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HEMATOPOIESIS
From the Division of Hematology/Oncology, Cedars Sinai
Research Institute, Los Angeles, CA, and the Department of Pathology,
University of California at Los Angeles School of Medicine; Research
Service, Veterans Affairs San Diego Healthcare System
(VASDHS), CA, and Division of Hematology/Oncology and Cancer
Center, University of California at San Diego School of Medicine, La
Jolla; and the Department of Hematology/Oncology, University Hospital,
Frankfurt/Main, Germany.
Gene patterns of expression in purified CD34+ bone
marrow cells from 7 patients with low-risk myelodysplastic syndrome
(MDS) and 4 patients with high-risk MDS were compared with expression data from CD34+ bone marrow cells from 4 healthy control
subjects. CD34+ cells were isolated by magnetic cell
separation, and high-density oligonucleotide microarray analysis was
performed. For confirmation, the expression of selected genes was
analyzed by real-time polymerase chain reaction. Class membership
prediction analysis selected 11 genes. Using the expression profile of
these genes, we were able to discriminate patients with low-risk from
patients with high-risk MDS and both patient groups from the control
group by hierarchical clustering (Spearman confidence). The
power of these 11 genes was verified by applying the algorithm to an
unknown test set containing expression data from 8 additional patients with MDS (3 at low risk, 5 at high risk). Patients at low risk could be
distinguished from those at high risk by clustering analysis. In
low-risk MDS, we found that the retinoic-acid-induced gene (RAI3), the radiation-inducible, immediate-early response
gene (IEX1), and the stress-induced phosphoprotein 1 (STIP1) were down-regulated. These data suggest that CD34+
cells from patients with low-risk MDS lack defensive proteins, resulting in their susceptibility to cell damage. In summary, we
propose that gene expression profiling may have clinical relevance for
risk evaluation in MDS at the time of initial diagnosis. Furthermore, this study provides evidence that in MDS, hematopoietic stem cells accumulate defects that prevent normal hematopoiesis.
(Blood. 2002;100:3553-3560) The underlying causes of primary
myelodysplastic syndrome (MDS) are still being defined. A proposal for
the multistep pathogenesis of MDS is that after initial damage to the
progenitor cell by a toxin or a spontaneous mutation, several
additional alterations may affect these cells and provide them with a
growth advantage. These alterations can influence the expression of
cell-cycle-related genes, including checkpoint and mismatch repair
genes, transcription factors, and tumor-suppressor genes. In
addition, early MDS was associated with an elevated ratio of apoptosis
to proliferation,1 but the mechanisms for this finding are
not yet established.
The defect of the hematopoietic stem cell in MDS is not well
characterized. One technical problem is that most of the experiments that use clinical samples from MDS patients were performed with low-density, nonadherent cells from the bone marrow of these patients. This may be adequate for high-risk MDS and acute myeloid leukemia (AML)
evolved from MDS because of the more uniform blast population in the
bone marrow. In contrast, in low-risk MDS, bone marrow cells are
heterogenous. Therefore, molecular abnormalities characteristic of
malignant cells are more difficult to find in low-risk MDS than in
high-risk MDS or AML. Because CD34 expression is a marker for
hematopoietic stem cells, experiments to detect abnormalities have focused on the CD34+ cells from patients with
MDS.2-4
To expand the understanding of genetic defects of the hematopoietic
stem cell in MDS, we performed oligonucleotide microarray analysis on
highly purified CD34+ cells from patients with MDS who were
clinically well characterized. All MDS samples used for our analyses
had the morphologic and immunologic features of MDS. In addition, the
defects in proliferation and differentiation of these CD34+
cells were demonstrated on a routine basis in suspension cultures (data
not shown).2 As a control, we investigated the gene
expression of CD34+ cells selected from the bone marrow of
healthy control subjects. We used 2 different strategies for analysis
of changes in gene expression. First, we singled out genes that are
highly predictive for the risk classification of the disease. Using
genes with a uniform expression pattern among subclasses, we were able
to distinguish MDS samples from those of healthy controls and to
differentiate low-risk from high-risk MDS patients according to their
gene expression profiles. Second, we focused on highly differentially
expressed genes in low-risk and high-risk MDS. Microarray results were
confirmed by real-time polymerase chain reaction (PCR) for selected genes.
