Comprehensive lipidomic analysis reveals regulation of glyceride metabolism in rat visceral adipose tissue by high-altitude chronic hypoxia.

Adipose tissue plays a central role in energy substrate homeostasis and is a key regulator of lipid flow throughout these processes. As hypoxia affects lipid metabolism in adipose tissue, we aimed to investigate the effects of high-altitude chronic hypoxia on lipid metabolism in the adipose tissue of rats using a lipidomic analysis approach. Visceral adipose tissues from rats housed in a high-altitude hypoxia environment representing 4,300 m with 14.07% oxygen (hypoxia group) and from rats housed in a low-altitude normoxia environment representing 41 m with 20.95% oxygen (normoxia group) for 8 weeks were analyzed using an ultra-performance liquid chromatography-Orbitrap mass spectrometry system. After 8 weeks, the body weight and visceral adipose tissue weight of the hypoxia group were significantly decreased compared to those of the normoxia group (p < 0.05). The area and diameter of visceral adipose cells in the hypoxia group were significantly smaller than those of visceral adipose cells in the normoxia group (p < 0.05). The results of lipidomic analysis showed a total of 21 lipid classes and 819 lipid species. The total lipid concentration of the hypoxia group was lower than that in the normoxia group (p < 0.05). Concentrations of diacylglycerols and triacylglycerols in the hypoxia group were significantly lower than those in the normoxia group (p < 0.05). Using univariate and multivariate analyses, we identified 74 lipids that were significantly altered between the normoxia and hypoxia groups. These results demonstrate that high-altitude chronic hypoxia changes the metabolism of visceral adipose glycerides, which may potentially modulate other metabolic processes.

Introduction

Hypoxia is a condition in which the body or specific tissues are deprived of oxygen [1]. This phenomenon can occur upon exposure to hypoxic environmental conditions, such as high altitude [2].

The Qinghai-Tibetan Plateau is the highest plateau in the world [3], with an average elevation of 4,000 meters above sea level [4] and an oxygen concentration of approximately 50–60% of that at sea level [5]. At high altitudes, the partial inspiratory pressure of oxygen decreases as the barometric pressure drops, resulting in hypobaric hypoxia. To ensure to survival under these conditions, the body elicits physiological acclimatization mechanisms alongside metabolic remodeling [6]. For example, Katie et al. [2] showed that lipid metabolism constitutes an important metabolic change in hypoxic environments by using metabolomics and lipidomics to investigate changes in the plasma profiles of human participants ascending to Everest Base Camp. In addition, several animal studies have indicatd that hypoxia affects adipose tissue functions as well as the blood lipid profile [7,8].

Adipose tissue plays an important role in energy substrate homeostasis [9,10]. It is composed of high amounts of lipids, which are essential constituents of the human body, including hydrophobic or amphoteric small molecules, fatty acids, glycerides, phospholipids, cholesterol esters, and other molecules. Among glycerides, diacylglycerols (DGs) and triacylglycerols (TGs) are the main types of glycerol [11]. However, hypoxia may disturb the balance of lipid storage and lipid mobilization in adipose tissue [1]. In particular, hypoxia induces decreased lipoprotein lipase activity in adipocytes [8,12] and has been shown to stimulate lipolysis of visceral adipocytes [13].

Lipidomics is a high-throughput analysis technique used to systematically analyze changes in lipid composition and expression in living organisms [14]. Lipidomics can efficiently determine the changes in lipid families and lipid molecules in various biological processes, enabling the elucidation of the related biological mechanisms and functions. Liquid chromatography-mass spectrometry (MS) has been generally applied in lipidomics analysis [15]. There are two main types of lipid detection methods: non-targeted analysis and targeted analysis [15]. The non-targeted analysis method can differentiate various types of lipids present in a sample without bias [16]. Absolute quantification of lipids with the use of internal standards can not only determine the differences in lipid levels between groups but can also determine the absolute concentrations of the lipids within each group.

At present, changes occurring at the level of the lipidome in adipose tissue under high-altitude chronic hypoxia have not been elucidated. Therefore, to effectivelydescribe the changes of lipid metabolism under conditions of hypobaric hypoxia and look for the characteristics of lipid metabolism under high altitude hypoxia adaptation, we used lipidomic analysis to compare metabolic changes in adipose tissue under normoxic and hypoxic conditions. In particular, we applied non-targeted lipidomics analysis based on the ultra-performance liquid chromatography (UPLC)-Orbitrap MS system, combined with LipidSearch software and an internal standard of 13 lipid molecules for lipid identification and data pretreatment to obtain the absolute content of lipid molecules in adipose tissue. We further applied LipidSearch software to carry out original data processing, peak extraction, lipid identification, peak alignment, and quantitative analysis to comprehensively identify the differences in lipid metabolism in adipose tissue of rats. This study provides the first broad lipidomics of the effects of exposure to environmental hypobaric hypoxia in the adipose tissue of rat.

Materials and methods

Lipidomics instruments and reagents

For high-resolution absolute quantitative lipidomics, the instruments used in this project were: Q Exactive Plus mass spectrometer (Thermo Scientific, Waltham, MA, USA); Nexera LC-30 ultra high performance liquid chromatography (UHPLC) (Shimadzu,Kyoto, Japan); a low-temperature high-speed centrifuge (Eppendorf 5430R, Hamburg, Germany); and a chromatographic column (Acquity UPLC CSH C18, 1.7 μm, 2.1 × 100 mm column; Waters, Milford, MA, USA). The reagents used in this project were: acetonitrile, isopropyl-alcohol, methyl-alcohol (all from Thermo Fisher Scientific), and 13 kinds of isotope internal standards.

