Biology subjects

Smith, A. P.

Publications and source records attributed to Smith, A. P..

12 recordsLinked to original sources

bioRxiv2024

The Tradeoffs Between Persistence and Mutation Rates at Sub-Inhibitory Antibiotic Concentrations in Staphylococcus aureus

The rational design of the antibiotic treatment of bacterial infections employs these drugs to reach concentrations that exceed the minimum needed to prevent the replication of the target bacteria. However, within a treated patient, spatial and physiological heterogeneity promotes antibiotic gradients such that the concentration of antibiotics at specific sites is below the minimum needed to inhibit bacterial growth. Here, we investigate the effects of sub-inhibitory antibiotic concentrations on three parameters central to bacterial infection and the success of antibiotic treatment, using in vitro experiments with Staphylococcus aureus and mathematical-computer simulation models. Our results, using drugs of six different classes, demonstrate that exposure to sub-inhibitory antibiotic concentrations not only alters the dynamics of bacterial growth but also increases the mutation rate to antibiotic resistance and decreases the rate of production of persister cells thereby reducing the persistence level. Understanding this trade-off between mutation rates and persistence levels resulting from sub-inhibitory antibiotic exposure is crucial for optimizing, and mitigating the failure of, antibiotic therapy.

microbiology↗

bioRxiv2023

The Evolution of Heteroresistance via Small Colony Variants in Escherichia coli Following Long Term Exposure to Bacteriostatic Antibiotics

Traditionally, bacteriostatic antibiotics are agents able to arrest bacterial growth. Despite being traditionally viewed as unable to kill bacterial cells, when they are used clinically the outcome of these drugs is frequently as effective as when a bactericidal drug is used. We explore the dynamics of Escherichia coli after exposure to two ribosome-targeting bacteriostatic antibiotics, chloramphenicol and azithromycin, for thirty days. The results of our experiments provide evidence that bacteria exposed to these drugs replicate, evolve, and generate a sub-population of small colony variants (SCVs) which are resistant to multiple drugs. These SCVs contribute to the evolution of heteroresistance and rapidly revert to a susceptible state once the antibiotic is removed. Stated another way, exposure to bacteriostatic drugs selects for the evolution of heteroresistance in populations previously lacking this trait. More generally, our results question the definition of bacteriostasis as populations exposed to bacteriostatic drugs are replicating despite the lack of net growth.

microbiology↗

bioRxiv2023

Theoretical Considerations and Empirical Predictions of the Pharmaco- and Population Dynamics of Heteroresistance

Antibiotics are considered one of the most important contributions to clinical medicine in the last 100 years. Due to the use and overuse of these drugs, there have been increasing frequencies of infections with resistant pathogens. One form of resistance, heteroresistance, is particularly problematic; pathogens appear sensitive to a drug by common susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. To quantitatively explore the processes contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment, we develop two distinct mathematical and computer-simulations models of heteroresistance. In our analysis of the properties of these models, we consider the factors that determine the response to antibiotic-mediated selection. In one model, heteroresistance is progressive, with each resistant state sequentially generating a higher resistance level. In the other model, heteroresistance is non-progressive, with a susceptible population directly generating populations with different resistance levels. The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rates of reversion from the resistant to the sensitive states are critically dependent on the transition rates and the fitness cost of resistance. Our results demonstrate that the standard test used to identify heteroresistance is insufficient. The predictions of our models are consistent with empirical results. Our results demand a reevaluation of the definition and criteria employed to identify heteroresistance. We recommend the definition of heteroresistance should include a consideration of the rate of return to susceptibility. Significance StatementThis mathematical modeling and computer-simulation study quantitatively explores two broadly different, previously undescribed, classes of heteroresistance. In our analysis of the properties of these models, we consider the response of heteroresistant populations to antibiotic exposure, focusing on the conditions where heteroresistance could lead to clinical treatment failure. We also provide novel consideration to the rate of reversion from a resistant to sensitive state. Our analysis illustrates the need to include the reversion rate from resistant to sensitive in the definition of heteroresistance and questions the sufficiency of the method currently used to identify heteroresistance.

microbiology↗

bioRxiv2022

What's the matter with MICs: The contribution of nutrients and limiting resources to the pharmacodynamics of antibiotics and bacteria

