Analysis of ROS-Responsive Genes in Mutant RAS Expressing Hematopoietic Progenitors Identifies the Glycolytic Pathway As a Major Target Promoting Both Proliferation and Survival
Authors: Hopkins Goitseone Lucy , Robinson Andrew J , Hole Paul S , Darley Richard L. , Tonks Alex 
Published: October 2019

Abstract
Acute myeloid leukemia (AML) is a heterogeneous clonal disorder with a generally poor clinical outcome. Previously we have shown that over-production of reactive oxygen species (ROS) occurs in >60% of AML patients due to NOX oxidase activation and that this promotes growth factor independent proliferation of AML blasts (Hole et al., 2013). Normal CD34+ cells overexpressing mutant NRAS serve as a model for this since these cells also overproduce ROS through NOX activation, which in turn promotes their proliferation (Hole et al., 2010). We used this model to investigate the mechanism by which ROS promote proliferation by examining the effect of ROS on gene expression. CD34+ expressing NRASG12D showed an 8-fold (p<0.05) increase in ROS production compared to empty vector controls. DPI, a NOX inhibitor, virtually ablated ROS production (>90%; p<0.05) and also selectively inhibited proliferation of CD34+-NRAS cells (>60%; p<0.05). We next compared the gene expression profile (GEP) of NRAS and control cells ±DPI to determine the ROS-specific gene expression profile (Affymetrix Human Exon 1.0ST). NRAS changed the expression of 342 genes (>1.2 fold; p<0.05) of which 24 were specifically attributed to ROS production (Table 1). Most of these were associated with metabolic change; particularly glycolysis (p<0.0001 n=4). Consistent with this we found a doubling in the level of extracellular lactate production (indicating increased glycolysis) from NRAS cells compared to controls (n=4). Extracellular ROS (generated from GOX) also directly promoted a 1.3 fold increase in lactate production (n=4). These data suggest that ROS directly promotes glycolysis in hematopoietic cells. To examine this in AML we analysed a GEP database of 139 AML patients; this showed that those with high ROS (defined by high NOX2 oxidase expression; Hole et al., 2013) had a distinct profile of glycolytic enzyme overexpression, particularly ALDOC (r=0.4; p=2x10-25), GPI (r=0.4; p=2x10-8) and FBP1(r=0.7; p=5x10-8). These are amongst the most significant ROS-responsive genes in Table 1 and suggest that promotion of glycolysis through extracellular ROS production is also seen in AML blasts.
To establish the functional significance of upregulated expression of glycolytic enzymes, we focused on the aldolase enzyme, ALDOC, since it showed the biggest induction with ROS and because overexpression of this enzyme has been recently associated with elevated glycolysis and poor prognosis in primary AML (Chen et al., 2014). We found that ALDOC was directly induced (2 fold) by physiological levels (150 nM/hour) of ROS in both normal CD34+ cells and in AML cell lines. We next examined the effect of stable ALDOC knockdown in 3 myeloid leukemia cell lines: Mv4;11 (1.5 fold reduction at the protein level), K562 (3.5 fold) and THP-1 (1.5 fold). Knockdown was associated with a reduction in proliferation in Mv4;11 and THP-1 cells (2- and 5-fold respectively; p<0.05 n=3) and also reduction in survival of THP-1 (1.7 fold; p<0.05, n=3).
These data have identified ROS-responsive genes in CD34+ hematopoietic cells and show for the first time that a major target of ROS are enzymes of the glycolytic pathway. We also show evidence that ROS promotes glycolysis in both cell lines and in AML patients and that myeloid leukemia cells show dependency on ALDOC, for their growth and survival. Given the frequent overexpression of ROS in primary AML, these data provide a plausible mechanism for the enhanced glycolysis seen in AML (Chen et al., 2014) and suggest that agents restoring the redox environment could be used to correct metabolic imbalances which contribute to treatment resistance in this disease.
Table 1. Effect of ROS on gene expression Gene Gene expression (fold and direction of change) p-value Protein validation (fold and direction of change where available) Process ALDOC +4.3 1×10-6 +2 Glycolysis ENO2 +2.6 1×10-4 +1.5 FBP1 +1.8 2×10-7 GPI +1.5 2×10-6 PFK-1 +1.4 9×10-6 GATM +2.8 3×10-5 +2 Metabolism SULF2 +2.1 2×10-5 CKB +2.1 7×10-5 ASPH +1.4 1×10-4 PTPRD +2.2 2×10-5 +2 Signal transduction KIT -2.1 5×10-5 0 CD32 +1.5 7×10-5 0 TNS1 +1.7 8×10-6 REC8 +1.4 6×10-5 STARD8 +1.4 3×10-5 CMTM8 -1.2 1×10-4 CNR2 -3.6 2×10-6 0 CD34 +1.7 4×10-5 0 Other CITED1 +1.7 6×10-5 +1.5 CYTL1 -1.9 6×10-6 CACNB1 +1.3 1×10-4 SLC6A8 +1.7 1×10-5 JAKMIP2 +1.2 3×10-5 WDR54 +1.8 5×10-6

Disclosures
No relevant conflicts of interest to declare.