Short- and Long-Term Transcriptomic Responses of Escherichia coli to Biocides: a Systems Analysis.

The mechanisms of the bacterial response to biocides are poorly understood, despite their broad application. To identify the genetic basis and pathways implicated in the biocide stress response, we exposed Escherichia coli populations to 10 ubiquitous biocides. By comparing the transcriptional responses between a short-term exposure (30 min) and a long-term exposure (8 to 12 h) to biocide stress, we established the common gene and pathway clusters that are implicated in general and biocide-specific stress responses. Our analysis revealed a temporal choreography, starting from the upregulation of chaperones to the subsequent repression of motility and chemotaxis pathways and the induction of an anaerobic pool of enzymes and biofilm regulators. A systematic analysis of the transcriptional data identified a zur-regulated gene cluster to be highly active in the stress response against sodium hypochlorite and peracetic acid, presenting a link between the biocide stress response and zinc homeostasis. Susceptibility assays with knockout mutants further validated our findings and provide clear targets for downstream investigation of the implicated mechanisms of action.IMPORTANCE Antiseptics and disinfectant products are of great importance to control and eliminate pathogens, especially in settings such as hospitals and the food industry. Such products are widely distributed and frequently poorly regulated. Occasional outbreaks have been associated with microbes resistant to such compounds, and researchers have indicated potential cross-resistance with antibiotics. Despite that, there are many gaps in knowledge about the bacterial stress response and the mechanisms of microbial resistance to antiseptics and disinfectants. We investigated the stress response of the bacterium Escherichia coli to 10 common disinfectant and antiseptic chemicals to shed light on the potential mechanisms of tolerance to such compounds.

Benzalkonium Chlorides: Uses, Regulatory Status, and Microbial Resistance.

Benzalkonium chlorides (BACs) are chemicals with widespread applications due to their broad-spectrum antimicrobial properties against bacteria, fungi, and viruses. This review provides an overview of the market for BACs, as well as regulatory measures and available data on safety, toxicity, and environmental contamination. We focus on the effect of frequent exposure of microbial communities to BACs and the potential for cross-resistant phenotypes to emerge. Toward this goal, we review BAC concentrations in consumer products, their correlation with the emergence of tolerance in microbial populations, and the associated risk potential. Our analysis suggests that the ubiquitous and frequent use of BACs in commercial products can generate selective environments that favor microbial phenotypes potentially cross-resistant to a variety of compounds. An analysis of benefits versus risks should be the guidepost for regulatory actions regarding compounds such as BACs.

Biocide-Induced Emergence of Antibiotic Resistance in Escherichia coli.

Biocide use is essential and ubiquitous, exposing microbes to sub-inhibitory concentrations of antiseptics, disinfectants, and preservatives. This can lead to the emergence of biocide resistance, and more importantly, potential cross-resistance to antibiotics, although the degree, frequency, and mechanisms that give rise to this phenomenon are still unclear. Here, we systematically performed adaptive laboratory evolution of the gut bacteria Escherichia coli in the presence of sub-inhibitory, constant concentrations of ten widespread biocides. Our results show that 17 out of 40 evolved strains (43%) also decreased the susceptibility to medically relevant antibiotics. Through whole-genome sequencing, we identified mutations related to multidrug efflux proteins (mdfA and acrR), porins (envZ and ompR), and RNA polymerase (rpoA and rpoBC), as mechanisms behind the resulting (cross)resistance. We also report an association of several genes (yeaW, pyrE, yqhC, aes, pgpA, and yeeP-isrC) and specific mutations that induce cross-resistance, verified through mutation repairs. A greater capacity for biofilm formation with respect to the parent strain was also a common feature in 11 out of 17 (65%) cross-resistant strains. Evolution in the biocides chlorophene, benzalkonium chloride, glutaraldehyde, and chlorhexidine had the most impact in antibiotic susceptibility, while hydrogen peroxide and povidone-iodine the least. No cross-resistance to antibiotics was observed for isopropanol, ethanol, sodium hypochlorite, and peracetic acid. This work reinforces the link between exposure to biocides and the potential for cross-resistance to antibiotics, presents evidence on the underlying mechanisms of action, and provides a prioritized list of biocides that are of greater concern for public safety from the perspective of antibiotic resistance. SIGNIFICANCE STATEMENT: Bacterial resistance and decreased susceptibility to antimicrobials is of utmost concern. There is evidence that improper biocide (antiseptic and disinfectant) use and discard may select for bacteria cross-resistant to antibiotics. Understanding the cross-resistance emergence and the risks associated with each of those chemicals is relevant for proper applications and recommendations. Our work establishes that not all biocides are equal when it comes to their risk of inducing antibiotic resistance; it provides evidence on the mechanisms of cross-resistance and a risk assessment of the biocides concerning antibiotic resistance under residual sub-inhibitory concentrations.

Tolerance to Glutaraldehyde in Escherichia coli Mediated by Overexpression of the Aldehyde Reductase YqhD by YqhC.

Glutaraldehyde is a widely used biocide on the market for about 50 years. Despite its broad application, several reports on the emergence of bacterial resistance, and occasional outbreaks caused by poorly disinfection, there is a gap of knowledge on the bacterial adaptation, tolerance, and resistance mechanisms to glutaraldehyde. Here, we analyze the effects of the independent selection of mutations in the transcriptional regulator yqhC for biological replicates of Escherichia coli cells subjected to adaptive laboratory evolution (ALE) in the presence of glutaraldehyde. The evolved strains showed improved survival in the biocide (11-26% increase in fitness) as a result of mutations in the activator yqhC, which led to the overexpression of the yqhD aldehyde reductase gene by 8 to over 30-fold (3.1-5.2 log2FC range). The protective effect was exclusive to yqhD as other aldehyde reductase genes of E. coli, such as yahK, ybbO, yghA, and ahr did not offer protection against the biocide. We describe a novel mechanism of tolerance to glutaraldehyde based on the activation of the aldehyde reductase YqhD by YqhC and bring attention to the potential for the selection of such tolerance mechanism outside the laboratory, given the existence of YqhD homologs in various pathogenic and opportunistic bacterial species.

Accelerated knowledge discovery from omics data by optimal experimental design.

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.