Spatial Heterogeneity of Autoinducer Regulation Systems

Autoinducer signals enable coordinated behaviour of bacterial populations, a phenomenon originally described as quorum sensing. Autoinducer systems are often controlled by environmental substances as nutrients or secondary metabolites (signals) from neighbouring organisms. In cell aggregates and biofilms gradients of signals and environmental substances emerge. Mathematical modelling is used to analyse the functioning of the system. We find that the autoinducer regulation network generates spatially heterogeneous behaviour, up to a kind of multicellularity-like division of work, especially under nutrient-controlled conditions. A hybrid push/pull concept is proposed to explain the ecological function. The analysis allows to explain hitherto seemingly contradicting experimental findings

Quorum sensing as a mechanism to harness the wisdom of the crowds

Bacteria release and sense small molecules called autoinducers in a process known as quorum sensing. The prevailing interpretation of quorum sensing is that by sensing autoinducer concentrations, bacteria estimate population density to regulate the expression of functions that are only beneficial when carried out by a sufficiently large number of cells. However, a major challenge to this interpretation is that the concentration of autoinducers strongly depends on the environment, often rendering autoinducer-based estimates of cell density unreliable. Here we propose an alternative interpretation of quorum sensing, where bacteria, by releasing and sensing autoinducers, harness social interactions to sense the environment as a collective. Using a computational model we show that this functionality can explain the evolution of quorum sensing and arises from individuals improving their estimation accuracy by pooling many imperfect estimates – analogous to the ‘wisdom of the crowds’ in decision theory. Importantly, our model reconciles the observed dependence of quorum sensing on both population density and the environment and explains why several quorum sensing systems regulate the production of private goods.</p

Quorum sensing dynamics in the alpha-proteobacterium Sinorhizobium meliloti at the single-cell and population level

In quorum sensing, bacteria produce and release so-called autoinducers that accumulate in the environment while the cells grow. Once these molecules reach a threshold concentration, they trigger major behavioral changes in the population. Since the triggered behaviors are thought to be effective only when performed by a large enough group, autoinducers are generally taken to indicate when this sufficient cell density has been reached. However, little is known about how these components interact dynamically at the single-cell level to fulfill their task of cell-cell communication. Furthermore, quorum sensing is often studied in well-shaken liquid cultures, but little is known about autoinducer dispersal and response dynamics over larger distances in physiological niches like the rhizosphere where active mixing is negligible. The aim of this work therefore was to investigate these aspects in the model organism Sinorhizobium meliloti. In (Bettenworth et al., 2022.), quorum sensing dynamics were investigated with respect to autoinducer synthase gene expression in single cells and the timing of the response in the respective colonies. Surprisingly, in S. meliloti the autoinducer synthase gene is not expressed continuously, but in discrete stochastic pulses. Stochasticity stems from scarcity and, presumably, low binding affinity of the essential transcription activator. Physiological factors modulate abundance of this activator or its binding affinity to the autoinducer synthase gene promoter and thereby modulate gene expression pulse frequency. Higher or lower pulse frequencies in turn trigger the onset of the quorum sensing response at lower or higher cell numbers, respectively. In other words: S. meliloti quorum sensing is based on a stochastic regulatory system that encodes each cell’s physiological condition in the pulse frequency with which it expresses its autoinducer synthase gene; pulse frequencies of all members of a population are then integrated in the common pool of autoinducers. Only once this vote crosses the threshold, the response behavior is initiated. Consequently, S. meliloti quorum sensing is not so much a matter of counting cell numbers as suggested by the analogy of the quorum, but more comparable to a voting in a local community, or the collective decision-making described for social insects (Bettenworth et al., 2022). In (Bettenworth et al., 2018), the dynamics of autoinducer dispersal by diffusion in a two-dimensional environment were explored. At first sight, diffusive spreading should yield a dilution of the molecules and, with increasing distance from the source, slow down progression of the concentration level necessary to trigger a response in distantly located receiver cells. In contrast to this expectation, however, this threshold concentration did not decelerate in respective experiments, but instead travelled with constant speed, comparable to front propagation in pattern-forming systems. According to a mathematical model, this effect was due to the exponential growth of the sender cells which yielded adding-up of an exponentially growing number of autoinducer concentration profiles, thus compensating for the thinning effect of diffusion. Consequently, even a single sender colony could induce a response in receiver cells up to 7 mm away (Bettenworth et al., 2018)

Quorum Sensing in the Context of Food Microbiology

Food spoilage may be defined as a process that renders a product undesirable or unacceptable for consumption and is the outcome of the biochemical activity of a microbial community that eventually dominates according to the prevailing ecological determinants. Although limited information are reported, this activity has been attributed to quorum sensing (QS). Consequently, the potential role of cell-to-cell communication in food spoilage and food safety should be more extensively elucidated. Such information would be helpful in designing approaches for manipulating these communication systems, thereby reducing or preventing, for instance, spoilage reactions or even controlling the expression of virulence factors. Due to the many reports in the literature on the fundamental features of QS, e.g., chemistry and definitions of QS compounds, in this minireview, we only allude to the types and chemistry of QS signaling molecules per se and to the (bioassay-based) methods of their detection and quantification, avoiding extensive documentation. Conversely, we attempt to provide insights into (i) the role of QS in food spoilage, (ii) the factors that may quench the activity of QS in foods and review the potential QS inhibitors that might “mislead” the bacterial coordination of spoilage activities and thus may be used as biopreservatives, and (iii) the future experimental approaches that need to be undertaken in order to explore the “gray” or “black” areas of QS, increase our understanding of how QS affects microbial behavior in foods, and assist in finding answers as to how we can exploit QS for the benefit of food preservation and food safety

