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Changes in the Genetic Requirements for Microbial Interactions with Increasing Community Complexity Manon Morin1, Emily C Pierce1, Rachel J Dutton1,2*

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Changes in the Genetic Requirements for Microbial Interactions with Increasing Community Complexity Manon Morin1, Emily C Pierce1, Rachel J Dutton1,2* RESEARCH ARTICLE Changes in the genetic requirements for microbial interactions with increasing community complexity Manon Morin1, Emily C Pierce1, Rachel J Dutton1,2* 1Division of Biological Sciences, University of California, San Diego, La Jolla, United States; 2Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, United States Abstract Microbial community structure and function rely on complex interactions whose underlying molecular mechanisms are poorly understood. To investigate these interactions in a simple microbiome, we introduced E. coli into an experimental community based on a cheese rind and identified the differences in E. coli’s genetic requirements for growth in interactive and non- interactive contexts using Random Barcode Transposon Sequencing (RB-TnSeq) and RNASeq. Genetic requirements varied among pairwise growth conditions and between pairwise and community conditions. Our analysis points to mechanisms by which growth conditions change as a result of increasing community complexity and suggests that growth within a community relies on a combination of pairwise and higher-order interactions. Our work provides a framework for using the model organism E. coli as a readout to investigate microbial interactions regardless of the genetic tractability of members of the studied ecosystem. DOI: https://doi.org/10.7554/eLife.37072.001 Introduction Microorganisms rarely grow as single isolated species but rather as part of diverse microbial commu- nities. In these communities, bacteria, archaea, protists, viruses and fungi can coexist and perform complex functions impacting biogeochemical cycles and human health (Falkowski et al., 2008; Flint et al., 2012). Deciphering microbial growth principles within a community is challenging due to *For correspondence: the intricate interactions between microorganisms, and between microorganisms and their environ- [email protected] ment. While interest in microbial communities has dramatically increased, our understanding of Competing interests: The microbial interactions within communities is lagging significantly behind our ability to describe the authors declare that no composition of a given community. competing interests exist. Approaches relying on 16S rDNA sequencing analyses of microbial communities can be used to Funding: See page 23 reconstruct ecosystem networks and detect patterns of co-occurrence to infer general interactions Received: 28 March 2018 such as competition, mutualism and commensalism (Faust and Raes, 2012). However, the molecular Accepted: 09 September 2018 mechanisms underlying these interactions remain largely uncharacterized. Further, the way in which Published: 13 September 2018 these interactions are organized within a community, such as whether they consist of predominantly pairwise or higher-order interactions, is even less clear. A more precise understanding of microbial Copyright Morin et al. This interactions, their underlying mechanisms, and how these interactions are structured within a com- article is distributed under the munity, are all necessary to elucidate the principles by which a community is shaped. In this study, terms of the Creative Commons Attribution License, which we combine genome-scale genetic and transcriptomic approaches within an experimentally tractable permits unrestricted use and model microbial community to begin to address these questions. redistribution provided that the Genome-scale approaches, such as transposon mutagenesis coupled to next-generation sequenc- original author and source are ing (TnSeq approaches) have been successfully used to quantify the contribution and thus the impor- credited. tance of individual genes to a given phenotype (van Opijnen and Camilli, 2013). These techniques Morin et al. eLife 2018;7:e37072. DOI: https://doi.org/10.7554/eLife.37072 1 of 26 Research article Microbiology and Infectious Disease eLife digest Microorganisms live almost everywhere on Earth. Whether it is rainforest soil or human skin, each environment hosts a unique community of microbes, referred to as its microbiome. There can be upwards of hundreds of species in a single microbiome, and these species can interact in a variety of ways; some cooperate, others compete, and some can kill other species. Deciphering the nature of these interactions is crucial to knowing how microbiomes work, and how they might be manipulated, for example, to improve human health. Yet studies into these interactions have proven difficult, not least because most of the species involved are difficult to grow in controlled