Nanotubemediated Crossfeeding Couples the Metabolism of Interacting Bacterial Cells
Philos Trans R Soc Lond B Biol Sci. 2020 May 11; 375(1798): 20190250.
The microbial exometabolome: ecological resource and architect of microbial communities
Angela E. Douglas
1Department of Entomology, Cornell University, Ithaca, NY 14853, USA
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
Abstract
All microorganisms release many metabolites, collectively known as the exometabolome. The resultant multi-way cross-feeding of metabolites among microorganisms distributes resources, thereby increasing total biomass of the microbial community, and promotes the recruitment and persistence of phylogenetically and functionally diverse taxa in microbial communities. Metabolite transfer can also select for evolutionary diversification, yielding multiple closely related but functionally distinct strains. Depending on starting conditions, the evolved strains may be auxotrophs requiring metabolic outputs from producer cells or, alternatively, display loss of complementary reactions in metabolic pathways, with increased metabolic efficiency. Metabolite cross-feeding is widespread in many microbial communities associated with animals and plants, including the animal gut microbiome, and these metabolic interactions can yield products valuable to the host. However, metabolite exchange between pairs of intracellular microbial taxa that share the same host cell or organ can be very limited compared to pairs of free-living microorganisms, perhaps as a consequence of host controls over the metabolic function of intracellular microorganisms. Priorities for future research include the development of tools for improved quantification of metabolite exchange in complex communities and greater integration of the roles of metabolic cross-feeding and other ecological processes, including priority effects and antagonistic interactions, in shaping microbial communities.
This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.
Keywords: facilitation, metabolite cross-feeding, microbiome, syntrophy, reciprocity
1. Introduction
This article is founded on two traits of microorganisms: first, microorganisms display a great diversity of metabolic capabilities and, second, they are social organisms that communicate extensively and often cooperate [1,2]. These superficially disparate traits are inextricably linked because many social interactions among microorganisms are mediated by metabolite exchange, with consequences for the composition of microbial communities and, more broadly, ecosystem function and stability. A further consideration is that although microbial interactions are usually described in ecological terms, e.g. facilitation, mutualism, competition, they can also exert strong selection pressures because the fitness of one organism depends on the traits of the other interacting organisms [2,3]. As discussed in this article, there is now abundant evidence that, for microorganisms, evolutionary change can occur over ecological timescales, and that the resultant eco-evolutionary feedbacks influence the patterns of metabolite exchange, with consequences for microbial community assembly.
Why is metabolite exchange so important for microorganisms? The reason is that all microbial cells release many metabolites, collectively known as the exometabolome [4], and many of these metabolites are a resource for other organisms. To date, most research on metabolite cross-feeding has been conducted on microorganisms in abiotic habitats, including sediments and the water column, as well as laboratory media. However, there is increasing interest in the ecology of microbial communities in biotic habitats, i.e. other living organisms, particularly animal and plant hosts [5–8]. By definition, biotic habitats have the special trait that they are subject to selection, such that the conditions and resources for microorganisms can evolve in a fashion that is adaptive for the host [9].
The purpose of this article is to review the current understanding of metabolite exchange among microorganisms, and how these interactions influence the diversity and functional traits of microbial communities. In §2, I summarize the processes by which metabolites are released from microbial cells. Then, in §3 I consider the three broad categories of metabolite cross-feeding among microbial cells in abiotic habitats: facilitation, syntrophy and reciprocity (figure 1), together with their effects on the taxonomic and functional composition of microbial communities. In §4, I address how biotic habitats can influence the pattern of metabolic interactions among microorganisms, focusing on microorganisms that are benign or beneficial for animal hosts. The article concludes with some key priorities for future research in §5.
Modes of metabolite exchange between pairs of microorganisms. (a) Facilitation, (b) syntrophy and (c) reciprocity. (Online version in colour.)
2. The exometabolome of microorganisms
All microorganisms have an exometabolome, meaning that they release metabolites into the surrounding environment [4,10,11]. The exometabolome of vigorously growing bacterial cultures can comprise hundreds of metabolites, the great majority of which are derived from living cells and not cell lysis [12,13]. Many of the exported metabolites are widely believed to be by-products of metabolism, also known as overflow metabolites. It is argued that the release of these compounds protects the cell against their uncontrolled accumulation in the intracellular environment, which would result in metabolic imbalance, such as perturbation of redox balance [13]. It has been inferred from genome-scale metabolic modelling on various facultative anaerobic bacterial species grown in silico on media conferring optimal growth that the export of many metabolites, including glycerol, acetate, lactate, malate and succinate, imposes no metabolic cost on the bacterial cells [14].
Two processes, passive transfer across the lipid bilayer (lipoidal diffusion) and transporters (facilitated diffusion), mediate the release of many compounds in the exometabolome, including various microbial fermentation products (ketones, alcohols, undissociated forms of carboxylic acids, etc.), nonpolar amino acids, sugars and sugar derivatives. The contribution of passive flux and facilitated diffusion varies among microbial taxa. Natural selection can favour genetic variants of transporters with channel-like function mediating passive flux [15], and the composition of membrane lipids can influence lipoidal diffusion, e.g. the much-reduced permeability of membranes enriched with ladderine lipids in anaerobic ammonium oxidizing bacteria [16]. In addition to these passive routes, some metabolites are exported against the concentration gradient via efflux pumps, particularly ABC transporters, and various secondary active transporters powered by the electrochemical gradient across the membrane. Although these systems are best known as routes for the extrusion of drugs and other xenobiotics, there are reported instances of active efflux of primary metabolites, e.g. organic acids, amino acids, sugars [13].
A further source of exometabolites for some microorganisms comes from enzymes that are associated with the external surface of microorganisms or secreted into the external environment, where they mediate the extracellular breakdown of complex biomolecules (e.g. chitin, plant fibre) to soluble monomers [17,18]. The degradation products are taken up by the microbial cells producing the enzymes, but these processes are not perfectly efficient as a result of diffusive loss and transport limitations, and products can be recovered in the exometabolome.
The composition of the exometabolome varies widely with the metabolic capabilities of different taxa and also environmental circumstance (e.g. nutrient resources, oxygen availability, pH). As a result, the exometabolome of one microbial cell can include resources valuable to other organisms, with consequences for the abundance of individual taxa and composition of microbial communities. The scale of these interactions is likely very great. To illustrate, a computational analysis of genome data for microbial communities in more than 800 habitats identified the potential for an extensive exchange of metabolites, especially recurring patterns of sugar and amino acid transfer involving taxonomically diverse taxa in divergent habitats [19]. The resultant metabolic networks can be complex, involving interactions of higher order than can be modelled readily. As a result, much of current research focuses on components of these networks, especially uni-directional and bi-directional transfer of metabolites between pairs of taxa.
3. The exometabolome as an ecological resource for microorganisms
The exometabolome of microorganisms is of great ecological significance because other microorganisms can use released metabolites as substrates for population growth. Two broad categories of uni-directional metabolite transfer are widely recognized in the literature [20]. Facilitation refers to interactions where the sole beneficiary of metabolite transfer is the recipient microbial cells; and the producer derives no direct benefit from the consumption of released metabolites by the recipient (figure 1 a). In syntrophy, the producer also benefits from the consumer-mediated depletion of the released metabolite (figure 1 b). The third category of metabolite cross-feeding is reciprocal exchange of metabolites between pairs of microbial cells with different metabolic capabilities (figure 1 c). (Note that some authors adopt a wider definition of syntrophy to comprise any metabolite transfer between living microbial cells, e.g. [21].) As considered below, each of these three categories of metabolite transfer can have substantial implications for the metabolic costs incurred by the consumer, the producer or both partners in the interaction.
