Asymmetric cellular memory in bacteria exposed to antibiotics
© The Author(s). 2017
Received: 23 July 2016
Accepted: 15 January 2017
Published: 9 March 2017
The ability to form a cellular memory and use it for cellular decision-making could help bacteria to cope with recurrent stress conditions. We analyzed whether bacteria would form a cellular memory specifically if past events are predictive of future conditions. We worked with the asymmetrically dividing bacterium Caulobacter crescentus where past events are expected to only be informative for one of the two cells emerging from division, the sessile cell that remains in the same microenvironment and does not migrate.
Time-resolved analysis of individual cells revealed that past exposure to low levels of antibiotics increases tolerance to future exposure for the sessile but not for the motile cell. Using computer simulations, we found that such an asymmetry in cellular memory could be an evolutionary response to situations where the two cells emerging from division will experience different future conditions.
Our results raise the question whether bacteria can evolve the ability to form and use cellular memory conditionally in situations where it is beneficial.
KeywordsBacterial memory Priming Caulobacter crescentus Asymmetry
In bacteria, as in every cellular organism, the current state and composition of a cell is determined by events in the past [1–4]. Factors like nutrient availability, physical and chemical conditions and biological signals in the recent past influence the current set of transcripts and proteins in a cell, its metabolic activity, and other aspects of its phenotype. We refer to this dependency of the current state of single cells on past conditions as ‘cellular memory’. We use the term ‘cellular memory’ in a phenomenological sense for situations where a phenotypic trait of a cell depends on past conditions, irrespective of the underlying molecular mechanism. One fundamental question is whether cellular memory is beneficial [1, 3, 4]: To what degree does it allow a cell to better cope with current and future conditions? Do bacteria ‘remember’ past environmental states and use this memory to anticipate future conditions?
This question is motivated by the fact that the degree of cellular memory is shaped by biological processes that are encoded by genes, and that can thus undergo evolutionary change in response to natural selection. Cellular memory depends on the half-lives and turnover of cellular components—for example RNAs and proteins—and on the architecture of gene-regulatory networks [1, 5–7]. Importantly, cellular memory can be differentially controlled for different phenotypic traits: for example, traits that are based on transcripts with long half-lives and stable proteins are expected to show a large degree of cellular memory, whereas other traits that are based on transcripts with short half-lives and unstable proteins are expected to have a low degree of cellular memory.
The ‘adaptive value’ of cellular memory—its effect on survival and reproduction of individual cells—is expected to depend on the specific situation. For some environmental factors the conditions in the past might be indicative of the conditions in the future, and cellular memory for phenotypic traits that determine the response to these environmental factors might thus be beneficial; for other factors this might not be the case. This raises the question whether bacteria (and other organisms) evolved high degrees of cellular memory specifically in traits and in circumstances where this is beneficial, and low degrees of cellular memory when it is not. We refer to this possibility as conditional cellular memory.
We investigated if organisms memorize past events specifically in situations where past events have predictive value about the future. To address this question, we used an experimental system where cellular memory is expected to be beneficial to some cells in a population, and less beneficial to other cells. Specifically, we worked with an asymmetrically dividing bacterium—Caulobacter crescentus—where one cell emerging from division remains in the same microenvironment, while the other migrates to a different microenvironment . For the first cell the future conditions are expected to be related to the past (since the cell is staying in the same microenvironment), and for this cell it might therefore be advantageous to memorize past events to prepare for future conditions. For the second cell future conditions are not related, or related to a lesser degree, to the past. It could thus be neutral or disadvantageous to prepare for these future conditions based on past events. Based on these considerations, one would expect the second cell to show a lower degree of cellular memory than the first cell. If in such a scenario we can observe differences in how these two cell types use past events to prepare for future environmental conditions, this would be consistent with the interpretation that this organism has evolved the ability to memorize past events, but to use this ability only if it is beneficial (we use the phrasing ‘consistent with the interpretation’ to emphasize that such a finding would not firmly establish that asymmetric memory is a direct consequence of natural selection on this trait, since asymmetric memory could also be caused by a range of other factors such as differences in the cellular composition due to the asymmetry of mother and daughter cell in this bacterium ).
