Transcriptional Profiling of Human Hematopoiesis During In Vitro Lineage-Specific Differentiation
http://www.100md.com
《干细胞学杂志》
a Department of Hematology and Oncology, University Hospital, Frankfurt, Germany;
b Department of Hematology, Oncology and Transfusion Medicine, University Hospital Benjamin Franklin, Berlin, Germany;
c Division of Hematology/Oncology, UCLA School of Medicine, Los Angeles, California, USA
Key Words. Gene expression ? Hematopoietic development ? Hematopoietic stem cell ? Microarray ? Transcription
Correspondence: Wolf-K. Hofmann, M.D., Department of Hematology, Oncology and Transfusion Medicine, University Hospital Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany. Telephone: 49-30-8445-3421; Fax: 49-30-8445-4468; e-mail: W.K.Hofmann@charite.de
ABSTRACT
Normal hematopoiesis is maintained by pluripotent, long-term repopulating stem cells that generate progenitors capable of differentiating into all three hematopoietic lineages. Only a few pluripotent hematopoietic stem cells possesses the ability to actively self-renew at any given time . Hematopoietic differentiation is strictly regulated by several exogenous factors . Lineage-specific growth factors, such as erythropoietin (EPO), granulocyte colony-stimulating factor (G-CSF), granulocyte macrophage colony-stimulating factor (GM-CSF), and thrombopoietin (TPO), induce proliferation and differentiation into functionally active peripheral blood (PB) cells by activating multiple genes in an orchestrated way . Other growth factors, such as stem cell factor (SCF), the ligand of the Flt3/Flt2 receptor tyrosine kinase (FL), interleukin (IL)-1, IL-3, and IL-6, support growth and survival of primitive progenitor cells .
Disruptions of these intricate sequences of transcriptional activation and suppression of multiple genes can cause hematological diseases, such as leukemias, myelodysplastic syndromes (MDS), or myeloproliferative syndromes (MPS). Elucidating the pattern and sequence of gene expression during normal hematopoietic cell development may help to unravel the disease-specific mechanisms in hematopoietic malignancies.
The first report describing sequential analysis of gene expression during hematopoietic differentiation in the mouse system has recently been published . Transcriptional profiling of murine multipotent hematopoietic progenitor cells (factor-dependent cell-Paterson -mix cell line) during multilineage differentiation revealed several known and as-yet unknown genes that are differentially expressed in erythroid and neutrophil differentiation as well as genes involved in self-renewal and differentiation .
We have established an in vitro model that enables the detailed analysis of human hematopoietic differentiation originating from unstimulated, steady-state bone marrow CD34+ cells. Using this system, we have analyzed gene expression patterns of the three hematopoietic lineages at defined time points throughout lineage-specific differentiation. Because we used primary human CD34+ cells, the experiments were done in triplicates for each of the time points and each of the conditions to minimize individual changes in expression levels of approximately 22,500 genes that were analyzed.
Our data provide several new insights into understanding differentiation and proliferation of human stem and progenitor cells. In addition, the data should be particularly suitable for comparative analysis of gene expression in hematopoietic malignancies, to characterize pathomechanisms of diseases, and, finally, to find genetically defined therapeutic targets.
MATERIALS AND METHODS
In Vitro Differentiation of CD34+ Cells
CD34+ cells of healthy individuals were plated on different culture conditions for lineage-specific differentiation to generate erythropoietic, granulopoietic, and megakaryopoietic cells (Fig. 1). Lineage-specific cells were characterized by morphology and by immunophenotype as described. Cells from each of the donors were cultured separately, and RNA was prepared from each of the conditions for microarray hybridization.
