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Introduction

Acute myeloid leukaemia (AML) with karyotypic discernible translocation (t)(8;21)(q22;q22) (RUNX1-RUNX1T1) is observed in approximately 7% of adults (1), and is the most frequent non-lymphoblastic leukaemia in children (2). This balanced translocation disrupts two functionally distinct genes, RUNX1 (previously termed AML1) and RUNX1T1 (commonly known as ETO or MTG8) (3). Physiologically, RUNX1 is a transcription factor that encodes to the alpha subunit of the core biding factor (CBFα) which forms a heterodimer with the CBFβ subunit and regulates differentiation of haematopoietic stem cells into committed mature cells. The translocated RUNX1T1 moiety gets activated as a result of the fusion with RUNX1 (4,5). Together, the hybridised RUNX1-RUNX1T1 (RR) oncoprotein functions as a dominant negative form of RUNX1. As empirically shown, RR exerts a repressive effect on the promoters of genes normally activated by the RUNX1 transcription factor through interactions of its RUX1T1 moiety with nuclear corepressors such as NCOR1 and Sin3A (6,7). Several studies have indicated that this altered transcription pattern impairs myeloid precursor cell differentiation, proliferation and apoptosis, and is therefore thought to be the underlying pathogenesis of myeloid leukaemia (8–13). However, this translocation itself is not necessarily sufficient for the development of AML. It appears that additional secondary mutations are required to impair haematopoietic differentiation to recapitulate the phenotypic features of AML. Based on recent studies there appears to be a fairly consistent association between the t(8;21) translocation and the classical MAPK signalling pathway (9,14).

One of many signalling pathways triggered by haematopoietic cytokines is the ERK (Ras/Raf/MEK/ERK) pathway. The MAP kinase ERK2 (MAPK1), encoded by MAPK1, plays an integral role in the ERK cascade in relaying extracellular biological signals from cell membrane to the nucleus. This mechanism modulates the normal haematopoietic cell proliferation, differentiation and prevention of apoptosis (15). The ERK pathway, most extensively studied of all MAPK pathways, is deregulated in a third of all human cancers, a frequent observation in the pathogenesis of haematological malignancies such as AML (16–18). In wide varieties of AMLs, constitutive activation that centres on the MEK/ERK node in the pathway has been attributed to the tumourigenic effects such as independence of proliferation and evasion of apoptosis (16–19). Previous study revealed that AML with t(8;21) encoded RR induces a microRNA-dependent mechanism that utilises the MAPK cascade to signal hyper-proliferation of myeloid progenitors and block their differentiation (20). Another group reported that Kasumi-1 cells that were refractory to apoptosis following RR silencing activate an array of signalling pathways involved in cell survival and proliferation, prominently the ERK2 pathway (21). These findings underscore the influence of RR on the MAPK pathway towards the pathobiology of AML.

Although the role of constitutive activation of the MEK/ERK pathway in the development of myeloid leukaemia is well established, RR- and MAPK-driven intricate molecular and cellular pathways that maintain the pathobiology of AML remain elusive. In the present study we used siRNA-mediated gene silencing approach to elucidate the transcription profiles of RR and MAPK1 suppressed AML cell lines and further analysed these expression signatures to explore the molecular mechanisms involved in cell proliferation, cell cycle distribution, apoptosis and differentiation. In the present study, we report differential regulation of gene expression by RR and MAPK1 as determined by genome-wide expression analysis responsible for the phenotypic features of AML.

Materials and methods

Cell culture and siRNA transfection

Two suspension leukaemic cell lines with t(8;21), Kasumi-1 and SKNO-1, were used in the present study. The former was obtained from the American Type Culture Collection (ATCC; Manassas, VA, USA) while the latter was a kind gift from Professor Olaf Heidenreich (Northern Institute for Cancer Research, Newcastle University, UK). Kasumi-1 and SKNO-1 cells were cultured in RPMI-1640 medium containing 10 and 20% foetal bovine serum (FBS) (Gibco; Thermo Fisher Scientific, Inc., Waltham, MA, USA), respectively. SKNO-1 was also supplemented with 10 ng/ml of granulocyte-macrophage colony stimulating factor (GM-CSF). Cell lines were incubated at 37°C in a humidified incubator with 5% CO2.

Kasum-1 and SKNO-1 cells cultivated to late log growth phase, concentrated to 107 cells/ml in culture medium without serum, were electroporated with siRNA using a Bio-Rad Gene Pulser Xcell™ at 330 V for 10 m. The sequences of siRNAs used are as follows: siRNA-MAPK1 (siMAPK1) sense, 5′-GUUCGAGUAGCUAUCAAGATT-3′ and antisense, 5′-UCUUGAUAGCUACUCGAACTT-3′; siRNA-RUNX1-RUNX1T1 (siRR) sense, 5′-CCUCGAAAUCGUACUGAGAAG-3′ and antisense, 5′-UCUCAGUACGAUUUCGAGGUU-3′; and scrambled mismatch siRNA (siMM) from Qiagen (Hilden, Germany).

For isolated downregulation of RR and MAPK1 gene expression, both cell types were electro-transfected with 200 nM of siRR and 100 nM of annealed siMAPK1 respectively; and for co-transfection a mixture of both siRR and siMAPK1 were used. These concentrations were derived from dose-dependent experiments performed in our laboratory. In experiments requiring extended transfection, cells were sequentially transfected on days 0, 4 and 7. Immediately after electro-transfection, cells were diluted 20-fold in complete culture medium and returned to the incubator. All cell groups were electroporated in triplicates.