(a) Facilitation
Facilitation enhances populations of the microorganisms that consume metabolites released from other microbial taxa. Quantitatively important members of microbial communities are commonly dependent on metabolites derived from other taxa. For example, an analysis of bacterial communities associated with chitin particles in seawater identified many taxa that can use neither chitin nor its monomer (N-acetyl glucosamine), but derive their carbon requirements from waste products, including organic acids, of the chitin-degrading taxa. As a result, both the biomass and the functional and taxonomic diversity of the community are increased [22]. In other words, the community is shaped not only by the abiotic resources (chitin), but also by metabolic interactions among the microorganisms. The relationships between different microbial taxa can, however, be strongly context-dependent [23]. This is illustrated by an elegant study using microfluidics technology to quantify the population increase of a focal bacterium Herbaspirillum frisingense cultured in isolation or cocultured with 14 other bacteria in every possible combination of one, two to seven taxa. On complex sugars, such as sucrose, which H. frisingense uses poorly, a subset of the test bacteria promoted the yield of H. frisingense through the release of monosaccharides that H. frisingense uses readily. However, when grown on glucose or fructose, the interactions with these bacteria were competitive, depressing H. frisingense yield [24].
As the examples in the previous paragraph illustrate, facilitation is the product of ecological fit (sensu [25]) and does not arise from coevolutionary interactions among taxa with a long history of co-association. Nevertheless, producer-derived metabolites can drive evolutionary change with profound ecological consequences. The exogenous supply of a nutrient is predicted to select for increased metabolite uptake and utilization by consumers. These changes can, further, be accompanied by loss of biosynthetic capability (i.e. auxotrophy) because the costs of acquiring a nutrient from the environment is lower than the cost of biosynthesis. Specifically, an auxotroph does not incur the cost of generating the enzymes, substrates and energy for the biosynthetic reactions, nor the metabolic inefficiency that can arise from conflicting demands for metabolites, redox, etc. between different metabolic pathways [26]. With exogenous supply, an auxotroph can also be fitter than a cell that synthesizes the nutrient only under limiting conditions because it does not bear the cost of sustaining the machinery to sense and respond to exogenous nutrient supply, e.g. [27]. In habitats where an exogenous nutrient is synthesized by a microorganism (a prototroph), negative frequency-dependent selection can arise, i.e. auxotrophy is advantageous in the presence of many producers, and prototrophy is advantageous at high auxotroph frequency. This scenario, in which prototrophs that leak nutrients that are costly to produce can diversify into mixed populations of prototrophs and auxotrophs, has been formalized as the Black Queen Hypothesis [28]. It has been applied to explain the functional diversity of closely related microorganisms in well-mixed environments, including Prochlorococcus in the ocean [28] and Actinobacteria in freshwater habitats [29]. These evolutionary events have also been demonstrated in experimental evolution studies, notably the evolution an ancestral catalase-positive Escherichia coli strain to a mixed community of catalase-positive and catalase-negative genotypes [30].
In summary, metabolic facilitation promotes biomass production (both growth rates and yield) and enhances the taxonomic and functional diversity of microbial communities, by the interplay of ecological and evolutionary processes. Producer-derived metabolites favour colonization and growth of auxotrophic microorganisms and also exert frequency-dependent selection for the evolution of auxotrophy. The net result can be interacting populations of multiple closely related strains with different metabolic capabilities. Importantly, many of these related strains probably go undetected by standard microbiological techniques. Specifically, the 16S rRNA gene sequences standardly used in community profiling can display minimal variation among strains and many consumers may be unculturable because they are dependent on specific metabolite released from producers.
(b) Syntrophy
The exometabolome of a microbial cell includes metabolic waste products that can accumulate in the external environment, perturbing metabolism and even causing autotoxicity of the producer. Syntrophy, i.e. the consumption of these waste products from the environment by microorganisms with complementary metabolic capabilities (figure 1 b), is extensively documented in energy-limited, anoxic habitats. For example, bacteria that ferment organic substrates with the net production of hydrogen are often associated with methanogens that consume the hydrogen as substrate for methane production. By acting as an electron sink, the methanogen favours sustained fermentation of its partner. As reviewed comprehensively in Morris et al. [20], the carbon substrates supporting these syntrophic associations are diverse, including processes of economic and environmental importance, e.g. biofuel production from complex plant polysaccharides, biodegradation of complex aromatic pollutants and refractory hydrocarbons. Many of these syntrophic associations are binary, comprising a single fermentative bacterium and methanogen, but others are more complex, involving one or more methanogens as well as bacteria that can act as both acceptor and donor of reducing equivalents [31]. This is illustrated by a three-member community of anaerobic bacteria and archaea that grew out when a complex community derived from a natural sediment was incubated anoxically with the fatty acid butyrate and inorganic ammonium as sole carbon and nitrogen sources. Butyrate degradation was mediated by the bacterium Syntrophomonas (Firmicutes), with the transfer of reducing equivalents to both the methanogen Methanoculleus (Archaea of the order Methanomicrobiales) and the bacterium Desulfovibrio (delta-proteobacteria) [32] (figure 2 a).
Metabolic interactions in a three-member microbial community supported by butyrate as carbon source and inorganic ammonium as nitrogen source under anoxic conditions. (a) Syntrophic cross-feeding of waste products of butyrate degradation by Syntrophomonas to the hydrogenotrophic methanogen Methanoculleus and Desulfovibrio. (although Desulfovibrio is potentially able to reduce sulphate with concomitant consumption of hydrogen, sulphate reduction was not observed in this system.). (b) Reciprocal exchange of amino acids among the community members, inferred from their genetic capacity to synthesize the 20 amino acids, indicated by one-letter abbreviations (D, E, N, Q and G, which are synthesized by all three taxa, are not shown). (Drawn from data in figs 2 and 3 of [32]). (Online version in colour.)
It has been noted previously in this article that, under certain conditions, the release of various organic acids imposes no discernible metabolic cost to the producer, such that their consumption by other microorganisms is facilitation. Nevertheless, these interactions can be syntrophic, i.e. advantageous to the producer, as is illustrated by long-term experiments cultivating single E. coli strains on glucose as sole carbon source. Whether in shaken flask culture, chemostat or biofilm culture on solid media, the single strain reproducibly evolves into two metabolic phenotypes, each comprising one or more genotypes: a glycolytic phenotype that generates acetate, and an aerobic phenotype that oxidizes the acetate via the TCA cycle and oxidative phosphorylation [33–35]. The latter phenotype is generally underpinned by mutations in the gene acs coding acetyl CoA synthetase [33]. Equivalent associations can be constructed from E. coli mutants engineered to produce and consume acetic acid [21]. These syntrophic partnerships display increased growth rates and yield, relative to a single glucose-utilizing strain, and the biofilm cultures are spatially organized with the acetate-consuming strain, which requires oxygen, external to the glycolytic strain [36].
The selective advantage and ecological success of syntrophic splitting of glucose utilization are often referred to as metabolic division of labour [37,38]. The likely basis for the enhanced fitness of both partners is the metabolic inefficiency of sustaining different metabolic processes within a single cell; each partner benefits from the metabolic segregation of metabolic pathways that would otherwise impose conflicting demands on metabolic intermediates, redox conditions, etc. As discussed in D'Souza et al. [39], the incidence and pattern of metabolic compartmentation among microbial taxa can vary with the substrates available and the structure of the metabolic network in different microbial taxa.
(c) Metabolic reciprocity
Given the variation in exometabolome composition both among different microbial taxa and with environmental conditions, we can reasonably anticipate widespread multi-way transfer of metabolites between members of a microbial community, including the reciprocal exchange of metabolites between pairs of microbial cells with different metabolic capabilities (figure 1 c). To a large extent, our understanding of these interactions comes from research on simple systems in laboratory culture. Many studies have focused on conditional lethal biosynthetic mutants, i.e. strains that can grow at rates comparable to wild-type strains when a specific nutrient is provided but cannot be supported in media lacking the nutrient. For example, two strains of E. coli that are auxotrophic for the amino acids leucine and lysine, respectively, can be grown on minimal medium (without amino acids) in co-culture but not in mono-culture [40]. Auxotrophs of different species also display metabolic reciprocity in laboratory culture, e.g. reciprocal auxotrophs for tryptophan and histidine in E. coli and Acinetobacter baylyi [41], and a methionine auxotroph of E. coli with Salmonella enterica dependent on E. coli-derived acetate [42]. These experimental systems elegantly demonstrate that reciprocal relationships can arise through ecological fit without extended coevolutionary interactions. Nevertheless, the fitness of the associations can be enhanced by persistent co-association. Notably, experimental evolution studies on co-cultures of multiple E. coli auxotrophic mutants yielded progressive increases in biomass production over approximately 1011 cell divisions [43]. Many of the underlying adaptive mutations in this analysis could be assigned to genes coding transporters for the cross-fed metabolites and the overall architecture of nitrogen metabolism.
