| An immune pathophysiology for acquired aplastic
anemia (AA) has been inferred from the responsiveness
of the patients to immunosuppressive therapies and
experimental laboratory data. To address the
transcriptome of hematopoietic cells in AA, we
undertook GeneChip analysis of the extremely limited numbers
of progenitor and stem cells in the marrow of patients
with this disease. We pooled total RNA from highly
enriched bone marrow CD34 cells of 36 patients with
newly diagnosed AA and 12 healthy volunteers for
analysis on oligonucleotide chips. A large number of
genes implicated in apoptosis and cell death showed
markedly increased expression in AA CD34 cells, and negative
proliferation control genes also had increased activity.
Conversely, cell cycle progress–enhancing genes
showed low expression in AA. Cytokine/chemokine
signal transducer genes, stress response genes, and
defense/immune response genes were up-regulated, as
anticipated from other evidence of the heightened immune
activity in AA patients' marrow. In summary, detailed
genetic analysis of small numbers of hematopoietic
progenitor cells is feasible even in marrow failure
states where such cells are present in very small
numbers. The gene expression profile of primary human
CD34 hematopoietic stem cells from AA was consistent
with a stressed, dying, and immunologically activated target
cell population. Many of the genes showing differential
expression in AA deserve further detailed analysis,
including comparison with other marrow failure states
and autoimmune disease.
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Introduction |
Acquired aplastic anemia (AA) is a bone marrow (BM)–failure
syndrome that is characterized by low blood cell counts
and bone marrow hypocellularity.1
On the basis of clinical observations of high
response rates to combined immunosuppressive therapy,
immune-mediated suppression of hematopoiesis has been considered
to play an important role in most cases of AA.2-5
Laboratory findings, including inhibition of
hematopoietic cell growth by patient lymphocytes and
their overproduction of myelosuppressive cytokines,
such as interferon-gamma (IFN- )
and tumor necrosis factor (TNF), have supported this
hypothesis.6-9
Similarly to other autoimmune diseases,
antigen-specific T cells in the BM of AA patients are
expanded; these lymphocytes are likely to mediate
organ-specific cytotoxicity for bone marrow hematopoietic
cells.10-14
To date, only limited information has been available
concerning the characteristics of stem cells in AA. The precise
antigenic targets of cytotoxic T cells are unknown, and
the effects of T-cell attack on hematopoietic target
cells are poorly characterized. Although the
expression levels of a few genes, such as FMS-related
tyrosine kinase3 ligand (FLT3L) and GATA2,
appear to be different in AA patients and healthy donors,15-17
a more general transcriptome pattern of CD34 cells in AA
patients has not been described.
Oligonucleotide microarrays allow quantitation of expression
levels of a large number of genes in a cell, and thus
provide a powerful tool to study the molecular
mechanisms of disease at the messenger RNA level.
Recently, the gene expression pattern in healthy
human CD34 stem/progenitor cells has been reported.18
Using microarray technology, Steidl et al19
successfully compared the gene expression profile in
CD34 cells derived from bone marrow or granulocyte
colony-stimulating factor (G-CSF)–mobilized
peripheral blood cells. Microarray has also provided an image
of gene expression in autoimmune disease, such as multiple
sclerosis lesions.20
Here we apply DNA chip technology to measure the gene
expression profile in CD34 cells from the bone marrow of
patients with newly diagnosed AA.
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Patients, materials, and methods
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Patients
Patients were evaluated at the Hematology Branch of the
Clinical Center of the National Institutes of Health.
The diagnosis of AA was established by bone marrow
biopsy and peripheral blood counts as recommended by
the International Study of Aplastic Anemia and
Agranulocytosis21; severity was
classified by the criteria of Camitta et al.22
Thirty-six patients with newly diagnosed moderate or
severe AA were selected for our experiments (Table
1). Controls were 12 healthy volunteers whose sex and
age were approximately matched. To obtain marrow, informed
consent was obtained according to protocols approved
by the Institutional Review Board of the National
Heart, Lung, and Blood Institute.
