between aplastic anemia patients and health ...

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Prepublished online September 22, 2003; doi:10.1182/blood-2003-02-0490

Gene expression profiling in CD34 cells identifies significant differences between aplastic anemia patients and health volunteers Weihua Zeng, Guibin Chen, Sachiko Kajigaya, Olga Nunez, Alexandra Charrow, Eric M Billings and Neal S Young

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From bloodjournal.hematologylibrary.org by guest on June 1,22, 2013. For personal use only. Blood First Edition Paper, prepublished online September 2003; DOI 10.1182/blood-2003-02-0490

Gene expression profiling in CD34 cells identifies significant differences between aplastic anemia patients and health volunteers Running title: Gene expression profiling in CD34 cells of aplastic anemia patients Weihua Zeng,* Guibin Chen, Sachiko Kajigaya, Olga Nunez, Alexandra Charrow, Eric M. Billings, and Neal S. Young Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland

* Corresponding author: Weihua Zeng, Ph.D. Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike Bethesda, Maryland USA Phone: 301-496-5093 Fax:

301-496-8396

Email: [email protected]

Word count: 4701 Scientific heading: Red Cells Key Words: bone marrow failure, GeneChip analysis, microarray, Real-Time PCR, hematopoiesis

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Copyright (c) 2003 American Society of Hematology

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Abstract 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 newly diagnosed AA patients and 12 normal volunteers for analysis on Affymetrix oligonucleotide chips (Human Genome U95A version 2 Array). 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.

Introduction Acquired aplastic anemia (AA) is a bone marrow (BM) failure syndrome that is characterized

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by low blood cell counts and bone marrow hypocellularity.1 Based on 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 have supported this hypothesis, 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).6-9 Similar to other autoimmune diseases, antigen-specific T-cells in the BM of AA patients are expanded; these lymphocytes likely 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 target(s) of cytotoxic T-cells is 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 different between AA patients and normal 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 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 normal human CD34 stem/

progenitor cells has been reported.18 Using microarray technology, Steidl and Kronenwett successfully compared the gene expression profile in CD34 cells derived from bone marrow or GCSF-mobilized peripheral blood cells.19 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 newly diagnosed AA 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 Agranulocytosis;21 severity was classified by the criteria by Camitta et al.22 Thirty-six newly diagnosed patients with moderate or severe AA were selected for our experiments (Table 1). Controls were twelve healthy volunteers whose gender and age were approximately matched. To obtain marrow, informed consent was obtained according to protocols and approved by the Institutional Review Board of the National Heart, Lung, and Blood Institute. Isolation of CD34 and CD4 cells BM mononuclear cells (BMMNC) were obtained by aspiration of the iliac crest of patients and normal donors, and prepared using lymphocyte separation medium (Cappel, Aurora, OH). CD34 and CD4 cells were positively selected using 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 less BMMNCs were washed twice and then suspended in 300 µL of sorting buffer composed of 1 × phosphate-buffered saline (PBS), 2 mM MEDTA, and 0.5% bovine serum albumin. Cells were incubated with 100 µL of human immunoglobulin-FcR blocking antibody and 100 µL of monoclonal hapten-conjugated CD34 antibody (clone QBEND/10; Miltenyi Biotec) for 15 minutes at 4°C. After washing, cells were resuspended in 400 µL of sorting buffer, and 100 µL of 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

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eluted with sorting buffer using a plunger and subjected to a second separation. Purity of CD34 cells was 90-97% by flow cytometry analysis. After washing, 107 or less of CD34- cells were resuspended in 80 µL of sorting buffer, 20 µL of CD4 MicroBeads was added and incubated for 15 minutes at 4°C. Washed cells were resuspended, passed through the column, and the subsequent steps were performed as described as above. RNA Preparation Total cellular RNA was extracted from CD34 cells using TRIzol reagent (Invitrogen, Carlsbad, CA) or the High Purity RNA Isolation Kit (Roche Diagnostics Corporation, Indianapolis, IN), according to the manufacturers’ protocols.