Experimental animals

The experiment protocol was approved by the Animal Protection and Use Institution Committee of Qinghai University and was carried out in accordance with the Animal Management Regulations of the Ministry of Health of China.A total of 20 Sprague Dawley male rats (weight: 180–200 g; age: 6 weeks) were obtained from Shanghai Xipu Bikai Experimental Animal Co., Ltd. (Shanghai, China). Rats were randomly divided into the normoxia group (altitude 41 m, Xuzhou, Jiangsu, China, n = 10) and hypoxia group (altitude 4,300 m, Maduo, Qinghai, China, n = 10) for 8 weeks. These two groups were fed a regular diet (Shoobree 1010010). All rats were maintained under a natural light cycle at room temperature (22 ± 2°C) and 50 ± 5% humidity. Water and food were freely given throughout the experiment.

Experimental process

After 8 weeks of hypoxia or normoxia exposure, rats were anesthetized using 3% isoflurane via the R510 animal anesthesia machine (RWD Life Science Co., Ltd., San Diego, USA).Hemoglobin was tested using an automatic blood analyzer (Mindray BC-5000Vet, Mindray Corporation, Shenzhen, China). The visceral adipose tissue was removed from the rats quickly sectioned, and immediately fixed in 4% paraformaldehyde. A section of the visceral adipose tissue was stored at −80°C for lipidomics analysis.

Histological analyses

Visceral adipose tissue was fixed in 4% paraformaldehyde, embedded in paraffin, and cut into 5 μM-thick sections that were stained with hematoxylin and eosin and observed using a microscope (Olympus, Tokyo, Japan) at 400× magnification.The cell sizes and areas of adipose tissues were measured by Image J.

Non-esterified fatty acids (NEFA) in serum

An enzyme-linked immunosorbent assay was used to measure the level of serum free fatty acid (Nanjin Jiancheng Bioengineering Institute, Nanjing, China).

Quantitative polymerase chain reaction (qPCR) analysis

Total RNA was extracted from adipose tissue using an RNA Simple Total RNA Kit (cat DP419; Tiangen Biotech, Beijing, China) and cDNA was synthesized using Fasting gDNA Dispelling RT SuperMix (cat KR118;Tiangen Biotech). qPCR was performed via the SYBR Green method using SuperReal Color Premix (cat No. FP205; Tiangen Biotech) on a QuantStudio 5 Real-Time PCR system (Thermo Fisher Scientific).

The mRNA content was detected by real-time quantitative PCR (qPCR) using the following forward and reverse primers:ATGL 5‘-GTTCGCTGGTTGTGGCTTCCTC-3’and 5’-GGCAAATCACAGAGCAAGCAACAG-3’

HSL 5‘- CTCACAGTTACCATCTCACCTC-3’and 5’-GATTTTGCCAGGCTGTTGAGTA-3’

18S rRNA, 5′-TTGACGGAAGGGCACCACCAG-3′ and 5′-GCACCACCACCCACGGAATCG-3′.

Visceral adipose tissue lipidomics profiling with Nexera LC-30A

Sample pretreatment UHPLC Each 200 mg visceral adipose tissue sample was processed by adding 200 μL water and 20 μL internal lipid standard mixture and vortexing; then, 800 μL methyl tert-butyl ether was added, followed by vortexing. Subsequently, 240 μL of pre-cooled methanol was added, and samples were mixed by vortexing. Before being placed at room temperature for 30 min, samples were sonicated in a low-temperature water bath for 20 min and centrifuged at 14,000 ×g at 10 ℃ for 15 min. The upper organic phase was collected and dried using nitrogen. For MS analysis, 200 μL of 90% isopropanol/acetonitrile solution was added for resolution and the mixture vortexed. The complex solution (90 μL) was collected and centrifuged at 14,000 ×g at 10 ℃ for 15 min. Finally, the supernatant was collected for sample analysis.

Chromatography conditions

Sample separation was performed using Nexera LC-30A UHPLC and a C18 chromatographic column (column temperature = 45 ℃; flow rate = 300 μL/min). Mobile phase composition A comprised acetonitrile aqueous solution (acetonitrile: water = 6:4 (v/v)), and mobile phase composition B was acetonitrile isopropanol solution (acetonitrile: isopropanol = 1:9 (v/v)). The gradient elution procedure was as follows: 0−2 min, 30% B; 2–25 min, 30% to 100% B; 25–35 min, 30% B [17]. The sample was placed in a 10 ℃ autosampler during the whole analysis process. To avoid variation caused by the fluctuation of the instrument’s detection signal, the continuous analysis of samples was carried out in a random order. One quality control (QC) sample was set, and every six samples were sampled to monitor the reliability of the experimental data.

MS conditions

Electrospray ionization (ESI) positive ion and negative ion modes were used for detection. Samples were separated using UHPLC and analyzed using MS with a Q Exactive series mass spectrometer. ESI conditions were as follows: heater temperature, 300°C; sheath gas flow rate, 45 arb; aux gas flow rate, 15 arb; sweep gas flow rate, 1 arb; spray voltage, 3.0 kV; capillary temperature, 350°C; S-Lens RF Level, 50%; and MS1 scan range, 200–1800. Mass charge ratios of lipid molecules and lipid fragments were collected as follows: ten fragment maps (MS2 scan, HCD) were collected after each full scan. MS1 had a resolution of 70,000 at M/Z 200, and MS2 had a resolution of 17,500 at M/Z 200.

Data analysis

LipidSearch software was used to identify the peak, extract it, and identify lipid molecules (secondary appraisal). The major parameters used were: precursor tolerance, 5 ppm; product tolerance, 5 ppm; and product ion threshold, 5%. Univariate analysis was performed on all detected lipid molecules, and the results were presented in the form of a volcano plot. For multi-dimensional statistical analysis, the principal component analysis (PCA) method was adopted to observe the overall distribution trend and difference in samples between groups. We also used partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to analyze different lipids. Hierarchical cluster analysis showed the relationship between samples and differences in lipid expression. Correlation analysis was used to measure the degree of metabolic closeness between lipids with significant differences.

Statistical analysis

Data were analyzed using Prism 8 software andexpressed as the mean ± standard deviation. Two groups were evaluated using the independent-samples t-test. Statistical significance was defined as p< 0.05.

