A passive mutualistic interaction promotes the evolution of spatial structure within microbial populations
- Marie Marchal1,
- Felix Goldschmidt1, 2,
- Selina N. Derksen-Müller1, 2,
- Sven Panke3,
- Martin Ackermann†1, 2Email author and
- David R. Johnson†1Email author
© The Author(s). 2017
Received: 7 October 2016
Accepted: 4 April 2017
Published: 24 April 2017
While mutualistic interactions between different genotypes are pervasive in nature, their evolutionary origin is not clear. The dilemma is that, for mutualistic interactions to emerge and persist, an investment into the partner genotype must pay off: individuals of a first genotype that invest resources to promote the growth of a second genotype must receive a benefit that is not equally accessible to individuals that do not invest. One way for exclusive benefits to emerge is through spatial structure (i.e., physical barriers to the movement of individuals and resources).
Here we propose that organisms can evolve their own spatial structure based on physical attachment between individuals, and we hypothesize that attachment evolves when spatial proximity to members of another species is advantageous. We tested this hypothesis using experimental evolution with combinations of E. coli strains that depend on each other to grow. We found that attachment between cells repeatedly evolved within 8 weeks of evolution and observed that many different types of mutations potentially contributed to increased attachment.
We postulate a general principle by which passive beneficial interactions between organisms select for attachment, and attachment then provides spatial structure that could be conducive for the evolution of active mutualistic interactions.
How mutualistic interactions emerge and persist is not clear [9–11]. A first question pertains to the origin of mutualistic interactions. Starting from a situation where two microbial genotypes exist in the same environment but do not affect each other’s growth, how could a mutant of one genotype emerge that excretes a metabolite that positively affects the growth of another genotype and increases in frequency, thus laying the foundation for a mutualistic interaction to evolve? In this scenario, the mutant might not receive an immediate benefit but would potentially carry a metabolic cost associated with excreting the metabolite. The mutant might therefore not be able to increase in frequency relative to its ancestor and a mutualistic interaction could not establish.
One possible solution to this problem is that mutually beneficial interactions could originally be passive in nature [12, 13]. We use the term ‘passive’ to refer to a behavior or property of an organism that, while potentially being beneficial to another organism, did not evolve because of its positive effects on that other organism. This is particularly evident for metabolic interactions between microorganisms, which often passively excrete metabolites that positively affect the growth of other microorganisms [12, 14–16]. For example, microorganisms might excrete metabolites via cell leakage or as side-products or end-products of their own metabolism [16–19], which could then be taken up by other microorganisms. If such metabolic interactions are reciprocal between two partners (Fig. 1a), they constitute as a mutually beneficial but passive mutualistic interaction.
While the passive scenario described above could be important for the origin of a mutualistic interaction, many mutualistic interactions in nature are not passive but are rather based on the active excretion of metabolites that positively affect the growth of a mutualistic partner (e.g. ). The active excretion of metabolites must, at least to some extent, divert cellular resources away from the growth and reproduction of the excreting microorganism [20–22]. These metabolic costs then lead to a second fundamental question: starting from a situation where two microorganisms are coupled by an initially passive mutualistic interaction, how could investment of metabolic resources into each mutualistic partner evolve? Such an evolutionary transition requires a mutant that actively excretes metabolites to increase in frequency. Again, this is not trivial to explain. A mutant that actively excretes metabolites would increase the growth of its mutualistic partner, which in turn would lead to a benefit that is accessible to both the mutant and its ancestor (Fig. 1b). While the benefits of an increased investment are thus distributed uniformly, the costs are borne alone by the actively excreting mutant. The mutant might therefore have a growth disadvantage relative to its ancestor and decrease in frequency.
Ultimately, the evolutionary transition of a passive into an active mutualistic interaction requires that the mutant that actively invests metabolic resources into the growth of its mutualistic partner receive an exclusive benefit [9, 23]. Such coupling between investment and return arises if individuals of different mutualistic partners are spatially associated with each other for long periods of time [24, 25]. In this case, a mutant that invests metabolic resources into the growth of its mutualistic partner would receive an increased return if the partner’s growth leads to an increased production of the benefit. This situation has been referred to as the “partner-fidelity model” . A general scenario under which such a long-term association between individuals could arise is in the presence of spatial structure [11, 20, 21, 23, 25–29] (Fig. 1c); that is, in a situation where physical barriers constrain the movement of individuals and metabolites. In spatially structured environments, the benefits arising from an investment into another organism are not distributed evenly but are instead disproportionally directed back towards the investor, thus allowing those individuals to potentially increase in frequency and spread [20, 21, 23, 26–29] (Fig. 1c).
While there is accumulating theoretical and experimental evidence to support the importance of spatial structure on the evolution of mutualistic interactions, these studies have largely focused on imposing abiotic spatial structure on a mutualistic consortium and analyzing the evolutionary outcomes (e.g. [20, 21, 26, 30]). Mutualistic interactions, however, are also observed in habitats with relatively little abiotic spatial structure, for example between microorganisms that reside within the water columns of open oceans and lakes (e.g. [7, 31]). This then underscores an important gap in our knowledge: How can we explain the evolution of an active mutualistic interaction in the absence of extensive abiotic spatial structure?
The central idea that we are addressing in this manuscript is that microorganisms can readily evolve to create their own spatial structure based on physical attachment between individuals (i.e., cell aggregation) . More specifically, we test the hypothesis that an initially passive mutualistic interaction selects for mutants that aggregate together with other individuals and thereby benefit from increased local concentrations of excreted metabolites. This scenario could have important consequences because cell aggregation leads to “partner fidelity” and could set the stage for the evolution of an active mutualistic interaction. Indeed, cell aggregates are prevalent within the mixed layers of oceans and non-stratified lakes and can harbor mutualistic interactions (e.g., [7, 31]). Whether a passive mutualistic interaction itself could promote the evolution of cell aggregation, however, is not clear.
To test this hypothesis, we experimentally created a passive mutualistic interaction between two auxotrophic strains of the bacterium Escherichia coli and tested for the evolution of cell aggregation. Each strain is defective in the biosynthesis of a different amino acid; they can only grow if the required amino acid is exogenously supplied from an abiotic source or if the strains are grown together in co-culture and passively excrete or release small amounts of the amino acid required by the other. This passive mutualistic interaction was based on a single genetic mutation in each mutualistic partner; it could therefore originate spontaneously via random mutation within large populations. We then propagated the mutualistic co-cultures in the absence of extensive abiotic spatial structure (i.e., in continuously-mixed batch reactors) and tested whether the passive mutualistic interaction itself promotes the evolution of cell aggregation. We are not investigating how spatial structure influences the evolutionary transition to an active mutualistic interaction in this manuscript. Instead we ask how spatial structure itself evolves, and thereby focus on a process that has potentially profound implications for interactions both within and between populations of organisms.
Bacterial strains and plasmids used in this study
Strain or plasmid
Reference or source
E. coli strain
BW25113 with ∆proC:Km R ; KmR
BW25113 with ∆trpC:Km R ; KmR
Used for replication of pUC18T derivatives; λpir80dlacZ ΔM15 Δ(lacZYA-argG)U169 recA1 hsdr17 deoR thi-1 supE44 gyrA96 relA
pUC18-based conditionally replicative delivery plasmid for mini-Tn7-LAC-Gm; ApR, GmR, mob+
pUC18T-mini-Tn7T-LAC-Gm containing egfp immediately downstream of P lac ; ApR, GmR, mob+, egfp+
pUC18T-mini-Tn7T-LAC-Gm containing echerry immediately downstream of P lac ; ApR, GmR, mob+, echerry+
We verified that the mutualistic partners are auxotrophic for the predicted amino acid by growing them in isolation with liquid minimal medium that was or was not supplemented with the required amino acid. The liquid minimal medium consisted of 6.8 g L−1 Na2HPO4 × 7H2O, 3 g L−1 KH2PO4, 0.5 g L−1 NaCl, 1 g L−1 NH4Cl, 3.6 g L−1 glucose, 0.24 g L−1 MgSO4, and 10 mg L−1 gentamycin (referred to as MM hereafter). We streaked each mutualistic partner onto a different lysogeny broth (LB) agar plate, picked three colonies from each LB agar plate, inoculated each colony into a different test tube containing 3 ml of MM, and incubated the test tubes for 24 h at 37 °C with continuous shaking (220 r. p. m.). As expected, neither mutualistic partner could grow in isolation with MM. However, strain BW25113 (∆proC) could grow in isolation when we supplemented MM with 50 mg L−1 L-proline while strain BW25113 (∆trpC) could grow in isolation when we supplemented MM with 20 mg L−1 L-tryptophan, thus verifying that each mutualistic partner is indeed auxotrophic for the predicted amino acid.
We introduced a different plasmid into each mutualistic partner that carries a gene encoding for a different florescent protein, thus allowing us to distinguish and individually quantify each mutualistic partner when they are grown together in co-culture. To accomplish this, we constructed two derivatives of the pUC18T-mini-Tn7T-LAC-Gm conditionally replicative plasmid (Table 1) . This plasmid contains an isopropyl-β-D-thiogalactopyranosid (IPTG)-inducible P lac promoter located immediately upstream of a multiple cloning site (MCS). We first purified the pUC18T-mini-Tn7T-LAC-Gm plasmid from an overnight culture of E. coli DH5α/λpir (Table 1) . We next used GoTaq DNA polymerase (Promega, Madison, WI, USA) to PCR amplify the egfp or echerry gene , which encode for green or red fluorescent protein respectively. The PCR amplification primers contain the BamHI and KpnI restriction sites that we used to clone the PCR products into the MCS of the pUC18T-mini-Tn7T-LAC-Gm plasmid (See Additional file 1). We then digested the PCR products and the pUC18T-mini-Tn7T-LAC-Gm plasmid with BamHI and KpnI (Thermo Fisher Scientific, Waltham, MA, USA) and ligated the PCR products into the pUC18T-mini-Tn7T-LAC-Gm plasmid. We designated the assembled derivative plasmids as pUC18T-mini-Tn7T-LAC-Gm-egfp and pUC18T-mini-Tn7T-LAC-Gm-echerry (Table 1). We finally replicated the assembled derivative plasmids in E. coli DH5α/λpir (Table 1) , introduced the derivative plasmids into the mutualistic partners via electroporation, and selected for transformants carrying the derivative plasmids by plating on LB agar plates supplemented with 10 μg ml−1 gentamycin and 1 mM IPTG. We introduced each derivative plasmid into each mutualistic partner, resulting in both egfp- and echerry-expressing variants of strains BW25113 (∆proC) and BW25113 (∆trpC).
We performed an evolution experiment with replicated co-cultures of the two mutualistic partners. We streaked the egfp-expressing BW25113 (∆proC), echerry-expressing BW25113 (∆trpC), echerry-expressing BW25113 (∆proC), and egfp-expressing BW25113 (∆trpC) strains onto different LB agar plates that were supplemented with 10 μg ml−1 gentamycin and 1 mM IPTG. We then picked one colony from each LB agar plate, inoculated each colony into a different test tube containing 2.7 ml of MM supplemented with 300 μl of liquid LB medium, and incubated the test tubes for 24 h at 37 °C with continuous shaking (220 r. p. m.). After reaching stationary phase, we centrifuged the cultures, discarded the spent medium, washed the cells twice with H2O containing 9 g L−1 NaCl, and suspended the washed cells in H2O containing 9 g L−1 NaCl. We next prepared two binary mixes (i.e. co-cultures) of the mutualistic partners (50:50 ratio based on optical density measurements at 600 nm [OD600]); one set of co-cultures consisted of the egfp-expressing BW25113 (∆proC) and echerry-expressing BW25113 (∆trpC) mutualistic partners while the other set of co-cultures consisted of the echerry-expressing BW25113 (∆proC) and egfp-expressing BW25113 (∆trpC) mutualistic partners. We finally inoculated 300 μl of each co-culture into eight replicated test tubes containing 2.7 ml of MM that was supplemented with 1 mM IPTG but not with amino acids, resulting in a total of 16 replicated mutualistic co-cultures. We designated the co-cultures consisting of the egfp-expressing BW25113 (∆proC) and echerry-expressing BW25113 (∆trpC) mutualistic partners as mutualistic co-cultures A1 to A8 and the co-cultures consisting of the echerry-expressing BW25113 (∆proC) and egfp-expressing BW25113 (∆trpC) mutualistic partners as mutualistic co-cultures B1 to B8. The initial OD600 of each mutualistic co-culture was approximately 0.12. After incubating the mutualistic co-cultures for 3.5 days at 37 °C with continuous shaking (220 r. p. m.), we transferred 300 μl of each co-culture to a new test tube containing 2.7 ml of fresh MM that was supplemented with 1 mM IPTG but not with amino acids to achieve a 1:10 (volume: volume) dilution. We then repeated the incubation and transfer steps in the same MM supplemented with 1 mM IPTG for a total of 16 serial transfers. Immediately before each transfer, we measured the OD600 of each mutualistic co-culture and archived a portion of each mutualistic co-culture in 20% glycerol at −80 °C for further analyses. We quantified the magnitude of cell aggregation within each mutualistic co-culture immediately before the fourteenth transfer as described below. If all cells in these co-cultures would have grown at the same rate, then the fourteen transfers with ten-fold dilution would correspond to approximately 46 generations of growth (14 × log210); however, if only a portion of the cells would have grown efficiently under our experimental conditions (for example mutants that attach to other cells), then the number of cell generations during the evolution experiment could have been potentially much larger. We performed control experiments with the ancestral mutualistic partners using growth conditions identical to those described above, except that the MM was supplemented with 50 mg L−1 L-proline and 20 mg L−1 L-tryptophan.
Quantification of cell aggregation
We imaged each mutualistic co-culture using a Leica TCS SP5 confocal laser-scanning microscope (CLSM) with a 63 × (1.4 NA) oil-immersion lens (Leica Microsystems, Wetzlar, Germany). We removed 5-μl liquid aliquots from each mutualistic co-culture immediately after removal from the shaking incubator and deposited the liquid aliquots onto the surface of a glass slide. We imaged egfp-expressing cells using 488 nm excitation wavelength and 500–530 nm emission wavelengths. We imaged echerry-expressing cells using 633 nm excitation wavelength and 657–757 nm emission wavelengths. We collected images at a resolution of 1024 × 1024 using LAS AF v2.7 software (Leica Microsystems).
We quantified cell aggregation using the StatColoc plugin of the Icy software . This algorithm computes the two-dimensional co-localization of different objects (in our case cells) using the Ripley’s K function . The resulting K-value measures the degree to which a set of objects deviates from spatial homogeneity. In our experiments with completely mixed batch reactors, a deviation from spatial homogeneity is most likely caused by cell aggregation, which we indeed confirmed by microscopy. We first detected e gfp- and echerry-expressing cells using the Spot Detector plugin of Icy and translated the images into binary data using the NIH ImageJ analysis software (http://rsbweb.nih.gov/ij/). We then applied the StatColoc plugin using a radius of 0.48 μm to 4.8 μm. We analyzed between five and nine randomly selected microscope fields for each mutualistic co-culture and obtained ten K-values as a function of distance for each microscope field. We finally identified the maximum observed K-value for each microscope field and tested whether the maximum observed K-values are significantly different between test and reference mutualistic co-cultures using the non-parametric Mann-Whitney U test. We performed the same statistical tests with the sum-of-the-ten K-values rather than the maximum observed K-value and obtained qualitatively identical results. We chose here to report the maximum observed K-values because they had better statistical properties. Namely, many of the microscopy fields did not contain any cell aggregates, which would be expected when aggregation is extensive and consists of a few sparsely distributed objects. This increases the variance when using the sum-of-the-ten K-values. We implemented the Mann-Whitney U test with the StatColoc plugin and considered a two-sided P < 0.05 to be statistically significant.
Isolation of evolved mutualistic partners
We isolated mutants emerging within each archived mutualistic co-culture that remained viable immediately before the fourteenth transfer of experimental evolution. We first streaked each archived mutualistic co-culture onto different LB agar plates that were supplemented with 10 μg ml−1 gentamycin and 1 mM IPTG. In many cases, the colonies expressed multiple fluorescent proteins, and we therefore had to perform a second streaking. After obtaining single colonies that only express one fluorescent protein, we qualitatively distinguished different evolved mutualistic partners within each co-culture based on colony morphology and the fluorescent gene that they expressed (egfp or echerry). For some co-cultures, we identified more than one colony morphology for each fluorescent gene. We finally picked one colony for each morphology, inoculated each colony into a different test tube containing 3 ml of liquid LB medium supplemented with 10 μg ml−1 gentamycin and 1 mM IPTG, incubated the test tubes for 24 h at 37 °C with continuous shaking (220 r. p. m.), and archived a portion of each culture in 20% glycerol at −80 °C for further analyses.