Isolation of CD34 and CD4 cells
BM mononuclear cells (BMMNCs) were obtained by aspiration of
the iliac crest of patients and healthy donors and
prepared with the use of lymphocyte separation medium
(Cappel, Aurora, OH). CD34 and CD4 cells were
positively selected by means of the mini-MACS
immunomagnetic separation system (Miltenyi Biotec,
Auburn, CA), according to the manufacturer's instructions. In
brief, to obtain normal CD34 cells, 108 or
fewer BMMNCs were washed twice and then suspended in
300 µL sorting buffer composed of 1
x phosphate-buffered saline
(PBS), 2 mM EDTA (ethylenediaminetetraacetic acid),
and 0.5% bovine serum albumin. Cells were incubated with
100 µL human immunoglobulin–Fc receptor (FcR) blocking
antibody and 100 µL monoclonal hapten-conjugated CD34
antibody (clone QBEND/10; Miltenyi Biotec) for 15 minutes
at 4°C. After washing, cells were resuspended in 400
µL sorting buffer, and 100 µL paramagnetic microbeads
conjugated to antihapten antibody were added,
followed by incubation for 15 minutes at 4°C. After
washing, cells were resuspended in sorting buffer,
passed through a 30-µm nylon mesh, and separated in a
column exposed to the magnetic field of the MACS
device. The column was washed twice with sorting buffer
and removed from the separator. Retained cells were eluted
with sorting buffer by means of a plunger and
subjected to a second separation. Purity of CD34
cells was 90% to 97% by flow cytometry analysis.
After washing, 107 or fewer of CD34– cells
were resuspended in 80 µL sorting buffer; 20 µL CD4
microbeads was added and incubated for 15 minutes at
4°C. Washed cells were resuspended and passed through
the column, and the subsequent steps were performed
as described.
RNA preparation
Total cellular RNA was extracted from CD34 cells by means of
TRIzol reagent (Invitrogen, Carlsbad, CA) or the High
Purity RNA Isolation Kit (Roche Diagnostics,
Indianapolis, IN), according to the manufacturers'
protocols. To provide sufficient total RNA for
processing, samples were pooled. An RNA pool from 24
AA patients (equal amounts of RNA from each individual) was
named pool-AA1, and pool-AA2 was
obtained from another cohort of 6 AA patients. For
controls, pool-N1 was prepared from 8
healthy individuals and pool-N2 from an additional 4
healthy individuals. In the initial oligonucleotide
array experiments, triplicate technical RNA aliquots
from pool-AA1 or pool-N1 were
prepared separately and subjected to subsequent cDNA synthesis,
labeling, hybridization, and analysis. For subsequent
oligonucleotide array analyses, biologic duplicates,
termed pool-AA2 and pool-N2,
were prepared from different patients and healthy volunteers,
respectively. In addition, pool-AA3 was
prepared from a further 6 AA patients for real-time
polymerase chain reaction (PCR) assay (TaqMan; PE
Applied Biosystems, Foster City, CA).
Affymetrix GeneChip assay
The GeneChip Eukaryotic 2 Cycles Small Sample Target Labeling
protocol developed by Affymetrix (Santa Clara, CA) was
employed to produce biotinylated cRNA from small
amounts of total RNA. This protocol uses 2 cycles of
cDNA synthesis combined with in vitro transcription
(IVT). In the first cycle, first-strand cDNA is
synthesized from total cellular RNA, which in turn becomes
a template to generate second-strand cDNA, resulting in
double-strand (ds) cDNA. As a final step in the first
cycle, unlabeled cRNA is created from the ds-cDNA. In
the second cycle, the unlabeled cRNA is converted
into ds-cDNA through first-strand and then
second-strand cDNA syntheses, followed by synthesis of
biotinylated cRNA. In our study, 500 ng pooled total
RNA was used as a template to generate first-strand
cDNA with the SuperScript Choice reagents
(Invitrogen) in combination with an oligo-deoxythymidine
(oligo-dT) primer containing the T7 RNA polymerase
binding site
(5'-GCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGG-(dT)24-3')
(Genset, La Jolla, CA), according to the manufacturer's
instructions. After generation of ds-cDNA from the
first-strand cDNA, unlabeled cRNA was synthesized by
in vitro transcription with the use of the Ambion
MEGAscript T7 Kit (Ambion, Austin, TX) in the
provided protocol. In the second cycle, first-strand cDNA was
synthesized with the use of the unlabeled cRNA as a
template and random primers (Invitrogen), and
subsequently converted into ds-cDNA. For probing on
Affymetrix arrays, biotinylated cRNA was generated
with the Enzo BioArray High Yield Transcript Labeling
Kit (Enzo Diagnostics, Farmingdale, NY). The biotinylated
cRNA was purified with the RNeasy Kit (Qiagen, Valencia,
CA), followed by fragmentation of an aliquot (15 µg)
of the biotinylated cRNA. Samples were frozen at
–20°C until use.
