Active Oxidative Metabolism and Impaired Glyoxalase System Under Increased Intracellular Oxidative Stress in Non-Small Cell Lung Cancer

Active Oxidative Metabolism and Impaired Glyoxalase System Under Increased Intracellular Oxidative Stress in Non-Small Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Active Oxidative Metabolism and Impaired Glyoxalase System Under Increased Intracellular Oxidative Stress in Non-Small Cell Lung Cancer Ruth Birner-Gruenberger, Tamara Tomin, Sophie Honeder, Laura Liesinger, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4535848/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jun, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Reactive oxygen species can oxidatively modify enzymes to reroute metabolic pathways according to tumor needs but we lack overview of all potential targets. Thiol groups are most susceptible to oxidative modifications but rarely analyzed in clinical settings due to their reactivity. To accurately address the cross-talk between redox signaling and metabolism we collected tumor and healthy tissue from 70 individuals with non-small cell lung cancer right after surgery into a thiol-quenching solution, then carried out redox-proteomics. As a result of such an unbiased approach, we for the first time show evidence of higher oxidation of a number of key metabolic enzymes in tumor (especially glucose-related); we demonstrate that cancer strives to maintain oxidative metabolism amid the rise of intracellular oxidative stress; and report both redox and protein level deactivation of the glyoxalase system, which might be compensated by higher excretion or lower production of toxic methylglyoxal, aiding cancer progression. Biological sciences/Cell biology/Post-translational modifications Health sciences/Oncology/Cancer/Cancer metabolism Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer Figures Figure 1 Figure 2 Figure 3 Introduction Oxidative stress plays a critical role in carcinogenesis of lung cancer 1 , 2 . Prolonged exposure to tobacco smoke, air pollution, pathogens and/or other risk-factors submits lungs to a prooxidative environment, correlates with a functional decline of the lung epithelium 2 – 4 and can actively contribute to neoplastic transformation. On a chemical level, the negative effects of oxidative stress can be traced to the impact of reactive oxygen species (ROS), a group of unstable, electron rich molecules which drive oxidation of diverse biomolecules. While a small amount of ROS is common and even needed for physiological signaling 3 , an excess of ROS can lead to permanent cell damage and cell death. In cancer, ROS act as a double-edged sword: while some cancer therapies are based on triggering intracellular ROS to the point of cellular destruction 5 , ROS derived genetic instability and protein oxidation can also work in favor of prooncogenic signaling by modulating metabolic pathways and supporting neoplastic transformation 5 – 7 . One possibility how cross-talk between ROS production and metabolism is achieved is via oxidative posttranslational modifications (oxPTMs), especially on cysteine residues. Cysteine oxPTMs can lead to both loss 8 and gain of enzymatic functions 9 , and correspondingly rewire cancer metabolism 10 , 11 . To obtain a deeper understanding of the crosstalk between oxidative stress, redox signaling and metabolism in lung cancer, we collected tumor and matched healthy tissue from 70 individuals with non-small cell lung carcinoma (NSCLC). NSCLC accounts for 84% of diagnosed lung cancers and is almost exclusively treated with surgical resection in early stages 12 , providing access to often chemotherapy-naïve tissue specimens. Considering the importance of cysteines in redox signaling 13 , 14 , our specific focus was the analysis of cysteine oxidation, which is inherently a challenging task. Thiol residues are extremely prone to artificial oxidation, raising the need for special precautions during sample collection, including instant thiol quenching 15 . Therefore, in the frame of this study, immediately upon collection, tissue samples (from both tumor and healthy sections of the incised lung piece) were preserved from post-sampling oxidation in neutrally buffered 80 % methanl containing the cysteine alkylating reagent N-ethylmaleimide (NEM) following our recently published method 15 , enabling an unbiased determination of both small molecular weight (e.g. glutathione) as well as protein thiols. By applying this approach, we demonstrate that lung tumors are indeed exposed to higher intracellular levels of oxidative stress which affects the redox state of a number of proteins, many of which are key players of cellular metabolism, especially related to glucose utilization. However, on protein abundance level, we did not observe a trend towards lessened oxidative glucose metabolism in tumor tissue, rather the opposite: the expression of many mitochondrial proteins (including those of the citrate cycle) was increased in tumor, suggestive of active mitochondrial function. Lastly, we identified a potentially redox-dependent hampering of the glyoxalase system in cancer, the main detoxifying route of methylglyoxal (MG), a reactive side-product of glycolysis. Our combined comprehensive ex vivo redox analysis suggests that lung cancers maintain their oxidative metabolism despite the intracellular rise of oxidative stress. Toxic byproducts of glucose metabolism (i.e. MG) appear to be eliminated from the intracellular environment by secretion and/or reduced MG formation by higher GAPDH activity preserving cancer progression. GAPDH is supposed to prevent accumulation of MG but can also be a target of MG modification 16 . Materials and methods Sample collection and patient information Tissue pieces were collected in the span of one and a half years from curative surgery for NSCLC at the Division of Thoracic and Hyperbaric Surgery at the Medical University/State hospital of Graz, Austria. The cohort consisted of 64% male and 36% female patients with an average age of 65 ± 9 years at the point of surgery. The dominant form of NSCLC was lung adenocarcinoma (LUAD; 59%) followed by squamous cell carcinoma (SCC; 30%). In 10% of the cases no further pathological stratification at the point of sample collection beyond “NSCLC” was available, and one case of large cell carcinoma was included. Individual patient information such as type of lung cancer, gender and patient age are listed in Suppl. Table S1. Tissue pieces were collected at the point of pathological examination (within the first hour upon surgery) and were immediately placed into sample tubes containing 1 ml of 80% methanol in 50 mM ammonium acetate and 2.5 mM N-ethylmaleimide (NEM; polar extract solution). Samples were homogenized with an Ultraturrax tissue homogenizer (IKA) and stored at − 80°C until further processing. Study Approval and Ethical Aspects The use of human biomaterials was approved by the ethics committee of the Medical University of Graz (30–354 ex 17/18) and conformed with all pertaining regulations and the principles of the Declaration of Helsinki 17 . Sample preparation for comprehensive redox analysis Homogenized tissue pieces were sonicated (2–5 s at 10% amplitude) in polar extract solution, then spun down to pellet proteins for 10 min at 16,000 g . Supernatants were transferred to a new collection tube and dried down (for subsequent glutathione analysis) while the protein pellets were resuspended in 100 µl 50% trifluoroethanol (TFE)/50 mM ammonium bicarbonate (ABC) and processed for redox and quantitative proteomics. Proteomics sample preparation Tissue protein extracts in 50% TFE/ABC were subjected to protein estimation (BCA; Thermo) after which 50 µg of protein was aliquoted for further processing. In the next step, samples were diluted with 100 mM ABC, then reduced with 5 mM TCEP (in 50 mM ABC) for 30 min at 37°C, re-alkylated with 10 mM d5-NEM, diluted with four volumes of 25 mM ABC and digested overnight with trypsin at 37°C, (shaking at 550 rpm; 1 µg of trypsin per sample). In the last step, 4 µg of protein was desalted offline using in house made stage tips (SDB-RPS, Empore-Supelco); and 300 ng of protein was injected into the LC-MS/MS system. Proteomics LCMS analysis Chromatography was carried out on an Ultimate 3000 RSLC Nano Dionex system (Thermo) equipped with an Ionopticks Aurora Ultimate Series UHPLC C18 column (250 mm x 75 µm, 1.6 µm) (Ionopticks). Solvent A was 0.1% formic acid in water and solvent B acetonitrile containing 0.1% formic acid. Total LC-MS/MS run time per sample was 86.5 min with the following gradient: 0-5.5 min: 2% B; 5.5–25.5 min: 2–10% B; 25.5–45.5 min: 10–25% B, 45.5–55.5 min: 25–37% B, 55.5–85.5 min: 37–80% B; 65.5–75.5 min: 80% B; 75.5–76.5 min: 80 − 2% B; 76.5–86.5: 2% B at a flow rate of 400 nl/min and 40°C. The timsTOF mass spectrometer (Bruker Daltonics) was operated in positive mode with enabled trapped Ion Mobility Spectrometry (TIMS) at 100% duty cycle (100 ms ramp and accumulation time). The source capillary voltage was set to 1600 V and the dry gas flow to 3 L/min at 180°C. The scan mode was set to parallel accumulation-serial fragmentation (PASEF) for the scan range of 100–1700 m/z. Precursor selection was based on their intensity (data dependent acquisition) and the precursors were allowed to accumulate for the total of four ramps per PASEF cycle, resulting in a total cycle time of 0.53 s. Proteomics data processing and statistical analysis All proteomics analysis included all 70 samples per group (healthy and tumor). Raw data files were processed as previously described 18 with some modifications. Data analysis including database search and label free protein quantitation (LFQ) was carried out with MaxQuant (v2.1.3.0) 19 , 20 . Here, methionine oxidation, NEM and d5-NEM on cysteine were selected as dynamic modifications and no static modifications were defined. Trypsin was set as digestion enzymes allowing for two missed cleavages. Redox proteomics was processed with FragPipe (version 18.0 containing MSFragger v.3.5 21 and IonQuant 1.8.0 22 ). Open search as well as targeted search for methylglyoxal modifications were also carried out with FragPipe (same version or 19.0 (containing MSFragger 3.6 and IonQuant 1.8.9)). All data except of directed MG-H1 search was searched against a Uniprot human protein database fasta file with 20398 entries downloaded on 15.08.2022, while for MG-H1 search a fasta file containing 20806 entries (downloaded on 16.03.2023) was used. All outputs were filtered at 1 % FDR for dentification. If not stated otherwise Perseus (v1.6.14.0) was used for downstream statistical analysis of resulting quantitative data 23 . All raw data including search engine output has been deposited to PRIDE 24 with the following identification: PXD052340 (for review only: Username: [email protected] ; Password: JXv1wdvF0QkP). Label-free (quantitative) proteomics For LFQ analysis, at least two peptides per each protein were required for quantification. Match between run feature was enabled with matching time window of 1 min and alignment time window of 20 min. In Perseus, the table with protein LFQ intensities was filtered for non-contaminants, then for at least 60% of valid values in at least one of the groups (healthy or tumor). Values were additionally normalized by subtracting the median value per column and the missing values were imputed from normal distribution (width 0.3, downshift 1.8), before carrying out multi-test corrected Student’s t-testing between the groups (healthy versus tumor; FDR 5%, S0 0.1). The results of the LFQ proteomics of the patient data are reported in Suppl. Table S2. Significantly more or less abundant proteins were further subjected to gene ontology enrichment analysis of biological process (GOBP) using StringApp (v1.6.0) 25 and were additionally visualized using EnrichmentMap (v3.3.1) 26 , all in Cytoscape v3.9.1 27 . For easier overview of the data, EnrichmentMap output was additionally manually curated by grouping terms based on the type of the biological process (see Suppl. Fig. S2). All individual terms encompassing such a “cluster” of GOBPs are reported in Suppl. Table S5 (with proteins significantly less abundant in cancer as input for enrichment) and Suppl. Table S6 (proteins significantly up in cancer as input for enrichment). Redox proteomics : For peptide L/H (light to heavy, NEM to d5-NEM) ratio estimation, NEM and d5-NEM were configured as a “light” (NEM) and “heavy” (d5-NEM) label pair in IonQuant. Peptide lists with calculated ratios were then processed through an in-house Python script that first annotated the cysteine residues then calculated a median value of peptides containing the same cysteine residue. The script was deposited on Github and made publicly available ( https://github.com/BernhardRetzl/cysteine_label ). Statistical analysis of obtained light/heavy (L/H) ratios for each cysteine (resembling ratios of reduced to oxidized cysteine residues) was performed in Perseus. There, the matrix was further filtered to contain only those cysteines with reported L/H ratios in at least 30% of the samples per each of the groups (healthy or tumor). On the resulting matrices two-tailed Student’s t-test were performed between healthy and cancer group groups (FDR corrected p-value < 0.05 or non-FDR corrected p-value < 0.05). The output of the redox proteomics analysis is reported in Suppl. Table S3, which includes the filtered output of the script (median value of the reported ratios per cysteine after filtering for valid values) as well as raw output from FragPipe containing all the detected cysteine containing peptides. Proteins whose oxidative state was significantly altered (p-value < 0.05) were further used for GO enrichment analysis of either cellular compartments (GOCC) or GOBP using the STRING database web-platform (v12.0). In case of GOCC significantly more or less oxidized proteins were separately used as input for enrichment, while for GOBP all significantly redox-altered proteins were used. Cut-off for all enrichment analysis was always FDR corrected p-value < 0.05. GOCC and GOBP enriched terms from the redox dataset are reported in Suppl. Table S4. Open search An open search was carried out according to the built in FragPipe settings 28 . Briefly, the precursor mass tolerance was set from − 150 to + 500 Da. Methionine oxidation, NEM and d5-NEM cysteine modifications were selected as dynamic modifications, and no static modifications were defined. Strict trypsin was set as digestion enzyme allowing for two missed cleavages. PTM-Shepherd was activated to search against the Unimod collection of modifications and to normalize the data on the number of peptide spectral matches (PSM). A minimum of 10 PSM was set as threshold to be required for detection of a modification. The modified precursor tolerance was set to 0.01 Da, and the peak picking width to 0.002 Da. Results of the open