Sensitivity of the quorum sensing system is achieved by low pass filtering

Pseudomonas fuscovaginae quorum sensing studies: 5% dominates cell-to-cell conversations

A common feature of N-acyl-l-homoserine lactone (AHL) quorum-sensing (QS) systems is that the AHL signal is autoinducing. Once induced, a cell will further amplify the signal via a positive feedback loop. Pseudomonas fuscovaginae UPB0736 has two fully functional AHL QS systems, called PfsI/R and PfvI/R, which are inactive in a standard laboratory setting. In this work, we induce the QS systems with exogenous AHL signals and characterize the AHL signal amplification effect and QS activation dynamics at community and single-cell level. While the cognate signal is in both cases significantly further amplified to physiologically relevant levels, we observe only a limited response in terms of AHL synthase gene promoter activity. Additionally, the PfsI/R QS system exhibits a unique dramatic phenotypic heterogeneity, where only up to 5% of all cells amplify the signal further and are, thus, considered to be QS active.

IMPORTANCE

Bacteria use N-acyl-l-homoserine lactone (AHL) quorum-sensing (QS) systems for population-wide phenotypic coordination. The QS configuration in Pseudomonas fuscovaginae is dramatically different from other model examples of AHL QS signaling and, thus, represents an important exception to the norm, which usually states that QS triggers population-wide phenotypic transitions in relation to cell density. We argue that the differences in QS dynamics of P. fuscovaginae highlight its different evolutionary purpose, which is ultimately dictated by the selective pressures of its natural habitat. We hope that this example will further expand our understanding of the complex and yet unknown QS-enabled sociomicrobiology. Furthermore, we argue that exemptions to the QS norm will be found in other plant-pathogenic bacterial strains that grow in similar environments and that molecularly similar QS systems do not necessarily share a similar evolutionary purpose; therefore, generalizations about bacterial cell-to-cell signaling systems function should be avoided.

A pore-scale reactive transport modeling study for quorum sensing-driven biofilm dispersal in heterogeneous porous media

Microorganisms regulate the expression of energetically expensive phenotypes via a collective decision-making mechanism known as quorum sensing (QS). This study investigates the intricate dynamics of biofilm growth and QS-controlled biofilm dispersal in heterogeneous porous media, employing a pore-scale reactive transport modeling approach. Model simulations carried out under various fluid flow conditions and biofilm growth scenarios reveal that QS processes are influenced not only by the biomass density of biofilm colonies but also by a complex interplay between pore architecture, flow velocity, and the rates of biofilm growth and dispersal. This study demonstrates that pore architecture controls the initiation of QS processes and advection gives rise to oscillatory growth of biofilms. Such oscillation is suppressed if biofilm dynamics are in favor of sustaining a sufficiently high signal concentration, such as fast growth or slow dispersal rates. By establishing a mathematical framework, this study contributes to the fundamental understanding of QS-controlled biofilm dynamics in complex environments.

Temporal Convolutional Network-Based Signal Detection for Magnetotactic Bacteria Communication System

Molecular communication (MC) aims to use signaling molecules as information carriers to achieve communication between biological entities. However, MC systems severely suffer from inter symbol interference (ISI) and external noise, making it virtually difficult to obtain accurate mathematical models. Specifically, the mathematically intractable channel state information (CSI) of MC motivates the deep learning (DL) based signal detection methods. In this paper, a modified temporal convolutional network (TCN) is proposed for signal detection for a special MC communication system which uses magnetotactic bacteria (MTB) as information carriers. Results show that the TCN-based detector demonstrates the best overall performance. In particular, it achieves better bit error rate (BER) performance than sub-optimal maximum a posteriori (MAP) and deep neural network (DNN) based detectors. However, it behaves similarly to the bidirectional long short term memory (BiLSTM) based detector that has been previously proposed and performs worse than the optimal MAP detector. When both BER performance and computational complexity are taken into account, the proposed TCN-based detector outperforms BiLSTM-based detectors. Furthermore, in terms of robustness evaluation, the proposed TCN-based detector outperforms all other DL-based detectors.

Parameters, architecture and emergent properties of the Pseudomonas aeruginosa LasI/LasR quorum-sensing circuit

Quorum sensing is a widespread process in bacteria that controls collective behaviours in response to cell density. Populations of cells coordinate gene expression through the perception of self-produced chemical signals. Although this process is well-characterized genetically and biochemically, quantitative information about network properties, including induction dynamics and steady-state behaviour, is scarce. Here we integrate experiments with mathematical modelling to quantitatively analyse the LasI/LasR quorum sensing pathway in the opportunistic pathogen Pseudomonas aeruginosa. We determine key kinetic parameters of the pathway and, using the parametrized model, show that quorum sensing behaves as a bistable hysteretic switch, with stable on and off states. We investigate the significance of feedback architecture and find that positive feedback on signal production is critical for induction dynamics and bistability, whereas positive feedback on receptor expression and negative feedback on signal production play a minor role. Taken together, our data-based modelling approach reveals fundamental and emergent properties of a bacterial quorum sensing circuit, and provides evidence that native quorum sensing can indeed function as the gene expression switch it is commonly perceived to be.

