The use of triclosan (TCS) in hygiene products and other materials raises concerns about increased antimicrobial resistance. Research has shown that bacteria can develop resistance to TCS, but there is limited understanding of how bacterial behavior changes after TCS is removed. This study focuses on how bacteria adapt to prolonged TCS exposure and recover after its removal. It aims to improve practices for using antimicrobial agents in sanitation products and emphasize the importance of understanding exposure and recovery times to prevent resistance development. The study evaluated the bacterial behavior, determining the minimum inhibition concentration of TCS that 90% of Escherichia coli was inhibited (MIC90), predicting bacterial growth kinetics using the Modified Gompertz model and membrane permeability recovery. Bacteria showed a significant increase in resistance levels during exposure, with the MIC90 steadily increasing over 30 days and peaking at 16 mg/L. Upon removal, there was a notable 2-fold decrease in resistance after 10 days of reculturing in a non-chemical medium, likely due to restructuring changes in cell membranes. Therefore, in addition to reducing the occurrence of antibacterial agents in the environment, advanced treatments for eliminating antibacterial resistance should be performed on wastewater before being discharged into the environment.

  • The reduction of MIC90 of triclosan (TCS) was observed despite no advanced treatment being applied.

  • The evolution of antibacterial resistance was observed in the increased lag duration time.

  • Exposure of bacterial cells to TCS led to an increase in the leakage of proteins and DNA, indicating significant damage to the cell membrane.

Triclosan (TCS, 2,4,4′-trichloro-2′-hydroxydiphenl ether) is a synthetic antibacterial agent widely used in hygiene and disinfection products such as soap, shampoo, and toothpaste. Since the outbreak of the COVID-19 epidemic, the higher demand for disinfection and personal wash products has led to the risks of a large volume of hygiene products leaking into the environment through various pathways. Chemically, TCS is an aromatic chloride compound with a high octanol–water partition coefficient (log10KOW = 4.8 at pH = 7.5) (Wang et al. 2014). Although most TCS is removed in wastewater plants, a small amount of TCS is still released into water sources and sediments, which is estimated to be over 0.2 kg/day (Lozano et al. 2013). It was reported that 79–99% of TCS was adsorbed by sludge or degradation in wastewater treatment plants (WWTPs), and the remainder entered aquatic environments (Middleton & Salierno 2013). For instance, the TCS concentration was detected at 0.1 μg/L (Juksu et al. 2019) and nearly 2 mg/g (Zhu et al. 2019) in effluent and waste sludge in China and Thailand, respectively. The widespread occurrence of TCS has attracted significant potential negative impacts on ecology, especially the impact of antimicrobial resistance, which is one of the most concerning in the world in recent years.

The continuous TCS released into the aquatic environment may induce specific bacterial tolerance/resistance mechanisms to the contaminant. Resistance to TCS is usually accomplished through different mechanisms: target gene mutation, overexpression of target genes, efflux pump induction, reduction of membrane permeability, and enzymatic degradation (Ciusa et al. 2012; Li et al. 2019; Zeng et al. 2020; Yang et al. 2024). Previous studies observed the antimicrobial resistance evolution of microorganisms to TCS in the habitats like Escherichia coli (Li et al. 2019; Zeng et al. 2020), Staphylococcus aureus (Ciusa et al. 2012; Suller 2000), and Klebsiella pneumoniae (Yang et al. 2024). It has been revealed that overuse of TCS could induce reactive oxygen species (ROS) overproduction, which is one of the main factors causing antimicrobial resistance (Lu et al. 2018). ROS are the by-products of normal cell metabolism in bacteria. At low levels, ROS plays an essential role in controlling cellular processes such as bacterial self-destruction or biofilm formation (Zhao & Drlica 2014; Dryden et al. 2017). In contrast, the overproduction of ROS can come with a risk of various macromolecule modifications (DNA, RND, proteins, and lipids) by oxidation (Markkanen 2017), of which the efflux pump system is the main system that involves reducing the sensitivity to TCS in E. coli, whereby the system pumps antimicrobial drugs out of the cell (Curiao et al. 2015). In addition, TCS can bind to the enoy-acyl carrier protein reductase enzyme and form a ternary complex that may disrupt lipid metabolism and biosynthesis of fatty acid, leading to changes in membrane permeability and resulting in cell death (Sonbol et al. 2019; Zeng et al. 2020).

