The computational modeling algorithm for the hierachical cellular organization of HSPC was

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1 Supplement Computational modeling algorithm The computational modeling algorithm for the hierachical cellular organization of HSPC was developed based on the assumption of increasing transcriptional silencing during differentiation of HSPC from HSC over CMP to GMP or MEP, respectively. Gene correlation matrices reflecting the complexity of gene interactions within the distinct HSPC subsets were generated. The complexity of the matrices is measured as message entropy which decreases from HSC over CMP to MEP and GMP in normal donors. Gene networks were considered as functional groups and identified based on network topology. Such networks are connected by hub nodes which were divided in order to form groups of subnetworks with the lowest possible number of interconnections. The node with the highest correlation entropy was used as root node. Once the network has been divided, expression differences in the subnetworks between different stem and progenitor cell subsets were tested for their potential to distinguish between subsets. The number of multiple changes in the subnetworks was then calculated for every possible combination of the four stem and progenitor cells subsets. To infer the most likely differentiation path, combinations were ranked according to the number of multiple changes in the subnetworks ranking the combination with the fewest multiple changes highest. A detailed description of the algorithm is available online at Methods PCR PCR primer sequences were as follows: GAPDH sense GAPDH antisense - TCCATGACAACTTTGGTATCG - GTCGCTGTTGAAGTCAGAGGA

2 TBRI sense TBRI antisense Smad2 sense Smad2 antisense - GGAACAAAAAGGTACATGGCC - TCAACTGATGGGTCAGAAGG - TATATTCCAGAAACGCCACCTC - TGAGTGAGGGCTGTGATGC For detection of TGF beta 1 the QuantiTect Primer Assay HS-TGFB1_1_SG (Qiagen, Hilden, Germany) was used. The fold change was calculated from the formula 2 Ct. Supplementary Figure legends Supplementary Figure 1. CLP, pdc and B cell precursors are reduced in the BM of MM patients. The distribution of CLP, pdc and B cell precursors was determined in BM samples of healthy donors and MM patients. Representative FACS plots showing CLP of healthy donors and MM patients are provided in (A). CLP concentrations in the BM were calculated based on the number of total nucleated cells (TNC) per µl BM aspirate. Bar chart represents the mean concentration of CLP in the BM of healthy donors and MM patients (B). Mean percentages of pdc, pro-b cells, pre-b cells, mature B cells and immature B cells in BM MNC of healthy donors (white bars) and MM patients (black bars) are given in C. Error bars represent SEM. The Student's t-test was used to detect statistically significant differences. Asterisks indicate p-values *p<0.05, **p<0.01, ***p< Supplementary Figure 2. Gene map of the TGF beta signaling pathway. Visualization of molecular interaction and reaction networks within deregulated molecular pathways obtained from the KEGG database. (A) TGF beta signaling. Red colour represents up-regulation, green colour represents down-regulation, and white colour represents no significant change in HSPC. Supplementary Figure 3. Concentrations of HSPC-inhibitory cytokines in the BMEF. Concentrations of MIP-1 alpha, IL-1 beta and TNF alpha were determine by ELISA. Mean concentrations of MIP-1 alpha, IL-1 beta and TNF alpha in the BMEF of healthy donors (white bars) and MM patients (black bars) are shown. The student's t-test was used to detect statistically significant differences. Asterisk indicates a p-values *p<0.05.

3 Supplementary Figure 4. TGF beta, TBRI and Smad2 gene expression levels in HSPC of healthy donors and MM patients. (A) Expression levels of TGF beta, TBRI and Smad2 were assessed in HSPC of 6 healthy donors (white bars) and 6 MM patients by real-time (RT)-PCR. Values are the average of duplicate experimental replicates. The healthy donor value was set to 1. The error bars represent the corresponding SEM. Asterisks indicate p- values *p<0.05, **p<0.01. Supplementary Figure 5. Effect of p38 MAPK and NFKB signaling pathway inhibition on the colony formation of BM HSPC of MM patients. Purified HSPC from the BM of 6 MM patients were seeded into semisolid ready-to-use methylcellulose growth medium at a concentration of 5 x 10 2 cells/ml after preincubation with vehicle, the p38 MAPK inhibitor SCIO-469 or the NFKB inhibitor TPCA-1. Colony numbers were counted after 2 weeks under an inverted light microscope. Proportions of colonies (BFU-E/CFU-E, CFU-G/CFU-M/CFU- GM, CFU-GEMM) derived from BM HSPC of MM patients treated with vehicle (black bars), SCIO-469 (white bars) and TPCA-1 (hedged bars) are shown. The error bars represent the corresponding SEM.

4 Supplementary Table 1. Patient # Age Sex Paraprotein Subtype Stage [DS] Stage [ISS] BMinfiltration [%] Hgb [g/dl] WBC [1000/µL] Platelets [1000/µL] Calcium [mm] 1 72 f IgG kappa IIIA I m IgG kappa IA II m IgG lambda IA I f IgG kappa IIIA III f LC lambda IIIB II m LC kappa IIIB III f IgA lambda IIIB II m IgG kappa IIIA II f IgG lambda IA I m IgA lambda IIIA I m IgG lambda IIIA n.a f IgG+IgA kappa IIIA II f IgA kappa IIIA II f IgG kappa IA II f IgG lambda IA n.a f IgG lambda IIIA n.a m LC kappa IA II f IgA kappa IIIB II m IgA lambda IIIB I f IgG kappa IIA II m IgG kappa IIIA n.a f IgG lambda IIIA n.a m IgG lambda IA I f IgG lambda IIIA II m IgG kappa IIIA III m IgG kappa IIIA II m IgG kappa IIIA II m IgA lambda IIIA I m LC kappa IIIA I f IgG kappa IA I m IgG lambda IIIA I f IgG lambda IIIA II f IgG lambda IIA I f IgA lambda IA I f IgA kappa IIA II m IgG kappa IIIA I m IgA kappa IIIA II f IgG kappa IIIA I m IgG kappa IA II f IgG kappa IIIA I m IgA kappa IIA II m IgA lambda IIIA I m IgA lambda IIIA III m IgG kappa IIIA II m IgG lambda IIIB III f IgG kappa IIIA I m IgG kappa IIIA III m LC lambda IIIB III m IgG lambda IIIA II m LC lambda IA II m LC kappa IIIA II m LC lambda IIIB III f IgA kappa IIIA II f LC kappa IIB III f IgG kappa IIIA II m IgG kappa IIIB II f IgG lambda IIIA III f IgG kappa IIIA III f IgA lambda IA I n.a f IgG lambda IIIA II f LC kappa IIIA I Osteolyses 0 1 = = 1 > 5 = 2

5 62 71 m IgG lambda I I m IgG kappa IIIB III f IgA kappa IA I f IgA kappa III III m IgG lambda IIIA III m FC kappa IIIA III m FC kappa IA I f IgG kappa IIIA II f IgG lambda IIIA III m IgG kappa IIIA I

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