Hybridization, washing, staining, and scanning of Affymetrix
probe arrays were performed as described in the standard
Affymetrix protocol (P/N 700 222 rev 4) for Human
Genome U95A version 2 Arrays (HG-U95AV2) with the use
of 15 µg fragmented RNA.
Data analysis
Gene expression levels were determined by means of
Affymetrix's Microarray Suite 5.0 (MAS 5.0); this
software's algorithms allow quantitative estimation
of a gene expression and a P value to
establish a confidence level that the mRNA of interest is
accurately measured. To correct for technical
variation between chips, the mean expression of the
50th percentile of each chip was scaled to a common
value of 1000. Scaled expression levels and P
values were exported for individual GeneChips for subsequent
analysis with the use of Silicon Genetics's GeneSpring
software (version 5.1) (Silicon Genetics, Redwood
City, CA). Once imported into GeneSpring, each gene
was normalized by using the median of its
measurements in all samples. The mRNA expression levels
for patients and controls were determined in 2 steps:
means of gene expressions among the 3 technical
replicates were used as the best estimate of
expression levels for pool-AA1 and pool-N1,
and these means were then averaged with the biologic
replicates, pool-AA2, and pool-N2,
respectively. The averaged expression level of the 2
biologic samples was used in subsequent analysis by
GeneSpring software.
Genes differentially expressed in the patients were
identified by normalizing the expression levels of
pooled AA by those of normal pools. Lists of genes
for further study were created by filtering genes
with at least a 2.0-fold change. As only 2 biologic
replicates were possible for each group, a rigorous
t test with a multiple testing correction produced no
significant genes. For exploratory analysis of the
data, the most reliable measurements were identified
with an uncorrected t test on individual
genes, and genes with P values less than .05 were
retained. An additional filter, based on the P
< .05 according to MAS, was added to eliminate genes
that were not accurately measured in at least one of
the samples used.
For some functional gene assignments, we also used the Cancer
Molecular Analysis Project of the National Cancer
Institute Web site (http://cmap.nci.nih.gov/.
Accessed October 1, 2003).
Quantitative real-time RT-PCR
TaqMan real-time reverse transcription–PCR (RT-PCR) was
performed to confirm expression levels of RNA transcripts
with sequence-specific oligonucleotide primers and
methylglyoxal bis(guanylhydrazone) (MGB) probes (Table
2), according to the manufacturer's instructions
(PE Applied Biosystems). For relative quantification,
beta-actin mRNA served as an external control. In
brief, first-strand cDNA was synthesized from total cellular
RNA with an oligo-dT 12-18 primer (Pharmacia, Piscataway,
NJ) with the use of the SuperScript Choice reagents.
The obtained cDNA was amplified in a final volume of
20 µL with 300 nM of each primer; 200 nM probe; 3.5
mM MgCl2; 1 x
TaqMan Buffer A; 200 µM deoxyadenosine triphosphate (dATP),
deoxycytidine triphosphate (dCTP), and deoxyguanosine
triphosphate (dGTP); 400 µM deoxyuridine triphosphate
(dUTP); 0.2 U AmpErase uracil N-glycosylase (UNG);
and 0.5 U AmpliTaq DNA polymerase. All PCR
consumables were purchased from PE Applied Biosystems.
Primers and probes were designed with the use of Primer
Express (PE Applied Biosystems) and synthesized by PE
Applied Biosystems. The thermal cycling included 2
minutes at 50°C and 10 minutes at 95°C, then
proceeded with 40 cycles at 95°C for 15 seconds and
60°C for 1 minute. All reactions were performed in
the Model 7700 sequence detector (PE Applied Biosystems).
Each target (pool-AA1, pool-AA3, or
pool-N1) was measured in the same plate
for the same gene, and every sample was examined in
duplicate. The threshold cycle (Ct) was used to quantify
mRNA levels of samples with beta-actin normalization. The
following equation was used for relative mRNA
calculation23: Relative
mRNA = 2– CT.
(CT
=
CT,X
–
CT,R;
X indicates the difference in threshold cycles for
target; R, housekeeping gene).