Due to the concern about its resistance, the European Chemicals Agency (ECHA) and the U.S. Food and Drug Administration (FDA) restricted TCS use in hygiene products in 2016 and 2010, respectively. In order to determine the antimicrobial susceptibility efficiency, the minimum inhibitory concentration (MIC) was defined as the ‘lowest concentration of antimicrobial that, under established in vitro condition, inhibits visible growth of a target bacterial population’ (Wiegand et al. 2008). There are different definitions of MIC based on its efficiency in inhibiting the growth percentage of test strains. For example, the MIC50 and MIC90 values represent the concentration of antibiotics required to inhibit the growth of 50 or 90% of the tested bacterial isolates. Previously, Salmonella was tested for the reversibility experiment, and the results were observed in the decreased MIC for TCS after 30-day growth in a non-TCS environment (Braoudaki & Hilton 2005). Furthermore, Li et al. (2019) determined that the sudden decline of TCS in living bacteria could not only have a slight effect on the resistance mechanism in reducing the MIC of E. coli for TCS but also led to the biofilm formation returning to the normal wild-type bacteria (Li et al. 2019). However, there is limited information about how the dynamics and metabolism of bacteria change from the initial exposure to TCS until the point at which the density of antibacterial-resistant bacteria (ARB) becomes widespread. In particular, the changes of those ARBs after being cultured in a chemical-free environment compared to bacteria exposed to TCS for different periods. The TCS resistance mechanism has mainly affected the cell membrane. Thus, a fundamental study focusing on growth kinetics and cell wall damage will provide basic information about changes in the metabolism of ARBs. Therefore, this study aims to investigate the physiological changes of bacteria during prolonged exposure to TCS and their recovery when TCS levels decline in the culture environment.

Strains and chemicals

E. coli (NBRC 3301) was used in this study. The strains of E. coli were cultivated in the Luria-Bertani (LB) medium, which is commonly used for culturing Enterobacteriaceae members due to its rapid growth and high plasmid yields. The culture was incubated at 37 °C for 20 h with 180 rpm agitation and marked as the control sample (initial bacterial strains, E0, that had not been exposed to any antibacterial drug yet). For long-term storage, the bacterial suspension was stored in LB broth containing 40% glycerol and frozen at −80 °C.

TCS (CAS Number 3380-34-5), purity ≥98%, was purchased from Combi-Blocks, Inc. (CA, USA). Dimethyl sulfoxide (DMSO, ≥99% purity), CAS Number 67-68-5, was used as the TCS solvent and was obtained from Wako Pure Chemical Industries, Ltd (Tokyo, Japan).

A stock solution of TCS (100 mg/L) was prepared daily in DMSO solution. For the evolution experiment, the concentration of 0.01 mg/L TCS was prepared by adding 1 μL of the stock solution into 10 mL of the LB medium.

Evolution and reversibility experiments

First, E. coli suspension was inoculated into 5 mL of LB liquid, including 0.01 mg/L TCS (the lowest contamination in water sources), at 37 °C on a rotary shaker with a speed of 180 rpm. Every 24 h, 100 μL of cell suspension was incubated in a fresh medium with the same concentration of TCS. The evolution experiment was conducted in 30 days, presenting as 30 subculture cycles. In order to study the time-varying association between TCS exposure and antimicrobial resistance, different harvesting times were observed for the development of TCS resistance and its impact on bacteria's metabolism and growth kinetics. Previous studies have shown that antibiotic resistance can develop after 7–20 cycles (Baym et al. 2016; Levin-Reisman et al. 2017). Therefore, bacterial cells were harvested at 10, 20, and 30 cycles (termed E10, E20, and E30) to investigate the evolution of TCS resistance over time. For further experiments, the cell suspension was centrifuged at 5,000 × g for 10 min at 4 °C, rinsed three times with saline (0.9% NaCl), and then resuspended in sterile saline to a cell density of 107–108 CFU/mL.

In order to study the reversibility of TCS resistance, after 30-day TCS exposure, the bacteria were harvested, washed, and reincubated in 5 mL of fresh LB liquid for 24 h at 37 °C with continuous shaking at 180 rpm. The bacteria were transferred into 5 mL fresh medium every 24 h for 10 days (R) and then collected, rinsed, and resuspended in saline for further experiments.

