Asymmetrical Transfer Benefits Transformable Cells

To explore potential benefits of different evolutionary strategies to bacterial cells, a stochastic compartmental model was developed to simulate genetic exchange through HDT (see Methods). The first simulations investigated scenarios in which transformation might facilitate the import of beneficial foreign DNA, corresponding to either the acquisition of novel loci or the reversal of Muller’s ratchet by repairing deleterious mutations. These featured two strains of different fitnesses, one of which was competent for transformation, growing and competing in a homogeneous environment. Increasing the rate of transformation, τ, was advantageous to the transformable strain so long as the nontransformable strain was fitter and, therefore, donating beneficial alleles (Fig 1A). The greater the fitness advantage of the donor, the greater τ needed to be for the transformable strain to acquire the beneficial allele before it was outcompeted. However, when the nontransformable strain was less fit, increasing τ was detrimental to the transformable strain, which could only diversify through the acquisition of deleterious alleles. This situation was exacerbated if transformation has an associated fitness cost, set at 5% in Fig 1B. In these simulations, the cost of expressing the competence machinery meant the transformable strain was outcompeted by a fitter competitor more quickly, limiting the opportunity for the acquisition of beneficial loci, and outcompeted a less fit competitor more slowly, increasing the opportunity for the acquisition of deleterious alleles. Hence, in these simulations, transformation’s ability to facilitate exchange between strains can only provide a long-term benefit to cells if they continuously encounter potential donors of alleles that increase their fitness to an extent that outweighs the cost of expressing the competence machinery, and those beneficial alleles can be acquired more quickly than the transformable cells are outcompeted by the potential donors.

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Fig 1. Variation in the effectiveness of transformation when importing DNA from a different strain.

(A) Heatmap showing the outcome of simulated competitions between two strains, of which only one is transformable. Each cell in the grid represents a specific transformation rate (τ) and relative fitness of the allele at an exchangeable locus within the transformable strain, displayed at the top of each column. Relative fitnesses greater than one indicate the transformable strain has an initial advantage over the nontransformable strain; relative fitnesses below one indicate the nontransformable strain has the initial fitness advantage. Each cell is split in two: the “S” component shows the outcome of simulations in which transformation is symmetrical, and the “A” component shows the outcome of simulations in which transformation is asymmetrical, with the lower fitness allele acquired at a rate 10-fold lower than that of the higher fitness allele. (B) Heatmap showing the outcome of simulated competitions between two strains, of which only one is transformable, when there is a cost associated with the expression of the competence machinery. This figure shows the outcome of simulations analogous to those displayed in panel A, except that the transformable strain has a growth rate, γ, 5% lower than the nontransformable strain to represent the cost of the competence machinery. This cost was constant across simulations and independent of the relative fitness difference between the alleles of the exchangeable locus. Raw data are tabulated in S1 Data.

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The symmetrical shuffling of alleles in this scenario only occurs if donor and recipient have similarly sized alleles of a shared locus. However, homologous recombination is able to transfer DNA when there is similar sequence only at both ends of an otherwise divergent locus, even if the intervening sequence is the length of a typical genomic island [60]. Homologous recombination can, therefore, change a genome’s content through integrating or deleting components of the accessory genome, depending on whether the intervening locus is present in the imported DNA or recipient genome, respectively (Fig 2A). If such allelic variation in the length of a locus exists, then all else being equal, transformation has a bias towards shorter alleles, resulting in a tendency to delete, rather than import, sequence [90–93]. This is a consequence of the necessity for similarity at each end of an imported DNA fragment for it to be stably integrated into a chromosome; any cleavage that separates one of these regions from the other, which becomes increasingly likely as the distance between the homologous arms lengthens, prevents the integration of the entire intervening imported sequence [94].

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Fig 2. Modelling the interaction of HDT mechanisms in a simple bacterial population.

(A) Definition of the asymmetry parameter, φ. (B) Illustration of the exchange of DNA within and between clonally related cells. (C) Description of the stochastic compartmental model of within-population HDT.

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The parameter φ is included in our model to represent the asymmetric integration of alleles of different lengths (model parameters are summarised in S1 Table). When φ = 1, both alleles of such loci are exchanged at an equal rate (symmetrical transformation); if φ < 1, then the shorter allele is imported at a higher rate than the longer allele, a situation reversed if φ > 1. Assuming imported DNA fragments have a geometric length distribution with a rate parameter of λ_R bp^-1 [60], then φ is a function of an insertion’s length, x, of the form φ_x = (1-λ_R)^x. The simulations were run as previously described, with a transformable and nontransformable strain initially distinguished by a biallelic locus, with one strain having a neutral short allele and the other a long allele associated with a fitness cost. The transformable cells acquired long alleles at a rate of φ = 0.1 relative to the short allele (see Methods). In simulations in which the deleterious insertion was initially present in the transformable strain, transformation was beneficial in facilitating removal of the locus. However, increasing rates of transformation were neutral when the transformable strain was fitter, as any rise in the rate at which the longer allele was acquired was compensated for by its more rapid removal, a pattern observed whether or not there was a fitness cost associated with the expression of the competence machinery (Fig 1A and 1B; S1 Data). Hence, asymmetric transformation can purge genomes of deleterious insertions.

Asymmetrical Transformation Counteracts MGE Spread

In these simulations, the deleterious long alleles were eventually removed from the population by transformation; following their elimination, there is no longer a benefit to retaining the competence system. However, deleterious insertions are continuously generated within bacterial populations through MGE movement [57]. We extended the stochastic compartmental model of bacterial HDT such that the deleterious MGEs transmitted horizontally within a community of cells at a rate parameterised by β, assumed to be much faster than the rate of MGE transmission between communities (β_i; Fig 2B and 2C). The model simulated the growth, competition, and DNA exchange between susceptible (S) and infected (I) bacteria, isogenic except for a single locus at which an MGE was present in the I bacteria. Transformation was only able to eliminate MGEs when they were integrated into the host chromosome. MGEs excised themselves from the host chromosome at rate f, which determined the rate at which they transitioned from vertical to horizontal transmission. Simulations were conducted with two MGE types: “more horizontal” (MH; β = 10⁻⁶ unless stated, b = 10, f = 0.05, c_M = 0.075, a = 1), which frequently transmitted between cells, and “more vertical” (MV; β = 10⁻³ unless stated, b = 5, f = 0.005, c_M = 0.0025, a = 0), which more stably associated with host chromosomes.

