Application of a quantitative framework to improve the accuracy of a bacterial infection model.

Laboratory models are critical to basic and translational microbiology research. Models serve multiple purposes, from providing tractable systems to study cell biology to allowing the investigation of inaccessible clinical and environmental ecosystems. Although there is a recognized need for improved model systems, there is a gap in rational approaches to accomplish this goal.

Impact of Growth Rate on the Protein-mRNA Ratio in Pseudomonas aeruginosa.

Our understanding of how bacterial pathogens colonize and persist during human infection has been hampered by the limited characterization of bacterial physiology during infection and a research bias toward , fast-growing bacteria. Recent research has begun to address these gaps in knowledge by directly quantifying bacterial mRNA levels during human infection, with the goal of assessing microbial community function at the infection site. However, mRNA levels are not always predictive of protein levels, which are the primary functional units of a cell.

The time scale of asymptomatic transmission affects estimates of epidemic potential in the COVID-19 outbreak.

The role of asymptomatic carriers in transmission poses challenges for control of the COVID-19 pandemic. Study of asymptomatic transmission and implications for surveillance and disease burden are ongoing, but there has been little study of the implications of asymptomatic transmission on dynamics of disease. We use a mathematical framework to evaluate expected effects of asymptomatic transmission on the basic reproduction number (i.

The time scale of asymptomatic transmission affects estimates of epidemic potential in the COVID-19 outbreak.

The role of asymptomatic carriers in transmission poses challenges for control of the COVID-19 pandemic. Study of asymptomatic transmission and implications for surveillance and disease burden are ongoing, but there has been little study of the implications of asymptomatic transmission on dynamics of disease. We use a mathematical framework to evaluate expected effects of asymptomatic transmission on the basic reproduction number R (i.

Quantitative Framework for Model Evaluation in Microbiology Research Using and Cystic Fibrosis Infection as a Test Case.

Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of cystic fibrosis (CF) lung infection.

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 modeling, we show that the evolution of such division of labor depends on the cost function of compound production.

transcriptome during human infection.

Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known.

Bacterial tweets and podcasts #signaling#eavesdropping#microbialfightclub.

Once thought to live independently, bacteria are now known to be highly social organisms. Their behaviors ranges from cooperatively forming complex multispecies communities to fiercely competing for resources. Work over the past fifty years has shown that bacteria communicate through diverse mechanisms, such as exchanging diffusible molecules, exporting molecules in membrane vesicles, and interacting through direct cell-cell contact.

Bacterial Cooperation Causes Systematic Errors in Pathogen Risk Assessment due to the Failure of the Independent Action Hypothesis.

The Independent Action Hypothesis (IAH) states that pathogenic individuals (cells, spores, virus particles etc.) behave independently of each other, so that each has an independent probability of causing systemic infection or death. The IAH is not just of basic scientific interest; it forms the basis of our current estimates of infectious disease risk in humans.

Combinatorial quorum sensing allows bacteria to resolve their social and physical environment.

Quorum sensing (QS) is a cell-cell communication system that controls gene expression in many bacterial species, mediated by diffusible signal molecules. Although the intracellular regulatory mechanisms of QS are often well-understood, the functional roles of QS remain controversial. In particular, the use of multiple signals by many bacterial species poses a serious challenge to current functional theories.

Competition sensing: the social side of bacterial stress responses.

The field of ecology has long recognized two types of competition: exploitative competition, which occurs indirectly through resource consumption, and interference competition, whereby one individual directly harms another. Here, we argue that these two forms of competition have played a dominant role in the evolution of bacterial regulatory networks. In particular, we argue that several of the major bacterial stress responses detect ecological competition by sensing nutrient limitation (exploitative competition) or direct cell damage (interference competition).

Synergy and group size in microbial cooperation.

Microbes produce many molecules that are important for their growth and development, and the exploitation of these secretions by nonproducers has recently become an important paradigm in microbial social evolution. Although the production of these public-goods molecules has been studied intensely, little is known of how the benefits accrued and the costs incurred depend on the quantity of public-goods molecules produced. We focus here on the relationship between the shape of the benefit curve and cellular density, using a model assuming three types of benefit functions: diminishing, accelerating, and sigmoidal (accelerating and then diminishing).

Evolution of virulence in opportunistic pathogens: generalism, plasticity, and control.

Standard virulence evolution theory assumes that virulence factors are maintained because they aid parasitic exploitation, increasing growth within and/or transmission between hosts. An increasing number of studies now demonstrate that many opportunistic pathogens (OPs) do not conform to these assumptions, with virulence factors maintained instead because of advantages in non-parasitic contexts. Here we review virulence evolution theory in the context of OPs and highlight the importance of incorporating environments outside a focal virulence site.

Erratic flu vaccination emerges from short-sighted behavior in contact networks.

The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models.