Dynamic assessment of the allocation of copper to cytochrome c oxidase using size-exclusion chromatography (SEC) combined with inductively coupled plasma mass spectrometry (ICP-MS)

TL;DR

The study uses a new method to track how copper is used in a key mitochondrial enzyme and finds that copper transporters change depending on copper availability in kidney cancer cells.

Contribution

A novel SEC-ICP-MS method is introduced to dynamically assess copper allocation to cytochrome c oxidase in renal cancer cells.

Findings

A high molecular weight copper-containing peak in SEC-ICP-MS correlates with cytochrome c oxidase activity under oxidative phosphorylation conditions.

Copper incorporation into cytochrome c oxidase in renal cancer cells is time- and dose-dependent under high copper conditions.

Copper transporter contributions to cytochrome c oxidase formation shift dynamically with copper availability.

Abstract

Copper (Cu) is an essential trace element required for mitochondrial respiration via its incorporation into cytochrome c oxidase (CuCOX), the terminal enzyme of the electron transport chain. Here, we employed size-exclusion chromatography coupled with inductively coupled plasma mass spectrometry (SEC-ICP-MS), UV-Vis spectroscopy, and immunoblotting to identify and validate a high molecular weight Cu-containing peak in the SEC-ICP-MS chromatogram as representative of CuCOX activity. We demonstrate that this CuCOX peak is enhanced under metabolic conditions inducing oxidative phosphorylation, such as high Cu supplementation or galactose-containing media, and correlates with increased mitochondrial respiration. Using exogenous 63Cu tracing, we characterized the time- and dose-dependent incorporation of newly acquired Cu into CuCOX under elevated Cu conditions in renal cancer cells, modeling advanced clear cell renal cell carcinoma (ccRCC). RNA interference experiments targeting key Cu transporters revealed that CuCOX formation is independent of the high-affinity Cu importer CTR1, but instead relies on alternative transporters, including DMT1, LAT1, and the mitochondrial carrier SLC25A3, with transporter contributions dynamically reshaped during chronic adaptation to high Cu availability. In contrast, under standard low-Cu conditions, CTR1 remains required for cellular Cu uptake and CuCOX metallation. Together, these findings define context-dependent Cu trafficking pathways in renal cancer and establish SEC-ICP-MS as a sensitive platform for assessing CuCOX metallation and mitochondrial metabolism, with potential applications in biomarker discovery and therapeutic targeting in RCC.

Figures

Full text

Copper (Cu) is an essential trace element that plays a role in numerous biological processes, including mitochondrial metabolism and enzymatic reactions. One of its most critical functions is within cytochrome c oxidase (CuCOX), the terminal enzyme and rate-limiting step of the mitochondrial electron transport chain, where it transfers electrons to molecular oxygen, to drive ATP production. CuCOX contains two key copper centers, CuA and CuB. The CuA site, a binuclear copper center, mediates electron transfer from cytochrome c to the enzyme’s catalytic core. In contrast, the CuB site, in conjunction with a heme a3 group, forms a dinuclear center responsible for binding oxygen and reducing it to water (1, 2). The assembly and activity of the CuCOX complex are tightly regulated by Cu availability (1).

Intracellular uptake and distribution of Cu and its speciation are robustly controlled (3). Extracellular Cu is imported through a plasma membrane high-affinity transporter, CTR1 (SLC31A1), which is internalized into endosomes in response to high Cu to protect cells from Cu toxicity (4). CTR1 transport requires reduction of the extracellular Cu^2+^ to Cu^+^ by STEAP metalloreductases. Additional Cu importers include divalent metal transporter, DMT1 (SLC11A2) (5), amino acid transporter LAT1 (SLC7A5) (6) and zinc exporter, ZNT1 (SLC30A1) (7). One study reported macropinocytosis as an exclusive Cu uptake mechanism in RAS-driven colon cancer tumors (8). Intracellular Cu is bound to metallothioneins (MTs) for storage (9) and to protein chaperones for transport to cuproenzymes (10). The labile, dynamic pool of Cu is bound to small molecules (e.g. GSH) and contributes to the allosteric effects of Cu (11). Cu enters mitochondria through the SLC25A3 transporter (12, 13) and is delivered for the formation of CuCOX by COX17 to downstream chaperones COX11/COX19 (2) and SCO1, which respectively transfer Cu to the CuB center in the COX subunit 1 and the CuA center in the COX subunit 2 (1). ATP7A/B transporters function as the primary Cu detoxification pathway in eukaryotic cells (14) exporting Cu to the Golgi for assembly into secreted cuproenzymes and into the extracellular environment at the plasma membrane (3).

Size-exclusion chromatography (SEC) coupled with inductively coupled plasma mass spectrometry (ICP-MS) analyzes metal-protein interactions in biological samples (15). SEC enables the separation of biomolecules based on their size and, with proper selection of the mobile phase, preserves their native state, while ICP-MS provides highly sensitive and specific detection of metal content at each eluting fraction. A diode array UV-Vis detector can be added between SEC column and the ICP-MS detector as a non-destructive detector, providing information related to chromophores typical of metal-binding proteins. This combination enables molecular mass-based characterization of metalloproteins, their metal-binding properties, and potential changes under physiological or pathological conditions.

Our recent studies identified elevated levels of Cu and Cu-loaded cytochrome c oxidase as hallmarks of tumor progression in clear cell renal cell carcinoma (ccRCC) (16). Canonically, ccRCC is characterized by high glycolytic and low mitochondrial activity due to the activation of the hypoxia-inducible factors (HIFs) following loss of the von Hippel-Lindau tumor suppressor (VHL) (17). However, more aggressive or relapsing tumors accumulate Cu and allocate it to CuCOX, driving a metabolic shift from glycolysis towards oxidative phosphorylation and thereby promoting tumor growth (16, 18).

