Life is sustained by an array of chemical reactions that is collectively referred to as metabolism. Some of these reactions break down complex substances to release energy and vital compounds, while others make new molecules from smaller building blocks.

Bacterial communities are regularly composed of heterogeneous species, several of which have lost one or more essential metabolic pathways. Nevertheless, these cells can still survive by making use of metabolic products released by their neighbouring cells.

Yeast are single-celled fungi that also form colonies and, as eukaryotes, they possess cells that are more similar to our own. However, in the laboratory, complementary metabolically deficient yeast cells do not survive when mixed together. It was presumed this is because yeast cells make only enough of each essential metabolite for themself, and so can’t replace those that are missing from their neighbouring cells. Campbell et al. now challenge this view by finding that yeast cells release a variety of metabolites, they use these released metabolites in preference to making their own, and possess the capacity to grow on the basis of a non-cell-autonomous metabolism.

This discovery came with the design of a new experimental test to study metabolite exchange interactions. This method uses yeast cells that have one or more of their own metabolic genes disabled, and instead have a copy of these genes on small circular DNA 'mini-chromosomes' (called plasmids). The gene on the plasmid can compensate for the yeast having its own gene missing, and allows the cell to still make the metabolic product it needs to survive. However, as a single cell divides to form a colony, cells randomly lose these plasmids, leaving some of the cells deficient for a particular metabolite. These cells can only survive if the other cells in the colony export the missing metabolite in the quantity needed for growth. Using this test, Campbell et al. found that yeast cells can export missing metabolites at levels needed to sustain these emerging metabolic mutants. Additionally, these yeast communities could grow at levels comparable to other yeast without metabolic deficiencies. The resulting colonies also feature one of several different genetic and metabolic profiles, which change in response to the metabolite that is missing.

These findings demonstrate that yeast cells can exchange high amounts of metabolites, sufficient to form cooperative colonies, and as metabolite concentrations are not altered compared to normal cells, it is likely that exchange of metabolites is ongoing between neighbours in yeast communities. The additional discovery that yeast stop making metabolites when they can obtain them from neighbouring cells has implications for research. This is because many yeast genetic studies use metabolically deficient strains that are supplemented in culture with metabolites. Future work could address whether such supplementation has kept certain functions of metabolism hidden.

All living cells possess a system for biochemical reactions, the metabolic network, which supplies cells with their necessary molecular constituents. The reactions participating in this network are highly conserved, so much so that all life is made up of a markedly similar set of metabolites (Braakman and Smith, 2013; Caetano-Anolles et al., 2009). The functionality of the metabolic system is bound to a series of transport reactions that facilitate the uptake of metabolites from the environment, as well as metabolite export. Metabolite export primarily occurs for the purpose of maintaining balance of the metabolic system ('overflow metabolism') and to maintain chemical and physical integrity of the metabolic network. This includes indiscriminate metabolite export through non-specific multi-drug transporters required in removal of toxic metabolites for cells (Paczia et al., 2012; Piedrafita et al., 2015). Co-growing cells can uptake the released metabolites and exploit their presence. Indeed, to a lower extent, metabolite export of metabolites can specifically occur for the purpose of establishing inter-cellular metabolic interactions in biosynthetic metabolism (Nigam, 2015; Paczia et al., 2012; Yazaki, 2005) and can lead to mutualistic situations in which cells profit from coexistence (Foster and Bell, 2012; Oliveira et al., 2014). In between species, mutually positive interactions can readily establish when exchange concerns an overflow metabolite, exemplified by yeast–algae interactions that can form on the basis of a CO₂ and sugar exchange (Hom and Murray, 2014), or between different cells of the same species or tissue, exemplified in tumours, when lactate produced in excess by one cell type is metabolised by another (Bonuccelli et al., 2010), or between neurons and glial cells that exchange sugar metabolites (Bélanger et al., 2011; Volkenhoff et al., 2015).

