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. 2008 Sep 18;8:264. doi: 10.1186/1471-2407-8-264

Expression profile of CREB knockdown in myeloid leukemia cells

Matteo Pellegrini 1,✉, Jerry C Cheng 2, Jon Voutila 2, Dejah Judelson 2, Julie Taylor 2, Stanley F Nelson 3, Kathleen M Sakamoto 4,5
PMCID: PMC2647550  PMID: 18801183

Abstract

Background

The cAMP Response Element Binding Protein, CREB, is a transcription factor that regulates cell proliferation, differentiation, and survival in several model systems, including neuronal and hematopoietic cells. We demonstrated that CREB is overexpressed in acute myeloid and leukemia cells compared to normal hematopoietic stem cells. CREB knockdown inhibits leukemic cell proliferation in vitro and in vivo, but does not affect long-term hematopoietic reconstitution.

Methods

To understand downstream pathways regulating CREB, we performed expression profiling with RNA from the K562 myeloid leukemia cell line transduced with CREB shRNA.

Results

By combining our expression data from CREB knockdown cells with prior ChIP data on CREB binding we were able to identify a list of putative CREB regulated genes. We performed extensive analyses on the top genes in this list as high confidence CREB targets. We found that this list is enriched for genes involved in cancer, and unexpectedly, highly enriched for histone genes. Furthermore, histone genes regulated by CREB were more likely to be specifically expressed in hematopoietic lineages. Decreased expression of specific histone genes was validated in K562, TF-1, and primary AML cells transduced with CREB shRNA.

Conclusion

We have identified a high confidence list of CREB targets in K562 cells. These genes allow us to begin to understand the mechanisms by which CREB contributes to acute leukemia. We speculate that regulation of histone genes may play an important role by possibly altering the regulation of DNA replication during the cell cycle.

Background

Several proto-oncogenes have been demonstrated to be deregulated in human cancer. In particular, the development of the hematologic malignancies such as leukemia, is associated with aberrant expression or function of proto-oncogenes such as c-myc, evi-1, and c-abl. Many translocations with cytogenetic abnormalities that characterize leukemias involve rearrangement of transcription factors, including AML-ETO and Nup98-hox. Some of these leukemia-associated fusion proteins predict prognosis, e.g. t(8,21), t(15,17), and inv(16) are associated with a good prognosis in acute myeloid leukemia (AML) [1]. Approximately 50% of adult patients have been noted to have specific cytogenetic abnormalities. The overall survival of patients with AML is less than 50%. Since half of the patients diagnosed with AML have normal cytogenetic profiles, it is critical to understand the molecular pathways leading to leukemogenesis.

We identified that the cyclic AMP Response Element Binding Protein (CREB) was overexpressed in the majority of bone marrow samples from patients with acute leukemia [2,3]. CREB is a leucine zipper transcription factor that is a member of the ATF/CREB family of proteins [4-6]. This transcription factor regulates proliferation, differentiation, and survival in a number of cell types, including neuronal and hematopoietic cells [4,5]. CREB has been shown to be critical in memory and hippocampal development in mice [7,8]. We previously described that CREB is phosphorylated at serine 133 downstream of signaling by the hematopoietic growth factor, Granulocyte Macrophage-Colony Stimulating Factor (GM-CSF) in myeloid cells [9-11]. We further demonstrated that CREB phosphorylation results from the activation of the Mitogen Activated Protein Kinase (MAPK) and pp90 Ribosomal S6 Kinase (pp90RSK) pathways in response to GM-CSF stimulation [9].

To understand the role of CREB in normal and neoplastichematopoiesis we investigated the expression of CREB in primary cells from patients with acute lymphoblastic (ALL) and myeloid leukemia and found that CREB was overexpressed in the majority of leukemia cells from patients with ALL and AML at the protein and mRNA levels [2,3,12]. Furthermore, overexpression of CREB was associated with a worse prognosis. We created CREB transgenic mice that overexpressed CREB in myeloid cells. These mice developed enlarged spleens, high monocyte count, and preleukemia (myeloproliferative disease) after one year. Bone marrow progenitor cells from CREB transgenic mice had increased proliferative capacity and were hypersensitive to growth factors compared to normal hematopoietic stems cells (HSCs). Overexpression of CREB in myeloid leukemia cell lines resulted in increased proliferation, survival, and numbers of cells in S phase [12]. Known target genes of CREB include the cyclins A1 and D [4,5,12,13]. Both of these genes were upregulated in CREB overexpressing cells from mice and human cell lines [4,5]. Thus, CREB is a critical regulator of leukemic proliferation and survival, at least in part, through its downstream target genes.

CREB target genes have been published on the website developed by Marc Montminy http://natural.salk.edu/CREB/ based on ChIP chip data [14]. Additional CREB target genes were described by Impey et al. [15]. In their studies, serial analysis of chromatin occupancy (SACO) was performed by combining chromatin immunoprecipitation (ChIP) with a modification of Serial Analysis of Gene Expression (SAGE). Using a SACO library derived from rat PC12 cells, approximately 41,000 genomic signature tags (GSTs) were identified that mapped to unique genomic loci. CREB binding was confirmed for all loci supported by multiple GSTs. Of the 6302 loci identified by multiple GSTs, 40% were within 2 kb of the transcriptional start of an annotated gene, 49% were within 1 kb of a CpG island, and 72% were within 1 kb of a putative cAMP-response element (CRE). A large fraction of the SACO loci delineated bidirectional promoters and novel antisense transcripts [15]. These studies suggest that CREB binds many promoters, but only a fraction of the associated genes are activated in any specific lineage. We therefore set out to measure the functional targets of CREB in a hematopoietic model system.