search are listed in the Suppl. Table S7. MG-H1 directed search : Methylglyoxal hydroimidazolone 1 (MG-H1) directed search was carried out according to the default parameters for closed search in MSFragger with the following adaptations: precursor match tolerance was set to ± 20 ppm, strict-trypsin was used as enzyme but since MG-H1 is expected to be on lysine and arginine affecting tryptic cleavage efficiency, missed cleavage allowance was increased from two to three. Here methionine oxidation, NEM and d5-NEM on cysteines and MG-H1 (+ 54.0106 Da) on arginine and lysine were selected as dynamic modifications, and no static modifications were defined. The obtained table of quantified peptides was then imported in Perseus (v2.0.1.0) and filtered to contain only the peptides with reported MG-H1 modification. Resulting matrix was further filtered to contain only those MG-H1 peptides with reported value in at least 10 samples in at least one group (healthy or tumor) on which then the GO annotation and statistical analysis was carried out (Student’s t-test FDR corrected p-value < 0.05, S0 = 0.1). The results of MG-H1 directed search are also reported in Suppl. Table S7. Immunoblotting 21 µg of protein from nine matched patients (tumor and healthy tissue) was loaded onto an SDS-gel and consequently transferred to an PVDB membrane using an iBlot system (Thermo). Membranes were stained with PonceauS, then blocked for 1 h in Protein-free blocking buffer (Thermo) prior to incubation with MG-H1 monoclonal antibodies (1:1000 dilution in protein free blocking buffer; STA-011; Cell Biolabs). Upon washing, membranes were incubated with HRP-linked anti-mouse secondary antibody (G21040; Invitrogen) and the proteins harboring the MG-H1 epitope were visualized with SuperSignal West Pico Plus reagent (Thermo) on a ChemiDoc (Biorad). Results Intra- and extracellular changes in redox-balance in lung cancer To address the impact of oxidative stress on tissue redox signaling in cancer we carried out redox proteomics profiling of the 70 matched healthy and lung tumor tissue pieces which, upon site annotation and filtering for valid values (see Materials and methods), enabled us to quantitatively address the oxidative status of 1834 individual cysteine residues across 921 different proteins (Suppl. Table S3). The differences in redox ratios between the tumor and healthy tissues were not substantial; besides a couple of outliers, the samples did not strongly separate in a principal component analysis (Suppl. Figure 1C) and the median Cys red /Cys ox ratio of all samples was always clustered around the value of 1 (Suppl. Figure 1D), suggesting that (a) redox ratios are biologically tightly controlled and (b) our instantaneous alkylation protocol yielded unbiased results. While 97% of all proteins contain cysteine, reportedly only 18% of all proteins are expected to harbor disulfide bonds 29 ; other thiols are then free or otherwise modified. Of the 1834 confidently quantified cysteine redox ratios, we found 170 cysteine residues to be differentially oxidized upon multi-testing corrected student t-testing (Fig. 1 A, marked in black) and 488 residues upon analysis with a less stringent significance threshold (no multi-testing correction; Suppl. Table S3). The two top most oxidized cysteine residues in tumors were Cys 156 of caveolin-1 (CAV1) and Cys 240 of receptor of activated protein C kinase 1 (RACK1) (Fig. 1 A, Suppl. Table S3). These are particularly intriguing findings as both proteins are direct interactors of potent oncogenes. Cys 156 of CAV1 is a known palmitoylation site 30 as well as a nitrosylation target. 31 When modified, it increases CAV1 interaction with proto-oncogene kinase SRC, which “activates” CAV1 and improves caveolae trafficking as well as anchorage-independent cell growth 30 , 31 . RACK1 interacts with both activated protein kinase C as well as SRC 32 . It is additionally noteworthy to mention that CAV1 expression is often reduced in lung cancer compared to healthy parenchyma and we observe the same on protein level 33 (Suppl. Table S2). On the other hand, among the most oxidized proteins in healthy tissue compared to tumor were different isoforms of myosin, including MYH8 and MYH11 (Fig. 1 A, Suppl. Table S3). Next to higher oxidation, there was a greater abundance of myosin in healthy tissue on protein level, of both conventional as well as non-muscle isoforms (Suppl. Table S2); with exception of MYO1E, a myosin we detected to be more present in tumor (Suppl. Table S2) and which was just recently proposed as an independent marker of mortality in lung cancer 34 . To obtain a better overview of oxidative-stress affected cellular compartments and gain a deeper understanding of the cross-talk between redox signaling and metabolism, the 488 significant hits from redox profiling were further subjected to gene ontology enrichment of cellular compartments (GOCC) as well as biological process (GOBP). GOCC analysis suggested that tumor tissue was exposed to higher intracellular oxidative burden: Tumors had relatively more oxidation-affected intracellular proteins than healthy tissue (matching to the term GO:0043229 - intracellular organelles; Fig. 1 B). With only the significantly more oxidized proteins as input (separately for tumor or healthy), more oxidized proteins in tumor tissue matched to GOCC terms related to intracellular compartments and complexes (Fig. 1 D and Suppl. Table S4) while almost three times more significantly more oxidized proteins in healthy tissue were extracellular or secreted (Fig. 1 B; matching the GOCC term GO: 0005576 (extracellular region)). Correspondingly, extracellular matrix and blood particles were the top matched GOCC terms more oxidized in healthy tissue (displayed in Fig. 1 C; all terms listed in Suppl. Table S4). Interestingly, this was also reflected on protein abundance level of antioxidative enzymes. For example, while cytosolic and mitochondrial superoxide dismutase (SOD1 and SOD2) were more abundant in tumor tissue (especially mitochondrial isoform, SOD2), SOD3, the excreted isoform, was significantly more abundant in healthy tissue (Suppl. Fig. S1F and Suppl. Table S2). Furthermore, blood-cell-related antioxidant enzymes such as myeloperoxidase (MPO) and eosinophil peroxidase (EPX) were significantly more abundant in healthy tissue (Suppl. Fig. S1G and Suppl. Table S2). In general, more antioxidative proteins were present in healthy compared to tumor tissue (Supp. Fig. S1E, marked in pink; Suppl. Table S2), despite indications of higher intracellular oxidative stress in tumors. In summary, our data suggests that tumor tissue experiences more intracellular oxidative stress coupled with potentially impaired or insufficient antioxidative support. On the contrary, healthy tissue displayed higher levels of extracellular oxidative stress, which could be a consequence of better vascularization and therefore oxygen availability in healthy tissue: while the reported oxygen concentration for healthy lung tissue is around 5.6%, for lung tumors this value drops to approximately 2%, and in some extreme cases even to as low as 0.1% 35,36 . Along these lines, our quantitative proteomics analysis of protein abundance revealed a prominent reduction in abundance of all subunits of hemoglobin in tumor tissue (Suppl Table S2 and S5) as well as a reduction in proteins responsible for maintaining proper lung function and integrity of the epithelial barrier, such as claudins 37 (claudin-18), caveolins (CAV1 and 2) 38 , angiotensin-converting enzyme (ACE1 and 2) 39 and others (Suppl. Fig. S1E and S2A and Suppl. Tables S2 and S5). Cross-talk between redox signaling and glucose metabolism in lung tumor tissue To answer whether higher intracellular oxidative stress and disturbed redox signaling affects metabolic enzymes of cancer cells, we also carried out a GOBP enrichment with the 488 redox-affected proteins as input. This approach revealed prominent changes in the redox state of enzymes involved in glucose metabolism, especially in glycolysis and in the pentose-phosphate pathway (PPP; Fig. 1 E, marked in green), as well as of enzymes involved in detoxification of detrimental side products of glycolysis (i.e. methylglyoxal (MG); Fig. 1 E (marked in pink) and Suppl. Table S4). Given the intertwining nature of these pathways, we merged our redox- (Fig. 1 A and Suppl. Table S3) and label-free quantitative proteomics data (Suppl. Table S2 and Suppl. Fig. S1E) from 70 lung cancer patients and depicted combined effects on enzymes involved in glucose metabolism in tumor (as compared to healthy tissue; Fig. 2 ). This analysis revealed that not only the protein levels of glycolytic enzymes were changed in lung cancer but also their redox states. It is well known that cancer cells rely on glucose metabolism but are also able to adapt it to their needs by changing protein expression and allosteric regulation of the enzymes involved 40 . In this large patient cohort, we for the first-time provide in vivo evidence that a number of glucose-related metabolic enzymes is also susceptible to oxPTMs which might affect their enzymatic function. We detected higher oxidized states of Cys 170 of phosphofructokinase (PFK), Cys 247 of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Cys 379/80 of phosphoglycerate kinase (PGK) and Cys 49 and Cys 326 of pyruvate kinase M (PKM), which were all reported to interfere with either primary of secondary function of these enzymes 41 – 44 . Any impairment in the activity of glycolytic enzymes might trigger flux rerouting towards antioxidant production e.g. to the pentose phosphate pathway (PPP) for NADPH regeneration (and with it, glutathione recycling via glutathione reductase (GR)) or through serine-biosynthesis and folate cycle to glutathione (GSH) biosynthesis 45 (Fig. 2 ). In line with the latter, in tumors we detected a prominent increase in protein levels of glutathione synthase (GSS), the enzyme responsible for addition of glycine to the γ-glutamyl-cysteine in GSH biosynthesis (Suppl. Fig. S3B and Fig. 2 ). Glutathione is the most abundant and arguably most important intracellular antioxidant. As the ratio of the reduced (GSH) to the oxidized form of glutathione (GSSG) is tightly controlled under physiological conditions, disturbances of the GSH/GSSG ratio can be considered a readout of cellular oxidative stress 46 – 48 . While the GSH/GSSG ratio was not affected in this patient cohort (arguably due to rise in GSH synthesis), in cancer tissue we did detect higher levels of GSSG suggesting increased exposure to oxidative stress (Suppl. Fig. S3A). Interestingly, for a number of glycolytic enzymes we did not find the reported “most critical” cysteine residue (i.e. active-site cysteine or key cysteine for enzymatic function) to be more oxidized in tumors. For example, while we detected higher oxidation of Cys 49 and Cys 326 of PKM, the oxidative state of Cys 358 , a cysteine residue which when oxidized leads to complete loss of PKM2 activity prompting metabolic flux routing towards PPP 8 , was not significantly changed (Supp. Fig. S1H). The same was true for the catalytic Cys 152 of GAPDH 49 , 50 (Suppl. Fig. S1I). Modifications of cysteines even distal from the active site might however impact enzymatic activity, and for both PKM2 as well as for GAPDH, protein levels were prominently increased in tumor tissue (Fig. 2 and Suppl. Table S2). Interestingly, the abundance of the enzymes whose key cysteine was observed to be less oxidized in tumor (marked in green in Fig. 2 ) remained unchanged, as was the case for phosphoglycerate mutase 1 (PGAM1), transaldolase 1 (TALDO1) and 6-phosphoglucolactonase (PGLS). Only aldolase A (ALDOA) was found to be both less oxidized on its potentially critical cysteine (Cys 339 ) and at the same time to be more abundant, which may corroborate its critical role in cancer survival and metastasis 51 . Strikingly, not only the abundance of enzymes involved in glycolysis but also of those acting in the tricarboxylic acid (TCA) cycle was largely affected. While the redox status of TCA enzymes was mainly unchanged (except for malate dehydrogenase at Cys 89 and α-ketoglutarate dehydrogenase at Cys 802 ), almost all TCA enzymes were more abundant in tumor cells. This was also reflected on GOBP enrichment of all more abundant tumor proteins as input: a prominent cluster of mitochondrial activity/oxidative respiration GOBP terms emerged as upregulated in tumor tissue (Suppl. Fig. S2B and Supp. Table S6). In summary, our analysis corroborates earlier in vivo tracing studies that lung cancer cells heavily rely on the TCA cycle for energy production 52 . Attempting to maintain mitochondrial functionality amid the lack of oxygen might be a significant contributor to the rising pool of ROS 53 . Mitochondrial resilience in response to nutrient stress was recently also shown in vitro in NSCLC cell models under conditions of cysteine starvation revealing that sustaining the mitochondrial cysteine pool by GSH catabolism can support Fe-S proteins and thus mitochondrial respiratory function 54 . Along these lines and as aforementioned, SOD2 (the mitochondrial isoform of superoxide-dismutase) protein levels were prominently increased in tumor tissue (Suppl. Fig. S1F), suggesting that tumor mitochondria are in greater need of antioxidative protection. Lastly, it is also noteworthy that two enzymes of the non-oxidative branch of the PPP were found less oxidized in cancer (Fig. 3 ), and protein levels of 6-phosphogluconate dehydrogenase (6PGD) to be increased (Fig. 2 ; Suppl. Table S2). 6PGD is one of the two critical enzymes for NADPH regeneration, and with it, for overall intracellular redox balance. 