Introduction

Standard hematopoietic stem/progenitor cell transformation to AML cells requires metabolic reprogramming ¹. AML cells rely heavily on glucose for unchecked proliferation. Using ¹⁸F-Fluoro-deoxy-Glucose (¹⁸FDG) as a marker, Cunningham et al. detected high glucose uptake in the bone marrow of AML patients, and pyruvate and 2-hydroxy-glutarate concentrations negatively correlate with patient survival rates². Dysregulated metabolic enzyme and mitochondria activities have been reported to be the causes of chemoresistance to AML ^3,4 as well as solid tumors ⁵. Metabolites such as acetyl-CoA, α-ketoglutarate, vitamin C (aka ascorbic acid), and S-adenosyl-L-methionine (SAM) are cofactors for histone and DNA modifications. Thus, metabolic reprogramming transforms the epigenetic landscape in leukemia cells. Mutations in isocitrate dehydrogenases, IDH1/2, produce 2-hydroxyl-glutarate (2-HG) instead of α-ketoglutarate. 2-HG inhibits demethylases that erase methylation marks on histones and DNA, and hydroxylases such as FIH (factor inhibiting HIF) in leukemia and glioblastoma ⁶. However, mutations of metabolic enzymes in cancer are relatively rare. Usually, metabolic reprogramming is achieved mainly by expressing oncogenic transcription factors such as p53 mutants, HIF1, and FOXOs ^7–10. Dysregulation of signaling pathways such as KRAS mutations ^5,11 and upregulation of the mTOR pathway during leukemogenesis ¹² also alter metabolic pathways in tumorigenesis. Nevertheless, how epigenetic regulators involved in leukemogenesis regulate metabolic reprogramming still needs more research.

The protein arginine methyltransferase (PRMT) family has nine members, with PRMT1 responsible for most of the enzymatic activity in mammalian cells. PRMT1 is an epigenetic regulator via methylation of histone H4 and transcription factor RUNX1 ^13,14. The oncogenic roles of PRMT1 has been demonstrated in many types of solid cancers ^15–24. The importance of PRMT1 in leukemia has been shown in FLT3-ITD, AML1-ETO and MLL-EEN-associated acute myeloid leukemia and lymphoid leukemia ^25–29. Targeting PRMT1 is effective in treating leukemia with splicing factor mutations ³⁰. Yet, how PRMT1 is involved in cancer metabolic reprogramming has not been explored, albeit the known role of PRMT1 in metabolic regulation in model organisms. Phosphorylation of Hmt1 (PRMT1 ortholog in yeast) controls cell cycle progression in response to nutrition signals ^31,32. PRMT1 in Caenorhabditis elegans and Trypanasome is responsible for methylation of proteins inside mitochondria, although PRMT1 is not inside mitochondria, while PRMT1-null worms have dysfunctional mitochondria ^33,34. In Trypanosomes, PRMT1 promotes glycolysis and is required for virulent infection ³⁵.

AMKL (acute megakaryoblastic leukemia) is a subtype of AML with leukemia cells stuck at the differentiation stage of immature megakaryocytes. AMKL is a rare leukemia often associated with Down syndrome. In cases not related to Down syndrome, AMKL is caused by chromosomal translocations. Although AMKL can occur in adults, AMKL occurs more commonly in children ³⁶. Chromosomal translocation t(1;22) that generates RBM15-MKL1 fusion protein was discovered in childhood AMKL ^37,38. RBM15-MKL1 is a fatal disease without available targeted therapy.

Copious expression of PRMT1 is a poor prognostic marker for AML ^39,40. Furthermore, PRMT1 is expressed at an even higher level in AMKL than in other types of AML⁴⁰. Constitutive expression of PRMT1 blocks terminal MK differentiation ⁴⁰, while inhibition of PRMT1 activity promotes terminal MK differentiation⁴¹. Thus, we hypothesize that inhibiting PRMT1 activity could be a pro-differentiation therapy for AMKL. A leukemia cell line called 6133 is derived from Rbm15-MKL1 knock-in mice. When transplanted, 6133 cells can cause AMKL with low penetrance ⁴². Using this leukemia mouse model; we report here that the elevated level of PRMT1 maintains the leukemic stem cells via upregulation of glycolysis and that leukemia cells with high PRMT1 expression are vulnerable to the inhibition of fatty acid metabolic pathways.