In our cohort of patients with ccRCC, serum Cu concentrations ranged from 20.32 to 67.33 μM (n = 31) and were significantly higher in patients with advanced stage 3 and 4 disease compared to stage 1 and 2 (16), whereas in healthy individuals, serum Cu levels typically range from 10 to 24 μM (19). To model the pathophysiological Cu conditions found in patients and to dissect the mechanisms governing Cu uptake and utilization, we chronically exposed and adapted 786-O and RCC4 renal cell carcinoma cell lines to 30 μM Cu. Given that standard culture media contain subphysiological 0.14 to 2 μM Cu, this approach enabled us to explore how high Cu exposure influences Cu import pathways, intracellular distribution, and its incorporation into mitochondrial cuproproteins (16).

Using SEC-ICP-MS, we established that the chromatogram's high-molecular-weight peak corresponds to an active CuCOX complex. This finding provides a means to monitor how Cu availability modulates COX metallation and activity in relation to metabolic state. Furthermore, using exogenous ^63^Cu as a tracer, we found that Cu-dependent CuCOX induction requires a 48-h exposure in renal cancer cells. Under elevated extracellular Cu conditions lasting 48 h, this process is independent of the high-affinity Cu importer CTR1 and instead relies on DMT1, the amino acid transporter LAT1, and the mitochondrial carrier SLC25A3. Moreover, the contributions of the transporters for Cu uptake are dynamically reshaped during chronic adaptation to high Cu, relevant to renal cancer, with DMT1 emerging as a dominant contributor. In contrast, under standard low-Cu conditions, CTR1 remains required for cellular Cu uptake and intracellular Cu distribution. Our findings position ICP-MS and SEC-ICP-MS as powerful tools for studying Cu uptake and CuCOX biogenesis and activity, with potential applications in analyzing primary tumors and patient sera to explore Cu and CuCOX as prognostic biomarkers in clear cell renal cell carcinoma.

Results

CuCOX activity identified by high molecular weight peak in SEC-ICP-MS chromatogram

Here, we employed SEC-ICP-MS to characterize the molecular distribution of Cu in total non-denatured lysates from renal carcinoma cells exposed for 48 h to either standard media containing 0.14 μM Cu or media supplemented with 30 μM Cu. Peaks were identified based on retention times, signal intensity, and comparison with mass calibration standards.

A standard Cu SEC-ICP-MS chromatogram of cell lysates includes the high-molecular-weight fraction (HMW, 5–8 min retention time) that measures Cu bound to proteins; Cu bound to the cysteine-rich storage proteins, metallothioneins (MT, 8–10 min retention time); and the low-molecular-weight fraction (LMW, after 12 min) of Cu bound to metabolites, such as GSH, and includes labile Cu (Fig. 1A, top). Importantly, a peak with retention time at 5 to 6 min represents mitochondrial CuCOX: this peak is induced by treatment of 786-O and RCC4 renal cancer cells with media containing 30 μM Cu for 48 h (Fig. 1, A and C). Analysis of UV-Vis absorbance isoplots in parallel with the SEC-ICP-MS chromatograms reveals increased absorbance at the 6 min time point, corresponding to the CuCOX peak in the chromatogram (Fig. 1A, bottom). This includes 420 to 450 and 600 nm absorbance bands corresponding to cytochrome c oxidase due to the presence of heme a and a3 (20, 21, 22, 23) (Fig. 1B). The absorbance peak detected at 7 min is not detected by SEC-ICP-MS, consistent with the absence of Cu in this fraction (Fig. 1A, bottom). Western blot analysis of eluates from the fraction corresponding to CuCOX peak shows Cu-induced enrichment for mitochondrially encoded, MTCO1 and MTCO2 subunits (Fig. 1D). Exposure to 30 μM Cu also increased Cu bound to MTs and the total Cu content in both cells (Fig. 1C). Functionally, exposure to 30 μM Cu resulted in enhanced mitochondrial oxygen consumption rate (OCR) and ATP production in 786-O and RCC4 RCC cells, which exhibit constitutively low basal OCR (Fig. 1E). Furthermore, to compare the effects of Cu in renal cancer cells with non-cancer cells, we expanded this analysis to two immortalized Renal Proximal Tubule Epithelial Cell (RPTEC) TH1 and TERT1. Interestingly, both RPTEC cell lines accumulated higher levels of Cu bound to MTs, but there was no effect on the CuCOX peak or OCR, possibly because these cells had higher basal respiration than RCC cells (Fig. 1F). These data indicate that Cu regulates mitochondrial electron transport chain and CuCOX metallation differently in renal cancer vs. epithelial cells during 48-h exposure.Figure 1**Identification and validation of CuCOX peak in SEC-ICP-MS chromatogram.**A, Top: representative SEC-ICP-MS chromatograms of lysates from 786-O and RCC4 cells grown in 0.14 μM or 30 μM Cu for 48 h. Bottom: UV-Vis absorbance isoplots collected throughout the entire chromatography run. The legend shows the logarithmic scale for absorbance in the isoplot. Note that, because absorbance was measured before the ICP-MS detector, there is a slight shift in the timeline between the chromatogram and the spectra. B, measurement of absorbance from the 5 min fraction presented on a linear scale shows Cu-induced peaks at 420 and 600 nm. C, box whisker plots show relative abundance of Cu measured by SEC-ICP-MS for CuCOX and MTs, and by ICP-MS for total Cu content in 786-O (3 biological and 2 technical replicates) and RCC4 (4 biological and 2 technical replicates) cells. D, Western blot of indicated proteins eluted from the CuCOX peak and the inputs are shown below. E, oxygen consumption rate (OCR) from representative seahorse mitochondrial stress tests in 786-O and RCC4 cells (3 biological replicates). Quantification of basal and maximal respiration (post-FCCP injection), and respiration coupled to ATP production. Means ± SD are shown. p-values for basal respiration in 786-O cells was calculated using a two-tailed, paired t test. F, SEC-ICP-MS chromatograms, quantification of CuCOX and MT peaks, total Cu level, and Seahorse measurements of OCR for RPTEC TH1 and TERT1 cell lines (3 biological replicates and 2 technical replicates). Box-and-whisker plots display median, minimum and maximum values and all individual data points. Unless otherwise indicated, p-values were determined using a two-tailed, unpaired t test. All Cu measurements are normalized to total phosphorus (P). HMW, high molecular weight fraction; LMW, low molecular weight fraction; MT, metallothioneins

Next, we investigated whether oxidative phosphorylation-inducing conditions, beyond high Cu availability, influence CuCOX dynamics as detected by SEC-ICP-MS. In this regard, replacing glucose with galactose in tissue culture media forces cells to rely on oxidative phosphorylation (24, 25). However, the biochemical mechanism underlying this established experimental observation is unclear. One possibility is that the conversion of galactose to glucose through the LeLoir pathway slows glycolytic generation of ATP and therefore requires activation of oxidative phosphorylation (Fig. 2A). Indeed, 48 h exposure of 786-O cells to media that contains 0.14 μM Cu and 10 mM galactose significantly increased OCR and mitochondrial ATP production as compared to the same media with 10 mM glucose. However, the total production of ATP was smaller in cells grown in galactose as compared to glucose-containing media (Fig. 2B). Consistently, with this functional readout, there was also an increase in CuCOX peak in SEC-ICP-MS chromatogram and corresponding UV-Vis absorbance isoplots (Fig. 2C).Figure 2**Galactose induces oxidative phosphorylation and increases Cu content and allocation to CuCOX in 786-O RCC cells.**A, schematic representation of the Leloir pathway converting galactose into glucose. B, OCR and mitochondrial ATP production in response to 48 h treatment with galactose (red) as compared to glucose (black) (3 biological replicates). C, SEC-ICP-MS chromatograms of Cu (top) and corresponding UV-Vis absorbance plots in lysates from 786-O cells treated with 10 mM glucose or 10 mM galactose for 48 h. The legend shows the logarithmic scale for absorbance in the isoplot. D, box whiskers show quantification of Cu allocated to CuCOX and MTs, and total Cu content in lysates from 786-O cells grown in glucose- or galactose-containing media (7 biological replicates with 2 technical replicates for each). E, Western blot of total input lysates and CuCOX eluates from 786-O grown in glucose or galactose media. F, Schematic model of mitochondrial electron transport chain along with localization of inhibitors for each complex. G, quantification of SEC-ICP-MS peaks corresponding to Cu in CuCOX and MTs, and total Cu in lysates of 786-O cells treated with media containing glucose, galactose, or galactose with the indicated electron transport chain inhibitors added for 3 h before collection. ROT, rotenone (200 nM, three biological replicates in technical duplicates), IACS, IACS-10759 (1 nM, 3biological replicates in technical duplicates), MET, metformin (25 mM, 3 biological replicates in technical duplicates), AM, antimycin A (1 nM, 4 biological replicates in technical duplicates), MYX, myxothiazol (1 nM, 4 biological replicates run in technical duplicates), KCN, potassium cyanide (0.5 mM, 4 biological replicates in technical duplicates), NaN3, sodium azide (0.5 mM, 4 biological replicates in technical duplicates). Box-and-whisker plots displayed the median, minimum and maximum values, and all individual data points. All p-values were calculated using a two-tailed, unpaired t test and compared the treatment groups with the galactose-only condition.

Notably, galactose exposure induced not only increased Cu allocation to CuCOX, but also a significant increase in Cu bound to MTs and total cellular Cu content (Fig. 2D), although these increases were smaller as compared to the exposure to 30 μM Cu shown in Figure 1. The eluate from the CuCOX peak in cells grown in galactose media showed enrichment of CuCOX subunit, MT-CO2 (Fig. 2E). This galactose-induced increase in Cu was abolished by treating cells with inhibitors of CuCOX, as well as of electron transport chain complex I and III (Fig. 2G), indicating coordinated regulation between overall ETC activity and Cu uptake and allocation. Collectively, these data indicate that increased demand for CuCOX biogenesis is sufficient to reshape cellular Cu uptake and allocation.

Kinetics of 63Cu incorporation into CuCOX complex

The natural relative abundances of the Cu stable isotopes, ^63^Cu and ^65^Cu, are 69.17% and 30.83%, respectively, reflecting an approximate 2:1 ratio in naturally occurring Cu samples (26). This allows for the use of exogenous, pure ^63^Cu to trace Cu uptake and allocation by monitoring both ^63^Cu and ^65^Cu, as well as their ratio, by SEC-ICP-MS, and at the same time, to monitor the fate of endogenous Cu (Fig. 3A). Here, we traced the incorporation of exogenous ^63^Cu into CuCOX over time. Cells were cultured in standard media containing 0.14 μM Cu (natural isotopic abundance) and then switched to media supplemented with 0.14, 10, or 30 μM ^63^Cu for durations ranging from 2 to 48 h (Fig. 3B). The concentration of 0.14 μM reflects Cu levels in DMEM/F12 media with 10% serum used for carrying the cells, while 10 μM Cu represents physiological serum Cu concentrations in healthy human subjects, and 30 μM Cu represents concentrations of Cu measured in sera from patients with ccRCC (16).Figure 3**Kinetics of allocation of 63Cu tracer into CuCOX.**A, schematic representation of tracing ^63^Cu. B, timeline of ^63^Cu tracking into CuCOX. C and D, SEC-ICP-MS chromatograms of exogenous ^63^Cu in lysates from 786-O and RCC4 cells at the indicated time points. E and F, quantification by SEC-ICP-MS of exogenous ^63^Cu in CuCOX, MTs and total exogenous ^63^Cu in 786-O and RCC4 cells. Cu abundance was normalized to Phosphorus (P). In all experiments: n = 4, SEM ± SD are shown; *p-*values were calculated using a two-tailed, unpaired t test and indicate significance of the difference between 10 or 30 μM Cu compared to 0.14 μM Cu. Experiments were performed in 3 technical replicates twice and a representative experiment is shown.

