Biology subjects

Brown, S. P.

Publications and source records attributed to Brown, S. P..

36 records · Page 2Linked to original sources

bioRxiv2022

Modular gene interactions drive modular pan-genome evolution in bacteria.

Depending on the scale of observation, bacterial genomes are both organized and fluid. While individual bacterial genomes show signatures of organization (e.g., operons), pan-genomes reveal genome fluidity, both in terms of gene content and order (synteny). Here we ask how mutational forces (including recombination and horizontal gene transfer) combine with selection and gene interactions to shape genome organization and variation both within and across strains. We first build an evolutionary simulation model to assess the impact of gene interactions on pan-genome structure. A neutral evolutionary model can produce transient co-segregation of initially linked genes but is vulnerable on longer time-scales to perturbing mutational events. In contrast, incorporation of modular gene fitness interactions can produce sustainable clusters of linked and co-segregating genes, with the network of co-segregation recapitulating the defined simulation ground-truth network of gene interactions. To test our model predictions, we exploit the increasing number of closed genomes in model species to define gene co-segregation networks in the pan-genomes of Escherichia coli and Pseudomonas aeruginosa. Using these highly curated pan-genomes, we identify modular clusters of physically linked and co-segregating genes and show that the resulting co-segregation networks map onto underlying gene-regulatory and metabolic gene interaction networks. The results imply that co-segregation networks can contribute to accessory genome annotation, and more generally that gene interactions are the primary force shaping genome structure and operon evolution.

evolutionary biology↗

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↗

bioRxiv2021

Antibiotics drive expansion of rare pathogens in a chronic infection microbiome model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment, and are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model, and benchmark on clinical data. We show that, in the absence of antibiotics, the microbiome structure in a synthetic sputum medium is highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path towards microbiome-informed conditional treatment strategies.

microbiology↗

bioRxiv2021

Building reusable phage and antibiotic treatments via exploitation of bacteria-phage coevolutionary dynamics.

People with chronic (long-lasting) infections face the problem that treatment options diminish in time as the pathogen evolves increasing resistance. To address this challenge, we exploit phage and bacterial co-evolution, producing dynamic selection pressures that can return the pathogen to a state of susceptibility to the initial (regulator-approved) therapy. We show that phage OMKO1 alone triggers Arms Race Dynamic (ARD) co-evolution with the pathogen Pseudomonas aeruginosa, leading to generalized phage resistance and crucially - failure at reuse. In contrast, co-administration of the phage with antibiotics triggers Fluctuating Selection Dynamics (FSD) co-evolution, allowing for effective reuse after 20 days of treatment. We pursue medical relevance in our experiments with the use of clinically important pathogens, antibiotics, phage, and a benchmarked synthetic sputum medium. Phenotypic and genomic characterization of evolved isolates demonstrates that efflux-targeting phage OMKO1 exerts continued selection for antibiotic susceptibility regardless of co-evolutionary dynamic or antibiotic co-treatment, opening the door for evolutionary robust phage therapy.

microbiology↗

bioRxiv2021

Pseudomonas aeruginosa reaches collective decisions via transient segregation of quorum sensing activities across cells

Bacteria engage in a cell-to-cell communication process called quorum sensing (QS) to coordinate expression of cooperative exoproducts at the group level. While population-level QS-responses are well studied, we know little about commitments of single cells to QS. Here, we use flow cytometry to track the investment of Pseudomonas aeruginosa individuals into their intertwined Las and Rhl QS-systems. Using fluorescent reporters, we show that QS gene expression (signal synthase, receptor and exoproduct) was heterogenous and followed a gradual instead of a sharp temporal induction pattern. The simultaneous monitoring of two QS genes revealed that cells transiently segregate into low receptor (lasR) expressers that fully commit to QS, and high receptor expressers that delay QS commitment. Our mathematical model shows that such gene expression segregation could mechanistically be spurred by transcription factor limitation. In evolutionary terms, temporal segregation could serve as a QS-brake to allow for a bet-hedging strategy in unpredictable environments.

microbiology↗

bioRxiv2020

Control of neurogenic competence in mammalian hypothalamic tanycytes.