We chose C. crescentus to address this question of conditional cellular memory because it is a well-established model organism to study asymmetric cell division. A surface attached cell divides asymmetrically into a sessile stalked cell and motile swarmer cell. We refer to the sessile stalked cell as ‘mother’ and the motile swarmer cell as ‘daughter’ as for example in [10, 11]. This organism thus provides an ideal opportunity to address the question whether this organism uses cellular memory depending on whether memory helps cells to anticipate future conditions or not. While the sessile mother cell remains attached to the surface at the same location as before division, the motile daughter cell can move to a different microenvironment before differentiating into a stalked cell and initiating division. For the sessile mother information about the recent past may thus be of value to predict upcoming environmental conditions. For the motile daughter the past is arguably less informative, due to the change of location .
Bacterial memory has been studied in different contexts such as changing nutrient conditions [1, 13–16] or cues that indicate upcoming adverse conditions [3, 17–20]. Here we exposed cells with a low level of a stressor as a ‘warning’ of an upcoming exposure to a higher level of the same stressor. In such a scenario a cell that uses the warning to prepare for an upcoming stressful event can have a higher probability to survive. We can thus ask whether cells keep a cellular memory of the warning only if they are staying in the same microenvironment, and do not keep a cellular memory if they are migrating to another microenvironment where the timing of stress is probably different.
We used a combination of single-cell experiments and computer simulations to address these questions. We experimentally exposed C. crescentus to the antibiotic ampicillin . C. crescentus carries six genes that confer—when expressed—a certain degree of resistance to ampicillin, a beta-lactam antibiotic . Resistance to antibiotics has been found to be common in bacteria living in freshwater, and might be an adaptation to antibiotics produced by other microorganisms [23–27]. C. crescentus is potentially exposed to naturally occurring beta-lactams in its freshwater environment . Antibiotics are an ideal stressor to test our hypothesis about asymmetric memory in bacteria: while a sessile bacterial cell is bound to ‘endure’ an exposure to antibiotics produced by other microbes in the same microenvironment; a motile cell, in contrast, might move away from these producers and into a new microenvironment with a different regime of antibiotics exposure. We thus analyzed, for both cell types, whether a previous warning event would increase the tolerance to a subsequent stress event. These experiments indeed revealed a small but statistically significant asymmetry in the distribution of the cellular memory in response to antibiotics: the warning increased the survival of the sessile mother but not of the motile daughter during subsequent exposure to high concentrations of antibiotics.
These experimental results motivated us to ask whether such asymmetry in how past events influence future behavior could indeed be an evolved response to a situation where the predictive value of past events differs for the two cell types emerging from division. We used computer simulations to test whether asymmetric memory is expected to evolve in a situation where one individual emerging from division remains in the same environment while the other individual migrates to a different environment. These simulations support the notion that the asymmetry in cellular memory that we observed in the single-cell experiments could be the result of differential selection on the sessile and motile cell type. More generally, our experimental and theoretical results suggest that analyzing what types of past events are stored in different cell types allows formulating hypotheses about the adaptive nature of bacterial memory.
Results and discussion
We used this system to test the main idea put forward above: that the sessile mothers—but not the motile daughters—would retain a cellular memory of past events. To do so, we compared how survival under antibiotic exposure of the two groups of cells depended on a past warning. The first group (which we call ‘mothers’) was the cohort of stalked cells that were already present at the time of the warning; the second group (which we call ‘daughters’) was the cohort of cells that were produced after the warning, and that stayed in the microfluidic device and could thus be observed. It is important to note that many of the daughters had already differentiated to stalked cells at the onset of the ‘stress event’. While the time-point of differentiation for individual cells cannot be determined in our experimental set-up, the swarmer cells that remain in the microfluidic device, and that can thus be observed, only take about 20 min longer to reach the next cell division than the stalked cells (Additional file 3: Figure S3). This indicates that they have completed differentiation to a stalked cell after about 20 min.
This outcome is consistent with the hypothesis that we put forward above, that cells only form a cellular memory if this is beneficial, i.e., in situation where past events are informative about future conditions. However, it is important to note that this finding is by no means sufficient to firmly establish that this hypothesis is correct. There are many other possible non-evolutionary reasons why swarmer cells would not retain the (unknown) cellular components upon cell division that make their stalked counterpart more tolerant to antibiotics following after the warning, as will be discussed further down. We interpret these results thus not as conclusive evidence for a beneficial asymmetric memory, but rather as an interesting observation that raises new questions. One of the questions that it raises, and that we addressed in the next part, is: do we indeed expect asymmetric memory to evolve in response to a situation where the predictive value of past events differs for the two cell types emerging from division? We used computer simulations to analyze how cellular memory evolves in situation where the predictive value of past events differs for the two cells emerging from division.
Motivated by the experimental results shown above, we used an agent-based simulation to analyze the evolution of cellular memory in simulated bacterial populations. While our main goal here was to investigate the evolution of asymmetric memory, we first asked a simpler question: under what circumstances do we expect evolution of the ability to form a cellular memory? Building on these results, we then analyzed the distribution of memory among asymmetrically dividing cells.
We simulated individual cells with evolvable traits that were genetically encoded. These traits determined the formation of a cellular memory that can provide protection against an external stressor. The level of protection of a given cell was a continuous trait that could change over time, and that determined its survival upon exposure to stress. The current level of protection, and its modulation by the environment, depended on three genetically encoded traits: ‘basal protection’ determined the minimal protection a cell maintained during all times; ‘protection increase’ determined the absolute amount of protection that was added to the current protection level when warning or stress conditions were sensed; ‘protection decrease’ referred to the absolute amount of protection that was subtracted from the current protection level during each time step. If an addition or subtraction of protection to the current level of protection resulted in a protection level higher than 1 or lower than 0, it was corrected in order to ensure that the protection level was always between 0 and 1. When a cell divided, these genetically encoded traits were copied to the two cells emerging from division. At cell division the protection level, which is a phenotypic property of the dividing cell, was divided into two equal parts, and both cells emerging from division received one of these parts (we relaxed this last assumption in the second version of the model, as described below).
In the context of our model, we interpreted the phenotypic trait of increasing the protection level in response to a warning or stress event as the ability to form a cellular memory, and the phenotypic trait of decreasing the protection level under favorable conditions as the ability to erase this memory. The current protection level of a cell then characterized the current state of the cellular memory; it was influenced both by the environment the cell was exposed to in the past as well as by the cell’s genotype that determined how these past events influenced the cellular state. The current protection level determined the probability that a cell would survive a stress event.
We extended the simulation framework to investigate the evolution of memory in organisms where the two cells emerging from division had different fates. Specifically, we were interested in situations where one cell would remain in the current environment, and the other could migrate and potentially colonize a different environment. We refer to the first cell as the ‘mother’, and the second as the ‘daughter’, as above. We then tested the idea developed above, that such a situation could select for organisms where the mother cell carries a memory, while the daughter cell would not keep a memory. The basis of this hypothesis was that the daughter cell would migrate to a new environment, so that past events in the birth environment were not informative. To address this question with our simulation framework we introduced an additional evolvable trait that we called ‘memory distribution factor’. The memory distribution factor controlled how memory (i.e., the current level of protection, which is a phenotypic trait) was distributed between the mother and the daughter at cell division. A memory distribution factor of 0 corresponded to a situation where the mother kept the entire memory (i.e., the current protection level), while a distribution factor of 1 corresponded to a situation where the daughter cell received the entire memory (i.e., the current protection level). In the previous simulations memory was split equally between these two cells. This corresponded to fixing the memory distribution factor to 0.5. By permitting the memory distribution factor to evolve we expected it to deviate from 0.5 when the past was only informative to the mother but not to the daughter cell.
We expected that this situation would select for organisms where memory, i.e. protection, would be distributed asymmetrically at cell division: for the mother, past events were informative about the future, and thus the mother was expected to maintain memory. For the daughter, past events were less predictive about the future (since it only stays in the same environment in 50% of the cases), and we thus expected that it would keep less memory. In the context of our model, such asymmetric distribution of memory would manifest as a memory distribution factor smaller than 0.5. As a control, we ran the simulations with the same evolvable traits in a single environment where both cell types emerging from a cell division were staying in the same environment (Fig. 8a).
In the control environment, where both mother and daughter remained in same environment, we found an unexpected result: while there was no consistent distribution of protection (Fig. 9a, orange) to either mother or daughter across the 20 replicate populations (which is to be expected because mother and daughters are equivalent in this setting), within most of the 20 replicate populations there was a clear tendency for protection to be either passed on to the mother cells (memory distribution factor close to 0) or to the daughter cells (memory distribution factor close to 1). This means that most of these 20 replicated populations evolved a type of asymmetric memory where cellular protection was passed on to one of the two cells emerging from division, rather than distributed to both types equally (see Additional file 4: S8.2 left panels, for memory distribution factor trait distributions, orange, per single simulation). We note that this outcome is not equivalent to the situation with two environments, where cellular protection consistently is passed on to the mother cell that stays in the same environment (orange, Fig. 9b and Additional file 4: S8.2 right panels), consistent with the idea put forward above. We see a possible interpretations for this asymmetry observed in the control populations: distributing protection asymmetrically leads to variation between the two cells emerging from division, and this could increase the long-term growth rate of these types by decreasing the temporal variation in survival. This decrease in temporal variation results from the fact that through this asymmetry a fraction of the individuals are well prepared for stress, while others are well prepared for favorable conditions. This effect is known as bet-hedging [34–36].
These simulations indicate that memory is expected to evolve in an informative environment (Fig. 7b) and that it is distributed asymmetrically to the cell types for which past conditions are informative about the future (Fig. 9b). Asymmetric segregation of cellular memory between the two cells emerging from division can thus be indicative of an adaptive role of cellular memory; this is because asymmetric segregation can be a manifestation of differences between cells in how informative events are to predict future events. However, it is important to note that we observed that asymmetric memory could also evolve for other reasons (see Fig. 9a), potentially because it can reduce temporal variation in survival through a bet-hedging mechanism.
While using these agent-based simulations to explore the evolution of cellular memory in different environments can be useful for testing verbal arguments and guiding experimental investigations, we want to emphasize that the behavior observed in biological systems is more complex than what is captured by our model. We tried to keep the number of parameters as low as possible to and make sensible choices (also see a discussion on the sensitivity on parameter values in Additional file 5: S9), to increase the probability of finding general patterns.
In conclusion, the asymmetry that we observed between mother and daughter cells regarding the influence of past events on future behavior is consistent with the view that cellular memory might be shaped by natural selection to increase survival and reproduction in the face of environmental fluctuations. Our computer simulations indicate that the ability to form a cellular memory can evolve in situations where the past is informative with respect to future events, and that asymmetric memory is expected to evolve if the past is predictive for one cell type but not for the other. Asymmetric cellular memory might indeed be a possible explanation of the experimental results, in the sense that a previous ‘warning’ increased survival during antibiotic exposure for the sessile mother but not for the motile daughter cells. However, we cannot rule out alternative hypotheses. C. crescentus stalked cells and swarmer cells differ in behavior and motility, and this results in differences in a large number of cellular features, including proteome composition , transcriptional [38, 39] and translational activity  and second-messenger signaling . It is well possible that the relative small asymmetry in cellular memory that we observed is an unselected consequence of these cellular differences, rather than a consequence of natural selection acting differently on history-dependent behavior of swarmer and stalked cells.
