LC-MS/MS-Based Serum Metabolomics and Transcriptome Analyses for the Mechanism of Augmented Renal Clearance

Abstract

Augmented Renal Clearance (ARC) refers to the increased renal clearance of circulating solute in critically ill patients. In this study, the analytical research method of transcriptomics combined with metabolomics was used to study the pathogenesis of ARC at the transcriptional and metabolic levels. In transcriptomics, 534 samples from 5 datasets in the Gene Expression Omnibus database were analyzed and 834 differential genes associated with ARC were obtained. In metabolomics, we used Ultra-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry to determine the non-targeted metabolites of 102 samples after matching propensity scores, and obtained 45 differential metabolites associated with ARC. The results of the combined analysis showed that purine metabolism, arginine biosynthesis, and arachidonic acid metabolism were changed in patients with ARC. We speculate that the occurrence of ARC may be related to the alteration of renal blood perfusion by LTB4R, ARG1, ALOX5, arginine and prostaglandins E2 through inflammatory response, as well as the effects of CA4, PFKFB2, PFKFB3, PRKACB, NMDAR, glutamate and cAMP on renal capillary wall permeability.

Keywords: augmented renal clearance; metabolomics; transcriptome; vancomycin.

Figure 1

TIC pattern of quality control samples during injection sequence.

Figure 2

Clustering Analysis Plot: (A) PCA plot; (B) orthogonal partial least squares discriminant analysis. The color of the dots represents their grouping; the range of ellipses represents a 95% confidence interval.

Figure 3

Permutation test results of OPLS-DA model. Used to validate the predictive powers of OPLS-DA models. For Q², the model is considered to be not overfitted if the outcome of the result on the y-axis does not exceed 0.05. When the Q² of the real model (far right) and the Q² of the random label modeling are close, it indicates that the model is overfitting.

Figure 4

Screening results for differential metabolites: (A) Volcano map. The abscissa represents the results of FC analysis. The ordinate represents the univariate analysis results. Dark blue: corrected p-value < 0.05, FC <−1.5; Light blue: corrected p-value < 0.05, −1.5 < FC < 0; Orange: corrected p-value < 0.05, 0 < FC < 1.5; Red: corrected p-value < 0.05, FC > 1.5. (B) Score plot of the OPLS-DA model. The horizontal axis displays the x-loadings p and Y-loadings q of the predictive component. The vertical axis displays the x-loadings p(o) and the loading s(o) for the y-orthogonal component. Red indicates compounds with VIP > 1.5; (C) Venn plot, representing the number of differential metabolites obtained through each data analysis method.

Figure 5

Heat map of differential metabolite expression. Each horizontal row represents a differential metabolite; each column represents a sample; the color indicates the relative expression of the differential metabolites in the individual samples. The clustering methods for horizontal rows and vertical columns are complete linkage agglomerative clustering.

Figure 6

Transcriptome sample processing visualization results: (A) Boxplot of sample expression; (B) sample cluster plot before processing; (C) sample cluster plot after processing.

Figure 7

Volcano map of DEGs in each dataset: (A) GSE37069; (B) GSE57065; (C) GSE37069; (D) GSE57065; (E) GSE28750.

Figure 8

Venn diagram of different genes in each dataset: (A) Upregulated genes; (B) downregulated genes.

Figure 9

Schematic diagram of the correlation coefficient and average number of connections under different soft thresholds.

Figure 10

Cluster dendrogram of DEGs. Different branches of the cluster tree represent different genes, and different colors represent different modules. Genes in the same color module indicate similar expression patterns.

Figure 11

Correlation heatmap: (A) Module-to-module dependencies; (B) correlation between the module and sample characteristics.

Figure 12

Gene–trait scatterplot: (A) Blue; (B) turquoise; (C) brown. Each dot represents a DEG, and the line is the correlation fitting curve of the module with the ARC trait.

Figure 13

Hub genes visualization network diagram: (A) Blue; (B) turquoise; (C) brown.

Figure 14

Metabolite-metabolite network diagram: A red dot indicates upregulation, a green dot indicates downregulation, and a gray dot indicates that this metabolite is not included in the metabolite that we ultimately annotated (Table 4), but is closely related to other differential metabolites. The size of the dot represents the magnitude of the FC.

Figure 15

Gene-metabolite network diagram: A square represents metabolites, and round shapes represent genes. Red indicates upregulation, and green indicates downregulation.

Figure 16

Pathway analysis enrichment bubble diagram: (A) Only differential metabolites were used for the results of the pathway analysis in KEGG; (B) Only differential metabolites were used for the results of pathway analysis in the SMPDB; (C) Joint-pathway analysis results of differential genes and differential metabolites in combination. The size of the bubbles is determined by the pathway impact values from the pathway topology analysis, and the larger the value, the larger the bubble. The color of the bubbles is determined by the p-values from the pathway enrichment analysis. The larger the p -value, the darker the color (reddish), while the smaller the p-value, the lighter the color (yellow or even white).

Figure 17

Schematic diagram of changes in the pathways of ARC patients. Solid lines represent direct interactions, and dashed lines represent indirect interactions. Red indicates metabolites or genes upregulated, green indicates metabolites or genes downregulated, and gray indicates omitted metabolites or genes that were not included in our study results.

Figure 18

Global metabolic network changes in ARC patients. Circles and bold lines indicate changes in metabolic pathways that may occur in patients with ARC.

Figure 19

ARC pathogenesis diagram.

Figure 20

Differential metabolite analysis process.