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Results |
Validation of the microarray procedures
We analyzed the gene expression profile of bone marrow CD34
cells from patients with newly diagnosed AA using
Affymetrix oligoarrays containing sequences of 12 627
genes. Highly enriched CD34 cells (purity, 90% to
97%) were isolated from AA patients and healthy
volunteers. In AA patients, the numbers of bone
marrow CD34 cells are extremely low, and it is impossible to
obtain sufficient mRNA from CD34 cells of a single patient
for individual testing. To account for differences
among individuals and to obtain adequate quantities
of RNA for the analysis, we pooled equal amounts of
CD34-cell RNA from patients (pool-AA1 or
pool-AA2) or healthy controls (pool-N1 or
pool-N2). Technical replicates were
subsequently created from pool-AA1 and pool-N1
to examine the reproducibility of the Small Sample
Protocol. The standard sample preparation Affymetrix
GeneChip protocol requires at least 5 µg total RNA as
a starting material for each target preparation
reaction. Owing to the extremely limited numbers of
CD34 cells in AA patients, we used the Small Sample
Protocol developed by Affymetrix, which provides for
2 cycles of standard cDNA synthesis, followed by IVT for
GeneChip target amplification. The principle of this
method is that the first cycle provides initial
amplification of total RNA, which results in
unlabeled cRNA. In the second cycle, during IVT synthesis,
biotin-ribonucleotides are incorporated to produce labeled
antisense cRNA target. To evaluate this method for
microarray expression analysis, we used several
parameters, including the yield of labeled cRNA,
expression levels of transcripts used as positive
controls, and reproducibility of expression levels among
technical replicates. The cRNA yield was compared in
the Small Sample and the standard protocols, with the
use of 500 ng or 5 µg total RNA of CD4 cells from
healthy donors, respectively (Table 3).
The quantities of cRNA obtained from 500 ng or 5 µg
RNA in 2 replicate experiments were 55.5 and 53.2 µg,
or 54.5 and 52.2 µg, respectively, indicating similar
yields. The 500 ng RNA samples resulted in 45.6% "present"
calls, comparable to 45% obtained with 5 µg starting
RNA labeled by the standard protocol. The correlation
of expression levels showed 91% reproducibility. The
Small Sample Protocol gave rise to a higher 3'-to-5'
ratio of individual genes, including control genes
such as GAPDH, presumably owing to the generation of
shorter products toward the 3' end of mRNA in the
second cycle of amplification. In this study, the
ratio was 1.5 to 3.27 for the Small Sample Protocol
and below 2 for the standard protocol. Our method therefore
met the quality control metrics provided by Affymetrix for
the Small Sample Protocol. All these parameters were
comparable in the Small Sample and standard
protocols, suggesting that results using the Small
Sample Protocol would be reliable.
To identify major sources of experimental variability, 3 technical
replicates were prepared with the use of 500 ng RNA
samples of CD34 cells from AA patients (pool-AA1-1,
pool-AA1-2, and pool-AA1-3) or
healthy volunteers (pool-N1-1, pool-N1-2,
and pool-N1-3), respectively. Each RNA
sample was converted to ds-cDNA, followed by
synthesis of the first-cycle cRNA. With the use of 3
µg cRNA as a template for the second cycle, ds-cDNA
and then biotinylated cRNA target were generated (Table
3). The "present" calls of the 8 pools were
between 41.9% and 48.5%. The technical replicates
showed that the Small Sample Protocol was highly
reproducible: the correlation coefficients between
replicates from pool-AA1 were 0.987, 0.990, and
0.994, and for replicates of pool-N1,
0.991, 0.991, and 0.996. There was modestly more
variation between biologic replicates: the correlation
coefficient was 0.919 between pool-AA1 and
pool-AA2, and 0.904 between samples pool-N1
and pool-N2.
A comparison of pool-AA1 with pool-AA2
showed 5542 genes were present in all 3 replicates
from pool-AA1, and 6116 genes were present
in the single pool-AA2. There were 5169 genes present
in both pool-AA1 and pool-AA2, which
represented 93.3% of the genes present in pool-AA1
and 84.5% of those in pool-AA2. For the
normal pools, 5291 or 5868 genes were present in pool-N1
or pool-N2, respectively. Venn diagram analysis
revealed that 4854 genes were present in both N1
and N2 pools, of which 91.7% of genes were
judged present in pool-N1 and 82.7% in pool-N2.
Genes identified as absent were not well correlated,
indicating that the reported hybridization data of
genes with low expression levels and/or absent calls
were unreliable. In contrast, a present call
indicates low experimental variability and high reproducibility.24
Differential gene expression profiles between AA
patients and healthy volunteers
Genes expressed differentially were identified by comparing
the average of the biologic pools. Overall, about 8% of
the total genes were differentially expressed in
patient samples, and most were up-regulated compared
with controls: 805 genes were increased in expression
compared with 238 genes decreased in expression. An
overview of the gene expression profile in AA
patients compared with healthy donors is shown in
Figure 1.