Results

PRMT1 promotes the progression of RBM15-MKL1-initiated leukemia

The 6133 cells can be transplanted into recipient mice to induce low penetrant leukemia with symptoms closely recapitulating human AMKL⁴². To find additional factors needed to transform 6133 cells fully, we have reported a fluorescent probe (E84) that can be used to sort live cells according to PRMT1 protein concentrations ⁴³ (Supplemental Figure 1). We sorted 6133 cells into two populations for bone marrow transplantation (BMT) according to E84 staining intensities (Figure 1A). All mice that received 6133 cells expressing higher levels of PRMT1 developed leukemia and died rapidly, while 6133 cells expressing lower levels of PRMT1 did not develop leukemia (Figure 1B). Consistently, a higher percentage of leukemia cells were detected in bone marrow and peripheral blood in recipient mice transplanted with E84-high (i.e., density staining of E84) 6133 cells according to FACS analysis (Figure 1C).

Figure 1.

Given that E84-high cells can initiate leukemia, we introduced PRMT1 into 6133 cells (aka 6133/PRMT1 cells) using a lentivirus vector. Overexpression of PRMT1 rendered 6133/PRMT1 cells to increase in a cytokine-independent fashion in cell culture (Figure 1 D), and recipient mice transplanted with 6133/PRMT1 cells developed leukemia and died within 25 days (Figure 1 E&F). Although PRMT1-mediated methylation triggers the degradation of RBM15, PRMT1 overexpression does not affect the stability of the RBM15-MKL1 fusion (Supplemental Figure 2 A&B). The leukemic mice displayed splenomegaly. Intriguingly, the leukemia mice were paralyzed with observable spinal bleeding during dissection, although the bone density was still normal (data not shown). In a xenograft model with human RBM15-MKL1 leukemia cells, leukemia also caused spinal bleeding ⁴⁴. Collectively, PRMT1 is required for the initiation of frank AMKL expressing RBM15-MKL1 fusion protein and leukemia initiated by 6133/PRMT1 cells in mice mimics several features of human AMKL.

MS023, a PRMT1 inhibitor, cures mice with Rbm15-MKL1-initiated leukemia

MS023 was reported to be a potent inhibitor of Type-I PRMTs including PRMT1 ^41,45. Furthermore, MS023 has been tested safe on mice at 80 mg/kg of body weight ^29,41. The 6133/PRMT1 cells were intravenously injected into sub-lethally irradiated recipient mice. A week after bone marrow transplantation (BMT), we injected intraperitoneally MS023 every other day for a month. Notably, while the mice in the untreated group exhibited rapid sickness and developed moribund symptoms, such as severe weight loss and rear limb paralysis, within 30 days, the majority of mice treated with MS023 remained healthy and symptom-free. MS023 treatment significantly alleviated leukemia burden, as shown by the FACS analysis of the percentages of leukemia cells in bone marrow and peripheral blood (Figure 2D). We also validated that the bone marrow cells from MS023-treated mice have reduced global levels of arginine methylation (Supplemental Figure 1C). Leukemia-associated splenomegaly was also relieved in MS023-treated mice (Figure 2D). Kaplan-Meir curves shew that the MS023-treated group was literally cured after120 days (Figure 2C), with only residual leukemia cells detected in bone marrow and peripheral blood (Figure 2D&E).

Figure 2.

