Convergent genetic adaptation of Escherichia coli in minimal media leads to pleiotropic divergence.

Adaptation in an environment can either be beneficial, neutral or disadvantageous in another. To test the genetic basis of pleiotropic behaviour, we evolved six lines of E. coli independently in environments where glucose and galactose were the sole carbon sources, for 300 generations. All six lines in each environment exhibit convergent adaptation in the environment in which they were evolved. However, pleiotropic behaviour was observed in several environmental contexts, including other carbon environments. Genome sequencing reveals that mutations in global regulators rpoB and rpoC cause this pleiotropy. We report three new alleles of the rpoB gene, and one new allele of the rpoC gene. The novel rpoB alleles confer resistance to Rifampicin, and alter motility. Our results show how single nucleotide changes in the process of adaptation in minimal media can lead to wide-scale pleiotropy, resulting in changes in traits that are not under direct selection.

Increased privatization of a public resource leads to spread of cooperation in a microbial population.

The phenomenon of cooperation is prevalent at all levels of life. In one such manifestation of cooperation in microbial communities, some cells produce costly extracellular resources that are freely available to others. These resources are referred to as public goods. Saccharomyces cerevisiae secretes invertase (public good) in the periplasm to hydrolyze sucrose into glucose and fructose, which are then imported by the cells. After hydrolysis of sucrose, a cooperator retains only 1% of the monosaccharides, while 99% of the monosaccharides diffuse into the environment and can be utilized by any cell. The non-producers of invertase (cheaters) exploit the invertase-producing cells (cooperators) by utilizing the monosaccharides and not paying the metabolic cost of producing the invertase. In this work, we investigate the evolutionary dynamics of this cheater-cooperator system. In a co-culture, if cheaters are selected for their higher fitness, the population will collapse. On the other hand, for cooperators to survive in the population, a strategy to increase fitness would likely be required. To understand the adaptation of cooperators in sucrose, we performed a coevolution experiment in sucrose. Our results show that cooperators increase in fitness as the experiment progresses. This phenomenon was not observed in environments which involved a non-public good system. Genome sequencing reveals duplication of several HXT transporters in the evolved cooperators. Based on these results, we hypothesize that increased privatization of the monosaccharides is the most likely explanation of spread of cooperators in the population.IMPORTANCEHow is cooperation, as a trait, maintained in a population? In order to answer this question, we perform a coevolution experiment between two strains of yeast-one which produces a public good to release glucose and fructose in the media, thus generating a public resource, and the other which does not produce public resource and merely benefits from the presence of the cooperator strain. What is the outcome of this coevolution experiment? We demonstrate that after ~200 generations of coevolution, cooperators increase in frequency in the co-culture. Remarkably, in all parallel lines of our experiment, this is obtained via duplication of regions which likely allow greater privatization of glucose and fructose. Thus, increased privatization, which is intuitively thought to be a strategy against cooperation, enables spread of cooperation.

Ecological disruptive selection acting on quantitative loci can drive sympatric speciation.

The process of speciation generates biodiversity. According to the null model of speciation, barriers between populations arise in allopatry, where, prior to biology, geography imposes barriers to gene flow. On the other hand, sympatric speciation requires that the process of speciation happen in the absence of a geographical barrier, where the members of the population have no spatial, temporal barriers. Several attempts have been made to theoretically identify the conditions in which speciation can occur in sympatry. However, these efforts suffer from several limitations. We propose a model for sympatric speciation based on adaptation for resource utilization. We use a genetics-based model to investigate the relative roles of prezygotic and postzygotic barriers, from the context of ecological disruptive selection, sexual selection, and genetic architecture, in causing and maintaining sympatric speciation. Our results show that sexual selection that acts on secondary sexual traits does not play any role in the process of speciation in sympatry and that assortative mating based on an ecologically relevant trait forces the population to show an adaptive response. We also demonstrate that understanding the genetic architecture of the trait under ecological selection is very important and that it is not required for the strength of ecological disruptive selection to be very high in order for speciation to occur in sympatry. Our results provide an insight into the kind of scenarios in which sympatric speciation can be demonstrated in the lab.

Rapid evolution of pre-zygotic reproductive barriers in allopatric populations.

