Identification of blood exosomal metabolomic profiling for high-altitude cerebral edema

High-altitude cerebral edema (HACE) is a severe neurological condition that can occur at high altitudes. It is characterized by the accumulation of fluid in the brain, leading to a range of symptoms, including severe headache, confusion, loss of coordination, and even coma and death. Exosomes play a crucial role in intercellular communication, and their contents have been found to change in various diseases. This study analyzed the metabolomic characteristics of blood exosomes from HACE patients compared to those from healthy controls (HCs) with the aim of identifying specific metabolites or metabolic pathways associated with the development of HACE conditions. A total of 21 HACE patients and 21 healthy controls were recruited for this study. Comprehensive metabolomic profiling of the serum exosome samples was conducted using ultraperformance liquid chromatography–tandem mass spectrometry (UPLC‒MS/MS). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to identify the metabolic pathways affected in HACE patients. Twenty-six metabolites, including ( +)-camphoric acid, choline, adenosine, adenosine 5′-monophosphate, deoxyguanosine 5′-monophosphate, guanosine, and hypoxanthine-9-β-D-arabinofuranoside, among others, exhibited significant changes in expression in HACE patients compared to HCs. Additionally, these differentially abundant metabolites were confirmed to be potential biomarkers for HACE. KEGG pathway enrichment analysis revealed several pathways that significantly affect energy metabolism regulation (such as purine metabolism, thermogenesis, and nucleotide metabolism), estrogen-related pathways (the estrogen signaling pathway, GnRH signaling pathway, and GnRH pathway), cyclic nucleotide signaling pathways (the cGMP-PKG signaling pathway and cAMP signaling pathway), and hormone synthesis and secretion pathways (renin secretion, parathyroid hormone synthesis, secretion and action, and aldosterone synthesis and secretion). In patients with HACE, adenosine, guanosine, and hypoxanthine-9-β-D-arabinofuranoside were negatively correlated with height. Deoxyguanosine 5′-monophosphate is negatively correlated with weight and BMI. Additionally, LPE (18:2/0:0) and pregnanetriol were positively correlated with age. This study identified potential biomarkers for HACE and provided valuable insights into the underlying metabolic mechanisms of this disease. These findings may lead to potential targets for early diagnosis and therapeutic intervention in HACE patients.


Participants
A total of 21 HACE patients diagnosed by professional doctors were recruited from The General Hospital of Tibet Military Command.Twenty-one healthy individuals were recruited as healthy control (HC) participants, and the HACE patients were matched in terms of age and sex.The main characteristics of all participants are shown in Table 1.Written informed consent was obtained from all participants.The research protocol was approved by the Ethics Committee of Minzu University of China, Beijing, China, and this study was conducted in accordance with the Declaration of Helsinki.

Exosome isolation
Blood samples were collected from both the control and HACE participants following an overnight fast.After allowing the blood to clot at room temperature for 2 h, the serum was obtained by centrifuging at 3000×g for 15 min.The serum was then stored at − 80 °C until further analysis.Serum exosomes were isolated using 70 nm qEVoriginal Columns method provided by Izon (Ser.#: 1,004,555, Oxford, UK).Subsequently, the collected exosomes were concentrated by centrifugation at 5000×g for 10 min using the 30 K MWCO PES protein concentrator Vivaspin (Sarorius, Goettingen, Germany).The concentrated exosomes were resuspended in 100 μl of PBS for subsequent analysis.

Metabolite measurements
Comprehensive metabolomic profiling of serum exosome samples was conducted using UPLC-MS/MS, as previously described 14 .Both first-and second-order mass spectrometry were employed for qualitative analysis utilizing a metabolomic data management system and public metabolite databases.The metabolites were measured using triple quadrupole mass spectrometry and a multiple-reaction monitoring method.

Differential expression analysis
Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was applied to assess the differential expression of metabolites.Variable importance in projection (VIP) values were extracted from the model 15 .Metabolites with VIP ≥ 1 16 and an absolute Log 2 FC (fold change) ≥ 2, as determined by the Mann-Whitney U test, were considered to be differentially expressed.