Since CREB is overexpressed in bone marrow cells from patients with acute leukemia compared to normal HSCs, this provides a potential target for leukemia therapy. To this end, we stably transduced myeloid leukemia cells with CREB shRNAlentivirus[16]. CREB knockdown by 80% resulted in decreased proliferation and differentiation of both normal myeloid cells and leukemia cells in vitro and in vivo [16]. However, downregulation of CREB did not affect short-term or long-term engraftment of normal HSCs in bone marrow transplantation assays [16]. To understand the pathways downstream of CREB, we investigated genes that were differentially regulated in CREB shRNA transduced cells. In this paper, we report expression profiling of genes that were differentially regulated in CREB knockdown K562 myeloid leukemia cells and could be potential targets for development of new therapies for acute leukemia.

Methods

Cell lines

The following human leukemia cell lines were transduced with shRNAs: K562 (Iscoves + 10% FCS) and TF-1 (RPMI + 10%FCS + rhGM-CSF. Cells were cultured at 37°C, 5% CO2 and split every 3 to 4 days. Primary AML bone marrow samples were processed as previously described [12]. All human samples were obtained with approval from the Institutional Review Board and consents were signed, according to the Helsinki protocol.

shRNA sequence design and constructs

The CREB specific shRNA sequences were selected and validated based on accepted parameters established by Tuschl et al. [17-19]http://www.rockefeller.edu/labheads/tuschl/sirna.html; CREB shRNA-1, CREB shRNA-2, CREB shRNA-3. Controls included empty vector, luciferaseshRNA, and scrambled shRNA. shRNA sequences are: CREB shRNA-1(5'GCAAATGACAGTTCAAGCCC3'), shRNA-2 (5'GTACAGCTGGCTAACAATGG3'), shRNA-3 (5'GAGAGAGGTCCGTCTAATG3'), LuciferaseshRNA (5'GCCATTCTATCCTCTAGAGGA3'), Scramble shRNA (5'GGACGAACCTGCTGAGATAT3'). Short-hairpin sequences were synthesized as oligonucleotides and annealed according to standard protocol. Annealed shRNAs were then subcloned into pSICO-R shRNA vectors from the Jacks laboratory at MIT [20]. The second generation SIN vector HIV-CSCG was used to produce human shRNA vectors [21].

Microarray analysis

Total RNA (10 μg) was extracted from K562 cells transduced with vector alone or CREB shRNA was submitted to the UCLA DNA Microarray Facility. RNA samples were labeled and hybridized by standard protocol to Affymetrix Gene Chip Human Genome U133+ Array Set HG-U133A array. Gene expression values were calculated using the MAS5 software. The expression values are quantile normalized across all arrays. We obtained the expression profiles for a control set and CREB downregulated K562 cells. A t-test is performed between the two groups to identify significantly differentially regulated genes. The analysis was performed using Matlab (Mathworks, Inc.). We find a significant number of differentially expressed genes, which are either direct or indirect targets of CREB.

To further characterize the data we have aligned CREB binding data from chromatin immunoprecipitation studies with our expression data. The chromatin immunoprecipitation data was obtained from the website http://natural.salk.edu/CREB/[14]. To identify genes that are most significantly bound by CREB and differentially expressed in our knockdown experiment we first filtered genes by their fold change (greater than 1.5 or less than 0.7). Finally, we ranked genes according to the product of the binding and expression P value (jerry_bind_data.xls) (see Additional file 1).

We characterize these genes using three types of analyses: Ingenuity Pathway Analysis (IPA), Gene Ontology term enrichment analysis and tissue distribution. For the former analysis, we used the Ingenuity Pathways Analysis tool on the lists of significant downregulated genes. We then identified functions that were overrepresented among these genes. For the second, we used the DAVID website http://david.abcc.ncifcrf.gov/home.jsp to identify Gene Ontology terms that were enriched in the list.

Finally, we compute the tissue distribution of the 200 genes we identified as functional CREB targets. The tissue specific expression profiles of each gene are obtained from HG_U133A/GNF1H and GNF1M Tissue Atlas Datasets.[22]. We first compute the logarithm of the ratio of the expression intensity of each gene in each tissue, divided by its average intensity across all tissues. We then perform hierarchical clustering of both the genes and the tissues.

Quantitative Real-time PCR

K562 transduced with CREBshRNA(5 × 106) were lysed in Trizol and stored at -80°C prior to RNA extraction. RNA extraction was performed according to a standard protocol supplied by the manufacturer (Invitrogen) and pellets were resuspended in RNAse free water. The cDNA was transcribed with a Superscript RT III based-protocol. DNAse treatment was not performed due to the selection of intron-spanning primers. Quantitative real-time PCR was performed with the SyberGreen reagent (Bio-Rad) in triplicates and analyzed by the standard curve method standardized to the housekeeping gene beta actin[23,24].