Figure 1. Morphology of CD34+ cells and cells generated by in vitro differentiation. Typical morphology is detectable in erythropoiesis (day 4, proerythroblasts; day 7, erythroblasts; day 11, normoblasts), granulopoiesis (incremental organization of nucleus resulting in polymorph nuclear cells), and megakaryopoiesis (increasing polyploidy). Abbreviations: EPO, erythropoietin; FL, ligand of the Flt3/Flt2 receptor tyrosine kinase; G-CSF, granulocyte colony-stimulating factor; GM-CSF, granulocyte macrophage colony-stimulating factor; IL-3, interleukin-3; SCF, stem cell factor; TPO, thrombopoietin.
On day 4 of culture, cell numbers increased markedly under conditions for erythropoiesis and granulopoiesis (>threefold). Because of the endomitotic development of megakaryocytic cells, the proliferation of these cells was slow compared with erythropoietic and granulopoietic cells. Table 1 shows a summary of cell numbers and the purities of the lineage-specific cells after positive selection used for gene expression analysis.
Table 1. Summary of cell numbers, purities of the in vitro–differentiated cells, and the "present" calls (P) of the hybridized microarrays
Oligonucleotide Microarrays
Gene expression in cells sampled at days 0, 4, 7, and 11 of hematopoietic differentiation was analyzed by oligonucleotide microarrays (HG-U133A).
As an indicator of high-quality hybridization results, we determined the value of expressed genes (P) of every array analyzed by MAS 5.0. The mean value of present calls (P-calls) was 10,074 out of 22,500 (44%; Table 1) and therefore above 25% in all of our experiments, as required by MIAME (minimum information about a microarray experiment) for a high-quality microarray experiment .
Horizontal Analysis of Gene Expression During Lineage-Specific Differentiation
Characterization of Genes Known to Be Lineage-Specific ? As a confirmation of the lineage-specific in vitro differentiation, we identified continuously upregulated genes that are already known to be associated with specific programs of hematopoietic differentiation. These genes were not expressed in CD34+ cells. Figure 2 shows the expression of specific genes for the erythrocytic (e.g., ANK, D9S57E, CD35), granulocytic (e.g., IRC1, FCGR2B), and megakaryocytic (e.g., SERPINE1, THBD, PDGF1, and CD42C) lineage in the course of differentiation.
Figure 2. Confirmation of the in vitro differentiation of CD34+ cells by microarray analysis. During lineage-specific differentiation, the expression of well-known marker genes for each hematopoietic lineage analyzed increased continuously. Expression of erythrocytic (ANK, erythrocytic Ankyrin 1; Z39IG, immunoglobulin superfamily protein; CD35, complement component receptor 1, including Knops blood group system; D9S57E, tropomodulin), granulocytic (IRC1, leukocyte membrane antigen; FCGR2B, Fc fragment of immunoglobulin G, low-affinity IIb, receptor for CD32) and mega-karyocytic marker genes (SERPINE1, serine proteinase inhibitor, clade E ; PHS1, prostaglandin-endoperoxide synthase 1; THBD, thrombomodulin; MSR1, macrophage scavenger receptor 1; PDGF1, platelet-derived growth factor alpha; FCGR2A, receptor for CD32; CD42C, glycoprotein Ib ) is shown.
Genes Strongly Associated with Specific Hematopoietic Lineages ? To investigate genes that are strongly associated with lineage-specific differentiation, we generated lists containing genes with continuously increasing or decreasing expression in each hematopoietic lineage. Continuously regulated genes of the hematopoietic lineages were hierarchically clustered (Figs. 3A–3C).
Figure 3. Cluster analysis of genes associated with (A) erythropoiesis, (B) granulopoiesis, and (C) megakaryopoiesis. One cluster of continuously upregulated and another cluster of continuously down-regulated genes for each of the three hematopoietic lineages are shown. Color code: blue, low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.
