mir-9 supplementary revision leukemia · targets, vsn-normalized values of the mir-sc condition...
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MIR-9 IN PEDIATRIC AML
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Supplementary Material
Emmrich et al. miR-9 in pediatric AML
Supplementary Figures
Supplementary Figure 1. miRNA expression stratifies different cytogenetic types of pediatric AML. Unsupervised hierarchical correlation clustering of AML samples (n=90) and most discriminative miRNA values (n=253). Colors at the patient sample branches denote AML cytogenetic subgroups as indicated at the top right. Heatmap colors indicate miRNA expression values as indicated at the top left.
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miR-126
10 -15 10 -10 10 -5 10 0 10 510 -15
10 -10
10 -5
10 0
10 5
Spearman R=0.95p<0.0001
TLDA
Sin
gle
miR-151-3p
10 -20 10 -15 10 -10 10 -5 10 010 -20
10 -15
10 -10
10 -5
10 0
Spearman R=0.68p=0.0018
TLDA
Sin
gle
miR-181a
10 -15 10 -10 10 -5 10 010 -15
10 -10
10-5
10 0
Spearman R=0.80p=0.0001
TLDA
Sin
gle
miR-181c
10 -20 10 -15 10 -10 10 -5 10 010-20
10-15
10-10
10 -5
10 0
Spearman R=0.10p=0.7115
TLDA
Sin
gle
miR-192
10 -15 10 -10 10 -5 10 010 -15
10 -10
10 -5
10 0
Spearman R=0.43p=0.0969
TLDA
Sin
gle
miR-500
10 -15 10 -10 10 -5 10 0
10 -15
10 -10
10 -5
10 0
Spearman R=0.75p=0.0002
TLDA
Sin
gle
miR-532-5p
10 -15 10 -10 10 -5 10 010 -15
10 -10
10 -5
10 0
Spearman R=0.62p=0.0050
TLDA
Sin
gle
miR-574-3p
10 -10 10 -8 10 -6 10 -4 10 -210 -20
10 -15
10 -10
10 -5
10 0
Spearman R=0.78p<0.0001
TLDA
Sin
gle
miR-660
10 -15 10 -10 10 -5 10 010 -15
10 -10
10 -5
10 0
Spearman R=0.78p<0.0001
TLDA
Sin
gle
miR-9
10 -3 10 -2 10 -1 10 0 10 110 -2
10 -1
10 0
10 1
10 2
10 3
Spearman R=0.91p<0.0001
TLDA
Sin
gle
Supplementary Figure 2. Correlation between single and multiplex microRNA assays for a selection of 10 microRNAs. Figures display expression measured by multiplex miRNA assay vs. expression measured by singleplex miRNA assay for 10 microRNAs. Reproducibility of miR-9 measurements is very good: Spearman R=0.91, p<0.0001.
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Supplementary Figure 3. Expression of miR-9. (a) Expression of miR-9-5p as measured by TLDA in CD34+-HSPCs (control) and blasts from patients with t(8;21) and MLL-rearrangement. Expression is normalized to t(8;21) patients. (b) Expression of miR-9-5p measured in duplicate by RT-qPCR after transduction of CB-HSPCs and KASUMI-1 with miR-SC (ctrl) or miR-9.
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Supplementary Figure 4. Sponge-mediated inhibition of miR-9 in cell lines. (a) Expression of miR-9-5p normalized to RNU44 measured in duplicate by RT-qPCR of the indicated cell lines. (b) Schematic map of the modified sponge (SP) vector used for miRNA inhibition.1 For the SP inserts each lined block represents a bulged target site for the respective miRNA; SP-sc sequence was adapted from Loya et al.2 (c) Fluorescence intensity for mCherry validating miRNA inhibition by SP-9 in THP1 cells. (d) Number of viable miR-9-transduced SKNO1, THP1 and NB4 cells relative to the miR-SC-transduced control on day 7 of culture. Mean±s.d. of replicates from one representative of two independent experiments are shown.