Patients
CD34+ cell selection and nucleic acid
preparation
Total RNA was extracted using TRIzol (Life Technologies, Grand Island, NY) according to the manufacturer's protocol with minor modifications. In addition, RNA was purified by the RNeasy system (Qiagen, Valencia, CA) following the manufacturer's advice. To ensure that the gene expression we measured by microarray assay was
not affected by degradation of the RNA extracted from the purified
CD34+ cells, we used denaturing gel electrophoresis to
evaluate the quality of the RNA. Furthermore, the expression level of
Oligonucleotide microarray A detailed protocol for the sample preparation and microarray processing is available from Affymetrix (Santa Clara, CA). Because of the limited number of CD34+ cells and the low content of RNA in these hematopoietic stem cells, a double in vitro transcription technique (nanogram scale assay) was used. To assay 50 ng total RNA, the standard Affymetrix target amplification protocol was modified by using first-round cRNA product to generate double-stranded cDNA that was then used for a second round of in vitro transcription for synthesis of the biotinylated cRNA.Fifteen micrograms fragmented, biotinylated cRNA was hybridized to an HG-U95Av2 microarray (Affymetrix) for 16 hours at 45°C with constant rotation at 60 rpm, according to the Affymetrix protocol. This high-density oligonucleotide array targets 9670 human genes as selected from the National Center for Biotechnology Information (NCBI) Gene Bank database, with a total of 12 000 oligonucleotide sets. Each microarray was used to assay a single sample. After hybridization, the microarray was washed and stained on an Affymetrix fluidics station and was scanned with an argon-ion confocal laser with 488-nm emission and detection at 570 nm. Fluorescence intensity normalized to the average fluorescence for the entire microarray.7 Data were imported into a Microsoft Excel 2000 (Microsoft, Redmond, WA) database. Data analysis GeneChip image analysis was performed using the Microarray Analysis Suites 4.0.6 and 5.0 (Affymetrix). Data analysis was performed with the GeneSpring software version 4.0 (Silicon Genetics, San Carlos, CA). To eliminate changes within the range of background noise and to select the most differentially expressed genes, restrictions to classify genes as up-regulated or down-regulated required that raw data values be greater than 1000, changes in expression be greater than 5-fold, and genes be present by Affymetrix data analysis. Statistical significance of changes was calculated by the nonparametric t test, with P < .05. We used the class membership prediction method8-10 to determine whether the pattern of gene expression could be used to classify MDS and healthy control samples into 3 classes (control, low-risk MDS, high-risk MDS). The maximum number of genes to predict the class membership was set to 50. Hierarchical clustering analysis with Spearman confidence correlation was used to identify gene clusters. The separation ratio was set at 0.5.Real-time PCR Quantification of RNA in CD34+ cells by real-time PCR was performed as described previously.11 Briefly, 500 ng total RNA was processed directly to cDNA by reverse transcription. PCR primers and TaqMan (Applied Biosystems, Foster City, CA) probes were designed using software PRIMER3 (http://www.genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi) with published sequence data from the NCBI database. Amplification reactions contained 5 µL diluted cDNA and 12.5 µL Universal TaqMan 2× PCR mastermix (Applied Biosystems). Concentrations of primer and TaqMan probes were 300 nM and 100 nM, respectively, in a final volume of 25 µL. All reactions were performed in triplicate in an iCycler system (Bio-Rad, Hercules, CA), and the thermal cycling conditions were as follows: 2 minutes at 50°C and 10 minutes at 95°C, followed by 45 cycles at 95°C for 15 seconds and at 60°C for 1 minute.Beta-actin (BAC) and 18S were used as active and endogenous references to correct for differences in the amount of total RNA added to a reaction and to compensate for different levels of inhibition during reverse transcription of RNA and during PCR.11-13 No difference in relative expression was noted using either 18S or BAC for the normalization of gene expression.
