A Recombinant Vaccine Effectively Induces C5a-Specific Neutralizing Antibodies and Prevents Arthritis

OBJECTIVES: To develop and validate a recombinant vaccine to attenuate inflammation in arthritis by sustained neutralization of the anaphylatoxin C5a. METHODS: We constructed and expressed fusion protein of C5a and maltose binding protein. Efficacy of specific C5a neutralization was tested using the fusion protein as vaccine in three different arthritis mouse models: collagen induced arthritis (CIA), chronic relapsing CIA and collagen antibody induced arthritis (CAIA). Levels of anti-C5a antibodies and anti-collagen type II were measured by ELISA. C5a neutralization assay was done using a rat basophilic leukemia cell-line transfected with the human C5aR. Complement activity was determined using a hemolytic assay and joint morphology was assessed by histology. RESULTS: Vaccination of mice with MBP-C5a led to significant reduction of arthritis incidence and severity but not anti-collagen antibody synthesis. Histology of the MBP-C5a and control (MBP or PBS) vaccinated mice paws confirmed the vaccination effect. Sera from the vaccinated mice developed C5a-specific neutralizing antibodies, however C5 activation and formation of the membrane attack complex by C5b were not significantly altered. CONCLUSIONS: Exploitation of host immune response to generate sustained C5a neutralizing antibodies without significantly compromising C5/C5b activity is a useful strategy for developing an effective vaccine for antibody mediated and C5a dependent inflammatory diseases. Further developing of such a therapeutic vaccine would be more optimal and cost effective to attenuate inflammation without affecting host immunity

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe

Predicting Chronic Heart Failure Using Diagnoses Graphs

Part 5: MAKE AALInternational audiencePredicting the onset of heart disease is of obvious importance as doctors try to improve the general health of their patients. If it were possible to identify high-risk patients before their heart failure diagnosis, doctors could use that information to implement preventative measures to keep a heart failure diagnosis from becoming a reality. Integration of Electronic Medical Records (EMRs) into clinical practice has enabled the use of computational techniques for personalized healthcare at scale. The larger goal of such modeling is to pivot from reactive medicine to preventative care and early detection of adverse conditions. In this paper, we present a trajectory-based disease progression model to detect chronic heart failure. We validate our work on a database of Medicare records of 1.1 million elderly US patients. Our supervised approach allows us to assign likelihood of chronic heart failure for an unseen patient’s disease history and identify key disease progression trajectories that intensify or diminish said likelihood. This information will be a tremendous help as patients and doctors try to understand what are the most dangerous diagnoses for those who are susceptible to heart failure. Using our model, we demonstrate some of the most common disease trajectories that eventually result in the development of heart failure