The first set of simulations (Fig 3A) compared different φ values against MGEs of varying transmissibility, with a fixed transformation rate of τ = 10⁻⁴. We found that simulations with φ > 1, which favoured the transfer of insertions, actively drove the spread of MGEs through the cell population. However, when asymmetry favoured the import of shorter alleles (φ < 1), transformation was highly effective at inhibiting the spread of MH MGEs over a narrow range β values, and MV MGEs across all tested β values. Similarly, when φ = 0.1, higher rates of τ were found to be more effective against MV across all tested values of β, whereas MH could still spread through the population if sufficiently transmissible (Fig 3B). Sensitivity analyses indicated elimination of MGEs was dependent upon a sufficiently high concentration of extracellular DNA for homologous recombination, facilitated by higher cell growth rates and greater stability of extracellular molecules (S1 Fig).

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Fig 3. Identifying the necessary conditions for constitutive competence to be effective in inhibiting MGE transmission.

(A) Heatmap showing the outcome of simulations in which MGEs infect cells competent for transformation at rate τ = 10⁻⁴. The colour of the heatmap represents the proportion of the population infected by MGEs over the course of each simulation. Each cell represents a specific MGE transmissibility (β) and transformation asymmetry (φ); the “MH” and “MV” components show simulations with MGEs having relatively greater propensities for horizontal and vertical transmission, respectively. (B) Heatmap, displayed as in panel A, but this time comparing the effects of varying β against changing the rate of transformation (τ) with a fixed value of φ = 0.1. (C) Conditions necessary for transformation to provide a fitness advantage to the population. This heatmap shows the ratio of the total number of cells in two independent sets of simulations: one in which cells were nontransformable, and another in which the cells were transformable and suffered an associated cost (c_C). In both sets of simulations, either the MH or MV MGEs were present in the populations, each of which was associated with a varying cost to the host (c_M); transformation was only effective at inhibiting the transmission of MV. The higher the value of the ratio, indicated by the heatmap colour, the relatively greater the size of the bacterial population when cells were transformable. Raw data are tabulated in S1 Data.

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This mechanism was also found to provide a benefit at the level of the population. Independent simulations were run in which cells were either nontransformable or transformable (φ = 0.1, τ = 10⁻⁴) and, therefore, affected by an associated cost of expressing the competence machinery (c_C). Each population featured either MH or MV, with the associated cost of being infected by such an MGE, c_M, varying between simulations. The heatmap summarizing the ratio of the total cell population recorded from the simulations with identical parameterization, but differing in whether or not the competence machinery was expressed, is shown in Fig 3C. In simulations involving MH, against which transformation was ineffective, either the total number of cells was similar in the matched sets of simulations, if c_C was negligible, or the nontransformable cells were detectably more successful, if c_C was high. However, transformation was effective at inhibiting the spread of MV, and, therefore, in simulations involving this MGE in which c_M was greater than c_C, transformation resulted in the cell population being more numerous when transformable, despite the associated costs of the competence machinery. Hence, constitutive asymmetric transformation inhibits the vertical transmission of deleterious MGEs, thereby allowing bacteria to purge such parasites from their genomes, potentially resulting in increased fitness of individual cells and populations.

Transient Competence Removes Deleterious MGEs

In many species, competence is only transiently expressed, rather than being constitutive. To test whether these expression patterns are compatible with inhibiting the spread of MGEs, populations were simulated in which cells were only competent in a “C state.” As competence is often regulated by diffusible signals, cells were assumed to enter C state upon an intercellular signal, a “C signal” (s_C) constitutively generated by all cells, surpassing a threshold, t_C. C-state cells suffered a cost to expressing competence, quantified as a growth inhibition c_C, and exited C state at the rate r_C (Fig 4A). These parameters were sufficient to define a cell-density-dependent C state (g_C = 10, r_C = 0, c_C = 0.1), in which cells became irreversibly competent above a particular density threshold, and a “bet hedging” strategy (g_C = 0.1, r_C = 0.5, c_C = 1), which resulted in a dynamic equilibrium in which approximately 13% of the population was in C state, with their growth completely arrested, at any point in time. Neither caused much change in the overall growth pattern of the population (Fig 4B).

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Fig 4. Inhibition of MGE transmission by transient competence.

(A) Model of a transient competent (“C”) state and cell–cell killing of non-C-state cells. (B) Graph showing the original cell growth curve (no C state), cells entering the C state in a cell-density-dependent manner, then never leaving (k_C = 0 and r_C = 0); a “bet hedging” strategy (k_C = 0, g_C = 0.1, r_C = 0.5) in which only a fraction of the population is competent for transformation at any one time; cells undergoing small population oscillations (k_C = 10⁻⁶) and cells undergoing large population oscillations (k_C = 10⁻³) at frequencies determined by r_C. (C) Heatmap summarising the outcomes of simulations comparing patterns of growth and competence expression in panel B with different MGEs. Colours are scaled as in Fig 3. Results for two representatives of MH and MV are shown, each associated with different rates of HDT. Transformation at the specified τ_C rate occurred in the C state, such that the cells that never entered the C state were never competent for transformation. Raw data are tabulated in S1 Data.

