Investigation Of Serum Metabolic Profiling On Active Tuberculosis

Objectives:Using ultra-high performance liquid chromatography mass spectrometry (UPLC-MS) technique in patients with active pulmonary tuberculosis metabolic profiling to analyze, exploring the application of pattern recognition method to establish discriminant model features metabolites characteristic metabolic markers screened with potential diagnostic value things, to assist the diagnosis and differential diagnosis of tuberculosis and to advance clinical metabolomics to lay the foundation.Methods:Using ultra performance liquid chromatography with mass spectrometry (UPLC-MS) metabolomics research platform on349cases of serum samples (active tuberculosis group, the healthy control group and non-tuberculous disease group) were analyzed using MZmine2.0software was pre-processing the data, and then processing the resulting data into the SIMCA-P+12.0.1.0(Umetrics Swedish company) for analysis. By constructing orthogonal pattern recognition method SIMCA-P+12.0.1.0partial least squares discriminant analysis (OPLS-DA) model of metabolic markers initial screening based on the value and VIP VIP confidence intervals, and using SPSS12.0(SPSS, USA) the resulting metabolic screening markers of non-parametric test for independent samples, excluding variable P values greater than0.05, the final screening of potential metabolic markers.Results:1. Establish OPLS-DA model that can better distinguish active TB group and the healthy control group, the group can not effectively distinguish between tuberculosis and non-tuberculous disease group, but tuberculosis group and non-tuberculous lung disease group in subgroups of COPD Asia between the groups and subgroups bronchiectasis obvious distinction, but not with pneumonia subgroup distinction was not significant.2. Looking to the27kinds of characteristics of metabolic markers identified13 species of active tuberculosis in serum kynurenine original squalene diphosphate and quinolinic acid in up trend, and the remaining10kinds downward trend. This13kinds of metabolic markers in the healthy group, there were significant differences between tuberculous and non-tuberculous disease group.3. Lysophosphatidylcholine Group (LysoPC (P-18:1(9Z)), LysoPC (P-16:0), LysoPC (16:0), LysoPC (18:0)), palmitic acid, phytanic acid, mountain Yu acid, threonyl-y-glutamic acid, tryptophan, kynurenine, quinolinic acid, vegetable base with the original squalene diphosphate and other13kinds of active metabolites is expected to become a means of diagnosis and differential diagnosis of pulmonary tuberculosis.4.Original squalene diphosphate, lysophosphatidylcholine Group, phytanic acid, behenic acid, vegetable-based, threonyl-y-glutamic acid, kynurenine and quinolinic acid in tuberculosis and lung cancer sub-group group significant difference (p<0.05).5. Palmitic acid, lysophosphatidylcholine Group, phytanic acid, behenic acid, vegetable base and threonyl-y-glutamic acid had significant difference (p<0.05) in the TB group and COPD subgroups.6. Palmitic acid, lysophosphatidylcholine (16:0), phytanic acid, behenic acid, vegetable base and lysophosphatidylcholine (P-16:0) There are significant differences in the TB group and subgroup bronchiectasis (p<0.05).7. Palmitic acid, lysophosphatidylcholine (16:0) There was a significant difference (p <0.05) in the TB group and subgroup pneumonia.8. The TB group and non-tuberculous disease group all subgroups for comparison,13kinds of metabolic markers only lysophosphatidylcholine (16:0) were statistically significant (p<0.05).Conclusion:Core Concepts of metabonomics are individual metabolic state can be very closely reflect their health status, this metabolic state is encoded by the genome and environmental modifications result of the dual role. This study used high-performance liquid chromatography-mass spectrometry analysis (UPLC-MS) for active tuberculosis group, the control group and the non-tuberculous disease group metabolomics studies, initially identified27kinds of metabolic markers, including13 confirmed the presence of specific kinds of changes in the development of active tuberculosis, and is expected to become active tuberculosis metabolic markers. The experiments show that OPLS-DA model built by the metabolic profiling can better distinguish between active pulmonary tuberculosis group, the control group with non-tuberculous disease group (except pneumonia subgroup), the results can be to find the characteristics of active tuberculosis and identification markers diagnostics to provide new ideas and methods.