1 The Cardeza Foundation for Hematologic Research and Division of Hematology, Department of Medicine, Jefferson Medical College
2 Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107
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ABSTRACT |
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gene expression profiling; hemin; cell differentiation; transcription regulatory elements
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INTRODUCTION |
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Currently available therapeutic agents for elevating Hb F levels in patients with SCD and ß-thalassemia include 5-azacytidine and derivatives, butyrate and derivatives, and hydroxyurea. The mechanisms of action of these drugs are not at all clear but are presumed to involve changes in DNA methylation, histone acetylation, and possibly stress-induced, accelerated erythropoiesis, respectively (48). Patient response to these agents varies widely, use of these compounds can cause neutropenia, and some are thought to have oncogenic potential. Therefore, the motivations for development of new drugs aimed at elevated fetal globin in adult life include 1) targeted mechanism of action, 2) high response rate, 3) low toxicity/oncogenicity.
To increase our understanding of the molecular basis of the responses of K562 cells to the fetal/embryonic hemoglobin inducer, hemin, and to identify rate-limiting steps in hemoglobinization, we performed transcriptome analyses of these cells. K562 cells, although a neoplastic cell line, represent a tool to study globin gene regulation, as demonstrated by recent studies showing similar effects of chemical treatment on globin gene expression in K562 and primary erythroid cells (60, 41, 28). We now describe changes in steady-state mRNA profiles before and after hemin treatment during 72 h in culture and provide insights into rate-limiting steps involved in appearance of heme-containing globins in these cells. In addition, we show that differentially expressed genes can be assigned to a limited number of expression patterns. We also present analyses of their 5'-flanking regions and suggest that specific transcription regulatory elements (TREs) could effect coordinate regulation of the putative target genes in these clusters.
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MATERIALS AND METHODS |
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Benzidine staining.
Phosphate-buffered saline-washed untreated and treated K562 cells were stained in a benzidine solution (43) containing 0.6% (wt/vol) benzidine base, 2% (vol/vol) hydrogen peroxide, and 12% (vol/vol) acetic acid. At least 500 cells were counted at each time point after treatment using a light microscope to assess the percent of cells that appeared blue due to the presence of heme-containing globin tetramers.
RNA isolation.
DNA-free total RNA was isolated from 5 x 107 cells from duplicate (A, B) untreated control (0 h) and hemin-treated cultures (after 6, 12, 24, 48, and 72 h) using the RNAqueous-4PCR kit as described by the manufacturer (Ambion, Austin, Texas). RNA was treated with DNase, purified by ethanol precipitation, resuspended in DEPC-treated water, and stored at 80°C.
Microarray analysis.
Microarray analysis was accomplished using the procedure recommended by the manufacturer (Affymetrix, Santa Clara, CA). Briefly, double-stranded cDNA was synthesized using a cDNA synthesis kit (Invitrogen, Carlsbad, CA), and biotinylated cRNA prepared using BioArray High Yield T7 RNA transcript labeling kit (Enzo Diagnostics, Farmingdale, NY). The cRNA was fragmented by heat and ion-mediated hydrolysis and hybridized to the Human Genome HU133A oligonucleotide array GeneChip (Affymetrix) containing 500,000 spots with 22,283 different probe sets representing 14,397 unique genes. Arrays were washed and stained using a GeneChip fluidics station (Affymetrix), and hybridization signals were amplified using antibody amplification with goat IgG (Sigma-Aldrich) and anti-streptavidin biotinylated antibody (Vector Laboratories, Burlingame, CA). GeneChips were scanned using a GeneArray scanner (Agilent Technologies, Palo Alto, CA). Data analysis was performed using Affymetrix MAS 5.0, Micro DB 3.0, Data Mining Tool (version 3.0), and GeneSpring 5.1 (Silicon Genetics, Redwood City, CA) software to identify expressed and differentially expressed genes. For absolute expression analysis, images were analyzed by MAS 5.0 to determine whether a gene is expressed ("present"). The target signal value used was 150 with the "all probe sets" choice for scaling to ensure consistency of the files to be compared. Expression patterns at each time point were compared with those of untreated cultures. Detection change call (i.e., increase, decrease, marginal, or no change) was determined by comparison expression analysis (CEA) using MAS 5.0 software. Four pair-wise comparisons were performed at each time point using duplicate treated and untreated cultures (i.e., 6A vs. 0A, 6B vs. 0A, 6A vs. 0B, and 6B vs. 0B). Only genes designated as differentially expressed in common from all four comparisons at each time point were selected for further analysis. The data sets discussed herein can be accessed at the Gene Expression Omnibus (GEO) web site under the submission numbers GSM16523GSM16529, GSM16531GSM16533, GSM16535, and GSM16536 (http://www.ncbi.nlm.nih.gov/geo/). Supplemental materials containing complete results of transcriptome analysis in untreated K562 cells (Supplemental Table S1) and results of differentially expressed genes in response to hemin (Supplemental Table S2) are available online at the Physiological Genomics web site.1
Self-organizing map clustering.
