Title: An untargeted LC–MS based approach for identification of altered metabolites in blood plasma of rheumatic heart disease patients
Journal: Scientific Reports
Published: 2022
Background
Rheumatic heart disease (RHD) is one of the commonest causes of cardiac disease below 25 years of age. Untargeted metabolite study is a hypothesis free approach where novel metabolites can be discovered whereas targeted metabolite approach is a hypothesis-based approach where metabolites are known. Thus, metabolomics approach will not only provide insights into the pathogenesis of disease progression but will also help in identifying new therapeutic targets. In the present study we aim to identify the putative metabolic biomarkers for RHD using high throughput non targeted ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) which can be useful in early diagnosis and monitoring of RHD, further helping in understanding the disease pathology.
Methods
Sample preparation
Frozen samples were thawed at room temperature. Metabolites were extracted using chilled methanol in ratio of 1:3 (plasma: methanol) followed by vertexing and centrifugation at 10,000 rpm for 10 min. The supernatant containing metabolites was then collected in a microcentrifuge tube and was lyophilized. The lyophilized samples were reconstituted in 15% methanol and 5 μL was injected for LC-MS analysis. Samples were run in randomized way and all the acquisition has been done in single batch.
Untargeted LC-MS metabolomic profiling
LC–MS acquisition was done using orbitrap Fusion (Thermo Fischer) coupled with ultimate 3000 UHPLC system.
Results
Untargeted LC–MS analysis. 351 metabolites were identified in blood plasma through UHPLC-MS/MS analysis. Metabolites with more than 20% missing values were removed from the study rest were replaced by LoDs (1/5 of the minimum positive value of each variable). The peak area data matrix was sum normalized, log transformed and pareto scaled. PCA analysis was performed to understand the aggregation and description of the samples (Fig. 1a.). Whereas PLS-DA score plot helped us to clearly discriminate between the two groups (R2 = 0.92 and Q2 = 0.76) (Fig. 1b). Cross validation analysis using 100 random permutations were done to prevent overfitting of the PLS-DA model. The R2 and Q2 values of the originally obtained model were better than the 100 randomly permutated models indicating good predictive capacity of the obtained PLS-DA model.
Figure 1. Chemometric analysis of metabolites among RHD and healthy controls. (a) Principal Component Analysis (PCA) score plot from RHD and healthy control. The green dots represent RHD patients and the red dots represent healthy controls in the 2D PCA score plots. (b) Partial least squares discriminant analysis (PLS-DA) score plot from RHD and healthy control.[1]
Other significant altered pathways discovered in RHD patients were d-Glutamine and d-glutamate metabolism and Linoleic acid metabolism (Fig. 2). Glutamate and glutamine are nonessential amino acids that are transformed into each other by glutamine synthase and glutaminase. Framingham heart study reported that the circulating glutamate levels lead to cardiometabolic risk factors whereas circulating level of glutamine and the glutamine: glutamate ratio exhibits opposite association with the cardiometabolic risk factors.
Figure 2. The MetPA analysis based on KEGG Analysis. The darker the red color of the metabolic pathway, the greater its-log (p) value, indicating a more significant difference.[1]
Reference
- Das S.; et al. An Untargeted LC-MS based approach for identification of altered metabolites in blood plasma of rheumatic heart disease patients. Sci Rep. 2022, 12(1):5238 https://www.nature.com/articles/s41598-022-09191-z