A 3D computational model for investigating the Spatial heterogeneity of Polymicrobial biofilm using K-means Clustering

Biofilm is considered for the cause of many microbial infections which range from Cystic fibrosis to middle ear infections. The biofilm mode of growth is more significant than planktonic as the biofilm mode of growth is protected from acidic PH, host immune responses, metal toxicity and antibiotics. Antibiotic resistance is becoming a bigger threat to mankind than cancer. The inter and intra species communications play a vital role in the growth dynamics and survival of the biofilm. The emergence of Small colony variants by phenotypic switching from S.aureus in presence of 4- Hydroxy-2-heptylquinoline N-oxide (HQNO) which is produced by P.aeruginosa play a superior role because of its inherent resilience and host adaptability

Transition to Quorum Sensing in an Agrobacterium Population: A Stochastic Model

Understanding of the intracellular molecular machinery that is responsible for the complex collective behavior of multicellular populations is an exigent problem of modern biology. Quorum sensing, which allows bacteria to activate genetic programs cooperatively, provides an instructive and tractable example illuminating the causal relationships between the molecular organization of gene networks and the complex phenotypes they control. In this work we—to our knowledge for the first time—present a detailed model of the population-wide transition to quorum sensing using the example of Agrobacterium tumefaciens. We construct a model describing the Ti plasmid quorum-sensing gene network and demonstrate that it behaves as an “on–off” gene expression switch that is robust to molecular noise and that activates the plasmid conjugation program in response to the increase in autoinducer concentration. This intracellular model is then incorporated into an agent-based stochastic population model that also describes bacterial motion, cell division, and chemical communication. Simulating the transition to quorum sensing in a liquid medium and biofilm, we explain the experimentally observed gradual manifestation of the quorum-sensing phenotype by showing that the transition of individual model cells into the “on” state is spread stochastically over a broad range of autoinducer concentrations. At the same time, the population-averaged values of critical autoinducer concentration and the threshold population density are shown to be robust to variability between individual cells, predictable and specific to particular growth conditions. Our modeling approach connects intracellular and population scales of the quorum-sensing phenomenon and provides plausible answers to the long-standing questions regarding the ecological and evolutionary significance of the phenomenon. Thus, we demonstrate that the transition to quorum sensing requires a much higher threshold cell density in liquid medium than in biofilm, and on this basis we hypothesize that in Agrobacterium quorum sensing serves as the detector of biofilm formation

A 3D individual-based model to investigate the spatially heterogeneous response of bacterial biofilms to antimicrobial agents

The response of bacterial biofilms to treatment with antimicrobial agents is often characterized by the emergence of recalcitrant cellular microcolonies. We present an individual-based model to investigate the biophysical mechanisms of the selective resistance that arises within the biofilm and leads to a spatially heterogeneous response upon treatment with antibiotics. The response occurs in 3 distinct phases. In the first phase, the subpopulation of metabolically active cells diminishes due to antibiotic-induced cell death. Subsequently, in the second phase, increased nutrient availability allows dormant cells in the lower layers of the biofilm to transform into metabolically active cells. In the third phase, survival of the biofilm is governed by the interplay between 2 contrasting factors: (1) rate of antibiotic-induced cell death and (2) rate of transformation of dormant cells into active ones. Metabolically active cells at the distal edge of the biofilm sacrifice themselves to protect the dormant cells in the interior by (1) reducing local antibiotic concentrations and (2) increasing nutrient availability. In the presence of quorum sensing, biofilms exhibit increased tolerance compared with the quorum sensing-negative strains. Extracellular polymeric substance (EPS) forms a protective layer at the top of the biofilm, thereby limiting antibiotic penetration. The surviving cells, in turn, produce EPS resulting in a feedback-like mechanism of resistance. Whereas resistance in QS- biofilms occurs because of transformation of dormant cells into metabolically active cells, this transformation is less pronounced in QS+ biofilms, and resistance is a consequence of the sequestration of the antibiotic by EPS

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model

This is the author accepted manuscript. The final version is available from American Chemical Society via the DOI in this record.We extend a spatially explicit agent based model (ABM) developed previously to investigate entrainment and control of the emergent behavior of a population of synchronized oscillating cells in a microfluidic chamber. Unlike most of the work in models of control of cellular systems which focus on temporal changes, we model individual cells with spatial dependencies which may contribute to certain behavioral responses. We use the model to investigate the response of both open loop and closed loop strategies, such as proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control), to heterogeinities and growth in the cell population, variations of the control parameters and spatial effects such as diffusion in the spatially explicit setting of a microfluidic chamber setup. We show that, as expected from the theory of phase locking in dynamical systems, open loop control can only entrain the cell population in a subset of forcing periods, with a wide variety of dynamical behaviors obtained outside these regions of entrainment. Closed-loop control is shown instead to guarantee entrainment in a much wider region of control parameter space although presenting limitations when the population size increases over a certain threshold. In silico tracking experiments are also performed to validate the ability of classical control approaches to achieve other reference behaviors such as a desired constant output or a linearly varying one. All simulations are carried out in BSim, an advanced agent-based simulator of microbial population which is here extended ad hoc to include the effects of control strategies acting onto the population.The authors declare no competing interests. We thank Dr. Nigel J. Savery at the University of Bristol for useful discussions around the subject of GRNs and for his help in developing the original ABM model. We also wish to thank Dr Gianfranco Fiore at the University of Bristol and the anonymous reviewers for reading the revised manuscript carefully and providing insightful comments that led to a consistent revision of the original manuscript. P.M. was supported by EPSRC Grant EP/E501214/1 and K.T.-A. by EPSRC Grant EP/I018638/1. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol, http://www.bris.ac.uk/acrc/