
==== Front
Proc Natl Acad Sci U S A
Proc Natl Acad Sci U S A
PNAS
Proceedings of the National Academy of Sciences of the United States of America
0027-8424
1091-6490
National Academy of Sciences

38513095
202319429
10.1073/pnas.2319429121
datasetDatasetresearch-articleResearch Articlemed-sciMedical Sciences422
Biological Sciences
Medical Sciences
Activation of polyamine catabolism promotes glutamine metabolism and creates a targetable vulnerability in lung cancer
Han Xinlu a 1
Wang Deyu a 1
Yang Liao a
Wang Ning b https://orcid.org/0000-0002-3188-1256

Shen Jianliang c https://orcid.org/0009-0008-5474-7405

Wang Jinghan a https://orcid.org/0000-0002-8932-0267

Zhang Lei d
Chen Li e
Gao Shenglan a
Zong Wei-Xing zongwx@pharmacy.rutgers.edu
c 2 https://orcid.org/0000-0002-3129-8220

Wang Yongbo wangyongbo@fudan.edu.cn
a f g 2 https://orcid.org/0000-0003-4383-9423

aDepartment of Cellular and Genetic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
bBio-med Big Data Center, Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
cDepartment of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers-The State University of New Jersey, Piscataway, NJ 08854
dInstitutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
eShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai 200433, China
fMinhang Hospital & Institutes of Biomedical Sciences, Fudan University, Shanghai, China
gShanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
2To whom correspondence may be addressed. Email: zongwx@pharmacy.rutgers.edu or wangyongbo@fudan.edu.cn.
Edited by Nada Y. Kalaany, Boston Children’s Hospital, Boston, MA; received November 6, 2023; accepted February 25, 2024 by Editorial Board Member David J. Mangelsdorf

1X.H. and D.W. contributed equally to this work.

21 3 2024
26 3 2024
21 9 2024
121 13 e231942912106 11 2023
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Copyright © 2024 the Author(s). Published by PNAS.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

Significance

Polyamines, encompassing putrescine, spermidine, and spermine, are small polycationic alkylamines essential for cell growth. Elevated polyamines are commonly observed and used as therapeutic targets in cancer. However, polyamine targeting agents often have limited anticancer efficacy, and the reasons behind are largely unclear. Here we uncover that activation of polyamine catabolism promotes glutamine metabolism as a metabolic vulnerability, and demonstrates the therapeutic potential of cotargeting polyamine catabolism and glutamine metabolism. This study unveils a mechanism by which cancer cells compromise the effects of polyamine catabolic drugs, and establishes efficacious combination treatment strategies in lung cancer.

Polyamines are a class of small polycationic alkylamines that play essential roles in both normal and cancer cell growth. Polyamine metabolism is frequently dysregulated and considered a therapeutic target in cancer. However, targeting polyamine metabolism as monotherapy often exhibits limited efficacy, and the underlying mechanisms are incompletely understood. Here we report that activation of polyamine catabolism promotes glutamine metabolism, leading to a targetable vulnerability in lung cancer. Genetic and pharmacological activation of spermidine/spermine N1-acetyltransferase 1 (SAT1), the rate-limiting enzyme of polyamine catabolism, enhances the conversion of glutamine to glutamate and subsequent glutathione (GSH) synthesis. This metabolic rewiring ameliorates oxidative stress to support lung cancer cell proliferation and survival. Simultaneous glutamine limitation and SAT1 activation result in ROS accumulation, growth inhibition, and cell death. Importantly, pharmacological inhibition of either one of glutamine transport, glutaminase, or GSH biosynthesis in combination with activation of polyamine catabolism synergistically suppresses lung cancer cell growth and xenograft tumor formation. Together, this study unveils a previously unappreciated functional interconnection between polyamine catabolism and glutamine metabolism and establishes cotargeting strategies as potential therapeutics in lung cancer.

polyamine catabolism
SAT1
glutamine metabolism
lung cancer
MOST | National Natural Science Foundation of China (NSFC) 501100001809 81871878 Yongbo Wang STCSM | Natural Science Foundation of Shanghai Municipality (上海市自然科学基金) 100007219 20ZR1406500 Yongbo Wang Foundation for the National Institutes of Health (FNIH) 100000009 R01CA129536 and R01CA224550 Wei-Xing Zong
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pmcMetabolic reprogramming is a molecular hallmark of cancer (1, 2). Cancer cells rewire metabolism to meet the requirements for rapid proliferation and survival under stress conditions (3, 4). As a notable example, glutamine metabolism is often repurposed by cancer cells to fulfill fundamental functions, including energy production, macromolecular synthesis, and generation of antioxidants to maintain redox homeostasis (5). Glutaminolysis is a key process of glutamine metabolism with well-established roles in cancer. It converts glutamine to glutamate catalyzed by glutaminase (GLS), and then to tricarboxylic acid (TCA) cycle metabolites, generating adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NADPH) (5). In addition, glutaminolysis also directly regulates reactive oxygen species (ROS) homeostasis by providing glutamate and cysteine precursors for glutathione (GSH) synthesis (5). Targeting rewired glutamine metabolism is actively being exploited as a cancer therapy. However, the outcome has not been satisfactory, due at least in part to the intricate compensatory metabolic changes in cancer (6, 7).

Polyamines, encompassing putrescine, spermidine, and spermine, are small polycationic alkylamines that are involved in many fundamental biological processes (8, 9). The amounts of intracellular polyamines are tightly controlled through their uptake, biosynthesis, and catabolism (8). Polyamines are synthesized from the precursor ornithine via stepwise reactions. The decarboxylation of ornithine by ornithine decarboxylase (ODC1) produces putrescine, which is consecutively converted to spermidine and spermine by spermidine synthase (SRM) and spermine synthase (SMS), respectively. Conversely, spermine and spermidine are converted by SAT1 into N1-acetylspermine and N1-acetylspermidine respectively, which are subsequently oxidized by polyamine oxidase (PAOX) to spermidine and putrescine. In addition, spermine can be oxidized by spermine oxidase (SMOX) to form spermidine. These oxidation reactions involved in polyamine catabolism generate the ROS hydrogen peroxide (H2O2).