^63^Cu concentration had a major effect on the magnitude of ^63^CuCOX and MT peaks, with rapid appearance of ^63^Cu in the MT-associated fraction and slower accumulation in the CuCOX fraction (Fig. 3, C–F). There were also cell line-specific differences between 786-O and RCC4 cells. In 786-O cells, allocation of Cu to CuCOX and MT, as well as total uptake of Cu were significantly larger, and these cells were very highly sensitive to the increase in ^63^Cu from 10 μM to 30 μM (Fig. 3, C and E). In contrast, in the case of RCC4 cells, only the amount of ^63^Cu in the CuCOX peak was sensitive to the change in Cu concentration between 10 and 30 μM, while the ^63^Cu bound to MTs and total uptake of exogenous ^63^Cu were similar between these high Cu concentrations (Fig. 3, D and F). RCC4 cells accumulated less ^63^Cu than 786-O cells, consistent with our previously published findings (16). This difference may relate to the more malignant phenotype of 786-O cells, which express only HIF2A but not HIF1A, whereas RCC4 cells are less malignant and express HIF2A and HIF1A, which is considered a tumor suppressor (27). Notably, 48 h of exposure to high Cu corresponded to the time point at which OCR induction was consistently observed (Fig. 1D). Shorter Cu exposures did not result in a reproducible increase in OCR (data not shown).

Next, we determined the effects of exogenous ^63^Cu exposure on intracellular ^63^Cu levels and on endogenous Cu, Zn, and Fe. 786-O and RCC4 cells, which were chronically grown either in standard low-Cu media (0.14 μM) or high-Cu media (30 μM), were treated with 30 μM ^63^Cu for 48 h (Fig. 4A). Cells chronically adapted to high Cu concentration exhibited higher baseline endogenous Cu levels and accumulated more exogenous Cu when compared to cells chronically grown in low Cu media (Fig. 4, B and C), a pattern extended to Cu allocated to CuCOX (Fig. 4, B and D). Interestingly, in 786-O and RCC4 cells chronically grown in low Cu media, the uptake of exogenous ^63^Cu during 48 h of exposure to 30 μM Cu resulted in a reduction in endogenous CuCOX, and in the case of 786-O cells, also in total endogenous Cu (Fig. 4, B–D). This effect was not observed in cells grown chronically in high-Cu media, suggesting an early adaptive response that transiently limits endogenous Cu pools during acute Cu exposure, which is not maintained following prolonged adaptation to high Cu. In both cell lines, exposure to Cu altered Zn levels (Fig. 4, E and F). Finally, cells chronically exposed to Cu showed increased Fe levels (Fig. 4, G and H), consistent with increased demand for iron-containing heme and iron–sulfur cluster proteins required for ETC and CuCOX activity (28).Figure 4**Allocation of tracer ^63^Cu to CuCOX in 786-O and RCC4 cells chronically grown in low or high Cu media.*A, timeline of the experiment: AC, exposure to 30 μM ^63^Cu for 48 h of cells grown continuously in low Cu media; CH, exposure to 30 μM ^63^Cu for 48 h of cells grown in high Cu media. B, representative examples of Cu SEC-ICP-MS chromatograms from cells grown in Cu low media and exposed to 30 μM ^63^Cu for 48 h (AC) or cells grown continuously in media containing 30 μM Cu and then treated with the same concentration of ^63^Cu (CH). C, quantification of ^63^Cu in total exogenous and endogenous Cu measured by SEC-ICP-MS. D, quantification of ^63^Cu in exogenous and endogenous CuCOX peak from SEC-ICP-MS chromatograms. E, representative examples of Zn SEC-ICP-MS chromatograms as described in B. F, quantification of total cellular Zn based on SEC-ICP-MS. G, representative examples of Fe SEC-ICP-MS chromatograms as described in B. H, quantification of total cellular Fe based on SEC-ICP-MS chromatograms. Metals’ abundance was normalized to Phosphorus (P). In all experiments: Means ± SD are shown; n = 4 biological replicates; p-*values were calculated using a two-tailed, unpaired t test.

Analysis of transporters contributing Cu for CuCOX

Extracellular Cu is primarily taken up by the high-affinity transporter CTR1, but other transporters, such as DMT1 and LAT1, have also been proposed (5, 6). Cu enters mitochondria via the phosphate transporter SLC25A3 (12, 13). To determine the role of these transporters in allocating exogenous ^63^Cu to CuCOX in cells grown in chronic low and high Cu conditions, we first knocked down CTR1, DMT1, LAT1, and SLC25A3 with siRNA. Cells were collected 72 h after the first siRNA transfection and 48 h after exposure to exogenous 30 μM or 0.14 μM ^63^Cu (Fig. 5A).Figure 5**Contribution of Cu transporters to the allocation of tracer ^63^Cu to CuCOX.*A, timeline of experiments for cells cultured under low or high Cu conditions and subsequently exposed to 30 μM ^63^Cu for 48 h (AC and CH, respectively) or to 0.14 μM ^63^Cu for 48 h (LO). B–E, effects of the knockdowns of indicated transporters on the allocation of exogenous ^63^Cu and total content of Zn and Fe measured by SEC-ICP-MS in AC conditions for 786-O (top) and RCC4 (bottom) cells. F-I, effects of the knockdowns of indicated transporters on the allocation of exogenous ^63^Cu and total content of Zn and Fe measured by SEC-ICP-MS in CH conditions for both cell lines. J and K, Western blots showing efficiency of the indicated knockdowns for AC (J) and CH (K) conditions. L and M, RT-PCR validation of CTR1 KD under AC (L) and CH (M) conditions. NT-non-targeting siRNA. Metals’ abundance was normalized to Phosphorus (P). N, Western blots showing expression of ZNT1 in RCC4 and 786-O cells and the effectiveness of the knockout. O, effects of the ZNT1 knockout on the total exogenous ^63^Cu uptake and ^63^Cu allocation to CuCOX, as well as total content of Zn, and Fe measured by SEC-ICP-MS in AC conditions for RCC4 cells. P, SEC-ICP-MS chromatograms of lysates from 786-O cells grown in 0.14 μM Cu, treated with non-targeting (NT) or CTR1 siRNAs, and exposed to 0.14 μM ^63^Cu for 48 h. Q, quantification of total ^63^Cu, and ^63^Cu allocated to CuCOX and MT measured by SEC-ICP-MS in response to CTR1 KD. R, DepMap gene-effect scores distribution and median across all RCC cell lines for five metal transporter genes. A blue dot marks 786-O cell line. Biological replicates: B–I and Q, n = 4 (except for NT treatment in RCC4 in B–E and CTR1 KD in 786-O in F–I where n = 3); L and M, n = 3; O, n = 6. Means ± SD are shown; p-*values were calculated using a two-tailed, unpaired t test and comparing the significance of changes caused by specific si/sgRNA to NT si/sgRNA.

