A Critical Role of Perinuclear Filamentous Actin in Spatial Repositioning and Mutually Exclusive Expression of Virulence Genes in Malaria Parasites

SummaryMany microbial pathogens, including the malaria parasite Plasmodium falciparum, vary surface protein expression to evade host immune responses. P. falciparium antigenic variation is linked to var gene family-encoded clonally variant surface protein expression. Mututally exclusive var gene expression is partially controlled by spatial positioning; silent genes are retained at distinct perinuclear sites and relocated to transcriptionally active locations for monoallelic expression. We show that var introns can control this process and that var intron addition relocalizes episomes from a random to a perinuclear position. This var intron-regulated nuclear tethering and repositioning is linked to an 18 bp nuclear protein-binding element that recruits an actin protein complex. Pharmacologically induced F-actin formation, which is restricted to the nuclear periphery, repositions intron-carrying episomes and var genes and disrupts mutually exclusive var gene expression. Thus, actin polymerization relocates var genes from a repressive to an active perinuclear compartment, which is crucial for P. falciparium phenotypic variation and pathogenesis

Infection‐driven activation of transglutaminase 2 boosts glucose uptake and hexosamine biosynthesis in epithelial cells

DATA AVAILABILITYThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD017117.International audienceTransglutaminase 2 (TG2) is a ubiquitously expressed enzyme with transamidating activity. We report here that both expression and activity of TG2 are enhanced in mammalian epithelial cells infected with the obligate intracellular bacteria Chlamydia trachomatis. Genetic or pharmacological inhibition of TG2 impairs bacterial development. We show that TG2 increases glucose import by up-regulating the transcription of the glucose transporter genes GLUT-1 and GLUT-3. Furthermore, TG2 activation drives one specific glucose-dependent pathway in the host, i.e., hexosamine biosynthesis. Mechanistically, we identify the glucosamine:fructose-6-phosphate amidotransferase (GFPT) among the substrates of TG2. GFPT modification by TG2 increases its enzymatic activity, resulting in higher levels of UDP-N-acetylglucosamine biosynthesis and protein O-GlcNAcylation. The correlation between TG2 transamidating activity and O-GlcNAcylation is disrupted in infected cells because host hexosamine biosynthesis is being exploited by the bacteria, in particular to assist their division. In conclusion, our work establishes TG2 as a key player in controlling glucose-derived metabolic pathways in mammalian cells, themselves hijacked by C. trachomatis to sustain their own metabolic needs

Hierarchical manifold learning for the interpretation of multi-level data -Application to cardiac imaging

International audienc

Hierarchical manifold learning for the interpretation of multi-level data -Application to cardiac imaging

International audienc

Hierarchical manifold learning for the interpretation of multi-level data -Application to cardiac imaging

International audienc

Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning

International audienceWe aim at better understanding the mechanisms of ischemia and reperfusion, in the context of acute myocardial infarction. For this purpose, imaging and in particular magnetic resonance imaging are of great value in the clinic, but the richness of the images is currently under exploited. In this paper, we propose to characterize myocardial ischemia and reperfusion patterns across a population beyond the scalar measurements used in the clinic. Specifically, we adapted representation learning techniques to not only characterize the population distribution in terms of scar and microvascular obstruction patterns, but also regarding the appearance of late gadolinium images which reflects tissue heterogeneity. To do so, we implemented a hierarchical manifold learning approach where the embedding from a higher-level content (the images) is guided by one from a lower-level content (the infarct and microvascular obstruction segmentations). We demonstrate its relevance on 1711 late gadolinium enhancement slices from 123 patients with acute ST-elevation myocardial infarction. We designed ways to balance the contribution of each level in the hierarchy, and quantify its impact on the overall distribution and on sample neighborhoods. We notably observe that the obtained latent space is a balanced contribution between the two levels of the hierarchy, and is more robust to challenging images subjected to artifacts or specific lesion patterns

Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning

International audienceWe aim at better understanding the mechanisms of ischemia and reperfusion, in the context of acute myocardial infarction. For this purpose, imaging and in particular magnetic resonance imaging are of great value in the clinic, but the richness of the images is currently under exploited. In this paper, we propose to characterize myocardial ischemia and reperfusion patterns across a population beyond the scalar measurements used in the clinic. Specifically, we adapted representation learning techniques to not only characterize the population distribution in terms of scar and microvascular obstruction patterns, but also regarding the appearance of late gadolinium images which reflects tissue heterogeneity. To do so, we implemented a hierarchical manifold learning approach where the embedding from a higher-level content (the images) is guided by one from a lower-level content (the infarct and microvascular obstruction segmentations). We demonstrate its relevance on 1711 late gadolinium enhancement slices from 123 patients with acute ST-elevation myocardial infarction. We designed ways to balance the contribution of each level in the hierarchy, and quantify its impact on the overall distribution and on sample neighborhoods. We notably observe that the obtained latent space is a balanced contribution between the two levels of the hierarchy, and is more robust to challenging images subjected to artifacts or specific lesion patterns

Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning

International audienceWe aim at better understanding the mechanisms of ischemia and reperfusion, in the context of acute myocardial infarction. For this purpose, imaging and in particular magnetic resonance imaging are of great value in the clinic, but the richness of the images is currently under exploited. In this paper, we propose to characterize myocardial ischemia and reperfusion patterns across a population beyond the scalar measurements used in the clinic. Specifically, we adapted representation learning techniques to not only characterize the population distribution in terms of scar and microvascular obstruction patterns, but also regarding the appearance of late gadolinium images which reflects tissue heterogeneity. To do so, we implemented a hierarchical manifold learning approach where the embedding from a higher-level content (the images) is guided by one from a lower-level content (the infarct and microvascular obstruction segmentations). We demonstrate its relevance on 1711 late gadolinium enhancement slices from 123 patients with acute ST-elevation myocardial infarction. We designed ways to balance the contribution of each level in the hierarchy, and quantify its impact on the overall distribution and on sample neighborhoods. We notably observe that the obtained latent space is a balanced contribution between the two levels of the hierarchy, and is more robust to challenging images subjected to artifacts or specific lesion patterns

Functional Imaging and Modeling of the Heart, 12th International Conference, FIMH 2023

International audienc

Caractérisation statistique de l'infarctus du myocarde pour la personnalisation de modèles géométriques de lésions

International audienceno abstractIntroduction: Afin de réduire l'impact sur le patient ischémique, des algorithmes d'apprentissage cherchent à caractériser la viabilité du myocarde à partir de sa déformation extraite de modalités non invasives. La génération de données synthétiques a un fort potentiel pour valider ces algorithmes, grâce à des modèles géométriques de lésion combinés à la simulation de la fonction cardiaque. Néanmoins, leur réalisme reste un problème ouvert