Cameron Smith

A core erythroid transcriptional network is repressed by a master regulator of myelo-lymphoid differentiation

Two mechanisms that play important roles in cell fate decisions are control of a “core transcriptional network” and repression of alternative transcriptional programs by antagonizing transcription factors. Whether these two mechanisms operate together is not known. Here we report that GATA-1, SCL, and Klf1 form an erythroid core transcriptional network by co-occupying >300 genes. Importantly, we find that PU.1, a negative regulator of terminal erythroid differentiation, is a highly integrated component of this network. GATA-1, SCL, and Klf1 act to promote, whereas PU.1 represses expression of many of the core network genes. PU.1 also represses the genes encoding GATA-1, SCL, Klf1, and important GATA-1 cofactors. Conversely, in addition to repressing PU.1 expression, GATA-1 also binds to and represses >100 PU.1 myelo-lymphoid gene targets in erythroid progenitors. Mathematical modeling further supports that this dual mechanism of repressing both the opposing upstream activator and its downstream targets provides a synergistic, robust mechanism for lineage specification. Taken together, these results amalgamate two key developmental principles, namely, regulation of a core transcriptional network and repression of an alternative transcriptional program, thereby enhancing our understanding of the mechanisms that establish cellular identity.

Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology.

Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the PluxI promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of “hidden interactions” on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context.

Fungal Virulence in a Lepidopteran Model Is an Emergent Property with Deterministic Features

ABSTRACT Virulence has been proposed to be an emergent property, which by definition implies that it is not reducible to its components, but this application of a philosophical concept to the host-microbe interaction has not been experimentally tested. The goals of our study were to analyze the correlation of the phenotype with the ability to cause disease and to determine the dynamics of an experimental cryptococcal infection in Galleria mellonella and Acanthamoeba castellanii. By studying the outcome of infection as host death, we showed that the dynamics of virulence in the G. mellonella/Cryptococcus neoformans interaction follow a predictable pattern. We also found that the experimental temperature and not the presence of virulence factors was a critical parameter defining the pathogenic potential of cryptococcal species. Our results established that cryptococcal species not considered pathogenic could be pathogens given suitable conditions. Our results support the idea that virulence is an emergent property that cannot be easily predicted by a reductionist approach and yet it behaves as a deterministic system in a lepidopteran cryptococcal infection. These findings provide a road map for evaluating whether host-microbe interactions in other systems are chaotic, deterministic, or stochastic, including those with public health importance. IMPORTANCE Virulence is a complex phenotype that cannot be easily studied by analyzing its individual components in isolation. By studying the outcome of infection as the death of the host, we found that a given microbial phenotype does not necessarily correlate with its ability to cause disease and that the presence of so-called virulence factors does not predict pathogenicity, consistent with the notion that virulence is an emergent property. This paper reports that the dynamics of virulence in Galleria mellonella larvae infected with the fungus Cryptococcus neoformans follows a predictable pattern. Establishing that virulence is an emergent property is important because it implies that it is not reducible to its components, and consequently, this phenomenon needs to be studied by a holistic approach. Virulence is a complex phenotype that cannot be easily studied by analyzing its individual components in isolation. By studying the outcome of infection as the death of the host, we found that a given microbial phenotype does not necessarily correlate with its ability to cause disease and that the presence of so-called virulence factors does not predict pathogenicity, consistent with the notion that virulence is an emergent property. This paper reports that the dynamics of virulence in Galleria mellonella larvae infected with the fungus Cryptococcus neoformans follows a predictable pattern. Establishing that virulence is an emergent property is important because it implies that it is not reducible to its components, and consequently, this phenomenon needs to be studied by a holistic approach.

Signal discrimination by differential regulation of protein stability in quorum sensing.

