Mouse expression data were normalized using the robust multiarray algorithm (1) using

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1 Supplementary Information Bioinformatics statistical analysis of microarray data Mouse expression data were normalized using the robust multiarray algorithm (1) using a custom probe set definition that mapped probes directly to Entrez Gene Ids ("MoGene10stv1_Mm_ENTREZG") (2). For the detection of differentially expressed genes, a linear model was fitted to the data and empirical Bayes moderated statistics were calculated using the limma package from Bioconductor (3). Adjustment of p- values was done by the determination of false discovery rates (FDR) using Benjamini- Hochberg procedure 3. Genes representing a change of 1.5-fold or greater and moderated p-value <0.05 were considered as differential expressed. GSEA was used in Pre-Ranked mode, where the list of genes was ranked according to the moderated t-statistic computed by limma. Gene-sets comprising all canonical pathways (c2.cp) categories defined by the Broad Institute Molecular Signatures Database (MSigDB) were tested. The significance of the gene sets each containing genes were estimated from 1000 permutations (4). Supplementary references 1. Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B., and Speed, T.P Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31:e Dai, M., Wang, P., Boyd, A.D., Kostov, G., Athey, B., Jones, E.G., Bunney, W.E., Myers, R.M., Speed, T.P., Akil, H. et al Evolving Gene/Transcript Definitions Significantly Alter the Interpretation of GeneChip Data. Nucleic Acids Res. 33: e175. 1

2 3. Smyth G K Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S. et al Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A 102:

3 Supplementary Figure Legends Figure S1.- Similar white blood cells and platelets count in peripheral blood from wild-type, Parp-2 -/-, and Parp-1 -/- mice. (A) Number of white blood cells (WBC) and (B) platelets in peripheral blood from mice were determined on an Abacus Junior Vet Haematology Blood Analyser. Each symbol represents a single mice, with the bar indicating the mean. Figure S2.- Representative dot-plots of biotin-labeled RBCs at different time points. Red blood cells (RBCs) from wild-type (WT) and Parp-2 -/- mice were biotin-labeled in vivo. Animals were bled at different time points, and biotinylated RBCs in circulating blood were defined by staining for Ter119 and streptavidin. Percentage of Ter119 + cells labelled for biotin/streptavidin is indicated in each quadrant. Values represent the mean ± SEM obtained from two independent experiments including at least 5 mice per genotype in each experiment. Figure S3.- Flow cytometry gating strategies. (A) Flow cytometry gating strategies used to analyze erythroblasts subsets. Cell doublets were excluded from all analyses by using the FSC-A and FSC-H parameters. Cell death was excluded by DAPI staining. Erythroid maturation was examined by using the expression of the cell-surface markers Ter119 and CD71 and the FSC-A parameter. (B) Flow cytometry gating strategies used to analyze cell cycle distribution in ProE, Ery.A, Ery.B and Ery.C cells from mouse bone marrow. Cell doublets were excluded from all analyses by using the FSC-A and FSC-H parameters. Erythroid maturation was examined by using the expression of the cell-surface markers Ter119 and CD71 and the FSC-A parameter as indicated in figure 3

4 A. The cell cycle status in each fixed erythroblast population was assessed by BrdU staining to detect DNA synthesis and with DAPI to gauge the amount of DNA per cell. Figure S4.- Normal erythroid differentiation and cell cycle profiles in Parp-1 -/- mice. (A) Representative dot-plots showing erythroblast differentiation in bone marrow from WT and Parp-1 -/- mice, defined by staining for CD71 and Ter119-differentiation markers and forward scatter (FSC) distribution. Percentage of cells in the individual subpopulations is indicated in each quadrant. Enucleated cells were lysed using ACK lysis buffer. (B) Graph showing the absolute number of ProE, Ery.A, Ery.B and Ery.C cells in bone marrow from WT and Parp-1 -/- mice. (C) Representative cell cycle profiles of WT and Parp-1 -/- erythroid progenitor cells. Bone marrow cells were isolated from mice that were injected 1 hour earlier with BrdU, surface stained for CD71 and Ter119, to define erythroblast subsets as indicate above, and analyzed for BrdU incorporation and DNA content (DAPI staining) in each population. The percentage of cells in each quadrant represents the mean from at least 6 mice in each group. (D) Graph showing percentage of cells that are in G0/G1, S, and G2/M phases of cell cycle. Values represent the mean ± SEM obtained from at least 6 mice per genotype. Figure S5.- Gene expression profile analysis of wild-type and Parp-2 -/- erythroblasts. (A) Heat map representing the average normalized intensity values of all genes that are differentially expressed between wild-type (WT) and Parp-2 -/- erythroblasts. Red indicates higher expression whereas green indicates lower expression. (B) Number of genes that were up-regulated (yellow bars) or downregulated (blue bars) in erythroblasts from Parp-2 -/- compared to WT cells. 4

5 Figure S6.- Decreased number of Ter119 + fetal liver cells in Parp-2 -/- embryos is strengthened by loss of p21. (A) Representative pictures of Ter119 immunohistochemistry in fetal livers from WT, Parp-2 -/- p21 +/+, Parp-2 +/+ p21 -/-, and Parp-2 -/- p21 -/- embryos. Ter119 is localized in the plasma membrane of erythroid cells. (B) Quantification of the percentage of Ter119-positive cells in the different embryos. Three different regions of fetal liver from 2 embryos from each genotype were analysed. *, Statistically significant difference (P<0.05). Figure S7.- Comprehensive model for genomic instability in Parp-2 -/- erythroblasts leading to chronic anemia. 5