Appl Microbiol Biotechnol (2015) 99:2861–2869 DOI 10.1007/s00253-014-6166-9
ENVIRONMENTAL BIOTECHNOLOGY
Chlorine stress mediates microbial surface attachment in drinking water systems Li Liu & Yang Le & Juliang Jin & Yuliang Zhou & Guowei Chen
Received: 4 July 2014 / Revised: 10 October 2014 / Accepted: 14 October 2014 / Published online: 31 October 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Microbial attachment to drinking water pipe surfaces facilitates pathogen survival and deteriorates disinfection performance, directly threatening the safety of drinking water. Notwithstanding that the formation of biofilm has been studied for decades, the underlying mechanisms for the origins of microbial surface attachment in biofilm development in drinking water pipelines remain largely elusive. We combined experimental and mathematical methods to investigate the role of environmental stress-mediated cell motility on microbial surface attachment in chlorination-stressed drinking water distribution systems. Results show that at low levels of disinfectant (0.0–1.0 mg/L), the presence of chlorine promotes initiation of microbial surface attachment, while higher amounts of disinfectant (>1.0 mg/L) inhibit microbial attachment. The proposed mathematical model further demonstrates that chlorination stress (0.0–5.0 mg/L)-mediated microbial cell motility regulates the frequency of cell-wall collision and thereby controls initial microbial surface attachment. The results reveal that transport processes and decay patterns of chlorine in drinking water pipelines regulate microbial cell motility and, thus, control initial surface cell attachment. It provides a mechanistic understanding of microbial attachment shaped by environmental disinfection stress and leads to new insights into microbial safety protocols in water distribution systems.
Keywords Microbial motility . Chlorination stress . Surface attachment . Drinking water systems
L. Liu : Y. Le : J. Jin : Y. Zhou : G. Chen (*) Department of Civil Engineering, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China e-mail: [email protected]
Introduction Despite the presence of disinfectant in drinking water distribution systems, microorganisms can attach to and colonize readily the surfaces of drinking water pipelines, forming biofilm (Douterelo et al. 2013). The formation of biofilm in drinking water systems involves complex interactions between microbes and their microenvironment, yielding severe water quality issues, such as deterioration of taste or color (Hoehn 1988), reduction of water pressure and flow rate (Flemming et al. 2002), spread of pathogenic diseases (Lehtola et al. 2004; Moritz et al. 2010), and accelerated corrosion of pipes (Beech and Sunner 2004). Interrupting cell attachment to surfaces is often implicated as an effective strategy for controlling biofilm formation; however, the underlying mechanisms for the initial biofilm development process in drinking water systems are not well understood yet (Palmer et al. 2007). Microbes survive under harsh conditions in drinking water systems, suffering from poor nutrients, chemical disinfection, as well as variable hydraulic situations. Microbial surface attachment is an important survival mode, which is often considered to be governed primarily by the growth medium (Srinivasan et al. 2008; Wang et al. 2012), flow regime (Manuel et al. 2007, 2010; Douterelo et al. 2013), pipe surface properties, and cell-cell interactions (Yu et al. 2010; Liu et al. 2013). Notwithstanding a multitude of factors involved in cell surface attachment, it is believed that disinfection (e.g., disinfectant type and concentration level) plays critical roles in regulating surface attachment and subsequent biofilm development in water distribution systems (Palmer et al. 2007; Srinivasan et al. 2008). Momba et al. (1998) found that biofilm formation on the flow-through glass tubes, stainless steel, and cement coupons is directly correlated to residual disinfectant concentration, and monochloramine and hydrogen peroxide have more persistent influence than free chlorine
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on controlling biofilm growth. Increase in disinfectant concentration typically results in decreased number of microbes in both bulk water and attached biofilm (Lehtola et al. 2005; Srinivasan et al. 2008). Numerous studies (Herson et al. 1987; Srinivasan et al. 2008) revealed that the fraction of microbes present in the biofilm (as compared with that in bulk water) increases with the increment in chlorine concentration and was suggested to be attributed to microbial survival strategies under disinfection stress. Microorganisms are capable of sensing environmental stresses and carrying out self-adjustment, adapting to harsh environments (Umehara et al. 2007; Ahmed et al. 2010; Taylor and Stocker 2012). For example, microbes may significantly increase cell motility, facilitating foraging upon depletion of the exogenous carbon source (Amsler et al. 1993). On the other hand, a sharp motility reduction was observed a few minutes after depletion of oxygen for aerobic bacteria (Douarche et al. 2009). In addition, many microbial taxa have evolved the ability to sense chemical gradients towards favorable conditions, moving towards gradients of attractants and away from gradients of repellents (Taylor and Stocker 2012; Stocker 2012). Cell motility (e.g., flagellum or chemotaxis mediated) was recognized to be linked to surface attachment and subsequent biofilm formation due to the increase in collision frequency (Morisaki et al. 1999; Merritt et al. 2007; Lemon et al. 2007). Nevertheless, understanding of the underlying mechanisms of disinfection-driven microbial attachment through cell motility regulation remains elusive. Quantitative modeling with consideration of physicochemical processes and microbial behaviors at the scale of single cells and microenvironments is essential for mechanistic understanding of the relation between disinfection stress and cell surface attachment. We study experimentally the impact of a common disinfectant, chlorine, on microbial surface attachment in drinking water systems. To elucidate the experimental results, we developed a simple individual-based mathematical model quantitatively linking initial surface attachment with chlorine transport and decay pattern-mediated microbial cell motility at the microscopic level.