In natural microbial communities, the patterns of reciprocal metabolite cross-feeding can be complex, and involve amino acids, sugars, nucleotides and B vitamins [38,44]. For example, each of the three taxa in a community selected on butyrate as carbon substrate (figure 2 a) [32] is auxotrophic for at least three amino acids, and requires the biosynthetic function of both of the other two taxa to meet its amino acid requirements. This three-way reciprocal exchange of amino acids (figure 2 b) overlays the syntrophic relationship between the three taxa, with the implication that community composition can be shaped by complex patterns of metabolite cross-feeding requiring functional complementarity in both carbon/energy metabolism and amino acid biosynthesis. The microbial communities supported by more energy-rich carbon substrates than butyrate are taxonomically more complex (four taxa on the fatty acid caprylate, and five taxa on the alkane hexadecane) and underlain by more complex patterns of amino acid and carbon exchange [32]. This study illustrates how reciprocal networks of metabolic exchange among microorganisms can promote both the taxonomic diversity of microbial communities and, where the different taxa are mutually dependent, communities of stable taxonomic composition.
It has long been argued that reciprocal relationships can be invaded by cheaters, thereby limiting both the incidence and persistence of these interactions. The argument rests on the assumption that it is costly for the participating organisms to provision the metabolic needs of the partner, selecting for variants that derive the benefit from the costly products of its partner(s), while avoiding the cost of delivering the costly reciprocated service [45,46]. However, we cannot apply this reasoning globally to metabolic reciprocity because, as discussed above, metabolite release is not invariably costly. This is illustrated by a recent study demonstrating that the growth of a prototrophic strain of E. coli is not significantly depressed by co-culture with an amino acid auxotroph, even though the prototroph provisioning of the amino acid requirements for auxotroph growth involves increased expression of the amino acid biosynthesis genes and increased intracellular pools of the amino acid [47]. It was inferred that, in these experiments, the amino acid overproduction by the prototroph is mediated by re-programming of flux through the metabolic network without detectable effects on fitness [47].
In reciprocal relationships, the cost of metabolite release can differ between the interacting organisms. For example, in the mutually required interaction between acetate-releasing E. coli and methionine-releasing S. enterica, E. coli incurs no costs in producing acetate at rates sufficient to support the total carbon requirement of S. enterica, but methionine production is costly for S. enterica [42]. Further empirical studies are needed to establish the incidence of cost-free (i.e. cheater-resistant) metabolite overproduction in reciprocal relationships, and how this trait varies with the identity of the released metabolites, the structure of the metabolic and regulatory networks of the producer, and environmental conditions.
Where metabolite exchange is costly for the producer, persistence is promoted by spatial structure, for example by self-organization of cells in biofilms or aggregations, which increases the proximity of cooperators and exclusion of cheats, essentially making the public goods 'less public' [45,46]. Furthermore, spatial structure can facilitate the evolution of costly metabolic traits in co-occurring microorganisms. This is illustrated by an experimental evolution study conducted on the association between acetate-releasing E. coli and methionine-releasing S. enterica described above. When the spatial structure was imposed by co-culturing the two bacteria on solid medium, E. coli cells evolved the costly trait of secreting the sugar galactose, supporting increased growth and methionine release from the S. enterica cells [48]. Nevertheless, the galactose-releasing E. coli never completely replaced the non-producing cells because, as they become more common, the resultant increase in S. enterica-derived methionine promoted the abundance of E. coli cells that did not incur the costs of galactose production. In this system, the increased functional diversity of the association, such that S. enterica interacted with both acetate-producing and galactose-producing E. coli, is driven not by selective advantage to S. enterica but by negative frequency-dependent selection on the two E. coli phenotypes [48]. This study also illustrates the significance of eco-evolutionary feedbacks in promoting functional complexity in microbial communities.