experiments. One environment that is home to a rich community of microbes is the outer surface of cheese, known as the cheese rind. The cheese rind microbiome is a useful system for laboratory experiments, because it is relatively easy to replicate and its microbes can be grown on their own or in combinations with others. To explore the nature of interactions in microbiomes, Morin et al. have now grown a large collection of E. coli mutants as members of simplified microbiomes based on the cheese rind. The mutant bacteria were grown on cheese either alone, paired with one other species, or alongside a community of three species. The aim was to see which mutants grew poorly when other species were present, thus allowing Morin et al. to identify specific genes that are important for interactions within the experimental microbiomes. Even in these simplified microbiomes, the microbes interacted in a variety of ways. Some microbes competed with E. coli for elements like iron and nitrogen; others cooperated by sharing the building blocks needed to make larger molecules. Many of the interactions that happened when E. coli was paired with one species were not seen when more species were added to the community. Similarly, some interactions were only seen when E. coli was grown alongside a community of microbes, and not when it was paired with any of the three species on their own. These findings show that complex interactions are present even in a simplified microbiome. This experimental approach can now be applied to other microbiomes that can be grown in the laboratory to examine whether the patterns of interactions seen are generalizable or specific to the cheese rind system. DOI: https://doi.org/10.7554/eLife.37072.002 use a pooled library of transposon insertion mutants whose frequency is measured to identify genes important for growth in a given condition. Recently, the generation and introduction of unique ran- dom barcodes into transposon mutant libraries made this approach more high-throughput, enabling screens of important genes across hundreds of conditions and for numerous genetically tractable microorganisms (Wetmore et al., 2015; Price et al., 2018). To investigate the genetic basis of microbial interactions, we have adapted this approach to iden- tify and compare genetic requirements in single-species (non-interactive) and multi-species (interac- tive) conditions. We used a large and diverse transposon library previously generated in the genetically-tractable model bacterium E. coli K12 (Wetmore et al., 2015) to characterize the genetic requirements of interactions within a model community based on the rind of cheese (Wolfe et al., 2014). The fact that the E. coli genome has undergone extensive characterization can help more effectively interpret the genetic requirements introduced by growth within communities. Although E. coli K12 is not a typical endogenous species of this particular microbiome, non-pathogenic E. coli strains can be found in raw milk and raw-milk cheese (Trmcˇic´ et al., 2016). Shiga-toxin-producing E. coli 0157:H7 and non-0157 pathogenic E. coli species are common invaders of the cheese environ- ment and can survive during cheese making causing mild to life-threatening symptoms after inges- tion (Coia et al., 2001; Montet et al., 2009; Frank et al., 1977). Using the E. coli transposon library, we (i) identified the set of genes important for growth alone in the cheese environment, (ii) identified the set of genes important for growth in pairwise conditions with each individual community member and (iii) identified the set of genes important for growth in the presence of the complete community. Characterization of the functions or pathways associated with growth in interactive versus non-interactive conditions were then used to infer the biological Morin et al. eLife 2018;7:e37072. DOI: https://doi.org/10.7554/eLife.37072 2 of 26 Research article Microbiology and Infectious Disease processes involved in interactions within the model microbiome. Additionally, we compared the set of genes important for growth in pairwise conditions with the ones important for growth in a com- munity to investigate how microbial interactions change depending on the complexity of the interac- tive context. We also performed a similar RB-TnSeq analysis during non-interactive and interactive conditions using a transposon library we generated in the cheese-endogenous species Pseudomonas psychrophila JB418. Finally, we measured changes in the transcriptional profile of E. coli during growth alone, growth in pairwise conditions, and within the community using RNAseq as a comple- mentary approach to RB-TnSeq in defining microbial interactions. This analysis revealed a deep reor- ganization of gene expression whenever E. coli is in the presence of other species. This work revealed numerous interactions between species, such as metabolic competition for iron and
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