Results and discussion

Since CREB has pleiotropic effects on cell function and potentially activates several genes in hematopoietic and leukemia cells, we performed microarray analysis with total RNA isolated from K562 chronic myeloid leukemia cells transduced with CREB or control shRNA. The comparison of transcriptional profiles in wild type and CREB shRNA transduced K562 cells revealed a large number of differentially expressed genes (see Additional file 2). Among these genes, some are direct targets of CREB, while others are indirect targets. To infer which of these genes was potentially directly regulated by CREB, we combined the expression data with the ChIP-chip data of CREB bound promoters as demonstrated by Marc Montminy[14]. As was previously observed CREB binding sites are highly conserved across different tissues. However, these sites are activated by cAMP in a tissues specific manner. Therefore by combining these two datasets we attempted to uncover the functional CREB sites in hematopoietic tissues.

Our hypothesis for discovering functional CREB sites in hematopoietic cells is that if a gene is found to be differentially expressed in the CREB shRNA K562 transduced cells, and bound by CREB it is likely to be a direct target. To identify these genes we developed a metric that accounts for both the significance of the expression change and binding data for each gene (described in detail in Methods).

Since CREB has been described as both a transcriptional activator (when phosphorylated) and a repressor, we were interested in genes that were both up and downregulated in CREB shRNA transduced cells. The resulting rank ordered list allows us to sort genes by their likelihood of being functional CREB targets in K562 cells. It is difficult to determine, however, where to draw a threshold between the true and false targets. We have decided to restrict our analysis to the top several hundred targets that had both significant changes in expression and binding, as we deemed these to be highly enriched for true versus false targets. However, we do not claim that these are the only functional CREB targets in K562 cells, as the exact number of true targets is difficult to determine. The top down and upregulated genes revealed by this analysis are listed in Tables 1 and 2, and the full list is found in the supplementary materials.

Table 1.

Potential CREB target genes.