Only a few genes increased or decreased continuously during erythropoietic (21 up, 58 down), granulopoietic (21 up, 30 down), and megakaryocytic differentiation (91 up, 37 down). Nine genes are regulated in both erythropoietic and granulopoietic cells (e.g., CD86 antigen, immunoglobulin superfamily protein, solute carrier family 7), three genes are regulated in erythropoiesis and megakaryopoiesis (FLJ20748, CD86, EST: 217678_AT), and three genes are regulated during granulopoietic and megakaryopoietic differentiation (potassium large-conductance calcium-activated channel, CD86, MGC5528). Only one gene is continuously regulated in all three differentiation programs (CD86), suggesting that it may be involved in commitment or differentiation of the hematopoietic stem cell. Table 2 lists genes with continuously increasing expression in erythropoiesis. Table 3 shows continuously decreasing genes in erythropoiesis. The data set for all lineages (continuously upregulated and downregulated genes) is given in the supplemental material (supplemental online Tables 1–6). In addition, the complete expression data of all samples will be available for downloading at http://knm1.ibe.med.uni-muenchen.de/kn_home/WKH/index.htm.
Table 2. Genes continuously regulated during erythropoietic differentiation with continuously increasing expression
Table 3. Genes continuously regulated during erythropoietic differentiation with continuously decreasing expression
Table 4. Genes differentially expressed in erythropoiesis
Table 5. Genes differentially expressed in granulopoiesis
Table 6. Genes differentially expressed in megakaryopoiesis
Vertical Analysis of Differentially Expressed Genes
A vertical analysis was performed to identify genes that are differentially expressed in a specific hematopoietic lineage at a defined time point. The analysis of genes expressed in cells on specific lineage-directed differentiation conditions that show a greater than threefold change at any defined time point compared with cells in the remaining conditions (e.g., erythropoiesis versus granulopoiesis and megakaryopoiesis) resulted in lists of differentially expressed genes for each of the hematopoietic lineages. Tables 4–6 show highly differentially expressed genes for each lineage (A, erythropoiesis; B, granulopoiesis; C, megakaryopoiesis). The specific time point of expression is indicated. We could not detect genes common to all three hematopoietic lineages, supporting the lineage-specificity of the identified genes.
Class Membership Prediction
The maximal number of genes to predict the class membership was found to be 53 using five neighbors. We defined this set of genes to be able to classify a particular sample according to its lineage. Figure 4 represents the results of hierarchical clustering with Spearman’s confidence correlation of 25 samples of in vitro–differentiated CD34+ cells. Three subclusters indicate the affiliation to one of the three hematological lineages. One misclassification (sample E_d04_2) occurred. In Table 7, the predictive genes are listed according to the appearance in the cluster.
Figure 4. Identification of genes expressed in differentiating CD34+ bone marrow cells, which are significantly correlated with each of the three analyzed hematopoietic lineages. Results represent the analysis by hierarchical clustering with Spearman’s confidence correlation of 25 samples of in vitro–differentiated CD34+ bone marrow cells. Fifty-three genes were selected to predict the class membership of each of the samples. The vertical list contains each of the samples. The horizontal list displays the 53 genes. Three clusters corresponding to the three hematological lineages were found, as indicated by the black bars. One misclassification occurred (sample ID: E_d04_2). For granulopoietic and megakaryopoietic differentiation, three and two samples are missing in the analysis, respectively. Color code: blue, low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.
Table 7. Genes that are predictive for the lineages of human in vitro–differentiating hematopoietic cells (see Fig. 4)
Confirmation of Expression of Continuously Regulated Genes During In Vitro Hematopoietic Differentiation by Real-Time PCR
To confirm the expression data from the oligonucleotide micro-array studies, we analyzed the expression of a selection of six significantly regulated genes (two in each lineage) in 25 samples by real-time PCR. The gene for PTGS2 was analyzed in erythropoietic and granulopoietic samples. All experiments were done in triplicate. The variance between the triplicates was less than 5%. The results were normalized to the expression of GPI in each of the samples. Figure 5 summarizes the expression data measured by real-time PCR for the selected genes. We could confirm our microarray results for both the continuously upregulated and downregulated genes not only for the single time points but also for the course of gene expression during hematopoietic differentiation.