In the case of cells grown continuously in low Cu and exposed to 30 μM ^63^Cu, the knockdown (KD) of CTR1, did not affect ^63^Cu accumulation and modestly increased the allocation of ^63^Cu to CuCOX in 786-O cells (Fig. 5B). In RCC4 cells, it had a small effect, decreasing ^63^Cu total content and did not affect the allocation of ^63^Cu to CuCOX (Fig. 5C). In contrast, there was a strong and consistent effect of DMT1 KD on ^63^Cu accumulation and allocation to CuCOX in both cell lines (Fig. 5, B and C). KD of LAT1 reduced total and ^63^Cu-COX in 786-O cells but not in RCC4 cells, supporting the roles of two transporters in ^63^Cu uptake and CuCOX allocation, contributing to higher Cu concentrations in 786-O cells (Fig. 5, B and C). Finally, SLC25A3 KD decreased the allocation of ^63^Cu to CuCOX in both cell lines, but in 786-O cells had an additional effect on total ^63^Cu accumulation, suggesting coupling between mitochondrial Cu import and overall cellular Cu homeostasis (Fig. 5, B and C). The DMT1 KD also decreased Zn levels in both cell lines and Fe levels in 786-O cells (Fig. 5, D and E), although these effects were smaller than those observed for Cu.

In the case of cells grown continuously in high Cu and exposed to 30 μM ^63^Cu, KDs of transporters showed a significant contribution of DMT1 to the total Cu content and CuCOX, and Zn in 786-O cells and a contribution of LAT1 to the total levels of Cu but not to CuCOX (Fig. 5, F–H). No significant effects on Fe concentrations were observed (Fig. 5J). In the case of RCC4 cells, none of the KDs affected Cu, CuCOX, Zn, or Fe (Fig. 5, G–J). The KDs of the transporters were validated by immunoblotting for DMT1, LAT1, and SLC25A3 (Fig. 5, K and L). Because validated antibodies for CTR1 are not available, the KDs were confirmed by RT-PCR (Fig. 5, M and N).

Recently, ZNT1, classically known as a zinc exporter, was also reported to function as a Cu importer (7). ZNT1 expression was undetectable in 786-O cells but readily detected in RCC4 cells (Fig. 5N). To assess its potential contribution to Cu uptake, RCC4 cells with a validated ZNT1 knockout (7) (Fig. 5N) were exposed to 30 μM ^63^Cu. Loss of ZNT1 led to a modest increase in total ^63^Cu accumulation and had no measurable effect on the allocation of exogenous ^63^Cu to CuCOX (Fig. 5O). Consistent with its canonical role in Zn export, ZNT1 knockout cells showed significantly elevated intracellular Zn levels, confirming the functional disruption of ZNT1, whereas iron concentrations remained unchanged (Fig. 5O).

An intriguing aspect of our findings is that the canonical high-affinity Cu importer, CTR1, plays a limited role in Cu uptake under elevated extracellular Cu conditions. We hypothesized that CTR1 may become functionally unnecessary under high-Cu conditions but remain active at low Cu concentrations. To test this, cells chronically maintained in 0.14 μM Cu were treated with CTR1 siRNA to induce knockdown and subsequently exposed to 0.14 μM ^63^Cu (Fig. 5A). CTR1 knockdown markedly reduced total ^63^Cu uptake and its allocation to CuCOX and MTs (Fig. 5, P and Q), demonstrating that CTR1 is required for Cu uptake and intracellular Cu distribution under low-Cu conditions.

Finally, we analyzed DepMap gene-effect scores for the five transporter genes across renal cell carcinoma cell lines to determine their relative importance to tumor cell survival (Fig. 5R). Genes with scores near zero are considered non-essential, indicating that loss of their function does not significantly affect RCC cell growth. In contrast, any transporter gene showing gene-effect scores approaching −1 can be considered essential, as its knockout markedly reduces cell viability. None of the Cu transporters analyzed exhibited essential gene-effect scores (Fig. 5R), indicating that Cu uptake in RCC cells is supported by multiple, redundant, and context-dependent transport mechanisms.

Discussion

We established that the high-molecular-weight peak identified by SEC-ICP-MS corresponds to the active CuCOX complex and is regulated by both Cu availability and metabolic conditions that promote oxidative phosphorylation, such as substituting galactose for glucose in cell culture media. The CuCOX peak is induced within 48 h under either condition, with extracellular Cu entering cells via transporters such as DMT1 and LAT1, and mitochondria via SLC25A3. The induction of this peak is accompanied by increased oxygen consumption rate and mitochondrial ATP production, which are functional markers of CuCOX activity.