Gene Name Fold Change CREB binding CREB site Gene Name Fold Change CREB binding CREB site
DKFZP434G222 0.551725 3.883395 ht h HSPC056 0.44548 1.892546 ht h
ABCG2 0.479066 2.244422 ht h HSU79303 0.573524 1.812829 ht
ALDH2 0.5604 1.989872 none ILVBL 0.675128 1.893295 ht h
ALDH7A1 0.62012 2.051646 h KIAA0103 0.682528 2.620283 ht h
ALS2CR19 0.46208 1.788188 ht HSU79303 0.573524 1.812829 ht
ANC_2H01 0.659044 1.991467 ht h ILVBL 0.675128 1.893295 ht h
ANG 0.693535 3.287977 ht KIAA0103 0.682528 2.620283 ht h
APLP2 0.636685 1.219917 h KIAA0141 0.689536 3.479426 h
APPL 0.668234 1.391059 h KIAA0408 0.595271 3.603389 none
ARFD1 0.524897 2.336962 ht KIAA0494 0.67838 5.420821 F
BCL2L11 0.589894 3.191337 H h KLF5 0.553523 2.062499 H
BECN1 0.600243 1.151217 H h KNSL8 0.468603 7.854334 HT ft
BMX 0.315984 1.072006 none KPNA5 0.562667 2.859517 none
C20orf133 0.635849 2.420642 h LANCL1 0.647544 1.020319 none
C6orf67 0.610619 2.665053 h LOC51668 0.500097 1.062053 ht h
CA2 0.592202 1.082939 ht LOC51762 0.599397 3.307553 ht h
CALB2 0.671562 1.894443 h LYPLA3 0.664078 2.379015 HT h
CCDC2 0.533032 1.529166 none MAF 0.597194 2.383458 FT
CENPE 0.306986 3.736367 FT ht MAPKAPK5 0.699356 2.053184 FH
CGI-77 0.664435 4.334985 H ht h MDM2 0.468991 2.523732 none
CLDN18 0.566707 4.30699 ht h MGC15419 0.617252 3.032433 h
CNN1 0.670957 1.150221 F ht h MPHOSPH1 0.423771 3.535138 ht h
CREB1 0.382751 1.816762 HT H ht h MSH2 0.592302 3.203985 h
CSPG6 0.573523 3.082765 h MVD 0.632896 3.854905 ht h
CUL5 0.683117 2.073118 H ht h MYL4 0.69963 1.010099 h
DBP 0.67969 2.805267 ft ht NEFL 0.343403 2.413823 HT h
DES 0.521516 1.509794 ht h NFKBIL1 0.695019 4.072353 ht
DIS3 0.692573 3.837304 HT ht NIPSNAP1 0.679129 1.215594 h
DNCI1 0.673721 2.195167 none NOX3 0.455479 2.60292 h
DNMT3A 0.679821 1.035348 h NR4A3 0.543361 5.002146 HT H h
DSIPI 0.40458 2.546212 HT NUDT5 0.673003 2.561752 h
DUSP19 0.674195 2.225933 none NUMB 0.675667 1.014954 HT ht
EIF2S1 0.631867 1.075696 H ht h PDE6B 0.66696 2.699363 h
EIF2S2 0.644661 3.313634 ht h PEX12 0.694707 6.199684 h
ESRRBL1 0.67914 4.633352 FH h PFDN4 0.507631 2.196535 none
FBXO22 0.688756 2.206273 ht PHC1 0.672187 1.053985 HT
FECH 0.516446 1.045191 h PKD2L2 0.513894 2.249593 h
FECH 0.658471 1.045191 h PLAA 0.603854 9.235476 none
FLJ10853 0.622952 3.981514 H ht PPP1R2 0.568734 2.04019 ft
FLJ10858 0.668758 1.523113 none PRDX3 0.615229 1.847784 none
FLJ10904 0.54026 1.085341 none PSAT1 0.47554 2.492965 ht
FLJ11011 0.610253 3.387879 ht h PSMAL/GCP 0.68221 1.341117 none
FLJ11342 0.683482 2.617474 ht PTGS2 0.684401 3.057276 ht h
FLJ11712 0.62618 2.776373 ht RAB31 0.698664 1.12667 ht
FLJ13491 0.633125 3.268155 none RB1CC1 0.533475 1.390318 none
FLJ20130 0.640787 2.766588 h RFC3 0.577787 6.745001 FH ht
FLJ20331 0.681859 8.752576 H RHEB 0.682202 3.47317 HT H h
FLJ20333 0.690542 1.946262 ht h RNASE4 0.436168 2.975774 ht h
FLJ20509 0.691949 1.96435 none SARS2 0.692149 5.455469 H h
FLJ23233 0.471676 1.517415 none SBBI26 0.683312 6.75719 H
FOXD1 0.593522 5.160553 HT ht SDP35 0.502432 2.320591 h
GCAT 0.656744 2.122675 ht h SERPINI1 0.31594 3.277692 ht
GCHFR 0.676365 2.188753 ht h SHMT1 0.658252 1.127084 ht h
GFI1B 0.671179 0.999255 h SILV 0.662805 2.130617 H
GMPR 0.672975 1.149663 ht SLC11A2 0.684325 1.842417 none
GOLGA4 0.567882 2.939327 ht h SLC22A5 0.657746 1.64513 none
GPNMB 0.410992 1.004344 none SLC27A6 0.547039 1.029816 ht
GRHPR 0.68706 2.454475 H ht SLC2A4 0.507466 2.273185 ht h
H2BFS 0.591569 2.358423 ht SLC39A8 0.201136 1.004832 none
HBE1 0.639376 0.947159 h SLC4A7 0.532067 1.262531 ht
HDGFRP3 0.65013 1.208322 none SMARCA1 0.519982 1.056916 HT ht
HDGFRP3 0.668211 1.208322 none SMC2L1 0.596288 2.916083 ht h
HEXA 0.54467 2.622927 none SRI 0.671893 0.826457 ht
HIST1H1C 0.590374 1.983514 h STK16 0.680797 6.555535 H h
HIST1H2AD 0.66909 4.768013 ht h SULT1C2 0.599235 3.511947 f h
HIST1H2AI 0.542518 2.801688 H ht h SURB7 0.498245 1.598812 ht
HIST1H2AJ 0.696531 3.066865 ft ht h SYN1 0.696375 3.016534 F h
HIST1H2AL 0.602018 2.600144 FHT ht h TAF1A 0.589389 2.689618 none
HIST1H2BB 0.590821 1.782458 ht h TBC1D7 0.692755 1.281463 ht
HIST1H2BD 0.674855 3.111055 HT ht h TCTE1L 0.368312 2.475611 ht
HIST1H2BE 0.546621 2.34815 ht TFDP2 0.670657 1.016413 ht
HIST1H2BF 0.543665 1.985466 ht TGDS 0.67197 1.523411 none
HIST1H2BH 0.617917 2.04185 none THRB 0.670555 2.256453 H ht h
HIST1H2BI 0.585897 1.443622 ht TMEM14A 0.656093 1.175355 ht h
HIST1H2BJ 0.493823 5.335159 HT ht h TOM1 0.64031 3.221137 h
HIST1H2BM 0.687469 3.533372 ft ht h TXN2 0.689274 1.893339 H ht h
HIST1H2BO 0.618862 4.014214 ht h UBE2B 0.663194 3.652863 H ht h
HIST1H3B 0.556438 4.260113 ft ht VRK1 0.650583 1.000406 h
HIST1H3H 0.641946 2.647758 H ht h WASPIP 0.572355 1.01892 none
HIST1H4E 0.608257 2.458831 FT h WDHD1 0.624889 4.984045 H ht h
HIST1H4I 0.612088 2.068983 ht WWOX 0.671866 1.882778 h
HIST2H2AA 0.560962 4.032876 ht ZNF134 0.677481 2.726853 ht h
HLA-DRA 0.365141 3.086303 ht h ZNF222 0.5618 4.09755 ht h
HLXB9 0.667926 1.006593 none ZNF230 0.410725 3.76825 ht h
HS2ST1 0.694429 1.032562 ht h ZNF235 0.38371 2.959812 none
HSBP1 0.671929 1.891961 ht h
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Top down-regulated genes that show significant CREB binding and changes in expression in the CREB knockdown cells. The detailed criteria for selecting these genes are described in the methods section. For each grouping of genes, from left to right, column 1 shows the gene symbols, column 2 the ratio of the expression change in wild type versus knockdown, column 3 the CREB binding ratio and column 4 the presence of CREB binding motifs. The key for column 4 is as follows: F is a full CREB motif (TGACGCTA) that is conserved from human to mouse, while f is not conserved, H is a conserved CREB half motif (TGACG or CGTCA), while h is not conserved, and T is the conserved presence of a TATA motif less than 300 base pairs downstream of the CREB motif, while t is not conserved.

Table 2.

Potential CREB target genes.