Polyamine levels are frequently elevated in cancers (9, 10). Pharmacological inhibitors targeting polyamine metabolism have been developed, including difluoromethylornithine (DFMO), a small molecule inhibitor of ODC1, and N1, N11-diethylnorspermine (DENSpm, also known as BENSpm), a small molecule agonist of SAT1, for cancer treatment (9, 11–13). However, despite certain anticancer effects in preclinical studies, these drugs alone produced limited efficacy in cancer patients (9). It is therefore crucial to understand how cancer cells mitigate the effects of targeting polyamine metabolism and identify new vulnerabilities that can be therapeutically leveraged.

Lung cancer is highly heterogeneous and causes the most cancer-related mortality globally (14). Approximately 85% lung cancer is histologically classified as non-small-cell lung cancer (NSCLC), among which lung adenocarcinoma (LUAD) is the most common subtype (14, 15). Targeted therapy based on oncogenic drivers (e.g., EGFR mutation and ALK fusion) and immunotherapy have significantly improved the overall outcome of NSCLC patients over the past two decades (14, 15). However, there are still a portion of NSCLC patients that lack clinically targetable driver mutations and do not respond well to current immunotherapies. In addition, even in the patients with initial responses, resistance and relapse inevitably develop, calling for the development of new treatment approaches (16).

In this study, metabolomic and transcriptomic profiling reveals that genetic and pharmacological activation of polyamine catabolism via SAT1 increased glutamine metabolism in NSCLC. SAT1 activation enhanced glutamine uptake and its conversion to glutamate, which is subsequently diverted for GSH synthesis to reduce oxidative stress and support cell growth. Importantly, inhibitors of glutamine transport, glutaminolysis or GSH synthesis together with the SAT1-agonist DENSpm synergistically inhibits NSCLC in cultured cells and in tumor xenografts. Together, this study identifies enhanced glutamine metabolism as a molecular consequence of the activation of polyamine catabolism, which helps cancer cells cope with the oxidative stress. Combined targeting of glutaminolysis and polyamine catabolism provides a promising therapeutic strategy for lung cancer.

Results

Genetic Activation of SAT1 Promotes Glutamine Metabolism.

SAT1 is the rate-limiting enzyme and key drug target of polyamine catabolism (17). Spermine and spermidine were converted by SAT1 into N1-acetylspermine and N1-acetylspermidine, which are either subsequently oxidized by PAOX to spermidine and putrescine, or secreted from the cell to the extracellular environment (Fig. 1A). To study the role of SAT1-mediated polyamine catabolism in lung cancer, we established a doxycycline inducible SAT1 expression LUAD A549 cell line. SAT1 protein was nearly undetectable in control, while first increased, and then decreased after inducible overexpression (OE) (Fig. 1B), indicating a tight regulation of SAT1 as previously reported (17). Untargeted metabolomics analysis was conducted at an early time point with strongest SAT1 expression (24 h post doxycycline treatment) to identify metabolic changes caused by SAT1 activation (Fig. 1B). Principal component analysis (PCA) of the metabolomics data showed that SAT1 OE and control samples clearly separated from each other (SI Appendix, Fig. S1A), indicating that SAT1 OE caused prominent metabolic changes. Among all detected metabolites, the levels of 44 were significantly upregulated and six downregulated in SAT1 OE comparing with the control cells (|log2(fold change)| > |log2(1.3)| and P < 0.05; Fig. 1C and SI Appendix, Fig. S1B). Spermidine was significantly downregulated upon SAT1 OE (Fig. 1C), consistent with SAT1-mediated activation of polyamine catabolism in previous studies (18, 19). Interestingly, glutamine and glutamate metabolism, as well as several related pathways including glutathione metabolism, were significantly enriched (Fig. 1D).

Fig. 1. Genetic and pharmacological induction of SAT1 augments glutamine metabolism in LUAD cells. (A) Schematic of the polyamine metabolic pathway. dcAdoMet, decarboxylated S-adenosylmethionine; MTA, 5′-methylthioadenosine. (B) Western blot analysis of SAT1 expression induced by 1 μg/mL doxycycline (Dox) at indicated time points in A549 cells inducibly overexpressing Flag-tagged SAT1 (SAT1 tet-on). The red arrow marks the time point for metabonomics and RNA sequencing analyses. h: hour. (C) Volcano plot showing the metabolic changes following SAT1 overexpression (OE) compared with control (Ctrl). (D) Top 15 enriched pathways for significantly changed metabolites upon SAT1 OE. Data were analyzed by MetaboAnalyst 5.0 using KEGG metabolite sets library. (E) Scatter plot showing the correlation of metabolic changes induced by SAT1 OE in A549 and PC9 cells. (F) Relative abundance of indicated metabolites in A549 and PC9 cells under Ctrl and SAT1 OE. The intensities were normalized by total protein level in each sample. (G) Western blot analysis of SAT1 expression in A549 cells treated with DENSpm (2.5 μM) for 3 d. Loading control: β-actin. (H) Scatter plot showing the correlation of metabolic changes resulted from SAT1 OE and DENSpm treatment in A549 cells. (I) Relative abundance of indicated metabolites in Ctrl and DENSpm-treated (2.5 μM, 3 d) A549 and PC9 cells. Mean changes from three biological replicates were shown in (C, E, and H). Data are shown as the mean ± SD in (F and I). *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant; unpaired Student’s t test in (F and I).

RNA sequencing analysis identified 47 significantly upregulated and 111 downregulated genes upon SAT1 OE (adjusted P value < 0.05; SI Appendix, Fig. S1C and Dataset S1). These differentially expressed genes were enriched in pathways encompassing polyamine biosynthesis, metabolism of amino acids and derivative, as well as response to oxygen levels (SI Appendix, Fig. S1D), reinforcing the results from metabolomic analysis.