Hypothalamic tanycytes, radial glial cells that share many features with neuronal progenitors, generate small numbers of neurons in the postnatal hypothalamus, but the identity of these neurons and the molecular mechanisms that control tanycyte-derived neurogenesis are unknown. In this study, we demonstrate that tanycyte-specific disruption of the NFI family of transcription factors (Nfia/b/x) robustly stimulates tanycyte proliferation and tanycyte-derived neurogenesis. Single-cell RNA- and ATAC-Seq analysis reveals that NFI factors repress Shh and Wnt signaling in tanycytes, and small molecule modulation of these pathways blocks proliferation and tanycyte-derived neurogenesis in Nfia/b/x-deficient mice. We show that Nfia/b/x-deficient tanycytes give rise to multiple mediobasal hypothalamic neuronal subtypes that can mature, integrate into hypothalamic synaptic circuitry, and selectively respond to changes in internal states. These findings identify molecular mechanisms that control tanycyte-derived neurogenesis, suggesting a new therapeutic approach to selectively remodel the hypothalamic neural circuitry that controls homeostatic physiological processes.

neuroscience↗

bioRxiv2020

Community lifespan, niche expansion and the evolution of interspecific cooperation

Natural selection favours individuals who maximise their own reproductive success and that of their close relatives. From this perspective, cooperation that benefits individuals of a different species represents an evolutionary conundrum. The theory of mutualism seeks to resolve this puzzle, and it posits that there must be downstream benefits to cooperators that offset any costs inherently associated with interspecific cooperation. Thus, individuals should only further the survival and fecundity of their interspecific partners if this generates additional return benefits, such as food, shelter or protection. A major challenge for the evolution of mutualism is when the ecological niches of partner species overlap, as this creates a tension between the benefits of exchanging services and the costs of competing for shared resources. Here we study the extent to which niche expansion, in which cooperation augments the common pool of resources, can resolve this problem. We find that niche expansion facilitates the evolution of mutualism, especially when populations are at high densities. Further, we show that niche expansion can promote the evolution of reproductive restraint, in which a focal species adaptively sacrifices its own growth rate to increase the density of partner species. We interpret these results in the context of microbial community interactions, which are often characterized by yield-enhancing exchanges of nutrients, termed cross-feeding. Our findings suggest that yield-enhancing mutualisms are more prevalent in stable habitats with a constant supply of resources, where populations typically live at high densities, but such mutualisms are particularly vulnerable to the emergence of cheats. In general, our findings highlight the need to integrate both temporal and spatial dynamics in the analysis of mutualisms.

evolutionary biology↗

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

Microbiome data enhances predictive models of lung function in people with CF

BackgroundMicrobiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but non-pathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). MethodsWe collected health information (lung function, age, BMI) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use Elastic Net regularization to train linear models predicting lung function and extract the most informative features. ResultsModels trained on whole microbiome quantitation outperform models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retain key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF related diabetes). In addition, our models select non-pathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. ConclusionsThese results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.

bioinformatics↗

bioRxiv2019

Allelic polymorphism shapes collective phenotypes in evolving Pseudomonas aeruginosa populations

Pseudomonas aeruginosa is an opportunistic pathogen that chronically infects the lungs of individuals with cystic fibrosis (CF) by forming antibiotic resistant biofilms. Emergence of phenotypically diverse isolates within CF P. aeruginosa populations has previously been reported, however, the impact of heterogeneity on social behaviors and community function is poorly understood. Here we describe how this heterogeneity impacts on behavioral traits by evolving the strain PAO1 in biofilms grown in a synthetic sputum medium for 50 days. We measured social trait production and antibiotic tolerance and used a metagenomic approach to analyze and assess genomic changes over the duration of the evolution experiment. We found that (i) evolutionary trajectories were reproducible in independently evolving populations; (ii) over 60% of genomic diversity occurred within the first 10 days of selection. We then focused on quorum sensing (QS), a well-studied P. aeruginosa trait that is commonly mutated in strains isolated from CF lungs. We found that at the population level (i) evolution in sputum medium selected for decreased production of QS and QS-dependent traits; (ii) there was a significant correlation between lasR mutant frequency, the loss of protease and the 3O-C12-HSL signal, and an increase in resistance to clinically relevant {beta}-lactam antibiotics, despite no previous antibiotic exposure. Overall, our findings provide insights into the effect of allelic polymorphism on community functions in diverse P. aeruginosa populations. Further, we demonstrate that P. aeruginosa population and evolutionary dynamics can impact on traits important for virulence and can lead to increased tolerance to {beta}-lactam antibiotics.