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C state can also mimic “fratricide” or “cannibalism” [15]. Lysis of surrounding bacteria, which are usually near-isogenic clonal descendants of a recent common ancestor, may be important in raising the concentration of DNA available for transformation. To explore the biological significance of this, C-state cells were enabled to kill non-C-state cells at a rate k_C (Fig 4A). This inclusion of transient arrest of cell growth and cell–cell killing (c_C = 1, k_C > 0) resulted in oscillatory growth patterns in which populations went through alternate phases of clonal growth and competence. At k_C = 10⁻⁶, competence was transient but associated with little change in population size, while k_C = 10⁻³ drove large population oscillations. Increasing r_C had the opposite effect on oscillation amplitude and frequency (Fig 4B). Large, unexplained population oscillations have been observed during the growth of S. pneumoniae in a chemostat in the absence of phage [95,96], and their occurrence during carriage could explain the discrepancy between census and effective population sizes in animal models [97].

Like constitutive competence, transient competence was also effective in inhibiting the spread of MV (Fig 4C). Irreversible cell-density-dependent C state eliminated MV from the population more quickly than bet hedging, although the latter strategy confined the costs of expressing competence to only a subset of the population. Oscillatory growth patterns were effective when transformation was rapid, necessitating a high value of τ and sufficient DNA release to sustain transformation throughout the competence period. This was facilitated by the cycles of cell–cell killing and rapid growth driven by k_c > 0, and a shorter period during which competence-associated arrest of growth applied (S2 Fig). By contrast, the spread of MH was only inhibited by large oscillations in cell population size (Fig 4C), also driven by higher values of k_C, although this effect was contingent upon the MGEs being relatively unstable outside the cell, such that they could not survive extracellularly from one population boom to the next (S3 Fig). Hence, we find that coregulation of cell–cell killing with asymmetric transformation, as modelled by this C state, can synergistically combine to block the transmission and spread of MGEs. Horizontal transmission is limited by the large reductions in the density of susceptible cells during population crashes, which are contemporaneous with large releases of DNA that can limit vertical transmission through transformation. Importantly, all simulated patterns of growth and competence were found to be compatible with the elimination of parasitic MGEs.

Some MGEs have spread successfully while carrying cargo, such as antibiotic resistance, that can be beneficial to their bacterial host [4,84]. To explore the spread of beneficial MGEs, we allow fitness “costs” to be negative, corresponding to advantages that increase the replication rate. Fig 5 shows the spread of beneficial and deleterious MV when the cell population undergoes irreversible density-dependent competence (Fig 5A), low-amplitude oscillations (Fig 5B), or high-amplitude oscillations (Fig 5C). Constitutive, density-dependent transformation is the most effective at eliminating MV, regardless of whether they are detrimental or beneficial. By contrast, transient competence is able to remove deleterious MV but permit those that are beneficial to the cell to spread over multiple orders of magnitude variation in τ. This is the consequence of the MGEs increasing in frequency between phases of competence through both transmission and selection. However, at the population level, asymmetric transformation is still required to preferentially acquire beneficial alleles, with tightly regulated expression of competence alone being insufficient (S3 Fig). Hence, transiently competent populations biased against acquiring insertions through transformation can often allow beneficial MGEs to spread, while eliminating deleterious MGEs.

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Fig 5. The effect of different patterns of competence expression on the transmission of MGEs that benefit their host.

(A) Heatmap summarising the outcome of simulations in which MV-type MGEs infected cells that entered the C state in a cell-density-dependent manner (r_C = 0). Each cell represents a specific transformation rate τ_C and “cost” of the MGE (c_M); negative costs imply the MGE benefits the cell. Each cell is split into two components, representing an MV MGE with high (β = 10⁻¹) or low (10⁻³) transmissibility. (B,C) Heatmaps, displayed as in panel A, but comparing cells transiently entering the C state with k_C = 10⁻⁶ (panel B) or k_C = 10⁻³ (panel C; r_C = 0.9 and φ = 0.5 in both cases). Compared to cell-density-dependent C state, these patterns of C state expression were still effective at inhibiting the spread of MV that were detrimental to the cell, but were a relatively lower impediment to the spread of MV that benefitted the cell. Raw data are tabulated in S1 Data.

https://doi.org/10.1371/journal.pbio.1002394.g005

Frequent Opportunities for MGE Removal by Transformation

For cells to derive a benefit from transformation acting to remove MGEs, there must be frequent instances of deleterious MGEs being polymorphic within otherwise isogenic cell populations. We explored the potential for such situations within a dataset of over 3,000 sequenced isolates of S. pneumoniae, of which 1,715 represented longitudinal sampling of 371 hosts [98]. By performing a systematic search for variation in the three major classes of MGEs found in the pneumococcus (phage, integrative conjugative elements, and phage-related chromosomal islands [PRCI] [57]), we uncovered multiple instances of MGE variation between closely related isolates from the same host sampled on different days, despite the comparative insensitivity of sampling a single colony per timepoint. All but one of the well-characterised examples involved changes in prophages, which integrate into the genome and do not typically contain beneficial cargo genes in S. pneumoniae [57].

Phylogenies were constructed that accounted for divergence through interstrain transformation, thereby reconstructing clonal descent (S4 Fig) [99]. Such analysis of the most common lineage in the sampled population, BAPS cluster (BC) 1-19F, demonstrated that the MGE variation within carriage represented changes in the prophage content of stably carried bacteria with very similar core genomes, rather than a host acquiring new genotypes with stable MGE content (Fig 6, S2 and S3 Tables). The detectable frequency of integrative MGE acquisition indicates these otherwise isogenic, recombining populations will frequently consist of coexisting S and I cells. That this coexistence may persist over weeks or longer is suggested by the repeated identification of the same MGE within a particular host, contrasting with its absence from other timepoints during individual carriage episodes. However, many bacteria were nonlysogenic, and there is little evidence of prophages being conserved over substantial proportions of the lineage’s overall evolutionary history, consistent with selection against cells infected with these parasites (S5 Fig) [57,100,101].

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Fig 6. Evidence for coexistence of susceptible and infected BC1-19F cells within individual carriage episodes.