As A549 contains KRAS activation mutation, we also examined polyamines and glutamine-related metabolites in another LUAD cell line PC9 that harbors EGFR activation mutation. Similar to A549 cells, SAT1 OE in the EGFR-mutated PC9 cells also increased the levels of glutamine-related metabolites (Fig. 1 E and F and SI Appendix, Fig. S1E). Collectively, the metabolomics and transcriptomics results demonstrate that genetic activation of SAT1 elevates polyamine catabolism, accompanied by an upregulation of glutamine metabolism.

Pharmaceutical Activation of SAT1 Recapitulates the Metabolic Changes from OE.

We then took a pharmacological approach to activate SAT1 followed by metabolomics analysis. A549 and PC9 cells were treated with the polyamine catabolic compound DENSpm that is known to induce SAT1 expression (20–23) (Fig. 1G and SI Appendix, Fig. S2 A and B). As exemplified by A549 cells, metabolic changes resulted from DENSpm treatment were positively correlated with those caused by SAT1 OE (Fig. 1H), supporting the notion that SAT1 expression and activation correlate with the cellular effects of DENSpm (24, 25). Notably, both SAT1 OE and DENSpm treatment significantly decreased the levels of spermidine, while increased glutamine, glutamate, and oxidized glutathione (GSSG), in A549 cells PC9 cells (Fig. 1 H and I and SI Appendix, Fig. S2 A and B). Contrastingly, inhibition of polyamine synthesis with DFMO, an ODC inhibitor, did not cause significant changes of glutamine and glutamate (SI Appendix, Fig. S2 C and D), indicating that the increase of glutamine metabolism following SAT1 activation is not due to decrease of spermidine or spermine. Consistent with the increased glutamine metabolism following SAT1 activation, levels of alanine-serine-cysteine transporter, type-2 (ASCT2, also known as SLC1A5, the primary transporter of glutamine) were increased upon SAT1 OE and DENSpm treatment, respectively (SI Appendix, Fig. S2 E and F). Taken together, these results demonstrate that activation of polyamine catabolism via SAT1 can lead to enhanced glutamine metabolism.

SAT1 Activation Enhances Glutamine Dependence in Lung Cancer Cells.

Lung cancer cells are known to be dependent on glutamine (26, 27). To interrogate whether the elevated glutamine metabolism can serve as a vulnerability for activation of polyamine catabolism, we deprived glutamine in A549 and PC9 cells together with SAT1 activation via OE or DENSpm treatment. SAT1 activation and glutamine deprivation individually suppressed cell growth, while in combination exhibited synergistic inhibitory effects (Fig. 2 A and B). Such synergistic effects were further observed in 6 out of 7 NSCLC cell lines (Fig. 2 C and D), manifesting the essential role of glutamine metabolism in supporting cell growth upon the activation of SAT1. Similarly, blocking the glutamine transport via ASCT2 knockdown synergized with DENSpm treatment to inhibit cell growth (Fig. 2E).

Fig. 2. Glutamine deprivation combined with SAT1 activation synergistically inhibits cell growth and induces cell death. (A and B) Heatmap demonstration of relative cell growth of (A) A549 and (B) PC9 SAT1 tet-on cells cultured in the medium with indicated concentrations of glutamine and doxycycline (Dox) or DENSpm. Cell biomass was assessed by crystal violet staining, n = 3 independent replicates. HSA Synergy score was analyzed using synergy finder (https://synergyfinder.org/). (C) Colony formation assay and crystal violet staining of different lung cancer cell lines under DENSpm treatment and glutamine deprivation alone or in combination. 1.25 to 2.5 μM DENSpm was added to the cells when reaching 30% confluence in plates, and cultured for 3.5 d. n = 3 independent replicates. (D) Quantification results of (C). (E) Colony formation assay and crystal violet staining of negative control (NC) and ASCT2-silenced A549 cells with vehicle or DENSpm (50 nM) treatment. ASCT2 knockdown (KD) efficiency was analyzed by western blot (Left). Loading control: β-actin. (F) Trypan blue analysis of cell death in A549 and PC9 SAT1 tet-on cells upon doxycycline (1,000 ng/mL) treatment with or without glutamine for 3.5 d. n = 3 independent replicates. (G) Trypan blue analysis of cell death in A549, PC9, and H1650 cells treated with vehicle or DENSpm (2.5 mM) and cultured with or without glutamine for 3.5 d. Effects of SAT1 knockdown on cell death under indicated conditions were analyzed in A549 cells (right part of the Left panel). n = 3 independent replicates. (H and I) Western blot analysis of SAT1 and cleaved-PARP of A549 and PC9 cells under conditions as indicated in (F and G). (J) Western blot analysis of SAT1 KD efficiency in (G) and effect of SAT1 KD on ASCT2 expression upon DENSpm treatment. Loading control: β-actin. Data are shown as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant; Ordinary one-way ANOVA with Tukey’s multiple comparisons tests in (D, F, and G).

Furthermore, concurrent SAT1 activation and glutamine deprivation significantly promoted cell death, while single perturbation had little effects (Fig. 2 F-I). Of note, SAT1 knockdown largely abrogated the increased cell death upon combination perturbations (Fig. 2 G and J) and partially reverted the increase of ASCT2 upon DENSpm treatment (Fig. 2J), suggesting that the effect on cell death is dependent on SAT1 activation. Collectively, these data demonstrate that SAT1 activation enhances glutamine dependence, providing a vulnerability in lung cancer cells.

SAT1 Activation Enhances GSH Synthesis via Glutamine Metabolic Rewiring.

Glutamine, upon its conversion to glutamate via glutaminolysis, contributes to the TCA cycle for energy production, and serves as a precursor for GSH biosynthesis to maintain the redox homeostasis (Fig. 3A). To investigate the utilization of glutamine upon SAT1 activation, we performed metabolic tracing experiment using stable isotope carbon-13 (13C)-labeled glutamine in A549 cells (Fig. 3A). Both labeled and total glutamine were increased in SAT1 OE cells (Fig. 3B), indicating an increased Gln uptake. Importantly, glutamate, GSH, and GSSG were also increased in SAT1 OE cells (Fig. 3B). In contrast, the tricarboxylic acid (TCA) cycle intermediates, including alpha-ketoglutarate (α-KG), succinate, malate and fumarate, either did not show significant change or only moderately increased (Fig. 3B). These results indicate that SAT1 activation promotes the conversion of glutamine to glutamate that facilitates biosynthesis of GSH and GSSG. Consistent with this notion, untargeted metabolomics showed increased GSSG levels as well as GSSG/GSH ratio following SAT1 OE or DENSpm treatment in A549 and PC9 cells (Fig. 3C). As polyamine catabolic process involves oxidation reactions that generate hydrogen peroxide (H2O2), these data indicate that, upon SAT1 activation, glutamine metabolism is elevated to enhance the production of GSH to mitigate ROS, thereby supporting cell survival and proliferation.