Figure 5. Validation of gene expression pattern of continuously regulated genes during lineage-specific differentiation by real-time polymerase chain reaction (PCR). The expression of a selection of six genes in 25 samples was analyzed by real-time PCR. Two genes per hematopoietic lineage were analyzed in samples of the appropriate lineage (the gene for PTGS2 was analyzed in erythropoietic and granulopoietic samples). We found with one exception (IRC1 should be upregulated in G) that for both the continuously upregulated and downregulated genes, the real-time PCR results are similar to the results detected by microarray analysis. X-axis, days; Y-axis, GPI-normalized expression levels as detected by real-time PCR.
Gene Ontology Analysis
Categorization of differentially expressed genes in all three hematopoietic lineages according to the molecular function yielded seven functional groups (genes with binding, catalytic, signal transducer, transcription regulator, structural molecule, enzyme regulator, and transporter activity; Fig. 6).
Figure 6. Classification of differentially expressed genes of normal hematopoiesis according to gene ontology. Genes were annotated according to FatiGO Data Mining with Gene Ontology. Annotations are available for a subset of genes (~68%) represented on the HG-U133A microarray. The functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.
Genes with binding (E, 36%; versus G, 37%; versus M, 38%), catalytic (E, 26%; versus G, 19%; versus M, 19%), and signal transducer activity (E, 22%; versus G, 29%; versus M, 23%) were comparable between the three hematopoietic lineages. The percentage of genes performing transcription regulator activity or structure molecule activity (E, 2%; versus G, 7%; versus M, 3%; E, 6%; versus G, 2%; versus M, 1%) was slightly elevated in the granulopoietic or erythropoietic lineage, whereas genes functioning as enzyme regulators (E, 4%; versus G, 3%; versus M, 6%) and transporters (E, 4%; versus G, 3%; versus M, 10%) were overrepresented in the megakaryopoietic lineage. Further, it should be noted that the functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.
DISCUSSION
This work was supported by the Deutsche Forschungsgemeinschaft (HO 2207/3-1), the BMBF Competence Network Leukemias, and the German Genome Research Network.
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b Department of Hematology, Oncology and Transfusion Medicine, University Hospital Benjamin Franklin, Berlin, Germany;
c Division of Hematology/Oncology, UCLA School of Medicine, Los Angeles, California, USA
Key Words. Gene expression ? Hematopoietic development ? Hematopoietic stem cell ? Microarray ? Transcription
Correspondence: Wolf-K. Hofmann, M.D., Department of Hematology, Oncology and Transfusion Medicine, University Hospital Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany. Telephone: 49-30-8445-3421; Fax: 49-30-8445-4468; e-mail: W.K.Hofmann@charite.de
ABSTRACT
Normal hematopoiesis is maintained by pluripotent, long-term repopulating stem cells that generate progenitors capable of differentiating into all three hematopoietic lineages. Only a few pluripotent hematopoietic stem cells possesses the ability to actively self-renew at any given time . Hematopoietic differentiation is strictly regulated by several exogenous factors . Lineage-specific growth factors, such as erythropoietin (EPO), granulocyte colony-stimulating factor (G-CSF), granulocyte macrophage colony-stimulating factor (GM-CSF), and thrombopoietin (TPO), induce proliferation and differentiation into functionally active peripheral blood (PB) cells by activating multiple genes in an orchestrated way . Other growth factors, such as stem cell factor (SCF), the ligand of the Flt3/Flt2 receptor tyrosine kinase (FL), interleukin (IL)-1, IL-3, and IL-6, support growth and survival of primitive progenitor cells .
Disruptions of these intricate sequences of transcriptional activation and suppression of multiple genes can cause hematological diseases, such as leukemias, myelodysplastic syndromes (MDS), or myeloproliferative syndromes (MPS). Elucidating the pattern and sequence of gene expression during normal hematopoietic cell development may help to unravel the disease-specific mechanisms in hematopoietic malignancies.
