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COMMUNICATION TO THE EDITOR Correlating Single Cell Motility With Population Growth Dynamics for Flagellated Bacteria Sucheta Arora, Vidya Bhat, Aditya Mittal Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India; telephone: 91 11 26591052; fax: 91 11 26582282; e-mail: [email protected] Received 20 September 2006; accepted 29 January 2007 Published online 1 February 2007 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/bit.21372 ABSTRACT: Many bacteria used for biotechnological applications are naturally motile. Their ‘‘bio-nanopropeller’’ driven movement allows searching for better environments in a process called chemotaxis. Since bacteria are extremely small in size compared to the bulk fluid volumes in bioreactors, single cell motility is not considered to influence bioreactor operations. However, with increasing interest in localized fluid flow inside reactors, it is important to ask whether individual motility characteristics of bacteria are important in bioreactor operations. The first step in this direction is to try to correlate single cell measurements with population data of motile bacteria in a bioreactor. Thus, we observed the motility behavior of individual bacterial cells, using video microscopy with 33 ms time resolution, as a function of population growth dynamics of batch cultures in shake flasks. While observing the motility behavior of the most intensively studied bacteria, Escherichia coli, we find that overall bacterial motility decreases with progression of the growth curve. Remarkably, this is due to a decrease in a specific motility behavior called ‘‘running’’. Our results not only have direct implications on biofilm formations, but also provide a new direction in bioprocess design research highlighting the role of individual bacterial cell motility as an important parameter. Biotechnol. Bioeng. 2007;97: 1644–1649. ß 2007 Wiley Periodicals, Inc. KEYWORDS: motility; growth curve; nanopropeller; chemotaxis; bioprocess design; video microscopy Introduction Industrial production of several important metabolites is achieved through growth of bacterial cultures in bioreactors (Glazer and Nikaido, 1995). Growth of bacteria is dependent on several factors including environmental parameters apart from the nutritional status of the culture medium. Bioreactors are designed and operated to ensure homogeneity in the system with the aim of maintaining identical growth conditions for each cell (Shuler and Kargi, 2002). Although the operational conditions are aimed at completely homogenizing the bulk fluid in the bioreactors, formation of localized fluid flow patterns is inevitable (Ranade, 2002). Many bacteria that are used for production of biotechnological products are naturally motile. For example, Escherichia coli, through the course of evolution, has developed an ability to move in search of favorable environments by using its own natural motor–propeller assembly—the flagellar motor system (Berg, 2003; Kojima and Blair, 2004). Bacterial cell movement is enabled by flagellar proteins on their surfaces that act as nanopropellers to direct the bacteria towards better living environments in a process called chemotaxis (Wadhams and Armitage, 2004). This nanopropeller system is embedded in the bacterial cell membrane and driven by ion gradients across the cell membrane (Gabel and Berg, 2003; Sowa et al., 2005). Since the bacteria are extremely small in size, compared to the bulk fluid volumes handled in bioreactors, motility of individual bacterial cells is not considered to influence bioreactor operations. However, with increasing interest in localized fluid flow inside reactors, along with the bulk flow, it is important to ask the question whether individual motility characteristics of bacteria are significant in bioreactor operations. Motion of a submerged body in a fluid depends on the ability of its propeller to overcome mainly two forces: the inertial and the viscous forces. For macroscopic bodies the inertial forces dominate. However, for microscopic bodies inertial forces are negligible and the viscous forces are the primary constraints in their motility (Behkam and Sitti, 2005). In principle, bacterial motility might appear to be non-relevant; for large-scale bioreactors, however, its relevance (or lack of) in bioreactor operations is not proven either way. Thus, it is important to ask whether bacterial motility is important in bioprocesses. It might be especially important in operational conditions of bioreac- Correspondence to: A. Mittal 1644 Biotechnology and Bioengineering, Vol. 97, No. 6, August 15, 2007 ß 2007 Wiley Periodicals, Inc. tors which deal with high viscosity media and low agitation rates to avoid shear stress on cells. Bacterial motility is also an extremely important parameter biofilm formation (Wood et al., 2006) and would certainly be relevant in microfluidic devices. While chemotactic motility has been intensely studied from microbiological (Berg, 2003; Kojima and Blair, 2004) and biophysical (Behkam and Sitti, 2005; Berg and Brown, 1972; Brown and Berg, 1974; DiLuzio et al., 2005; Lauga et al., 2006) perspectives in the last three decades, correlating single cell motility with population studies (in bioreactor systems) of motile bacteria is still an unexplored territory (but see Staropoli and Alon, 2000). In this work we hope to fill this gap by providing the first experimental evidence on the possible variations in motion characteristics of individual bacterial cells as a function of the growth cycle of the motile bacteria in shake flask bioreactors. Materials and Methods E. coli strain JM109 was used for this study. The strain JM109 was chosen since it has been reported to be a fairly motile strain of E. coli compared to other strains (Wood et al., 2006). Initial studies were also carried out with E. coli strain DH5-a. However, the motility was not quantifiable for most experiments (less than 1%). The lack of quantifiable motility is in agreement with the results obtained by Wood et al. (2006). Nutrient broth of analytical grade was obtained from HiMedia Laboratories Pvt. Ltd (Mumbai, India). For inoculum preparation, bacterial cells were grown to exponential phase in 50 mL conical flasks, until the turbidity reached 1 at 600 nm (approximately 4 h). These cultures were then inoculated into 250 mL of final culture volumes to be aerobically grown in 500 mL shake flasks. Specifically, 250 mL of freshly prepared Nutrient Broth media (HiMedia Laboratories Pvt. Ltd., Mumbai, India) was inoculated with 10% of the inoculum culture in a 500 mL conical flask and grown at 378C, 200 rpm in an incubator-shaker (ORBITEK AID Electronics, Chennai, India) for 9 h. Samples for analysis were collected using pipettes under aseptic conditions every half an hour, throughout the growth period i.e., 9 h. The culture flask was restored in its original conditions as soon as possible. Thirty-three millisecond (ms) resolution video microscopy to record bacterial motility was done as described earlier (Gupta et al., 2006; Sharma et al., 2007). Briefly, for each sample, the collected sample was transferred to a glass slide immediately (within a few seconds), covered with a cover slip and digital videos of bacterial motion were recorded under the 100 oil immersion objective of Motic B1 upright microscope (MOTIC Microscopes) equipped with a digital camera. The camera is capable of acquiring images at the rate of 30 frames per second (i.e., 33 ms time resolution) using the software Motic Images Plus 2.0 ML. Note that our motility recordings were done within 30–60 s of the time when the sample was extracted from the culture flask to eliminate the possibility of any change in motility of bacteria due to change in its surrounding environment (from shaking inside a flask to that on a glass slide). It must be noted that the microscope stage temperature was not maintained at the same temperature as that of the shake flasks (378C). However, there was no large change in environment upon going from 378C to ambient (microscope stage), which was at 27–288C at all times. Whatever small changes were experienced during the transfer of the culture, were expected to be normalized over all the experiments. Simultaneously, an aliquot of each sample was stored at 48C for optical density measurement at 600 nm, to get the growth kinetics, using a Manual Simultaneous Spectrophotometer (SPEKOL-1200 Analytik Jena AG, Jena, Germany), which was carried out at the end of the experiment. The experiments were done in triplicates; with each video recording 20–200 bacterial cells simultaneously (less number of cells was essentially seen only in the very initial phases of growth due to very low number of cells). In one of the experiments the optical density of samples was measured immediately when the samples were taken out from the flask. This was done to check if there was any variation in the results due to storage of samples. Growth curve shown in Figure 1 confirmed that the absorbance was independent of whether it was measured for fresh samples or it was measured after a few hours of storage at 48C. Videos were then analyzed for motility data of individual cells by extracting the frames (images) with 33 ms resolution. Figure 1. Growth curve for batch culture of bacteria. Growth is expressed in absorbance of the sample at 600 nm and shown by solid circles representing mean SD of three independent experiments. The smooth curve is drawn to visually guide through different stages of the growth curve, approximately demarcated by arrows into lag phase, log phase; and stationary phase. Arora et al.: Single Cell Motility in Bioreactor Operations Biotechnology and Bioengineering. DOI 10.1002/bit 1645 Figure 2. Motility characteristics of single bacterial cells studied via 33 ms resolution video microscopy. If (A) is considered to be an image taken at time t, (B) shows the same field at time ¼ t þ 33 ms, and (C) shows the same field at time ¼ t þ 66 ms. The bacterial cell shown in the rectangle exhibits running motion relative to a fixed point in the field of view marked by the arrow and the encircled bacterial cell shows tumbling motion. Results Experiments were carried out with E. coli strains DH5-a and JM109. However, the motility was not quantifiable for experiments done with the former strain (less than 1%). The lack of quantifiable motility for the E. coli strain DH5-a is in agreement with the results obtained by Wood et al. (2006). Therefore, all the results are reported for the E. coli strain JM109 only. Figure 1 shows the bacterial growth curve obtained by us. It also represents the growth curve of a typical batch culture of bacteria in a reactor (Bailey and Ollis, 1986). The various stages of culture growth in a bioreactor are shown in the figure as the lag phase, the log phase and the stationary phase. Note that that our experiments were carried out up to the onset of stationary phase only and Figure 1 does not include the growth curve of bacteria in the death phase. The above is due to the facts that (a) we assume that the motility behavior of the bacterial cells at the onset of the stationary phase represents the behavior for the remaining stationary phase and (b) death phase is not relevant for the purposes of this study. We also measured the number of viable bacterial cells at a few points, by the CFU (colony forming units) assay to ensure the basic features of the batch culture growth curve. The CFU numbers correlated well with the absorbance data shown in Figure 1 (not shown), thus confirming that up to the time of our measurements, we did not have a significant number of dead cells contributing to our batch culture growth kinetics data. Note that absorbance at 600 nm, as shown in Figure 1, is a standard measure representing estimates for cell numbers in E. coli batch cultures (Bailey and Ollis, 1986; Olfosson et al., 2003), and for the rest of this work, we will use this absorbance to compare motility characteristics. Individual bacterial cells could be essentially classified into four categories: non-motile, runners (that swam in linear trajectories for several decades of microns), tumblers (that tumbled or rotated at a somewhat fixed position) and tumbling-runners (that displayed short runs with tumbling behavior). Figure 2 shows three successive frames of a video Figure 3. Percentage of bacteria that are motile in a batch culture as a function of growth in a batch culture w.r.t. time (A), and, absorbance at 600 nm (B). The percentage was calculated by counting the total bacterial cells in the field of view and then finding the number of motile bacteria amongst them. The smooth curve in (A) and the smooth line in (B) are shown to visually guide through the negative correlation (see text for details). Results are expressed as mean SD of three independent triplicate experiments. 1646 Biotechnology and Bioengineering, Vol. 97, No. 6, August 15, 2007 DOI 10.1002/bit Figure 4. Percentage of runners as a function of growth in a batch culture w.r.t. time (A), and, absorbance at 600 nm (B). The percentage was calculated by counting the total motile bacterial cells in the field of view and then finding the number of runners amongst them. The smooth curve in (A) and the smooth line in (B) are shown to visually guide through the negative correlation (see text for details). Results are expressed as mean SD of three independent triplicate experiments in (A) and only the mean is shown in (B). recording for a field in a sample. Relative to a fixed point in the field of view (marked by an arrow), the bacterial cell enclosed in a square is clearly seen to be a runner in the upward direction from A to C. In contrast, the encircled bacterium is a tumbler. Having recorded motility of bacteria at each point of the growth curve shown in Figure 1, we decided to investigate the motility characteristics of bacteria as a function of the growth curve. Figure 3 shows that the percentage of motile bacteria (percentage of bacteria showing any Figure 5. Percentage of tumblers (A and B) and tumbling-runners (C and D) as a function of growth in a batch culture w.r.t. time (A and C), and, absorbance at 600 nm (B and D). The percentage was calculated by counting the total motile bacterial cells in the field of view and then finding the number of tumblers and tumbling-runners amongst them. The smooth line in (B) and (D) are shown to visually guide through a lack of correlation (see text for details). Results are expressed as mean SD of three independent triplicate experiments in (A and C) and only the mean is shown in (B and D). Arora et al.: Single Cell Motility in Bioreactor Operations Biotechnology and Bioengineering. DOI 10.1002/bit 1647 motility), tend to decrease with both time (Fig. 3A) and increasing absorbance (Fig. 3B). These results were statistically corroborated by the fact that fairly strong negative correlation coefficients were obtained between absorbance and percentage of motile bacteria (i.e. for data shown in Fig. 3B) in the growth curve. While the overall average percent of bacteria that were motile (i.e. averages of triplicates) showed a correlation coefficient (CC) value of 0.58 against absorbance, the average CC, when considering individual experiments, was found to be 0.42 0.18. Note that investigating the latter was important, since experiment to experiment variations result in large standard deviations as seen in Figure 3B. Was there a specific kind of motile bacteria that were decreasing as growth progresses in a growth curve, or all kinds of individual bacterial motion decrease with time and cell numbers in a batch culture? To answer this question we first plotted percentage of runners out of the total motile bacteria (and not total bacteria in the field of view as in Fig. 3), against time and absorbance as shown in Figure 4. We observed a strong decline in the number of runners with the progression of the batch culture growth (Fig. 4). The statistical strength of our observations was shown by strong negative CCs between absorbance and percentage of runners (i.e. for data shown in Fig. 4B) in the growth curve. While the overall average percent of runners had a very high CC value of 0.70 against absorbance, the average CC, when considering individual experiments, was found to be 0.58 0.06. These results certainly indicate that ability of motile bacteria to ‘‘run’’ sharply decreases with the growth curve in a batch culture. Next we plotted percentage of tumblers and tumblingrunners out of the total motile bacteria (as in Fig. 4), against time and absorbance as shown in Figure 5. We observed that percentage of tumblers and tumbling-runners