Fig. 3. 13C5-labeled glutamine tracing demonstrates the elevation of glutamine utilization and GSH production following SAT1 OE. (A) Schematic of carbon flow derived from 13C5-glutamine. (B) The 13C5-glutamine-derived and total abundance of indicated metabolites in control (Ctrl) and SAT1 overexpression (OE) A549 cells. SAT1 tet-on A549 cells were cultured in normal medium with or without doxycycline (1 μg/mL) for 20 h, and then incubated in labeling media containing 1 mM L-13C5-Glutamine for 4 h. Intensities were normalized to total protein level in each sample, n = 3 independent experiments. (C) Relative abundance of GSH and GSSG, and ratio of GSSG/GSH upon SAT1 OE or DENSpm treatment in A549 and PC9 cells as indicated in Fig. 1 F and I. Individual data points and mean ± SD were shown. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant; unpaired Student’s t test in (B and C).

Concurrent Glutamine Deprivation and SAT1 Activation Induces Cell Death and Growth Inhibition Due to ROS Accumulation.

Based on the observations that glutamine metabolism was enhanced and mobilized for GSH synthesis following SAT1 activation (Fig. 4A), we reasoned that glutamine deprivation under SAT1 activation would induce a devastating level of ROS. Indeed, simultaneous glutamine deprivation and SAT1 activation significantly increased ROS levels comparing with each perturbation alone and the untreated control cells (Fig. 4B). Consistently, combined perturbations markedly increased cell death in both A549 and PC9 cells (Fig. 4C, Ctrl group). Of note, supplementation with cell permeable GSH-ethyl-ester (GSH-EE) or N-acetyl cysteine (NAC), a precursor for GSH synthesis, significantly reversed the increased ROS and cell death under combination condition (Fig. 4 C and D). Moreover, addition of sodium pyruvate (NA-PRV), the H2O2 scavenger, significantly reverted the cell death (Fig. 4E). In contrast, addition of the cell-permeable, dimethyl ester form of α-KG (dm-α-KG) had no rescuing effect, suggesting the increased cell death is not due to the reduction of glutamate-derived α-KG (SI Appendix, Fig. S3A). Cell growth assays showed that supplementation with either one of GSH-EE, NAC, and NA-PRV partly but significantly rescued the cell growth inhibition from combined glutamine deprivation and SAT1 activation (Fig. 4 F and G). Moreover, supplementation of putrescine, spermidine or spermine also partially reversed the cell growth inhibition (SI Appendix, Fig. S3 B and C), presumably due to the protective roles of polyamines in H2O2-mediated oxidative stress (28).

Fig. 4. Concurrent glutamine deprivation and SAT1 activation exacerbate ROS oxidative stress to trigger cell death and growth inhibition in LUAD cells. (A) Schematic of GSH biosynthesis pathway and actions of several antioxidants (Green). (B) ROS production detected by DCFH-DA staining and flow cytometry in A549 SAT1 tet-on cells upon SAT1 OE or DENSpm (2.5 μM) treatment with or without glutamine for 3.5 d. n = 3 independent replicates. (C) Effects of GSH-EE (2 mM) or NAC (2.5 mM) supplementation on cell death induced by SAT1 OE or DENSpm (2.5 μM) treatment and glutamine deprivation in A549 and PC9 SAT1 tet-on cells. n = 3 independent replicates. (D) Effects of GSH-EE (2 mM) or NAC (2.5 mM) supplementation on ROS production following SAT1 OE or DENSpm (2.5 μM) treatment and glutamine deprivation in A549 and PC9 SAT1 tet-on cells. n = 3 independent replicates. (E) Effects of sodium-pyruvate (NA-PRV, 10 mM) on cell death under conditions as in (D). n = 3 independent replicates. (F) Effects of GSH-EE (2 μM), NAC (1 mM), and NA-PRV (1 mM) addition on the growth of A549 SAT1 tet-on cells under indicated conditions. Cell biomass was assessed by crystal violet staining, n = 3 independent replicates. (G) Quantification result for the combination of a representative glutamine concentration and SAT1 OE with prominent changes in (F). Data are shown as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant; unpaired Student’s t test in (B), Ordinary one-way ANOVA with Tukey’s multiple comparisons test in (D and G), and two-way ANOVA with Sidak’s multiple comparisons test in (C and E).

In addition to potent induction of SAT1, DENSpm was reported to also induce PAOX and SMOX that catalyze the polyamine oxidation reactions and produce H2O2 (23, 29–31). We thus examined expression of the two enzymes under DENSpm treatment in A549 and PC9 cells. Immunoblotting analysis showed that PAOX moderately increased while SMOX seemingly did not change following DENSpm treatment in both cells (SI Appendix, Fig. S3D). To further explore the functional roles of PAOX and SMOX, we respectively inhibited PAOX and SMOX genetically via siRNAs or pharmacologically via small molecule inhibitors MDL 72527 and JNJ-9350 under DENSpm treatment and glutamine deprivation (SI Appendix, Fig. S3 E–G). Interestingly, inhibition of either PAOX or SMOX partially reverted the cell growth inhibition and cell death induced by concurrent DENspm treatment and glutamine deprivation (SI Appendix, Fig. S3 F and G). This suggests that both H2O2-producing enzymes contribute to the cellular phenotypes resulting from combination perturbations. Together, these observations indicate that concurrent glutamine deprivation and SAT1 activation produces excessive ROS, leading to cell death and growth inhibition in lung cancer cells.

Inhibition of Glutamine Metabolism or GSH Synthesis Synergizes with DENSpm to Inhibit Lung Cancer Cell Growth.