Growth kinetics and MIC90 determination

The evolutionary success of microbial resistance is determined by the fitness cost of adapting bacteria without contaminants (Hall 2004). The behavior of microbes in different environmental conditions is generally performed in its growth kinetics, and the lag phase before the exponential growth step is the most common pattern. This phase presents the adaptation ability of microbes to new environmental conditions (Chatterjee et al. 2015). The prolonged lag phase expresses the time required for the microorganism to reach the exponential phase, which is also defined as the antibiotic treatment duration. A 100 μL of harvested cells (E10, E20, and E30) after the resuspension were grown in 10 mL of sterilized LB liquid with or without 0.02 mg/L TCS (double the concentration of evolution experiment to investigate the behavior changing of E. coli with and without TCS). The cell growth was monitored for 12 h by withdrawing aseptically at each 2-h interval and measuring the optical density at 600 nm (OD600) with a Shimadzu UV Spectrometer. The initial suspension of E. coli was used as the control sample. The OD600 results were assumed to be the density of the bacteria populations. The kinetic growth of E. coli in the TCS-LB medium and the TCS-free medium was defined by the modified Gompertz equation (Zwietering et al. 1990).
(1)
where y is the optical density of E. coli concentration; t represents the time (h); A represents the upper asymptotic curve (concentration of bacteria in the stationary stage) (OD600); μm represents the maximum growth rate (OD/h); L represents lag time (h).

MIC90 determination

The MIC90 was determined as the minimum TCS concentration that inhibited 90% of bacteria growth after 24 h of incubation. (A detailed description of the MIC90 method was described in the Supplementary Information.) A 96-well microtiter plate-based method was used to determine the concentration of TCS, ranging from 0 to 64 mg/L. The densities of colonies were investigated by measuring the optical density at 600 nm by Shimadzu's UV spectrometer. (Detailed information was given in the Supplementary Information.) MIC90 was determined when the density in a particular well was ≤10% compared with that of the growth control (0 mg/L TCS); the proportion of survival bacteria was estimated by Equation (2).
(2)
where Ai is the optical density of E. coli suspension at i concentration (i = 0–64 mg/L) and A0 is the optical density of E. coli suspension at 0 mg/L TCS.

DNA and protein leakage detection

After collecting, E10, E20, E30, and R were centrifuged at 12,000 rpm for 3 min, and the supernatants were used for the detection of DNA and protein. DNA and protein detection in supernatants refers to the reduction of bacterial membranes due to the long exposure of bacteria to TCS. The Shimadzu's UV spectrometer was used to determine the leakage of DNA and protein with the OD260 nm and OD280 nm, respectively.

The DNA concentration was calculated using Equation (3) adopted by Zeng et al. (2010):
(3)

Statistical analysis

Each experiment was conducted three times. The growth kinetics was established via nonlinear regression (logistic) using the software IBM SPSS Statistics for Windows (version 25.0, IBM Corp, Armonk, NY, USA). Values for A, L, and μm were obtained under each condition by fitting the modified Gomperz model analyzed by the statistical SPSS software, and the coefficient of determination (R2) was evaluated for the fitting goodness. All results were expressed as ±SD, and an independent t-test was determined as statistical differences. The unpaired T-test was performed using the SPSS software to examine the differences between the control and evolved groups. Statistical significance was determined with a threshold of p-values <0.05, indicating a statistically significant difference between the experimental conditions.

Development of antimicrobial resistance

The study emphasizes that prolonged exposure to TCS leads to increased antimicrobial resistance in bacteria. Even in the wastewater treatment process, small amounts of TCS remain in the effluent and enter water systems. The fact that resistance develops and intensifies over time suggests that even low-level contamination with antimicrobial agents can substantially impact bacterial populations. It was observed in the development of MIC90 that even though a low concentration of TCS (0.01 mg/L) was used, antimicrobial resistance occurred over a long term of exposure (30 days). The MIC90 of E. coli increased relatively 2-fold with 10 days of exposure (Figure 1). From the 10th to the 30th day, the MIC90 of E. coli increased by a 2-log fold relative. After 30 days of TCS exposure, the MIC90 for E. coli was 16 mg/L, and the percentage of E. coli inhibition was 4.87%. Regarding the reversibility process, after 10 days of declining TCS in the culture environment, the MIC90 decreased to 8 mg/L. (The percentage was mainly equal to the rate of E. coli surviving on the 20th day: 6.2 and 5.39%, respectively).
Figure 1

The 90% minimum inhibition concentration of E. coli with TCS.

Figure 1

The 90% minimum inhibition concentration of E. coli with TCS.

Close modal

After declining the prevalence of TCS in the culture environment, the MIC90 decreased 2-fold, but it cannot return to the MIC90 of the parent strain (0.5 mg/L). Similar results were reported by Braoudaki & Hilton with Salmonella (decreased 2-fold compared to the parent strain after 30 days) (Braoudaki & Hilton 2005) and Li et al. (2019) with E. coli (slightly reduced after 10 days). The reversibility of resistance after the removal of TCS indicates that antimicrobial resistance can be somewhat mitigated if the hygiene consumption pressure is reduced. It highlights the need for careful management and regulation of antimicrobial use across various settings, including healthcare, agriculture, and consumer products.

