From Natural Microbe Screening to Sustained Chitinase Activity in Exogenous Hosts.

Genetic parts and hosts can be sourced from nature to realize new functions for synthetic biology or to improve performance in a particular application environment. Here, we proceed from the discovery and characterization of new parts to stable expression in new hosts with a particular focus on achieving sustained chitinase activity. Chitinase is a key enzyme for various industrial applications that require the breakdown of chitin, the second most abundant biopolymer on the earth. Diverse microbes exhibit chitinase activity, but for applications, the environmental conditions for optimal enzyme activity and microbe fitness must align with the application context. Achieving sustained chitinase activity under broad conditions in heterologous hosts has also proven difficult due to toxic side effects. Toward addressing these challenges, we first screen ocean water samples to identify microbes with chitinase activity. Next, we perform whole genome sequencing and analysis and select a chitinase gene for heterologous expression. Then, we optimize transformation methods for target hosts and introduce chitinase. Finally, to achieve robust function, we optimize ribosome binding sites and discover a beneficial promoter that upregulates chitinase expression in the presence of colloidal chitin in a sense-and-respond fashion. We demonstrate chitinase activity for >21 days in standard (Escherichia coli) and nonstandard (Roseobacter denitrificans) hosts. Besides enhancing chitinase applications, our pipeline is extendable to other functions, identifies natural microbes that can be used directly in non-GMO contexts, generates new parts for synthetic biology, and achieves weeks of stable activity in heterologous hosts.

Screening microbiota for effects on host tissues.

The assembly and function of microbial communities depends on many factors including the local environment and the metabolic properties of the colonizing organisms. Chemical communications or other secreted factors also play a role and are used by different microbial strains both cooperatively and competitively. The spectrum of microbial secretions have various effects on the microbe's respective hosts, both positive and negative. Thus, characterizing the roles of microbial community members and their secretions can yield key mechanistic insights into microbiome function and can lead to new intervention strategies. Focusing on the simple, yet important functional impact of toxicity, we quantify supernatant dosage responses with image data and examine the morphological effects of microbial secretions on skin-associated host cells. Since the diversity of microbial communities, coupled with the multiplicity of host tissues requires scalable methods, we develop and demonstrate a microfluidic device that enables high-content screening of microbial secretion effects on adherent cell types.

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

BACKGROUND: Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. RESULTS: We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. CONCLUSIONS: Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

Examining the effect of wound cleansing on the microbiome of venous stasis ulcers.

Common treatment for venous leg wounds includes topical wound dressings with compression. At each dressing change, wounds are debrided and washed; however, the effect of the washing procedure on the wound microbiome has not been studied. We hypothesized that wound washing may alter the wound microbiome. To characterize microbiome changes with respect to wound washing, swabs from 11 patients with chronic wounds were sampled before and after washing, and patient microbiomes were characterized using 16S rRNA sequencing and culturing. Microbiomes across patient samples prior to washing were typically polymicrobial but varied in the number and type of bacterial genera present. Proteus and Pseudomonas were the dominant genera in the study. We found that washing does not consistently change microbiome diversity but does cause consistent changes in microbiome composition. Specifically, washing caused a decrease in the relative abundance of the most highly represented genera in each patient cluster. The finding that venous leg ulcer wound washing, a standard of care therapy, can induce changes in the wound microbiome is novel and could be potentially informative for future guided therapy strategies.

A mixed community of skin microbiome representatives influences cutaneous processes more than individual members.

BACKGROUND: Skin, the largest organ of the human body by weight, hosts a diversity of microorganisms that can influence health. The microbial residents of the skin are now appreciated for their roles in host immune interactions, wound healing, colonization resistance, and various skin disorders. Still, much remains to be discovered in terms of the host pathways influenced by skin microorganisms, as well as the higher-level skin properties impacted through these microbe-host interactions. Towards this direction, recent efforts using mouse models point to pronounced changes in the transcriptional profiles of the skin in response to the presence of a microbial community. However, there is a need to quantify the roles of microorganisms at both the individual and community-level in healthy human skin. In this study, we utilize human skin equivalents to study the effects of individual taxa and a microbial community in a precisely controlled context. Through transcriptomics analysis, we identify key genes and pathways influenced by skin microbes, and we also characterize higher-level impacts on skin processes and properties through histological analyses. RESULTS: The presence of a microbiome on a 3D skin tissue model led to significantly altered patterns of gene expression, influencing genes involved in the regulation of apoptosis, proliferation, and the extracellular matrix (among others). Moreover, microbiome treatment influenced the thickness of the epidermal layer, reduced the number of actively proliferating cells, and increased filaggrin expression. Many of these findings were evident upon treatment with the mixed community, but either not detected or less pronounced in treatments by single microorganisms, underscoring the impact that a diverse skin microbiome has on the host. CONCLUSIONS: This work contributes to the understanding of how microbiome constituents individually and collectively influence human skin processes and properties. The results show that, while it is important to understand the effect of individual microbes on the host, a full community of microbes has unique and pronounced effects on the skin. Thus, in its impacts on the host, the skin microbiome is more than the sum of its parts. Video abstract.