Having established the essential role of glutamine metabolism and subsequent GSH biosynthesis upon polyamine catabolic activation, we next investigated the therapeutic potential of the combinational treatments of inhibition of glutamine/GSH metabolism and activation of polyamine catabolism. Lung cancer cells were cotreated with DENSpm in combination with one of the following compounds: i) V-9302, an inhibitor of ASCT2 (32), ii) CB-839, a potent and noncompetitive allosteric GLS1 inhibitor (33), and iii) buthionine sulfoximine (BSO), an irreversible inhibitor of glutamate-cysteine ligase (GCL), the rate-limiting enzyme of GSH synthesis (34), respectively (Fig. 5A). Notably, all three combinations exhibited synergistic or additive inhibitory effects in all 7 NSCLC cell lines (Fig. 5 B and C). Consistent with the decreased cell growth, cotreatments concertedly decreased the cell proliferation marker protein PCNA compared to individual treatments and the untreated controls (Fig. 5D). Metabolic profiling in A549 cells showed that, upon V-9302 and DENSpm cotreatment, the levels of intracellular glutamate decreased, while extracellular glutamate increased, compared to single treatment and control (SI Appendix, Fig. S4 A–C), corroborating the inhibition of glutamate utilization by cotreatment. These data demonstrate that inhibitors of glutamine metabolism or GSH synthesis synergize with DENSpm to suppress lung cancer cell growth.

Fig. 5. Inhibition of glutamine metabolism or GSH synthesis synergizes with polyamine catabolism activation to suppress LUAD cell growth. (A) Schematic of targeting glutamine metabolism and GSH synthesis pathway. (B) Heatmap demonstration of relative cell growth of LUAD cell lines treated with vehicle, or indicated concentrations of DENSpm and V-9302 or CB-839 or BSO individually or in combination. Cell biomass was assessed by crystal violet staining, n = 3 independent replicates. (C) HSA synergy score of indicated combination treatments in (B). (D) Western blot analysis of PNCA and SAT1 expression in A549 cells treated with vehicle, or DENSpm (D, 2.5 μM) and V-9302 (V, 20 μM) individually or in combination for 3 d.

Combined Treatment Potently Inhibits Lung Tumor Growth In Vivo.

To further evaluate the therapeutic potential of the abovementioned combination treatments in vivo, PC9 cells were subcutaneously implanted into nude mice to form tumors. After tumor size reached approximately 100 to 300 mm3, mice were treated with the vehicle, DENSpm, V-9302, or the combination of the two (Fig. 6A). Notably, the combined treatment significantly inhibited the growth of xenograft tumors and reduced the tumor weights, while the single treatment only exhibited mild tumor regression (Fig. 6 B–D). The tumor-inhibitory effect of the combined treatment was accompanied by increased SAT1, decreased PCNA, and increased cleaved-PARP (Fig. 6E), corroborating the inhibition of proliferation and induction of cell death in vivo. Slight reduction of body weight was observed in the treatment groups, due in part to the shrinkage of tumor (SI Appendix, Fig. S4D), indicating tolerable toxicities.

Fig. 6. Inhibition of glutamine metabolism in combination with DENSpm treatment potently suppresses LUAD tumor growth in vivo. (A) Experimental scheme of combination treatment with DENSpm and V-9302 HCL. DENSpm was administrated at 100 mg/kg via intraperitoneal injection (i.p.) twice weekly, V9302-HCL was administrated via i.p. at 30 mg/kg once daily, and the duration of administration was 2 wk. (B) Growth of tumors from PC9 cells subcutaneously transplanted in BALB/c-nude mice treated with vehicle, or DENSpm and V9302-HCL individually or in combination. n = 6 tumors. (C) Schematic illustration of surgically resected tumors and (D) tumor weight at the endpoint in (B). (E) Western blot analysis of indicated proteins in the tumor samples from (C). (F) Experimental scheme of combination treatments with DENSpm and CB-839 or BSO. DENSpm was administrated at 100 mg/kg via i.p. twice weekly, CB-839 was administrated at 200 mg/kg via intragastric injection (i.g.) once or twice per day alternately, BSO was administrated at 500 mg/kg every 2 d, and the duration of administration was 2 wk. (G) Growth of tumors from PC9 cells subcutaneously transplanted in BALB/c-nude mice treated with vehicle, or DENSpm and CB-839/BSO individually or in combination. n = 6 to 8 tumors. (H) Schematic illustration of surgically resected tumors and (I) tumor weight at endpoint in (G). Data are shown as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant, Ordinary one-way ANOVA with Tukey’s multiple comparisons test in (B, D, G, and I).

We further examined the in vivo effectiveness of combining DENSpm with CB-839 or BSO (Fig. 6F). Akin to the results from V-9302, the combination of DENSpm with either CB-839 or BSO significantly inhibited tumor growth compared to single treatment and vehicle control (Fig. 6 G and I and SI Appendix, Fig. S4E). Immunoblotting analysis of PAOX and SMOX expression revealed that, aside from induction of SAT1, DENSpm treatment alone or together with CB-839/BSO also enhanced the expression of PAOX and SMOX (SI Appendix, Fig. S4 F and G), further supporting the functional roles of the two polyamine oxidation enzymes under DENSpm-induced activation of SAT1 and polyamine catabolism. Collectively, these results demonstrate the combination of DENSpm with either one of V-9302/CB-839/BSO effectively inhibits lung tumor growth in vivo, providing potentially therapeutic strategies for lung cancer.

Discussion

Targeting cancer cell metabolism has been actively pursued as anticancer therapeutics. However, to date, only very few metabolic drugs have been successfully applied for cancer therapy in clinic (6, 7). The limited success is largely due to the intricate metabolic rewiring and adaptability accompanied with treatment that compromise the effectiveness (7). Therefore, identifying the metabolic reprogramming and vulnerabilities induced by metabolic drugs is crucial for rational design of more efficacious therapies. Here, we report that activation of polyamine catabolism enhances glutamine metabolism to facilitate the generation of GSH, hence elevating glutamine dependence in lung cancer cells (Fig. 7, Left part). Notably, inhibitors of glutamine transport, glutaminolysis, or GSH biosynthesis (i.e., V-9302, CB-839, and BSO, respectively) synergize with polyamine catabolic drug DENSpm to inhibit growth of lung cancer cells and tumor xenografts (Fig. 7, Right part). Our study uncovers an interconnection between polyamine catabolism and glutamine metabolism, and suggests combination therapeutic strategies in lung cancer.