Data from all time points, using MAS 5.0 absolute analysis, were published to Micro DB 3.0. Self-organizing map (SOM) clustering (8, 49) was performed after averaging of duplicate signals of each time point by Data Mining Tool 3.0 (Affymetrix). The SOM algorithm distributes genes into an arbitrarily selected number of patterns according to their expression profile after hemin treatment. The details and description of the parameters can be found in the Affymetrix Data Mining Tools Users Guide, version 3.0.
Gene annotation.
Differentially expressed genes from microarray analysis were annotated using the NetAffx Analysis Center (27) and http://www.affymetrix.com.
Real-time RT-PCR.
A selected subset of differentially expressed genes was chosen for independent confirmation by SYBR Green-based, real-time RT-PCR using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an internal reference gene. Primer pairs (Table 1) (Integrated DNA Technologies, Coralville, IA) for representative genes from the SOM-defined clusters were designed to span or bridge exons when possible, using Primer Express software [Applied Biosystems (ABI)]. Melting curve analysis with Dissociation software (ABI) was used to confirm generation of a single product.
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Transcriptional regulatory network analysis (TRNA) using PAINT v3.0.
Promoter analysis was performed using PAINT v3.0 (50). PAINT v3.0 contains a database of promoters (UpstreamDB) constructed for all the annotated genes (known and putative) in the Ensembl genome database for Homo sapiens, version 16.33, containing 24,261 genes. The transcription start site (TSS) (first exon) of a gene does not necessarily correspond to the start of the open reading frame (ORF). The 5'-untranslated region (UTR) needs to be considered to identify the TSS. For 10,000 genes, 5'-UTR sequence information is available in the Ensembl human genome database. For the remaining genes, the start of the ORF is considered as an estimate of the TSS. For each gene, 5,000 base pairs (bp) upstream (5' to the TSS) were retrieved from the genome and placed in UpstreamDB. A total of 20,291 genes contain cross-references to information in the UniGene database, thus allowing queries using the UniGene ClusterIDs for the genes of interest in this study. Because of the nature of the cross-referencing between the UniGene and genome assembly, in several cases, multiple genes correspond to a single UniGene cluster.
Promoter analysis for putative TREs, i.e., binding sites for known transcription factors (TFs) is performed by MATCH tool using TRANSFAC database version 7.2 (31). The results are transformed into a candidate interaction matrix (CIM) in which each row corresponds to a promoter, each column represents a TRE, and each element cij of the matrix contains a 1 if ith promoter has jth TRE, 0 otherwise. For any functionally related group or "cluster" of genes, PAINT v3.0 first retrieves the cognate genomic sequences, next analyzes these sequences for presence of known TREs, and finally compares results to those from random samples of genes to analytically test for enrichment of specific TREs. In addition, PAINT v3.0 can combine TRNA with results of gene expression analysis, in particular, clustering based on microarray data. Identification of statistically significant enrichment of a specific TRE within a particular expression cluster may indicate a role for the cognate transcription factor in coordinate regulation of genes in that cluster.
