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Expression profile of CREB knockdown in myeloid leukemia cells

  • Matteo Pellegrini1Email author,
  • Jerry C Cheng2,
  • Jon Voutila2,
  • Dejah Judelson2,
  • Julie Taylor2,
  • Stanley F Nelson3 and
  • Kathleen M Sakamoto4, 5
BMC Cancer20088:264

DOI: 10.1186/1471-2407-8-264

Received: 31 January 2008

Accepted: 18 September 2008

Published: 18 September 2008

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 [46]. 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 [911]. 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. [1719]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

    

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

    

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) [2528]. 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).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-8-264/MediaObjects/12885_2008_Article_1202_Fig1_HTML.jpg
Figure 1

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

 

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-8-264/MediaObjects/12885_2008_Article_1202_Fig2_HTML.jpg
Figure 2

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

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-8-264/MediaObjects/12885_2008_Article_1202_Fig3_HTML.jpg
Figure 3

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-8-264/MediaObjects/12885_2008_Article_1202_Fig4_HTML.jpg
Figure 4

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.

Declarations

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.

Authors’ Affiliations

(1)
Department of Molecular, Cellular, and Developmental Biology, University of California
(2)
Division of Hematology-Oncology, Department of Pediatrics, Kaiser Permanente Medical Center
(3)
Department of Human Genetics, David Geffen School of Medicine at UCLA
(4)
Division of Hematology-Oncology, Department of Pediatrics, Gwynne Hazen Cherry Laboratories, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA
(5)
Division of Biology, California Institute of Technology

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  32. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/8/264/prepub

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© Pellegrini et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.