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Figure 1.
Overview of differential gene expression
patterns in CD34 cell of AA patients compared
with healthy volunteers. Gene expression
profiles of CD34 cells from 2 independent pools
of patients and controls were generated by means
of Affymetrix Human Genome U95A version 2
arrays, and the results analyzed by GeneSpring
software. A gene within each category was
considered differentially expressed if at least
a 2.0-fold difference was observed between AA
and controls in both biologic pools. The numbers
of genes in each functional category in which
transcripts were more abundant in AA patients
than in healthy volunteers are shown to the
right, and genes less expressed in AA patients
compared with controls are shown on the left.
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The 805 genes up-regulated at least 2.0-fold in AA patients
belonged mainly in the functional categories of defense/immune
response, cell death and apoptosis, cell cycle/cell
proliferation, cytokine/chemokine, signal transducer,
metabolism, transport, stress response, transcription
factor, and cell adhesion. The 238 genes showing at
least 2.0-fold down-regulation in AA patients were
grouped into cell cycle/cell proliferation, growth factor,
cell growth and maintenance, antiapoptosis, nucleic acid
binding, cell adhesion, oncogenes/transcription
factor, signal transduction, enzyme/enzyme inhibitor,
metabolism, immune response, and genes of unknown
function categories. (Figures 1 and
2)
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Figure 2.
Differential gene expression profiles in AA
patients and healthy volunteers. Genes were
grouped and displayed in the following
categories: immune response, apoptosis-related,
cell cycle and cell proliferation, stress
response, cell growth and maintenance, and cell
adhesion. Relative expression (normalized to the
median) is displayed by color: genes at
significantly higher levels are shown in red;
those with significantly lower expression in
green. Two biologic pools were tested. For
pool-AA1 and pool-N1,
sufficient RNA was available to create 3
technical replicates; for pool-AA2
and pool-N2, only a single chip could
be tested. Immune response, apoptosis-related,
and stress response genes were largely
up-regulated while cell cycle and cell growth
and maintenance genes were down-regulated in AA
patients compared with controls.
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The most striking results were obtained for the gene categories
related to immunity and cell death. A large number of
immune/defense response genes were highly expressed
in CD34 cells from AA patients. In Affymetrix
HG-U95AV2 arrays, 150 of the 290 genes (56%) related
to the immune response were at least 2.0-fold changed in their
expression in AA; almost all (141) were upregulated: 20
genes for cytokines and cytokine receptors, 21 genes
for chemokines and chemokine receptors, 36 signal
transduction-mediation genes, and 64 other immune
response genes (antibodies, enzymes, complement/component
receptors, IGFBP4, and toll-like receptors). In
contrast, lower expression in AA was observed for a
small number (9) of immune response genes, including
FCE1A, pro-platelet basic protein, PF4,
and PPBP.
Apoptosis genes also were differentially expressed in
patients' samples at a much higher rate than in the
global pattern of the transcriptome. Sixty-seven out
of 356 (19%) apoptosis genes, including 9 death
receptor pathway genes, 3 caspase-related genes (CASPER,
CASP1, and CASP8), 5 granzyme and perforin pathway
genes, 21 other signal transduction-related pathway genes
(JUN, JUNB, KBF1, TNFSF2, and MAP4K4),
and 26 genes otherwise involved in other apoptosis
pathways (serine/threonine kinase 17a and 17b, and
TOSO), were up-regulated. In contrast, 3 genes including
TIAF1, which has been implicated in antiapoptotic
regulation, were down-regulated in AA. In the death
pathway, 5 death receptors and 4 death ligands showed
enhanced expression in AA.
Cell cycle and cell proliferation genes (54 out of 348; 16%)
also showed differences between AA patients and healthy
volunteers. Eleven signal transduction–related genes,
including STAT1 and IGF1; 17 cell
proliferation-negative control genes; and 6 other
cell cycle-related genes were up-regulated. Of these
genes, most are believed to exert negative effects on cell
proliferation and to inhibit entry into cell cycle.
In contrast, several genes that exert positive
effects on cell cycle progress and cell proliferation
control were down-regulated: 2 members of the
cyclin-dependent kinase (CDK) family; 3 of the cell
division cycle (CDC) family; and 15 signal
transduction or other cell cycle control genes,
including M-phase phosphoprotein 9, MYC, and
BUB1.
