Memory and fitness optimization of bacteria under fluctuating environments

Optimizing denitrification with volatile fatty acids from hydrolysis acidification-treated domestic wastewater: Comparative effects of nitrate and nitrite using immobilized biofiller

By embedding immobilized biofiller, 100% denitrification efficiency was achieved with nitrate or nitrite as electron acceptors utilizing volatile fatty acids (VFAs) from domestic wastewater after hydrolysis acidification. The consumption patterns of VFAs by functional bacteria and differences in nitrogen metabolic gene expression were thoroughly analyzed. Total consumption of acetic and propionic acids with >95% VFAs utilization was achieved utilizing nitrate, whereas the consumption of butyric and valeric acids was enhanced utilizing nitrite. Denitrification-related genes were all upregulated, particularly nosZ, indicating systemic N2O emission reduction potential. Electron acceptor changes dynamically shifted microbial dominance from Thauera (19.4%) to Thiobacillus (7.2%). These results provide valuable insights into the adaptability and ecological niche characterization of denitrifying bacteria, contributing to improving nitrogen removal efficiency, optimizing carbon source utilization, and reducing sludge production.

Actionable Forecasting as a Determinant of Biological Adaptation

Organisms continuously adapt to changing environments to survive. Here, contrary to the prevailing view that predictive strategies are essential for perfect adaptation, it is shown that biological systems can precisely track their optimal state by adapting to a non-anticipatory actionable target that integrates the current optimum with its rate of change. Predictive mechanisms, such as circadian rhythms, are beneficial for accurately inferring the actionable target when environmental sensing is slow or unreliable. A new mathematical framework is developed, showing that dynamics-informed neural networks embodying these principles can efficiently capture biological adaptation even in noisy environments. These results provide fundamental insights into the interplay between forecasting, control, and inference in biological systems, redefining adaptation strategies and guiding the design of advanced adaptive biomolecular circuits.

Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges

SUMMARYThe development of multicellularity represents a key evolutionary transition that is crucial for the emergence of complex life forms. Although multicellularity has traditionally been studied in eukaryotes, it originates in prokaryotes. Coordinated aggregation of individual cells within the confines of a colony results in emerging, higher-level functions that benefit the population as a whole. During colony differentiation, an almost infinite number of ecological and physiological population-forming forces are at work, creating complex, intricate colony structures with divergent functions. Understanding the assembly and dynamics of such populations requires resolving individual cells or cell groups within such macroscopic structures. Addressing how each cell contributes to the collective action requires pushing the resolution boundaries of key technologies that will be presented in this review. In particular, single-cell techniques provide powerful tools for studying bacterial multicellularity with unprecedented spatial and temporal resolution. These advancements include novel microscopic techniques, mass spectrometry imaging, flow cytometry, spatial transcriptomics, single-bacteria RNA sequencing, and the integration of spatiotemporal transcriptomics with microscopy, alongside advanced microfluidic cultivation systems. This review encourages exploring the synergistic potential of the new technologies in the study of bacterial multicellularity, with a particular focus on individuals in differentiated bacterial biofilms (colonies). It highlights how resolving population structures at the single-cell level and understanding their respective functions can elucidate the overarching functions of bacterial multicellular populations.

Carbon upshift in Lactococcus cremoris elicits immediate initiation of proteome-wide adaptation, coinciding with growth acceleration and pyruvate dissipation switching

Fitness optimization in a dynamic environment requires bacteria to adapt their proteome in a tightly regulated manner by altering protein production and/or degradation. Here, we investigate proteome adaptation in Lactococcus cremoris following a sudden nutrient upshift (e.g., nutrients that allow faster growth) and focus especially on the fate of redundant proteins after the shift. Protein turnover analysis demonstrated that L. cremoris cultures shifted from galactose to glucose, immediately accelerate growth and initiate proteome-wide adjustment toward glucose-optimized composition. Redundant proteins were predominantly adjusted by lowering (or stopping) protein production combined with dilution by growth. However, pyruvate formate lyase activator (PflA) was actively degraded, which appears correlated to reduced 4Fe-4S cofactor availability. Active PflA removal induces the shutdown of galactose-associated mixed acid fermentation to accelerate the switch toward glucose-associated homolactic fermentation. Our work deciphers molecular adjustments upon environmental change that drive physiological adaptation, including growth rate and central energy metabolism.IMPORTANCEBacteria adapt to their environment by adjusting their molecular makeup, in particular their proteome, which ensures fitness optimization under the newly encountered environmental condition. We present a detailed analysis of proteome adaptation kinetics in Lactococcus cremoris following its acute transition from galactose to glucose media, as an example of a sudden nutrient quality upshift. Analysis of the replacement times of individual proteins after the nutrient upshift established that the entire proteome is instantly adjusting to the new condition, which coincides with immediate growth rate acceleration and metabolic adaptation. The latter is driven by the active removal of the pyruvate formate lyase activator protein that is pivotal in controlling pyruvate dissipation in L. cremoris. Our work exemplifies the amazing rate of molecular adaptation in bacteria that underlies physiological adjustments, including growth rate and carbon metabolism. This mechanistic study contributes to our understanding of adaptation in L. cremoris during the dynamic conditions it encounters during (industrial) fermentation, even though environmental transitions in these processes are mostly more gradual than the acute shift studied here.

Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity

A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.

Evolution of pH-sensitive transcription termination in Escherichia coli during adaptation to repeated long-term starvation

Fluctuating environments that consist of regular cycles of co-occurring stress are a common challenge faced by cellular populations. For a population to thrive in constantly changing conditions, an ability to coordinate a rapid cellular response is essential. Here, we identify a mutation conferring an arginine-to-histidine (Arg to His) substitution in the transcription terminator Rho. The rho R109H mutation frequently arose in Escherichia coli populations experimentally evolved under repeated long-term starvation conditions, during which the accumulation of metabolic waste followed by transfer into fresh media results in drastic environmental pH fluctuations associated with feast and famine. Metagenomic sequencing revealed that populations containing the rho mutation also possess putative loss-of-function mutations in ydcI, which encodes a recently characterized transcription factor associated with pH homeostasis. Genetic reconstructions of these mutations show that the rho allele confers plasticity via an alkaline-induced reduction of Rho function that, when found in tandem with a ΔydcI allele, leads to intracellular alkalization and genetic assimilation of Rho mutant function. We further identify Arg to His substitutions at analogous sites in rho alleles from species that regularly experience neutral to alkaline pH fluctuations in their environments. Our results suggest that Arg to His substitutions in Rho may serve to rapidly coordinate complex physiological responses through pH sensing and shed light on how cellular populations use environmental cues to coordinate rapid responses to complex, fluctuating environments.

Multigenerational inheritance drives symbiotic interactions of the bacterium Bacillus subtilis with its plant host

Bacillus subtilis is a beneficial bacterium that supports plant growth and protects plants from bacterial, fungal, and viral infections. Using a simplified system of B. subtilis and Arabidopsis thaliana interactions, we studied the fitness and transcriptome of bacteria detached from the root over generations of growth in LB medium. We found that bacteria previously associated with the root or exposed to its secretions had greater stress tolerance and were more competitive in root colonization than bacteria not previously exposed to the root. Furthermore, our transcriptome results provide evidence that plant secretions induce a microbial stress response and fundamentally alter signaling by the cyclic nucleotide c-di-AMP, a signature maintained by their descendants. The changes in cellular physiology due to exposure to plant exudates were multigenerational, as they allowed not only the bacterial cells that colonized a new plant but also their descendants to have an advance over naive competitors of the same species, while the overall plasticity of gene expression and rapid adaptation were maintained. These changes were hereditary but not permanent. Our work demonstrates a bacterial memory manifested by multigenerational reversible adaptation to plant hosts in the form of activation of the stressosome, which confers an advantage to symbiotic bacteria during competition.

Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments

Evolution in a static laboratory environment often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. Here we find that fitness in fluctuating environments indeed often deviates from the time average, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments. We use a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results show that environmental fluctuations impact fitness and suggest that variance in static environments can explain these impacts.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Protein overabundance is driven by growth robustness

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Protein overabundance is driven by growth robustness

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

AI-2 quorum sensing controlled delivery of cytolysin-A by tryptophan auxotrophic low-endotoxic Salmonella and its anticancer effects in CT26 mice with colon cancer

INTRODUCTION

The limitations of conventional cancer therapies necessitate target-oriented, highly invasive, and safe treatment approaches. Hence, the intrinsic anti-tumor activity of Salmonella can offer better options to combat cancers.

OBJECTIVES

This study aims to utilize attenuated Salmonella and deliver cytolytic protein cytolysin A (ClyA) under quorum sensing (QS) signaling for precise localized expression in tumors but not in healthy organs.

METHODS

The therapeutic delivery strain was imposed with tryptophan auxotroph for selective colonization in tumors by trpA and trpE deletion, and lipid-A and O-antigen were altered by pagL and rfaL deletions using lambda red recombination method. The strain was transformed with the designed QS-controlled ClyA expression vector which was validated by western blot. The in vivo passaged therapeutic strain was used for treatment four times at a weekly interval, with a dose of 5 × 106 CFU/mouse for cancer therapy.

RESULTS

The attenuated strain induced minimal endotoxicity-related cytokines TNF-α, IL-1β, and IFN-γ and exhibited adequate colonization despite earlier exposure in mice. The QS-controlled ClyA expression was confirmed by western blot from bacterial cultures grown at different cell densities. The results demonstrated that the in vivo passaged strain preferentially colonized the tumor after vacating the spleen, liver, and lung, leaving no outward histological scars. The anti-cancer effect of the designed construct was evaluated in the murine CT26 colon cancer model. The expression of ClyA increased tumoricidal activity by 67 % compared to vector control.

CONCLUSION

Hence, the anti-tumor effect of the engineered Salmonella strain was improved by ClyA expression via QS activation after achieving the threshold bacterial cell density. Further, immunohistochemical staining of the tumor and other organs corroborated the QS-controlled tumor-specific expression of ClyA. Overall, the results imply that the developed anti-cancer Salmonella has low endotoxicity and QS-controlled expression of ClyA as beneficial safety elements and supports recurrent Salmonella inoculation by O-antigen deficiency.