Low-risk MDS versus normal CD34+ cells Analysis of the genes expressed in CD34+ marrow cells, which were more than 5-fold down-regulated in low-risk MDS than in those of healthy controls, resulted in a list of 161 highly differentially expressed genes (Figure 1). We examined this gene list using public databases and noted that 32 of these genes were involved in cell growth and signaling (Table 2). In low-risk MDS, 117 genes were more than 5-fold up-regulated compared with healthy CD34+ cells. A detailed analysis of these genes identified 27 that were probably involved in the regulation of hematopoiesis (Table 2).
High-risk MDS versus normal CD34+ cells Comparing gene expression in CD34+ marrow cells of patients with high-risk MDS versus healthy controls, 72 genes were more than 5-fold down-regulated. Twenty probably are important for hematopoiesis (Table 3), including HLF (hepatic leukemia factor) and STAT2 (signal transducer and activator of transcription). In CD34+ marrow cells from patients with high-risk MDS, 53 genes were more than 5-fold up-regulated compared with CD34+ marrow cells from healthy controls. A more detailed analysis of these genes identified 10 of them (Table 3) associated with hematopoiesis.
High-risk versus low-risk MDS Using the restrictions described and comparing high-risk and low-risk MDS, 210 genes showed higher expression in CD34+ cells from patients with high-risk MDS, whereas 134 genes were down-regulated. Further analysis identified 24 genes up-regulated and 25 genes down-regulated in CD34+ cells from high- versus low-risk MDS (Table 4), which are likely involved in the regulation of hematopoietic proliferation and differentiation.
Class membership prediction and hierarchical clustering Using the first data set (Table 1, samples 275-286), we identified, by class membership prediction, 11 genes (Table 5) whose expression can be used to differentiate with high accuracy between patients with low-risk MDS, those with high-risk MDS, and healthy controls. To verify the power of the genes selected by class membership prediction, we used the 11 genes for hierarchical clustering with Spearman confidence correlation (Figure 2A). We generated 3 clusters. All controls were in one cluster (with a maximum of 2 subclusters). Furthermore, patients with high-risk MDS and low-risk MDS were separated into different clusters, with a maximum of 3 subclusters. As a control experiment, unsupervised hierarchical cluster analysis using expression data from 1000 randomly selected genes did not result in any specific subclusters (data not shown) underlining the importance of the 11 selected genes for the prediction of MDS risk groups.
We used the 11 predictive genes to perform clustering analysis in a second data set obtained from CD34+ cells of patients with low-risk and high-risk MDS (Table 1, samples 3000-13 591). The aim of these additional experiments was to evaluate independently the predictive power of the 11 selected genes using a new series of patient samples. As shown in Figure 2B, we found 2 clusters corresponding to the IPSS classifications high-risk and low-risk. No misclassification of any of the samples of the test set occurred, demonstrating the power of the selected genes for risk-group prediction. One high-risk sample was, however, classified in a separate subcluster (sample 3883). Gene expression analysis by real-time PCR We tested 14 genes that were differentially expressed in CD34+ cells from patients with low-risk MDS compared to CD34+ cells from healthy controls (data not shown). Real-time PCR analysis was performed in 2 steps. First, we evaluated the expression of genes selected by a raw data restriction of 1000. Of 7 genes IEX1, RAI3, FNTB, ATF3, TM4SF1,
STIP1, and DNMwe confirmed the expression of 3 (43%) of them. Therefore, we increased our stringency level from a raw
data cutoff of 1000 to 2500. The second set of genes for confirmation
included BTUB, KDELR, STRAT,
TXNR, OCT3, CARD2, and
ANXA2. In this cohort of more stringently selected genes, the differential expression measured by oligonucleotide microarray was
confirmed by real-time PCR in 6 of 7 genes (86% confirmation rate).