Bioinformatics analysis
To gain insight into the biological functions of the identified differentially expressed metabolites, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation was performed using MetaboAnalyst software 17 .The metabolic pathways with a significance level of P < 0.05 were considered significantly enriched.

Statistical analysis
Unsupervised principal component analysis (PCA) was conducted using the prcomp statistical function in R software 18 .Receiver operating characteristic (ROC) curve analysis 17 was employed to assess the ability of exosomal metabolites to distinguish between the HCs and HACE patients.

Ethics approval and consent to participate
Written informed consent was obtained from all participants.The research protocol was approved by the Ethics Committee of Minzu University of China, Beijing, China, and this study was conducted in accordance with the Declaration of Helsinki.

Results
The results of exosome identification are presented in Supplementary Fig. 1.Nanoparticle tracking analysis (NTA) revealed that the peak particle size of the extracted exosomes was 86.7 nm, with over 98% falling within the range of 30-150 nm (Supplementary Fig. 1A).Western blot analysis confirmed the presence of specific exosome markers-CD63 and TSG101 (Supplementary Fig. 1B).Additionally, the concentration of exosomes in serum samples was quantified using the BCA protein assay, as shown in Supplementary Fig. 2.There was no significant difference in serum exosome concentration between individuals with HC and HACE (2.472 ± 0.913 mg/mL, 2.284 ± 1.376 mg/mL; P = 0.6968).Furthermore, an analysis of blood parameters in individuals with HACE and HC revealed a significant increase in WBC, NEUT, MONO, and CRP levels in HACE compared to HC.Conversely, there was a notable decrease in RBC, LYM, and EO levels in individuals with HACE.Detailed data are provided in Supplementary Table 1.
The results of our study revealed significant differential expression of blood exosomal metabolites in HACE patients.We conducted a data normalization process, followed by cluster heatmap analysis of all the samples www.nature.com/scientificreports/(Fig. 1A).Subsequently, PCA was performed on the samples grouped for differential comparison, revealing distinct metabolic profiles between the HACE and HC groups (Fig. 1B).An OPLS-DA model was applied to identify the differentially expressed metabolites between these two groups (Fig. 1C), revealing three upregulated and twenty-three downregulated metabolites in patients with HACE compared to HCs (Fig. 1D).Additionally, the fold changes in the quantitative information of the metabolites between the HC and HACE groups are depicted in Fig. 1E,F.The differentially abundant metabolites, including ( +)-camphoric acid, choline, adenosine, adenosine 5′-monophosphate, deoxyguanosine 5′-monophosphate, guanosine, and hypoxanthine-9-β-D-arabinofuranoside, are visually presented (Fig. 2).Furthermore, this study explored the potential of exosomal metabolites as biomarkers for HACE.The analysis of the ROC curve using 26 exosomal metabolites revealed promising results in differentiating between HACE patients and HCs (Fig. 3).
Based on the KEGG information of the differentially abundant metabolites identified using the aforementioned filtering criteria, KEGG metabolic pathways that contained at least one differentially abundant metabolite were selected.We subsequently classified metabolites that exhibited significant differences according to the pathway types in the KEGG database.Among them, there were three metabolic categories with three or more differentially abundant metabolites enriched in the KEGG pathway, concentrated in the energy metabolism pathway (Fig. 4A).The top 20 metabolic pathways related to purine metabolism, regulation of lipolysis in adipocytes, vascular smooth muscle contraction, thermogenesis, renin secretion, platelet activation, parathyroid hormone synthesis, secretion, and action were found to be significantly affected in the HACE group compared to the HCs.The difference abundance score plot revealed the downregulation of pathways related to the regulation of lipolysis in adipocytes, purine metabolism, nucleotide metabolism, the cGMP-PKG signaling pathway, and morphine addiction, while pathways related to GnRH secretion, endocrine and other factor-regulated calcium reabsorption, and the estrogen signaling pathway were upregulated in HACE patients compared to HCs (Fig. 4B,C).
MSEA enrichment analysis of 84 KEGG pathway metabolic sets and 339 exosomal metabolic sets enabled us to visualize the differentially abundant metabolites and conduct pathway searches and regulatory interaction network analysis (Fig. 4D,E).The network plot showed that 5′-nucleotidase (EC3.1.3.5) had the most common connections with the differentially abundant metabolites and played a crucial role in energy metabolism (Fig. 4F).
The Pearson correlation analysis revealed several significant associations (Fig. 5A).Due to the inability to measure height and weight in some patients in a coma and bedridden state, there were fewer than 21