In order 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-AA

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was obtained from another cohort of six AA

patients. For controls, a normal pool-N1 was prepared from eight healthy individuals and pool-N2 from an additional four 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, biological 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 six AA patients for real-time PCR assay (TaqMan, PE Applied Biosystems, Foster City, CA). Affymetrix GeneChip Assay The GeneChip Eukaryotic Two 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 utilizes two 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

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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 of pooled total RNA was used as a template to generate first strand cDNA using the SuperScript Choice reagents in combination with an oligo-dT primer containing the T7 RNA polymerase binding site [5’GCCAGTGAATTGTAATACGACTCACT ATAGGGAGGCGG - (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 using the Ambion MEGAscript T7 Kit (Ambion, Austin, TX) in the provided protocol. In the second cycle, first strand cDNA was synthesized using 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 – 20ºC until use. Hybridization, washing, staining, and scanning of Affymetrix probe arrays were performed as described in the standard Affymetrix protocol (P/N 700222 rev. 4) for human Genome U95A version 2 Arrays using 15 µg of fragmented RNA. Data Analysis Gene expression levels were determined using Affymetrix’ 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

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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 using Silicon Genetics’ GeneSpring software (version 5.1). Once imported into GeneSpring, each gene was normalized using the median of its measurements in all samples. The mRNA expression levels for patients and controls were determined in two steps: means of gene expressions among the three 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 biological replicates, pool-AA2 and pool-N2, respectively. The averaged expression level of the two biological 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 pooled normal. Lists of genes for further study were created by filtering genes with at least a 2.0-fold change. As only two biological 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 those with p-values below 0.05 were retained. An additional filter, based on the MAS 5.0 p-value, 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 the Cancer Molecular Analysis Project of the National Cancer Institute website (http://cmap.nci.nih.gov/). Quantitative Real-Time RT-PCR TaqMan real-time RT-PCR was performed to confirm expression levels of RNA transcripts with sequence-specific oligonucleotide primers and MGB probes (Table III), according to the manufacturer’s instruction (PE Applied Biosystems, Foster City, CA). For relative quantification,

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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) using the SuperScript Choice reagents. The obtained cDNA was amplified in a final volume of 20 µL using 300 nM of each primer, 200 nM probe, 3.5 mM MgCl2, 1 x TaqMan Buffer A, 200 µM dATP, dCTP and dGTP, 400 µM dUTP, 0.2 units of AmpErase uracil N-glycosylase (UNG), and 0.5 units of AmpliTaq DNA polymerase. All PCR consumables were purchased from PE Applied Biosystems. Primers and probes were designed using 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 sec and 60°C for 1 minutes. 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 calculation.23 Relative mRNA = 2 – (

C T

CT= CT, X - CT, R; X: the difference in threshold cycles for target, R: housekeeping gene)

Results Validation of the Microarray Procedures We analyzed the gene expression profile of bone marrow CD34 cells from newly diagnosed AA patients using Affymetrix oligoarrays containing sequences of 12,627 genes. Highly enriched CD34 cells (purity 90%-97%) were isolated from AA patients and normal 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

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CD34-cell RNA from patients (pool-AA1 or pool-AA2), or normal 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 of total RNA as a starting material for each target preparation reaction. Due to the extremely limited numbers of CD34 cells in AA patients, we used the Small Sample Protocol developed by Affymetrix, which provides for two 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 between the Small Sample and the standard protocols, using 500 ng or 5 µg of total RNA of CD4 cells from normal donors, respectively (Table II). The quantities of cRNA obtained from 500 ng or 5 µg of RNA in two replicate experiments were 55.5 and 53.2 µg, or 54.5 and 52.2 µg, respectively, indicating similar yields. The 500 ng of RNA samples resulted in 45.6 % “present” calls, comparable to 45% obtained with 5 µg of 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 due to shorter products towards the 3' end of mRNA generated in the second cycle of amplification. In this study, the ratio was 1.5 to 3.27 for the Small Sample Protocol and under 2 for the standard protocol. Our method therefore met the quality control metrics provided by Affymetrix for the Small Sample

