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. 2026 Jul 25;27:874. doi: 10.1186/s12864-026-13228-2

Interactions between kidney proteins and metabolism in Tibetan sheep at different altitudes

Kejiang Liu 1, Jiang Zhang 1, Xueqin Yu 2, Pengfei Zhao 1,✉
PMCID: PMC13685116  PMID: 42902137

Abstract

When oxygen supply to the kidneys is restricted, it may trigger a series of acid-base imbalances and pathological states, subsequently inducing hypoxic adaptive mechanisms in the kidneys. However, the protein-metabolism interaction mechanisms underlying this process remain unclear. Therefore, this study conducted proteomic and metabolomic analyses on Tibetan sheep kidneys collected at three distinct altitudes (2500 m, 3500 m and 4500 m) to elucidate the protein-metabolism interaction mechanisms during hypoxic adaptation. The results demonstrate a synergistic relationship between proteins and metabolism, revealing dynamic expression patterns. Key DEPs (ALDH2, COX5A, GLUD1, NDUFS2, NDUFS8 and SLC17A1) and key DEMs (Wedelolactone, Cytidine, and 3-hydroxybenzoic acid) primarily participated in the antioxidant defense, anti-inflammatory, and energy supply functions of Tibetan sheep kidney hypoxia adaptation through signaling pathways such as the citrate cycle (TCA cycle) and oxidative phosphorylation. In summary, the adaptive mechanisms to renal hypoxia are not influenced by a single protein or metabolite, but rather are related to the interactions between proteins and metabolites. These interactions may collectively influence the adaptive mechanisms, ensuring that the kidneys maintain normal function in high-altitude environments.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12864-026-13228-2.

Keywords: Hypoxia adaptation, Antioxidant, Proteins, Metabolites, Anti-inflammatory

Introduction

Extreme environments are common natural conditions in high-altitude regions, characterized by low pressure, low oxygen levels, and low temperatures [1]. As altitude increases, atmospheric pressure decreases. This leads to a reduction in the partial pressure of oxygen at each node of the oxygen transport chain—from the environment to the mitochondria within tissues. Conversely, this decrease in oxygen partial pressure triggers corresponding changes in multiple organs and tissues. These adaptations help the body counteract the functional impacts of rising altitude and maintain normal physiological levels [2]. The duration of exposure at high altitudes has varying effects on kidney function. Short-term exposure can cause acid-base and sodium imbalances, while prolonged exposure allows the kidneys to gradually adapt and restore normal physiological function. Therefore, organisms living at high altitudes must develop corresponding adaptive mechanisms to ensure normal physiological functions in such environments. As plateau animals, Tibetan sheep have drawn widespread attention for their hypoxic adaptation to unique environments.

Hypoxia adaptation is a specialized physiological phenomenon, referring to the adaptive responses an organism adopts to sustain basic life functions when exposed to low-oxygen environments. In high-altitude regions, numerous species have evolved unique survival strategies and biological mechanisms for adapting to hypoxia due to the combined effects of long-term natural and artificial selection [3, 4]. The kidneys play a crucial role in the body’s adaptation to hypoxic conditions and in maintaining acid-base balance and electrolyte homeostasis. When oxygen supply is restricted, it can trigger a series of acid-base imbalances, such as pathological states like metabolic acidosis or alkalosis [5]. In response, the kidneys initiate compensatory mechanisms by precisely regulating the excretion of bicarbonate in urine, thereby helping to alleviate hypoxia-induced metabolic acidosis [6]. Specifically, under hypoxic conditions, renal proximal tubule cells can activate calcium and potassium ion channel mechanisms. This mechanism not only helps maintain appropriate cellular membrane potential stability, but this critical ion regulation also exerts a significant protective effect by inhibiting apoptosis and enhancing cellular tolerance to the hypoxic environment [7]. When confronted with hypoxic challenges, the kidneys also modulate their water and salt reabsorption mechanisms to maintain stable blood volume and blood pressure. The kidneys are highly sensitive to hypoxia, particularly the renal medulla, where the partial pressure of oxygen is low. Hypoxia activates renal afferent nerves, directly causing an increase in blood pressure and stimulating the production of erythropoietin, which increases the blood’s oxygen-carrying capacity. At the same time, the kidneys and the carotid bodies work together to detect hypoxia; mediated by the RAAS and HIF pathways, this creates a positive feedback loop that drives excessive sympathetic nervous system activation, ultimately leading to and maintaining hypertension [8]. Research has shown that the interaction between hypoxia and the renin-angiotensin system (RAS) is complex: under hypoxic conditions, the RAS is activated, and the activated RAS, in turn, exacerbates hypoxia [9]. This process is crucial for ensuring adequate oxygen and nutrient supply to the kidneys during hypoxia, enabling them to maintain normal physiological function under low-oxygen conditions. However, due to the extreme environment at high altitudes, when the kidneys’ adaptive mechanisms are disrupted, this can lead to kidney tissue damage. Prolonged hypoxia at high altitudes leads to increased cellular proliferation and impaired endothelial cell function [10]. Simultaneously, persistent hypoxia causes abnormal increases in serum uric acid levels [11]. This increase in uric acid may be linked to a decrease in ATP levels caused by adenine nucleotides and the subsequent activation of xanthine oxidase [12]. Research has also found that prolonged exposure to hypoxic environments leads to glomerular hypertrophy, a phenomenon observed in both humans and animals [13, 14]. This condition may be associated with a reduction in nephron number and glomerular hypertension [15, 16].

Therefore, this study selected Tibetan sheep as an ideal model for plateau research and conducted investigations on kidney tissue at three different altitudes. The adaptive mechanisms to hypoxic environments are complex, involving the coordinated action of multiple organ systems. Beyond core life-sustaining organs such as the heart, lungs, and central nervous system, the kidneys also play a crucial role in responding to hypoxic challenges. When confronted with hypoxic stress, the kidneys can effectively adapt to these adverse conditions through finely tuned adjustments to energy metabolism pathways-such as enhancing anaerobic glycolysis or regulating oxidative phosphorylation-while maintaining acid-base balance and other critical metabolic activities. This study aims to investigate the effects of long-term exposure to high-altitude environments on the kidneys, elucidate the role of kidney-related proteins in high-altitude hypoxia, and examine changes in metabolites. It also analyzes the protein-metabolite interactions involved in renal adaptation to high-altitude hypoxia, providing new insights into the adaptive mechanisms of kidneys in high-altitude animals and humans.

Materials and methods

Sample collection

Samples were collected from three distinct altitudes: Mado County (H: 4500 m), Guoluo Prefecture, Qinghai Province; Maqu County (M: 3500 m); and Xiahe County (L: 2500 m). All these animals were purchased from local herders, with their consent. The flock is divided into three groups, each containing five Tibetan sheep. All are four-year-old grazing ewes in good condition and healthy. Kidney tissue samples were collected into cryopreservation tubes and transported back to the laboratory in liquid nitrogen for subsequent experiments. All samples were collected in the summer of 2024 from naturally grazed, well-nourished sheep. The flock consisted of sheep from different elevations but all from the same herd; the individuals were not related to one another and were in good physiological condition. Kidney samples were collected from the entire thickness of the left kidney. Slaughter was conducted in accordance with ethical standards (Stun with an electric shock and then bleed out quickly); the sheep had no history of obvious disease and were assessed as healthy.

Proteomic data

After sample extraction and separation, DDA library preparation and DIA data acquisition and analysis were performed using the Bruker tims TOF Pro2 mass spectrometer (Bruker Daltonics) for protein quantification and identification. Using a filtering criterion of FDR ≤ 0.01, identify the proteins and peptides that meet the requirements. Based on quantitative proteomics data, we employed Principal Component Analysis (PCA) using R (http://www.r-project.org/) to explore distance relationships among samples through dimensionality reduction. Differential protein analysis was performed based on quantitative results, screening proteins exhibiting significant abundance changes between comparison groups. Student’s t-tests were conducted to obtain p-values. To reduce false positive rates, multiple hypothesis testing corrections were applied to p-values using the Benjamini-Hochberg (BH) method to derive FDR values. Differential expression protein screening thresholds were set as follows: absolute fold change (FC) greater than 1.5-fold, and a corrected P-value < 0.05. This process identifies proteins exhibiting significant differences between groups. Perform GO functional annotation (http://www.geneontology.org) on all identified proteins. Then, for each GO class involved in the three ontologies, list all corresponding protein IDs and the number of proteins. KEGG is the primary public database for pathways [17]. Pathway analysis identifies the major biochemical metabolic pathways and signal transduction pathways in which proteins participate. Then select gene sets with specific biological characteristics from the clustering results. Perform trend analysis using the ShortTime-series Expression Miner software [18] (Details on proteomics sequencing are provided in the supplementary materials).

Metabolomic data

After sample pretreatment, metabolites were detected using the AB SCIEX Triple TOF 6600 mass spectrometer (Shanghai Applied Protein Technology Co., Ltd.), followed by bioinformatics analysis. During our testing, we simultaneously employed both positive ion mode (POS) and negative ion mode (NEG) ionization methods. This approach enhances metabolite coverage and improves detection performance. Subsequent data analysis processes will separately analyze data from the positive and negative ion modes. Perform OPLS-DA analysis using the R package ropls [19]. This method effectively reduces model complexity and enhances interpretability without compromising predictive capability, thereby maximizing the visualization of intergroup differences. We combined the VIP values from multivariate statistical analysis (OPLS-DA) with the P-values from univariate statistical analysis (T-test) to screen for significantly different metabolites between different comparison groups [20]. The threshold for significant differences was set as: VIP ≥ 1 in the OPLS-DA model and T-test P < 0.05. Pathway significance enrichment analysis employs the hypergeometric test at the KEGG pathway level to identify pathways significantly enriched in metabolic pathways relative to the entire genomic background [21]. This analysis determines the primary biochemical metabolic pathways and signal transduction pathways in which differentially expressed metabolites participate. To examine the expression pattern of DEMs, the expression data of each sample (in the order of treatment) were normalized to 0, log2 (v1/v0), log2(v2/v0), and then clustered by Short Time-series Expression Miner software (STEM) [18] (Details on metabolomic sequencing are provided in the supplementary materials).

Integrated analysis of multi-omics data

To identify sets of associated proteins and metabolites that influence sample grouping, analyze association characteristics, and perform O2PLS (Bidirectional orthogonal projections to latent structures) model analysis based on two datasets: protein expression levels and metabolite abundances. Based on all proteomics and metabolomics data, we performed O2PLS analysis using the OmicsPLS package [22]. Additionally, differentially expressed proteins and metabolites with absolute correlation coefficients greater than 0.5 were identified, and an interaction network diagram was constructed using Cytoscape21. The Sankey diagram was generated by the online data analysis and visualization platform https://www.bioinformatics.com.cn (last accessed: April 1, 2026).

Statistical analysis

All results of this study were analysed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) statistical software, and pearson correlation analysis was used to determine the Protein and metabolism intergroup correlations, with P < 0.05 indicates a significant difference.

Results

Analysis of kidneys proteomic data of Tibetan sheep at different altitudes

A proteomics study of kidney tissues from Tibetan sheep at three different altitudes was conducted using directDIA technology. To ensure the results are usable and reliable, protein identification was performed on the sequencing data, revealing 102,120 Precursors, 92,062 Peptides, 7,180 Protein_Groups, and 7,408 Proteins (Figure. S1A). Simultaneously, peptide identification was performed on the identified proteins, revealing 3,029 peptides with a count of ≥ 11 (Figure. S1B). Statistical analysis of annotation results from GO, KEGG, and KOG revealed that 3,254 proteins were annotated in all three databases (Figure.S1C). Principal component analysis showed that kidney samples from Tibetan sheep in three regions at different elevations exhibited distinct clustering patterns and high intra-group reproducibility, indicating that the data are suitable for further analysis (Fig. 1 A). Statistical analysis of differentially expressed proteins across comparison groups identified 1,859 differentially expressed proteins (DEPs) in L vs. M (939 upregulated, 920 downregulated), 794 in L vs. H (456 upregulated, 338 downregulated), and 1,322 in M vs. H (798 upregulated, 524 downregulated) (Fig. 1B). Further expression trend analysis of DEPs revealed that all DEPs were assigned to eight expression trend modules, with the top three being modules 2, 5, and 6 (Fig. 1 C). We intersected the DEPs across the three comparison groups using a Venn diagram, identifying 78 DEPs common to all three groups (Fig. 1D). At the same time, we present the expression trends of key proteins, with a focus on the Trend 2 module (Table S1).

Fig. 1.

Fig. 1

Proteome data of kidneys from Tibetan sheep at different altitudes. A PCA plot. B Multi-group differential scatter plot. C Expression trend plot. D Venn diagram. L: 2500 m, M: 3500 m, H:4500 m

Enrichment analysis of proteomic data from Tibetan sheep kidneys at different altitudes

GO enrichment analysis of all DEPs revealed that the three comparison groups showed significant enrichment in biological processes including immune system processes, metabolic processes, developmental processes, and response to stimulus (Fig. 2 A). We further performed KEGG pathway enrichment analysis, revealing 27 significantly enriched pathways in the L vs. H group, 46 significantly enriched pathways in the L vs. M group, and 42 significantly enriched pathways in the M vs. H group. Significant enrichment was observed across multiple pathways—including the Citrate cycle (TCA cycle), Oxidative phosphorylation, Carbon metabolism, Steroid hormone biosynthesis, and Thermogenesis—in all three control groups (Fig. 2B-D).

Fig. 2.

Fig. 2

Enrichment Analysis of Differentially Expressed Proteins in Kidneys of Tibetan Sheep at Different Altitudes. A GO enrichment analysis. B-D KEGG enrichment analysis in L vs. H, L vs. M and in M vs. H

Statistical analysis of kidney metabolites in Tibetan sheep at different altitudes

A metabolomics study of Tibetan sheep kidneys at different altitudes revealed 11,881 characteristic peaks in positive ion mode and 11,519 characteristic peaks in negative ion mode, as detected by high-resolution mass spectrometry (HRMS) (Table S2). To validate the reliability of the OPLS-DA model, cross-validation was also performed. Results showed that for different comparison groups, R²X > 0.5 and R²Y > 0.9 in both positive and negative ion modes may participate, indicating the model’s reliability (Table S3). Similarly, OPLS-DA results revealed significant intergroup separation and intragroup clustering in both positive and negative ion modes, indicating reliable outcomes (Fig. 3A-F). We conducted a further analysis of the data and added substitution tests for different groups in both the cation and anion modes; the results showed no signs of overfitting, indicating that the findings are reliable (Figure S2).

Fig. 3.