Materials and methods
Appl Microbiol Biotechnol (2015) 99:2861–2869 Table 1 Measured physical, chemical, and microbiological parameters of water sample (mean±SD of three replicates) Parameter
Value
T (°C) pH O2 (mg O2/L) TOC, total organic carbon (mg/L)
17.8±0.5 6.53±0.04 4.21±0.06 4.06±0.64
TDS, total dissolved solid (mg/L) Chlorine (mg/L) Ammonia (mg N/L) Nitrate (mg N/L) Nitrite (mg N/L) R2A-cultivable cell density (cell/mL) Total Fe (mg/L) SO42− (mg/L) Cl− (mg/L) Alkalinity (mmol/L) Manganese (mg/L)
94.7±0.6 0.16±0.03 0.076±0.015 0.457±0.289 0.010±0.002 4.0×103 ±1.6×102 0.0181±0.003 17.961±0.442 15.629±0.5877 1.47±0.036 1.0 mg/L) inhibited microbial activities leading to limited microbial attachment, and microbial coverage as well as cluster size peaked at a chlorine concentration of 1.0 mg/L, a favorable condition supporting microbial attachment in this study. Effect of chlorine on shaping attached microbial viability Figure 2 describes Live/Dead cells’ profiles under various chlorine conditions, showing close patterns for attached live and dead cell numbers. For example, at increased chlorine
800
3.0
(D)
600
Live 2.5 Dead Live/Dead 2.0
400
1.5 1.0
200
Live/Dead
(C) 1.0 mg/L
(B) 1.0 mg/L
2
(A) 0.0 mg/L
Cell numbers (cell/cm )
(B) 0.5 mg/L
Microbial attaching rate (h-1)
(A) 0.0 mg/L
0.5 0
0
1
2
3
4
5
0.0
Chlorine (mg/L)
Fig. 2 Fluorescence microscopy images of live/dead cells at a 0.0 mg/L, b 1.0 mg/L, and c 3.0 mg/L chlorine. d Quantitative live/dead cell profiles (mean±SD of three replicates) at 15 min after exposure (green, live cells; red, dead cells)
level from 0.0 to 1.0 mg/L, the live cell number was found increased from 232 to 607 cells/cm2, similar to that of the dead cells (from 98 to 473 cells/cm2) (Fig. 2a, b, d). An opposite trend in attached cell numbers was observed for both live and dead cells at chlorine concentration higher than 1.0 mg/L (Fig. 2c). Not surprisingly, an increase in chlorine level led to a decrease in the ratio of live/dead attached cell numbers, while this trend was not conspicuous at higher chlorine levels (Fig. 2d). The results are consistent with that of microbial coverage patterns (seeing Fig. 1), evidencing that chlorine disinfection at lower levels promotes microbial surface attachment, while high chlorine concentration inhibits microbial activity and attachment.
Simulation results of microbial attachment under various chlorine levels To elucidate the mechanisms through which chlorine mediates microbial surface attachment and to offer a predictive tool for understanding the microbial attachment process in drinking water distribution systems, a simple mechanistic model was proposed by linking microbial motility with chlorine decay and diffusion. Figure 3 depicts snapshots of simulated microbial distribution and attachment patterns under chlorine concentrations of 0.0, 1.0, and 3.0 mg/L, respectively, at 15 min after inoculation. The experimentally observed patterns (Figs. 1 and 2) were reproduced in the model’s prediction (Fig. 3). Simulation results showed an optimal chlorine condition of 1.0 mg/L for microbial surface attachment and aggregation, associating with an uptrend in dead cell numbers at increased chlorine concentration from 0.0 to 3.0 mg/L (Figs. 3a–e). Under all experimental conditions, the number of attached cells increased nonlinearly over time (Fig. 3e). Interestingly, there was no statistical difference
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Appl Microbiol Biotechnol (2015) 99:2861–2869
level of the individual cells, we quantitatively estimated microbial clustering dynamics under various chlorine levels. Figure 4 illustrates the percentage of microbial clusters (defined as a ratio of the number of clusters with multiple cells to the number of the entire microbial entity—including multiplecell clusters and individual cells) under various chlorine levels at 15 min after inoculations. Under chlorine conditions of 0.0, 1.0, 3.0, and 5.0 mg/L, the percentage of microbial clusters was about 45.0, 48.2, 47.6, and 46.5 %, respectively. It reflected that chlorine concentration at moderate level (1.0 mg/L) promoted microbial cluster formation (large number of clusters), with such enhancement fading out with increasing chlorine concentration. In addition, the average cell number per microbial cluster was estimated as 1.92 at the chlorine concentration of 1.0 mg/L and 1.78, 1.88, and 1.82 for 0.0, 3.0, and 5.0 mg/L, respectively, suggesting that the moderate chlorine level (1.0 mg/L) favors microbial collision and thus sufficient cluster formation. It is remarkable that the simulated results support the experimental observations of the microbial aggregation and surface attachment patterns.