For each group of genes analyzed, the P values for significance of enrichment of each TRE in that group are calculated using hypergeometric distribution, by comparing the abundance of each TRE to that from a reference set of randomly selected genes (15, 50). For each TRE V$XYZ, given 1) a reference CIM of n promoters of which l promoters contain V$XYZ, and 2) a CIM of interest with m promoters of which h contain V$XYZ, the associated P value for enrichment is given as:
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For TRNA analysis, there are multiple choices of reference sets (whole genome, microarray or untreated, "control" expressed genes). Each comparison of promoter set to reference addresses a different scientific question, and the reference set acts as a filter for predicting significantly enriched TREs corresponding to that filter.
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RESULTS |
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Microarray analyses before and after varying times of hemin treatment.
As depicted in Fig. 1, transcriptome analyses were performed by expression profiling to define the number and identity of expressed genes and to monitor for changes in steady-state mRNA levels as a function of time in culture after hemin treatment. Array data reproducibility was documented by comparison of spot intensities at each register on duplicate arrays using targets derived from duplicate untreated cultures (Fig. 2A, slope = 0.9294, R2 = 0.9922). The total number of probe sets designated as a "present" (P) on arrays from duplicate untreated cultures was 10,471, which decreased to 7,282 when average intensities <100 were excluded. This represents 32% of arrayed probe sets (7,282/22,283) and corresponds to 5,184 unique genes. The number of expressed probe sets showed minimal change comparing untreated to 72-h treated cultures (7,282 vs. 7,424, respectively). Many of the highest values were mRNAs coding for ribosomal proteins and
-globin. A complete listing of values on duplicate arrays, averages, and annotations for the 7,282 probe sets expressed in untreated cells is in Supplemental Table S1.
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One such plot is shown in Fig. 2B underscoring the number of false positives when stringency for defining a gene as differentially expressed changes from 1.5-fold, to 1.75-fold, to 2-fold up or down. Average intensities >100 on duplicate arrays were chosen, and false positives were defined as spots located outside the limits of two lines parallel to the x-axis corresponding to each of the three different arbitrary y-axis ratio cutoffs. Those limits/lines correspond to y = log2 of ±0.5849 for 1.5-fold, y = log2 of ±0.8074 for 1.75-fold, and y = log2 of ±1.0000 for 2-fold up or down. The number of false positives increases from 11 (0.2%) to 33 (0.4%) to 162 (0.8%) when going from 2- to 1.75- to 1.5-fold as the cutoff for definition of a differentially expressed gene. If only P calls in common from both arrays are used in the analysis, then there were no false positives using a cutoff of 2-fold up or down. In addition, the graph shows increased scatter from the ideal line of y = log2 ratio of 0 at lower spot intensities, whereas the data at higher intensities is much tighter. Such increased scatter of the ratios from ideal at low intensities indicates a potentially higher number of false positives can be expected when ratios are derived from low-intensity comparisons, emphasizing the need for independent follow-up confirmation on differentially expressed candidate genes. Based on these self-versus-self results
2-fold up or down (log2 ratio greater/equal to +1 and less/equal to 1) relative to RNA from untreated cultures was used as the definition of a differentially expressed gene. Using this criteria, truly differentially expressed genes with high intensities which may change between only 1.5- to 2-fold up or down after treatment may be missed, but the expected number of false positives at lower intensities is minimized.
We next performed overlap analysis, comparing arrays from duplicate cultures from five different time points against RNA from duplicate untreated cultures using intensities with no cutoff, thus allowing detection of significant changes in low-abundance transcripts. We required that all four comparisons (e.g., 48A vs. 0A, 48B vs. 0A, 48A vs. 0B, 48B vs. 0B) at each time point showed a 2-fold up or down change to classify a gene as differentially expressed; this was accomplished using CEA and overlap analysis. A total of 1,209 probe sets including 899 known unique genes were classified as differentially expressed. The number of differentially expressed probe sets as a function of time after hemin treatment was 124, 202, 293, 548, and 847 at 6, 12, 24, 48, and 72 h, respectively.
Identification of common expression patterns for differentially expressed genes.