Gene Name Fold Change CREB binding CREB site Gene Name Fold Change CREB binding CREB site
ACOX1 2.110674 2.911283 H ht LDLR 1.678587 1.525499 ht
ADAT1 1.410234 3.769574 ht f h LGALS3BP 2.131291 3.615437 none
APEH 1.400261 2.527266 h LIM 1.696177 1.097432 none
APPBP2 1.486616 2.151867 H ht h LIM 1.849989 1.097432 none
ARHB 2.758453 2.77377 H ht LRRFIP1 1.941595 1.122307 h
ATP6V1A 1.446867 3.016595 HT ht h METAP2 1.916632 2.635425 ht
BCL6 1.640646 6.084626 HT ht METTL2 1.593867 3.474639 none
BDKRB2 1.600927 2.601219 none MGC2731 1.588545 2.80081 HT h
BTN3A2 1.465264 3.426679 ht MGC4054 1.502743 2.777966 ht
C20orf12 1.511854 3.12999 h MOCS3 1.796255 5.213295 none
C20orf121 1.456022 3.532969 H MRPS10 1.410471 1.834794 ht f
C20orf172 1.463616 4.659037 H h NCOA3 1.495237 2.715807 ht
C20orf23 1.528396 2.622103 none NDRG1 2.030896 2.312257 ht h
CD44 9.531947 1.335178 ht h NEDF 1.567662 4.268912 ft ht
CDH12 3.296441 1.178959 none NPR2L 1.618864 6.397355 ht h
CDKAL1 1.735322 3.445022 none ODZ1 1.448279 2.310975 ht
CDKN1A 2.216725 1.778747 H ht h OPA3 1.474233 7.631458 FHT ht h
CELSR3 1.546375 3.175919 H ht OTC 1.693003 4.881484 ht
CENPF 1.415064 2.654622 ht PAFAH2 1.67217 4.584628 none
CHRNB1 1.55045 1.412576 H h PAFAH2 1.631066 4.584628 none
CLECSF2 1.747573 1.251667 none PHC3 1.42261 1.747154 ht
CML2 1.47905 3.427882 ht PHLDA1 3.92008 2.003171 h
COL15A1 2.56792 1.394566 none PLAT 1.668223 1.95203 none
CREM 1.793497 3.67068 H PLEKHB2 1.568395 4.611748 f
CRKL 1.690269 3.051845 H h PPARGC1 2.268458 2.972107 HT F ht h
CSMD1 1.647116 1.61907 ht PPFIBP1 1.852526 2.550633 ht h
CTMP 1.548763 3.386235 none PPP1R10 1.870902 2.447557 H h
DBT 1.518604 4.292329 none PPP1R3B 1.693114 1.622596 h
DCLRE1C 1.41992 3.010944 none PSMAL/GCP 1.506527 2.707076 none
DDOST 1.582101 2.508459 ht RAB7L1 1.638378 1.15364 ht h
DDX3X 1.817009 3.42975 none RABL2B 1.486054 2.496157 h
DEGS 1.488221 1.464348 none RASSF1 1.431271 4.04395 none
DIAPH1 1.412484 2.96506 none RBL1 1.529652 2.451247 h
DUSP1 1.578824 2.102797 FT HT ht h REL 1.944847 1.143935 H h
EGR2 5.148023 2.036633 HT ht h RHOBTB3 1.63057 2.813465 none
EIF5 1.422558 4.208549 ht h RIOK3 1.40951 2.008376 none
ELK1 1.405171 4.088789 ht RNASE6PL 1.561704 2.252099 ht
ENC1 1.957151 1.549567 h RNF32 1.954396 1.603905 H ht
F2R 1.804785 1.098488 ht h SAS 1.768493 7.735178 HT ht h
FAM13A1 1.780869 2.014276 none SERPINB9 2.244605 1.418097 ht h
FAT 2.00051 1.816506 F ht SFPQ 1.477265 3.428149 ht
FKBP14 1.78994 3.042488 ht SHARP 1.558516 1.078188 H ht
FLJ10781 1.463332 1.113364 ht h SLC31A1 1.491104 3.803168 FH ht
FLJ10803 1.726196 2.63943 ht SLC35E3 1.716026 1.969928 ht
FLJ11029 1.422001 3.085667 ht h SLC38A2 1.497716 1.914154 H ht
FLJ11151 2.413055 1.840398 h SLC39A6 1.477678 3.119807 h
FLJ20507 1.730068 2.922871 H ht h SMA3 1.414595 2.654203 ht
FOSL1 2.220086 1.929543 HT ht h SMARCF1 1.537978 1.046929 none
FRSB 1.423607 2.982919 ht SNAP29 1.521481 2.454502 h
FXC1 1.423019 5.02095 HT H ht SON 1.42477 4.933417 H
GALNS 1.772331 2.592543 h SPG4 1.413533 3.160161 none
GCA 1.690161 2.92801 H h SUFU 1.661693 2.275704 ht h
GTF2H3 1.593421 10.587057 H TAP1 1.435113 3.105625 H h
GYS1 1.418699 2.559154 h TIGD6 1.772719 3.636168 h
HBS1L 1.475369 3.891767 ht TIMP1 1.791155 1.848154 HT h
HIP1 1.537214 2.114631 ht h TNFRSF21 1.498482 2.635088 ht
HLA-C 1.429002 3.2916 h TP53AP1 1.527339 3.493111 ht h
HSPG2 1.708361 1.453039 none TPM4 2.201468 1.33368 H ht
ICAM1 2.20462 1.198603 ht h TRIM26 1.400065 6.12308 ht
ID1 1.521685 2.3068 FT ht TSSC3 1.879281 2.01021 H ht h
IDS 1.508286 1.1848 h TTF1 1.513382 3.461645 ht h
IER5 1.66867 2.847755 HT ht TUBA3 1.481437 2.500545 none
IL10RA 1.64246 2.830231 f U2AF1L1 2.758542 3.548509 ht
IL10RB 1.410005 1.192048 ht h U5-116KD 2.223148 2.779884 h
IL1R1 1.812093 1.329947 ht USP2 2.35423 3.920336 HT H h
IL6 1.980266 1.460112 HT ht VPS4B 1.474465 6.693871 H ht
IL6ST 1.54702 3.418269 none YME1L1 1.441837 1.843132 F ht h
INPP1 2.071508 1.550135 ht h ZFP37 1.572207 4.659572 ht h
ITGA5 2.028008 1.315131 none ZNF142 1.50914 3.028386 h
JM4 1.606813 2.392743 HT h ZNF155 1.69746 4.195939 none
KIAA0266 1.504796 2.986155 none ZNF189 1.625836 4.104303 ht h
KIF14 1.453888 4.181899 none ZNF221 1.777122 3.569536 none
KIF3B 1.623133 1.560467 none ZNF324 1.488601 4.205703 h
LCMT2 1.587221 2.338943 H ht h
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Top up-regulated genes that show significant CREB binding and changes in expression in the CREB knockdown cells. The detailed criteria for selecting these genes are described in the methods section. The column descriptions are the same as in Table 1.

