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International Journal of Molecular Medical Science
12
in the Discovery program and a Monte Carlo strategy
in the Affinity program. Each energy-minimized final
docking position of the complex was evaluated using
the interactive score function in the LUDI module,
visual inspection and interface analyses including
contact surface area, steric tension, improper rotamer
positions by GRASP, CHAIN and O. The final
binding position of the complex was determined based
on the evaluation of favorable binding interactions
using the LUDI score function. The parameters used
in this docking strategy included searching for five
unique structures, 1 000 minimization steps for each
structure, energy range of 10.0 kcal/mol, maximum
translation of the ligand of 3.0 Å, maximum rotation
of the ligand of 10°, and an energy tolerance of 1500
kcal/mol.
2.4 Bioinformatics and Statistical Analysis of Gene
Expression Profiles
The publically available archived GSE32311 database
was used to compare gene expression changes in
CD4
+
CD8
+
double-positive wild type (N=3;
GSM800500, GSM800501, GSM800502) vs.
IKZF1
null mouse thymocytes (N=3,
GSM800503,
GSM800504, GSM800505) from the same genetic
background of (C57BL/6 x129S4/SvJae). Probe level
RMA signal intensity values were obtained from the
mouse 430_2.0 Genome Array. Up-regulated and
down-regulated transcripts in
IKZF1
knockout mice
were identified by filtering changes greater than 2-fold
and T-test P-values less than 0.05 (T-test, Unequal
Variances, Excel formula). We identified 1 158
transcripts representing 924 genes that were
down-regulated in
IKZF1
null mice with a subset of
201 transcripts representing 137 genes exhibiting
>2-fold decreased expression levels.
By
cross-referencing this IK-regulated gene set with the
archived CHiPseq data (GSM803110) using the
Integrative Genomics Browser (Robinson et al., 2011),
we further identified 45
Ikaros
target genes that
harbored IK binding sites (Uckun et al., 2012). The
Gene
Pattern web based software
(http://www.broadinstitute.org/cancer/software/genepa
ttern) was used to extract expression values from the
National Center for Biotechnology Information (NCBI)
Gene Expression Omnibus (GEO) database to compile
gene expression profiles of human lymphocyte
precursors in 1 104 primary leukemia specimens from
pediatric ALL patients (GSE3912, N=113; GSE18497,
N=82; GSE4698, N=60; GSE7440, N=99; GSE13159,
N=750). We focused our analysis on 45 validated
IK
target genes (Uckun et al., 2012). Expression values
expressed as Standard Deviation units were compiled
for the 5 studies and rank ordered according to the
mean expression of three highly correlated transcripts
(208642_s_at (
XRCC5
), 208643_s_at (
XRCC5
),
200792_at (
XRCC6
). Prospective power analysis was
utilized to determine the Standard Deviation cut-off
for “high
Ku
expression” and “low
Ku
expression” in
the data sets. To control for False Positive Rate (FPR)
to detect for differences in 3
Ku
transcripts out of
approximately 20 000 transcripts common across the 5
Affymetrix platforms, we set the unadjusted P-value at
2.5×10
-6
(FPR = 0.05). Sample size greater than 132
would provide sufficient to detect a difference of 1
standard deviation units with 99.9% power. Therefore,
samples were assigned to the “high
Ku
expression”
group if their expression level was >0.5 standard
deviations units higher than the mean expression level
(N=314) and to the “low
Ku
expression” group if their
expression level was >0.5 standard deviations units
lower than the mean expression level (N=324). These
samples were also rank ordered according to
IKZF1
expression level
(205038_at,
205039_s_at,
216901_s_at, 227344_at and 227346_at; 3 of these
were common in all Affymetrix platforms - 205038_at,
205039_s_at, 216901_s_at) resulting in 302 ALL
samples with high
IKZF1
expression and 318 samples
with low
IKZF1
expression. T-tests were performed
for the combined Standard Deviation units from the 5
datasets (2-sample, Unequal variance correction,
p-values<0.05 deemed significant) and revealed 27
transcripts representing 19 IK target genes (Table 1)
and 13 transcripts representing 12 lymphoid-priming
genes (Uckun et al., 2012; Ma et al., 2013) that were
significantly up-regulated in samples with both high
Ku
and high
IKZF1
expression levels. We used a
one-way agglomerative hierarchical
clustering
technique to organize expression patterns using the
average distance linkage method such that genes
(rows) having similar expression across patients were
grouped together (average distance metric).
Dendrograms were drawn to illustrate similar
gene-expression profiles from joining pairs of closely
Molecular Medical Science, Int’l Journal of