Non-linear population dynamics in chemostats associated with live–dead cell cycling in Escherichia coli strain K12-MG1655

Bacterial populations conditionally display non-linear dynamic behaviour in bioreactors with steady inputs, which is often attributed to varying habitat conditions or shifting intracellular metabolic activity. However, mathematical modelling has predicted that such dynamics also might simply result from staggered birth, growth, and death events of groups of cells within the population, causing density oscillations and the cycling of live and dead cells within the system. To assess this prediction, laboratory experiments were performed on Escherichia coli strain K12-MG1655 grown in chemostats to first define fine-scale population dynamics over time (minutes) and then determine whether the dynamics correlate with live–dead cell cycles in the system. E. coli populations displayed consistent oscillatory behaviour in all experiments. However, close synchronisation between OD600 and live–dead cell oscillations (within ~33–38 min cycles) only became statistically significant (p < 0.01) when pseudo-steady state operations approaching carrying capacity existed in the bioreactor. Specifically, live cells were highest at local OD600 maxima and lowest at local OD600 minima, showing that oscillations followed live–dead cell cycles as predicted by the model and also consistent with recent observations that death is non-stochastic in such populations. These data show that oscillatory dynamic behaviour is intrinsic in bioreactor populations, which has implications to process operations in biotechnology.

[1]  L. Raskin,et al.  Diversity and dynamics of microbial communities in engineered environments and their implications for process stability. , 2003, Current opinion in biotechnology.

[2]  K. Lewis,et al.  Programmed Death in Bacteria , 2000, Microbiology and Molecular Biology Reviews.

[3]  B. Bassler,et al.  Quorum sensing: cell-to-cell communication in bacteria. , 2005, Annual review of cell and developmental biology.

[4]  H. Engelberg-Kulka,et al.  Bacterial Programmed Cell Death and Multicellular Behavior in Bacteria , 2006, PLoS genetics.

[5]  Frederik Hammes,et al.  Assessment and Interpretation of Bacterial Viability by Using the LIVE/DEAD BacLight Kit in Combination with Flow Cytometry , 2007, Applied and Environmental Microbiology.

[6]  D. Moinier,et al.  Existence of Abnormal Protein Aggregates in Healthy Escherichia coli Cells , 2007, Journal of bacteriology.

[7]  T. Nyström A Bacterial Kind of Aging , 2007, PLoS genetics.

[8]  M. Watve,et al.  Aging may be a conditional strategic choice and not an inevitable outcome for bacteria , 2006, Proceedings of the National Academy of Sciences.

[9]  Satoshi Okabe,et al.  Application of a direct fluorescence-based live/dead staining combined with fluorescence in situ hybridization for assessment of survival rate of Bacteroides spp. in drinking water. , 2005, Biotechnology and bioengineering.

[10]  John D. Baines,et al.  Burial and the dead in ancient Egyptian society , 2002 .

[11]  François Taddei,et al.  Asymmetric segregation of protein aggregates is associated with cellular aging and rejuvenation , 2008, Proceedings of the National Academy of Sciences.

[12]  Sabine Pruggnaller,et al.  Quantitative and spatio‐temporal features of protein aggregation in Escherichia coli and consequences on protein quality control and cellular ageing , 2010, The EMBO journal.

[13]  Steven N. Austad,et al.  Why do we age? , 2000, Nature.

[14]  François Taddei,et al.  In Brief , 2003, Nature Reviews Microbiology.

[15]  Katie Bloor,et al.  Experimental demonstration of chaotic instability in biological nitrification , 2007, The ISME Journal.

[16]  Omar E. Cornejo,et al.  Oscillations in continuous culture populations of Streptococcus pneumoniae: population dynamics and the evolution of clonal suicide , 2008, Proceedings of the Royal Society B: Biological Sciences.

[17]  Conrad L Woldringh Is Escherichia coli getting old? , 2005, BioEssays : news and reviews in molecular, cellular and developmental biology.

[18]  H. Kitano,et al.  Regulation of yeast oscillatory dynamics , 2007, Proceedings of the National Academy of Sciences.

[19]  Andrew Wright,et al.  Robust Growth of Escherichia coli , 2010, Current Biology.

[20]  Jared Rutter,et al.  Metabolism and the control of circadian rhythms. , 2002, Annual review of biochemistry.

[21]  Sam Dukan,et al.  Protein Aggregates: an Aging Factor Involved in Cell Death , 2008, Journal of bacteriology.

[22]  Martin Ackermann,et al.  Senescence in a Bacterium with Asymmetric Division , 2003, Science.

[23]  M. Yarmolinsky,et al.  Programmed cell death in bacterial populations , 1995, Science.

[24]  Vasile Lavric,et al.  Birth, growth and death as structuring operators in bacterial population dynamics. , 2010, Journal of theoretical biology.

[25]  M. Stephens EDF Statistics for Goodness of Fit and Some Comparisons , 1974 .

[26]  A. Walden,et al.  Spectral analysis for physical applications : multitaper and conventional univariate techniques , 1996 .

[27]  Smith,et al.  Mathematics of the Discrete Fourier Transform (DFT) with Audio Applications , 2007 .

[28]  T. Nyström Aging in bacteria. , 2002, Current opinion in microbiology.

[29]  T. Naganuma Differential enumeration of intact and damaged marine planktonic bacteria based on cell membrane integrity , 1996 .

[30]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

[31]  K. Rice,et al.  Molecular Control of Bacterial Death and Lysis , 2008, Microbiology and Molecular Biology Reviews.