Programming stress-induced altruistic death in engineered bacteria

Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics

The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. We previously found that growth productivity, which captures the relationship between [ATP] and growth, can account for the strength of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. We show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. Specifically, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for β-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect and paves the way for intervention strategies to reduce the inoculum effect in the clinic.

Granulation characteristics of anammox sludge in response to different signal-molecule-stimulants; mediated through programmed cell death

During the anammox process, mitigation of biomass washout to increase sludge retention is an important parameter of process efficiency. Signal molecular stimulants (SMS) initiate the sludge granulations controlled by programmed cell death (PCD) of microorganisms. In this study, the aerobic granular sludge (AGS), cell fragments, extracellular polymeric substances (EPS), and AGS process effluent were tested as SMS to identify their effect on anammox granulation. The results showed that the addition of SMS increased the nitrogen removal efficiency to varying degrees, whereas the addition of AGS process supernatant, as SMS, increased the ammonia removal efficiency up to 96%. The addition of SMS was also found to increase EPS production and contributed to sludge granulation. In this process, the proportion of PCD increased and both Gaiella and Denitratisoma abundance increased from 3.54% to 5.59%, and from 1.8% to 3.42%, respectively. In conclusion, PCD was found important to increase anaerobic ammonia oxidation performance through the granulation mechanism.

The concepts and origins of cell mortality

Organismal death is foundational to the evolution of life, and many biological concepts such as natural selection and life history strategy are so fashioned only because individuals are mortal. Organisms, irrespective of their organization, are composed of basic functional units-cells-and it is our understanding of cell death that lies at the heart of most general explanatory frameworks for organismal mortality. Cell death can be exogenous, arising from transmissible diseases, predation, or other misfortunes, but there are also endogenous forms of death that are sometimes the result of adaptive evolution. These endogenous forms of death-often labeled programmed cell death, PCD-originated in the earliest cells and are maintained across the tree of life. Here, we consider two problematic issues related to PCD (and cell mortality generally). First, we trace the original discoveries of cell death from the nineteenth century and place current conceptions of PCD in their historical context. Revisions of our understanding of PCD demand a reassessment of its origin. Our second aim is thus to structure the proposed origin explanations of PCD into coherent arguments. In our analysis we argue for the evolutionary concept of PCD and the viral defense-immunity hypothesis for the origin of PCD. We suggest that this framework offers a plausible account of PCD early in the history of life, and also provides an epistemic basis for the future development of a general evolutionary account of mortality.

No more tears from surgical site infections in interventional pain management

As the field of interventional pain management (IPM) grows, the risk of surgical site infections (SSIs) is increasing. SSI is defined as an infection of the incision or organ/space that occurs within one month after operation or three months after implantation. It is also common to find patients with suspected infection in an outpatient clinic. The most frequent IPM procedures are performed in the spine. Even though primary pyogenic spondylodiscitis via hematogenous spread is the most common type among spinal infections, secondary spinal infections from direct inoculation should be monitored after IPM procedures. Various preventive guidelines for SSI have been published. Cefazolin, followed by vancomycin, is the most commonly used surgical antibiotic prophylaxis in IPM. Diagnosis of SSI is confirmed by purulent discharge, isolation of causative organisms, pain/tenderness, swelling, redness, or heat, or diagnosis by a surgeon or attending physician. Inflammatory markers include traditional (C-reactive protein, erythrocyte sedimentation rate, and white blood cell count) and novel (procalcitonin, serum amyloid A, and presepsin) markers. Empirical antibiotic therapy is defined as the initial administration of antibiotics within at least 24 hours prior to the results of blood culture and antibiotic susceptibility testing. Definitive antibiotic therapy is initiated based on the above culture and testing. Combination antibiotic therapy for multidrug-resistant Gram-negative bacteria infections appears to be superior to monotherapy in mortality with the risk of increasing antibiotic resistance rates. The never-ending war between bacterial resistance and new antibiotics is continuing. This article reviews prevention, diagnosis, and treatment of infection in pain medicine.

Growth productivity as a determinant of the inoculum effect for bactericidal antibiotics

Understanding the mechanisms by which populations of bacteria resist antibiotics has implications in evolution, microbial ecology, and public health. The inoculum effect (IE), where antibiotic efficacy declines as the density of a bacterial population increases, has been observed for multiple bacterial species and antibiotics. Several mechanisms to account for IE have been proposed, but most lack experimental evidence or cannot explain IE for multiple antibiotics. We show that growth productivity, the combined effect of growth and metabolism, can account for IE for multiple bactericidal antibiotics and bacterial species. Guided by flux balance analysis and whole-genome modeling, we show that the carbon source supplied in the growth medium determines growth productivity. If growth productivity is sufficiently high, IE is eliminated. Our results may lead to approaches to reduce IE in the clinic, help standardize the analysis of antibiotics, and further our understanding of how bacteria evolve resistance.

