Parp1 facilitates alternative NHEJ, whereas Parp2 suppresses IgH/c-myc translocations during immunoglobulin class switch recombination

Immunoglobulin class switch recombination (CSR) is initiated by DNA breaks triggered by activation-induced cytidine deaminase (AID). These breaks activate DNA damage response proteins to promote appropriate repair and long-range recombination. Aberrant processing of these breaks, however, results in decreased CSR and/or increased frequency of illegitimate recombination between the immunoglobulin heavy chain locus and oncogenes like c-myc. Here, we have examined the contribution of the DNA damage sensors Parp1 and Parp2 in the resolution of AID-induced DNA breaks during CSR. We find that although Parp enzymatic activity is induced in an AID-dependent manner during CSR, neither Parp1 nor Parp2 are required for CSR. We find however, that Parp1 favors repair of switch regions through a microhomology-mediated pathway and that Parp2 actively suppresses IgH/c-myc translocations. Thus, we define Parp1 as facilitating alternative end-joining and Parp2 as a novel translocation suppressor during CSR

Additional file 3: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S3. Quantile-quantile (QQ) plots for CpG site-specific analysis with respect to smoking using standard adjustment (a), residuals (b), ComBat (c) and SVA (d) correcting methods for the M values. The inflation factor λ is defined as the ratio of the median of the observed log10 transformed p values from the CpG site-specific analysis and the median of the expected log10 transformed p values. (PDF 110 kb

Additional file 2: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S2. Quantile-quantile (QQ) plots for CpG site-specific analysis with respect to smoking using standard adjustment (a), residuals (b), ComBat (c) and SVA (d) correcting methods for the β values. The inflation factor λ is defined as the ratio of the median of the observed log10 transformed p values from the CpG site-specific analysis and the median of the expected log10 transformed p values. (PDF 110 kb

Identifying and correcting epigenetics measurements for systematic sources of variation

Abstract Background Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features. In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. Results A sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals’ methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to ‘batch’ (1.3%) and ‘sample position’ (0.6%), and in a diminished variability attributable to ‘chip’ within a batch (0.9%). After ComBat or the residuals’ corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96). Conclusions The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation