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Metabonomic profiling of serum and urine by (1)H NMR-based spectroscopy discriminates patients with chronic obstructive pulmonary disease and healthy individuals.

Wang L, Tang Y, Liu S, Mao S, Ling Y, Liu D, He X, Wang X - PLoS ONE (2013)

Bottom Line: Samples were analyzed by high resolution (1)H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis.Moreover, metabolic differences in urine were more significant than in serum.Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects.

View Article: PubMed Central - PubMed

Affiliation: Department of Respiratory Medicine, The Fourth Affiliated Hospital of China Medical University, Shenyang, People's Republic of China.

ABSTRACT
Chronic obstructive pulmonary disease (COPD) has seriously impacted the health of individuals and populations. In this study, proton nuclear magnetic resonance ((1)H NMR)-based metabonomics combined with multivariate pattern recognition analysis was applied to investigate the metabolic signatures of patients with COPD. Serum and urine samples were collected from COPD patients (n = 32) and healthy controls (n = 21), respectively. Samples were analyzed by high resolution (1)H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis. Consistent metabolic differences have been found in serum as well as in urine samples from COPD patients and healthy controls. Compared to healthy controls, COPD patients displayed decreased lipoprotein and amino acids, including branched-chain amino acids (BCAAs), and increased glycerolphosphocholine in serum. Moreover, metabolic differences in urine were more significant than in serum. Decreased urinary 1-methylnicotinamide, creatinine and lactate have been discovered in COPD patients in comparison with healthy controls. Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects. Our results illustrate the potential application of NMR-based metabonomics in early diagnosis and understanding the mechanisms of COPD.

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Coefficient loading plots obtained from serum and urine.Coefficient loading plots calculated from OPLS-DA modeling of (A) serum and (B) urine. Peaks in the positive direction indicate metabolites with increased expression levels in healthy controls, whereas the negative direction peaks denote metabolites display enhanced expression levels in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. Keys of the assignments were shown in Figure 1 and 2, respectively.
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pone-0065675-g005: Coefficient loading plots obtained from serum and urine.Coefficient loading plots calculated from OPLS-DA modeling of (A) serum and (B) urine. Peaks in the positive direction indicate metabolites with increased expression levels in healthy controls, whereas the negative direction peaks denote metabolites display enhanced expression levels in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. Keys of the assignments were shown in Figure 1 and 2, respectively.

Mentions: To identify the main metabolites responsible for discriminating COPD patients from healthy control subjects, OPLS-DA was carried out with a unit variance scaling strategy to further analyze the metabolite profiles obtained from serum and urine samples. The OPLS-DA score plots and corresponding coefficient loading plots based on the collected NMR data are presented in Figure 3 and Figure 5, respectively. The separation between COPD patients and healthy controls was further improved both for serum (R2X = 20.5%, Q2 = 0.265) (Figure 3E) and urine (R2X = 48.6%, Q2 = 0.154) (Figure 3F) samples after OPLS-DA was employed. Moreover, the color-coded coefficient loading plots revealed more detailed information about metabolic differences between COPD patients and healthy controls (Figure 5). Here, the direction of the signals associates with the relative variations of metabolites in COPD patients compared to the healthy controls. For instance, peaks in the positive direction indicate metabolites that are more abundant in healthy controls, whereas the negative direction peaks denote metabolites significantly higher in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. For example, red indicates a more significant contribution to the separation between these two groups than blue. According to the loading plots, increased expression levels of phenylalanine, tyrosine, alanine, valine, leucine, isoleucine and high density lipoprotein (HDL) were found in the serum of healthy controls (Figure 5A). However, only glycerolphosphocholine (GPC) was elevated in COPD patients (Figure 5A). Interestingly, there are more significant metabolic differences in urine between COPD patients and healthy controls. As shown in Figure 5B, metabolites, including 1-methylnicotinamide (1-MN), creatinine and lactate exhibited an elevated expression level in healthy controls. On the other hand, enhanced expression levels of carnosine, phenylacetyglycine, pyruvate, m-hydroxyphenylacetate, α-ketoglutarate, acetate, acetoacetate and acetone were found in the urine of COPD patients relative to healthy controls (Figure 5B). The coefficients indicating the significance of the metabolites contributing to the separation in serum and urine were summarized in Tables 3 and 4, respectively. Here, the coefficient of 0.4 was used as the cut-off value which was calculated based on discrimination significance at the level of 0.05.