Expression of c-MPLW515L mutant in 6133 cells can render the 6133 cells fully penetrant for leukemia ⁴², and in vitro MS023 treatment reduced proliferation (Figure 2F). We then transplanted 6133/cMPLW515L cells into the congenic mice. In Figure 2 G-I, we demonstrated that MS023 also cures leukemia, which argues that PRMT1 can be a valid target for leukemia or myeloid proliferative disease driven by c-MPLW515L. Collectively, these data showed that PRMT1 is required to maintain Rbm15-MKL1-initiated leukemia in mice; targeting PRMT1 pharmacologically is an effective approach to treat leukemia.

PRMT1 promotes glycolysis in leukemia cells

Given that the role of PRMT1 in metabolic regulation has been well-documented, we then investigated whether PRMT1 is involved in transforming 6133 cells through metabolic reprogramming. To assess this, we conducted Seahorse assays to measure the changes in ECAR (extracellular acidification rate) in 6133/PRMT1 cells compared to the parental 6133 cells. The ECAR curve of 6133/PRMT1 cells was elevated compared to that of 6133 cells (Figure 3A). Interestingly, the addition of FCCP, which uncouples oxidative phosphorylation from the TCA cycle, did not increase acidification levels.

Figure 3.

Similarly, antimycin did not cause a significant drop in acid concentration. Remarkably, we treated 6133 cells with MS023 overnight prior to Seahorse assays and observed a reduction in ECAR levels (Figure 3B). These findings suggest that PRMT1 is responsible for the observed increase in acidification. In this experiment, glycolysis contributes the most to acidification in the 6133 leukemia cells, as the addition of mitochondrial respiratory inhibitors only moderately reduces acidification levels according to the principles outlined by Divakaruni et. al. ⁴⁶. Lactate dehydrogenase A (LDHA), the final key enzyme in glycolysis that converts pyruvate to lactate for NAD+ regeneration, was found to be influenced by PRMT1 overexpression. Specifically, PRMT1 overexpression stimulated the tyrosine phosphorylation of LDHA, thereby activating its enzymatic activity ⁴⁷ despite an overall decrease in LDHA levels (Figure 3C). Consistently, when we directly measured the intracellular and extracellular lactate levels of 6133 and 6133/PRMT1 cells, we observed that 6133/PRMT1 cells not only released more lactate into the medium but also had higher intracellular lactate levels (Figure 3D). We therefore concluded that PRMT1 promotes cellular glycolysis and lactate production, which predominantly contributes to the observed increase in acidification.

PRMT1 reduces oxygen consumption in mitochondria

The Seahorse assays have demonstrated that the cellular OCR (oxygen consumption rate) was reduced in 6133 cells. Additionally, it was observed that these cells had a limited capacity for reserve respiration, as the maximum respiration level was nearly identical to the basal OCAR level, regardless of the expression levels of PRMT1 (Figure 4A). Conversely, when the 6133/PRMT1 cells were pre-treated with MS023 overnight before the Seahorse assays, there was an increase in mitochondrial oxygen consumption compared to the non-treated controls (Figure 4B). These findings further support the notion that PRMT1 plays a role in mediating metabolic reprogramming by enhancing glycolysis and reducing mitochondrial oxygen consumption.

Figure 4.

We have demonstrated that PRMT1 increases the number of mitochondria in MEG-01 cells, an acute megakaryocytic leukemia cell line (Zhang et al., 2015). In this experiment, FACS analysis with MitoTracker staining (Figure 4C) and real-time PCR analysis of mitochondrial DNA (Figure 4D) further validated that PRMT1 upregulated the mitochondria biogenesis in 6133 cells. H2DCFDA staining demonstrated that PRMT1 elevated global ROS levels in 6133 cells (Figure 4F). Intriguingly, when we performed Mito-Sox staining for ROS levels inside mitochondria, we found reduced levels of ROS (Figure 4G). The data implies that most of the ROS detected by H2DCFA may be generated from cytoplasm rather than from mitochondria, which is also agreeable with the Seahorse data that oxidation consumption in mitochondria is reduced (Figure 4A). Next, we performed TMRE staining for mitochondrial membrane potential. 6133/PRMT1 cells have reduced staining, which implies that PRMT1 reduces membrane potential (Figure 4E). This result also cross-validated the Seahorse findings that PRMT1 reduced mitochondrial oxygen consumption.