Isolation and characterization of diverse microbial representatives from the human skin microbiome.

BACKGROUND: The skin micro-environment varies across the body, but all sites are host to microorganisms that can impact skin health. Some of these organisms are true commensals which colonize a unique niche on the skin, while open exposure of the skin to the environment also results in the transient presence of diverse microbes with unknown influences on skin health. Culture-based studies of skin microbiota suggest that skin microbes can affect skin properties, immune responses, pathogen growth, and wound healing. RESULTS: In this work, we greatly expanded the diversity of available commensal organisms by collecting > 800 organisms from 3 body sites of 17 individuals. Our collection includes > 30 bacterial genera and 14 fungal genera, with Staphylococcus and Micrococcus as the most prevalent isolates. We characterized a subset of skin isolates for the utilization of carbon compounds found on the skin surface. We observed that members of the skin microbiota have the capacity to metabolize amino acids, steroids, lipids, and sugars, as well as compounds originating from personal care products. CONCLUSIONS: This collection is a resource that will support skin microbiome research with the potential for discovery of novel small molecules, development of novel therapeutics, and insight into the metabolic activities of the skin microbiota. We believe this unique resource will inform skin microbiome management to benefit skin health. Video abstract.

Trait-based analysis of the human skin microbiome.

BACKGROUND: The past decade of microbiome research has concentrated on cataloging the diversity of taxa in different environments. The next decade is poised to focus on microbial traits and function. Most existing methods for doing this perform pathway analysis using reference databases. This has both benefits and drawbacks. Function can go undetected if reference databases are coarse-grained or incomplete. Likewise, detection of a pathway does not guarantee expression of the associated function. Finally, function cannot be connected to specific microbial constituents, making it difficult to ascertain the types of organisms exhibiting particular traits-something that is important for understanding microbial success in specific environments. A complementary approach to pathway analysis is to use the wealth of microbial trait information collected over years of lab-based, culture experiments. METHODS: Here, we use journal articles and Bergey's Manual of Systematic Bacteriology to develop a trait-based database for 971 human skin bacterial taxa. We then use this database to examine functional traits that are over/underrepresented among skin taxa. Specifically, we focus on three trait classes-binary, categorical, and quantitative-and compare trait values among skin taxa and microbial taxa more broadly. We compare binary traits using a Chi-square test, categorical traits using randomization trials, and quantitative traits using a nonparametric relative effects test based on global rankings using Tukey contrasts. RESULTS: We find a number of traits that are over/underrepresented within the human skin microbiome. For example, spore formation, acid phosphatase, alkaline phosphatase, pigment production, catalase, and oxidase are all less common among skin taxa. As well, skin bacteria are less likely to be aerobic, favoring, instead, a facultative strategy. They are also less likely to exhibit gliding motility, less likely to be spirillum or rod-shaped, and less likely to grow in chains. Finally, skin bacteria have more difficulty at high pH, prefer warmer temperatures, and are much less resilient to hypotonic conditions. CONCLUSIONS: Our analysis shows how an approach that relies on information from culture experiments can both support findings from pathway analysis, and also generate new insights into the structuring principles of microbial communities.

Detecting interaction networks in the human microbiome with conditional Granger causality.

Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because "correlation is neither necessary nor sufficient to establish causation"; environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiome to decipher the networks of correlation and causation of human-associated microbial genera. We show that correlation is not a good proxy for biological interaction; we observed a weak negative relationship between correlation and causality. Strong interspecific interactions are disproportionately positive, whereas almost all strong intraspecific interactions are negative. Interestingly, intraspecific interactions also appear to act at a short timescale causing vast majority of the effects within 1-3 days. We report how different taxa are involved in causal relationships with others, and show that strong interspecific interactions are rarely conserved across two body sites whereas strong intraspecific interactions are much more conserved, ranging from 33% between the gut and right-hand to 70% between the two hands. Therefore, in the absence of guiding assumptions about ecological interactions, Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.