Genome sequencing of evolved mutualistic partners
We sequenced the genomes of all the isolated evolved mutualistic partners (see Additional file 2). In parallel, we sequenced the genomes of all the ancestral mutualistic partners (i.e., the egfp-expressing BW25113 (∆proC), echerry-expressing BW25113 (∆trpC), echerry-expressing BW25113 (∆proC), and egfp-expressing BW25113 (∆trpC) strains). We first streaked each mutualistic partner from the glycerol-archived samples onto different LB agar plates supplemented with 10 μg ml−1 gentamycin and 1 mM IPTG. We then picked one colony from each LB agar plate, inoculated each colony into a test tube containing 3 ml of liquid LB medium, incubated the test tubes for 24 h at 37 °C with continuous shaking (220 r. p. m.), and extracted genomic DNA from each culture using the ArchivePure DNA Purification kit (5prime, Hilden, Germany). We then prepared one sequence library for each mutualistic partner using 1 ng of genomic DNA, the Nextera XT DNA Sample Preparation kit (Illumina Inc., San Diego, CA, USA), and a different sample-specific multiplex adapter. We next pooled the libraries together, loaded the pool onto a single MiSeq flow cell (Ilumina Inc.), and sequenced the libraries using a MiSeq sequencer (Illumina Inc.) operated by the Genomic Diversity Center at ETH Zürich (Zürich, Switzerland.) We performed paired-end 150-cycle sequencing with the MiSeq Reagent Kit (version 2) (Illumina Inc.). All of the sequence reads are publically available in the NCBI Sequence Read Archive (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/sra) under Bioproject ID number SUB2512304.
We analyzed the resulting sequence reads by first binning the raw sequence reads into libraries using the automated run protocol on the MiSeq sequencer (Illumina Inc.). We then performed quality control with FastQC version 0.10.1 and quality filtering using PrinSeq Lite version 0.20.4 software . The parameters used for quality filtering are as follows: out_format, 3; min_qual_mean, 28; min_len, 50; range_gc 15–85; ns_max_n, 1; derep, 14; derep_min, 2; trim_ns_left, 1; trim_ns_right, 1; trim_qual_left, 28; trim_qual_right, 28; trim_left, 1. In summary, we trimmed or discarded all sequence reads with mean quality scores below 28 or had ambiguous nucleotides. We further discarded all sequence reads that were shorter than 50 bases. We finally applied the breseq pipeline (version 0.24rc1) and the utility program gdtools [39, 40] to predict genetic changes between each evolved mutualistic partner and its corresponding ancestral mutualistic partner. This included nucleotide polymorphisms, deletions, insertions, and multiplications. We used the genome sequence of E. coli K-12 MG1655  as a reference for the mapping and assembly of the sequence reads. The parameters used for calling genetic changes are as follows: reference, NC_000913; base-quality-cutoff, 15; require-match-length, 30.
We randomly selected the B2 mutualistic co-culture to test whether one or more of the evolved mutualistic partners were responsible for the evolution of cell aggregation. Based on colony morphology, fluorescent protein production, and genome analyses, we identified one evolved mutualistic partner of strain BW25113 (∆proC) and two different evolved mutualistic partners of strain BW25113 (∆trpC) within the B2 mutualistic co-culture immediately before the fourteenth transfer of experimental evolution (see Additional file 2). We first streaked each of the evolved mutualistic partners and their corresponding ancestral mutualistic partners from the archived isolate samples onto different LB agar plates that were supplemented with 10 μg ml−1 gentamycin and 1 mM IPTG. We then picked one colony from each LB agar plate, inoculated each colony into a different test tube containing 2.7 ml of MM supplemented with 300 μl of liquid LB medium, and incubated the test tubes for 24 h at 37 °C with continuous shaking (220 r. p. m.). After reaching stationary phase, we washed and suspended the cells in water containing 9 g L−1 NaCl as described above for the evolution experiment. We next prepared different binary (50:50 ratio based on OD600 measurements) or ternary (33:33:33 ratio based on OD600 measurements) mixes of different evolved and ancestral mutualistic partners as described in the results section. We finally inoculated 300 μl of each mix into replicated test tubes containing 2.7 ml of MM that was supplemented with 1 mM IPTG but not with amino acids. The initial OD600 of each mutualistic co- or tri-culture was approximately 0.12. After incubating the mutualistic co- or tri-cultures for 7 days at 37 °C with continuous shaking (200 r. p. m.), we measured the OD600 of each co-or tri-culture and quantified the magnitude of cell aggregation as described above.
Results and discussion
Experimental creation of a passive mutualistic interaction
We created a passive and obligate mutualistic interaction by inoculating pairs of the BW25113 (∆proC) and BW25113 (∆trpC) mutualistic partners (Table 1) together into MM that was not supplemented with amino acids. We found that the mutualistic partners could grow when they were inoculated together but could not grow when they were inoculated in isolation, thus demonstrating that we could indeed create the expected passive mutualistic interaction. We use the term ‘passive’ because the interaction emerges spontaneously when inoculating the two auxotrophic strains together that were neither engineered (e.g., as in ) nor evolved to actively excrete the amino acid that the other strain requires. We further tested whether access to these two amino acids limits the growth of the mutualistic co-cultures. We found that the co-cultures reached stationary phase approximately four-times more rapidly when we provided exogenous supplements of the required amino acids (50 mg L−1 L-proline and 20 mg L−1 L-tryptophan) (< 18 h) than when we did not (> 3.5 days), thus verifying that the supply of the required amino acids did indeed limit the growth of the mutualistic co-cultures.
We next investigated the evolutionary dynamics of the mutualistic co-cultures. We performed an evolution experiment by serially transferring the 16 replicated mutualistic co-cultures every 3.5 days into fresh MM that was not supplemented with amino acids, corresponding to 8 weeks of experimental evolution. Mutualistic co-cultures A1-A8 consisted of the egfp-expressing BW25113 (∆proC) and echerry-expressing BW25113 (∆trpC) mutualistic partners while mutualistic co-cultures B1 to B8 consisted of the echerry-expressing BW25113 (∆proC) and egfp-expressing BW25113 (∆trpC) mutualistic partners. We used two different combinations of fluorescent protein-encoding genes for two reasons. First, it allowed us to assess whether differences in the fluorescent protein-encoding genes themselves affect the outcome of the evolution experiment. Second, it allowed us to monitor for cross-contamination among the mutualistic co-cultures during the evolution experiment, which we never detected. We measured the OD600 of each mutualistic co-culture immediately before each subsequent transfer as a proxy of total cell numbers.