Unexpectedly, our investigation revealed that exposing cancer cells to galactose induced CuCOX and oxidative phosphorylation, and increased intracellular Cu levels. The mechanism by which galactose stimulates oxidative phosphorylation remains incompletely understood. While galactose is metabolized via the Leloir pathway to glucose (29), which then enters glycolysis to yield 2 ATP molecules, this process may occur at a lower rate than glucose metabolism, potentially reducing ATP availability and thereby promoting mitochondrial respiration as a compensatory mechanism. Our data support an additional, non-mutually exclusive interpretation: galactose may stimulate mitochondrial respiration in part by enhancing Cu uptake and directing it toward CuCOX biogenesis. This interpretation requires future mechanistic studies. This effect could be mediated via glycosylation of membrane proteins or transporters, a pathway that utilizes UDP-galactose (29). Consistent with this possibility, the Cu transporters investigated here are reported or predicted to be glycosylated (https://www.genecards.org), although this mechanism was not directly tested.

The effects of galactose increasing total cellular Cu accumulation, together with the observation that knockdown of the mitochondrial Cu transporter SLC25A3 reduces overall uptake of exogenous Cu, support the hypothesis that mitochondrial Cu utilization and electron transport chain activity can influence the broader cellular Cu uptake and transport network. This reflects regulation of Cu homeostasis at the global cellular level rather than effects attributable to individual transporters.

The ^63^Cu tracer experiments are more flexible than traditional radioactive ^64^Cu tracer experiments, which are incompatible with chromatographic methods due to radiation safety protocols. The use of a stable isotope enables a quantitative, time-resolved view of Cu dynamics, revealing a reproducible temporal sequence in which newly imported Cu is first detected in MT-associated fractions, whereas accumulation in CuCOX occurs later. At present, it remains unresolved whether Cu initially detected in MT-associated fractions subsequently contributes to CuCOX metalation and therefore respiration, or whether CuCOX is supplied by a distinct Cu pool. In this context, overexpression of MT2A has been reported to decrease oxidative phosphorylation capacity in HEK293 cells, consistent with an inhibitory role of excess MTs on CuCOX function rather than a requirement for MTs in Cu delivery (30).

Interpretation of MT-associated Cu should consider that digitonin solubilization disrupts intracellular membranes and could permit limited post-lysis redistribution of Cu from organelles to MTs. Accordingly, while some MT-associated Cu detected by SEC-ICP-MS may arise from post-lysis binding, this effect is unlikely to account for the full magnitude or reproducibility of the observed MT-associated Cu signal. Concurrently, we observe a decline in pre-existing Cu-bound CuCOX under high Cu conditions, consistent with turnover and replacement of CuCOX subunits. Collectively, these findings define a dynamic and time-resolved framework for Cu sequestration and utilization that constrains models of CuCOX homeostasis and biogenesis in response to elevated Cu availability.

Our results provide new insights into the roles of Cu transporters in renal cancer cells. Unexpectedly, we found that the canonical high-affinity Cu importer CTR1, while essential for Cu uptake under low-Cu conditions, is dispensable under high-Cu conditions in renal cell lines exposed to elevated extracellular Cu. The loss of dependence on CTR1-mediated uptake under prolonged high-Cu conditions is likely due to transporter inactivation, which protects cells from excess Cu (31, 32). Nevertheless, there are robust alternative pathways of Cu acquisition, mediated by DMT1 and LAT1. Among these, DMT1 emerged as the most consistently involved in Cu import across experimental conditions, suggesting it serves as a key mediator of cellular Cu entry. These findings highlight context-dependent adaptation of Cu trafficking under Cu-rich pathophysiological conditions. The idea that CTR1-mediated Cu uptake in cancer may not be the main source of cellular Cu is supported by observations in ovarian cancer, where low CTR1 levels are associated with resistance to platinum-based drugs, yet, paradoxically, a Cu-dependent phenotype is also linked to tumor resistance and aggressiveness (33, 34). Collectively, these findings expand our understanding of Cu trafficking in cancer and implicate metal-ion transporters traditionally associated with iron and amino acid metabolism in the regulation of mitochondrial function through Cu delivery.

Our data point to an adaptive process in Cu uptake during the transition from a relatively short Cu exposure (48 h) to a chronic state. During short exposure, disruption of distinct copper transporters (DMT1, LAT1, or SLC25A3) converges on a similar cellular outcome, reduced total Cu and CuCOX metallation, reflecting a system-level homeostatic response rather than transporter-specific control. In contrast, chronic adaptation to high copper reshapes this network, engaging compensatory mechanisms that buffer loss of LAT1 or mitochondrial Cu import while preserving reliance on DMT1. Thus, copper homeostasis in renal cancer cells is dynamic and context-dependent, transitioning from an acute stress response to an adapted state with redistributed transporter dependencies.

Copper uptake significantly influences the homeostasis of other essential metals, including Zn and Fe. We interpret these changes as secondary consequences of altered metal homeostasis rather than direct effects on metal transport. Notably, chronic exposure to elevated Cu levels is associated with increased intracellular Fe accumulation, consistent with coordinated regulation of metal availability required for heme biosynthesis and iron–sulfur cluster formation, both of which are essential for Cu-driven electron transport chain activity. This interplay is particularly relevant in advanced clear cell renal cell carcinoma (ccRCC), a malignancy characterized by high Cu content and potentially elevated Fe levels. Such metal imbalance may sensitize cancer cells to ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, when exposed to specific therapeutic interventions (35), highlighting a potential metabolic vulnerability that could be exploited by strategies targeting metal metabolism. In this context, perturbations in heme biosynthesis have been shown to destabilize cytochrome c oxidase and trigger copper-dependent cell death (cuproptosis), underscoring the tightly intertwined regulation of Cu, Fe, and mitochondrial metabolism (36).

Measuring the CuCOX complex by SEC-ICP-MS in primary tumors or biopsies from both primary and metastatic lesions, combined with total Cu quantification in serum and tumor tissues by ICP-MS, could offer significant clinical utility. This combined approach may serve as a prognostic biomarker for disease progression, providing insights into tumor oxidative metabolism and potentially guiding therapeutic decisions. Such applications remain prospective and will require validation in primary human samples. The use of metallomic analysis of tumor Fe, another metal crucial for oxidative phosphorylation, is hindered by iron's presence in many more proteins than Cu, particularly in high-molecular-mass fractions, and by Fe abundance in red blood cells.