The displayed phylogeny was generated based on point mutations, excluding base substitutions likely to have been introduced by recombination (S4 Fig). Leaf nodes are annotated to indicate whether the comYC gene, required for efficient transformation, is intact (green dash) or disrupted by a prophage insertion (orange dash; see key in Figs 8 and S5). Specific cases of changes in prophage content within what are likely to represent individual carriage episodes are highlighted. Each box displays the annotation of a specific prophage (S2 Table), along with sequence read mapping heatmaps beneath showing the depth of coverage across the viral sequence for individual isolates from a single host: blue for low levels of mapping, indicating the sequence is absent, and red for high levels of mapping, indicating it is present (see scale in Fig 8). The isolates are ordered by the date of isolation. Epidemiological data are summarised in S3 Table.

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The precise mechanisms by which the MGEs may be eliminated are difficult to differentiate in populations with few distinguishing genetic markers. However, in one of the rare cases in which interstrain DNA transfer was detected within a single carriage episode, a transformation event was associated with the loss of an otherwise stable PRCI (S6 Fig) [57]. This observation of an MGE apparently being removed by transformation demonstrates the feasibility of the mechanism underlying our model.

Recurrent Inhibition of Transformation by Prophage

The leaf nodes of the phylogenies shown in Fig 6 are marked according to the status of their comYC gene, required for effective transformation [4,15]. S5 Fig shows that all these instances of comYC disruption result from the insertion of a prophage into the coding sequence (CDS). Whereas the integrases of most MGEs target them into the small noncoding fraction of the bacterial genome, thereby minimising the selective cost imposed on the host, this was the only example of an MGE disrupting a CDS observed in genomic data from a S. pneumoniae population [57]. Past investigation of resistant S. pneumoniae have found a second instance of a genomic island disrupting a CDS; a gene cassette encoding a mefA macrolide resistance determinant was observed to inhibit transformation through insertion into comEC [102], which encodes a protein critical for forming the DNA import pore.

The benefit to the MGE of disrupting the competence system of the host cell is illustrated in Fig 7A and 7B. These display repeats of the simulations shown in Fig 3A and 3B, except that infection by either MH or MV prevented their host undergoing transformation. The results indicate MGEs derive an advantage from such abrogation of host competence when the transformation rate was sufficiently high (τ > 10⁻⁷), and asymmetry in favour of shorter alleles sufficiently strong (φ ≤ 0.1), to impede their transmission through the population. Hence, in this model, transformation’s potential to prevent the spread of MGEs provides the selection pressure for such mobile elements to target competence genes for disruption when they integrate into the genome.

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Fig 7. The benefit to MGEs of inhibiting rapid, asymmetric transformation.

Panels A and B repeat the simulations displayed in Fig 3A and 3B, respectively, with the difference that the MH and MV MGEs in these simulations disrupt the host’s ability to undergo transformation when they insert into the cell’s chromosome. Raw data are tabulated in S1 Data.

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A second lineage from the same population, BC4-6B, included clade A that diversified similarly to BC1-19F, with intermittent import of diversity through transformation and disruption of comYC (Fig 8). Yet clade B shows little evidence of diversification through transformation or MGE variation (S7 and S8 Figs). This reduction in all HDT mechanisms is associated with the stable inheritance of two prophages within clade B, one of which disrupts comYC (S8 Fig), exemplifying the efficient vertical transmission of prophages in the absence of transformation. However, as is necessary for competence to be preserved in the species [74], most insertions into comYC were not successful. Instead, across BC1-19F and clade A of BC4-6B, noncompetent bacteria appear to have been removed by selection at a faster rate than diversification through interstrain transformation, with “clonal” lineages only rarely found to transmit between multiple hosts.

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Fig 8. Evidence for coexistence of susceptible and infected BC4-6B cells within individual carriage episodes.

The displayed phylogeny was generated based on point mutations, excluding base substitutions likely to have been introduced by recombination (S7 Fig). Leaf nodes are annotated to indicate whether the comYC gene, required for efficient transformation, is intact or disrupted by a prophage insertion (see key and S8 Fig). Specific cases of changes in prophage content within what are likely to represent individual carriage episodes are highlighted. Each box displays the annotation of a specific prophage (S2 Table), along with sequence read mapping heatmaps beneath showing the depth of coverage across the viral sequence for individual isolates from a single host, ordered by the date of isolation. Epidemiological data are summarised in S3 Table.

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MGEs Inhibit Transformation in Many Species

The insertion of a prophage into comYC is not restricted to S. pneumoniae. Searches for orthologues of the relevant phages’ integrases, which determine the site into which a phage inserts, identified examples of a prophage targeting the same gene in several other related species; these included Streptococcus mutans and Streptococcus parauberis, from the same genus, as well as an example in Lactococcus lactis (Figs 9A and S9). These all inserted at an orthologous, but not perfectly conserved, site within the gene (S10 Fig). Unexpectedly, an orthologous integrase was found in Streptococcus agalactiae, a species not considered to be naturally competent (Fig 9B) [28,103]. This integrase was part of a phage that inserted into the cas3 gene of S. agalactiae’s CRISPR2 locus [104]. While disruption of loci that inhibit infection of the cell would not be expected to benefit a prophage, CRISPR systems are capable of targeting integrated prophages, resulting in cell suicide, or the post-activation excised, replicating form of the phage [105–107]. Under either scenario, depleting the cytosol of functional CRISPR proteins would provide the phage with an advantage prior to transmitting horizontally to the next cell. Hence, cellular countermeasures to both defences against horizontal and vertical transmission of MGEs are targeted by similar integrases directing the insertion of prophages in streptococci and related genera.

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Fig 9. Selected examples of MGE insertions disrupting chromosomal protein coding sequences.