The first report describing sequential analysis of gene expression during hematopoietic differentiation in the mouse system has recently been published . Transcriptional profiling of murine multipotent hematopoietic progenitor cells (factor-dependent cell-Paterson -mix cell line) during multilineage differentiation revealed several known and as-yet unknown genes that are differentially expressed in erythroid and neutrophil differentiation as well as genes involved in self-renewal and differentiation .
We have established an in vitro model that enables the detailed analysis of human hematopoietic differentiation originating from unstimulated, steady-state bone marrow CD34+ cells. Using this system, we have analyzed gene expression patterns of the three hematopoietic lineages at defined time points throughout lineage-specific differentiation. Because we used primary human CD34+ cells, the experiments were done in triplicates for each of the time points and each of the conditions to minimize individual changes in expression levels of approximately 22,500 genes that were analyzed.
Our data provide several new insights into understanding differentiation and proliferation of human stem and progenitor cells. In addition, the data should be particularly suitable for comparative analysis of gene expression in hematopoietic malignancies, to characterize pathomechanisms of diseases, and, finally, to find genetically defined therapeutic targets.
MATERIALS AND METHODS
In Vitro Differentiation of CD34+ Cells
CD34+ cells of healthy individuals were plated on different culture conditions for lineage-specific differentiation to generate erythropoietic, granulopoietic, and megakaryopoietic cells (Fig. 1). Lineage-specific cells were characterized by morphology and by immunophenotype as described. Cells from each of the donors were cultured separately, and RNA was prepared from each of the conditions for microarray hybridization.
Figure 1. Morphology of CD34+ cells and cells generated by in vitro differentiation. Typical morphology is detectable in erythropoiesis (day 4, proerythroblasts; day 7, erythroblasts; day 11, normoblasts), granulopoiesis (incremental organization of nucleus resulting in polymorph nuclear cells), and megakaryopoiesis (increasing polyploidy). Abbreviations: EPO, erythropoietin; FL, ligand of the Flt3/Flt2 receptor tyrosine kinase; G-CSF, granulocyte colony-stimulating factor; GM-CSF, granulocyte macrophage colony-stimulating factor; IL-3, interleukin-3; SCF, stem cell factor; TPO, thrombopoietin.
On day 4 of culture, cell numbers increased markedly under conditions for erythropoiesis and granulopoiesis (>threefold). Because of the endomitotic development of megakaryocytic cells, the proliferation of these cells was slow compared with erythropoietic and granulopoietic cells. Table 1 shows a summary of cell numbers and the purities of the lineage-specific cells after positive selection used for gene expression analysis.
Table 1. Summary of cell numbers, purities of the in vitro–differentiated cells, and the "present" calls (P) of the hybridized microarrays
Oligonucleotide Microarrays
Gene expression in cells sampled at days 0, 4, 7, and 11 of hematopoietic differentiation was analyzed by oligonucleotide microarrays (HG-U133A).
As an indicator of high-quality hybridization results, we determined the value of expressed genes (P) of every array analyzed by MAS 5.0. The mean value of present calls (P-calls) was 10,074 out of 22,500 (44%; Table 1) and therefore above 25% in all of our experiments, as required by MIAME (minimum information about a microarray experiment) for a high-quality microarray experiment .
Horizontal Analysis of Gene Expression During Lineage-Specific Differentiation
Characterization of Genes Known to Be Lineage-Specific ? As a confirmation of the lineage-specific in vitro differentiation, we identified continuously upregulated genes that are already known to be associated with specific programs of hematopoietic differentiation. These genes were not expressed in CD34+ cells. Figure 2 shows the expression of specific genes for the erythrocytic (e.g., ANK, D9S57E, CD35), granulocytic (e.g., IRC1, FCGR2B), and megakaryocytic (e.g., SERPINE1, THBD, PDGF1, and CD42C) lineage in the course of differentiation.
