Distinct types of multicellular aggregates in Pseudomonas aeruginosa liquid cultures.

Pseudomonas aeruginosa forms suspended multicellular aggregates when cultured in liquid media. These aggregates may be important in disease, and/or as a pathway to biofilm formation. The polysaccharide Psl and extracellular DNA (eDNA) have both been implicated in aggregation, but previous results depend strongly on the experimental conditions. Here we develop a quantitative microscopy-based method for assessing changes in the size distribution of suspended aggregates over time in growing cultures. For exponentially growing cultures of P. aeruginosa PAO1, we find that aggregation is mediated by cell-associated Psl, rather than by either eDNA or secreted Psl. These aggregates arise de novo within the culture via a growth process that involves both collisions and clonal growth, and Psl non-producing cells do not aggregate with producers. In contrast, we find that stationary phase (overnight) cultures contain a different type of multicellular aggregate, in which both eDNA and Psl mediate cohesion. Our findings suggest that the physical and biological properties of multicellular aggregates may be very different in early-stage vs late-stage bacterial cultures.

Active layer dynamics drives a transition to biofilm fingering.

The emergence of spatial organisation in biofilm growth is one of the most fundamental topics in biofilm biophysics and microbiology. It has long been known that growing biofilms can adopt smooth or rough interface morphologies, depending on the balance between nutrient supply and microbial growth; this 'fingering' transition has been linked with the average width of the 'active layer' of growing cells at the biofilm interface. Here we use long-time individual-based simulations of growing biofilms to investigate in detail the driving factors behind the biofilm-fingering transition. We show that the transition is associated with dynamical changes in the active layer. Fingering happens when gaps form in the active layer, which can cause local parts of the biofilm interface to pin, or become stationary relative to the moving front. Pinning can be transient or permanent, leading to different biofilm morphologies. By constructing a phase diagram for the transition, we show that the controlling factor is the magnitude of the relative fluctuations in the active layer thickness, rather than the active layer thickness per se. Taken together, our work suggests a central role for active layer dynamics in controlling the pinning of the biofilm interface and hence biofilm morphology.

Amplification Free Detection of SARS-CoV-2 Using Multi-Valent Binding.

We present the development of electrochemical impedance spectroscopy (EIS)-based biosensors for sensitive detection of SARS-CoV-2 RNA using multi-valent binding. By increasing the number of probe-target binding events per target molecule, multi-valent binding is a viable strategy for improving the biosensor performance. As EIS can provide sensitive and label-free measurements of nucleic acid targets during probe-target hybridization, we used multi-valent binding to build EIS biosensors for targeting SARS-CoV-2 RNA. For developing the biosensor, we explored two different approaches including probe combinations that individually bind in a single-valent fashion and the probes that bind in a multi-valent manner on their own. While we found excellent biosensor performance using probe combinations, we also discovered unexpected signal suppression. We explained the signal suppression theoretically using inter- and intra-probe hybridizations which confirmed our experimental findings. With our best probe combination, we achieved a LOD of 182 copies/µL (303 aM) of SARS-CoV-2 RNA and used these for successful evaluation of patient samples for COVID-19 diagnostics. We were also able to show the concept of multi-valent binding with shorter probes in the second approach. Here, a 13-nt-long probe has shown the best performance during SARS-CoV-2 RNA binding. Therefore, multi-valent binding approaches using EIS have high utility for direct detection of nucleic acid targets and for point-of-care diagnostics.

Model for Quorum-Sensing Mediated Stochastic Biofilm Nucleation.

Surface-attached bacterial biofilms cause disease and industrial biofouling, as well as being widespread in the natural environment. Density-dependent quorum sensing is one of the mechanisms implicated in biofilm initiation. Here we present and analyze a model for quorum-sensing triggered biofilm initiation. In our model, individual, planktonic bacteria adhere to a surface, proliferate, and undergo a collective transition to a biofilm phenotype. This model predicts a stochastic transition between a loosely attached, finite layer of bacteria near the surface and a growing biofilm. The transition is governed by two key parameters: the collective transition density relative to the carrying capacity and the immigration rate relative to the detachment rate. Biofilm initiation is complex, but our model suggests that stochastic nucleation phenomena may be relevant.

A computational model for microbial colonization of an antifouling surface.