RNA isolation and integrity evaluation

Quantitative RT-PCR total RNA was isolated using RNeasy Mini kit (Qiagen) according to the manufacturer's protocol. RNA integrity was analysed on an Agilent 2100 Bioanalyser RNA 6000 NanoLabChip (Agilent Technologies, Palo Alto, CA, USA) to produce an electrophoresis trace of 18S and 28S peaks using 2100 Expert Software (Agilent Technologies). The RNA integrity number (RIN) was calculated directly from the peak areas, a value of 10 corresponded to intact RNA while 1 indicates a total degradation. Our optimised methods showed a 28S:18S ratio of 1.9–2.1:1 and an RIN of 9–10.

Quantitative real-time polymerase chain reaction (q-RT-PCR)

Quantitative real-time-polymerase chain reaction (qRT-PCR) was performed on RNA isolated at various time points post-transfection to quantify the mRNA expression levels of MAPK1 and RR genes in cells treated with respective siRNAs.

Gene-specific primers for MAPK1 (forward, 5′-CTGCTGCTCAACACCACCT-3′ and reverse, 5′-GCCACATATTCTGTCAGGAACC-3′); and B2M (forward, 5′-GGCATTCCTGAAGCTGACAG-3′ and reverse, 5′-TCTGCTGGATGACGTGAGTAA-3′) were designed using PrimerPlex software (Premier Biosoft, Palo Alto, CA, USA). Primers to quantify RR expression were: Forward, 5′-AATCACAGTGGATGGGCCC-3′ and reverse, 5′-TGCGTCTTCACATCCACAGG-3′ as described by Heidenreich et al (5).

A mixture of 50 ng of total RNA together with 0.5 µM of MAPK1 and B2M, and 0.2 µM of RR primers was reverse transcribed and amplified simultaneously in the same reaction tube using the QuantiFast SYBR®-Green RT-PCR One-Step PCR Master Mix (Qiagen) in a final volume of 20 µl on a StepOnePlus Real-Rime thermocycler (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The thermocycler was programmed at 50°C for 10 min for RNA reverse transcription into complementary cDNA followed by 95°C for 5 min for the initial DNA polymerase activation step. Quantitative amplification was carried out in 35 cycles of denaturation at 95°C for 10 sec, and annealing and extension at 60°C for 30 sec. All reactions were performed in triplicates.

Relative quantity of MAPK1 and RR mRNA expression was computed using the comparative cycle threshold (2−∆∆Ct) method normalised with the β2 microglobulin B2M expression as endogenous reference gene. Mock cells were used as a reference sample for normalisation and AllStars negative siRNA (Qiagen) as a mismatch (siMM) control for validation purposes.

Cellular differentiation assay

Differentiation ability of RR- and MAPK1-suppressed Kasumi-1 cells was monitored by CD34 expression. Kasumi-1 cells sequentially transfected on days 0, 4 and 7 with either siRR, siMAPK1 or a combination of siRR + siMAPK1 were double stained on day 9 with allophycocyanin conjugated anti-CD34:APC and peridinin-chlorophyll A protein conjugated anti-CD45:PerCP (Becton-Dickinson, Franklin Lakes, NJ, USA).

Briefly, ~105 transfected Kasumi-1 cells were treated with Fc blocking reagent [phosphate-buffered saline (PBS) containing 0.02% sodium azide and 1% BSA] and incubated on ice for 30 min to block non-specific antibody binding. After incubation, the cells were washed and re-suspended in 100 µl of PBS. Cells were subsequently stained with 0.5 µl of allophycocyanin conjugated anti-CD34:APC and 2 µl of peridinin-chlorophyll A protein conjugated anti-CD45:PerCP (Becton-Dickinson). Cells were sorted on a FACSCanto II equipped with BD FACSCanto Clinical Software for acquisition and analyses. Mock cells and siMM-transfected Kasumi-1 cells were used as controls.

Cell apoptosis assay

To determine the suppressive effects of MAPK1 and RUNX1-RUNX1T1 genes on apoptosis induction, Kasumi-1 cells were sequentially transfected on days 0, 4 and 7 with siMAPK1 and siRR singly, and in combination. Apoptosis was quantified on day 9 using an Annexin V/fluorescein isothiocyanate (FITC) assay kit (AbD Serotec®; Bio-Rad Laboratories, Raleigh, NC, USA) using a FACSCanto™ II (BD Biosciences; Becton-Dickinson and Company, Franklin Lakes, NJ, USA) flow cytometer. In brief, cells in logarithmic growth phase after treatment with siRNA were washed once in cold 1X PBS and once in cold 1X binding buffer. Cells were suspended to a density of 1×105 cells in 100 µl of 1X binding buffer and treated with 5 µl of Annexin V:FITC for 10 min in the dark at room temperature. On completion, the cells were washed again in 1X binding buffer and re-suspended in 190 µl of 1X binding buffer with 10 µl of propidium iodide (PI) and directly analysed on a flow cytometer. siMM-transfected cells were used as a negative control.

Cell cycle analysis

Distribution of Kasumi-1 cells in different phases of the cell cycle were examined using EZCell™ cell cycle analysis kit (BioVision, Milpitas, CA, USA) according to the manufacturer's protocol. Three aliquots of Kasumi-1 cells were eletrotransfected on days 0 and 4 with siRR and siMAPK1, and in combination respectively. Kasumi-1 cells electroporated with siMM and mock-transfected cells were used as controls. Cell cycle analysis was performed on day 6 by FACS analysis. Briefly, cells were washed in PBS, adjusted to a cell density of 1×106 and fixed in 100% ice-cold ethanol for 24 h. On the next day, PBS washed cells were digested with 100 µl of RNase A for 30 min at 37°C followed by staining with 400 µl of PI and subsequently analysed using FACSCanto II flow cytometer at 488 nm wavelength using FL3 filters with wavelength of 670 nm for the proportion cell cycle distribution in the G0/G1, S and G2/M phase.

Cell viability and proliferation assays

The rate of viable cells was determined using trypan blue (Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) staining method that selectively stains dead cells. Cell counting was carried out using a haemocytometer.

