Face à la rapidité des changements climatiques, les arbres doivent faire preuve de plasticité phénotypique. Les mécanismes épigénétiques font partie des pistes de recherche actuelles pour expliquer la plasticité phénotypique. Cette thèse visait à évaluer le rôle de la méthylation de l’ADN dans la plasticité phénotypique d’un organisme pérenne séquencé, le peuplier, en réponse à des variations de disponibilité en eau du sol. Les travaux, combinant écophysiologie et épigénomique, se sont focalisés sur le méristème apical caulinaire, centre de la morphogenèse de la tige feuillée. Trois résultats majeurs sont issus de cette thèse : i) Chaque état hydrique est associé à un méthylome et un transcriptome spécifiques, ii) Certaines régions différentiellement méthylées sont conservées dans le temps et entre contextes environnementaux, iii) Des lignées RNAi hypométhylées soumises à différents contextes hydriques présentent une réponse modifiée. Les résultats acquis lors de cette thèse appuient une contribution de la méthylation de l’ADN à la plasticité phénotypique et suggèrent un rôle des mécanismes épigénétiques dans la mémoire d’un stress chez les arbres. / Due to rapid climate changes, trees must exhibit phenotypic plasticity. Epigenetic mechanisms are part of current research to explain phenotypic plasticity. This thesis aimed to evaluate the role of DNA methylation in phenotypic plasticity of a perennial sequenced organism, poplar, in response to variations in soil water availability. The work, combining ecophysiology and epigenomics, focused on the shoot apical meristem, the center of morphogenesis of the leafy stem. Three major results emerge from this thesis: (i) Each hydric state is associated with a specific methylome and transcriptome, (ii) Some differentially methylated regions are conserved in time and between environmental contexts, (iii) Hypomethylated RNAi lines subjected to different contexts show a modified response. The results obtained during this thesis support a contribution of DNA methylation to phenotypic plasticity and suggest a role of epigenetic mechanisms in stress memory in trees.

Indiana University-Purdue University Indianapolis (IUPUI) / Colon rectal cancer (CRC) is one of the most common cancers worldwide. It is characterized by the successive accumulation of mutations in genes controlling epithelial cell growth and differentiation leading to genomic in-stability. This results in the activation of proto-oncogene(K-ras), loss of tumor suppressor gene activity and ab-normality in DNA repair genes. Targeted therapy is a new generation of cancer treatment in which drugs attack targets which are specific for the cancer cell and are critical for its survival or for its malignant behavior. Survival of metastatic CRC patients has approximately doubled due to the development of new combinations of stan-dard chemotherapy, and the innovative targeted therapies, such as monoclonal antibodies against epidermal growth factor receptor (EGFR) or monoclonal antibodies against vascular endothelial growth factor (VEGFR).The study is to exhibit the need for right drug-right target and provides a proof of principle for potent efficacy of molecular targeted therapy for CRC. We have performed the weighted gene co-expression network analysis for three different patient cohort treated with different targeted therapy drugs. The results demonstrates the variation across different treatment regime in context of transcription factor networks. New significant tran-scription factors have been identified as potential biomarker for CRC cancer including EP300, STAT6, ATF3, ELK1, HNF4A, JUN, TAF1, IRF1, TP53, ELF1 and YY1. The results provides guidance for future omic study on CRC and additional validation work for potent biomarker for CRC.

<p dir="ltr">The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects but does not necessarily indicate the initial point of interference within a network. The objective of this project is to take advantage of large scale and genome-wide perturbational or Perturb-Seq datasets by using them to pre-train a generalist machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of single cell RNA sequencing data collected from CRISPR knock out screens in cell culture. The advent of generative machine learning algorithms, particularly transformers, make it an ideal time to re-assess large scale data libraries in order to grasp cell and even organism-wide genomic expression motifs. By tailoring an algorithm to learn the downstream effects of the genetic perturbations, we present a pre-trained generalist model capable of predicting the effects of multiple perturbations in combination, locating points of origin for perturbation in new datasets, predicting the effects of known perturbations in new datasets, and annotation of large-scale network motifs. We demonstrate the utility of this model by identifying key perturbational signatures in RNA sequencing data from spaceflown biological samples from the NASA Open Science Data Repository.</p>