Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics

Abstract

To uncover metal toxicity targets and defense mechanisms of the facultative anaerobe Pantoea sp. strain MT58 (MT58), we used a multiomic strategy combining two global techniques, random bar code transposon site sequencing (RB-TnSeq) and activity-based metabolomics. MT58 is a metal-tolerant Oak Ridge Reservation (ORR) environmental isolate that was enriched in the presence of metals at concentrations measured in contaminated groundwater at an ORR nuclear waste site. The effects of three chemically different metals found at elevated concentrations in the ORR contaminated environment were investigated: the cation Al³⁺, the oxyanion CrO₄^2-, and the oxycation UO₂²⁺. Both global techniques were applied using all three metals under both aerobic and anaerobic conditions to elucidate metal interactions mediated through the activity of metabolites and key genes/proteins. These revealed that Al³⁺ binds intracellular arginine, CrO₄^2- enters the cell through sulfate transporters and oxidizes intracellular reduced thiols, and membrane-bound lipopolysaccharides protect the cell from UO₂²⁺ toxicity. In addition, the Tol outer membrane system contributed to the protection of cellular integrity from the toxic effects of all three metals. Likewise, we found evidence of regulation of lipid content in membranes under metal stress. Individually, RB-TnSeq and metabolomics are powerful tools to explore the impact various stresses have on biological systems. Here, we show that together they can be used synergistically to identify the molecular actors and mechanisms of these pertubations to an organism, furthering our understanding of how living systems interact with their environment. IMPORTANCE Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In this study, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism, Pantoea sp. strain MT58, and metals contaminating a site where the organism can be located. A number of novel metal-microbe interactions were uncovered, including Al³⁺ toxicity targeting arginine synthesis, which could lead to a deeper understanding of the impact Al³⁺ contamination has on microbial communities as well as its impact on higher-level organisms, including plants for whom Al³⁺ contamination is an issue. Using multiomic approaches like the one described here is a way to further our understanding of microbial interactions and their impacts on the environment overall.

Keywords: metabolomics; metal resistance; transposon mutagenesis.

FIG 1

Aerobic and anaerobic Al³⁺ challenge results in negative fitness changes for multiple arginine synthesis genes and the decrease of key arginine synthetic pathway metabolites. Arginine synthesis pathway enzymes and metabolites that are impacted by Al³⁺ challenge are shown in red. Proteins encoded by genes with negative fitness changes are indicated in boxes with the change in parentheses, and metabolite fold changes are indicated in parentheses above or below the metabolite, with the negative sign indicating downward dysregulation. Aerobic Al³⁺ challenge effects are shown in blue, while anaerobic Al³⁺ challenge effects are shown in purple.

FIG 2

Arginine corrects the growth defect caused by Al³⁺ in MT58. (A) Gene fitness value comparisons between aerobic base and aerobic Al³⁺ challenge conditions. Data points shown in red correspond to arginine synthetic genes with large changes in fitness values upon Al³⁺ challenge, while those in black correspond to other amino acid synthetic genes with large changes in fitness values upon Al³⁺ challenge. (B) Endpoint growth at 22 h of MT58 grown in the absence (black) and presence (gray) of 3 mM Al³⁺ with and without the addition of various amino acids (5 mM).

FIG 3

Aerobic CrO₄²⁻ challenge impacts the sulfate assimilatory pathway. The genes encoding enzymes in red have negative fitness changes with the metal challenge, while those in green have positive fitness changes, with gene fitness change values in parentheses. Multiple fitness change values indicate the genes encoding multiple subunits of the protein. Metabolites in red are dysregulated in a downward fashion with the metal challenge, and the fold change is in parentheses below the metabolite, with the negative sign indicating downward dysregulation.