Adaptive immune system | EPFL Graph Search

The adaptive immune system, also known as the acquired immune system, or specific immune system is a subsystem of the immune system that is composed of specialized, systemic cells and processes that eliminate pathogens or prevent their growth. The acquired immune system is one of the two main immunity strategies found in vertebrates (the other being the innate immune system). Like the innate system, the adaptive immune system includes both humoral immunity components and cell-mediated immunity components and destroys invading pathogens. Unlike the innate immune system, which is pre-programmed to react to common broad categories of pathogen, the adaptive immune system is highly specific to each particular pathogen the body has encountered. Adaptive immunity creates immunological memory after an initial response to a specific pathogen, and leads to an enhanced response to future encounters with that pathogen. Antibodies are a critical part of the adaptive immune system. Adaptive imm

Evidence of dysregulation of dendritic cells in primary HIV infection

Jacques Fellay, Nan Hu, Marie Larsson

Myeloid and plasmacytoid dendritic cells (DCs) are important mediators of both innate and adaptive immunity against pathogens such as HIV. During the course of HIV infection, blood DC numbers fall substantially. In the present study, we sought to determine how early in HIV infection the reduction occurs and whether the remaining DC subsets maintain functional capacity. We find that both myeloid DC and plasmacytoid DC levels decline very early during acute HIV infection. Despite the initial reduction in numbers, those DCs that remain in circulation retain their function and are able to stimulate allogeneic T-cell responses, and up-regulate maturation markers plus produce cytokines/chemokines in response to stimulation with TLR7/8 agonists. Notably, DCs from HIV-infected subjects produced significantly higher levels of cytokines/chemokines in response to stimulation with TLR7/8 agonists than DCs from uninfected controls. Further examination of gene expression profiles indicated in vivo activation, either directly or indirectly, of DCs during HIV infection. Taken together, our data demonstrate that despite the reduction in circulating DC numbers, those that remain in the blood display hyperfunctionality and implicates a possible role for DCs in promoting chronic immune activation.

2010

Investigation of transcriptional partners and genes downstream of Foxn1

Zineb Bekkali

The thymus is the second fundamental organ of the immune system after the bone marrow; it is the essential for T cell maturation and repertoire selection. The function of the thymus critically depends on the thymic epithelium, which is structured in two distinct regions: The cortex and the medulla. The knowledge about molecular mechanisms involved in thymus development is limited. However, some evidences suggest that the Hox, Pax, Six, Eya, Gcm2, FGFs and Foxn1 are key players in thymus organogenesis. Foxn1 is essential for thymic epithelial cell development but its exact role is still unknown. The null mutation in the transcription factor Foxn1 generates the nude phenotype of athymia and hairlessness. The aim of our laboratory is to better understand thymus development and specially focuses on the Foxn1 function. The aim of this project is then to investigate target genes and transcription partners of Foxn1 as a transcriptional factor. As a transcription factor, Foxn1 protein should be able to bind with DNA but should also interact with other proteins such as cofactors in order to activate its target genes. At present, the transcriptional partners of Foxn1 are completely unknown in the literature and the proteinprotein interaction databases. The experimental approach proposed here is the standard method for identification of unknown transcriptional partners: Immunoprecipitation (IP) followed by a mass spectrometry analysis. The list of protein candidates can then be validated by Western blotting. We identified the right antibodies to use for this procedure and optimised protocols for the IP. We also performed a mass spectrometry analysis that was not conclusive, however, the platform is now established to complete this analysis. Only little is known about genes up- or down-stream Foxn1. Some genes such as FgfR2IIIb, DLL4 and CCL25 are believed to be up-regulated by Foxn1. Our laboratory has generated a revertible hypomorphic allele of Foxn1 and have used this to obtain a series of six allelic combinations at Foxn1. These mice, expressing different levels of Foxn1, are a very useful tool in order to investigate target genes of Foxn1. In particular, we wish to investigate whether different Foxn1 targets are activated at different Foxn1 levels. 6 To reach this objective, the proposed approach is the comparison of gene expression profiles of thymic epithelial and mesenchymal cells of the allelic series at two different time points E13.5 and E15.5 using a Solexa tool called SAGE (Serial Analysis of Gene Expression). As preliminary analysis before the global gene expression analysis, we have determined expression profiles of several genes across the allelic series by quantitative PCR. Our findings suggest that DLL4, CCL25, FgfR2IIIb, FGF10 and possible Pax1 are direct or indirect targets of Foxn1 activated at different levels of Foxn1. DLL4 and CCL25 seem to be involved in lymphocyte attraction and maturation. FgfR2IIIb appears to be important for thymic epithelium differentiation and development by increasing the sensitivity of epithelial cells to FGF10 signalling. Finally, Pax1 is possibly required downstream of Foxn1 for terminal differentiation of cortical thymic epithelial cells

2009

Statistical Analysis of Chao’s Immune System Simulation

Eric Winnington

Modeling the immune system (IS) means putting together a set of assumptions about its components (cells and organs) and their interactions. Simulations of a model show joint behavior of the components, which for complex realistic models is often impossible to find analytically. Simulations allow us to experiment on how initial concentrations and properties of the immune cells and viruses impact the IS behavior, and gain better quantitative and qualitative insight into how the IS works and why different behavior patterns occur. A simulation, once it has been created, must be reviewed both statistically and analytically as well as validated from the biological point of view. We analyzed Chao’s immune system simulation [1][2] from a statistical and analytical point. We explicited both the Markov chain which was simulated and the underlying process on which Chao’s stage-structured approach was built. Furthermore, we established a test protocol for timestep validation which Chao’s simulator passed. We evaluated Chao’s simulator’s dependence on the random number generator, which was shown to be negligible. Finally, we evaluated the simulator output and our major result is the discovery of a secondary response to a primary infection, an occurrence is not shown in Chao’s dissertation. A tertiary response to the infection is never possible due to the size of the secondary response caused by memory cells.

2005

Investigation of transcriptional partners and genes downstream of Foxn1

Zineb Bekkali

2009