Allele-dependent interaction of LRRK2 and NOD2 in leprosy.

Leprosy, caused by Mycobacterium leprae, rarely affects children younger than 5 years. Here, we studied a multiplex leprosy family that included monozygotic twins aged 22 months suffering from paucibacillary leprosy. Whole genome sequencing identified three amino acid mutations previously associated with Crohn's disease and Parkinson's disease as candidate variants for early onset leprosy: LRRK2 N551K, R1398H and NOD2 R702W. In genome-edited macrophages, we demonstrated that cells expressing the LRRK2 mutations displayed reduced apoptosis activity following mycobacterial challenge independently of NOD2. However, employing co-immunoprecipitation and confocal microscopy we showed that LRRK2 and NOD2 proteins interacted in RAW cells and monocyte-derived macrophages, and that this interaction was substantially reduced for the NOD2 R702W mutation. Moreover, we observed a joint effect of LRRK2 and NOD2 variants on Bacillus Calmette-Guérin (BCG)-induced respiratory burst, NF-κB activation and cytokine/chemokine secretion with a strong impact for the genotypes found in the twins consistent with a role of the identified mutations in the development of early onset leprosy.

Grid-based International Network for Flu observation (g-INFO).

The 2009 H1N1 outbreak has demonstrated that continuing vigilance, planning, and strong public health research capability are essential defenses against emerging health threats. Molecular epidemiology of influenza virus strains provides scientists with clues about the temporal and geographic evolution of the virus. In the present paper, researchers from France and Vietnam are proposing a global surveillance network based on grid technology: the goal is to federate influenza data servers and deploy automatically molecular epidemiology studies. A first prototype based on AMGA and the WISDOM Production Environment extracts daily from NCBI influenza H1N1 sequence data which are processed through a phylogenetic analysis pipeline deployed on EGEE and AuverGrid e-infrastructures. The analysis results are displayed on a web portal (http://g-info.healthgrid.org) for epidemiologists to monitor H1N1 pandemics.

Innovative in silico approaches to address avian flu using grid technology.

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper proposes new approaches for the integration of existing data sources towards a global surveillance network for molecular epidemiology and in silico drug discovery.