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.

Direct Growth of Bacteria in Headspace Vials Allows for Screening of Volatiles by Gas Chromatography Mass Spectrometry.

Bacterially produced volatile organic compounds (VOCs) can modify growth patterns of eukaryotic hosts and competing/cohabiting microbes. These compounds have been implicated in skin disorders and attraction of biting pests. Current methods to detect and characterize VOCs from microbial cultures can be laborious and low-throughput, making it difficult to understand the behavior of microbial populations. In this work we present an efficient method employing gas chromatography/mass spectrometry with autosampling to characterize VOC profiles from solid-phase bacterial cultures. We compare this method to complementary plate-based assays and measure the effects of growth media and incubation temperature on the VOC profiles from a well-studied Pseudomonas aeruginosa PAO1 system. We observe that P. aeruginosa produces longer chain VOCs, such as 2-undecanone and 2-undecanol in higher amounts at 37°C than 30°C. We demonstrate the throughput of this method by studying VOC profiles from a representative collection of skin bacterial isolates under three parallel growth conditions. We observe differential production of various aldehydes and ketones depending on bacterial strain. This generalizable method will support screening of bacterial populations in a variety of research areas.

Metabolic division of labor in microbial systems.

Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established. Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

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.

Preservation of protein expression systems at elevated temperatures for portable therapeutic production.

Many biotechnology capabilities are limited by stringent storage needs of reagents, largely prohibiting use outside of specialized laboratories. Focusing on a large class of protein-based biotechnology applications, we address this issue by developing a method for preserving cell-free protein expression systems for months above room temperature. Our approach realizes unprecedented long-term stability at elevated temperatures by leveraging the sugar alcohol trehalose, a simple, low-cost, open-air drying step, and strategic separation of reaction components during drying. The resulting preservation capacity enables efficient production of a wide range of on-demand proteins under adverse conditions, for instance during emergency outbreaks or in remote locations. To demonstrate application potential, we use cell-free reagents subjected to months of exposure at 37°C and atmospheric conditions to produce sufficient concentrations of a pyocin protein to kill Pseudomonas aeruginosa, a troublesome pathogen for traumatic and burn wound injuries. Our work makes possible new biotechnology applications that demand ruggedness and scalability.

Cell-free synthetic biology for environmental sensing and remediation.

The fields of biosensing and bioremediation leverage the phenomenal array of sensing and metabolic capabilities offered by natural microbes. Synthetic biology provides tools for transforming these fields through complex integration of natural and novel biological components to achieve sophisticated sensing, regulation, and metabolic function. However, the majority of synthetic biology efforts are conducted in living cells, and concerns over releasing genetically modified organisms constitute a key barrier to environmental applications. Cell-free protein expression systems offer a path towards leveraging synthetic biology, while preventing the spread of engineered organisms in nature. Recent efforts in the areas of cell-free approaches for sensing, regulation, and metabolic pathway implementation, as well as for preserving and deploying cell-free expression components, embody key steps towards realizing the potential of cell-free systems for environmental sensing and remediation.

Invasion speeds in microbial systems with toxin production and quorum sensing.

The theory of invasions and invasion speeds has traditionally been studied in macroscopic systems. Surprisingly, microbial invasions have received less attention. Although microbes share many of the features associated with competition between larger-bodied organisms, they also exhibit distinctive behaviors that require new mathematical treatments to fully understand invasions in microbial systems. Most notable is the possibility for long-distance interactions, including competition between populations mediated by diffusible toxins and cooperation among individuals of a single population using quorum sensing. In this paper, we model bacterial invasion using a system of coupled partial differential equations based on Fisher's equation. Our model considers a competitive system with diffusible toxins that, in some cases, are expressed in response to quorum sensing. First, we derive analytical approximations for invasion speeds in the limits of fast and slow toxin diffusion. We then test the validity of our analytical approximations and explore intermediate rates of toxin diffusion using numerical simulations. Interestingly, we find that toxins should diffuse quickly when used offensively, but that there are two optimal strategies when toxins are used as a defense mechanism. Specifically, toxins should diffuse quickly when their killing efficacy is high, but should diffuse slowly when their killing efficacy is low. Our approach permits an explicit investigation of the properties and characteristics of diffusible compounds used in non-local competition, and is relevant for microbial systems and select macroscopic taxa, such as plants and corals, that can interact through biochemicals.