To quantitatively determine the number of viable cells in proliferation on days 0, 4 and 8 following electrotransfection, the siRR, siMAK1 and combined siRR/siMAPK1-treated Kasumi-1 cells were seeded separately, on the day of the experiment, into a 96-well plate at a density of 5×104/well in 100 µl complete growth medium in quadruples. Twenty microliters of 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium MTS/PMS solution (as instructed in the Cell Titer 96® AQueousNon-Radioactive Cell Proliferation Assay kit literature; Promega Corp., Madison, WI, USA) was added and the plate was incubated for 2 h at 37°C in a humidified 5% CO2 incubator. The absorbance was measured at 490 nm using an ELISA plate reader. Data are represented as mean ± SD of four independent experiments.

Microarray experiments

Post electrotransfection, genome wide expression (GWE) levels were determined in a series of four experimental conditions set up for each cell line, Kasumi-1 and SNKO-1. They include cells; co-transfected with siRR/siMAPK1; singly transfected with siRR; siMM; and mock-transfected cells. A total of 24 arrays, three replicates for each of the transfection experiment, were processed using Illumina HumanHT-12 v3 Expression BeadChip™ array (Illumina, San Diego, CA, USA) targeting 48,804 human transcripts per array with an average of 15-fold transcript redundancy.

Pre-hybridisation sample preparation used a total of 400–500 ng of RNA from each experiment, which was reverse transcribed to cDNA followed by amplification and in vitro transcription of cDNA to bio-11-dUTP-labelled complementary RNA (cRNA). Quantified (500 ng) and labelled cRNA was then hybridised to BeadChip arrays at 55°C overnight. BeadChips were then wash-cleaned with Illumina high-stringent wash buffer. The hybridisations were scanned after staining with 1 µg/ml streptavidin-Cy3 (Amersham Biosciences, Piscatawy, NJ, USA) on the Illumina BeadArray Reader confocal scanner and BeadScan software to produce bead level data.

Background subtracted bead summary data were exported to GeneSpring™ GX 11.0 software (Agilent Technologies, Santa Clara, CA, USA) for transformation and normalisation, and differential expression analyses. Microarray expression data were normalised to the most stable three reference genes (ACTB, B2M and UBC). Probe sets returning a P-value <0.05 (unpaired t-test) in comparison to the mock cells and experiment classes were considered to be differentially expressed. The differentially expressed genes were then filtered for gene sets that had fold-changes >1.1, and >1.5 together with false discovery rate (FDR) adjusted P-value ≤0.05.

The processed data were used for GO annotation and KEGG pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery [DAVID; (22)].

Statistical analysis

All statistical analysis was performed using SPSS software version 17 (SPSS, Inc., Chicago, IL, USA). Paired t-test was used to measure mean differences between two variables whereas one-way ANOVA was used to measure mean differences between three variables.

Results

siRNA induces the silencing of RUNX1-RUNX1T1 and MAPK1 in Kasumi-1 cells

To identify the silencing efficacies of siMAPK1 and siRR, we transfected Kasumi-1 cells with 100 and 200 nM of the respective siRNAs separately in triplicates. Time course suppression of RR and MAPK1 genes in the experimental cells were compared with mock-transfected cells. The results of qRT-PCR showed that siMAPK1 inhibited the expression of MAPK1 by 85% on day one, and lasted for at least three days with 2% recovery (83%) on day two and 6% recovery (77%) on day three (Fig. 1A). siRR-treated Kasumi-1 cells exhibited up to 78% suppression of RR expression at 24 h and continued to further suppress to 82% at 48 h before 16% recovery was observed at 72 h. These observations as measured by qRT-PCR validates that significant knockdown (>80%) of RR and MAPK1 was achieved with respective doses of 200 nM of siRR and 100 nM of siMAPK1 at 48 h post-transfection. Melting curves for RUNX1-RUNX1T1 and MAPK1 are included in Fig. 1B and C, respectively.

Figure 1. (A) Time-course depletion effect of the target mRNA concentration assessed using qRT-PCR at 24, 48 and 72 h after single transfection of Kasumi-1 cells with 100 nM of siMAPK1 and 200 nM of siRR. Reductions in MAPK1 and RR transcripts at three time points are shown. The mean values with error bars to indicate standard deviations were calculated from three independent experiments. Student's t-test was performed on each data set relative to mock data (*P<0.05). (B) Melt curve analysis of AML/ETO (RR). A single peak is observed at TM 81.31. (C) Melt curve analysis of MAPK1. A single peak is observed at TM 82.65. AML, acute myeloid leukaemia. siMAPK1, siRNA-MAPK1; siRR, siRNA-RUNX1-RUNX1T1.

Whole genome expression profile

We conducted gene expression analysis on mRNA extracts obtained at 48 h post-transfection using Illumina's HumanHT-12 v3 Expression BeadChips. A total of 24 arrays, three replicates for each of the four experimental conditions for both Kasumi-1 and SKNO-1 cells (as described above) were profiled for at least 48,804 transcripts to characterise the genes modulated when RR and MAPK1 were downregulated.

Microarray data for Kasumi-1 cells co-transfected with siMARK1 and siRR showed a total of 4,788 differentially expressed genes (DEG) as compared to the mock cells, accounting for 9.8% of the transcriptome probed in our array. However, when the 4,788 DEGs were conditioned to a fold change (FC) ≥1.5 and P<0.05 (t-test), the gene set condensed to 466 representing 262 upregulations and 204 downregulations. Relative to Kasumi-1 cells, co-knockdown of RR and MAPK1 genes in SKNO-1 cells revealed a more pronounced gene modulation presenting with 6,465 DEGs accounting for 13.2% of the transcriptome studied. Of these 6,465 DEGs, 732 sequences had a FC of ≥1.5 (at P<0.05) representing 415 upregulations and 317 downregulations.

