MCU controls melanoma progression through a redox‐controlled phenotype switch

Abstract Melanoma is the deadliest of skin cancers and has a high tendency to metastasize to distant organs. Calcium and metabolic signals contribute to melanoma invasiveness; however, the underlying molecular details are elusive. The MCU complex is a major route for calcium into the mitochondrial matrix but whether MCU affects melanoma pathobiology was not understood. Here, we show that MCUA expression correlates with melanoma patient survival and is decreased in BRAF kinase inhibitor‐resistant melanomas. Knockdown (KD) of MCUA suppresses melanoma cell growth and stimulates migration and invasion. In melanoma xenografts, MCUA_KD reduces tumor volumes but promotes lung metastases. Proteomic analyses and protein microarrays identify pathways that link MCUA and melanoma cell phenotype and suggest a major role for redox regulation. Antioxidants enhance melanoma cell migration, while prooxidants diminish the MCUA_KD‐induced invasive phenotype. Furthermore, MCUA_KD increases melanoma cell resistance to immunotherapies and ferroptosis. Collectively, we demonstrate that MCUA controls melanoma aggressive behavior and therapeutic sensitivity. Manipulations of mitochondrial calcium and redox homeostasis, in combination with current therapies, should be considered in treating advanced melanoma.


Introduction
Melanoma is the most aggressive of all skin cancers (Schadendorf et al, 2018). Despite recent therapy improvements, advanced melanoma is difficult to cure and many patients still succumb to the disease; this is mostly influenced by low response rates to immunotherapies and drug resistance which commonly occur due to the high heterogeneity and plasticity of melanoma cells (Ribas & Wolchok, 2018;O'Donnell et al, 2019;Jenkins & Fisher, 2020).
Mitochondria were identified as important regulators of melanoma pathobiology (Haq et al, 2013;Roesch et al, 2013;Theodosakis et al, 2014;Zhang et al, 2019). Moreover, mitochondria are essential determinants of melanoma response to targeted therapies and immune checkpoint blockade (Vazquez et al, 2013;Harel et al, 2019). It is also becoming increasingly apparent that the tight interplay between mitochondria, bioenergetics, and reactive oxygen species (ROS), plays an important role in melanoma biology and therapeutic sensitivity (Roesch et al, 2013;Piskounova et al, 2015;Chio & Tuveson, 2017). Our previous findings showed that highly aggressive, tumor-maintaining, and therapy-resistant melanoma cells rely on mitochondrial bioenergetic output (Roesch et al, 2010(Roesch et al, , 2013Cappello et al, 2021). Other studies also demonstrated that increased mitochondrial capacity and resistance to oxidative stress define aggressive melanoma cell subsets (Haq et al, 2013;Vazquez et al, 2013) and that antioxidants and the environmental redox status control melanoma but also lung cancer cell metastatic spread (Sayin et al, 2014;Le Gal et al, 2015;Piskounova et al, 2015;Ubellacker et al, 2020). In addition, inhibitors of the antioxidant enzyme thioredoxin reductase 1, which is elevated in cancer, efficiently eliminated cancer cells including melanoma without affecting healthy cells (Stafford et al, 2018).
Similar to the redox signals, ionic Ca 2+ is an important regulator of cancer cell biology (Prevarskaya et al, 2011;Hoth, 2016;Marchi et al, 2020). In mitochondria, the mitochondrial Ca 2+ uniporter (MCU) complex is the main transporter of Ca 2+ across the inner mitochondrial membrane (IMM). The MCU complex is composed of several proteins located within the IMM or in the mitochondrial intermembrane space (IMS) (Fig 1A). These include the bona fide channel pore MCU A , its inhibiting isoform MCU B , EMRE, MCUR1, and the three regulators MICU1-3, which sense the Ca 2+ concentration in the IMS and thus control the activity of the whole MCU complex (Fig 1A) (Foskett & Philipson, 2015;Mammucari et al, 2018;Nemani et al, 2018;Pathak & Trebak, 2018;Pallafacchina et al, 2021). Given the functional importance of this complex, the relevance of MCU in cancers such as breast and hepatocellular carcinoma was investigated in the past (summarized in Vultur et al, 2018). These studies identified several important cancer-relevant mechanisms controlled by the MCU complex. However, they also presented opposing results, thus indicating that the role of MCU in tumor biology is far more complex and demands additional investigation.
Using an array of experimental techniques, proteomic and microarray screens, and bioinformatic approaches, we examined the role of the MCU A in a panel of genetically diverse melanoma cells, in melanoma xenografts and in melanoma patient samples and datasets. Our findings demonstrate that MCU A impacts aggressive melanoma cell behavior. Furthermore, we identify redox and metabolic signals as essential links between mitochondrial Ca 2+ and melanoma cell aggressiveness. We also show that MCU A controls melanoma cell sensitivity to immunotherapies and to inducers of ferroptotic cell death. Collectively, we propose that MCU A can be utilized as a prognostic biomarker as well as a therapeutic target in treating melanoma.