Phenotypic memory in quorum sensing

Quorum sensing (QS) is a regulatory mechanism used by bacteria to coordinate group behavior in response to high cell densities. During QS, cells monitor the concentration of external signals, known as autoinducers, as a proxy for cell density. QS often involves positive feedback loops, leading to the upregulation of genes associated with QS signal production and detection. This results in distinct steady-state concentrations of QS-related molecules in QS-ON and QS-OFF states. Due to the slow decay rates of biomolecules such as proteins, even after removal of the initial stimuli, cells can retain elevated levels of QS-associated biomolecules for extended periods of time. This persistence of biomolecules after the removal of the initial stimuli has the potential to impact the response to future stimuli, indicating a memory of past exposure. This phenomenon, which is a consequence of the carry-over of biomolecules rather than genetic inheritance, is known as "phenotypic" memory. This theoretical study aims to investigate the presence of phenotypic memory in QS and the conditions that influence this memory. Numerical simulations based on ordinary differential equations and analytical modeling were used to study gene expression in response to sudden changes in cell density and extracellular signal concentrations. The model examined the effect of various cellular parameters on the strength of QS memory and the impact on gene regulatory dynamics. The findings revealed that QS memory has a transient effect on the expression of QS-responsive genes. These consequences of QS memory depend strongly on how cell density was perturbed, as well as various cellular parameters, including the Fold Change in the expression of QS-regulated genes, the autoinducer synthesis rate, the autoinducer threshold required for activation, and the cell growth rate.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Proteolytic stability and aggregation in a key metabolic enzyme of bacteria

Proteins that are kinetically stable are thought to be less prone to both aggregation and proteolysis. We demonstrate that the classical lac system of Escherichia coli can be leveraged as a model system to study this relation. β-galactosidase (LacZ) plays a critical role in lactose metabolism and is an extremely stable protein that can persist in growing cells for multiple generations after expression has stopped. By attaching degradation tags to the LacZ protein, we find that LacZ can be transiently degraded during lac operon expression but once expression has stopped functional LacZ is protected from degradation. We reversibly destabilize its tetrameric assembly using α-complementation, and show that unassembled LacZ monomers and dimers can either be degraded or lead to formation of aggregates within cells, while the tetrameric state protects against proteolysis and aggregation. We show that the presence of aggregates is associated with cell death, and that these proteotoxic stress phenotypes can be alleviated by attaching an ssrA tag to LacZ monomers which leads to their degradation. We unify our findings using a biophysical model that enables the interplay of protein assembly, degradation, and aggregation to be studied quantitatively in vivo. This work may yield approaches to reversing and preventing protein-misfolding disease states, while elucidating the functions of proteolytic stability in constant and fluctuating environments.

Evolution of pH-sensitive transcription termination during adaptation to repeated long-term starvation

Fluctuating environments that consist of regular cycles of co-occurring stress are a common challenge faced by cellular populations. For a population to thrive in constantly changing conditions, an ability to coordinate a rapid cellular response is essential. Here, we identify a mutation conferring an arginine-to-histidine (Arg to His) substitution in the transcription terminator Rho. The rho R109H mutation frequently arose in E. coli populations experimentally evolved under repeated long-term starvation conditions, during which feast and famine result in drastic environmental pH fluctuations. Metagenomic sequencing revealed that populations containing the rho mutation also possess putative loss-of-function mutations in ydcI, which encodes a recently characterized transcription factor associated with pH homeostasis. Genetic reconstructions of these mutations show that the rho allele confers a plastic alkaline-induced reduction of Rho function that, when found in tandem with a ΔydcI allele, leads to intracellular alkalinization and genetic assimilation of Rho mutant function. We further identify Arg to His substitutions at analogous sites in rho alleles from species originating from fluctuating alkaline environments. Our results suggest that Arg to His substitutions in global regulators of gene expression can serve to rapidly coordinate complex responses through pH sensing and shed light on how cellular populations across the tree of life use environmental cues to coordinate rapid responses to complex, fluctuating environments.

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.

Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking

Gut microbiota can adapt to their host environment by rapidly acquiring new mutations. However, the dynamics of this process are difficult to characterize in dominant gut species in their complex in vivo environment. Here we show that the fine-scale dynamics of genome-wide transposon libraries can enable quantitative inferences of these in vivo evolutionary forces. By analyzing >400,000 lineages across four human Bacteroides strains in gnotobiotic mice, we observed positive selection on thousands of cryptic variants - most of which were unrelated to their original gene knockouts. The spectrum of fitness benefits varied between species, and displayed diverse tradeoffs over time and in different dietary conditions, enabling inferences of their underlying function. These results suggest that within-host adaptations arise from an intense competition between numerous contending variants, which can strongly influence their emergent evolutionary tradeoffs.

Bacterial Adaptive Memory in Methicillin-Resistant Staphylococcus aureus from Endotracheal Tubes

OBJECTIVES

To evaluate the expression dynamics of biofilm genes in methicillin-resistant Staphylococcus aureus (MRSA) retrieved from endotracheal tubes (ETT) and to determine how gene regulation is attenuated in vitro where host-environmental factors are no longer present.

METHODS

Biofilm was grown (24 h) in tryptic broth soy plus 0.25% glucose for a clinical MRSA isolate in planktonic state and after sessile growth named ETT-MRSA (S2, S3, S4, S5, S6, S7). Gene expression of five biofilm-related genes (icaC, clfB, ebps, fnbB, and RNA III) was assessed consecutively from day 1 to day 4 after ETT growth through real-time PCR. 16S rRNA was used as a control.

RESULTS

The MRSA isolates retrieved from ETT were capable of producing biofilms dependent on ica. The gene expression dynamics of ETT-MRSA changed progressively compared to planktonic MRSA gene expression under both ambient air (p < 0.001) and ambient air with 5% CO2 (p < 0.001). Dynamic assessment of icaC expression in both atmospheric conditions showed progressive downregulation in vitro compared to in vivo ETT biofilms. The expression patterns of clfB and ebps genes were similar to icaC. In contrast, the expression of the RNA III gene showed progressive upregulation from day 1 to day 4 (p < 0.001).