Simulated results of cell velocity and chlorine distribution variation resulting from initial chlorine conditions (H) 0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
50 40
1.0
Cell viability(%)
Attached cells
60
30 20 10 0
0
5
10
Time (min)
15
0.8 0.6
0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
0.4 0.2
0
5
10
Time (min)
15
Fig. 3 Simulation results of microbial attachment under various chlorine conditions. Simulated microbial distribution patterns of live and dead cells under a, b 0.0 mg/L; c, d 1.0 mg/L; and e, f 3.0 mg/L chlorine conditions at 15 min after exposure. g Time course of attached cell numbers. h Cell variability under various chlorine concentrations. Circles represent individual cells, with colors marking the degree of aggregation
in attached cell numbers induced by chlorine conditions of 0.0, 3.0, and 5.0 mg/L at the first 10 min after inoculations. At 10–15 min, the 0.0 mg/L chlorine scenario led to a relatively larger number of cells attached. As shown in Fig. 3h, cell viability continually decreased with exposure time and higher concentration of chlorine, showing a rapid inactivation effect as expected, which is in agreement with experimental observations (Helbling and VanBriesen 2007). Simulated results of effects of chlorine concentration on microbial clustering patterns To explore the microbial attachment process resulting from the combined effect of countless interactions occurring at the
We investigated the time course of cell velocity for microbes suspended in the medium with different initial chlorine concentrations. The microbial motility profiles under various chlorine levels are depicted in Fig. 5a. In the absence of chlorine disinfection, the mean cell velocity remained constant at around 20 μm/s over time, implying that microbes tend to move randomly without motility alteration, inhabiting a constant environment. For the chlorine condition of 1.0 mg/ L, the microbes moved at velocities close to the maximum value of 24.2 μm/s, with cell velocities slightly decreasing 0.4
Percentage in total clusters (%)
(G)
0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
0.3 0.016 0.012
0.2 0.008 0.004
0.1 0.000
6
7
8
9
10
7
8
9
10
0.0 1
2
3
4
5
6
11
Cell numbers per cluster
Fig. 4 Simulated results of microbial cluster distribution with different cell numbers (mean±SD, n=10) under various chlorine levels at 15 min after exposure
Appl Microbiol Biotechnol (2015) 99:2861–2869 35
0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
(A)
Cell velocity ( m/s)
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30 25 20 15 10 5
Discussion
0
Distribution variation (mg/L)
interactions, promising mechanisms for successful microbial surface attachment. Further increase in chlorine concentration (to 3.0 and 5.0 mg/L, respectively) did not facilitate microbial surface attachment, albeit a higher variation of chlorine distribution was simulated. This can be explained by the fact that higher chlorine level can cause more cell inactivation.
0
5
10
15
0.30
(B)
0.25 0.20 0.15 0.10 0.05 0.00 -0.05 0
5
10
Time (min)
15
Fig. 5 Simulated results of a mean cell velocity and b distribution variation of chlorine concentration (mean±SD, n=10) within 15 min after inoculations
over time. In contrast, the average cell velocity decreased from 21.5 to 10.4 μm/s with increasing chlorine concentration from 1.0 to 5.0 mg/L, respectively. The simulated results show a decline in microbial velocity over time under moderate chlorine concentration (1.0 mg/L), which may be explained by a reduction in chlorine gradient at later stages. More significant decreasing trends were observed under higher chlorine levels (3.0 and 5.0 mg/L (Fig. 5a)). This phenomenon was attributed to cell inactivation and thus inhibition of motility by higher chlorine conditions. To help understand the microbial velocity in response to the concentration gradient of chlorine, we calculated the standard deviation of the chlorine concentration for each grid, as a measure of the variation of its distribution. Under all simulated conditions, chlorine distribution variation experienced a sharp increase followed by a gradual decrease, except in the control scenario, as shown in Fig. 5b. It reflects that, as a result of the diffusion and reaction process, the temporal and spatial discrepancies of the chlorine fields shaped by chlorine level generated repellant gradients and thereof provoked a negative chemotaxis response. The initial chlorine condition of 1.0 mg/ L produced larger variations of chlorine distribution as compared with the control scenario, indicating that an increase in chlorine concentration at low levels enhanced microbial chemotactic motility that favors cell-cell and cell-surface
Chlorine disinfection has been reported to contribute to surface attachment and subsequent biofilm formation in water distribution systems (Herson et al. 1987; Srinivasan et al. 2008; Rathi and Satheesh 2012; Xue et al. 2012). In contrast to the current understanding that chlorination affects some of the important characteristics involved in biofilm formation and hence reduces microbial adhesion rate to surfaces (Rathi and Satheesh 2012), the experiment results reflected that chlorine disinfection at moderate levels promoted microbial surface attachment, while at high chlorine levels, the inhibition of microbial motility reduced cell collision frequency and thereof limited chances for successful surface attachment. This is in agreement with the experimental observations in which Escherichia coli exhibited negative chemotaxis in gradients of repellents, with their motility being promoted at low concentrations, while suppressed at high values (Benov and Fridovich 1996). The results