Invasion of epithelial cells by the obligate intracellular bacterium Chlamydia trachomatis results in its enclosure inside a membrane-bound compartment termed an inclusion. The bacterium quickly begins manipulating interactions between host intracellular trafficking and the inclusion interface, diverging from the endocytic pathway and escaping lysosomal fusion. We have isolated a mutant strain that shows several developmental defects. The C3BI will contribute to the statistical analysis of the data.

Notre but est de trouver un test statistique capable de dire si deux matrices de nombres sont différents. Ces matrices correspondent à l’utilisation de fragments de gènes (V ou J) pour créer un gène ré-arrangé (V-J) codant pour un anticorps de spécificité particulière. Ici nous avons représenté pour chaque gène V la famille de gène J utilisé (parmi J1, J2, J3, J4). Le nombre correspondant au nombre d’occurrences que nous avons trouvé après séquençage du répertoire d’anticorps spécifiques à partir de souris immunisées contre l’un de ces antigènes. Les matrices ont été réalisées pour les deux chaines codant la spécificité d’un anticorps: la chaine lourde (VH) ou la chaine légère (VL). Le but est de comparer les deux matrices VH l’une avec l’autre, et les deux matrices VL l’une avec l’autre.

The present work is to systematically investigate the role of TLRs and NODs in the host defense during C. trachomatis infection using KO animals. The inflammatory cytokines and bacterial burden will be measured using Bio-Plex ELISA kit. C3BI will provide help in the data analysis.

Non human primates are an important reservoir for zoonotic disease. Here we analyze in Cameroon how human activities in the forest influence contact with non human primates to better understand processes of emergence.

Quantitatively understanding the stochastic dynamics of gene expression requires measurements at the level of single cells. A common approach to follow the expression of genes in single cells and in real time is to make use of fluorescent reporter proteins and to record the cells' fluorescence by microscopy. However, this provides only an indirect readout of the biological processes that are of interest such as the regulation mechanisms at the promoter. A possible way to uncover the unobservable biological processes is to infer the hidden dynamics from the available data through the use of mechanistic models of gene expression. The goal of this project is to develop methods for state estimation and parameter inference for such models and to test these methods on real data.

Réalisation d'un interactome entre des protéines de mammifères et une protéine bactérienne de virulence.

Mood disorders such as bipolar and major depressive illnesses are among the most severe psychiatric disorders. They have high prevalence and chronic course, and are associated with significant mental and somatic comorbidities and high personal and societal costs (lost productivity and increased medical expenses). Patients with bipolar disorder (BD), for example, exhibit a reduced lifespan compared with the general population, a finding that cannot only be explained by high suicide risk, reduced access to medical care and lifestyle factors. However, the pathophysiological mechanisms of BD are poorly understood, and patients often have incomplete treatment response. Advanced mathematical approaches such as machine learning techniques are increasingly being used to generate predictions based on complex data, and it has been successfully used to detect a number of clinical outcomes and to predict behaviours. In combination with mobile technologies (e.g. smartphones, wearables) to collect behavioural, physiological and environmental data, these big data predictive approaches may provide a much richer and deeper understanding of phenomenology and pathophysiological mechanisms of mood and bipolar disorders. By taking advantage of the high-standard bioinformatics expertise offered by the C3BI, this multidisciplinary, collaborative project aims to explore how clinical and biological factors, may contribute for better characterizing BD patients as well as to identify predictors of treatment response in BD. Our project also aims to explore how daily behavioural and physiological parameters may influence mood and behaviour in individuals at-risk or suffering from mood disorders.

Our goal is to have a bioinformatic tool that performs the 2 x 2 comparison of matrices of numbers in matrix batches. Each matrix corresponds to the use of gene fragments (V or J) to create a re-arranged gene (VJ) encoding a specific antibody, identified by droplet microfluidic or flow cytometry techniques followed by high throughput sequencing (NGS). ). The matrices are made for the two chains encoding the specificity of an antibody: the heavy chain (VH) or the light chain (VL). The goal is to compare VH matrices one against another, and VL matrices one against another for antibody gene rearrangements in mice, llama, and humans.

Because of the fast-growing number of assembled genomes available in the public repositories (GenBank/RefSeq), it is now quite common to end up with many genome assemblies that belong to the same species. In this current context, determining the type strain/species of any new species/genus requires to identify the representive one(s) within a large collection of genomes. The medoid of a genome collection being the one(s) whose sum of distances to all other genomes is minimal, it is an excellent candidate to be the representative genome of a collection. We therefore aim at procuring a bioinformatic tool able to quickly and accurately identify the medoid of any genome collections.