Results

Hemoglobin, body weight, food intake and visceral adipose tissue weightin the hypoxia versus normoxia groups

After 8 weeks, hemoglobin levels in the hypoxia group were significantly higher than those in the normoxia group (p < 0.05, Fig 1A). At the beginning of the experiment, there was no significant difference in body weight between the two groups (p > 0.05, Fig 1B). After 8 weeks, the body weight of rats in the hypoxia group was significantly lower than that in the normoxia group (p < 0.05, Fig 1C). In addition, the weight of visceral adipose tissue in the hypoxia group was lower than that in the normoxia group (p < 0.05, Fig 1E). However, there was no significant difference in total food intake between the two groups (p < 0.05, Fig 1D), suggesting that the weight loss in rats may be due to hypoxia.

Fig 1

Changes in body weight, adipose tissue, and blood indexes in the hypoxia group versus the normoxia group.

Hemoglobin, (B) 0-week body weight, (C) 8-week body weight, (D) total food intake, (E) visceral adipose tissue weight. A: n = 5 rats per group. B-D: n = 6 rats per group. E: n = 4 rats per group. Independent-samples t-test was used for statistical analysis.

Changes in lipids in the hypoxia versus normoxia groups based on lipidomics

The base peak spectra (BPC) of QC samples were compared for spectral overlap, as shown in S1 Fig1 and S1 Fig2 in S1 File. The chromatographic peak response strength and retention time of each QC sample overlapped, indicating that the experiment had good reproducibility. Pearson correlation analysis was performed on QC samples, as shown in S1 Fig3 in S1 File. Generally, a relation index >0.9 indicates a good correlation. The relation indices between QC samples were all > 0.9, indicating that the experiment had good reproducibility. PCA analysis was then performed on the ion peaks extracted from all experimental samples and QC samples following Pareto-scaling, as shown in S1 Fig4 in S1 File. The QC samples were closely clustered together, indicating good reproducibility.

In addition, PCA was conducted using all the ion peaks, which introduced some degree of noise interference. To better display the sample distribution, PLS-DA was also performed (S1 Fig5 in S1 File). Outlier samples were designated using Hotelling’s T2 test, which evaluates the samples via multivariate variable modeling and defines 95 or 99% confidence intervals. The experimental results showed that all QC samples were within a 99% confidence interval [18](S1 Fig6 in S1 File), indicating good repeatability of the experiment.

The stability of instrument status was monitored and evaluated using Multivariate Control Chart (MCC), a multivariate statistical model based on the ion peaks detected in QC samples that serves as a quality management tool. The points in the diagram, each representing a single QC sample and arrayed along the X-axis according to loading sequence, fluctuate up and down owing to fluctuations in the state of the instrument. The normal range is generally within ± 3 standard deviations. The multivariate control diagram of QC samples in this study is shown in S1 Fig 7 in S1 File. The experimental results show that the fluctuation of QC samples was within the range of ± 3 standard deviations, which reflects that the fluctuation of the instrument was within the normal range and the data could be used for subsequent analysis.

Relative standard deviation (RSD) of the ion peak abundance of QC samples was applied as an important indicator of data quality, as the smaller the RSD, the better the instrument stability. In this experiment, the number of peaks with RSD ≤30% in QC samples accounted for >80% of the total peak number in QC samples, as shown in S1 Fig8 in S1 File. indicating that the instrumental analysis system had good stability and the data could be used for subsequent analyses [19].

Identification of lipid compounds

The numbers of lipid compounds in samples identified by positive and negative ion modes in this experiment is shown in Fig 2A. A total of 21 lipid classes and 819 lipid species were identified. Among these, the TG lipid class was the largest, with 508 lipid species. The total content of lipid molecules of a sample is the sum of all quantified lipid molecules identified in that sample, and the total lipid content of samples from two groups can be compared. As shown in Fig 2B, the total content of lipid molecules in the normoxia group was significantly higher than that in the hypoxia group. For different lipid classes, the content of lipid species in the two groups was compared, as shown in Fig 2C and 2D. As compared to the normoxia group, DGs and TGs were significantly decreased in the hypoxia group (p < 0.05, Fig 2D). However, no significant differences were observed for other lipid classes (p > 0.05, Fig 2C).

Fig 2

Changes in lipids in the hypoxia group versus the normoxia group.

Lipids were classified according to the guidelines of the International Lipid Classification and Nomenclature committee. (A) Number of lipid classes and lipid species, (B) total lipid molecular content, and (C and D) content of different lipid subgroups. Independent-samples t-test was used for the statistical analysis of data presented in panels B, C, and D.

Univariate and multivariate analyses of lipid molecules for all samples

Lipid molecule multivariate analysis for all samples was performed in three steps: PCA, PLS-DA, and OPLS-DA. The PCA reflects the overall distribution trend and difference degree for the two groups of samples (Fig 3A). PCA model parameters were obtained via seven-fold cross-validation. The model interpretation rate was 0.6 (S1 Table 1 in S1 File), indicating that the model was reliable. Through the established discriminant model, PLS-DA can screen out differential lipids related to grouping from data sets. As shown in Fig 3B, the PLS-DA model could distinguish the two groups of samples. The PLS-DA model parameters, explanation rate of the model for variable X (R2Y), and predictive power (Q2) were obtained through seven-fold cross-validation (S1 Table 2 in S1 File). Generally, a Q2 vaule >0.5 indicates that the model is stable and reliable. In this experiment, the Q2 was 0.879, confirming the reliability of the model. To avoid overfitting of supervised models, a permutation test (Fig 3C) was used. As the replacement retention gradually decreased, the R2 and Q2 of the random model decreased gradually, demonstrating that the original model did not have an overfitting phenomenon and that the model was good. As shown in Fig 3D, OPLS-DA functions as an analytical method that can be applied to modify PLS-DA. In this experiment, the Q2 was 0.882 (S1 Table 3 in S1 File), confirming that the model was stable and reliable. Retesting of the model usinga permutation test indicated that the replacement retention R2 and Q2 decreased gradually, showing that the model was valid (Fig 3E).