Biofouling of marine surfaces such as ship hulls is a major industrial problem. Antifouling (AF) paints delay the onset of biofouling by releasing biocidal chemicals. We present a computational model for microbial colonization of a biocide-releasing AF surface. Our model accounts for random arrival from the ocean of microorganisms with different biocide resistance levels, biocide-dependent proliferation or killing, and a transition to a biofilm state. Our computer simulations support a picture in which biocide-resistant microorganisms initially form a loosely attached layer that eventually transitions to a growing biofilm. Once the growing biofilm is established, immigrating microorganisms are shielded from the biocide, allowing more biocide-susceptible strains to proliferate. In our model, colonization of the AF surface is highly stochastic. The waiting time before the biofilm establishes is exponentially distributed, suggesting a Poisson process. The waiting time depends exponentially on both the concentration of biocide at the surface and the rate of arrival of resistant microorganisms from the ocean. Taken together our results suggest that biofouling of AF surfaces may be intrinsically stochastic and hence unpredictable, but immigration of more biocide-resistant species, as well as the biological transition to biofilm physiology, may be important factors controlling the time to biofilm establishment.

Dynamic Diatom-Bacteria Consortia in Synthetic Plankton Communities.

Microalgae that form phytoplankton live and die in a complex microbial consortium in which they co-exist with bacteria and other microorganisms. The dynamics of species succession in the plankton depends on the interplay of these partners. Bacteria utilize substrates produced by the phototrophic algae, while algal growth can be supported by bacterial exudates. Bacteria might also use chemical mediators with algicidal properties to attack algae. To elucidate whether specific bacteria play universal or context-specific roles in the interaction with phytoplankton, we investigated the effect of cocultured bacteria on the growth of 8 microalgae. An interaction matrix revealed that the function of a given bacterium is highly dependent on the cocultured partner. We observed no universally algicidal or universally growth-promoting bacteria. The activity of bacteria can even change during the aging of an algal culture from inhibitory to stimulatory or vice versa. We further established a synthetic phytoplankton/bacteria community with the centric diatom, Coscinodiscus radiatus, and 4 phylogenetically distinctive bacterial isolates, Mameliella sp., Roseovarius sp., Croceibacter sp., and Marinobacter sp. Supported by a Lotka-Volterra model, we show that interactions within the consortium are specific and that the sum of the pairwise interactions can explain algal and bacterial growth in the community. No synergistic effects between bacteria in the presence of the diatom was observed. Our survey documents highly species-specific interactions that are dependent on algal fitness, bacterial metabolism, and community composition. This species specificity may underly the high complexity of the multi-species plankton communities observed in nature. IMPORTANCE The marine food web is fueled by phototrophic phytoplankton. These algae are central primary producers responsible for the fixation of ca. 40% of the global CO2. Phytoplankton always co-occur with a diverse bacterial community in nature. This diversity suggests the existence of ecological niches for the associated bacteria. We show that the interaction between algae and bacteria is highly species-specific. Furthermore, both, the fitness stage of the algae and the community composition are relevant in determining the effect of bacteria on algal growth. We conclude that bacteria should not be sorted into algicidal or growth supporting categories; instead, a context-specific function of the bacteria in the plankton must be considered. This functional diversity of single players within a consortium may underly the observed diversity in the plankton.

Lineage dynamics in growing biofilms: Spatial patterns of standing vs. de novo diversity.

Microbial biofilms show high phenotypic and genetic diversity, yet the mechanisms underlying diversity generation and maintenance remain unclear. Here, we investigate how spatial patterns of growth activity within a biofilm lead to spatial patterns of genetic diversity. Using individual-based computer simulations, we show that the active layer of growing cells at the biofilm interface controls the distribution of lineages within the biofilm, and therefore the patterns of standing and de novo diversity. Comparing biofilms of equal size, those with a thick active layer retain more standing diversity, while de novo diversity is more evenly distributed within the biofilm. In contrast, equal-sized biofilms with a thin active layer retain less standing diversity, and their de novo diversity is concentrated at the top of the biofilm, and in fewer lineages. In the context of antimicrobial resistance, biofilms with a thin active layer may be more prone to generate lineages with multiple resistance mutations, and to seed new resistant biofilms via sloughing of resistant cells from the upper layers. Our study reveals fundamental "baseline" mechanisms underlying the patterning of diversity within biofilms.

Tracking the stochastic growth of bacterial populations in microfluidic droplets.