55 , 56 Redox and protein-level downregulation of glyoxalase system Next to glycolysis one of the top enriched GOBP terms from our lung cancer redox-proteomics dataset was methylglyoxal metabolism (Fig. 1 E, marked in pink). Methylglyoxal (MG) is a highly reactive and toxic side product of glucose, lipid and protein metabolism, with glycolysis being its major source 57 – 59 . MG is mainly detoxified by a two-enzyme system: in the first step, glyoxalase 1 (GLO1) with the help of a molecule of GSH creates the intermediate product S-lactoylglutathione (sLG) – a metabolite consequently hydrolyzed by the activity of hydroxyacylglutathione hydrolase (GLO2; HAGH ), which produces D-lactate and recycles the used molecule of GSH (Figs. 2 and 3 A). Resulting D-lactate can be consequently converted to pyruvate with the help of lactate dehydrogenase D 60 (LDHD; Fig. 2 ). In our dataset, we detected prominent oxidation of Cys 139 of GLO1 in lung tumors (Fig. 1 A, 2 and 3 A, Suppl. Table S3), a cysteine residue deemed critical for the enzyme’s activity in vitro 61 . To validate if some degree of GLO1 inhibition is present in tumor tissue, we addressed levels of sLG, the direct GLO1 product, and accordingly observed reduced levels of sLG in tumors (n = 54; Fig. 3 A), further indicating potentially lower activity/lower flux through the GLO1 enzyme in our patient cohort. Interestingly, contrary to Cys 139 of GLO1, Cys 201 of GLO2 was significantly less oxidized in tumor tissue (Figs. 2 and 3 A, Suppl. Table S3). While Cys 201 of GLO2 was identified as an S-nitrosylation target 62 , nothing is known with regard to its effect on enzyme activity. Overall, the more oxidized state of GLO1 together with lower sLG levels suggests a potentially reduced ability of tumor cells to metabolize MG or perhaps less need of the cells to metabolize it. Although GLO1 is reported to be higher expressed in a number of different cancer types including the lung 57 , 63 , 64 , in our cohort of 70 patients we did not observe such a clear trend: almost half of the patients had a higher and the other half a lower GLO1 protein expression in tumor compared to the surrounding healthy tissue (Fig. 3 B; Suppl. Table S2). On the other hand, GLO2 expression was consistently reduced in tumor (Fig. 3 B; Suppl. Table S2). Of note, LDHD, the last enzyme involved, was detected in pairwise manner only in very few samples of our cohort with no striking change in abundance (Fig. 2 ; Suppl. Table S2). Since almost nothing is known regarding GLO2 expression in lung cancer, we examined publicly available cancer genome atlas datasets (TCGA; source: Xena browser; TARGET GTEx dataset) for GLO1 and GLO2 mRNA expression across normal, healthy lung, tumor-adjacent lung tissue as well as primary tumor of the lung. According to the TCGA public datasets, while GLO1 mRNA expression was increased in both tumors and tumor-adjacent tissue compared to healthy lung (Fig. 3 C), the opposite was the case for GLO2: GLO2 mRNA expression was prominently decreased in primary tumor compared to both normal and tumor-adjacent tissue (Fig. 3 C). Same was true for LDHD (Suppl. Fig. S4A). In all cases however, tumor adjacent tissue demonstrated higher mRNA transcript levels of both GLO enzymes as well as LDHD compared to healthy lungs, indicating that the cells in the tumor microenvironment might have to cope with higher MG levels than usual. Strikingly, low expression of GLO2 as well as of LDHD was prominently connected with poorer five-year overall survival across different cancers (TCGA PANCAN dataset; Suppl. Fig. S4B). Contrary to GLO2 and LDHD, expression of GLO1 had minimal effect on survival (Suppl. Fig. S4B). As one key property which distinguishes tumor to normal tissue is hypoxia, we wondered if hypoxia itself might play a role in inhibition of the glyoxalase pathway as hypoxia-driven GLO1 deactivation was suggested already by previous studies 65 , 66 . In this regard the effect of hypoxia might be twofold: (a) Electron decoupling and rise of ROS in hypoxia could affect the redox state of GLO enzymes potentially inhibiting them (especially GLO1); (b) Activity of hypoxia-dependent transcription factors (such as hypoxia inducible factor 1 (HIF1A)) could dictate GLO gene expression levels. To test the latter hypothesis, again using publicly available TCGA datasets, we carried out a correlation analysis between GLO1/HIF1A and GLO2/HIF1A expression in both lung cancer (LUNG; N = 1129), lung adenocarcinoma (LUAD; N = 585) as well as across all cancer types (PANCAN dataset; N = 11768). While GLO1 gene expression did not seem to be influenced by HIF1A gene expression (Fig. 3 D, upper panel), GLO2 gene expression was inversely correlated with HIF1A gene expression (Fig. 3 D, lower panel) suggesting a strong, hypoxia driven in vivo down-regulation of GLO2 gene expression (Pearson’s r ≥ 0.4). Similar was true for LDHD, especially in lung cancer datasets (Suppl. Fig. S4C). MG is potentially cleared from the tumor through excretion The key role of the glyoxalase system is to detoxify MG. Thus a lower activity of the glyoxylate system through ROS deactivation and/or hypoxia-driven downregulation is expected to lead to local MG accumulation in tumor tissue, especially since solid tumors are known to possess a high level of glycolytic dependency 67 . One of the ways to validate if that indeed is the case is to read out secondary effects of intracellular MG accumulation and track the resulting most common MG-driven protein modifications, namely MG-derived hydroimidazolone (MG-H1; +54.0106 Da) and dihydroxy MG/MG-derived carboxyethyl lysine (CEL; +72.0211) 68 . To this end, we ran an open search on the proteomics data of the collected tumor and healthy lung tissue and to our surprise observed the opposite: tumors had drastically less MG-driven modifications (namely MG-H1 and CEL) compared to healthy tissue (Fig. 3 E; N = 70 per group). The result of the open search was further corroborated by a specific search targeting MG-H1 as variable modification on peptides (Fig. 3 F and Suppl. Table S7), which confirmed a significantly higher abundance of MG-H1 modified peptides in healthy tissue compared to tumor (MG-H1 peptides more abundant in healthy are marked in violet). While the majority of the significantly more abundant MG-H1 modified peptides in both tumor and healthy tissue were intracellular, quite a large number of hemoglobin peptides were found to harbor MG-H1 modifications in healthy tissue (Suppl. Table S7). This further points towards better vascularization in healthy compared to tumor tissue but also might indicate a higher presence of MG in the blood stream of lung cancer patients, potentially due to secretion of MG by the tumor itself. Lastly, the output from the open and direct MG-H1 search was additionally validated by immunoblotting with an anti-MG-H1 antibody which confirmed a much stronger signal for MG-H1 protein modification in the healthy tissue compared to the tumor (Fig. 3 G). The MG-driven increase in AGEs is known to be able to upregulate the receptor for AGEs (RAGE), present at the surface of endothelial cells and other cell types and highly expressed in lungs. RAGE activation coupled with RAGE overexpression amplifies intracellular pro-inflammatory responses via induction of signaling pathways 69 – 71 . In line with lower intercellular presence of MG (reflected by lower abundance of AGE-modified proteins and lower metabolism of MG in tumor tissue) coupled with a potential increase in MG excretion by the tumor, in the healthy tissue taken from the distal part of the same explanted lung piece we observe a stark upregulation of RAGE, as well as its most prominent ligands - members of the S100 family (Suppl. Fig. S1E; marked in blue and Suppl. Table S2). Such loss of RAGE expression in tumor compared to the healthy epithelium is a known characteristic of lung cancer, and was even brought in direct correlation with oncogenic transformation 72 , 73 , which might suggest that mechanisms to evade AGE-driven cellular damage might be a common yet vital trait of lung cancer cells. Discussion ROS play an important role in development and progression of lung cancer orchestrating an interplay between oncogenes, oxPTM driven redox signaling and metabolic adaptations 74 . Tumorigenesis is a process driven by oncogenes, some of which are even known to boost oxidative metabolism (e.g. KRAS ), and with it, increase cellular stress exposure 75 . Tumors might employ targeted oxPTMs to both sustain cancer pro-oncogenic signaling and metastasis 31 , 32 , but also to directly influence metabolic pathways. For example, to lessen their oxidative burden, cancer cells are able to use oxPTMs to reroute metabolic pathways according to their needs, e.g. for antioxidant production/recycling. Accordingly, inhibition of key glycolytic enzymes such as PKM2 and GAPDH through oxPTMs (on Cys 358 and Cys 152 , respectively) is known to divert glucose away from glycolysis towards PPP for regeneration of redox equivalents which can be forwarded to enzymes such as GR to enable glutathione recycling 8 , 76 (Fig. 2 ). While we did not observe a significantly higher oxidation of these two cysteine residues of PKM and GAPDH in lung tumors of our patient cohort (Suppl. Fig. S1FG and Suppl. Table S3), we detected prominent oxidation of other cysteine residues of both GAPDH and PKM (Fig. 2 ; Suppl. Table S3), some of which have been implicated with loss of enzymatic function 42 , 43 . Next to GAPDH and PKM2, other glycolytic enzymes such as PFK, PGK1 and enolase 1 (ENO1) were found to be more oxidized but also higher abundant in tumors (Fig. 2 , Suppl. Tables S2-3). Oxidation of these enzymes has also been reported to lead to a reduction of their activities 41 , 44 , 77 , enabling diversion of carbon flux from glycolysis towards nucleotide and glutathione synthesis 77 : Inhibition of glycolysis enables for glycolytic intermediates to be pushed towards serine synthesis and consequently folate cycle and/or transsulfuration pathway 78 to provide glycine and cysteine, which are building blocks for glutathione production (Fig. 3 ) 45 . In line with this, we also observe a prominent increase in the expression of glutathione synthase in tumor (GSS; Suppl. Fig. S3B; Fig. 2 ; Suppl. Table S2), suggesting that there is indeed a higher need for glutathione production in cancer. In general, the goal of such a metabolic rerouting in tumor is often to improve antioxidant defense. Cancers, and especially lung cancer, are known to accumulate both reduced as well as oxidized glutathione 57 . We observed a similar trend in our cohort of 70 patients: compared to neighboring healthy tissue, tumors had drastically higher levels of GSSG which they appeared to balance by GSH production, reflected by a trend of an increase of GSH as well as a prominent rise in abundance of several enzymes responsible for GSH synthesis (Suppl. Fig. S3). Overall, our snapshot into the redox-proteomic landscape of tumor suggests that cancer cells are enduring higher levels of oxidative stress and might regulate glycolysis through redox-signaling to divert carbon flux towards antioxidant production. Another reason for cancer cells to slow down glycolysis would be to reduce accumulation of toxic glycolytic intermediates, such as methylglyoxal (MG). Accumulation of AGEs and dicarbonyl stress arising from uncontrolled MG reactivity can act as a double edged sword in cancers - sometimes supporting tumor growth, while other times triggering detrimental inflammation and apoptosis 79 , 80 . In lung cancer, specifically, it has been shown that tumors tend to upregulate GLO1, the main enzyme for MG metabolic clearance, and accumulate S-lactoylglutathione (sLG), the intermediate product of methylglyoxal catabolism 57 . Accumulation of sLG could be either due to a higher activity/abundance of GLO1 57 or lower activity/abundance of the subsequent enzyme in the pathway - GLO2 81 . However, in our large cohort of 70 patients we observed the opposite: lung tumors had lower sLG content compared to the surrounding tissue (Fig. 3 A). This could mean that less MG is produced in cancer cells (due to hypoxia and/or redox stress driven inactivation of the GLO system) or that the cancer cells tend to excrete MG rather than metabolizing it (Figs. 2 and 3 ). Lower production is quite plausible: similar to previous findings, we also observe that tumor tissue seems to maintain oxidative glucose metabolism which might reduce the pool of available DHAP for MG production 52 , 54 . Along those lines, tumors had dramatically higher levels of GAPDH as well as of all other downstream glycolytic and TCA enzymes (Fig. 2 and Suppl. Table S2). Interestingly, according to the TCGA data, GAPDH expression in lung cancer is inversely correlated with GLO2 and LDHD expression but not with GLO1 (Suppl. Fig. S4D) and higher expression of GAPDH is prominently connected to worse five-year survival across all cancer types (PANCAN dataset; Suppl. Fig. S4B). On the other hand, MG is a small, cell permeable molecule 82 . In line with the hypothesis that MG is excreted by tumors, higher MG content was reported for peritumor tissue of 40 individuals with colorectal carcinoma 83 and MG itself was proposed as a putative metabolic biomarker of cancer, as its blood levels consistently increased with tumor size in a rat colon adenocarcinoma model 84 . Furthermore, in tumor tissue of our patient cohort we also observe a stark downregulation of RAGE, the receptor for AGEs, which triggers an intracellular inflammatory and signaling cascade. RAGE is a known mediator of inflammation 85 and its loss is connected with higher cancer staging as well as metastatic potential of lung cancer cells 73 . The fact that RAGE seems more active in healthy tissue (with a number of its ligands of the S100 protein family being more expressed in healthy lungs) further suggests that contrary to the tumor itself, healthy lung tissue of lung cancer patients might be exposed to a higher presence of AGEs and dicarbonyl stress and correspondingly react to it. As aforementioned, observed perturbations of GLO1 activity/abundance and MG accumulation could be related to hypoxia. GLO1 activity seems to be lower in hypoxia 65 and malignant properties of the breast cancer cell line MCF-7 can be preserved under hypoxic conditions by GLO1 overexpression 86 . Similar beneficial effects of GLO1 overexpression have been shown to ameliorate the effects of ischemia in critical limb disease 87 as well as improve neovascularization upon tissue ischemia in diabetes 88 . Moreover, GLO1 activity seems to decline with cancer progression, as higher stages of colorectal cancer had dramatically reduced GLO1 activity 89 . Furthermore, GLO1 has been particularly proposed as a drug target in leukemic cells exposed to chronic hypoxia 90 . While we did not observe changes in GLO1 protein abundance, we did find it to be more oxidized and likely less active in tumors. Furthermore, we revealed GLO2 protein to be both less oxidized as well as less abundant in lung tumor tissue of 70 individuals (compared to healthy tissue from the same lung explant; Fig. 3AB), which is in accordance with publicly available datasets demonstrating lower GLO2 gene expression in lung tumor (compared to normal and tumor-adjacent lung tissue). Moreover, GLO2 and LDHD but not GLO1 gene expression is inversely correlated with HIF1A gene expression in both lung cancer as well as across different cancer types (Fig. 3BCD). We therefore conclude that GLO2 is a hypoxia-sensitive enzyme in cancer. Low GLO2 and LDHD expression was also correlated with poorer survival in cancer patients, while GLO1 expression had minimal effect on the survival (Suppl. Fig. S4B). Interestingly, a GLO2 gene knock-out was also associated with metabolic rewiring. In the absolute lack of GLO2, accumulated sLG can non-enzymatically react with lysine residues of glycolytic enzymes reducing glycolytic output 81 , suggesting that GLO2 downregulation in cancer in vivo might resemble a balancing act at the cross-roads of metabolism and redox signaling and with it, an important drug target. Conclusion We carried out for the first-time immediate ex vivo redox analysis of lung tumor and tumor adjacent tissue of seventy individuals, which revealed a complex interlace of redox signaling and glucose metabolism, counteracting oxidative stress and formation of advanced glycation end-products. We found several glycolytic enzymes to be more oxidized suggesting metabolic rerouting of glycolytic metabolites to glutathione regeneration and glutathione and nucleotide synthesis. We demonstrate that lung cancer suffers from increased intracellular oxidative stress while remaining highly dependent on mitochondrial metabolism, reflected by strong upregulation of oxidized glutathione, active glutathione biosynthesis and TCA cycle enzymes, as well as intracellular antioxidative enzymes. Lastly, we show hypoxia-dependent downregulation of the glyoxalase system and less of its intermediate product S-lactoylglutathione and AGEs in lung cancer, which could be due to reduced DHAP levels and/or efficient excretion of MG. Declarations Competing interests Authors have no competing interest to report. Materials & Correspondence Co-corresponding authors contact: Matthias Schittmayer ( [email protected] ) and Ruth Birner-Gruenberger ( [email protected] ). Author contributions T.T., M.S. and R.G.B. devised the study and wrote the manuscript. T.T. and D.G. carried out the experiments. L.B. and J.L. provided the samples and carried pathological analysis. S.H. and L.L. collected and prepared the samples for redox analysis. T.T. did the data analysis and statistical evaluation. B.R. contributed to the data and statistical analysis. Acknowledgements We gratefully acknowledge the support of the TU Wien, PhD program Metabolic and Cardiovascular Disease (DK-MCD) of the Medical University of Graz, the Austrian Science Fund (FWF) through project grants 10.55776/W1226 , 10.55776/COE7 and 10.55776/FG12 to R. Birner-Gruenberger and 10.55776/F73 (P09) to R. Birner-Gruenberger and T. Tomin, the City of Vienna grant H-867676/2022 to T. Tomin and the Marietta Blau fellowship to Sophie Honeder. 