(A) Comparison between Lactococcus lactis isolates IL1403 and KLDS 4.0325, the latter of which has a prophage inserted into the comYC gene, encoding the major structural component of the competence pilus. The sequences’ accession codes are given in brackets underneath the isolate names. Blue and orange boxes represent CDSs, with the direction of their transcription indicated by their vertical position relative to the horizontal line; pink boxes indicate putative MGE CDSs in the same way. Brown boxes linked by dashed lines mark the fragments of a pseudogene disrupted by MGE insertion. The red bands link regions of similar sequence in the two loci, as identified by BLAST-like alignment tool (BLAT); the intensity of the colour indicates the strength of the match. The prophage integrase has ~51% identity with the protein that drives integration into the orthologous gene in S. pneumoniae 670-6B (SP670_2190). (B) Comparison between Streptococcus agalactiae isolates COH1 and FSL S3-277, the latter of which has a prophage inserted into the cas3 gene of the S. agalactiae CRISPR2 locus. This prophage integrase is ~76% identical with that of the prophage disrupting the comYC gene of S. pneumoniae 670-6B. (C) Comparison of Streptococcus suis isolates P1/7 and 89–590, the latter of which has a prophage inserted into the 3’ half of the comFA competence gene. This prophage integrase is ~46% identical with that of the prophage inserted into the orthologous gene in Streptococcus equi (SEQ_1765). (D) Comparison between Bacillus cereus isolates MHI 226 and VD214, the latter of which has a prophage inserted into the 5’ half of the comFA competence gene. The prophage’s integrase is ~44% identical with that of the prophage inserted into the comK competence gene in Listeria monocytogenes (LMRG_01511).

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A different example was observed in Streptococcus equi [108], in which a prophage inserted into the comFA gene. Orthologues of this distinct integrase were identified in other species including the zoonotic pathogen Streptococcus suis, in which the protein again directed a prophage to insert into the host cell’s comFA gene (Fig 9C). Similarly, some strains of Listeria monocytogenes have a prophage inserted into their comK genes [109], encoding a regulator of competence. A similar insertion was identified in a representative of Listeria innocua [110], a further example of which is shown in S11 Fig. Searching for proteins similar to the L. monocytogenes prophage integrase identified a prophage inserted into comFA in representatives of the Bacillus cereus and thuringiensis group (Figs 9D and S11). However, codon alignments demonstrated that the insertion site within the Bacillus comFA genes was distant from that of the distinct prophage identified in S. suis (S10 Fig), suggesting the targeting of this gene represented convergent evolution between the two MGEs. Another orthologue of the integrase found in L. monocytogenes was present in a phage of Enterococcus faecalis, this time targeting the MGE to insert into radC (S11 Fig); the same gene was reported to be targeted by a prophage inserted into B. subtilis that inhibited transformation [111], although the insertion site was not demonstrated to be the cause of this inability to integrate exogenous DNA. While RadC levels increase during competence, it does not always appear to be essential for transformation in the laboratory [112].

Another previously identified example of MGEs inhibiting competence was the observation from Aggregatibacter actinomycetemcomitans genomes that some MGEs inserted into comM [113], which encodes a protein important for efficient incorporation of DNA into the chromosome through homologous recombination [22]. MGEs were also inserted into comM in a single representative of Acinetobacter baumannii [114], and a large insertion was identified in the same gene in Mannheimia succiniciproducens [115]. Searching for orthologues of the integrase targeting comM from A. actinomycetemcomitans identified examples in other strains of A. baumannii (S12 Fig), and similar insertions were observed in genes encoding orthologues of ComM across a diverse set of species, including the animal pathogen Mannheimia haemolytica, the human pathogen Francisella philomiragia, and the plant pathogen Pseudomonas syringae.

The Effect of Competition between MGEs

Despite the potential advantage to individual mobile elements, the disruption of competence by MGEs is not ubiquitous. To investigate the reasons underlying this, we modelled an MGE “MI,” intermediate in properties between MV and MH (β = 5×10⁻⁶, b = 7, f = 0.01, c_M = 0.005, a = 1), which is able to spread through a nontransformable cell population but is eliminated by cell-density-dependent, bet-hedging, and transient patterns of competence expression (Fig 10A). However, “MI_NT,” which has the same properties but disrupts the competence system of the host cell, is able to spread regardless of the type and rate of transformation of the host cell. When MI and MI_NT coinfect the same cell population, both MGEs achieve similar levels of transmission, mirroring the stability of both prophages within BC4-6B clade B, despite only one inhibiting competence (S8 Fig). This limits the advantage of strategies such as disrupting comYC, as the affected cell cannot eliminate superinfecting MGEs, benefitting all other elements infecting the same host.

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Fig 10. The effectiveness of MGE strategies for reducing elimination by transformation.

(A) Heatmap summarising the outcomes of simulations comparing the patterns of cellular competence shown in Fig 4C in the presence of the MGE MI (top row), which has properties intermediate between those of MH and MV. The colour of each cell represents the proportion of the population infected by the MGE over the course of the simulations. In the second row, the same simulations are performed, but in this case the MGE MI_NT inhibits transformation in the host cell. The bottom two rows show the outcome of simulations in which both MI and MI_NT infect the same population. (B) Heatmap summarising the outcomes of simulations comparing cell growth patterns with different MI activation patterns: f, the normal rate of activation, is either low (0.005) or high (0.5), as is f_C, the rate of activation in C state. (C) Heatmap summarising the same simulations shown in panel B, but for MI_NT. (D) Competition between MI_NT operating with its optimal strategy (f and f_C low) and MI operating with its optimal strategy (f low, f_C high). Both MGEs were allowed to infect the same population in these simulations. The heatmap shows the ratio of strains infected with MI_NT to those infected with MI when cells grew and expressed competence for transformation under different strategies. Raw data are tabulated in S1 Data.

https://doi.org/10.1371/journal.pbio.1002394.g010

Individual MGEs can also escape the consequences of transformation by switching from vertical to horizontal transmission when a cell becomes competent. This is achieved through increasing the activation rate, f, to an elevated value f_C during C state. Evidence for the relevance of this mechanism is the observation that many prophages [116–118] and integrative and conjugative elements (ICEs) [119,120] excise from the chromosome in response to elevated levels of RecA, the protein required for homologous recombination. Simulating the spread of MI with low (f or f_C = 0.005) or high (f or f_C = 0.5) rates of activation during either clonal growth or C state shows that relying heavily on vertical transmission (f = f_C = 0.005) makes the MGE highly susceptible to transformation (Fig 10B). By contrast, high levels of activation outside of C state (f = 0.5) results in substantial costs to the host population, with the consequent low host cell density reducing the efficiency of horizontal MGE transmission (S13 Fig). However, activating only at a high rate during C state permits stable vertical transmission when cells are not competent, while greatly reducing elimination through transformation. Nevertheless, cells’ synergistic coupling of reduced cell density with transformation can still strongly inhibit the spread of MGEs adopting this optimal strategy.