We then examined the 466 and 732 gene sets with FC >1.5 for shared common genes modulated in both Kasumi-1 and SKNO-1 cells using the two-way Venn diagram analysis. In total, there were 148 overlapping genes, which included 109 upregulations and 39 downregulations (Fig. 2A). We also looked at the shared genes between the two cell lines when the fold changes were relaxed to 1.1. At this fold change, we observed 4,855 and 4,974 genes that were modulated in their respective co-transfected Kasumi-1 and SKNO-1 cell lines when compared to their respective mock-transfected cells. When used on two-way Venn diagram, these two enriched gene sets presented with 884 overlapping genes, which included 478 upregulations and 406 downregulations (Fig. 2B).

Figure 2. The two-way Venn-diagrams display the total number of unique and overlapping gene expression changes as a result of RR and MAPK1 gene supression in Kasumi-1 cells (dark grey circles) and SKNO-1 cells (light grey circles). (A) Two-way Venn diagram shows the number of shared 109 upregulated (left) and 39 downregulated (right) gene changes between cell lines co-transfected with siRR and siMAPK1 when absolute fold change was set for 1.5 with P≤0.05 by one-way ANOVA. (B) Similar to A but with the fold changes relaxed to 1.1. (C) Venn diagram shows the number of shared upregulated (left) and downregulated (right) gene changes between cell lines singly transfected with siRR with absolute fold change set for 1.5 with P≤0.05 by one-way ANOVA. (D) Similar to C with fold change adjusted to 1.1. siMAPK1, siRNA-MAPK1; siRR, siRNA-RUNX1-RUNX1T1.

Whole genome expression studies from isolated RR-depleted Kasumi-1 cells presented with 5,178 DEGs relative to mock-transfected cells, accounting for 10.6% of the transcriptome probed. One-tenth of these, 552 genes, had a fold change expression ≥1.5 including 300 upregulations and 252 downregulations. In turn, RR-depleted SKNO-1 altered the expression of 9,049 genes. Of these, expression of 2,600 genes was significantly changed at a ≥1.5-fold cut-off including 1,267 upregulations and 1,333 downregulations. Two-way Venn diagram analysis enrolling the gene sets observed at fold changes ≥1.5 from Kasumi-1 cells (552) and SKNO-1 cells (2,600) revealed that a subset of 284 DEGs (including 197 upregulations and 87 downregulations) were common between the two cell lines (Fig. 2C), and support their association with the RR knockdown. Moreover, when the absolute fold change cut-off was adjusted to ≥1.1, we observed the number of overlapping genes from the two cell lines rose to 1,891, with 971 upregulations and 920 downregulations (Fig. 2D).

Gene ontology analysis

To reveal the potential function of differentially expressed genes common for both Kasumi-1 and SKNO-1 cell lines in terms of their associated biological processes, cellular components and molecular functions, we used the online data-mining tools of DAVID software (23,24). Uploading the 148 overlapping gene set, derived from the siRR + siMAPK experiment as depicted on the Venn diagram Fig. 2A, into DAVID with the default gene ontology (GO) setting (P<0.01) indicated association to 267 biological processes (BP), 49 cellular components (CC) and 52 molecular functions (MF). GO enrichment analysis for the 284 overlapping gene set identified from the Venn diagram analysis (Fig. 2C) from siRR experiments on Kasumi-1 and SKNO-1 cell lines identified 269 BP, 85 CC and 69 MF.

qRT-PCR validation of microarray data

In order to validate the altered gene expression results of the BeadChip experiments, we performed quantitative PCR following reverse transcription (qRT-PCR) on the same RNA isolates used on BeadChips. We chose to quantify the expression levels of MAPK1 and RR, and compared the fold changes.

qRT-PCR assessment of RR and MAPK1 gene expression levels from the co-transfection experiment showed comparable downregulated fold changes validating the microarray data. As illustrated in Fig. 3, RR expression in co-transfected Kasumi-1 and SKNO-1 cells revealed a fold change between 1.75–1.85 and 1.4–1.6, respectively on qRT-PCR, while the same on the BeadChip had fold change downregulations between 2.4–2.6 and 1.9–2.3, respectively. Similar agreements were observed for MAPK1 in the co-transfected experiments. MAPK1 had a downregulated fold changes between 2.4–2.6 and 2.6–3.0 by qRT-PCR and 3.8–3.9 and 2.8–3.6 by BeadChip in Kasumi-1 and SKNO-1 cells, respectively.

Figure 3. Validation of microarray data for Kasumi-1 and SKNO-1 cell lines by qRT-PCR. Microarray signal intensity and qRT-PCR MAPK1 and RUNX1-RUNX1T1 mRNA copy number were determined in triplicates, normalised to B2M expression and reported as means with error bars to indicate standard deviations. siMAPK1, siRNA-MAPK1; siRR, siRNA-RUNX1-RUNX1T1.

The results from the isolated RR knockdown experiment were also consitent with those of BeadChip. On a log2 scale, qRT-PCR revealed that RR expression was downregulated by 1.5–1.7- and 1.6–1.7-fold, while the same on microarray was 2.9–3.3 and 3.2–3.7 in Kasumi-1 and SKNO-1 cells, respectively.

RUNX1-RUNX1T1 and MAPK1 suppression reduces the proliferation rate of t(8;21) cells

To determine the effect of RUNX1-RUNX1T1 and MAPK1 suppression and their combined influence on the proliferation rate, we transfected Kasumi-1 cells three successive times in a gap of three days with their respective siRNAs individually and in combination. MTS assays were performed on days 0, 4 and 8 to determine the proliferation activity. siMM and mock-transfected Kasumi-1 cells served as controls.