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Cell Death Differ 17:1211–1220. 10.1038/cdd.2010.6 Additional Declarations There is NO Competing Interest. Supplementary Files Suppl.TableS1.Patientdata.xlsx Supplemental Table 1 Suppl.TableS2.LFQProteomicsLungTissue.xlsx Supplemental Table 2 Suppl.TableS3.RedoxProteomics.xlsx Supplemental Table 3 Suppl.TableS4.GOBPpvaluesignredoxproteomics.xlsx Supplemental Table 4 Suppl.TableS5.EnrichmentMapDowninCancer.xlsx Supplemental Table 5 Suppl.TableS6.EnrichmentMapUpinCancer.xlsx Supplemental Table 6 Suppl.TableS7.OpenSearchMGH1search.xlsx Supplemental Table 7 SupplementaryMethods.docx Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4535848","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315877546,"identity":"5245b2e6-dcb3-412f-b930-3a0278956843","order_by":0,"name":"Ruth Birner-Gruenberger","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3950-0312","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Birner-Gruenberger","suffix":""},{"id":315877547,"identity":"f6ee0725-7d4b-43ce-a489-db0bb050d5cd","order_by":1,"name":"Tamara Tomin","email":"","orcid":"","institution":"Technische Universität Wien","correspondingAuthor":false,"prefix":"","firstName":"Tamara","middleName":"","lastName":"Tomin","suffix":""},{"id":315877548,"identity":"4eb7aa43-537c-4530-b940-3c018863874e","order_by":2,"name":"Sophie Honeder","email":"","orcid":"","institution":"Technische Universität Wien","correspondingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Honeder","suffix":""},{"id":315877549,"identity":"01a7d77a-40eb-430b-8300-a473957fd383","order_by":3,"name":"Laura Liesinger","email":"","orcid":"","institution":"Technische Universität Wien","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Liesinger","suffix":""},{"id":315877550,"identity":"9d4bf2aa-de8d-4163-8476-44923f412fd6","order_by":4,"name":"Daniela Gremel","email":"","orcid":"","institution":"Technische Universität Wien","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Gremel","suffix":""},{"id":315877551,"identity":"0ba7112e-818e-4231-9581-2ce58807a593","order_by":5,"name":"Bermhard Retzl","email":"","orcid":"","institution":"Technische Universität Wien","correspondingAuthor":false,"prefix":"","firstName":"Bermhard","middleName":"","lastName":"Retzl","suffix":""},{"id":315877552,"identity":"d43a01cf-d310-46ec-88e2-064d1548251e","order_by":6,"name":"Joerg Lindenmann","email":"","orcid":"","institution":"Medical University of Graz","correspondingAuthor":false,"prefix":"","firstName":"Joerg","middleName":"","lastName":"Lindenmann","suffix":""},{"id":315877553,"identity":"52dd3f35-f7d4-4e8c-9353-e3eaff5ad96b","order_by":7,"name":"Luka Brcic","email":"","orcid":"https://orcid.org/0000-0002-9098-8416","institution":"Medical University of Graz","correspondingAuthor":false,"prefix":"","firstName":"Luka","middleName":"","lastName":"Brcic","suffix":""},{"id":315877554,"identity":"2688291d-9777-4fa3-b237-97b7ad0ba72f","order_by":8,"name":"Matthias Schittmayer","email":"","orcid":"","institution":"Technische Universität Wien","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Schittmayer","suffix":""}],"badges":[],"createdAt":"2024-06-05 18:40:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4535848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4535848/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-60326-y","type":"published","date":"2025-06-03T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58673198,"identity":"5020db18-305f-41ae-b588-82b7778e99fb","added_by":"auto","created_at":"2024-06-19 15:14:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":338053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRedox proteomics of lung tumor and tumor-adjacent healthy tissue reveals intra- and extracellular differences.\u003c/strong\u003e A. Volcano plot depicting proteins whose redox state was significantly affected in tumor versus healthy tissue. Each dot represents an individual cysteine residue (labelled black: significantly changed proteins (FDR corrected p-value \u0026lt; 0.05, S0 = 0.1); specific pathways of interest e.g. glucose and methylglyoxal metabolism are highlighted in green and pink, respectively); B. According to GOCC annotation, healthy tissue exhibited more oxidation of extracellular proteins compared to tumor tissue (33 % (92 out of 274) versus 14 % (31 out of 214) oxidized proteins matched to GOCC term for extracellular region in healthy versus tumor). Conversely, tumors percentage-wise displayed slightly higher oxidation of intracellular proteins (73 % (158 out of 214) in tumor compared to 73 % (160 of 274) in healthy); C. Similar findings were revealed by GOCC enrichment of more oxidized proteins in healthy tissue (274 proteins) as input with top terms matching to extracellular space/blood particles (extracellular GOCC terms marked in bold); D. The opposite was the case for more oxidized proteins in tumor tissue (214 proteins), with top most GOCC terms matching to intracellular compartments. For both C and D, the size of circles represents the number of significantly matched genes divided by the size of the term in percent. E. The top 25 non-redundant significantly enriched terms upon GOBP enrichment analysis with all 488 redox affected proteins as input (FDR for enrichments was always \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/bfd52ba5f4e01b55874e6bfd.png"},{"id":58673193,"identity":"21ff8892-ae22-41b8-9f4d-91d9b8378748","added_by":"auto","created_at":"2024-06-19 15:14:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":292448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOxidative and abundance changes of enzymes involved in glucose and methylglyoxal metabolism in lung cancer patients. \u003c/strong\u003eGlycolytic enzymes were not only more abundant (marked with a thick margin and bold writing) but also often more oxidized (represented in red) in lung tumors. Oxidation might interfere with functionality of these enzymes. If that is the case, accumulated glycolytic intermediates might be rerouted towards either pentose-phosphate pathway for NADPH regeneration (aiding glutathione recycling) or glutathione production (via serine \u003cem\u003ede novo\u003c/em\u003e synthesis and folate cycle). With high glycolytic rates increase in methylglyoxal (MG), a toxic side product of glycolysis is to be expected. However, its main detoxifying enzymes glyoxalase 1 (GLO1) and glyoxalase 2 (GLO2) seem to be less active (GLO1) as well as less abundant in tumor (GLO2), suggesting the excess of methylglyoxal might be secreted. In addition, although mainly not redox affected, almost all enzymes of the citrate cycle were found to be more abundant in tumor, suggesting that the cancer maintains functional mitochondrial metabolism. For a cysteine residue to be considered significantly altered the Student’s t-test p-value had to be less than 0.05, while for a protein to be considered more or less abundant its FDR corrected p-value (healthy \u003cem\u003eversus \u003c/em\u003etumor) had to be less than 0.05. 1,3BPG - 1,3-Bisphosphoglycerate; 2PG - 2-phosphoglycerate; 3PG - 3-phosphoglycerate; 3PHP - 3-hydroxypyruvate; 6PG - 6-phosphogluconate; 6PGD - 6-phosphogluconate dehydrogenase; 6PGL - 6-phosphogluconolactone; \u0026nbsp;ACO2 - aconitase; ALDOA/B - aldolase A/B; ACLY - citrate synthase; αKG - α-ketoglutarate; DHAP - dihydroxyacetone phosphate; E4P/F6P - erythrose 4-phosphate or fructose-6-phosphate; ENO1 - enolase 1; F1,6BP - Fructose 1,6-bisphosphate; FH - fumarase; G3P - glycerol-3-phosphate; G3P/S7P - G3P or sedoheptulose 7-phosphate; G6P - glucose-6-phosphate; GAPDH - Glyceraldehyde-3-phosphate dehydrogenase; GLO1 - lactoylglutathione lyase (glyoxalase 1); GLO2 - hydroxyacylglutathione hydrolase (glyoxalase 2); GPD - Glycerol-3-phosphate dehydrogenase; G6PD - glucose-6-phosphate dehydrogenase; GPI - Glucose-6-phosphate isomerase; GR - glutathione reductase; GSH- glutathione; GSS - glutathione synthetase; GSSG - glutathione disulfide; HK2 - hexokinase 2; IDH1/2 - isocitrate dehydrogenase; LDHA - lactate dehydrogenase A; LDHD - probable D-lactate dehydrogenase; MDH2 - malate dehydrogenase 2; MG - methylglyoxal; OGDH - α-ketoglutarate dehydrogenase; OxaloAc - oxaloacetate; PC - pyruvate carboxylase; PDH - Pyruvate dehydrogenase; PEP - phosphoenolpyruvate; PFK - phosphofructokinase; PGAM1 - Phosphoglycerate mutase 1; PGK1 - Phosphoglycerate kinase 1; PGLS - 6-phosphogluconolactonase; PHGDHA - phosphoglycerate dehydrogenase; PKM2 - pyruvate kinase M2; R5P/X5P - ribose or xylulose 5-phosphate; RPE - Ribulose-phosphate 3-epimerase; RPI - \u0026nbsp;Ribose 5-phosphate isomerase; SDHA - succinate dehydrogenase; sLG - S-lactoylglutathione; Succ-CoA - succinyl CoA; SUCLG2 - succinate-CoA ligase; TALDO - transaldolase; TKT - transketolase; TPI - triosephosphate isomerase.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/aa2e60ad877e97d58297ccdb.png"},{"id":58673200,"identity":"7b76c581-0994-4253-908d-77233d91497f","added_by":"auto","created_at":"2024-06-19 15:14:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":323685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of GLO2 was lower in tumor tissue and the downregulation might be driven by hypoxia. \u003c/strong\u003eA. Depiction of the main cellular methylglyoxal clearance pathway with redox status of the critical cysteine residue of GLO1 (Cys\u003csup\u003e138\u003c/sup\u003e; N\u0026nbsp;=\u0026nbsp;70 per group) as well as S-lactoylglutathione levels (the main GLO1 product; N\u0026nbsp;=\u0026nbsp;54 per group) in tumor versus healthy tissue. Contrary to GLO1, GLO2 (Cys\u003csup\u003e201\u003c/sup\u003e) was found to be significantly less oxidized in tumors (N\u0026nbsp;=\u0026nbsp;70 per group); B. While GLO1 protein levels were unchanged, GLO2 protein was significantly less abundant in tumor tissues (N\u0026nbsp;=\u0026nbsp;70 per group). C. GLO1 gene expression tends to be higher while GLO2 gene expression appears to be lower in lung tumor (N\u0026nbsp;=\u0026nbsp;1011) versus normal lung (N\u0026nbsp;=\u0026nbsp;287) and tumor-adjacent lung tissue (N=109). However, both GLO1 and GLO2 gene expression were markedly higher in tumor-adjacent tissue compared to normal lung tissue (source: Xena browser); D. Correlation analysis between GLO1 and GLO2 versus HIF1A gene expression in lung carcinoma (LUNG; N\u0026nbsp;=\u0026nbsp;1129), lung adenocarcinoma (LUAD; N\u0026nbsp;=\u0026nbsp;585) and all-cancer datasets (PANCAN; N\u0026nbsp;=\u0026nbsp;11768). E. An open search of LFQ proteomics data detected a lower quantity of MG driven modifications (MG-H1 on arginine and CEL on lysine) in tumor compared to healthy adjacent tissue (N = 70 per group). F. Targeted search for MG-H1 modification corroborated the open search output (N = 70 per group). Orange represents MG-H1 modified peptides more abundant in tumor, whereas purple represents those more abundant in healthy. G. Immunoblot analysis with MG-H1 antibody (N = 9 per group; healthy or tumor). **** Student’s t-test p-value \u0026lt; 0.0001; *** p-value \u0026lt; 0.001; ** p-value \u0026lt; 0.01; * p-value \u0026lt; 0.05; ns - not significant. For Fig. 4A and 4B the t-tests were paired. GLO1 - Lactoylglutathione lyase (glyoxalase 1), GLO2 - Hydroxyacylglutathione hydrolase (HAGH; glyoxylase 2), MG-H1 - methylglyoxal hydroimidazolone 1, CEL - N-ε-(carboxyethyl)lysine.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/92f134e65141f5b14327e22f.png"},{"id":83887068,"identity":"2466098d-eea7-4843-b330-7558c2ec8687","added_by":"auto","created_at":"2025-06-04 07:05:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2031617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/2f660960-ca61-4257-b74d-5dc43f22f709.pdf"},{"id":58673895,"identity":"5d887722-78e0-46c4-a0b7-df956813c7f5","added_by":"auto","created_at":"2024-06-19 15:22:19","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12829,"visible":true,"origin":"","legend":"Supplemental Table 1","description":"","filename":"Suppl.TableS1.Patientdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/e895c86ce3ad52652b2c265d.xlsx"},{"id":58673201,"identity":"b5eed77a-1fe7-46df-a0bf-c06799a20305","added_by":"auto","created_at":"2024-06-19 15:14:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10599103,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table 2\u003c/p\u003e","description":"","filename":"Suppl.TableS2.LFQProteomicsLungTissue.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/e9f7694a8960b60f0451aa2d.xlsx"},{"id":58673190,"identity":"fede339b-c097-49e1-ada8-96e1ddfad0d4","added_by":"auto","created_at":"2024-06-19 15:14:17","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12242226,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table 3\u003c/p\u003e","description":"","filename":"Suppl.TableS3.RedoxProteomics.