Environmental drivers of metabolic heterogeneity in clonal microbial populations

Carbon and Nutrient Limitations of Microbial Metabolism in Xingkai Lake, China: Abiotic and Biotic Drivers

Microbial communities are crucial for water quality and biogeochemical cycling in freshwaters. Microbes secrete extracellular enzymes to decompose organic matter for their needs of nutrients and scarce elements. Yet, there is a lack of knowledge on microbial metabolic limitations in freshwaters, especially in lake sediments. Here, we examined the carbon, nitrogen, and phosphorus-acquiring extracellular enzyme activities and the bacterial and fungal communities of 30 sediments across Xingkai Lake, the largest freshwater lake in Northeast Asia. We further analyzed the microbial metabolic limitations via extracellular enzyme stoichiometry and explored the direct and indirect effects of abiotic and biotic factors on the limitations. We found that microbial metabolisms were primarily limited by phosphorus in Xingkai Lake. For instance, microbial carbon and phosphorus limitations were closely correlated to abiotic factors like water depth, total dissolved solids, sediment total carbon, and conductivity. The metabolic limitations were also affected by biotic factors, such as showing positive relationships with the alpha and beta diversity of bacteria, and with the beta diversity of fungi. In addition, community compositions of bacteria and fungi were mainly correlated to abiotic factors such as total carbon and dissolved organic carbon, respectively. Collectively, microbial metabolic limitations were affected directly or indirectly by abiotic factors and microbial communities. Our findings indicate that microbial metabolic limitations are not only driven by bacteria and fungi but also by abiotic factors such as water depth and total nitrogen, and thus provide empirical evidence for effective management of freshwater lakes under climate warming and intensified human activities.

Space and genealogy determine inter-individual differences in siderophore gene expression in bacterial colonies

Heterogeneity in gene expression is common among clonal cells in bacteria, although the sources and functions of variation often remain unknown. Here, we track cellular heterogeneity in the bacterium Pseudomonas aeruginosa during colony growth by focusing on siderophore gene expression (pyoverdine versus pyochelin) important for iron nutrition. We find that the spatial position of cells within colonies and non-genetic yet heritable differences between cell lineages are significant sources of cellular heterogeneity, while cell pole age and lifespan have no effect. Regarding functions, our results indicate that cells adjust their siderophore investment strategies along a gradient from the colony center to its edge. Moreover, cell lineages with below-average siderophore investment benefit from lineages with above-average siderophore investment, presumably due to siderophore sharing. Our study highlights that single-cell experiments with dual gene expression reporters can identify sources of gene expression variation of interlinked traits and offer explanations for adaptive benefits in bacteria.

Interplay between environmental yielding and dynamic forcing modulates bacterial growth

Many bacterial habitats-ranging from gels and tissues in the body to cell-secreted exopolysaccharides in biofilms-are rheologically complex, undergo dynamic external forcing, and have unevenly distributed nutrients. How do these features jointly influence how the resident cells grow and proliferate? Here, we address this question by studying the growth of Escherichia coli dispersed in granular hydrogel matrices with defined and highly tunable structural and rheological properties, under different amounts of external forcing imposed by mechanical shaking, and in both aerobic and anaerobic conditions. Our experiments establish a general principle: that the balance between the yield stress of the environment that the cells inhabit, σy, and the external stress imposed on the environment, σ, modulates bacterial growth by altering transport of essential nutrients to the cells. In particular, when σy<σ, the environment is easily fluidized and mixed over large scales, providing nutrients to the cells and sustaining complete cellular growth. By contrast, when σy>σ, the elasticity of the environment suppresses large-scale fluid mixing, limiting nutrient availability and arresting cellular growth. Our work thus reveals a new mechanism, beyond effects that change cellular behavior via local forcing, by which the rheology of the environment may modulate microbial physiology in diverse natural and industrial settings.