are independent of the progression of the batch culture growth (Fig. 5). Our observations were confirmed by very low CCs between absorbance and percentage of tumblers and tumblingrunners (i.e. for data shown in Figure 5B and D respectively) in the growth curve. While the overall average percent of tumblers and tumbling-runners had CC values of 0.19 and 0.02 respectively against absorbance, the average CC, when considering individual experiments, was found to be 0.31 0.24 for tumblers and 0.10 0.63 for tumblingrunners. These results certainly indicate that ability of motile bacteria to exhibit tumbling or combined tumbling-running behavior does not change with the growth curve in a batch culture. Discussion and Conclusions Bioprocess engineering assumes that the growth curve for a batch culture represents identical bacterial cells multiplying inside a bioreactor. Are they identical indeed? We clearly demonstrate that this is not the case with a physico-chemical parameter of bacterial motility. The E. coli strain JM109 was 1648 used in our studies, since this strain has been shown to be one of the most motile strains (Wood et al., 2006). While there is no set standard assay for establishing motility of bacterial strains, it has been studied using either video microscopy (Berg and Brown, 1972; Diluzio et al., 2005; Gupta et al., 2006) or measuring the spread of bacterial ‘‘halo’’ on agar plates (Wood et al., 2006). Considering the fact that the motility of bacteria in fluid would be different than on an agar plate, we used video microscopy to study bacterial motility. The bacteria used throughout experimentation should naturally move towards a favorable nutrient environment (chemotaxis). Therefore, the natural expectation during a batch culture would be that the overall bacterial population should include more motile bacteria with advancing growth due to overall depletion of nutrients in the culture broth. We report a finding exactly opposite to the one expected above, that the percentage of motile bacteria decreases with increasing population in a batch culture. Remarkably, this decrease is due to a significant decrease in a specific motility behavior of bacteria call ‘‘running.’’ This, on the one hand is in complete contrast to the expected behavior in which one would expect an increase in motility with progression of the growth curve due to constant reduction in nutrient levels of the culture. On the other hand, it could simply be a result of increasing population of bacterial cells leading to individual cells impeding the ‘‘running’’ motion for each other. If the latter was the case, then one would expect it only beyond the mid-log phase of the batch culture, when the bacterial cell concentration starts becoming substantial, which is clearly not the case. Thus, while we do not know at this stage as to why motility decreases with the progression of the growth curve, it does raise the possibility that bacterial motility can be utilized as an important parameter in bioprocess operations, apart from the normally accepted parameters of aeration, agitation and other physiological factors. While cell synchronization on a biochemical level was recognized to be of utmost importance in bioprocess engineering many years ago (Bailey and Ollis, 1986), we present the first evidence that the physical parameter of cell motility may be equally important. Staropoli and Alon (2000) have reported for E. coli K-12 strain RP437 that run speeds of bacterial cells peak in mid-exponential phase and tumbling frequency increases over the course of growth in a batch culture in 125 mL flasks. However, their elegant experiments were not designed to investigate motility of fresh cultures, i.e. substantial time was provided for biological adaptation to the bacterial samples to undergo chemotaxis driven motility under various conditions while observing under the microscope. Thus, their results were quite reasonable in terms of the expected outcome of the experiments. We, on the other hand, seem to have stumbled upon counterintuitive results, highlighting the importance of microbial motility in bioreactor operations, which opens a new line of thinking that is testable for other strains of motile bacteria. As of today, motility of microbial cells is not a parameter considered in bioreactor design and operation. By learning Biotechnology and Bioengineering, Vol. 97, No. 6, August 15, 2007 DOI 10.1002/bit from different motility behaviors exhibited by bacterial cells in different phases of the growth curve, it may be possible to enhance the bioprocess productivity by incorporating strategies to normalize chemotaxis driven motility over the complete growth profile during an operation. References Bailey JE, Ollis DF. 1986. Biochemical Engineering Fundamentals, 2nd ed. (International). Singapore: McGraw Hill Book Co. Behkam B, Sitti M. 2005. Modelling and testing of a biomimetic flagellar propulsion method for microscale biomedical swimming robots. Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 37–42. Berg HC. 2003. The rotary motor of bacterial flagella. Ann Rev Biochem 72:19–54. Berg HC, Brown DA. 1972. Chemotaxis in Escherichia coli analysed by three-dimensional tracking. Nature 239:500–504 Brown DA, Berg HC. 1974. 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Wood TK, Barrios AFG, Herzberg M, Lee J. 2006. Motility influences biofilm architecture in Escherichia coli. Appl Microbiol Biotechnol 72:361–367. Arora et al.: Single Cell Motility in Bioreactor Operations Biotechnology and Bioengineering. DOI 10.1002/bit 1649