MCU A expression determines melanoma patient survival
To evaluate the clinical relevance of the MCU complex, and thus mitochondrial Ca 2+ homeostasis in melanoma, we performed bioinformatic processing of publicly available melanoma patient datasets (TCGA). To this end, we examined MCU A expression and classified melanoma patients as high and low expressers. Using Kaplan-Meier analyses, we found that melanoma patients with lower MCU A expression have a strongly reduced survival expectancy ( Fig 1B). Further analyses suggested that this MCU A association with patient survival is more pronounced in patients with mutations in the BRAF kinase, which is the most commonly altered molecule in cutaneous melanoma patients (Fig 1C). The association of MCU A expression with patient survival was also observed in BRAF WT patients but this was less evident ( Fig 1D). As BRAF kinase inhibitors such as vemurafenib or dabrafenib elevate cellular ROS production (Cesi et al, 2017), this finding indicated a potential role of redox signaling in the context of MCU A expression and patient survival. The important role of MCU A expression was also evident in the early stages (I-II) and in the late stages (III-IV) of the disease (Fig EV1A and B). These findings were intriguing since an inverse correlation between MCU A expression and patient survival (high expression = low survival) was suggested in several other cancers such as breast and liver (Li et al, 2020;Zheng et al, 2020). To understand the role of MCU A in a broader cancer-related context, we evaluated the influence of its expression on patient survival in a panel of additional cancer types. Our results showed that in some cancers such as kidney, bladder, cervical, colorectal, stomach, and thyroid, similarly to melanoma, MCU A expression correlates with patient survival (i.e., low expression = low survival; Figs 1E and EV1C-G). We also found that in other cancer types such as pancreas, breast, and liver, this correlation is inverse (high expression = low survival) (Figs 1F and EV1H and I) and in cancers such as lung, brain, head and neck, ovarian, uterine, prostate, and testicular, MCU A expression did not significantly affect patient survival . Given the potential clinical relevance of these findings, we sought to identify factors that regulate MCU A expression and, more importantly, parameters that link low MCU A levels with decreased melanoma patient survival. We thus first tested whether key genetic alterations such as BRAF or NRAS play a role in this context. Grouping melanomas by BRAF or NRAS status did not indicate that driver mutations play a role in the regulation of the MCU A expression (Fig EV1P and Q). Moreover, our analyses also showed that other parameters such as disease stage, patients' age, or gender do not influence MCU A expression (Fig EV1R-T). Considering the possible link between BRAF kinase inhibitors and MCU A , we examined whether resistance to these therapeutics might affect the expression of MCU components. As seen in Fig 1H and I, MCU A expression was decreased in both BRAF kinase inhibitor-resistant cell lines (451Lu-BR3 and WM983B-BR) as compared with their control cell lines (451Lu and WM983B). Notably, also EMRE and MICU1 were decreased in the resistant lines to some extent, suggesting that mito Ca 2+ is an important parameter of acquired therapeutic resistance in melanoma.
Taken all together, we conclude that the role of MCU A in cancer is tumor-type specific, and that low MCU A expression is associated with decreased melanoma patient survival and therapeutic resistance. (Petrungaro et al, 2015). To test whether this regulation applies to melanoma cells, we quantified mito Ca 2+ using the 4mt-D 3 cpV FRET-based biosensor (Fig 2A; Zhang  resistance to BRAF inhibitors ("BR"), normalized to housekeeping gene TBP, quantified by RT-qPCR (n ≥ 3 biological replicates). Data are presented as mean AE SEM. Statistical significance was assessed using unpaired, two-tailed Student's t-test, *P < 0.05; **P < 0.01; ***P < 0.005; no asterisk means no statistical significance (P > 0.05).
thapsigargin-induced mitochondrial Ca 2+ uptake in both 1205Lu and WM3734 cell lines (Fig 2B-G). The KD efficiency was confirmed by RT-qPCR and Western blot (WB) analyses and showed an inhibition of around 70% at the mRNA level and around 95% at the protein level (Fig EV2A-D). siRNA-based silencing of MCU A in WM3734 cells also decreased mito Ca 2+ (Fig 2H), while transient overexpression caused elevation of the resting mito Ca 2+ concentration ( Fig 2I). The efficiency of the siRNA transfection was again confirmed by RT-qPCR and WB analyses (Fig EV2E-H). Importantly, downregulation of MCU A did not significantly affect the expression levels of other MCU complex components (Fig EV2I-L).
To examine the contribution of MCU A on mito Ca 2+ following physiological stimulation, we first tested the effect of different stimuli on the store-operated Ca 2+ entry (SOCE) in melanoma cells. As depicted in Fig 2J, ATP, histamine, and noradrenaline induced increase in SOCE. Because the effect of ATP was the strongest, we next evaluated its effect on mito Ca 2+ in both 1205Lu and WM3734 cells. Our findings demonstrate that physiological stimulationinduced mito Ca 2+ uptake is also strongly suppressed in the MCU A_KD cells (Fig 2K-P).
Given that MCU A expression is decreased in the BRAF kinase inhibitor-resistant cells, we next measured mito Ca 2+ in the resistant (451Lu-BR3 and WM983B-BR) and the control (451Lu and WM983B) melanoma cells. Our results showed that ATP-induced mito Ca 2+ uptake is decreased in the resistant lines (Fig 2Q-V). These data thus strengthen the theory that low mito Ca 2+ promotes melanoma aggressiveness.
In immune cells, mitochondrial Ca 2+ uptake and thus MCU control SOCE by buffering Ca 2+ and by preventing premature closure of the Orai Ca 2+ channels (Hoth et al, 1997;Samanta et al, 2014). However, in other cell types, this mitochondria-controlled SOCE regulation was not observed. Because STIM-gated Orai channels affect melanoma cell biology (Stanisz et al, 2016), we measured SOCE in WM3734 MCU A_KD cells and found no overt differences between the control and the MCU A_KD cells. These results suggest that STIMgated Orai channels are not involved in the MCU A -controlled melanoma cell phenotype (Fig EV2M and N).
In summary, we confirm that MCU A and thereby the MCU complex are central regulators of the mitochondrial Ca 2+ homeostasis in melanoma cells. Moreover, we demonstrated that mito Ca 2+ is activated by physiological stimuli and is decreased in BRAF kinase inhibitor-resistant cells.

MCU A controls melanoma cell growth and invasion in vitro
Given that MCU A expression determines mito Ca 2+ , melanoma patient survival and therapeutic resistance, we explored its effect on cell growth and invasive potential. To this end, we determined cell proliferation of the WM3734 MCU A_KD and 1205Lu MCU A_KD cells. After 24 h, we recorded a mild increase in cell growth in the MCU A_KD cells. However, after 72 h, three out of four MCU A_KD cell lines displayed significantly decreased cell proliferation (Fig 3A and  B). To test the invasive potential of the MCU A_KD cells, we performed transwell migration assays and observed a robust increase in transwell migration of 1205Lu MCU A_KD cell lines (Fig 3C and D). As many cellular traits are affected by cell culture conditions and the microenvironment, especially when comparing 2D and 3D cellular cultures (Pampaloni et al, 2007;Kapałczyńska et al, 2018), we next examined the role of MCU A in 3D collagen-embedded melanoma spheroids. As shown in Fig 3E-H, the spheroid size was reduced in all MCU A_KD cell lines. Importantly, quantification of melanoma cell collagen invasion indicated that following a longer period (10 days), MCU A_KD promotes melanoma cell invasive properties ( Fig 3I). Next, we examined migration and invasion potentials of the BRAF kinase inhibitor-resistant cell lines and found that in both resistant lines, the transwell migration and spheroid invasion were elevated as compared with the control lines (Fig 3J-N).
Collectively, our findings suggested that lowered MCU A levels cause decreased melanoma cell growth but augmented migration and invasion.

MCU A regulates melanoma growth and invasion in vivo
The importance of MCU A in vivo was examined using melanoma xenografts in immunodeficient mice. Two clones of the 1205Lu MCU A_KD cells and a control transduced cell line were inoculated, and the tumor volume was measured over 36 days. As seen in Fig 3O-Q, both MCU A_KD clones resulted in smaller tumors. The difference in tumor volume and weight was detected 2 week after inoculation and reached 40% smaller volumes after 6 week (Fig 3P and  Q). These findings confirmed the in vitro observations regarding the role of MCU A in melanoma cell growth. To examine whether this conclusion also applies to the invasive behavior, we evaluated the metastatic burden in a postmortem examination of the mouse lungs. We found that the number of metastatic lesions per mouse, the ◀ Figure 2. MCU A controls mitochondrial calcium dynamics in melanoma cells.