Genes within the downregulated list were BECLIN 1, UBE2B. Both these genes have a cAMP responsive element binding site(s) in their promoters. These genes were selected for further validation because they are known to be involved in autophagy/apoptosis (BECLIN 1), cell cycle/DNA repair (UBE2B) [25-28]. Quantitative real time-polymerase chain reaction (qRT-PCR) with mRNA from AML cell lines (K562 and TF-1) and primary leukemic blasts from a patient with M4-AML was performed. UBE2B expression was significantly reduced in CREB shRNA transduced TF-1 and K562 myeloid leukemia cells compared to controls (Figure 1, p < 0.05). BECLIN and UBE2B were downregulated in primary AML cells transduced with CREB shRNA (Figure 1, p < 0.05).

Figure 1.

Figure 1

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Expression of potential target genes downstream of CREB in myeloid leukemia cells. Primers specific for the UBE2B, BECLIN1, and CREB genes were generated and utilized for quantitative real-time PCR by SyberGreen method (Bio-Rad Inc.) Relative gene expression normalized to the housekeeping gene actin is shown for the following transduced cells: (A) K562 myeloid leukemia cells, (B) TF-1 myeloid leukemia cells, and (C) Human AML-M4 blasts.

Having confirmed the validity of our microarray results in these two test cases we set out to characterize the function of the complete list of CREB target genes using two annotation schemes. The first utilizes the annotation contained in the Ingenuity Pathway Analysis software (IPA). This analysis showed that there is a significant enrichment for cell cycle (P < 1e-3) and cancer (P < 1e-3) genes. The full list of genes associated with cancer is shown in Table 3. Many of these genes regulate cell cycle, signaling, DNA repair, or metabolism, which are consistent with previously published results [5,15]. Furthermore, the role of CREB in the pathogenesis of leukemias has also been described in the literature [2,3,12,29].

Table 3.

The subset of CREB target genes associated with cancer according to Ingenuity Pathways Analysis.

