James Q. Boedicker

Defined spatial structure stabilizes a synthetic multispecies bacterial community

This paper shows that for microbial communities, “fences make good neighbors.” Communities of soil microorganisms perform critical functions: controlling climate, enhancing crop production, and remediation of environmental contamination. Microbial communities in the oral cavity and the gut are of high biomedical interest. Understanding and harnessing the function of these communities is difficult: artificial microbial communities in the laboratory become unstable because of “winner-takes-all” competition among species. We constructed a community of three different species of wild-type soil bacteria with syntrophic interactions using a microfluidic device to control spatial structure and chemical communication. We found that defined microscale spatial structure is both necessary and sufficient for the stable coexistence of interacting bacterial species in the synthetic community. A mathematical model describes how spatial structure can balance the competition and positive interactions within the community, even when the rates of production and consumption of nutrients by species are mismatched, by exploiting nonlinearities of these processes. These findings provide experimental and modeling evidence for a class of communities that require microscale spatial structure for stability, and these results predict that controlling spatial structure may enable harnessing the function of natural and synthetic multispecies communities in the laboratory.

Microfluidic Confinement of Single Cells of Bacteria in Small Volumes Initiates High-Density Behavior of Quorum Sensing and Growth and Reveals Its Variability

One is a quorum: As few as one to three cells of Pseudomonas aeruginosa bacteria are confined in small volumes by the use of microfluidics. These small numbers of cells are able to activate quorum sensing (QS) pathways and achieve QS-dependent growth. The results also show that at low numbers of cells, initiation of QS is highly variable within a clonal population.

DNA sequence-dependent mechanics and protein-assisted bending in repressor-mediated loop formation

As the chief informational molecule of life, DNA is subject to extensive physical manipulations. The energy required to deform double-helical DNA depends on sequence, and this mechanical code of DNA influences gene regulation, such as through nucleosome positioning. Here we examine the sequence-dependent flexibility of DNA in bacterial transcription factor-mediated looping, a context for which the role of sequence remains poorly understood. Using a suite of synthetic constructs repressed by the Lac repressor and two well-known sequences that show large flexibility differences in vitro, we make precise statistical mechanical predictions as to how DNA sequence influences loop formation and test these predictions using in vivo transcription and in vitro single-molecule assays. Surprisingly, sequence-dependent flexibility does not affect in vivo gene regulation. By theoretically and experimentally quantifying the relative contributions of sequence and the DNA-bending protein HU to DNA mechanical properties, we reveal that bending by HU dominates DNA mechanics and masks intrinsic sequence-dependent flexibility. Such a quantitative understanding of how mechanical regulatory information is encoded in the genome will be a key step towards a predictive understanding of gene regulation at single-base pair resolution.

Comparison and Calibration of Different Reporters for Quantitative Analysis of Gene Expression

Absolute levels of gene expression in bacteria are observed to vary over as much as six orders of magnitude. Thermodynamic models have been proposed as a tool to describe the expression levels of a given transcriptional circuit. In this context, it is essential to understand both the limitations and linear range of the different methods for measuring gene expression and to determine to what extent measurements from different reporters can be directly compared with one aim being the stringent testing of theoretical descriptions of gene expression. In this article, we compare two protein reporters by measuring both the absolute level of expression and fold-change in expression using the fluorescent protein EYFP and the enzymatic reporter β-galactosidase. We determine their dynamic and linear range and show that they are interchangeable for measuring mean levels of expression over four orders of magnitude. By calibrating these reporters such that they can be interpreted in terms of absolute molecular counts, we establish limits for their applicability: autofluorescence on the lower end of expression for EYFP (at ∼10 molecules per cell) and interference with cellular growth on the high end for β-galactosidase (at ∼20,000 molecules per cell). These qualities make the reporters complementary and necessary when trying to experimentally verify the predictions from the theoretical models.

Queuing Models for Abstracting Interactions in Bacterial Communities

Microbial communities play a significant role in bioremediation, plant growth, human and animal digestion, global elemental cycles including the carbon-cycle, and water treatment. They are also posed to be the engines of renewable energy via microbial fuel cells, which can reverse the process of electrosynthesis. Microbial communication regulates many virulence mechanisms used by bacteria. Thus, it is of fundamental importance to understand interactions in microbial communities and to develop predictive tools that help control them, in order to aid the design of systems exploiting bacterial capabilities. This position paper explores how abstractions from communications, networking and information theory can play a role in understanding and modeling bacterial interactions. In particular, two forms of interactions in bacterial systems will be examined: electron transfer and quorum sensing. While the diffusion of chemical signals has been heavily studied, electron transfer occurring in living cells and its role in cell-cell interaction is less understood. Recent experimental observations open up new frontiers in the design of microbial systems based on electron transfer, which may coexist with the more well-known interaction strategies based on molecular diffusion. In quorum sensing, the concentration of certain signature chemical compounds emitted by the bacteria is used to estimate the bacterial population size, so as to activate collective behaviors. In this position paper, queuing models for electron transfer are summarized and adapted to provide new models for quorum sensing. These models are stochastic, and thus capture the inherent randomness exhibited by cell colonies in nature. It is shown that queuing models allow the characterization of the state of a single cell as a function of interactions with other cells and the environment, thus enabling the construction of an information theoretic framework, while being amenable to complexity reduction using methods based on statistical physics and wireless network design.