A
Representative images of mitochondrial Ca 2+ measurements in WM3734 cells before and after thapsigargin (Tg; 1 μM) stimulation. Scale bar: 10 μm. B-D Mitochondrial calcium uptake (represented as FRET ratio) in stable 1205Lu shCTRL (black; n = 72 cells from nine biological replicates), 1205Lu shMCU_1 (darker blue; n = 67 cells from three biological replicates) and 1205Lu shMCU_2 (lighter blue; n = 64 cells from nine biological replicates) cells upon stimulation with thapsigargin (Tg; 1 μM). (C, D) Quantification of basal levels (C) and mitochondrial Ca 2+ uptake (Δpeak) (D) in 1205Lu stable lines with or without MCU A_KD . E-G Mitochondrial Ca 2+ uptake (represented as FRET ratio) in stable WM3734 shCTRL (black; n = 143 cells from nine biological replicates), WM3734 shMCU_1 (darker orange; n = 156 cells from nine biological replicates) and WM3734 shMCU_2 (lighter orange; n = 135 cells from nine biological replicates) cells upon stimulation with thapsigargin (Tg; 1 μM). Data information: Data in (B-I) were obtained by imaging single cells in Ringer's buffer containing 1 mM Ca 2+ , data in (J-V) were obtained in Ringer's buffer containing 0.5 mM Ca 2+ and are presented as mean AE SEM. Statistical significance was determined using unpaired, two-tailed Student's t-test (shMCU, siMCU, O/E or BR cells were compared to their respective control), *P < 0.05; **P < 0.01; ***P < 0.005; no asterisk means no statistical significance (P > 0.05). number of metastasis-positive mice, the total metastatic area, and the area/metastatic lesion ratio were higher in the mice inoculated with 1205Lu MCU A_KD cells compared with the control cell line (Fig 3R and S). The MCU A_KD efficiency was maintained in vivo over the 6 week after inoculation, as confirmed by WB analyses of tumor lysates (Fig EV3A and B). Our in vivo experiments suggest that MCU A and thereby the mitochondrial Ca 2+ homeostasis play an important role as regulators of melanoma pathobiology.

Proteomic analyses identify proteins and signaling pathways regulated by MCU A
To identify molecular determinants and signaling mechanisms that are influenced by the abundance and activity of MCU A , we performed proteomic and microarray analyses of MCU A_KD cells and the corresponding control cell lines. The volcano plot depicting the data from proteome analyses indicates that the abundance of a number of proteins is significantly altered (Fig 4A, red and green, see also Dataset EV1) following MCU A knockdown in the WM3734 cells. A "proteomap"-based pathway analysis indicated a high contribution of cellular metabolism and bioenergetics, protein folding, sorting and translation, as well as vesicular transport and cell adhesion, in the MCU A_KD -regulated proteins (Fig 4B and C). Similar findings were observed when we analyzed the proteomic data obtained from control and MCU A_KD 1205Lu cells (Fig EV3C-E, see also Dataset EV2). The subsequent KEGG-based pathway analysis depicted a robust melanoma cell metabolic reprograming as a number of metabolic pathways such as the pentose phosphate pathway, the TCA cycle, and the glutathione metabolism were significantly enriched following MCU A downregulation (Figs 4D and EV3F). In addition, the evaluation of the affected biological processes and molecular functions identified response and binding to ascorbic acid, dioxygenase activity as well as NADPH regeneration and NAD binding as processes controlled by MCU A . These data thus suggested that in the context of the MCU A_KD -induced metabolic reprograming, redox regulation plays a central role in melanoma cell phenotype control (Fig 4E and F). We also assessed the proteomic data according to the "Hallmarks of Cancer" classification and calculated the fraction of MCU A -controlled proteins into each hallmark as defined by Hanahan & Weinberg (2011) (Figs 4G and EV3G). This analysis identified MCU A -controlled proteins involved in several cancer hallmarks such as reprograming energy metabolism, activating invasion and metastasis and resisting cell death and further strengthened the hypothesis that MCU A is a critical regulator of melanoma cell pathobiology.
To further decipher the role of MCU A in melanoma, we performed reverse phase protein array (RPPA), an antibody-based screening tool that allows evaluation of abundance and/or activation state of cancer-relevant proteins (Lu et al, 2016). As shown in the cluster map ( Fig 4H, see also Dataset EV3), MCU A_KD significantly affected the abundance and/or activity of several proteins. The proteomaps and KEGG-based pathway analyses again suggested a significant contribution of MCU A in cell metabolism but also in transcription, signaling, protein quality control and cell growth, death, and invasion (Fig 4I and J). The subsequent pathway and functional analyses (Fig 4K-M) indicated that a number of metabolic and signaling pathways, T cell-relevant biological processes, kinase and phosphatase activity and cell death mechanisms are under control of or are influenced by the mitochondrial Ca 2+ homeostasis. The Hallmarks of Cancer analyses indicated a high occurrence of the identified "hits" and confirmed the proteomic findings ◀ Figure 3. MCU A knockdown suppresses tumor growth and promotes invasion in vitro and in vivo.
A, B Proliferation of 1205Lu (A) and WM3734 (B) stable MCU A_KD lines over 72 h, shown as percent of the respective control (shCTRL at 24 h) (n ≥ 7 biological replicates/condition/day). C Representative images of the migrated stained 1205Lu shCTRL (black frame), shMCU_1 (darker blue frame) and shMCU_2 (lighter blue frame) cells on the lower side of the insert. D Quantification of the transwell migration in 1205Lu stable MCU A_KD lines, based on the number of stained cells (n = 4 biological replicates/condition, shown also by individual data points). E Representative images of 1205Lu shCTRL (black frame), shMCU_1 (darker blue frame) and shMCU_2 (lighter blue frame) melanoma spheroids after 72-h invasion in collagen. Live cells are shown in green. Scale bar: 100 μm. F Quantification of 1205Lu stable MCU A_KD spheroid core size (n = 6 biological replicates/condition, shown also by individual data points). G Representative images of WM3734 shCTRL (black frame), shMCU_1 (darker orange frame) and shMCU_2 (lighter orange frame) melanoma spheroids. Live cells are shown in green. Scale bar: 100 μm. H Quantification of WM3734 stable MCU A_KD spheroid core size (n = 5 biological replicates/condition, shown also by individual data points). I Invasion potential of WM3734 shCTRL (black), shMCU_1 (darker orange) and shMCU_2 (lighter orange) over a period of 10 days (n ≤ 4 biological replicates/condition/day, shown also by individual data points). J, K Migration potential of 451Lu and 451Lu BRAF inhibitor-resistant (451Lu-BR3) (J) and WM983B and WM983B BRAF inhibitor-resistant (WM983B-BR) cells (K) over 24 h (n = 3 biological replicates/condition, shown also by individual data points). L Representative images of 451Lu wild-type (gray frame), 451Lu-BR3 (lighter green frame), WM983B wild-type (gray frame) and WM983B-BR (darker green frame) melanoma spheroids after 72-h invasion in collagen. Live cells are shown in green. Scale bar: 100 μm. M, N Quantification of 451Lu versus 451Lu-BR3 (M) and WM983B versus WM983B-BR (N) spheroid core size (n ≥ 9 biological replicates/condition, shown also by individual data points). O Representative images of mice in the shCTRL group and the shMCU_1 group after 36 days of tumor growth (n = 10 mice/group). P Tumor volume growth over 36 days postinoculation. Q Average weight of the tumors at the end point (36 days) upon removal (individual values shown as points). R Exemplary image of 1205Lu shCTRL (black frame) and shMCU (blue frame) metastases in mouse lung tissue sections; paraffin sections were made from the removed lungs; H&E staining was carried out and the sections were scanned using an Axio Scan.Z1 microscope. Scale bar: 100 μm. S Quantitative evaluation of the incidence (shown in percent) of lung metastases, total amount of positive animals identified with metastases, total area of all metastases in a group, and the average area per metastasis in shCTRL group (black) and shMCU group (blue).
Data information: Data are presented as mean AE SEM. Statistical significance was determined using unpaired, two-tailed Student's t-test (shMCU cells were compared with their respective control, shCTRL), *P < 0.05; **P < 0.01; ***P < 0.005; no asterisk means no statistical significance (P > 0.05). that MCU A is involved in hallmarks such as activating invasion and metastasis, sustaining proliferation, and reprograming energy metabolism ( Fig 4N).