Name Location Type Drugs
Downregulated Cancer Genes
ABCG2 Plasma Membrane transporter
ANG Extracellular Space enzyme
BCL2L11 Cytoplasm other
BECN1 Cytoplasm other
BMX Cytoplasm kinase
CA2 Cytoplasm enzyme methazolamide, hydrochlorothiazide, acetazolamide, trichloromethiazide, dorzolamide, chlorothiazide, dorzolamide/timolol, brinzolamide, chlorthalidone, benzthiazide, sulfacetamide, topiramate
CENPE Nucleus other
CNN1 Cytoplasm other
CREB1 Nucleus transcription regulator
CUL5 Nucleus ion channel
GFI1B Nucleus transcription regulator
KLF5 Nucleus transcription regulator
MDM2 (includes EG:4193) Nucleus transcription regulator
MPHOSPH1 Nucleus enzyme
MSH2 Nucleus enzyme
MVD Cytoplasm enzyme
NR4A3 Nucleus ligand-dependent nuclear receptor
NUMB Plasma Membrane other
PPP1R2 Cytoplasm phosphatase
PTGS2 Cytoplasm enzyme acetaminophen/pentazocine, acetaminophen/clemastine/pseudoephedrine, aspirin/butalbital/caffeine,
RB1CC1 Nucleus other
SILV Plasma Membrane enzyme
SMC2 Nucleus transporter
SMC3 Nucleus other
TFDP2 Nucleus transcription regulator
THRB Nucleus ligand-dependent nuclear receptor 3,5-diiodothyropropionic acid, amiodarone, thyroxine, L-triiodothyronine
UBE2B Cytoplasm enzyme
VRK1 Nucleus kinase
WWOX Cytoplasm enzyme
Upregulated cancer Genes
ACOX1 Cytoplasm enzyme
ARID1A Nucleus transcription regulator
BCL6 Nucleus transcription regulator
BDKRB2 Plasma Membrane G-protein coupled receptor anatibant, icatibant
CD44 Plasma Membrane other
CDKN1A Nucleus kinase
COL15A1 Extracellular Space other collagenase
CREM Nucleus transcription regulator
CRKL Cytoplasm kinase
DCLRE1C Nucleus enzyme
DEGS1 Plasma Membrane enzyme
DIAPH1 Cytoplasm other
DUSP1 Nucleus phosphatase
EGR2 Nucleus transcription regulator
ELK1 Nucleus transcription regulator
ENC1 Nucleus peptidase
F2R Plasma Membrane G-protein coupled receptor chrysalin, argatroban, bivalirudin
FOSL1 Nucleus transcription regulator
HIP1 Cytoplasm other
HSPG2 (includes EG:3339) Plasma Membrane other
ICAM1 Plasma Membrane transmembrane receptor
ID1 Nucleus transcription regulator
IL6 Extracellular Space cytokine tocilizumab
IL1R1 Plasma Membrane transmembrane receptor anakinra
IL6ST Plasma Membrane transmembrane receptor
ITGA5 Plasma Membrane other
KIF14 Cytoplasm other
METAP2 Cytoplasm peptidase PPI-2458
NCOA3 Nucleus transcription regulator
NDRG1 Nucleus kinase
PHLDA1 Cytoplasm other
PLAT Extracellular Space peptidase
RASSF1 Nucleus other
RBL1 Nucleus other
REL Nucleus transcription regulator
RHOB Cytoplasm enzyme
SERPINB9 Cytoplasm other
SUFU Nucleus transcription regulator
TIMP1 Extracellular Space other
TNFRSF21 Plasma Membrane other
USP2 Cytoplasm peptidase
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Column 1 is the gene name, column 2 the localization, column 3 is a description of the protein function and column 4 are compounds that target the protein.

IPA also allows us to study CREB target genes in the context of protein-protein interactions networks. A network for downregulated genes interacting with CREB is shown in Figure 2, with a subset of the downregulated targets shown in grey, while other genes not in the target list that interact with these, shown in white. Here we see that there is prior literature supporting our analysis that CREB1 regulates PTGS2 (COX2), NR4A3 and TOM1, as depicted by the blue lines. Interestingly, COX2 is an important drug target, and suggests that commonly used COX2 inhibitors may provide a target for acute leukemia.

Figure 2.

Figure 2

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A network depicting interactions between direct CREB targets (shown in grey) and proteins that these interact with (shown in white). PTGS2, NR4A3 and TOM1 are direct CREB targets whose regulation by CREB was previously described in the literature (clue lines). PTGS2 (COX2) emerges as a central player in this network, and is thus implicated as a potential regulator of leukemias.

The second analysis that we performed used the terms from Gene Ontology to identify common characteristics among the top K562 CREB targets. Here we find the striking and unexpected result that ten percent of the downregulated targets code for histone genes (P < 1e-10, Table 4). We also performed an analysis of the top upregulated genes but did not find any significant GO terms. Although there is some prior literature indicating that CREB or CREB-related pathways may play a role in regulating histone modifications primarily through the histone acetylase CREB Binding Protein (CBP)[5,30,31], the fact that CREB directly regulates the transcription of histone genes in these cells is unexpected.

Table 4.

Gene Ontology terms that are enriched among the top CREB targets.

Category Term Count % PValue
GOTERM_CC_ALL nucleosome 11 6.88% 6.22E-10
GOTERM_CC_ALL chromosome 17 10.62% 2.39E-09
GOTERM_BP_ALL nucleosome assembly 11 6.88% 6.60E-09
GOTERM_CC_ALL chromatin 13 8.12% 7.56E-09
GOTERM_BP_ALL chromatin assembly 11 6.88% 1.66E-08
GOTERM_BP_ALL protein complex assembly 15 9.38% 2.19E-07
GOTERM_BP_ALL chromatin assembly or disassembly 11 6.88% 3.84E-07
GOTERM_BP_ALL chromosome organization and biogenesis 15 9.38% 5.56E-07
GOTERM_BP_ALL chromosome organization and biogenesis (sensu Eukaryota) 14 8.75% 1.63E-06
GOTERM_CC_ALL membrane-bound organelle 75 46.88% 1.93E-06
GOTERM_CC_ALL intracellular membrane-bound organelle 74 46.25% 4.63E-06
GOTERM_CC_ALL organelle 83 51.88% 5.39E-06
GOTERM_MF_ALL DNA binding 38 23.75% 6.17E-06
GOTERM_BP_ALL cellular physiological process 118 73.75% 8.86E-06
GOTERM_BP_ALL establishment and/or maintenance of chromatin architecture 12 7.50% 1.02E-05
GOTERM_CC_ALL intracellular organelle 82 51.25% 1.28E-05
GOTERM_BP_ALL DNA packaging 12 7.50% 1.38E-05
GOTERM_BP_ALL organelle organization and biogenesis 22 13.75% 1.59E-05
GOTERM_CC_ALL nucleus 56 35.00% 2.46E-05
GOTERM_BP_ALL DNA metabolism 19 11.88% 2.63E-05
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Column 1 is the ontology used (BP is biological process, CC is cellular localization and MF is molecular function), column 2 is the term, column 3 is the number of genes in the target list associated wit the term, column 4 is the percentage of genes in the target list associated with the term and column 5 is the P value for observing this number genes associated with the term.

