1 Section of Molecular Microbiology, BioCentrum DTU, Technical University of Denmark DTU, Building 301, DK-2800 Lyngby, Denmark
2 Department of Microbiology, E. C. Slater Institute, BioCentrum Amsterdam, University of Amsterdam, 1018 WS Amsterdam, The Netherlands
Correspondence
Ole Michelsen
Ole.Michelsen{at}BioCentrum.dtu.dk
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ABSTRACT |
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INTRODUCTION |
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The majority of the research into the cell cycle periods of bacteria has focused on relatively fast-growing cultures, and only a few studies have been concerned with slowly growing cultures (Helmstetter, 1996). But bacteria such as E. coli do experience conditions of substrate limitation resulting in slow growth in parts of their natural environment, and it is therefore of interest to analyse to what extent the cell cycle periods change in response to slow growth on poor carbon sources or to substrate-limited growth in the chemostat.
The study by Skarstad et al. (1985) strongly suggests that computer simulation of DNA distributions obtained by flow cytometry might be a precise way to determine the B, C and D cell cycle periods in slowly growing cultures. However, except for a few determinations of the C and D periods in relatively fast-growing cultures of E. coli strains (Allman et al., 1991
; Skarstad et al., 1985
) computer simulation of DNA distributions obtained by flow cytometry has not been used for a systematic study of cell cycle periods in E. coli. We have developed new computer software for the accurate cell cycle analysis of DNA distributions obtained by flow cytometry, and here we describe the use of our software for a comprehensive analysis of cell cycle periods in slowly growing cultures of E. coli B/r and K-12.
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METHODS |
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Computer simulation of DNA distributions.
Our computer software to simulate experimental DNA distributions of slowly growing bacterial cultures is described in Results and Discussion. For faster-growing bacterial cultures where initiation of replication is moved into the preceding cell cycles the variation in doubling times for the individual bacteria in a culture should be taken into account (Skarstad et al., 1985). This was done as follows. The culture was divided into up to 41 subpopulations of cells which initiated chromosome replication at slightly different times in the cell cycle, and it was assumed that the numbers of cells in these subpopulations were normally distributed around the normal initiation time with a standard deviation which can be chosen as a percentage of the generation time. Additionally, and in contrast to Skarstad et al. (1985)
, who kept the C period constant and varied the D period according to two different models, it was assumed that the C and D periods were affected proportionally in cells which initiated replication too early or too late in the cell cycle and therefore had a longer or a shorter period (C+D) to cell division. Input parameters for the software are standard deviations for the lower and upper parts of the DNA distribution, or standard deviations obtained from rifampicin-treated samples, and the length of the C and D periods. These values are varied by manual iteration until a best fit of the calculated distribution to the DNA histogram was obtained (see Results and Discussion for further details). To evaluate our simulations we calculated the deviation s using the formula presented by Skarstad et al. (1985)
:
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The program can be used to determine accurate cell cycle periods as long as C+D<2; for faster-growing cultures the program can be used and it was used in this study but other methods are recommended. The computer program can be obtained from F. G. Hansen (FGH@BioCentrum.dtu.dk).
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RESULTS AND DISCUSSION |
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Preferably, and as is done above, standard deviations should be determined for cells with different amounts of DNA obtained from rifampicin-treated cultures. These samples should be measured immediately before or after that of the exponential culture from which the DNA histogram should be simulated. However, this poses a problem for the reanalysis of older data where only the DNA distribution of the exponential culture is available, and for the data of all of the batch and chemostat cultures of strain PJ4004, which will be analysed later in this article, as it turned out that this strain continued initiation of chromosome replication in the presence of rifampicin.
The background subtraction feature of the software allows the user to approximate normal distributions to the left (1 chromosome) and right (2 chromosome) peaks, to subtract cells (signals) which are outside of the main DNA distribution, and to calculate the corresponding standard deviations. These standard deviations' will be slightly higher (515 % for the 1 chromosome and 1525 % for the 2 chromosome peaks) than the standard deviations obtained by analysing corresponding rifampicin- and cephalexin-treated samples containing cells with either 1 or 2 chromosomes simply because the left and right curvatures of an exponential DNA distribution are formed by the presence of non-replicating as well as replicating cells (cf. Fig. 2a). Thus, in cases where rifampicin samples are not available (and often in our daily use of the software also when rifampicin samples are available) the standard deviations' obtained from the DNA distribution of the exponentially growing cells are used as a guide for making qualified guesses' of the true standard deviations, which are then used in the simulation. In the present example the standard deviation for the 1 chromosome peak in the rifampicin-treated sample (Fig. 2b
) was 9 channels and the standard deviation obtained from the left curvature of the cells containing preferentially 1 chromosome was 9·5 channels. To demonstrate the necessity to use a non-proportional increase in standard deviations we also present simulations where a proportional increase in standard deviations (constant CV) was used (Fig. 2c
e). The CV values were based on the standard deviations determined from Fig. 2(b)
of either the 1 (Fig. 2c
) or the 2 (Fig. 2d
) chromosome peaks or the mean of these standard deviations (Fig. 2e
). It is clear that we obtain the best curve fit (the lowest s) for the simulation presented in Fig. 2(a)
(variable CV); however, the calculated cell cycle periods are similar for the calculations based on a constant CV.
It is our experience that only when correct standard deviations are used will it be possible to get a perfect fit of the calculated to the experimental DNA distribution.