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Supplementary Figure 5. RT-qPCR and luciferase reporter assay for target validation of miR-9. (a) RT-qPCR of granulocytic and monocytic differentiation markers in miR-9-transduced t(8;21) (N=2) and MLL-rearranged (N=2) patient blasts. Data were normalized to housekeeping genes and miR-SC control of the same samples. (b) Representative photomicrographs of transduced MLL-rearranged blasts (scale bars 100µm) showing less adherence upon miR-9 transduction.
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Supplementary Figure 6. RT-qPCR and luciferase reporter assay for target validation of miR-9. (a) Number of CFUs in collagen-based colony-forming assays of miR-9 and miR-SC (ctrl) transduced HSCPs. (b) Representative photographs of the slides of (a); arrows indicate normal sized CFU-GMs in the miR-SC control, missing upon miR-9 expression. (c) Luciferase reporter assay with indicated 3’UTR fragments in 293T cells. Data are presented as mean±s.d. of replicates of five independent experiments (**p<0.01). (d) mRNA levels of MAF and its isoform B (isoB) measured during in vitro monocytic differentiation of miR-9- and miR-SC (ctrl)-transduced CD34+-HSPCs on different time points (****p<0.0001). (e) RT-qPCR of miR-9 targets FBN2, RUNX2, NOTCH2, NFKB1, NCOA3, NAP1L1, CDH1, ESR1, PRUNE, PAK4, LAMP1, ATF1, and SHC1 in transduced HSPCs on day 7 of monocytic in vitro differentiation. Data were normalized to housekeeping genes and miR-SC sample and are presented as relative change. (a, b, d, e) Data are presented as mean±s.d. of replicates of two independent experiments (*p<0.05; **p<0.01; ***p<0.001).
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Supplementary Figure 7. Validation of miR-9 target genes. (a) Western Blot of LIN28B and HMGA2 in MLL-rearranged patient blasts (N=1); Actin was used as loading control. (b) Expression of LIN28B cDNA 3 days after transduction using three LIN28B shRNAs (#1 - #3) relative to shNSC (%). (c) Expression of HMGA2 cDNA 3 days after transduction using three HMGA2 shRNAs (#1 - #3) relative to shNSC. (d) Methylcellulose colony assay of shRNA-transduced Kasumi-1 cells enumerated on day 14 of culture. (**p<0.01)
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Supplementary Methods
Cytogenetic and molecular analysis
Bone marrow or peripheral blood samples of pediatric patients with AML were routinely investigated for cytogenetic abnormalities by standard chromosome-banding analysis. Screening for recurrent nonrandom genetic abnormalities characteristic for AML included t(15;17), inv(16), t(8;21), t(7;12), and MLL-gene rearrangements, using either RT-qPCR and/or fluorescent in situ hybridization. Patients lacking cytogenetic abnormalities are designated as cytogenetically normal (CN) and patients with miscellaneous cytogenetic abnormalities are designated as patients with other cytogenetics (OC). The distribution of morphological, cytogenetic and molecular subgroups illustrates a representative pediatric AML cohort which was enriched for t(7;12) cases , as previously described.3
miRNA and mRNA expression profiling and RT-qPCR
RNA quality was assessed using the RNA 6000 nano assay on the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). In short, expression of 665 microRNAs was measured using two 384-wells Taqman Low Density Cards. Input was 500ng total RNA per card. Raw Ct-values were analyzed, summarized and exported per plate using SDS 2.3 (Applied Biosystems). Baseline was manually set at 0.15 for all plates. All data mining and analyses were done in R 2.11.1.4 Per card type, Ct-values were imported (readCtData) into a qPCR-set using the package HTqPCR.5 The average cycle threshold (Ct) value was used to calculate microRNA expression levels relative to the expression level of the small reference RNAs (mean of (mean) Mamm-U6, RNU44 and RNU48) by use of the comparative cycle time (ΔCt) method.6 Validity of the multiplex system was tested by singleplex assays for 10 differentially expressed miRNAs (miR-9, miR-126, miR-151-3p, miR-181a, miR-181c, miR-192, miR-500, miR-532-5p, miR-574-3p, and miR-660) by duplicate RT-qPCR TaqMan MiRNA assays (Applied Biosystems) on an Applied Biosystems 7900HT RT-PCR system. The average cycle threshold (Ct) value was used to calculate microRNA expression levels relative to the expression level of the reference (RNU44) using the comparative Ct method. Correlation between singleplex and multiplex assays was assessed by the calculation of Spearman Correlation Coefficient. Data acquisition and normalization was performed in the statistical data environment R (version 2.12.0 (2010-10-15)) as described previously.7 Results were validated by RT-qPCR using FAST SYBR Green dye on a StepOnePlus (Applied Biosystems) according to the manufacturer’s instructions. Primers are available upon request. Data analysis was performed with use of geNorm algorithms comparative Ct method.8 Analyses for differentially expressed microRNAs were performed on -ΔCt values in the environment R using the limma package. Non detected miRNAs were substituted by an artificial low level of expression of -25. MicroRNAs were considered differentially expressed when the FDR-adjusted moderated t statistic p-value was smaller than 0.05. For prediction of miR-9 targets, VSN-normalized values of the miR-sc condition were subtracted by the VSN-normalized values of the paired miR-9 condition. Probes downregulated in all three experiments and annotated by hgu133plus2.db (version 2.4.5) were compared to validated and predicted miR-9 target genes as supplied by miRecords.9
Quality control for miRNA expression experiments
Quality control was performed using a combination of three approaches in three steps. This combination is summarized in a flowchart (Supplemental Figure 1).