Discussion
HACE is a condition characterized by cerebral edema and neurological impairment that occurs in low-pressure, hypoxic environments.It leads to damage to the endothelial cells of the blood-brain barrier and results in abnormal metabolic phenomena 19 .Exosomes contain proteins, lipids, amino acids, and metabolites and play crucial roles in cell communication 20 .We hypothesized that HACE may cause functional changes in the brain, leading to alterations in blood exosome metabolism.To explore this phenomenon, we utilized UPLC-MS/ MS to analyze the metabolomic profile of blood exosomes in HACE.Our findings revealed differential expression of metabolites in the blood exosomes of individuals with HACE.Specifically, 29 metabolites, including ( +)-camphoric acid, choline, adenosine, adenosine 5′-monophosphate, deoxyguanosine 5′-monophosphate, guanosine, hypoxanthine-9-β-D-arabinofuranoside, and others, exhibited significant changes in expression in HACE patients compared to HCs.Furthermore, enrichment pathway analysis revealed several pathways, such as purine metabolism, regulation of lipolysis in adipocytes, vascular smooth muscle contraction, thermogenesis, renin secretion, platelet activation, parathyroid hormone synthesis, secretion, and action, that were significantly affected in HACE.Collectively, the data from this study indicate an imbalance in metabolite levels in the serum exosomes of individuals with HACE.HACE is associated with prolonged exposure to hypobaric hypoxia in high-altitude environments 21 .Hypobaric hypoxia can cause severe brain damage and impair mitochondrial function, contributing to hypoxic brain injury 22 .Mitochondria primarily utilize inhaled oxygen for oxidative phosphorylation, which generates adenosine triphosphate (ATP).Hypoxia significantly affects mitochondria, jeopardizes cellular energy availability, damages mitochondrial components, alters mitochondrial quality and dynamics, and influences mitochondrial cell death pathways 23 .Our study identified differential expression profiles related to energy metabolism in the blood exosome-derived metabolites of individuals with HACE.Metabolites such as adenosine, adenosine 5′-monophosphate, deoxyguanosine 5′-monophosphate, guanosine, and hypoxanthine-9-β-D-arabinofuranoside were found to be significantly decreased.Furthermore, it is well established that hypoxia-induced release of exosomes in tumor cells can significantly impact their secretion, composition, and function, potentially resulting in alterations in the proteins, lipids, and metabolites encapsulated within exosomes 24,25 .However, our research findings revealed no significant difference in serum exosome concentrations between the HC group and patients with HACE.This lack of distinction may be due to the diverse cell types present in the human body, as well as the various types of extracellular vesicles present in serum.Although tumor cells demonstrate increased exosome release under hypoxic conditions, it remains uncertain whether other cells may reduce exosome release under hypoxia, potentially explaining the consistent serum exosome concentrations observed.
Hypoxia-induced dysfunction of the blood-brain barrier and subsequent cerebral edema are considered potential mechanisms underlying HACE 26 .During the early stages of cerebral ischemia and hypoxia, edema can develop through a process involving increased secretion of sodium, chloride, and permeable water, which cross the blood-brain barrier from the blood into the brain 27 .Consequently, surrounding nerve cells rapidly swell as they absorb ions and water, resulting in cytotoxic edema 28 .Estrogen has been shown to protect against brain damage caused by ischemic stroke 29 .Specifically, estradiol acts on the endothelial cells of the blood-brain barrier to reduce the formation of brain edema mediated by Na-K-Cl cotransporter proteins 30 .Our findings indicated that the levels of 17β-estradiol were upregulated during HACE.This finding suggested that, during the progression of HACE, the upregulation of estradiol in patients may play a critical role.Furthermore, we observed the differential expression of metabolites such as ( +)-camphoric acid, Ser-Ser, 20,26-dihydroxyecdysone, glycerophospho-Narachidonoyl ethanolamine, choline, and acylglycine during HACE.Although these metabolites have rarely been reported in previous studies, they may play important roles in mediating signal transfer during HACE.
Based on the results of differentially abundant metabolite analysis, we conducted KEGG pathway enrichment analysis and identified several significantly affected metabolic pathways.These pathways included pathways related to energy metabolism regulation (such as purine metabolism, thermogenesis, and nucleotide metabolism), estrogen-related pathways (estrogen signaling pathway, GnRH signaling pathway, and GnRH pathway), cyclic nucleotide signaling pathways (cGMP-PKG signaling pathway and cAMP signaling pathway), and hormone synthesis and secretion (renin secretion, parathyroid hormone synthesis, secretion and action, and aldosterone synthesis and secretion).These abnormalities in metabolic pathways are primarily associated with ischemiahypoxia, energy metabolism 31,32 , estrogen secretion 33 , and hormone secretion 34,35 and have been previously identified in relevant studies on ischemia and hypoxia.Additionally, the analysis of regulatory networks of differentially abundant metabolites revealed the interconnectedness of these metabolites within the context of HACE.Therefore, our metabolomic data provide the basis for adopting a multitarget protection strategy for HACE.
We examined the associations between various factors and HACE scores using Pearson correlation analysis.The results revealed several significant associations, which provide valuable insights into the potential risk factors for HACE.We found an inverse relationship between stature and three metabolites: adenosine, guanosine, and hypoxanthine-9-β-D-arabinofuranoside.This finding suggested that individuals with a shorter stature may be at a greater risk for developing HACE.These metabolites have been previously implicated in oxygen deprivation 36 and may play a role in the development of HACE.Additionally, we observed a negative correlation between BMI and deoxyguanosine 5′-monophosphate.This finding implies that individuals with a higher BMI may be less susceptible to HACE.The underlying mechanisms for this association need further investigation, but they may be related to the protective effects of higher BMI on oxygen deprivation.Furthermore, we found that the age of the subjects with HACE was positively correlated with LPE and pregnanetriol.This finding suggested that older individuals may be at a greater risk for developing HACE.LPE and pregnanetriol are substances involved in various physiological processes 37,38 , and their association with HACE warrants further research.It is important to note that correlation does not imply causation, and the directionality of the relationships observed in this study cannot be established.Additionally, the sample size of our study was relatively small, which may limit the

