Research article

Impact of chlorhexidine digluconate and temperature on curli production in Escherichia coli—consequence on its adhesion ability

  • Chlorhexidine-Digluconate (CHX-Dg) is a biocide widely used as disinfectant or antiseptic in clinical and domestic fields. It is often found in the formulation of solutions to treat superficial wounds. Nevertheless, few studies have focused on its effects on Escherichia coli while this bacterium is commonly involved in mixed infections. Therefore, the impact of CHX-Dg and temperature on E. coli was investigated; particularly the curli production. In accordance with bibliographic data, the curli production decreased when the temperature of the culture was shift from 30 °C to 37 °C. The bacterial adhesion to abiotic surfaces was also reduced. Surprisingly, the curli production at 37 °C was maintained in presence of antiseptic and the bacterial adhesion was improved at a very low concentration (1 µg ml1) of CHX-Dg. Complementary investigations with a cpxR mutant demonstrated that the CpxA/R-TCS (Two-Component System) is involved in the temperature-dependent control of the curli expression. Indeed, the curli production was not altered by the growth temperature in the mutant. Otherwise, no relationship between CHX-Dg and the Cpx-TCS was shown. A subsequent proteomic investigation revealed the alteration of the production of 44 periplasmic and outer membrane proteins in presence of CHX-Dg. These proteins are involved in the transport of small molecules, the envelope integrity, the stress response as well as the protein folding.

    Citation: Laurent Coquet, Antoine Obry, Nabil Borghol, Julie Hardouin, Laurence Mora, Ali Othmane, Thierry Jouenne. Impact of chlorhexidine digluconate and temperature on curli production in Escherichia coli—consequence on its adhesion ability[J]. AIMS Microbiology, 2017, 3(4): 915-937. doi: 10.3934/microbiol.2017.4.915

    Related Papers:

    [1] Sherry Towers, Katia Vogt Geisse, Chia-Chun Tsai, Qing Han, Zhilan Feng . The impact of school closures on pandemic influenza: Assessing potential repercussions using a seasonal SIR model. Mathematical Biosciences and Engineering, 2012, 9(2): 413-430. doi: 10.3934/mbe.2012.9.413
    [2] Jinlong Lv, Songbai Guo, Jing-An Cui, Jianjun Paul Tian . Asymptomatic transmission shifts epidemic dynamics. Mathematical Biosciences and Engineering, 2021, 18(1): 92-111. doi: 10.3934/mbe.2021005
    [3] Paula A. González-Parra, Sunmi Lee, Leticia Velázquez, Carlos Castillo-Chavez . A note on the use of optimal control on a discrete time model of influenza dynamics. Mathematical Biosciences and Engineering, 2011, 8(1): 183-197. doi: 10.3934/mbe.2011.8.183
    [4] Boqiang Chen, Zhizhou Zhu, Qiong Li, Daihai He . Resurgence of different influenza types in China and the US in 2021. Mathematical Biosciences and Engineering, 2023, 20(4): 6327-6333. doi: 10.3934/mbe.2023273
    [5] Rodolfo Acuňa-Soto, Luis Castaňeda-Davila, Gerardo Chowell . A perspective on the 2009 A/H1N1 influenza pandemic in Mexico. Mathematical Biosciences and Engineering, 2011, 8(1): 223-238. doi: 10.3934/mbe.2011.8.223
    [6] Majid Jaberi-Douraki, Seyed M. Moghadas . Optimal control of vaccination dynamics during an influenza epidemic. Mathematical Biosciences and Engineering, 2014, 11(5): 1045-1063. doi: 10.3934/mbe.2014.11.1045
    [7] Gerardo Chowell, Catherine E. Ammon, Nicolas W. Hengartner, James M. Hyman . Estimating the reproduction number from the initial phase of the Spanish flu pandemic waves in Geneva, Switzerland. Mathematical Biosciences and Engineering, 2007, 4(3): 457-470. doi: 10.3934/mbe.2007.4.457
    [8] Eunha Shim . Prioritization of delayed vaccination for pandemic influenza. Mathematical Biosciences and Engineering, 2011, 8(1): 95-112. doi: 10.3934/mbe.2011.8.95
    [9] Jie Bai, Xiunan Wang, Jin Wang . An epidemic-economic model for COVID-19. Mathematical Biosciences and Engineering, 2022, 19(9): 9658-9696. doi: 10.3934/mbe.2022449
    [10] Julien Arino, Fred Brauer, P. van den Driessche, James Watmough, Jianhong Wu . A final size relation for epidemic models. Mathematical Biosciences and Engineering, 2007, 4(2): 159-175. doi: 10.3934/mbe.2007.4.159
  • Chlorhexidine-Digluconate (CHX-Dg) is a biocide widely used as disinfectant or antiseptic in clinical and domestic fields. It is often found in the formulation of solutions to treat superficial wounds. Nevertheless, few studies have focused on its effects on Escherichia coli while this bacterium is commonly involved in mixed infections. Therefore, the impact of CHX-Dg and temperature on E. coli was investigated; particularly the curli production. In accordance with bibliographic data, the curli production decreased when the temperature of the culture was shift from 30 °C to 37 °C. The bacterial adhesion to abiotic surfaces was also reduced. Surprisingly, the curli production at 37 °C was maintained in presence of antiseptic and the bacterial adhesion was improved at a very low concentration (1 µg ml1) of CHX-Dg. Complementary investigations with a cpxR mutant demonstrated that the CpxA/R-TCS (Two-Component System) is involved in the temperature-dependent control of the curli expression. Indeed, the curli production was not altered by the growth temperature in the mutant. Otherwise, no relationship between CHX-Dg and the Cpx-TCS was shown. A subsequent proteomic investigation revealed the alteration of the production of 44 periplasmic and outer membrane proteins in presence of CHX-Dg. These proteins are involved in the transport of small molecules, the envelope integrity, the stress response as well as the protein folding.