While we thus cannot currently exclude alternative explanations for this finding, these experimental and theoretical results raise the interesting question whether microorganisms might evolve specificity in the cellular memory, in the sense that they store information about past events specifically in cases where these past events are informative with respect to future conditions. Asymmetrically dividing cells are an interesting model system to further investigate the nature and function of bacterial memory.
The microfluidic devices used were adapted from the ‘mother machine’ design . Masks for photolithography were ordered from ML&C GmbH, Jena, Germany. Two-step photolithography was used to obtain silicon wafers. Silicone elastomers (Sylgard 184 Silicone Elastomer Kit, Dow Corning) were prepared by mixing the two components in a ratio of 10:1, poured on the dust-free wafer, de-aired in a desiccator to eliminate air bubbles, and incubated overnight at 80 °C for curing. Polydimethylsiloxane elastomer (PDMS) chips of approximately 2.5 cm × 4 cm were cut out around the structures of the wafer. Each PDMS chip featured 8 separate channels (we did not make use of the narrow side channels of the ‘mother machine’ design because C. crescentus attaches to the glass slide naturally). This enabled us to test 8 conditions in one experiment (Fig. 1b). The channels were 22 μm deep and 100 μm wide. Holes for medium supply and outlet were punched using a 20 G needle (1.2 mm × 40 mm) that was modified by breaking off the beveled tip and sharpening the edges of the now straight tip. The surface residues on the PDMS chips and on the round (50 mm diameter) glass coverslips (Menzel-Gläser, Braunschweig, Germany) were chemically activated by treating both surfaces for 6 min in a UV-Ozone cleaner (Novascan PSD-UV). The PDMS chips were then placed on the glass cover slips, the exposed sides facing each other, and heated at 100 °C for 1 h to ensure binding. Before the experiment, the chips were rinsed with PYE medium (see below) with a flow rate of 3.5 mL/h until the channels were filled. This was done using 1 mL syringes (Codan/Once Primo, Huberlab) with a single-syringe pump (NE-300, NewEra Pump Systems).
We constructed a transcriptional fusion of the blaA gene with egfp (green fluorescent protein). The chromosomal setup suggests that blaA (CC2139) is part of an operon with three other genes (CC2141, CC2140, CC2138) . We amplified the promoter region of CC2141 and fused it with egfp (green fluorescent protein). The amplified region was inserted into pMR10 background. The resulting plasmid was transformed into wildtype C. crescentus CB15 , ATCC 19089. GFP expression from this plasmid was used to assess induction of blaA (see Additional file 6: Figure S6). The GFP signal was not used for data analysis except for the findings reported in Additional file 6: Figure S6.
Bacteria were grown overnight in culture tubes (100 mm × 16 mm PP reaction tube, Sarstedt, Nümbrecht, Germany) in 5 mL PYE complex medium  shaking at 220 rpm at 30 °C, and then diluted 1:10’000 in 25 mL PYE in a 50 mL Falcon tube to obtain exponentially growing cells. When the culture reached an OD600 of 0.2, 12.5 mL of cells were centrifuged for 3 min at 4600 × g in a 15 mL Falcon tube. Supernatant was discarded and cells were resuspended in the remaining 500 μL and loaded into a 1 mL syringe (Codan/Once Primo, Huberlab). The cells were then pumped into the channel of the microfluidic chip for 1 min at a rate of 3.5 mL/h using a single-syringe pump (NE-300, NewEra Pump Systems). The cells were incubated in the chip for 20 min at 30 ° C. During that time swarmer cells attached to the surfaces of the main channel of the chip (Fig. 3) and later differentiated to stalked cells and started to divide.
For the experiment two pumps (NE-1800, NewEra Pump Systems) with 8 syringes in parallel were used. This ensured constant growth conditions, keeping the sessile cells in exponential phase and preventing the formation of biofilm. 50 mL syringes (Pic Solution) were loaded with 50 mL PYE and used for the non-stress periods. For the ampicillin warning and stress events, 10 mL syringes (Soft-Ject) were loaded with 2 mL of PYE + ampicillin (2, 10 or 2000 μL/mL depending on the condition applied). Tubing (Microbore Tygon X74HL, ID 0.76 mm, OD 2.29 mm, Fisher Scientific) was connected to the syringes using 20G needles (0.9 mm × 70 mm, Huberlab). Smaller tubing (PTFE, ID 0.3 mm, OD 0.76 mm, Fisher Scientific) was then inserted into the bigger tubing (Tygon S54HL), and directly connected to the inlet hole in the PDMS chip. Medium change was performed by disconnecting and reconnecting the tubing from the PYE to PYE + ampicillin. Pumping speed during the experiments was set to 2 mL/h.
Microscopy was performed using an Olympus IX81 inverted microscope system with automated stage, shutters, and laser based autofocus system. Several positions were monitored in parallel on the same device, and phase contrast images of every position were taken every 5 min. Images were acquired using an UPLFLN40x phase contrast objective (Olympus) and a cooled CCD camera (Olympus XM10). For image acquisition, the Xcellence Pro software package (Olympus, Version 1.2) was used. To keep temperature at 30 ° C the microscope was placed in an incubated box (Life Imaging Services, Reinach, Switzerland). Fluorescence images were acquired using a 120 W mercury short arc lamp (Xcite 120PC Q) and the U-N49002 EGFP filter set (450–490 nm ex/500–550 em/495 dichroic mirror, Chroma).
Images were analyzed with ImageJ. A plugin (CellCounter, Kurt de Vos, University of Sheffield, http://rsb.info.nih.gov/ij/plugins/cell-counter.html) was adapted such that cells could be marked across all time frames. Division frames of each cell were stored in a XML file upon visual inspection. Simulations were programmed in C++. Matlab and R were used for data analysis. The complete code of the computational model can be obtained from the corresponding author upon request.
We thank Yves Barral for helpful discussions and comments. Daan Kiviet adapted the mother machine design to fit eight channels on one chip and produced the silicon wafers. This work was supported by a grant from the Swiss National Science Foundation to M.A., by ETH Zurich and by Eawag.
ETH Zurich, Eawag and Swiss National Science Foundation, Grant no. 31003A_149267 to M.A.
Availability of data and materials
The datasets analysed during the current study are on labarchives.com: https://mynotebook.labarchives.com/share/Caulobacter%2520Asymmetric%2520Memory/MTMuMHwyMzYwODgvMTAvVHJlZU5vZGUvNDAwNjkwMzI3MHwzMy4w.