Emergent robustness of bacterial quorum sensing in fluid flow

Bacteria use intercellular signaling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment; consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans. To understand how QS genetic architectures in cells promote appropriate population-level phenotypes throughout the bacterial life cycle requires knowledge of how these architectures determine the QS response in realistic spatiotemporally varying flow conditions. Here we develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. We predict that when a subset of the population meets these conditions, cell-level positive feedback promotes a robust collective response by overcoming flow-induced autoinducer concentration gradients. By accounting for a dynamic flow in our theory, we predict that positive feedback in cells acts as a low-pass filter at the population level in oscillatory flow, allowing a population to respond only to changes in flow that occur over slow enough timescales. Our theory is readily extendable and provides a framework for assessing the functional roles of diverse QS network architectures in realistic flow conditions.

Burst statistics in an early biofilm quorum sensing model: the role of spatial colony-growth heterogeneity

Quorum-sensing bacteria in a growing colony of cells send out signalling molecules (so-called “autoinducers”) and themselves sense the autoinducer concentration in their vicinity. Once—due to increased local cell density inside a “cluster” of the growing colony—the concentration of autoinducers exceeds a threshold value, cells in this clusters get “induced” into a communal, multi-cell biofilm-forming mode in a cluster-wide burst event. We analyse quantitatively the influence of spatial disorder, the local heterogeneity of the spatial distribution of cells in the colony, and additional physical parameters such as the autoinducer signal range on the induction dynamics of the cell colony. Spatial inhomogeneity with higher local cell concentrations in clusters leads to earlier but more localised induction events, while homogeneous distributions lead to comparatively delayed but more concerted induction of the cell colony, and, thus, a behaviour close to the mean-field dynamics. We quantify the induction dynamics with quantifiers such as the time series of induction events and burst sizes, the grouping into induction families, and the mean autoinducer concentration levels. Consequences for different scenarios of biofilm growth are discussed, providing possible cues for biofilm control in both health care and biotechnology.

Ecology and evolution of metabolic cross-feeding interactions in bacteria

Literature covered: early 2000s to late 2017Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has shown that bacterial genotypes can significantly benefit from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight general principles that underlie the adaptive evolution of interconnected microbial metabolic networks as well as the evolutionary consequences that result for cells living in such communities.

Towards Concurrent Data Transmission: Exploiting Plasmid Diversity by Bacterial Conjugation

The progress of molecular communication (MC) is tightly connected to the progress of nanomachine design. State-of-the-art states that nanomachines can be built either from novel nanomaterials by the help of nanotechnology or they can be built from living cells which are modified to function as intended by synthetic biology. With the growing need of the biomedical applications of MC, we focus on developing bio-compatible communication systems by engineering the cells to become MC nanomachines. Since this approach relies on modifying cellular functions, the improvements in the performance can only be achieved by integrating new biological properties. A previously proposed model for molecular communication is using bacteria as information carriers between transmitters and receivers, also known as bacterial nanonetworks. This approach has suggested encoding information into the plasmids inserted into the bacteria which leads to extra overhead for the receivers to decode and analyze the plasmids to obtain the encoded information. Another scheme, which is proposed in this paper, is to determine the digital information transmitted based on the quantity of bacteria emitted. While this scheme has its simplicity, the major drawback is the low-data rate resulting from the long propagation of the bacteria. To improve the performance, this paper proposes a distributed modulation scheme utilizing three bacterial properties, namely, engineering of plasmids, conjugation, and bacterial motility. In particular, genetic engineering allows us to engineer the different combinations of genes representing the different series of bits. When compared with binary density modulation and the M-ary density modulation, it is shown that the distributed modulation scheme outperforms the other two approaches in terms of bit error probability as well as the achievable rate for varying quantity of bacteria transmitted, distances, as well as time slot length.

Single-cell resolution of intracellular T cell Ca2+ dynamics in response to frequency-based H2O2 stimulation

Adaptive immune cells, such as T cells, integrate information from their extracellular environment through complex signaling networks with exquisite sensitivity in order to direct decisions on proliferation, apoptosis, and cytokine production. These signaling networks are reliant on the interplay between finely tuned secondary messengers, such as Ca2+ and H2O2. Frequency response analysis, originally developed in control engineering, is a tool used for discerning complex networks. This analytical technique has been shown to be useful for understanding biological systems and facilitates identification of the dominant behaviour of the system. We probed intracellular Ca2+ dynamics in the frequency domain to investigate the complex relationship between two second messenger signaling molecules, H2O2 and Ca2+, during T cell activation with single cell resolution. Single-cell analysis provides a unique platform for interrogating and monitoring cellular processes of interest. We utilized a previously developed microfluidic device to monitor individual T cells through time while applying a dynamic input to reveal a natural frequency of the system at approximately 2.78 mHz stimulation. Although our network was much larger with more unknown connections than previous applications, we are able to derive features from our data, observe forced oscillations associated with specific amplitudes and frequencies of stimuli, and arrive at conclusions about potential transfer function fits as well as the underlying population dynamics.