Genes encoding proteins that bind to DNA were also
differentially regulated in AA patients compared with
controls. In patients, 25 DNA-binding protein genes,
including members of the zinc finger protein family,
and RNA-binding genes, were down-regulated.
Conversely, 53 genes of these types were up-regulated, including
RNA polymerase II, which is overexpressed in cells
undergoing apoptosis. Genes for several cell adhesion
molecules and cell adhesion receptors were
up-regulated in AA, including VCAM1 and
ICAM1, expression of which is increased following T-cell
engagement. Two genes related to platelet differentiation,
CD62P and CD42b, were down-regulated in
patients. Growth factor and cytokine genes, such as
FLT3, GATA2, and PF4, were down-regulated
in AA patients, as well as several oncogenes including
c-myb. A large number of other genes involved in
signal transduction pathways, such as transcription
factors, membrane proteins, and enzymes, also showed
differential expression in AA.
Validation of microarray by quantitative real-time
gene amplification
For quantitative analysis using TaqMan Quantitative PCR, we
selected 9 genes from the initial GeneChip analysis: 5
genes appeared to be up-regulated and 4 were
down-regulated, over a range of 2.7- to 77.4-fold.
Three pools were assayed: the original samples
prepared for the GeneChip analysis (pool-AA1
and pool-N1) as well as RNA from a new group of
patients (pool-AA3). TNFR2 and
IL-8 showed 3.2- and 77.4-fold increases, respectively,
in chip analysis of pool-AA1; with the use of
real-time PCR, these genes were increased 1.8- and
13-fold in pool-AA1, and 9.6- and 12-fold
in pool-AA3. Similarly, CD34, c-myc, GATA2,
and FLT3, which were all decreased by GeneChip
analysis of AA CD34 cells, were down-regulated in
real-time PCR analysis (Figure 3).
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Figure 3.
Validation of GeneChip results by real-time
RT-PCR. Experiments were performed with the
use of 3 pools (pool-AA1, pool-N1,
and pool-AA3): pool-AA1
and pool-N1 had been subjected to
GeneChip analysis, and pool-AA3 was
prepared from a fresh corhort of patients. Nine
genes that showed differential expression in AA
patients in the GeneChip analysis were selected:
5 were up-regulated and 4 were down-regulated.
Six genes showed a consistent differential
change in real-time PCR. Another 3 genes showed
no changes between AA patients and healthy
donors by this assay. Upward- and
downward-pointing bars represent higher or lower
expression levels in CD34 cells of AA patients
compared with those of healthy volunteers. Black
bar indicates GeneChip results; hatched bar,
real-time PCR results; P1, pool-AA1;
P3, pool-AA3. Mean values of 2
independent experiments in duplicate are
indicated.
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Discussion |
In spite of the extremely limited numbers of CD34 cells present
in the bone marrow of patients with AA, we were able to
analyze the transcriptome pattern in these cells by
combining the use of pooled RNA samples and a Small
Sample amplification technique. Because of the small
numbers of cells, the use of pooled samples, and the
Small Sample amplification method, there was a strong
possibility of error and of generating misleading data. However,
we showed, first, the high reproducibility of results
among replicate samples from the same pool of RNA of
either AA patients or healthy individuals. Second, we
found a high correlation in gene up- and
down-regulation in patient samples as compared with
healthy individuals when separate patient and control pools
were compared. Third, the ratio of representation of the
3' and 5' ends of the genes assessed, a measure of
the adequacy of RNA synthesis, was within the
parameters specified for this technique and close to
that obtained with standard GeneChip analyses.
Finally, we selected individual genes for comparison
using real-time PCR amplification. While a minority of genes
could not be confirmed to be dysregulated in AA with the
use of this more rigorous methodology, the majority
of the genes that we identified by chip analysis were
similarly up- or down-regulated in a third pool of AA
patient samples. Therefore, we believe that our
method is an adequate screening technique for the scant
numbers of CD34 cells in bone marrow failure patients and
should be capable of providing data for hypothesis
generation, with the understanding that initial
results should be confirmed by gene amplification or
other methods.