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↗

bioRxiv2018

Resistance diagnostics as a public health tool to combat antibiotic resistance: A model-based evaluation

Rapid point-of-care resistance diagnostics (POC-RD) are thought to be a key tool in the fight against antibiotic resistance. By tailoring drug choice to infection genotype, doctors can improve treatment efficacy while limiting costs of inappropriate antibiotic prescription. Here we combine epidemiological theory and data to assess the potential of POC-RD innovations in a public health context, as a means to limit or even reverse selection for antibiotic resistance. POC-RD can be used to impose a non-biological fitness cost on resistant strains, by triggering targeted interventions that reduce their opportunities for transmission. We assess this diagnostic-imposed fitness cost in the context of a spectrum of bacterial population biologies and POC-RD conditional strategies, and find that the expected impact varies from selection against resistance for obligate pathogens to marginal public health improvements for opportunistic pathogens with high bystander antibiotic exposure during asymptomatic carriage (e.g. the pneumococcus). We close by generalizing the notion of RD-informed strategies to incorporate both POC and carriage surveillance information, and illustrate that coupling transmission control interventions to the discovery of resistant strains in carriage can potentially select against resistance in a broad range of opportunistic pathogens.

epidemiology↗

bioRxiv2018

Combinatorial quorum sensing in Pseudomonas aeruginosa allows for novel cheating strategies

In the opportunistic pathogen Pseudomonas aeruginosa, quorum sensing (QS) is a social trait that is exploitable by non-cooperating cheats. Previously it has been shown that by linking QS to the production of both public and private goods, cheats can be prevented from invading populations of cooperators and this has been termed a metabolic incentive to cooperate. We hypothesized P. aeruginosa could evolve novel cheating strategies to circumvent private goods metabolism by rewiring its combinatorial response to two QS signals (3O-C12-HSL and C4-HSL). We performed a selection experiment that cycled P. aeruginosa between public and private goods growth media and evolved an isolate which rewired its control of cooperative protease expression from a synergistic (AND-gate) response to dual signal input, to a 3O-C12-HSL only response. We show that this isolate circumvents metabolic incentives to cooperate and acts as a combinatorial signaling cheat, with a higher fitness in competition with its ancestor. Our results show three important principles; first, combinatorial QS allows for diverse social strategies to emerge; second, that restrictions levied by private goods are not sufficient to explain the maintenance of cooperation in natural populations and third that modifying combinatorial QS responses could result in important physiological outcomes in bacterial populations.

microbiology↗

bioRxiv2017

Modified antibiotic adjuvant ratios can slow and steer the evolution of resistance: co-amoxiclav as a case study