Redox signals underlie the MCU A -driven melanoma cell phenotype
Our proteomic and RPPA screens indicated that MCU A and thereby mito Ca 2+ play a distinctive role in several cancer-relevant signaling pathways and cellular functions. Based on these findings and given that mito Ca 2+ is an important regulator of mitochondrial metabolic output and redox signaling ( Fig 5G). On the contrary, overexpression of MCU A , which led to elevated mito Ca 2+ , induced an increase in the cytosolic H 2 O 2 concentration ( Fig 5H). Measurements of the mitochondrial glutathione redox potential with another mitochondria-targeted biosensor ( mito Grx1-roGFP2) demonstrated a more reducing redox state in the mitochondrial matrix of all four MCU A_KD cell lines ( Fig 5I-K). Measurements of mitochondrial H 2 O 2 in the BRAF kinase inhibitor-resistant cells showed decreased mitochondrial H 2 O 2 levels relative to the control cells ( Fig 5L and M). Of note, the control SypHer-based measurements indicated that mitochondrial and intracellular pH was not altered in the MCU A_KD cells (Fig EV4A-F). Collectively, the redox measurements strengthen the hypothesis that MCU A is an important regulator of the mitochondrial and cellular redox homeostasis. Redox signals have been identified as regulators of melanoma invasive potential and metastatic spread in vitro and in vivo (Le Gal et al, 2015;Piskounova et al, 2015); hence, we hypothesized that MCU A -controlled H 2 O 2 production, that is, redox regulation, is a molecular mechanism that links decreased mito Ca 2+ and increased melanoma cell invasiveness. We tested this theory by measuring transwell migration of melanoma cells treated with antioxidants such as N-acety-cystein (NAC) and prooxidants such as buthionine sulphoximine (BSO) and H 2 O 2 . Our results confirmed that antioxidants promote melanoma cell migration and also showed that this increase can be reversed by the addition of oxidants (Fig 5 N and O). Moreover, we found that the MCU A_KD -induced increase in melanoma cell migration is diminished when cells are pretreated with H 2 O 2 ( Fig 5P). Of note, the BSO, NAC, and H 2 O 2 concentrations used for these experiments did not affect the viability of 1205Lu and WM3734 melanoma cells following 4 h of stimulation (Fig EV4G-I) but NAC and the mitochondria-targeted antioxidant mitoTEMPO suppressed the mitochondrial H 2 O 2 production ( Fig  EV4J and K). These findings thus corroborated the hypothesis that MCU A -controlled redox signals are involved in regulating melanoma aggressive behavior.

MCU A controls mitochondrial ATP production
To evaluate the impact of MCU A on mitochondrial function, we determined the inner mitochondria membrane (IMM) potential. Our single-cell-based fluorescence measurements showed that in the MCU A_KD melanoma cells, the IMM potential is significantly reduced (Fig 6A-D). These findings suggested that mitochondrial ATP production might also be affected by MCU A_KD . We thus measured ATP levels using a fluorescent dye as well as a genetically encoded mitochondrial ATP sensor, mito-A TEAM . Both measurements demonstrated that mitochondrial ATP levels are diminished in the 1205Luand the WM3734 MCU A_KD cells (Fig 6E-I).
Mitochondrial Ca 2+ induces dehydrogenase activity in the matrix and thus fuels ATP synthesis. Accordingly, the decreased ATP levels following MCU A_KD were not an unexpected finding; however, MCU A could have additional effects that may influence mitochondrial bioenergetics. For example, MCU A could modulate mitochondrial dynamics and structure. We explored this possibility by performing confocal-and electron microscopy. As seen in Fig 6J-O, neither the overall morphology of the mitochondrial network, nor the parameters such as mitochondrial diameters and the distances between cristae were altered in the MCU A_KD melanoma cells, indicating that the regulation of melanoma biology by MCU A is mostly governed by the mito Ca 2+ -ATP-redox axis.

A
Volcano plot of proteomics data for WM3734 MCU A _ KD versus shCTRL. Protein hits with significant P-values (P < 0.05) are marked in red. Protein hits with both significant P-value and FDR (false discovery rate) (P < 0.05; FDR < 0.05) are marked in green. B, C Proteomap analyses in WM3734 MCU A_KD cells, based on data shown in (A) (red dots). D KEGG-based analysis of cellular components and processes based on protein hits that show differential expression between in WM3734 MCU A_KD and shCTRL cells, revealed via proteomics. E, F Enrichment analyses of GO terms and pathways in WM3734 MCU A_KD cells, based on proteomic analysis. GO analyses were performed according to two GO categories: biological processes (E) and molecular function ( Jenkins & Fisher, 2020). Given the effects of MCU A expression and its effects on melanoma cell growth, invasion, and patient survival, we assessed the involvement of MCU A in melanoma cell therapeutic sensitivity. To this end, we first identified transcripts that correlate with those of MCU A in the TCGA-derived melanoma patient dataset (Fig 1). The proteomaps (Fig 7A and B) showed that in addition to metabolism, genetic information processing and signal transduction are processes whose expression correlated with MCU A and are also involved in regulating immune responses (Dataset EV4). The subsequent KEGG-based pathway analysis further strengthened these findings ( Fig 7C). As shown, the correlating genes were involved in a number of immune cell-relevant and metabolic pathways. This was further corroborated by the GO term-based evaluation of biological processes and molecular functions which demonstrated a robust enrichment of transcripts involved in antigen presentation, MHC activity, cytokine secretion, and other functions that can determine melanoma cell interaction with T-and NK cells (Fig 7D and E). These findings thus suggested that MCU A is an important determinant of melanoma cell therapeutic responses. To test this hypothesis, we first treated MCU A_KD cells with clinically relevant small molecule inhibitors, that is, trametinib (MEK inhibitor) and vemurafenib (BRAF inhibitor). As expected, both drugs reduced melanoma cell viability in a concentration-dependent manner (Figs 7F and G,and EV5A and B). However, we did not observe differences between the parental and the MCU A_KD cells. To test their sensitivity to immunotherapies, we used primary human natural killer (NK) cells from healthy human donors and determined NK cell-mediated melanoma cell killing (NKmK; Cappello et al, 2021). Notably, NKmK of MCU A_KD cells was diminished compared with the control cells (Fig 7H and I). Next, we treated the cells with the antineoplastic chemotherapeutic agent mitoxantrone, which is not used to treat melanoma but was recently reported as a potent inhibitor of MCU (Arduino et al, 2017). To this end, we examined its effect on melanoma cell viability and found that MCU A_KD cells are less sensitive to this inhibitor ( Fig 7J). Based on our findings regarding the role of MCU A on the redox status of melanoma cells and the fact that ferroptosis is a cell death mechanism controlled by oxidative stress (Jiang et al, 2021), we treated both control and MCU A_KD cells with two ferroptosis inducers (FINs). As demonstrated, MCU A_KD cells were more resistant to BSO, an agent that lowers glutathione levels and thus induces ferroptosis ( Fig 7K). This ferroptotic resistance was even more prominent when melanoma cells were treated with RSL3 (RAS-selective lethal), a drug that induces ferroptosis by inhibiting the glutathione peroxidase 4 (GPX4) (Jiang et al, 2021) (Figs 7L and EV5C). In summary, the presented bioinformatic-and experimental data indicate that MCU A regulates the immunogenicity of melanoma cells and thus their sensitivity to immune cell-based therapies. In addition, the findings that MCU A abundance determines the sensitivity to ferroptosis established a direct link between the altered metabolism, that is, redox status and melanoma aggressive behavior.