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Bacterial quorum sensing (QS) refers to the process of cell-to-cell bacterial communication enabled through the production and sensing of the local concentration of small molecules called autoinducers to regulate the production of gene products (e.g. enzymes or virulence factors). Through autoinducers, bacteria interact with individuals of the same species, other bacterial species, and with their host. Among QS-regulated processes mediated through autoinducers are aggregation, biofilm formation, bioluminescence, and sporulation. Autoinducers are therefore "master" regulators of bacterial lifestyles. For over 10 years, mathematical modelling of QS has sought, in parallel to experimental discoveries, to elucidate the mechanisms regulating this process. In this review, we present the progress in mathematical modelling of QS, highlighting the various theoretical approaches that have been used and discussing some of the insights that have emerged. Modelling of QS has benefited almost from the onset of the involvement of experimentalists, with many of the papers which we review, published in non-mathematical journals. This review therefore attempts to give a broad overview of the topic to the mathematical biology community, as well as the current modelling efforts and future challenges.

Bright mutants of Vibrio fischeri ES114 reveal conditions and regulators that control bioluminescence and expression of the lux operon

Vibrio fischeri ES114, an isolate from the Euprymna scolopes light organ, produces little bioluminescence in culture but is ∼1,000-fold brighter when colonizing the host. Cell-density-dependent regulation alone cannot explain this phenomenon, because cells within colonies on solid medium are much dimmer than symbiotic cells despite their similar cell densities. To better understand this low luminescence in culture, we screened ∼20,000 mini-Tn5 mutants of ES114 for increased luminescence and identified 28 independent "luminescence-up" mutants with insertions in 14 loci. Mutations affecting the Pst phosphate uptake system led to the discovery that luminescence is upregulated under low-phosphate conditions by PhoB, and we also found that ainS, which encodes an autoinducer synthase, mediates repression of luminescence during growth on plates. Other novel luminescence-up mutants had insertions in acnB, topA, tfoY, phoQ, guaB, and two specific tRNA genes. Two loci, hns and lonA, were previously described as repressors of bioluminescence in transgenic Escherichia coli carrying the light-generating lux genes, and mutations in arcA and arcB were consistent with our report that Arc represses lux. Our results reveal a complex regulatory web governing luminescence and show how certain environmental conditions are integrated into regulation of the pheromone-dependent lux system.

A mathematical model of the Gac/Rsm quorum sensing network in Pseudomonas fluorescens

I present a deterministic model of the dynamics of signal transduction and gene expression in the Gac/Rsm network of the soil-dwelling bacterium Pseudomonas fluorescens. The network is involved in quorum sensing and governs antifungal production in this important biocontrol agent. A central role is played by small untranslated RNAs, which sequester regulatory mRNA-binding proteins. The model provides a reasonable match to the available data, which consists primarily of time series from reporter gene fusions. I use the model to investigate the information-processing properties of the Gac/Rsm network, in part by comparing it to a simplified model capable of quorum sensing. The results suggest that the complexity and redundancy of the Gac/Rsm network have evolved to meet the conflicting requirements of high sensitivity to environmental conditions and a conservative, robust response to variability in parameter values. Similar systems exist in a wide variety of bacteria, where they control a diverse set of population-dependent behaviors. This makes them important subjects for mathematical models that can help link empirical understanding of network structure to theoretical insights into how these networks have evolved to function under natural conditions.

A critical quantity for noise attenuation in feedback systems

Feedback modules, which appear ubiquitously in biological regulations, are often subject to disturbances from the input, leading to fluctuations in the output. Thus, the question becomes how a feedback system can produce a faithful response with a noisy input. We employed multiple time scale analysis, Fluctuation Dissipation Theorem, linear stability, and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus, and we obtained a critical quantity in noise attenuation, termed as "signed activation time". We then studied the signed activation time for a system of two positive feedback loops, a system of one positive feedback loop and one negative feedback loop, and six other existing biological models consisting of multiple components along with positive and negative feedback loops. An inverse relationship is found between the noise amplification rate and the signed activation time, defined as the difference between the deactivation and activation time scales of the noise-free system, normalized by the frequency of noises presented in the input. Thus, the combination of fast activation and slow deactivation provides the best noise attenuation, and it can be attained in a single positive feedback loop system. An additional positive feedback loop often leads to a marked decrease in activation time, decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation. On the other hand, a negative feedback loop may increase the activation and deactivation times. The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops. This principle may be applicable to other feedback systems.