Given that the cytoplasmic ROS level was significantly raised upon PRMT1 expression, we measured the ratios of two redox pairs: glutathione and NADP⁺. Intriguingly, activation of PRMT1 increased the ratios of both GSH/GSSG and NADPH/NADP⁺. Given the essential roles of GSH and NAPDH in biomass synthesis, PRMT1 upregulation may support anabolism, consistent with PRMT1’s role in supporting cell proliferation.

Metabolomic analysis of 6133/PRMT1 cells

To further probe the complexity of PRMT1-mediated metabolic reprogramming, we established a new 6133 cell line that can conditionally express PRMT1 upon adding doxycycline to the medium. We compared the metabolomic status of 6133 cells before and after 12 hours of PRMT1 induction. The cells were grown in standard RPMI1640 medium with 10% fetal bovine serum, with high concentrations of glucose and essential amino acids for 12-hour growth. We repeated the metabolomic analysis strictly five times. Principle core analysis indicated that PRMT1 caused a significant shift in metabolite profiles (Figure 5A,B). Notably, PRMT1 activation led to increased ATP production and the accumulation of succinyl-CoA, alanine, serine, and short-chain fatty acids. Conversely, it resulted in a depletion of SAM, aspartic acid, nicotinamide,succinyl-homoserine, oxidized glutathione (which is consistent with the increased ratio of GSH/GSSG shown in Figure 4H), and alpha-ketoglutarate (aka oxoglutaric acid) (Figure 5C). These findings suggest that the beta-oxidation of fatty acids may be impaired, leading to the accumulation of fatty acids such as docosadiencic acid, caproic acid and suberic acid, while the levels of palmitoyl-carnitine are reduced. Furthermore, the reduced levels of alpha-ketoglutarate indicate potential alterations in the TCA cycle and glutaminolysis. Overall, our data highlights the profound metabolic changes induced by PRMT1, particularly in amino acid biosynthesis, one-carbon metabolism as shown in Figure 5D generated by the metaboAnalyst software ⁴⁸.

Figure 5.

PRMT1-transformed leukemia cells are highly dependent on glucose consumption

After determining that glycolysis was enhanced in PRMT1-overexpressing cells, we performed glucose colorimetric assays to measure the glucose concentrations in the cell culture medium when the 6133 and 6133/PRMT1 cells grew exponentially. More significant reduction of glucose in 6133/PRMT1 cells was observed than that in parental 6133 cells (Figure 6A), indicating that elevated glucose consumption via glycolysis is caused by PRMT1 upregulation. Next, we grew the cells both in glucose-containing and glucose-depleted media. Cell viability assays showed the slower growth of 6133/PRMT1 cells in a glucose-free medium, while the development of parental cells was less affected (Figure 6B). Accordingly, we used 2-deoxy-D-glucose (2-DG), a glucose analog that competes with glucose in glycolysis. The proliferation of 6133/PRMT1 cells was notably inhibited by 2-DG, while the proliferation of 6133 cells was barely affected (Figure 6C). These results are consistent with Seahorse analysis that 6133/PRMT1 cells rely on glycolysis for growth (Figure 3). Next, we tested whether glucose blockage can impede AMKL progression in mice. Four days after transplantation of the 6133/PRMT1 cells, we administered 2-DG to the mice at 0.5g/Kg body weight every other day for a month. 2-DG treatment significantly postponed disease progression (Figure 6D). The splenomegaly was alleviated, and the burden of leukemia cells in bone marrow and peripheral blood was notably decreased in 2-DG treated mice (Figure 6E&F). Taken together, the in vitro and in vivo data indicate that PRMT1-mediated metabolic reprogramming makes leukemia cells highly addicted to glucose.

Figure 6.