As the spread of antibiotic resistance outstrips the introduction of new antibiotics, reusing existing antibiotics is increasingly important. One promising method is to combine antibiotics with synergistically acting adjuvants that inhibit resistance mechanisms, allowing drug killing. Here we use co-amoxiclav (a commonly used and clinically important drug combination of the {beta}-lactam antibiotic amoxicillin and the {beta}-lactamase inhibitor clavulanate) to ask whether treatment efficacy and resistance evolution can be decoupled via component dosing modifications.\n\nA simple mathematical model predicts that different ratios of these two drug components can produce distinct evolutionary responses despite similar initial levels of control. We test this hypothesis by selecting Escherichia coli with a plasmid encoded {beta}-lactamase (ESBL CTX-M-14), against different proportions of amoxicillin and clavulanate. Consistent with our theory, we found that while resistance evolved under all conditions, the component ratio influenced both the rate and mechanism of resistance evolution. Specifically, we found that the current clinical practice of high amoxicillin to clavulanate ratios resulted in the most rapid failure due to the evolution of gene dosing responses. Increased plasmid copy number allowed E. coli to increase {beta}-lactamase dosing and effectively titrate out the low quantities of clavulanate, restoring amoxicillin resistance. In contrast, we found high clavulanate ratios were more robust - plasmid copy number did not increase, although porin or efflux resistance mechanisms were found, as in all drug ratios. Our results indicate that by changing the ratio of adjuvant to antibiotic we can slow and steer the path of resistance evolution. We therefore suggest the use of increased clavulanate dosing regimens to slow the rate of resistance evolution.

microbiology↗

bioRxiv2017

Variation in neuronal activity state, axonal projection target, and position principally define the transcriptional identity of individual neocortical projection neurons.

Single-cell RNA sequencing technologies have generated the first catalogs of transcriptionally defined neuronal subtypes of the brain. However, the biologically informative cellular processes that contribute to neuronal subtype specification and transcriptional heterogeneity remain unclear. By comparing the gene expression profiles of single layer 6 corticothalamic neurons in somatosensory cortex, we show that transcriptional subtypes primarily reflect axonal projection pattern, laminar position within the cortex, and neuronal activity state. Pseudotemporal ordering of 1023 cellular responses to manipulation of sensory input demonstrates that changes in expression of activity-induced genes both reinforced cell-type identity and contributed to increased transcriptional heterogeneity within each cell type. This is due to cell-type specific biases in the choice of transcriptional states following manipulation of neuronal activity. These results reveal that axonal projection pattern, laminar position, and activity state define significant axes of variation that contribute both to the transcriptional identity of individual neurons and to the transcriptional heterogeneity within each neuronal subtype.

neuroscience↗

bioRxiv2017

Division of labor, bet hedging, and the evolution of mixed biofilm investment strategies

Bacterial cells, like many other organisms, face a tradeoff between longevity and fecundity. Planktonic cells are fast growing and fragile, while biofilm cells are often slower growing but stress resistant. Here we ask: why do bacterial lineages invest simultaneously in both fast and slow growing types? We develop a population dynamical model of lineage expansion across a patchy environment, and find that mixed investment is favored across a broad range of environmental conditions, even when transmission is entirely via biofilm cells. This mixed strategy is favored because of a division of labor, where exponentially dividing planktonic cells can act as an engine for the production of future biofilm cells, which grow more slowly. We use experimental evolution to test our predictions, and show that phenotypic heterogeneity is persistent even under selection for purely planktonic or purely biofilm transmission. Furthermore, simulations suggest that maintenance of a biofilm subpopulation serves as a cost-effective hedge against environmental uncertainty, which is also consistent with our experimental findings.

microbiology↗

bioRxiv2016

Individual- versus group-optimality in the production of secreted bacterial compounds

How unicellular organisms optimize the production of compounds is a fundamental biological question. While it is typically thought that production is optimized at the individual-cell level, secreted compounds could also allow for optimization at the group level, leading to a division of labor where a subset of cells produces and shares the compound with everyone. Using mathematical modelling, we show that the evolution of such division of labor depends on the cost function of compound production. Specifically, for any trait with saturating benefits, linear costs promote the evolution of uniform production levels across cells. Conversely, production costs that diminish with higher output levels favor the evolution of specialization - especially when compound shareability is high. When experimentally testing these predictions with pyoverdine, a secreted iron-scavenging compound produced by Pseudomonas aeruginosa, we found linear costs and, consistent with our model, detected uniform pyoverdine production levels across cells. We conclude that for shared compounds with saturating benefits, the evolution of division of labor is facilitated by a diminishing cost function. More generally, we note that shifts in the level of selection from individuals to groups do not solely require cooperation, but critically depend on mechanistic factors, including the distribution of compound synthesis costs.

evolutionary biology↗