Conclusion
In conclusion, a metabolomic analysis of blood-derived exosomes from HACE patients highlighted significant alterations in metabolic pathways and identified potential biomarkers associated with this disease.The differential expression of metabolites linked to energy metabolism, hormone regulation, and hypoxia-induced dysfunction of the blood-brain barrier provides a comprehensive understanding of the pathophysiology of HACE.The findings from this study could lead to the development of new strategies for early diagnosis and potential therapeutic targets for the management of HACE, contributing to improved clinical interventions in high-altitude environments.Further research with larger sample sizes and longitudinal designs is warranted to validate and expand on these findings for the advancement of HACE diagnosis and treatment.

Figure 1 .Figure 2 .Figure 3 .Figure 4 .
Figure 1.Bioinformatic screening for the differential expression of metabolites in blood exosomes of individuals with high-altitude cerebral edema (HACE).(A) Cluster heatmap illustrating all substances; (B) Principal component analysis (PCA) and (C) orthogonal partial least squares-discriminant analysis (OPLS-DA) model plot in the training participant set; (D) Volcano plot depicting differences in metabolites between HACE participants and healthy controls; (E) Cluster heatmap of differential substances.

Figure 5 .
Figure 5. Pearson correlation analysis of various differentially abundant metabolite raw intensities with the height, BMI, weight, and age of HACE participants.(A) A heatmap displaying correlation P values; (B) 9 Correlation analysis diagrams of P values < 0.05.

Table 1 .
Demographic characteristics of the subjects.BMI, Body mass index; NA, Not available.