The Molecular Role of HIF1α Is Elucidated in Chronic Myeloid Leukemia

Abstract

Chronic myeloid leukemia (CML) is potentially fatal blood cancer, but there is an unmet need to discover novel molecular biomarkers. The hypothesis of this study aimed to elucidate the relationship of HIF1α with the redox system, Krebs cycles, notch1, and other regulatory proteins to better understand the pathophysiology and clinical relevance in chronic myeloid leukemia (CML) patients, as the molecular mechanism of this axis is still not clear. This study included CML patient samples (n = 60; 60: blood; 10: bone marrow tissues) and compared them with healthy controls (n = 20; blood). Clinical diagnosis confirmed on bone marrow aspiration, marrow trephine biopsy, and BCR/ABL1 translocation. Cases were subclassified into chronic, accelerated, and blast crises as per WHO guidelines. Molecular experiments included redox parameters, DNA fragmentation, Krebs cycle metabolites, and gene expression by RT-PCR/Western blot/LC-MS, PPI (STRING), Pearson correlation, and ROC curve analysis. Here, our findings show that p210/p190BCR/ABL1 translocation is common in all blast crisis phases of CML. Redox factor/Krebs oncometabolite concentrations were high, leading to upregulation and stabilization of HIF1α. HIF1α leads to the pathogenesis in CML cells by upregulating their downstream genes (Notch 2/4/Ikaros/SIRT1/Foxo-3a/p53, etc.). Whereas, downregulated ubiquitin proteasomal and apoptotic factors in CML pateints, can trigger degradation of HIF1α through proline hydroxylation. However, HIF1α showed a negative corelation with the notch1 pathway. Notch1 plays a tumor-suppressive role in CML and might have the potential to be used as a diagnostic marker along with other factors in CML patients. The outcome also revealed that oxidant treatment could not be effective in augmentation with conventional therapy because CML cells can enhance the levels of antioxidants for their survival. HIF1α might be a novel therapeutic target other than BCR/ABL1 translocation.

Keywords: BCR/ABL1 translocation; HIF1α; Krebs metabolite; chronic myeloid leukemia (CML); hypoxia; notch signaling pathway; redox system.

Figure 1

Clinical diagnosis was confirmed on bone marrow aspiration, marrow trephine biopsy, and BCR/ABL1 translocation. (A, B) Representative images of Leishman staining of the blood of CML patients, where blast crisis cells are shown by the red arrow. (C) Flow cytometry results show the blast cells in various patients of CML patients, which were plotted on CD45 with side scatter and plotted on the marker and marker vs marker for the confirmation of CML (see also Figure S1 ). (D) the first two rows of the scattered plot show the myeloid blast crisis cells, and other two rows show lymphoid blast crisis cells. (E) the scattered plot shows the blast crisis phase of chronic myeloid leukemia. (F) shows BCR/ABL1 translocation in CML patients, where lanes 1-5 show p210/p190 translocation, and lane M shows the standard of all kinds of translocation. (G) CD64 marker plot showing that it is present in all kinds of cells in CML patients and responsible for the production of ROS intermediates.

Figure 2

Redox system in the pathogenesis of CML and DNA fragmentation. (A–I) Significant oxidative parameters and their overexpression were found in CML patients. (J–O) Antioxidant levels were also found to be high in CML due to their pathogenesis and protection from apoptosis. Both oxidative and antioxidative factors were found to be high and make an equilibrium for survival. (P, Q) shows that the integrity of DNA was maintained when both parameters were upregulated, which triggered morphological changes in CML cells but was not able to damage DNA integrity. All the data are the mean ± SD, ****p<0.00001, ***p<0.0001, **p<0.001; Student’s t test (paired).

Figure 3

Krebs metabolite in CML progression. (A–C) Krebs cycle oncometabolites in CML were quantified by colorimetry. Malate, fumarate, and succinate levels were found to be high in CML patients, with fold changes of 1.3609, 1.709, and 1.7231, respectively. All data are mean ± SD, ****p<0.00001, Student’s t-test (paired).