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/2eee97a48c65eeff882192ee.xlsx"},{"id":58673202,"identity":"ee51fef2-272b-4367-94b7-b73242eac458","added_by":"auto","created_at":"2024-06-19 15:14:22","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":179149,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table 4\u003c/p\u003e","description":"","filename":"Suppl.TableS4.GOBPpvaluesignredoxproteomics.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/983e9632fb30b8721e8673e9.xlsx"},{"id":58673192,"identity":"3310378a-5fdc-4cf8-bd04-ec5fec7a7f07","added_by":"auto","created_at":"2024-06-19 15:14:18","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":107986,"visible":true,"origin":"","legend":"Supplemental Table 5","description":"","filename":"Suppl.TableS5.EnrichmentMapDowninCancer.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/cf642f22872f858b3da5634c.xlsx"},{"id":58673195,"identity":"a077ac50-e473-48c0-a6d0-83020221e46a","added_by":"auto","created_at":"2024-06-19 15:14:19","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":80240,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table 6\u003c/p\u003e","description":"","filename":"Suppl.TableS6.EnrichmentMapUpinCancer.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/f0c8cc2ef45844ffb7c0c9f6.xlsx"},{"id":58673199,"identity":"d5b428ef-2638-4dae-89c0-75c656942970","added_by":"auto","created_at":"2024-06-19 15:14:20","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":7158319,"visible":true,"origin":"","legend":"Supplemental Table 7","description":"","filename":"Suppl.TableS7.OpenSearchMGH1search.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/93a7e59b42a64f84260ad8ba.xlsx"},{"id":58673203,"identity":"0c564edf-a099-4a75-9751-b36d17224fcd","added_by":"auto","created_at":"2024-06-19 15:14:22","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1904494,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-4535848/v1/5eaa9c5cbfdc63a4f6abdea7.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Active Oxidative Metabolism and Impaired Glyoxalase System Under Increased Intracellular Oxidative Stress in Non-Small Cell Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOxidative stress plays a critical role in carcinogenesis of lung cancer \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Prolonged exposure to tobacco smoke, air pollution, pathogens and/or other risk-factors submits lungs to a prooxidative environment, correlates with a functional decline of the lung epithelium \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and can actively contribute to neoplastic transformation. On a chemical level, the negative effects of oxidative stress can be traced to the impact of reactive oxygen species (ROS), a group of unstable, electron rich molecules which drive oxidation of diverse biomolecules. While a small amount of ROS is common and even needed for physiological signaling \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, an excess of ROS can lead to permanent cell damage and cell death.\u003c/p\u003e \u003cp\u003eIn cancer, ROS act as a double-edged sword: while some cancer therapies are based on triggering intracellular ROS to the point of cellular destruction \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, ROS derived genetic instability and protein oxidation can also work in favor of prooncogenic signaling by modulating metabolic pathways and supporting neoplastic transformation \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. One possibility how cross-talk between ROS production and metabolism is achieved is via oxidative posttranslational modifications (oxPTMs), especially on cysteine residues. Cysteine oxPTMs can lead to both loss \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and gain of enzymatic functions \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and correspondingly rewire cancer metabolism \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo obtain a deeper understanding of the crosstalk between oxidative stress, redox signaling and metabolism in lung cancer, we collected tumor and matched healthy tissue from 70 individuals with non-small cell lung carcinoma (NSCLC). NSCLC accounts for 84% of diagnosed lung cancers and is almost exclusively treated with surgical resection in early stages \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, providing access to often chemotherapy-na\u0026iuml;ve tissue specimens. Considering the importance of cysteines in redox signaling \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, our specific focus was the analysis of cysteine oxidation, which is inherently a challenging task. Thiol residues are extremely prone to artificial oxidation, raising the need for special precautions during sample collection, including instant thiol quenching \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Therefore, in the frame of this study, immediately upon collection, tissue samples (from both tumor and healthy sections of the incised lung piece) were preserved from post-sampling oxidation in neutrally buffered 80 % methanl containing the cysteine alkylating reagent N-ethylmaleimide (NEM) following our recently published method \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, enabling an unbiased determination of both small molecular weight (e.g. glutathione) as well as protein thiols.\u003c/p\u003e \u003cp\u003eBy applying this approach, we demonstrate that lung tumors are indeed exposed to higher intracellular levels of oxidative stress which affects the redox state of a number of proteins, many of which are key players of cellular metabolism, especially related to glucose utilization. However, on protein abundance level, we did not observe a trend towards lessened oxidative glucose metabolism in tumor tissue, rather the opposite: the expression of many mitochondrial proteins (including those of the citrate cycle) was increased in tumor, suggestive of active mitochondrial function. Lastly, we identified a potentially redox-dependent hampering of the glyoxalase system in cancer, the main detoxifying route of methylglyoxal (MG), a reactive side-product of glycolysis. Our combined comprehensive \u003cem\u003eex vivo\u003c/em\u003e redox analysis suggests that lung cancers maintain their oxidative metabolism despite the intracellular rise of oxidative stress. Toxic byproducts of glucose metabolism (i.e. MG) appear to be eliminated from the intracellular environment by secretion and/or reduced MG formation by higher GAPDH activity preserving cancer progression. GAPDH is supposed to prevent accumulation of MG but can also be a target of MG modification \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and patient information\u003c/h2\u003e \u003cp\u003eTissue pieces were collected in the span of one and a half years from curative surgery for NSCLC at the Division of Thoracic and Hyperbaric Surgery at the Medical University/State hospital of Graz, Austria. The cohort consisted of 64% male and 36% female patients with an average age of 65\u0026thinsp;\u0026plusmn;\u0026thinsp;9 years at the point of surgery. The dominant form of NSCLC was lung adenocarcinoma (LUAD; 59%) followed by squamous cell carcinoma (SCC; 30%). In 10% of the cases no further pathological stratification at the point of sample collection beyond \u0026ldquo;NSCLC\u0026rdquo; was available, and one case of large cell carcinoma was included. Individual patient information such as type of lung cancer, gender and patient age are listed in Suppl. Table S1. Tissue pieces were collected at the point of pathological examination (within the first hour upon surgery) and were immediately placed into sample tubes containing 1 ml of 80% methanol in 50 mM ammonium acetate and 2.5 mM N-ethylmaleimide (NEM; polar extract solution). Samples were homogenized with an Ultraturrax tissue homogenizer (IKA) and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Approval and Ethical Aspects\u003c/h2\u003e \u003cp\u003eThe use of human biomaterials was approved by the ethics committee of the Medical University of Graz (30\u0026ndash;354 ex 17/18) and conformed with all pertaining regulations and the principles of the Declaration of Helsinki \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation for comprehensive redox analysis\u003c/h2\u003e \u003cp\u003eHomogenized tissue pieces were sonicated (2\u0026ndash;5 s at 10% amplitude) in polar extract solution, then spun down to pellet proteins for 10 min at 16,000 \u003cem\u003eg\u003c/em\u003e. Supernatants were transferred to a new collection tube and dried down (for subsequent glutathione analysis) while the protein pellets were resuspended in 100 \u0026micro;l 50% trifluoroethanol (TFE)/50 mM ammonium bicarbonate (ABC) and processed for redox and quantitative proteomics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProteomics sample preparation\u003c/h2\u003e \u003cp\u003eTissue protein extracts in 50% TFE/ABC were subjected to protein estimation (BCA; Thermo) after which 50 \u0026micro;g of protein was aliquoted for further processing. In the next step, samples were diluted with 100 mM ABC, then reduced with 5 mM TCEP (in 50 mM ABC) for 30 min at 37\u0026deg;C, re-alkylated with 10 mM d5-NEM, diluted with four volumes of 25 mM ABC and digested overnight with trypsin at 37\u0026deg;C, (shaking at 550 rpm; 1 \u0026micro;g of trypsin per sample). In the last step, 4 \u0026micro;g of protein was desalted offline using in house made stage tips (SDB-RPS, Empore-Supelco); and 300 ng of protein was injected into the LC-MS/MS system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eProteomics LCMS analysis\u003c/h2\u003e \u003cp\u003eChromatography was carried out on an Ultimate 3000 RSLC Nano Dionex system (Thermo) equipped with an Ionopticks Aurora Ultimate Series UHPLC C18 column (250 mm x 75 \u0026micro;m, 1.6 \u0026micro;m) (Ionopticks). Solvent A was 0.1% formic acid in water and solvent B acetonitrile containing 0.1% formic acid. Total LC-MS/MS run time per sample was 86.5 min with the following gradient: 0-5.5 min: 2% B; 5.5\u0026ndash;25.5 min: 2\u0026ndash;10% B; 25.5\u0026ndash;45.5 min: 10\u0026ndash;25% B, 45.5\u0026ndash;55.5 min: 25\u0026ndash;37% B, 55.5\u0026ndash;85.5 min: 37\u0026ndash;80% B; 65.5\u0026ndash;75.5 min: 80% B; 75.5\u0026ndash;76.5 min: 80\u0026thinsp;\u0026minus;\u0026thinsp;2% B; 76.5\u0026ndash;86.5: 2% B at a flow rate of 400 nl/min and 40\u0026deg;C. The timsTOF mass spectrometer (Bruker Daltonics) was operated in positive mode with enabled trapped Ion Mobility Spectrometry (TIMS) at 100% duty cycle (100 ms ramp and accumulation time). The source capillary voltage was set to 1600 V and the dry gas flow to 3 L/min at 180\u0026deg;C. The scan mode was set to parallel accumulation-serial fragmentation (PASEF) for the scan range of 100\u0026ndash;1700 m/z. Precursor selection was based on their intensity (data dependent acquisition) and the precursors were allowed to accumulate for the total of four ramps per PASEF cycle, resulting in a total cycle time of 0.53 s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProteomics data processing and statistical analysis\u003c/h2\u003e \u003cp\u003eAll proteomics analysis included all 70 samples per group (healthy and tumor). Raw data files were processed as previously described\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e with some modifications. Data analysis including database search and label free protein quantitation (LFQ) was carried out with MaxQuant (v2.1.3.0) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Here, methionine oxidation, NEM and d5-NEM on cysteine were selected as dynamic modifications and no static modifications were defined. Trypsin was set as digestion enzymes allowing for two missed cleavages. Redox proteomics was processed with FragPipe (version 18.0 containing MSFragger v.3.5\u003csup\u003e21\u003c/sup\u003e and IonQuant 1.8.0 \u003csup\u003e22\u003c/sup\u003e). Open search as well as targeted search for methylglyoxal modifications were also carried out with FragPipe (same version or 19.0 (containing MSFragger 3.6 and IonQuant 1.8.9)). All data except of directed MG-H1 search was searched against a Uniprot human protein database fasta file with 20398 entries downloaded on 15.08.2022, while for MG-H1 search a fasta file containing 20806 entries (downloaded on 16.03.2023) was used. All outputs were filtered at 1 % FDR for dentification. If not stated otherwise Perseus (v1.6.14.0) was used for downstream statistical analysis of resulting quantitative data \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. All raw data including search engine output has been deposited to PRIDE\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e with the following identification: PXD052340 (for review only: Username: [email protected]; Password: JXv1wdvF0QkP).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLabel-free (quantitative) proteomics\u003c/strong\u003e \u003cp\u003eFor LFQ analysis, at least two peptides per each protein were required for quantification. Match between run feature was enabled with matching time window of 1 min and alignment time window of 20 min. In Perseus, the table with protein LFQ intensities was filtered for non-contaminants, then for at least 60% of valid values in at least one of the groups (healthy or tumor). Values were additionally normalized by subtracting the median value per column and the missing values were imputed from normal distribution (width 0.3, downshift 1.8), before carrying out multi-test corrected Student\u0026rsquo;s t-testing between the groups (healthy \u003cem\u003eversus\u003c/em\u003e tumor; FDR 5%, S0 0.1). The results of the LFQ proteomics of the patient data are reported in Suppl. Table S2. Significantly more or less abundant proteins were further subjected to gene ontology enrichment analysis of biological process (GOBP) using StringApp (v1.6.0) \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and were additionally visualized using EnrichmentMap (v3.3.1) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, all in Cytoscape v3.9.1 \u003csup\u003e27\u003c/sup\u003e. For easier overview of the data, EnrichmentMap output was additionally manually curated by grouping terms based on the type of the biological process (see Suppl. Fig. S2). All individual terms encompassing such a \u0026ldquo;cluster\u0026rdquo; of GOBPs are reported in Suppl. Table S5 (with proteins significantly less abundant in cancer as input for enrichment) and Suppl. Table S6 (proteins significantly up in cancer as input for enrichment).