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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. 

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. 

Abstract 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 ofEscherichia 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. 

Political polarization impeded public support for policies to reduce the spread of COVID-19, much as polarization hinders responses to other contemporary challenges. Unlike previous theory and research that focused on the United States, the present research examined the effects of political elite cues and affective polarization on support for policies to manage the COVID-19 pandemic in seven countries ( n = 12,955): Brazil, Israel, Italy, South Korea, Sweden, the United Kingdom, and the United States. Across countries, cues from political elites polarized public attitudes toward COVID-19 policies. Liberal and conservative respondents supported policies proposed by ingroup politicians and parties more than the same policies from outgroup politicians and parties. Respondents disliked, distrusted, and felt cold toward outgroup political elites, whereas they liked, trusted, and felt warm toward both ingroup political elites and nonpartisan experts. This affective polarization was correlated with policy support. These findings imply that policies from bipartisan coalitions and nonpartisan experts would be less polarizing, enjoying broader public support. Indeed, across countries, policies from bipartisan coalitions and experts were more widely supported. A follow-up experiment replicated these findings among US respondents considering international vaccine distribution policies. The polarizing effects of partisan elites and affective polarization emerged across nations that vary in cultures, ideologies, and political systems. Contrary to some propositions, the United States was not exceptionally polarized. Rather, these results suggest that polarizing processes emerged simply from categorizing people into political ingroups and outgroups. Political elites drive polarization globally, but nonpartisan experts can help resolve the conflicts that arise from it. 

Abstract People believe they should consider how their behavior might negatively impact other people, Yet their behavior often increases others’ health risks. This creates challenges for managing public health crises like the COVID-19 pandemic. We examined a procedure wherein people reflect on their personal criteria regarding how their behavior impacts others’ health risks. We expected structured reflection to increase people's intentions and decisions to reduce others’ health risks. Structured reflection increases attention to others’ health risks and the correspondence between people's personal criteria and behavioral intentions. In four experiments during COVID-19, people (N  = 12,995) reported their personal criteria about how much specific attributes, including the impact on others’ health risks, should influence their behavior. Compared with control conditions, people who engaged in structured reflection reported greater intentions to reduce business capacity (experiment 1) and avoid large social gatherings (experiments 2 and 3). They also donated more to provide vaccines to refugees (experiment 4). These effects emerged across seven countries that varied in collectivism and COVID-19 case rates (experiments 1 and 2). Structured reflection was distinct from instructions to carefully deliberate (experiment 3). Structured reflection increased the correlation between personal criteria and behavioral intentions (experiments 1 and 3). And structured reflection increased donations more among people who scored lower in cognitive reflection compared with those who scored higher in cognitive reflection (experiment 4). These findings suggest that structured reflection can effectively increase behaviors to reduce public health risks.