Discussion
The importance of Ca 2+ and redox regulation in cancer is nowadays widely accepted, and the number of genes and proteins involved in controlling molecular mechanisms that link Ca 2+ and redox signals with cancer biology is constantly increasing (Chio & Tuveson, 2017;Monteith et al, 2017;Marchi et al, 2020). In this context, several studies suggest that the MCU complex is an important determinant of cancer biology (summarized in Vultur et al, 2018). However, the current knowledge regarding the role of MCU in cancer is incomplete, and the molecular mechanisms involved and controlled by the Ca 2+ -transporting complex are not fully understood.
Our findings revealed that MCU A and thereby mito Ca 2+ control melanoma patient survival and melanoma cell aggressive behavior.
In this context, we demonstrated that acquired resistance to BRAF kinase inhibitors causes reduced MCU A expression and reduced mitochondrial Ca 2+ uptake. We also found that melanoma cell phenotype switching, a concept known to control melanoma progression and therapeutic sensitivity, is of central importance in this regard (Rambow et al, 2019). Indeed, we observed a decreased cell growth and increased invasive properties of MCU A_KD cells in vitro and in vivo. The proteomic and RPPA-based screens demonstrated that the highest fraction of proteins affected by MCU A_KD are involved in controlling cellular metabolism and redox signaling, in addition to environmental-and genetic information processing. Moreover, measurements of parameters, such as ATP production, IMM potential, and mitochondrial structure, suggest that MCU A controls mitochondrial function and bioenergetic output in melanoma cells. Given that bioenergetics, metabolism, and redox signaling play a critical role in cancer pathobiology (Diebold & Chandel, 2016;Panieri & Santoro, 2016;Hempel & Trebak, 2017;Zhang et al, 2019;Tasdogan et al, 2020), we sought to decipher the role of MCU A in melanoma by exploring the contribution of the involved signaling mechanisms and pathways. In addition, we pursued this direction based on studies which suggested that redox signals determine the melanoma cell invasive phenotype, whereby ROS, antioxidants, and the redox state of the cellular environment, that is, the blood and the lymph system control melanoma metastatic spread and sensitivity to ferroptotic cell death (Le Gal et al, 2015;Piskounova et al, 2015;Ubellacker et al, 2020).
Our findings are in line with these studies but also provide novel insights and identify molecular players that are involved in the Ca 2+ -controlled metabolic regulation of melanoma. This study also indicates that manipulations of mito Ca 2+ as a tool to modulate redox signaling might be applicable in treating melanoma and other ◀ Figure 5. MCU A controls redox signaling in melanoma cells.
cancers. More concretely, our results suggest that mitochondrial Ca 2+ uptake alters the cellular redox status and causes a more proliferative but less invasive melanoma cell phenotype. Given that such growth-oriented phenotypes are in general more responsive to conventional anticancer therapies, we hypothesize that the activation of MCU will increase the therapeutic sensitivity of melanoma cells and Our therapeutic assays demonstrate that MCU A affects melanoma cell sensitivity to immunotherapies and ferroptosis. To this end, the bioinformatic analyses of melanoma patient datasets identified genes whose expression correlates with the one of MCU A . Notably, most of these genes are involved in shaping the immunogenicity of melanoma tumors. These molecular determinants, together with MCU A, have thus the potential to be explored as biomarkers and predictors of patient therapeutic responses. In addition, the identification of the molecular determinants underlying the robust resistance toward ferroptosis inducers in cells lacking MCU A might provide novel therapeutic targets for treating metastatic melanoma.
Our study demonstrates that in melanoma, MCU A inhibits tumor aggressiveness by controlling a signaling cascade which encompasses mitochondrial-Ca 2+ dynamics, bioenergetics, and ROS production. Based on this model, increased MCU A expression causes elevated mito Ca 2+ , increased ATP synthesis but also higher ROS production that ultimately causes oxidative stress. Through a regulation of several pathways and cellular functions, this transformation induces a switch that drives melanoma cells toward a more proliferative and less invasive phenotype. Moreover, this MCU-Ca 2+ -ATP-ROS axis-induced phenotypic transformation will influence the immunogenicity and the therapeutic sensitivity of melanoma cells. According to this concept, melanomas with high MCU A abundance/ activity are associated with higher therapeutic sensitivity and will manifest into a less aggressive disease (Fig 8).
Within this study, we demonstrate that MCU A controls melanoma aggressive behavior and therapeutic sensitivity. We identify novel biomarkers and signaling pathways that control melanoma cell aggressiveness and suggest that manipulations of mitochondrial Ca 2+ and redox homeostasis, in combination with current therapies, should be considered in treating advanced melanoma.

Protein knockdown
Transient KD was performed using siRNA sequences from Qiagen (Hilden, Germany) and MicroSynth (Balgach, Switzerland), delivered to the cells by nucleofection (Amaxa Nucleofector, Lonza GmbH, Cologne, Germany). Two million cells and 4 μl of 20 pmol siRNA were used for each transfection. Measurements were taken 48 h post-transfection. All siRNA sequences are listed in Table EV1.

Fluorescence microscopy
Imaging experiments were performed at 37°C in 0.5 mM or 1 mM Ca 2+ Ringer's buffer (as indicated in the figure legends) using either a Zeiss Cell Observer Z1 microscope equipped with a 40× oil "Fluar" (N.A.: 1.3) objective, multi-filter system, fast acquisition EMCCD camera (Evolve ® 512 Delta) and LED system (Colibri, Zeiss) or a High MCU A abundance results in elevated mito Ca 2+ , which in turn enhances mitochondrial ROS and ATP production. This Ca 2+ -induced alterations induce metabolic and redox reprograming of melanoma cells and promote a more proliferative yet less invasive cellular phenotype. The high mito Ca 2+ -induces phenotype sensitizes melanoma cells to therapeutic treatments, decreases metastatic spread and has a positive effect on melanoma patient survival. Conversely, low MCU A abundance causes lower mito Ca 2+ , lower ROS and ATP production, promotes a less proliferative yet more invasive melanoma cell phenotype, promotes melanoma cell resistance to therapeutic treatments and decreases patient survival rate.
*CF: correction factor for donor (d) and acceptor (a) bleed-through.