Fig 3

Univariate and multivariate analyses of lipids in rat visceral adipose tissue.

(A) PCA score plot, (B) PLS-DA score plot, (C) PLS-DA permutation test, (D) OPLS-DA score plot, (E) OPLS-DA permutation test, and (F) volcano plot of the normoxia and hypoxia groups.Differences in lipid molecules with FC >1.5 or FC <0.67 and p-value <0.05 are represented by different colors. The rose-red dots in the figure represent significantly different lipids.

In this experiment, a univariate statistical analysis method was used to analyze the differences between the two groups of samples, involving fold change (FC) analysis and the t-test. Using the univariate analysis method, differences were analyzed for all detected lipid molecules, and the analysis results were presented in the form of a volcano plot, as shown in Fig 3F. Variable importance for the projection (VIP) > 1 and p < 0.05 were considered significant differences for lipids. A total of 74 lipids were identified, including 73 TGs and1 DG. Details of the 75 lipids are presented in S1 Table 4 in S1 File. Compared to the normoxia group,the levels of TGs and DGs were decreased in the hypoxia group (Fig 3F and S1 Table 4 in S1 File).

Effects of hypoxia on markers of lipolysis

The area and diameter of visceral adipose cells in the hypoxia group were significantly smaller than those in the normoxia group (p < 0.05, Fig 4A–4C). Conversely, the content of NEFA in serum, reflecting lipid breakdown products, was increased in the hypoxia group (Fig 4F). Consistent with this, the gene expression of hormone-sensitive lipase (HSL) and adipose TG lipase (ATGL), key enzymes of lipolysis [20,21], were significantly increased under hypoxia compared to that in the normoxia group (Fig 4D and 4E).

Fig 4

Effects of hypoxia on markers of lipolysis.

Haematoxylin and eosin staining, scale bar:10μm, (B) adipocyte diameter, and (C) adipocyte area changes in the visceral adipose tissue in the hypoxia group versus the normoxia group. The mRNA levels of lipolysis related-genes,including (D) adipose TG lipase (ATGL), and (E) hormone-sensitive lipase (HSL). (F) The content of NEFA. A-C: n = 3 rats per group.D-E: n = 6 rats per group. Independent-samples t-test was used for statistical analysis.

Lipid molecule chain length, chain saturation, hierarchical clustering, and correlation analysis

Compared with the normoxia group, the lipid molecule chain length and chain saturation of DGs (Fig 5A and 5B) and TGs (Fig 5C and 5D) were significantly decreased in the hypoxia group. In this study, we used the expression levels of significantly different lipids to perform hierarchical clustering for each group to evaluate the relationship between lipid samples and the differences in lipid expression patterns in different samples. Fig 6A shows the patterns of correlation of different lipid molecules in the normoxia and hypoxia groups. The lipids clustered together showed similar expression patterns.

Fig 5

Analysis of significantly different lipids between the normoxia and hypoxia groups.

(A) DG carbon chain length analysis. (B) DG saturation analysis. (C) TG carbon chain length analysis. (D) TG saturation analysis. **p <0.01 versus the normoxia group; *p <0.05 versus the normoxia group.

Fig 6

Clustering and correlation analyses of lipids in rat visceral adipose tissue.

Correlation clustering heat map. Red represents positive correlation and purple represents negative correlation. Color intensity is related to the absolute value of the correlation coefficient; i.e., the higher the positive or negative correlation, the darker the color. (B) Hierarchical clustering heat map.

To measure the degree of metabolic proximities between significantly different lipids and to evaluate the mutual regulation relationship between lipids, correlation analysis was used. Fig 6B shows the correlation of different lipids using a correlation clustering heatmap.

Discussion

Visceral adipose, a type of body adipose located primarily around the organs in the abdominal cavity, is hormonally active and important for health. In this study, we characterized lipid profiles in the visceral adipose tissue of rats to estimate whether changes in geographical altitude influence lipid metabolism. Specifically, we determined the effect of chronic hypoxia on visceral adipose tissue lipid composition. Animal models present the advantage of controlling confounding factors such as environment, genetic background, or dietary habits, which can influence lipid metabolism. We used rats as an animal model, and the experimental conditions were strictly controlled. We housed rats in a high-altitude hypoxia environment for 8 weeks and used absolute quantitative lipidomics analysis to compare their lipid profiles with those of rats that were exposed to low-altitude normoxia. After 8 weeks, the body weight and visceral adipose weight of rats in the hypoxic group were lower than those in the normoxic group, although there was no significant difference in total food intake between the two groups. These results suggested that hypoxia caused enhanced mobilization of visceral adipose. Moreover, the composition and concentration of lipid compounds in the normoxia and hypoxia groups differed significantly.

To our knowledge, this is the first lipidomics study of the visceral adipose tissue of rats exposed to high-altitude chronic hypoxia. Non-targeted lipidomics analysis based on the UPLC-Orbitrap system has previously been performed to analyze lipid quantity, composition, and differences [22]. In our lipid analysis, the content, chain length, and chain saturation changes were assessed. In particular,lipidomic analysis showed that DGs and TGs were significantly different in the hypoxic versus normoxia groups. Glycerides play an important role in visceral adipose tissue. All glycerol, fatty acids, including saturated and unsaturated fatty acids, and esters produced by esterification belong to the glycerol ester class. Conversely, our study showed that high-altitude hypoxia did not change sterol and sphingolipid metabolism in the visceral adipose tissue of rats.