To test whether both these strategies for avoiding elimination by transformation might be synergistic, the analysis of activation rates was repeated for MI_NT. This found that the optimal strategy for MI_NT was different; as the MGE could not be removed by transformation, it could achieve fixation in a population without elevated rates of activation in C state, and instead benefitted from a low rate of activation regardless of the cell’s behaviour (Fig 10C). However, when both MI and MI_NT were allowed to compete in the same population, both operating under optimal strategies (f low and f_C high for MI; f and f_C low for MI_NT), MI was more successful in a greater number of scenarios (Fig 10D). MI_NT, by contrast, was more successful only if there were large changes in population size that inhibited horizontal transmission. This was the consequence of the rapid activation of MI, following the onset of C state, allowing it to spread horizontally at a higher rate; additionally, this behaviour facilitated killing C-state cells in which MI_NT was also inserted but not yet activated. Hence, elevated f_C provides an advantage to MGEs over competitors in the same cell, regardless of whether they inhibit transformation or not. This contrasts with the disruption of host cell competence, which is an effective strategy for individual MGEs, but provides the same benefit to all MGEs parasitizing the same host. Hence the effectiveness of transformation against MGEs is likely to be partly maintained by competition between MGEs that infect the same cell.

Transformable Streptococci Harbour Fewer Prophages

Our hypothesis predicts that parasitic MGEs integrated into chromosomes that have not achieved fixation should be vertically transmitted at a lower rate in recombining populations of naturally transformable bacteria relative to equivalent nontransformable bacteria. The implication is, all else being equal, that transformable bacteria should harbour fewer MGEs. This is difficult to test with draft assemblies of bacterial genomes, which are fragmentary, often particularly so in regions encoding MGEs; this situation is exacerbated when multiple similar elements are present in the same genome [57]. An alternative approach is to use complete and high quality draft genomes from species or genera containing a mix of transformable and nontransformable bacteria. There are over 140 suitable genome assemblies available for streptococci, of which a subset have been demonstrated to be naturally transformable [28,32,84,121–123]; additionally, Streptococcus pseudopneumoniae was assumed to be naturally transformable, based on its close relationship with S. pneumoniae and Streptococcus mitis. In agreement with a previous comment by Beres et al. [124] based on the small number of genomes available at the time, streptococci demonstrated to be naturally transformable had significantly fewer prophages than nontransformable streptococci (mean number of prophages per transformable genome: 0.74; mean number of prophages per nontransformable genome: 1.85; Wilcoxon rank sum test: W = 3065.5, p = 0.00021; S4 Table and S14 Fig) despite tending to have larger genomes (mean size of transformable genome = 2,073,450 bp; mean size of nontransformable genome = 2,002,130 bp; Wilcoxon rank sum test: W = 1702, p = 0.013; S4 Table). However, there are several caveats to such an analysis. Firstly, the sampling of genomes is nonrandom, something that this analysis partly seeks to address (see Methods). Secondly, all samples are filtered by selection, and, therefore, low-fitness isolates that accumulate large numbers of MGEs are likely to be lost from the population quickly, thereby limiting the opportunity for sampling. Thirdly, there are other systems that inhibit the transmission of MGEs, thereby making the comparison uncontrolled; it is noteworthy, for instance, that an analogous comparison of MGEs and CRISPR systems in streptococci did not find evidence for these systems providing a protective effect against phage infection [125]. In another example, a within-species analysis of A. actinomycetemcomitans cells, of which a subset were rendered nontransformable by MGE insertions into the comM gene, reported noncompetent isolates to have an increased susceptibility to further MGE infection, although this was confounded by the associated loss of some CRISPR functionality [113].

Description of the Microevolutionary Model of HDT

We developed a stochastic compartmental model that included four types of compartments: cells, MGEs, DNA, and a signalling molecule, “C signal.” The overall structure of the model is displayed in S15 Fig.

Bacterial cell growth (green arrows in S15 Fig) followed a logistic growth model. In the absence of MGE infections, cells grew at a constant rate γ (set to 0.2 t^-1, unless otherwise specified). Analyses of the model output sensitivity to different values of γ are shown in S1 and S3 Figs. Cells died at a density-dependent rate (brown arrows in S15 Fig) determined by γ and a carrying capacity, κ (10⁶ in all simulations). For the k cell compartments in the model, the number of cells (N_i) in compartment i at time t changed at time t+1 through the demographic processes of birth and death by P_i, which was distributed as:

In all simulations, the starting inoculum of each distinct genotype was 100 cells. All cell compartments immutably belonged to one of two strains, each of which could be independently parameterised. The “plastic” aspect of cells’ genotypes was defined by two biallelic loci: the first locus could either be “empty” (allele E1) or have an inserted MGE, M1; analogously, the second could be empty (allele E2) or contain a different inserted MGE, M2. Upon density-dependent cell death, one DNA molecule was released from each locus, the type of which depended on the host cell genotype (S15 Fig; blue arrows).

