Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium

Microbial consortia form when multiple species colocalize and communally generate a function that none is capable of alone. Consortia abound in nature, and their cooperative metabolic activities influence everything from biodiversity in the global food chain to human weight gain. Here, we present an engineered consortium in which the microbial members communicate with each other and exhibit a “consensus” gene expression response. Two colocalized populations of Escherichia coli converse bidirectionally by exchanging acyl-homoserine lactone signals. The consortium generates the gene-expression response if and only if both populations are present at sufficient cell densities. Because neither population can respond without the other's signal, this consensus function can be considered a logical AND gate in which the inputs are cell populations. The microbial consensus consortium operates in diverse growth modes, including in a biofilm, where it sustains its response for several days

Synthetic biology: new engineering rules for an emerging discipline

Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development

A Stoichioproteomic Analysis of Samples from the Human Microbiome Project

Ecological stoichiometry (ES) uses organism-specific elemental content to explain differences in species life histories, species interactions, community organization, environmental constraints and even ecosystem function. Although ES has been successfully applied to a range of different organisms, most emphasis on microbial ecological stoichiometry focuses on lake, ocean, and soil communities. With the recent advances in human microbiome research, however, large amounts of data are being generated that describe differences in community composition across body sites and individuals. We suggest that ES may provide a framework for beginning to understand the structure, organization, and function of human microbial communities, including why certain organisms exist at certain locations, and how they interact with both the other microbes in their environment and their human host. As a first step, we undertake a stoichioproteomic analysis of microbial communities from different body sites. Specifically, we compare and contrast the elemental composition of microbial protein samples using annotated sequencing data from 690 gut, vaginal, oral, nares, and skin samples currently available through the Human Microbiome Project. Our results suggest significant differences in both the median and variance of the carbon, oxygen, nitrogen, and sulfur contents of microbial protein samples from different locations. For example, whereas proteins from vaginal sites are high in carbon, proteins from skin and nasal sites are high in nitrogen and oxygen. Meanwhile, proteins from stool (the gut) are particularly high in sulfur content. We interpret these differences in terms of the local environments at different human body sites, including atmospheric exposure and food intake rates

Stochastic Turing patterns in a synthetic bacterial population

The origin of biological morphology and form is one of the deepest problems in science, underlying our understanding of development and the functioning of living systems. In 1952, Alan Turing showed that chemical morphogenesis could arise from a linear instability of a spatially uniform state, giving rise to periodic pattern formation in reaction–diffusion systems but only those with a rapidly diffusing inhibitor and a slowly diffusing activator. These conditions are disappointingly hard to achieve in nature, and the role of Turing instabilities in biological pattern formation has been called into question. Recently, the theory was extended to include noisy activator–inhibitor birth and death processes. Surprisingly, this stochastic Turing theory predicts the existence of patterns over a wide range of parameters, in particular with no severe requirement on the ratio of activator–inhibitor diffusion coefficients. To explore whether this mechanism is viable in practice, we have genetically engineered a synthetic bacterial population in which the signaling molecules form a stochastic activator–inhibitor system. The synthetic pattern-forming gene circuit destabilizes an initially homogenous lawn of genetically engineered bacteria, producing disordered patterns with tunable features on a spatial scale much larger than that of a single cell. Spatial correlations of the experimental patterns agree quantitatively with the signature predicted by theory. These results show that Turing-type pattern-forming mechanisms, if driven by stochasticity, can potentially underlie a broad range of biological patterns. These findings provide the groundwork for a unified picture of biological morphogenesis, arising from a combination of stochastic gene expression and dynamical instabilities. Keywords: Turing patterns; biofilm; synthetic biology; signaling molecules; stochastic gene expressionNational Science Foundation (U.S.) (Grant CCF-1521925)National Institutes of Health (U.S.) (Grant CCF-1521925)National Science Foundation (U.S.) (Grant CNS-1446474)National Institutes of Health (U.S.) (Grant CNS-1446474

Additional file 2 of A mixed community of skin microbiome representatives influences cutaneous processes more than individual members

Additional file 1. Microorganism Isolate Information. Table containing information for the bacterial microorganisms used in this study. Table containing the identifying genus, the isolate identifier used by the collecting study, the 16S rRNA sequence, and the least common ancestors of the bacterial isolate determined by classification to the Greengenes, RDP, and SILVA databases [42]