Adipose tissue contains a large number of adipocytes in which TGs are synthesized and stored. Hypoxia has been shown to increase activity of the sympathetic nervous system [23-25]together with lipolysis and catecholamine secretion [26]; notably, HSL and ATGL are mainly controlled by catecholamines [20,21]. Moreover, hypoxia induced lipolysis of visceral adipocytes, leads to preferential NEFA efflux into the circulation [13]. A study have showeded increased sympathetic activation and up-regulation lipolysis under hypoxia exposure [27]. Consistent with this hypoxia exposure for 14 days in mice significantly increased adipocyte lipolysis and elevated NEFA levels [1]. Several studies have also demonstrated that reduced oxygen supply in the air could increase adipocyte lipolysis both in vivo and in vitro [28-32]. In our study, compared with normoxia group, we observed that the mRNA expression of the HSL and ATGL genes were increased in the hypoxia group. compared to that in the normoxia group. In turn, measurement of the size of lipid droplets, special organelles for the storage of neutral lipids, revealed a significant reduction in the lipid droplet diameter of adipocytes. Together, these findings are consistent with hypoxia-mediated stimulation of lipolysis in visceral adipose tissue. Furthermore, the content of NEFA in the hypoxia group was increased compared with that in normoxia group. Increased levels of circulating fatty acids may contribute to the reported impairment of β-oxidation capacity at high altitudes [33].

TGs, which are composed of 3 fatty acids and 1 glycerol molecule, represent the most energy-dense lipolysis substrate [1]. In addition to adipocytes, almost all types of cells have the ability to store excess energy in the form of TGs in lipid droplets [34].

With exposure to reduced oxygen partial pressure during ascent to Everest Base Camp altitude, Katie et al. [2] study showed that lipidomic analysis revealed alterations to the main constituent of body fat, TGs. In the present study, lipidomics of adipose tissue showed that the contents of both TGs and DGs were significantly decreased under hypoxia.

Chain length refers to the total carbon atoms of fatty acid chains in lipid molecules. The length of lipid chains affects the thickness of cell membranes, further affecting their fluidity, as well as the activity and function of related lipid transporters and target proteins [35]. Chain saturation is the sum of the number of double bonds in the fatty acid chain of a lipid molecule. Lipid saturation plays an important role in the occurrence of disease and stress responses by also affecting cell membrane fluidity, along with cell division, migration, and signal transduction [36,37]. Some studies showed that TGs with 48–50 carbons are usually associated with de novo adipogenesis, a process in which excess carbohydrates are converted to fatty acids, which are then converted to TGs for storage [38]. In human studies, the concentrations of TGs 48:1 and 50:1 decreased with the increase of Everest Base Camp altitude, which may be related to the inhibition of adipogenesis mediated by hypoxia-inducible factor 1-a [39,40]. In our study, we discovered that TGs containing 48–50 carbons were significantly decreased in the hypoxia group, compared to those in the normoxia group.Together, these findings suggested that fat storage may be activated by sympathetic nerve activity stimulated by hypoxia.

Conclusions

We used a UHPLC-Q Exactive Plus MS method-based lipidomics strategy to characterize the lipid profiles of visceral adipose tissue after exposure to high-altitude chronic hypoxia. Based on the reproducibility of the QC results, the method was deemed good. Univariate and multivariate analyses showed that lipid profiles were significantly different between the normoxia and hypoxia groups and that the differently expressed lipids were concentrated in DGs and TGs. Therefore, high-altitude chronic hypoxia affects lipid metabolism in visceral adipose tissue by regulating glycerides.

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4 Feb 2022

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Reviewer #1: In this manuscript, the authors assessed the changes in lipid profiles by high-altitude chronic hypoxia in visceral adipose tissue of rats. Using a lipidomic approach, seventy-four significantly altered lipids between the normoxia and hypoxia groups were identified. The results showed the total lipid concentration of the hypoxia group was lower than that in the normoxia group, and in particular diacylglycerols and triacylglycerols in the hypoxia group were significantly lower than those in the normoxic condition.

The authors need to address the following issues and questions before the manuscript is suitable for publication in the journal.

1. Is the lipolysis activity enhanced by hypoxia? The authors shall provide further evidence (such as to examine the expression or lipolytic activity of the adipose tissue) to support their observation in lipidomics.

2. The lipid molecule chain length and chain saturation of DGs and TGs were significantly decreased in hypoxia group. What is the possible mechanism? Does fatty acid oxidation is increased and desaturation activity decreased upon hypoxia? If no activity assays are performed, at least the mRNA levels or protein levels of the key enzymes shall be examined to see whether the expression of the molecules in these pathways are altered by hypoxia.

3. From Figure 1F, I cannot see obvious difference on adipocyte size. It seems more likely a change in fibrosis? The measurement/quantification of adipocyte size and diameter shall be presented as a Gaussian curve, instead of simple bar chart.

4. Legend for tables are missing. For example, the fold change is hypoxia vs. normoxia or normoxia vs. hypoxia?

5. In addition to TG and DG, how about other lipid species?

Minor points:

1. The authors should include more literatures in introduction or discussion on previous studies on lipidomics changes in response to hypoxia. For example, a previous study assessed the serum lipid and metabolomics profiles in human in responses to progressive environmental hypoxia (Scientific Reports volume 9, Article number: 2297 (2019) ), and there are many other studies performed in other samples/tissues in response to hypoxia, and it is worthwhile to compare between the current findings with previous ones.

Reviewer #2: The manuscript, entitled “ Comprehensive lipidomic analysis reveals regulation of glyceride metabolism in rat visceral adipose tissue by high-altitude chronic hypoxia”, aims to utilize lipidomic profiling of visceral adipose to address the effect of high-altitude. The result is interesting but there are several issues in this manuscript.

1. The authors didn’t clearly describe the issue they would like to address. It seems to me that the authors aim to study the effect of hypobaric hypoxia on high altitude acclimatization. However, they didn’t provide the rationale for studying visceral adipose. It is not clear why the authors have decided to focus only on visceral adipose. The reference, cited in the introduction, shows liver also plays an important role. The author may re-organize this manuscript to help the general readers realize the importance of the issue.

2. Figure 2 can be moved to the supplementary data. It only talks about the QC of the mass spectrum data. Moreover, I think that Figure 2D can’t represent good reproducibility.

3. Similar to Figure 2, Figure 3A only shows the total number of lipid species identified in this study instead of presenting the difference in the number of expressed lipids between the two groups (like Figure 3B).

4. Figure 3 shows the two lipid class, TG and DG, are significantly different between the normoxia and hypoxia groups. However, it is strange that Supplementary Table 4 shows only one significant lipid species belongs to DG, the others are TG.

