Anti-CALM1 polyclonal antibody

Objectives - The purpose of this study was to identify genetic variants associated with severe coronary artery disease (CAD). Methods and Results - We used 3 case-control studies of white subjects whose severity of CAD was assessed by angiography. The first 2 studies were used to generate hypotheses that were then tested in the third study. We tested 12 077 putative functional single nucleotide polymorphisms (SNPs) in Study 1 (781 cases, 603 controls) and identified 302 SNPs nominally associated with severe CAD. Testing these 302 SNPs in Study 2 (471 cases, 298 controls), we found 5 (in LPA, CALM1, HAP1, AP3B1, and ABCG2) were nominally associated with severe CAD and had the same risk alleles in both studies. We then tested these 5 SNPs in Study 3 (554 cases, 373 controls). We found 1 SNP that was associated with severe CAD: LPA I4399M (rs3798220). LPA encodes apolipoprotein(a), a component of lipoprotein(a). I4399M is located in the protease-like domain of apolipoprotein(a). Compared with noncarriers, carriers of the 4399M risk allele (2.7% of controls) had an adjusted odds ratio for severe CAD of 3.14 (confidence interval 1.51 to 6.56), and had 5-fold higher median plasma lipoprotein(a) levels (P = 0.003). Conclusions - The LPA I4399M SNP is associated with severe CAD and plasma lipoprotein(a) levels.

With the advent of rapid sequencing technologies, making sense of all the genomic variations that we see among us has been a major challenge. A plethora of algorithms and methods exist that try to address genome interpretation through genotype-phenotype linkage analysis or evaluating the loss of function/stability mutations in protein. Critical Assessment of Genome Interpretation (CAGI) offers an exceptional platform to blind-test all such algorithms and methods to assess their true ability. We take advantage of this opportunity to explore the use of molecular dynamics simulation as a tool to assess alteration of phenotype, loss of protein function, interaction, and stability. The results show that coarse-grained dynamics based protein flexibility analysis on 34 CHEK2 and 1719 CALM1 single mutants perform reasonably well for class-based predictions for phenotype alteration and two-thirds of the predicted scores return a correlation coefficient of 0.6 or more. When all-atom dynamics is used to predict altered stability due to mutations for Frataxin protein (8 cases), the predictions are comparable to the state-of-the-art methods. The competitive performance of our straightforward approach to phenotype interpretation contrasts with heavily trained machine learning approaches, and open new avenues to rationally improve genome interpretation.