Data from all time points following hemin treatment were analyzed by SOM clustering where expression is statistically evaluated for change over time in which patterns can emerge showing similar expression profiles for the different genes. Results show 517 probe sets representing 425 genes were clustered into 8 distinct timed expression patterns (Fig. 3). Of these 425 genes generated from SOM analysis, 322 (399 probe sets) or 76% were in common with the list of 899 genes found to be differentially expressed by CEA. A sampling of the probe sets in the eight expression clusters is shown in Table 2; the entire listing with expression ratios at different time points is presented in Supplemental Table S2. This listing represents a conservative estimate of differentially expressed genes since we required they be categorized as differentially expressed by both analysis methods.
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Transcriptional regulatory network analysis.
The nonredundant set of 322 differentially expressed genes (based on GenBank accession number) was computationally mapped to 311 unique UniGene clusters (DIFF311). TRNA was performed for the genes corresponding to these clusters using PAINT v3.0 ("promoter analysis and interaction network generation tool") (54) to identify biologically relevant transcription factor binding sites, referred to herein as transcription regulatory elements or "TREs," found in the regulatory regions of these genes.
As listed in Supplemental Table S1, 7,282 probe sets representing 5,184 unique genes were expressed in untreated cells (designated U5184). We found that 227 genes of DIFF311 were also detectable in untreated cells (DIFF227). The remaining 84 differentially expressed genes were not expressed in untreated cells (DIFF84). Therefore, TRNA was done as follows: 1) DIFF227 was compared with U5184, and 2) DIFF84 was compared with the entire microarray (A12432). Comparison of the differentially expressed gene set (DIFF227) to those expressed in untreated cells (U5184) affords a filter, since only 32% of arrayed probe sets are represented in these cells. This comparison will most likely identify TREs involved in erythroid commitment in these cells following hemin treatment. The remaining 84 differentially expressed genes (DIFF84) were not expressed in untreated cultures and therefore could not be similarly analyzed and were compared with the total array (A12432).
A total of 4,862 promoter entries were found for the U5814 set. TRNA identified a total of 213 TREs as present, at least once, in promoters from DIFF227 out of 456 motifs tested. TREs identified as significantly enriched in DIFF227 compared with the U5184 set (P < 0.1) and occurring in at least 5% of promoters are listed in Table 3A, with a graphical representation of sample TRE distributions in Fig. 5. The DIFF84 set was represented by 85 promoter entries for 81 genes. Significantly enriched TREs in the 84 genes turned on by hemin treatment are listed in Table 3B. Only TREs with both a P value <0.1 and an occurrence rate >5% are presented in the DISCUSSION.
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DISCUSSION |
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In addition, genes involved in leukemogenesis and proliferation were affected, including the apoptosis inhibitor survivin (BIRC5) (49) (downregulated >2-fold at 72 h) and the apoptosis inducer TAT interactive protein-2 (HTATIP2) (57) (upregulated 11-fold at 72 h). Cathepsin H (CTSH) and protein tyrosine phosphatase PTP4A3, both of which are overexpressed in leukemic cell lines (35, 43), were downregulated 3-fold at 72 h. mRNAs for cathepsins B, D, and L are upregulated (see CTSL, Fig. 4), consistent with increased enzymatic activity for cathepsins B and D in K562 cells following hemin treatment (35). Also, IL-8 (cluster 6, Table 2), a recently described substrate of cathepsin L (39), is dramatically upregulated more than 50-fold at 48 h. Interestingly, suppressor of cytokine signaling 2 (SOCS2), which is overexpressed in CML (48), was downregulated upon hemin treatment (>2-fold at 72 h). LM02, the cysteine-rich, LIM domain-containing protein rhombotin 2, which is critical to early hematopoiesis (58) and previously found to be differentially expressed in K562 cells treated with sodium butyrate (44), is downregulated (>2-fold at 72 h) in our study.