The minimum inhibitory concentration (MIC) of an antibiotic required to prevent replication is used both as a measure of the susceptibility/resistance of bacteria to that drug and as the single pharmacodynamic parameter for the rational design of antibiotic treatment regimes. MICs are estimated in vitro under conditions optimal for the action of the antibiotic. However, bacteria rarely grow in these optimal conditions. Using a mathematical model of the pharmacodynamics of antibiotics, we make predictions about the nutrient dependency of bacterial growth in the presence of antibiotics. We test these predictions with experiments in a rich media and a glucose-limited minimal media with Escherichia coli and eight different antibiotics. Our experiments uncover properties that question the sufficiency of using MICs and simple pharmacodynamic functions as measures of the pharmacodynamics of antibiotics under the nutritional conditions of infected tissues. To an extent that varies among drugs: (i) The estimated MICs obtained in rich media are greater than those estimated in minimal media. (ii) Exposure to these drugs increases the time before logarithmic growth starts, their lag. (iii) The stationary phase density of E. coli populations declines with greater sub-MIC antibiotic concentrations. We postulate a mechanism to account for the relationship between the sub-MIC concentration of antibiotics and the stationary phase density of bacteria and provide evidence in support of this hypothesis. We discuss the implications of these results to our understanding of the MIC as the unique pharmacodynamic parameter used to design protocols for antibiotic treatment.

microbiology↗

bioRxiv2022

Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infection during SARS-CoV-2 Infection

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate SARS-CoV-2 infection increased the risk of pneumococcal coinfection in a time-dependent, but sexindependent, manner in the transgenic K18-hACE mouse model of COVID-19. Bacterial coinfection was not established at 3 d post-virus, but increased lethality was observed when the bacteria was initiated at 5 or 7 d post-virus infection (pvi). Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.

microbiology↗

bioRxiv2021

Multiple paths lead to salt tolerance - pre-adaptation vs dynamic responses from two closely related extremophytes

Salt tolerance is a complex trait with much of the underlying genetic variation and integrated response strategies yet to be discovered from stress adapted plants. Schrenkiella parvula and Eutrema salsugineum are salt-tolerant extremophytes related to Arabidopsis thaliana in Brassicaceae. We investigated their response strategies contrasted against the salt-sensitive model, A. thaliana to cope with salt stresses via transcriptomic, metabolomic, and ionomic adjustments. The extremophytes exemplified divergent routes to achieve nutrient balance, build osmotolerance, boost antioxidant capacity, and extend transcriptomic support for modified ion transport and stress signaling. Those led to similar molecular phenotypes adapted to salt stress in the extremophytes, absent in A. thaliana. The predominant transcriptomic signals in all three species were associated with salt stress. However, root architecture modulation mediated by negative regulation of auxin and ABA signaling supported minimally-affected root growth unique to each extremophyte during salt treatments. Overall, E. salsugineum exhibited pre-adapted responses at the metabolome level, whereas S. parvula showed dynamic metabolomic responses coupled to a pre-adapted transcriptome to survive salt stress. Our work shows that the two extremophytes share common salt tolerance features, but differ substantially in pathways leading to the convergent, adaptive traits.

plant biology↗

bioRxiv2021

Balancing growth amidst salinity stress-lifestyle perspectives from the extremophyte model Schrenkiella parvula

Schrenkiella parvula, a leading extremophyte model in Brassicaceae, can grow and complete its life cycle under multiple environmental stresses, including high salinity. While foundational genomic resources have been created for S. parvula, a comprehensive physiological or structural characterization of its salt stress responses is absent. We aimed to identify the influential traits that lead to stress-resilient growth of this species. We examined salt-induced changes in the physiology and anatomy of S. parvula throughout its lifecycle across multiple tissues. We found that S. parvula maintains or even exhibits enhanced growth during various developmental stages at salt stress levels known to inhibit growth in Arabidopsis and most crops. The resilient growth of S. parvula was associated with key traits that synergistically allow continued primary root growth, expansion of xylem vessels across the root-shoot continuum, and a high capacity to maintain tissue water levels by developing larger and thicker leaves while facilitating continued photosynthesis during salt stress. These traits at the vegetative phase were followed by a successful transition to the reproductive phase via early flowering, development of larger siliques, and production of viable seeds during salt stress. Additionally, the success of self-fertilization during early flowering stages was dependent on salt-induced filament elongation in flowers that aborted in the absence of salt. Our results suggest that the maintenance of leaf water status and enhancement of selfing in early flowers to ensure reproductive success, are among the most influential traits that contribute to the extremophyte lifestyle of S. parvula in its natural habitat. One sentence summarySchrenkiella parvula salt-resilient growth is facilitated by uncompromised primary root growth, expansion of xylem vessels, maintenance of leaf water status and photosynthesis, and early flowering.

plant biology↗

bioRxiv2021

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. Author summaryUnderstanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.

immunology↗

bioRxiv2020

Cross species multi-omics reveals cell wall sequestration and elevated global transcription as mechanisms of boron tolerance in plants

