SUPPLEMENTARY DATA TO THE ARTICLE TITLED:
“PREDNISOLONE EXERTS LATE MITOGENIC AND BIPHASIC EFFECTS ON RESISTANT
ACUTE LYMPHOBLASTIC LEUKEMIA CELLS: RELATION TO EARLY GENE EXPRESSION”
George I Lambrou1, Spiros Vlahopoulos1, Chrisanthi Papathanasiou1, Maria Papanikolaou1, Michael
Karpusas2, Emmanouil Zoumakis3, Fotini Tzortzatou-Stathopoulou1
1
Hematology/Oncology Unit, First Department of Pediatrics, University of Athens Medical School,
“Aghia Sophia” Children’s Hospital, Athens, Greece
2
University Research Institute for the Study and Treatment of Childhood Genetic and Malignant Diseases
3
Choremeio Research Laboratory, First Department of Pediatrics, University of Athens Medical School,
“Aghia Sophia” Children’s Hospital, Athens, Greece
MICROARRAY EXPERIMENTATION AND DATA ANALYSIS
cDNA microarray chips (1,200 genes) were obtained from TAKARA (Human Cancer Chip v.40).[1, 2]
Hybridization was performed with the CyScribe Post-Labeling kit (RPN5660, Amersham) as described by
the manufacturer, utilizing the Cy3 and Cy5 fluorescent dyes. cDNAs were purified with QIAGEN PCR
product clean-up kit (Cat # 28104). Slides were activated at 55o C for 30 min in 1% BSA. Samples were
applied on the slides, and let to hybridize overnight at 55o C. The following day, slides were washed in 200
ml 0.1× SSC and 0,1% SDS for 3× 5 min, in 200 ml 0.1× SSC for 2× 5 min and in 200 ml ddH2O for 30
sec. Slides were dried by centrifugation at 1500 rpm for 3 min and scanned with a microarray scanner
(ScanArray 4000XL). Images were generated with ScanArray microarray acquisition software (GSI
Lumonics, USA). To obtain the highest possible experimental accuracy, cDNAs were used from three
experimental setups, each one consisting of three independent experiments. In particular, the experimental
setups consisted of the three following pairs: control (0 μM prednisolone) (Cy3) vs 10nM prednisolone
(Cy5) (designated as 0vs1), 10nM prednisolone (Cy3) vs 701μM prednisolone (Cy5) (designated as 1vs3),
control (0 μM prednisolone) (Cy3) vs 701μM prednisolone (Cy5) (designated as 0vs3).
This is a ‘simple loop’ experimental design, taking into account all possible combinations between
samples, as previously described.[3]
Microarray Data Analysis and Statistics:
Microarray data analysis was performed with ImaGene v.6.0 Software (BioDiscovery Inc, CA).
Normalization was performed by dividing each data value with the median (50% percentile) and
background correction was performed with global background correction, sub-grid based and negative
control spots.[4-6] Genes were filtered first for their spot quality. This was done by acquiring a ‘0’ flag
marking on each spot as assessed by ImaGene software (Signal-to-Noise Ratio, SNR) in at least two
experiments for each concentration (notice that SNR is calculated by the software from the equation
1
SNR 
 R ,G   B
, where μR,G and μB is the mean value intensity for the respective channel (Cy3 or Cy5)
B
and mean background intensity respectively and σB is the background mean signal standard deviation. A
threshold of 2 has been set as a cut-off value, meaning that spot intensity for at least one channel should be
twice as much as that of the background). The mean ratio of each triplicate of the ith gene was calculated
from R 
ch2i
1 j
log 2 (
) , where R is the mean expression ratio of a gene among a triplicate, ch2i and

j i 1
ch1i
ch1i are the normalized intensities of the two channels (Cy5 and Cy3 respectively). Or equivalently stated
it is the geometric mean where R  log 2 (GM )  log 2 ( j
j
ch2i
 ch1
i 1
), j  3 . Furthermore, genes with a
i
good spot quality were tested against their differential expression consistency between the experimental
setups i.e. genes in the third experiment (“0vs3”) should manifest a behavior consistent to the previous
two. For example, a gene that is 2-fold overexpressed in the “0vs1” experiment and 2-fold overexpressed
in the “1vs3” experiment should be approximately 4-fold overexpressed in the “0vs3” experiment, in
logarithmic scale. This was calculated by the observation that the experimental setup followed a pattern of
additive expressions. More specifically, fold expression in the first two experiments should be reflected as
a sum in the third experiment. To explain this in more detail an example is given: a two-fold expression in
a gene between control and 10nM prednisolone (0vs1) should give a log2 of 1. An another two- or fourfold expression between 10nM prednisolone and 701μM prednisolone (1vs3) equals to log2 of 1 (21) and
2 (22) respectively. The fold expression for the comparison between control and 701μM prednisolone
(0vs3) that should be observed will be then log2(22)=2 and log2(23)=3 respectively. This fold expression in
the 0vs3 experimental setup equals to the sum of the previous two experiments. In other words it should be
R0,3=R0,1+R1,3 where Ri,j (j=1,2,3) is the ratio of the ith gene in the jth experiment (practically the third
vector R0,3 should be the sum of the vectors R0,1 and R1,3 ). Taking the standard deviation (σ) between
the calculated R0,3 and the experimental R0,3 a criterion of the consistency between the experiments is
drawn. Genes that did not conform to this rule, specifically those with σ>1, were excluded from further
analysis. Furthermore, each gene was tested for its significance in differential expression using a z-test.
Since there were 9 independent experiments (three for each concentration combinations i.e. 0vs1, 1vs3,
0vs3) a meta-analysis was performed where p-values were combined in order to obtain a single p-value. pvalue combination was performed using the Fisher’s transformation for combining tests of significance. pvalue combinations have a chi-square distribution which is given by the equation x22k  2
k
 ln( p ) with
i 1
i
2k degrees of freedom. Genes were considered to be significantly differentially expressed if they obtained
a p-value <0.01 in all experiments. The False Discovery Rate has been calculated as described
previously.[7-9] There was a FDR of 0.5% for a combined p<0.01 for experimental setup 0vs1, 0.6% for a
2
combined p<0.01 for experimental setup 1vs3 and 0.9% for a combined p<0.01 for the experimental setup
0vs3. Clustering analysis and chromosome mapping [10, 11] were performed with Genesis 1.7.2
(Technische Universitaet-Graz, Austria)[12] using Pearson’s correlation (r) and Spearman rank order
correlation (ρ). Pearson’s correlation was calculated by
order correlation was calculated by
and Spearman rank
For clustering analysis hierarchical clustering by
Euclidian distance was used.[13] Also, a methodology of Self Organizing Maps (SOMs) was used in order
to determine patterns of expression and it was compared to the hierarchical clustering results.
3
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