RM and MA designed research; RM performed research; RM analyzed data; and RM and MA wrote the paper. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Lambert G, Kussel E. Memory and fitness optimization of bacteria under fluctuating environments. PLoS Genet. 2014;10:e1004556. dx.plos.org.View ArticlePubMedPubMed CentralGoogle Scholar
- Wolf DM, Fontaine-Bodin L, Bischofs I, Price G, Keasling J, Arkin AP. Memory in microbes: quantifying history-dependent behavior in a bacterium. PLoS One. 2008;3:e1700.View ArticlePubMedPubMed CentralGoogle Scholar
- Veening J-W, Stewart EJ, Berngruber TW, Taddei F, Kuipers OP, Hamoen LW. Bet-hedging and epigenetic inheritance in bacterial cell development. Proc Natl Acad Sci U S A. 2008;105:4393–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Casadesús J, D’Ari R. Memory in bacteria and phage. Bioessays. 2002;24:512–8.View ArticlePubMedGoogle Scholar
- Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet. 2007;8:450–61.View ArticlePubMedGoogle Scholar
- Zordan RE, Miller MG, Galgoczy DJ, Tuch BB, Johnson AD. Interlocking transcriptional feedback loops control white-opaque switching in Candida albicans. PLoS Biol. 2007;5:e256.View ArticlePubMedPubMed CentralGoogle Scholar
- Fritz G, Buchler NE, Hwa T, Gerland U. Designing sequential transcription logic: a simple genetic circuit for conditional memory. Syst Synth Biol. 2007;1:89–98.View ArticlePubMedPubMed CentralGoogle Scholar
- Poindexter JS. The caulobacters: ubiquitous unusual bacteria. Microbiol Rev. 1981;45:123–79.PubMedPubMed CentralGoogle Scholar
- Tsokos CG, Laub MT. Polarity and cell fate asymmetry in Caulobacter crescentus. Curr Opin Microbiol. 2012;15:744–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Ackermann M, Schauerte A, Stearns SC, Jenal U. Experimental evolution of aging in a bacterium. BMC Evol Biol. 2007;7:126.View ArticlePubMedPubMed CentralGoogle Scholar
- Ackermann M, Chao L, Bergstrom CT, Doebeli M. On the evolutionary origin of aging. Aging Cell. 2007;6:235–44.View ArticlePubMedPubMed CentralGoogle Scholar
- Norman TM, Lord ND, Paulsson J, Losick R. Memory and modularity in cell-fate decision making. Nature. 2013;503:481–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Tagkopoulos I, Liu Y-C, Tavazoie S. Predictive behavior within microbial genetic networks. Science. 2008;320:1313–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Schild S, Tamayo R, Nelson EJ, Qadri F, Calderwood SB, Camilli A. Genes induced late in infection increase fitness of Vibrio cholerae after release into the environment. Cell Host Microbe. 2007;2:264–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, Kupiec M, et al. Adaptive prediction of environmental changes by microorganisms. Nature. 2009;460:220–4. Nature Publishing Group.View ArticlePubMedGoogle Scholar
- Hoffer SM, Westerhoff HV, Hellingwerf KJ, Postma PW, Tommassen JAN. Autoamplification of a Two-Component Regulatory System Results in “Learning” Behavior. Society. 2001;183:4914–7.Google Scholar
- Mathis R, Ackermann M. Response of single bacterial cells to stress gives rise to complex history dependence at the population level. Proc Natl Acad Sci U S A. [Internet]. 2016; Available from: http://dx.doi.org/10.1073/pnas.1511509113
- Mitchell A, Pilpel Y. A mathematical model for adaptive prediction of environmental changes by microorganisms. Proc Natl Acad Sci U S A. 2011;108:7271–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Wolf DM, Vazirani VV, Arkin AP. Diversity in times of adversity: probabilistic strategies in microbial survival games. J Theor Biol. 2005;234:227–53.View ArticlePubMedGoogle Scholar
- Lou Y, Yousef AE. Adaptation to sublethal environmental stresses protects Listeria monocytogenes against lethal preservation factors. Appl Environ Microbiol. 1997;63:1252–5.PubMedPubMed CentralGoogle Scholar
- Zeng X, Lin J. Beta-lactamase induction and cell wall metabolism in Gram-negative bacteria. Front Microbiol. 2013;4:128.View ArticlePubMedPubMed CentralGoogle Scholar
