Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments

Phenotypic heterogeneity in Streptomyces colonies

Streptomyces are a large genus of multicellular bacteria best known for their prolific production of bioactive natural products. In addition, they play key roles in the mineralisation of insoluble resources, such as chitin and cellulose. Because of their multicellular mode of growth, colonies of interconnected hyphae extend over a large area that may experience different conditions in different parts of the colony. Here, we argue that within-colony phenotypic heterogeneity can allow colonies to simultaneously respond to divergent inputs from resources or competitors that are spatially and temporally dynamic. We discuss causal drivers of heterogeneity, including competitors, precursor availability, metabolic diversity and division of labour, that facilitate divergent phenotypes within Streptomyces colonies. We discuss the adaptive causes and consequences of within-colony heterogeneity, highlight current knowledge (gaps) and outline key questions for future studies.

Optimal phenotypic adaptation in fluctuating environments

Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.

Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it.

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.

Quantitative biochemical phenotypic heterogeneity of macrophages after myelin debris phagocytosis at a single cell level by synchrotron radiation fourier transform infrared microspectroscopy

During the immuno-inflammatory pathophysiological process of spinal cord injury, traumatic brain injury, and ischemic stroke, macrophages play an important role in phagocytizing and clearing degenerated myelin debris. After phagocytizing myelin debris, the biochemical phenotypes related to the biological function of macrophages show vast heterogeneity; however, it is not fully understood. Detecting biochemical changes after myelin debris phagocytosis by macrophages at a single-cell level is helpful to characterize phenotypic and functional heterogeneity. In this study, based on the cell model of myelin debris phagocytosis by macrophages in vitro, the biochemical changes in macrophages were investigated using Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy. Infrared spectrum fluctuations, principal component analysis, and cell-to-cell Euclidean distance statistical analysis of specific spectrum regions revealed dynamic and significant changes in proteins and lipids within macrophages after myelin debris phagocytosis. Thus, SR-FTIR microspectroscopy is a powerful identification toolkit for exploring biochemical phenotype heterogeneity transformation that may be of great importance to providing an evaluation strategy for studying cell functions related to cellular substance distribution and metabolism.

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

Escherichia coli survival in response to ciprofloxacin antibiotic stress correlates with increased nucleoid length and effective misfolded protein management

The evolution of antibiotic resistance is a fundamental problem in disease management but is rarely quantified on a single-cell level owing to challenges associated with capturing the spatial and temporal variation across a population. To evaluate cell biological phenotypic responses, we tracked the single-cell dynamics of filamentous bacteria through time in response to ciprofloxacin antibiotic stress. We measured the degree of phenotypic variation in nucleoid length and the accumulation of protein damage under ciprofloxacin antibiotic and quantified the impact on bacterial survival. Increased survival was correlated with increased nucleoid length and the variation in this response was inversely correlated with antibiotic concentration. Survival time was also increased through clearance of misfolded proteins, an unexpected mechanism of stress relief deployed by the filamentous bacteria. Our results reveal a diverse range of survival tactics employed by bacteria in response to ciprofloxacin and suggest potential evolutionary routes to resistance.

Genomes comparison of two Proteus mirabilis clones showing varied swarming ability

BACKGROUND

Proteus mirabilis is a Gram-negative bacteria most noted for its involvement with catheter-associated urinary tract infections. It is also known for its multicellular migration over solid surfaces, referred to as 'swarming motility'. Here we analyzed the genomic sequences of two P. mirabilis isolates, designated K38 and K39, which exhibit varied swarming ability.

METHODS AND RESULTS

The isolates genomes were sequenced using Illumina NextSeq sequencer, resulting in about 3.94 Mbp, with a GC content of 38.6%, genomes. Genomes were subjected for in silico comparative investigation. We revealed that, despite a difference in swarming motility, the isolates showed high genomic relatedness (up to 100% ANI similarity), suggesting that one of the isolates probably originated from the other.

CONCLUSIONS

The genomic sequences will allow us to investigate the mechanism driving this intriguing phenotypic heterogeneity between closely related P. mirabilis isolates. Phenotypic heterogeneity is an adaptive strategy of bacterial cells to several environmental pressures. It is also an important factor related to their pathogenesis. Therefore, the availability of these genomic sequences will facilitate studies that focus on the host-pathogen interactions during catheter-associated urinary tract infections.

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

Case Studies in the Assessment of Microbial Fitness: Seemingly Subtle Changes Can Have Major Effects on Phenotypic Outcomes

Following the completion of an adaptive evolution experiment, fitness evaluations are routinely conducted to assess the magnitude of adaptation. In doing so, proper consideration should be given when determining the appropriate methods as trade-offs may exist between accuracy and throughput. Here, we present three instances in which small changes in the framework or execution of fitness evaluations significantly impacted the outcomes. The first case illustrates that discrepancies in fitness conclusions can arise depending on the approach to evaluating fitness, the culture vessel used, and the sampling method. The second case reveals that variations in environmental conditions can occur associated with culture vessel material. Specifically, these subtle changes can greatly affect microbial physiology leading to changes in the culture pH and distorting fitness measurements. Finally, the last case reports that heterogeneity in CFU formation time can result in inaccurate fitness conclusions. Based on each case, considerations and recommendations are presented for future adaptive evolution experiments.

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

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.

Stochastic population dynamics of cancer stemness and adaptive response to therapies

Intratumoral heterogeneity can exist along multiple axes: Cancer stem cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states, and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple stable gene expression patterns ('attractors'). These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

Dynamics of hepatocyte-cholangiocyte cell-fate decisions during liver development and regeneration

The immense regenerative potential of the liver is attributed to the ability of its two key cell types – hepatocytes and cholangiocytes – to trans-differentiate to one another either directly or through intermediate progenitor states. However, the dynamic features of decision-making between these cell-fates during liver development and regeneration remains elusive. Here, we identify a core gene regulatory network comprising c/EBPα, TGFBR2, and SOX9 which is multistable in nature, enabling three distinct cell states – hepatocytes, cholangiocytes, and liver progenitor cells (hepatoblasts/oval cells) – and stochastic switching among them. Predicted expression signature for these three states are validated through multiple bulk and single-cell transcriptomic datasets collected across developmental stages and injury-induced liver repair. This network can also explain the experimentally observed spatial organization of phenotypes in liver parenchyma and predict strategies for efficient cellular reprogramming. Our analysis elucidates how the emergent dynamics of underlying regulatory networks drive diverse cell-fate decisions in liver development and regeneration.

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Identified minimal regulatory network to model liver development and regeneration

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Changes in phenotypic landscapes by in-silico perturbations of regulatory networks

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Ability to explain physiological spatial patterning of liver cell types

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Decoded strategies for efficient reprogramming among liver cell phenotypes

Diversity of bet-hedging strategies in microbial communities-Recent cases and insights

Microbial communities are continuously exposed to unpredictable changes in their environment. To thrive in such dynamic habitats, microorganisms have developed the ability to readily switch phenotypes, resulting in a number of differently adapted subpopulations expressing various traits. In evolutionary biology, a particular case of phenotypic heterogeneity that evolved in an unpredictably changing environment has been defined as bet‐hedging. Bet‐hedging is a risk‐spreading strategy where isogenic populations stochastically (randomly) diversify their phenotypes, often resulting in maladapted individuals that suffer lower reproductive success. This fitness trade‐off in a specific environment may have a selective advantage upon the sudden environmental shift. Thus, a bet‐hedging strategy allows populations to persist in very dynamic habitats, but with a particular fitness cost. In recent years, numerous examples of phenotypic heterogeneity in different microorganisms have been observed, some suggesting bet‐hedging. Here, we highlight the latest reports concerning bet‐hedging phenomena in various microorganisms to show how versatile this strategy is within the microbial realms.

This article is categorized under:

Infectious Diseases > Molecular and Cellular Physiology

Transient Antibiotic Tolerance Triggered by Nutrient Shifts From Gluconeogenic Carbon Sources to Fatty Acid

Nutrient shifts from glycolytic-to-gluconeogenic carbon sources can create large sub-populations of extremely antibiotic tolerant bacteria, called persisters. Positive feedback in Escherichia coli central metabolism was believed to play a key role in the formation of persister cells. To examine whether positive feedback in nutrient transport can also support high persistence to β-lactams, we performed nutrient shifts for E. coli from gluconeogenic carbon sources to fatty acid (FA). We observed tri-phasic antibiotic killing kinetics characterized by a transient period of high antibiotic tolerance, followed by rapid killing then a slower persister-killing phase. The duration of transient tolerance (3-44 h) varies with pre-shift carbon source and correlates strongly with the time needed to accumulate the FA degradation enzyme FadD after the shift. Additionally, FadD accumulation time and thus transient tolerance time can be reduced by induction of the glyoxylate bypass prior to switching, highlighting that two interacting feedback loops simultaneously control the length of transient tolerance. Our results demonstrate that nutrient switches along with positive feedback are not sufficient to trigger persistence in a majority of the population but instead triggers only a temporary tolerance. Additionally, our results demonstrate that the pre-shift metabolic state determines the duration of transient tolerance and that supplying glyoxylate can facilitate antibiotic killing of bacteria.

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

While being genetically the same, a population of cells can show phenotypic variability even under homogeneous environments. Often advantageous under heterogeneous environments, this phenotypic heterogeneity is highly relevant in the studies of antibiotic resistance evolution and cancer resurgence. Numerous theoretical models exist applying a simple model of phenotypic switching. Experimental measurements on phenotypic heterogeneity have increased in precision over the past decade, and the simple models are inadequate to explain the new observations. In this paper, we explore the role of cellular memory as a crucial component of phenotypic switching. We see that memory helps account for the hitherto unexplained observations and fundamentally extend our understanding of phenotypic heterogeneity.

Multi-omic Characterization of Intraspecies Variation in Laboratory and Natural Environments

Investigation of microbial communities has led to many advances in our understanding of ecosystem function, whether that ecosystem is a subglacial lake or the human gut. Within these communities, much emphasis has been placed on interspecific variation and between-species relationships. However, with current advances in sequencing technology resulting in both the reduction in sequencing costs and the rise of shotgun metagenomic sequencing, the importance of intraspecific variation and within-species relationships is becoming realized. Our group conducts multi-omic analyses to understand how spatial structure and resource availability influence diversification within a species and the potential for long-term coexistence of multiple ecotypes within a microbial community. Here, we present examples of ecotypic variation observed in the lab and in the wild, current challenges faced when investigating intraspecies diversity, and future developments that we expect to define the field over the next 5 years.

Visualization and Quantification of Genetically Adapted Microbial Cells During Preculture

As culture history is known to affect the length of the lag phase and microbial cell growth, precultures are often grown in the same medium as the main culture for physiological adaptation and to reduce a prolonged lag time in some microbial cells. To understand the adaptation process of microbial cells during transfer from Luria-Bertani medium to minimal medium, we used the growth of Escherichia coli BL21(DE3) in succinate minimal medium as a model system. We observed that only one or two sequential transfers from minimal medium to fresh minimal medium accelerated the growth rate of BL21(DE3) cells. In addition, the number of large colonies (diameter ≥0.1 cm) on succinate agar increased with the number of transfers. Genome and transcript analyses showed that the C-to-T point mutation in large colony cells converted the inactive promoter of kgtP (known to encode α-ketoglutarate permease) to the active form, allowing efficient uptake of exogenous succinate. Moreover, we visualized the occurrence of genetically adapted cells with better fitness in real time and quantified the number of those cells in the microbial population during transfer to the same medium. Fluorescence microscopy showed the occurrence and increase of adapted mutant cells, which contain intracellular KgtP-fused green fluorescent proteins, as a result of the C-to-T mutation in the promoter of a fused kgtP-sfgfp during transfer to fresh medium. Flow cytometry revealed that the proportion of mutant cells increased from 1.75% (first transfer) to 12.16% (second transfer) and finally 70.79% (third transfer), explaining the shortened lag time and accelerated growth rate of BL21(DE3) cells during adaptation to the minimal medium. This study provides new insights into the genetic heterogeneity of microbial populations that aids microbial adaptability in new environments.

Short-term effects of controlled mating and selection on the genetic variance of honeybee populations

Directional selection in a population yields reduced genetic variance due to the Bulmer effect. While this effect has been thoroughly investigated in mammals, it is poorly studied in social insects with biological peculiarities such as haplo-diploidy or the collective expression of traits. In addition to the natural adaptation to climate change, parasites, and pesticides, honeybees increasingly experience artificial selection pressure through modern breeding programs. Besides selection, many honeybee breeding schemes introduce controlled mating. We investigated which individual effects selection and controlled mating have on genetic variance. We derived formulas to describe short-term changes of genetic variance in honeybee populations and conducted computer simulations to confirm them. Thereby, we found that the changes in genetic variance depend on whether the variance is measured between queens (inheritance criterion), worker groups (selection criterion), or both (performance criterion). All three criteria showed reduced genetic variance under selection. In the selection and performance criteria, our formulas and simulations showed an increased genetic variance through controlled mating. This newly described effect counterbalanced and occasionally outweighed the Bulmer effect. It could not be observed in the inheritance criterion. A good understanding of the different notions of genetic variance in honeybees, therefore, appears crucial to interpreting population parameters correctly.

Genome-Wide Identification and Expression Analysis of SOS Response Genes in Salmonella enterica Serovar Typhimurium

A bioinformatic search for LexA boxes, combined with transcriptomic detection of loci responsive to DNA damage, identified 48 members of the SOS regulon in the genome of Salmonella enterica serovar Typhimurium. Single cell analysis using fluorescent fusions revealed that heterogeneous expression is a common trait of SOS response genes, with formation of SOS^OFF and SOS^ON subpopulations. Phenotypic cell variants formed in the absence of external DNA damage show gene expression patterns that are mainly determined by the position and the heterology index of the LexA box. SOS induction upon DNA damage produces SOS^OFF and SOS^ON subpopulations that contain live and dead cells. The nature and concentration of the DNA damaging agent and the time of exposure are major factors that influence the population structure upon SOS induction. An analogy can thus be drawn between the SOS response and other bacterial stress responses that produce phenotypic cell variants.

Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

Phenotypic (non-genetic) heterogeneity has significant implications for the development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unraveled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other, but members of a team activate one another, forming a 'toggle switch' between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their 'latent' design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.

The impact of mistranslation on phenotypic variability and fitness

Phenotypic variation is widespread in natural populations, and can significantly alter population ecology and evolution. Phenotypic variation often reflects underlying genetic variation, but also manifests via non-heritable mechanisms. For instance, translation errors result in about 10% of cellular proteins carrying altered sequences. Thus, proteome diversification arising from translation errors can potentially generate phenotypic variability, in turn increasing variability in the fate of cells or of populations. However, the link between protein diversity and phenotypic variability remains unverified. We manipulated mistranslation levels in Escherichia coli, and measured phenotypic variability between single cells (individual-level variation), as well as replicate populations (population-level variation). Monitoring growth and survival, we find that mistranslation indeed increases variation across E. coli cells, but does not consistently increase variability in growth parameters across replicate populations. Interestingly, although any deviation from the wild-type (WT) level of mistranslation reduces fitness in an optimal environment, the increased variation is associated with a survival benefit under stress. Hence, we suggest that mistranslation-induced phenotypic variation can impact growth and survival and has the potential to alter evolutionary trajectories.

Single-Cell Infrared Microspectroscopy Quantifies Dynamic Heterogeneity of Mesenchymal Stem Cells during Adipogenic Differentiation

The central relevance of cellular heterogeneity to biological phenomena raises the rational needs for analytical techniques with single-cell resolution. Here, we developed a single-cell FTIR microspectroscopy-based method for the quantitative evaluation of cellular heterogeneity by calculating the cell-to-cell similarity distance of the infrared spectral data. Based on this method, we revealed the infrared phenotypes might reflect the dynamic heterogeneity changes in the cell population during the adipogenic differentiation of the human mesenchymal stem cells. These findings provide an alternative label-free optical approach for quantifying the cellular heterogeneity, and the combination with other single-cell analysis tools will be very helpful for understanding the genotype-to-phenotype relationship in cellular populations.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.

Gene expression noise can promote the fixation of beneficial mutations in fluctuating environments

Nongenetic phenotypic variation can either speed up or slow down adaptive evolution. We show that it can speed up evolution in environments where available carbon and energy sources change over time. To this end, we use an experimentally validated model of Escherichia coli growth on two alternative carbon sources, glucose and acetate. On the superior carbon source (glucose), all cells achieve high growth rates, while on the inferior carbon source (acetate) only a small fraction of the population manages to initiate growth. Consequently, populations experience a bottleneck when the environment changes from the superior to the inferior carbon source. Growth on the inferior carbon source depends on a circuit under the control of a transcription factor that is repressed in the presence of the superior carbon source. We show that noise in the expression of this transcription factor can increase the probability that cells start growing on the inferior carbon source. In doing so, it can decrease the severity of the bottleneck and increase mean population fitness whenever this fitness is low. A modest amount of noise can also enhance the fitness effects of a beneficial allele that increases the fraction of a population initiating growth on acetate. Additionally, noise can protect this allele from extinction, accelerate its spread, and increase its likelihood of going to fixation. Central to the adaptation-enhancing principle we identify is the ability of noise to mitigate population bottlenecks, particularly in environments that fluctuate periodically. Because such bottlenecks are frequent in fluctuating environments, and because periodically fluctuating environments themselves are common, this principle may apply to a broad range of environments and organisms.

Scale-Free Biology: Integrating Evolutionary and Developmental Thinking

When the history of life on earth is viewed as a history of cell division, all of life becomes a single cell lineage. The growth and differentiation of this lineage in reciprocal interaction with its environment can be viewed as a developmental process; hence the evolution of life on earth can also be seen as the development of life on earth. Here, in reviewing this field, some potentially fruitful research directions suggested by this change in perspective are highlighted. Variation and selection become, for example, bidirectional information flows between scales, while the notions of "cooperation" and "competition" become scale relative. The language of communication, inference, and information processing becomes more useful than the language of causation to describe the interactions of both homogeneous and heterogeneous living systems at any scale. Emerging scale-free theoretical frameworks such as predictive coding and active inference provide conceptual tools for reconceptualizing biology as the study of a unified, multiscale dynamical system.

Lineage EM algorithm for inferring latent states from cellular lineage trees

SUMMARY

Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivorship bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation-maximization (EM) algorithm, which we call lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data.

AVAILABILITY AND IMPLEMENTATION

An implementation of LEM is available at https://github.com/so-nakashima/Lineage-EM-algorithm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

Extrinsic Noise and Heavy-Tailed Laws in Gene Expression

Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our ansatz allows us to incorporate the effects of extrinsic noise-encompassing factors external to the transcription of the individual gene-and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

A riboswitch gives rise to multi-generational phenotypic heterogeneity in an auxotrophic bacterium

Auxotrophy, the inability to produce an organic compound essential for growth, is widespread among bacteria. Auxotrophic bacteria rely on transporters to acquire these compounds from their environment. Here, we study the expression of both low- and high-affinity transporters of the costly amino acid methionine in an auxotrophic lactic acid bacterium, Lactococcus lactis. We show that the high-affinity transporter (Met-transporter) is heterogeneously expressed at low methionine concentrations, resulting in two isogenic subpopulations that sequester methionine in different ways: one subpopulation primarily relies on the high-affinity transporter (high expression of the Met-transporter) and the other subpopulation primarily relies on the low-affinity transporter (low expression of the Met-transporter). The phenotypic heterogeneity is remarkably stable, inherited for tens of generations, and apparent at the colony level. This heterogeneity results from a T-box riboswitch in the promoter region of the met operon encoding the high-affinity Met-transporter. We hypothesize that T-box riboswitches, which are commonly found in the Lactobacillales, may play as-yet unexplored roles in the predominantly auxotrophic lifestyle of these bacteria.

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp

Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. To assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifier whose overall accuracy reached 85% on the test dataset, with per-class specificity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classification. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. These can be promptly identified and characterized using computational models so that future work may unveil morphological associations with yeast virulence.

Modeling population heterogeneity from microbial communities to immune response in cells

Heterogeneity is universally observed in all natural systems and across multiple scales. Understanding population heterogeneity is an intriguing and attractive topic of research in different disciplines, including microbiology and immunology. Microbes and mammalian immune cells present obviously rather different system-specific biological features. Nevertheless, as typically occurs in science, similar methods can be used to study both types of cells. This is particularly true for mathematical modeling, in which key features of a system are translated into algorithms to challenge our mechanistic understanding of the underlying biology. In this review, we first present a broad overview of the experimental developments that allowed observing heterogeneity at the single cell level. We then highlight how this "data revolution" requires the parallel advancement of algorithms and computing infrastructure for data processing and analysis, and finally present representative examples of computational models of population heterogeneity, from microbial communities to immune response in cells.

Bacterial Flow Cytometry and Imaging as Potential Process Monitoring Tools for Industrial Biotechnology

Minimizing process variations by early identification of deviations is one approach to make industrial production processes robust. Cell morphology is a direct representation of the physiological state and an important factor for the cell’s survival in harsh environments as encountered during industrial processing. The adverse effects of fluctuating process parameters on cells were studied using flow cytometry and imaging. Results showed that altered pH caused a shift in cell size distribution from a heterogeneous mix of elongated and short cells to a homogenous population of short cells. Staining based on membrane integrity revealed a dynamics in the pattern of cluster formation during fermentation. Contradictory findings from forward scatter and imaging highlight the need for use of complementary techniques that provide visual confirmation to interpret changes. An atline flow cytometry or imaging capable of identifying subtle population deviations serves as a powerful monitoring tool for industrial biotechnology.

BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells. These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic framework for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty. Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

Plasticity of Cyanobacterial Thylakoid Microdomains Under Variable Light Conditions

Photosynthetic light reactions proceed in thylakoid membranes (TMs) due to the activity of pigment-protein complexes. These complexes are heterogeneously organized into granal/stromal thylakoids (in plants) or into recently identified cyanobacterial microdomains (MDs). MDs are characterized by specific ratios of photosystem I (PSI), photosystem II (PSII), and phycobilisomes (PBS) and they are visible as sub-micrometer sized areas with different fluorescence ratios. In this report, the process of long-term plasticity in cyanobacterial thylakoid MDs has been explored under variable growth light conditions using Synechocystis sp. PCC6803 expressing YFP tagged PSI. TM organization into MDs has been observed for all categorized shapes of cells independently of their stage in cell cycle. The heterogeneous PSI, PSII, and PBS thylakoid areas were also identified under two types of growth conditions: at continuous light (CL) and at light-dark (L-D) cycle. The acclimation from CL to L-D cycle changed spatial distribution of photosystems, in particular PSI became more evenly distributed in thylakoids under L-D cycle. The process of the spatial PSI (and partially also PSII) redistribution required 1 week and was accompanied by temporal appearance of PBS decoupling probably caused by the re-organization of photosystems. The overall acclimation we observed was defined as TM plasticity as it resembles higher plants grana/stroma reorganization at variable growth light conditions. In addition, we observed large cell to cell variability in the actual MDs organization. It leads us to suggest that the plasticity, and cell to cell variability in MDs could be a manifestation of phenotypic heterogeneity, a recently broadly discussed phenomenon for prokaryotes.

Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Scientists tend to appreciate microbes for their simplicity and predictability: a population of genetically identical cells inhabiting a uniform environment is expected to behave in a uniform way. However, counter-examples to this assumption are frequently being discovered, forcing a re-examination of the relationship between genotype and phenotype. In most such examples, bacterial cells are found to split into two discrete populations, for instance growing and non-growing. Here, we report the discovery of a novel example of microbial phenotypic heterogeneity in which cells are distributed along a gradient of phenotypes, ranging from low to high tolerance of a toxic chemical. Furthermore, we demonstrate that the distribution of phenotypes changes in different growth conditions, and we use mathematical modeling to show that cells may change their phenotype either randomly or in a particular direction in response to the environment. Our work expands our understanding of how a bacterial cell's genome, family history, and environment all contribute to its behavior, with implications for the diverse situations in which we care to understand the growth of any single-celled populations.

Trade-offs between gene expression, growth and phenotypic diversity in microbial populations

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Limitations in molecular resources for gene expression influence bacterial physiology.

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Bacteria optimise trade-offs between resource allocation and growth.

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Resource allocation plays a role in the emergence of phenotypic heterogeneity.

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Trade-offs between bet-hedging and growth can be harnessed in biotechnology.

Bacterial cells have a limited number of resources that can be allocated for gene expression. The intracellular competition for these resources has an impact on the cell physiology. Bacteria have evolved mechanisms to optimize resource allocation in a variety of scenarios, showing a trade-off between the resources used to maximise growth (e.g. ribosome synthesis) and the rest of cellular functions. Limitations in gene expression also play a role in generating phenotypic diversity, which is advantageous in fluctuating environments, at the expenses of decreasing growth rates. Our current understanding of these trade-offs can be exploited for biotechnological applications benefiting from the selective manipulation of the allocation of resources.

The cellular microbiology of Salmonellae interactions with macrophages

Salmonellae are important enteric pathogens that cause gastroenteritis and systemic illnesses. Macrophages are important components of both the innate and acquired immune system, acting as phagocytes with significant antimicrobial killing activities that present antigen to the adaptive immune system. Macrophages can also be cultured from a variety of sites as primary cells, and the study of the survival and interactions of Salmonellae with these cells is a very early model of infection and cellular microbiology. This review traces the history of discoveries made using Salmonellae infection of macrophages and addresses the possibility of future research in this area, in particular with regards to understanding the complexity of individual bacteria and macrophage cell variability and how such heterogeneity may alter the outcome of infection.

Aggregation of Nontuberculous Mycobacteria Is Regulated by Carbon-Nitrogen Balance

Free-living bacteria can assemble into multicellular structures called biofilms. Biofilms help bacteria tolerate multiple stresses, including antibiotics and the host immune system. Nontuberculous mycobacteria are a group of emerging opportunistic pathogens that utilize biofilms to adhere to household plumbing and showerheads and to avoid phagocytosis by host immune cells. Typically, bacteria regulate biofilm formation by controlling expression of adhesive structures to attach to surfaces and other bacterial cells. Mycobacteria harbor a unique cell wall built chiefly of long-chain mycolic acids that confers hydrophobicity and has been thought to cause constitutive aggregation in liquid media. Here we show that aggregation is instead a regulated process dictated by the balance of available carbon and nitrogen. Understanding that mycobacteria utilize metabolic cues to regulate the transition between planktonic and aggregated cells reveals an inroad to controlling biofilm formation through targeted therapeutics.

Nontuberculous mycobacteria (NTM) are emerging opportunistic pathogens that colonize household water systems and cause chronic lung infections in susceptible patients. The ability of NTM to form surface-attached biofilms in the nonhost environment and corded aggregates in vivo is important to their ability to persist in both contexts. Underlying the development of these multicellular structures is the capacity of mycobacterial cells to adhere to one another. Unlike most other bacteria, NTM spontaneously and constitutively aggregate in vitro, hindering our ability to understand the transition between planktonic and aggregated cells. While culturing a model NTM, Mycobacterium smegmatis, in rich medium, we fortuitously discovered that planktonic cells accumulate after ∼3 days of growth. By providing selective pressure for bacteria that disperse earlier, we isolated a strain with two mutations in the oligopeptide permease operon (opp). A mutant lacking the opp operon (Δopp) disperses earlier than wild type (WT) due to a defect in nutrient uptake. Experiments with WT M. smegmatis revealed that growth as aggregates is favored when carbon is replete, but under conditions of low available carbon relative to available nitrogen, M. smegmatis grows as planktonic cells. By adjusting carbon and nitrogen sources in defined medium, we tuned the cellular C/N ratio such that M. smegmatis grows either as aggregates or as planktonic cells. C/N-mediated aggregation regulation is widespread among NTM with the possible exception of rough-colony Mycobacterium abscessus isolates. Altogether, we show that NTM aggregation is a controlled process that is governed by the relative availability of carbon and nitrogen for metabolism.

Cell-length heterogeneity: a population-level solution to growth/virulence trade-offs in the plant pathogen Dickeya dadantii

Necrotrophic plant pathogens acquire nutrients from dead plant cells, which requires the disintegration of the plant cell wall and tissue structures by the pathogen. Infected plants lose tissue integrity and functional immunity as a result, exposing the nutrient rich, decayed tissues to the environment. One challenge for the necrotrophs to successfully cause secondary infection (infection spread from an initially infected plant to the nearby uninfected plants) is to effectively utilize nutrients released from hosts towards building up a large population before other saprophytes come. In this study, we observed that the necrotrophic pathogen Dickeya dadantii exhibited heterogeneity in bacterial cell length in an isogenic population during infection of potato tuber. While some cells were regular rod-shape (<10μm), the rest elongated into filamentous cells (>10μm). Short cells tended to occur at the interface of healthy and diseased tissues, during the early stage of infection when active attacking and killing is occurring, while filamentous cells tended to form at a later stage of infection. Short cells expressed all necessary virulence factors and motility, whereas filamentous cells did not engage in virulence, were non-mobile and more sensitive to environmental stress. However, compared to the short cells, the filamentous cells displayed upregulated metabolic genes and increased growth, which may benefit the pathogens to build up a large population necessary for the secondary infection. The segregation of the two subpopulations was dependent on differential production of the alarmone guanosine tetraphosphate (ppGpp). When exposed to fresh tuber tissues or freestanding water, filamentous cells quickly transformed to short virulent cells. The pathogen adaptation of cell length heterogeneity identified in this study presents a model for how some necrotrophs balance virulence and vegetative growth to maximize fitness during infection.

Virulence and vegetative growth are two distinct lifestyles in pathogenic bacteria. Although virulence factors are critical for pathogens to successfully cause infections, producing these factors is costly and imposes growth penalty to the pathogen. Although each single bacterial cell exists in one lifestyle or the other at any moment, we demonstrated in this study that a bacterial population could accomplish the two functions simultaneously by maintaining subpopulations of cells in each of the two lifestyles. During the invasion of potato tuber, the soft rot pathogen Dickeya dadantii formed two distinct subpopulations characterized by their cell morphology. The population consisting of short cells actively produced virulence factors to break down host tissues, whereas the other population, consisting of filamentous cells, was only engaged in vegetative growth and was non-virulent. We hypothesize that this phenotypic heterogeneity allows D. dadantii to break down plant tissues and release nutrients, while efficiently utilizing nutrients needed to build up a large pathogen population at the same time. Our study provides insights into how phenotypic heterogeneity could grant bacteria abilities to “multi-task” distinct functions as a population.

Ribosome Provisioning Activates a Bistable Switch Coupled to Fast Exit from Stationary Phase

Observations of bacteria at the single-cell level have revealed many instances of phenotypic heterogeneity within otherwise clonal populations, but the selective causes, molecular bases, and broader ecological relevance remain poorly understood. In an earlier experiment in which the bacterium Pseudomonas fluorescens SBW25 was propagated under a selective regime that mimicked the host immune response, a genotype evolved that stochastically switched between capsulation states. The genetic cause was a mutation in carB that decreased the pyrimidine pool (and growth rate), lowering the activation threshold of a preexisting but hitherto unrecognized phenotypic switch. Genetic components surrounding bifurcation of UTP flux toward DNA/RNA or UDP-glucose (a precursor of colanic acid forming the capsules) were implicated as key components. Extending these molecular analyses-and based on a combination of genetics, transcriptomics, biochemistry, and mathematical modeling-we show that pyrimidine limitation triggers an increase in ribosome biosynthesis and that switching is caused by competition between ribosomes and CsrA/RsmA proteins for the mRNA transcript of a positively autoregulated activator of colanic acid biosynthesis. We additionally show that in the ancestral bacterium the switch is part of a program that determines stochastic entry into a semiquiescent capsulated state, ensures that such cells are provisioned with excess ribosomes, and enables provisioned cells to exit rapidly from stationary phase under permissive conditions.

Evolutionary Stability of Salmonella Competition with the Gut Microbiota: How the Environment Fosters Heterogeneity in Exploitative and Interference Competition

Following ingestion, gastrointestinal pathogens compete against the gastrointestinal microbiota and overcome host immune defenses in order to cause infections. Besides employing direct killing mechanisms, the commensal microbiota occupies metabolic niches to outcompete invading pathogens. Salmonella enterica serovar Typhimurium (S. Typhimurium) uses several strategies to successfully colonize the gut and establish infection, of which an increasing number is based on phenotypic heterogeneity within the S. Typhimurium population. The utilization of myo-inositol (MI) and the production of colicin confer a selective advantage over the microbiota in terms of exploitative and interference competition, respectively. In this review, we summarize the genetic basis underlying bistability of MI catabolism and colicin production. As demonstrated by single-cell analyses, a stochastic switch in the expression of the genes responsible for colicin production and MI degradation constitutes the heterogeneity of the two phenotypes. Both genetic systems are tightly regulated to avoid their expression under non-appropriate conditions and possible detrimental effects on bacterial fitness. Moreover, evolutionary mechanisms underlying formation and stability of these phenotypes in S. Typhimurium are discussed. We propose that both MI catabolism and colicin production create a bet-hedging strategy, which provides an adaptive benefit for S. Typhimurium in the fluctuating environment of the mammalian gut.

Growth-dependent recombinant product formation kinetics can be reproduced through engineering of glucose transport and is prone to phenotypic heterogeneity

Background

Escherichia coli W3110 and a group of six isogenic derivatives, each displaying distinct specific rates of glucose consumption were characterized to determine levels of GFP production and population heterogeneity. These strains have single or combinatory deletions in genes encoding phosphoenolpyruvate:sugar phosphotransferase system (PTS) permeases as PtsG and ManX, as well as common components EI, Hpr protein and EIIA, also the non-PTS Mgl galactose/glucose ABC transporter. They have been transformed for expressing GFP based on a lac-based expression vector, which is subject to bistability.

Results

These strains displayed specific glucose consumption and growth rates ranging from 1.75 to 0.45 g/g h and 0.54 to 0.16 h⁻¹, respectively. The rate of acetate production was strongly reduced in all mutant strains when compared with W3110/pV21. In bioreactor cultures, wild type W3110/pV21 produced 50.51 mg/L GFP, whereas strains WG/pV21 with inactive PTS IICBGlc and WGM/pV21 with the additional inactivation of PTS IIABMan showed the highest titers of GFP, corresponding to 342 and 438 mg/L, respectively. Moreover, we showed experimentally that bistable expression systems, as lac-based ones, induce strong phenotypic segregation among microbial populations.

Conclusions

We have demonstrated that reduction on glucose consumption rate in E. coli leads to an improvement of GFP production. Furthermore, from the perspective of phenotypic heterogeneity, we observed in this case that heterogeneous systems are also the ones leading to the highest performance. This observation suggests reconsidering the generally accepted proposition stating that phenotypic heterogeneity is generally unwanted in bioprocess applications.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1073-5) contains supplementary material, which is available to authorized users.

Intrinsic and extrinsic noise of gene expression in lineage trees

Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the population dynamics. While these sources are usually studied separately, we develop an agent-based framework that accounts for both factors while tracking every single cell of a growing population. Apart from the common intrinsic variability, the framework also predicts extrinsic noise without the need to introduce fluctuating rate constants. Instead, extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age. We provide explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics in two-colour experiments. We find that these statistics differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) population snapshots with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots with unknown cell ages as measured from static images or flow cytometry. Applying the method to models of stochastic gene expression and feedback regulation elucidates that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.