Comparative transcriptomics of pathogenic and non-pathogenic Listeria species

Comparative RNA-seq analysis of two related pathogenic and non-pathogenic bacterial strains reveals a hidden layer of divergence in the non-coding genome as well as conserved, widespread regulatory structures called ‘Excludons', which mediate regulation through long non-coding antisense RNAs

Auto-antibodies targeting brain antigens in Plasmodium falciparum infected patients as biomarkers of Cerebral Malaria

International audiencen.

An RNA-Binding Protein Secreted by a Bacterial Pathogen Modulates RIG-I Signaling.

RNA-binding proteins (RBPs) perform key cellular activities by controlling the function of bound RNAs. The widely held assumption that RBPs are strictly intracellular has been challenged by the discovery of secreted RBPs. However, extracellular RBPs have been described in eukaryotes, while secreted bacterial RBPs have not been reported. Here, we show that the bacterial pathogen Listeria monocytogenes secretes a small RBP that we named Zea. We show that Zea binds a subset of L. monocytogenes RNAs, causing their accumulation in the extracellular medium. Furthermore, during L. monocytogenes infection, Zea binds RIG-I, the non-self-RNA innate immunity sensor, potentiating interferon-β production. Mouse infection studies reveal that Zea affects L. monocytogenes virulence. Together, our results unveil that bacterial RNAs can be present extracellularly in association with RBPs, acting as "social RNAs" to trigger a host response during infection

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Reduction de dimensionalité et analyse des réseaux de voies de signalisation pour les données de transcriptome: Appliquation à la caractérisation des cellules T.

In the context of whole-genome expression (transcriptome) data analysis, different tools already exist today. One class of tools, called dimensionality reduction techniques, seeks for general patterns and important components of the system which can help to summarize the data. During my thesis I extensively studied the different state-of-the-art techniques existing in this field. We then developed our own approach based on the combination of Singular Value Decomposition and Multidimensional Scaling. We proved its usefulness and accuracy. In addition to gene expression-specific data analysis tools, we developed a software which allows to map different gene expression patterns to protein-protein networks. In order to link the gene expression scale to the protein scale (proteome). Those protein-protein networks are built based on curated ontology-based pathway models. The tools developed here and many others were used in order to analyze different "omics" data. The first application was on the analysis of experiments measuring autoantibodies and cytokine expression in the human body during Malaria infection. We determined specific markers of Cerebral Malaria, which will help to better detect the disease. The larger analysis we have performed, consisted in defining the transcriptome profile of regulatory T-cell subsets (Treg). These cells are depleted during HIV infection, for this reason a good molecular characterization of the different subsets would help find more accurate markers to, for example, follow their evolution during the treatment with novel drugs to fight AIDS. Among the new molecular markers of Treg we identified, a new transcription factor FOXLF was discovered which may play an important role in the regulation of the "regulatory" function of those cells.Dans le contexte de l'étude pan-génomique de données d'expression des gènes (transcriptome), différents outils existent déjà. Parmi eux, les techniques de réduction de dimensionnalité cherchent les formes remarquables et les composants importants du système qui peuvent aider à résumer les données. Au cours de ma thèse, j'ai étudié en profondeur les différentes techniques existantes dans ce domaine. Nous avons ensuite développé notre propre approche basée sur la combinaison de la décomposition en valeurs singulières (Singular Value Decomposition) et le Multidimensional Scaling. Nous avons prouvé son utilité et sa précision. En plus des outils d'analyse de données spécifiques à l'étude de l'expression des gènes, nous avons développé un logiciel qui permet de correler l'expression des gènes à des réseaux d'interactions protéine-protéine. Et ceci afin de lier l'information sur l'expression des gènes à celle des interactions entre protéines (protéome) qui ont lieu au sein de la cellule. Tous les outils venant d'être décrits et de nombreux autres ont été utilisés afin d'analyser différent types de données biologiques. La première application a été de corréler l'expression d'auto-anticorps et de cytokines dans le corps humain lors d'une infection au paludisme. Nous avons déterminé des marqueurs spécifiques du paludisme cérébral, permetant à termes de prévenir et détecter plus tôt la maladie. La plus grande analyse que nous avons réalisé visait à définir le profil du transcriptome des cellules T régulatrices (Treg). Ces cellules sont détruites au cours d'une infection par le VIH, une bonne caractérisation moléculaire de celles-ci permettrait par exemple de mieux suivre l'évolution des Treg au cours des traitements pour le SIDA. Parmi les nouveaux marqueurs moléculaires de Treg que nous avons étudié, un nouveau facteur de transcription FOXLF a été découvert, qui pourrait jouer un rôle important dans l'apparition du caractère de "regulation" chez les Treg