Boron toxicity is a worldwide problem for crop production, yet we have only a limited understanding of the genetic responses and adaptive mechanisms to this environmental stress in plants. Here we identified responses to excess boron in boron stress-sensitive Arabidopsis thaliana and its boron stress-tolerant extremophyte relative Schrenkiella parvula using comparative genomics, transcriptomics, metabolomics, and ionomics. S. parvula maintains a lower level of total boron and free boric acid in its roots and shoots and sustains growth for longer durations than A. thaliana when grown with excess boron. S. parvula likely excludes boron more efficiently than A. thaliana, which we propose is partly driven by BOR5, a boron transporter that we functionally characterized in the current study. Both species allocate significant transcriptomic and metabolomic resources to enable their cell walls to serve as a partial sink for excess boron, particularly discernable in A. thaliana shoots. We provide evidence that the S. parvula transcriptome is pre-adapted to boron toxicity, exhibiting substantial overlap with the boron-stressed transcriptome of A. thaliana. Our transcriptomic and metabolomics data also suggest that RNA metabolism is a primary target of boron toxicity. Cytoplasmic boric acid likely forms complexes with ribose and ribose-containing compounds critical to RNA and other primary metabolic functions. A model depicting some of the cellular responses that enable a plant to grow in the presence of normally toxic levels of boron is presented.

plant biology↗

bioRxiv2019

Defining chromatin state transitions predicts a network that modulates cell wall remodeling in phosphate-starved rice shoots

Phosphorus (P) is an essential plant macronutrient vital to fundamental metabolic processes. Plant-available P is low in most soils, making it a frequent limiter of growth. Declining P reserves for fertilizer production exasperates this agricultural challenge. Plants modulate complex responses to fluctuating P levels via global transcriptional regulatory networks. Although chromatin structure plays a substantial role in controlling gene expression, the chromatin dynamics involved in regulating P homeostasis have not been determined. Here we define distinct chromatin states across the rice genome by integrating multiple aspects of chromatin structure, including the H2A.Z histone variant, H3K4me3 modification, and nucleosome positioning. In response to P starvation, 40% of all protein-coding genes exhibit a transition from one chromatin state to another at their transcription start site. Several of these transitions are enriched in subsets of genes differentially expressed by P deficiency. The most prominent subset supports the presence of a coordinated signaling network that targets cell wall structure and is regulated in part via a decrease of H3K4me3 at the transcription start site. The P-starvation induced chromatin dynamics and correlated genes identified here will aid in enhancing P-use efficiency in crop plants, benefitting global agriculture.\n\nOne sentence summaryCombining data for three components of chromatin structure from control and phosphate-starved rice shoots reveals specific chromatin state transitions that correlate with subsets of functionally distinct differentially-expressed genes.

plant biology↗

bioRxiv2019

Dynamic pneumococcal genetic adaptations support bacterial growth and inflammation during coinfection with influenza

Streptococcus pneumoniae (pneumococcus) is one of the primary bacterial pathogens that complicates influenza virus infections. These bacterial coinfections increase influenza-associated morbidity and mortality through a number of immunological and viral-mediated mechanisms, but the specific bacterial genes that contribute to post-influenza pathogenicity are not known. Here, we used genome-wide transposon mutagenesis (Tn-Seq) to reveal bacterial genes that confer improved fitness in influenza-infected hosts. The majority of the 32 identified genes are involved in bacterial metabolism, including nucleotide biosynthesis, amino acid biosynthesis, protein translation, and membrane transport. We generated single-gene deletion (SGD) mutants of five identified genes: SPD1414, SPD2047 (cbiO1), SPD0058 (purD), SPD1098, and SPD0822 (proB), to investigate their effect on in vivo fitness, disease severity, and host immune responses. Growth of SGD mutants was slightly attenuated in vitro and in vivo, but each still grew to high titers in the lungs of mock- and influenza-infected hosts. Despite high bacterial loads, mortality was significantly reduced or delayed with all SGD mutants. Time-dependent reductions in pulmonary neutrophils, inflammatory macrophages, and select proinflammatory cytokines and chemokines were also observed. Immunohistochemical staining further revealed that neutrophil phenotype and distribution was altered in the lungs of influenza-SGD coinfected animals. These studies demonstrate a critical role for specific bacterial genes and for bacterial metabolism in driving virulence and modulating immune function during influenza-associated bacterial pneumonia.

microbiology↗

bioRxiv2019

The Nonlinear Relations that Predict Influenza Viral Dynamics, CD8+ T cell-Mediated Clearance, Lung Pathology, and Disease Severity

Influenza viruses cause a significant amount of morbidity and mortality. Understanding host immune control efficacy and how different factors influence lung injury and disease severity are critical. Here, we established dynamical connections between viral loads, infected cells, CD8+ T cell-mediated clearance, lung injury, inflammation, and disease severity using an integrative model-experiment exchange. The model was validated through CD8 depletion and whole lung histomorphometry, which showed that the infected area matched the model-predicted infected cell dynamics and that the resolved area paralleled the relative CD8 dynamics. Inflammation could further be predicted by the infected cell dynamics, and additional analyses revealed nonlinear relations between lung injury, inflammation, and disease severity. These links between important pathogen kinetics and host pathology enhance our ability to forecast disease progression, potential complications, and therapeutic efficacy.

microbiology↗