To further validate the hypothesis that CREB is an activator of these 20 histone genes, we utilized previously published analyses of the gene promoters to identify consensus CREB binding sequences. The results shown in Table 1 demonstrate that nearly all the histone genes contain CREB half sites along with a TATA box in the vicinity of these. Thus three lines of evidence support the assignment of these 20 histone genes as CREB targets in K562 cells: expression, binding and sequence based.

We examined the distribution of expression of these 20 histone genes across human tissues. The expression data were obtained from the GNF body atlas. We were able to extract expression profiles for 81 histone genes contained in the human genome. Fifteen of these overlapped with the 20 histone CREB targets. We show the expression of all 81 histone genes in Figure 3, where the identity of the 15 CREB target genes is shown in the last row. We see that the 15 genes are clustered into two groups containing more than one gene, with a third group consisting of a single histone HIST1H1C. One of the groups contains histones that are broadly expressed across human tissues, and particularly in all hematopoietic tissues. The second group is instead expressed in a very narrow range of tissues including K562 cells, bone marrow, prostate and thymus.

Figure 3.

Figure 3

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The tissue specific expression of histone genes. Each row of the figure represents a tissue from the GNF Body Atlas (see methods). We show only the top 30 tissues with highest variance of expression of histone genes. Each column represents a histone gene. We use hierarchical clustering to order the rows and columns according to their similarity. Red indicates that the gene is over expressed relative to its mean expression levels across all tissues, and green that it is under expressed. The histone genes that we identify as direct targets of CREB are shown in red in the last row of the figure. We see that many of these are only expressed in a small subset of rapidly dividing tissues along with K562 cells.

We examined the expression of three histones that are putative targets of CREB by real time PCR with mRNA from K562, TF-1, and primary cells from patients with AML. The three histones selected were based on our microarray analyses. Our results demonstrated a statistically significant decrease in histonesHIST1H2Bj, HIST1H3B, and HIST2H2AA in K562 and TF-1 cells (Figure 4). Interestingly, in primary cells from a patient with AML, only HIST1H3B and HIST2H2AA, but not HIST1H2BJ expression was decreased with CREB knockdown. These results suggest that histones are differentially expressed in AML and that specific histones are potential targets of CREB. This analysis supports the hypothesis that CREB regulates a subset of histone genes that are normally expressed in a small set of rapidly dividing tissues. These genes are presumably aberrantly activated in K562 and other leukemia cells, and could potentially contribute to the malignant phenotype.

Figure 4.

Figure 4

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Expression of target histone genes is decreased in CREB knockdown myeloid leukemia cells. Primers specific for HIST1H2BJ, HIST1H3B, and HIST2H2AA were generated and utilized for quantitative real-time PCR by the SYBR Green method (Applied Biosystems). Relative gene expression normalized to the housekeeping gene actin is shown for the following transduced cells: (A) K562 myeloid leukemia cells, (B) TF-1 myeloid leukemia cells, and (C) primary AML cells.

Conclusion

We have identified a high confidence list of CREB target genes in K562 myeloid leukemia cells. Several important CREB target genes that function in DNA repair, signaling, oncogenesis, and autophagy were identified. These genes provide potential mechanisms by which CREB contributes to the pathogenesis of acute leukemia. Expression of the genes beclin-1 and ube2b was found to be decreased in myeloid leukemia cell lines and primary AML cells in which CREB was downregulated. In addition, we speculate that CREB may have more global effects on transcription, primarily through the regulation of histone genes thereby altering the regulation of DNA replication during the cell cycle.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MP and SFN analyzed the microarray data, performed the statistical analysis, and drafted the manuscript. JCC, JC, DJ, and JT performed the real-time PCR experiments. KMS supervised the experiments and wrote the manuscript. All authors read and approved the final manuscript.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2407/8/264/prepub

Supplementary Material

Additional File 1

Supplementary table 1

Click here for file (1.9MB, xls)
Additional File 2

Supplementary table 2

Click here for file (14.7MB, txt)

Acknowledgments

Acknowledgements

We would like to thank Nori Kasahara and the Core Vector Laboratory for assistance with the CREB shRNA lentivirus. This work was supported by National Institutes of Health grants HL75826 (K.M.S.), HL83077 (K.M.S.), F32HL085013 (J.C.), American Cancer Society grant RSG-99-081-01-LIB (K.M.S.), and Department of Defense grant CM050077 (K.M.S.). Microarray experimentation was supported by the UCLA NHLBI Shared Microarray Resource grant R01HL72367 (S.F.N.). K.M.S. is a scholar of the Leukemia and Lymphoma Society.

Contributor Information

Matteo Pellegrini, Email: matteop@mcdb.ucla.edu.

Jerry C Cheng, Email: jerryccheng@gmail.com.

Jon Voutila, Email: jvoutila@ucla.edu.

Dejah Judelson, Email: dejahjudelson@ucla.edu.

Julie Taylor, Email: janntaylor@ucla.edu.

Stanley F Nelson, Email: snelson@ucla.edu.

Kathleen M Sakamoto, Email: kms@ucla.edu.

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Associated Data

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Supplementary Materials

Additional File 1

Supplementary table 1

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Additional File 2

Supplementary table 2

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