We observed that a single co-transfection with siRR and siMAPK1 or isolated transfection of siRR was not sufficient to inhibit the proliferation of Kasumi-1 cells when compared with siMM (Fig. 4). However, sequential electroporations on every third or fourth day attenuated the proliferation viabilities on days 4 and 8 in both the experiments. Similarly, two successive transfections of siMAPK1 were required to inhibit Kasumi-1 cell proliferation by 18.5% on day 4. The third successive transfection was only sufficient to sustain the proliferation at the same level on day 8. These data suggest that the fusion protein RR plays a more substantial role than MAPK1 in supporting the proliferation of leukaemic cells with t(8;21).

Figure 4. Effect of each siRNA on the suppression of cell proliferation. MTS assays on days 0, 4 and 8 show the time course inhibition of proliferation of Kasumi-1 cells upon knockdown of MAPK1 and RUNX1-RUNX1. Error bars indicate standard deviations of two independent experiments. All experiments were carried out in four replicates. siMM, scrambled mismatch siRNA; siMAPK1, siRNA-MAPK1; siRR, siRNA-RUNX1-RUNX1T1.

Our GO enrichment analysis revealed that the observed growth suppression upon RR knockdown was accompanied by deregulated expression of classical positive regulator genes that otherwise are known to enhance cell proliferation. They include significant downregulation of FLT3, HIF1A, KIT, MYCN, LYN, UBE2A, TBRG4, RPS9, RPS15A, RPA1, PRDX3, PDF, ODC1, IL13RA1, ITGB1, GRN, FABP1, CDK6, F2R, CDC123, CAPNS1, MARCKSL1, CD40, BCL2L1, AGPAT1, MXI1, CAV2, ENG, EMP3, IL8, MFN2 and TRIM24 genes, and upregulation of CEBPA, S100A11, ING4 and SSTR2 genes. On comparison, GO enrichment analysis for category of proliferation from RR knockdown and RR/MAPK1 co-knockdown experiments revealed several shared differentially expressed genes. On exclusion of the simultaneously altered genes, we observed that downregulation of JUN, NRAS, and STAT1 genes and upregulation of VHL, ZBTB16 and BTG1 genes were unique to MAPK1 depletion. The results of the GO enrichment analysis are summarised in Table I.

Table I. Summary of Gene Ontology Enrichment Analysis according to pathways indicated when genes were silenced using the respective siRNAs.

Table I.

Summary of Gene Ontology Enrichment Analysis according to pathways indicated when genes were silenced using the respective siRNAs.

Pathways	RUNX1-RUNX1T1 (siRR)	MAPK1 (siMAPK1)
Cell	Upregulated	Upregulated
proliferation	genes	genes
	CEBPA	VHL
	S100A11	ZBTB16
	ING4	BTG1
	SSTR2
	Downregulated	Downregulated
	genes	genes
	FLT3	FLT3
	HIF1A	NRAS
	KIT	STAT1
	MYCN
	LYN
	UBE2A
	TBRG4
	PRDX3
	CD40
	IL8
	BCL2L1
Apoptosis	Upregulated genes	Upregulated genes
	BCL2	TP53
	SPHK2	TNFSF10
	CFLAR	ADA
	CD24
	NOTCH1
	ADAM17
	BIRC3
	HMGB1
	Downregulated genes	Downregulated genes
	IP6K2	JUN
	BECN1	NRAS
Differentiation	Upregulated genes	Upregulated genes
	CD24	ADA
	NOTCH1
	DNMT38
	CEBPA
	CEBPE
	ID2
	JMJD6
	IK2F1
	Downregulated genes	Downregulated genes
	RHOA	JUN
	CBFB
	KIT
	CDK6
	FLT3
Cell cycle	Upregulated genes	Upregulated genes
	FOXO4	RASSF1
	ING4	GADD45A
		p53
		FBX06
	Downregulated genes	Downregulated genes
	TPX2	MAPK11
	CDC2
	CKAP5
	IL8
	MAD21L
	CCNE2
	CCNG2
	CDK6
	TBRG4

[i] siMAPK1, siRNA-MAPK1; siRR, siRNA-RUNX1-RUNX1T1.

RUNX1-RUNX1T1 suppression exerts an anti-apoptotic effect while MAPK1 suppression induces apopotosis in t(8;21) cells

Next, we examined the consequences of isolated and co-depletions of RR and MAPK1 on cell apoptosis. Kasumi-1 cells harvested on day 9, after three consecutive transfections, were double stained using Annexin V/FITC and PI.

Three consecutive siRR treatments significantly (P=0.0001) lowered the live cells by 24% when compared with the siMM control indicating that RUNX1-RUNX1T1 suppression has an apoptotic effect on Kasumi-1 cells (Fig. 5). Notably, pronounced apoptosis (30±1.6%) was observed upon isolated MAPK1 deletion, and revealed a significant difference (P<0.0001) when compared with control cells. However, co-depeletion demonstrated no difference when compared with the siMM control cells. FACS results for the apoptosis analysis are shown in Fig. 5B-E.

Figure 5. (A) Histogram represents the knockdown effects of MAPK1, RUNX1-RUNX1T1 and their combined suppression on apoptosis induction in Kasumi-1 cells. Error bars indicate standard deviations of two independent experiments each with triplicate technical replicates. P-value of silenced genes vs. control siRNA was determined by one way ANOVA. (B) FACS results of apoptosis induction by co-knockdown of MAPK1 and RUNX1-RUNX1T1 (siMAPK1 + siRR). (C) FACS results of apoptosis induction by knockdown of MAPK1 (siMAPK1). (D) FACS results of apoptosis induction by knockdown of RUNX1-RUNX1T1 (siRR). (E) FACS results of apoptosis induction by knockdown with negative control siRNA (siMM).