    The vaccination and drug treatment play important roles in mitigating strategies when influenza outbreaks [1]. However, these strategies might lose effectiveness in the case when a novel influenza virus emerges, as new vaccines and drugs will take a lot of time to be explored. During the H1N1 outbreaks occurred in 2009, non-pharmaceutical strategies were adopted, such as school closures, social distancing or facemasks. Several studies illustrated that facemasks could be a useful non-pharmaceutical strategy. For example, it is shown in [2] that if N95 respirators are 20% effective in reducing susceptibility and infectivity, 10% of the population would have to wear them to reduce the number of influenza A (H1N1) cases by 20%. The study on wearing facemasks in [3] had showed a reduction of 10-50% in transmission of influenza. Furthermore, experiments [2,4] drew a conclusion that N95 respirators are more efficient to control transmission of influenza than surgical masks.

    Mathematical models have been used to analyze the effectiveness of facemasks in reducing the spread of novel influenza A (H1N1) [2,5,6]. The model in [2] is a Susceptible-Exposed-Infectious-Recovered (SEIR) model. Because a large proportion of infections might be asymptomatic [7], the asymptomatic infections are significant in influenza transmission dynamics [8]. Therefore, in this paper, the asymptomatic class is introduced to the SEIR model. An improved model (SEIAR) is proposed to analyze the influence of wearing masks on the final size and the basic reproduction number of H1N1. Furthermore, the sensitivity analysis of the initial time of wearing facemasks, which compartment wears facemasks and the proportion of asymptomatic individuals will be discussed.

    Two sub-populations (or sub-groups) are considered: one does not wear facemasks and one does (indicated by a subscript M). Susceptible (S, SM), exposed (E, EM), infectious (I, IM), asymptomatic (A, AM), and recovered (R), For influenza H1N1, there is some infectivity during the exposed period [2,6]. This may be modeled by assuming infectivity reduced by a fraction α during the exposed period. The infectious of exposed individuals are less than that of symptomatic individuals [2,6]. These model parameters are also listed in Table 1, and a transition diagram is shown in Figure 1. Let β denote the transmission rate of individuals in the I class, α and δ denote the reduction in infection rate of individuals in the E and A classes, respectively, ω is the rate at which individuals leave the exposed class, of which p and 1p proportions will enter the A(AM) and I(IM) classes, respectively, γ and γ denote the recovery rate of infectious and asymptomatic individuals, respectively. The parameters φSM, φEM, φIM and φAM are the transition rates from S, E, I and A to SM, EM, IM and AM, respectively; similarly, the parameters φS, φE, φI and φA are the transition rates from SM, EM, IM and AM to S, E, I and A, respectively, as shown in Figure 1.