It is more difficult to assess whether metabolite exchange is indicative of non-cell-autonomous metabolism, when exchange concerns metabolites that are needed by both exchange partners, amino acids, and nucleobases for instance. Exchange of costly intermediates is associated with a significant risk, as exported metabolites can be lost through diffusion, chemical damage, or cheating (Dobay et al., 2014; Oliveira et al., 2014; Wintermute and Silver, 2010). Despite these constraints, exchange of intermediates is frequently observed within bacterial microbial communities. Many bacterial species lose essential biosynthetic pathways, disabling them from living autonomously, which may explain why more than 90% of bacteria cannot be cultivated in the absence of a community environment (Costerton et al., 1994; Johnson et al., 2012). The energetic benefit and selective advantage associated with non-autonomous cellular metabolism is often not clear but might involve, for example, the ability to reduce genome size which would in turn facilitate faster proliferation. This may explain why bacteria frequently appear to cooperate in the biosynthesis of more costly and biosynthetically complex metabolites, such as aromatics (Mee et al., 2014).

While metagenomics has boosted the knowledge of metabolite exchange strategies in bacteria (Blaser et al., 2013; Manor et al., 2014; Zelezniak et al., 2015), relatively little is known about eukaryotic species. This includes yeast, a popular single cellular eukaryotic model organism, whose metabolic capacities are regularly exploited in biotechnology. Yeast cells are known to participate in multi-species communities (e.g. on human skin (Findley et al., 2013)), but as wild yeast isolates usually maintain similar prototrophic genomes (Jeffares et al., 2015; Liti et al., 2009), metagenomic data are not conclusive about yeast's metabolite exchange strategies. In laboratory experiments, yeast cultures were, however, not effective in supporting co-growth of auxotrophs that have complementary defects in amino acid and nucleotide metabolism, unless they were genetically modified to increase metabolite export (Müller et al., 2014; Shou et al., 2007). This contrasts with analogous studies in bacterial species, in which such growth experiments regularly show that co-cultured cells can overcome complementary metabolic deficiencies (Foster and Bell, 2012; Oliveira et al., 2014; Pande et al., 2014; Vetsigian et al., 2011). In the absence of quantitative metabolite data, this observation has triggered the conclusion that co-growing prototrophic yeast cells produce amino acid and nucleotide metabolites predominantly for themselves and export them at insufficient quantities to support growth of co-growing cells (Momeni et al., 2013; Shou et al., 2007).

Conflicting with this interpretation, we here report that yeast colonies maintain a rich exometabolome and that cells exploit this metabolic pool preferentially over their own biosynthetic capacities, which implies that metabolite exchange establishes as a natural property of yeast growth. To test whether yeast indeed possesses the capacity for metabolite exchange at growth relevant quantities, we established an alternative method to co-culture experiments. We exploited the stochastic segregation of episomes to randomly and progressively introduce metabolic auxotrophies into a yeast population which self-establishes from an initially prototrophic cell. This strategy enabled co-growing auxotrophs to enter an efficient state of metabolic cooperation, named self-establishing metabolically cooperating communities (SeMeCos). Despite an auxotrophic cell composition of up to 97%, SeMeCos achieve metabolic efficiency, growth parameters, and cell viability similar to that of genetically prototrophic cells, revealing a natural capacity of yeast to exchange metabolites at growth relevant quantities. In a SeMeCo that possesses auxotrophies in histidine, leucine, uracil and methionine metabolism, we distinguish up to eight cell types, each of which is unable to survive on its own, or in co-culture studies, however, could adapt effectively and cooperatively to overcoming metabolic deficiencies, once self-established in a community structure. Communities have a stable population composition as well as distinct spatial heterogeneity, which was, however, not essential for metabolite exchange, as SeMeCos maintain growth in liquid suspension. Self-establishing, complex communities thus demonstrate that yeast is not only exchanging metabolites, but is also able to do so at growth relevant quantities, to facilitate growth on the basis of a non-cell-autonomous metabolism.