5. In Figure 6, the lipid names should be displayed in the regular format, such TG(6:0/14:0/16:1) shown in Supplementary Table 4.

6. Recently, several web tools, such LipidSig and LipidSuite, are developed for analyzing lipidomic data. I suggest the authors can utilize such tools to interpret their data.

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23 Mar 2022

Reviewer #1

1. Is the lipolysis activity enhanced by hypoxia? The authors shall provide further evidence (such as to examine the expression or lipolytic activity of the adipose tissue) to support their observation in lipidomics.

Response: We thank the Reviewer for this thoughtful comment. Adipose tissue contains a large number of adipocytes in which triglycerides (TG) are stored. Hypoxia stimulates lipolysis activity [1]. Hormone-sensitive lipase (HSL) and the adipose TG lipase (ATGL) constitute key enzymes of lipolysis[2, 3]. The expression levels of HSL and ATGL genes were measured by quantitative polymerase chain reaction (qPCR) to determine the effect of hypoxia on lipolysis of adipose tissue. The mRNA expression levels of HSL and ATGL genes of adipose tissue were significantly increased under hypoxia, suggesting an increase in lipolysis. These data are shown below and have been added to the manuscript (page 15-16, line 325-340) and as the new Fig. 4D, E. In turn, the increase in lipolysis led to a decrease in the TG levels in adipocytes. As the new Fig. 4A-C (previously Fig 1F-H), measurement of lipid droplet size revealed a significant reduction in the lipid droplet diameter and area of adipocytes. Furthermore, lipidomic of adipose tissue showed that the contents of TG was significantly decreased under hypoxia.

2. The lipid molecule chain length and chain saturation of DGs and TGs were significantly decreased in hypoxia group. What is the possible mechanism? Does fatty acid oxidation is increased and desaturation activity decreased upon hypoxia? If no activity assays are performed, at least the mRNA levels or protein levels of the key enzymes shall be examined to see whether the expression of the molecules in these pathways are altered by hypoxia.

Response: We thank the Reviewer for this relevant comment. Hypoxia may disturb the balance between lipid storage and lipid mobilization in adipose tissues (AT). Hypoxia has been shown to increase activity of the sympathetic nervous system [4, 5] together with catecholamine secretion [6]. HSL and ATGL are mainly controlled by catecholamines. Weiszenstein et al.[7]have elegantly demonstrated increased sympathetic activation and up-regulation of intracellular lipolysis in response to hypoxia exposure. As shown in the previous question.

Hypoxia induced lipolysis of visceral adipocytes, leads to preferential NEFA efflux into the circulation[8]. Consistent with this hypoxia exposure for 14 days in mice significantly increased adipocyte lipolysis and elevated NEFA levels [9]. In our study, compared with the normoxia group, we observed increased plasma NEFA in the hypoxia group. These data are shown below and have been added to the manuscript (page 16, line 329-339) and as the new Fig. 4F.

Lipolysis causes the fat in adipocytes to decompose into free fatty acids and glycerin, which are released into the blood and taken up and used by tissues such as the liver and muscle. Liver and muscle are the most active tissues for fatty acid oxidation. In this study, we focused on the lipidomic of visceral adipose tissue, with preferential attention paid to adipose mobilization. The issue of β-oxidation of adipose tissue under hypoxia is currently an active area of study by other members of our group and is beyond the scope of the current study.

3. From Figure 1F, I cannot see obvious difference on adipocyte size. It seems more likely a change in fibrosis? The measurement/quantification of adipocyte size and diameter shall be presented as a Gaussian curve, instead of simple bar chart.

Response: We appreciate this helpful comment. I am very sorry that the picture in the original manuscript is not well representative. Therefore, the adipose tissue section experiment was repeated. New Figure 4A in the revised manuscript showed histological sections of adipose tissues between normoxia and hypoxia group.

The adipocyte sizes and diameter of adipose tissues were measured by Image J. The calculation method and resultant bar chart were based on the following publication:

[1] Li B, Po S S, Zhang B , et al. Metformin regulates adiponectin signalling in epicardial adipose tissue and reduces atrial fibrillation vulnerability. Journal of Cellular and Molecular Medicine, 2020 (Pt 3).[2] Zhang S, Cao H, Li Y, et al. Metabolic benefits of inhibition of p38α in white adipose tissue in obesity. PLoS Biology, 2018, 16(5): e2004225.

[3] Famulla S, Schlich R, Sell H, Eckel J. Differentiation of human adipocytes at physiological oxygen levels results in increased adiponectin secretion and isoproterenol-stimulated lipolysis. Adipocyte. 2012;1: 132-181.

4. Legend for tables are missing. For example, the fold change is hypoxia vs. normoxia or normoxia vs. hypoxia?

Response: We apologize for this omission. We have added the fold-change specification “normoxia vs. hypoxia” to the table legend as indicated by the Reviewer.

5. In addition to TG and DG, how about other lipid species?

Response: We agree with the Reviewer that information regarding other lipid species is of interest and have emphasized the findings in the revised manuscript. The results of the lipidomic analysis showed a total of 21 lipid classes and 819 lipid species. DG and TG concentrations in the hypoxia group were significantly lower than those in the normoxia group (P < 0.05). Statistical analysis showed no difference in other lipid classes between normoxia and hypoxia (P > 0.05, Fig. 2C, D) (Page 13-14, line 268-285).

Minor points:

1. The authors should include more literatures in introduction or discussion on previous studies on lipidomics changes in response to hypoxia. For example, a previous study assessed the serum lipid and metabolomics profiles in human in responses to progressive environmental hypoxia (Scientific Reports volume 9, Article number: 2297 (2019)), and there are many other studies performed in other samples/tissues in response to hypoxia, and it is worthwhile to compare between the current findings with previous ones.

Response: We thank the Reviewer for this comment. As suggested, relevant content have been added in the revised manuscript (page 3, lines 54-59, lines 66–68, page 19, lines 397–415, page 20, lines 419–423, page 21, lines 431–439).

