Evolutionary pressures on microbial metabolic strategies in the chemostat

Proteome allocation and the evolution of metabolic cross-feeding

In a common instance of metabolic cross-feeding (MCF), an organism incompletely metabolizes nutrients and releases metabolites that are used by another to produce energy or building blocks. Why would the former waste edible food, and why does this preferentially occur at specific locations in a metabolic pathway have challenged evolutionary theory for decades. To address these questions, we combine adaptive dynamics with an explicit model of cell metabolism, including enzyme-driven catalysis of metabolic reactions and the cellular constraints acting on the proteome that may incur a cost to expressing all enzymes along a pathway. After pointing out that cells should in principle prioritize upstream reactions when metabolites are restrained inside the cell, we show that the occurrence of permeability-driven MCF is rare and requires that an intermediate metabolite be extremely diffusive. Indeed, only at very high levels of membrane permeability (consistent with those of acetate and glycerol, for instance) and under distinctive sets of parameters should the population diversify and MCF evolve. These results help understand the origins of simple microbial communities, such as those that readily evolve in short-term evolutionary experiments, and may later be extended to investigate how evolution has progressively built up today's extremely diverse ecosystems.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Evolutionary coexistence in a fluctuating environment by specialization on resource level

Microbial communities in fluctuating environments, such as oceans or the human gut, contain a wealth of diversity. This diversity contributes to the stability of communities and the functions they have in their hosts and ecosystems. To improve stability and increase production of beneficial compounds, we need to understand the underlying mechanisms causing this diversity. When nutrient levels fluctuate over time, one possibly relevant mechanism is coexistence between specialists on low and specialists on high nutrient levels. The relevance of this process is supported by the observations of coexistence in the laboratory, and by simple models, which show that negative frequency dependence of two such specialists can stabilize coexistence. However, as microbial populations are often large and fast growing, they evolve rapidly. Our aim is to determine what happens when species can evolve; whether evolutionary branching can create diversity or whether evolution will destabilize coexistence. We derive an analytical expression of the invasion fitness in fluctuating environments and use adaptive dynamics techniques to find that evolutionarily stable coexistence requires a special type of trade-off between growth at low and high nutrients. We do not find support for the necessary evolutionary trade-off in data available for the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae on glucose. However, this type of data is scarce and might exist for other species or in different conditions. Moreover, we do find evidence for evolutionarily stable coexistence of the two species together. Since we find this coexistence in the scarce data that are available, we predict that specialization on resource level is a relevant mechanism for species diversity in microbial communities in fluctuating environments in natural settings.

Oropharyngeal, proximal colonic, and vaginal microbiomes of healthy Korean native black pig gilts

BACKGROUND

Exploring the microbiome in multiple body sites of a livestock species informs approaches to promote its health and performance through efficient and sustainable modulation of these microbial ecosystems. Here, we employed 16S rRNA gene sequencing to describe the microbiome in the oropharyngeal cavity, proximal colon, and vaginal tract of Jeju Black pigs (JBP), which are native to the Korean peninsula.

RESULTS

We sampled nine 7-month-old JBP gilts raised under controlled conditions. The most abundant phyla that we found within the oropharyngeal microbiota were Proteobacteria, Bacteroidetes, Fusobacteria and Firmicutes, collectively providing core features from twenty-five of their genera. We also found a proximal colonic microbial core composed of features from twenty of the genera of the two predominant phyla, Firmicutes, and Bacteroidetes. Remarkably, within the JBP vaginal microbiota, Bacteroidetes dominated at phylum level, contrary to previous reports regarding other pig breeds. Features of the JBP core vaginal microbiota, came from seventeen genera of the major phyla Bacteroidetes, Firmicutes, Proteobacteria, and Fusobacteria. Although these communities were distinct, we found some commonalities amongst them. Features from the genera Streptococcus, Prevotella, Bacillus and an unclassified genus of the family Ruminococcaceae were ubiquitous across the three body sites. Comparing oropharyngeal and proximal colonic communities, we found additional shared features from the genus Anaerorhabdus. Between oropharyngeal and vaginal ecosystems, we found other shared features from the genus Campylobacter, as well as unclassified genera from the families Fusobacteriaceae and Flavobacteriaceae. Proximal colonic and vaginal microbiota also shared features from the genera Clostridium, Lactobacillus, and an unclassified genus of Clostridiales.

CONCLUSIONS

Our results delineate unique and ubiquitous features within and across the oropharyngeal, proximal colonic and vaginal microbial communities in this Korean native breed of pigs. These findings provide a reference for future microbiome-focused studies and suggest a potential for modulating these communities, utilizing ubiquitous features, to enhance health and performance of the JBP.

Towards evolutionary predictions: Current promises and challenges

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.

Metabolically active organelles compete for cytosolic space and resources during metabolism rewiring. Here, the authors develop a computational model of yeast metabolism and resource allocation to predict condition- and compartment-specific proteome constraints that govern metabolic strategies.

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Model‐based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time‐variable metabolic capacities that lead to a considerable model‐plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model‐based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off‐gas measurements. Additional information on current parameter importance is derived from time‐resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long‐term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify time‐variable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic by‐product streams.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Regulation of Eukaryote Metabolism: An Abstract Model Explaining the Warburg/Crabtree Effect

Adaptation of metabolism is a response of many eukaryotic cells to nutrient heterogeneity in the cell microenvironment. One of these adaptations is the shift from respiratory to fermentative metabolism, also called the Warburg/Crabtree effect. It is a response to a very high nutrient increase in the cell microenvironment, even in the presence of oxygen. Understanding whether this metabolic transition can result from basic regulation signals between components of the central carbon metabolism are the the core question of this work. We use an extension of the René Thomas modeling framework for representing the regulations between the main catabolic and anabolic pathways of eukaryotic cells, and formal methods for confronting models with known biological properties in different microenvironments. The formal model of the regulation of eukaryote metabolism defined and validated here reveals the conditions under which this metabolic phenotype switch occurs. It clearly proves that currently known regulating signals within the main components of central carbon metabolism can be sufficient to bring out the Warburg/Crabtree effect. Moreover, this model offers a general perspective of the regulation of the central carbon metabolism that can be used to study other biological questions.

Induction of amylase and protease as antibiofilm agents by starch, casein, and yeast extract in Arthrobacter sp. CW01

BACKGROUND

In unfavourable environment, such as nutrient limitation, some bacteria encased themselves into a three dimensional polymer matrix called biofilm. The majority of microbial infections in human are biofilm related, including chronic lung, wound, and ear infections. The matrix of biofilm which consists of extracellular polymeric substances (EPS) causes bacterial colonization on medical implanted device in patients, such as catheter and lead to patient's death. Biofilm infections are harder to treat due to increasing antibiotic resistance compared to planktonic microbial cells and escalating the antibiotic concentration may result into in vivo toxicity for the patients. Special compounds which are non-microbicidal that could inhibit or destroy biofilm formation are called antibiofilm compounds, for example enzymes, anti-quorum sensing, and anti-adhesins. Arthrobacter sp. CW01 produced antibiofilm compound known as amylase. This time our preliminary study proved that the antibiofilm compound was not only amylase, but also protease. Therefore, this research aimed to optimize the production of antibiofilm agents using amylase and protease inducing media. The five types of production media used in this research were brain heart infusion (BHI) (Oxoid), BHI with starch (BHIS), casein with starch (CS), yeast extract with starch (YS), and casein-yeast extract with starch (CYS). Biofilm eradication and inhibition activities were assayed against Pseudomonas aeruginosa (ATCC 27,853) and Staphylococcus aureus (ATCC 25,923).

RESULTS

The results showed that different production media influenced the antibiofilm activity. Addition of starch, casein and yeast extract increased the production of amylase and protease significantly. Higher amylase activity would gradually increase the antibiofilm activity until it reached the certain optimum point. It was shown that crude extracts which contained amylase only (BHI, BHIS and YS) had the optimum eradication activity against P. aeruginosa and S. aureus biofilm around 60-70 %. Meanwhile, CS and CYS crude extracts which contained both amylase and protease increased the biofilm eradication activity against both pathogens, which were around 70-90 %.

CONCLUSIONS

It was concluded that the combination of amylase and protease was more effective as antibiofilm agents against P. aeruginosa and S. aureus rather than amylase only.

Selection for rapid uptake of scarce or fluctuating resource explains vulnerability of glycolysis to imbalance

Glycolysis is a conserved central pathway in energy metabolism that converts glucose to pyruvate with net production of two ATP molecules. Because ATP is produced only in the lower part of glycolysis (LG), preceded by an initial investment of ATP in the upper glycolysis (UG), achieving robust start-up of the pathway upon activation presents a challenge: a sudden increase in glucose concentration can throw a cell into a self-sustaining imbalanced state in which UG outpaces LG, glycolytic intermediates accumulate and the cell is unable to maintain high ATP concentration needed to support cellular functions. Such metabolic imbalance can result in "substrate-accelerated death", a phenomenon observed in prokaryotes and eukaryotes when cells are exposed to an excess of substrate that previously limited growth. Here, we address why evolution has apparently not eliminated such a costly vulnerability and propose that it is a manifestation of an evolutionary trade-off, whereby the glycolysis pathway is adapted to quickly secure scarce or fluctuating resource at the expense of vulnerability in an environment with ample resource. To corroborate this idea, we perform individual-based eco-evolutionary simulations of a simplified yeast glycolysis pathway consisting of UG, LG, phosphate transport between a vacuole and a cytosol, and a general ATP demand reaction. The pathway is evolved in constant or fluctuating resource environments by allowing mutations that affect the (maximum) reaction rate constants, reflecting changing expression levels of different glycolytic enzymes. We demonstrate that under limited constant resource, populations evolve to a genotype that exhibits balanced dynamics in the environment it evolved in, but strongly imbalanced dynamics under ample resource conditions. Furthermore, when resource availability is fluctuating, imbalanced dynamics confers a fitness advantage over balanced dynamics: when glucose is abundant, imbalanced pathways can quickly accumulate the glycolytic intermediate FBP as intracellular storage that is used during periods of starvation to maintain high ATP concentration needed for growth. Our model further predicts that in fluctuating environments, competition for glucose can result in stable coexistence of balanced and imbalanced cells, as well as repeated cycles of population crashes and recoveries that depend on such polymorphism. Overall, we demonstrate the importance of ecological and evolutionary arguments for understanding seemingly maladaptive aspects of cellular metabolism.

Fungal evolution: cellular, genomic and metabolic complexity

The question of how phenotypic and genomic complexity are inter-related and how they are shaped through evolution is a central question in biology that historically has been approached from the perspective of animals and plants. In recent years, however, fungi have emerged as a promising alternative system to address such questions. Key to their ecological success, fungi present a broad and diverse range of phenotypic traits. Fungal cells can adopt many different shapes, often within a single species, providing them with great adaptive potential. Fungal cellular organizations span from unicellular forms to complex, macroscopic multicellularity, with multiple transitions to higher or lower levels of cellular complexity occurring throughout the evolutionary history of fungi. Similarly, fungal genomes are very diverse in their architecture. Deep changes in genome organization can occur very quickly, and these phenomena are known to mediate rapid adaptations to environmental changes. Finally, the biochemical complexity of fungi is huge, particularly with regard to their secondary metabolites, chemical products that mediate many aspects of fungal biology, including ecological interactions. Herein, we explore how the interplay of these cellular, genomic and metabolic traits mediates the emergence of complex phenotypes, and how this complexity is shaped throughout the evolutionary history of Fungi.

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.

Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.

Statistical mechanics of interacting metabolic networks

We cast the metabolism of interacting cells within a statistical mechanics framework considering both the actual phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of high-dimensional spin vectors, whose values will be constrained by the stochiometry and the energy requirements of the metabolism. Within this picture, finding the phenotypic states of the population turns out to be equivalent to searching for the equilibrium states of a disordered spin model. We provide a general solution of this problem for arbitrary metabolic networks and interactions. We apply this solution to a simplified model of metabolism and to a complex metabolic network, the central core of Escherichia coli, and demonstrate that the combination of selective pressure and interactions defines a complex phenotypic space. We also present numerical results for cells fixed in a grid. These results reproduce the qualitative picture discussed for the mean-field model. Cells may specialize in producing or consuming metabolites complementing each other, and this is described by an equilibrium phase space with multiple minima, like in a spin-glass model.

Rapid Colonization of Uranium Mining-Impacted Waters, the Biodiversity of Successful Lineages of Phytoplankton Extremophiles

Anthropogenic extreme environments are emphasized as interesting sites for the study of evolutionary pathways, biodiversity, and extremophile bioprospection. Organisms that grow under these conditions are usually regarded as extremophiles; however, the extreme novelty of these environments may have favor adaptive radiations of facultative extremophiles. At the Iberian Peninsula, uranium mining operations have rendered highly polluted extreme environments in multiple locations. In this study, we examined the phytoplankton diversity, community structure, and possible determining factors in separate uranium mining-impacted waters. Some of these human-induced extreme environments may be able to sustain indigenous facultative extremophile phytoplankton species, as well as alleged obligate extremophiles. Therefore, we investigated the adaptation capacity of three laboratory strains, two Chlamydomonas reinhardtii and a Dictyosphaerium chlorelloides, to uranium-polluted waters. The biodiversity among the sampled waters was very low, and despite presenting unique taxonomic records, ecological patterns can be identified. The microalgae adaptation experiments indicated a gradient of ecological novelty and different phenomena of adaptation, from acclimation in some waters to non-adaptation in the harshest anthropogenic environment. Certainly, phytoplankton extremophiles might have been often overlooked, and the ability to flourish in extreme environments might be a functional feature in some neutrophilic species. Evolutionary biology and microbial biodiversity can benefit the study of recently evolved systems such as uranium-polluted waters. Moreover, anthropogenic extremophiles can be harnessed for industrial applications.

Continual evolution through coupled fast and slow feedbacks

Continual evolution describes the unceasing evolution of at least one trait involving at least one organism. The Red Queen Hypothesis is a specific case in which continual evolution results from coevolution of at least two species. While microevolutionary studies have described examples in which evolution does not cease, understanding which general conditions lead to continual evolution or to stasis remains a major challenge. In many cases, it is unclear which experimental features or model assumptions are necessary for the observed continual evolution to emerge, and whether the described behavior is robust to variations in the given setup. Here, we aim to find the minimal set of conditions under which continual evolution occurs. To this end, we present a theoretical framework that does not assume any specific functional form and, therefore, can be applied to a wide variety of systems. Our framework is also general enough to make predictions about both monomorphic and polymorphic populations. We show that the combination of a fast positive and a slow negative feedback between environment, population, and evolving traits causes continual evolution to emerge even from the evolution of a single evolving trait, provided that the ecological timescale is sufficiently faster than the timescales of mutation and the negative feedback. Our approach and results thus contribute to a deeper understanding of the evolutionary dynamics resulting from biotic interactions.

Trusting the hand that feeds: microbes evolve to anticipate a serial transfer protocol as individuals or collectives

BACKGROUND

Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe "wild types" (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms.

RESULTS

We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode.

CONCLUSIONS

All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes.

Segregostat: a novel concept to control phenotypic diversification dynamics on the example of Gram-negative bacteria

Controlling and managing the degree of phenotypic diversification of microbial populations is a challenging task. This task not only requires detailed knowledge regarding diversification mechanisms but also advanced technical set-ups for the real-time analyses and control of population behaviour on single-cell level. In this work, set-up, design and operation of the so called segregostat are described which, in contrast to a traditional chemostat, allows the control of phenotypic diversification of microbial populations over time. Two exemplary case studies will be discussed, i.e. phenotypic diversification dynamics of Eschericia coli and Pseudomonas putida based on outer membrane permeabilization, emphasizing the applicability and versatility of the proposed approach. Upon nutrient limitation, cell population tends to diversify into several subpopulations exhibiting distinct phenotypic features (non-permeabilized and permeabilized cells). Online analysis leads to the determination of the ratio between cells in these two states, which in turn triggers the addition of glucose pulses in order to maintain a predefined diversification ratio. These results prove that phenotypic diversification can be controlled by means of defined pulse-frequency modulation within continuously running bioreactor set-ups. This lays the foundation for systematic studies, not only of phenotypic diversification but also for all processes where dynamics single-cell approaches are required, such as synthetic co-culture processes.

New developments in online OUR monitoring and its application to animal cell cultures

The increasing demand for biopharmaceuticals produced in mammalian cells has driven the industry to enhance the productivity of bioprocesses through intensification of culture process. Fed-batch and perfusion culturing strategies are considered the most attractive choices, but the application of these processes requires the availability of reliable online measuring systems for the estimation of cell density and metabolic activity. This manuscript reviews the methods (and the devices used) for monitoring of the oxygen consumption, also known as oxygen uptake rate (OUR), since it is a straightforward parameter to estimate viable cell density and the physiological state of cells. Furthermore, as oxygen plays an important role in the cell metabolism, OUR has also been very useful to estimate nutrient consumption, especially the carbon (glucose and glutamine) and nitrogen (glutamine) sources. Three different methods for the measurement of OUR have been developed up to date, being the dynamic method the golden standard, even though DO and pH perturbations generated in the culture during each measurement. For this, many efforts have been focused in developing non-invasive methods, such as global mass balance or stationary liquid mass balance. The low oxygen consumption rates by the cells and the high accuracy required for oxygen concentration measurement in the gas streams (inlet and outlet) have limited the applicability of the global mass balance methodology in mammalian cell cultures. In contrast, stationary liquid mass balance has successfully been implemented showing very similar OUR profiles compared with those obtained with the dynamic method. The huge amount of studies published in the last years evidence that OUR have become a reliable alternative for the monitoring and control of high cell density culturing strategies with very high productivities.

Cell population heterogeneity driven by stochastic partition and growth optimality

A fundamental question in biology is how cell populations evolve into different subtypes based on homogeneous processes at the single cell level. Here we show that population bimodality can emerge even when biological processes are homogenous at the cell level and the environment is kept constant. Our model is based on the stochastic partitioning of a cell component with an optimal copy number. We show that the existence of unimodal or bimodal distributions depends on the variance of partition errors and the growth rate tolerance around the optimal copy number. In particular, our theory provides a consistent explanation for the maintenance of aneuploid states in a population. The proposed model can also be relevant for other cell components such as mitochondria and plasmids, whose abundances affect the growth rate and are subject to stochastic partition at cell division.

Noise propagation in an integrated model of bacterial gene expression and growth

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield

Microbes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM) and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism in E. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.

Predicting metabolic adaptation from networks of mutational paths

Competition for substrates is a ubiquitous selection pressure faced by microbes, yet intracellular trade-offs can prevent cells from metabolizing every type of available substrate. Adaptive evolution is constrained by these trade-offs, but their consequences for the repeatability and predictability of evolution are unclear. Here we develop an eco-evolutionary model with a metabolic trade-off to generate networks of mutational paths in microbial communities and show that these networks have descriptive and predictive information about the evolution of microbial communities. We find that long-term outcomes, including community collapse, diversity, and cycling, have characteristic evolutionary dynamics that determine the entropy, or repeatability, of mutational paths. Although reliable prediction of evolutionary outcomes from environmental conditions is difficult, graph-theoretic properties of the mutational networks enable accurate prediction even from incomplete observations. In conclusion, we present a novel methodology for analyzing adaptive evolution and report that the dynamics of adaptation are a key variable for predictive success.The structure and dynamics of microbial communities reflect trade-offs in the ability to use different resources. Here, Josephides and Swain incorporate metabolic trade-offs into an eco-evolutionary model to predict networks of mutational paths and the evolutionary outcomes for microbial communities.