Electron microscopy
Cells grown on glass coverslips were immobilized using a prewarmed solution consisting of 2.5% glutaraldehyde in 0.1 M cacodylic buffer at pH 7.4. Sample immobilization was continued for 1 h at room temperature and was completed at 4°C overnight. Staining and secondary fixation was started using 1% osmium tetroxide in 1.5% K 4 [Fe(CN) 6 ] in 0.1 M cacodylic buffer at pH 7.4 for 1 h at room temperature. Without any washing in-between, the solution was replaced with 1% osmium tetroxide in 0.1 M cacodylic buffer at pH 7.4 and incubated for an additional hour at room temperature. During three extensive washing steps using double distilled water for 10 min each, the Walton's lead aspartate solution was prepared. Therefore, 40 mg aspartic acid was dissolved in double distilled water, and the solution brought up to 60°C in an oven. Next, 66 mg lead nitrate was added to the solution. The pH of the mixture was then adjusted using 1N sodium hydroxide solution.
The samples were then immersed in the lead aspartate solution and kept at 60°C for 1 h. After three washing steps with double distilled water for 10 min each, the samples were dehydrated with a graded ethanol series starting with 30% over 50, 70 up to 100% with several exchanges in-between. Final dehydration prior to resin infiltration was facilitated with two exchanges of propylene oxide for 5 min each. Resin infiltration was mediated with a starting solution of 1:1 Epon resin and propylene oxide for 1 h at room temperature followed by placing the samples into fresh 100% Epon resin for an additional hour. For overnight infiltration, the samples were placed again in fresh Epon resin on a rocker table overnight at room temperature. The following day, the samples were embedded in BEEM capsules, and resin polymerization took place over 48 h at 60°C. Thin sections of 60 nm thickness were prepared from the final resin blocks and collected on formvar-coated mesh grids. The electron micrographs were recorded on a Philips CM120 transmission electron microscope equipped with a LaB6-source and a TVIPS 2x2 slow-scan CCD camera at an original magnification of 8,600×.

Immunolabeling and confocal microscopy
Cells were cultured on glass coverslips and fixed with prewarmed (37°C) 8% formaldehyde in PBS for 10 min at room temperature. After washing with PBS, cells were permeabilized with 0.5% Triton X-100 in PBS for 5 min and blocked with 5% BSA in PBS for 5 min. Anti-TOMM20 antibody coupled to Alexa Fluor 488 (1:100, #ab205486, Abcam) was diluted in 5% BSA in PBS and incubation was performed in dark for 1 hour at room temperature. Cells were washed in PBS and 2.5 μg/ml 40,6-Diamidin-2-phenylindol (DAPI) (Sigma-Aldrich) was added to the PBS during one washing step. Cells were then mounted in Mowiol with 0.1% 1,4-Diazabicyclo [2.2.2]octan (DABCO).
Confocal microscopy was performed with a TCS SP8 (Leica, Wetzlar, Germany) with a 63X oil immersion objective (HC PL APO 63×/1,40 oil CS2). No image processing was applied except for adjustment of brightness and contrast.

Proliferation, viability, migration, and invasion assays
Melanoma cell proliferation was evaluated by seeding 10,000 cells/ well in 24-well plates and incubating them at 37°C over a period of 72 h. Following cell growth, cells were fixed with methanol for 15 min and stained with a 0.05% crystal violet solution for 30 min at room temperature. Next, cells were destained with a 40% acetic acid solution, and absorbance at 595 nm was measured using a Mithras LB 940 plate reader (Berthold Technologies, Bad Wildbad, Germany). Cell viability was assessed using the CellTiter-Blue ® Cell Viability Assay kit (Promega GmbH, Walldorf, Germany), according to the manufacturer's instructions. Briefly, 5,000 cells/well were seeded in 96-well plates and allowed to attach overnight. Cells were afterward treated with small molecule inhibitors or ferroptosis inducers for 24-72 h. The resazurin-based reagent was added 3 h prior to fluorescence measurement using a CLARIOstar ® plate reader (BMG LABTECH, Ortenberg, Germany).
To assess the in vitro migration ability of cells, a transwell migration setup featuring 8 μm pore size inserts (Corning ® , Kennebunk ME, USA) was used. Briefly, 200,000 cells in 150 μl of fresh, serum-free medium were pipetted into the top compartment of an insert and were allowed to migrate for 4-48 h toward preconditioned medium containing 10% FCS. Prior to imaging, nonmigrated cells were washed out from the inserts, and the remaining migrated cells were stained with either 1 μM calcein-AM (#C1430, Thermo Fisher Scientific, Waltham, Massachusetts, USA) or with 0.5 μg/ml Hoechst 33342 (#H1399, Thermo Fisher Scientific, Waltham, Massachusetts, USA). Fluorescence intensity was measured using an Infinite 200 PRO plate reader (Tecan, M€ annedorf, Switzerland), or images of migrated cells were acquired using a Zeiss Axiovert S100TV inverted microscope (Oberkochen, Germany) featuring a sCMOS pco.edge camera and later analyzed using the ImageJ software.
Spheroids were generated as previously described (Roesch et al, 2013). Shortly, 5,000 cells/well were seeded in 96-well plates on top of a nonadhesive layer of agar. Following 96 h and the formation of a 3D structure, spheroids were harvested and embedded in a collagen I mixture and were allowed to invade for a given period of time. Spheroids were then stained with the Live/DeadÔ Viability/Cytotoxicity Kit (#L3224, Invitrogen, Carlsbad, California, USA) and were imaged using a Zeiss Primo Vert and a Zeiss Axiovert S100TV microscope equipped with a 10× objective. Images were acquired using the AxioVision and the VisiView ® softwares. Spheroid size evaluation was determined using the ImageJ software. Spheroid invasion was measured by subtracting the mask for the core of each spheroid from the total area covered by all the cells of a given spheroid (invasion area [μm 2 ] = total area − spheroid core) using ImageJ.

RT-qPCR
The total RNA (800 ng of template RNA was used per condition) was isolated and reverse transcribed to cDNA using SuperscriptÔ II (#18064022, Invitrogen), according to the manufacturer's instructions. 0.5 μl of cDNA was used for RT-qPCR using the QuantiTect SYBR Green Kit (#204145, Qiagen) and Bio-Rad CFX96Ô Real-Time System. The CT values of the target mRNAs were normalized to the CT values of TATA box binding protein (TBP) which was used as the housekeeping gene. Data were quantified using the 2 −ΔCT method. Gene-specific primer sets were purchased from Qiagen or Sigma-Aldrich. Primer sequences are listed in Table EV3.