Metabonomic profiling of serum and urine by (1)H NMR-based spectroscopy discriminates patients with chronic obstructive pulmonary disease and healthy individuals.

Wang L, Tang Y, Liu S, Mao S, Ling Y, Liu D, He X, Wang X - PLoS ONE (2013)

Coefficient loading plots obtained from serum and urine.Coefficient loading plots calculated from OPLS-DA modeling of (A) serum and (B) urine. Peaks in the positive direction indicate metabolites with increased expression levels in healthy controls, whereas the negative direction peaks denote metabolites display enhanced expression levels in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. Keys of the assignments were shown in Figure 1 and 2, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3675021&req=5

pone-0065675-g005: Coefficient loading plots obtained from serum and urine.Coefficient loading plots calculated from OPLS-DA modeling of (A) serum and (B) urine. Peaks in the positive direction indicate metabolites with increased expression levels in healthy controls, whereas the negative direction peaks denote metabolites display enhanced expression levels in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. Keys of the assignments were shown in Figure 1 and 2, respectively.
Mentions: To identify the main metabolites responsible for discriminating COPD patients from healthy control subjects, OPLS-DA was carried out with a unit variance scaling strategy to further analyze the metabolite profiles obtained from serum and urine samples. The OPLS-DA score plots and corresponding coefficient loading plots based on the collected NMR data are presented in Figure 3 and Figure 5, respectively. The separation between COPD patients and healthy controls was further improved both for serum (R2X = 20.5%, Q2 = 0.265) (Figure 3E) and urine (R2X = 48.6%, Q2 = 0.154) (Figure 3F) samples after OPLS-DA was employed. Moreover, the color-coded coefficient loading plots revealed more detailed information about metabolic differences between COPD patients and healthy controls (Figure 5). Here, the direction of the signals associates with the relative variations of metabolites in COPD patients compared to the healthy controls. For instance, peaks in the positive direction indicate metabolites that are more abundant in healthy controls, whereas the negative direction peaks denote metabolites significantly higher in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. For example, red indicates a more significant contribution to the separation between these two groups than blue. According to the loading plots, increased expression levels of phenylalanine, tyrosine, alanine, valine, leucine, isoleucine and high density lipoprotein (HDL) were found in the serum of healthy controls (Figure 5A). However, only glycerolphosphocholine (GPC) was elevated in COPD patients (Figure 5A). Interestingly, there are more significant metabolic differences in urine between COPD patients and healthy controls. As shown in Figure 5B, metabolites, including 1-methylnicotinamide (1-MN), creatinine and lactate exhibited an elevated expression level in healthy controls. On the other hand, enhanced expression levels of carnosine, phenylacetyglycine, pyruvate, m-hydroxyphenylacetate, α-ketoglutarate, acetate, acetoacetate and acetone were found in the urine of COPD patients relative to healthy controls (Figure 5B). The coefficients indicating the significance of the metabolites contributing to the separation in serum and urine were summarized in Tables 3 and 4, respectively. Here, the coefficient of 0.4 was used as the cut-off value which was calculated based on discrimination significance at the level of 0.05.

Bottom Line: Samples were analyzed by high resolution (1)H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis.Moreover, metabolic differences in urine were more significant than in serum.Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects.

View Article: PubMed Central - PubMed

Affiliation: Department of Respiratory Medicine, The Fourth Affiliated Hospital of China Medical University, Shenyang, People's Republic of China.

ABSTRACT
Chronic obstructive pulmonary disease (COPD) has seriously impacted the health of individuals and populations. In this study, proton nuclear magnetic resonance ((1)H NMR)-based metabonomics combined with multivariate pattern recognition analysis was applied to investigate the metabolic signatures of patients with COPD. Serum and urine samples were collected from COPD patients (n = 32) and healthy controls (n = 21), respectively. Samples were analyzed by high resolution (1)H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis. Consistent metabolic differences have been found in serum as well as in urine samples from COPD patients and healthy controls. Compared to healthy controls, COPD patients displayed decreased lipoprotein and amino acids, including branched-chain amino acids (BCAAs), and increased glycerolphosphocholine in serum. Moreover, metabolic differences in urine were more significant than in serum. Decreased urinary 1-methylnicotinamide, creatinine and lactate have been discovered in COPD patients in comparison with healthy controls. Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects. Our results illustrate the potential application of NMR-based metabonomics in early diagnosis and understanding the mechanisms of COPD.

Show MeSH
Related in: MedlinePlus