Metabolic exchange interactions occur frequently among cells that grow in proximity to one another, as metabolites are constantly released from cells for different reasons, such as overflow metabolism, metabolite repair, as well as  export to facilitate metabolite exchange. In bacteria, a subset of such metabolite exchanges are of a cooperative nature in the sense that all exchange partners profit from this situation (Oliveira et al., 2014), whereas for the majority of eukaryotic organisms, metabolite exchange strategies remain unclear. Despite yeast auxotrophs being viable in supplemented and rich media (Mülleder et al., 2012), in the absence of amino acid supplementation, they fail to complement metabolic deficiencies in several pairs or higher order co-culture experiments (Figure 1A,B, [Müller et al., 2014; Shou et al., 2007]), a clear difference to bacterial studies, where similar experiments were effective (Foster and Bell, 2012; Freilich et al., 2011; Harcombe, 2010; Pande et al., 2014; Ramsey et al., 2011; Vetsigian et al., 2011). This led to speculations that yeast might, in contrast to many bacterial species, lack the required export capacities to enable growth relevant exchange of intermediary metabolites such as amino acids and nucleobases (Shou et al., 2007). Analysing the intra-colony exometabolome we could, however, detect the required metabolites; in fact, we found that cells within a colony are surrounded by a rich exometabolome. We also found that yeast would efficiently exploit the nutrients when available, to the extent that they solely rely on these extracellular metabolites. This result implied that metabolite exchange among co-growing yeast cells is frequent by nature; the lack of complementation in the co-culture experiments could thus reflect a limit of the experiment itself, and not represent the metabolite exchange capacities of yeast cells.

To circumvent combining two or more cultures, we chose an approach of synthetic biology and exploited the stochastic loss of plasmids to progressively introduce the metabolic deficiencies in random combination from an initially single cell. The progressive loss of prototrophy allowed cells to maintain cell growth on the basis of metabolite exchange, resulting in a community with 73% auxotrophy, which increased to 97% upon supplementation with the most limiting metabolite, uracil. Despite its dominant auxotrophic composition, the SeMeCo community could maintain a wild-type like exometabolome, metabolic efficiency, as well as cell viability, implying that this type of cooperation is a robust physiological property. Hence yeast's natural metabolite export and import capacities are wholly sufficient to support co-growth on the basis of metabolite exchange.

The establishment of SeMeCos was not facilitated by mixing the auxotrophs in a higher order combination either. This is consistent with the notion that losing more metabolic genes reduces biochemical capabilities and does not add new ones. The key of the SeMeCo system is instead to allow the progressive self-establishment of the community starting from the single cell (Figure 2). Metabolic feedback regulatory systems therefore do not inhibit metabolite export in general but prevent cooperative co-growth when already pre-established co-cultures are mixed (Müller et al., 2014; Shou et al., 2007). A possible role of these mechanisms could perhaps prevent the spread of foreign, potentially cheating, cells that derive from a competing yeast colony. We could replicate behaviour which is in favour of such an assumption; By spiking into SeMeCo a cell culture possessing the same genotype as a frequent (HIS3 LEU2 ura3Δ MET15) and rare (his3Δ leu2Δ URA3 MET15) genotype (Figure 5A), we observed that both genotypes were rapidly depleted from the pre-established SeMeCo, irrespective of the frequency of the respective genotype in SeMeCo (Figure 5—figure supplement 1).

Studying the genotypic composition of SeMeCo implied that there are a defined set of interactions underlying the properties of the cooperating community, which maintained a similar population composition involving eight reproducibly concentrated metabotypes when established independently. This indicates that this quantitatively defined community composition was most effective in metabolic cooperation. This finding may close an important gap in the understanding of the evolution of multicellularity; If a defined composition is most effective in cooperative growth, a selection advantage could be provided by any sort of physical bonding which can maintain cooperation partners in the defined equilibria, and would in addition, provide additional protection against the invasion of cheating cell types. Indeed, the exometabolome data implies that the number of metabolite exchange interactions among co-growing cells could be significant. The colony exometabolome contained a vast array of biomolecules, including the majority of amino acids (Figure 1C) (Castrillo et al., 2007; Paczia et al., 2012; Silva and Northen, 2015). The finding that yeast cells prefer uptake over synthesis of amino acids and uracil, even when they are genetically prototrophic, shows that exchange interactions will readily establish once the cellular environment has acquired a critical concentration of metabolites. This has implications for the interpretation of metagenomic studies and cheating/benefactor experiments, as for this reason, it cannot be concluded from the genetic presence or absence of a single metabolic pathway, or from following the synthesis of a single metabolite, how many other metabolites are being exchanged as well.

