Microvesicle inhibition enhances the therapeutic effects of ATRA in acute promyelocytic leukemia cells via changes in miRNAs: the promising antileukemic potential of imipramine

Abstract

Extracellular vesicles (EVs) represent an essential role in cancer progression through intercellular communication. Therefore, the use of EV formation inhibitors could be a profitable therapeutic strategy in various types of cancer, including leukemia. Imipramine, a tricyclic antidepressant, can block EV formation by inhibiting acid sphingomyelinase. Additionally, other crucial players in cancer progression are microRNAs, which regulate molecular mechanisms at the post-transcriptional level. Here, to potentiate the therapeutic effect of all-trans retinoic acid (ATRA) in acute promyelocytic leukemia (APL), we investigated the effect of imipramine as a microvesicle inhibitor in combination with ATRA for the treatment of APL-derived NB4 cells. Our results declared that imipramine reduced the viability and metabolic activity of ATRA-treated NB4 cells after 48 h. In addition, flow cytometry results highlighted that imipramine induced cytotoxicity through G2/M phase arrest followed by apoptosis. Moreover, we discovered that the antileukemic effects of imipramine were associated with inhibiting microvesicle release and miRNA alteration. Based on bioinformatics methods, we predicted two miRNAs, including hsa-miR-4498 and hsa-miR-3156-5p, which target PML. Additionally, we selected miR-23a-5p, miR-19a-3p, and miR-181b-5p based on relevant studies and subsequently predicted their target genes. The real-time PCR results revealed that the expression level of these miRNAs increased after treatment with imipramine. Moreover, functional enrichment analysis of target genes demonstrated that these genes are involved in cancer-related pathways, including MAPK, FOXO, AMPK, and cellular senescence. Given the significant efficacy of imipramine in potentiating the anti-tumor effects of chemotherapeutic drugs, it can be considered as a potential treatment agent for APL.

Keywords: Bioinformatics; Extracellular vesicle; Imipramine; Leukemia; MicroRNA.

Fig. 1

Cytotoxic effect of imipramine in combination with ATRA on viability and metabolic activity of NB cells. A The effect of various doses of imipramine on NB4 cells for 24, 48, and 72h. B Treatment of NB4 cells with imipramine and ATRA reduced the cell viability and C metabolic activity. D The calculation of the CI index represented a synergism effect of imipramine at the concentrations of 15 and 20 μM with ATRA (1 μM). E Imipramine and ATRA had no cytotoxic effect on PBMCs (normal cells). F Imipramine and combinational dose decreased the cell count of NB4 cells after 48h. Values are given as mean ± standard deviation of three independent experiments (*P < 0.05, **P < 0.01, ***P < 0.001, relative to untreated cells)

Fig. 2

Imipramine could increase the apoptotic effect of ATRA. A,B The flow cytometry assay indicated that imipramine enhanced the apoptotic cells from 3.82% in ATRA-treated to 35.63% in imipramine + ATRA-treated group. Values are given as mean ± standard deviation of three independent experiments (**** P < 0.0001, relative to untreated cells)

Fig. 3

The effect of imipramine/ATRA on the proliferative capacity of APL cell line (NB4). The DNA content analysis showed that imipramine reduced the proportion of NB4 cells in the S phase and induced G2/M arrest. Values are given as mean ± standard deviation of three independent experiments (*P < 0.05, ****P < 0.0001, relative to untreated cells)

Fig. 4

Imipramine reduced the MV release of NB4 cells. A The result of DLS revealed that the size of isolated microvesicles is 450–580 nm. B The BCA assay indicated that imipramine reduced the MV release (**** P < 0.0001, relative to untreated cells)

Fig. 5

Imipramine changed the expression of 5 miRNAs. A Imipramine and combinational dose increased the expression level of predicted miRNAs (miR-4498 and miR-3156). B Expression level of miR-23a-5p, miR-19a-3p, and miR-181b-5p. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, relative to untreated cells)

Fig. 6

Identification of target genes and PPI network construction. A The Venn diagram indicated that 561 genes are the common between miRTarBase and TarBase databases. B PPI network analysis represented the relation between target genes. Hub genes have different color

Fig. 7

Functional enrichment analyses of target genes. KEGG pathway and GO enrichment analyses of target genes revealed the signaling pathways, molecular function (MF), biological process (BP), and cellular component (CC). The dot size showed the number of target genes, and the color of dots represented the p.adjust