the nodes and the paths with statistically significant centrality score can be considered as putative members of molecular pathways leading to the studied phenotype. In case experimental scores and p-values are available for a large number of nodes in the network, the method can also calculate paths’ experiment-based scores (as an average of the experimental scores of the nodes in the path) and experiment-based p-values (by aggregating p-values of the nodes in the path using Fisher’s combined probability test and permutation approach). The method is illustrated by analyzing the results of miRNA loss-of-function screening and transcriptomic profiling of terminal muscle differentiation and of ‘druggable’ loss-of-function screening of the DNA repair process. The Java source code is available on GitHub page https://github.com/daggoo/masterPATH

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