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The first step is visual inspection of correlation plots. Correlation plots were called by the plotCtCor command (HTqPCR). This method provides a quick first impression of the data as well as an opportunity for pattern recognition. The correlation coefficient is based on the number of failed measurements for each card. Measurements are considered failed when flagged by the SDS-program or when the Ct values are higher than 38 or not acquired (NA). Failed measurements can be due to either technical failure of the reaction or expression below the detection level. Cards that correlated poorly to the other cards of the same type (Pearson R<0.6) were omitted from further analyses. The second step uses the number of failed measurements (undetermined, Ct>38, or flagged in any way by SDS software) per card to determine quality. The number of failed measurements per card was summarized with the featureCategory command (HTqPCR). Of this number the mean, standard deviation and 95% confidence interval were calculated for all A and all B cards to obtain the upper limits of the number of failed measurements (A card n= 265, B card n=285). If the number of failed measurements on a card exceeded the limit, this card was omitted from further analyses. The third quality measure uses the expression levels of the control reactions to determine card quality. In the R environment, the median Ct-value of each control was calculated per card. With these values, the mean value for all A and B cards was calculated per control. In addition, the standard deviation and 95% confidence interval were calculated to obtain the upper limits of expression per control per card type. If the Ct-value of two out of three control reactions per card exceeded the upper limit of expression, the card was omitted from further analyses. If both card A and B for one sample were retained after going through the steps of quality control, expression values were merged into one dataset. In total, 28 cards were excluded.
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Supplementary References
1. Maetzig T, Galla M, Brugman MH, Loew R, Baum C, Schambach a. Mechanisms controlling titer and expression of bidirectional lentiviral and gammaretroviral vectors. Gene Ther. 2010;17(3):400–11.
2. Loya CM, Lu CS, Van Vactor D, Fulga T a. Transgenic microRNA inhibition with spatiotemporal specificity in intact organisms. Nat. Methods. 2009;6(12):897–903.
3. Creutzig U, van den Heuvel-Eibrink MM, Gibson B, et al. Diagnosis and management of acute myeloid leukemia in children and adolescents: recommendations from an international expert panel. Blood. 2012;120(16):3187–205.
4. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
5. Dvinge H, Bertone P. HTqPCR: high-throughput analysis and visualization of quantitative real-time PCR data in R. Bioinformatics. 2009;25(24):3325–6.
6. Stam RW, den Boer ML, Meijerink JPP, et al. Differential mRNA expression of Ara-C-metabolizing enzymes explains Ara-C sensitivity in MLL gene-rearranged infant acute lymphoblastic leukemia. Blood. 2003;101(4):1270–6.
7. Kuipers JE, Coenen E a, Balgobind B V, et al. High IGSF4 expression in pediatric M5 acute myeloid leukemia with t(9;11)(p22;q23). Blood. 2011;117(3):928–35.
8. Vandesompele J, Preter K De, Pattyn F, et al. Accurate normalization of real-time quantitative RT -PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):1–12.
9. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37(Database issue):D105–10.