Biology subjects

Rattray, J. B.

Publications and source records attributed to Rattray, J. B..

5 recordsLinked to original sources

bioRxiv2023

The las and rhl Quorum Sensing Systems in Pseudomonas aeruginosa Form a Multi-Signal Reciprocal Network Which Can Tune Reactivity to Variations in Physical and Social Environments

Researchers often view the multi-signal quorum sensing systems of Pseudomonas aeruginosa as a hierarchy, topped by the las system which acts as a master regulator. By experimentally controlling the concentration of auto-inducer signals in a signal null strain (PAO1{Delta}lasI{Delta}rhlI), we show that the two primary quorum sensing systems--las and rhl--act reciprocally rather than hierarchically. Just as the las systems 3-oxo-C12-HSL can induce increased expression of rhlI, the rhl systems C4-HSL increases the expression level of lasI. We develop a mathematical model to quantify relationships both within and between the las and rhl quorum sensing systems and the downstream genes they influence. The results show that not only do the systems interact reciprocally, but they do so cooperatively and nonlinearly, with the combination of C4-HSL and 3-oxo-C12-HSL increasing expression level far more than the sum of their individual effects. We computationally assess how our parameterized model responds to variation in social (population density) and physical (mass transfer) environment and demonstrate that a reciprocal architecture is more responsive to density and more robust to mass transfer than a strict hierarchy.

microbiology↗

bioRxiv2022

The dynamic response of quorum-sensing to density is robust to signal supplementation and signal synthase knockouts

Quorum sensing (QS) is a widespread mechanism of environment sensing and behavioral coordination in bacteria. At its core, QS is based on the production, sensing and response to small signaling molecules. Previous work with Pseudomonas aeruginosa shows that QS can be used to achieve quantitative resolution and deliver a dosed response to the bacterias density environment, implying a sophisticated mechanism of control. To shed light on how the mechanistic signal components contribute to graded responses to density, we assess the impact of genetic (AHL signal synthase deletion) and/or signal supplementation (exogenous AHL and PQS addition) perturbations on lasB reaction-norms to changes in density. Our approach condenses data from 2,000 timeseries (over 74,000 individual observations) into a comprehensive view of QS-controlled gene expression across variation in genetic, environmental, and signal determinants of lasB expression. We first confirm that deleting either ({Delta}lasI, {Delta}rhlI) or both ({Delta}lasIrhlI) signal synthase genes attenuates QS response. In the {Delta}rhlI background we show persistent yet attenuated density-dependent lasB expression due to native 3-oxo-C12 signaling. We then test if density-independent quantities of signal (3-oxo-C12, C4, PQS or combined) added to the WT either flatten or increase the reaction norm and find that the WT response is robust to all tested concentrations of signal, alone or in combination. We then move to progressively supplementing the genetic knockouts and find that cognate signal supplementation ({Delta}lasI+3-oxo-C12, {Delta}rhlI+C4) is sufficient to restore lasB expression and as well as reactivity to density encoded by the intact signal synthase. We also find that dual supplementation of the double synthase knockout restores expression but does not flatten the reaction norm. Despite adding a density-independent amount of AHL, the double signal synthase can still quantitively sense density. Our results show that a positive reaction norm to density is robust to multiple combinations of gene deletion and density-independent signal supplementation and that while density-independent signal supplementation can increase mean expression, the WT QS still retains the ability to quantitatively respond to density. Our work develops a modular approach to query the robustness and mechanistic bases of the central environmental sensing phenotype of quorum sensing.

microbiology↗

bioRxiv2022

Machine learning identification of Pseudomonas aeruginosa strains from colony image data

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual fingerprints that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data. Author SummarySince the birth of microbiology, scientists have looked at the patterns of bacterial growth on agar (colony morphology) as a key tool for identifying bacterial species. We return to this traditional approach with modern tools of computer vision and deep learning and show that we can achieve high levels of classification accuracy on a within-species scale, despite what is considered a data-starved dataset. Our results show that strains of the environmental generalist and opportunistic pathogen Pseudomonas aeruginosa have a characteristic morphological fingerprint that enables accurate strain classification via a custom deep convolutional neural network. Our work points to extensions towards predicting phenotypes of interest (e.g. antibiotic resistance, virulence), and suggests that sample size limitations may be less restrictive than previously thought for deep learning applications in biology, given appropriate use of data augmentation and transfer-learning tools.

microbiology↗

bioRxiv2019

Bacterial quorum sensing allows graded responses to variations in density, on both individual and population scales

Quorum sensing (QS) is a mechanism of cell-cell communication that connects gene expression to environmental conditions (e.g. density) in many bacterial species, mediated by diffusible signal molecules. Current functional studies focus on a dichotomy of QS on/off (or, quorate / sub-quorate) states, overlooking the potential for intermediate, graded responses to shifts in the environment. Here, we track QS regulated protease (lasB) expression and show that Pseudomonas aeruginosa can deliver a graded behavioral response to fine-scale variation in population density, on both the population and single-cell scales. On the population scale, we see a graded response to variation in environmental population density. On the single-cell scale, we see significant bimodality at higher densities, with separate OFF and ON sub-populations that respond differentially to changes in density; static OFF cells and increasing intensity of expression among ON cells. Together these results indicate that QS can tune gene expression to graded environmental change, with no critical cell mass or quorum at which behavioral responses are activated on either the individual cell or population scale. In an infection context, our results indicate there is not a hard threshold separating sub-quorate stealth mode and a quorate attack mode.

microbiology↗

bioRxiv2019

In silico bacteria evolve robust cooperation via complex quorum-sensing strategies

Many species of bacteria collectively sense and respond to their social and physical environment via quorum sensing (QS), a communication system controlling extracellular cooperative traits. Despite detailed understanding of the mechanisms of signal production and response, there remains considerable debate over the functional role(s) of QS: in short, what is it for? Experimental studies have found support for diverse functional roles: density sensing, mass-transfer sensing, genotype sensing, etc. While consistent with theory, these results cannot separate whether these functions were drivers of QS adaption, or simply artifacts or spandrels of systems shaped by distinct ecological pressures. The challenge of separating spandrels from drivers of adaptation is particularly hard to address using extant bacterial species with poorly understood current ecologies (let alone their ecological histories). To understand the relationship between environmental challenges and trajectories of QS evolution, we used an agent-based simulation modeling approach. Given genetic mixing, our simulations produce behaviors that recapitulate features of diverse microbial QS systems, including coercive (high signal / low response) and generalized reciprocity (signal auto-regulation) strategists -- that separately and in combination contribute to QS-dependent resilience of QS-controlled cooperation in the face of diverse cheats. We contrast our in silico results with bacterial QS architectures that have evolved under largely unknown ecological contexts, highlighting the critical role of genetic constraints in shaping the shorter term (experimental evolution) dynamics of QS. More broadly, we see experimental evolution of digital organisms as a complementary tool in the search to understand the emergence of complex QS architectures and functions.\n\nAuthor summaryBacteria communicate and cooperate using complex cell-cell signaling systems known as quorum-sensing (QS). While the molecular mechanisms are often well understood, the reasons why bacteria use QS are less clear -- how has QS aided survival and growth? The answer to this question is dependent on the environment of adaptation, and unfortunately our current understanding of QS bacterial ecology is broadly lacking. To address this gap, we studied the evolution of digital organisms, individual-based computer simulations of bacterial populations evolving under defined environmental contexts. Our results pinpoint how simple environmental challenges (variable density and genetic mixing) can lead to the emergence of complex strategies that recapitulate features of bacterial QS, and open a path towards reverse-engineering the environmental drivers of QS.

microbiology↗