CONCLUSIONS

MRSA loses its biofilm gene expression in vitro, by adaptive features across multiple generations, as evidenced by the progressive downregulation of icaC and upregulation of RNA III. These findings underscore the significance of host-environment dependence in regulating bacterial biofilm genes, highlighting its importance in diagnostics. Bacterial strains lose their host-specific characteristics as they are cultured in vitro.

Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing

Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.

Dynamic responses of Salmonella Typhimurium to re-exposure to sublethal ciprofloxacin

This study was designed to evaluate the history-dependent behaviors of Salmonella Typhimurium re-exposed to sublethal levels of ciprofloxacin. The S. Typhimurium cells were pre-exposed to 0 (CON), 1/16 (LOW), 1/8 (MED), and 1/4 (HIGH) minimum inhibitory concentrations (MICs) of ciprofloxacin, followed by re-exposure to the same concentrations. The bacterial growth, postantibiotic effect (PAE), relative fitness, and swimming motility of treatments were evaluated in the absence of ciprofloxacin. The lag phase duration (LPD) was estimate to assess bacterial recovery under ciprofloxacin exposure. A disk diffusion assay was used to determine the cross-resistance and collateral sensitivity of CON, LOW, MED, and HIGH treatments to ciprofloxacin (CIP), ceftriaxone (CEF), erythromycin (ERY), gentamicin (GEN), and polymyxin B (POL). The S. Typhimurium cells pre-exposed to ciprofloxacin were susceptible in antibiotic-free media, showing delayed growth. The highest PAE (>1 h) and bacterial fluctuation (CV = 5%) were observed at the High treatment compared to the CON. The HIGH treatment had the lowest relative fitness levels (0.87) and swimming motility (55 mm). The LPD was significantly decreased at the LOW treatment (1.8 h) when re-exposed to 1/16 × MIC of ciprofloxacin. The LOW, MED, and HIGH treatments showed the cross-resistance to POL and the collateral sensitivity to CEF, ERY, and GEN. The pre-exposure to ciprofloxacin could induce phenotypic diversity, corresponding to the history-dependent behaviors. These results provide important insights for the dynamic nature of bacterial populations when re-exposed to sublethal concentrations of antibiotics.

The hierarchy of sugar catabolization in Lactococcus cremoris

IMPORTANCE

The availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate-typically growth on glucose-is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in Lactococcus cremoris. For this, we determined the proteome and carbohydrate utilization capacity after growth on different sugars. The results show that the preparedness of cells for the utilization of "slower" sugars is not strictly determined by the growth rate. The data point to individual proteins relevant for various sugar transitions and suggest that the evolutionary history of the organism might be responsible for deviations from a strictly growth rate-related sugar catabolization hierarchy.

Effective fitness under fluctuating selection with genetic drift

The natural environment fluctuates for virtually every population of organisms. As a result, the fitness of a mutant may vary temporally. While commonly used for summarizing the effect of fluctuating selection on the mutant, geometric mean fitness can be misleading under some circumstances due to the influence of genetic drift. Here, we show by mathematical proof and computer simulation that, with genetic drift, the geometric mean fitness does not accurately reflect the overall effect of fluctuating selection. We propose an alternative measure based on the average expected allele frequency change caused by selection and demonstrate that this measure-effective fitness-better captures the overall effect of fluctuating selection in the presence of drift.

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.

Minorities drive growth resumption in cross-feeding microbial communities

Microbial communities are fundamental to life on Earth. Different strains within these communities are often connected by a highly connected metabolic network, where the growth of one strain depends on the metabolic activities of other community members. While distributed metabolic functions allow microbes to reduce costs and optimize metabolic pathways, they make them metabolically dependent. Here, we hypothesize that such dependencies can be detrimental in situations where the external conditions change rapidly, as they often do in natural environments. After a shift in external conditions, microbes need to remodel their metabolism, but they can only resume growth once partners on which they depend have also adapted to the new conditions. It is currently not well understood how microbial communities resolve this dilemma and how metabolic interactions are reestablished after an environmental shift. To address this question, we investigated the dynamical responses to environmental perturbation by microbial consortia with distributed anabolic functions. By measuring the regrowth times at the single-cell level in spatially structured communities, we found that metabolic dependencies lead to a growth delay after an environmental shift. However, a minority of cells-those in the immediate neighborhood of their metabolic partners-can regrow quickly and come to numerically dominate the community after the shift. The spatial arrangement of a microbial community is thus a key factor in determining the communities' ability to maintain metabolic interactions and growth in fluctuating conditions. Our results suggest that environmental fluctuations can limit the emergence of metabolic dependencies between microorganisms.

Coupled environmental and demographic fluctuations shape the evolution of cooperative antimicrobial resistance

There is a pressing need to better understand how microbial populations respond to antimicrobial drugs, and to find mechanisms to possibly eradicate antimicrobial-resistant cells. The inactivation of antimicrobials by resistant microbes can often be viewed as a cooperative behaviour leading to the coexistence of resistant and sensitive cells in large populations and static environments. This picture is, however, greatly altered by the fluctuations arising in volatile environments, in which microbial communities commonly evolve. Here, we study the eco-evolutionary dynamics of a population consisting of an antimicrobial-resistant strain and microbes sensitive to antimicrobial drugs in a time-fluctuating environment, modelled by a carrying capacity randomly switching between states of abundance and scarcity. We assume that antimicrobial resistance (AMR) is a shared public good when the number of resistant cells exceeds a certain threshold. Eco-evolutionary dynamics is thus characterised by demographic noise (birth and death events) coupled to environmental fluctuations which can cause population bottlenecks. By combining analytical and computational means, we determine the environmental conditions for the long-lived coexistence and fixation of both strains, and characterise a fluctuation-driven AMR eradication mechanism, where resistant microbes experience bottlenecks leading to extinction. We also discuss the possible applications of our findings to laboratory-controlled experiments.

Long-term history dependence of growth rates of E. coli after nutrient shifts

According to a widely accepted paradigm of microbiology, steady-state growth rates are determined solely by current growth conditions1-3 and adaptations between growth states are rapid, as recently recapitulated by simple resource allocation models4. However, even in microbes overlapping regulatory networks can yield multi-stability or long-term cellular memory. Species like Listeria monocytogenes5 and Bacillus subtilis "distinguish" distinct histories for the commitment to sporulation6, but it is unclear if these states can persist over many generations. Remarkably, studying carbon co-utilization of Escherichia coli, we found that growth rates on combinations of carbon sources can depend critically on the previous growth condition. Growing in identical conditions, we observed differences in growth rates of up to 25% and we did not observe convergence of growth rates over 15 generations. We observed this phenomenon occurs across combinations of different phosphotransferase (PTS) substrates with various gluconeogenic carbon sources and found it to depend on the transcription factor Mlc.

A Reduction of Transcriptional Regulation in Aquatic Oligotrophic Microorganisms Enhances Fitness in Nutrient-Poor Environments

In this review, we consider the regulatory strategies of aquatic oligotrophs, microbial cells that are adapted to thrive under low-nutrient concentrations in oceans, lakes, and other aquatic ecosystems. Many reports have concluded that oligotrophs use less transcriptional regulation than copiotrophic cells, which are adapted to high nutrient concentrations and are far more common subjects for laboratory investigations of regulation. It is theorized that oligotrophs have retained alternate mechanisms of regulation, such as riboswitches, that provide shorter response times and smaller amplitude responses and require fewer cellular resources. We examine the accumulated evidence for distinctive regulatory strategies in oligotrophs. We explore differences in the selective pressures copiotrophs and oligotrophs encounter and ask why, although evolutionary history gives copiotrophs and oligotrophs access to the same regulatory mechanisms, they might exhibit distinctly different patterns in how these mechanisms are used. We discuss the implications of these findings for understanding broad patterns in the evolution of microbial regulatory networks and their relationships to environmental niche and life history strategy. We ask whether these observations, which have emerged from a decade of increased investigation of the cell biology of oligotrophs, might be relevant to recent discoveries of many microbial cell lineages in nature that share with oligotrophs the property of reduced genome size.

Nearly maximal information gain due to time integration in central dogma reactions

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we demonstrate that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain because of time integration while also keeping the loss because of stochasticity in translation relatively low (<0.5 bits).

The impact of metabolism on the adaptation of organisms to environmental change

Since Jacob and Monod's discovery of the lac operon ∼1960, the explanations offered for most metabolic adaptations have been genetic. The focus has been on the adaptive changes in gene expression that occur, which are often referred to as "metabolic reprogramming." The contributions metabolism makes to adaptation have been largely ignored. Here we point out that metabolic adaptations, including the associated changes in gene expression, are highly dependent on the metabolic state of an organism prior to the environmental change to which it is adapting, and on the plasticity of that state. In support of this hypothesis, we examine the paradigmatic example of a genetically driven adaptation, the adaptation of E. coli to growth on lactose, and the paradigmatic example of a metabolic driven adaptation, the Crabtree effect in yeast. Using a framework based on metabolic control analysis, we have reevaluated what is known about both adaptations, and conclude that knowledge of the metabolic properties of these organisms prior to environmental change is critical for understanding not only how they survive long enough to adapt, but also how the ensuing changes in gene expression occur, and their phenotypes post-adaptation. It would be useful if future explanations for metabolic adaptations acknowledged the contributions made to them by metabolism, and described the complex interplay between metabolic systems and genetic systems that make these adaptations possible.

Counterintuitive properties of evolutionary measures: A stochastic process study in cyclic population structures with periodic environments

Local environmental interactions are a major factor in determining the success of a new mutant in structured populations. Spatial variations in the concentration of genotype-specific resources change the fitness of competing strategies locally and thus can drastically change the outcome of evolutionary processes in unintuitive ways. The question is how such local environmental variations in network population structures change the condition for selection and fixation probability of an advantageous (or deleterious) mutant. We consider linear graph structures and focus on the case where resources have a spatial periodic pattern. This is the simplest model with two parameters, length scale and fitness scales, representing heterogeneity. We calculate fixation probability and fixation times for a constant population birth-death process as fitness heterogeneity and period vary. Fixation probability is affected by not only the level of fitness heterogeneity but also spatial scale of resources variations set by period of distribution T. We identify conditions for which a previously a deleterious mutant (in a uniform environment) becomes beneficial as fitness heterogeneity is increased. We observe cases where the fixation probability of both mutant and resident types are more than their neutral value, 1/N, simultaneously. This coincides with exponential increase in time to fixation which points to potential coexistence of resident and mutant types. Finally, we discuss the effect of the 'fitness shift' where the fitness function of two types has a phase difference. We observe significant increases (or decreases) in the fixation probability of the mutant as a result of such phase shift.

Dynamic proteome trade-offs regulate bacterial cell size and growth in fluctuating nutrient environments

Bacteria dynamically regulate cell size and growth to thrive in changing environments. While previous studies have characterized bacterial growth physiology at steady-state, a quantitative understanding of bacterial physiology in time-varying environments is lacking. Here we develop a quantitative theory connecting bacterial growth and division rates to proteome allocation in time-varying nutrient environments. In such environments, cell size and growth are regulated by trade-offs between prioritization of biomass accumulation or division, resulting in decoupling of single-cell growth rate from population growth rate. Specifically, bacteria transiently prioritize biomass accumulation over production of division machinery during nutrient upshifts, while prioritizing division over growth during downshifts. When subjected to pulsatile nutrient concentration, we find that bacteria exhibit a transient memory of previous metabolic states due to the slow dynamics of proteome reallocation. This allows for faster adaptation to previously seen environments and results in division control which is dependent on the time-profile of fluctuations.

Internal cues for optimizing reproduction in a varying environment

In varying environments, it is beneficial for organisms to utilize available cues to infer the conditions they may encounter and express potentially favourable traits. However, external cues can be unreliable or too costly to use. We consider an alternative strategy where organisms exploit internal sources of information. Even without sensing environmental cues, their internal states may become correlated with the environment as a result of selection, which then form a memory that helps predict future conditions. To demonstrate the adaptive value of such internal cues in varying environments, we revisit the classic example of seed dormancy in annual plants. Previous studies have considered the germination fraction of seeds and its dependence on environmental cues. In contrast, we consider a model of germination fraction that depends on the seed age, which is an internal state that can serve as a memory. We show that, if the environmental variation has temporal structure, then age-dependent germination fractions will allow the population to have an increased long-term growth rate. The more the organisms can remember through their internal states, the higher the growth rate a population can potentially achieve. Our results suggest experimental ways to infer internal memory and its benefit for adaptation in varying environments.

Amino Acids Drive the Deterministic Assembly Process of Fungal Community and Affect the Flavor Metabolites in Baijiu Fermentation

Nutrient fluctuation is ubiquitous in fermentation ecosystems. However, the microbial community assembly mechanism and metabolic characteristics in response to nutrient variation are still unclear. Here, we used Baijiu fermentation as a case example to study the responses of microbial community assembly and metabolic characteristics to the variation of amino acids using high-throughput sequencing and metatranscriptomics analyses. We chose two fermentation groups (group A with low amino acid and group B with high amino acid contents). The two groups showed similar succession patterns in the bacterial community, whereas they showed different succession in the fungal community wherein Pichia was dominant in group A and Zygosaccharomyces was dominant in group B. The β-nearest taxon index (βNTI) revealed that bacterial community was randomly formed, whereas fungal community assembly was a deterministic process. Variance partitioning analysis and redundancy analysis revealed that amino acids showed the largest contribution to the fungal community (37.64%, P = 0.005) and were more tightly associated with it in group B. Further study revealed that serine was positively related to Zygosaccharomyces and promoted its growth and ethanol production. Metatranscriptomic analysis revealed that the differential metabolic pathways between the two groups mainly included carbohydrate metabolism and amino acid metabolism, which explained the differences of ethanol production and volatile metabolites (such as isoamylol, isobutanol, and 2-methyl-1-butanol). Then these metabolic pathways were constructed and related gene expression and active microorganisms were listed. Our study provides a systematical understanding of the roles of amino acids in both ecological maintenance and flavor metabolism in fermentation ecosystems. IMPORTANCE In spontaneous fermented foods production, nutrient fluctuation is a critical factor affecting microbial community assembly and metabolic function. Revealing the microbial community assembly mechanism and how it regulates its metabolic characteristics in response to nutrient variation is helpful to the management of the fermentation process. This study provides a systematical understanding of the effect of amino acids on the microbial community assembly and flavor metabolisms using Baijiu fermentation as a case example. The data of this study highlight the importance of the nutrient management in fermentation ecosystems.

Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As³⁺ and 6.8 pM for Cr⁶⁺, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

Prophylactic and therapeutic insights into trained immunity: A renewed concept of innate immune memory

Trained immunity is a renewed concept of innate immune memory that facilitates the innate immune system to have the capacity to remember and train cells via metabolic and transcriptional events to enable them to provide nonspecific defense against the subsequent encounters with a range of pathogens and acquire a quicker and more robust immune response, but different from the adaptive immune memory. Reversing the epigenetic changes or targeting the immunological pathways may be considered potential therapeutic approaches to counteract the hyper-responsive or hypo-responsive state of trained immunity. The efficient regulation of immune homeostasis and promotion or inhibition of immune responses is required for a balanced response. Trained immunity-based vaccines can serve as potent immune stimuli and help in the clearance of pathogens in the body through multiple or heterologous effects and confer protection against nonspecific and specific pathogens. This review highlights various features of trained immunity and its applications in developing novel therapeutics and vaccines, along with certain detrimental effects, challenges as well as future perspectives.

Identifying Bacterial Lineages in Salmonella by Flow Cytometry

Advances in technologies that permit high-resolution analysis of events in single cells have revealed that phenotypic heterogeneity is a widespread phenomenon in bacteria. Flow cytometry has the potential to describe the distribution of cellular properties within a population of bacterial cells and has yielded invaluable information about the ability of isogenic cells to diversify into phenotypic subpopulations. This review will discuss several single-cell approaches that have recently been applied to define phenotypic heterogeneity in populations of Salmonella enterica.

Growth condition-dependent differences in methylation imply transiently differentiated DNA methylation states in Escherichia coli

DNA methylation in bacteria frequently serves as a simple immune system, allowing recognition of DNA from foreign sources, such as phages or selfish genetic elements. However, DNA methylation also affects other cell phenotypes in a heritable manner (i.e. epigenetically). While there are several examples of methylation affecting transcription in an epigenetic manner in highly localized contexts, it is not well-established how frequently methylation serves a more general epigenetic function over larger genomic scales. To address this question, here we use Oxford Nanopore sequencing to profile DNA modification marks in three natural isolates of Escherichia coli. We first identify the DNA sequence motifs targeted by the methyltransferases in each strain. We then quantify the frequency of methylation at each of these motifs across the entire genome in different growth conditions. We find that motifs in specific regions of the genome consistently exhibit high or low levels of methylation. Furthermore, we show that there are replicable and consistent differences in methylated regions across different growth conditions. This suggests that during growth, E. coli transiently differentiate into distinct methylation states that depend on the growth state, raising the possibility that measuring DNA methylation alone can be used to infer bacterial growth states without additional information such as transcriptome or proteome data. These results show the utility of using Oxford Nanopore sequencing as an economic means to infer DNA methylation status. They also provide new insights into the dynamics of methylation during bacterial growth and provide evidence of differentiated cell states, a transient analog to what is observed in the differentiation of cell types in multicellular organisms.

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

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

Cellular memory of rapid growth is sensitive to nutrient depletion during starvation

Bacteria frequently encounter nutrient fluctuations in natural environments, yet we understand little about their ability to maintain physiological memory of previous food sources. Starvation is a particularly acute case, in which cells must balance adaptation to stresses with limited nutrient supply. Here, we show that Escherichia coli cells immediately accelerate and decelerate in growth upon transitions from spent to fresh media and vice versa, respectively, and memory of rapid growth can be maintained for many hours under constant flow of spent medium. However, after transient exposure of stationary-phase cells to fresh medium, subsequent aerobic incubation in increasingly spent medium led to lysis and limited growth when rejuvenated in fresh medium. Growth defects were avoided by incubation in anaerobic spent medium or water, suggesting that defects were caused by respiration during the process of nutrient depletion in spent medium. These findings highlight the importance of respiration for stationary phase survival and underscore the broad range of starvation outcomes depending on environmental history.

Do microbes have a memory? History-dependent behavior in the adaptation to variable environments

Microbes are constantly confronted with changes and challenges in their environment. A proper response to these environmental cues is needed for optimal cellular functioning and fitness. Interestingly, past exposure to environmental cues can accelerate or boost the response when this condition returns, even in daughter cells that have not directly encountered the initial cue. Moreover, this behavior is mostly epigenetic and often goes hand in hand with strong heterogeneity in the strength and speed of the response between isogenic cells of the same population, which might function as a bet-hedging strategy. In this review, we discuss examples of history-dependent behavior (HDB) or “memory,” with a specific focus on HDB in fluctuating environments. In most examples discussed, the lag time before the response to an environmental change is used as an experimentally measurable proxy for HDB. We highlight different mechanisms already implicated in HDB, and by using HDB in fluctuating carbon conditions as a case study, we showcase how the metabolic state of a cell can be a key determining factor for HDB. Finally, we consider possible evolutionary causes and consequences of such HDB.

Novel prokaryotic system employing previously unknown nucleic acids-based receptors

The present study describes a previously unknown universal system that orchestrates the interaction of bacteria with the environment, named the Teazeled receptor system (TR-system). The identical system was recently discovered within eukaryotes. The system includes DNA- and RNA-based molecules named "TezRs", that form receptor's network located outside the membrane, as well as reverse transcriptases and integrases. TR-system takes part in the control of all major aspects of bacterial behavior, such as intra cellular communication, growth, biofilm formation and dispersal, utilization of nutrients including xenobiotics, virulence, chemo- and magnetoreception, response to external factors (e.g., temperature, UV, light and gas content), mutation events, phage-host interaction, and DNA recombination activity. Additionally, it supervises the function of other receptor-mediated signaling pathways. Importantly, the TR-system is responsible for the formation and maintenance of cell memory to preceding cellular events, as well the ability to "forget" preceding events. Transcriptome and biochemical analysis revealed that the loss of different TezRs instigates significant alterations in gene expression and proteins synthesis.

Overcoming Leak Sensitivity in CRISPRi Circuits Using Antisense RNA Sequestration and Regulatory Feedback

The controlled binding of the catalytically dead CRISPR nuclease (dCas) to DNA can be used to create complex, programmable transcriptional genetic circuits, a fundamental goal of synthetic biology. This approach, called CRISPR interference (CRISPRi), is advantageous over existing methods because the programmable nature of CRISPR proteins in principle enables the simultaneous regulation of many different targets without crosstalk. However, the performance of dCas-based genetic circuits is limited by both the sensitivity to leaky repression within CRISPRi logic gates and retroactive effects due to a shared pool of dCas proteins. By utilizing antisense RNAs (asRNAs) to sequester gRNA transcripts as well as CRISPRi feedback to self-regulate asRNA production, we demonstrate a mechanism that suppresses unwanted repression by CRISPRi and improves logical gene circuit function in Escherichia coli. This improvement is particularly pronounced during stationary expression when CRISPRi circuits do not achieve the expected regulatory dynamics. Furthermore, the use of dual CRISPRi/asRNA inverters restores the logical performance of layered circuits such as a double inverter. By studying circuit induction at the single-cell level in microfluidic channels, we provide insight into the dynamics of antisense sequestration of gRNA and regulatory feedback on dCas-based repression and derepression. These results demonstrate how CRISPRi inverters can be improved for use in more complex genetic circuitry without sacrificing the programmability and orthogonality of dCas proteins.

Connecting single-cell ATP dynamics to overflow metabolism, cell growth, and the cell cycle in Escherichia coli

Adenosine triphosphate (ATP) is an abundant and essential metabolite that cells consume and regenerate in large amounts to support growth. Although numerous studies have inferred the intracellular concentration of ATP in bacterial cultures, what happens in individual bacterial cells under stable growth conditions is less clear. Here, we use the QUEEN-2m biosensor to quantify ATP dynamics in single Escherichia coli cells in relation to their growth rate, metabolism, cell cycle, and cell lineage. We find that ATP dynamics are more complex than expected from population studies and are associated with growth-rate variability. Under stable nutrient-rich condition, cells can display large fluctuations in ATP level that are partially coordinated with the cell cycle. Abrogation of aerobic acetate fermentation (overflow metabolism) through genetic deletion considerably reduces both the amplitude of ATP level fluctuations and the cell-cycle trend. Similarly, growth in media in which acetate fermentation is lower or absent results in the reduction of ATP level fluctuation and cell-cycle trend. This suggests that overflow metabolism exhibits temporal dynamics, which contributes to fluctuating ATP levels during growth. Remarkably, at the single-cell level, growth rate negatively correlates with the amplitude of ATP fluctuation for each tested condition, linking ATP dynamics to growth-rate heterogeneity in clonal populations. Our work highlights the importance of single-cell analysis in studying metabolism and its implication to phenotypic diversity and cell growth.

Control of phenotypic diversification based on serial cultivations on different carbon sources leads to improved bacterial xylanase production

Thermobacillus xylanilyticus is a thermophilic and hemicellulolytic bacterium of interest for the production of thermostable hemicellulases. Enzymes' production by this bacterium is challenging, because the proliferation of a cheating subpopulation of cells during exponential growth impairs the production of xylanase after serial cultivations. Accordingly, a strategy of successive cultivations with cells transfers in stationary phase and the use of wheat bran and wheat straw as carbon sources were tested. The ratio between subpopulations and their corresponding metabolic activities were studied by flow cytometry and the resulting hemicellulases production (xylanase, acetyl esterase and β-xylosidase) followed. During serial cultivations, the results pointed out an increase of the enzymatic activities. On xylan, compared to the first cultivation, the xylanase activity increases by 7.15-fold after only four cultivations. On the other hand, the debranching activities were increased by 5.88-fold and 57.2-fold on wheat straw and by 2.77-fold and 3.34-fold on wheat bran for β-xylosidase and acetyl esterase, respectively. The different enzymatic activities then stabilized, reached a plateau and further decreased. Study of the stability and reversibility of the enzyme production revealed cell-to-cell heterogeneities in metabolic activities which could be linked to the reversibility of enzymatic activity changes. Thus, the strategy of successive transfers during the stationary phase of growth, combined with the use of complex lignocellulosic substrates as carbon sources, is an efficient strategy to optimize the hemicellulases production by T. xylanilyticus, by preventing the selection of cheaters.

A plasmid system with tunable copy number

Plasmids are one of the most commonly used platforms for genetic engineering and recombinant gene expression in bacteria. The range of available copy numbers for cloning vectors is largely restricted to the handful of Origins of Replication (ORIs) that have been isolated from plasmids found in nature. Here, we introduce two systems that allow for the continuous, finely-tuned control of plasmid copy number between 1 and 800 copies per cell: a plasmid with an anhydrotetracycline-controlled copy number, and a parallelized assay that is used to generate a continuous spectrum of 1194 ColE1-based copy number variants. Using these systems, we investigate the effects of plasmid copy number on cellular growth rates, gene expression, biosynthesis, and genetic circuit performance. We perform single-cell timelapse measurements to characterize plasmid loss, runaway plasmid replication, and quantify the impact of plasmid copy number on the variability of gene expression. Using our assay, we find that each plasmid imposes a 0.063% linear metabolic burden on their hosts, hinting at a simple relationship between metabolic burdens and plasmid DNA synthesis. Our systems enable the precise control of gene expression, and our results highlight the importance of tuning plasmid copy number as a powerful tool for the optimization of synthetic biological systems.

Targeting primary and metastatic tumor growth in an aggressive breast cancer by engineered tryptophan auxotrophic Salmonella Typhimurium

The global cancer burden is growing and accounted for 10 million deaths in 2020. The resurgence of chemo- and radiation resistance have contributed to the treatment failures in many cancer types. Therefore, alternative strategies are desired for the effective cancer therapy. Bacteria-mediated cancer therapy presents an attarctive alternative option for the treatment and diagnosis of cancers. Herein, we describe an engineered Salmonella Typhimurium (ST) auxotrophic for tryptophan as a cancer therapeutic. The tryptophan auxotrophy was sufficient to render ST avirulent and highly safe to mice. The auxotroph recovered from the infected tumors had improved ability to target and colonize the tumors. We show that tryptophan auxotrophy reduced the fitness of ST in healthy tissues, but not in the tumors. We evaluated the auxotroph in highly aggressive metastatic 4T1 breast cancer model to inhibit primary tumor growth and lung metastases. The therapy greatly suppressed the primary growth with tumor-free survival of 40% mice. Importantly, therapy abolished the metastatic dissemination of tumor to lungs. Further, therapy markedly diminished the macrophage population in the tumors that may have contributed to the therapeutic benefit recorded. Collectively, results highlight the therapeutic efficacy of the tryptophan auxotrophic ST in an aggressive metastatic cancer model.

Probabilistic Inference with Polymerizing Biochemical Circuits

Probabilistic inference-the process of estimating the values of unobserved variables in probabilistic models-has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially "predictive" behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.