The lack of suitable experimental models for MDS (eg, cell lines or animal systems) hampers progress in understanding the biology of this disease. Therefore, new approaches to elucidate the underlying abnormalities are needed. We and others2,14 have shown that to begin to characterize the cellular and molecular defects of clinical samples from patients with MDS requires the analysis of freshly isolated hematopoietic stem cells. Even those highly purified cells may demonstrate broad biologic variability, especially in low-risk MDS because the CD34+ cells in this subgroup of MDS may not always be monoclonal. In addition, the selection procedure of stem cells from whole bone marrow cells might influence biologic behavior and gene expression in these selected cells. We did focus on optimizing the cell selection procedure and the use of small amounts of RNA. The nanogram scale assay used in this work enabled amplification of small amounts of total RNA available for the analysis of the gene expression profile of CD34+ cells from patients with low-risk and high-risk MDS in comparison to those from healthy, unstimulated CD34+ cells. We were able to correlate the gene expression patterns in CD34+ cells with the clinical course of MDS. In addition, by analyzing the expression data of 9670 different genes, we identified key genes whose expression was aberrant in hematopoietic stem cells from patients with low-risk and high-risk MDS. Class membership prediction analysis and hierarchical clustering based on expression data of 11 selected genes enabled us to discriminate between patients with low-risk and high-risk MDS and to discriminate between each of these subgroups and healthy controls according to their gene expression profiles (Figure 2A). No misclassification of any of the samples occurred. The low number of subclusters substantiates the importance of the selected genes to discriminate between the 3 different groups. Analysis of a paired sample from a patient with low-risk MDS at the time of initial diagnosis and after transformation to high-risk MDS (samples 277 and 284) demonstrates that the power of the 11 selected genes to classify disease status is stronger than the similarity of gene expression within paired samples from the same patient (Figure 2A). A groundbreaking study showed that global gene expression analysis can distinguish with high accuracy between tumor samples from patients with AML or with acute lymphoblastic leukemia (ALL).9 Expression data from 51 selected genes were used for classification of AML versus ALL. These initial results were extended by class membership prediction analysis of breast cancers carrying mutations of 2 different genes (BRCA1 and BRCA2).10 The expression data of 9 and 11 genes, respectively, were successfully used to categorize the tumor samples to the correct group having either a BRCA1 or a BRCA2 mutation. Recently, we and others11,15-21 have shown that microarray analysis can provide sufficient data to detect genes or gene patterns associated with alterations of specific cellular pathways or signal cascades in tumor cells. For example, several new genes regulated by p53 have been identified that are important in p53-induced cell cycle arrest.22 Likewise, the regulation of gene expression in response to ionizing radiation was carefully dissected in human cells.23 In addition, the knowledge of the genes involved in specific pathways facilitates the discovery of those coding for interactive proteins, giving insight into their functional importance. Several redox and mitochondrial elements that control either resistance or sensitivity to apoptosis have been identified by microarray.7 Recent studies in genome-wide identification of putative DNA-binding sites for transcription factors have been accomplished using the microarray approach.24 We showed earlier11 that the application of restrictions (raw data values and -fold changes) to the microarray data resulted in the selection of genes whose expression could be confirmed by 2 independent techniques (real-time PCR and immunohistochemistry) in approximately 90% of genes. This may be true when using standard in vitro transcription to generate cRNA for hybridization with oligonucleotide microarrays. This report is the first to describe the double in vitro transcription for microarray analysis of small amounts of RNA from isolated CD34+ cell samples. Our results show that genes selected from the microarray data should have raw data of at least 2500. We focused on genes associated with cell growth and survival, apoptosis, and transcriptional factors because their dysregulation is a likely target of MDS. In low-risk MDS, enhanced intramedullary apoptosis may contribute to
ineffective hematopoiesis.25-27 We found that the
expression of the IEX1 gene was decreased in low-risk MDS.