One limitation of our analysis above is our use of OD600 measures as proxy measures of total cell numbers, as cell aggregation can prevent a linear correspondence between OD600 and total cell numbers. In general, however, cell aggregation tends to decrease OD600. Thus, the fact that we observed a general increase in OD600 over evolutionary time as cell aggregation emerged suggests that total cell numbers also increased over the same evolutionary time. The increase in total cell numbers, however, should be interpreted as a qualitative observation rather than a quantitative measure.
Evolution of cell aggregation
Comparison of the K-values for the test and ancestral mutualistic consortia
Panc + Tevol,a
(mutualistic consortium B2)
Panc + Tevol,b
Pevol + Tanc
Pevol + Tevol,a
Pevol + Tevol,b
Pevol + Tevol,a + Tevol,b
One alternative explanation is that the cell aggregates consisted of dead cells rather than viable cells. To test this, we routinely plated the cultures onto agar plates. After incubation, we always observed individual colonies that expressed both fluorescent proteins (i.e., they contained mixtures of cells that express gfp or echerry). This indicates that the mutualistic partners were indeed physically attached to each other and that cells within the aggregates were viable. The cell aggregation phenotype therefore clearly contributed towards population-level phenotype. However, as shown in Fig. 3, individual planktonic cells remained. Thus, while aggregation significantly contributed to population-level phenotype, it remained incomplete.
While we observed the emergence of cell aggregation, we note that our results do not suggest that the emergence of cell aggregation was directly caused by the mutualistic interaction itself. This could occur via mechanisms such as specific partner recognition, where each mutualistic type aggregates with its partner. However, such adaptations would likely require long evolutionary times. Instead, the emergence of cell aggregation in our study is likely an indirect consequence of the small amounts of amino acids that leak or are released from the mutualistic partners. In other words, the mutualistic interaction creates an environment with low concentrations of amino acids, which then promotes the evolution of non-specific cell aggregation. Regardless, non-specific cell aggregation might then set the stage for further evolutionary changes, such as the emergence of partner recognition and specific attachment between the different mutualistic partners.
Genetic changes during emergence of cell aggregation
We investigated the genetic changes that occurred during the evolution experiment. The goal here was not to identify the specific genetic changes that cause cell aggregation, but rather to investigate whether each lineage accumulated similar or different genetic changes during the acquisition of the cell aggregation phenotype. This then allows us to hypothesize whether there is a single or multiple evolutionary pathways to cell aggregation. To accomplish this, we reconstructed the ancestral and evolved mutualistic co-cultures from isolates. We first grew each ancestral or evolved mutualistic partner on amino acid-rich LB agar plates, assembled the mutualistic partners together into mutualistic co-cultures, and inoculated the co-cultures into MM that was not supplemented with amino acids. We found that the reconstructed evolved mutualistic co-cultures formed cell aggregates after 7 days of incubation while the ancestral mutualistic co-cultures did not (i.e., the ancestral cells were completely planktonic in microscopy images). Thus, cell aggregation was heritable and therefore likely had a genetic basis.
Genetic changes within or upstream of genes that have experimentally verified roles in E. coli biofilm formation in other studies
bEvolved mutualistic partner
Gene(s) or intergenic region
Role of gene(s) in E. coli biofilm formation
non-synonymous point mutation, A- > G
Δ16 bp, intergenic region
upstream of flhD
Δ1 bp, coding region
Δ1 bp, coding region
non-synonymous point mutation, G- > A
Δ627 bp, coding and intergenic regions
Δ627 bp, coding and intergenic regions
non-synonymous point mutation, C- > T
point mutation, intergenic region
upstream of yqcC
Δ15 bp, coding region
Δ15 bp, coding region
Δ15 bp, coding region
Δ3 bp, coding region
Δ3 bp, coding region
Δ3 bp, coding region
Δ6 bp, coding region
Δ11 bp, coding region
Δ10 bp, intergenic region
upstream of bluF and ycg
Δ5 bp, coding region
Δ3 bp, intergenic region
upstream of yliE
113 bp duplication of coding region
non-synonymous point mutation, G- > T
Δ1 bp, coding region
While we observed limited evolutionary parallelism in the genetic changes, a few genes or upstream regions were changed in mutualistic partners from more than one co-culture. Genetic changes in spoT or its upstream region occurred in mutualistic partners from three co-cultures (Table 3). spoT affects biofilm formation by modifying levels of (p) ppGpp [51, 52] and, under certain conditions, the inactivation of spoT can enhance biofilm formation . Genetic changes in glmU occurred in mutualistic partners from two co-cultures (Table 3). glmU affects biofilm formation by controlling the biosynthesis of surface adhesion molecules [53, 54]. We note here, however, that genetic changes in spoT and glmU have been reported in other evolution experiments where cell aggregation did not emerge , and that further molecular work would therefore be required to test their role here. Genetic changes in three flagellar genes (flhC, flhD, and hdfR) occurred in mutualistic partners from two co-cultures (Table 3). These genes affect biofilm formation and surface attachment by regulating the biosynthesis of flagella . Finally, genetic changes in three other genes or upstream regions (bluR, bluF, ycgG) occurred in mutualistic partners from two co-cultures (Table 3). These genes affect biofilm formation by activating the Rcs system, which regulates the biosynthesis of surface adhesion molecules and curli fimbre . We did observe some mutations in lacI, which is involved with the transcriptional regulation of the fluorescent proteins. These mutations were not unique to one mutualistic partner or another, and they are therefore unlikely to have created confounding factors that compromise our main conclusions.