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Protocol. All the above parameters were comparable between the Small Sample and standard protocols, suggesting that results using the Small Sample Protocol would be reliable. To identify major sources of experimental variability, three technical replicates were prepared using 500 ng of RNA samples of CD34 cells from AA patients (pool-AA1-1, pool-AA1-2, and poolAA1-3) or normal 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. Using 3 µg of cRNA as a template for the second cycle, ds-cDNA and then biotinylated cRNA target were generated (Table II). The “present” calls of the eight 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 biological replicates: the correlation coefficient between pool-AA1 and pool-AA2 was 0.919, and 0.904 between samples pool-N1 and pool-N2. A comparison of pool-AA1 to pool-AA2 showed 5,542 genes were present in all three replicates from pool-AA1 and 6,116 genes were “present” in the single pool-AA2. There were 5,169 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, 5,291 or 5,868 genes were

“present” in pool-N1 or pool-N2, respectively. Venn Diagram analysis revealed that 4,854 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 with “absent” calls were unreliable. In contrast, “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 biological

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pools. Overall, about 8% of the total genes were differentially expressed in patient samples, and most were up-regulated compared to controls: as 805 genes were increased in expression compared to 238 genes decreased in expression. An overview of the gene expression profile in AA patients compared to healthy donors is shown in Figure 1. The 805 genes up-regulated

2.0-fold in AA patients mainly belonged 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 2.0-fold down-regulation in AA patients were grouped into cell cycle/cell proliferation, growth factor, cell growth and maintenance, anti-apoptosis, nucleic acid binding, cell adhesion, oncogenes/transcription factor, signal transduction, enzyme/enzyme inhibitor, metabolism, immune response, and genes of unknown function categories. (Figure 1 and 2) 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 HU-U95 A version 2 Arrays, 150 of the 290 or 56% of genes related to the immune response were 2.0-fold changed in their expression in AA; almost all (141) were upregulated, including 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 nine death receptor pathway genes, three caspase-related genes (CASPER, CASP1, and

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CASP8), five 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 b, and TOSO) were up-regulated. In contrast, 3 genes including TIAF1, which has been implicated in anti-apoptotic regulation, were down-regulated in AA. In the death pathway, five death receptors and four 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 six other cell cycle-related genes were upregulated. Of these genes, most are believed to exert negative effects on cell proliferation and to inhibit entry into cell cycle. In contrast, several genes which exert positive effects on cell cycleprogress and cell proliferation control were down-regulated: two members of the cyclin-dependent kinase (CDK) family, three 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 compared to normal. 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 upregulated, including RNA polymerase II, which is over-expressed 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

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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 nine genes from the initial GeneChip analysis: five genes appeared to be up-regulated and four were down-regulated, over a range of 2.7- to 77.4-fold. Three pools were assayed, including the original samples prepared for the GeneChip analysis (pool-AA1 and pool-N1) as well as RNA from a new group of patients (poolAA3). TNFR2 and IL-8 showed 3.2- and 77.4-fold increases, respectively, in chip analysis of poolAA1; using real-time PCR, these genes were increased 1.8- and 13-fold in pool-AA1, and 9.6- and 12fold in pool-AA3, respectively.

