Pulmonary arterial hypertension (PAH), a chronic and fatal disease with a poor prognosis, is characterized by elevated pulmonary arterial pressure which could lead to right side heart failure [
Recently, metabonomic technique has been applied to clinical practice including diagnosis, evaluation of severity, progression and prognosis of diseases, and estimation of efficacy of surgical and pharmacological treatment [
When peripheral blood returns to the heart and then passes through the lungs, it contains almost the same metabolites as in both organs. Metabonomic analyses of circulating plasma may, to some degree, reflect the metabolic profiles indicating pulmonary arterial disease. In our previous studies, we had established a rat PAH model by intraperitoneal injection of monocrotaline (MCT) [
PAH rat was induced by a single intraperitoneal injection of MCT in our laboratory as described previously according to Xie and colleagues [
The mean pulmonary artery pressure (mPAP) and right ventricle hypertrophy index (RVHI) of all rats were measured weekly as described previously [
The serum samples (300
To exploit the metabolic information embedded in the spectra, each free induction decay (FID) was zero-filled to 64 k points and all 1H NMR spectra were multiplied by a 0.3 Hz exponential line-broadening function prior to Fourier transform according to the method previously described [
The resulting bucketed data matrices were imported into SIMCA-P+ 12.0 software package (Umetrics AB, Sweden) for chemometric analysis. Pareto scaling was used to increase the importance of low-concentration metabolites without significant amplification of noise. Principle components analysis (PCA) was performed for identifying differences among the metabolic profiles of all samples. The first three principle components (PCs) were used to generate a score plot displaying the correlation matrices. Orthogonal signal correction partial least-squares discriminant analysis (OPLS-DA) was applied for one-to-one classification between any two groups using MATLAB (Version MATLAB 2011b, MathWorks, USA). The reliability of the OPLS-DA model was tested as described previously [
For relative quantification, the intensities of each metabolite were calculated by using the relative integrals of each NMR spectrum and were represented as mean ± standard deviation. Distributions of the metabolite values were tested. The Student’s
At the end of the first week, WT% but not mPAP, RVHI, and WA% in rats was significantly altered compared to the W0 rats (
Changes in mPAP (a), RVHI (b), WT% (c), and WA% (d) in monocrotaline-induced rat PAH model. mPAP: mean pulmonary artery pressure; RVHI: right ventricle hypertrophy index; WT%: the ratio of vessel wall thickness and wall diameter; WA%: the ratio of vessel wall area and total vessel area.
An exploratory PCA has been applied to obtain a comprehensive comparison of metabolic profiles of the samples (Figure
PCA score plots of 1H NMR data from serum of PAH rats. 3D PCA score plot generated from unsupervised PCA of NMR spectra of aqueous metabolites showing separate grouping for samples. Each point in the PCA score plot represents a specific individual sample, and samples with similar metabolic profiles are grouped together in clusters. (a) All rats; (b) W0 versus W1; (c) W1 versus W2-3; (d) W2-3 versus W4; (e) W4 versus W0.
OPLS-DA score plots ((a), (c), and (e)) and OPLS-DA coefficients-coded loading plots ((b), (d), and (f)). Colors on the loading plots are used to identify the altered metabolites between two groups. When the
We then identified importance of metabolites for class discrimination, as described in Materials and Methods. Metabolites with larger contribution in the discrimination between each stage of PAH and baseline (W0) were selected, respectively, by OPLS-DA loadings and their VIP values. The VIP values and the direction of variation (either increased or decreased) were listed in Table
Changes of metabolites after MCT injection.
Chemical shift |
Metabolites | W1 versus W0 | W2-3 versus W0 | W4 versus W0 | |||||||||
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VIP |
Vary |
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|
VIP |
Vary |
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|
VIP |
Vary |
| ||
3.70 | Glucose | 0.72 | 3.21 | ↑↑ | 0.52 | 4.04 | ↑ | 0.58 | 3.03 | ↓ | |||
1.34 | Lactate | — | — | — | 0.58 | 4.23 | ↓↓ | 0.73 | 8.77 | ↑↑ | 0.008 | ||
2.37 | Pyruvate | 0.69 | 1.1 | ↑↑ | 0.62 | 2.15 | ↓↓ | 0.79 | 4.15 | ↑↑ | 0.050 | ||
0.92 | LDL/VLDL | — | — | — | 0.64 | 2.4 | ↑↑ | 0.58 | 1.77 | ↓ | |||
3.67 | Glycerol | 0.85 | 3.27 | ↑↑ | <0.001 | 0.89 | 4.18 | ↑↑ | 0.016 | 0.59 | 2.22 | ↑↑ | 0.001 |
1.20 | 3-Hydroxybutyrate (3-HB) | 0.8 | 1.18 | ↓↓ | — | — | — | — | — | — | |||
2.23 | Acetone | 0.76 | 1.26 | ↑↑ | 0.56 | 1.26 | ↑↑ | 0.039 | — | — | — | ||
2.29 | Acetoacetate | 0.82 | 1.74 | ↑↑ | 0.62 | 1.74 | ↓↓ | — | — | — | 0.042 | ||
1.92 | Acetate | 0.86 | 2.59 | ↑↑ | 0.004 | — | — | — | — | — | — | ||
2.45 | Carnitine | 0.84 | 1.24 | ↓↓ | — | — | — | 0.52 | 1.33 | ↑↑ | |||
3.23 | Taurine | — | — | — | 0.48 | 3.11 | ↓ | 0.56 | 2.48 | ↓ | 0.025 | ||
2.14 | Methionine | 0.77 | 1.96 | ↓↓ | — | — | — | — | — | — | |||
3.20 | Choline | 0.81 | 2 | ↓↓ | 0.57 | 1.68 | ↑↑ | 0.69 | 2.29 | ↑↑ | |||
3.89 | Betaine | 0.8 | 4.95 | ↑↑ | 0.51 | 2.76 | ↑ | 0.039 | 0.54 | 3.8 | ↑ | ||
3.56 | Glycine | 0.64 | 2.79 | ↑↑ | 0.043 | 0.54 | 1.87 | ↑↑ | <0.001 | 0.62 | 3.19 | ↑ | <0.001 |
3.04 | Creatine | 0.66 | 2.63 | ↓↓ | — | — | — | 0.57 | 4.48 | ↑ | 0.018 | ||
0.99 | Leucine | — | — | — | — | — | — | 0.54 | 1.44 | ↑ | |||
1.02 | Isoleucine | — | — | — | — | — | — | 0.68 | 1.86 | ↑↑ | 0.044 | ||
1.04 | Valine | 0.69 | 1.65 | ↑↑ | 0.47 | 2.01 | ↓ | — | — | — | |||
2.04 | N-Acetyl-l-cysteine (Nac) | 0.69 | 2.31 | ↓↓ | — | — | — | — | — | — |
After the multivariate analysis, we also analyzed statistic differences of the relative concentrations of these selected metabolites among groups, as seen in Table
A color heat map of the Pearson’s correlation coefficients computed for the characteristic metabolites observed during the PAH progress. The correlation coefficients between the metabolites at each stage: W0 (a), W1 (b), W2-3 (c), and W4 (d). Correlation coefficients were shown with continuous gradient colors, where significant positive correlation is marked in red, negative is in blue, and grey represents no significant correlation being found. Color bars represent the significance of the correlation coefficients.