Recalculation of cell cycle data
The B, C and D periods of slowly growing E. coli B/r A and K strains were determined by flow cytometry by Skarstad et al. (1983). These estimations were based on the number of cells present in the peaks representing cells with one and two chromosomes, respectively. However, replicating cells will also contribute to the number of cells in these two peaks, making it very difficult to put precise borders between cells which have not started replication, and cells which have just started. The same will be true for cells which are just about to finish and cells that have finished replication. Later these authors (Skarstad et al., 1985
) developed a computer program to simulate DNA distribution data obtained by flow cytometry. This program can handle flow cytometric DNA distributions for both slowly growing and fast-growing bacteria and the B, C and D periods were determined for an E. coli B/r A strain growing with a 330 min doubling time. The B, C and D periods determined in these two articles (Skarstad et al., 1983
, 1985
), are presented in Table 2
, together with the values obtained using our newly developed simulation routine to estimate the cell cycle periods from the same DNA distributions. The new simulations (Fig. 4a, b
) of the data presented in the early article (Skarstad et al., 1983
) gave cell cycle periods (Table 2
) which were significantly different from the results presented earlier. It was clear that the B and D periods were overestimated and thus the C period became too short. However, we also found significant differences from the results presented in the article where Skarstad et al. (1985)
took advantage of a computer program to simulate the DNA distribution. Our simulation suggests that the D period might be overestimated. Fig. 5
(a) shows two simulations of the data obtained from an E. coli B/r culture growing with 330 min doubling time (Skarstad et al., 1985
) using our program. The enlarged area (Fig. 5b
) emphasizes the differences in the two simulations. For the light grey thick curve we varied the C and D periods as well as the standard deviations until we obtained the best fit to the experimental data (deviation s=0·35). For the simulation presented by the black curve we used the C and D periods estimated by Skarstad et al. (1985)
and we varied the standard deviations with the restriction that the CV should be constant. This latter simulation obtained with our program was identical to the simulation presented by Skarstad et al. (1985)
, but the fit to the experimental data was not as good (s=1·01) as for the one where we used a variable CV. Thus, we suggest that the approach we have taken to simulate DNA histograms obtained by flow cytometry might give more precise data for the different cell cycle periods.
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Variation in the cell cycle periods
We observed minor and major adaptations or mutations of the cultures during prolonged growth in the chemostat. As can be seen from Table 4 the duration of the individual cell cycle periods changed during cultivation in the chemostat, and from Fig. 6
it can be seen that the values for the different periods scatter compared to the simple linear relationship. Part of this scatter is probably real and might be a reflection of adaptive mutations in the chemostats (Notley-McRobb & Ferenci, 2000
). As the most extreme example, Fig. 7
shows two histograms of samples from chemostat 2 (generation times 105 and 103 min, Table 4
). There are 140 generations between the two samples and during this time only a small change in dilution rate (growth rate) had occurred. However, the last sample showed a dramatic change in the shape of the DNA histograms. The calculations indicate that this is due to a significant decrease in the C period and a concomitant increase in the D period. We have not investigated this phenomenon in more detail. However, it is known that insertional inactivation of the ihfA or ihfB alleles (von Freiesleben et al., 2000
) and of the hns gene (Atlung & Hansen, 2002
) can decrease the C period. Although the DNA distributions of the two cultures look very different, the durations of the cell cycle periods do fall within the general scatter in Fig. 6
.
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We find that the flow cytometric approach is very accurate for determining the C period for slowly growing bacteria. Marker frequency analysis of the origin to terminus ratio by hybridization is another method, which gives very precise measurements of the C period (Atlung & Hansen, 1993). However, this method is especially good for determining the C period in fast-growing bacteria where the origin to terminus ratio is high. In slow-growing bacteria with a long B period the origin to terminus ratio is very low and the method becomes less accurate. Although we have used flow cytometry in this study to estimate cell cycle periods also from faster-growing cultures, we recommend the use of different methods for bacteria growing at different generation times. Flow cytometry can be used successfully if the C+D period is less than two doubling times, and marker frequency analysis if the C period is longer than the doubling time.
As mentioned before, many of the previous determinations of the D period indicate that D is constant (Helmstetter, 1996). Some of these determinations are based on the assumption that protein synthesis is not required for cell division after termination of chromosome replication (Kubitschek, 1974
); others were calculated based on the fraction of replicating cells (Kubitschek & Newman, 1978
). However, early studies using the membrane elution technique (Helmstetter et al., 1968
) suggested that the D period increases with increasing generation time at generation times above 60 min. Also, a few flow cytometric determinations of D suggested that the D period increases with increasing generation time in E. coli B/r (Skarstad et al., 1983
, 1985
). In the light of these data and our measurements of the D periods in many different strains growing with different generation times, we conclude that the D period increases with increasing generation time.
However, there are clear differences between the lengths of the D period in the different strains at the long generation times, and the scatter of lengths of D periods is much greater than that for C periods, possibly caused by carbon-source-dependent variations of the D period. In general it is clear that the D period is much shorter in B/r strains than in K-12 strains. Compare for example the D period of 42 min in B/r A at 17 h doubling time with the D period of 90 min in B/r K at 16 h doubling time and with the 210 min D period we observed in the K-12 strain (PJ4004) at 13 h doubling time. We suggest that flow cytometric analysis is the best (and the easiest) way to get precise measurements of the D period.
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ACKNOWLEDGEMENTS |
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REFERENCES |
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Received 17 October 2002;
revised 9 December 2002;
accepted 24 December 2002.
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