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eRedox proteomics\u003c/span\u003e: For peptide L/H (light to heavy, NEM to d5-NEM) ratio estimation, NEM and d5-NEM were configured as a \u0026ldquo;light\u0026rdquo; (NEM) and \u0026ldquo;heavy\u0026rdquo; (d5-NEM) label pair in IonQuant. Peptide lists with calculated ratios were then processed through an in-house Python script that first annotated the cysteine residues then calculated a median value of peptides containing the same cysteine residue. The script was deposited on Github and made publicly available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BernhardRetzl/cysteine_label\u003c/span\u003e\u003cspan address=\"https://github.com/BernhardRetzl/cysteine_label\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical analysis of obtained light/heavy (L/H) ratios for each cysteine (resembling ratios of reduced to oxidized cysteine residues) was performed in Perseus. There, the matrix was further filtered to contain only those cysteines with reported L/H ratios in at least 30% of the samples per each of the groups (healthy or tumor). On the resulting matrices two-tailed Student\u0026rsquo;s t-test were performed between healthy and cancer group groups (FDR corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or non-FDR corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The output of the redox proteomics analysis is reported in Suppl. Table S3, which includes the filtered output of the script (median value of the reported ratios per cysteine after filtering for valid values) as well as raw output from FragPipe containing all the detected cysteine containing peptides. Proteins whose oxidative state was significantly altered (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were further used for GO enrichment analysis of either cellular compartments (GOCC) or GOBP using the STRING database web-platform (v12.0). In case of GOCC significantly more or less oxidized proteins were separately used as input for enrichment, while for GOBP all significantly redox-altered proteins were used. Cut-off for all enrichment analysis was always FDR corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. GOCC and GOBP enriched terms from the redox dataset are reported in Suppl. Table S4.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOpen search\u003c/strong\u003e \u003cp\u003eAn open search was carried out according to the built in FragPipe settings \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Briefly, the precursor mass tolerance was set from \u0026minus;\u0026thinsp;150 to +\u0026thinsp;500 Da. Methionine oxidation, NEM and d5-NEM cysteine modifications were selected as dynamic modifications, and no static modifications were defined. Strict trypsin was set as digestion enzyme allowing for two missed cleavages. PTM-Shepherd was activated to search against the Unimod collection of modifications and to normalize the data on the number of peptide spectral matches (PSM). A minimum of 10 PSM was set as threshold to be required for detection of a modification. The modified precursor tolerance was set to 0.01 Da, and the peak picking width to 0.002 Da. Results of the open search are listed in the Suppl. Table S7.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMG-H1 directed search\u003c/span\u003e: Methylglyoxal hydroimidazolone 1 (MG-H1) directed search was carried out according to the default parameters for closed search in MSFragger with the following adaptations: precursor match tolerance was set to \u0026plusmn;\u0026thinsp;20 ppm, strict-trypsin was used as enzyme but since MG-H1 is expected to be on lysine and arginine affecting tryptic cleavage efficiency, missed cleavage allowance was increased from two to three. Here methionine oxidation, NEM and d5-NEM on cysteines and MG-H1 (+\u0026thinsp;54.0106 Da) on arginine and lysine were selected as dynamic modifications, and no static modifications were defined. The obtained table of quantified peptides was then imported in Perseus (v2.0.1.0) and filtered to contain only the peptides with reported MG-H1 modification. Resulting matrix was further filtered to contain only those MG-H1 peptides with reported value in at least 10 samples in at least one group (healthy or tumor) on which then the GO annotation and statistical analysis was carried out (Student\u0026rsquo;s t-test FDR corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, S0\u0026thinsp;=\u0026thinsp;0.1). The results of MG-H1 directed search are also reported in Suppl. Table S7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmunoblotting\u003c/h2\u003e \u003cp\u003e21 \u0026micro;g of protein from nine matched patients (tumor and healthy tissue) was loaded onto an SDS-gel and consequently transferred to an PVDB membrane using an iBlot system (Thermo). Membranes were stained with PonceauS, then blocked for 1 h in Protein-free blocking buffer (Thermo) prior to incubation with MG-H1 monoclonal antibodies (1:1000 dilution in protein free blocking buffer; STA-011; Cell Biolabs). Upon washing, membranes were incubated with HRP-linked anti-mouse secondary antibody (G21040; Invitrogen) and the proteins harboring the MG-H1 epitope were visualized with SuperSignal West Pico Plus reagent (Thermo) on a ChemiDoc (Biorad).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntra- and extracellular changes in redox-balance in lung cancer\u003c/h2\u003e \u003cp\u003eTo address the impact of oxidative stress on tissue redox signaling in cancer we carried out redox proteomics profiling of the 70 matched healthy and lung tumor tissue pieces which, upon site annotation and filtering for valid values (see Materials and methods), enabled us to quantitatively address the oxidative status of 1834 individual cysteine residues across 921 different proteins (Suppl. Table S3). The differences in redox ratios between the tumor and healthy tissues were not substantial; besides a couple of outliers, the samples did not strongly separate in a principal component analysis (Suppl. Figure\u0026nbsp;1C) and the median Cys\u003csub\u003ered\u003c/sub\u003e/Cys\u003csub\u003eox\u003c/sub\u003e ratio of all samples was always clustered around the value of 1 (Suppl. Figure\u0026nbsp;1D), suggesting that (a) redox ratios are biologically tightly controlled and (b) our instantaneous alkylation protocol yielded unbiased results. While 97% of all proteins contain cysteine, reportedly only 18% of all proteins are expected to harbor disulfide bonds \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e; other thiols are then free or otherwise modified.\u003c/p\u003e \u003cp\u003eOf the 1834 confidently quantified cysteine redox ratios, we found 170 cysteine residues to be differentially oxidized upon multi-testing corrected student t-testing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, marked in black) and 488 residues upon analysis with a less stringent significance threshold (no multi-testing correction; Suppl. Table S3). The two top most oxidized cysteine residues in tumors were Cys\u003csup\u003e156\u003c/sup\u003e of caveolin-1 (CAV1) and Cys\u003csup\u003e240\u003c/sup\u003e of receptor of activated protein C kinase 1 (RACK1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Suppl. Table S3). These are particularly intriguing findings as both proteins are direct interactors of potent oncogenes. Cys\u003csup\u003e156\u003c/sup\u003e of CAV1 is a known palmitoylation site \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e as well as a nitrosylation target. \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e When modified, it increases CAV1 interaction with proto-oncogene kinase SRC, which \u0026ldquo;activates\u0026rdquo; CAV1 and improves caveolae trafficking as well as anchorage-independent cell growth \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. RACK1 interacts with both activated protein kinase C as well as SRC \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. It is additionally noteworthy to mention that CAV1 expression is often reduced in lung cancer compared to healthy parenchyma and we observe the same on protein level \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e (Suppl. Table S2). On the other hand, among the most oxidized proteins in healthy tissue compared to tumor were different isoforms of myosin, including MYH8 and MYH11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Suppl. Table S3). Next to higher oxidation, there was a greater abundance of myosin in healthy tissue on protein level, of both conventional as well as non-muscle isoforms (Suppl. Table S2); with exception of MYO1E, a myosin we detected to be more present in tumor (Suppl. Table S2) and which was just recently proposed as an independent marker of mortality in lung cancer \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo obtain a better overview of oxidative-stress affected cellular compartments and gain a deeper understanding of the cross-talk between redox signaling and metabolism, the 488 significant hits from redox profiling were further subjected to gene ontology enrichment of cellular compartments (GOCC) as well as biological process (GOBP). GOCC analysis suggested that tumor tissue was exposed to higher intracellular oxidative burden: Tumors had relatively more oxidation-affected intracellular proteins than healthy tissue (matching to the term GO:0043229 - intracellular organelles; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). With only the significantly more oxidized proteins as input (separately for tumor or healthy), more oxidized proteins in tumor tissue matched to GOCC terms related to intracellular compartments and complexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and Suppl. Table S4) while almost three times more significantly more oxidized proteins in healthy tissue were extracellular or secreted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; matching the GOCC term GO: 0005576 (extracellular region)). Correspondingly, extracellular matrix and blood particles were the top matched GOCC terms more oxidized in healthy tissue (displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC; all terms listed in Suppl. Table S4). Interestingly, this was also reflected on protein abundance level of antioxidative enzymes. For example, while cytosolic and mitochondrial superoxide dismutase (SOD1 and SOD2) were more abundant in tumor tissue (especially mitochondrial isoform, SOD2), SOD3, the excreted isoform, was significantly more abundant in healthy tissue (Suppl. Fig. S1F and Suppl. Table S2). Furthermore, blood-cell-related antioxidant enzymes such as myeloperoxidase (MPO) and eosinophil peroxidase (EPX) were significantly more abundant in healthy tissue (Suppl. Fig. S1G and Suppl. Table S2). In general, more antioxidative proteins were present in healthy compared to tumor tissue (Supp. Fig. S1E, marked in pink; Suppl. Table S2), despite indications of higher intracellular oxidative stress in tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, our data suggests that tumor tissue experiences more intracellular oxidative stress coupled with potentially impaired or insufficient antioxidative support. On the contrary, healthy tissue displayed higher levels of extracellular oxidative stress, which could be a consequence of better vascularization and therefore oxygen availability in healthy tissue: while the reported oxygen concentration for healthy lung tissue is around 5.6%, for lung tumors this value drops to approximately 2%, and in some extreme cases even to as low as 0.1% \u003csup\u003e35,36\u003c/sup\u003e. Along these lines, our quantitative proteomics analysis of protein abundance revealed a prominent reduction in abundance of all subunits of hemoglobin in tumor tissue (Suppl Table S2 and S5) as well as a reduction in proteins responsible for maintaining proper lung function and integrity of the epithelial barrier, such as claudins \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e (claudin-18), caveolins (CAV1 and 2) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, angiotensin-converting enzyme (ACE1 and 2) \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and others (Suppl. Fig. S1E and S2A and Suppl. Tables S2 and S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCross-talk between redox signaling and glucose metabolism in lung tumor tissue\u003c/h2\u003e \u003cp\u003eTo answer whether higher intracellular oxidative stress and disturbed redox signaling affects metabolic enzymes of cancer cells, we also carried out a GOBP enrichment with the 488 redox-affected proteins as input. This approach revealed prominent changes in the redox state of enzymes involved in glucose metabolism, especially in glycolysis and in the pentose-phosphate pathway (PPP; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, marked in green), as well as of enzymes involved in detoxification of detrimental side products of glycolysis (i.e. methylglyoxal (MG); Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE (marked in pink) and Suppl. Table S4). Given the intertwining nature of these pathways, we merged our redox- (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Suppl. Table S3) and label-free quantitative proteomics data (Suppl. Table S2 and Suppl. Fig. S1E) from 70 lung cancer patients and depicted combined effects on enzymes involved in glucose metabolism in tumor (as compared to healthy tissue; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis analysis revealed that not only the protein levels of glycolytic enzymes were changed in lung cancer but also their redox states. It is well known that cancer cells rely on glucose metabolism but are also able to adapt it to their needs by changing protein expression and allosteric regulation of the enzymes involved\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In this large patient cohort, we for the first-time provide \u003cem\u003ein vivo\u003c/em\u003e evidence that a number of glucose-related metabolic enzymes is also susceptible to oxPTMs which might affect their enzymatic function. We detected higher oxidized states of Cys\u003csup\u003e170\u003c/sup\u003e of phosphofructokinase (PFK), Cys\u003csup\u003e247\u003c/sup\u003e of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Cys\u003csup\u003e379/80\u003c/sup\u003e of phosphoglycerate kinase (PGK) and Cys\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and Cys\u003csup\u003e326\u003c/sup\u003e of pyruvate kinase M (PKM), which were all reported to interfere with either primary of secondary function of these enzymes \u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Any impairment in the activity of glycolytic enzymes might trigger flux rerouting towards antioxidant production e.g. to the pentose phosphate pathway (PPP) for NADPH regeneration (and with it, glutathione recycling via glutathione reductase (GR)) or through serine-biosynthesis and folate cycle to glutathione (GSH) biosynthesis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In line with the latter, in tumors we detected a prominent increase in protein levels of glutathione synthase (GSS), the enzyme responsible for addition of glycine to the γ-glutamyl-cysteine in GSH biosynthesis (Suppl. Fig. S3B and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Glutathione is the most abundant and arguably most important intracellular antioxidant. As the ratio of the reduced (GSH) to the oxidized form of glutathione (GSSG) is tightly controlled under physiological conditions, disturbances of the GSH/GSSG ratio can be considered a readout of cellular oxidative stress \u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. While the GSH/GSSG ratio was not affected in this patient cohort (arguably due to rise in GSH synthesis), in cancer tissue we did detect higher levels of GSSG suggesting increased exposure to oxidative stress (Suppl. Fig. S3A).