Fig. 3

Metabolome OPLS-DA Scores of Tibetan Sheep Kidneys at Different Altitudes. A-C Positive ion mode; D-F Negative ion mode

Analysis of kidneys metabolome of Tibetan sheep at different altitudes

In positive ion mode, statistical analysis of differentially expressed metabolites between groups identified 150 differentially expressed metabolites (DEMs) in the comparison between L vs. M comparison (104 up-regulated, 46 down-regulated); In the L vs. H comparison, 130 DEMs (96 up-regulated, 34 down-regulated) were identified; in the M vs. H comparison, 210 DEMs (96 up-regulated, 114 down-regulated) were identified (Fig. 4A). In Negative ion mode, statistical analysis of differentially expressed metabolites between groups identified 180 DEMs in the comparison between L vs. M comparison (129 up-regulated, 51 down-regulated); In the L vs. H comparison, 174 DEMs (105 up-regulated, 69 down-regulated) were identified; in the M vs. H comparison, 260 DEMs (112 up-regulated, 148down-regulated) were identified (Fig. 4B). In the positive ion mode, the Venn diagram revealed that 4 metabolites (4-aminovaleric acid betaine, Bioresmethrin, Diphenylamine and Wedelolactone) were enriched in all three comparison groups (Fig. 4C). In the Negative ion mode, the Venn diagram revealed that 20 metabolites (3-hydroxybenzoic acid, Cytidine and etc.) were enriched in all three comparison groups (Fig. 4D). Expression trend analysis of DEMs revealed eight distinct expression patterns, with significant enrichment in modules 6 and 7 under positive ion mode and in modules 5 and 3 under negative ion mode (Fig. 4E, F).

Fig. 4.

Fig. 4

Analysis of kidneys metabolome of Tibetan sheep at different altitudes. A, C, E Positive ion mode; B, D, F Negative ion mode. A, B Multi-group differential scatter plot, C, D Venn diagram, E, F Expression trend plot. L: 2500 m, M: 3500 m, H:4500 m

Enrichment analysis of metabolome data from Tibetan Sheep kidneys at different altitudes

In the positive ion mode, KEGG enrichment analysis of DEMs revealed significant enrichment in the L vs. H group for the Cholinergic synapse, Glycerophospholipid metabolism, and Glycosaminoglycan biosynthesis - heparan sulfate/heparin signaling pathways (Fig. 5A). Significantly enriched in the HMG-CoA reductase inhibitors signaling pathway in the L vs. M group (Fig. 5B). In the M vs. H group, significant enrichment was observed in the Steroid biosynthesis and HMG-CoA reductase inhibitors signaling pathways (Fig. 5C).

Fig. 5.

Fig. 5

KEGG enrichment analysis of differentially expressed metabolites in kidneys of Tibetan sheep at different altitudes. A-C Positive ion mode in L vs. H, L vs. M and M vs. H. D-F Negative ion mode in L vs. H, L vs. M and M vs. H

In the negative ion mode, KEGG enrichment analysis of DEMs revealed significant enrichment in the L vs. H group for the Biosynthesis of unsaturated fatty acids, Fc epsilon RI signaling pathway, and Secondary bile acid biosynthesis signaling pathways (Fig. 5D). No significant pathways were enriched in the L vs. M group. (Fig. 5E). In the M vs. H group, significant enrichment was observed in the Sulfur metabolism and Biosynthesis of unsaturated fatty acids signaling pathways (Fig. 5F).

Analysis of kidney metabolomics results at different altitudes in merge mode

We further combined the positive and negative ion modes for analysis of the metabolomic data. The OPLS-DA analysis results showed significant separation between groups and clustering within groups in the combined mode, indicating that the analysis results are reliable (Fig. 6 A–C). The Venn diagram results show that, when comparing the three groups in all possible pairwise combinations, a total of 24 differentially expressed metabolites were common to all groups (Wedelolactone, Cytidine, and 3-hydroxybenzoic acid), and these common differentially expressed metabolites were consistent with those previously identified through separate analyses under our two models (Fig. 6D). A trend analysis was performed on the differentially expressed metabolites, which were classified into eight trend modules (Fig. 6E). We further performed KEGG enrichment analysis on the differentially expressed metabolites, which showed significant enrichment in Glycerophospholipid metabolism pathway (Fig. 6 F) (P < 0.05).

Fig. 6.