Fig. 7. A mechanistic model for the interconnection between polyamine catabolism and glutamine metabolism and cotargeting strategies in lung cancer. Gln: glutamine, Glu: glutamate, SPD: spermidine, SPM: spermine.

Interconnections between metabolism of polyamine and amino acid have been identified to exert important physiopathological functions (8, 35–37). For instance, a recent study showed that glutamate contributes to polyamine biosynthesis by serving as a resource for ornithine through ornithine aminotransferase (OAT) to support tumor growth of pancreatic cancer (38). In comparison to polyamine biosynthesis, how polyamine catabolism connects to amino acid metabolism is less understood. Our study reveals an interplay between polyamine catabolism and glutamine metabolism via redox homeostasis, which is critical for lung cancer cell survival and proliferation and can be therapeutically targeted.

SAT1 levels are highly regulated by polyamines or their analogs at multiple steps, including transcription, splicing, translation, and protein degradation (17). As a polyamine analog, DENSpm was reported to activate the translation of SAT1 by promoting degradation and release of a translation repressor protein nucleolin from SAT1 mRNA (22, 39). DENSpm was also shown to induce SAT1 via RNA splicing or to enhance its enzymatic activity (40, 41). The in-depth mechanism of DENSpm-mediated SAT1 activation remains to be fully elucidated.

Although the cellular effects of DENSpm have been primarily attributed to its effect on SAT1 expression and activation (12, 24, 42), DENSpm has been shown to have additional effects including PAOX and SMOX activation, as well as inhibition of polyamine biosynthesis (12, 30, 43). Indeed, DENSpm treatment led to upregulation of PAOX and SMOX in our xenograft tumors (SI Appendix, Fig. S4 F and G), and moderate increase of PAOX while no obvious change of SMOX in cultured cells (SI Appendix, Fig. S3D). In contrast, our RNA-Seq results indicate that SAT1 OE led to decreased SMOX transcript (Dataset S1), suggesting an extra compensatory response other than the elevated polyamine biosynthesis following SAT1-induced polyamine catabolism. Therefore, DENSpm treatment and SAT1 OE may have nonoverlapping metabolic consequences. Along with this line, we observed that spermine that is also oxidized by SMOX drastically decreased upon DENSpm treatment, but did not show marked change upon SAT1 OE (Fig. 1 F and I). This is coinciding with previous studies involving DENSpm treatment (23, 29) and SAT1 OE (18, 19). In addition, variable decreases of both spermidine and spermine have also been found upon SAT1 OE (44, 45). We postulate that the differing changes of spermidine and spermine induced by SAT1 activation in different studies are likely dependent on multiple factors, including the cellular levels of polyamines and their regulating enzymes, the extent and duration of SAT1 activation, as well as the magnitude of compensatory increase of polyamine biosynthesis.

Based on our results, we propose that SAT1 activation and subsequent polyamine oxidation produce H2O2 as a source of ROS that triggers an adaptive elevation of GSH synthesis from glutamine metabolism, thereby maintaining cell proliferation and survival (Fig. 7). The results obtained from pharmacological or genetic inhibition of PAOX or SMOX (SI Appendix, Fig. S3 E–G) suggest that both H2O2-producing polyamine oxidation enzymes contribute to the cellular phenotypes induced by concurrent DENSpm treatment and glutamine deprivation. The individual contribution of PAOX or SMOX to ROS production may rely on cell intrinsic and extrinsic contexts (28, 43, 46). In addition to ROS production, SAT1 activation has been shown to increase the flux of polyamine metabolism, leading to the consumption of ATP and acetyl-CoA, and then possibly bioenergetic crisis and defective lipid biosynthesis, which may also be compensated by increased glutaminolysis (5, 17, 45, 47, 48).

Different inhibitory effects were observed across NSCLC cell lines (Fig. 5 B and C), likely due to distinct utilization and dependence of glutamine in these cell lines. Studies have shown that cancer cells with specific mutations, including KEAP1 and ARID1A inactivation mutations that frequently occur in NSCLC (49), exhibited heightened glutamine dependence and were more sensitive to GLS inhibitors (50–52). Accordingly, it is reasonable to speculate that NSCLC patients containing KEAP1 and ARID1A inactivation mutations may benefit more from targeting glutamine metabolism, and the effects are likely to be further enhanced by combining with polyamine catabolic drugs. Monitoring the metabolic status of tumors by technologies such as isotope-labeled glutamine-based positron emission tomography (PET) scan (26), together with genetic mutation screening, should help selecting optimal combined therapy.

Targeting either polyamine catabolism or glutamine metabolism exhibited anticancer effects across different cancers in preclinical studies (9, 53). Monotherapies using DENSpm or CB-839 were quite tolerated but had poor efficacy in clinical trials (9, 53). Previous studies demonstrated the improved effectiveness of combination strategies in several cancers (23, 54–57). We thus postulate that the combination therapeutic strategies established here may be effective in lung cancer, and possibly be extended to other cancer types. In summary, our study not only reveals a mechanism by which cancer cells adapt to dysregulated polyamine catabolism, but also provides therapeutic strategies with potentially broad clinical significance.

Materials and Methods

Cell Lines and Culturing.

Lung cancer cell lines A549 (RRID: CVCL_0023), PC9 (RRID: CVCL_B260), H1650 (RRID: CVCL_1483), H1944 (RRID: CVCL_1508), H358 (RRID: CVCL_1559), H1975 (RRID: CVCL_1511), H1792 (RRID: CVCL_1495) were cultured in RPMI 1640 supplemented with 10% FBS and 1% penicillin streptomycin, and maintained in standard cell culture conditions at 37 °C and 5% CO2. All cell lines were purchased from COBIOER or the Cell Bank of Chinese Academy of Sciences and authenticated based on STR genotyping. Cells were checked for absence of Mycoplasma by PCR.