Characteristic metabolites and their concentrations were imported to the web-based tool MetaboAnalyst, to exploit the most disturbed metabolic pathways via Pathway enrichment analysis (Figure
Metabolic abnormality may be a fundamental mechanism of the uncontrolled proliferative and antiapoptotic pulmonary artery smooth muscle cells in the pathogenesis of PAH. (a) Pathway enrichment analysis for determining which pathways are more likely to be involved in the PAH development. (b) Change of metabolite levels during progression of PAH. (c) Hypothetical pathways for choline, betaine, methionine, and energy metabolism dysfunction in PAH. Increased levels of choline and betaine could cause energy metabolism abnormality by affecting mitochondrial function. In PAH, PASMCs proliferation maximizes the usage of methionine for protein synthesis which might reduce the methionine level (choline dehydrogenase: CHDH; methylene tetrahydrofolate dehydrogenase: MTHFD), although methionine is the metabolic production of betaine.
In this study, it was shown that serum metabolite levels varied and closely related to the pathophysiological changes in different stages of PAH. Changes of metabolic profile of glycolysis, lipid metabolism, and methionine were the most significant determinants in different stages of PAH. Correlation between these metabolites and other pathological indexes from different stages of PAH was quite strong, especially in the fourth week, in which glycolysis, lipid, and methionine metabolism pathways were found to be highly activated.
Although there were some studies focusing on the metabolic dysfunction in PAH [
Generally speaking, we found that most of the characteristic metabolites changed significantly in the first week after MCT treatment but tended back to control level in the following weeks. In the first week, the possible explanation for metabolic shifting might be the fact that MCT injection provokes an acute stress response. This could be reflected by reduced level of N-acetyl-L-cysteine, which was believed to play a key role in preventing and suppressing oxidative stress and inhibiting the apoptotic pathways [
In the early stage of PAH (weeks 2-3), there was an increase in glucose level; however, there was a decrease in both lactate and pyruvate levels, indicating that both mitochondrial glucose oxidation and glycolytic metabolism were downregulated during this stage.
Furthermore, we found that enhanced lipid metabolism was one of the main biochemical characteristics which may be used to indicate an early onset of PAH. Our data suggested that the disorder of metabolism that occurred in the development of pulmonary hypertension shifted from glucose metabolism to fatty acid usage dominant on the whole. Based on our data, it is believed that a well-known mechanism, Randle cycle, defined as the glucose oxidation switching the energy production from carbohydrates to fatty acid [
In agreement with previous works [
Interestingly, we also found an increase in the level of choline, betaine, methionine, and glycine. Furthermore, betaine and methionine pathway was identified as vital pathway in PAH. Importantly, imbalance of metabolism in choline, betaine, and methionine is involved in the pathways of cells proliferation and energy metabolism [
Although the statistical correlations and the pathway enrichment analysis did not provide cause-effect relationship directly, accumulating data suggested a possible underlying mechanism. Choline is known as a precursor for formation of the neurotransmitter acetylcholine which can be oxidized to form betaine (catalyzed by choline dehydrogenase, CHDH). It is well-known that betaine is a methyl donor in the formation of methionine, a critical step in the formation of methyltetrahydrofolate catalyzed by methylene tetrahydrofolate dehydrogenase (MTHFD) [
Given that PAH is a fetal disease with very poor survival prognosis, the research of its specific metabolic mechanism may shed light on potential therapeutic strategies for PAH [
At this stage, we used 1H NMR-based metabonomics to scan metabolic shifts in the sera of PAH rats during progression of vascular remodeling. The preliminary results provided valuable knowledge on the biochemistry during PAH process and highlighted the betaine and methionine pathway in the onset of PAH, which could be helpful for further research of PAH pathogenetic mechanism and treatment.
The authors alone are responsible for the content and writing of the paper.
The authors report no conflict of interests.
Taijie Lin and Jinping Gu contributed equally to this work.
This work was supported in part by grants from National Natural Science Foundation of China (no. 81270111/H0109 and no. 91129713) and from The Key Clinical Program of Fujian Medical University (no. XK201107).