Fig. 6

Analysis of Kidney Metabolomics Results at Different Altitudes in Merge Mode. A-C Metabolome OPLS-DA Scores in L vs. H, L vs. M and M vs. H. D Venn diagram, E Expression trend plot. F KEGG Enrichment Analysis

Integrated analysis of proteomics and metabolomics

By correlating the screened metabolites and proteins, we plotted loadings for different omics data on the variables in the joint section, revealing associations between the two omics datasets (Fig. 7A) (Table S4, 5). To further investigate correlations, we performed heatmap visualization of differentially expressed proteins and metabolites. This revealed strong correlations between two key metabolites (Cytidine, and 3-hydroxybenzoic acid) and multiple key proteins, as well as a correlation between one key metabolite (Wedelolactone) and a key protein (GUSB) (Fig. 7B). Subsequently, network diagrams were constructed for key proteins and metabolites, revealing a positive correlation between Wedelolactone and GUSB. Cytidine exhibited negative correlations with most proteins, while 3-hydroxybenzoic acid showed positive correlations with the majority of proteins (Fig. 7C). An analysis of the relationships between key proteins and metabolites in the skin reveals that, as shown in the network diagram, most key proteins influence metabolites by participating in metabolic pathways (Fig. 7D).

Fig. 7.

Fig. 7

Integrated Analysis of Proteomics and Metabolomics. A Different omics loadings diagram. B Key protein and metabolite heatmap. C Key protein and metabolite network interaction diagram. Solid lines indicate positive correlations, while dashed lines indicate negative correlations. D Sankey diagram illustrating the relationships between key proteins and metabolites in the Kidney

Discussion

High-altitude animals, living long-term in the low-oxygen environment of plateaus, undergo adaptive changes in the morphological structure of their tissues and organs, as well as in their physiological and biochemical indicators. This study aims to investigate kidney tissues from Tibetan sheep at different altitudes using proteomic and metabolomic approaches. The goal is to elucidate the hypoxic adaptation-related protein-metabolite interaction mechanisms in high-altitude animal kidneys, thereby providing novel insights into renal health in such environments. Using proteomics techniques to study the kidney, we identified a total of 78 differentially expressed proteins, which revealed proteins closely associated with kidney function. Research has found that ALDH2 is primarily expressed in kidney tissue and localized to renal tubular epithelial cells. Additionally, studies in rats indicate that ALDH2 expression contributes to reduced damage in kidney tissue [23]. Research has also revealed a relationship between ALDH2 and renal tissue fibrosis, specifically that enhancing ALDH2 expression can inhibit the progression of fibrosis [24]. Another study indicates that suppressing ALDH2 expression exacerbates damage to kidney tissue [25]. This also demonstrates that ALDH2 exerts a protective effect on the kidneys. Research has demonstrated that COX5A plays a crucial regulatory role in mitochondrial energy metabolism. Furthermore, a study in mice revealed that COX5A exerts a protective effect during mitochondrial oxidative stress [26]. Reactive oxygen species (ROS) serve as the primary source of oxidative stress in high-altitude hypoxia, and ROS imbalance can trigger various physiological and pathological conditions. Research has revealed that COX5A modulates the expression of antioxidant genes by regulating YAP1, thereby exerting resistance to oxidative stress mechanisms [27]. This also indicates that COX5A plays a crucial role in defending against oxidative stress within the hypoxic adaptation mechanism of Tibetan sheep kidney tissue. Additionally, another study found that miR-30a-5p can target and inhibit GLUD1, thereby further suppressing oxidative stress and apoptosis in renal tubular epithelial cells under hypoxic conditions [28]. A study on humans found that disrupting NDUFS2 leads to decreased glycolytic capacity and ATP production in renal cells, while conversely increasing ROS levels [29]. This finding bears similarities to NUDFS8: silencing NUDFS8 reduces ATP production in endothelial cells, leading to increased ROS production and oxidative stress [30]. SLC17A1 is highly expressed in renal epithelial cells and has been demonstrated to interact with Lac-Phe. Knockout of SLC17A1 in mice resulted in reduced urinary Lac-Phe levels [31]. This also confirms that SLC17A1 participates in renal hypoxic adaptation mechanisms through metabolic processes, thereby protecting kidney health. The above studies confirm that the hypoxic adaptation mechanism identified in Tibetan sheep kidney tissue involves not a single protein but a synergistic action of multiple proteins. Specifically, ALDH2 and GLUD1 protect the kidneys by reducing inflammation; COX5A counters oxidative stress; NDUFS2 and NDUFS8 supply ATP energy to the kidneys; and SLC17A1 participates in metabolic processes. Collectively, these proteins ensure kidney health in animals living in high-altitude environments. Due to the kidneys’ high energy demands, they are highly dependent on mitochondrial function, with the TCA cycle serving as the core of energy metabolism, primarily responsible for energy production [32]. Meanwhile, mitochondria, acting as powerhouses, produce adenosine triphosphate (ATP) through oxidative phosphorylation via the electron transport chain to supply cells [29]. This finding is consistent with the results of this study, which indicate that Tibetan sheep kidneys significantly enrich key components of the TCA cycle and oxidative phosphorylation pathways in their hypoxic adaptation mechanisms, playing a crucial role in these processes. Additionally, the study revealed that NDUFS8 and NDUFS2 are core components of the ubiquinone oxidoreductase (NADH) complex, playing a pivotal role in energy production within the oxidative phosphorylation pathway. Their primary function is to facilitate the transfer of electrons from NADH to ubiquinone [33–35]. These studies also demonstrate that NDUFS2 and NDUFS8 provide energy through the oxidative phosphorylation pathway, while GLUD1 and ALDH2 participate as key enzymes in the TCA cycle (supplying acetyl-CoA and NADH). Meanwhile, SLC17A1 indirectly influences energy metabolism and oxidative stress status by regulating urate levels within the body. However, research on sheep kidneys has certain limitations. Much of the evidence cited above comes from models other than sheep and the kidney—primarily from studies on humans, rats, and mice—and this evidence cannot serve as direct confirmation of hypoxic adaptation in Tibetan sheep kidneys. Therefore, in future studies, we will conduct direct experimental research on sheep kidneys to validate our findings.