Both mutualistic partners are required for cell aggregation
We found that heritable changes in both evolved mutualistic partners are required to maximize cell aggregation. Mutualistic consortia of Panc and Tevol,a or Tevol,b produced significantly more cell aggregation than mutualistic consortia of Panc and Tanc (Fig. 5 and Table 2; Mann-Whitney U test, two-sided P < 0.05). In contrast, mutualistic consortia of Pevol and Tanc did not form significantly more cell aggregation than mutualistic consortia of Panc and Tanc (Fig. 5 and Table 2; Mann-Whitney U test, two-sided P > 0.05). However, mutualistic consortia of Pevol and Tevol,a or Tevol,b produced the most significant increase in cell aggregation (Fig. 5 and Table 2; Mann-Whitney U test, two-sided P < 0.05). Thus, within the B2 mutualistic consortium, the most substantial increase in cell aggregation occurred when mutations in both mutualistic partners were present together within the consortium.
Only one mutualistic partner contributes towards increased cell numbers
We propose that the scenario we investigated here might be applicable to different types of organisms and interactions: for many organisms, proximity to members of the same or another genotype might be advantageous, for example because these other individuals provide resources or protection. This is expected to impose selection for biological traits that ensure proximity, through physical attachment, behavior, or by other means. This would lead to a situation that is equivalent to spatial structure; that is, a situation where individuals within or between genotypes are associated with each other for extended periods of time, as assumed in the “partner fidelity” model . According to this scenario, mutually beneficial interactions within and between organisms are expected to evolve more readily than often assumed, because the organisms themselves can readily generate the spatial structure that is necessary for these beneficial interactions to emerge and be stable. While this scenario is consistent with our results, a conclusive test would require tracking the spatial positioning of individual cells over evolutionary time, which is not possible with our current data set. However, recent developments in analytical microbiology should now allow for such tracking and for explicit tests of this hypothesis.
We thank Jan Roelof van der Meer and Victor de Lorenzo for generously providing the plasmids used in this study. We thank the Genetic Diversity Center at ETH Zurich for technical support with the genomic analyses.
This work was supported by grants from Eawag (Category: Seed), the Swiss National Science Foundation (31003A_149304), and SystemsX.ch (Category: IPP). The authors declare no conflict of interest related to this work.
Availability of data and materials
All of the sequence reads are publically available in the NCBI Sequence Read Archive (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/sra) under Bioproject ID number SUB2512304.
All authors conceived and designed the research project and wrote the manuscript. MM performed all the experiments. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Boucher DH, James S, Keeler KH. The ecology of mutualism. Annu Rev Ecol Syst. 1982;13:315–47.View ArticleGoogle Scholar
- Mougi A, Kondoh M. Diversity of interaction types and ecological community stability. Science. 2012;337:349–51.View ArticlePubMedGoogle Scholar
- Little AE, Robinson CJ, Peterson SB, Raffa KF, Handelsman J. Rules of engagement: interspecies interactions that regulate microbial communities. Annu Rev Microbiol. 2008;62:375–401.View ArticlePubMedGoogle Scholar
- Schink B. Energetics of syntrophic cooperation in methanogenic degradation. Microbiol Mol Biol Rev. 1997;61:262–80.PubMedPubMed CentralGoogle Scholar
- Pernthaler A, Dekas AE, Brown CT, Goffredi SK, Embaye T, Orphan VJ. Diverse syntrophic partnerships from deep-sea methane vents revealed by direct cell capture and metagenomics. Proc Natl Acad Sci U S A. 2008;105:7052–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Men Y, Feil H, Verberkmoes NC, Shah MB, Johnson DR, Lee PK, et al. Sustainable syntrophic growth of Dehalococcoides ethenogenes strain 195 with Desulfovibrio vulgaris Hildenborough and Methanobacterium congolense: global transcriptomic and proteomic analyses. ISME J. 2012;6:410–21.View ArticlePubMedGoogle Scholar
- Thompson AW, Foster RA, Krupke A, Carter BJ, Musat N, Vaulot D, et al. Unicellular cyanobacterium symbiotic with a single-celled eukaryotic alga. Science. 2012;337:1546–50.View ArticlePubMedGoogle Scholar
- Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci U S A. 2015;112:6449–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Doebeli M, Knowlton N. The evolution of interspecific mutualisms. Proc Natl Acad Sci U S A. 1998;95:8676–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferriere R, Bronstein JL, Rinaldi S, Law R, Gauduchon M. Cheating and the evolutionary stability of mutualisms. Proc Biol Sci. 2002;269:773–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Nahum JR, Harding BN, Kerr B. Evolution of restraint in a structured rock-paper-scissors community. Proc Natl Acad Sci U S A. 2011;108:10831–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Sachs JL, Hollowell AC. The origins of cooperative bacterial communities. MBio. 2012;3:e00099–12.View ArticlePubMedPubMed CentralGoogle Scholar
- Estrela S, Morris JJ, Kerr B. Private benefits and metabolic conflicts shape the emergence of microbial interdependencies. Environ Microbiol. 2015; doi:10.1111/1462-2920.13028.
- Duan K, Sibley CD, Davidson CJ, Surette MG. Chemical interactions between organisms in microbial communities. Contrib Microbiol. 2009;16:1–17.View ArticlePubMedGoogle Scholar
- Zengler K, Palsson BO. 2012. A road map for the development of community systems (CoSy) biology. Nat Rev Microbiol. 2012;10:366–72.PubMedGoogle Scholar
- Morris JJ. Black queen evolution: the role of leakiness in structuring microbial communities. Trends Genet. 2015;31:475–82.View ArticlePubMedGoogle Scholar
- Ovádi J. Physiological significance of metabolic channelling. J Theor Biol. 1991;152:1–22.View ArticlePubMedGoogle Scholar
- Wolfe AJ. The acetate switch. Microbiol Mol Biol Rev. 2005;69:12–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Lilja EE, Johnson DR. Segregating metabolic processes into different microbial cells accelerates the consumption of inhibitory substrates. ISME J. 2015; doi:10.1038/ismej.2015.243.
