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Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.

Feng S, Du YQ, Zhang L, Zhang L, Feng RR, Liu SY - Chin. Med. J. (2015)

Bottom Line: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups.The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms.The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Laboratory, Haihe Hospital, Respiratory Disease Research Institute, Tianjin 300350, China.

ABSTRACT

Background: Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.

Methods: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.

Results: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.

Conclusion: The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

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Related in: MedlinePlus

Heat map based on the differences in the abundance of small metabolic compounds among sera from the tuberculosis (TB) group, healthy group and non-TB group. Yellow color indicates higher abundance of metabolites; Orange color indicates lower abundance of metabolites.
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Figure 5: Heat map based on the differences in the abundance of small metabolic compounds among sera from the tuberculosis (TB) group, healthy group and non-TB group. Yellow color indicates higher abundance of metabolites; Orange color indicates lower abundance of metabolites.

Mentions: The successful use of UPLC-MS metabolomics analysis and the OPLS-DA model described above to distinguish between the TB group and control groups led us to search for the specific metabolites that contributed to the metabolomic differences. Based on the OPLS-DA model, the signals that were highly correlated and had high signal-to-noise ratio values were selected. The metabolites of >400 small molecules in the sera of patients in the three groups were explored. The molecules responsible for these signals were identified and differences in the abundance of these small molecules were determined by applying Mann–Whitney U-test for each of the three possible comparisons, using a P value threshold of 0.05. Twenty-seven metabolites were detected at significantly different levels between the active TB group and the control groups. Of those metabolites identified to differ significantly between groups, 12 metabolites [Table 2 and Figure 6], were clustered in the fatty acid, phospholipids, amino acids, and terpenoid compounds metabolite sets. To confirm that the three groups differed in terms of the serum levels of these metabolites, a heat map, a graphical representation of data where the individual values are represented as colors, was drawn [Figure 5]. Heat maps are 2D displays of the measured experimental values in the data matrix. The relatively high abundance of any specific metabolite is represented by yellow-colored squares (pixels) and a low abundance is represented by orange-colored squares.


Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.

Feng S, Du YQ, Zhang L, Zhang L, Feng RR, Liu SY - Chin. Med. J. (2015)

Heat map based on the differences in the abundance of small metabolic compounds among sera from the tuberculosis (TB) group, healthy group and non-TB group. Yellow color indicates higher abundance of metabolites; Orange color indicates lower abundance of metabolites.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837832&req=5

Figure 5: Heat map based on the differences in the abundance of small metabolic compounds among sera from the tuberculosis (TB) group, healthy group and non-TB group. Yellow color indicates higher abundance of metabolites; Orange color indicates lower abundance of metabolites.
Mentions: The successful use of UPLC-MS metabolomics analysis and the OPLS-DA model described above to distinguish between the TB group and control groups led us to search for the specific metabolites that contributed to the metabolomic differences. Based on the OPLS-DA model, the signals that were highly correlated and had high signal-to-noise ratio values were selected. The metabolites of >400 small molecules in the sera of patients in the three groups were explored. The molecules responsible for these signals were identified and differences in the abundance of these small molecules were determined by applying Mann–Whitney U-test for each of the three possible comparisons, using a P value threshold of 0.05. Twenty-seven metabolites were detected at significantly different levels between the active TB group and the control groups. Of those metabolites identified to differ significantly between groups, 12 metabolites [Table 2 and Figure 6], were clustered in the fatty acid, phospholipids, amino acids, and terpenoid compounds metabolite sets. To confirm that the three groups differed in terms of the serum levels of these metabolites, a heat map, a graphical representation of data where the individual values are represented as colors, was drawn [Figure 5]. Heat maps are 2D displays of the measured experimental values in the data matrix. The relatively high abundance of any specific metabolite is represented by yellow-colored squares (pixels) and a low abundance is represented by orange-colored squares.

Bottom Line: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups.The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms.The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Laboratory, Haihe Hospital, Respiratory Disease Research Institute, Tianjin 300350, China.

ABSTRACT

Background: Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.

Methods: Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.

Results: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.

Conclusion: The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Show MeSH
Related in: MedlinePlus