Figure 4

Gene expression of HIF1α, Notch1, and their associated genes in CML. (A–X) Gene expression was quantified by RT-PCR in CML (n = 60) and healthy controls (n = 20), where HIF1α and its associated genes were found to be upregulated and Notch 1 was downregulated in blast crisis cells of CML patients. (Y) The heat map shows the relative expression of all the genes in CML (class indicated Control and CML), and the intensity of color shows the expression of genes. All quantitative data are the mean ± SD, ****p<0.00001, Student’s t-test (paired).

Figure 5

Protein expression of HIF1α, Notch1, and their associated genes in CML. (A–D) Western blot and densitometric analysis of the proteins. In healthy controls, we performed Western blotting in blood and serum, wherein CML was performed Western blotting in bone marrow tissue, serum, and blood and compared all the results with healthy controls. All quantitative data are mean ± SD, ***p<0.001, ANOVA.

Figure 6

High-throughput screening of HIF1α, Notch1, and all the proteins in CML by LC/MSMS. (A) SDS-PAGE shows the integrity of the protein, lanes 1 and 2 show the healthy control, lanes 3 and 4 show the CML, and lane M shows the protein ladder. (B, C) Shows the 37 protein sets that are relative compared in both groups through a heatmap. (D–F) Comparison of HIF1α, Notch1, and their associated proteins, where the graph shows that apoptotic and ubiquitin proteasomal protein sets are downregulated under CML conditions. (G, H) Vanin 1 expression was negligible in CML cases, which are connecting links between inflammation and oxidative stress, and their equilibrium was disrupted in blast crisis cells. (I) The graph shows the proteins that were only expressed in CML cases. (J) Estimation plot shows that the peptides of the abovementioned proteins were significantly lower under CML conditions. (K) Protein-protein interaction of all the proteins involved in this study and divided into the different domains where HIF1α acts as a master regulator of all the genes. All quantitative data are the mean ± SD, ****p<0.0001, ***p<0.001, ANOVA and chi-square test.

Figure 7

Pearson correlation between HIF1α and all the factors. (A–G) HIF1α has a strong positive correlation with ROS (0.922, p = 0.01), total NO (0.99, p = 0.01), nitrite (0.94, p = 0.01), nitrate (0.873, p = 0.01), and superoxide ions (0.827, p = 0.01), while HIF1α does not show a strong correlation with other redox factors. However, HIF1α had a positive correlation with malate (0.751, p = 0.01) and fumarate (0.871, p = 0.01) and a moderate positive correlation with succinate (0.562, p = 0.01). HIF1α also showed a strong positive correlation with Notch 2, Notch 4, Ikaros, p53, Snail 1, CD-11d, TNFα, CCAR1, SIRT1, Foxo-3a, HSF1, IL-1β, UQCR2, and PSMB6 (1.00, p = 0.001) and a strong negative correlation with Notch1, Lgd, CDH1, GAPDH, UBQLN2, RPS18/18a, and PGGT1B (-1.00, p = 0.001). **p >0.001; ***p >0.0001.

Figure 8

ROC for all the factors in CML samples. (A–S) AUROC shows that HIF1α, Notch 1, Notch 4, Lgd, Foxo-3a, p53, TNFα, CDH1, CD11d, GAPDH, malate, and fumarate have 100% sensitivity and 100% specificity, while Notch 2 (sensitivity- 80%, specificity- 90%), Ikaros, (sensitivity- 100%, specificity- 90%), HSF1 (sensitivity- 90%, specificity- 100%), CCAR1 (sensitivity- 90%, specificity- 90%), Snail1 (sensitivity- 60%, specificity- 90%), succinate (sensitivity- 90%, specificity- 90%), and Redox indicator Fe²⁺ (sensitivity- 90%, specificity- 100%). See also Figure S2 . (T) Overall mechanism explained in HIF1α/Notch1 pathway.