Copper is one of the essential trace elements for living beings, influencing several critical processes like cellular energy production, antioxidant defense, communication within cells, and the functioning of enzymes^[1]. The daily intake of copper is 0.7−3.0 mg/d, and copper homeostasis is strictly regulated by physiological processes, including duodenal and small intestinal uptake, blood transport, liver storage and release, and bile excretion, thereby maintaining copper homeostasis in the body^[2], and many studies have confirmed that copper disorders in the body are associated with neurodegenerative, metabolic, and genetic diseases^[3]. Currently, copper is widely used in mining, construction, medical equipment, power generation and other industries, resulting in increasing environmental copper exposure levels. The World Health Organization has set the safety standard for copper concentration in drinking water at 2 mg/L, and more and more industries will discharge a large amount of copper-containing wastewater in a short period of time, causing the copper concentration in the water to reach more than 100 mg/L, or even more than 1,000 mg/L^[4]. Therefore, effective prevention and mitigation of copper toxicity are urgently needed. Untargeted metabolomics by gas chromatography-mass spectrometry (GC-MS), through the comprehensive analysis of metabolites or small-molecule substrates within organisms, provides a powerful tool for understanding disease mechanisms and conducting systems biology analyses^[5]. In this study, we used this technology to analyze the changes in metabolites in the serum and tissues of mice exposed to high-dose copper, aiming to systematically analyze and explore the toxic mechanisms of copper.

Eight-week-old mice were randomly divided into two groups (n = 7). After adaptivefeeding, weselected CuSO₄·5H₂O as the Cu source and exposed them to Cu for 4 weeks. The Cu-exposed group drank water containing 500 mg/L of sulfate, while the control group drank the same amount of tap water. This model can simulate high-dose Cu pollution in an actual environment. The dosage and period of exposure were selected based on previous research and relevant scholarly articles^[6]. All mice survived until the end of the experiment, and there was no statistically significant difference in body weight between the two groups (Figure 1A). As the central organ for maintaining Cu homeostasis, approximately 20% of Cu is distributed in the liver, and blood is the transportation center of various substances in the human body; approximately 5%–10% of Cu is distributed in the blood. Therefore, selecting serum and liver samples to monitor changes in Cu levels can reflect the overall Cu exposure of the body and important changes in physiological status (Figure 1B–C). Cu ions accumulated significantly in the blood and liver of mice in the Cu-exposure group compared with the control group. H&E staining revealed obvious pathological changes in the livers of mice after exposure to high-dose Cu, with congestion of the central and portal veins in the hepatic lobules (red arrows), hydropic degeneration of a large number of hepatocytes (blue arrows), swollen cells, and loose and lightly stained cytoplasm (Figure 1D), which provided sufficient verification for subsequent research.

Figure 1.  After copper exposure (A) weight, (B–C) Cu ion concentrations in serum and liver, (D) pathological examination, and (E) TIC images of QCs. Data are presented as mean ± standard deviation. ^*P < 0.05, ^**P < 0.01, and ^***P < 0.001 indicate differences between different groups, respectively.

Second, to further systematically analyze and explore the toxicity mechanism of Cu, we used GC-MS to quantitatively analyze the metabolite content of serum and different tissue samples. The chromatograms of each sample extracted from the GC-MS data showed excellent signal response and reproducibility (Figure 1E). OPLS-DA analysis using SIMCA-P (version 14.1) software observed that the two groups of samples were independently separated and clustered within the group, indicating that the metabolite differences between the groups were significant and the differences within the group were small. Supplementary Table S1 (available in www.besjournal.com) introduces the coefficient of determination (R²) and predictive ability (Q²) of serum and various tissues and organs. When the R² value was closer to 1, the model fit the data better. Q² values greater than 0.5 are generally considered to have good predictive ability. The higher the value, the better is the prediction effect of the model. Through 200 permutation tests, it was observed that the intercept of the Q² regression line on the y-axis was less than zero, and all Q² values on the left were lower than the original points on the right, which reflected the reliability of the model and further supported the conclusion of differences between the groups (Supplementary Figure S1, available in www.besjournal.com).

Figure S1.  OPLS-DA score plots and 200 permutation tests tables: (A) serum, (B) liver, (C) hippocampus, (D) cortex, (E) kidney, (F) pancreas, (G) lung, and (H) muscle.

A t-test was performed using SPSS 27.0 to identify potential metabolites with significant differences in expression between the two groups. The primary differential metabolites screened according to VIP > 1, P < 0.05 criteria were imported into MetaboAnalyst 5.0, and it was finally determined that 65 metabolites were significantly different between the control group and the Cu exposure group (Supplementary Table S2, available in www.besjournal.com). Heat maps and cluster analyses were used to more intuitively display the differences in metabolite levels between the two groups (Figure 2).