- West L, Yang D, Stephens C. Use of the Caulobacter crescentus genome sequence to develop a method for systematic genetic mapping. Society. 2002;184:2155–66.Google Scholar
- Baquero F, Martínez J-L, Cantón R. Antibiotics and antibiotic resistance in water environments. Curr Opin Biotechnol. 2008;19:260–5.View ArticlePubMedGoogle Scholar
- Brook I. The role of beta-lactamase-producing-bacteria in mixed infections. BMC Infect Dis. 2009;9:202.View ArticlePubMedPubMed CentralGoogle Scholar
- Ash RJ, Mauck B, Morgan M. Antibiotic resistance of gram-negative bacteria in rivers, United States. Emerg Infect Dis. 2002;8:713–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Abrudan MI, Smakman F, Grimbergen AJ, Westhoff S, Miller EL, van Wezel GP, et al. Socially mediated induction and suppression of antibiosis during bacterial coexistence. Proc Natl Acad Sci U S A. 2015;112:11054–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Raaijmakers JM, Mazzola M. Diversity and natural functions of antibiotics produced by beneficial and plant pathogenic bacteria. Annu Rev Phytopathol. 2012;50:403–24.View ArticlePubMedGoogle Scholar
- Poindexter JS. Biological properties and classification of the Caulobacter Group. Bacteriol Rev. 1964;28:231–95.PubMedPubMed CentralGoogle Scholar
- Ackermann M, Stearns SC, Jenal U. Senescence in a bacterium with asymmetric division. Science. 2003;300:1920.View ArticlePubMedGoogle Scholar
- Kussell E, Leibler S. Phenotypic diversity, population growth, and information in fluctuating environments. Science. 2005;309:2075–8.View ArticlePubMedGoogle Scholar
- Donaldson-Matasci MC, Bergstrom CT, Lachmann M. The fitness value of information. Oikos. 2010;119:219–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Acar M, Mettetal JT, van Oudenaarden A. Stochastic switching as a survival strategy in fluctuating environments. Nat Genet. 2008;40:471–5.View ArticlePubMedGoogle Scholar
- Arnoldini M, Mostowy R, Bonhoeffer S, Ackermann M. Evolution of stress response in the face of unreliable environmental signals. PLoS Comput Biol. 2012;8:e1002627.View ArticlePubMedPubMed CentralGoogle Scholar
- Donaldson-Matasci MC, Lachmann M, Bergstrom CT. Phenotypic diversity as an adaptation to environmental uncertainty. Evol Ecol Res. 2008;10:493–515. Evolutionary Ecology, Ltd.Google Scholar
- Haccou P, Iwasa Y. Optimal mixed strategies in stochastic environments. Theor Popul Biol. 1995;47:212–43.View ArticleGoogle Scholar
- Cohen D. Optimizing reproduction in a randomly varying environment. J Theor Biol. 1966;12:119–29.View ArticlePubMedGoogle Scholar
- Werner JN, Chen EY, Guberman JM, Zippilli AR, Irgon JJ, Gitai Z. Quantitative genome-scale analysis of protein localization in an asymmetric bacterium. Proc Natl Acad Sci U S A. 2009;106:7858–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Laub MT, McAdams HH, Feldblyum T, Fraser CM, Shapiro L. Global analysis of the genetic network controlling a bacterial cell cycle. Science. 2000;290:2144–8.View ArticlePubMedGoogle Scholar
- Skerker JM, Laub MT. Cell-cycle progression and the generation of asymmetry in Caulobacter crescentus. Nat Rev Microbiol. 2004;2:325–37.View ArticlePubMedGoogle Scholar
- Schrader JM, Li G-W, Childers WS, Perez AM, Weissman JS, Shapiro L, et al. Dynamic translation regulation in Caulobacter cell cycle control. Proc Natl Acad Sci U S A. 2016;113:E6859–67.View ArticlePubMedGoogle Scholar
- Christen M, Kulasekara HD, Christen B, Kulasekara BR, Hoffman LR, Miller SI. Asymmetrical distribution of the second messenger c-di-GMP upon bacterial cell division. Science. 2010;328:1295–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang P, Robert L, Pelletier J, Dang WL, Taddei F, Wright A, et al. Robust growth of Escherichia coli. Curr Biol. 2010;20:1099–103.View ArticlePubMedPubMed CentralGoogle Scholar
- Ely B.  Genetics of Caulobacter crescentus. Methods Enzymol. 1991;204:372–84. Elsevier.View ArticlePubMedGoogle Scholar