A modified Gompertz model predicted the kinetic growth of bacteria in the presence and absence of contaminants, and the data are listed in Table 1. (The bacterial growth trend curves fitted with the Modified Gompertz model are provided in the Supplementary Information).

Table 1

Kinetic growth of E. coli in the TCS-LB medium and the TCS-free medium calculated by the modified Gompertz model

SampleParameters
A
μm
L
R2
Non-TCSInduced TCSNon-TCSInduced TCSNon-TCSInduced TCSNon-TCSInduced TCS
Control 2.169 2.058 0.303 0.27 0.795 0.72 0.984 0.984 
10 2.46 2.352 0.261 0.233 1.714 1.241 0.973 0.967 
20 2.294 2.271 0.236 0.257 1.224 1.372 0.966 0.96 
30 2.252 2.322 0.256 0.232 0.802 0.461 0.981 0.988 
10_Rev 2.2 1.983 0.289 0.221 1.49 1.237 0.982 0.974 
SampleParameters
A
μm
L
R2
Non-TCSInduced TCSNon-TCSInduced TCSNon-TCSInduced TCSNon-TCSInduced TCS
Control 2.169 2.058 0.303 0.27 0.795 0.72 0.984 0.984 
10 2.46 2.352 0.261 0.233 1.714 1.241 0.973 0.967 
20 2.294 2.271 0.236 0.257 1.224 1.372 0.966 0.96 
30 2.252 2.322 0.256 0.232 0.802 0.461 0.981 0.988 
10_Rev 2.2 1.983 0.289 0.221 1.49 1.237 0.982 0.974 

In the evolution experiment, it is observed that E10 and E20 had the longest lag duration time both in the presence (1.6 ± 0.16 and 1.35 ± 0.17, respectively) and absence (1.29 ± 0.07 and 1.55 ± 0.25, respectively) of TCS in the medium (Figure 1(a)). However, after 30 days of TCS exposure, the lag phase was shortened to 0.73 ± 0.1 in free TCS broth and 0.05 ± 0.08 in the TCS medium.

The relative maximum growth rate of E10, E20, E30, and R to Control was determined by comparing the fitness of the growth trend curve (Figure 2(b) and 2(c)). The concept of bacterial fitness is defined as the ability of a bacterium to survive and grow in different environments. In this study, it was used to compare the growth rate of evolved bacteria to the parent strain. R had the highest fitness rate for the TCS-free medium with 96.4 ± 1.4%, followed by E10 (84.1 ± 2.9%) and E30 (82.8 ± 2.4%). Although E20 had the lowest value in the TCS-free medium (78.7 ± 1.1%), the highest percentage was recorded in the 0.02 mg/L TCS medium (98.7 ± 5%). The fitness rate of E10, E30, and R mainly was higher than 85%. It was indicated that there were fewer physiological changes in antimicrobial resistance.
Figure 2

(a) Lag duration time of E. coli in different durations of TCS exposure; bacterial growth rate fitness of E. coli in the TCS-free medium (b) and with 0.02 mg/L TCS (c).

Figure 2

(a) Lag duration time of E. coli in different durations of TCS exposure; bacterial growth rate fitness of E. coli in the TCS-free medium (b) and with 0.02 mg/L TCS (c).

Close modal

DNA and protein leakage detection

The findings about bacterial membrane changes due to TCS exposure highlight the complex interaction between antimicrobials and bacterial cell structures. Understanding these interactions can help in the development of more effective disinfection strategies and in predicting how bacteria might evolve in response to different types of antimicrobial stress. The longer the time of exposure to TCS, the more protein and DNA released were detected (Figure 3). After 20 and 30 days, the harvested bacteria were observed to be the highest leakage for both protein and DNA-releasing concentrations (0.144 ± 0.003 and 8.467 ± 0.093 for E20, 0.147 ± .004 and 8.661 ± .208 for E30, respectively). The release of intracellular components supposed that the cell membrane was damaged, reducing the permeability of membranes. The leakage of protein and DNA decreased when TCS was removed from the medium in the R sample (0.118 ± 0.013 for protein and 6.894 ± .699 for DNA).
Figure 3

The leakage of protein (a) and DNA (b) of E. coli in different durations of TCS exposure.

Figure 3

The leakage of protein (a) and DNA (b) of E. coli in different durations of TCS exposure.