Several genes known to play a role in response to oxidative stress or heavy metals were differentially expressed. These include the genes for metallothionein 1G and 2A and NAD(P)H dehydrogenase, quinone 1 (upregulated 2.4-fold at 6 h, 2.7-fold at 12 h, and 7.4-fold at 48 h, respectively). Several genes involved in heme biosynthesis showed differential expression upon hemin treatment, including the genes for ferrochetalase (upregulated >2-fold at 24 h) and coproporphyrinogen oxidase (downregulated >2-fold at 48 h). In agreement with other studies of hemin treatment of K562 cells (44), the gene for ferritin was differentially expressed (heavy chain upregulated >4-fold at 48 h). The gene for chaperone protein HSPA5/grp78, implicated in the control of apoptosis through interaction with caspase 7 (45) was upregulated >3-fold at 72 h.
Differential expression of transcriptional regulators.
Expression of transcriptional regulators was also affected by hemin treatment, including several molecules that play a role in globin-gene regulation. The genes for CCAAT/enhancer binding proteins C/EBP-ß and - (59) were induced (>4-fold and >2-fold at 72 h, respectively). Previous studies showed moderate overexpression of C/EBP-
in the erythroid lineage in mice containing the human ß-like globin cluster transgene led to increased expression of
-globin relative to ß-globin (59). V-maf, homolog G, which binds maf recognition elements (MAREs) such as those present in the ß-globin locus control region HS2 enhancer (21), was upregulated early, peaking at >3-fold at 72 h. Interestingly, maf "activity" is increased by heme binding to the transcriptional repressor Bach1 (38). Heme binding inhibits formation of hetero-oligomers of Bach1 and maf-family proteins, thereby effectively increasing levels of maf-family proteins for dimerization with transcriptional activators like those possibly required for increased embryonic/fetal globin expression. Of note, Bach1 is expressed in hematopoietic cells and has MARE-binding activity in MEL cells (23). Paradoxically, nuclear transcription factor-Y
, another protein known to bind the CCAAT box of
-globin (13), was downregulated upon hemin treatment (>2-fold downregulated at 72 h). Gfi-1B, the zinc finger-containing transcriptional repressor recently found to be involved in regulation of erythroblast proliferation (38), is downregulated by hemin (>2-fold at 72 h). Also of interest is the finding that MCM5, a protein recently shown to play a role in transcriptional repression (14), is upregulated eightfold in K562 cells after 72 h of hemin treatment.
Comparison of microarray and real-time RT-PCR analysis data.
In general, these results comparing microarray vs. real-time RT-PCR data are in agreement in the relative expression patterns of those genes tested. It is not unusual to see differences in absolute fold change in expression comparing microarray and RT-PCR data; however, the overall expression pattern (i.e., up or down over time) was similar. The discrepancy between array and RT-PCR data for - and
-globin transcripts highlights the limitation of Affymetrix microarray analysis for studies of gene families. Since these microarrays use the average intensities at multiple spots with distinct, not necessarily gene-specific oligonucleotides derived from the mRNA, gene expression studies of members of highly homologous gene families (e.g., globin) require use of other methods such as gene-specific real-time RT-PCR.
Regulatory regions of differentially expressed genes provide insight into potential coordinate regulation.
PAINT analysis identified binding sites for transcription factors of known importance in hematopoiesis, erythropoiesis, and megakaryopoiesis (GATA-1, AML-1, GATA-2, Lmo2, Evi-1, NF-E2) (27, 56). The DIFF84 TRE list includes transcription factors identified as differentially expressed genes (i.e., GATA-2), as well as several known to be involved in hematopoiesis, such as NF-B (12) and CREB (34). Interpretation of TRNA data is complicated by several factors. First, P values indicate enrichment relative to the large reference set. Thus a TRE can be significantly enriched yet still rare within a cluster. Second, some TREs are found at high frequency in the genome, making enrichment difficult to discern. Finally, sample sizes are small (<10) for some clusters, further weakening inference. Thus the desired result would be a TRE significantly enriched with a high frequency in a given cluster. An example of such a result is AML1, a TRE present in all 5'-flanking regions of cluster 8 members. Our microarray data show low expression of AML1 at all time points. AML1 is expressed in K562 cells and implicated in erythroid and megakaryocytic differentiation (15, 37); however, it is not known if AML1 activity is regulated transcriptionally or posttranscriptionally. Some care must be taken in interpretation of the enrichment of the AML1 TRE in this analysis, since the TRE for AML1 is short, consisting of only six nucleotides. Although AML1 sites would be predicted to be common in the genome, cluster 8 still shows an enrichment greater than what would be found by chance, as is depicted in Fig. 5. A complete graphical representation is available (Supplemental Fig. S1).