Targeting fatty acid metabolic pathways for leukemia therapy

Rapidly growing cancer cells need de novo synthesis of fatty acids to meet the demand for building cell membranes ⁴⁹. De novo fatty acid synthesis uses acetyl-CoA generated from fatty acid oxidation. Inhibition of fatty acid oxidation has been shown to kill leukemia cells ⁵⁰. Carnitine-palmitoyl-transferase (CPT1A) catalyzes the rate-limiting step of fatty acid oxidation by transporting fatty acids across the mitochondrial outer membrane. RBM15 binds to 3’UTR of CPT1A mRNA (Zhang et al., 2015). Furthermore, PRMT1 controls the stability of the RBM15 protein. We demonstrated that the expression of both isoforms of PRMT1 lowered the mRNA levels of CPT1A (Figure 7A). We then performed Western blotting and confirmed that the protein level of CPT1A was accordingly reduced in 6133 cell lines ectopically expressing PRMT1 isoforms (Figure 6B). Downregulation of CPT1A causes less consumption of long-chain fatty acids. We consistently found more lipid accumulation in 6133/PRMT1 cells than in 6133 cells by FACS analysis with lipid-binding fluorescent dye (Figure 7C). Metabolomic analysis also found the accumulation of fatty acids such as suberic acid and caproic acid while downregulation of palmitoyl-carnitine which is a product of CPT1A (see supplemental table 1 for metabolomic data). We then treated leukemia cells with etomoxir, which inhibits CPT1A enzymatic activity. The 6133/PRMT1 cells were more sensitive to etomoxir than the 6133 cells (Fig 6D). Orlistat, a FASN inhibitor, also impeded the proliferation of 6133/PRMT1 cells more effectively than that of 6133 cells (Fig 6E). While CPT1A is responsible for transporting long-chain fatty acids, short-chain fatty acids can be directly transported to mitochondria. We added acetate, propionate, and butyrate in the form of triglycerides to a glucose-free medium. Strikingly, all three forms of short-chain fatty acids could support the proliferation of 6133/PRMT1 cells better than the parental 6133 cells (Fig 6F). The data implies that leukemia cells with high levels of PRMT1 expression can use short-chain fatty acids to compensate for the need for glucose.

Figure 7.

We then used a lentivirus to express CPT1A in 6133/PRMT1 cells ectopically. CPT1A wild type and mutant (H473A) that does not have succinylation activity ⁵¹ dampens the proliferation rates of 6133/PRMT1 cells grown in regular culture conditions. Intriguingly, the CPT1A enzymatic dead mutant (G710E) blocks the proliferation of the 6133/PRMT1 cells, which implies that the 6133 cells still need fatty acid oxidation for de novo synthesis of lipids for proliferation (Figure 7G). Taken together, we reveal that although PRMT1 downregulates fatty acid oxidation as an energy source, PRMT1 upregulates the usage of short-chain fatty acids as an alternative energy source for mitochondria.

Discussion

This report demonstrated that PRMT1 expression is required for RBM15-MKL1-initiated leukemia in mice. Given that only leukemia cells expressing elevated levels of PRMT1 can cause leukemia (Figure 1) and that normal hematopoietic stem cells express low levels of PRMT1⁴³, targeting PRMT1 may spare the normal hematopoietic stem cells. Given the essential roles of PRMT1 in AML with wide range of gene mutations such as RBM15-MKL1, FLT3-ITD, MLL-EEN, and AML1-ETO fusions as well as AML with splicing factor mutations ³⁰, the mechanism on how PRMT1 drives leukemia becomes particularly important to investigate further. Here, we demonstrated the critical regulation of metabolic pathways by PRMT1.