Figure 2.  Differential metabolites identified in (A) serum, (B) liver, (C) hippocampus, (D) cortex, (E) kidney, (F) pancreas, (G) lung, and (H) muscle samples through cluster analysis and heatmap representation, comparing the Cu-exposed group with the control group. The heatmap colors indicate the metabolite regulation levels (blue for downregulation, red for upregulation). In this representation, the rows correspond to individual samples, whereas the columns represent specific metabolites.

Finally, the analysis observed that 14 metabolic pathways were significantly differentially expressed between the control group and the Cu-exposed group (P < 0.05, impact > 0.05), indicating that the changes in metabolite levels in the serum, tissues, and organs caused by Cu exposure were related to toxicological processes such as oxidative stress, decreased detoxification capacity, mitochondrial dysfunction, energy metabolism disorder, and amino acid metabolism disorder. The details of the metabolic pathway analysis are summarized in Supplementary Table S3 (available in www.besjournal.com) and are supplemented with a visual representation in Figure 3. Cu exposure may cause an imbalance in oxidative and antioxidant stress responses in the body, reduce detoxification capacity, and damage tissues and organs. The results showed that the levels of L-glutamic acid, L-5-Oxoproline, and Glycine in the livers of mice in the Cu-exposed group decreased. Glutathione (GSH) is an important cellular antioxidant that can remove free radicals and peroxides and protect cells from oxidative damage^[7]. Glutamate and glycine are the key precursors of the GSH metabolic pathway. Conversely, when GSH is degraded, glutamate and L-5-oxoproline are produced, and the latter can be converted into glutamate and participate in the GSH synthesis cycle. Therefore, we inferred that Cu exposure may increase the consumption of the antioxidant defense system in the liver, interfere with the normal GSH metabolic pathway, and cause the liver to suffer the toxic effects of oxidative stress damage. In addition, we observed that the tyrosine content in the kidney decreased after Cu exposure. Cu, as an oxidant, can catalyze the generation of reactive oxygen species, leading to oxidative stress, which may interfere with the activity of phenylalanine, tyrosine, and tryptophan biosynthesis, affect the conversion of phenylalanine to tyrosine, and reduce the detoxification ability of the kidney, leading to the accumulation of free radicals and other metabolic wastes in the kidney and causing nephrotoxicity.

Figure 3.  KEGG enrich in serum and different organs.

Cu exposure may damage the body through toxic mechanisms such as mitochondrial damage and energy metabolism disorders. The pyruvate metabolism plays a key role in energy production. Pyruvate enters the mitochondria under aerobic conditions and is converted to acetyl-CoA through a series of reactions. The latter then enters the tricarboxylic acid cycle to produce (S)malate. (S)-Lactic acid is produced from pyruvate through lactic acid fermentation under hypoxic conditions, thereby maintaining the energy supply of cells under hypoxic conditions^[8]. After Cu exposure, the levels of (S)-malate and (S)-lactate in the serum of mice decreased, which may be due to the damage caused by Cu to mitochondrial function, interfering with the pyruvate metabolic pathway and affecting overall energy metabolism. Cu exposure caused changes in the levels of metabolites in the pancreas of mice. The results showed that the citrate content in the glyoxylate and dicarboxylic acid metabolic pathways related to energy metabolism was reduced. Cu exposure can induce oxidative stress, damage mitochondrial function, and lead to toxic mechanisms such as energy metabolism disorders. Faced with the toxic effects of Cu exposure, the body may increase antioxidant defense and damage repair mechanisms, such as serine and L-glutamic acid, to cope with the toxic effects caused by oxidative stress and energy metabolism disorders.

Cu exposure has been shown to alter the amino acid profiles of various tissues and organs in mice. L-glutamate has been shown to possess antioxidant and detoxification properties and act as a primary excitatory neurotransmitter in the central nervous system. Moreover, it is involved in numerous intricate cell signaling pathways, including arginine biosynthesis, arginine and proline metabolism, and butyrate metabolism. However, excessive Glu levels can lead to excitotoxicity^[9]. The elevated expression levels of related metabolites (L-glutamate, L-serine, L-proline, and 4-aminobutyrate) in our study may indicate that Cu exposure can cause damage to the cortex and hippocampus of mice through toxic mechanisms, such as disruption of the balance between related amino acid metabolism and interference with intercellular signaling. The arginine and proline metabolic pathways, which are rich in gamma-aminobutyric acid and L-glutamate, are vital for collagen synthesis. Collagen is an essential component of lung tissue structure and is associated with pulmonary fibrosis and lung injury repair processes^[10]. The glycine, serine, and threonine metabolic pathways play important roles in protein synthesis and cell signaling. The increase in glycine and l-threonine content in the muscle after Cu exposure may be due to Cu interfering with normal glycine, serine, and threonine metabolic pathways, thereby affecting the synthesis and degradation of muscle proteins.

This study revealed that the toxic mechanisms of Cu exposure in different tissues and organs may include oxidative stress, decreased detoxification capacity, mitochondrial dysfunction, and energy and amino acid metabolism disorders. This study deepens our understanding of the systemic toxicity mechanism caused by Cu exposure and will help formulate strategies for the prevention and treatment of Cu poisoning.