Syntrophic exchange in synthetic microbial communities PNAS PLUS Michael T. Meea,b, James J. Collinsa,c,d,e, George M. Churchb,c,1, and Harris H. Wangf,1 aDepartment of Biomedical Engineering, Boston University, Boston, MA 02215; bDepartment of Genetics, Harvard Medical School, Boston, MA 02115; cWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115; dHoward Hughes Medical Institute and eCenter of Synthetic Biology, Boston University, Boston, MA 02215; and fDepartment of Systems Biology, Columbia University, New York, NY 10032 Contributed by George M. Church, April 1, 2014 (sent for review January 30, 2014) Metabolic crossfeeding is an important process that can broadly amino acids in natural interkingdom and interspecies exchange shape microbial communities. However, little is known about networks (9–11). Recent comparative analyses of microbial specific crossfeeding principles that drive the formation and genomes suggest that a significant proportion of all bacteria lack maintenance of individuals within a mixed population. Here, we essential pathways for amino acid biosynthesis (2). These auxo- devised a series of synthetic syntrophic communities to probe the trophic microbes thus require extracellular sources of amino complex interactions underlying metabolic exchange of amino acids. acids for survival. Understanding amino acid exchange therefore We experimentally analyzed multimember, multidimensional com- presents an opportunity to gain new insights into basic principles munities of Escherichia coli of increasing sophistication to assess the in metabolic crossfeeding. Recently, several studies have used outcomes of synergistic crossfeeding. We find that biosynthetically model systems of Saccharomyces cerevisiae (12), Saccharomyces costly amino acids including methionine, lysine, isoleucine, arginine, enterica (13), and E. coli (14–16) to study syntrophic growth of and aromatics, tend to promote stronger cooperative interactions amino acid auxotrophs in coculture environments. Numerous than amino acids that are cheaper to produce. Furthermore, cells that quantitative models have also been developed to describe the share common intermediates along branching pathways yielded behavior of these multispecies systems, including those that in- more synergistic growth, but exhibited many instances of both pos- tegrate dynamics (17, 18), metabolism (19–21), and spatial co- itive and negative epistasis when these interactions scaled to higher ordination (22). Although these efforts have led to an improved dimensions. In more complex communities, we find certain members understanding of the dynamics of syntrophic pairs and the en- exhibiting keystone species-like behavior that drastically impact ergetic and benefits of cooperativity in these simple systems (23), the community dynamics. Based on comparative genomic analysis larger more complex syntrophic systems have yet to be explored. of >6,000 sequenced bacteria from diverse environments, we present SYSTEMS BIOLOGY Here, we use engineered E. coli mutants to study syntrophic evidence suggesting that amino acid biosynthesis has been broadly crossfeeding, scaling to higher-dimensional synthetic ecosystems optimized to reduce individual metabolic burden in favor of en- hanced crossfeeding to support synergistic growth across the bio- of increasing sophistication. We first devised pairwise syntrophic sphere. These results improve our basic understanding of microbial communities that show essential and interesting dynamics that syntrophy while also highlighting the utility and limitations of cur- can be predicted by simple kinetic models. We then increased rent modeling approaches to describe the dynamic complexities un- the complexity of the interaction in three-member synthetic derlying microbial ecosystems. This work sets the foundation for consortia involving crossfeeding of multiple metabolites. To future endeavors to resolve key questions in microbial ecology and further increase the complexity of our system, we devised a 14- evolution, and presents a platform to develop better and more ro- member community to understand key drivers of population dy- bust engineered synthetic communities for industrial biotechnology. namics over short and evolutionary timescales. Finally, we provide evidence for widespread trends of metabolic crossfeeding based synthetic ecosystem | amino acid exchange | population modeling on comparative genomic analysis of aminoacidbiosynthesisacross icrobes are abundantly found in almost every part of the Significance Mworld, living in communities that are diverse in many facets. Although it is clear that cooperation and competition Metabolic exchange between microbes is a crucial process within microbial communities is central to their stability, main- driving the development of microbial ecosystems. The ex- tenance, and longevity, there is limited knowledge about the change of essential amino acids presents an opportunity to general principles guiding the formation of these intricate sys- investigate the guiding principles underlying microbial trade in tems. Understanding the underlying governing principles that nature. In this study, we devised synthetic communities of shape a microbial community is key for microbial ecology but is Escherichia coli bacteria of increasing complexity to measure also crucial for engineering synthetic microbiomes for various general properties enabling metabolic exchange of amino acids. biotechnological applications (1–3). Numerous such examples We identified numerous syntrophic interactions that enable co- have been recently described including the bioconversion of operative growth, which exhibited both positive and negative unprocessed cellulolytic feedstocks into biofuel isobutanol using epistasis with increasing community complexity. Our results fungal–bacterial communities (4) and biofuel precursor methyl suggest that amino acid auxotrophy may be an evolutionarily halides using yeast–bacterial cocultures (5). Other emerging optimizing strategy to reduce biosynthetic burden while pro- applications in biosensing and bioremediation against environ- moting cooperative interactions between different bacteria in mental toxins such as arsenic (6) and pathogens such as Pseu- the microbiome. domonas aeruginosa and Vibrio cholerae have been demonstrated using engineered quorum-sensing Escherichia coli (7, 8). These Author contributions: M.T.M., J.J.C., G.M.C., and H.H.W. designed research; M.T.M. and H.H.W. performed research; M.T.M. and H.H.W. contributed new reagents/analytic tools; advances paint an exciting future for the development of sophis- M.T.M. and H.H.W. analyzed data; and M.T.M., J.J.C., G.M.C., and H.H.W. wrote ticated multispecies microbial communities to address pressing the paper. challenges and the crucial need to understand the basic principles The authors declare no conflict of interest. that enables their design and engineering. Freely available online through the PNAS open access option. An important process that governs the growth and composi- 1To whom correspondence may be addressed. E-mail: [email protected] tion of microbial ecosystems is the exchange of essential or [email protected]. metabolites, known as metabolic crossfeeding. Entomological This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. studies have elucidated on a case-by-case basis the importance of 1073/pnas.1405641111/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1405641111 PNAS | Published online April 28, 2014 | E2149–E2156 Downloaded by guest on September 28, 2021 thousands of sequenced genomes. Our large-scale and systematic glucose minimal media after 4 d and growth only when supple- efforts represent an important foray into forward and reverse en- mented with each amino acid needed. Using a microplate gineering synthetic microbial communities to gain key governing spectrophotometer, we performed kinetic growth curve analysis principles of microbial ecology and systems microbiology. for each of the 14 auxotrophs grown in M9-glucose supple- mented with varying initial amino acid levels. Under these amino Results acid-limiting conditions, an auxotrophic strain will grow expo- Our overall goal is to develop and understand a simple microbial nentially until the amino acid supplementation is exhausted (Fig. model of metabolic crossfeeding that can be scaled in a tractable S1B). Saturating cell densities (i.e., carrying capacities) plotted stepwise manner, toward reconstituting the complexity and dy- against initial seeding amino acid levels show a strong linear namics exhibited by natural ecosystems (Fig. 1A). To this end, we relationship (Fig. S1B) indicating that external amino acid levels devised a series of syntrophic microbial communities of in- can determine cell growth in a linear and predictable manner. creasing diversity and complexity using the simple model bacte- We estimated the number of extracellular amino acids needed to rium E. coli. Our system is based on the syntrophic behavior of generate a cell for each of the 14 amino acids (Fig. S1A and amino acid exchange between auxotrophic mutants to facilitate Dataset S1). The estimated biosynthetic cost to produce each coculture growth. We first investigated the energetics involved amino acid (24) shows a strong inverse relationship with the in amino acid utilization and exchange. Starting from a pro- amount of amino acids needed to produce a cell (Fig. 1B), totrophic E. coli