Figure 2. Confirmation of the in vitro differentiation of CD34+ cells by microarray analysis. During lineage-specific differentiation, the expression of well-known marker genes for each hematopoietic lineage analyzed increased continuously. Expression of erythrocytic (ANK, erythrocytic Ankyrin 1; Z39IG, immunoglobulin superfamily protein; CD35, complement component receptor 1, including Knops blood group system; D9S57E, tropomodulin), granulocytic (IRC1, leukocyte membrane antigen; FCGR2B, Fc fragment of immunoglobulin G, low-affinity IIb, receptor for CD32) and mega-karyocytic marker genes (SERPINE1, serine proteinase inhibitor, clade E ; PHS1, prostaglandin-endoperoxide synthase 1; THBD, thrombomodulin; MSR1, macrophage scavenger receptor 1; PDGF1, platelet-derived growth factor alpha; FCGR2A, receptor for CD32; CD42C, glycoprotein Ib ) is shown.
Genes Strongly Associated with Specific Hematopoietic Lineages ? To investigate genes that are strongly associated with lineage-specific differentiation, we generated lists containing genes with continuously increasing or decreasing expression in each hematopoietic lineage. Continuously regulated genes of the hematopoietic lineages were hierarchically clustered (Figs. 3A–3C).
Figure 3. Cluster analysis of genes associated with (A) erythropoiesis, (B) granulopoiesis, and (C) megakaryopoiesis. One cluster of continuously upregulated and another cluster of continuously down-regulated genes for each of the three hematopoietic lineages are shown. Color code: blue, low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.
Only a few genes increased or decreased continuously during erythropoietic (21 up, 58 down), granulopoietic (21 up, 30 down), and megakaryocytic differentiation (91 up, 37 down). Nine genes are regulated in both erythropoietic and granulopoietic cells (e.g., CD86 antigen, immunoglobulin superfamily protein, solute carrier family 7), three genes are regulated in erythropoiesis and megakaryopoiesis (FLJ20748, CD86, EST: 217678_AT), and three genes are regulated during granulopoietic and megakaryopoietic differentiation (potassium large-conductance calcium-activated channel, CD86, MGC5528). Only one gene is continuously regulated in all three differentiation programs (CD86), suggesting that it may be involved in commitment or differentiation of the hematopoietic stem cell. Table 2 lists genes with continuously increasing expression in erythropoiesis. Table 3 shows continuously decreasing genes in erythropoiesis. The data set for all lineages (continuously upregulated and downregulated genes) is given in the supplemental material (supplemental online Tables 1–6). In addition, the complete expression data of all samples will be available for downloading at http://knm1.ibe.med.uni-muenchen.de/kn_home/WKH/index.htm.
Table 2. Genes continuously regulated during erythropoietic differentiation with continuously increasing expression
Table 3. Genes continuously regulated during erythropoietic differentiation with continuously decreasing expression
Table 4. Genes differentially expressed in erythropoiesis
Table 5. Genes differentially expressed in granulopoiesis
Table 6. Genes differentially expressed in megakaryopoiesis
Vertical Analysis of Differentially Expressed Genes
A vertical analysis was performed to identify genes that are differentially expressed in a specific hematopoietic lineage at a defined time point. The analysis of genes expressed in cells on specific lineage-directed differentiation conditions that show a greater than threefold change at any defined time point compared with cells in the remaining conditions (e.g., erythropoiesis versus granulopoiesis and megakaryopoiesis) resulted in lists of differentially expressed genes for each of the hematopoietic lineages. Tables 4–6 show highly differentially expressed genes for each lineage (A, erythropoiesis; B, granulopoiesis; C, megakaryopoiesis). The specific time point of expression is indicated. We could not detect genes common to all three hematopoietic lineages, supporting the lineage-specificity of the identified genes.
Class Membership Prediction
The maximal number of genes to predict the class membership was found to be 53 using five neighbors. We defined this set of genes to be able to classify a particular sample according to its lineage. Figure 4 represents the results of hierarchical clustering with Spearman’s confidence correlation of 25 samples of in vitro–differentiated CD34+ cells. Three subclusters indicate the affiliation to one of the three hematological lineages. One misclassification (sample E_d04_2) occurred. In Table 7, the predictive genes are listed according to the appearance in the cluster.