We have proposed that the pathophysiology of AA can be
simplified to T-cell–mediated, organ-specific attack
of cytotoxic lymphocytes on CD34 hematopoietic stem
and progenitor cells.25 Most
obviously in the current analysis, CD34 cells from AA patients
showed ample evidence of the expression of genes involved
in the signal transduction pathways for apoptosis and
terminal cytolytic enzyme generation. Conversely,
antiapoptotic genes appeared to be expressed at lower
levels in patients' CD34 cells as compared with
healthy voluteers. Among the up-regulated genes
involved in the death receptor pathway were several receptors
and ligands, such as the death receptors Fas, DR3,
and DR5, TNFRII, and TRAIL. High
expression of TNFR2 has been associated with the
pathogenesis of other immune-mediated diseases.26,27
Other apoptosis-related genes were increased in patients:
stress- and cytokine-inducible GADD45 B family
proteins, which function as specific activators of
mitogen-activated protein three kinase 1 (MTK1) (a
mitogen-activated protein kinase kinase kinase [MAPKKK]
upstream in the p38 pathway that can induce apoptosis),28,29
and nuclear factor kappa-B (NFKB) inhibitory protein
NFKBIA (nuclear factor of kappa light polypeptide
gene enhancer in B cells inhibitor alpha), which
could influence the function of NFKB and enhance
apoptosis.30
Direct evidence of immune system attack was also inferred
from increased expression of a large number of
defense and immune response genes in patient samples.
Anticipated to be increased in expression were a
number of interferon-response genes, stress-related
genes, and chaperone protein genes, such as HSP40. However,
a number of cytokine, chemokine, and T-cell effector
protein genes also were apparently active in
patients, including IFN-,
TNF- ,
perforin, and granzyme protein genes. These results are
consistent with some reported data suggesting that CD34
cells are capable of cytokine production and release,19,31
but they also could be explained by contamination of
even our relatively purified CD34 populations,
especially from scanty cell samples of marrow failure
patients, with effector lymphocytes themselves, the
presumed source of these inhibitory or cytotoxic cytokines
and perforin family members. IL-1 ,
IL-6, and IL-8 also showed
up-regulation in patient samples. The receptor for IL-10 was
increased in expression consistent with an IFN-
effect; IL-10 inhibits in vitro hematopoietic
suppression as well as production of IFN-
and TNF-
by peripheral blood MNCs (PBMNCs) from patients with
AA.32 IL-10 is also thought to
play a role in limiting immune-mediated pathology
during the host response to pathogens.33
We observed up-regulation of several chemokine genes
including CXC (IL-8 and SDF1)
and CC (MCP-2 and MCP-1), increased
expression of which occurs in other autoimmune
diseases.34,35
Finally, a large number of genes involved in signal
transduction following immune activation were
increased in patient samples. In total, the
expression pattern of immune response genes in our chip
analysis was supportive of the hypothesis of
immune-mediated marrow destruction in AA.
Thirty-four of 54 genes in the class of cell proliferation
and cell cycle were up-regulated in AA CD34 cells; 17
of these genes were assigned a negative regulatory
function in the software and publicly available
databases that we employed for annotation (only 1
up-regulated gene was characterized as a positive proliferation
regulator, and the remainder were of mixed or
indeterminate function). Conversely, of the 20 genes
in this class that were down-regulated in AA, 14 were
identifed as positive promoters of cell proliferation
and cycling (with the remainder of mixed or
indeterminate function [Figure 2]). These
data imply suppression of proliferation of CD34 cells
as well as direct induction of cell death by T-cell
attack. Of some interest, genes for several
constitutive centromere proteins that are essential for
spindle-pole body duplication showed markedly
decreased expression in AA, a suggestive finding
given the propensity of patients to develop
aneuploidy over time. Cell cycle control genes that were
down-regulated included, for example, CDK6,
which plays an essential role in controlling the G1/S
transition, and cell cycle regulators like cyclins E
and A.36,37
CDK2, important in the initiation of both
centrosome duplication and DNA synthesis, was down-regulated.
In summary, the pattern of involvement of multiple genes
that control cell cycle progression might explain the
inability of remaining stem and progenitor cells to
competently replicate and ultimately compensate for
destruction within the hematopoietic cell
compartment, despite the abundance of hematopoietic growth
factors and even after seemingly successful
immunosuppression has removed extrinsic inhibitory
factors. Down-regulation of several cell cycle
"checkpoint" genes, such as FANCG, c-myb, and
c-myc, would also be consistent with the ultimate
development of premalignant or aneuploid cells in
survival patients, who are susceptible to conversion
to myelodysplasia or frank leukemic transformation.