Bacterial growth in microfluidic droplets is relevant in biotechnology, in microbial ecology, and in understanding stochastic population dynamics in small populations. However, it has proved challenging to automate measurement of absolute bacterial numbers within droplets, forcing the use of proxy measures for population size. Here we present a microfluidic device and imaging protocol that allows high-resolution imaging of thousands of droplets, such that individual bacteria stay in the focal plane and can be counted automatically. Using this approach, we track the stochastic growth of hundreds of replicateEscherichia colipopulations within droplets. We find that, for early times, the statistics of the growth trajectories obey the predictions of the Bellman-Harris model, in which there is no inheritance of division time. Our approach should allow further testing of models for stochastic growth dynamics, as well as contributing to broader applications of droplet-based bacterial culture.

Staff and patient perceptions of a community urinary catheter service.

INTRODUCTION: Urinary catheters are used extensively throughout healthcare for various reasons including management of urinary tract dysfunction. The purpose of this study was to simultaneously explore both catheter user experience and staff perception of catheter services within community urinary catheter care. METHODS: A questionnaire was conducted to investigate the views of community nursing staff. During the same time period, patients were interviewed about i) catheter-care standards and adherence to guidelines ii) patients' feelings towards their catheter and iii) potential improvements to catheter practices and design. RESULTS: Sixty-nine staff were surveyed. Although 97% of staff indicated they used local guidelines, in up to 62% of cases findings suggested practices in sending urine samples for culture did not comply with guidelines. Seventy-five percent of staff were satisfied with catheter care, but weaknesses were identified in handover processes, communication between staff and patients, and excessive documentation. Staff results were compared with the findings from interviews of 29 long-term urinary catheter users, demonstrating a higher level of satisfaction with catheter care amongst patients (86%). Patients and staff agreed that generally the impacts of their catheter on personal hygiene, sense of independence, sense of dignity and of patient happiness, were neutral (neither positive nor negative). However, regarding improvements to catheter practices and catheter design; 73% of staff but only 45% of patients suggested improvements in service, while 76% of patients but only 49% of staff suggested improvement in design. CONCLUSION: The study reveals general satisfaction with community catheter care, but indicates areas of potential improvements regarding communication, documentation and catheter design. When compared to patient responses, staff overall had a less positive view of patients perception of their relationship with their catheter.

Stability of ß-lactam antibiotics in bacterial growth media.

Laboratory assays such as MIC tests assume that antibiotic molecules are stable in the chosen growth medium-but rapid degradation has been observed for antibiotics including ß-lactams under some conditions in aqueous solution. Degradation rates in bacterial growth medium are less well known. Here, we develop a 'delay time bioassay' that provides a simple way to estimate antibiotic stability in bacterial growth media, using only a plate reader and without the need to measure the antibiotic concentration directly. We use the bioassay to measure degradation half-lives of the ß-lactam antibiotics mecillinam, aztreonam and cefotaxime in widely-used bacterial growth media based on MOPS and Luria-Bertani (LB) broth. We find that mecillinam degradation can occur rapidly, with a half-life as short as 2 hours in MOPS medium at 37°C and pH 7.4, and 4-5 hours in LB, but that adjusting the pH and temperature can increase its stability to a half-life around 6 hours without excessively perturbing growth. Aztreonam and cefotaxime were found to have half-lives longer than 6 hours in MOPS medium at 37°C and pH 7.4, but still shorter than the timescale of a typical minimum inhibitory concentration (MIC) assay. Taken together, our results suggest that care is needed in interpreting MIC tests and other laboratory growth assays for ß-lactam antibiotics, since there may be significant degradation of the antibiotic during the assay.

A Roadblock-and-Kill Mechanism of Action Model for the DNA-Targeting Antibiotic Ciprofloxacin.