Our metabolomics study of Tibetan sheep at different altitudes identified 24 key DEMs. Trend analysis of these metabolites revealed dynamic expression patterns. The detected differentially expressed metabolites included Wedelolactone, Diphenylamine, and Bioresmethrin; these compounds may originate from environmental exposure, pesticide residues, or feed, and their biological significance requires further validation. Research has found that, as a natural product, Wedelolactone possesses excellent antioxidant properties [36]. Its presence in sheep kidneys may be attributed to feed (such as sheep grazing on Asteraceae plants containing this compound) or other exogenous sources. However, further experimental verification is needed to determine whether Wedelolactone exerts anti-inflammatory or antioxidant effects on sheep kidneys. Cytidine is a component of RNA and a precursor to uridine. Research has shown that metabolites produced by the breakdown of uridine enable cells to grow normally in glucose-deficient environments. Additionally, studies indicate that uridine can provide energy in cases of mitochondrial oxidative phosphorylation defects [37]. These studies also corroborate our findings that metabolites participate in the establishment of renal hypoxic adaptation mechanisms in Tibetan sheep at high altitudes, providing antioxidant, anti-inflammatory, and energy supply functions. Further KEGG enrichment analysis of DEMs revealed that the HMG-CoA reductase inhibitors signaling pathway and the Biosynthesis of unsaturated fatty acids pathway primarily contribute to renal hypoxic adaptation mechanisms. Research has found that the HMG-CoA reductase inhibitors signaling pathway primarily functions to reduce the occurrence of inflammation, particularly by blocking inflammatory mediators in microglia following hypoxia [38]. Our research confirms that changes in metabolites influence alterations in metabolic pathways, leading to the upregulation of anti-inflammatory pathways. This represents one of the mechanisms by which Tibetan sheep kidneys maintain renal health under hypoxic conditions. At the same time, after conducting an integrated analysis of the positive and negative ion patterns in the metabolome, we found that the results were consistent with those of previous studies, which confirms the reliability of our analysis. We further investigated the relationship between proteins and metabolites, revealing correlations between the renal proteome and metabolome. Subsequent analysis of DEPs and DEMs demonstrated significant positive/negative correlations between proteins and metabolites. At the same time, research has shown that most key proteins influence metabolites by participating in metabolic pathways. This indicates that renal hypoxic adaptation mechanisms are not driven by isolated proteins or metabolites, but rather by synergistic interactions between proteins and metabolites. Together, these interactions influence adaptive mechanisms, ensuring normal renal function in high-altitude environments. However, the lack of independent experimental validation is a limitation of this study. Furthermore, we would like to emphasize that future research will focus on conducting further functional validation of the identified key candidate molecules to elucidate their causal mechanisms in high-altitude hypoxic adaptation. In addition, an ideal study design should include multiple independent sampling points at each elevation gradient and, to the extent possible, control for or record key covariates (such as rearing methods and genetic background) to reduce confounding bias and enhance the reliability of causal inferences. Future studies will further optimize their designs in this direction.