Together, our data point to a close interplay between copper metabolism and mitochondrial function in renal cancer cells, uncovering potential metabolic liabilities that may be particularly relevant to Cu-dependent cancers such as ccRCC.

Experimental procedures

Cell culture and treatments

Human renal carcinoma cell lines 786-O (RRID: CVCL_1051, ATCC) and RCC4 (RRID: CVCL_0498) and human immortalized renal proximal tubule cells RPTEC cells TH1 (kerafast ECH001) (RRID: CVCL_K278) and TERT1 (ATCC CRL-4031were cultured in DMEM/F12 medium (Cytiva, SH30023) supplemented with 10% fetal bovine serum (FBS; Gibco, 6000–044) at 37 °C in a humidified atmosphere containing 5% CO_2_. To generate high-copper (Cu) media, 300 μM CuSO_4_ was incubated with 100% FBS overnight at 4 °C, then diluted 10-fold into complete culture media. Chronic copper-high cells were generated by gradually increasing copper concentrations over a 2-week period, as described previously (16). The 30 μM ^63^Cu-enriched media were prepared using Copper-63 metal (Cambridge Isotope Laboratories, CULM-463-PK). Media Cu concentrations were routinely validated using ICP-MS. Cell line authentication was performed regularly by short tandem repeat (STR) profiling (LabCorp, Burlington, NC), and all cultures were screened for mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit (Lonza). Cells were used for experiments up to passage 15.

SEC-ICP-MS

Cells were washed 3 times (4 if media contained 10 or 30 μM Cu) in PBS buffer, pelleted at 8,000 g for 20 min and frozen. Cell pellets were lysed in an ice bath with a buffer containing 1% digitonin, 0.1% SDS, 10 mM NaCl, and 50 mM Tris-HCl (pH 7.4), filtered through a 0.45 μm membrane, and injected into an Agilent 1290 HPLC system equipped with a thermostated autosampler (4 °C), vacuum degasser, binary pump, column oven, and UV-Vis diode array detector. The size-exclusion chromatography (SEC) column was a TSK gel QC-PAK GFC 200, 7.8 × 150 mm, 5 μm particle size (Tosoh Global), with 50 mM ammonium acetate and 0.5% MeOH at pH 7.4, at a flow rate of 0.565 ml min^-1^ and an injection volume of 80 μl. The outlet of the HPLC system was connected to the Agilent diode array UV-Vis detector with a 10 mm optical path to acquire full UV-Vis spectra from 220 to 750 nm every second, and then to the ICP-MS-MS nebulizer by a 65 cm PEEK capillary of 0.17 mm internal diameter. The ICP-MS was operated in time-resolved analysis under oxygen reaction mode (at 2 ml/min), with an integration time of 0.1 s per isotope, including ^63^Cu, ^65^Cu, ^66^Zn, ^57^Fe and ^31^P → ^47^PO isotopes. For quality control analysis, the column retention times were calibrated using gel filtration standard (GFS, Bio-Rad Laboratories, 1511901). The intensity of the chromatographic peaks for Cu, Zn and Fe were normalized to the total phosphorus for each chromatogram. To analyze protein content at the individual chromatographic peaks, equal amounts of total cell lysates were injected for SEC-ICP-MS analysis, and fractions corresponding to CuCOX peaks were collected based on the retention time. Samples were then freeze-dried (Millrock), resuspended in RIPA buffer, and analyzed by immunoblotting.

The integration of the SEC-ICP-MS chromatograms was performed in Origin X (OriginLabA) after exporting them from Agilent MassHunter ICP-MS. Quantification of the peaks was performed using Origin software and an in-house Excel script developed to compute isotope-dilution results. The exogenous ^63^Cu was determined by measuring the ^65^Cu signal (counts per second, CPS) at each point along the chromatogram to calculate the expected ^63^Cu signal based on the natural isotopic abundance ratio. This calculated ^63^Cu contribution was subtracted from the experimentally established ^63^Cu signal (in CPS). The remaining CPS values represent an excess of ^63^Cu at each point, enabling the generation of a new chromatogram specifically for exogenous ^63^Cu for analysis. Endogenous Cu was calculated as Total ^63^Cu minus Extra ^63^Cu. As a negative control, samples without ^63^Cu treatment were included, resulting in a flat chromatogram. The areas under the chromatogram peaks were used to quantify the metal content in the SEC fractions. Quantification was performed by applying the sensitivity for ^63^Cu obtained from a gel filtration standard of ovalbumin Cu content. Finally, the metal content values were normalized to the total phosphorus in each sample. In Figure 1C, the total Cu content was measured independently by ICP-MS. In all other figures, the total Cu level was calculated from SCE-ICP-MS chromatograms as the total area under the curve.

The external calibration method was used to quantify the total Cu concentration in cell culture media and cell pellets, as needed, after acid digestion in 10% nitric acid at 50 °C, using an Agilent 8900 ICP-MS/MS system (RRID: SCR_019460).

Western blots

Total cell lysates were prepared using RIPA buffer (Thermo Scientific, 89,901) supplemented with 1 × protease and phosphatase inhibitor cocktail (PPI; Thermo Fisher Scientific, 1,861,281) and 10 μM phenylmethanesulfonyl fluoride (PMSF; Sigma, P7626). Lysates were centrifuged at 14,000g for 20 min at 4 °C. Protein concentration in the supernatant was quantified using the DC Protein Assay (Bio-Rad, 5000114). Equal amounts of protein were separated by SDS-PAGE.

For samples subjected to SEC-ICP-MS, fractions were collected from the HPLC system at previously established retention times. These samples were filtered, freeze-dried, and resuspended in RIPA buffer containing PPI and PMSF. 10% of the original volume was used for Western blotting, as described below.