The references added in the revised draft are respectively:1-2,7-8,12-13,21-22,36-40.

Reviewer #2

1.The authors didn’t clearly describe the issue they would like to address. It seems to me that the authors aim to study the effect of hypobaric hypoxia on high altitude acclimatization. However, they didn’t provide the rationale for studying visceral adipose. It is not clear why the authors have decided to focus only on visceral adipose. The reference, cited in the introduction, shows liver also plays an important role. The author may re-organize this manuscript to help the general readers realize the importance of the issue.

Response: We thank the Reviewer for this important comment. As recommended, a detailed rationale for studying visceral adipose with regard to high-altitude acclimatization has been added to the introduction and discussion section in the revised manuscript (Introduction pages 3–4, lines 51–60, lines 80-85; Discussion pages 18–21, lines 371– 440).

2. Figure 2 can be moved to the supplementary data. It only talks about the QC of the mass spectrum data. Moreover, I think that Figure 2D can’t represent good reproducibility.

Response: We thank the Reviewer for the careful consideration of our data presentation. As recommended, we have moved Figure 2 to the supplementary data. In this experiment, six quality control (QC) items were used to evaluate the stability of the instrument, the reproducibility of the experiment, and the reliability of the data quality. Details are as follows 1 to 6.

Revised S1 Fig.4 (original Figure 2D): The QC samples are mix samples of the same amount of all samples, which are repeated for three times. The QC samples in the figure are closely overlapped together, indicating good stability of the instrument.

A PCA diagram can reflect the reproducibility of the samples within the group and the differences between the samples. As can be seen from the figure, the first principal component was able to distinguish the two groups of samples.

However, owing to the large heterogeneity between individuals in different samples, the differences between samples within the group are consistent with the law of biological repetition [10]. In addition, PCA was performed using all the ion peaks, which introduced some noise interference. Better display of the sample distribution could be obtained using partial least squares discriminant analysis (PLS-DA) (S1 Fig.5).

Overall, the results of QC analysis demonstrated good reproducibility; a detailed discussion of these points has been included in the revised manuscript (page 11-13; lines 230-265).

(1) Comparison of base peak spectra (BPC) of QC samples

The BPC map of QC samples was used for spectral overlap comparison, as shown in S1 Fig.1 and S1 Fig.1 2, below. The experimental results showed that the chromatographic peak response intensity and retention time of each QC sample essentially overlapped, indicating good repeatability of the experiment.

S1 Fig.1 Typical base peak intensity chromatograms for the visceral adipose tissue of rats, derived from QC samples in positive ion mode.

S1 Fig.2 Typical base peak intensity chromatograms for the visceral adipose tissue of rats, derived from QC samples in negative ion mode.

(2) Correlation map of QC samples

Pearson correlation analysis was conducted on QC samples, as shown in S1 Fig.3. A general correlation coefficient >0.9 indicates a good correlation. The experimental results showed that the correlation coefficients between QC samples were all >0.9, indicating good repeatability of the experiment.

S1 Fig.3 Correlation map of QC samples

(3) Principal component analysis (PCA) of all samples

Principal component analysis was performed on the ion peaks extracted from all experimental and QC samples following Pareto-scaling, as shown in S1 Fig.4. The experimental results showed that the QC samples were closely clustered together, indicating good repeatability of the experiment.

S1 Fig.4 PCA analysis of all samples

S1 Fig.5 PLS-DA score plot

(4) Hotelling's T2 test of all samples

Hotelling's T2 test evaluates the samples via multivariate variable modeling and defines 95% or 99% confidence intervals, which can be used for the determination of outlier samples. The experimental results showed that all QC samples were within 99% confidence intervals [11], indicating good repeatability of the experiment. The results are shown in S1 Fig.6.

S1 Fig.6 Hotelling's T2 test of all samples.

(5) Multivariate control chart (MCC) for QC samples

MCC is a multivariate statistical model based on the ion peaks detected in QC samples. It is a quality management tool used to monitor and judge whether the instrument status is stable.

Each point in the MCC represents a QC sample; the X-axis is the loading sequence of all QC samples. The points in the figure fluctuate up and down owing to fluctuations in the state of the instrument. The normal range is usually within ±3 standard deviations. The multivariate control diagram of QC samples in this project is shown in S1 Fig.7. The experimental results show that the fluctuation of QC samples is within the range of ±3 standard deviations, which reflects that the fluctuation of the instrument is within the normal range and the data can be used for subsequent analysis.

S1 Fig.7 MCC diagram of QC samples.

(6) Relative standard deviation (RSD) of QC samples

The smaller the RSD of the ion peak abundance of QC samples, the better the stability of the instrument; thus, RSD is an important indicator to reflect the quality of data. In this experiment, peak numbers with RSD ≤30% among QC samples accounted for >80% of the total peak number in QC samples, as shown in S1 Fig.8, indicating that the instrumental analysis system has good stability and the data can be used for subsequent analysis.

S1 Fig.8 Relative standard deviation of QC samples.

3. Similar to Figure 2, Figure 3A only shows the total number of lipid species identified in this study instead of presenting the difference in the number of expressed lipids between the two groups (like Figure 3B).

Response: We appreciate this helpful comment. Revised Figure 2A (original Figure 3A) shows the content differences of each lipid subclass between the two groups according to the guidelines of the International Lipid Classification and Nomenclature Committee. The X-axis represents each lipid subclass, different groups are distinguished by different colors, and the Y-axis represents the content of lipid subclass. There was no difference in lipid subclass between the two groups, only in content.

Revised Figure 2B (original Figure 3B) shows a comparison of the total content of the two groups of samples. Revised Figure 2C and 2D (original Figures 3C and 3D ) visually show the content differences of each lipid subclass between normoxia group and hypoxia group in the form of bar charts.

4. Figure 3 shows the two lipid class, TG and DG, are significantly different between the normoxia and hypoxia groups. However, it is strange that Supplementary Table 4 shows only one significant lipid species belongs to DG, the others are TG.

