Usnic acid impacts energy production and iron metabolism in Mycobacterium tuberculosis H37Rv

Abstract

Mycobacterium tuberculosis has developed a wide array of response mechanisms to various stress factors. Usnic acid has been demonstrated to be a potent antimycobacterial agent that induces stress responses and growth inhibition in many mycobacterial species. Previous studies have shown that it alters the expression of stress-responsive sigma factors, as well as the metabolites and lipid profile in M. tuberculosis H37Ra. This study was designed to examine potential differences in the strain-specific susceptibility of the virulent H37Rv strain to usnic acid. By combining lipidomic and transcriptomic analyses, we uncovered the impact of usnic acid on bacterial metabolism. The observed downregulation of key lipid classes suggested reduced metabolic activity. The simultaneous elevation of mycobactins-siderophores used by members of the genus Mycobacterium to transport free extracellular iron ions into the cytoplasm-indicated the involvement of iron in the stress response generated by usnic acid. The repressed tricarboxylic acid (TCA) cycle and oxidative phosphorylation were compensated by the upregulation of alternative energy production pathways, such as cytochrome P450 and the ferredoxin reductase system. This indicates that mycobacteria may switch to alternative electron transport mechanisms under usnic acid stress using iron-sulfur clusters to generate energy. From a therapeutic perspective, the study highlights iron metabolism as an essential drug target in mycobacteria. Simultaneously, the results confirm the strain-specific metabolic response of sister strains against the same stressing agent.

Importance: A previous study on the influence of usnic acid on the avirulent H37Ra strain revealed that the early bacterial response was associated with redox homeostasis, lipid synthesis, and nucleic acid repair. The response of bacteria to antimicrobials is specific to each species and strain. Given the genetic and phenotypic differences between the avirulent H37Ra strain and the virulent H37Rv strain, we combined lipidomics and global transcriptomics to uncover the mechanism of action of usnic acid against H37Rv. The study identified strain-specific differences between the virulent H37Rv and avirulent H37Ra. The H37Ra strain exhibited increased metabolic activity, while the H37Rv strain showed a reduction in basic metabolic processes and activated alternative iron-dependent energy production. These differences highlight the varying susceptibility of sister strains within the same species to the same antibacterial agent.

Keywords: antimicrobials; iron metabolism; lipidomics; mechanism of action; metabolomics; mycobactins; natural products; transcriptomics; tuberculosis.

Fig 1

The score plot of PLS-DA of lipidomic profiles recorded for whole-cell extracts (A) and extracellular fraction (B) shows the separation between classes. The ellipse represents the Hotelling T 2 with a 95% CI; R2X explains variation in X by the component. PLS-DA loading plots of whole-cell extracts (C) show that the usnic acid-treated group has lower levels of lipids, while in the extracellular fraction (D) downregulation was less pronounced; control (+) vs treated sample (−) values; Ex, extracellular; UA, usnic acid-treated bacteria.

Fig 2

VIP plot of extracellular (A) fraction and whole-cell fraction (B). The most influential variables, with scores greater than 1, were considered significant: mainly glycerophospholipids with lipid chains having more than 30 carbons in the extracellular fraction, glycerophospholipids, glycerolipids, their lyso form, and mycobactins in cellular extracts.

Fig 3

The expression profiles of genes coding proteins involved in various cellular processes in M. tuberculosis H37Rv under the influence of usnic acid. After 24 h of exposure, the genetic material was extracted and subjected to RNAseq. The expression was presented as a fold change of the test group (usnic acid treated, 512 µg/mL) in relation to the control group (untreated). The visualization was done using the Omics Dashboard (BioCyc). The panels represent top-level cellular systems, each containing a set of plots that represent component subsystems. These subsystems map to one or more pathways or pathway classes, Gene Ontology (GO) terms, or computed categories based on transport or regulatory activity. Global transcriptomic response (A); transport proteins (B); cell wall biogenesis/organization proteins (C); enzyme cofactors, carriers (D); biosynthesis processes (E); energy generation (F).