IMPORTANCE: A population diversifies into two or more species-such a process is known as speciation. In sexually reproducing microorganisms, which barriers arise first-pre-mating or post-mating? In this work, we quantify the relative strengths of these barriers and demonstrate that pre-mating barriers arise first in allopatrically evolving populations of yeast, Saccharomyces cerevisiae. These defects arise because of the altered kinetics of mating of the participating groups. Thus, our work provides an understanding of how adaptive changes can lead to diversification among microbial populations.

Exploring the Influence of pH on the Dynamics of Acetone-Butanol-Ethanol Fermentation.

Clostridium acetobutylicum is an anaerobic bacterium that is extensively studied for its ability to produce butanol. Over the past two decades, various genetic and metabolic engineering approaches have been used to investigate the physiology and regulation system of the biphasic metabolic pathway in this organism. However, there has been a relatively limited amount of research focused on the fermentation dynamics of C. acetobutylicum. In this study, we developed a pH-based phenomenological model to predict the fermentative production of butanol from glucose using C. acetobutylicum in a batch system. The model describes the relationship between the dynamics of growth and the production of desired metabolites and the extracellular pH of the media. Our model was found to be successful in predicting the fermentation dynamics of C. acetobutylicum, and the simulations were validated using experimental fermentation data. Furthermore, the proposed model has the potential to be extended to represent the dynamics of butanol production in other fermentation systems, such as fed-batch or continuous fermentation using single and multi-sugars.

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae.

Saccharomyces cerevisiae is a widely used model organism in genetics, evolution, and molecular biology. In recent years, it has also become a popular model organism to study problems related to speciation. The life cycle of yeast involves both asexual and sexual reproductive phases. The ease of performing evolution experiments and the short generation time of the organism allow for the study of the evolution of reproductive barriers. The efficiency with which the two mating types (a and α) mate to form the a/α diploid is referred to as the mating efficiency. Any decrease in the mating efficiency between haploids indicates a pre-zygotic barrier. Thus, to quantify the extent of reproductive isolation between two haploids, a robust method to quantify the mating efficiency is required. To this end, a simple and highly reproducible protocol is presented here. The protocol involves four main steps, which include patching the haploids on a YPD plate, mixing the haploids in equal numbers, diluting and plating for single colonies, and finally, calculating the efficiency based on the number of colonies on a drop-out plate. Auxotrophic markers are employed to clearly make the distinction between haploids and diploids.

Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population.

Understanding the basis of biological diversity remains a central problem in evolutionary biology. Using microbial systems, adaptive diversification has been studied in (a) spatially heterogeneous environments, (b) temporally segregated resources, and (c) resource specialization in a homogeneous environment. However, it is not well understood how adaptive diversification can take place in a homogeneous environment containing a single resource. Starting from an isogenic population of yeast Saccharomyces cerevisiae, we report rapid adaptive diversification, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good enzyme α-galactosidase, which hydrolyzes melibiose into glucose and galactose. The diversification is driven by mutations at a single locus, in the GAL3 gene in the S. cerevisiae GAL/MEL regulon. We show that metabolic co-operation involving public resources could be an important mode of generating biological diversity. Our study demonstrates sympatric diversification of yeast starting from an isogenic population and provides detailed mechanistic insights into the factors and conditions responsible for generating and maintaining the population diversity.

Selection in a growing colony biases results of mutation accumulation experiments.

Mutations provide the raw material for natural selection to act. Therefore, understanding the variety and relative frequency of different type of mutations is critical to understanding the nature of genetic diversity in a population. Mutation accumulation (MA) experiments have been used in this context to estimate parameters defining mutation rates, distribution of fitness effects (DFE), and spectrum of mutations. MA experiments can be performed with different effective population sizes. In MA experiments with bacteria, a single founder is grown to a size of a colony (~ 108). It is assumed that natural selection plays a minimal role in dictating the dynamics of colony growth. In this work, we simulate colony growth via a mathematical model, and use our model to mimic an MA experiment. We demonstrate that selection ensures that, in an MA experiment, fraction of all mutations that are beneficial is over-represented by a factor of almost two, and that the distribution of fitness effects of beneficial and deleterious mutations are inaccurately captured in an MA experiment. Given this, the estimate of mutation rates from MA experiments is non-trivial. We then perform an MA experiment with 160 lines of E. coli, and show that due to the effect of selection in a growing colony, the size and sector of a colony from which the experiment is propagated impacts the results. Overall, we demonstrate that the results of MA experiments need to be revisited taking into account the action of selection in a growing colony.

GAL Regulon in the Yeast S. cerevisiae is Highly Evolvable via Acquisition in the Coding Regions of the Regulatory Elements of the Network.

GAL network in the yeast S. cerevisiae is one of the most well-characterized regulatory network. Expression of GAL genes is contingent on exposure to galactose, and an appropriate combination of the alleles of the regulatory genes GAL3, GAL1, GAL80, and GAL4. The presence of multiple regulators in the GAL network makes it unique, as compared to the many sugar utilization networks studied in bacteria. For example, utilization of lactose is controlled by a single regulator LacI, in E. coli's lac operon. Moreover, recent work has demonstrated that multiple alleles of these regulatory proteins are present in yeast isolated from ecological niches. In this work, we develop a mathematical model, and demonstrate via deterministic and stochastic runs of the model, that behavior/gene expression patterns of the cells (at a population level, and at a single-cell resolution) can be modulated by altering the binding affinities between the regulatory proteins. This adaptability is likely the key to explaining the multiple GAL regulatory alleles discovered in ecological isolates in recent years.

Cell Growth Model with Stochastic Gene Expression Helps Understand the Growth Advantage of Metabolic Exchange and Auxotrophy.

During cooperative growth, microbes often experience higher fitness by sharing resources via metabolite exchange. How competitive species evolve to cooperate is, however, not known. Moreover, existing models (based on optimization of steady-state resources or fluxes) are often unable to explain the growth advantage for the cooperating species, even for simple reciprocally cross-feeding auxotrophic pairs. We present here an abstract model of cell growth that considers the stochastic burst-like gene expression of biosynthetic pathways of limiting biomass precursor metabolites and directly connect the amount of metabolite produced to cell growth and division, using a "metabolic sizer/adder" rule. Our model recapitulates Monod's law and yields the experimentally observed right-skewed long-tailed distribution of cell doubling times. The model further predicts the growth effect of secretion and uptake of metabolites by linking it to changes in the internal metabolite levels. The model also explains why auxotrophs may grow faster when supplied with the metabolite they cannot produce and why two reciprocally cross-feeding auxotrophs can grow faster than prototrophs. Overall, our framework allows us to predict the growth effect of metabolic interactions in independent microbes and microbial communities, setting up the stage to study the evolution of these interactions. IMPORTANCE Cooperative behaviors are highly prevalent in the wild, but their evolution is not understood. Metabolic flux models can demonstrate the viability of metabolic exchange as cooperative interactions, but steady-state growth models cannot explain why cooperators grow faster. We present a stochastic model that connects growth to the cell's internal metabolite levels and quantifies the growth effect of metabolite exchange and auxotrophy. We show that a reduction in gene expression noise can explain why cells that import metabolites or become auxotrophs can grow faster and why reciprocal cross-feeding of metabolites between complementary auxotrophs allows them to grow faster. Furthermore, our framework can simulate the growth of interacting cells, which will enable us to understand the possible trajectories of the evolution of cooperation in silico.

Experimental Evolution of Anticipatory Regulation in Escherichia coli.

Environmental cues in an ecological niche are often temporal in nature. For instance, in temperate climates, temperature is higher in daytime compared to during night. In response to these temporal cues, bacteria have been known to exhibit anticipatory regulation, whereby triggering response to a yet to appear cue. Such an anticipatory response in known to enhance Darwinian fitness, and hence, is likely an important feature of regulatory networks in microorganisms. However, the conditions under which an anticipatory response evolves as an adaptive response are not known. In this work, we develop a quantitative model to study response of a population to two temporal environmental cues, and predict variables which are likely important for evolution of anticipatory regulatory response. We follow this with experimental evolution of Escherichia coli in alternating environments of rhamnose and paraquat for ∼850 generations. We demonstrate that growth in this cyclical environment leads to evolution of anticipatory regulation. As a result, pre-exposure to rhamnose leads to a greater fitness in paraquat environment. Genome sequencing reveals that this anticipatory regulation is encoded via mutations in global regulators. Overall, our study contributes to understanding of how environment shapes the topology of regulatory networks in an organism.

Phenotypic Heterogeneity in Tumor Progression, and Its Possible Role in the Onset of Cancer.

Heterogeneity among isogenic cells/individuals has been known for at least 150 years. Even Mendel, working on pea plants, realized that not all tall plants were identical. However, Mendel was more interested in the discontinuous variation between genetically distinct individuals. The concept of environment dictating distinct phenotypes among isogenic individuals has since been shown to impact the evolution of populations in numerous examples at different scales of life. In this review, we discuss how phenotypic heterogeneity and its evolutionary implications exist at all levels of life, from viruses to mammals. In particular, we discuss how a particular disease condition (cancer) is impacted by heterogeneity among isogenic cells, and propose a potential role that phenotypic heterogeneity might play toward the onset of the disease.

Evolution of Escherichia coli in different carbon environments for 2,000 generations.

Cellular energetics is thought to have played a key role in dictating all major evolutionary transitions in the history of life on Earth. However, how exactly cellular energetics and metabolism come together to shape evolutionary paths is not well understood. In particular, when an organism is evolved in different energy environments, what are the phenomenological differences in the chosen evolutionary trajectories, is a question that is not well understood. In this context, starting from an Escherichia coli K-12 strain, we evolve the bacterium in five different carbon environments-glucose, arabinose, xylose, rhamnose and a mixture of these four sugars (in a predefined ratio) for approximately 2,000 generations. At the end of the adaptation period, we quantify and compare the growth dynamics of the strains in a variety of environments. The evolved strains show no specialized adaptation towards growth in the carbon medium in which they were evolved. Rather, in all environments, the evolved strains exhibited a reduced lag phase and an increased growth rate. Sequencing results reveal that these dynamical properties are not introduced via mutations in the precise loci associated with utilization of the sugar in which the bacterium evolved. These phenotypic changes are rather likely introduced via mutations elsewhere on the genome. Data from our experiments indicate that evolution in a defined environment does not alter hierarchy in mixed-sugar utilization in bacteria.

Distribution of fitness effects of mutations obtained from a simple genetic regulatory network model.

Beneficial and deleterious mutations change an organism's fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.

Phenomenological models as effective tools to discover cellular design principles.

Microbes have proved useful to us in many different ways. To utilize microbes, we have mostly focused on maximizing growth, to improve yield of chemicals derived from the microbes. However, to truly tap into their potential, we should also aim to understand microbial physiology. We present a historical perspective of the developments in the field of Microbial Biotechnology, focusing on how the growth-modelling approaches have changed. Starting from simple empirical growth models, we have evolved towards mechanistic and phenomenological models which use molecular and physiological details to drastically improve prediction power of these models. Lastly, we explore the as of yet unsolved questions in microbial physiology, and discuss how the ability to monitor microbial growth at single cell resolution using the lab-on-a-chip technologies is uncovering previously unobservable causal principles underlying microbial growth.

Mathematical modeling of movement on fitness landscapes.

BACKGROUND: Movement of populations on fitness landscapes has been a problem of interest for a long time. While the subject has been extensively developed theoretically, reconciliation of the theoretical work with recent experimental data has not yet happened. In this work, we develop a computational framework and study evolution of the simplest transcription network between a single regulator, R and a single target protein, T. RESULTS: Through our simulations, we track evolution of this transcription network and comment on its dynamics and statistics of this movement. Significantly, we report that there exists a critical parameter which controls the ability of a network to reach the global fitness peak on the landscape. This parameter is the fraction of all permissible values of a biochemical parameter that can be accessed from its current value via a single mutation. CONCLUSIONS: Overall, through this work, we aim to present a general framework for analysis of movement of populations (and particularly regulatory networks) on landscapes.

Dynamics and Control of Flagella Assembly in Salmonella typhimurium.