Response: We thank the Reviewer for this comment. Revised Figure 2 (original Figure 3) shows the results of the lipid component analysis, including lipid class, lipid species, and the contents of the different lipid subgroups.

Based on the above results, the content of TG and DG in the two groups was found to be different. S1 Table 4 further divides TG and DG into different lipid molecules according to their specific fatty acid chain length, saturation and binding position, as shown in Table 4. For example, TG (6:0/14:0/16:1) +NH4, etc.

S1 Table 4 shows the results of analysis of lipid differences according to their specific fatty acid chain length, saturation and binding position between the two groups. Lipid molecules with variable importance for the projection (VIP)>1 are considered to contribute significantly to model interpretation. In this study, OPLS-DA VIP >1 and P value < 0.05 were used as screening criteria for significantly different lipid subfractions. Significant differences in lipid molecules between groups are listed in S1 Table 4. Hence, S1 Table 4 shows only one significant lipid species belongs to DG, the others are TG. The above results are further illustrated that hypoxia mainly affects the lipolysis of triglycerides.

5. In Figure 6, the lipid names should be displayed in the regular format, such TG(6:0/14:0/16:1) shown in Supplementary Table 4.

Response: We thank the Reviewer for this helpful comment. The lipids names I Figure 6 have been changed as recommended in the revised manuscript.

6. Recently, several web tools, such LipidSig and LipidSuite, are developed for analyzing lipidomic data. I suggest the authors can utilize such tools to interpret their data.

Response: We appreciate this recommendation. LipidSig or LipidSuite are web-based platforms that integrate biogenic analyses of lipidome data, including lipid profile data with different characteristics, chain length, unsaturated, hydroxyl, and fatty acid composition[12]. All of the above can be done with the biogenic R packages, as shown in the results of the paper in a similar form. our article. However, receiver-operating characteristic (ROC) analysis is more suitable for clinical sample marker screening than for screening animal samples. This study is mainly screening animal samples. We'll use it later when we transition to clinical sample studies.

References

1. Pasarica M, Sereda O, Redman L, Albarado D, Hymel D, Roan L, et al. Reduced adipose tissue oxygenation in human obesity: evidence for rarefaction, macrophage chemotaxis, and inflammation without an angiogenic response. Diabetes. 2009;58(3):718-25. doi: 10.2337/db08-1098. PubMed PMID: 19074987.

2. Lafontan M, Langin D. Lipolysis and lipid mobilization in human adipose tissue. Progress in lipid research. 2009;48(5):275-97. doi: 10.1016/j.plipres.2009.05.001. PubMed PMID: 19464318.

3. Young S, Zechner R. Biochemistry and pathophysiology of intravascular and intracellular lipolysis. Genes & development. 2013;27(5):459-84. doi: 10.1101/gad.209296.112. PubMed PMID: 23475957.

4. Somers V, Mark A, Zavala D, Abboud F. Influence of ventilation and hypocapnia on sympathetic nerve responses to hypoxia in normal humans. Journal of applied physiology (Bethesda, Md : 1985). 1989;67(5):2095-100. doi: 10.1152/jappl.1989.67.5.2095. PubMed PMID: 2513315.

5. Somers V, Dyken M, Clary M, Abboud F. Sympathetic neural mechanisms in obstructive sleep apnea. The Journal of clinical investigation. 1995;96(4):1897-904. doi: 10.1172/jci118235. PubMed PMID: 7560081.

6. Mesarwi O, Loomba R, Malhotra A. Obstructive Sleep Apnea, Hypoxia, and Nonalcoholic Fatty Liver Disease. American journal of respiratory and critical care medicine. 2019;199(7):830-41. doi: 10.1164/rccm.201806-1109TR. PubMed PMID: 30422676.

7. Weiszenstein M, Shimoda L, Koc M, Seda O, Polak J. Inhibition of Lipolysis Ameliorates Diabetic Phenotype in a Mouse Model of Obstructive Sleep Apnea. American journal of respiratory cell and molecular biology. 2016;55(2):299-307. doi: 10.1165/rcmb.2015-0315OC. PubMed PMID: 26978122.

8. O'Rourke R, Meyer K, Gaston G, White A, Lumeng C, Marks D. Hexosamine biosynthesis is a possible mechanism underlying hypoxia's effects on lipid metabolism in human adipocytes. PloS one. 2013;8(8):e71165. doi: 10.1371/journal.pone.0071165. PubMed PMID: 23967162.

9. Morin R, Goulet N, Mauger J, Imbeault P. Physiological Responses to Hypoxia on Triglyceride Levels. Frontiers in physiology. 2021;12:730935. doi: 10.3389/fphys.2021.730935. PubMed PMID: 34497541.

10. Chen P, Wang C, Ren Y, Ye Z, Jiang C, Wu Z. Alterations in the gut microbiota and metabolite profiles in the context of neuropathic pain. Molecular brain. 2021;14(1):50. doi: 10.1186/s13041-021-00765-y. PubMed PMID: 33750430.

11. Siskos A, Jain P, Römisch-Margl W, Bennett M, Achaintre D, Asad Y, et al. Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Analytical chemistry. 2017;89(1):656-65. doi: 10.1021/acs.analchem.6b02930. PubMed PMID: 27959516.

12. Lin W, Shen P, Liu H, Cho Y, Hsu M, Lin I, et al. LipidSig: a web-based tool for lipidomic data analysis. Nucleic acids research. 2021;49:W336-W45. doi: 10.1093/nar/gkab419. PubMed PMID: 34048582.

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11 Apr 2022

Comprehensive lipidomic analysis reveals regulation of glyceride metabolism in rat visceral adipose tissue by high-altitude chronic hypoxia

PONE-D-21-40070R1

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Reviewer #1: All comments have been addressed

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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22 Apr 2022

PONE-D-21-40070R1

Comprehensive lipidomic analysis reveals regulation of glyceride metabolism in rat visceral adipose tissue by high-altitude chronic hypoxia

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