Fluoroquinolones, antibiotics that cause DNA damage by inhibiting DNA topoisomerases, are clinically important, but their mechanism of action is not yet fully understood. In particular, the dynamical response of bacterial cells to fluoroquinolone exposure has hardly been investigated, although the SOS response, triggered by DNA damage, is often thought to play a key role. Here, we investigated the growth inhibition of the bacterium Escherichia coli by the fluoroquinolone ciprofloxacin at low concentrations. We measured the long-term and short-term dynamical response of the growth rate and DNA production rate to ciprofloxacin at both the population and single-cell levels. We show that, despite the molecular complexity of DNA metabolism, a simple roadblock-and-kill model focusing on replication fork blockage and DNA damage by ciprofloxacin-poisoned DNA topoisomerase II (gyrase) quantitatively reproduces long-term growth rates in the presence of ciprofloxacin. The model also predicts dynamical changes in the DNA production rate in wild-type E. coli and in a recombination-deficient mutant following a step-up of ciprofloxacin. Our work highlights that bacterial cells show a delayed growth rate response following fluoroquinolone exposure. Most importantly, our model explains why the response is delayed: it takes many doubling times to fragment the DNA sufficiently to inhibit gene expression. We also show that the dynamical response is controlled by the timescale of DNA replication and gyrase binding/unbinding to the DNA rather than by the SOS response, challenging the accepted view. Our work highlights the importance of including detailed biophysical processes in biochemical-systems models to quantitatively predict the bacterial response to antibiotics.

Predictable properties of fitness landscapes induced by adaptational tradeoffs.

Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape.

Drug resistant bacteria pose a major threat to public health systems all over the world. Darwinian evolution is at the heart of this drug resistance: a mutation that allows bacteria to divide in the presence of a drug appears initially in a single cell. This mutation makes this cell and its descendants more likely to survive, so they can end up taking over the population. The evolution of resistance can be thought of in terms of 'bacterial fitness landscapes'. These landscapes visualise the relationship between the mutations present in a population of bacteria and how quickly the bacteria divide or reproduce. They are called landscapes because they can be represented as a series of mountains and valleys. The peaks of this landscape represent combinations of mutations that give bacteria the greatest chance of dividing (the greatest fitness). In a landscape with multiple peaks, some peaks will be higher than others. If the landscape is smooth, bacteria can easily acquire mutations for drug resistance. However, in a rugged landscape, bacteria may get stuck at sub-optimal peaks, because the mutations that would enable them to reach a higher peak would first lead them to losing fitness. Several studies on the evolution of antibiotic resistance exist for specific bacteria and specific drugs, but relatively little is known about the general properties of the underlying fitness landscapes. Do these landscapes have features that can help explain the rapid evolution of high levels of resistance? Antibiotic resistance often comes at a cost ­ more resistant strains of bacteria tend to grow more slowly when the drug is absent. To build a model of antibiotic resistance landscapes, Das et al. performed growth experiments on several strains of Escherichia coli exposed to a drug called ciprofloxacin. They measured how the rate at which the bacteria divided changed at different antibiotic concentrations, and combined this with the observation about resistant strains growing slower to formulate a mathematical model of antibiotic resistance landscapes. The landscapes that resulted were found to be very rugged, but unexpectedly, the bacteria could still evolve to access all fitness peaks. This means that landscape ruggedness does not constrain the evolution of resistance. Understanding how and when resistance evolves is important both for the design of new drugs and the development of treatment protocols. A specific prediction of the model is that resistance evolution in fitness landscapes where resistant strains divide more slowly is reversible. This implies that the bacteria could regain their susceptibility to treatment when the drug concentration decreases, but this would depend on the specific bacteria and drug in question. More broadly, the model provides a framework for addressing the evolution of resistance in clinical and environmental settings, where drug concentrations vary widely in time and space.

Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications.

Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

Computational design of probes to detect bacterial genomes by multivalent binding.

Rapid methods for diagnosis of bacterial infections are urgently needed to reduce inappropriate use of antibiotics, which contributes to antimicrobial resistance. In many rapid diagnostic methods, DNA oligonucleotide probes, attached to a surface, bind to specific nucleotide sequences in the DNA of a target pathogen. Typically, each probe binds to a single target sequence; i.e., target-probe binding is monovalent. Here we show using computer simulations that the detection sensitivity and specificity can be improved by designing probes that bind multivalently to the entire length of the pathogen genomic DNA, such that a given probe binds to multiple sites along the target DNA. Our results suggest that multivalent targeting of long pieces of genomic DNA can allow highly sensitive and selective binding of the target DNA, even if competing DNA in the sample also contains binding sites for the same probe sequences. Our results are robust to mild fragmentation of the bacterial genome. Our conclusions may also be relevant for DNA detection in other fields, such as disease diagnostics more broadly, environmental management, and food safety.

A simulation study of aggregation mediated by production of cohesive molecules.

Mechanical interactions between biological cells can be mediated by secreted products. Here, we investigate how such a scenario could affect the cells' collective behaviour. We show that if the concentration field of secreted products around a cell can be considered to be in steady state, this scenario can be mapped onto an effective attractive interaction that depends on the local cell density. Using a field-theory approach, this density-dependent attraction gives rise to a cubic term in the Landau-Ginzburg free energy density. In continuum field simulations this can lead to "nucleation-like" appearance of homogeneous clusters in the spinodal phase separation regime. Implementing the density-dependent cohesive attraction in Brownian dynamics simulations of a particle-based model gives rise to similar "spinodal nucleation" phase separation behaviour.

Growth-dependent drug susceptibility can prevent or enhance spatial expansion of a bacterial population.

As a population wave expands, organisms at the tip typically experience plentiful nutrients while those behind the front become nutrient-depleted. If the environment also contains a gradient of some inhibitor (e.g. a toxic drug), a tradeoff exists: the nutrient-rich tip is more exposed to the inhibitor, while the nutrient-starved region behind the front is less exposed. Here we show that this can lead to complex dynamics when the organism's response to the inhibitory substance is coupled to nutrient availability. We model a bacterial population which expands in a spatial gradient of antibiotic, under conditions where either fast-growing bacteria at the wave's tip, or slow-growing, resource-limited bacteria behind the front are more susceptible to the antibiotic. We find that growth-rate dependent susceptibility can have strong effects on the dynamics of the expanding population. If slow-growing bacteria are more susceptible, the population wave advances far into the inhibitory zone, leaving a trail of dead bacteria in its wake. In contrast, if fast-growing bacteria are more susceptible, the wave is blocked at a much lower concentration of antibiotic, but a large population of live bacteria remains behind the front. Our results may contribute to understanding the efficacy of different antimicrobials for spatially structured microbial populations such as biofilms, as well as the dynamics of ecological population expansions more generally.

Bacterial growth: a statistical physicist's guide.

Bacterial growth presents many beautiful phenomena that pose new theoretical challenges to statistical physicists, and are also amenable to laboratory experimentation. This review provides some of the essential biological background, discusses recent applications of statistical physics in this field, and highlights the potential for future research.

Oxic-anoxic regime shifts mediated by feedbacks between biogeochemical processes and microbial community dynamics.

Although regime shifts are known from various ecosystems, the involvement of microbial communities is poorly understood. Here we show that gradual environmental changes induced by, for example, eutrophication or global warming can induce major oxic-anoxic regime shifts. We first investigate a mathematical model describing interactions between microbial communities and biogeochemical oxidation-reduction reactions. In response to gradual changes in oxygen influx, this model abruptly transitions between an oxic state dominated by cyanobacteria and an anoxic state with sulfate-reducing bacteria and phototrophic sulfur bacteria. The model predictions are consistent with observations from a seasonally stratified lake, which shows hysteresis in the transition between oxic and anoxic states with similar changes in microbial community composition. Our results suggest that hysteresis loops and tipping points are a common feature of oxic-anoxic transitions, causing rapid drops in oxygen levels that are not easily reversed, at scales ranging from small ponds to global oceanic anoxic events.The role of microbial communities in regime shifts is poorly understood. Here, the authors use a mathematical model and field data from a seasonally stratified lake to show that gradual environmental changes can induce oxic-anoxic regime shifts mediated by microbial community dynamics and redox processes.

Assembly of microbial communities in replicate nutrient-cycling model ecosystems follows divergent trajectories, leading to alternate stable states.

We studied in detail the reproducibility of community development in replicate nutrient-cycling microbial microcosms that were set up identically and allowed to develop under the same environmental conditions. Multiple replicate closed microcosms were constructed using pond sediment and water, enriched with cellulose and sulphate, and allowed to develop over several months under constant environmental conditions, after which their microbial communities were characterized using 16S rRNA gene sequencing. Our results show that initially similar microbial communities can follow alternative - yet stable - trajectories, diverging in time in a system size-dependent manner. The divergence between replicate communities increased in time and decreased with larger system size. In particular, notable differences emerged in the heterotrophic degrader communities in our microcosms; one group of steady state communities was enriched with Firmicutes, while the other was enriched with Bacteroidetes. The communities dominated by these two phyla also contained distinct populations of sulphate-reducing bacteria. This biomodality in community composition appeared to arise during recovery from a low-diversity state that followed initial cellulose degradation and sulphate reduction.

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics.

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance-yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding ('low-affinity antibiotic') or, in contrast, irreversible transport and/or high affinity ribosome binding ('high-affinity antibiotic'). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.