Conversely, transforming growth factor–1
(TGF-1)
was up-regulated; the gene product inhibits G1 and G2
cyclin-dependent kinesis.36
CDK2, which is regulated by TGF-1,
was markedly decreased in AA. Cell cycle progression
through the G1 phase into S is a major
checkpoint for proliferating cells and is under
multiple levels of control by p21.38
Of the growth factor genes and their receptors, we
confirmed previously described FLT3 and FLT3 ligand
changes in AA,16 showing
especially markedly elevated FLT3 ligand expression.
Decreased FLT3 receptor expression suggests
impairment of FLT ligand signaling in this disease.
Also, a number of insulin growth factor genes and genes
for their receptors were elevated in patient samples,
implicating this important family of mitogens for the
first time in marrow aplastic. We also confirmed
down-regulation of GATA-2 in AA patients;17
C-myb also was down-regulated, and decreased expression
of c-myb and GATA-2 probably affects the growth and
differentiation of CD34 cells in marrow failure.
Finally, a large number of genes that were apparently
abnormally up- or down-regulated in patients have not
been previously suspected as involved in AA. Examples
include vascular cell adhesion molecules, such as
VCAM-1, and intercellular adhesion molecule ICAM-1,
both of which were greatly increased in patients'
CD34 cells. Other adhesion molecules, some of which
have been associated with platelet function (CD62P
and PF4), were down-regulated. These
aberrations in gene expressions need to be confirmed by
appropriate studies, but they suggest further
experimental approaches for both the understanding of
the pathophysiology of AA and the improvement of
therapy. For example, expressions of some adhesion
molecules are altered by T-cell engagement, and interruption
of this interaction may be generally beneficial in
autoimmune diseases.39
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Footnotes |
Submitted February 13, 2003; accepted September 6, 2003.
Prepublished online as Blood
First Edition Paper, September 22, 2003; DOI
10.1182/blood-2003-02-0490.
The publication costs of this
article were defrayed in part by page charge payment.
Therefore, and solely to indicate this fact, this
article is hereby marked "advertisement" in accordance
with 18 U.S.C. section 1734.
Reprints: Weihua Zeng, Hematology Branch, National
Heart, Lung, and Blood Institute, National Institutes of Health,
9000 Rockville Pike, Bethesda, MD 20892; e-mail:
zengw@nhlbi.nih.gov
.
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References |
- Young NS. Acquired aplastic anemia. Ann Intern
Med. 2002;136: 534-546.[Abstract/Free
Full Text]
- Frickhofen N, Heimpel H, Kaltwasser JP,
Schrezenmeier H; German Aplastic Anemia Study Group.
Antithymocyte globulin with or without cyclosporin A:
11-year follow-up of a randomized trial comparing treatments
of aplastic anemia. Blood. 2003;101: 1236-1242.[Abstract/Free
Full Text]
- Rosenfeld S, Follmann D, Nunez O, Young NS.
Antithymocyte globulin and cyclosporine for severe aplastic
anemia: association between hematologic response and
long-term outcome. JAMA. 2003;289: 1130-1135.[Abstract/Free
Full Text]
- Bacigalupo A, Bruno B, Saracco P, et al.
Antilymphocyte globulin, cyclosporine, prednisolone, and
granulocyte colony-stimulating factor for severe aplastic
anemia: an update of the GITMO/EBMT study on 100 patients.
European Group for Blood and Marrow Transplantation (EBMT)
Working Party on Severe Aplastic Anemia and the Gruppo
Italiano Trapianti di Midolio Osseo (GITMO). Blood. 2000;95:
1931-1934.[Abstract/Free
Full Text]
- Maciejewski JP, Sloand EM, Nunez O, Boss C,
Young NS. Recombinant humanized anti-IL-2l receptor antibody
(Daclizumab) produces responses in patients with moderate
aplastic anemia. Blood. 2003;102: 3584-3586.[Abstract/Free
Full Text]
- Geissler K, Kabrna E, Kollars M, et al.
Interleukin-10 inhibits in vitro hematopoietic suppression
and production of interferon-gamma and tumor necrosis
factor-alpha by peripheral blood mononuclear cells from
patients with aplastic anemia. Hematol J. 2002;3: 206-213.[CrossRef][Medline]
[Order article via Infotrieve]
- Sloand E, Kim S, Maciejewski JP, Tisdale J,
Follmann D, Young NS. Intracellular interferon-gamma in
circulating and marrow T cells detected by flow cytometry
and the response to immunosuppressive therapy in patients
with aplastic anemia. Blood. 2002;100: 1185-1191.[Abstract/Free
Full Text]
- Nakao S, Ya
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