Metabolic engineering of human gut microbiome: Recent developments and future perspectives

Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities

Metagenomes of tropical soil-derived anaerobic switchgrass-adapted consortia with and without iron

Tropical forest soils decompose litter rapidly with frequent episodes of anoxia, making it likely that bacteria using alternate terminal electron acceptors (TEAs) such as iron play a large role in supporting decomposition under these conditions. The prevalence of many types of metabolism in litter deconstruction makes these soils useful templates for improving biofuel production. To investigate how iron availability affects decomposition, we cultivated feedstock-adapted consortia (FACs) derived from iron-rich tropical forest soils accustomed to experiencing frequent episodes of anaerobic conditions and frequently fluctuating redox. One consortium was propagated under fermenting conditions, with switchgrass as the sole carbon source in minimal media (SG only FACs), and the other consortium was treated the same way but received poorly crystalline iron as an additional terminal electron acceptor (SG + Fe FACs). We sequenced the metagenomes of both consortia to a depth of about 150 Mb each, resulting in a coverage of 26× for the more diverse SG + Fe FACs, and 81× for the relatively less diverse SG only FACs. Both consortia were able to quickly grow on switchgrass, and the iron-amended consortium exhibited significantly higher microbial diversity than the unamended consortium. We found evidence of higher stress in the unamended FACs and increased sugar transport and utilization in the iron-amended FACs. This work provides metagenomic evidence that supplementation of alternative TEAs may improve feedstock deconstruction in biofuel production

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tool

Computational Design of Synthetic Microbial Communities

In naturally occurring microbial systems, species rarely exist in isolation. There is strong ecological evidence for a positive relationship between species diversity and the functional output of communities. The pervasiveness of these communities in nature highlights that there may be advantages for engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigate issues often found in engineering a monoculture, especially when functional complexity is increasing. The establishment of synthetic microbial communities is a major challenge we must overcome in order to implement coordinated multicellular systems. Here I present computational tools that help us design engineering strategies for establishing synthetic microbial communities. Using these tools I identify promising candidates for several design scenarios. This work highlights the importance of parameter inference and model selection to build robust communities. The findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and tuning the community composition. Additionally, I show that fundamental interactions in small synthetic communities can produce chaotic behaviour that is unforecastable. Together these findings have important ramifications for how we build synthetic communities in the lab, and the considerations of interactions in microbiomes we manipulate

Oral Health by Using Probiotic Products

One of the most prevalent and important health problems in the world is periodontal and plaque-related diseases for which antibiotic drugs with their associated side effects are used as treatment. With increasing resistance to antibiotics and a desire from the general public for "natural" therapies, there is a need to minimize antibiotic use and develop new treatments for oral diseases without antimicrobial agents. Probiotics are viable microorganisms that provide a health benefit to the host when administered in adequate amounts; studies show that probiotics have the potential to modify the oral microbiota and decrease the colony-forming unit counts of the oral pathogens being investigated to prevent or treat oral diseases, such as dental caries and the periodontal diseases. In addition, the identification of specific strains with probiotic activity is required for any oral infectious disease to determine the exact dose, the time of treatment, and the ideal vehicle

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.We gratefully acknowledge funding from the Defense Advanced Research Projects Agency (Purchase Request No. HR0011515303, Contract No. HR0011-15-C-0091), the U.S. Department of Energy (Grants DE-SC0004962 and DE-SC0012627), the NIH (Grants 5R01DE024468 and R01GM121950), the national Science Foundation (Grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office ARC grant on Systems Biology Approaches to Microbiome Research. We also thank Dr Kirill Korolev and members of the Segre Lab for their invaluable feedback on this work. (HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - national Science Foundation; NSFOCE-BSF 1635070 - national Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office ARC)Published versio

Machine learning for microbial ecology: predicting interactions and identifying their putative mechanisms

Microbial communities are key components of Earth’s ecosystems and they play important roles in human health and industrial processes. These communities and their functions can strongly depend on the diverse interactions between constituent species, posing the question of how such interactions can be predicted, measured and controlled. This challenge is particularly relevant for the many practical applications enabled by the rising field of synthetic microbial ecology, which includes the design of microbiome therapies for human diseases. Advances in sequencing technologies and genomic databases provide valuable datasets and tools for studying inter-microbial interactions, but the capacity to characterize the strength and mechanisms of interactions between species in large consortia is still an unsolved challenge. In this thesis, I show how machine learning methods can be used to help address these questions. The first portion of my thesis work was focused on predicting the outcome of pairwise interactions between microbial species. By integrating genomic information and observed experimental data, I used machine learning algorithms to explore the predictive relationship between single-species traits and inter-species interaction phenotypes. I found that organismal traits (e.g. annotated functions of genomic elements) are sufficient to predict the qualitative outcome of interactions between microbes. I also found that the relative fraction of possible experiments needed to build acceptable models drastically shrinks as the combinatorial space grows. In the second part of my thesis work, I developed an algorithmic method for identifying putative interaction mechanisms by scoring combinations of variables that random forest uses in order to predict interaction outcomes. I applied this method to a study of the human microbiome and identified a previously unreported combination of microbes that are strongly associated with Crohn’s disease. In the last part of my thesis, I utilized a regression approach to first identify and then quantify interactions between microbial species relevant to community function. The work I present in this dissertation provides a general framework for understanding the myriad interactions that occur in natural and synthetic microbial consortia

Automated design of synthetic microbial communities

Microbial species rarely exist in isolation. In naturally occurring microbial systems there is strong evidence for a positive relationship between species diversity and productivity of communities. The pervasiveness of these communities in nature highlights possible advantages for genetically engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigate issues often found in engineering a monoculture, especially as functional complexity increases. Here, we demonstrate a methodology for designing robust synthetic communities that include competition for nutrients, and use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We computationally explore all two- and three- strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and further tuning the community composition