    Table 1.  Parameter Definitions and Values.
    Notation Description Baseline Reference
    N Total population 1 million See text
    ω Rate of progression from exposed to infectious 1/6 [9]
    β Transmission rate 0.271 See text
    p Proportion of of infections being asymptomatic 0.46 [10]
    α Relative infectiousness of exposed individuals 0.5 [2,6]
    δ Relative infectiousness of asymptomatic infections 0.5 [1,11,12]
    γ Recover rate of symptomatic 1/5.2 [10]
    γ Recover rate of the asymptomatic 1/4.1 [1,11,12]
    ηs Decrease in susceptibility when masks are used 0.2 [4]
    ηi Decrease in infectivity when masks are used 0.5 [4]
    φi Transition rate between mask and non-mask 0.1, 0.25, 0.5 See text

     | Show Table
    DownLoad: CSV
    Figure 1.  Flow diagram of the SEIAR compartmental model.

    The forces of infection for susceptible individuals without or with facemasks, denoted by λ and λM, respectively,

    λ=β(I+αE+δAN+(1ηi)IM+αEM+δAMN),λM=(1ηs)λ,

    where N:=S+SM+E+EM+I+IM+A+AM+R is the total population, 1ηi and 1-ηs represent the reductions in infectivity and susceptibility, respectively, due to wearing facemasks, respectively.

    The model consists of the following nonlinear differential equations:

    dSMdt=(φS+λM)SM+φSMS,dEMdt=φEEMωEM+φEME+λMSM,dIMdt=(γ+φI)IM+(1p)ωEM+φIMI,dAMdt=(γ+φA)AM+φAMA+pωEM,dSdt=(λ+φSM)S+φSSM,dEdt=φEMEωE+φEEM+λS,dIdt=(γ+φIM)I+(1p)ωE+φIIM,dAdt=(γ+φAM)A+pωE+φAAM,dRdt=γ(I+IM)+γ(A+AM). (1.1)

    The individuals switch rates between the two groups who wear or do not wear facemasks are assumed to be the following step-functions:

    φi={a,i=SM,EM,IM,AM,b,i=S,E,I,A,

    where the parameters a and b are positive constants. In this study, a is set to be 0.1, 0.25 or 0.5, and b is set to be 0.1 [2].

    In this section, the basic and control reproduction numbers (R0 and Rc, respectively) are calculated, which can be used to evaluate disease control strategies. The effect of wearing masks on reduction in final epidemic size is evaluated.

    In the absence of mask wearing, that is to say, in the early stage of infection, the basic reproduction number R0 is as follow:

    R0=β(αω+1pγ+pδγ). (2.1)

    R0 includes three terms representing contributions from infected individuals in the E, I, and A classes. Each term is a product of the transmission rate per unit of time (β), relative infectiousness (α, 1, δ), and the average period of infection (1/ω, 1/γ, 1/γ), weighted by the proportions p and 1p, respectively, for the A and I stages.

    For the derivation of Rc, we use the approach of the next generation generation matrix [13]. Let ˆηi=1ηi, ˆηs=1ηs, σ=S0+S0M,

    F=βσ(αS0αˆηiS0S0ˆηiS0δS0ˆηiδS0αˆηsS0MαˆηiˆηsS0MˆηsS0MˆηiˆηsS0MδˆηsS0MδˆηiˆηsS0M000000000000000000000000),

    and

    V=(r1φE0000φEMr20000(p1)ω0r3φI000(p1)ωφIMr400pω000r5φA0pω00φAMr6),

    where

    r1=φEM+ω,r2=φE+ω,r3=γ+φIM,r4=γ+φI,r5=γ+φAM,r6=γ+φA.

    Then the control reproduction number Rc is the largest eigenvalue of matrix FV1 [13] and can be written as:

    Rc=βS0σ(A+B+C)+βS0M(1ηs)σ(D+E+F),

    where

    A=αr2+(1ηi)φEMω(φE+φEM+ω),B=(1p)ωr2r4+φIφEM+(1ηi)r2φIM+(1ηi)r3φEMω(φE+φEM+ω)γ(φI+φIM+γ),C=δpωr2r6+φAφEM+(1ηi)r2φAM+(1ηi)r5φEMω(φE+φEM+ω)γ(φA+φAM+γ),D=α(1ηs)φE+(1ηi)(1ηs)r1ω(φE+φEM+ω),E=(1p)ω(1ηs)r1φI+(1ηs)r4φE+(1ηi)(1ηs)(r1r3+φEφIM)ω(φE+φEM+ω)γ(φI+φIM+γ),F=δpωr1φA+r6φE+(1ηi)(1ηs)(r1r5+φEφAM)ω(φE+φEM+ω)γ(φA+φAM+γ).