Many substrates of PRMT1 have been characterized, including metabolic enzymes such as GAPDH⁵² and PHGDH⁵³ and regulators such as PGC1, FOXOs, and RBM15 ⁵⁴. PGC1 and FOXO1 are known regulators of metabolism ^55,56. RBM15 regulates hematopoietic stem cell homeostasis and megakaryocyte differentiation ^57,58. We demonstrated that almost 50% of RBM15 targets are metabolic enzymes whose 3’UTR regions on mRNAs are bound by RBM15⁴⁰. The CPT1A mRNA is one of them. Since our metabolomic analysis demonstrated that PRMT1 overexpression caused the accumulation of intracellular lipids, we examined the CPT1A protein levels in the two cell lines. PRMT1 downregulates CPT1A protein level, which may partially explain lipid accumulation. Nevertheless, PRMT1 does not turn off TCA cycle or fatty acid oxidation, since the 6133/PRMT1 cells still support proliferation using galactose and glycerol in combination with acetate (triacetin), propionate (tripropionin) and butyrate (tributyrin) (Figure 7G). Transporting short-chain fatty acids across mitochondria does not require CPT1A expression. Thus, these molecules can be converted to acetyl CoA and propionyl-CoA and butyl-CoA to feed into TCA cycle. Although reduced oxygen consumption and membrane potential in mitochondria suggest the reduced activity of respiratory chain in mitochondria when long-chain fatty acids are supplied as fuels, the respiratory chain per se is still intact so the 6133/PRMT1 cells can adapt to the consumption of short chain fatty acid and non-fermentable carbon sources such as glycerol and galactose. We then rescued the growth of 6133/PRMT1 cells in a glucose-minus medium by ectopically expressing CPT1A, which argues that CPT1A-mediated fatty acid oxidation is the major pathway regulated by PRMT1. RBM15 also binds to several mRNAs coding for enzymes involved in glycolysis: LDHA and HK1. Although we did not detect increased protein expression of HK1 and LDHA, we did observe the increase of tyrosine phosphorylation of lactate dehydrogenase A (LDHA) by PRMT1. Since phosphorylation activates LDHA, upregulation of PRMT1 activates glycolysis. The decreased ratio of NADP/NADPH reduces aspartate concentration, which is another strong indicator of prominent levels of glycolysis in proliferating cells^59,60. Consistently, we also observed downregulation of aspartate as well as decreased ratio of NADP/NADPH in the 6133/PRMT1 cells. In conclusion, our data reveal the profound effect of PRMT1 upregulation on metabolic reprogramming.

Another intriguing phenomenon is that PRMT1 overexpression promotes mitochondria biogenesis as we reported before and further validated here (Figure 4C and D). However, the increase of mitochondria number did not translate to higher oxygen consumption. Actually, the ROS level inside mitochondria per cell is reduced, which is consistent with reduced membrane potential and lower oxygen consumption we observed. How PRMT1 regulates electron transportation chain at transcription and posttranscription levels needs more studies in the future. Intriguingly, we detected an increase of the total level of ROS, which implies that PRMT1 upregulates the ROS in the cytoplasm. Since lipids can stimulate the generation of ROS⁶¹, we speculate that intracellular accumulation of lipids by PRMT1 may be responsible for ROS increase in the cytoplasm. We posit that in cancer cells expressing copious levels of PRMT1, the main function of mitochondria is to generate intermediate metabolites for biomass synthesis. Understanding how PRMT1 regulates metabolic pathways may open new therapeutic avenues for cancer and other chronic diseases such as diabetes.

Methods and methods

Cell Culture and metabolite measurement

6133 cells were cultured in RPMI1640 medium supplemented with 10% FBS100 U/mL penicillin and 100 μg/mL streptomycin. Addition or withdrawal of mSCF (10 ng/mL) in culture were performed accordingly. L-Lactate Assay Kit (Cayman, Ann Arbor, Michigan) was used to measure the intracellular and extracellular lactate levels of cultured cells: approximately 1×10⁷ cells were cultured in 10 mL fresh medium for 24 hours; cell pellet and medium were collected and processed separately as instructed. Glucose Colorimetric Assay Kit (Cayman, Ann Arbor, MI) was used to quality the remaining glucose in culture medium: 1.25×10⁵ cells were cultured in fresh medium overnight; supernatant/medium was collected and processed as instructed. The fluorescent or colorimetric signals of the assays above were measured using a microplate reader (Biotek, Winooski, VT).