Figure 4. Identification of genes expressed in differentiating CD34+ bone marrow cells, which are significantly correlated with each of the three analyzed hematopoietic lineages. Results represent the analysis by hierarchical clustering with Spearman’s confidence correlation of 25 samples of in vitro–differentiated CD34+ bone marrow cells. Fifty-three genes were selected to predict the class membership of each of the samples. The vertical list contains each of the samples. The horizontal list displays the 53 genes. Three clusters corresponding to the three hematological lineages were found, as indicated by the black bars. One misclassification occurred (sample ID: E_d04_2). For granulopoietic and megakaryopoietic differentiation, three and two samples are missing in the analysis, respectively. Color code: blue, low expression; red, high expression. The intensity of the color reflects the reliability of the expression data.
Table 7. Genes that are predictive for the lineages of human in vitro–differentiating hematopoietic cells (see Fig. 4)
Confirmation of Expression of Continuously Regulated Genes During In Vitro Hematopoietic Differentiation by Real-Time PCR
To confirm the expression data from the oligonucleotide micro-array studies, we analyzed the expression of a selection of six significantly regulated genes (two in each lineage) in 25 samples by real-time PCR. The gene for PTGS2 was analyzed in erythropoietic and granulopoietic samples. All experiments were done in triplicate. The variance between the triplicates was less than 5%. The results were normalized to the expression of GPI in each of the samples. Figure 5 summarizes the expression data measured by real-time PCR for the selected genes. We could confirm our microarray results for both the continuously upregulated and downregulated genes not only for the single time points but also for the course of gene expression during hematopoietic differentiation.
Figure 5. Validation of gene expression pattern of continuously regulated genes during lineage-specific differentiation by real-time polymerase chain reaction (PCR). The expression of a selection of six genes in 25 samples was analyzed by real-time PCR. Two genes per hematopoietic lineage were analyzed in samples of the appropriate lineage (the gene for PTGS2 was analyzed in erythropoietic and granulopoietic samples). We found with one exception (IRC1 should be upregulated in G) that for both the continuously upregulated and downregulated genes, the real-time PCR results are similar to the results detected by microarray analysis. X-axis, days; Y-axis, GPI-normalized expression levels as detected by real-time PCR.
Gene Ontology Analysis
Categorization of differentially expressed genes in all three hematopoietic lineages according to the molecular function yielded seven functional groups (genes with binding, catalytic, signal transducer, transcription regulator, structural molecule, enzyme regulator, and transporter activity; Fig. 6).
Figure 6. Classification of differentially expressed genes of normal hematopoiesis according to gene ontology. Genes were annotated according to FatiGO Data Mining with Gene Ontology. Annotations are available for a subset of genes (~68%) represented on the HG-U133A microarray. The functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.
Genes with binding (E, 36%; versus G, 37%; versus M, 38%), catalytic (E, 26%; versus G, 19%; versus M, 19%), and signal transducer activity (E, 22%; versus G, 29%; versus M, 23%) were comparable between the three hematopoietic lineages. The percentage of genes performing transcription regulator activity or structure molecule activity (E, 2%; versus G, 7%; versus M, 3%; E, 6%; versus G, 2%; versus M, 1%) was slightly elevated in the granulopoietic or erythropoietic lineage, whereas genes functioning as enzyme regulators (E, 4%; versus G, 3%; versus M, 6%) and transporters (E, 4%; versus G, 3%; versus M, 10%) were overrepresented in the megakaryopoietic lineage. Further, it should be noted that the functional classification of a specific gene may be redundant, resulting from the assignment of one gene to more than one category.
DISCUSSION
This work was supported by the Deutsche Forschungsgemeinschaft (HO 2207/3-1), the BMBF Competence Network Leukemias, and the German Genome Research Network.
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