GO enrichment analysis on shared DEGs from both Kasumi-1 and SKNO-1 cells with fold change of ≥1.5 in response to RR depletion exhibited several changes to genes involved in negative regulation of apoptosis. They included significant upregulation of BCL2, SPHK2, CFLAR, CD24, NOTCH1, ADAM17, BIRC3 and HMGB1, and downregulation of IP6K2 and BECN1 genes. Exclusion of overlapping differentially expressed genes involved in apoptosis between the isolated RR knockdown and the co-knockdown experiments revealed five genes specific for MAPK1 depletion. They include upregulated expression of TP53, TNFSF10 and ADA genes as well as downregulated expression of JUN and NRAS genes. The results of the GO enrichment analysis are summarised in Table I.

RUNX1-RUNX1T1 suppression supports, while MAPK1 depletion inhibits the differentiation of t(8;21) cells

To investigate the effect of RUNX1-RUNX1T1 and MAPK1 depletion on the progenitor status of Kasumi-1 cells, we analysed the CD34 expression levels as an indicator of differentiation. Compared with the cells, RR suppression showed statistically significant (P=0.0001) loss of CD34-expressing cells by ~23±1.1% 9 days after the first of the three successive electroporations indicating that the cells were undergoing apparent differentiation (Fig. 6A). The loss of CD34-expressing cells corresponded with an increase in cells expressing CD45 which indicated differentiation. The FACS results are shown in Fig. 6B-E, respectively. Based on the gene ontology analysis on the RR-suppressed microarray data, we identified several genes involved in cell differentiation to be activated or repressed. Upregulated genes included CD24, NOTCH1, DNMT3B, CEBPA, CEBPE, ID2, JMJD6 and IKZF1, while RHOA, CBFB, KIT, CDK6 and FLT3 were downregulated.

Figure 6. (A) Effects of MAPK1, RUNX1-RUNX1T1 and combination of MAPK1 and RUNX1-RUNX1T1 suppression on differentiation induction/suppression. Error bars indicate standard deviations of two independent experiments each with triplicate technical replicates. P-value of silenced genes vs. mock control was determined by one way ANOVA. (B) FACS results of differentiation induction/suppression by co-knockdown of MAPK1 and RUNX1-RUNX1T1 (siMAPK1 + siRR). (C) FACS results of differentiation induction/suppression by knockdown of MAPK1 (siMAPK1). (D) FACS results of differentiation induction/suppression by knockdown of RUNX1-RUNX1T1 (siRR). (E) FACS results of differentiation induction/suppression by Kasumi-1 cells without siRNA (Mock). (F) FACS results of differentiation induction by knockdown with negative control siRNA (siMM).

When both MAPK1 and RR were co-suppressed, the expression of CD34 in Kasumi-1 cells were relatively less pronounced (13±2.3%) (Fig. 6A). However, CD34 suppression revealed a significant difference (23±1.1%), when compared with the control cell expression in which only RR was suppressed. This implies that MAPK1 abrogated the effect of RR by ~40% suggesting that tandem MAPK1 suppression exerted an anti-differentiation effect. Due to this antagonist effect of MAPK1 and RR co-suppression, we used the same strategy mentioned in the apoptosis section to identify the genes more specific for MAPK1 knockdown. After exclusion, MAPK1 knockdown revealed significant downregulation of c-JUN and upregulation of ADA, both identified to have anti-differentiation effect.

FACS experiment on isolated MAPK1 suppression showed no significant loss of CD34 expression when compared with mock and AllStar siRNA-treated control cells (Fig. 6A). The results of the GO enrichment analysis are summarised in Table I.

RUNX1-RUNX1T1 and MAPK1 depletion promotes cell cycle arrest

Finally, we examined the effect of different siRNAs on cell cycle progression. Kasumi-1 cells were electro-transfected twice o a gap of three days with either siRR, siMAPK1 or co-transfected with siRR/siMAPK1. Cell phase distributions were analysed on day 6 by flow cytometry. In comparison with the mock-transfected cells, RR suppression showed a reduction in the percentage of cells in the S and G2M phases (Fig. 7A). This was correlated with increased cells in the G0/G1 phase by ~13%. Gene expression data following RR depletion in the Kasumi-1 and SKNO-1 cell lines showed that several genes involved in positive and negative regulation of the cell cycle were variably expressed. Upregulated genes included FOXO4 and ING4, and downregulated genes were TPX2, CDC2, CKAP5, IL8, MAD2L1, CCNE2, CCNG2, CDK6, TBRG4 and KPNA2. Isolated MAPK1 suppression also caused an increased growth arrest. This was accompanied with reduction in G2M and S phases, while cells in the G0/G1 phase were increased (Fig. 7A). FACS results for the cell cycle analysis are included in Fig. 7B-F, respectively.

Figure 7. (A) MAPK1, RUNX1-RUNX1T1 and their co-suppressions induces growth arrest. Error bars indicate standard deviations of two independent experiments. P-value of silenced genes vs. mock control was determined by one way ANOVA. (B) FACS results of cell cycle shifts induced by co-knockdown of MAPK1 and RUNX1-RUNX1T1 (siMAPK1 + siRR). (C) FACS results of cell cycle shifts induced by knockdown of MAPK1 (siMAPK1). (D) FACS results of cell cycle shifts induced by knockdown of RUNX1-RUNX1T1 (siRR). (E) FACS results of cell cycle shifts induced by knockdown with negative control siRNA (siMM). (F) FACS results of cell cycle shifts induced by Kasumi-1 cells without siRNA (Mock).

Combined targeting of RR and MAPK1 yielded further reduction of cells in S and in G2M phase by ~40 and 70%, respectively, when compared to the mock cells. This also led to an increased number of cells in the G0/G1 phase by ~25%. Gene ontology for double knockdown of RR and MAPK1, after exclusion of common target genes, demonstrated that several protein coding genes were uniquely responsive to the depletion of MAPK1; they included upregulation of RASSF1, GADD45A, P53 and FBXO6, and downregulation of MAPK11. The results of the GO enrichment analysis are summarised in Table I.