\u003c/p\u003e \u003cp\u003eInterestingly, for a number of glycolytic enzymes we did not find the reported \u0026ldquo;most critical\u0026rdquo; cysteine residue (i.e. active-site cysteine or key cysteine for enzymatic function) to be more oxidized in tumors. For example, while we detected higher oxidation of Cys\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and Cys\u003csup\u003e326\u003c/sup\u003e of PKM, the oxidative state of Cys\u003csup\u003e358\u003c/sup\u003e, a cysteine residue which when oxidized leads to complete loss of PKM2 activity prompting metabolic flux routing towards PPP \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, was not significantly changed (Supp. Fig. S1H). The same was true for the catalytic Cys\u003csup\u003e152\u003c/sup\u003e of GAPDH \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e (Suppl. Fig. S1I). Modifications of cysteines even distal from the active site might however impact enzymatic activity, and for both PKM2 as well as for GAPDH, protein levels were prominently increased in tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Suppl. Table S2).\u003c/p\u003e \u003cp\u003eInterestingly, the abundance of the enzymes whose key cysteine was observed to be less oxidized in tumor (marked in green in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) remained unchanged, as was the case for phosphoglycerate mutase 1 (PGAM1), transaldolase 1 (TALDO1) and 6-phosphoglucolactonase (PGLS). Only aldolase A (ALDOA) was found to be both less oxidized on its potentially critical cysteine (Cys\u003csup\u003e339\u003c/sup\u003e) and at the same time to be more abundant, which may corroborate its critical role in cancer survival and metastasis \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStrikingly, not only the abundance of enzymes involved in glycolysis but also of those acting in the tricarboxylic acid (TCA) cycle was largely affected. While the redox status of TCA enzymes was mainly unchanged (except for malate dehydrogenase at Cys\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e and α-ketoglutarate dehydrogenase at Cys\u003csup\u003e802\u003c/sup\u003e), almost all TCA enzymes were more abundant in tumor cells. This was also reflected on GOBP enrichment of all more abundant tumor proteins as input: a prominent cluster of mitochondrial activity/oxidative respiration GOBP terms emerged as upregulated in tumor tissue (Suppl. Fig. S2B and Supp. Table S6). In summary, our analysis corroborates earlier \u003cem\u003ein vivo\u003c/em\u003e tracing studies that lung cancer cells heavily rely on the TCA cycle for energy production \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Attempting to maintain mitochondrial functionality amid the lack of oxygen might be a significant contributor to the rising pool of ROS \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Mitochondrial resilience in response to nutrient stress was recently also shown \u003cem\u003ein vitro\u003c/em\u003e in NSCLC cell models under conditions of cysteine starvation revealing that sustaining the mitochondrial cysteine pool by GSH catabolism can support Fe-S proteins and thus mitochondrial respiratory function \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Along these lines and as aforementioned, SOD2 (the mitochondrial isoform of superoxide-dismutase) protein levels were prominently increased in tumor tissue (Suppl. Fig. S1F), suggesting that tumor mitochondria are in greater need of antioxidative protection.\u003c/p\u003e \u003cp\u003eLastly, it is also noteworthy that two enzymes of the non-oxidative branch of the PPP were found less oxidized in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and protein levels of 6-phosphogluconate dehydrogenase (6PGD) to be increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Suppl. Table S2). 6PGD is one of the two critical enzymes for NADPH regeneration, and with it, for overall intracellular redox balance. \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRedox and protein-level downregulation of glyoxalase system\u003c/h2\u003e \u003cp\u003eNext to glycolysis one of the top enriched GOBP terms from our lung cancer redox-proteomics dataset was methylglyoxal metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, marked in pink). Methylglyoxal (MG) is a highly reactive and toxic side product of glucose, lipid and protein metabolism, with glycolysis being its major source \u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. MG is mainly detoxified by a two-enzyme system: in the first step, glyoxalase 1 (GLO1) with the help of a molecule of GSH creates the intermediate product S-lactoylglutathione (sLG) \u0026ndash; a metabolite consequently hydrolyzed by the activity of hydroxyacylglutathione hydrolase (GLO2; \u003cem\u003eHAGH\u003c/em\u003e), which produces D-lactate and recycles the used molecule of GSH (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Resulting D-lactate can be consequently converted to pyruvate with the help of lactate dehydrogenase D \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e (LDHD; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our dataset, we detected prominent oxidation of Cys\u003csup\u003e139\u003c/sup\u003e of GLO1 in lung tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Suppl. Table S3), a cysteine residue deemed critical for the enzyme\u0026rsquo;s activity \u003cem\u003ein vitro\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. To validate if some degree of GLO1 inhibition is present in tumor tissue, we addressed levels of sLG, the direct GLO1 product, and accordingly observed reduced levels of sLG in tumors (n\u0026thinsp;=\u0026thinsp;54; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), further indicating potentially lower activity/lower flux through the GLO1 enzyme in our patient cohort. Interestingly, contrary to Cys\u003csup\u003e139\u003c/sup\u003e of GLO1, Cys\u003csup\u003e201\u003c/sup\u003e of GLO2 was significantly less oxidized in tumor tissue (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Suppl. Table S3). While Cys\u003csup\u003e201\u003c/sup\u003e of GLO2 was identified as an S-nitrosylation target \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, nothing is known with regard to its effect on enzyme activity.\u003c/p\u003e \u003cp\u003eOverall, the more oxidized state of GLO1 together with lower sLG levels suggests a potentially reduced ability of tumor cells to metabolize MG or perhaps less need of the cells to metabolize it. Although GLO1 is reported to be higher expressed in a number of different cancer types including the lung \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, in our cohort of 70 patients we did not observe such a clear trend: almost half of the patients had a higher and the other half a lower GLO1 protein expression in tumor compared to the surrounding healthy tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Suppl. Table S2). On the other hand, GLO2 expression was consistently reduced in tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Suppl. Table S2). Of note, LDHD, the last enzyme involved, was detected in pairwise manner only in very few samples of our cohort with no striking change in abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Suppl. Table S2). Since almost nothing is known regarding GLO2 expression in lung cancer, we examined publicly available cancer genome atlas datasets (TCGA; source: Xena browser; TARGET GTEx dataset) for GLO1 and GLO2 mRNA expression across normal, healthy lung, tumor-adjacent lung tissue as well as primary tumor of the lung. According to the TCGA public datasets, while GLO1 mRNA expression was increased in both tumors and tumor-adjacent tissue compared to healthy lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), the opposite was the case for GLO2: GLO2 mRNA expression was prominently decreased in primary tumor compared to both normal and tumor-adjacent tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Same was true for LDHD (Suppl. Fig. S4A). In all cases however, tumor adjacent tissue demonstrated higher mRNA transcript levels of both GLO enzymes as well as LDHD compared to healthy lungs, indicating that the cells in the tumor microenvironment might have to cope with higher MG levels than usual. Strikingly, low expression of GLO2 as well as of LDHD was prominently connected with poorer five-year overall survival across different cancers (TCGA PANCAN dataset; Suppl. Fig. S4B). Contrary to GLO2 and LDHD, expression of GLO1 had minimal effect on survival (Suppl. Fig. S4B).\u003c/p\u003e \u003cp\u003eAs one key property which distinguishes tumor to normal tissue is hypoxia, we wondered if hypoxia itself might play a role in inhibition of the glyoxalase pathway as hypoxia-driven GLO1 deactivation was suggested already by previous studies \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In this regard the effect of hypoxia might be twofold: (a) Electron decoupling and rise of ROS in hypoxia could affect the redox state of GLO enzymes potentially inhibiting them (especially GLO1); (b) Activity of hypoxia-dependent transcription factors (such as hypoxia inducible factor 1 (HIF1A)) could dictate GLO gene expression levels. To test the latter hypothesis, again using publicly available TCGA datasets, we carried out a correlation analysis between GLO1/HIF1A and GLO2/HIF1A expression in both lung cancer (LUNG; N\u0026thinsp;=\u0026thinsp;1129), lung adenocarcinoma (LUAD; N\u0026thinsp;=\u0026thinsp;585) as well as across all cancer types (PANCAN dataset; N\u0026thinsp;=\u0026thinsp;11768). While GLO1 gene expression did not seem to be influenced by HIF1A gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, upper panel), GLO2 gene expression was inversely correlated with HIF1A gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, lower panel) suggesting a strong, hypoxia driven \u003cem\u003ein vivo\u003c/em\u003e down-regulation of GLO2 gene expression (Pearson\u0026rsquo;s r\u0026thinsp;\u0026ge;\u0026thinsp;0.4). Similar was true for LDHD, especially in lung cancer datasets (Suppl. Fig. S4C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMG is potentially cleared from the tumor through excretion\u003c/h2\u003e \u003cp\u003eThe key role of the glyoxalase system is to detoxify MG. Thus a lower activity of the glyoxylate system through ROS deactivation and/or hypoxia-driven downregulation is expected to lead to local MG accumulation in tumor tissue, especially since solid tumors are known to possess a high level of glycolytic dependency \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. One of the ways to validate if that indeed is the case is to read out secondary effects of intracellular MG accumulation and track the resulting most common MG-driven protein modifications, namely MG-derived hydroimidazolone (MG-H1; +54.0106 Da) and dihydroxy MG/MG-derived carboxyethyl lysine (CEL; +72.0211) \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. To this end, we ran an open search on the proteomics data of the collected tumor and healthy lung tissue and to our surprise observed the opposite: tumors had drastically less MG-driven modifications (namely MG-H1 and CEL) compared to healthy tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE; N\u0026thinsp;=\u0026thinsp;70 per group). The result of the open search was further corroborated by a specific search targeting MG-H1 as variable modification on peptides (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and Suppl. Table S7), which confirmed a significantly higher abundance of MG-H1 modified peptides in healthy tissue compared to tumor (MG-H1 peptides more abundant in healthy are marked in violet). While the majority of the significantly more abundant MG-H1 modified peptides in both tumor and healthy tissue were intracellular, quite a large number of hemoglobin peptides were found to harbor MG-H1 modifications in healthy tissue (Suppl. Table S7). This further points towards better vascularization in healthy compared to tumor tissue but also might indicate a higher presence of MG in the blood stream of lung cancer patients, potentially due to secretion of MG by the tumor itself. Lastly, the output from the open and direct MG-H1 search was additionally validated by immunoblotting with an anti-MG-H1 antibody which confirmed a much stronger signal for MG-H1 protein modification in the healthy tissue compared to the tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eThe MG-driven increase in AGEs is known to be able to upregulate the receptor for AGEs (RAGE), present at the surface of endothelial cells and other cell types and highly expressed in lungs. RAGE activation coupled with RAGE overexpression amplifies intracellular pro-inflammatory responses via induction of signaling pathways \u003csup\u003e\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In line with lower intercellular presence of MG (reflected by lower abundance of AGE-modified proteins and lower metabolism of MG in tumor tissue) coupled with a potential increase in MG excretion by the tumor, in the healthy tissue taken from the distal part of the same explanted lung piece we observe a stark upregulation of RAGE, as well as its most prominent ligands - members of the S100 family (Suppl. Fig. S1E; marked in blue and Suppl. Table S2). Such loss of RAGE expression in tumor compared to the healthy epithelium is a known characteristic of lung cancer, and was even brought in direct correlation with oncogenic transformation \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, which might suggest that mechanisms to evade AGE-driven cellular damage might be a common yet vital trait of lung cancer cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eROS play an important role in development and progression of lung cancer orchestrating an interplay between oncogenes, oxPTM driven redox signaling and metabolic adaptations \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Tumorigenesis is a process driven by oncogenes, some of which are even known to boost oxidative metabolism (e.g. \u003cem\u003eKRAS\u003c/em\u003e), and with it, increase cellular stress exposure \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Tumors might employ targeted oxPTMs to both sustain cancer pro-oncogenic signaling and metastasis \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, but also to directly influence metabolic pathways. For example, to lessen their oxidative burden, cancer cells are able to use oxPTMs to reroute metabolic pathways