Metabolite Dysregulation by Pranlukast in Mycobacterium tuberculosis

Abstract

Mycobacterium tuberculosis has been infecting millions of people worldwide over the years, causing tuberculosis. Drugs targeting distinct cellular mechanisms including synthesis of the cell wall, lipids, proteins, and nucleic acids in Mtb are currently being used for the treatment of TB. Although extensive research is being carried out at the molecular level in the infected host and pathogen, the identification of suitable drug targets and drugs remains under explored. Pranlukast, an allosteric inhibitor of MtArgJ (Mtb ornithine acetyltransferase) has previously been shown to inhibit the survival and virulence of Mtb. The main objective of this study was to identify the altered metabolic pathways and biological processes associated with the differentially expressed metabolites by PRK in Mtb. Here in this study, metabolomics was carried out using an LC-MS/MS-based approach. Collectively, 50 metabolites were identified to be differentially expressed with a significant p-value through a global metabolomic approach using a high-resolution mass spectrometer. Metabolites downstream of argJ were downregulated in the arginine biosynthetic pathway following pranlukast treatment. Predicted human protein interactors of pranlukast-treated Mtb metabolome were identified in association with autophagy, inflammation, DNA repair, and other immune-related processes. Further metabolites including N-acetylglutamate, argininosuccinate, L-arginine, succinate, ergothioneine, and L-phenylalanine were validated by multiple reaction monitoring, a targeted mass spectrometry-based metabolomic approach. This study facilitates the understanding of pranlukast-mediated metabolic changes in Mtb and holds the potential to identify novel therapeutic approaches using metabolic pathways in Mtb.

Keywords: antagonist; bacteria; mass spectrometer; targeted metabolomics; untargeted metabolomics.

Figure 1

Schematic representation of the metabolomics experimental pipeline. The details of samples and the experimental pipeline employed for targeted and untargeted approach are illustrated.

Figure 2

Data visualization plots. PCA clustering of PRK-treated and untreated Mtb H37Rv samples in (A) positive mode and (B) negative mode. Volcano plots showing the distribution of identified features in (C) positive mode and (D) negative mode. The differentially expressed metabolites with p-value ≤ 0.05 are highlighted in blue and red in the respective plots.

Figure 3

Pathway enrichment with significant p-value and FDR is shown as a bubble plot. The size of the bubble represents the p-value and color scale represents FDR for each pathway.

Figure 4

Gene Ontology classification of predicted protein targets—(A) protein classes and (B) biological processes. (C) Pathway enrichment showed with an alluvial diagram. The thickness of the correlation lines connecting genes to pathways represents a significant p-value (≤0.05).

Figure 5

Interaction network map of protein targets of altered metabolites showing clustering of proteins into three groups. The edges connecting the nodes represent high confidence. Proteins associated with apoptosis and immune response are highlighted with pink and blue stars, respectively, while proteins associated with inflammation are highlighted with green stars.

Figure 6

Box plots of validated metabolites showing differential expression of metabolites in control and PRK-treated Mtb H37Rv groups. (A) L-Ergothioneine, (B) L-Arginine (C) Argininosuccinate, (D) N-acetylglutamate, (E) Succinate, and (F) L-Phenylalanine.

Figure 7

Pathway map of arginine metabolism and GABA shunt. MRM-validated metabolites are highlighted in yellow, while the metabolites dysregulated in the global analysis are highlighted in blue in the pathway.