Discussion

The consequence of RR translocation in haematopoietic precursors is characterised by clonal proliferation with reduced capacity to differentiate into committed progenitors and therefore functional myeloid cells. Although the primary genetic aberration t(8;21) found in self-renewing malignant haematopoietic stem cells significantly alters the regulatory processes controlling the growth and differentiation programmes, an associated secondary lesion is often essential for the leukaemic transformation and maintenance. Taken together, both primary and secondary lesions confer a proliferation advantage and impair haematopoietic differentiation in AML. Deregulated signalling in the RAS/RAF/MEK/ERK pathway has often been implicated with leukaemia, particularly in the RAS-RAF node of the pathway (25,26). In this study, our focus was to identify the patterns and networks of genes that were either upregulated or downregulated driving the tumourigenic processes including proliferation, apoptosis, cell cycle and differentiation when RR and MAPK1 were suppressed. Gene expression profiling has played a key role in providing us with important clues to these processes.

We showed that continuous suppression of RR is important for inhibiting cell proliferation, a finding extensively validated and characterised by many researchers (21,27). Our GO enrichment analysis showed that this dampened proliferation was largely due to the downregulation of several genes that are otherwise known to enhance cell proliferation. Most important among them were KIT and FLT3 genes that encode to class III transmembrane receptor tyrosine kinases (RTK), which play a crucial role in haematopoiesis. While the expression of FLT3 in haematopoietic stem cells and progenitors are important to maintain cell survival (28), c-Kit expression by myeloblasts observed in ~80% of patients with AML typically enhances proliferation (29). A negative correlation between the level of c-Kit expression intensity on myeloblasts and the number of leukocytes in the blood of AML patients has been reported suggesting its role in myeloblast egression into peripheral circulation (30). As these RTKs are immediately upstream of the Ras/RAF/MAPK/ERK pathway, their constitutive activation may lead to aberrant signalling, proliferation and differentiation. Leveraging on this abnormal signalling mechanism, several small-molecule tyrosine kinase inhibitors have been developed and investigated as potential FLT3- and c-KIT-targeted therapies for RTKs expressed on myeloblasts in AML. It appears that patients with AML benefit from a synergistic approach of kinase inhibitors used in combination with conventional chemotherapy in controlling proliferation (31). On further dissection, the same set of genes involved in inhibiting proliferation is also involved in other biological processes. Based on the functional and biological connectivity analysis on shared DEGs between cell lines in response to RR suppression, at least eight (HIF1A, ITGB1, BCL2L1, CDK6, CEBPA, IL8, FLT3 and KIT) DEGs were involved in cancer pathways, five genes (IL13RA1, CD40, KIT, FLT3 and IL8) in cytokine-cytokine receptor interactions and three genes (CEBPA, KIT and FLT3) in the AML pathway.

We also showed that the antiproliferative consequence of the co-suppression of MAPK1 and RR was biologically no different from the effect exerted by isolated repression of RR in Kasumi-1 cells. This failure of MAPK1 depletion to augment the level of the antiproliferation effect resulting from RR suppression suggests the lack of coordinative synergy between the DEG sets. Thus delivery of MAPK1 with RR siRNA may not serve as an attractive gene therapy modality to control abnormal proliferation in t(8;21) AML cells. GO enrichment analysis of the BeadChip data from the MAPK1 suppressed Kasumi-1 and SKNO-1 cell lines demonstrated six (6) DEGs with the biological process of regulating cell proliferation. Among them, we found that the expression of the NRAS oncogene (an upstream GTPase in the classical ERK pathway) was suppressed in a MAPK-1-dependent manner illustrating a possible feedback loop within the ERK pathway. A previous study reported that ERK exerts a negative feedback by interfering with RAS activation by phosphorylating son of sevenless (SOS), an immediate early gene in the ERK cascade (32). Another interesting target of MAPK1 associated with the proliferation and apoptosis in Kasumi-1 cells was the transcription factor JUN, a subunit of the activator protein-1 (AP-1) important for transactivation of CDKN2D. Downregulated expression of JUN, in the absence of MAPK1 suggests a cross-talk between the ERK and JNK pathways. A similar observation has been made where ERK and Jun kinases underlie the vascular endothelial cell growth factor-induced proliferation in endothelial cells (33). These observations suggest that MAPK1, the terminal kinase in the ERK signalling pathway, may play a less significant role in the progression of AML cells, and could be less effective as a druggable target than RR in terms of impeding cell proliferation. Another target of MAPK1 was the downregulation of ZBTB16, a transcription factor that regulates the expression of genes involved in cell growth and apoptosis, and may have complemented the reduced proliferation observed in our study.

This study identified a panel of 10 genes whose differential expression exerted an anti-apoptotic effect on leukaemic myeloblast secondary to selective RR silencing. These identified included upregulated BCL2, SPHK2, CFLAR, CD24, NOTCH1, ADAM17, BIRC3 and HMGB1, and downregulated IP6K2 and BECN1 genes. More important were the overexpression of anti-apoptotic BCL2, BIRC3 and CFLAR genes. Studies have shown that enhanced expression of BCL2 in leukaemic myeloblasts offers them a survival advantage by regulating the release of apoptogenic proteins from mitochondria, notably cytochrome c, and resistance to chemotherapy (34,35). This anti-apoptotic characteristic is further strengthened by the increased activity of BIRC3, a member of the IAP family of proteins, that acts as endogenous inhibitor of caspases (36). Furthermore, the loss of RR caused increased expression of CFLAR, a critical anti-apoptotic regulator of apoptosis.