4. Metabolite cross-feeding in biotic habitats
To what extent is the pattern of metabolite cross-feeding among microorganisms in biotic habitats modified by interactions with the host? As described in this section, this question is starting to be answered with experimental data and metabolic modelling that collectively indicate that host intervention in metabolic exchange among microorganisms is more evident in associations with greater host control over microbial composition and access to nutrients. I consider two widespread groups of associations in animals: the gut microbiota, which can comprise hundreds of taxa and has access to nutritional resources from ingested food as well as host metabolism; and intracellular microorganisms which, of necessity, derive their total nutritional requirement from the surrounding host cell and where host control is pervasive.
(a) Microbial communities in the animal gut
Generally, the animal host has relatively weak control over the composition of the complex microbial communities in the gut. The abundance and prevalence of many taxa conform to the predictions of the neutral model, i.e. determined by passive dispersal between hosts and ecological drift (chance loss from individual hosts) [49–52]. Furthermore, the microbial communities are repeatedly challenged by incoming taxa ingested with food, and the gut residence time varies widely among microbial taxa [53,54], leading to variation in community composition among individual hosts and, over time, within an individual host. Despite this variation, there is a broad consistency in the genetic capacity for many functional traits, including metabolic traits, of the gut microbiota [55] and considerable functional redundancy in the communities [56,57]. A key prediction that emerges from these patterns is that metabolic cross-feeding is widespread and shaped largely by ecological fit.
Both genome-scale metabolic modelling and empirical studies confirm the widespread incidence of metabolic cross-feeding in the gut microbiota. In one analysis, the metabolic network of 773 human gut bacteria was reconstructed and the growth in silico of these bacteria was determined in isolation and paired with one other bacterium. Between 31 and 45% of the nearly 300 000 pairwise interactions tested yielded cross-feeding of metabolites that promoted the growth of one or both taxa [58]. Furthermore, the metabolic cross-feeding yields products that single taxa either cannot produce or produce at lower rates. For example, pairs of human gut bacteria have the capacity to transform host-derived primarily bile acids to 12 secondary bile acids, of which only six could be synthesized by individual bacterial taxa [59]; and the gut bacteria Eubacterium hallii and Lactobacillus reuteri synthesize the short-chain fatty acid propionic acid from 1,2-propanediol, a cross-fed product of sugar fermentation from various other bacteria in the human gut [60]. Thus, many of the insights gained from metabolite cross-feeding in abiotic habitats can be applied to complex microbial communities in hosts. This general expectation cannot, however, be applied to intracellular microorganisms, as is considered next.
(b) Intracellular microorganisms
Many intracellular associations in invertebrate animals include two or more taxa localized to the same host cell or different host cells within a single organ [61,62]. Most research on the metabolic relations in these systems has concerned cross-feeding between the the microorganisms and host, and very little is known about the extent or importance of transfer between the microbial symbionts. Exceptionally, a metaproteomic analysis of the four bacterial symbionts, two sulphur-oxidizing chemoautotrophic γ-proteobacteria and two sulphate-reducing δ-proteobacteria, in the annelid worm Olavius algarvensis has demonstrated the supply of reduced sulphur compound(s) from the δ-proteobacterial partners to the γ-proteobacteria, ensuring that the latter can sustain substantial carbon fixation rates despite low sulphur availability in the external habitat [63]. To investigate the extent of cross-feeding between intracellular symbionts, genome-scale metabolic modelling was conducted on three xylem-feeding insects, revealing just a few or no metabolites transferred between the two bacterial partners, despite extensive inputs of host-derived metabolites to each bacterium, including potentially competition for compounds required by both bacteria [64] (figure 3 a). Although further research is required to assess their generality, these results raise the possibility that limited metabolite cross-feeding between intracellular microorganisms may contribute to the overall host control over the metabolic function of each of their intracellular symbionts.