are consistent with the modeling predictions that accelerated microbial chemotactic motility at moderate chlorination levels enhanced cell-surface collision opportunities and thereby facilitated cell attachment to the surface of drinking water pipelines. It provides a promising mechanism for untangling early-stage biofilm development processes in drinking water systems. Many bacteria actively alter their motility and chemotaxis in response to environmental stresses, which is clearly of a great adaptive value to bacterial cells for down-streaming stressful areas or up-streaming favorable microenvironments for survival and reproduction (Amsler et al. 1993; Wang and Or 2010; Stocker 2012). The mechanism of environmental stress-mediated cell motility regulating frequency of cell-wall collision and thereby controlling initial microbial surface attachment conforms to previous reports. For example, Lemon et al. (2007) found that flagellar motility is critical for Listeria monocytogenes in both initial surface attachment and subsequent biofilm formation. The attachment of Vibrio alginolyticus to glass surfaces was observed to be dependent on the swimming speed and frequency of cell collision to the surface (Kogure et al. 1998). Our results demonstrate that increasing cell motility favors cell collision and thus facilitates surface attachment, as well as cluster formation. The present results indicate that, as compared to the control scenario without disinfection, the moderate chlorine level
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generated temporal and spatial discrepancies of the chlorine fields and thereby provoked negative chemotactic response. The environments that microbes experienced are spatially and temporally heterogeneous at microscale, and simulation results showed that the initial chlorine concentration level shaped the chlorine decay process and resulted in a landscape to which chemotactic microbes respond in water distribution systems, which conforms to a previous study that chlorine demand increased with initial concentration of free chlorine and cells (Helbing and VanBriesen 2007). Despite the higher variation of chlorine distribution generated, a further increase in chlorine concentration reduced (average) cell velocity due to cell inactivation and thus inhibited microbial motility (Adler and Templeton 1967; Gagnon et al. 2005). In addition to motility alteration, other microbial responses to environmental stresses, such as production of extracellular polymeric substances (EPS), cell-cell signals, and cell surface features, may contribute to cell adhesion (Palmer et al. 2007; Xue and Seo 2013). While previous reports have shown that the presence of EPS could act as bonding material initiating attractive bridging interactions and thereby lead to surface attachment, chlorine disinfection also causes functional group deformation in carbohydrate polymers, which may hinder the bridging effect of EPS (Xue and Seo 2013). Recently, there is growing understanding of the considerable role of cell-cell signaling that contributes to biofilm formation (Davies et al. 1998; Heydorn et al. 2002; Shrout et al. 2011). Typically, these studies have identified nutritional, chemical, and physical environments to have a significant effect on microbial quorum sensing and thereby motility controls (Shrout et al. 2011), and thus provide another bridge linking environmental stress with cell motility. Notably, Xue and Seo (2013) reported that chlorine exposure made the surface charge more negative for three Pseudomonas aeruginosa strains in both planktonic cells and detached clusters and thereby suppressed surface adhesion, which provides indirect evidence that cell surface attachment promotion is likely regulated by chlorine (at moderate levels)-mediated cell motility. Acknowledgments This study was financially supported by the Natural Science Foundation of China (51479046, 51108148) and Anhui Provincial Natural Science Foundation (1208085ME75).
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Appl Microbiol Biotechnol (2015) 99:2861–2869 Ayoub GM, Malaeb L (2006) Impact of intermittent water supply on water quality in Lebanon. Int J Environ Pollut 26:379–397 Beech WB, Sunner J (2004) Biocorrosion: towards understanding interactions between biofilms and metals. Curr Opin Biotechnol 15:181– 186 Benov L, Fridovich I (1996) Escherichia coli exhibits negative chemotaxis in gradients of hydrogen peroxide, hypochlorite, and Nchlorotaurine: products of the respiratory burst of phagocytic cells. Proc Natl Acad Sci U S A 93:4999–5002 Boulos L, Prevost M, Barbeau B, Coallier J, Desjardins R (1999) LIVE/DEAD® BacLight™: application of a new rapid staining method for direct enumeration of viable and total bacteria in drinking water. J Microbiol Methods 37:77–86 Davies DG, Parsek MR, Pearson JP, Iglewski BH, Costerton JW, Greenberg EP (1998) The involvement of cell-to-cell signals in the development of a bacterial biofilm. Science 280:295–298 Douarche C, Buguin A, Salman H, Libchaber A (2009) E. coli and oxygen: a motility transition. Phys Rev Lett 102:198101 Douterelo I, Sharpe RL, Boxall JB (2013) Influence of hydraulic regimes on bacterial community structure and composition in an experimental drinking water distribution system. Water Res 47:503–516 Flemming HC, Percival SL, Walker JT (2002) Contamination potential of biofilms in water distribution systems. Water Sci Technol: Water Supply 2:271–280 Gagnon GA, Rand JL, O’Leary KC, Rygel AC, Chauret C, Andrews RC (2005) Disinfectant efficacy of chlorite and chlorine dioxide in drinking water biofilms. Water Res 39:1809–1817 Graham AG, Robin MS (1999) An efficient biofilm removal method for bacterial