The finding that V$BACH2_01, the TRE for the transcriptional repressor Bach2, is significant in cluster 5 is intriguing, since this cluster behaves differently than others in that gene members are upregulated late. The TREs for Bach1 and Bach2 share a core of 7 of 11 bases, suggesting overlapping functions. Bach2 is known to interact with small Maf transcription factors and with MAZR to alter gene expression (26), and interestingly, Bach2 expression is regulated by BCR-ABL (55). Recent studies show that Bach2 expression is induced by oxidative stress, and Bach2 protein accumulates in the nucleus and is associated with inhibition of proliferation and apoptosis (36). Our findings that hemin induction enriches for genes with Bach-binding elements in their 5'-flank and that Bach factors are not differentially expressed following hemin treatment support a model in which Bach1/2 is a transcriptional repressor whose activity is diminished upon hemin induction. This could occur by direct heme binding to Bach1/2, inhibiting its ability to dimerize with maf-like proteins (36) as well as by enrichment for genes with Bach1/2 binding sites. Interestingly, recent studies show that heme positively regulates ß-globin gene expression in MEL cells by blocking Bach1/MARE interactions in the locus-control region (52). Furthermore, changing the dimerization partner of MafK from Bach1 to NF-E2p45 is critical for globin expression in MEL cells (7).
It is noteworthy that TREs for several candidate factors not known to be expressed in hematopoietic cells were found to be significantly enriched in this analysis (see Table 4). Examples include HNF1 and HNF4 (clusters 3 and 2, respectively) expressed in liver (10), Hand1/E47 (cluster 1), involved in heart and neural crest development (18), and Pax-4 (clusters 1 and 3), expressed in pancreatic B cells (24). There are several possible explanations for these findings: 1) the result represents a false positive; 2) these genes may play a role completely unrelated to hematopoiesis that is regulated using these TREs; or, 3) there are as yet unidentified factors expressed in hematopoietic cells that utilize these TREs.
Our findings using the PAINT v3.0 software to examine 5'-flanking regions of differentially expressed genes suggest that factors binding to the identified TREs may be influential in the expression pattern of a particular cluster. Our findings of distinct clusters for the differentially expressed genes may be useful in investigations probing for functional relationships between genes in each cluster, genes in different clusters, and their timing of expression. Further studies of promoter occupancy and transcription factor perturbation are now required to functionally validate these sites and the factors that bind to them.
Hemin-induced hemoglobinization in K562 cells.
In this report, changing steady-state mRNA profiles are characterized in K562 cells before and after varying times of hemin treatment. Several questions emerge as to the effects of hemin on globin induction in these cells. For example, our microarray analyses agree with real-time RT-PCR data of others (50) showing that steady-state -globin mRNA levels are high and do not appear to increase >2-fold with hemin treatment of K562 cells. Our real-time RT-PCR results, however, show an
4-fold increase in
-globin mRNA at 48 h (Fig. 4).
It is unclear whether -globin mRNA is translated maximally in untreated cells. If translated, then do
4 tetramers form? And, if so, why is there minimal benzidine staining in untreated cells? One possibility is that heme may be rate limiting; and, a specific translational block may be in effect for globin synthesis thereby protecting cells against accumulation of unstable and nonfunctional apoglobin tetramers (22). In support of this, our results show the gene for heat shock 70-kDa protein HSPA1B (cluster 7), involved in protein stability, is induced >10-fold at 72 h. HSPA1A is known to interact with the gene for NAD(P)H dehydrogenase, quinone 1 (2), and expression of both peaks at 48 h. In addition, translational (5) and posttranscriptional (25) controls relating to butyrate-, hydroxyurea-, and 5-azacytidine-induced appearance of hemoglobins in K562 cells have been proposed.