Similarly, CD34, c-myc, GATA2, and FLT3, which were all

decreased by GeneChip analysis of AA CD34 cells, also were down-regulated in real-time PCR analysis(Figure 3). 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 samples-amplification method, there was a strong possibility of error and the generation of 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 normal individuals. Second, we found a high correlation in gene up- and down-regulation in patient samples as compared to normal individuals when separate patient and normal 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

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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 using 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 cells25. 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, anti-apoptotic genes appeared to be expressed at lower levels in patients’ CD34 cells as compared to health 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: stressand cytokine-inducible GADD45 B family proteins, which function as specific activators of MTK1 (a MAPKKK upstream in the p38 pathway that can induce apoptosis 28, 29), and NFKB inhibitory protein NFKBIA, which could influence the function of NFKB and enhance apoptosis. 30 Direct evidence of immune system attack also was 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

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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 cellular 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 PBMNC 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.34,35Finally, 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 upregulated in AA CD34 cells; 17 of these genes were assigned a negative regulatory function in the software and publicly available databases which we employed for annotation (only one upregulated gene was characterized as a positive proliferation regulator, and the remainders were of mixed or indeterminate role). Conversely, of the 20 genes in this class which were downregulated 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

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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.

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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 “check point” genes, such as FANCG, c-myb and c-myc, would also be consistent with the ultimate development of pre-malignant or aneuploid cells in survivals patients, who are susceptible to conversion to myelodysplasia or frank leukemic transformation. Conversely, 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, markedly elevated FLT3 ligand expression.

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showing especially

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;

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C-myb also was down-regulated, and decreased expression of c-myb and GATA-2

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likely 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 as well as improve 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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12. Zeng, W., J. P. Maciejewski, G. Chen, and N. S. Young. 2001. Limited heterogeneity of T cell receptor BV usage in aplastic anemia. J Clin Invest 108:765-773. 13. Zeng, W., S. Nakao, H. Takamatsu, A. Yachie, A. Takami, Y. Kondo, N. Sugimori, H. Yamazaki, Y. Miura, S. Shiobara, and T. Matsuda. 1999. Characterization of T-cell repertoire of the bone marrow in immune- mediated aplastic anemia: evidence for the involvement of antigen- driven T-cell response in cyclosporine-dependent aplastic anemia. Blood 93:30083016. 14. Nakao, S., A. Takami, H. Takamatsu, W. Zeng, N. Sugimori, H. Yamazaki, Y. Miura, M. Ueda, S. Shiobara, T. Yoshioka, T. Kaneshige, M. Yasukawa, and T. Matsuda. 1997. Isolation of a T-cell clone showing HLA-DRB1*0405-restricted cytotoxicity for hematopoietic cells in a patient with aplastic anemia. Blood 89:3691-3699. 15. Pfister, O., E. Chklovskaia, W. Jansen, K. Meszaros, C. Nissen, C. Rahner, N. Hurwitz, N. Bogatcheva, S. D. Lyman, and A. Wodnar-Filipowicz. 2000. Chronic overexpression of membrane-bound flt3 ligand by T lymphocytes in severe aplastic anaemia. Br J Haematol. 109:211-220. 16. Lyman, S. D., M. Seaberg, R. Hanna, J. Zappone, K. Brasel, J. L. Abkowitz, J. T. Prchal, J. C. Schultz, and N. T. Shahidi. 1995. Plasma/serum levels of flt3 ligand are low in normal individuals and highly elevated in patients with Fanconi anemia and acquired aplastic anemia. Blood 86:4091-4096. 17. Fujimaki, S., H. Harigae, T. Sugawara, N. Takasawa, T. Sasaki, and M. Kaku. 2001. Decreased expression of transcription factor GATA-2 in haematopoietic stem cells in patients with aplastic anaemia. Br J Haematol. 113:52-57. 18. Zhou, G., J. Chen, S. Lee, T. Clark, J. D. Rowley, and S. M. Wang. 2001. The pattern of gene expression in human CD34(+) stem/progenitor cells. Proc.Natl.Acad.Sci.U.S.A 98:1396613971. 19. Steidl, U., R. Kronenwett, U. P. Rohr, R. Fenk, S. Kliszewski, C. Maercker, P. Neubert, M. Aivado, J. Koch, O. Modlich, H. Bojar, N. Gattermann, and R. Haas. 2002. Gene expression profiling identifies significant differences between the molecular phenotypes of bone marrowderived and circulating human CD34+ hematopoietic stem cells. Blood 99:2037-2044. 20. Lock, C., G. Hermans, R. Pedotti, A. Brendolan, E. Schadt, H. Garren, A. Langer-Gould, S. Strober, B. Cannella, J. Allard, P. Klonowski, A. Austin, N. Lad, N. Kaminski, S. J. Galli, J. R. Oksenberg, C. S. Raine, R. Heller, and L. Steinman. 2002. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat.Med 8:500-508. 21. Kaufman, D. W., J. P. Kelly, J. M. Jurgelon, T. Anderson, S. Issaragrisil, B. E. Wiholm, N. S. Young, P. Leaverton, M. Levy, and S. Shapiro. 1996. Drugs in the aetiology of agranulocytosis and aplastic anaemia. Eur.J Haematol.Suppl 60:23-30.

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22. Camitta, B. M., E. D. Thomas, D. G. Nathan, R. P. Gale, K. J. Kopecky, J. M. Rappeport, G. Santos, E. C. Gordon-Smith, and R. Storb. 1979. A prospective study of androgens and bone marrow transplantation for treatment of severe aplastic anemia. Blood 53:504-514. 23. Livak, K. J. and T. D. Schmittgen. 2001. Analysis of relative gene expression data using realtime quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25:402-408. 24. Tan, F. L., C. S. Moravec, J. Li, C. Apperson-Hansen, P. M. McCarthy, J. B. Young, and M. Bond. 2002. The gene expression fingerprint of human heart failure. Proc.Natl.Acad.Sci.U.S.A 99:11387-11392. 25. Young, N. S. and J. Maciejewski. 1997. The pathophysiology of acquired aplastic anemia. N.Engl.J.Med. 336:1365-1372. 26. Dieude, P., E. Petit, S. Cailleau-Moindrault, J. Osorio, C. Pierlot, M. Martinez, S. Faure, O. Alibert, S. Lasbleiz, C. De Toma, T. Bardin, B. Prum, and F. Cornelis. 2002. Association between tumor necrosis factor receptor II and familial, but not sporadic, rheumatoid arthritis: evidence for genetic heterogeneity. Arthritis Rheum. 46:2039-2044. 27. Dittel, B. N. 2000. Evidence that Fas and FasL contribute to the pathogenesis of experimental autoimmune encephalomyelitis. Arch Immunol.Ther.Exp.(Warsz.) 48:381-388. 28. Takekawa, M. and H. Saito. 1998. A family of stress-inducible GADD45-like proteins mediate activation of the stress-responsive MTK1/MEKK4 MAPKKK. Cell 95:521-530. 29. Mita, H., J. Tsutsui, M. Takekawa, E. A. Witten, and H. Saito. 2002. Regulation of MTK1/MEKK4 kinase activity by its N-terminal autoinhibitory domain and GADD45 binding. Mol.Cell Biol. 22:4544-4555. 30. Curran, J. E., S. R. Weinstein, and L. R. Griffiths. 2002. Polymorphic variants of NFKB1 and its inhibitory protein NFKBIA, and their involvement in sporadic breast cancer. Cancer Lett. 188:103-107. 31. Geissler, K., E. Kabrna, M. Kollars, L. Ohler, A. Berer, H. Burgmann, S. Winkler, M. Willheim, W. Hinterberger, and K. Lechner. 2002. Interleukin-10 inhibits in vitro hematopoietic suppression and production of interferon-gamma and tumor necrosis factoralpha by peripheral blood mononuclear cells from patients with aplastic anemia. Hematol.J 3:206-213. 32. Moore, K. W., M. R. de Waal, R. L. Coffman, and A. O'Garra. 2001. Interleukin-10 and the interleukin-10 receptor. Annu.Rev Immunol. 19:683-765. 33. Hitchon, C., K. Wong, G. Ma, J. Reed, D. Lyttle, and H. El Gabalawy. 2002. Hypoxia-induced production of stromal cell-derived factor 1 (CXCL12) and vascular endothelial growth factor by synovial fibroblasts. Arthritis Rheum. 46:2587-2597. 34. Elenkov, I. J. and G. P. Chrousos. 2002. Stress hormones, proinflammatory and antiinflammatory cytokines, and autoimmunity. Ann N Y.Acad.Sci. 966:290-303.