Stable cell lines inducibly expressing SAT1 were obtained using the Lenti-X Tet-On Advanced Inducible Expression System (Clontech) as previously described (58). Overexpression of SAT1 was induced by doxycycline treatment (Sigma Aldrich, D3072).

Plasmids and Lentiviruses.

Tet-on lentiviral plasmid expressing Flag-tagged SAT1 (Flag-SAT1) was constructed by cloning the SAT1 coding sequence into pLVX-Tight-puro (Clontech) using Phanta Super-Fidelity DNA Polymerase (vazyme, P501-d1). Lentiviral particles were generated by ViraPower Lentiviral Expression Systems (Thermo Fisher Scientific) following the manufacturer’s manual.

Chemicals.

DENSpm (CAS NO. 156886-85-0) was purchased from Tocris Bioscience (0468) and MedChemExpress (HY-13610A). V-9302 (HY-112683, CAS NO. 1855871-76-9) was purchased from MedChemExpress. CB-839 (HY-13610A, CAS NO.1439399-58-2) was purchased from Selleck. BSO (19176, CAS NO. 5072-26-4) was purchased from Sigma-Aldrich. NAC (MB1735, CAS NO. 616-91-1) and Sodium pyruvate (MB4185, CAS NO. 113-24-6) was purchased from Meilunbio. L-Glutamine-13C5 (CLM-1822, CAS NO. 184161-19-1) was purchased from Cambridge Isotope Laboratories, JNJ-9350 (HY-Q36392, CAS NO. 326923-09-5) and MDL 72527 dihydrochloride (HY-100621, CAS NO.93565-01-6) was purchased from MedChemExpress.

Cell Growth and Death Assay.

Cell proliferation was analyzed by crystal violet staining. A total of 5,000 cells were seeded in a 24-well plate, and the next day medium was replaced with fresh medium for different treatment conditions. After 5 d incubation, cells were fixed by 4% paraformaldehyde (PFA) and stained with 0.1% crystal violet for 20 min. Next, the plate was rinsed with tap water and followed by crystal violet extraction using 10% acetic acid. Finally, crystal violet absorbance at 590 nm was measured by microplate reader (BioTek).

Cell death was analyzed by trypan blue staining. Cells were digested from the plate and reresuspended by fresh complete medium. Cell suspension was mixed with Trypan blue solution (Meilunbio, MA0130) by a 1:1 ratio, and cell death was assessed by cell counting based on Trypan blue positive staining.

Reactive Oxygen Species (ROS) Detection.

ROS production was analyzed using the ROS Assay Kit (Beyotime, S0033S). Cells were digested and resuspended by fresh medium without FBS, and the DCFH-DA probe was diluted in the medium without FBS by 1:1,000 ratio (volume). Cells were then incubated with diluted DCFH-DA solution at 37 °C for 25 min, and the mixture was shaken up every 5 min during incubation. After probe incubation, the cells were rinsed with fresh medium without FBS for three times followed by flow cytometry analysis.

RNA Interference.

Transient gene silencing for SAT1 and ASCT2 was performed using small interfering RNA (siRNA). In brief, siRNA oligos at final concentration (20 nM) were transfected into cells with RNAiMAX transfection reagent (Thermo Fisher Scientific, 13778075) according to the manufacturer’s instructions. Western blots were performed 48 h after transfection to assess the knockdown efficiency.

Western Blot.

Western blot was performed as described previously (58). Briefly, the whole cell lysate was prepared in RIPA buffer supplemented with protease and phosphatase inhibitors cocktail (MCE Chemicals), and the protein concentration was determined by BCA protein assay (Meilunbio, MA0082). 30 μg proteins were separated on 8 to 12% SDS-PAGE gels, transferred to PVDF membranes (Millipore), and detected with antibodies as follows:

Primary antibodies.

ASCT2 (Cell Signaling Technology, 8057S, 1:2,000), GLS (KGA/GAC, Proteintech, 12855-1-AP, 1:1,000), SLC7A11 (Proteintech, 12855-1-AP, 1:1,000), SAT1 (CST, 61586S, 1:2,000), SLC3A2 (Proteintech, 66883-1-Ig, 1:2,000), β-actin (Proteintech, 81115-1-RR, 1:2,000), Cleaved PARP (CST, 5625S, 1:2,000), PAOX (Proteintech, 18972-1-AP, 1:1,000), SMOX(15052-1-AP, 1:1,000) and PCNA (CST, 2586S, 1:2,000).

Secondary antibodies.

Goat anti-rabbit IgG HRP (Abmart, M21002,1:5,000), Goat anti-mouse IgG HRP (Abmart, M21001, 1:10,000).

RNA Sequencing and Data Analysis.

Total RNA was extracted from cells by Trizol reagent (Invitrogen) and then the RNA quality was assessed by Bioanalyzer 2100 (Agilent). The RNA with RIN (RNA integrity number) >7.0 was used for cDNA library construction. mRNA-Seq library was prepared using the Stranded mRNA-seq Library Prep Kit (Vazyme) following the manufacturer’s manual. Prepared libraries with different sequence indexes were pooled at equal molar and sequenced on Illumina X-ten using a standard protocol.

RNA-Seq Data Analysis.

The RNA-seq reads were mapped to the human genome reference (ENSEMBL genome browser hg38) using STAR version 2.4.2a with default parameters (59). Gene-level quantification was counted by RSEM version1.3.3 with the parameter configuration “--paired-end --alignments,” based on bam files obtained from STAR alignment (60). Differentially expressed gene analysis was conducted using the R package “DESeq2” (61), applying a threshold of |log2FoldChange| > log2(1.5) and P-value < 0.05. Functional enrichment analysis was performed using the Metascape online tool (https://metascape.org/gp/index.html#/main/step1).

Metabonomics Using Liquid Chromatography Tandem Mass Spectrometry (LC–MS/MS).