Proteins were transferred to PVDF membranes, which were then blocked for 1 h at room temperature in 5% non-fat dry milk prepared in PBS-T (PBS + 0.1% Tween-20). Membranes were incubated with primary antibodies overnight at 4 °C with gentle rocking. Following three washes in PBS-T, membranes were incubated with HRP-conjugated secondary antibodies (1:5000 in 5% milk/PBS-T) for 1 h at room temperature. After additional washes, signals were developed using one of the following chemiluminescent substrates: Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific, 32,106), SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific, 34,095), or SuperSignal West Atto Ultimate Sensitivity Substrate (Thermo Fisher Scientific, A38556). Blots were imaged using a Bio-Rad ChemiDoc system. Primary antibodies used: MT-CO1 (Abcam, ab203912; 1:1000; RRID: AB_2801537); MT-CO2 (Abbexa, abx125706; 1:1000; RRID: AB_3076223); GAPDH (Abcam, ab8245; 1:8000; RRID: AB_2107448); DMT1 (Proteintech, 20507-1-AP; 1:1000; RRID: AB_10694284); LAT1 (Cell Signaling Technology, 5347; 1:1000; RRID: AB_10695104); SLC25A3 (Gift from P. Combine and S. Leary (37); 1:1000); ZNT1 (Proteintech, 22661-1-AP; 1:5000). Secondary antibodies: Anti-Rabbit IgG HRP-linked (Cell Signaling Technology, 7073; 1:5000; RRID: AB_2099233); Anti-Mouse IgG HRP-linked (Cell Signaling Technology, 7076; 1:5000; RRID: AB_330924).

RT-qPCR

Total RNA was extracted from cells using 1 ml of TriReagent (MRC, TR 118) following the manufacturer’s protocol. RNA was resuspended in 20 μl of nuclease-free water and quantified using a NanoDrop spectrophotometer; the 260/280 absorbance ratio was recorded to assess purity. Complementary DNA (cDNA) was synthesized using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368814). Quantitative PCR was performed using the ΔΔCt method with 1 × Fast SYBR Green Master Mix (Applied Biosystems) on an Applied Biosystems QuantStudio 7 Real-Time PCR System (RRID: SCR_020245), using 20 ng of cDNA per reaction. Gene expression was normalized to the housekeeping gene PP1A, and expression levels were calculated relative to the Non-Targeting control sample. Primer sequences were as follows: CTR1 (SLC31A1): Forward: 5′-CCC TTA CTC TGT TGT CCT TTC-3′ Reverse:5′-CAC AGC ATA GCA CTG TCT AC-3′; PP1A: Forward: 5′-ACC GCC GAG GAA AAC CG.

Gene silencing

Cells were transfected with small interfering RNA (siRNA) using Lipofectamine 3000 Transfection Kit (Invitrogen, L3000015) following the manufacturer’s instructions. siRNAs were used at a final concentration of 50 nM. Control cells were treated with non-targeting siRNA. Culture media were replaced 24 h after the first transfection. A second siRNA transfection (50 nM) was performed the following day. Twenty-four hours after the second transfection, cells were treated with ^63^Cu-containing media for 48 h. The following siRNA pools were used: Non-Targeting pool (Dharmacon, D-001810–10–20); SLC31A1 (CTR1): Dharmacon, L-007531-02 to 0005; SLC11A2 (DMT1): Dharmacon, L-007381-00 to 0010; SLC7A5 (LAT1): Dharmacon, L-004953-01 to 0005; SLC25A3: Dharmacon, L-007484-00 to 0010 (Table S1). Knockout of ZNT1 in RCC4 cells was performed using pLV{CRISPR}-hcas9:T2A:Puro-U6>hSLC30A1[gRNA] (Vector ID:VB250805–1442jeh) and non-targeting control pLV{CRISPR}-hcas9:T2A:Puro-U6>Scramble_gRNA (Vector ID:VB010000–9527dpk) form VectorBuilder (Table S1). Puromycin-selected cell pools were used for subsequent experiments.

Seahorse metabolic assays

Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured using an Agilent Seahorse XFe96 Analyzer. For galactose treatment conditions, cells were pre-treated for 48 h in DMEM (Gibco, A14430–01) supplemented with 10% dialyzed fetal bovine serum (dFBS), 10 mM glucose or 10 mM galactose, and 4 mM glutamine. On the day of the experiment, cells were incubated for 1 h in Seahorse XF Base Medium (Agilent) without serum, containing 10 mM glucose or 10 mM galactose and 4 mM glutamine. Basal respiration, maximal respiration, and ATP-linked OCR were determined using the Agilent Seahorse Mitochondrial Stress Test. Measurements were taken before treatment and after sequential injections of: Oligomycin A (1.5 μM); FCCP at cell line-specific concentrations: 786-O: 0.25 μM, RCC4: 1 μM, TH1: 1 μM, TERT: 0.5 μM, Antimycin A/rotenone (0.5 μM each). Data were analyzed using the Agilent Mitochondrial Stress Test Report Generator and normalized to cell number using Hoechst fluorescence staining. To determine mitochondrial vs. glycolytic ATP production, OCR and ECAR were measured before treatment and after sequential injections of oligomycin A (1.5 μM), followed by antimycin A/rotenone (0.5 μM each). Data were analyzed using the Agilent ATP Rate Assay Report Generator.

DepMap analysis

To analyze gene effect in the DepMap, CRISPR screen data were used. The analysis was restricted to cell lines annotated with renal cell carcinoma (RCC) as the primary disease to maintain clinical relevance to this cancer type. For each gene, the median gene effect score was then calculated across all RCC cell lines.

Statistics

Significance was determined by using two-tailed unpaired or paired t tests in GraphPad Prism version 10. Box and whisker plots displayed the median, minimum and maximum values, as well as all data points. Bar graphs displayed the mean with standard deviation, and all data values.

Data availability

All metalomics data can be provided upon request from the corresponding authors.

Supporting information

This article contains supporting information.

Conflict of interest

The authors declare that they do not have any conflicts of interest with the content of this article.