Conclusion

This study identified altitude-associated proteomic and metabolomic patterns and screened for key DMPs (ALDH2, COX5A, GLUD1, NDUFS2, NDUFS8, and SLC17A1) as well as key metabolites (Wedelolactone, Cytidine, and 3-hydroxybenzoic acid). These findings pertain to energy metabolism, oxidative phosphorylation, and candidate antioxidant/anti-inflammatory pathways. These findings require validation in controlled or replicable study designs.

Supplementary Information

12864_2026_13228_MOESM1_ESM.docx (523KB, docx)

Supplementary Material 1: Figure S1 Protein Identification and Annotation of Tibetan Sheep Kidneys at Different Altitudes. Figure S2 Metabolome Substitution test diagram of Tibetan Sheep Kidneys at Different Altitudes. Table S1 Trends in Key Protein Expression. Table S2 Metabolite Identification Results Table. Table S3 OPLS-DA Model Validation Parameters. Table S4 target Metabolite loading. Table S5 target Metabolite loading

Acknowledgements

The authors would like to thank all the reviewers who participated in the review.

Authors' contributions

KL performed the data analysis and wrote the manuscript. JZ performed investigation and collected the samples. XY performed the procedures and formal analysis using software. PZ did the project administration and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was financially supported by the 2024 Gansu Province Universities Industrial Support Plan Projects (2024CYZC-59), 2024 Gansu Province Youth Science and Technology Fund (24JRRP006), 2025 Gansu Province Universities Young Doctor Support Projects (2025QB-098).

Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD075530.

Declarations

Ethics approval and consent to participate

The animal study was reviewed and approved by the Ethics Committee of Gansu Minzu Normal University (Hezuo, China; Ethic approval file No. GMNU-EC-2024-022, 11 April 2024). These animals were purchased from local herders, and informed consent was obtained from all owners. All experimental procedures and sample collection methods were performed in accordance with approved guidelines and regulations to ensure animal welfare.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12864_2026_13228_MOESM1_ESM.docx (523KB, docx)

Supplementary Material 1: Figure S1 Protein Identification and Annotation of Tibetan Sheep Kidneys at Different Altitudes. Figure S2 Metabolome Substitution test diagram of Tibetan Sheep Kidneys at Different Altitudes. Table S1 Trends in Key Protein Expression. Table S2 Metabolite Identification Results Table. Table S3 OPLS-DA Model Validation Parameters. Table S4 target Metabolite loading. Table S5 target Metabolite loading

Data Availability Statement

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD075530.


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