Quorum sensing (QS) is a communication mechanism exploited by a large variety of bacteria to coordinate gene expression at the population level. In Gram-negative bacteria, QS occurs via synthesis and detection of small chemical signals, most of which belong to the acyl-homoserine lactone class. In such a system, binding of an acyl-homoserine lactone signal to its cognate transcriptional regulator (R-protein) often induces stabilization and subsequent dimerization of the R-protein, which results in the regulation of downstream gene expression. Existence of diverse QS systems within and among species of bacteria indicates that each bacterium needs to distinguish among a myriad of structurally similar chemical signals. We show, using a mathematical model, that fast degradation of an R-protein monomer can facilitate discrimination of signals that differentially stabilize it. Furthermore, our results suggest an inverse correlation between the stability of an R-protein and the achievable limits of fidelity in signal discrimination. In particular, an unstable R-protein tends to be more specific to its cognate signal, whereas a stable R-protein tends to be more promiscuous. These predictions are consistent with experimental data on well-studied natural and engineered R-proteins and thus have implications for understanding the functional design of QS systems.

Disease-Enhancing Antibodies Improve the Efficacy of Bacterial Toxin-Neutralizing Antibodies

During infection, humoral immunity produces a polyclonal response with various immunoglobulins recognizing different epitopes within the microbe or toxin. Despite this diverse response, the biological activity of an antibody (Ab) is usually assessed by the action of a monoclonal population. We demonstrate that a combination of monoclonal antibodies (mAbs) that are individually disease enhancing or neutralizing to Bacillus anthracis protective antigen (PA), a component of anthrax toxin, results in significantly augmented protection against the toxin. This boosted protection is Fc gamma receptor (FcγR) dependent and involves the formation of stoichiometrically defined mAb-PA complexes that requires immunoglobulin bivalence and simultaneous interaction between PA and the two mAbs. The formation of these mAb-PA complexes inhibits PA oligomerization, resulting in protection. These data suggest that functional assessments of single Abs may inaccurately predict how the same Abs will operate in polyclonal preparations and imply that potentially therapeutic mAbs may be overlooked in single Ab screens.

Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures

Constraints placed upon the phenotypes of organisms result from their interactions with the environment. Over evolutionary time scales, these constraints feed back onto smaller molecular subnetworks comprising the organism. The evolution of biological networks is studied by considering a network of a few nodes embedded in a larger context. Taking into account this fact that any network under study is actually embedded in a larger context, we define network architecture, not on the basis of physical interactions alone, but rather as a specification of the manner in which constraints are placed upon the states of its nodes. We show that such network architectures possessing cycles in their topology, in contrast to those that do not, may be subjected to unsatisfiable constraints. This may be a significant factor leading to selection biased against those network architectures where such inconsistent constraints are more likely to arise. We proceed to quantify the likelihood of inconsistency arising as a function of network architecture finding that, in the absence of sampling bias over the space of possible constraints and for a given network size, networks with a larger number of cycles are more likely to have unsatisfiable constraints placed upon them. Our results identify a constraint that, at least in isolation, would contribute to a bias in the evolutionary process towards more hierarchical -modular versus completely connected network architectures. Together, these results highlight the context dependence of the functionality of biological networks.

Process Information and Evolution

Universal semantic communication (USC) is a theory that models communication among agents without the assumption of a fixed protocol. We demonstrate a connection, via a concept we refer to as process information, between a special case of USC and evolutionary processes. In this context, one agent attempts to interpret a potentially arbitrary signal produced within its environment. Sources of this effective signal can be modeled as a single alternative agent. Given a set of common underlying concepts that may be symbolized differently by different sources in the environment, any given entity must be able to correlate intrinsic information with input it receives from the environment in order to accurately interpret the ambient signal and ultimately coordinate its own actions. This scenario encapsulates a class of USC problems that provides insight into the semantic aspect of a model of evolution proposed by Rivoire and Leibler. Through this connection, we show that evolution corresponds to a means of solving a special class of USC problems, can be viewed as a special case of the multiplicative weights updates algorithm, and that infinite population selection with no mutation and no recombination conforms to the Rivoire–Leibler model. Finally, using process information we show that evolving populations implicitly internalize semantic information about their respective environments.