- Bever JD, Richardson SC, Lawrence BM, Holmes J, Watson M. Preferential allocation to beneficial symbiont with spatial structure maintains mycorrhizal mutualism. Ecol Lett. 2009;12:13–21.View ArticlePubMedGoogle Scholar
- Harcombe W. Novel cooperation experimentally evolved between species. Evolution. 2010;64:2166–72.PubMedGoogle Scholar
- Johnson DR, Goldschmidt F, Lilja EE, Ackermann M. Metabolic specialization and the assembly of microbial communities. ISME J. 2012;6:1985–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Werner GD, Strassmann JE, Ivens AB, Engelmoer DJ, Verbruggen E, Queller DC, et al. Evolution of microbial markets. Proc Natl Acad Sci U S A. 2014;111:1237–44.View ArticlePubMedPubMed CentralGoogle Scholar
- Bull JJ, Rice WR. Distinguishing mechanisms for the evolution of co-operation. J Theor Biol. 1991;149:63–74.View ArticlePubMedGoogle Scholar
- Foster KR, Wenseleers T. A general model for the evolution of mutualisms. J Evol Biol. 2006;19:1283–93.View ArticlePubMedGoogle Scholar
- Kim HJ, Boedicker JQ, Choi JW, Ismagilov RF. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc Natl Acad Sci U S A. 2008;105:18188–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Wakano JY, Nowak MA, Hauert C. Spatial dynamics of ecological public goods. Proc Natl Acad Sci U S A. 2009;106:7910–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Allen B, Nowak MA. Cooperation and the fate of microbial societies. PLoS Biol. 2013;11:e1001549.View ArticlePubMedPubMed CentralGoogle Scholar
- Pande S, Kaftan F, Lang S, Svatoš A, Germerodt S, Cost C. Privatization of cooperative benefits stabilizes mutualistic cross-feeding interactions in spatially structured environments. 2015;ISME J. doi:10.1038/ismej.2015.212.
- Hol FJ, Galajda P, Nagy K, Woolthuis RG, Dekker C, Keymer JE. Spatial structure facilitates cooperation in a social dilemma: empirical evidence from a bacterial community. PLoS One. 2013;8:e77042.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu Z, Müller J, Li T, Alvey RM, Vogl K, Frigaard NU, et al. Genomic analysis reveals key aspects of prokaryotic symbiosis in the phototrophic consortium “Chlorochromatium aggregatum”. Genome Biol. 2013;14:R127.View ArticlePubMedPubMed CentralGoogle Scholar
- Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol. 2006;2:2006.0008.View ArticlePubMedPubMed CentralGoogle Scholar
- Choi KH, Gaynor JB, White KG, Lopez C, Bosio CM, Karkhoff-Schweizer RR, et al. A Tn7-based broad-range bacterial cloning and expression system. Nat Methods. 2005;2:443–8.View ArticlePubMedGoogle Scholar
- Miller VL, Mekalanos JJ. A novel suicide vector and its use in construction of insertion mutations: osmoregulation of outer membrane proteins and virulence determinants in Vibrio cholerae requires toxR. J Bacteriol. 1998;170:2575–83.View ArticleGoogle Scholar
- Minoia M, Gaillard M, Reinhard F, Stojanov M, Sentchilo V, van der Meer JR. Stochasticity and bistability in horizontal transfer control of a genomic island in Pseudomonas. Proc Natl Acad Sci U S A. 2008;105:20792–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Lagache T, Lang G, Sauvonnet N, Olivo-Marin JC. Analysis of the spatial organization of molecules with robust statistics. PLoS One. 2013;8:e80914.View ArticlePubMedPubMed CentralGoogle Scholar
- Ripley BD. The second-order analysis of stationary point processes. J Appl Prob. 1976;13:255–66.View ArticleGoogle Scholar
- Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Barrick JE, Lenski RE. Genome-wide mutational diversity in an evolving population of Escherichia coli. Cold Spring Harb Symp Quant Biol. 2009;74:119–29.View ArticlePubMedPubMed CentralGoogle Scholar
- Barrick JE, Yu DS, Yoon SH, Jeong H, Oh TK, Schneider D, et al. Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature. 2009;461:1243–7.View ArticlePubMedGoogle Scholar
- Hayashi K, Morooka N, Yamamoto Y, Fujita K, Isono K, Choi S, et al. Highly accurate genome sequences of Escherichia coli K-12 strains MG1655 and W3110. Mol Syst Biol. 2006;2:2006.0007.View ArticlePubMedPubMed CentralGoogle Scholar
- Pande S, Merker H, Bohl K, Reichelt M, Schuster S, de Figueiredo LF, et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. ISME J. 2014;8:953–62.View ArticlePubMedGoogle Scholar
- Hillesland KL, Stahl DA. Rapid evolution of stability and productivity at the origin of a microbial mutualism. Proc Natl Acad Sci U S A. 2010;107:2124–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Gerstein AC, Lo DS, Otto SP. Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast. Genetics. 2012;192:241–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Tenaillon O, Rodríguez-Verdugo A, Gaut RL, McDonald P, Bennett AF, Long AD, et al. The molecular diversity of adaptive convergence. Science. 2012;335:457–61.View ArticlePubMedGoogle Scholar
- Le Gac M, Cooper TF, Cruviller S, Edigue CM, Schneider D. Evolutionary history and genetic parallelism affect correlated responses to evolution. Mol Ecol. 2013;22:3292–303.View ArticlePubMedGoogle Scholar
- Blank D, Wolf L, Ackermann M, Silander OK. The predictability of molecular evolution during functional innovation. Proc Natl Acad Sci U S A. 2014;111:3044–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Pratt LA, Kolter R. Genetic analyses of bacterial biofilm formation. Curr Opin Microbiol. 1999;2:598–603.View ArticlePubMedGoogle Scholar
- Wood TK. Insights on Escherichia coli biofilm formation and inhibition from whole-transcriptome profiling. Environ Microbiol. 2009;11:1–15.View ArticlePubMedPubMed CentralGoogle Scholar
- Hung C, Zhou Y, Pinkner JS, Dodson KW, Crowley JR, Heuser J, et al. Escherichia coli Biofilms have an organized and complex extracellular matrix structure. MBio. 2013;4:e00645–13.PubMedPubMed CentralGoogle Scholar
- Balzer GJ, McLean RJ. The stringent response genes relA and spoT are important for Escherichia coil biofilms under slow-growth conditions. Can J Microbiol. 2002;48:675–80.View ArticlePubMedGoogle Scholar
- Boehm A, Steiner S, Zaehringer F, Casanova A, Hamburger F, Ritz D, et al. Second messenger signalling governs Escherichia coli biofilm induction upon ribosomal stress. Mol Microbiol. 2009;72:1500–16.View ArticlePubMedGoogle Scholar
- Itoh Y, Wang X, Hinnebusch BJ, Preston JF, Romeo T. Depolymerization of beta-1, 6-N-acetyl-D-glucosamine disrupts the integrity of diverse bacterial biofilms. J Bacteriol. 2005;187:382–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Burton E, Gawande PV, Yakandawala N, LoVetri K, Zhanel GG, Romeo T, et al. Antibiofilm activity of GlmU enzyme inhibitors against catheter-associated uropathogens. Antimicrob Agents Chemother. 2006;50:1835–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Pratt LA, Kolter R. Genetic analysis of Escherichia coli biofilm formation: roles of flagella, motility, chemotaxis and type I pili. Mol Microbiol. 1998;30:285–93.View ArticlePubMedGoogle Scholar
- Tschowri N, Lindenberg S, Hengge R. Molecular function and potential evolution of the biofilm-modulating blue light-signalling pathway of Escherichia coli. Mol Microbiol. 2012;85:893–906.View ArticlePubMedPubMed CentralGoogle Scholar
- Claret L, Hughes C. Interaction of the atypical prokaryotic transcription activator FlhD2C2 with early promoters of the flagellar gene hierarchy. J Mol Biol. 2002;321:185–99.View ArticlePubMedGoogle Scholar
- Prüss BM, Besemann C, Denton A, Wolfe AJ. A complex transcription network controls the early stages of biofilm development by Escherichia coli. J Bacteriol. 2006;188:3731–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Ko M, Park C. H-NS-dependent regulation of flagellar synthesis is mediated by a LysR family protein. J Bacteriol. 2000;182:4670–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Sule P, Horne SM, Logue CM, Prüss BM. Regulation of cell division, biofilm formation, and virulence by FlhC in Escherichia coli O157:H7 grown on meat. Appl Environ Microbiol. 2011;77:3653–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Beloin C, Valle J, Latour-Lambert P, Faure P, Kzreminski M, Balestrino D, et al. Global impact of mature biofilm lifestyle on Escherichia coli K-12 gene expression. Mol Microbiol. 2004;51:659–74.View ArticlePubMedGoogle Scholar
- Tenorio E, Saeki T, Fujita K, Kitakawa M, Baba T, Mori H, et al. Systematic characterization of Escherichia coli genes/ORFs affecting biofilm formation. FEMS Microbiol Lett. 2003;225:107–14.View ArticlePubMedGoogle Scholar
- Majdalani N, Heck M, Stout V, Gottesman S. Role of RcsF in signaling to the Rcs phosphorelay pathway in Escherichia coli. J Bacteriol. 2005;187:6770–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferrières L, Clarke DJ. The RcsC sensor kinase is required for normal biofilm formation in Escherichia coli K-12 and controls the expression of a regulon in response to growth on a solid surface. Mol Microbiol. 2003;50:1665–82.View ArticlePubMedGoogle Scholar
- Wu T, Malinverni J, Ruiz N, Kim S, Silhavy TJ, Kahne D. Identification of a multicomponent complex required for outer membrane biogenesis in Escherichia coli. Cell. 2005;121:235–45.View ArticlePubMedGoogle Scholar
- Ma Q, Wood TK. OmpA influences Escherichia coli biofilm formation by repressing cellulose production through the CpxRA two-component system. Environ Microbiol. 2009;11:2735–46.View ArticlePubMedGoogle Scholar
- Sanchez-Torres V, Maeda T, Wood TK. Global regulator H-NS and lipoprotein NlpI influence production of extracellular DNA in Escherichia coli. Biochem Biophys Res Commun. 2010;401:197–202.View ArticlePubMedPubMed CentralGoogle Scholar
- Magnusson LU, Gummesson B, Joksimović P, Farewell A, Nyström T. Identical, independent, and opposing roles of ppGpp and DksA in Escherichia coli. J Bacteriol. 2007;189:5193–202.View ArticlePubMedPubMed CentralGoogle Scholar
- Sommerfeldt N, Possling A, Becker G, Pesavento C, Tschowri N, Hengge R. Gene expression patterns and differential input into curli fimbriae regulation of all GGDEF/EAL domain proteins in Escherichia coli. Microbiology. 2009;155:1318–31.View ArticlePubMedGoogle Scholar
- Hara A, Sy J. Guanosine 5′-triphosphate, 3′-diphosphate 5′-phosphohydrolase. Purification and substrate specificity. J Biol Chem. 1983;258:1678–83.PubMedGoogle Scholar