Mycobacterial Mutagenesis and Drug Resistance Are Controlled by Phosphorylation- and Cardiolipin-Mediated Inhibition of the RecA Coprotease.

Infection with Mycobacterium tuberculosis continues to cause substantial human mortality, in part because of the emergence of antimicrobial resistance. Antimicrobial resistance in tuberculosis is solely the result of chromosomal mutations that modify drug activators or targets, yet the mechanisms controlling the mycobacterial DNA-damage response (DDR) remain incompletely defined. Here, we identify RecA serine 207 as a multifunctional signaling hub that controls the DDR in mycobacteria. RecA S207 is phosphorylated after DNA damage, which suppresses the emergence of antibiotic resistance by selectively inhibiting the LexA coprotease function of RecA without affecting its ATPase or strand exchange functions. Additionally, RecA associates with the cytoplasmic membrane during the mycobacterial DDR, where cardiolipin can specifically inhibit the LexA coprotease function of unmodified, but not S207 phosphorylated, RecA. These findings reveal that RecA S207 controls mutagenesis and antibiotic resistance in mycobacteria through phosphorylation and cardiolipin-mediated inhibition of RecA coprotease function.

Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait.

Many unicellular organisms live in multicellular communities that rely on cooperation between cells. However, cooperative traits are vulnerable to exploitation by non-cooperators (cheaters). We expand our understanding of the molecular mechanisms that allow multicellular systems to remain robust in the face of cheating by dissecting the dynamic regulation of cooperative rhamnolipids required for swarming in Pseudomonas aeruginosa. We combine mathematical modeling and experiments to quantitatively characterize the integration of metabolic and population density signals (quorum sensing) governing expression of the rhamnolipid synthesis operon rhlAB. The combined computational/experimental analysis reveals that when nutrients are abundant, rhlAB promoter activity increases gradually in a density dependent way. When growth slows down due to nutrient limitation, rhlAB promoter activity can stop abruptly, decrease gradually or even increase depending on whether the growth-limiting nutrient is the carbon source, nitrogen source or iron. Starvation by specific nutrients drives growth on intracellular nutrient pools as well as the qualitative rhlAB promoter response, which itself is modulated by quorum sensing. Our quantitative analysis suggests a supply-driven activation that integrates metabolic prudence with quorum sensing in a non-digital manner and allows P. aeruginosa cells to invest in cooperation only when the population size is large enough (quorum sensing) and individual cells have enough metabolic resources to do so (metabolic prudence). Thus, the quantitative description of rhlAB regulatory dynamics brings a greater understating to the regulation required to make swarming cooperation stable.

Cell-Size Homeostasis and the Incremental Rule in a Bacterial Pathogen.

How populations of growing cells achieve cell-size homeostasis remains a major question in cell biology. Recent studies in rod-shaped bacteria support the "incremental rule" where each cell adds a constant length before dividing. Although this rule explains narrow cell-size distributions, its mechanism is still unknown. We show that the opportunistic pathogen Pseudomonas aeruginosa obeys the incremental rule to achieve cell-length homeostasis during exponential growth but shortens its cells when entering the stationary phase. We identify a mutant, called frik, which has increased antibiotic sensitivity, cells that are on average longer, and a fraction of filamentous cells longer than 10 µm. When growth slows due to entry in stationary phase, the distribution of frik cell sizes decreases and approaches wild-type length distribution. The rare filamentous cells have abnormally large nucleoids, suggesting that a deficiency in DNA segregation prevents cell division without slowing the exponential elongation rate.

Hyperswarming adaptations in a bacterium improve collective motility without enhancing single cell motility.

Pseudomonas aeruginosa is a monoflagellated bacterium that can use its single polar flagellum to swim through liquids and move collectively over semisolid surfaces, a behavior called swarming. Previous studies have shown that experimental evolution in swarming colonies leads to the selection of hyperswarming bacteria with multiple flagella. Here we show that the advantage of such hyperswarmer mutants cannot be explained simply by an increase in the raw swimming speed of individual bacteria in liquids. Cell tracking of time-lapse microscopy to quantify single-cell swimming patterns reveals that both wild-type and hyperswarmers alternate between forward and backward runs, rather than doing the run-and-tumble characteristic of enteric bacteria such as E. coli. High-throughput measurement of swimming speeds reveals that hyperswarmers do not swim faster than wild-type in liquid. Wild-type reverses swimming direction in sharp turns without a significant impact on its speed, whereas multiflagellated hyperswarmers tend to alternate fast and slow runs and have wider turning angles. Nonetheless, macroscopic measurement of swimming and swarming speed in colonies shows that hyperswarmers expand faster than wild-type on surfaces and through soft agar matrices. A mathematical model explains how wider turning angles lead to faster spreading when swimming through agar. Our study describes for the first time the swimming patterns in multiflagellated P. aeruginosa mutants and reveals that collective and individual motility in bacteria are not necessarily correlated. Understanding bacterial adaptations to surface motility, such as hyperswarming, requires a collective behavior approach.

The ecological basis of morphogenesis: branching patterns in swarming colonies of bacteria.

Understanding how large-scale shapes in tissues, organs and bacterial colonies emerge from local interactions among cells and how these shapes remain stable over time are two fundamental problems in biology. Here we investigate branching morphogenesis in an experimental model system, swarming colonies of the bacterium Pseudomonas aeruginosa. We combine experiments and computer simulation to show that a simple ecological model of population dispersal can describe the emergence of branching patterns. In our system, morphogenesis depends on two counteracting processes that act on different length-scales: (1) colony expansion, which increases the likelihood of colonizing a patch at a close distance and (2) colony repulsion, which decreases the colonization likelihood over a longer distance. The two processes are included in a kernel based mathematical model using an integro-differential approach borrowed from ecological theory. Computer simulations show that the model can indeed reproduce branching, but only for a narrow range of parameter values, suggesting that P. aeruginosa has a fine-tuned physiology for branching. Simulations further show that hyperswarming, a process where highly dispersive mutants reproducibly arise within the colony and disrupt branching patterns, can be interpreted as a change in the spatial kernel.

High-resolution time series of Pseudomonas aeruginosa gene expression and rhamnolipid secretion through growth curve synchronization.

BACKGROUND: Online spectrophotometric measurements allow monitoring dynamic biological processes with high-time resolution. Contrastingly, numerous other methods require laborious treatment of samples and can only be carried out offline. Integrating both types of measurement would allow analyzing biological processes more comprehensively. A typical example of this problem is acquiring quantitative data on rhamnolipid secretion by the opportunistic pathogen Pseudomonas aeruginosa. P. aeruginosa cell growth can be measured by optical density (OD600) and gene expression can be measured using reporter fusions with a fluorescent protein, allowing high time resolution monitoring. However, measuring the secreted rhamnolipid biosurfactants requires laborious sample processing, which makes this an offline measurement. RESULTS: Here, we propose a method to integrate growth curve data with endpoint measurements of secreted metabolites that is inspired by a model of exponential cell growth. If serial diluting an inoculum gives reproducible time series shifted in time, then time series of endpoint measurements can be reconstructed using calculated time shifts between dilutions. We illustrate the method using measured rhamnolipid secretion by P. aeruginosa as endpoint measurements and we integrate these measurements with high-resolution growth curves measured by OD600 and expression of rhamnolipid synthesis genes monitored using a reporter fusion. Two-fold serial dilution allowed integrating rhamnolipid measurements at a ~0.4 h-1 frequency with high-time resolved data measured at a 6 h-1 frequency. We show how this simple method can be used in combination with mutants lacking specific genes in the rhamnolipid synthesis or quorum sensing regulation to acquire rich dynamic data on P. aeruginosa virulence regulation. Additionally, the linear relation between the ratio of inocula and the time-shift between curves produces high-precision measurements of maximum specific growth rates, which were determined with a precision of ~5.4%. CONCLUSIONS: Growth curve synchronization allows integration of rich time-resolved data with endpoint measurements to produce time-resolved quantitative measurements. Such data can be valuable to unveil the dynamic regulation of virulence in P. aeruginosa. More generally, growth curve synchronization can be applied to many biological systems thus helping to overcome a key obstacle in dynamic regulation: the scarceness of quantitative time-resolved data.

Optimization and quantification of protein synthesis inside liposomes.

Synthetic biology aims at reprogramming existing, or creating new, biological systems, with the ultimate aim to obtain artificial cells whose functions can be tailored. For the latter, encapsulation of complex biochemical reactions into cell-sized compartments, such as liposomes, is required. Recently, several groups have demonstrated that proteins of interest can be produced de novo within liposomes by entrapping cell-free protein-synthesis systems and DNA templates inside liposomes. Although detectable, intraliposomal protein synthesis was generally poor. Here, we have optimized intraliposomal cell-free protein synthesis by changing several variables, including lipid composition as well as liposome, pyrophosphatase, and T7 RNA polymerase concentration. Further, by using an activity-based assay, we have quantified the amount of full-length protein that was produced from DNA templates inside liposomes before and after optimization of aforementioned variables. Based on the model protein beta-galactosidase, it is demonstrated that liposomal protein synthesis can yield microgram quantities of protein (30-40 microg/mL liposomes).

Seeing is believing: what experiments with microbes reveal about evolution.

Darwin's theory of natural selection is among the most powerful ideas in science, yet evolutionary ideas remain challenged to this day. This is in part because evolution often cannot be directly observed. Simple experiments with microbes can change that by enabling direct observation of evolutionary processes.

Exploiting social evolution in biofilms.

Bacteria are highly social organisms that communicate via signaling molecules, move collectively over surfaces and make biofilm communities. Nonetheless, our main line of defense against pathogenic bacteria consists of antibiotics-drugs that target individual-level traits of bacterial cells and thus, regrettably, select for resistance against their own action. A possible solution lies in targeting the mechanisms by which bacteria interact with each other within biofilms. The emerging field of microbial social evolution combines molecular microbiology with evolutionary theory to dissect the molecular mechanisms and the evolutionary pressures underpinning bacterial sociality. This exciting new research can ultimately lead to new therapies against biofilm infections that exploit evolutionary cheating or the trade-off between biofilm formation and dispersal.

Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria.

Most bacteria in nature live in surface-associated communities rather than planktonic populations. Nonetheless, how surface-associated environments shape bacterial evolutionary adaptation remains poorly understood. Here, we show that subjecting Pseudomonas aeruginosa to repeated rounds of swarming, a collective form of surface migration, drives remarkable parallel evolution toward a hyperswarmer phenotype. In all independently evolved hyperswarmers, the reproducible hyperswarming phenotype is caused by parallel point mutations in a flagellar synthesis regulator, FleN, which locks the naturally monoflagellated bacteria in a multiflagellated state and confers a growth rate-independent advantage in swarming. Although hyperswarmers outcompete the ancestral strain in swarming competitions, they are strongly outcompeted in biofilm formation, which is an essential trait for P. aeruginosa in environmental and clinical settings. The finding that evolution in swarming colonies reliably produces evolution of poor biofilm formers supports the existence of an evolutionary trade-off between motility and biofilm formation.