    It is difficult to explain the biological meaning of Rc since the flow chart is bidirectional. In order to explain Rc, we simplify the model under the assumption that the individuals of group M can not transfer the other group, that is φS,E,I,A=0. Then, we obtain

    Rc=βS0σ(K1+K2+K3)+βS0M(1ηs)σ(KM1+KM2)

    where

    K1=αr1,K2=φEMr1α(1ηi)r2+(1p)ωr11r3+pωr1δr5,K3=(1ηi)φEMr1(1p)ωr21r4+(1p)ωr1φIMr31ηir4+(1ηi)φEMr1+δpωr21r4+pωr1(1ηi)φAMr51r6,KM1=α(1ηi)(1ηs)r2,KM2=(1ηs)(1p)ωr2(1ηi)r4+(1ηs)pωr2δ(1ηi)r6.

    The control reproduction number Rc includes six terms representing contributions from infected individuals in the E, I, A, EM, IM and AM classes. The average duration of E is 1/r1, transmission rate is αβ, then the βS0K1/σ represents one infected individual introduced to the susceptible population without facemasks in the E class. The ratio from E to EM is φEM/r1, the average duration of EM is 1/r2, transmission rate is α(1ηi)β. Similarly, βS0K2/σ represents one infected individual introduced to the susceptible population without facemasks from E to EM, I and A. In this way, the control reproduction number Rc can be explained by the biological meaning.

    Based on the best available data, the parameter values shown in Table 1 have been chosen. The incubation period for H1N1 in 2009 is about 2-10 days, hence we use 1/ω to be 6 days [9]. The mean periods of symptomatic and asymptomatic infections are 5.2 and 4.1 days, respectively [1,9,12,14], thus γ=1/5.20.1923 and γ=1/4.10.2439. The proportion of asymptomatic individuals is p=0.46 [10]. The infectivity of asymptomatic individuals is assumed to be about half that of symptomatic [1,11,12], that is, δ=0.5. And α=0.5 from [2,6]. With these parameter values, we can get R0=1.83 [2], and from equation (2.1) we can obtain β=0.271. The total population size N for the model is set at one million people. For initial conditions, we choose N=106, I=104. The time of wearing facemasks is focused on, for example, the first day, the sixth day and the eleventh day. Becaus the effectiveness of facemasks in reducing the susceptibility and infectivity of infected person is greater [4,15], other parameters ηs=0.2 and ηi=0.5.

    Numerical simulations of model (1.1) show that, in the absence of facemasks wearing, the cumulative number of infections will be 73%. To demonstrate the effect of the asymptomatic class in the model, we carry out numerical simulations of model (1.1) as 10%, 25% and 50% of the population wear them under various levels of effectiveness of masks in reducing susceptibility and infectivity. The final size is defined as the proportion of the cumulative number of infected cases [5]. The results are illustrated in Figure 2.

    Figure 2.  The final size for wearing N95 facemasks on the sixth day.

    From Figure 2 and Table 2, it can be seen that the final size is 73% without any intervention. The more effective masks are and the more masks are worn, the more quickly final size will be decreased. In detail, when the proportion of people wearing facemasks is 10%, 25%, and 50%, ηs=ηi=0.2, the final size of H1N1 will be reduced by 17%, 28%, and 35%, respectively. Wearing masks can effectively decrease the final size. These results are also listed in Table 2 and Figure 2, which is similar to Figure 3 but includes various level of reduction in susceptibility due to the use of masks.

    Table 2.  Simulation results of reductions in final size under various scenarios.
    Mask effectiveness % of population using facemasks
    Susceptible Infectious 10% 25% 50%
    None None 73 73 73
    ηs=0.2 ηi=0.2 56 45 38
    ηs=0.2 ηi=0.5 37 14 7.5
    ηs=0.5 ηi=0.5 20 5 2.7

     | Show Table
    DownLoad: CSV
    Figure 3.  The final size for N95 facemasks of wearing masks on the sixth day.

    In addition to using the final size reduction as a measure to compare scenarios, we can also examine the effect of facemasks wearing on the reduction in the control reproduction number Rc. The results are listed in Table 3. For example, when facemasks are 20% effective in reducing both susceptibility and infectivity and the wearing masks people is 10%, Rc can be reduced from 1.83 to 1.52.

    Table 3.  Reduction in Rc under various scenarios of facemasks use.
    Mask effectiveness % of population using facemasks
    Susceptible Infectious 10% 25% 50%
    None None 1.83 1.83 1.83
    ηs=0.2 ηi=0.2 1.71 1.63 1.57
    ηs=0.2 ηi=0.5 1.52 1.31 1.17
    ηs=0.5 ηi=0.5 1.52 1.31 1.17

     | Show Table
    DownLoad: CSV

    The epidemic is sensitivity to the time of wearing facemasks. The result is shown in Figure 4. that the sooner masks are worn, the more the reduction in the final size will be. With facemasks on the first day, the sixth day and the eleventh day as an example. Without any intervention, there will be 73% for the final size. With wearing masks on the first day, the final size will be nearly reduced by 70%.

    Figure 4.  Sensitivity to the time of wearing masks (with the 50% population wear masks and ηs=ηi=0.5).

    The model is sensitive to which compartment wears facemasks. There is no significant reduction of the final size if only infected individuals wear facemasks. It is important for susceptible, exposed, symptomatic and asymptomatic individuals to wear facemasks and there is a 70% reduction in the final size from Figure 5.

    Figure 5.  Sensitivity to who wears masks (with the 50% population wear masks and ηs=ηi=0.5).

    The sensitivity of parameter p is studied for the SEIAR model. The epidemic is sensitive to the proportion of asymptomatic individuals as seen in Figure 6. There is a obvious difference between the final size when p changes from 0 to 0.7. Asymptomatic individuals can not be neglected in the study of influenza H1N1.

    Figure 6.  Sensitivity to the population of asymptomatic individuals.

    In this paper, the influence of N95 respirators on reducing the spread of H1N1 in 2009 is analyzed. Compared with the model of [2], we add an asymptomatic compartment and quantify the influence of the wear of facemasks during an epidemic. Because of the high infectivity and the general susceptibility of the population, without any measures, the basic reproduction number R0=1.83 and the final size will be 73%. The final sizes of H1N1 are compared when population wearing facemasks are 10%, 25% and 50%. When ηS=ηi=0.5 and 50% of population wears facemasks on the sixth day, the basic reproduction number will be decreased from 1.83 to 1.17 (Table 3) and the final size will be reduced from 73% to 2.7% (Figures 2-3 and Table 2).

    Among the exposed individuals of influenza H1N1, the proportion of people with p became asymptomatic individuals. We assume that p changes from 0 to 0.7 and other parameters are fixed, the final size will be decreased from 84% to 65% (Figure 6). Therefore, asymptomatic individuals are extremely important and can not be ignored. For the time of wearing masks, there is an obvious difference among wearing facemasks on the first day, the sixth day and the eleventh day. The final size will be reduced to 2%, 2.7% and 3.8% with wearing facemasks on the first day, the sixth day and the eleventh day (Figure 4). Wearing facemasks can effectively reduce the final size. And in a short time, the Centers for Disease Control can reduce the spread of the influenza to take measures such as vaccination and drug treatment. When influenza outbreaks occur, it is almost ineffective to wear facemasks only for infected individuals. In order to effectively reduce infection, all people need to wear facemasks (Figure 5).

    Facemask is an effective tool and can be used for counties with limited vaccines. The model is improved to evaluate the effectiveness of facemasks during pandemic influenza.

    This work was supported in part by the National Natural Science Foundation of China (11871093), the General Program of Science and Technology Development Project of Beijing Municipal Education Commission (KM201910016001), the Fundamental Research Funds for Beijing Universities (X18006, X18080, X18017).

    The authors declare there is no conflict of interest in this paper.

    [1] Pinzauti S (1983) Chlorhexidine in dentistry, preservation of contact lens and skin disinfection. Farmaco Prat 38: 21–42.
    [2] Lee I, Agarwal RK, Lee BY, et al. (2010) Systematic review and cost analysis comparing use of chlorhexidine with use of iodine for preoperative skin antisepsis to prevent surgical site infection. Infect Cont Hosp Ep 31: 1219–1229. doi: 10.1086/657134
    [3] Garrett TR, Bhakoo M, Zhang Z (2008) Bacterial adhesion and biofilms on surfaces. Prog Nat Sci 18: 1049–1056. doi: 10.1016/j.pnsc.2008.04.001
    [4] Goulter RM, Gentle IR, Dykes GA (2009) Issues in determining factors influencing bacterial attachment: a review using the attachment of Escherichia coli to abiotic surfaces as an example. Lett Appl Microbiol 49: 1–7. doi: 10.1111/j.1472-765X.2009.02591.x
    [5] Zhou Y, Smith DR, Hufnagel DA, et al. (2013) Experimental manipulation of the microbial functional amyloid called curli. Methods Mol Biol 966: 53–75. doi: 10.1007/978-1-62703-245-2_4
    [6] Zogaj X, Nimtz M, Rohde M, et al. (2001) The multicellular morphotypes of Salmonella typhimurium and Escherichia coli produce cellulose as the second component of the extracellular matrix. Mol Microbiol 39: 1452–1463. doi: 10.1046/j.1365-2958.2001.02337.x
    [7] Barnhart MM, Chapman MR (2006) Curli biogenesis and function. Annu Rev Microbiol 60: 131–147. doi: 10.1146/annurev.micro.60.080805.142106
    [8] Jubelin G, Vianney A, Beloin C, et al. (2005) CpxR/OmpR interplay regulates curli gene expression in response to osmolarity in Escherichia coli. J Bacteriol 187: 2038–2049. doi: 10.1128/JB.187.6.2038-2049.2005
    [9] Olsén A, Arnqvist A, Hammar M, et al. (1993) Environmental regulation of curli production in Escherichia coli. Infect Agents Dis 2: 272–274.
    [10] Hufnagel DA, Depas WH, Chapman MR (2015) The biology of the Escherichia coli extracellular matrix. Microbiol Spectrum 3.
    [11] Evans ML, Chapman MR (2014) Curli biogenesis: order out of disorder. BBA-Mol Cell Res 1843: 1551–1558.
    [12] Prigent-Combaret C, Brombacher E, Vidal O, et al. (2001) Complex regulatory network controls initial adhesion and biofilm formation in Escherichia coli via regulation of the csgD gene. J Bacteriol 183: 7213–7223. doi: 10.1128/JB.183.24.7213-7223.2001
    [13] Dorel C, Lejeune P, Rodrigue A (2006) The Cpx system of Escherichia coli, a strategic signaling pathway for confronting adverse conditions and for settling biofilm communities? Res Microbiol 157: 306–314. doi: 10.1016/j.resmic.2005.12.003
    [14] Vidal O, Longin R, Prigent-Combaret C, et al. (1998) Isolation of an Escherichia coli K-12 mutant strain able to form biofilms on inert surfaces: involvement of a new ompR allele that increases curli expression. J Bacteriol 180: 2442–2449.
    [15] Uhlich GA, Cooke PH, Solomon EB (2006) Analyses of the red-dry-rough phenotype of an Escherichia coli O157:H7 strain and its role in biofilm formation and resistance to antibacterial agents. Appl Environ Microb 72: 2564–2572. doi: 10.1128/AEM.72.4.2564-2572.2006
    [16] Saldaña Z, Xicohtencatl-Cortes J, Avelino F, et al. (2009) Synergistic role of curli and cellulose in cell adherence and biofilm formation of attaching and effacing Escherichia coli and identification of Fis as a negative regulator of curli. Environ Microbiol 11: 992–1006. doi: 10.1111/j.1462-2920.2008.01824.x
    [17] Macé C, Seyer D, Chemani C, et al. (2008) Identification of biofilm-associated cluster (bac) in Pseudomonas aeruginosa involved in biofilm formation and virulence. PLoS One 3: e0003897.
    [18] Coquet L, Cosette P, Quillet L, et al. (2002) Occurrence and phenotypic characterization of Yersinia ruckeri strains with biofilm-forming capacity in a rainbow trout farm. Appl Environ Microb 68: 470–475. doi: 10.1128/AEM.68.2.470-475.2002
    [19] Obry A, Lequerré T, Hardouin J, et al. (2014) Identification of S100A9 as biomarker of responsiveness to the methotrexate/etanercept combination in rheumatoid arthritis using a proteomic approach. PLoS One 9: e115800. doi: 10.1371/journal.pone.0115800
    [20] Amorim CVG, Aun CE, Mayer MPA (2004) Susceptibility of some oral microorganisms to chlorhexidine and paramonochlorophenol. Braz Oral Res 18: 242–246. doi: 10.1590/S1806-83242004000300012
    [21] Shivaji S, Prakash JSS (2010) How do bacteria sense and respond to low temperature? Arch Microbiol 192: 85–95. doi: 10.1007/s00203-009-0539-y
    [22] Wojnicz D, Tichaczek-Goska D (2013) Effect of sub-minimum inhibitory concentrations of ciprofloxacin, amikacin and colistin on biofilm formation and virulence factors of Escherichia coli planktonic and biofilm forms isolated from human urine. Braz J Microbiol 44: 259–265. doi: 10.1590/S1517-83822013000100037
    [23] Kikuchi T, Mizunoe Y, Takade A, et al. (2005) Curli fibers are required for development of biofilm architecture in Escherichia coli K-12 and enhance bacterial adherence to human uroepithelial cells. Microbiol Immunol 49: 875–884. doi: 10.1111/j.1348-0421.2005.tb03678.x