Although the selective RR silencing inflicts an anti-apoptotic phenotype in t(8;21) myeloblasts, MAPK1 suppression reciprocally induces apoptosis while their co-suppression ameliorates the effect (Fig. 5). This relative refractory survival of t(8;21)-positive blasts upon enforced silencing of MAPK1 may be due to the apoptotic mitochondrial changes stimulated by the activity of upregulated TP53 and TNFSF10, and downregulated JUN gene, which regulates the release of cytochrome c and drives the proteolytic caspases. Additionally, NRAS and JUN, members of the classical MAP kinase and JNK pathways, respectively, were downregulated following the treatment of siMAPK1, demonstrating a link between the two pathways. It was previously shown that oncogenic RAS activates c-JUN via a separate pathway from the activation of extracellular signal-regulated kinases (37).

The significant loss of CD34 expression in the RR-suppressed cells suggested that the fusion gene expression exerted an anti-differentiation effect. Biological analysis revealed that 13 genes, in response to RR suppression, were involved in driving this process, and among them at least eight genes (CEBPA, CEBPE, ID2, JMJD6 and IKZF1, CBFB, KIT and CDK6) were directly responsible for myeloid progenitor cell differentiation. Both CEBPA (CCAAT enhancer binding protein α) and CEPBE are myeloid-specific transcription factors that function as crucial regulators of granulopoiesis. Their respective knockout mice have shown to block the differentiation of common myeloid progenitor cells into granulocyte-macrophage progenitor cells accompanied by granulocytopaenia (38,39). Apart from its role in differentiation, the consistent upregulation of myeloid transcription factor CEBPA has been shown to arrests t(8;21) AML cell proliferation through direct inhibition of CDK2 and CDK4 (40). A similar mechanism may be partly responsible for reduced proliferation in our study as RR knockdown resulted in upregulation of CEBPA and downregulation of the G1 kinase CDK6, the homolog of CDK4. The observed increased expression of ID2 subsequent to RR suppression may function at later stages of differentiation of t(8;21) to silence myeloblasts. This conclusion is proposed as previous research has shown that ID2 mRNA is constitutively expressed in more mature myeloid blast cells and the level markedly increased towards terminal myeloid differentiation (41). Notably, we observed that CBFB, which is required for myeloid and lymphoid differentiation, was downregulated with the suppression of RR suggesting that its deficiency does not block the differentiation of myeloid progenitors. The observed loss of CD34 expression is further intensified by the downregulation of CDK6 which otherwise is known to block myeloid differentiation by interfering with RUNX1 DNA binding and RUNX1-C/EBPα interaction (42).

Knockdown of MAPK1, but not RR, exhibited elevated expression of ADA and downregulation of JUN; both of which have been known to confer anti-differentiation effects (43). Therefore, it is not surprising that MAPK1-mediated differential expression of ADA and JUN did not alter the differentiation characteristics of the AML blasts (Fig. 6).

We showed that reduced proliferation upon isolated RR and MAPK1 depletion was paralleled by cell cycle arrest at the G0/G1 phase. A tightly regulated molecular mechanism that controls the accurate transition from G1 phase of the cell cycle to S phase is crucial for eukaryotic cell proliferation, and its de-regulation promotes oncogenesis. Upon siRNA-mediated RR depletion, we showed that eight genes (TBRG4, CCNE2, FOXO4, CDK6, ING4, IL8, MAD2L1 and CCNG2) under the GO category of negative regulation of the cell cycle were downregulated except FOXO4. Noteworthy is that the accumulation of Kasumi-1 cells in the G1 phase of the cell cycle in response to RR suppression was important due to the combined downregulated expression of CDK6 and CCNE2 (cyclin E2). Regulatory protein CDK6 is a catalytic subunit of the protein kinase complex that is important for cell cycle G1 phase progression and G1/S transition. GO enrichment analysis shows that the observed G1 arrest in response to MAPK1 depletion in t(8;21) myeloblasts was the result of increased expression of negative regulators of the cell cycle: RASSF1, FBXO6, DADD45A and P53. RASSF1A is an important human tumour-suppressor protein that inhibits the accumulation of cyclin D1 and induces cell cycle arrest acting at the level of G1/S phase cell cycle progression by engaging the Rb protein family (44). GADD45α-induced cell cycle arrest leads to a reduced cell proliferation rate. This is achieved by its interaction with cyclin-dependent kinase inhibitor p21waf/cip/mda-6 leading to cell cycle arrest at G1/S transition due to disruption in the formation of a CDK/cyclin complex (45). Most importantly, depletion of MAPK1 significantly increased the expression of P53 and leads to cell cycle arrest and apoptosis as observed in this study.

The present study primarily utilised siRNAs to silence target genes. Recently, more elaborate methods to induce gene knockdown have been utilized. These include gene editing techniques such as CRISPR (46–48) which gives higher specificity and minimises off targets. Nevertheless, gene silencing using small interfering RNA (siRNA) should not be discounted altogether given its simplicity and reproducibility. Other factors that should be considered in gene silencing experiments for minimizing off targets include using more than one target siRNA for each targeted gene or having a mismatch control. Overexpression studies of MAPK1 merits further investigation and could be carried out to ascertain the link between RR and MAPK1. Furthermore, the expression of other partners in the Ras/Raf/MEK/ERK pathways could be explored both at the gene and protein levels to shed some mechanistic insight regarding the interactions between MAPK1, RR and leukemogenesis.

In conclusion, in the present study, we showed that MAPK1 simultaneously promotes myeloid cell proliferation and differentiation by cell cycle progression and blocking apoptosis. GO enrichment analysis showed that the processes involved in cell cycle, cell proliferation, and cell differentiation were affected by MAP1 downregulation.

Acknowledgements

Not applicable.

Funding

This study was supported by Research University grants 1001/CIPPT/812095 and 1001/CIPPT/813064 from Universiti Sains Malaysia.

Availability of data and materials

The datasets used during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

SAASB, OH and NMY conceived and designed the study. SAASB performed the experiments. MS and EJM wrote the paper and analysed the data. EJM, NMY and OH reviewed and edited the manuscript. NMY funded the project. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors state that they have no competing interests.

References