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Environ Sci Ecotechnol
Environ Sci Ecotechnol
Environmental Science and Ecotechnology
2096-9643
2666-4984
Elsevier

S2666-4984(24)00089-9
10.1016/j.ese.2024.100475
100475
Short Communication
Nonlinear impacts of temperature on antibiotic resistance in Escherichia coli
Zhao Wenya ab
Zheng Shikan ab
Ye Chengsong ab
Li Jianguo ab
Yu Xin xyu@xmu.edu.cn
ab⁎
a College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China
b Fujian Key Laboratory of Coastal Pollution Prevention and Control, Xiamen, 361102, China
⁎ Corresponding author. College of the Environment and Ecology, Xiamen University, Xiamen,361102, China. xyu@xmu.edu.cn
03 8 2024
11 2024
03 8 2024
22 10047513 2 2024
1 8 2024
2 8 2024
© 2024 Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The increase in bacterial antibiotic resistance poses a significant threat to the effectiveness of antibiotics, and there is growing evidence suggesting that global warming may speed up this process. However, the direct influence of temperature on the development of antibiotic resistance and the underlying mechanisms is not yet fully understood. Here we show that antibiotic resistance exhibits a nonlinear response to elevated temperatures under the combined stress of temperatures and antibiotics. We find that the effectiveness of gatifloxacin against Escherichia coli significantly diminishes at 42 °C, while resistance increases 256-fold at 27 °C. Additionally, the increased transcription levels of genes such as marA, ygfA, and ibpB with rising temperatures, along with gene mutations at different sites, explain the observed variability in resistance patterns. These findings highlight the complexity of antibiotic resistance evolution and the urgent need for comprehensive studies to understand and mitigate the effects of global warming on antibiotic resistance.

Graphical abstract

Image 1

Highlights

• E. coli presents significant gatifloxacin resistance due to increased cellular activity at 42 °C.

• Gatifloxacin resistance increases 256-fold when exposed to gatifloxacin at 27 °C.

• E. coli exhibits multidrug resistance mutations when exposed to gatifloxacin at 27 °C.

• Gatifloxacin exposure at various temperatures results in different target gene mutation sites.

Keywords

Antibiotic resistance
Temperature
Gatifloxacin
Resistance evolution
Fitness costs
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pmc1 Introduction

Global warming has escalated environmental problems, such as high temperatures, droughts, wildfires, and torrential rainfall. It has also amplified human health risks, such as increased bacterial resistance, regarded as one of the greatest threats to human health in the 21st century [1,2]. The selective pressure of antibiotics is generally considered the main reason for disseminating and conserving bacterial resistance in the environment [3].

In addition to the selective effect of antibiotics, many physicochemical factors have been reported to play a role in the occurrence of bacterial antibiotic resistance in the environment, such as heavy metals [4], salinity [5], mutagenic compounds [6], and temperature changes [7]. Big data analysis has shown a 1.14-fold increase in carbapenem-resistant Klebsiella pneumoniae for every 1 °C rise in average temperature [3]. Keba stated that temperature contributes more to antibiotic resistance than dosage [8]. Linear regression is often used to analyze the relationship between temperature rise and antibiotic resistance. However, this pattern is influenced by multiple factors, including antibiotic usage, population density, economic development, and corruption levels [1,3]. The specific impact of temperature remains unclear. Given the current rise in global temperatures, it is crucial to determine the role of temperature in this context.

Temperature plays an important role in bacterial survival, transmission, and infection [9]. It can persistently stress bacteria regarding general physio-biochemical characteristics, such as growth and intracellular chemical reaction rates, and characteristics closely related to resistance, such as gene transfer [[10], [11], [12]]. Some researchers have found that Escherichia coli (E. coli) and Staphylococcus aureus have distinct sensitivities to various antibiotics at different temperatures [13,14]. For example, high-temperature adapted E. coli has increased resistance to sulfonamide and nitrofuran antibiotics and may also acquire resistance to rifampicin [15].

