Multiomics of azacitidine-treated AML cells reveals variable and convergent targets that remodel the cell-surface proteome

Abstract

Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are diseases of abnormal hematopoietic differentiation with aberrant epigenetic alterations. Azacitidine (AZA) is a DNA methyltransferase inhibitor widely used to treat MDS and AML, yet the impact of AZA on the cell-surface proteome has not been defined. To identify potential therapeutic targets for use in combination with AZA in AML patients, we investigated the effects of AZA treatment on four AML cell lines representing different stages of differentiation. The effect of AZA treatment on these cell lines was characterized at three levels: the DNA methylome, the transcriptome, and the cell-surface proteome. Untreated AML cell lines showed substantial overlap at all three omics levels; however, while AZA treatment globally reduced DNA methylation in all cell lines, changes in the transcriptome and surface proteome were subtle and differed among the cell lines. Transcriptome analysis identified five commonly up-regulated coding genes upon AZA treatment in all four cell lines, TRPM4 being the only gene encoding a surface protein, and surface proteome analysis found no commonly regulated proteins. Gene set enrichment analysis of differentially regulated RNA and surface proteins showed a decrease in metabolic pathways and an increase in immune defense response pathways. As such, AZA treatment led to diverse effects at the individual gene and protein levels but converged to common responses at the pathway level. Given the heterogeneous responses in the four cell lines, we discuss potential therapeutic strategies for AML in combination with AZA.

Keywords: AML; azacitidine; multiomics; surface proteomics; target discovery.

Fig. 1.

AZA treatment drives global DNA demethylation among all four AML cell lines. (A) Vehicle-treated cell lines have a bimodal distribution of genome-wide beta values (kernel density estimation). (B) Vehicle-treated cells share a high proportion of hypermethylated and hypomethylated sites. Overlapping hypermethylated sites (red, beta values >0.8) and hypomethylated sites (blue, beta values <0.2) are indicated by upward and downward bars, respectively, in the vertical bar graph. The specific overlapping groups are indicated by the black solid points below the bar graph. Total hypermethylated and hypomethylated sites found in each cell line are indicated in the horizontal bar graph. (C) AZA-treated cells have decreased hypermethylated beta values indicating DNA demethylation. (D) DNA demethylation in AZA-treated cells is shown by median change in beta value for KG1a (−0.097), HL60 (−0.28), HNT34 (−0.132), and AML193 (−0.099). (E) A high proportion of demethylated sites are common among the four cell lines, indicated by downward bars in the vertical bar graph (decrease in beta value >0.1, false discovery rate adjusted P < 0.05).

Fig. 2.

AZA treatment induces unique and subtle transcriptome changes in the four AML cell lines. (A) Gene expression profiles are similar among the four cell lines at baseline. Overlapping highly (top tertile) and lowly (bottom tertile) expressed genes are indicated by upward (red) and downward (blue) bars, respectively, in the vertical bar graph (n = 20,517 unique genes mapped from >54,000 probesets). (B) Distinct biological states shown by gene set variation analysis (GSVA) of the four cell lines at baseline levels using the MSigDB hallmark gene sets (variation score >0.2 in at least one cell line). (C) Most differentially regulated genes induced by AZA are unique to each cell line, and only six genes are common among the four cell lines. Up-regulated genes and down-regulated genes are indicated by the upward (red) and downward (blue) bars, respectively (fold change >2 and adjusted P value <0.05). Total differentially regulated genes for each cell line are indicated in the horizontal bar graph. (D) Common biological processes are enriched upon AZA treatment. Gene set enrichment analysis (GSEA) was performed using Hallmark (H), Reactome (R), and KEGG (K) pathways (GSEA normalized effect size >1 or <−1).

Fig. 3.

Surface proteome changes induced by AZA treatment in the four AML cell lines. (A) Surface proteins identified in the four vehicle-treated AML cell lines. Overlapping proteins identified are indicated in the vertical bar graph and the specific overlapping groups are indicated by the black solid points below the bar graph. Total surface proteins identified in each cell line are indicated in the horizontal bar graph. (B) CD markers identified by surface proteomics in vehicle-treated sample. The heat map is shaded from yellow to red to reflect estimated abundance (logarithmic sum of peptide intensities for each protein). (C) AZA induced unique changes on the cell-surface proteome. Overlapping up-regulated and down-regulated proteins are indicated by upward and downward bars, respectively, in the vertical bar graph [median stable isotope labeling by amino acids in cell culture (SILAC) ratio >2 or <2, P value <0.05]. The specific overlapping groups are indicated by the black solid points below the bar graph. Total differentially regulated surface proteins for each cell line are indicated in the horizontal bar graph showing variable surface proteome regulation by AZA. No commonly regulated protein was identified among the AZA-treated cell lines. (D) Proteins with significant changes in at least two cell lines are shown to illustrate distinct regulation of surface proteins by AZA (n = 13). (E) Comparison of surface proteomics data between AZA treatment and all-trans retinoic acid (ATRA) treatment in HL60 cells. Pearson correlation between the two datasets is 0.44. Data for ATRA treatment in HL60 was obtained from Hofmann et al. (18).

Fig. 4.

Omics comparison between methylome, transcriptome, and surface proteome in AML cells treated by AZA. (A) Hierarchal clustering of methylome, transcriptome, and surface proteome data were dominated by differences between cell lines rather than differences between vehicle and AZA treatment. (B) Representative RNA expression profiles of all genes (green), of all genes annotated to be surface proteins (blue), and of genes identified by mass spectrometry experiment (cyan) illustrate that surface proteins have higher gene expression levels. (C) Correlation of changes between gene and protein expression range from 0.44 for HL60 cells to 0.71 for HNT34 cells (r, Pearson correlation). Significantly up- and down-regulated genes and proteins are highlighted in red and green, respectively (P value <0.05 for both gene and protein expression profile). For KG1a and HL60, correlation was calculated after removing two and one outlier points with ${\mathrm{l}\mathrm{o}\mathrm{g}}_2$ (fold change of protein expression) >0, respectively. Dashed lines (y = x) are drawn for reference. (D) Comparison of omics datasets for genes with significant protein changes in at least one cell line. ${\mathrm{L}\mathrm{o}\mathrm{g}}_2$ fold changes are plotted for protein and gene expression, and scaled average changes in beta values for CpG sites within 1,500 bp of the transcriptional start site are plotted for methylation changes.