ALDOA-mediated metabolic reprogramming is a targetable vulnerability for ferroptosis sensitization in cancer
Description
Ferroptosis presents great potential for cancer therapy, either alone or in combination with classical therapy. However, inducing ferroptosis by targeting canonical ferroptosis suppressors that directly inhibit lipid peroxidation non-selectively induces ferroptosis in both cancerous and normal cells, thereby limiting its therapeutic potential. In this study, we reveal that aldolase A (ALDOA) reprograms lipid metabolism to resist ferroptosis in cancer cells and identify ALDOA as a targetable vulnerability for ferroptosis sensitization. Cancer cells with ALDOA suppression exhibit increased susceptibility to ferroptosis-a response less obvious in normal cells. Mechanistically, ALDOA depletion induces significant accumulation of fructose 1,6-bisphosphate in cancer cells, thereby enhancing autophagy-dependent degradation of phospholipid-modifying enzymes. These alterations increase the ratio of phospholipids containing pro-ferroptotic polyunsaturated fatty acids over anti-ferroptotic monounsaturated fatty acids, culminating in heightened ferroptosis sensitivity. Moreover, ALDOA inhibitors selectively promote ferroptosis in cancer cells, both in vitro and in vivo. Collectively, our findings reveal that ALDOA-mediated metabolic reprogramming is a targetable vulnerability for ferroptosis sensitization in cancer.
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Quantitative lipidomic profiling and data analyses Sample preparation and lipid extraction. For sample preparation, equal numbers of cells were then collected and lipids were extracted according to the methyl tert-butyl ether (MTBE) method. For the internal lipid standards, SPLASH® LIPIDOMIX® Internal Standard (330707-1EA) and C15-Ceramide-d7 (d18:1-d7/15:0) (Avanti, 860681P-1mg) were used. The mixture was adequately vortexed, sonicated for 20 min at 4 °C and then kept for 30 min at room temperature. Subsequently, 200 μl of MS-grade water was added, and the mixture was vortexed and centrifuged at 14,000 rpm for 15min at 4 °C. The upper organic solvent layer was collected and dried under nitrogen. For LC-MS analysis, the samples were re-dissolved in 200 μl of IPA/ACN (9:1, v/v) solvent and centrifuged at 14,000 rpm at 4 °C for 15 min, and then the supernatant was injected. LC-MS/MS method. Lipidomic analysis was performed on a UHPLC system (LC-30AD, Shimadzu) coupled with QTRAP 6500+ mass spectrometer (Sciex). Separation was achieved using either a HILIC column (Phenomenex Luna NH₂, 2.0 × 100 mm, 3 µm) or a C18 column (Phenomenex Kinetex C18, 2.1 × 100 mm, 2.6 µm). MRM method was used for mass spectrometry quantitative data acquisition. Polled quality control (QC) samples were set in the sample queue to evaluate the stability and repeatability of the system. Data processing. Raw data were processed using Sciex OS software. The QCs were processed together with the biological samples. Metabolites in QCs with coefficient of variation (CV) less than 30% were denoted as reproducible measurements. Lipid identification was based on matching to authentic standards, and lipid composition and differential abundance analyses were subsequently performed. Isotope labeling and isotope tracing analysis. For glucose tracing analysis, U-13C-glucose (Cambridge Isotope Laboratories, CLM-1396) was used. After 0.5 h of labeling, cells were washed once with ice-cold DPBS, quenched with pre-chilled methanol/water (80:20, v/v), and held at –80 °C for 2 h. Cells were scraped, and extracts were centrifuged at 14,000 g for 20 min (4 °C). The supernatant was used for metabolite analysis and pellets were dissolved in KOH for BCA quantification for normalization. LC–MS/MS tracing was conducted using a Vanquish™ Flex UPLC system coupled to a TSQ Quantiva Ultra triple-quadrupole mass spectrometer (Thermo Fisher, CA), equipped with a heated ESI (HESI) source. Extracts were separated by a Synergi Hydro-RP column (2.0×100mm, 2.5 μm, phenomenex). Data analysis and natural isotope abundance correction were performed with MassHunter Profinder software (Agilent). Relative levels of M+n isotopologues were quantified from corrected peak areas and normalized to total biomass.
Institutions
- Tsinghua University
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Funders
- National Key R&D Program of ChinaGrant ID: 2022YFA1103704
- Beijing Natural Science FoundationGrant ID: JQ22016
- New Cornerstone Investigator Program
- Tsinghua-Peking Center for Life Sciences