Overcoming Antibiotic Resistance with Novel Paradigms of Antibiotic Selection

Conventional antimicrobial susceptibility tests, including phenotypic and genotypic methods, are insufficiently accurate and frequently fail to identify effective antibiotics. These methods predominantly select therapies based on the antibiotic response of only the lead bacterial pathogen within pure bacterial culture. However, this neglects the fact that, in the majority of human infections, the lead bacterial pathogens are present as a part of multispecies communities that modulate the response of these lead pathogens to antibiotics and that multiple pathogens can contribute to the infection simultaneously. This discrepancy is a major cause of the failure of antimicrobial susceptibility tests to detect antibiotics that are effective in vivo. This review article provides a comprehensive overview of the factors that are missed by conventional antimicrobial susceptibility tests and it explains how accounting for these methods can aid the development of novel diagnostic approaches.

Optimal dose of lactoferrin reduces the resilience of in vitro Staphylococcus aureus colonies

The rise in antibiotic resistance has stimulated research into adjuvants that can improve the efficacy of broad-spectrum antibiotics. Lactoferrin is a candidate adjuvant; it is a multifunctional iron-binding protein with antimicrobial properties. It is known to show dose-dependent antimicrobial activity against Staphylococcus aureus through iron sequestration and repression of β–lactamase expression. However, S. aureus can extract iron from lactoferrin through siderophores for their growth, which confounds the resolution of lactoferrin’s method of action. We measured the minimum inhibitory concentration (MIC) for a range of lactoferrin/ β–lactam antibiotic dose combinations and observed that at low doses (< 0.39 μM), lactoferrin contributes to increased S. aureus growth, but at higher doses (> 6.25 μM), iron-depleted native lactoferrin reduced bacterial growth and reduced the MIC of the β-lactam-antibiotic cefazolin. This differential behaviour points to a bacterial population response to the lactoferrin/ β–lactam dose combination. Here, with the aid of a mathematical model, we show that lactoferrin stratifies the bacterial population, and the resulting population heterogeneity is at the basis of the dose dependent response seen. Further, lactoferrin disables a sub-population from β-lactam-induced production of β-lactamase, which when sufficiently large reduces the population’s ability to recover after being treated by an antibiotic. Our analysis shows that an optimal dose of lactoferrin acts as a suitable adjuvant to eliminate S. aureus colonies using β-lactams, but sub-inhibitory doses of lactoferrin reduces the efficacy of β-lactams.

Plant-beneficial functions and interactions of Bacillus subtilis SL-44 and Enterobacter cloacae Rs-2 in co-culture by transcriptomics analysis

The development of mixed microbial agents can reduce the use of pesticides and fertilizers in agriculture. However, most previous studies focused only on the overall effects of mixed microbial agents and ignored the interactions between bacteria in mixed systems. In this study, Bacillus subtilis SL-44 and Enterobacter cloacae Rs-2 were used to explore the interactions between two different functional plant growth-promoting rhizobacteria (PGPR). The plant growth-promotion properties and inhibition rate of Rhizoctonia solani were determined, and the mechanism of the interactions under single and co-culture conditions was elucidated via transcriptomics analysis under single and co-culture conditions. Results showed that the co-culture was not conducive to B. subtilis SL-44 growth. Furthermore, the differentially expressed genes related to B. subtilis SL-44 developmental process and cell differentiation were downregulated by 82.7% and 84.8% respectively. Moreover, among the properties, only siderophore production by the mixed culture was higher than that of single cultures because of the upregulation of the siderophore-related genes of B. subtilis SL-44. In addition, results revealed the altruistic relationship between the two strains, and the chemical and non-chemical signals of their interaction. This study provides unique insights into PGPR interactions and offers guidance for the development and application of mixed microbial agents.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Automated design of synthetic microbial communities

Microbial species rarely exist in isolation. In naturally occurring microbial systems there is strong evidence for a positive relationship between species diversity and productivity of communities. The pervasiveness of these communities in nature highlights possible advantages for genetically engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigate issues often found in engineering a monoculture, especially as functional complexity increases. Here, we demonstrate a methodology for designing robust synthetic communities that include competition for nutrients, and use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We computationally explore all two- and three- strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and further tuning the community composition.

Bistable Bacterial Growth Dynamics in the Presence of Antimicrobial Agents

The outcome of an antibiotic treatment on the growth capacity of bacteria is largely dependent on the initial population size (Inoculum Effect). We characterized and built a model of this effect in E. coli cultures using a large variety of antimicrobials, including conventional antibiotics, and for the first time, cationic antimicrobial peptides (CAMPs). Our results show that all classes of antimicrobial drugs induce an inoculum effect, which, as we explain, implies that the dynamic is bistable: For a range of anti-microbial densities, a very small inoculum decays whereas a larger inoculum grows, and the threshold inoculum depends on the drug concentration. We characterized three distinct classes of drug-induced bistable growth dynamics and demonstrate that in rich medium, CAMPs correspond to the simplest class, bacteriostatic antibiotics to the second class, and all other traditional antibiotics to the third, more complex class. These findings provide a unifying universal framework for describing the dynamics of the inoculum effect induced by antimicrobials with inherently different killing mechanisms.

Dataset for transcriptome analysis of Salmonella enterica subsp. enterica serovar Typhimurium strain 14028S response to starvation

Salmonella enterica is an ubiquitous pathogen throughout the world causing gastroenteritis in humans and animals. Survival of pathogenic bacteria in the external environment may be associated with the ability to overcome the stress caused by starvation. The bacterial response to starvation is well understood in laboratory cultures with a sufficiently high cell density. However, bacterial populations often have a small size when facing this challenge in natural biotopes. The aim of this work was to find out if there are differences in the transcriptomes of S. enterica depending on the factor of cell density during starvation. Here we present transcriptome data of Salmonella enterica subsp. enterica serovar Typhimurium str. 14028S grown in carbon rich or carbon deficient medium with high or low cell density. These data will help identify genes involved in adaptation of low-density bacterial populations to starvation conditions.

The mechanisms and cell signaling pathways of programmed cell death in the bacterial world

While programmed cell death was once thought to be exclusive to eukaryotic cells, there are now abundant examples of well regulated cell death mechanisms in bacteria. The mechanisms by which bacteria undergo programmed cell death are diverse, and range from the use of toxin-antitoxin systems, to prophage-driven cell lysis. Moreover, some bacteria have learned how to coopt programmed cell death systems in competing bacteria. Interestingly, many of the potential reasons as to why bacteria undergo programmed cell death may parallel those observed in eukaryotic cells, and may be altruistic in nature. These include protection against infection, recycling of nutrients, to ensure correct morphological development, and in response to stressors. In the following chapter, we discuss the molecular and signaling mechanisms by which bacteria undergo programmed cell death. We conclude by discussing the current open questions in this expanding field.

Versatile biomanufacturing through stimulus-responsive cell-material feedback

Small-scale production of biologics has great potential for enhancing the accessibility of biomanufacturing. By exploiting cell-material feedback, we have designed a concise platform to achieve versatile production, analysis and purification of diverse proteins and protein complexes. The core of our technology is a microbial swarmbot, which consists of a stimulus-sensitive polymeric microcapsule encapsulating engineered bacteria. By sensing the confinement, the bacteria undergo programmed partial lysis at a high local density. Conversely, the encapsulating material shrinks responding to the changing chemical environment caused by cell growth, squeezing out the protein products released by bacterial lysis. This platform is then integrated with downstream modules to enable quantification of enzymatic kinetics, purification of diverse proteins, quantitative control of protein interactions and assembly of functional protein complexes and multienzyme metabolic pathways. Our work demonstrates the use of the cell-material feedback to engineer a modular and flexible platform with sophisticated yet well-defined programmed functions.

Timing between successive introduction events determines establishment success in bacteria with an Allee effect

Propagule pressure is a leading determinant of population establishment. Yet, an experimental understanding of how propagule size and number (two principal parts of propagule pressure) determine establishment success remains incomplete. Theoretical studies suggest that the timing between introduction events, a component of propagule number, can influence establishment success. However, this dynamic has rarely been explored experimentally. Using Escherichia coli engineered with an Allee effect, we investigated how the timing of two introduction events influences establishment. For populations introduced below the Allee threshold, establishment occurred if the time between two introduction events was sufficiently short, with the length of time between events further reduced by reducing growth rate. Interestingly, we observed that as the density of bacteria introduced in one introduction event increased, the time between introduction events that allowed for establishment increased. Using a mathematical model, we provide support that the mechanism behind these trends is the ability of the first population to modify the environment, which can pave the way for establishment of the second population. Our results provide experimental evidence that the temporal distribution of introduction events regulates establishment, furthering our understanding of propagule pressure and may have implications in invasion biology and infectious disease.

Functional identification behind gravity-separated sludge in high concentration organic coking wastewater: Microbial aggregation, apoptosis-like decay and community

Functional identification and elimination of activity-decayed sludge are helpful for improving the performance of biological treatment process. However, cell decay-associated changes in biological functions have not been explored for gravity-separated sludge. In this work, sludge flocs from the aerobic basin of a wastewater treatment plant treating high-concentration organic coking wastewater was fractionated according to settling velocity, i.e. sludge F (fast settling), sludge M (moderate settling) and sludge S (slow settling). Sludge volume index (SVI), mean floc size, dehydrogenase activity, specific oxygen uptake rate (SOUR), extracellular polymeric substances (EPS) content and aggregation interaction were investigated in the fractionated sludges. Apoptosis-like decayed cell distribution (ALDCD), a novel property of sludge, was proposed to describe sludge decay based on cell membrane variation. ALDCD of sludge F was 6.64% and 13.5% lower than sludge M and S, respectively. Microbial community and functional prediction revealed that sludge F exhibited the highest microbial potential for organic removal and sludge M had the highest potential for nitrogen metabolism while sludge S had the lowest potential for both. Our analysis suggests that the treatment efficiency might be enhanced by retaining compact sludge flocs while eliminating dispersive sludge flocs. This study also facilitates the identification and elimination of functional microbial groups from decayed sludge in wastewater treatment.

A unifying framework for interpreting and predicting mutualistic systems

Coarse-grained rules are widely used in chemistry, physics and engineering. In biology, however, such rules are less common and under-appreciated. This gap can be attributed to the difficulty in establishing general rules to encompass the immense diversity and complexity of biological systems. Furthermore, even when a rule is established, it is often challenging to map it to mechanistic details and to quantify these details. Here we report a framework that addresses these challenges for mutualistic systems. We first deduce a general rule that predicts the various outcomes of mutualistic systems, including coexistence and productivity. We further develop a standardized machine-learning-based calibration procedure to use the rule without the need to fully elucidate or characterize their mechanistic underpinnings. Our approach consistently provides explanatory and predictive power with various simulated and experimental mutualistic systems. Our strategy can pave the way for establishing and implementing other simple rules for biological systems.

Biological complexity has impeded our ability to predict the dynamics of mutualistic interactions. Here, the authors deduce a general rule to predict outcomes of mutualistic systems and introduce an approach that permits making predictions even in the absence of knowledge of mechanistic details.

Physiological, Genetic, and Transcriptomic Analysis of Alcohol-Induced Delay of Escherichia coli Death

When Escherichia coli K-12 is inoculated into rich medium in batch culture, cells experience five phases. While the lag and logarithmic phases are mechanistically fairly well defined, the stationary phase, death phase, and long-term stationary phase are less well understood. Here, we characterize a mechanism of delaying death, a phenomenon we call the "alcohol effect," where the addition of small amounts of certain alcohols prolongs stationary phase for at least 10 days longer than in untreated conditions. We show that the stationary phase is extended when ethanol is added above a minimum threshold concentration. Once ethanol levels fall below a threshold concentration, cells enter the death phase. We also show that the effect is conferred by the addition of straight-chain alcohols 1-propanol, 1-butanol, 1-pentanol, and, to a lesser degree, 1-hexanol. However, methanol, isopropanol, 1-heptanol, and 1-octanol do not delay entry into death phase. Though modulated by RpoS, the alcohol effect does not require RpoS activity or the activities of the AdhE or AdhP alcohol dehydrogenases. Further, we show that ethanol is capable of extending the life span of stationary-phase cultures for non-K-12 E. coli strains and that this effect is caused in part by genes of the glycolate degradation pathway. These data suggest a model where ethanol and other shorter 1-alcohols can serve as signaling molecules, perhaps by modulating patterns of gene expression that normally regulate the transition from stationary phase to death phase.IMPORTANCE In one of the most well-studied organisms in the life sciences, Escherichia coli, we still do not fully understand what causes populations to die. This is largely due to the technological difficulties of studying bacterial cell death. This study provides an avenue to studying how and why E. coli populations, and perhaps other microbes, transition from stationary phase to death phase by exploring how ethanol and other alcohols delay the onset of death. Here, we demonstrate that alcohols are acting as signaling molecules to achieve the delay in death phase. This study not only offers a better understanding of a fundamental process but perhaps also provides a gateway to studying the dynamics between ethanol and microbes in the human gastrointestinal tract.

Applying ecological resistance and resilience to dissect bacterial antibiotic responses

An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a notable change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, although their interpretations are often subject to debate. Here, we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended-spectrum β-lactamases (ESBLs) when treated by a β-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a β-lactam and a β-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

In vitro antibacterial effects of statins against bacterial pathogens causing skin infections

With financial considerations impeding research and development of new antibiotics, drug repurposing (finding new indications for old drugs) emerges as a feasible alternative. Statins are extensively prescribed around the world to lower cholesterol, but they also possess inherent antimicrobial properties. This study identifies statins with the greatest potential to be repurposed as topical antibiotics and postulates a mechanism of action for statins' antibacterial activity. Using broth microdilution, the direct antibacterial effects of all seven parent statins currently registered for human use and three selected statin metabolites were tested against bacterial skin pathogens Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Serratia marcescens. Simvastatin and pitavastatin lactone exerted the greatest antibacterial effects (minimum inhibitory concentrations of 64 and 128 μg/mL, respectively) against S. aureus. None of the statins tested were effective against E. coli, P. aeruginosa, or S. marcescens, but simvastatin hydroxy acid acid might be active against S. aureus, E. coli, and S. marcescens at drug concentrations > 256 μg/mL. It was found that S. aureus may exhibit a paradoxical growth effect when exposed to simvastatin; thus, treatment failure at high drug concentrations is theoretically probable. Through structure-activity relationship analysis, we postulate that statins' antibacterial action may involve disrupting the teichoic acid structures or decreasing the number of alanine residues present on Gram-positive bacterial cell surfaces, which could reduce biofilm formation, diminish bacterial adhesion to environmental surfaces, or impede S. aureus cell division.

Morphodynamics of a growing microbial colony driven by cell death

Bacterial cells can often self-organize into multicellular structures with complex spatiotemporal morphology. In this work, we study the spatiotemporal dynamics of a growing microbial colony in the presence of cell death. We present an individual-based model of nonmotile bacterial cells which grow and proliferate by consuming diffusing nutrients on a semisolid two-dimensional surface. The colony spreads by growth forces and sliding motility of cells and undergoes cell death followed by subsequent disintegration of the dead cells in the medium. We model cell death by considering two possible situations: In one of the cases, cell death occurs in response to the limitation of local nutrients, while the other case corresponds to an active death process, known as apoptotic or programmed cell death. We demonstrate how the colony morphology is influenced by the presence of cell death. Our results show that cell death facilitates transitions from roughly circular to highly branched structures at the periphery of an expanding colony. Interestingly, our results also reveal that for the colonies which are growing in higher initial nutrient concentrations, cell death occurs much earlier compared to the colonies which are growing in lower initial nutrient concentrations. This work provides new insights into the branched patterning of growing bacterial colonies as a consequence of complex interplay among the biochemical and mechanical effects.

Oscillatory dynamics in a bacterial cross-protection mutualism

Cooperation between microbes can enable microbial communities to survive in harsh environments. Enzymatic deactivation of antibiotics, a common mechanism of antibiotic resistance in bacteria, is a cooperative behavior that can allow resistant cells to protect sensitive cells from antibiotics. Understanding how bacterial populations survive antibiotic exposure is important both clinically and ecologically, yet the implications of cooperative antibiotic deactivation on the population and evolutionary dynamics remain poorly understood, particularly in the presence of more than one antibiotic. Here, we show that two Escherichia coli strains can form an effective cross-protection mutualism, protecting each other in the presence of two antibiotics (ampicillin and chloramphenicol) so that the coculture can survive in antibiotic concentrations that inhibit growth of either strain alone. Moreover, we find that daily dilutions of the coculture lead to large oscillations in the relative abundance of the two strains, with the ratio of abundances varying by nearly four orders of magnitude over the course of the 3-day period of the oscillation. At modest antibiotic concentrations, the mutualistic behavior enables long-term survival of the oscillating populations; however, at higher antibiotic concentrations, the oscillations destabilize the population, eventually leading to collapse. The two strains form a successful cross-protection mutualism without a period of coevolution, suggesting that similar mutualisms may arise during antibiotic treatment and in natural environments such as the soil.

Horizontal transfer of DNA methylation patterns into bacterial chromosomes

Horizontal gene transfer (HGT) is the non-inherited acquisition of novel DNA sequences. HGT is common and important in bacteria because it enables the rapid generation of new phenotypes such as antibiotic resistance. Here we show that in vivo and in vitro DNA methylation patterns can be horizontally transferred into bacterial chromosomes to program cell phenotypes. The experiments were performed using a synthetic system in Escherichia coli where different DNA methylation patterns within the cis-regulatory sequence of the agn43 gene turn on or off a fluorescent reporter (CFP). With this system we demonstrated that DNA methylation patterns not only accompany the horizontal transfer of genes into the bacterial cytoplasm but can be transferred into chromosomes by: (i) bacteriophage P1 transduction; and (ii) transformation of extracellular synthetic DNA. We also modified the experimental system by replacing CFP with the SgrS small RNA, which regulates glucose and methyl α-D-glucoside uptake, and showed that horizontally acquired DNA methylation patterns can increase or decrease cell fitness. That is, horizontally acquired DNA methylation patterns can result in the selection for and against cells that have HGT. Findings from these proof-of-concept experiments have applications in synthetic biology and potentially broad implications for bacterial adaptation and evolution.

Orthocaspase and toxin-antitoxin loci rubbing shoulders in the genome of Microcystis aeruginosa PCC 7806

Programmed cell death in multicellular organisms is a coordinated and precisely regulated process. On the other hand, in bacteria we have little clue about the network of interacting molecules that result in the death of a single cell within a population or the death of almost complete population, such as often observed in cyanobacterial blooms. With the recent discovery that orthocaspase MaOC1 of the cyanobacterium Microcystis aeruginosa is an active proteolytic enzyme, we have gained a possible hint about at least one step in the process, but the picture is far from complete. Interestingly, the genomic context of MaOC1 revealed the presence of multiple copies of genes that belong to toxin-antitoxin modules. It has been speculated that these also play a role in bacterial programmed cell death. The discovery of two components linked to cell death within the same genomic region could open new ways to deciphering the underlying mechanisms of cyanobacterial cell death.

Coupling spatial segregation with synthetic circuits to control bacterial survival

Engineered bacteria have great potential for medical and environmental applications. Fulfilling this potential requires controllability over engineered behaviors and scalability of the engineered systems. Here, we present a platform technology, microbial swarmbot, which employs spatial arrangement to control the growth dynamics of engineered bacteria. As a proof of principle, we demonstrated a safeguard strategy to prevent unintended bacterial proliferation. In particular, we adopted several synthetic gene circuits to program collective survival in Escherichia coli: the engineered bacteria could only survive when present at sufficiently high population densities. When encapsulated by permeable membranes, these bacteria can sense the local environment and respond accordingly. The cells inside the microbial swarmbot capsules will survive due to their high densities. Those escaping from a capsule, however, will be killed due to a decrease in their densities. We demonstrate that this design concept is modular and readily generalizable. Our work lays the foundation for engineering integrated and programmable control of hybrid biological–material systems for diverse applications.

Stress-response balance drives the evolution of a network module and its host genome

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two-component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra-module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra- and extra-module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine-tune the module's noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.

Isolated cell behavior drives the evolution of antibiotic resistance

Bacterial antibiotic resistance is typically quantified by the minimum inhibitory concentration (MIC), which is defined as the minimal concentration of antibiotic that inhibits bacterial growth starting from a standard cell density. However, when antibiotic resistance is mediated by degradation, the collective inactivation of antibiotic by the bacterial population can cause the measured MIC to depend strongly on the initial cell density. In cases where this inoculum effect is strong, the relationship between MIC and bacterial fitness in the antibiotic is not well defined. Here, we demonstrate that the resistance of a single, isolated cell—which we call the single-cell MIC (scMIC)—provides a superior metric for quantifying antibiotic resistance. Unlike the MIC, we find that the scMIC predicts the direction of selection and also specifies the antibiotic concentration at which selection begins to favor new mutants. Understanding the cooperative nature of bacterial growth in antibiotics is therefore essential in predicting the evolution of antibiotic resistance.

The art of destruction: revealing the proteolytic capacity of bacterial caspase homologs

Caspases are proteases that initiate and execute programmed cell death in animal tissues, thereby facilitating multicellular development and survival. While caspases are unique to metazoans and specifically cleave substrates at aspartic acid residues, homologs are found in protozoa, plants, algae, fungi, bacteria and archaea, and show specificity for basic residues. In this issue of Molecular Microbiology, Klemenčič and colleagues present the first biochemical characterization of a bacterial caspase homolog, classified as an orthocaspase. By expressing the gene MaOC1 from the cyanobacterium Microcystis aeruginosa PCC 7806 in Escherichia coli, the authors discovered specificity for substrates with arginine in the P1 position. The protein requires autocatalytic processing to become active and is dependent on an intact histidine-cysteine dyad. These results significantly extend our knowledge of the specificities of bacterial caspase homologs, which are known to be highly diverse in protein domain architectures and active site mutations. Although bacterial programmed cell death is one possible area of action, the function of most bacterial caspase homologs remains unexplored. Cyanobacteria represent the best studied group in terms of prokaryotic caspase-like proteins both genomically and experimentally, and thereby provide a suitable platform for further investigations into activation, regulation and physiological roles of orthocaspases.

Dynamic control and quantification of bacterial population dynamics in droplets

Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput. However, difficulties in manipulating the droplet environment and monitoring encapsulated bacterial population for long-term experiments limit its utilization. To overcome these limitations, we used an electrode-free injection technology to modulate the chemical environment in droplets. This ability is critical for precise control of bacterial dynamics in droplets. Moreover, we developed a trapping device for long-term monitoring of population dynamics in individual droplets for at least 240 h. We demonstrated the utility of this new microfluidic system by quantifying population dynamics of natural and engineered bacteria. Our approach can further improve the analysis for systems and synthetic biology in terms of manipulability and high temporal resolution.

Bacterial temporal dynamics enable optimal design of antibiotic treatment

There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. β-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum β-lactamase (ESBL) producing bacteria has limited the use of β-lactams. Due to the concern of complex drug responses, many β-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some β-lactams. Using quantitative modeling, we show that β-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics.

Antibiotic resistance is a growing problem that the World Health Organization describes as “one of the top three threats to global health.” To date, bacteria have developed resistance to all antibiotics used in clinical settings. Unfortunately, the evolution of antibiotic resistant bacteria is accelerating, as antibiotics continue to be misused and overused. As the antibiotic pipeline is drying up, it becomes increasingly critical to utilize the antibiotics already on the market more effectively. The key to designing better regimens lies in the ability to predict how bacteria will respond to a particular antibiotic treatment. Because of this, we need a simple metric that characterizes this pathogen-antibiotic interaction that can be easily measured and used to design dosing protocols that will effectively clear an infection. To help guide the design of effective protocols, we use quantitative modeling to develop a metric that is easy to measure and quantifies the pathogen-antibiotic interaction. Through optimized antibiotic regimens, our strategy could extend the use of first-line antibiotics, improve treatment outcome, and preserve last-resort antibiotics.

Collective antibiotic tolerance: mechanisms, dynamics and intervention

Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses. Past studies have focused on survival in the presence of antibiotics by individual cells, as genetic mutants or persisters. Also important, however, is the fact that a population of bacterial cells can collectively survive antibiotic treatments lethal to individual cells. This tolerance can arise by diverse mechanisms, including resistance-conferring enzyme production, titration-mediated bistable growth inhibition, swarming and interpopulation interactions. These strategies can enable rapid population recovery after antibiotic treatment and provide a time window during which otherwise susceptible bacteria can acquire inheritable genetic resistance. Here, we emphasize the potential for targeting collective antibiotic tolerance behaviors as an antibacterial treatment strategy.

Die for the community: an overview of programmed cell death in bacteria

Programmed cell death is a process known to have a crucial role in many aspects of eukaryotes physiology and is clearly essential to their life. As a consequence, the underlying molecular mechanisms have been extensively studied in eukaryotes and we now know that different signalling pathways leading to functionally and morphologically different forms of death exist in these organisms. Similarly, mono-cellular organism can activate signalling pathways leading to death of a number of cells within a colony. The reason why a single-cell organism would activate a program leading to its death is apparently counterintuitive and probably for this reason cell death in prokaryotes has received a lot less attention in the past years. However, as summarized in this review there are many reasons leading to prokaryotic cell death, for the benefit of the colony. Indeed, single-celled organism can greatly benefit from multicellular organization. Within this forms of organization, regulation of death becomes an important issue, contributing to important processes such as: stress response, development, genetic transformation, and biofilm formation.

Advances in bioprocessing for efficient bio manufacture

The strategies involving molecular, cellular and community levels for improving various bioprocesses are reviewed with specific examples presented.

Enhancing stress-resistance for efficient microbial biotransformations by synthetic biology

Chemical conversions mediated by microorganisms, otherwise known as microbial biotransformations, are playing an increasingly important role within the biotechnology industry. Unfortunately, the growth and production of microorganisms are often hampered by a number of stressful conditions emanating from environment fluctuations and/or metabolic imbalances such as high temperature, high salt condition, strongly acidic solution, and presence of toxic metabolites. Therefore, exploring methods to improve the stress tolerance of host organisms could significantly improve the biotransformation process. With the help of synthetic biology, it is now becoming feasible to implement strategies to improve the stress-resistance of the existing hosts. This review summarizes synthetic biology efforts to enhance the efficiency of biotransformations by improving the robustness of microbes. Particular attention will be given to strategies at the cellular and the microbial community levels.

A microbial model of economic trading and comparative advantage

The economic theory of comparative advantage postulates that beneficial trading relationships can be arrived at by two self-interested entities producing the same goods as long as they have opposing relative efficiencies in producing those goods. The theory predicts that upon entering trade, in order to maximize consumption both entities will specialize in producing the good they can produce at higher efficiency, that the weaker entity will specialize more completely than the stronger entity, and that both will be able to consume more goods as a result of trade than either would be able to alone. We extend this theory to the realm of unicellular organisms by developing mathematical models of genetic circuits that allow trading of a common good (specifically, signaling molecules) required for growth in bacteria in order to demonstrate comparative advantage interactions. In Conception 1, the experimenter controls production rates via exogenous inducers, allowing exploration of the parameter space of specialization. In Conception 2, the circuits self-regulate via feedback mechanisms. Our models indicate that these genetic circuits can demonstrate comparative advantage, and that cooperation in such a manner is particularly favored under stringent external conditions and when the cost of production is not overly high. Further work could involve implementing the models in living bacteria and searching for naturally occurring cooperative relationships between bacteria that conform to the principles of comparative advantage.

Linear population allocation by bistable switches in response to transient stimulation

Many cellular decision processes, including proliferation, differentiation, and phenotypic switching, are controlled by bistable signaling networks. In response to transient or intermediate input signals, these networks allocate a population fraction to each of two distinct states (e.g. OFF and ON). While extensive studies have been carried out to analyze various bistable networks, they are primarily focused on responses of bistable networks to sustained input signals. In this work, we investigate the response characteristics of bistable networks to transient signals, using both theoretical analysis and numerical simulation. We find that bistable systems exhibit a common property: for input signals with short durations, the fraction of switching cells increases linearly with the signal duration, allowing the population to integrate transient signals to tune its response. We propose that this allocation algorithm can be an optimal response strategy for certain cellular decisions in which excessive switching results in lower population fitness.

Bacteria and phenoptosis

Genetically programmed death of an organism, or phenoptosis, can be found not only in animals and plants, but also in bacteria. Taking into account intrapopulational relations identified in bacteria, it is easy to imagine the importance of phenoptosis in the regulation of a multicellular bacterial community in the real world of its existence. For example, autolysis of part of the population limits the spread of viral infection. Destruction of cells with damaged DNA contributes to the maintenance of low level of mutations. Phenoptosis can facilitate the exchange of genetic information in a bacterial population as a result of release of DNA from lysed cells. Bacteria use a special "language" to transmit signals in a population; it is used for coordinated regulation of gene expression. This special type of regulation of bacterial gene expression is usually active at high densities of bacteria populations, and it was named "quorum sensing" (QS). Different molecules can be used for signaling purposes. Phenoptosis, which is carried out by toxin-antitoxin systems, was found to depend on the density of the population; it requires a QS factor, which is called the extracellular death factor. The study of phenoptosis in bacteria is of great practical importance. The components that make up the systems ensuring the programmed cell death, including QS factor, may be used for the development of drugs that will activate mechanisms of phenoptosis and promote the destruction of pathogenic bacteria. Comparative genomic analysis revealed that the genes encoding several key enzymes involved in apoptosis of eukaryotes, such as paracaspases and metacaspases, apoptotic ATPases, proteins containing NACHT leucine-rich repeat, and proteases similar to mitochondrial HtrA-like protease, have homologs in bacteria. Proteomics techniques have allowed for the first time to identify the proteins formed during phenoptosis that participate in orderly liquidation of Streptomyces coelicolor and Escherichia coli cells. Among these proteins enzymes have been found that are involved in the degradation of cellular macromolecules, regulatory proteins, and stress-induced proteins. Future studies involving methods of biochemistry, genetics, genomics, proteomics, transcriptomics, and metabolomics should support a better understanding of the "mystery" of bacterial programmed cell death; this knowledge might be used to control bacterial populations.

First principles of Hamiltonian medicine

We introduce the field of Hamiltonian medicine, which centres on the roles of genetic relatedness in human health and disease. Hamiltonian medicine represents the application of basic social-evolution theory, for interactions involving kinship, to core issues in medicine such as pathogens, cancer, optimal growth and mental illness. It encompasses three domains, which involve conflict and cooperation between: (i) microbes or cancer cells, within humans, (ii) genes expressed in humans, (iii) human individuals. A set of six core principles, based on these domains and their interfaces, serves to conceptually organize the field, and contextualize illustrative examples. The primary usefulness of Hamiltonian medicine is that, like Darwinian medicine more generally, it provides novel insights into what data will be productive to collect, to address important clinical and public health problems. Our synthesis of this nascent field is intended predominantly for evolutionary and behavioural biologists who aspire to address questions directly relevant to human health and disease.

Survival of bactericidal antibiotic treatment by a persister subpopulation of Listeria monocytogenes

Listeria monocytogenes can cause the serious infection listeriosis, which despite antibiotic treatment has a high mortality. Understanding the response of L. monocytogenes to antibiotic exposure is therefore important to ensure treatment success. Some bacteria survive antibiotic treatment by formation of persisters, which are a dormant antibiotic-tolerant subpopulation. The purpose of this study was to determine whether L. monocytogenes can form persisters and how bacterial physiology affects the number of persisters in the population. A stationary-phase culture of L. monocytogenes was adjusted to 10(8) CFU ml(-1), and 10(3) to 10(4) CFU ml(-1) survived 72-h treatment with 100 μg of norfloxacin ml(-1), indicating a persister subpopulation. This survival was not caused by antibiotic resistance as regrown persisters were as sensitive to norfloxacin as the parental strain. Higher numbers of persisters (10(5) to 10(6)) were surviving when older stationary phase or surface-associated cells were treated with 100 μg of norfloxacin ml(-1). The number of persisters was similar when a ΔsigB mutant and the wild type were treated with norfloxacin, but the killing rate was higher in the ΔsigB mutant. Dormant norfloxacin persisters could be activated by the addition of fermentable carbohydrates and subsequently killed by gentamicin; however, a stable surviving subpopulation of 10(3) CFU ml(-1) remained. Nitrofurantoin that has a growth-independent mode of action was effective against both growing and dormant cells, suggesting that eradication of persisters is possible. Our study adds L. monocytogenes to the list of bacterial species capable of surviving bactericidal antibiotics in a dormant stage, and this persister phenomenon should be borne in mind when developing treatment regimens.

Synthetic biological networks

Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics.

Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids

Inactivation of β-lactam antibiotics by resistant bacteria is a 'cooperative' behavior that may allow sensitive bacteria to survive antibiotic treatment. However, the factors that determine the fraction of resistant cells in the bacterial population remain unclear, indicating a fundamental gap in our understanding of how antibiotic resistance evolves. Here, we experimentally track the spread of a plasmid that encodes a β-lactamase enzyme through the bacterial population. We find that independent of the initial fraction of resistant cells, the population settles to an equilibrium fraction proportional to the antibiotic concentration divided by the cell density. A simple model explains this behavior, successfully predicting a data collapse over two orders of magnitude in antibiotic concentration. This model also successfully predicts that adding a commonly used β-lactamase inhibitor will lead to the spread of resistance, highlighting the need to incorporate social dynamics into the study of antibiotic resistance.

Towards a whole-cell modeling approach for synthetic biology

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

Programmed cell death in bacteria and implications for antibiotic therapy

It is now well appreciated that programmed cell death (PCD) plays critical roles in the life cycle of diverse bacterial species. It is an apparently paradoxical behavior as it does not benefit the cells undergoing PCD. However, growing evidence suggests that PCD can be 'altruistic': the dead cells may directly or indirectly benefit survivors through generation of public goods. This property provides a potential explanation on how PCD can evolve as an extreme form of cooperation, although many questions remain to be addressed. From another perspective, as PCD plays a critical role in bacterial pathogenesis, it has been proposed as a potential target for new antibacterial therapy. To this end, understanding the population and evolutionary dynamics resulting from PCD and public goods production may be a key to the success of designing effective antibiotic treatment.

A bird's-eye view of autophagy

Autophagy is a process in which a eukaryotic (but not prokaryotic) cell destroys its own components through the lysosomal machinery. This tightly regulated process is essential for normal cell growth, development, and homeostasis, serving to maintain a balance between synthesis and degradation, resulting in the recycling of cellular products. Here we try to expand the concept of autophagy and define it as a general mechanism of regulation encompassing various levels of the biosphere. Interestingly, one of the consequences of such an approach is that we must presume an existence of the autophagic processes in the prokaryotic domain.