The food-borne pathogen Salmonella typhimurium is a common cause of infections and diseases in a wide range of hosts. One of the major virulence factors associated to the infection process is flagella, which helps the bacterium swim to its preferred site of infection inside the host, the M-cells (Microfold cells) lining the lumen of the small intestine. The expression of flagellar genes is controlled by an intricate regulatory network. In this work, we investigate two aspects of flagella regulation and assembly: (a) distribution of the number of flagella in an isogenic population of bacteria and (b) dynamics of gene expression post cell division. More precisely, in a population of bacteria, we note a normal distribution of number of flagella assembled per cell. How is this distribution controlled, and what are the key regulators in the network which help the cell achieve this? In the second question, we explore the role of protein secretion in dictating gene expression dynamics post cell-division (when the number of hook basal bodies on the cell surface is reduced by a factor of two). We develop a mathematical model and perform stochastic simulations to address these questions. Simulations of the model predict that two accessory regulators of flagella gene expression, FliZ and FliT, have significant roles in maintaining population level distribution of flagella. In addition, FliT and FlgM were predicted to control the level and temporal order of flagellar gene expression when the cell adapts to post cell division consequences. Further, the model predicts that, the FliZ and FliT dependent feedback loops function under certain thresholds, alterations in which can substantially affect kinetics of flagellar genes. Thus, based on our results we propose that, the proteins FlgM, FliZ, and FliT, thought to have accessory roles in regulation of flagella, likely play a critical role controlling gene expression during cell division, and frequency distribution of flagella.

Logic of two antagonizing intra-species quorum sensing systems in bacteria.

Bacteria release signaling molecules into the surrounding environment and sense them when present in their proximity. Using this strategy, a cell estimates the number of neighbors in its surrounding. Upon sensing a critical number of individuals, bacteria coordinate a number of cellular processes. This density-dependent control of gene expression and physiology is called quorum sensing (QS). Quorum sensing controls a wide variety of functions in bacteria, including those related to motility, growth, virulence etc. Quorum sensing has been widely observed in bacteria while the individuals of the same species or different species compete and cooperate each other. Interestingly, many species possess more than one QS system (intra-species) and these QS systems interact each other to perform quorum sensing. Thus, several logical arrangements can be possible based on the interaction among intra-species QS systems - parallel, series, antagonizing, and agonizing. In this work, we perform simulations to understand the logic of interaction between two antagonizing intra-species QS systems. In such an interaction, one QS system gets fully expressed and the other only gets partially expressed. This is found to be dictated by the interplay between autoinducer's diffusivity and antagonizing strength. In addition, we speculate an important role of the intracellular regulators (eg. LuxR) in maintaining the uniform response among the individual cells from the different localities. We also expect the interplay between the autoinducer's diffusivity and distribution of cells in fine tuning the collective response. Interestingly, in a localized niche with a heterogeneous cell distribution, the cells are expected to perform a global quorum sensing via fully expressed QS system and a local quorum sensing via partially expressed QS system.

Robustness versus evolvability analysis for regulatory feed-forward loops.

From the definition, it appears that phenotypic robustness and evolvability of an organism are inversely related to each other. However, a number of studies exploring this question have found conflicting evidences in this regard. This question motivated the current work where we explore the relationship between robustness and evolvability. As a model system, we pick the Feed Forward Loops (FFLs), and develop a framework to characterize their performance in terms of their ability to resist changes to steady state expression (robustness), and their ability to evolve towards novel phenotypes (evolvability). We demonstrate that robustness and evolvability are positively correlated in some FFL topologies. We compare this against other small regulatory topologies, and show that the same trend does not hold among them. We postulate that the ability to positively link robustness and evolvability could be an additional reason for over-representation of FFLs in living organisms, as compared to other regulatory topologies.

Optimal parameter values for the control of gene regulation.

How does a transcription network arrive at the particular values of biochemical interactions defining it? These interactions define DNA-transcription factor interaction, degradation rates of proteins, promoter strengths, and communication of the environmental signal with the network. What is the structure of the fitness landscape that is defined by the space that these parameters can take on? To answer these questions, we simulate the simplest regulatory network: a transcription factor, R, and a target protein, T. We use a cost-benefit analysis to evolve the network and eventually arrive at values of parameters which maximize fitness. We show that for a given topology, multiple parameter sets exist which confer maximal fitness to the cell, and that pairwise correlations exist between parameters in optimal sets. In addition, our results indicate that in the parameter space defining the interactions in a topology, a highly rugged fitness landscape exists.