    [24] Ryu JH, Kim H, Frank JF, et al. (2004) Attachment and biofilm formation on stainless steel by Escherichia coli O157:H7 as affected by curli production. Lett Appl Microbiol 39: 359–362. doi: 10.1111/j.1472-765X.2004.01591.x
    [25] Kaplan JB (2011) Antibiotic-induced biofilm formation. Int J Artif Organs 34: 737–751. doi: 10.5301/ijao.5000027
    [26] Houari A, Martino PD (2007) Effect of chlorhexidine and benzalkonium chloride on bacterial biofilm formation. Lett Appl Microbiol 45: 652–656. doi: 10.1111/j.1472-765X.2007.02249.x
    [27] White-Ziegler CA, Um S, Pérez NM, et al. (2008) Low temperature (23 degrees C) increases expression of biofilm-, cold-shock- and RpoS-dependent genes in Escherichia coli K-12. Microbiology 154: 148–166. doi: 10.1099/mic.0.2007/012021-0
    [28] Yoon SH, Han MJ, Lee SY, et al. (2003) Combined transcriptome and proteome analysis of Escherichia coli during high cell density culture. Biotechnol Bioeng 81: 753–767. doi: 10.1002/bit.10626
    [29] Cheung HY, Wong MMK, Cheung SH, et al. (2012) Differential actions of chlorhexidine on the cell wall of Bacillus subtilis and Escherichia coli. PLoS One 7: e36659. doi: 10.1371/journal.pone.0036659
    [30] Condell O, Power KA, Händler K, et al. (2014) Comparative analysis of Salmonella susceptibility and tolerance to the biocide chlorhexidine identifies a complex cellular defense network. Front Microbiol 5: 373.
    [31] Goemans C, Denoncin K, Collet JF (2014) Folding mechanisms of periplasmic proteins. BBA-Mol Cell Res 1843: 1517–1528.
    [32] Vertommen D, Ruiz N, Leverrier P, et al. (2009) Characterization of the role of the Escherichia coli periplasmic chaperone SurA using differential proteomics. Proteomics 9: 2432–2443. doi: 10.1002/pmic.200800794
    [33] Zhang D, Sweredoski MJ, Graham RLJ, et al. (2012) Novel proteomic tools reveal essential roles of SRP and importance of proper membrane protein biogenesis. Mol Cell Proteomics 11: M111.011585.
    [34] Price NL, Raivio TL (2009) Characterization of the Cpx Regulon in Escherichia coli strain MC4100. J Bacteriol 191: 1798–1815. doi: 10.1128/JB.00798-08
    [35] Surmann K, Ćudić E, Hammer E, et al. (2016) Molecular and proteome analyses highlight the importance of the Cpx envelope stress system for acid stress and cell wall stability in Escherichia coli. MicrobiologyOpen 5: 582–596. doi: 10.1002/mbo3.353
    [36] Carter MQ, Brandl MT, Louie JW, et al. (2011) Distinct acid resistance and survival fitness displayed by Curli variants of enterohemorrhagic Escherichia coli O157:H7. Appl Environ Microb 77: 3685–3695. doi: 10.1128/AEM.02315-10
    [37] Alexander DM, John ACS (1994) Characterization of the carbon starvation-inducible and stationary phase-inducible gene slp encoding an outer membrane lipoprotein in Escherichia coli. Mol Microbiol 11: 1059–1071. doi: 10.1111/j.1365-2958.1994.tb00383.x
    [38] Mates AK, Sayed AK, Foster JW (2007) Products of the Escherichia coli acid fitness island attenuate metabolite stress at extremely low pH and mediate a cell density-dependent acid resistance. J Bacteriol 189: 2759–2768. doi: 10.1128/JB.01490-06
    [39] Shimizu K (2013) Regulation systems of bacteria such as Escherichia coli in response to nutrient limitation and environmental stresses. Metabolites 4: 1–35. doi: 10.3390/metabo4010001
    [40] Conter A, Menchon C, Gutierrez C (1997) Role of DNA supercoiling and rpoS sigma factor in the osmotic and growth phase-dependent induction of the gene osmE of Escherichia coli K12. J Mol Biol 273: 75–83. doi: 10.1006/jmbi.1997.1308
    [41] Bäumler AJ, Hantke K (1992) A lipoprotein of Yersinia enterocolitica facilitates ferrioxamine uptake in Escherichia coli. J Bacteriol 174: 1029–1035. doi: 10.1128/jb.174.3.1029-1035.1992
    [42] Plesa M, Hernalsteens JP, Vandenbussche G, et al. (2006) The SlyB outer membrane lipoprotein of Burkholderia multivorans contributes to membrane integrity. Res Microbiol 157: 582–592. doi: 10.1016/j.resmic.2005.11.015
    [43] Murata M, Noor R, Nagamitsu H, et al. (2012) Novel pathway directed by σ E to cause cell lysis in Escherichia coli. Genes Cells 17: 234–247. doi: 10.1111/j.1365-2443.2012.01585.x
    [44] Rahfeld JU, Rücknagel KP, Stoller G, et al. (1996) Isolation and amino acid sequence of a new 22-kDa FKBP-like peptidyl-prolyl cis/trans-isomerase of Escherichia coli. Similarity to Mip-like proteins of pathogenic bacteria. J Biol Chem 271: 22130–22138.
    [45] Ishihama A (2010) Prokaryotic genome regulation: multifactor promoters, multitarget regulators and hierarchic networks. FEMS Microbiol Rev 34: 628–645. doi: 10.1111/j.1574-6976.2010.00227.x
    [46] Rodgers N, Murdaugh A (2016) Chlorhexidine-induced elastic and adhesive changes of Escherichia coli cells within a biofilm. Biointerphases 11: 031011. doi: 10.1116/1.4962265
  • This article has been cited by:

    1. Daniela Coclite, Antonello Napoletano, Silvia Gianola, Andrea del Monaco, Daniela D'Angelo, Alice Fauci, Laura Iacorossi, Roberto Latina, Giuseppe La Torre, Claudio M. Mastroianni, Cristina Renzi, Greta Castellini, Primiano Iannone, Face Mask Use in the Community for Reducing the Spread of COVID-19: A Systematic Review, 2021, 7, 2296-858X, 10.3389/fmed.2020.594269
    2. Tongqian Zhang, Junling Wang, Yuqing Li, Zhichao Jiang, Xiaofeng Han, Dynamics analysis of a delayed virus model with two different transmission methods and treatments, 2020, 2020, 1687-1847, 10.1186/s13662-019-2438-0
    3. Xia Ma, Xiao-Feng Luo, Li Li, Yong Li, Gui-Quan Sun, The influence of mask use on the spread of COVID-19 during pandemic in New York City, 2022, 34, 22113797, 105224, 10.1016/j.rinp.2022.105224
    4. Lubna Pinky, Hana M. Dobrovolny, Epidemiological Consequences of Viral Interference: A Mathematical Modeling Study of Two Interacting Viruses, 2022, 13, 1664-302X, 10.3389/fmicb.2022.830423
    5. Sourav Kumar Sasmal, Yasuhiro Takeuchi, Editorial: Mathematical Modeling to Solve the Problems in Life Sciences, 2020, 17, 1551-0018, 2967, 10.3934/mbe.2020167
    6. José M. Garrido, David Martínez-Rodríguez, Fernando Rodríguez-Serrano, Sorina-M. Sferle, Rafael-J. Villanueva, Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months, 2021, 9, 2227-7390, 1132, 10.3390/math9101132
    7. Gorakh Sawant, Kumar Gaurav, Gaurav Mittal, Ashish Karn, 2022, Chapter 13, 978-981-16-8269-8, 179, 10.1007/978-981-16-8270-4_13
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6084) PDF downloads(922) Cited by(7)

Figures and Tables

Figures(5)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog