Unravelling the mechanisms of antibiotic and heavy metal resistance co-selection in environmental bacteria

Abstract

The co-selective pressure of heavy metals is a contributor to the dissemination and persistence of antibiotic resistance genes in environmental reservoirs. The overlapping range of antibiotic and metal contamination and similarities in their resistance mechanisms point to an intertwined evolutionary history. Metal resistance genes are known to be genetically linked to antibiotic resistance genes, with plasmids, transposons, and integrons involved in the assembly and horizontal transfer of the resistance elements. Models of co-selection between metals and antibiotics have been proposed, however, the molecular aspects of these phenomena are in many cases not defined or quantified and the importance of specific metals, environments, bacterial taxa, mobile genetic elements, and other abiotic or biotic conditions are not clear. Co-resistance is often suggested as a dominant mechanism, but interpretations are beset with correlational bias. Proof of principle examples of cross-resistance and co-regulation has been described but more in-depth characterizations are needed, using methodologies that confirm the functional expression of resistance genes and that connect genes with specific bacterial hosts. Here, we comprehensively evaluate the recent evidence for different models of co-selection from pure culture and metagenomic studies in environmental contexts and we highlight outstanding questions.

Keywords: antibiotics; evolution; metals; plasmids; resistance; selection.

Figure 1.

Bacterial sensing and resistance mechanisms for metals and antibiotics. (A) Metal efflux by the Czc of Cupriavidus metallidurans CH34. (B) Antibiotic efflux by AcrAB–TolC from Escherichia coli. (C) Chemical modification by Cue and Cus from E. coli. (D) Cleavage of β-lactam antibiotic by CTX-M in Enterobacteriaceae. (E) Sequestration of metal by Smt of Synechococcus PCC 7942. (F) Sequestration of mitomycin by Mrd, followed by export by Mct in Streptomyces lavendulae. Metal ions are shown as small spheres, coloured grey (Zn²⁺), blue (Cu⁺), or green (Cu²⁺). The pill symbol (red/white) represents antibiotics, specifically azithromycin, ciprofloxacin, gentamicin, and β-lactams in (B), β-lactams in (D), and mitomycin in (F). Genes are shown as block arrows with the corresponding proteins in the same colour. Icons created with smart.servier.com.

Figure 2.

Comparison of co-selection mechanisms. (A) Co-resistance demonstrated by ARG and MRG genes residing on the same plasmid, and thus are inherited simultaneously. (B) Cross-resistance demonstrated by an efflux pump ejecting both antibiotics and metals. (C) Co-regulation demonstrated by an ARG and MRG expressed together after induction by a metal. Grey spheres represent metal ions, and red and white pills represent antibiotics. Icons created with smart.servier.com.

Figure 3.

Relationships between MIC, MSC, and MCC as a function of concentration. Selection pressure from agent A increases with concentration from the MSC until it approaches the MIC, after which it rapidly declines due to cytotoxicity. Co-selection pressure on agent B increases from the agent A MCC until the MIC is reached.

Figure 4.

Methodologies for studies investigating co-selection. Environmental samples or environmental simulations (microcosms and mesocosms) are sources of communities or pure cultures for study. qPCR measures relative gene abundance, while the culturable resistant isolates can be enumerated via plating. Pure cultures can have MIC, MSC, or MCC profiled through disk diffusion, microbroth dilution, or E tests. Growth rates and yields under varying selective pressures can be quantified. Gene knockouts or knockdowns can assign genotypes to phenotypes. The occurrence and genetic context of resistance genes can be revealed through DNA sequencing, while expression levels of these genes can be tested via transcriptomics, RT-qPCR, microarrays, proteomics, enzyme assays, ELISA, or western blots. Plasmid capture and/or plasmid curing experiments can help to infer the function of plasmid-encoded genes. Finally, bioinformatic data can be mined for resistance genes and MGEs, which in turn informs experimental characterization. Icons were created with BioRender.com and smart.servier.com.

Figure 5.

Network analysis of correlations between MRGs and ARGs. Analysis based on correlations of ARGs and MRGs from co-selection studies in environmental contexts from 2010 onwards. Size of nodes is proportional to the number of primary studies investigating that specific resistance gene. Weight of edges is proportional to the number of studies finding a positive correlation. Only positive correlations are shown. Blue indicates ARG, and red indicates MRG. Network visualized with Gephi v 0.9.7.

Figure 6.

Network analysis of correlations between heavy metals and ARGs. Analysis based on correlations of ARGs and MRGs from co-selection studies in environmental contexts from 2010 onwards. Size of nodes is proportional to the number of primary studies investigating that specific resistance gene. Weight of edges is proportional to the number of studies finding a positive correlation. Only positive correlations are shown. Blue indicates ARG, and red indicates heavy metal. Network visualized with Gephi v 0.9.7.