Comparison of the theoretical and real-world evolutionary potential of a genetic circuit.

With the development of next-generation sequencing technologies, many large scale experimental efforts aim to map genotypic variability among individuals. This natural variability in populations fuels many fundamental biological processes, ranging from evolutionary adaptation and speciation to the spread of genetic diseases and drug resistance. An interesting and important component of this variability is present within the regulatory regions of genes. As these regions evolve, accumulated mutations lead to modulation of gene expression, which may have consequences for the phenotype. A simple model system where the link between genetic variability, gene regulation and function can be studied in detail is missing. In this article we develop a model to explore how the sequence of the wild-type lac promoter dictates the fold-change in gene expression. The model combines single-base pair resolution maps of transcription factor and RNA polymerase binding energies with a comprehensive thermodynamic model of gene regulation. The model was validated by predicting and then measuring the variability of lac operon regulation in a collection of natural isolates. We then implement the model to analyze the sensitivity of the promoter sequence to the regulatory output, and predict the potential for regulation to evolve due to point mutations in the promoter region.

The Contribution of High-Order Metabolic Interactions to the Global Activity of a Four-Species Microbial Community

The activity of a biological community is the outcome of complex processes involving interactions between community members. It is often unclear how to accurately incorporate these interactions into predictive models. Previous work has shown a range of positive and negative metabolic pairwise interactions between species. Here we examine the ability of a modified general Lotka-Volterra model with cell-cell interaction coefficients to predict the overall metabolic rate of a well-mixed microbial community comprised of four heterotrophic natural isolates, experimentally quantifying the strengths of two, three, and four-species interactions. Within this community, interactions between any pair of microbial species were positive, while higher-order interactions, between 3 or more microbial species, slightly modulated community metabolism. For this simple community, the metabolic rate of can be well predicted only with taking into account pairwise interactions. Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales.

Predicting the impact of promoter variability on regulatory outputs.

The increased availability of whole genome sequences calls for quantitative models of global gene expression, yet predicting gene expression patterns directly from genome sequence remains a challenge. We examine the contributions of an individual regulator, the ferrous iron-responsive regulatory element, BqsR, on global patterns of gene expression in Pseudomonas aeruginosa. The position weight matrix (PWM) derived for BqsR uncovered hundreds of likely binding sites throughout the genome. Only a subset of these potential binding sites had a regulatory consequence, suggesting that BqsR/DNA interactions were not captured within the PWM or that the broader regulatory context at each promoter played a greater role in setting promoter outputs. The architecture of the BqsR operator was systematically varied to understand how binding site parameters influence expression. We found that BqsR operator affinity was predicted by the PWM well. At many promoters the surrounding regulatory context, including overlapping operators of BqsR or the presence of RhlR binding sites, were influential in setting promoter outputs. These results indicate more comprehensive models that include local regulatory contexts are needed to develop a predictive understanding of global regulatory outputs.

Spatial dispersal of bacterial colonies induces a dynamical transition from local to global quorum sensing.

Bacteria communicate using external chemical signals called autoinducers (AI) in a process known as quorum sensing (QS). QS efficiency is reduced by both limitations of AI diffusion and potential interference from neighboring strains. There is thus a need for predictive theories of how spatial community structure shapes information processing in complex microbial ecosystems. As a step in this direction, we apply a reaction-diffusion model to study autoinducer signaling dynamics in a single-species community as a function of the spatial distribution of colonies in the system. We predict a dynamical transition between a local quorum sensing (LQS) regime, with the AI signaling dynamics primarily controlled by the local population densities of individual colonies, and a global quorum sensing (GQS) regime, with the dynamics being dependent on collective intercolony diffusive interactions. The crossover between LQS to GQS is intimately connected to a trade-off between the signaling networks latency, or speed of activation, and its throughput, or the total spatial range over which all the components of the system communicate.

Microbial Communication via Quorum Sensing

Chemical communication enables microbes to probe local cell density and coordinate collective behavior through a process known as quorum sensing (QS). In QS, microbes produce and detect small molecule signals, and the expression levels of many genes change in response to these signals. QS signals, known as autoinducers, potentially accumulate around the cell and relay information about environmental conditions, transport dynamics, and the number and identity of microbial neighbors. In this paper, we focus on the history of QS, the variety of molecular networks used by microbes to achieve QS, modeling approaches, and applications of QS control.