IEX1 was initially identified in human squamous carcinoma
cells, and its expression was induced by radiation.28
Tumor necrosis factor alpha (TNF- Overexpression of DLK1 was described previously in patients with high-risk MDS, and it was implicated as being involved in MDS.14 The function of this gene in hematopoiesis is unknown, but it is genomically imprinted and expressed only from the paternal allele.31 The mechanism of imprinting of this gene follows the same pattern known from the reciprocally imprinted genes IGF2 (insulinlike growth factor 2) and H19. We found that this gene was markedly up-regulated in our series of patients with low-risk MDS compared with healthy CD34+ marrow cells. We propose that this gene is associated with cellular proliferation and that possible loss of imprinting of this gene in MDS enhances the expression of DLK1, giving the abnormal clone a growth advantage. This study provides evidence that microarray analysis can detect gene expression profiles strongly associated with different risk groups in MDS. Therefore, we believe that prognoses for patients with this disease may be predicted by using the gene expression of purified CD34+ cells. We have shown that microarray analysis can be used with small amounts of RNA, which can be obtained from cells during routine diagnostic bone marrow aspirate. This method may facilitate therapeutic decisions for patients in whom diagnosis or risk evaluation is not possible through morphologic and classical cytogenic techniques. Lastly, such analysis of global gene expression provides new insights into alterations of cellular pathways in early hematopoietic stem cells in low-risk MDS.
We thank Caroline Zander and Sandra Wagner for technical help and Dietrich Henne for advice in several computer techniques. We appreciate the expertise of the VASDHS Gene Chip Core Laboratory and the University of California at San Diego (UCSD) Cancer Center Microarray Facility for technical assistance with the microarray assays.
Submitted November 9, 2001; accepted July 23, 2002.
Supported by the National Institutes of Health (H.P.K.), the C. and H. Koeffler Fund, Parker Hughes Trust, Fredrich Begell Foundation, and the Joseph Troy Leukemia Fund. W.K.H. is a recipient of a scholarship from the Deutsche Forschungsgemeinschaft (HO2207/1-1). S.d.V. was supported by the University of California at Los Angeles (UCLA) Specialty Training and Research (STAR) Program as a Hematology/Oncology fellow and as an Advanced Research fellow. H.P.K. is a member of the Jonsson Comprehensive Cancer Center and holds the endowed Mark Goodson Chair of Oncology Research at Cedars Sinai Medical Center/UCLA School of Medicine.
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: Wolf-K. Hofmann, Department of Hematology, University Hospital Theodor-Stern-Kai 7, 60596 Frankfurt/Main, Germany; e-mail: w.k.hofmann{at}em.uni-frankfurt.de.
1.
Parker JE, Mufti GJ, Rasool F, Mijovic A, Devereux S, Pagliuca A.
The role of apoptosis, proliferation, and the Bcl-2-related proteins in the myelodysplastic syndromes and acute myeloid leukemia secondary to MDS.
Blood.
2000;96:3932-3938 2. Hofmann WK, Kalina U, Wagner S, et al. Characterization of defective megakaryocytic development in patients with myelodysplastic syndromes. Exp Hematol. 1999;27:395-400[CrossRef][Medline] [Order article via Infotrieve]. 3. Kalina U, Hofmann WK, Koschmieder S, et al. Alteration of c-mpl-mediated signal transduction in CD34(+) cells from patients with myelodysplastic syndromes. Exp Hematol. 2000;28:1158-1163[CrossRef][Medline] [Order article via Infotrieve]. 4. Preisler HD, Li B, Chen H, et al. P15INK4B gene methylation and expression in normal, myelodysplastic, and acute myelogenous leukemia cells and in the marrow cells of cured lymphoma patients. Leukemia. 2001;15:1589-1595[CrossRef][Medline] [Order article via Infotrieve].
5.
Greenberg P, Cox C, LeBeau MM, et al.
International scoring system for evaluating prognosis in myelodysplastic syndromes.
Blood.
1997;89:2079-2088 6. Bennett JM. World Health Organization classification of the acute leukemias and myelodysplastic syndrome. Int J Hematol. 2000;72:131-133[Medline] [Order article via Infotrieve].
7.
Voehringer DW, Hirschberg DL, Xiao J, et al.
Gene microarray identification of redox and mitochondrial elements that control resistance or sensitivity to apoptosis.
Proc Natl Acad Sci U S A.