Viral Production and Cell Line Selection

For lentivirus production, viral vectors were co-transfected with envelope vector pMD2.G and packaging vector ps-PAX2 into 293T cells. Fresh or concentrated viruses were used to infect target 6133 cell lines. Stable cell lines were selected via GFP-based flow-cytometry sorting by BD FACSAria II system (BD, Franklin Lakes, NJ).

Murine Leukemia Model and Treatments

GFP-positive 6133 cells were intravenously transferred into 8-12 weeks old sub-lethally irradiated (6 Gy) C57BL/6 mice. Disease progression was closely monitored on a daily basis. Moribund mice were sacrificed for further analysis of GFP-positive donor leukemia cells in peripheral blood, bone marrow, and spleen. For PRMT1 inhibitor treatment, 80 mg/Kg body weight of inhibitor solution (16 mg/mL) was intraperitoneally injected into recipient mice every other day for a month, starting from the fourth-day post-cell transfer. PRMT1 inhibitor was dissolved in saline with 20% Captisol, 20% PEG-400 and 5% NMP (v/v). For 2-DG treatment, 0.25 g/Kg body weight of 2-DG (saline solution) was intraperitoneally injected into recipient mice every other day for a month, starting the fifth-day post cell transfer.

Flow Cytometry Analysis

FACS analysis was performed on BD LSRFortessa (BD, Franklin Lakes, NJ). Mitotracker DeepRed FM stain and TMRE stain (Molecular Probes, Eugene, OR) were used to assess cellular mitochondria.

SDS-PAGE and Western Blotting

6133 cells were collected from culture and lysed in 1 mL H-Lysis buffer (20 mM HEPES pH 7.9, 150 mM NaCl, 1 mM MgCl₂, 0.5% NP40, 10 mM NaF, 0.2 mM NaVO₄, 10 mM β-glycerol phosphate and 5% glycerol) with freshly added dithiothreitol/DTT (1 mM), PMSF (100 μM) and protease inhibitor cocktail (Roche, Branford, CT). Cells were incubated for 30 minutes on ice and sonicated in Bioruptor Ultra-sonication system (Diagenode, Denville, NJ). SDS-PAGE sample buffer was added to sonicated extracts and boiled. Samples were resolved by SDS-PAGE and transferred to PVDF membranes (Millipore, Billerica, MA). Membranes were blotted with antibodies then visualized by Immobilon Western Chemiluminescent reagent (Millipore) using Bio-Rad ChemiDoc MP system (Bio-Rad, Hercules, CA). Antibodies used in this study: PRMT1 (Cat# 07404, Millipore), LDHA (Cat#MA5-17246, Invitrogen), p-LDHA (Cat# 8176, Cell Signaling), CPT1a (Cat#66039, Proteintech).

Quantitative Real-time PCR

Total RNA was prepared using Direct-Zol RNAprep Kit (Zymo research, Irvine, CA). cDNA was generated by the Verso cDNA synthesis Kit (Thermo Scientific, Walthum, MA) with random hexamer priming. Real-time PCR assays were performed with Absolute Blue qPCR SYBR green Mix (Thermo Scientific) on a ViiA 7 system (Applied Biosystems, Walthum, MA). Relative quantity of gene expression was calculated by the ΔΔCt method.

Housekeeping gene beta-Actin was used for normalization.

Primer list:

• Prmt1-F CCCGTGGAGAAGGTGGACAT

• Prmt1-R CTCCCACCAGTGGATCTTGT

• Cpt1a-F GGCATAAACGCAGAGCATTCCTG

• Cpt1a-R CAGTGTCCATCCTCTGAGTAGC

• Idha-F GAATTACGATGGGGATGTGC

• Idha-R GACGTCTCTTGCCCTTTCTG

• Fasn-F AAGTTCGACGCCTCCTTTTT

• Fasn-R TGCCTCTGAACCACTCACAC

Cell Viability Assays

Cell viability was measured by using CellTiter-Glo Viability Assay Kit (Promega, Madison, WI). One thousand 6133 cells were seeded in 96-well plates (100 μL per well) with or without treatment. At the indicated time post culture setup, 100 μL of CellTiter-Glo reagent was added into each well. The luminescent signal was measured using a microplate reader (Biotek, Winooski, VT).