cells exposed to drinking water. J Microbiol Methods 34: 203–214 Helbing DE, VanBriesen JM (2007) Free chlorine demand and cell survival of microbial suspensions. Water Res 41:4424–4434 Helbing DE, VanBriesen JM (2008) Continuous monitoring of residual chlorine concentrations in response to controlled microbial intrusions in a laboratory-scale distribution system. Water Res 42:3162– 3172 Herson DS, McGonigle B, Payer MA, Baker KH (1987) Attachment as a factor in the protection of Enterobacter cloacae from chlorination. Appl Environ Microbiol 53:1178–1180 Heydorn A, Ersboll B, Kato J, Hentzer M, Parsek MR, Tolker-Nielsen T, Givskov M, Molin S (2002) Statistical analysis of Pseudomonas aeruginosa biofilm development: impact of mutations in genes involved in twitching motility, cell-to-cell signaling, and stationary-phase sigma factor expression. Appl Environ Microbiol 68:2008–2017 Hoehn RC (1988) Biological causes of tastes and odors in drinking water supplies. Water Qual Bull 13:46–52 Ilhan-Sungur E, Çotuk A (2010) Microbial corrosion of galvanized steel in a simulated recirculating cooling tower system. Corros Sci 52: 161–171 Kogure K, Ikemoto E, Morisaki H (1998) Attachment of Vibrio alginolyticus to glass surfaces is dependent on swimming speed. J Bacteriol 180:932–937 Kreft JU, Booth G, Wimpenny JWT (1998) BacSim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 144:3275–3287 Kreft JU, Picioreanu C, Wimpenny JWT, van Loosdrecht MCM (2001) Individual-based modelling of biofilms. Microbiology 147:2897– 2912 Lehtola MJ, Miettinen KT, Keinanen MM, Kekki TK, Laine O, Hir vo n en A , Vart iai ne n T, Ma r t i k a i ne n PJ ( 2 00 4 ) Microbiology, chemistry and biofilm development in a pilot drinking water distribution system with copper and plastic pipes. Water Res 38:3769–3779 Lehtola MJ, Miettinen IT, Lampola T, Hirvonen A, Vartiainen T, Martikainen PJ (2005) Pipeline materials modify the effectiveness
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2869 Skellam JG (1991) Random dispersal in theoretical populations. B Math Biol 53:135–165 Srinivasan S, Harrington GW, Xagoraraki I, Goel R (2008) Factors affecting bulk to total bacteria ratio in drinking water distribution systems. Water Res 42:3393–3404 Stocker R (2012) Marine microbes see a sea of gradients. Science 338: 628–633 Taylor JR, Stocker R (2012) Trade-offs of chemotactic foraging in turbulent water. Science 338:675 Umehara S, Hattori A, Inoue I, Yasuda K (2007) Asynchrony in the growth and motility responses to environmental changes by individual bacterial cells. Biochem Biophys Res Commun 356:464–469 van der Kooij D, Veenendaal HR, Baars-Lorist C, van der Klift DW, Drost YC (1995) Biofilm formation on surfaces of glass and Teflon exposed to treated water. Water Res 29:1655– 1662 Vickery K, Pajkos A, Cossart Y (2004) Removal of biofilm from endoscopes: evaluation of detergent efficiency. Am J Infect Control 32: 170–176 Wadhawan T, Maruska ZB, Siripattanakul S, Hill CB, Prüss BM, McEvoy JM, Khan E (2011) A new method to determine initial viability of entrapped cells using fluorescent nucleic acid staining. Bioresour Technol 102:1622–1627 Wang G, Or D (2010) Aqueous films limit bacterial cell motility and colony expansion on partially saturated rough surfaces. Environ Microbiol 12:1363–1373 Wang H, Hu C, Hu X, Yang M, Qu J (2012) Effects of disinfectant and biofilm on the corrosion of cast iron pipes in a reclaimed water distribution system. Water Res 46:1070–1078 Xue Z, Seo Y (2013) Impact of chlorine disinfection on redistribution of cell clusters from biofilms. Environ Sci Technol 47: 1365–1372 Xue Z, Sendamangalam VR, Gruden CL, Seo Y (2012) Multiple roles of extracellular polymeric substances on resistance of biofilm and detached clusters. Environ Sci Technol 46: 13212–13219 Yu J, Kim D, Lee T (2010) Microbial diversity in biofilms on water distribution pipes of different materials. Water Sci Technol 61:163– 171
ENVIRONMENTAL BIOTECHNOLOGY
Chlorine stress mediates microbial surface attachment in drinking water systems Li Liu & Yang Le & Juliang Jin & Yuliang Zhou & Guowei Chen
Received: 4 July 2014 / Revised: 10 October 2014 / Accepted: 14 October 2014 / Published online: 31 October 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Microbial attachment to drinking water pipe surfaces facilitates pathogen survival and deteriorates disinfection performance, directly threatening the safety of drinking water. Notwithstanding that the formation of biofilm has been studied for decades, the underlying mechanisms for the origins of microbial surface attachment in biofilm development in drinking water pipelines remain largely elusive. We combined experimental and mathematical methods to investigate the role of environmental stress-mediated cell motility on microbial surface attachment in chlorination-stressed drinking water distribution systems. Results show that at low levels of disinfectant (0.0–1.0 mg/L), the presence of chlorine promotes initiation of microbial surface attachment, while higher amounts of disinfectant (>1.0 mg/L) inhibit microbial attachment. The proposed mathematical model further demonstrates that chlorination stress (0.0–5.0 mg/L)-mediated microbial cell motility regulates the frequency of cell-wall collision and thereby controls initial microbial surface attachment. The results reveal that transport processes and decay patterns of chlorine in drinking water pipelines regulate microbial cell motility and, thus, control initial surface cell attachment. It provides a mechanistic understanding of microbial attachment shaped by environmental disinfection stress and leads to new insights into microbial safety protocols in water distribution systems.