Genomic and metabolic instability during long-term fermentation of an industrial Saccharomyces cerevisiae strain engineered for C5 sugar utilization

The genetic stability and metabolic robustness of production strains is one of the key criteria for the production of bio-based products by microbial fermentation on an industrial scale. These criteria were here explored in an industrial ethanol-producer strain of Saccharomyces cerevisiae able to co-ferment D-xylose and L-arabinose with glucose through the chromosomal integration of several copies of pivotal genes for the use of these pentose (C5) sugars. Using batch sequential cultures in a controlled bioreactor that mimics long-term fermentation in an industrial setting, this strain was found to exhibit significant fluctuations in D-xylose and L-arabinose consumption as early as the 50th generation and beyond. These fluctuations seem not related to the few low-consumption C5 sugar clones that appeared throughout the sequential batch cultures at a frequency lower than 1.5% and that were due to the reduction in the number of copies of transgenes coding for C5 sugar assimilation enzymes. Also, subpopulations enriched with low or high RAD52 expression, whose expression level was reported to be proportional to homologous recombination rate did not exhibit defect in C5-sugar assimilation, arguing that other mechanisms may be responsible for copy number variation of transgenes. Overall, this work highlighted the existence of genetic and metabolic instabilities in an industrial yeast which, although modest in our conditions, could be more deleterious in harsher industrial conditions, leading to reduced production performance.

Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics

Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

Single-cell analysis in hypersaline brines predicts a water-activity limit of microbial anabolic activity

Hypersaline brines provide excellent opportunities to study extreme microbial life. Here, we investigated anabolic activity in nearly 6000 individual cells from solar saltern sites with water activities (aw) ranging from 0.982 to 0.409 (seawater to extreme brine). Average anabolic activity decreased exponentially with aw, with nuanced trends evident at the single-cell level: The proportion of active cells remained high (>50%) even after NaCl saturation, and subsets of cells spiked in activity as aw decreased. Intracommunity heterogeneity in activity increased as seawater transitioned to brine, suggesting increased phenotypic heterogeneity with increased physiological stress. No microbial activity was detected in the 0.409-aw brine (an MgCl2-dominated site) despite the presence of cell-like structures. Extrapolating our data, we predict an aw limit for detectable anabolic activity of 0.540, which is beyond the currently accepted limit of life based on cell division. This work demonstrates the utility of single-cell, metabolism-based techniques for detecting active life and expands the potential habitable space on Earth and beyond.

Revisiting the Role of Genetic Variation in Adaptation

AbstractEvolutionary biologists have thought about the role of genetic variation during adaptation for a very long time-before we understood the organization of the genetic code, the provenance of genetic variation, and how such variation influenced the phenotypes on which natural selection acts. Half a century after the discovery of the structure of DNA and the unraveling of the genetic code, we have a rich understanding of these problems and the means to both delve deeper and widen our perspective across organisms and natural populations. The 2022 Vice Presidential Symposium of the American Society of Naturalists highlighted examples of recent insights into the role of genetic variation in adaptive processes, which are compiled in this special section. The work was conducted in different parts of the world, included theoretical and empirical studies with diverse organisms, and addressed distinct aspects of how genetic variation influences adaptation. In our introductory article to the special section, we discuss some important recent insights about the generation and maintenance of genetic variation, its impacts on phenotype and fitness, its fate in natural populations, and its role in driving adaptation. By placing the special section articles in the broader context of recent developments, we hope that this overview will also serve as a useful introduction to the field.

Single-cell isotope tracing reveals functional guilds of bacteria associated with the diatom Phaeodactylum tricornutum

Bacterial remineralization of algal organic matter fuels algal growth but is rarely quantified. Consequently, we cannot currently predict whether some bacterial taxa may provide more remineralized nutrients to algae than others. Here, we quantified bacterial incorporation of algal-derived complex dissolved organic carbon and nitrogen and algal incorporation of remineralized carbon and nitrogen in fifteen bacterial co-cultures growing with the diatom Phaeodactylum tricornutum at the single-cell level using isotope tracing and nanoSIMS. We found unexpected strain-to-strain and cell-to-cell variability in net carbon and nitrogen incorporation, including non-ubiquitous complex organic nitrogen utilization and remineralization. We used these data to identify three distinct functional guilds of metabolic interactions, which we termed macromolecule remineralizers, macromolecule users, and small-molecule users, the latter exhibiting efficient growth under low carbon availability. The functional guilds were not linked to phylogeny and could not be elucidated strictly from metabolic capacity as predicted by comparative genomics, highlighting the need for direct activity-based measurements in ecological studies of microbial metabolic interactions.

Exploiting Information and Control Theory for Directing Gene Expression in Cell Populations

Microbial populations can adapt to adverse environmental conditions either by appropriately sensing and responding to the changes in their surroundings or by stochastically switching to an alternative phenotypic state. Recent data point out that these two strategies can be exhibited by the same cellular system, depending on the amplitude/frequency of the environmental perturbations and on the architecture of the genetic circuits involved in the adaptation process. Accordingly, several mitigation strategies have been designed for the effective control of microbial populations in different contexts, ranging from biomedicine to bioprocess engineering. Technically, such control strategies have been made possible by the advances made at the level of computational and synthetic biology combined with control theory. However, these control strategies have been applied mostly to synthetic gene circuits, impairing the applicability of the approach to natural circuits. In this review, we argue that it is possible to expand these control strategies to any cellular system and gene circuits based on a metric derived from this information theory, i.e., mutual information (MI). Indeed, based on this metric, it should be possible to characterize the natural frequency of any gene circuits and use it for controlling gene circuits within a population of cells.

Varied interspecies interactions between anammox and denitrifying bacteria enhanced nitrogen removal in a single-stage simultaneous anammox and denitrification system

The simultaneous anammox and denitrification (SAD) system has received growing interest for the enhanced nitrogen removal, while the ecological traits of microbial community including spatial distribution characteristics, assembly processes and interspecies interactions have not been fully unraveled. The present study applied metagenomics and ecological analysis methods to gain the ecological traits of microbial communities in the SAD system across different organic substrate loadings. Results showed that organic matter significantly affected the bioreactor performance, and the optimal total nitrogen removal efficiency reached 93.4 ± 0.7% under the COD concentrations of 180 ± 18.2 mg/L. Functional organisms including Candidatus Brocadia (3.9%), Denitratisoma (1.6%), Dokdonella (4.4%) and Thauera (4.6%) obviously enriched under the optimal organic loading conditions. Moreover, microbial communities were significantly governed by deterministic process under high organic concentrations, and the denitrifying organisms displayed important ecological roles in the communities. Although anammox bacteria obviously enriched at the middle of bioreactor, it possessed the highest expression activities at both bottom and middle sites. Denitrifying bacteria that enriched at the bottom sites strongly achieved nitrate reduction and provided nitrite for anammox bacteria, while these organisms trended to compete nitrite with anammox bacteria at the middle site. These findings highlight the importance of microbial ecology in the SAD systems, which may expand our understanding of the synergistic patterns between anammox and denitrifying bacteria.

Emergence of Phenotypically Distinct Subpopulations Is a Factor in Adaptation of Recombinant Saccharomyces cerevisiae under Glucose-Limited Conditions

Cells cultured in a nutrient-limited environment can undergo adaptation, which confers improved fitness under long-term energy limitation. We have shown previously how a recombinant Saccharomyces cerevisiae strain, producing a heterologous insulin product, under glucose-limited conditions adapts over time at the average population level. Here, we investigated this adaptation at the single-cell level by application of fluorescence-activated cell sorting (FACS) and showed that the following three apparent phenotypes underlie the adaptive response observed at the bulk level: (i) cells that drastically reduced insulin production (23%), (ii) cells with reduced enzymatic capacity in central carbon metabolism (46%), and (iii) cells that exhibited pseudohyphal growth (31%). We speculate that the phenotypic heterogeneity is a result of different mechanisms to increase fitness. Cells with reduced insulin productivity have increased fitness by reducing the burden of the heterologous insulin production, and the populations with reduced enzymatic capacity of the central carbon metabolism and pseudohyphal growth have increased fitness toward the glucose-limited conditions. The results highlight the importance of considering population heterogeneity when studying adaptation and evolution. IMPORTANCE The yeast Saccharomyces cerevisiae is an attractive microbial host for industrial production and is used widely for manufacturing, e.g., pharmaceuticals. Chemostat cultivation mode is an efficient cultivation strategy for industrial production processes as it ensures a constant, well-controlled cultivation environment. Nevertheless, both the production of a heterologous product and the constant cultivation environment in the chemostat impose a selective pressure on the production organism, which may result in adaptation and loss of productivity. The exact mechanisms behind the observed adaptation and loss of performance are often unidentified. We used a recombinant S. cerevisiae strain producing heterologous insulin and investigated the adaptation occurring during chemostat growth at the single-cell level. We showed that three apparent phenotypes underlie the adaptive response observed at the bulk level in the chemostat. These findings highlight the importance of considering population heterogeneity when studying adaptation in industrial bioprocesses.

Environmental structure impacts microbial composition and secondary metabolism

Determining the drivers of microbial community assembly is a central theme of microbial ecology, and chemical ecologists seek to characterize how secondary metabolites mediate these assembly patterns. Environmental structure affects how communities assemble and what metabolic pathways aid in that assembly. Here, we bridged these two perspectives by addressing the chemical drivers of community assembly within a spatially structured landscape with varying oxygen availability. We hypothesized that structured environments would favor higher microbial diversity and metabolite diversity. We anticipated that the production of a compound would be more advantageous in a structured environment (less mixing) compared to an unstructured environment (more mixing), where the molecule would have a diminished local effect. We observed this to be partially true in our experiments: structured environments had similar microbial diversity compared to unstructured environments but differed significantly in the metabolites produced. We also found that structured environments selected for communities with higher evenness, rather than communities with higher richness. This supports the idea that when characterizing the drivers of community assembly, it matters less about who is there and more about what they are doing. Overall, these data contribute to a growing effort to approach microbial community assembly with interdisciplinary tools and perspectives.

On the Ecological Significance of Phenotypic Heterogeneity in Microbial Populations Undergoing Starvation

To persist in variable environments, populations of microorganisms have to survive periods of starvation and be able to restart cell division in nutrient-rich conditions. Typically, starvation signals initiate a transition to a quiescent state in a fraction of individual cells, while the rest of the cells remain nonquiescent. It is widely believed that, while quiescent (Q) cells help the population to survive long starvation, the nonquiescent (NQ) cells are a side effect of imperfect transition. We analyzed the regrowth of starved monocultures of Q and NQ cells compared to that of mixed, heterogeneous cultures from simple and complex starvation environments. Our experiments, as well as mathematical modeling, demonstrate that Q monocultures benefit from better survival during long starvation and from a shorter lag phase after resupply of rich medium. However, when the starvation period is very short, the NQ monocultures outperform Q and mixed cultures due to their short lag phase. In addition, only NQ monocultures benefit from complex starvation environments, where nutrient recycling is possible. Our study suggests that phenotypic heterogeneity in starved populations could be a form of bet hedging that is adaptive when environmental determinants, such as the length of the starvation period, the length of the regrowth phase, and the complexity of the starvation environment, vary over time.

IMPORTANCE Nongenetic cell heterogeneity is present in glucose-starved yeast populations in the form of quiescent (Q) and nonquiescent (NQ) phenotypes. There is evidence that Q cells help the population survive long starvation. However, the role of the NQ cell type is not known, and it has been speculated that the NQ phenotype is just a side effect of the imperfect transition to the Q phenotype. Here, we show that, in contrast, there are ecological scenarios in which NQ cells perform better than monocultures of Q cells or naturally occurring mixed populations containing both Q and NQ cells. NQ cells benefit when the starvation period is very short and environmental conditions allow nutrient recycling during starvation. Our experimental and mathematical modeling results suggest a novel hypothesis: the presence of both Q and NQ phenotypes within starved yeast populations may reflect a form of bet hedging where different phenotypes provide fitness advantages depending on the environmental conditions.

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

Use of qPCR for the analysis of population heterogeneity and dynamics during Lactobacillus delbrueckii spp. bulgaricus batch fculture

Direct molecular methods such as real-time polymerase chain reaction (qPCR) and propidium monoazide (PMA)-qPCR have been successfully used for quantifying viable microorganisms in the food industry. This study attempted to use qPCR and PMA-qPCR for quantifying Lactobacillus delbrueckii spp. bulgaricus sp1.1 physiological states. The qPCR standards of the 16S rRNA gene were employed to calibrate the qPCR assay, which contributed to an amplification efficiency of 98.42%. The number of copies of the 16S rRNA gene was linearly related to cell density, and this linear relationship was used to construct a quantitative curve (R² =0.9981) with a detection limit of 15.1 colony-forming units mL^-1·reaction^-1. qPCR in combination with an optimal PMA concentration (60 μM) helped in discriminating and quantifying the viable cells, without any interference by heat-killed cells. Compared with the conventional methods, the population heterogeneity of viable, culturable, dormant-like and membrane-permeabilized cells were well identified and quantified using qPCR during L. delbrueckii spp. bulgaricus sp1.1 batch culture. Despite the restriction in the enumeration of lysed cells, qPCR-based methods facilitated reliable identification and quantification of bacterial physiological states and provided additional knowledge on the dynamics of L. delbrueckii spp. bulgaricus sp1.1 physiological states.

Metabolic history and metabolic fitness as drivers of anabolic heterogeneity in isogenic microbial populations

Microbial populations often display different degrees of heterogeneity in their substrate assimilation, that is, anabolic heterogeneity. It has been shown that nutrient limitations are a relevant trigger for this behaviour. Here we explore the dynamics of anabolic heterogeneity under nutrient replete conditions. We applied time-resolved stable isotope probing and nanoscale secondary ion mass spectrometry to quantify substrate assimilation by individual cells of Pseudomonas putida, P. stutzeri and Thauera aromatica. Acetate and benzoate at different concentrations were used as substrates. Anabolic heterogeneity was quantified by the cumulative differentiation tendency index. We observed two major, opposing trends of anabolic heterogeneity over time. Most often, microbial populations started as highly heterogeneous, with heterogeneity decreasing by various degrees over time. The second, less frequently observed trend, saw microbial populations starting at low or very low heterogeneity, and remaining largely stable over time. We explain these trends as an interplay of metabolic history (e.g. former growth substrate or other nutrient limitations) and metabolic fitness (i.e. the fine-tuning of metabolic pathways to process a defined growth substrate). Our results offer a new viewpoint on the intra-population functional diversification often encountered in the environment, and suggests that some microbial populations may be intrinsically heterogeneous.

Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

Genetic variation is associated with differences in facilitative and competitive interactions in the Rhizobium leguminosarum species complex

Competitive and facilitative interactions influence bacterial community composition, diversity and functioning. However, the role of genetic diversity for determining interactions between coexisting strains of the same, or closely related, species remains poorly understood. Here, we investigated the type (facilitative/inhibitory) and potential underlying mechanisms of pairwise interactions between 24 genetically diverse bacterial strains belonging to three genospecies (gsA,C,E) of the Rhizobium leguminosarum species complex. Interactions were determined indirectly, based on secreted compounds in cell-free supernatants, and directly, as growth inhibition in cocultures. We found supernatants mediated both facilitative and inhibitory interactions that varied greatly between strains and genospecies. Overall, gsE strains indirectly suppressed growth of gsA strains, while their own growth was facilitated by other genospecies' supernatants. Similar genospecies-level patterns were observed in direct competition, where gsA showed the highest susceptibility and gsE the highest inhibition capacity. At the genetic level, increased gsA susceptibility was associated with a non-random distribution of quorum sensing and secondary metabolite genes across genospecies. Together, our results suggest that genetic variation is associated with facilitative and competitive interactions, which could be important ecological mechanisms explaining R. leguminosarum diversity.

High-Throughput and Continuous Chaotic Bioprinting of Spatially Controlled Bacterial Microcosms

Microorganisms do not work alone but instead function as collaborative microsocieties. The spatial distribution of different bacterial strains (micro-biogeography) in a shared volumetric space and their degree of intimacy greatly influences their societal behavior. Current microbiological techniques are commonly focused on the culture of well-mixed bacterial communities and fail to reproduce the micro-biogeography of polybacterial societies. Here, we bioprinted fine-scale bacterial microcosms using chaotic flows induced by a printhead containing a static mixer. This straightforward approach (i.e., continuous chaotic bacterial bioprinting) enables the fabrication of hydrogel constructs with intercalated layers of bacterial strains. These multilayered constructs are used to analyze how the spatial distributions of bacteria affect their social behavior. For example, we show that bacteria within these biological microsystems engage in either cooperation or competition, depending on the degree of shared interface. The extent of inhibition in predator-prey scenarios (i.e., probiotic-pathogen bacteria) increases when bacteria are in greater intimacy. Furthermore, two Escherichia coli strains exhibit competitive behavior in well-mixed microenvironments, whereas stable coexistence prevails for longer times in spatially structured communities. We anticipate that chaotic bioprinting will contribute to the development of a greater complexity of polybacterial microsystems, tissue-microbiota models, and biomanufactured materials.

Generation of Genetic Tools for Gauging Multiple-Gene Expression at the Single-Cell Level

Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple-gene expression at the single-cell level has limited the understanding of phenotypic heterogeneity. To investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple-gene expression at the single-cell level has been generated. This tool, named pRGC, consists of a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP), and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single-cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population without the need for image processing for spectral cross talk correction. In addition, two pRGC variants have been generated for either (i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome that is suitable for long-term experiments in the absence of antibiotic selection or (ii) replication in bacterial genera other than Pseudomonas The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple-gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity.IMPORTANCE Within a bacterial population, single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial subpopulations with distinct phenotypes. The analysis of gene expression at the single-cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation, and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple-gene expression at the single-cell level by fluorescence microscopy without the need for advanced image-processing procedures. A proof of concept has been provided by investigating iron uptake and iron storage gene expression in response to iron availability in P. aeruginosa.

A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux

RNA-based sensors for intracellular metabolites are a promising solution to the emerging issue of metabolic heterogeneity. However, their development, i.e., the conversion of an aptamer into an in vivo-functional intracellular metabolite sensor, still harbors challenges. Here, we accomplished this for the glycolytic flux-signaling metabolite, fructose-1,6-bisphosphate (FBP). Starting from in vitro selection of an aptamer, we constructed device libraries with a hammerhead ribozyme as actuator. Using high-throughput screening in yeast with fluorescence-activated cell sorting (FACS), next-generation sequencing, and genetic-environmental perturbations to modulate the intracellular FBP levels, we identified a sensor that generates ratiometric fluorescent readout. An abrogated response in sensor mutants and occurrence of two sensor conformations-revealed by RNA structural probing-indicated in vivo riboswitching activity. Microscopy showed that the sensor can differentiate cells with different glycolytic fluxes within yeast populations, opening research avenues into metabolic heterogeneity. We demonstrate the possibility to generate RNA-based sensors for intracellular metabolites for which no natural metabolite-binding RNA element exits.

Long-term iron deprivation and subsequent recovery uncover heterogeneity in the response of cyanobacterial populations

Cyanobacteria are globally important primary producers and nitrogen fixers. They are frequently limited by iron bioavailability in natural environments that often fluctuate due to rapid consumption and irregular influx of external Fe. Here we identify a succession of physiological changes in Synechocystis sp. PCC 6803 occurring over 14-16 days of iron deprivation and subsequent recovery. We observe several adaptive strategies that allow cells to push their metabolic limits under the restriction of declining intracellular Fe quotas. Interestingly, cyanobacterial populations exposed to prolonged iron deprivation showed discernible heterogeneity in cellular auto-fluorescence during the recovery process. Using FACS and microscopy techniques we revealed that only cells with high auto-fluorescence were able to grow and reconstitute thylakoid membranes. We propose that ROS-mediated damage is likely to be associated with the emergence of the two subpopulations, and, indeed, a rapid increase in intracellular ROS content was observed during the first hours following iron addition to Fe-starved cultures. These results suggest that an increasing iron supply is a double-edged sword - posing both an opportunity and a risk. Therefore, phenotypic heterogeneity within populations is crucial for the survival and proliferation of organisms facing iron fluctuations within natural environments.

Plant Single-Cell Metabolomics-Challenges and Perspectives

Omics approaches for investigating biological systems were introduced in the mid-1990s and quickly consolidated to become a fundamental pillar of modern biology. The idea of measuring the whole complement of genes, transcripts, proteins, and metabolites has since become widespread and routinely adopted in the pursuit of an infinity of scientific questions. Incremental improvements over technical aspects such as sampling, sensitivity, cost, and throughput pushed even further the boundaries of what these techniques can achieve. In this context, single-cell genomics and transcriptomics quickly became a well-established tool to answer fundamental questions challenging to assess at a whole tissue level. Following a similar trend as the original development of these techniques, proteomics alternatives for single-cell exploration have become more accessible and reliable, whilst metabolomics lag behind the rest. This review summarizes state-of-the-art technologies for spatially resolved metabolomics analysis, as well as the challenges hindering the achievement of sensu stricto metabolome coverage at the single-cell level. Furthermore, we discuss several essential contributions to understanding plant single-cell metabolism, finishing with our opinion on near-future developments and relevant scientific questions that will hopefully be tackled by incorporating these new exciting technologies.

Microbial metabolic noise

From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.

Aspects of Multicellularity in Saccharomyces cerevisiae Yeast: A Review of Evolutionary and Physiological Mechanisms

The evolutionary transition from single-celled to multicellular growth is a classic and intriguing problem in biology. Saccharomyces cerevisiae is a useful model to study questions regarding cell aggregation, heterogeneity and cooperation. In this review, we discuss scenarios of group formation and how this promotes facultative multicellularity in S. cerevisiae. We first describe proximate mechanisms leading to aggregation. These mechanisms include staying together and coming together, and can lead to group heterogeneity. Heterogeneity is promoted by nutrient limitation, structured environments and aging. We then characterize the evolutionary benefits and costs of facultative multicellularity in yeast. We summarize current knowledge and focus on the newest state-of-the-art discoveries that will fuel future research programmes aiming to understand facultative microbial multicellularity.

Metabolic Heterogeneity and Cross-Feeding in Bacterial Multicellular Systems

Cells in assemblages differentiate and perform distinct roles. Though many pathways of differentiation are understood at the molecular level in multicellular eukaryotes, the elucidation of similar processes in bacterial assemblages is recent and ongoing. Here, we discuss examples of bacterial differentiation, focusing on cases in which distinct metabolisms coexist and those that exhibit cross-feeding, with one subpopulation producing substrates that are metabolized by a second subpopulation. We describe several studies of single-species systems, then segue to studies of multispecies metabolic heterogeneity and cross-feeding in the clinical setting. Many of the studies described exemplify the application of new techniques and modeling approaches that provide insights into metabolic interactions relevant for bacterial growth outside the laboratory.