Metabolite extraction

Cells were washed twice with ice-cold PBS, followed by metabolite extraction in −70 °C in a 20% methanol solution (LC-MS grade methanol, Fisher Scientific). The tissue–methanol mixture was subjected to bead-beating for 45 seconds using a Tissue/cell disrupter (Qiagen). Extracts were centrifuged for 5 min at 2000Xg to pellet insoluble material and supernatants were transferred to clean tubes. The extraction procedure was repeated two additional times and all three supernatants were pooled, dried in a Vacufuge (Eppendorf) and stored at −80 °C until analysis. The methanol-insoluble protein pellet was solubilized in 0.2 M NaOH at 95 °C for 20 min and protein was quantified using a BioRad DC assay. On the day of metabolite analysis, dried cell extracts were reconstituted in 70% acetonitrile at a relative protein concentration of 1 μg/ml, and 4 μl of this reconstituted extract was injected for LC/MS-based untargeted metabolite profiling.

LC/MS metabolomics

Cell extracts were analyzed by LC/MS platform comprised of an Agilent Model 1290 Infinity II liquid chromatography system coupled to an Agilent 6550 iFunnel time-of-flight MS analyzer. Chromatography of metabolites utilized aqueous normal phase (ANP) chromatography on a Diamond Hydride column (Microsolv). Mobile phases consisted of: (A) 50% isopropanol, containing 0.025% acetic acid, and (B) 90% acetonitrile containing 5 mM ammonium acetate. To eliminate the interference of metal ions on chromatographic peak integrity and electrospray ionization, EDTA was added to the mobile phase at a final concentration of 6 μM. The mobile phase gradient used was: 0–1.0 min, 99% B; 1.0–15.0 min, to 20% B; 15.0 to 29.0, 0% B; 29.1 to 37 min, 99% B. Raw data were analyzed using MassHunter Profinder 8.0 and MassProfiler Professional (MPP) 15.1 software (Agilent technologies).

Metabolite structure specification

To ascertain the identities of differentially expressed metabolites (P < 0.05), LC/MS data were searched against an in-house annotated personal metabolite database, created using MassHunter PCDL manager 8.0 (Agilent Technologies) based on monoisotopic neutral mass (<5 ppm mass accuracy) and chromatographic retention times of pure standards. A molecular formula generator (MFG) algorithm in MPP was used to generate and score empirical molecular formulae, based on a weighted consideration of monoisotopic mass accuracy, isotope abundance ratios, and spacing between isotope peaks. A tentative compound ID was assigned when the PCDL database and MFG scores concurred for a given candidate molecule. Tentatively assigned molecules were confirmed based on a match of LC retention times and/or MS/MS fragmentation spectra for pure molecular standards.

Seahorse Assays

Measurement of metabolic activities of leukemia cells was performed using the Seahorse XF24 analyzers (Agilent Technologies, Santa Clara, CA). 1.5-2 ×10⁵ cells per well were seeded in 24-well plate coated with Cell-Tal reagent (Corning, Corning NY). Assay started at basal condition, followed with Oligomycin (500nM), FCCP (5 µM) and antimycin A (1 µM) injected sequentially to culture.

Supplemental figures

Supplemental Figure 1.

Supplemental Figure 2.

Supplemental Figure 3.

Acknowledgements

The project was partially supported by Leukemia Research Foundation to XZ, NCI R21 CA202390 to XZ, and Elsa Pardee Foundation to XZ. We would like to thank Dr. Taro Hitosugi (Mayo Clinic) for the CPT1A cDNAs.