Cells in 10-cm dish under different culturing conditions were replaced with fresh medium 4 h before collection. Cells were rinsed with cold PBS, lysed with 80% methanol: 20% water and scraped into an Eppendorf tube. The extracts were frozen-thawed for three times in liquid nitrogen followed by overnight extraction at −80 °C, and centrifuged at 14,000 rpm and 4 °C for 15 min. The insoluble fraction after centrifugation was discarded, and the supernatant was collected to a new tube and evaporated to dryness using a SpeedVac concentrator (Thermo Fisher Scientific). Metabolites were reconstituted in 0.03% formic acid in LC–MS grade water and centrifuged to remove debris. LC–MS/MS was performed on a 6500 QRTAP hybrid triple quadrupole/liner ion trap mass spectrometry (AB Sciex, CA) interfaced with HPLC systems (SHIMADSU, Tokyo) with a 2.1 × 250 mm column (5 μm id). Each sample was loaded and separated at a flow rate of 200 μL/min using a 15 min gradient, consisting of 20% to 90% solution B (0.1% formic acid in acetonitrile) for 5 min, holding at 90% solution B for 3 min, 90 to 20% solution B for 0.5 min, holding at 20% solution B for 6.5 min. data were processed using Skyline 22.2, and intensities of metabolites were normalized to protein concentration of the corresponding sample. The Student’s t test was used to identify difference between two groups, the multivariate analyses and modeling of normalized areas were performed using MetaboAnalyst V5.0. Differential metabolites (|log2 (fold change)| > |log2(1.3)|, P < 0.05) were used for pathway enrichment analysis using MetaboAnalyst V5.0 (https://www.metaboanalyst.ca/).

L-13C5-Glutamine Labeling.

A549 SAT1 tet-on cells at 60 to 80% confluence in 10 cm dish was replaced with fresh medium containing 1 mM glutamine, with or without doxycycline (1 μg/mL), and cultured for 20 h to induce SAT1 expression. Cells were then incubated in labeling media containing 1 mM L-13C5-Glutamine with or without doxycycline (1 μg/mL) for 4 h. Cells were subsequently rinsed with cold PBS, lysed with 40% methanol:40% acetonitrile:20% water. The metabolites were extracted using procedures described in the above LC–MS/MS section. LC–MS/MS and data acquisition was performed using ExionLC AC (AB SCIEX) interfaced with TripleTOF 6600+ mass spectrometer (AB SCIEX). iHILIC-(P) Classic HPLC HILIC column (150 × 2.1 mm, 5 μm) was used for metabolite separation. TripleTOF 6600+ mass spectrometer is equipped with an electrospray ionization (ESI) probe with parameters set as follows: source temperature, 550°(ESI+) and 450 °C (ESI−); ion spray voltage, 5,500 V or −4,500 V in positive or negative modes;Curtain Gas(CUR), 35 psi; Ion Source Gas 1 (GS1), 60 psi; Ion Source Gas 2 (GS2), 60 psi. Data were processed using EI-MAVEN software.

Xenograft Tumor Formation and Drug Treatment.

Male Athymic Balb/c nude mice at 5 to 6 wk of age were purchased from Gempharmatech. 1 × 107 PC9 cells were suspended in 200 μL PBS and implanted subcutaneously into upper flanks of mice. For drug treatment, mice were randomly assigned to each group when the tumor reached 100 to 300 mm3. DENSpm was administrated intraperitoneally (i.p.) at 100 mg/kg twice weekly (23), V9302-HCL was administrated intraperitoneally (i.p.) at 30 mg/kg once daily (32), CB-839 was administrated intragastrically (i.g.) at 200 mg/kg once or twice per day (62), BSO was administrated intraperitoneally (i.p.) at 500 mg/kg every 2 d (63), and mice for single treatment also received the vehicle of other drugs. Tumor volume was calculated by the following formula: width × width × length × 0.5. The weight of the mice was measured every day to monitor drug toxicity. Treatment was completed after 2 wk and tumor was dissected after the mice were sacrificed for subsequent analyses.

All the animal experiments were performed in compliance with the NIH Guide for the Care and Use of Laboratory Animals (National Academies Press, 2011) and were approved by the Animal Ethics Committee of School of Basic Medical Sciences at Fudan University.

Primer and Oligo Sequences.

Primer and oligo sequences were provided in SI Appendix, Table S1.

Statistical Analysis.

Statistical analysis for experimental data was performed using GraphPad Prism 8 software. Data were presented as mean ± SD unless indicated. Unpaired Student’s t test was used to compare between two groups, and Ordinary one-way ANOVA with Tukey’s multiple comparisons test or two-way ANOVA with Sidak’s multiple comparisons test was used to compare among three or more groups.

Supplementary Material

Appendix 01 (PDF)

Dataset S01 (XLSX)

This work was supported by the National Natural Science Foundation of China (81871878 and 82100293), the Shanghai Municipal Natural Science Fund (20ZR1406500), the Innovation Research Team of High-level Local Universities in Shanghai, and NIH (R01CA129536 and R01CA224550). The metabolomics analyses were supported by The Professional Technical Service Platform for Critical Diseases in Shanghai, China (No. 22142202400), and members from the Single Cell Quantitative Metabolomics and Lipidomics Core Facility of Institute of Metabolism & Integrative Biology at Fudan University. We also thank Dr. Xianfu Gao from Shanghai ProfLeader Biotech Co., Ltd. for technical support on metabonomics and stable-isotope glutamine tracing experiments.

Author contributions

W.-X.Z. and Y.W. designed research; X.H., D.W., L.Y., J.S., J.W., L.Z., L.C., S.G., and Y.W. performed research; L.Z., L.C., and S.G. contributed new reagents/analytic tools; X.H., D.W., N.W., W.-X.Z., and Y.W. analyzed data; and X.H., W.-X.Z., and Y.W. wrote the paper.

Competing interests

The authors declare no competing interest.

Data, Materials, and Software Availability

RNA sequencing data have been deposited in National Omics Data Encyclopedia (Accession number: OEP005018) (64). All other data are included in the manuscript and/or supporting information.

Supporting Information

This article is a PNAS Direct Submission. N.Y.K. is a guest editor invited by the Editorial Board.
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