However, it is unclear how bacterial antibiotic resistance and its mechanisms evolve when antibiotic selective pressure coexists with the stress of raised temperature, a widespread scenario in climate change. To shed light on this, the present study explored the resistance evolution in wild-type E. coli under the combined effect of temperature (five temperatures varying from 22 to 42 °C) and antibiotic exposure.

2 Materials and methods

2.1 Strain, medium, and antibiotics

The ancestor E. coli K12 was stored at −80 °C and later revived and cultured using Luria-Bertani (LB) broth and a solid medium. The antibiotics mentioned in the text were sourced from Macklin, and their corresponding Chemical Abstracts Service (CAS) numbers are: gatifloxacin (112811-59-3), ampicillin (69-53-4), methicillin (132-92-3), erythromycin (114-07-8), rifampicin (13292-46-1), streptomycin (3810-74-0), tetracycline (64-75-5), and chloramphenicol (56-75-7).

2.2 Experimental evolution

The evolution experiment was conducted in a 24-well plate with 2 mL of liquid LB. The strains were first passaged twice without exposure to antibiotics at temperature in evolutionary experiments. The strains were passaged (BMG Labtech, Germany) after the microplate reader determined that the bacterial solution had an optical density at 600 nm (OD600) greater than 0.8. Subsequently, 20 μL of bacterial solution and gradient-increased gatifloxacin solution were continuously transferred to liquid LB, with a total volume of 2 mL. Four replicates were prepared. The concentrations of the antibiotics were increased in a 1, 2, 5, 10, 20, 50, 100, 200, 500, and 1000 μg L−1 gradient at different temperatures (22, 27, 32, 37, and 42 °C) (schematic, Supplementary Material Fig. S1). The passage was repeated three times at each antibiotic concentration. The last passage of the population was saved and designated as the antibiotic-evolved population at different temperatures (denoted as “temperature-G″ when evolved under gatifloxacin and “temperature-A″ if the antibiotic is ampicillin). Meanwhile, the ancestor strains that evolved continuously in LB without antibiotics, maintaining the same frequency of passages, were considered the populations that evolved at different temperatures as controls, referred to in the text as “temperature-0.” The evolved strains obtained at different stages were stored at −80 °C in 25% glycerol.

Before each passage, it was ensured that at least one of the four replicates had an OD600 greater than 0.8. The passage interval of the temperature-only populations was consistent with those exposed to antibiotics at different temperatures. E. coli was grown at 180 rpm at a constant temperature. OD600 was measured and used to construct growth curves at hourly intervals throughout the incubation. The E. coli grown at 17 °C stopped growing when the antibiotic concentration reached 50 μg L−1, and the treatment stopped.

2.3 Phenotypic characterization

The phenotypic characterization of the E. coli evolved from gatifloxacin exposure at different temperatures was determined, along with the minimum inhibitory concentration (MICs, by referring to CLSI), the heritability of antibiotic resistance and the tolerance of the evolved strains, the intracellular reactive oxygen species (ROS) through 2′,7′-dichlorodihydrofluorescein diacetate, the cell membrane formation ability using the crystal violet staining method, and the mutation rate of the evolved strains using the fluctuation assay method. More experimental details can be found in the Methods section of the Supplementary Materials.

2.4 Fitness costs

According to the data model based on the growth curve [16], the growth data of the last evolutionary stage of E. coli were fitted by curveball (http://curveball.yoavram.com), including four different parallels and the optimal model was selected to simulate the competition experiment. The growth of the evolved strain and wild-type E. coli under antibiotic-free conditions at 37 °C was predicted to obtain the fitness costs of the strain. The lag times of the strains at 22 °C were too long to compare, and it was assumed that the strains’ adaptability was poor.

2.5 DNA extraction, whole-genome sequencing, and bioinformatics analysis

The whole-genome DNA of the strain exposed to gatifloxacin at different temperatures was extracted. According to the kit instructions, total bacterial DNA was extracted using a Tiangen kit. The reference genome was the National Center of Biotechnology Information (NCBI) Reference Sequence NZ_CP071521.1, sequencing the evolved bacterial genome (Novogene Bioinformatics Technology Co., Ltd., Tianjin, China). The original image data file obtained through Illumina sequencing was transformed into the original sequenced reads via base calling analysis. The experimental details, sequencing, and bioinformatic analysis data can be found in the Methods section of the Supplementary Materials.

2.6 Ribonucleic acid extraction and bioinformatics analysis

Ribonucleic acid (RNA) was extracted from the evolved endpoint strains exposed to gatifloxacin at different temperatures. The process was carried out at 4 °C (Tiangen kit). Immediately after extraction, complementary deoxyribonucleic acid (cDNA) was reverse transcribed as described, and the concentrations were recorded with Nanodrop (B-500 BioPhotometer, Shanghai) and stored at −20 °C. The expression levels of the 43 target genes were then quantified, including stress response, biofilm formation, heat shock, antibiotic resistance, and redox. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed according to the manufacturer's instructions (TransGen Biotech, China) using the ABI Q6 qPCR system (Life Technology, Singapore). The experiments set up three parallels, and the primers are shown in Supplementary Materials.

2.7 Molecular docking

To further understand the contribution of mutations at different temperatures to quinolone resistance at the molecular structure level, molecular docking of the protein DNA gyrase subunit A (GyrA) and gatifloxacin was performed. First, protein homology modeling of the mutant was performed in SWISS-MODEL (https://swissmodel.expasy.org/interactive# sequence) based on the E. coli K12 protein (PDB ID: P0AES5), and GROMACS was used to calculate the optimal structure of the mutant protein and ligand. UCSF DOCK 6.10 [17] was used for the molecular docking of proteins and ligands, and the grid fraction (binding energy [kcal mol−1]) was calculated to screen for the optimal binding state. PyMol was used to visualize the binding process of GyrA and the ligand.

3 Results and discussion

In the evolutionary experiment, wild-type E. coli K12 was exposed to a gradient of gatifloxacin concentrations (1 μg L−1–1 mg L−1) at different temperatures. Each treatment was cultured for about 2000 generations. The MICs of the E. coli increased with antibiotic dosage, but this increase did not follow a simple positive or linear correlation with elevated temperature. Gatifloxacin resistance was the highest at 27 °C, with a 256-fold increase, and the lowest at 42 °C, with an eight-fold increase. Resistance to antibiotics other than gatifloxacin was also induced at various temperatures (Fig. 1a). The MICs of the E. coli were essentially stable over the temperature control without antibiotics. In addition, gatifloxacin resistance in 27-G and 42-G could be heritable under antibiotic-free conditions (Fig. 1b). We also conducted experiments with increasing concentrations of antibiotics at 17 °C. The growth of E. coli ceased when the antibiotic level reached 50 μg L−1. We propose that 17 °C is not an optimal growth condition. Additionally, the strategies for resistance evolution at lower temperatures may differ from those at higher temperatures.Fig. 1 Antibiotic resistance and phenotype variations of Escherichia coli after antibiotic exposure at different temperatures. a, Fold changes in the MICs of eight antibiotics in E. coli evolved with and without gatifloxacin at different temperatures compared with wild-type E. coli. b, Gatifloxacin-evolved E. coli exhibit heritable antibiotic resistance in the absence of antibiotics at 37 °C. The heritability of resistance in E. coli that evolved with (solid line) and without (dashed line) gatifloxacin at different temperatures is indicated by changes in MICs during the cultivation of these strains at 37 °C without antibiotics. c, Changes in the lag phase time in E. coli during exposure to elevated gatifloxacin at different temperatures. d, Change in the inhibition rate of E. coli during exposure to elevated gatifloxacin at different temperatures. e–f, The fitness costs of evolved strains in gatifloxacin (e) and ampicillin (f) under different temperature treatments compared to wild-type strains. The solid and dashed lines indicate the position of the growth curves of the evolved E. coli and wild-type E. coli when co-cultured in competition. A lower position signifies a poorer competitive ability, i.e., a higher fitness cost. g–i, Phenotypic characterization of E. coli after evolving in gatifloxacin at different temperatures: biofilm forming ability (g); mutation rate (h); cellular ROS (i). The significance level was P < 0.01. Tmp: trimethoprim, Gat: gatifloxacin, Ery: erythromycin, Rif: rifampicin, Amp: ampicillin, Str: streptomycin, Tet: tetracycline, Chl: chloramphenicol.

Fig. 1

Optimizing the lag time is considered the primary adaptive change produced by bacteria in the face of stress [18] and can provide the basis for acquiring multiple resistance [19]. In the present study, the lag time of E. coli at different temperatures increased with the gatifloxacin concentration gradient, but it was more pronounced at lower temperatures. For example, the lag time at 22 °C increased from 6 h (under antibiotic-free conditions) to 28.2 h (1 mg L−1) (Fig. 1c). The E. coli grown at all temperatures exhibited “hormesis” during the stationary phase and was least inhibited at 42 °C (Fig. 1d). It was also least inhibited at 42 °C when another antibiotic ampicillin was used (Supplementary Materials S4). Furthermore, the biofilm formation ability of the evolved E. coli was generally enhanced (Fig. 1j), and the intracellular ROS levels and mutation rates increased, contributing to the development of resistance (Fig. 1k and l). Differences in the growth response of E. coli to antibiotic stress at various temperatures were observed, such as pronounced mutation rates in 27-G and 42-G (Fig. 1k). These differences may underlie the evolution of distinct levels of antibiotic resistance in E. coli.

Fitness costs are essential factors limiting the dissemination of antibiotic resistance [20]. We simulated the growth of strains that evolved separately in two antibiotics at 37 °C in an antibiotic-free environment. Except for 27-G, which was exposed to gatifloxacin, and 42-A, which was exposed to ampicillin, E. coli showed significant fitness costs (Fig. 1e and f). The two exceptions above are consistent with the results of the resistance heritable test (Fig. 1b). The mechanism may be that stepwise mutation and compensatory mutation at the evolutionary temperature reduce fitness costs. Mutations in E. coli can improve fitness and dramatically lower antibiotic sensitivity in acquiring quinolone resistance [21]; this may be analogous.

We investigated the underlying causes of the varying resistance phenotypes observed at different temperatures. First, the transcript levels of most genes generally increase with temperature. The expression of 43 genes in evolved E. coli was determined by RT-qPCR. The proportion of upregulated genes increased significantly as the temperature rose to 37 and 42 °C (Fig. 2b), and some genes, such as the heat shock gene ibpB, were upregulated almost a thousandfold. The upregulation of efflux pumps and global transcriptional regulators (marA, rpoS, and ygfA) was beneficial for E. coli in conferring multiple resistance and adaptability. There are also individual genes whose expression is negatively correlated with temperature, such as ompC, which tends to reduce the uptake of external substances at low temperatures, making bacteria more tolerant to antibiotics [22].Fig. 2 Gene expression, gene mutation, and molecular docking of mutant GyrA in Escherichia coli after evolving in gatifloxacin at different temperatures. a, The expression levels of some E. coli genes were changed after evolution. 16 S rRNA gene was used as the reference. The abbreviation consists of E. coli evolved in temperature-treated conditions: the number represents the temperature during evolution, and the following “0” and “G” indicate E. coli evolved without and with gatifloxacin. b, With (G) or without (0) gatifloxacin treatment, the increased temperature was correlated with the upregulation of several types of gene expression levels. c, Whole genome sequencing revealed effective genetic variation in the E. coli genome after evolution. The outer circle represents the size of the reference genome. Different colored dots represent the types of variation in the genomic response sites. Green indicates that the gene is a confirmed antibiotic-resistance mutation, and grey indicates that it has not been identified. d, Molecular docking was performed between the GyrA of both wild-type and evolved mutant strains with the antibiotic gatifloxacin. The mutant strains were those that evolved under different temperatures and increasing gradients of gatifloxacin. GyrA-22-G represents the docking structure of gatifloxacin with mutated E. coli GyrA that evolved under 22 °C and gatifloxacin. By analogy, GyrA-27-G, GyrA-32-G, GyrA-37-G, and GyrA-42-G represent the docking structures of the gatifloxacin and mutated E. coli GyrA that evolved with gatifloxacin at 27, 32, 37, and 42 °C, respectively.

