Ecological strategies and fitness tradeoffs inEscherichia coli mutants adapted to prolonged starvation

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Trade-offs, trade-ups, and high mutational parallelism underlie microbial adaptation during extreme cycles of feast and famine

Microbes are evolutionarily robust organisms capable of rapid adaptation to complex stress, which enables them to colonize harsh environments. In nature, microbes are regularly challenged by starvation, which is a particularly complex stress because resource limitation often co-occurs with changes in pH, osmolarity, and toxin accumulation created by metabolic waste. Often overlooked are the additional complications introduced by eventual resource replenishment, as successful microbes must withstand rapid environmental shifts before swiftly capitalizing on replenished resources to avoid invasion by competing species. To understand how microbes navigate trade-offs between growth and survival, ultimately adapting to thrive in environments with extreme fluctuations, we experimentally evolved 16 Escherichia coli populations for 900 days in repeated feast/famine conditions with cycles of 100-day starvation before resource replenishment. Using longitudinal population-genomic analysis, we found that evolution in response to extreme feast/famine is characterized by narrow adaptive trajectories with high mutational parallelism and notable mutational order. Genetic reconstructions reveal that early mutations result in trade-offs for biofilm and motility but trade-ups for growth and survival, as these mutations conferred positively correlated advantages during both short-term and long-term culture. Our results demonstrate how microbes can navigate the adaptive landscapes of regularly fluctuating conditions and ultimately follow mutational trajectories that confer benefits across diverse environments.

Adaptive laboratory evolution of a thermophile toward a reduced growth temperature optimum

Thermophily is an ancient trait among microorganisms. The molecular principles to sustain high temperatures, however, are often described as adaptations, somewhat implying that they evolved from a non-thermophilic background and that thermophiles, i.e., organisms with growth temperature optima (TOPT) above 45°C, evolved from mesophilic organisms (TOPT 25-45°C). On the contrary, it has also been argued that LUCA, the last universal common ancestor of Bacteria and Archaea, may have been a thermophile, and mesophily is the derived trait. In this study, we took an experimental approach toward the evolution of a mesophile from a thermophile. We selected the acetogenic bacterium T. kivui (TOPT 66°C) since acetogenesis is considered ancient physiology and cultivated it at suboptimal low temperatures. We found that the lowest possible growth temperature (TMIN) under the chosen conditions was 39°C. The bacterium was subsequently subjected to adaptive laboratory evolution (ALE) by serial transfer at 45°C. Interestingly, after 67 transfers (approximately 180 generations), the adapted strain Adpt45_67 did not grow better at 45°C, but a shift in the TOPT to 60°C was observed. Growth at 45°C was accompanied by a change in the morphology as shorter, thicker cells were observed that partially occurred in chains. While the proportion of short-chain fatty acids increased at 50°C vs. 66°C in both strains, Adpt45_67 also showed a significantly increased proportion of plasmalogens. The genome analysis revealed 67 SNPs compared to the type strain, among these mutations in transcriptional regulators and in the cAMP binding protein. Ultimately, the molecular basis of the adaptation of T. kivui to a lower TOPT remains to be elucidated. The observed change in phenotype is the first experimental step toward the evolution of thermophiles growing at colder temperatures and toward a better understanding of the cold adaptation of thermophiles on early Earth.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Osmotrophy of dissolved organic carbon by coccolithophores in darkness

The evolutionary and ecological story of coccolithophores poses questions about their heterotrophy, surviving darkness after the end-Cretaceous asteroid impact as well as survival in the deep ocean twilight zone. Uptake of dissolved organic carbon might be an alternative nutritional strategy for supply of energy and carbon molecules. Using long-term batch culture experiments, we examined coccolithophore growth and maintenance on organic compounds in darkness. Radiolabelled experiments were performed to study the uptake kinetics. Pulse-chase experiments were used to examine the uptake into unassimilated, exchangeable pools vs assimilated, nonexchangeable pools. We found that coccolithophores were able to survive and maintain their metabolism for up to 30 d in darkness, accomplishing about one cell division. The concentration dependence for uptake was similar to the concentration dependence for growth in Cruciplacolithus neohelis, suggesting that it was taking up carbon compounds and immediately incorporating them into biomass. We recorded net incorporation of radioactivity into the particulate inorganic fraction. We conclude that osmotrophy provides nutritional flexibility and supports long-term survival in light intensities well below threshold for photosynthesis. The incorporation of dissolved organic matter into particulate inorganic carbon, raises fundamental questions about the role of the alkalinity pump and the alkalinity balance in the sea.

A universal trade-off between growth and lag in fluctuating environments

The rate of cell growth is crucial for bacterial fitness and drives the allocation of bacterial resources, affecting, for example, the expression levels of proteins dedicated to metabolism and biosynthesis1,2. It is unclear, however, what ultimately determines growth rates in different environmental conditions. Moreover, increasing evidence suggests that other objectives are also important3-7, such as the rate of physiological adaptation to changing environments8,9. A common challenge for cells is that these objectives cannot be independently optimized, and maximizing one often reduces another. Many such trade-offs have indeed been hypothesized on the basis of qualitative correlative studies8-11. Here we report a trade-off between steady-state growth rate and physiological adaptability in Escherichia coli, observed when a growing culture is abruptly shifted from a preferred carbon source such as glucose to fermentation products such as acetate. These metabolic transitions, common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. Metabolomic analysis reveals that long lags result from the depletion of key metabolites that follows the sudden reversal in the central carbon flux owing to the imposed nutrient shifts. A model of sequential flux limitation not only explains the observed trade-off between growth and adaptability, but also allows quantitative predictions regarding the universal occurrence of such tradeoffs, based on the opposing enzyme requirements of glycolysis versus gluconeogenesis. We validate these predictions experimentally for many different nutrient shifts in E. coli, as well as for other respiro-fermentative microorganisms, including Bacillus subtilis and Saccharomyces cerevisiae.

Wide lag time distributions break a trade-off between reproduction and survival in bacteria

Many microorganisms face a fundamental trade-off between reproduction and survival: Rapid growth boosts population size but makes microorganisms sensitive to external stressors. Here, we show that starved bacteria encountering new resources can break this trade-off by evolving phenotypic heterogeneity in lag time. We quantify the distribution of single-cell lag times of populations of starved Escherichia coli and show that population growth after starvation is primarily determined by the cells with shortest lag due to the exponential nature of bacterial population dynamics. As a consequence, cells with long lag times have no substantial effect on population growth resumption. However, we observe that these cells provide tolerance to stressors such as antibiotics. This allows an isogenic population to break the trade-off between reproduction and survival. We support this argument with an evolutionary model which shows that bacteria evolve wide lag time distributions when both rapid growth resumption and survival under stressful conditions are under selection. Our results can explain the prevalence of antibiotic tolerance by lag and demonstrate that the benefits of phenotypic heterogeneity in fluctuating environments are particularly high when minorities with extreme phenotypes dominate population dynamics.

Density-Dependent Recycling Promotes the Long-Term Survival of Bacterial Populations during Periods of Starvation

The amount of natural resources in the Earth’s environment is in flux, which can trigger catastrophic collapses of ecosystems. How populations survive under nutrient-poor conditions is a central question in ecology. Curiously, some bacteria persist for a long time in nutrient-poor environments. Although this survival may be accomplished through cell death and the recycling of dead cells, the importance of these processes and the mechanisms underlying the survival of the populations have not been quantitated. Here, we use microbial laboratory experiments and mathematical models to demonstrate that death and recycling are essential activities for the maintenance of cell survival. We also show that the behavior of the survivors is governed by population density feedback, wherein growth is limited not only by the available resources but also by the population density. The numerical simulations suggest that population density-dependent recycling could be an advantageous behavior under starvation conditions.

How organisms survive after exhaustion of resources is a central question in ecology. Starving Escherichia coli constitute a model system to understand survival mechanisms during long-term starvation. Although death and the recycling of dead cells might play a key role in the maintenance of long-term survival, their mechanisms and importance have not been quantitated. Here, we verified the significance of social recycling of dead cells for long-term survival. We also show that the survivors restrained their recycling and did not use all available nutrients released from dead cells, which may be advantageous under starvation conditions. These results indicate that not only the utilization of dead cells but also restrained recycling coordinate the effective utilization of limited resources for long-term survival under starvation.

Survival kinetics of starving bacteria is biphasic and density-dependent

In the lifecycle of microorganisms, prolonged starvation is prevalent and sustaining life during starvation periods is a vital task. In the literature, it is commonly assumed that survival kinetics of starving microbes follows exponential decay. This assumption, however, has not been rigorously tested. Currently, it is not clear under what circumstances this assumption is true. Also, it is not known when such survival kinetics deviates from exponential decay and if it deviates, what underlying mechanisms for the deviation are. Here, to address these issues, we quantitatively characterized dynamics of survival and death of starving E. coli cells. The results show that the assumption--starving cells die exponentially--is true only at high cell density. At low density, starving cells persevere for extended periods of time, before dying rapidly exponentially. Detailed analyses show intriguing quantitative characteristics of the density-dependent and biphasic survival kinetics, including that the period of the perseverance is inversely proportional to cell density. These characteristics further lead us to identification of key underlying processes relevant for the perseverance of starving cells. Then, using mathematical modeling, we show how these processes contribute to the density-dependent and biphasic survival kinetics observed. Importantly, our model reveals a thrifty strategy employed by bacteria, by which upon sensing impending depletion of a substrate, the limiting substrate is conserved and utilized later during starvation to delay cell death. These findings advance quantitative understanding of survival of microbes in oligotrophic environments and facilitate quantitative analysis and prediction of microbial dynamics in nature. Furthermore, they prompt revision of previous models used to analyze and predict population dynamics of microbes.

Use of adaptive laboratory evolution to discover key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal medium

Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.

Cancer treatment as a game: integrating evolutionary game theory into the optimal control of chemotherapy

Chemotherapy for metastatic cancer commonly fails due to evolution of drug resistance in tumor cells. Here, we view cancer treatment as a game in which the oncologists choose a therapy and tumors 'choose' an adaptive strategy. We propose the oncologist can gain an upper hand in the game by choosing treatment strategies that anticipate the adaptations of the tumor. In particular, we examine the potential benefit of exploiting evolutionary tradeoffs in tumor adaptations to therapy. We analyze a math model where cancer cells face tradeoffs in allocation of resistance to two drugs. The tumor 'chooses' its strategy by natural selection and the oncologist chooses her strategy by solving a control problem. We find that when tumor cells perform best by investing resources to maximize response to one drug the optimal therapy is a time-invariant delivery of both drugs simultaneously. However, if cancer cells perform better using a generalist strategy allowing resistance to both drugs simultaneously, then the optimal protocol is a time varying solution in which the two drug concentrations negatively covary. However, drug interactions can significantly alter these results. We conclude that knowledge of both evolutionary tradeoffs and drug interactions is crucial in planning optimal chemotherapy schedules for individual patients.

Frequent beneficial mutations during single-colony serial transfer of Streptococcus pneumoniae

The appearance of new mutations within a population provides the raw material for evolution. The consistent decline in fitness observed in classical mutation accumulation studies has provided support for the long-held view that deleterious mutations are more common than beneficial mutations. Here we present results of a study using a mutation accumulation design with the bacterium Streptococcus pneumoniae in which the fitness of the derived populations increased. This rise in fitness was associated specifically with adaptation to survival during brief stationary phase periods between single-colony population bottlenecks. To understand better the population dynamics behind this unanticipated adaptation, we developed a maximum likelihood model describing the processes of mutation and stationary-phase selection in the context of frequent population bottlenecks. Using this model, we estimate that the rate of beneficial mutations may be as high as 4.8×10⁻⁴ events per genome for each time interval corresponding to the pneumococcal generation time. This rate is several orders of magnitude higher than earlier estimates of beneficial mutation rates in bacteria but supports recent results obtained through the propagation of small populations of Escherichia coli. Our findings indicate that beneficial mutations may be relatively frequent in bacteria and suggest that in S. pneumoniae, which develops natural competence for transformation, a steady supply of such mutations may be available for sampling by recombination.

Beneficial mutations have long been considered extremely rare events and were thought to occur with a frequency of approximately one out of a billion times that a bacterium replicates its genome. Rare beneficial mutations would then be amplified by natural selection from the more frequent background of harmful mutations. Mutation accumulation experiments probe the nature of these spontaneous mutations by monitoring changes in fitness of model organisms propagated in the laboratory through numerous generations under conditions where the effects of selection are minimal. Previous mutation accumulation experiments have shown that organisms under study have declined in fitness as random mutations accrue in their genomes, consistent with a predominance of deleterious mutations. We conducted a mutation accumulation study with the bacterial pathogen S. pneumoniae in which a broad measure of fitness instead rose. We demonstrate that this unexpected adaptation was due to frequent beneficial mutations that were further amplified by selection in stationary-phase bacterial colonies. Together with recent work using E. coli, these results demonstrate that beneficial mutations can be common in bacteria and may contribute to our understanding of the evolution of traits such as antibiotic resistance and virulence.

Some consequences of demographic stochasticity in population genetics

Much of population genetics is based on the diffusion limit of the Wright-Fisher model, which assumes a fixed population size. This assumption is violated in most natural populations, particularly for microbes. Here we study a more realistic model that decouples birth and death events and allows for a stochastically varying population size. Under this model, classical quantities such as the probability of and time before fixation of a mutant allele can differ dramatically from their Wright-Fisher expectations. Moreover, inferences about natural selection based on Wright-Fisher assumptions can yield erroneous and even contradictory conclusions: at small population densities one allele will appear superior, whereas at large densities the other allele will dominate. Consequently, competition assays in laboratory conditions may not reflect the outcome of long-term evolution in the field. These results highlight the importance of incorporating demographic stochasticity into basic models of population genetics.

Experimental adaptation of Burkholderia cenocepacia to onion medium reduces host range

It is unclear whether adaptation to a new host typically broadens or compromises host range, yet the answer bears on the fate of emergent pathogens and symbionts. We investigated this dynamic using a soil isolate of Burkholderia cenocepacia, a species that normally inhabits the rhizosphere, is related to the onion pathogen B. cepacia, and can infect the lungs of cystic fibrosis patients. We hypothesized that adaptation of B. cenocepacia to a novel host would compromise fitness and virulence in alternative hosts. We modeled adaptation to a specific host by experimentally evolving 12 populations of B. cenocepacia in liquid medium composed of macerated onion tissue for 1,000 generations. The mean fitness of all populations increased by 78% relative to the ancestor, but significant variation among lines was observed. Populations also varied in several phenotypes related to host association, including motility, biofilm formation, and quorum-sensing function. Together, these results suggest that each population adapted by fixing different sets of adaptive mutations. However, this adaptation was consistently accompanied by a loss of pathogenicity to the nematode Caenorhabditis elegans; by 500 generations most populations became unable to kill nematodes. In conclusion, we observed a narrowing of host range as a consequence of prolonged adaptation to an environment simulating a specific host, and we suggest that emergent pathogens may face similar consequences if they become host-restricted.

Long-term survival during stationary phase: evolution and the GASP phenotype

The traditional view of the stationary phase of the bacterial life cycle, obtained using standard laboratory culture practices, although useful, might not always provide us with the complete picture. Here, the traditional three phases of the bacterial life cycle are expanded to include two additional phases: death phase and long-term stationary phase. In many natural environments, bacteria probably exist in conditions more akin to those of long-term stationary-phase cultures, in which the expression of a wide variety of stress-response genes and alternative metabolic pathways is essential for survival. Furthermore, stressful environments can result in selection for mutants that express the growth advantage in stationary phase (GASP) phenotype.

Escherichia coli evolution during stationary phase

The process of evolution by natural selection has been known for a century and a half, yet the mechanics of selection are still poorly understood. In most cases where natural selection has been studied, the genetic and physiological bases of fitness variation that result in population changes were not identified, leaving only a partial understanding of selection. Starved cultures of the bacterium Escherichia coli present a model system with which to address the genetic and physiological bases of natural selection. This is a model system that also reflects the prevalent state of bacteria in the natural world; due to intense competition for nutrients, microorganisms spend the majority of their lives under starvation conditions. Genetic analyses of a single survivor of starvation identified four adaptive mutations(1). Investigation of these mutations has revealed insights into the molecular and physiological bases of evolution during prolonged starvation stress.

Evolutionary cheating in Escherichia coli stationary phase cultures

Starved cultures of Escherichia coli are highly dynamic, undergoing frequent population shifts. The shifts result from the spread of mutants able to grow under conditions that impose growth arrest on the ancestral population. To analyze competitive interactions underlying this dynamic we measured the survival of a typical mutant and the wild type during such population shifts. Here we show that the survival advantage of the mutant at any given time during a takeover is inversely dependent on its frequency in the population, its growth adversely affects the survival of the wild type, and its ability to survive in stationary phase at fixation is lower than that of its ancestor. These mutants do not enter, or exit early, the nondividing stationary-phase state, cooperatively maintained by the wild type. Thus they end up overrepresented as compared to their initial frequency at the onset of the stationary phase, and subsequently they increase disproportionately their contribution in terms of progeny to the succeeding generation in the next growth cycle, which is a case of evolutionary cheating. If analyzed through the game theory framework, these results might be explained by the prisoner's dilemma type of conflict, which predicts that selfish defection is favored over cooperation.

Developmental system drift and flexibility in evolutionary trajectories

The comparative analysis of homologous characters is a staple of evolutionary developmental biology and often involves extrapolating from experimental data in model organisms to infer developmental events in non-model organisms. In order to determine the general importance of data obtained in model organisms, it is critical to know how often and to what degree similar phenotypes expressed in different taxa are formed by divergent developmental processes. Both comparative studies of distantly related species and genetic analysis of closely related species indicate that many characters known to be homologous between taxa have diverged in their morphogenetic or gene regulatory underpinnings. This process, which we call "developmental system drift" (DSD), is apparently ubiquitous and has significant implications for the flexibility of developmental evolution of both conserved and evolving characters. Current data on the population genetics and molecular mechanisms of DSD illustrate how the details of developmental processes are constantly changing within evolutionary lineages, indicating that developmental systems may possess a great deal of plasticity in their responses to natural selection.