Keywords Microbial motility . Chlorination stress . Surface attachment . Drinking water systems
L. Liu : Y. Le : J. Jin : Y. Zhou : G. Chen (*) Department of Civil Engineering, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China e-mail: [email protected]
Introduction Despite the presence of disinfectant in drinking water distribution systems, microorganisms can attach to and colonize readily the surfaces of drinking water pipelines, forming biofilm (Douterelo et al. 2013). The formation of biofilm in drinking water systems involves complex interactions between microbes and their microenvironment, yielding severe water quality issues, such as deterioration of taste or color (Hoehn 1988), reduction of water pressure and flow rate (Flemming et al. 2002), spread of pathogenic diseases (Lehtola et al. 2004; Moritz et al. 2010), and accelerated corrosion of pipes (Beech and Sunner 2004). Interrupting cell attachment to surfaces is often implicated as an effective strategy for controlling biofilm formation; however, the underlying mechanisms for the initial biofilm development process in drinking water systems are not well understood yet (Palmer et al. 2007). Microbes survive under harsh conditions in drinking water systems, suffering from poor nutrients, chemical disinfection, as well as variable hydraulic situations. Microbial surface attachment is an important survival mode, which is often considered to be governed primarily by the growth medium (Srinivasan et al. 2008; Wang et al. 2012), flow regime (Manuel et al. 2007, 2010; Douterelo et al. 2013), pipe surface properties, and cell-cell interactions (Yu et al. 2010; Liu et al. 2013). Notwithstanding a multitude of factors involved in cell surface attachment, it is believed that disinfection (e.g., disinfectant type and concentration level) plays critical roles in regulating surface attachment and subsequent biofilm development in water distribution systems (Palmer et al. 2007; Srinivasan et al. 2008). Momba et al. (1998) found that biofilm formation on the flow-through glass tubes, stainless steel, and cement coupons is directly correlated to residual disinfectant concentration, and monochloramine and hydrogen peroxide have more persistent influence than free chlorine
2862
on controlling biofilm growth. Increase in disinfectant concentration typically results in decreased number of microbes in both bulk water and attached biofilm (Lehtola et al. 2005; Srinivasan et al. 2008). Numerous studies (Herson et al. 1987; Srinivasan et al. 2008) revealed that the fraction of microbes present in the biofilm (as compared with that in bulk water) increases with the increment in chlorine concentration and was suggested to be attributed to microbial survival strategies under disinfection stress. Microorganisms are capable of sensing environmental stresses and carrying out self-adjustment, adapting to harsh environments (Umehara et al. 2007; Ahmed et al. 2010; Taylor and Stocker 2012). For example, microbes may significantly increase cell motility, facilitating foraging upon depletion of the exogenous carbon source (Amsler et al. 1993). On the other hand, a sharp motility reduction was observed a few minutes after depletion of oxygen for aerobic bacteria (Douarche et al. 2009). In addition, many microbial taxa have evolved the ability to sense chemical gradients towards favorable conditions, moving towards gradients of attractants and away from gradients of repellents (Taylor and Stocker 2012; Stocker 2012). Cell motility (e.g., flagellum or chemotaxis mediated) was recognized to be linked to surface attachment and subsequent biofilm formation due to the increase in collision frequency (Morisaki et al. 1999; Merritt et al. 2007; Lemon et al. 2007). Nevertheless, understanding of the underlying mechanisms of disinfection-driven microbial attachment through cell motility regulation remains elusive. Quantitative modeling with consideration of physicochemical processes and microbial behaviors at the scale of single cells and microenvironments is essential for mechanistic understanding of the relation between disinfection stress and cell surface attachment. We study experimentally the impact of a common disinfectant, chlorine, on microbial surface attachment in drinking water systems. To elucidate the experimental results, we developed a simple individual-based mathematical model quantitatively linking initial surface attachment with chlorine transport and decay pattern-mediated microbial cell motility at the microscopic level.
Materials and methods
Appl Microbiol Biotechnol (2015) 99:2861–2869 Table 1 Measured physical, chemical, and microbiological parameters of water sample (mean±SD of three replicates) Parameter
Value
T (°C) pH O2 (mg O2/L) TOC, total organic carbon (mg/L)
17.8±0.5 6.53±0.04 4.21±0.06 4.06±0.64
TDS, total dissolved solid (mg/L) Chlorine (mg/L) Ammonia (mg N/L) Nitrate (mg N/L) Nitrite (mg N/L) R2A-cultivable cell density (cell/mL) Total Fe (mg/L) SO42− (mg/L) Cl− (mg/L) Alkalinity (mmol/L) Manganese (mg/L)
94.7±0.6 0.16±0.03 0.076±0.015 0.457±0.289 0.010±0.002 4.0×103 ±1.6×102 0.0181±0.003 17.961±0.442 15.629±0.5877 1.47±0.036 1.0 mg/L) inhibited microbial activities leading to limited microbial attachment, and microbial coverage as well as cluster size peaked at a chlorine concentration of 1.0 mg/L, a favorable condition supporting microbial attachment in this study. Effect of chlorine on shaping attached microbial viability Figure 2 describes Live/Dead cells’ profiles under various chlorine conditions, showing close patterns for attached live and dead cell numbers. For example, at increased chlorine
800
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Fig. 2 Fluorescence microscopy images of live/dead cells at a 0.0 mg/L, b 1.0 mg/L, and c 3.0 mg/L chlorine. d Quantitative live/dead cell profiles (mean±SD of three replicates) at 15 min after exposure (green, live cells; red, dead cells)
level from 0.0 to 1.0 mg/L, the live cell number was found increased from 232 to 607 cells/cm2, similar to that of the dead cells (from 98 to 473 cells/cm2) (Fig. 2a, b, d). An opposite trend in attached cell numbers was observed for both live and dead cells at chlorine concentration higher than 1.0 mg/L (Fig. 2c). Not surprisingly, an increase in chlorine level led to a decrease in the ratio of live/dead attached cell numbers, while this trend was not conspicuous at higher chlorine levels (Fig. 2d). The results are consistent with that of microbial coverage patterns (seeing Fig. 1), evidencing that chlorine disinfection at lower levels promotes microbial surface attachment, while high chlorine concentration inhibits microbial activity and attachment.