Western blots
For Western blot (WB), proteins were extracted as previously described in (Stanisz et al, 2014). Shortly, 40-75 μg of protein were resolved on a 10% SDS-polyacrylamide gel and transferred onto a 0.45 μm nitrocellulose membrane (#10600003, Amersham Protran Premium, GE Healthcare). After transfer, membranes were blocked in 5% BSA or 5% skim milk powder solution, followed by an overnight incubation with primary antibodies. Membranes were incubated with secondary antibodies for 1 h in the dark at room temperature. Imaging and quantification of the blots was performed using an Odyssey infrared imaging system (LI-COR, Lincoln, Nebraska, USA). Primary and secondary antibodies used for immunoblotting are listed in Tables EV4 and EV5.

Real-time killing assay
Human primary NK cell isolation and the subsequent melanoma killing assay were performed as described in (Cappello et al, 2021). Briefly, NK cells were obtained by negative bead isolation (#11349D, Dyn-abeadsÔ Untouched Human NK cell Kit) from human peripheral blood mononuclear cells (PBMCs) of healthy thrombocyte donors of the local blood bank (University Medical Center Göttingen, Ethics approval 2/3/ 18). Melanoma cells were seeded in a black, clear bottom 96-well plates and loaded with 0.5 μM calcein-AM (#C1430, Thermo Fisher Scientific, Waltham, MA, USA). Interleukin-2-stimulated (0.05 μg/ml; #15596-026, Thermo Fisher Scientific GmbH, Schwerte, Germany) primary human NK cells were added to melanoma cells in a NK cell to target cell ratio of~5:1. NK cell-mediated melanoma killing was measured for 4 h at 37°C in 5% CO 2 using a CLARIOstar ® plate reader (BMG LABTECH, Ortenberg, Germany). Killing efficiency was evaluated by fluorescence signal decrease.

Melanoma xenografts
All animal experiments were performed in accordance with The Wistar IACUC in NOD/LtSscidIL2Rγnull mice (NSG). Mice were kept in the same room, in germ-free environment, socially caged as five mice/unit cage and fed with sterile food pellets and water. Wistar Animal Facility has a quality control program in place wherein 5% of mice in each holding room were periodically tested serologically for common murine viruses, Mycoplasma pulmonis and Helicobacter. Human melanoma cells were injected with the MCU A_KD cells and their respective control (1205Lu shCTRL, shMCU_1 or shMCU_2); 10 animals were used per group, randomized. Each mouse was inoculated subcutaneously with 400,000 melanoma cells in a 1:1 suspension of Matrigel (#354230, BD MatrigelÔ Basement Membrane Matrix, Growth Factor Reduced; Becton Dickinson, Franklin Lakes, NJ, USA) and complete media. Seven days after cell injection, tumor growth was measured every 2-3 days, for 36 days, using a caliper and volumes calculated according to the formula V = (W × D × H)/2 (mm 3 ). On the last experimental day, a final measurement of the tumor was taken before tumor extraction. Other organs such as liver, lung, kidneys, and brain were isolated for further examinations. Tumor samples were snap-frozen in liquid nitrogen for subsequent protein analyses or fixed in formalin for the generation of paraffin blocks for immunostaining.

Immunohistochemistry
Hematoxylin and eosin (H&E) staining was performed on sections of lung tissue from mice; these were deparaffinized and returned to Ó 2022 The Authors EMBO reports 23: e54746 | 2022 an aqueous medium by consecutively immersing them in xylene, 100% isopropanol, and 70% isopropanol. For the hematoxylin staining, a hemalum solution diluted 1:10 in water was prepared. Following dipping for 6-10 times into the hemalum solution, tissue sections were rinsed with tap water which resulted in a color change from reddish-brown to blue-violet. Tissue sections were consequently counterstained with eosin and were scanned using an Axio Scan.Z1 microscope.

Reverse Phase Protein Array (RPPA)
The RPPA assay was performed by the MD Anderson Center RPPA core facility, as previously described (Paweletz et al, 2001;Grote et al, 2008). Shortly, serial-diluted lysates were printed on nitrocellulose-coated slides using a 2470 Microarray printer from Aushon Biosystems (Billerica, Massachusetts, USA). Slides were probed with primary antibodies plus biotin-conjugated secondary antibodies. The signal obtained was amplified using a Dako signal amplification system (Copenhagen, Denmark) and visualized by DAB colorimetric reaction. The intensity of each spot was calculated using the MicroVigeneTM (VigeneTech, Billerica, Massachusetts, USA). The data obtained contained normalized and linearized values of protein. The values of the KD cells were normalized to the associated control. Primary and secondary antibodies used for the RPPA assay can be found on the website of the MD Anderson Cancer Center (https://www.mdanderson.org/research/researchresources/core-facilities/functional-proteomics-rppa-core/antibodyinformation-and-protocols.html).

Mass spectrometric proteome analysis
A total of 50 μg of protein per sample were loaded onto a 4-12% NuPAGE Novex Bis-Tris Minigels (Invitrogen) and run into the gel for 1.5 cm. Following Coomassie staining, the protein areas were cut out, diced, and reduced with dithiothreitol, alkylation with iodoacetamide, and digested overnight with trypsin. Tryptic peptides were extracted from the gel, the solution dried in a Speedvac, and kept at −20°C for further analyses (Atanassov & Urlaub, 2013). For the generation of a peptide library, equal amount of aliquots from each sample were pooled to the total amount of 80 μg, and separated into eight fractions using a reversed phase spin column (Pierce High pH Reversed-Phase Peptide Fractionation Kit, Thermo Fisher Scientific). Digested proteins were analyzed on a nanoflow chromatography system (Eksigent nanoLC425) hyphenated to a hybrid triple quadrupole-TOF mass spectrometer (TripleTOF 5600+) equipped with a Nanospray III ion source (Ionspray Voltage 2400 V, Interface Heater Temperature 150°C, Sheath Gas Setting 12) and controlled by Analyst TF 1.7.1 software build 1163 (all AB Sciex). In brief, peptides were dissolved in loading buffer (2% acetonitrile, 0.1% formic acid in water) to a concentration of 0.3 μg/μl. For each analysis, 1.5 μg of digested protein was enriched on a self-packed precolumn (0.15 mm ID x 20 mm, Reprosil-Pur120 C18-AQ 5 μm, Dr. Maisch, Ammerbuch-Entringen, Germany) and separated on an analytical RP-C18 column (0.075 mm ID x 250 mm, Reprosil-Pur 120 C18-AQ, 3 μm, Dr. Maisch) using a 100-min linear gradient of 5-35% acetonitrile/0.1% formic acid (v:v) at 300 nl min −1 .
Qualitative LC/MS/MS analysis was performed using a Top30 data-dependent acquisition method with an MS survey scan of m/z 380-1,250 accumulated for 250 ms at a resolution of 35,000 full width at half maximum (FWHM). MS/MS scans of m/z 180-1,500 were accumulated for 100 ms at a resolution of 17,500 FWHM and a precursor isolation width of 0.7 FWHM, resulting in a total cycle time of 3.4 s. Precursors above a threshold MS intensity of 200 cps with charge states 2+, 3+, and 4+ were selected for MS/MS, and the dynamic exclusion time was set to 15 s. MS/MS activation was achieved by CID using nitrogen as a collision gas and the manufacturer's default rolling collision energy settings. Two technical replicates per reversed phase fraction were analyzed to construct a spectral library.
For quantitative SWATH analysis, MS/MS data were acquired using 100 variable size windows across the 400-1,200 m/z range . Fragments were produced using rolling collision energy settings for a charge state of 2+, and fragments acquired over an m/z range of 180-1,500 for 40 ms per segment. An overall cycle time of 4.3 s was the result of a 250 ms survey scan. Two replicate injections were acquired for each biological sample.
Protein identification was achieved using ProteinPilot Software version 5.0 build 4769 (AB Sciex) at the appropriate settings. A total of 126,301 MS/MS spectra from the combined qualitative analyses were searched against the UniProtKB Homo sapiens reference proteome (revision 04-2018, 93,610 entries) augmented with a set of 51 known common laboratory contaminants to identify proteins at a false discovery rate (FDR) of 1%.
Spectral library generation and SWATH peak extraction were achieved in PeakView Software version 2.1 build 11041 (AB Sciex) using the SWATH quantitation microApp version 2.0 build 2003. Following retention time correction on endogenous peptides spanning the entire retention time range, peak areas were extracted using information from the MS/MS library at a FDR of 1% (Lambert et al, 2013). The resulting peak areas were then summed to peptide and finally protein area values, which were used for further statistical analyses. A 1.698 proteins could be quantified consistently across all samples at a FDR of 1%