according to their needs, e.g. for antioxidant production/recycling. Accordingly, inhibition of key glycolytic enzymes such as PKM2 and GAPDH through oxPTMs (on Cys\u003csup\u003e358\u003c/sup\u003e and Cys\u003csup\u003e152\u003c/sup\u003e, respectively) is known to divert glucose away from glycolysis towards PPP for regeneration of redox equivalents which can be forwarded to enzymes such as GR to enable glutathione recycling \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While we did not observe a significantly higher oxidation of these two cysteine residues of PKM and GAPDH in lung tumors of our patient cohort (Suppl. Fig. S1FG and Suppl. Table S3), we detected prominent oxidation of other cysteine residues of both GAPDH and PKM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Suppl. Table S3), some of which have been implicated with loss of enzymatic function \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Next to GAPDH and PKM2, other glycolytic enzymes such as PFK, PGK1 and enolase 1 (ENO1) were found to be more oxidized but also higher abundant in tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Suppl. Tables S2-3). Oxidation of these enzymes has also been reported to lead to a reduction of their activities \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, enabling diversion of carbon flux from glycolysis towards nucleotide and glutathione synthesis \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e: Inhibition of glycolysis enables for glycolytic intermediates to be pushed towards serine synthesis and consequently folate cycle and/or transsulfuration pathway \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e to provide glycine and cysteine, which are building blocks for glutathione production (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In line with this, we also observe a prominent increase in the expression of glutathione synthase in tumor (GSS; Suppl. Fig. S3B; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Suppl. Table S2), suggesting that there is indeed a higher need for glutathione production in cancer. In general, the goal of such a metabolic rerouting in tumor is often to improve antioxidant defense. Cancers, and especially lung cancer, are known to accumulate both reduced as well as oxidized glutathione \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. We observed a similar trend in our cohort of 70 patients: compared to neighboring healthy tissue, tumors had drastically higher levels of GSSG which they appeared to balance by GSH production, reflected by a trend of an increase of GSH as well as a prominent rise in abundance of several enzymes responsible for GSH synthesis (Suppl. Fig. S3). Overall, our snapshot into the redox-proteomic landscape of tumor suggests that cancer cells are enduring higher levels of oxidative stress and might regulate glycolysis through redox-signaling to divert carbon flux towards antioxidant production.\u003c/p\u003e \u003cp\u003eAnother reason for cancer cells to slow down glycolysis would be to reduce accumulation of toxic glycolytic intermediates, such as methylglyoxal (MG). Accumulation of AGEs and dicarbonyl stress arising from uncontrolled MG reactivity can act as a double edged sword in cancers - sometimes supporting tumor growth, while other times triggering detrimental inflammation and apoptosis \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. In lung cancer, specifically, it has been shown that tumors tend to upregulate GLO1, the main enzyme for MG metabolic clearance, and accumulate S-lactoylglutathione (sLG), the intermediate product of methylglyoxal catabolism \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Accumulation of sLG could be either due to a higher activity/abundance of GLO1 \u003csup\u003e57\u003c/sup\u003e or lower activity/abundance of the subsequent enzyme in the pathway - GLO2 \u003csup\u003e81\u003c/sup\u003e. However, in our large cohort of 70 patients we observed the opposite: lung tumors had lower sLG content compared to the surrounding tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This could mean that less MG is produced in cancer cells (due to hypoxia and/or redox stress driven inactivation of the GLO system) or that the cancer cells tend to excrete MG rather than metabolizing it (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Lower production is quite plausible: similar to previous findings, we also observe that tumor tissue seems to maintain oxidative glucose metabolism which might reduce the pool of available DHAP for MG production \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Along those lines, tumors had dramatically higher levels of GAPDH as well as of all other downstream glycolytic and TCA enzymes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Suppl. Table S2). Interestingly, according to the TCGA data, GAPDH expression in lung cancer is inversely correlated with GLO2 and LDHD expression but not with GLO1 (Suppl. Fig. S4D) and higher expression of GAPDH is prominently connected to worse five-year survival across all cancer types (PANCAN dataset; Suppl. Fig. S4B).\u003c/p\u003e \u003cp\u003eOn the other hand, MG is a small, cell permeable molecule \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. In line with the hypothesis that MG is excreted by tumors, higher MG content was reported for peritumor tissue of 40 individuals with colorectal carcinoma \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e and MG itself was proposed as a putative metabolic biomarker of cancer, as its blood levels consistently increased with tumor size in a rat colon adenocarcinoma model \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Furthermore, in tumor tissue of our patient cohort we also observe a stark downregulation of RAGE, the receptor for AGEs, which triggers an intracellular inflammatory and signaling cascade. RAGE is a known mediator of inflammation \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e and its loss is connected with higher cancer staging as well as metastatic potential of lung cancer cells \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. The fact that RAGE seems more active in healthy tissue (with a number of its ligands of the S100 protein family being more expressed in healthy lungs) further suggests that contrary to the tumor itself, healthy lung tissue of lung cancer patients might be exposed to a higher presence of AGEs and dicarbonyl stress and correspondingly react to it.\u003c/p\u003e \u003cp\u003eAs aforementioned, observed perturbations of GLO1 activity/abundance and MG accumulation could be related to hypoxia. GLO1 activity seems to be lower in hypoxia \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and malignant properties of the breast cancer cell line MCF-7 can be preserved under hypoxic conditions by GLO1 overexpression \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Similar beneficial effects of GLO1 overexpression have been shown to ameliorate the effects of ischemia in critical limb disease \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e as well as improve neovascularization upon tissue ischemia in diabetes \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Moreover, GLO1 activity seems to decline with cancer progression, as higher stages of colorectal cancer had dramatically reduced GLO1 activity \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Furthermore, GLO1 has been particularly proposed as a drug target in leukemic cells exposed to chronic hypoxia \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. While we did not observe changes in GLO1 protein abundance, we did find it to be more oxidized and likely less active in tumors. Furthermore, we revealed GLO2 protein to be both less oxidized as well as less abundant in lung tumor tissue of 70 individuals (compared to healthy tissue from the same lung explant; Fig.\u0026nbsp;3AB), which is in accordance with publicly available datasets demonstrating lower GLO2 gene expression in lung tumor (compared to normal and tumor-adjacent lung tissue). Moreover, GLO2 and LDHD but not GLO1 gene expression is inversely correlated with HIF1A gene expression in both lung cancer as well as across different cancer types (Fig.\u0026nbsp;3BCD). We therefore conclude that GLO2 is a hypoxia-sensitive enzyme in cancer. Low GLO2 and LDHD expression was also correlated with poorer survival in cancer patients, while GLO1 expression had minimal effect on the survival (Suppl. Fig. S4B). Interestingly, a GLO2 gene knock-out was also associated with metabolic rewiring. In the absolute lack of GLO2, accumulated sLG can non-enzymatically react with lysine residues of glycolytic enzymes reducing glycolytic output \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, suggesting that GLO2 downregulation in cancer \u003cem\u003ein vivo\u003c/em\u003e might resemble a balancing act at the cross-roads of metabolism and redox signaling and with it, an important drug target.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe carried out for the first-time immediate \u003cem\u003eex vivo\u003c/em\u003e redox analysis of lung tumor and tumor adjacent tissue of seventy individuals, which revealed a complex interlace of redox signaling and glucose metabolism, counteracting oxidative stress and formation of advanced glycation end-products. We found several glycolytic enzymes to be more oxidized suggesting metabolic rerouting of glycolytic metabolites to glutathione regeneration and glutathione and nucleotide synthesis. We demonstrate that lung cancer suffers from increased intracellular oxidative stress while remaining highly dependent on mitochondrial metabolism, reflected by strong upregulation of oxidized glutathione, active glutathione biosynthesis and TCA cycle enzymes, as well as intracellular antioxidative enzymes. Lastly, we show hypoxia-dependent downregulation of the glyoxalase system and less of its intermediate product S-lactoylglutathione and AGEs in lung cancer, which could be due to reduced DHAP levels and/or efficient excretion of MG.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eAuthors have no competing interest to report.\u003c/p\u003e \u003ch2\u003eMaterials \u0026amp; Correspondence\u003c/h2\u003e \u003cp\u003eCo-corresponding authors contact: Matthias Schittmayer ([email protected]) and Ruth Birner-Gruenberger ([email protected]).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eT.T., M.S. and R.G.B. devised the study and wrote the manuscript. T.T. and D.G. carried out the experiments. L.B. and J.L. provided the samples and carried pathological analysis. S.H. and L.L. collected and prepared the samples for redox analysis. T.T. did the data analysis and statistical evaluation. B.R. contributed to the data and statistical analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe gratefully acknowledge the support of the TU Wien, PhD program Metabolic and Cardiovascular Disease (DK-MCD) of the Medical University of Graz, the Austrian Science Fund (FWF) through project grants \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.55776/W1226\u003c/span\u003e\u003cspan address=\"10.55776/W1226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.55776/COE7\u003c/span\u003e\u003cspan address=\"10.55776/COE7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.55776/FG12\u003c/span\u003e\u003cspan address=\"10.55776/FG12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e to R. Birner-Gruenberger and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.55776/F73\u003c/span\u003e\u003cspan address=\"10.55776/F73\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (P09) to R. Birner-Gruenberger and T. Tomin, the City of Vienna grant H-867676/2022 to T. Tomin and the Marietta Blau fellowship to Sophie Honeder. Additionally, our thanks go to Shimadzu Austria for supporting this research through instrument access given in the Metabolomics and Bioprocess Analytics laboratory (TU Wien).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eValavanidis A, Vlachogianni T, Fiotakis K, Loridas S (2013) Pulmonary oxidative stress, inflammation and cancer: respirable particulate matter, fibrous dusts and ozone as major causes of lung carcinogenesis through reactive oxygen species mechanisms. 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Cell Death Differ 17:1211\u0026ndash;1220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/cdd.2010.6\u003c/span\u003e\u003cspan address=\"10.1038/cdd.2010.6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4535848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4535848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReactive oxygen species can oxidatively modify enzymes to reroute metabolic pathways according to tumor needs but we lack overview of all potential targets. Thiol groups are most susceptible to oxidative modifications but rarely analyzed in clinical settings due to their reactivity.\u003c/p\u003e \u003cp\u003eTo accurately address the cross-talk between redox signaling and metabolism we collected tumor and healthy tissue from 70 individuals with non-small cell lung cancer right after surgery into a thiol-quenching solution, then carried out redox-proteomics.\u003c/p\u003e \u003cp\u003eAs a result of such an unbiased approach, we for the first time show evidence of higher oxidation of a number of key metabolic enzymes in tumor (especially glucose-related); we demonstrate that cancer strives to maintain oxidative metabolism amid the rise of intracellular oxidative stress; and report both redox and protein level deactivation of the glyoxalase system, which might be compensated by higher excretion or lower production of toxic methylglyoxal, aiding cancer progression.\u003c/p\u003e","manuscriptTitle":"Active Oxidative Metabolism and Impaired Glyoxalase System Under Increased Intracellular Oxidative Stress in Non-Small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 15:14:10","doi":"10.21203/rs.3.rs-4535848/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a10f75c-c6db-48ed-aa89-9055341481b1","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33403868,"name":"Biological sciences/Cell biology/Post-translational modifications"},{"id":33403869,"name":"Health sciences/Oncology/Cancer/Cancer metabolism"},{"id":33403870,"name":"Biological sciences/Cancer/Lung cancer/Non-small-cell lung cancer"}],"tags":[],"updatedAt":"2025-06-04T07:05:17+00:00","versionOfRecord":{"articleIdentity":"rs-4535848","link":"https://doi.org/10.1038/s41467-025-60326-y","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-06-03 04:00:00","publishedOnDateReadable":"June 3rd, 2025"},"versionCreatedAt":"2024-06-19 15:14:10","video":"","vorDoi":"10.1038/s41467-025-60326-y","vorDoiUrl":"https://doi.org/10.1038/s41467-025-60326-y","workflowStages":[]},"version":"v1","identity":"rs-4535848","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4535848","identity":"rs-4535848","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}