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35. Ewen, M. E. 2000. Where the cell cycle and histones meet. Genes Dev. 14:2265-2270. 36. Reed, S. I. 1997. Control of the G1/S transition. Cancer Surv. 29:7-23. 37. Ravitz, M. J. and C. E. Wenner. 1997. Cyclin-dependent kinase regulation during G1 phase and cell cycle regulation by TGF-beta. Adv.Cancer Res 71:165-207. 38. Sherr, C. J. and J. M. Roberts. 1999. CDK inhibitors: positive and negative regulators of G1phase progression. Genes Dev. N Engl J Med 348:68-72.

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Table I. Clinical Features of Patients Patient

Age/Sex

Diagnosis

Neutrophils 103/ L

Reticulocyte 103/ L

Platelet 103/mL

1

22/F

MAA

0.903

31.3

65

2

9/F

SAA

0.803

58.1

111

3

16/M

SAA

0.152

22.7

5

4

23/M

SAA

0.296

31.9

36

5

30/M

SAA

0.041

9.3

25

6

26/M

MAA

0.802

29.2

21

7

69/M

SAA

0.390

17.2

54

8

45/F

SAA

1.116

15.8

13

9

25/F

MAA

2.550

83.8

136

10

66/M

MAA

1.341

72.7

51

11

22/M

MAA

0.788

55.0

48

12

42/M

SAA

0.802

25.0

22

13

22/F

SAA

0.903

31.3

49

14

17/F

SAA

0.058

3.1

58

15

21/M

SAA

0.885

72.9

8

16

50/M

SAA

0.315

16.6

8

17

33/F

MAA

2.486

46.3

239

18

68/M

SAA

0.461

16.2

24

19

28/F

SAA

0.620

22.9

44

20

19/M

SAA

N/A

41.3

27

21

30/M

SAA

0.014

2.7

21

22

51/M

SAA

0.024

2.6

1

23

11/F

SAA

0.431

50.2

13

24

28/F

SAA

1.131

15.5

8

25

55/F

SAA

0.650

7.1

53

26

40/F

MAA

3.050

72.3

23

27

37/M

MAA

2.010

39.3

35

28

74/M

SAA

N/A

31.4

8

22

.


29

48/M

SAA

0.180

8.0

13

30

65/M

SAA

0.790

21.1

25

31

27/M

SAA

0.050

3.5

34

32

82/M

SAA

0.220

18.2

25

33

17/F

MAA

1.930

35.3

57

34

66/M

SAA

3.350

0.7

38

35

57/M

MAA

1.190

56.4

124

36

56/F

SAA

0.090

50.2

84

All patients are new diagnosed, not treated. SAA, severe AA; MAA, moderate AA.

Table II. Characterization of GeneChip Small Sample Target Labeling Assay Cell source

Initially used total RNA

cRNA yield (µ µg)

Percentage of “present” call

3'- 5' Ratio (GAPDH)

N CD4

500 ng

54.2

44.4

2.13

N CD4

500 ng

53.4

47.7

2.84

N CD4#

5 µg

52.2

46.7

1.83

N CD4#

5 µg

54.3

44.9

1.44

Pool-AA1-1

500 ng

51.8

45.4

2.17

Pool-AA1-2

500 ng

49.5

46.6

2.68

Pool-AA1-3

500 ng

50.6

44.1

2.67

Pool-AA2

500 ng

50.6

48.5

3.27

Pool-N1-1

500 ng

53.3

41.9

2.55

Pool-N1-2

500 ng

51.7

44.7

1.92

Pool-N1-3

500 ng

54.8

47.0

1.94

Pool-N2

500 ng

48.9

46.7

2.80

#, standard protocol; N, normal donor

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Table III. Sequences of the Primers and Probes used in Real-Time PCR Gene Name

Sense Primer

Antisense Primer

MGB Probe

GATA2

5’- CAA GCC CAA GCG AAG ACT GT - 3’

5’- CGC CAT AAG GTG GTG GTT G - 3’

5’- CCG GCA CCT GTT GTG CAA ATT GTC - 3’

FLT3

5’- TTT ACC CCA CTT TCC AAT CAC AT - 3’

5’- CGA GTC CGG GTG TAT CTG AAC - 3’

5’- CAA ATT CCA GCA TGC CTG GTT CAA GAG - 3’

CD34

5’- GGC TGG ACC GCG CTT T - 3’

5’- AGT ACC GTT GTT GTC AAG ACT CAT G - 3’

5’- ACC CAG AAG GCA GCA AAC TCA GCA AG 3’

c-myc

5’-TCA AGA GGT GCC ACG TCT CC - 3’

5’- TCT TGG CAG CAG GAT AGT CCT T - 3’

5’- CAG CAC AAC TAC GCA GCG CCT CC - 3’

IL-8

5’-CTC TTG GCA GCC TTC CTG ATT - 3’

5’- TAT GCA CTG ACA TCT AAG TTC TTT AGC A - 3’

5’- CTT GGC AAA ACT GCA CCT TCA CAC AGA - 3’

TNF-RII

5’- ACA ATG GGA GAC ACA GAT TCC A - 3’

5’- TGA CCG AAA GGC ACA TTC CT - 3’

5’- CCT CGG AGT CCC CGA AGG ACG A - 3’

STAT1

5’- CCA GCC TGG TTT GGT AAT TGA - 3’

5’- GCT GGC TGA CGT TGG AGA TC - 3’

5’- AGA CGA CCT CTC TGC CCG TTG TGG - 3’

P63

5’- CCG GCC CAT GTC CTC TCT - 3’

5’- AGA ACC CAA GGA CTC CCC TTT - 3’

5’- CCA AGG AAT GCA CAG GTT TCG ACT ACC A - 3’

5’- GCA GAG AGC CAT GGT GCA G - 3’

5’- CAG GTC TCC ACT GCT GCC CTT GC - 3’

LD78

5’- CCG TCAC CTG CTC AGA ATC A - 3’

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Figure Legend Figure 1. Overview of differential gene expression patterns in CD34 cell of AA patients compared to healthy volunteers. Gene expression profiles of CD34 cells from two independent pools of patients and controls were generated using 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 biological pools. The numbers of genes in each functional category in which transcripts were more abundant in AA than in healthy volunteers is shown to the right, and genes less expressed in AA compared to controls are shown on the left.

Figure 2. Differential genes expression profiles between AA patients and healthy volunteers. Genes were grouped and displayed in categories of 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 a color code: genes at significantly higher

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levels are shown in red, those with significantly lower expression in green. Two biological pools were tested. For pool-AA1 and pool-N1, sufficient RNA was available to create three 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 to controls.

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Figure 3. Validation of GeneChip results by Real-Time RT-PCR. Experiments were performed using three 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, which showed differential expression in AA patients in the GeneChip analysis, were selected: five were up-regulated and four were down-regulated. Six genes showed a consistent differential change in real-time PCR. Another three genes showed no changes between AA patients and normal donors by this assay. Upward- and downward-pointing bars represent

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higher or lower expression levels in CD34 cells of AA patients compared to those of healthy volunteers, respectively. Black bar, GeneChip results; white bar, Real-Time PCR results; P1, poolAA1; P3, pool-AA3. Mean values of two independent experiments in duplicate are indicated.

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