Even without selective pressure, both wild-type yeasts  and SeMeCo established an amino-acid-rich exometabolome on minimal media (Figure 1C) and engaged in metabolite uptake when nutrients were available (Figure 1E). This implies that these features are a natural property of yeast and raises the question of why natural yeast communities are not composed of co-growing, genetic, auxotrophs. To answer this question, one needs to keep in mind that possessing metabolic genes in the genome is not equal to the pathway being constantly active; Indeed, prototrophs can flexibly switch from self-synthesis to amino acid uptake (Figure 1). Being genetically prototrophic hence gives a higher level of metabolic flexibility, as prototrophic cells can re-activate a synthetic pathway when required. Unlike in SeMeCo, the genotype of a cell in a natural community is not in essence reflecting its metabotype or its metabolic role in the community. Additionally, the natural life cycle of yeast involves the formation of endospores, which are important for enduring starvation, and to spread between habitats. Without a prototrophic genotype, a single spore can no longer establish a colony on its own as genetic auxotrophy would interrupt the yeast life cycle. Second, only a fraction of the natural yeast life cycle occurs under exponential growth that requires abundant carbohydrate and nitrogen supply. The maintenance of a prototrophic genotype both in S. pombe and in S. cerevisiae wild isolates (Jeffares et al., 2015; Liti et al., 2009) is hence fully compatible with the presence of elaborate amino acid and nucleotide exchange mechanisms.

The finding that these cells fully shift from self-synthesis to uptake for histidine, leucine, methionine, and uracil, once these metabolites are provided, has direct implications on research using yeast, a primary eukaryotic model organism in genome-scale studies. A majority of yeast genetic experiments are conducted in auxotrophic strains, requiring amino-acid supplemented or rich media compositions. Important parts of biosynthetic metabolism (amino acid biosynthesis can account for up to 50% of metabolic flux towards biomass) may have thus stayed silent in a significant amount of functional genomics experiments. The effects of metabolic–genetic interactions on cellular physiology could thus substantially exceed our current knowledge and could be discovered upon switching to minimal nutrient supplementations. In this context, SeMeCos are simple to handle, establish rapidly and are easy to analyse, and therefore represent an effective and broadly applicable eukaryotic model system to study both cooperativity and effects of metabolism in the laboratory.

In summary, using histidine, leucine, methionine, and uracil as model metabolic pathways for exchangeable metabolites, we found that S. cerevisiae cells prefer these nutrients' uptake over their self-synthesis and maintain an amino-acid-rich exometabolome in the extracellular colony space, indicators of ongoing inter-cellular metabolite exchange. Although yeast is known to fail in compensating for auxotrophy in pairwise and higher order co-culture experiments, the cells did successfully enter a state of  metabolic cooperative growth upon exploiting stochastic plasmid segregation so that a single cell could progressively develop into a complex heterogeneous community. Composed of auxotrophic cell types that are non-viable on their own, SeMCo communities were able to overcome metabolic deficiencies and maintain metabolite concentrations and robustness similar to wild-type cells. Additionally, cooperation had imposed different metabolic roles on contributing cells. Progressive community formation thus reveals that yeast possesses full capacity to exchange anabolic metabolites at growth relevant quantities and readily establishes a non-cell-autonomous metabolism within complex but defined community structures.

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Author details

1. Kate Campbell

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   KC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Jakob Vowinckel

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   JV, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Michael Mülleder

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   MM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Silke Malmsheimer

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   SM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Nicola Lawrence

   The Wellcome Trust Gurdon Institute, University of Cambridge, Cambridge, United Kingdom

   Contribution

   NL, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Enrica Calvani

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   EC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Leonor Miller-Fleming

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   LMF, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Mohammad T Alam

   1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

   Contribution

   MTA, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Stefan Christen

   Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

   Contribution

   SC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Markus A Keller

    1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
    2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom

    Contribution

    MAK, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Markus Ralser

    1. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
    2. Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom
    3. Mill Hill Laboratory, The Francis Crick Institute, London, United Kingdom

    Contribution

    MR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    mr559@cam.ac.uk

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-9535-7413

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