Simulation results of microbial attachment under various chlorine levels To elucidate the mechanisms through which chlorine mediates microbial surface attachment and to offer a predictive tool for understanding the microbial attachment process in drinking water distribution systems, a simple mechanistic model was proposed by linking microbial motility with chlorine decay and diffusion. Figure 3 depicts snapshots of simulated microbial distribution and attachment patterns under chlorine concentrations of 0.0, 1.0, and 3.0 mg/L, respectively, at 15 min after inoculation. The experimentally observed patterns (Figs. 1 and 2) were reproduced in the model’s prediction (Fig. 3). Simulation results showed an optimal chlorine condition of 1.0 mg/L for microbial surface attachment and aggregation, associating with an uptrend in dead cell numbers at increased chlorine concentration from 0.0 to 3.0 mg/L (Figs. 3a–e). Under all experimental conditions, the number of attached cells increased nonlinearly over time (Fig. 3e). Interestingly, there was no statistical difference
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level of the individual cells, we quantitatively estimated microbial clustering dynamics under various chlorine levels. Figure 4 illustrates the percentage of microbial clusters (defined as a ratio of the number of clusters with multiple cells to the number of the entire microbial entity—including multiplecell clusters and individual cells) under various chlorine levels at 15 min after inoculations. Under chlorine conditions of 0.0, 1.0, 3.0, and 5.0 mg/L, the percentage of microbial clusters was about 45.0, 48.2, 47.6, and 46.5 %, respectively. It reflected that chlorine concentration at moderate level (1.0 mg/L) promoted microbial cluster formation (large number of clusters), with such enhancement fading out with increasing chlorine concentration. In addition, the average cell number per microbial cluster was estimated as 1.92 at the chlorine concentration of 1.0 mg/L and 1.78, 1.88, and 1.82 for 0.0, 3.0, and 5.0 mg/L, respectively, suggesting that the moderate chlorine level (1.0 mg/L) favors microbial collision and thus sufficient cluster formation. It is remarkable that the simulated results support the experimental observations of the microbial aggregation and surface attachment patterns.
Simulated results of cell velocity and chlorine distribution variation resulting from initial chlorine conditions (H) 0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
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15
Fig. 3 Simulation results of microbial attachment under various chlorine conditions. Simulated microbial distribution patterns of live and dead cells under a, b 0.0 mg/L; c, d 1.0 mg/L; and e, f 3.0 mg/L chlorine conditions at 15 min after exposure. g Time course of attached cell numbers. h Cell variability under various chlorine concentrations. Circles represent individual cells, with colors marking the degree of aggregation
in attached cell numbers induced by chlorine conditions of 0.0, 3.0, and 5.0 mg/L at the first 10 min after inoculations. At 10–15 min, the 0.0 mg/L chlorine scenario led to a relatively larger number of cells attached. As shown in Fig. 3h, cell viability continually decreased with exposure time and higher concentration of chlorine, showing a rapid inactivation effect as expected, which is in agreement with experimental observations (Helbling and VanBriesen 2007). Simulated results of effects of chlorine concentration on microbial clustering patterns To explore the microbial attachment process resulting from the combined effect of countless interactions occurring at the
We investigated the time course of cell velocity for microbes suspended in the medium with different initial chlorine concentrations. The microbial motility profiles under various chlorine levels are depicted in Fig. 5a. In the absence of chlorine disinfection, the mean cell velocity remained constant at around 20 μm/s over time, implying that microbes tend to move randomly without motility alteration, inhabiting a constant environment. For the chlorine condition of 1.0 mg/ L, the microbes moved at velocities close to the maximum value of 24.2 μm/s, with cell velocities slightly decreasing 0.4
Percentage in total clusters (%)
(G)
0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
0.3 0.016 0.012
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Fig. 4 Simulated results of microbial cluster distribution with different cell numbers (mean±SD, n=10) under various chlorine levels at 15 min after exposure
Appl Microbiol Biotechnol (2015) 99:2861–2869 35
0.0 mg/L 1.0 mg/L 3.0 mg/L 5.0 mg/L
(A)
Cell velocity ( m/s)
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Discussion
0
Distribution variation (mg/L)
interactions, promising mechanisms for successful microbial surface attachment. Further increase in chlorine concentration (to 3.0 and 5.0 mg/L, respectively) did not facilitate microbial surface attachment, albeit a higher variation of chlorine distribution was simulated. This can be explained by the fact that higher chlorine level can cause more cell inactivation.