2000;97:2680-2685 8. Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE Trans Information Theory. 1967;13:21-27[CrossRef].
9.
Golub TR, Slonim DK, Tamayo P, et al.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Science.
1999;286:531-537
10.
Hedenfalk I, Duggan D, Chen Y, et al.
Gene-expression profiles in hereditary breast cancer.
N Engl J Med.
2001;344:539-548
11.
Hofmann WK, de Vos S, Tsukasaki K, et al.
Altered apoptosis pathways in mantle cell lymphoma detected by oligonucleotide microarray.
Blood.
2001;98:787-794
12.
Peters UR, Tschan MP, Kreuzer KA, et al.
Distinct expression patterns of the p53-homologue p73 in malignant and normal hematopoiesis assessed by a novel real-time reverse transcription-polymerase chain reaction assay and protein analysis.
Cancer Res.
1999;59:4233-4236 13. Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25:169-193[Abstract].
14.
Miyazato A, Ueno S, Ohmine K, et al.
Identification of myelodysplastic syndrome-specific genes by DNA microarray analysis with purified hematopoietic stem cell fraction.
Blood.
2001;98:422-427
15.
Tamayo P, Slonim D, Mesirov J, et al.
Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.
Proc Natl Acad Sci U S A.
1999;96:2907-2912
16.
Khan J, Bittner ML, Saal LH, et al.
cDNA microarrays detect activation of a myogenic transcription program by the PAX3-FKHR fusion oncogene.
Proc Natl Acad Sci U S A.
1999;96:13264-13269 17. DeRisi J, van den Hazel B, Marc P, et al. Genome microarray analysis of transcriptional activation in multidrug resistance yeast mutants. FEBS Lett. 2000;470:156-160[CrossRef][Medline] [Order article via Infotrieve].
18.
Kaminski N, Allard JD, Pittet JF, et al.
Global analysis of gene expression in pulmonary fibrosis reveals distinct programs regulating lung inflammation and fibrosis.
Proc Natl Acad Sci U S A.
2000;97:1778-1783
19.
De Vos J, Couderc G, Tarte K, et al.
Identifying intercellular signaling genes expressed in malignant plasma cells by using complementary DNA arrays.
Blood.
2001;98:771-780
20.
Neiman PE, Ruddell A, Jasoni C, et al.
Analysis of gene expression during myc oncogene-induced lymphomagenesis in the bursa of Fabricius.
Proc Natl Acad Sci U S A.
2001;98:6378-6383
21.
Lee YT, Miller LD, Gubin AN, et al.
Transcription patterning of uncoupled proliferation and differentiation in myelodysplastic bone marrow with erythroid-focused arrays.
Blood.
2001;98:1914-1921
22.
Zhao R, Gish K, Murphy M, et al.
Analysis of p53-regulated gene expression patterns using oligonucleotide arrays.
Genes Dev.
2000;14:981-993
23.
Tusher VG, Tibshirani R, Chu G.
Significance analysis of microarrays applied to the ionizing radiation response.
Proc Natl Acad Sci U S A.
2001;98:5116-5121
24.
Bulyk ML, Huang X, Choo Y, Church GM.
Exploring the DNA-binding specificities of zinc fingers with DNA microarrays.
Proc Natl Acad Sci U S A.
2001;98:7158-7163 25. Parker JE, Mufti GJ. Ineffective haemopoiesis and apoptosis in myelodysplastic syndromes. Br J Haematol. 1998;101:220-230[CrossRef][Medline] [Order article via Infotrieve].
26.
Shetty V, Hussaini S, Broady-Robinson L, et al.
Intramedullary apoptosis of hematopoietic cells in myelodysplastic syndrome patients can be massive: apoptotic cells recovered from high-density fraction of bone marrow aspirates.
Blood.
2000;96:1388-1392 27. Novitzky N, Mohamed R, Finlayson J, duToit C. Increased apoptosis of bone marrow cells and preserved proliferative capacity of selected progenitors predict for clinical response to anti-inflammatory therapy in myelodysplastic syndromes. Exp Hematol. 2000;28:941-949[CrossRef][Medline] [Order article via Infotrieve].
28.
Kondratyev AD, Chung KN, Jung MO.
Identification and characterization of a radiation-inducible glycosylated human early-response gene.
Cancer Res.
1996;56:1498-1502 29. Kitagawa M, Saito I, Kuwata T, et al. Overexpression of tumor necrosis factor (TNF)-alpha and interferon (IFN)-gamma by bone marrow cells from patients with myelodysplastic syndromes. Leukemia. 1997;11:2049-2054[CrossRef][Medline] [Order article via Infotrieve].
30.
Wu MX, Ao Z, Prasad KV, Wu R, Schlossman SF.
IEX-1L, an apoptosis inhibitor involved in NF-
31.
Schmidt JV, Matteson PG, Jones BK, Guan XJ, Tilghman SM.
The Dlk1 and Gtl2 genes are linked and reciprocally imprinted.
Genes Dev.
2000;14:1997-2002
© 2002 by The American Society of Hematology.
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|
  D. P. Steensma The spectrum of molecular aberrations in myelodysplastic syndromes: in the shadow of acute myeloid leukemia Haematologica, June 1, 2007; 92(6): 723 - 727. [Full Text] [PDF]
|
|
|
|
 |
|
 B. M. Abdallah, P. Boissy, Q. Tan, J. Dahlgaard, G. A. Traustadottir, K. Kupisiewicz, J. Laborda, J.-M. Delaisse, and M. Kassem dlk1/FA1 Regulates the Function of Human Bone Marrow Mesenchymal Stem Cells by Modulating Gene Expression of Pro-inflammatory Cytokines and Immune Response-related Factors J. Biol. Chem., March 9, 2007; 282(10): 7339 - 7351. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Zhang, P. Hopwood, C. C. Abrams, A. Downing, F. Murray, R. Talbot, A. Archibald, S. Lowden, and L. K. Dixon Macrophage Transcriptional Responses following In Vitro Infection with a Highly Virulent African Swine Fever Virus Isolate. J. Virol., November 1, 2006; 80(21): 10514 - 10521. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Pellagatti, M. Cazzola, A. A. N. Giagounidis, L. Malcovati, M. G. D. Porta, S. Killick, L. J. Campbell, L. Wang, C. F. Langford, C. Fidler, et al. Gene expression profiles of CD34+ cells in myelodysplastic syndromes: involvement of interferon-stimulated genes and correlation to FAB subtype and karyotype Blood, July 1, 2006; 108(1): 337 - 345. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. C. Briggs, K. E. Shults, L. A. Flye, S. A. McClintock-Treep, M. H. Jagasia, S. A. Goodman, F. I. Boulos, J. W. Jacobberger, G. T. Stelzer, and D. R. Head Dysregulated Human Myeloid Nuclear Differentiation Antigen Expression in Myelodysplastic Syndromes: Evidence for a Role in Apoptosis. Cancer Res., May 1, 2006; 66(9): 4645 - 4651. [Abstract] [Full Text] [PDF]
|
|
|
|
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|
V. Hamelin, C. Letourneux, P.-H. Romeo, F. Porteu, and M. Gaudry Thrombopoietin regulates IEX-1 gene expression through ERK-induced AML1 phosphorylation Blood, April 15, 2006; 107(8): 3106 - 3113. [Abstract] [Full Text] [PDF]
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|
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|
|
A. Sternberg, S. Killick, T. Littlewood, C. Hatton, A. Peniket, T. Seidl, S. Soneji, J. Leach, D. Bowen, C. Chapman, et al. Evidence for reduced B-cell progenitors in early (low-risk) myelodysplastic syndrome Blood, November 1, 2005; 106(9): 2982 - 2991. [Abstract] [Full Text] [PDF]
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Top
Abstract
Introduction
-actin, as determined by GeneChip assay, was required to be
greater than 30 000 (raw data) in all of our MDS and control samples.
), which is overexpressed in bone
marrow cells from patients with MDS, also induced the expression of
IEX1,
B-mediated cell survival.
Science.
1998;281:998-1001