Bioinformatic analyses
Patient survival data To generate survival plots, melanoma patient datasets (TCGA) were downloaded from cBioPortal on 26.08.2020. Data on other cancers were downloaded from Human Protein Atlas on 18.03.2021. Kaplan-Meier plots were calculated using lifelines (10.5281/zenodo.4816284). Patients were divided into patients with either low or high MCU A expression by scanning 20-80% of MCU A 's expression value and determining the optimal separation in terms of survival based on a log-rank test. For detailed analyses, patients were separated by different melanoma tumor stages (I-II and III-IV), by age, gender, or by BRAF-and NRAS-wild-type or -mutant. Melanoma patient survival data (low and high MCU A ) were utilized for further analyses (based on hits evaluation). Hits were determined by calculating P-values (< 0.001) using Wilcoxon test adjusted by the Benjamini-Hochberg procedure, (FDR < 0.05) and selected according to an absolute logarithmic fold change higher than 1.

Volcano plots
Volcano plots were created to display protein hits. Student's t-tests were calculated for each shCTRL and MCU A _ KD cell line independently. Hits were shown in red if the unadjusted P-value was significant (P < 0.05) and the absolute logarithmical fold change was higher than 0.5. Hits were shown in green if the adjusted (Benjamini-Hochberg procedure, FDR < 0.05) P-value was significant and an absolute logarithmical fold change higher than 1 occurred. Only the hits shown in red in the volcano plot were further analyzed.

Proteomaps
Proteomaps were generated via (http://bionic-vis.biologie.unigreifswald.de/) by importing patient data-, proteomic-, and RPPA hits. All proteomaps were generated considering all human genes as a background. The size of each polygon represents the protein abundance.
KEGG-based enrichment analyses KEGG (Kyoto encyclopedia of genes and genomes)-based enrichment analysis were generated using DAVID bioinformatics resources that analyzed patient data-, proteomic-, and RPPA hits (red) (Huang da et al, 2009). All results were filtered by FDR adjusted P-value threshold of 0.05 and sorted by fold enrichment.

GO Terms
Patient data-, proteomic-, and RPPA hits were analyzed using GO (Gene Ontology) terms (Ashburner et al, 2000). All results were filtered by FDR adjusted P-value threshold of 0.05 and sorted by fold enrichment.

Hallmarks
Cancer Hallmarks Gene sets were taken from . Proteomic-and RPPA hits were analyzed considering their occurrence in each gene set. The occurrence of each hallmark was calculated and shown in percentage. P-values were calculated using hygeometric tests.

Statistical analyses
Data were analyzed and processed using Zeiss AxioVision, Zeiss Zen, VisiView ® , TILLVISION, Bio-Rad Quantity One, ImageJ/FIJI and Microsoft Excel. Data are shown as mean AE SEM. Unpaired, two-tailed Student's t-tests were carried out to test statistical significance, unless otherwise specified. The significant differences are marked with asterisks: *P < 0.05, **P < 0.01, and ***P < 0.005; a P-value higher than 0.05 means that no significant difference was observed.

Data availability
The datasets produced in this study are available in the following databases: RPPA: These data are available in Dataset EV3. Mass spectrometry proteomics: These data are available in Datasets EV1 and EV2 and on the ProteomeXchange Consortium via the PRIDE partner repository, dataset identifier: PXD029132 (http:// www.ebi.ac.uk/pride/archive/projects/PXD029132).
Expanded View for this article is available online. Figure EV1. MCU A expression affects cancer patient survival (related to Fig 1).

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A-O Kaplan-Meier survival plots depicting the correlation between MCU A mRNA expression levels and survival probability of stage I-II (A) and stage III-IV (B) melanoma patients and of patients with indicated cancer types (C-O). BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD, colon adenocarcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; LIHC, liver hepatocellular carcinoma; OV, ovarian serous cystadenocarcinoma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; UCEC, uterine corpus endometrial carcinoma. P-values are determined by log-rank test. P-T MCU expression levels in wild-type versus BRAF-mutant patients (P) and WT versus NRAS-mutant melanoma patients (Q), in melanoma patients with different tumor stages (R), in melanoma patients of different age intervals (S) and in male versus female melanoma patients (T). NOS, not otherwise specified; TPM, transcripts per million reads.

A, B mRNA expression of MCU
Data information: Data are presented as mean AE SEM. Statistical significance was assessed using unpaired, two-tailed Student's t-test (KD cells were compared with their respective control), *P < 0.05; **P < 0.01; ***P < 0.005; no asterisk means no statistical significance (P > 0.05). Source data are available online for this figure.

EV3
EMBO reports e54746 | 2022 Ó 2022 The Authors A, B Validation of MCU A knockdown in melanoma xenografts. WB for MCU A in 1205Lu MCU A _ KD cells isolated from xenograft tumors (A). Corresponding band intensity, shown as the ratio between MCU A and the loading control plasma membrane Ca 2+ ATPase (PMCA) (B). C Volcano plot of proteomics data for 1205Lu MCU A _ KD cells versus MCU A_ CTRL. Protein entities with significant P-values (P < 0.05) are marked with red dots. Protein entities with significance in both P-value and FDR (false discovery rate) (P < 0.05; FDR < 0.05) are marked with green dots. D, E Proteomap analyses with protein entities that display significant difference (absolute Log2 fold change > 0.5 and P < 0.05) between 1205Lu MCU A_KD and 1205Lu shCTRL cells. F KEGG-based analysis of cellular components and processes based on protein hits that show differential expression between in 1205Lu MCU A_KD and shCTRL cells, revealed via proteomics. G Cancer hallmark-based enrichment analysis of proteins differentially expressed in 1205Lu MCU A _ KD versus shCTRL cells, based on proteomics data shown in (A). Percentage of hits in the specific gene set is displayed.
Source data are available online for this figure.  Figure EV4. Effect of redox agents on melanoma cell viability and mitochondrial ROS (related to Fig 5).