Antibiotics as a selective driver for conjugation dynamics.

It is generally assumed that antibiotics can promote horizontal gene transfer. However, because of a variety of confounding factors that complicate the interpretation of previous studies, the mechanisms by which antibiotics modulate horizontal gene transfer remain poorly understood. In particular, it is unclear whether antibiotics directly regulate the efficiency of horizontal gene transfer, serve as a selection force to modulate population dynamics after such gene transfer has occurred, or both. Here, we address this question by quantifying conjugation dynamics in the presence and absence of antibiotic-mediated selection. Surprisingly, we find that sublethal concentrations of antibiotics from the most widely used classes do not significantly increase the conjugation efficiency. Instead, our modelling and experimental results demonstrate that conjugation dynamics are dictated by antibiotic-mediated selection, which can both promote and suppress conjugation dynamics. Our findings suggest that the contribution of antibiotics to the promotion of horizontal gene transfer may have been overestimated. These findings have implications for designing effective antibiotic treatment protocols and for assessing the risks of antibiotic use.

Multi-input regulation and logic with T7 promoters in cells and cell-free systems.

Engineered gene circuits offer an opportunity to harness biological systems for biotechnological and biomedical applications. However, reliance on native host promoters for the construction of circuit elements, such as logic gates, can make the implementation of predictable, independently functioning circuits difficult. In contrast, T7 promoters offer a simple orthogonal expression system for use in a variety of cellular backgrounds and even in cell-free systems. Here we develop a T7 promoter system that can be regulated by two different transcriptional repressors for the construction of a logic gate that functions in cells and in cell-free systems. We first present LacI repressible T7lacO promoters that are regulated from a distal lac operator site for repression. We next explore the positioning of a tet operator site within the T7lacO framework to create T7 promoters that respond to tet and lac repressors and realize an IMPLIES gate. Finally, we demonstrate that these dual input sensitive promoters function in an E. coli cell-free protein expression system. Our results expand the utility of T7 promoters in cell based as well as cell-free synthetic biology applications.

Probing cell-free gene expression noise in femtoliter volumes.

Cell-free systems offer a simplified and flexible context that enables important biological reactions while removing complicating factors such as fitness, division, and mutation that are associated with living cells. However, cell-free expression in unconfined spaces is missing important elements of expression in living cells. In particular, the small volume of living cells can give rise to significant stochastic effects, which are negligible in bulk cell-free reactions. Here, we confine cell-free gene expression reactions to cell-relevant 20 fL volumes (between the volumes of Escherichia coli and Saccharomyces cerevisiae ), in polydimethylsiloxane (PDMS) containers. We demonstrate that expression efficiency varies widely among different containers, likely due to non-Poisson distribution of expression machinery at the observed scale. Previously, this phenomenon has been observed only in liposomes. In addition, we analyze gene expression noise. This analysis is facilitated by our use of cell-free systems, which allow the mapping of the measured noise properties to intrinsic noise models. In contrast, previous live cell noise analysis efforts have been complicated by multiple noise sources. Noise analysis reveals signatures of translational bursting, while noise dynamics suggest that overall cell-free expression is limited by a diminishing translation rate. In addition to offering a unique approach to understanding noise in gene circuits, our work contributes to a deeper understanding of the biophysical properties of cell-free expression systems, thus aiding efforts to harness cell-free systems for synthetic biology applications.

Expression optimization and synthetic gene networks in cell-free systems.

Synthetic biology offers great promise to a variety of applications through the forward engineering of biological function. Most efforts in this field have focused on employing living cells, yet cell-free approaches offer simpler and more flexible contexts. Here, we evaluate cell-free regulatory systems based on T7 promoter-driven expression by characterizing variants of TetR and LacI repressible T7 promoters in a cell-free context and examining sequence elements that determine expression efficiency. Using the resulting constructs, we then explore different approaches for composing regulatory systems, leading to the implementation of inducible negative feedback in Escherichia coli extracts and in the minimal PURE system, which consists of purified proteins necessary for transcription and translation. Despite the fact that negative feedback motifs are common and essential to many natural and engineered systems, this simple building block has not previously been implemented in a cell-free context. As a final step, we then demonstrate that the feedback systems developed using our cell-free approach can be implemented in live E. coli as well, illustrating the potential for using cell-free expression to fast track the development of live cell systems in synthetic biology. Our quantitative cell-free component characterizations and demonstration of negative feedback embody important steps on the path to harnessing biological function in a bottom-up fashion.