Fig. 2

Second, mutation is evolution's main driver, indicating that resistance is likely to change significantly. By comparing the gene sequences of wild-type and evolved E. coli, a total of 12 genes were found to be mutated, with seven genes undergoing resistance mutations (Fig. 2c). The genes involved in the mutation were acrR and marR, which regulate the overall resistance of E. coli and are part of the negative regulation of the multidrug efflux pump regulator [23]. There were also mutations in the target genes gyrA, gyrB, and parC, which can confer high levels of quinolone resistance by reducing antibiotic affinity [24]. The gyrA mutation occurred at any temperature of gatifloxacin exposure and twice in 42-G. The Tus and rpoC mutations in 42-G and 37-G indicated the acquisition of β-lactam and rifampicin resistance [25]. Intriguingly, we identified several genetic variations in the 22-0 and 42-0 controls, where no antibiotics were added. These metabolic genes associated with zinc ion transport, movement, and adhesion will likely confer a greater selective advantage (Supplementary Material Table S3), easing the metabolic constraints on growth [25]. The unique gene mutations and different mutation sites at 22 and 42 °C indicated specific responses at different temperatures.

Temperature affects how resistance mutations occur in bacteria and their strength. The molecular docking model evaluated the affinity between GyrA with different mutation sites and gatifloxacin at the structural level [26]. The binding sites of GyrA and gatifloxacin were found to shift at various exposure temperatures. Compared to the binding energy of −35.209 kcal mol−1 in the wild type, the evolved strains in each treatment showed varying degrees of reduced binding energy, with the most significant reduction observed in 27-G. The double mutation of Asp87Gly and IIe798Ser in 42-G also reduced the electrostatic force, but it increased the polarity effect and ranked it backward. This shows that the induction mechanism at different temperatures is distinct but may have some preference.

4 Conclusion

In summary, global warming has been widely reported to contribute to increasing bacterial resistance to antibiotics [8] and to the spreading and maintaining of ARGs [3]. However, the results of the present study show that the role of temperature in the emergence and development of resistance is uncertain. Global warming may primarily drive the development of antibiotic resistance by escalating antibiotic usage. Higher temperatures are likely to increase the incidence, frequency, and duration of epidemics, inevitably leading to more antibiotic use [2,3]. Additionally, other issues arising from global warming induce or keep bacterial resistance, including the increased use of heavy metals and pesticides, as well as frequent natural disasters [2,4].

CRediT authorship contribution statement

Wenya Zhao: Writing - Review & Editing, Writing - Original Draft, Formal Analysis, Data Curation, Conceptualization. Shikan Zheng: Methodology, Data Curation. Chengsong Ye: Validation, Supervision, Investigation, Data Curation. Jianguo Li: Supervision, Data Curation, Conceptualization. Xin Yu: Validation, Supervision, Funding Acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article.Multimedia component 1

Multimedia component 1

Acknowledgments

This research was supported by the 10.13039/501100001809 National Natural Science Foundation of China (U2005206 ) and the 10.13039/501100012166 National Key R&D Program of China (2023YFE0112100 ).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ese.2024.100475.
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