Metabolic cross-feeding between intracellular bacterial symbionts of insects. (a) Number of metabolites exchanged between bacteria (solid lines) and derived from host (dashed lines) in three species of xylem-feeding insects, as predicted from genome-scale metabolic models. (b) Genetic capacity of two bacterial symbionts (Tremblaya and Moranella) in the mealybug Planococcus citri for enzymes in the tryptophan biosynthesis pathway (cross, absent). (c) Genetic capacity of two genotypes of the bacterium Hodgkinia (I and II) in the cicada Tettigades undata for reactions in histidine biosynthesis. (Drawn from data in [64], fig. 2 of [65] and fig. 2 of [66]). (Online version in colour.)
Cross-feeding has, however, been invoked in some exceptional systems, where different genes of individual metabolic pathways are coded by different bacteria, implying that metabolic intermediates are shuttled between the two taxa. For example, tryptophan is synthesized by the nested association between the bacteria Tremblaya and Moranella in the mealybug Planococcus citri [65] (figure 3 b), and histidine is produced by two Hodgkinia strains with complementary functions in the cicada Tettigades undata [66] (figure 3 c). There is abundant evidence that the gene loss underpinning this metabolic complementarity is caused by genomic decay of the bacterial symbionts, a consequence of their vertical transmission with very small effective population size [67,68]. Nevertheless, the near-exclusive restriction of these interactions to amino acids (such as tryptophan and histidine) that are energetically expensive to synthesize [61] raises the possibility that the resultant metabolic compartmentation of reactions may promote metabolic efficiency. More generally, further investigation is needed to establish whether selection operating at the level of the entire association may contribute to the patterns of metabolism gene loss and their metabolic consequences in intracellular microorganisms.
5. Outlook
The consensus from different lines of research, including studies of natural communities and laboratory co-cultures, together with metabolic modelling, is that metabolic cross-feeding among microorganisms is widespread and tends to promote the taxonomic and functional diversity of microbial communities. This perspective should be tempered, however, by two considerations. The first is the indications that metabolic interactions between intracellular microorganisms in some associations may be very limited (figure 3), although further research is needed to establish the generality of these findings. The second issue is that much of our current understanding comes from research on interactions between two (or a few) taxa. Although it can be argued that these simplified communities represent modules within a larger network of interacting microorganisms, complex communities may not necessarily display high modularity or include modules with the taxonomic and functional structure that match the simplified communities [69]. Indications that translating from simple to complex communities may be unreliable come from studies revealing highly significant interaction terms as synthetic and self-assembled communities are built from one to two and larger numbers of partners [70,71]. Furthermore, in some systems, the sign of interactions among community members can shift from generally positive (either facilitative or reciprocal) in binary co-culture towards competitive interactions as community complexity increases [70].
These issues create the imperative to expand analysis of metabolite cross-feeding in complex communities, despite the technical and computational challenges. Encouragingly, recent technical developments in microfluidics and robotics are enabling the parallel analysis of many thousands of co-culture combinations [24], enabling the systematic analysis of metabolic interactions and outcomes in increasingly complex communities. In parallel, computational tools are being developed to integrate metabolomic and metagenomic data for improved predictions of metabolic function and among-microbe exchange [43,72–74] and for sophisticated in silico analysis of metabolic interactions [75–79]. These advances can, additionally, expedite the investigation of factors that are important for community assembly but are often neglected in metabolic modelling and laboratory co-culture experiments. Of particular importance is to design studies that include priority effects, specifically the extent to which the identity of the first colonist(s) define the subsequent community composition and function [80], and to address the relative contributions of metabolic cross-feeding and antagonistic interactions in microbial communities [81]. Overall, these approaches provide the opportunity to solve a key challenge in microbial community ecology: to understand and predict the contribution of different processes to microbial community assembly.
Acknowledgements
I thank Nana Ankrah (Cornell University), Victoria Orphan (California Institute of Technology) and Anja Spang (Royal Netherlands Institute for Sea Research) for helpful discussions.
Data accessibility
This article has no additional data.
Competing interests
The author has no competing interests.
Funding
The author is supported by NIH grant no. R01GM095372.
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