A second possibility for lack of appreciable benzidine staining in untreated K562 cells is that -globin mRNA is translated but degraded in the absence of free heme and/or its normal hetero-chain partners
- and/or
-globin. Alternatively, apo
4 tetramers indeed might form in untreated cells, but in the absence of heme, do not stain with benzidine. Accumulation of these heme-lacking tetramers in high concentrations might be unstable and precipitate. However, if apoglobin tetramers were present, then the timing for appearance of benzidine-positive cells might be expected to occur sooner than the observed 48 h, since heme derived directly from hemin can be inserted in vitro into apoglobins (1). Interestingly, expression of ferrochelatase, which catalyzes chelation of ferrous iron and protoporphyrin to form heme (22), is upregulated during hemin treatment in our studies (Table 2). Taken together, these results suggest that heme may be rate limiting for benzidine staining in untreated K562 cells. Interestingly, previous studies indicate heme biosynthesis is rate limiting in early stages of differentiation of MEL cells (17). Others have shown that heme-containing
4 tetramers and/or embryonic and fetal globin heterotetramers are detected by benzidine staining upon treatment with hemin (5, 47). Our results show steady-state
-globin mRNA and to a lesser extent
-globin mRNA are upregulated after treatment (Fig. 4). Previous radiolabeled chain-synthesis studies demonstrated synthesis of radiolabeled homo- and heterotetramers in untreated K562 cells (42), suggesting that lack of appreciable benzidine staining prior to treatment may be in part due to absence of heme-containing chains/tetramers; or alternatively, absolute amounts of tetramers may be rate limiting, thereby precluding their detection by benzidine staining. Further studies are required to identify rate-limiting steps for detection of benzidine-positive globins in untreated compared with treated K562 cells, such as detection of apoglobin chains and tetramers in untreated cells, as well as studies on determining whether globin mRNAs are maximally translated in these cells.
Our results also show that the total number of different probe sets expressed as assessed by microarray monitoring of the steady-state mRNA population remains about the same comparing untreated to 72-h treated cultures. In addition, we found that the majority of genes are expressed at low levels and that the percentage of expressed probe sets in different arbitrarily binned intensity classes does not change appreciably between untreated and 72-h treated cultures. This implies that hemin treatment of these cells leads to minimal overall complexity change in the total expressed mRNA population. Earlier estimates indicate that 85% of the total mRNA mass in a cell is contained within the complex mRNA class (e.g., mRNAs present in 15 copies per cell) (11). Deciphering the molecular control effecting changes in steady-state mRNA abundance and the relationship between transcriptomes and proteosomes are central to our understanding of development and differentiation.
These studies address one aspect of the information defining overall cell phenotype and response to inducer (e.g., steady-state mRNA levels). Translational and posttranslational events have not been evaluated here. Protein studies are needed to evaluate the relationship between changes in the transcriptome and the cells proteome. Notwithstanding these caveats, characterization of K562 transcriptomes with and without hemin should facilitate studies on defining the molecular basis of leukemogenesis. In addition, transcriptome information will aid in our understanding of the lineage commitment from a bipotential cell to an erythroid cell and help define the molecular basis of increased hemoglobin expression in these cells. This information will be important for the identification of rate-limiting changes required for upregulation of fetal and embryonic globins in these cells and may identify new targets for therapeutic intervention in SCD and ß-thalassemia. Selection of such targets might involve study of the differentially expressed genes we identified involved in signaling, chromatin remodeling, and transcriptional activation or repression, followed by studies of their overexpression or knockdown in erythroid progenitors while monitoring effects on fetal globin expression. The differentially expressed genes identified with hemin-induced commitment now can be compared with those from other inducers to delineate common or distinct mechanisms for upregulating fetal globin expression.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: S. Surrey, Cardeza Foundation for Hematologic Research and Division of Hematology, Dept. of Medicine, Jefferson Medical College, Thomas Jefferson Univ., Curtis Bldg., Rm. 703, 1015 Walnut St., Philadelphia PA 19107 (E-mail: saul.surrey{at}jefferson.edu).
1 The Supplementary Material for this article (Supplemental Tables S1 and S2 and Supplemental Fig. S1) is available online at http://physiolgenomics.physiology.org/cgi/content/full/00028.2004/DC1.
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REFERENCES |
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