Close modal

Champlin et al. (2005) suggested that the outer membrane permeability is involved in the resistance to low-level hydrophobic antibacterials (Champlin et al. 2005). Sonbol et al. (2019) also reported a decrease in both the inner and outer membrane permeability of E. coli, with daily exposure to 50% of the minimum bactericidal concentration of TCS (Sonbol et al. 2019).

The observation that bacteria showed a significant increase in resistance levels during exposure but also a notable decrease in resistance upon removal suggests that environmental management practices should consider both exposure and withdrawal phases to minimize resistance. During 30 days of exposure to a low level of TCS, antimicrobial resistance occurred by day 10, with a significant MIC90, eight times higher than the concentration of the control sample, as well as the parent strain, and the resistance continuously increased. The evolution of TCS resistance can be expressed through the lag phase. Recent studies have shown that evolved mutants can prolong their lag phase, which increases their chances of survival and allows for the generation of more mutants during exponential growth (Li et al. 2016; Levin-Reisman et al. 2017). Some of these mutants are effectively killed by antibiotics, and the remains will become antibiotic-resistant bacteria. In Figure 2, the lag time of E10 and E20 was observed to increase in the presence of TCS, indicating that the population of evolved mutants grew over time. For E30, the lag duration time decreased to under 0.5 h, signifying that resistant bacteria had become the predominant species in this cycle, and they were able to survive to TCS without extending the lag time.

Even though the reversible sample (R) observed a decrease of the MIC90 to nearly the same results as E20, there was a remarkable difference in that the bacterial growth kinetics of the R sample had inverse patterns compared with those of the E20 samples. In the kinetic growth model, the antibacterial resistance of E. coli can be expressed through the lag phase. The difference in bacterial growth patterns between the evolved strains and the reversible sample underscores the complexity of antimicrobial resistance. This suggests that bacterial adaptation to antimicrobials is not uniform and may involve different mechanisms depending on the duration and type of exposure. However, this effect is not evident when the maximum growth rate is almost unaffected by the bacterial culture environment. It has been assumed that the prolonged lag phase confers a survival advantage to the bacteria and promotes regrowth upon antimicrobial removal (Li et al. 2016); therefore, the shorter the lag time, the greater the chance of antimicrobial resistance. The inverse patterns between R and E20 are that it took 20 days for bacteria to adapt to the contamination-included medium and only half of the duration to recover with the fresh medium. It is supposed that microbes can recombine and reduce antibacterial resistance in non-contaminant environments. Nevertheless, it indicates that resistant bacteria may still thrive in environments without antimicrobials, potentially leading to environmental and public health concerns if these strains spread.

The study demonstrated that the exposure of bacterial cells to TCS led to an increase in the leakage of proteins and DNA, indicating significant damage to the cell membrane. This information is crucial for understanding the mechanisms behind antimicrobial resistance and can inform the development of alternative disinfection strategies that minimize such damage. Previous studies proposed that the outer and the inner membranes of bacteria were affected by TCS over a long period, whereby the outer membranes serve as a selective barrier by allowing the nutrients to enter the cell without the toxic compounds. In contrast, the inner membrane is a permeability barrier for most molecules transported into the cells (Sonbol et al. 2019). Li et al. (2019) reported the returnable biofilm formation in E. coli after the cessation of antimicrobial exposure, but resistance is still inheritable via genetic mutations and efflux pump gene coding systems. This phenomenon was observed as the DNA and protein were still released. It revealed that the cell's membrane damage could be recovered when the toxic compound is removed from the living environment; however, compared with the parent strain, it can be noticed that only a part of the damage had been recovered, and the membrane properties could not completely return to the initial condition.

Chronic exposure to hydrophobic agents promotes antimicrobial resistance even in the slightest contaminant concentrations. In addition, long-term exposure to TCS can decrease bacterial membrane permeability, affecting the bactericidal efficiency as the antibacterial agent can be pumped out before contact with cells and causes antibacterial resistance. It revealed that TCS is pumped out of the cell, and other antibacterial substances are also at risk of being pumped into the environment before affecting bacteria. The dynamic factors of the influence of antibacterial substances on the ability of bacteria growth need to be studied further.

Short-term removal of antibacterial substances from the culture medium helps reduce the antibacterial resistance of E. coli to TCS and partly restores physiology to the previous state. However, the study showed that the recovery efficiency was negligible when antibacterial resistance was still present at high concentrations. Therefore, it is suggested that besides reducing the occurrence of antibacterial agents in the environment, advanced treatments for eliminating antibacterial resistance, such as ozonation, should be performed on wastewater before being discharged into the environment.

The authors gratefully acknowledge the anonymous reviewers for valuable suggestions for improving this manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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