0
5
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(B)
0.25 0.20 0.15 0.10 0.05 0.00 -0.05 0
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Fig. 5 Simulated results of a mean cell velocity and b distribution variation of chlorine concentration (mean±SD, n=10) within 15 min after inoculations
over time. In contrast, the average cell velocity decreased from 21.5 to 10.4 μm/s with increasing chlorine concentration from 1.0 to 5.0 mg/L, respectively. The simulated results show a decline in microbial velocity over time under moderate chlorine concentration (1.0 mg/L), which may be explained by a reduction in chlorine gradient at later stages. More significant decreasing trends were observed under higher chlorine levels (3.0 and 5.0 mg/L (Fig. 5a)). This phenomenon was attributed to cell inactivation and thus inhibition of motility by higher chlorine conditions. To help understand the microbial velocity in response to the concentration gradient of chlorine, we calculated the standard deviation of the chlorine concentration for each grid, as a measure of the variation of its distribution. Under all simulated conditions, chlorine distribution variation experienced a sharp increase followed by a gradual decrease, except in the control scenario, as shown in Fig. 5b. It reflects that, as a result of the diffusion and reaction process, the temporal and spatial discrepancies of the chlorine fields shaped by chlorine level generated repellant gradients and thereof provoked a negative chemotaxis response. The initial chlorine condition of 1.0 mg/ L produced larger variations of chlorine distribution as compared with the control scenario, indicating that an increase in chlorine concentration at low levels enhanced microbial chemotactic motility that favors cell-cell and cell-surface
Chlorine disinfection has been reported to contribute to surface attachment and subsequent biofilm formation in water distribution systems (Herson et al. 1987; Srinivasan et al. 2008; Rathi and Satheesh 2012; Xue et al. 2012). In contrast to the current understanding that chlorination affects some of the important characteristics involved in biofilm formation and hence reduces microbial adhesion rate to surfaces (Rathi and Satheesh 2012), the experiment results reflected that chlorine disinfection at moderate levels promoted microbial surface attachment, while at high chlorine levels, the inhibition of microbial motility reduced cell collision frequency and thereof limited chances for successful surface attachment. This is in agreement with the experimental observations in which Escherichia coli exhibited negative chemotaxis in gradients of repellents, with their motility being promoted at low concentrations, while suppressed at high values (Benov and Fridovich 1996). The results are consistent with the modeling predictions that accelerated microbial chemotactic motility at moderate chlorination levels enhanced cell-surface collision opportunities and thereby facilitated cell attachment to the surface of drinking water pipelines. It provides a promising mechanism for untangling early-stage biofilm development processes in drinking water systems. Many bacteria actively alter their motility and chemotaxis in response to environmental stresses, which is clearly of a great adaptive value to bacterial cells for down-streaming stressful areas or up-streaming favorable microenvironments for survival and reproduction (Amsler et al. 1993; Wang and Or 2010; Stocker 2012). The mechanism of environmental stress-mediated cell motility regulating frequency of cell-wall collision and thereby controlling initial microbial surface attachment conforms to previous reports. For example, Lemon et al. (2007) found that flagellar motility is critical for Listeria monocytogenes in both initial surface attachment and subsequent biofilm formation. The attachment of Vibrio alginolyticus to glass surfaces was observed to be dependent on the swimming speed and frequency of cell collision to the surface (Kogure et al. 1998). Our results demonstrate that increasing cell motility favors cell collision and thus facilitates surface attachment, as well as cluster formation. The present results indicate that, as compared to the control scenario without disinfection, the moderate chlorine level
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generated temporal and spatial discrepancies of the chlorine fields and thereby provoked negative chemotactic response. The environments that microbes experienced are spatially and temporally heterogeneous at microscale, and simulation results showed that the initial chlorine concentration level shaped the chlorine decay process and resulted in a landscape to which chemotactic microbes respond in water distribution systems, which conforms to a previous study that chlorine demand increased with initial concentration of free chlorine and cells (Helbing and VanBriesen 2007). Despite the higher variation of chlorine distribution generated, a further increase in chlorine concentration reduced (average) cell velocity due to cell inactivation and thus inhibited microbial motility (Adler and Templeton 1967; Gagnon et al. 2005). In addition to motility alteration, other microbial responses to environmental stresses, such as production of extracellular polymeric substances (EPS), cell-cell signals, and cell surface features, may contribute to cell adhesion (Palmer et al. 2007; Xue and Seo 2013). While previous reports have shown that the presence of EPS could act as bonding material initiating attractive bridging interactions and thereby lead to surface attachment, chlorine disinfection also causes functional group deformation in carbohydrate polymers, which may hinder the bridging effect of EPS (Xue and Seo 2013). Recently, there is growing understanding of the considerable role of cell-cell signaling that contributes to biofilm formation (Davies et al. 1998; Heydorn et al. 2002; Shrout et al. 2011). Typically, these studies have identified nutritional, chemical, and physical environments to have a significant effect on microbial quorum sensing and thereby motility controls (Shrout et al. 2011), and thus provide another bridge linking environmental stress with cell motility. Notably, Xue and Seo (2013) reported that chlorine exposure made the surface charge more negative for three Pseudomonas aeruginosa strains in both planktonic cells and detached clusters and thereby suppressed surface adhesion, which provides indirect evidence that cell surface attachment promotion is likely regulated by chlorine (at moderate levels)-mediated cell motility. Acknowledgments This study was financially supported by the Natural Science Foundation of China (51479046, 51108148) and Anhui Provincial Natural Science Foundation (1208085ME75).
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