Any cell with an “M1” or “M2” allele therefore carried an MGE and, consequently, grew at a rate γ(1-c_M), where c_M was the reduced growth of the host cell owing to the cost of the inserted MGE. This factor only applied to the growth term of the demographic model; cells carrying MGEs were killed through cell density-dependent death at the same rate as noncarriers. Analogously, cells infected with two MGEs grew at a rate γ(1-c_M1) (1-c_M2). For cells in the ith compartment carrying MGE M_q, the number of MGEs that activated per timestep interval at time t, A_q,i,t, was distributed according to the number of cells N_i at time t and activation rate of the MGE type q in cell type i f_q,i:

The number of MGEs of type M_q released by activations occurring in cell type i, R_q,i, at time t (dark blue and purple arrows in S15 Fig) depended on the mean burst size, b_q:

As A_q,t determines M_q,t, rather than M_q,t+n where n > 0, the activation and packaging of MGEs is effectively instantaneous in this model. This means there is no eclipse period. If included, this would limit the fitness of horizontal transfer by slowing the rate of transmission between cells, but as this study focused on inhibition of vertical transmission, it did not form part of this model. The parameter a determined the consequence of MGE activation for the cell; if a = 1, then MGE activation killed the host cell, as is typical for prophages; but if a = 0, then MGE activation did not affect the host cell, as is typical for ICEs. As MGE activation involves excision from the host chromosome and packaging of the MGE DNA, any cells killed through this mechanism did not release DNA molecules corresponding to the activated MGE, but the appropriate allele was released from the other locus (dark blue and purple arrows in S15 Fig).

Horizontal DNA transfer was modelled as a two-step process using one of two association parameters: β, the infectivity associated with a particular MGE, and τ, a transformation rate associated with a particular cell type. The noncellular components of the model were removed from the environment at a constant “washout” rate, ω, set at 0.6 t^-1 unless otherwise specified (grey arrows in S15 Fig). Analyses of the model output’s sensitivity to different values of ω are shown in S1 and S3 Figs. Hence, the overall rate at which noncellular agents were removed from the simulation was a composite of cellular binding and elimination from the extracellular environment. For the qth DNA compartment, molecules were removed at a composite rate r_q representing “washout” and binding to each of the k cell compartments, each containing N_i cells and undergoing transformation at a cell-determined rate τ_i:

Similarly, for the qth MGE compartment, elements were removed at a composite rate r_q representing washout and binding to each of the k cell compartments, each containing N_i cells, at the MGE-determined rate β_q:

For the qth type of noncellular agent in the model, the number of agents of that type (N_q) decreased by d_q per timestep as determined by the relevant value of r_q:

The noncellular agents were then assigned to cell compartments through a multinomial distribution, which allowed for differences in τ between cell types. Within each compartment, the bound DNA molecules and MGEs were then randomly assigned to individual cells. In cases in which a single cell was bound to multiple noncellular agents, a single noncellular agent was randomly selected for interaction at that timestep; MGEs interacted through causing infection (maroon arrows in S15 Fig), while DNA interacted through causing a transformation event (orange arrows in S15 Fig). This structure permitted a single association constant to parameterise interactions between noncellular agents and cells in a manner that could be limited by either partner in the interaction. However, this structure has the disadvantage of artefactual antagonism between MGEs and DNA in the cases where both are bound to a single cell, but only one is selected to interact with the cell. This effect is small unless there are large numbers of noncellular agents interacting with individual cells per timestep. Simulations mirroring those in Figs 3B and 4C were carried out in which cells preferentially interacted with MGEs if DNA was also bound to the cell, rather than the selection being random; the results are shown in S13E and S13F Fig, demonstrating that any artefactual antagonism between DNA transformation and MGE infection was negligible at the dt interval used in all simulations (10⁻³).

In cases in which MGEs were introduced at a constant rate, such as S13A–S13D Fig, the number of MGEs of type q entering at each timestep, E_q, was determined by the entry rate, e_q, and the MGE burst size:

The relative rate at which the “M” and “E” alleles at the two loci were exchanged was also determined by the asymmetry parameter, φ. In the case of symmetrical transformation (φ = 1), the rate at which the ith cellular compartment underwent transformation was determined by τ_i and the number of available DNA molecules. These factors also determined the rate at which any transformation in which the donor and recipient alleles were the same occurred; such recombinations did not affect cell genotype, but nevertheless depleted DNA molecules. When φ > 1, favouring the import of longer alleles, if B_i,q complexes of a DNA molecule of the qth compartment, corresponding to an “E” allele DNA molecule, were bound to a cell of the ith compartment, with an “M” allele at the relevant locus, the number of transformation events T_i,q was distributed as:

Correspondingly, when φ < 1, favouring the import of shorter alleles, if B_i,q complexes of a DNA molecule of the qth compartment, corresponding to an “M” allele DNA molecule, bound to a cell of the ith compartment, with an “E” allele at the relevant locus, the number of transformation events T_i,q was distributed as:

In all other cases, T_i,q = B_i,q. The default value of φ used in these simulations, 0.1, is a conservative estimate assuming a geometric distribution of imported DNA lengths parameterised according to the typical length of shorter classes of MGEs (~15 kb) [57] and an estimate of the transformation length distribution biased away from shorter transformation events (mean length of ~6.6 kb) [4,60].

The transformation rate also varied through the simulations in cases where the “C state” was included (S15 Fig), representing the regulated, transient competence state observed in many bacterial species. The trigger for entering C state was the “C signal,” the levels of which (S) were determined by the rate of production by all k compartments of cells at a rate of e_C per cell (set to 10 t^-1 per cell; light green arrows in S15 Fig), and elimination at the extracellular washout rate, ω (grey arrow in S15 Fig):

When the C signal surpassed the threshold t_C (set to 10⁷), the number of non-C-state cells (N) of compartment i entering C state (C_i^entry) was distributed according to a rate g_C (set to 10 t^-1 unless stated; red arrow in S15 Fig):

Independently of the C signal, cells of compartment i in C state (C_i) exited C state at a rate r_C (set between 0 and 0.9 t^-1, depending on the growth pattern):

Cells in the C state grew at a reduced rate γ(1-c_C), in which c_C was the cost associated with the C state. In oscillatory growth patterns, C-state cells were subject to growth arrest (c_C = 1), as has been observed in some species in which competence is regulated [37,38]. C-state cells underwent transformation at a higher rate (τ_C) than cells not in C state (orange arrows in S15 Fig), and drove cell–cell killing of cells not in the “C state” as a mass action process at a rate k_C (dashed lines in S15 Fig); this mirrors “fratricide” in some streptococcal species or “cannabilism” in B. subtilis [15]. For the k cell compartments in the model, the number of non-C-state cells (N_i) in the ith compartment killed by this mechanism (K_i) depended on the total population of C-state cells across all compartments (C_j), as well as k_C:

Therefore, the overall change in the population of compartment i, if such cells were not in the C state, could be summarised as: where P_i is the demographic change resulting from cell growth and death; K_i is the reduction as a consequence of cell-cell killing; A_i is the loss of cells due to MGE activation; C_i^entry is the number of cells entering C state, while C_i^exit is the number of cells of the same genotype exiting C state; T_i→!i is the consequence of transformation driving divergence to different genotypes, while T_!i→i is the number of cells being converted to compartment i by transformation; and M_i→!i represents MGE infection converting compartment i cells to other genotypes, whereas M_!i→i is the reciprocal conversion of other genotypes to compartment i through infection. If compartment i corresponds to C-state cells, the formula for the population change no longer includes cell–cell killing, and the impact of the state change terms is reversed:

For MGEs of compartment q (M_q) interacting with k cell compartments, the change between timesteps can be summarised as: where R_q represents the release of MGEs from host cells through activation, E_q is the spontaneous entry of MGEs into the model, and d_q represents MGEs lost to infection of cells and MGE washout and degradation.

For DNA of compartment q (D_q) interacting with k cell compartments, the change between timesteps can be summarised as: where L_q represents the release of DNA through cell lysis (either a consequence of cell-density-dependent death, MGE activation or cell–cell killing), and d_q represents the uptake of DNA by competent cells and DNA washout and degradation.

The model was implemented using C++, with the GNU scientific library. Each simulation was run from t = 0 to t = 1,000, with 10³ timesteps per unit time. Neither increasing the number of timesteps per unit time 10-fold, nor doubling the endpoint value of t, substantially altered the displayed results of simulations involving different MGEs and growth patterns. In some simulations involving high rates of transformation (τ ≥ 0.01), the number of timesteps per unit time resulted in detectable approximation errors in some model compartments, but validatory simulations confirmed this did not affect reported summary statistics. At t = 0, each genotype started at a frequency of 100 cells. If a single MGE featured in the simulation, then the two genotypes were uninfected and infected; if two MGEs featured in the simulation, then the starting cells were the two singly infected genotypes. Within each timestep, all cells were considered capable of replication at the appropriate growth rate. Molecules were first bound to cells and underwent transformation and MGE infection. Those cells remaining unbound to molecules entered and exited the C state, were eliminated through cell–cell killing, activation of some MGEs, or died through cell-density-dependent death at the appropriate per capita rates. Reported summary values were calculated over the full extent of the simulation and represent the mean of three simulations. The source code is available for download from https://github.com/nickjcroucher/mgeTransformation. Parameters are summarised, along with typical values, in S1 Table; simulation outputs are recorded in S1 Data.

Characterising Within-Host Pneumococcal Variation

The original dataset comprised 3,085 de novo assemblies of pneumococcal isolates from the Mae La refugee camp [98]. In order to detect short-term changes in mobile genetic element content, this study identified 374 hosts associated with two or more isolates of the same multilocus sequence type [98] and considered all 1,751 genomes from bacteria carried by these individuals. A quality threshold of a draft assembly N50 greater than 10 kb and a total draft assembly length between 1.75 Mb and 2.75 Mb was imposed on this set; genomes that did not meet these criteria were reassembled with Velvet as described previously [4]. This produced a final set of 1,715 sequences from 371 hosts. CDSs within these genomes were annotated using Prodigal [141] with a model trained on the reference sequence of S. pneumoniae ATCC 700669 [142] and translated to generate a database of 3,660,212 proteins. Using BLASTP [143] with an E-value threshold of 10⁻¹⁰, this database was searched with a single representative sequence from each of the 355 clusters of orthologous proteins previously found to be specific for ICEs [57]. This process was repeated using 590 proteins specific for prophages, 142 proteins specific for phage-related chromosomal islands, and three proteins specific for a particular prophage remnant [57]. Putative variation in MGE content was inferred where two isolates of the same sequence type varied by at least five BLASTP matches to proteins characteristic of a single type of MGE; this identified 281 hosts with candidate short-term accessory genome variation. The original Illumina sequence reads were then mapped against the variable protein coding sequences using BWA [144]. This allowed the many cases likely representing the variable results of de novo assembly to be distinguished from genuine cases of MGE acquisition or loss. Of the genuine cases, almost all corresponded to changes in prophage content.

In order to determine whether such changes were likely to have occurred within a single carriage episode, it was necessary to construct a phylogeny to determine the level of relatedness between isolates from the same host. This required focusing on lineages commonly identified within the dataset; based on the candidate instances of within-host MGE variation, BCs 1-19F and 4-6B were selected (S3 Table), along with one particular case in BC14. A reference genome assembly was needed for each BC being analysed; S. pneumoniae Taiwan^19F-14 was appropriate for BC1-19F [145], whereas novel references were required for the other two clusters. These were constructed by combining the original Velvet assemblies [98] with SGA assemblies [146] using Zorro [147], then ordering the contigs using ACT [148], as described previously [138]. For all cases in which at least two isolates of the relevant BAPS cluster had been isolated from a single host, the Illumina reads were then mapped against the reference sequence using SMALT [149] as described previously [138]. The resulting whole genome alignment was then analysed using Gubbins [99] to generate a maximum likelihood phylogeny while accounting for the frequent transformation events occurring in pneumococcal lineages. These analyses each corresponded well with the isolates’ metadata, identifying closely related clusters of isolates from individual hosts that represented likely individual carriage episodes.

In the instances in which these matched probable cases of MGE variation, regions of similarity between the de novo assemblies of the relevant isolates were identified through a BLAT [150] comparison of all the contigs in each sequence, using standard settings. This comparison file was then used to inspect the assemblies using ACT [148]. The originally identified MGE-associated sequences within the assemblies were then located, and, if part of a larger insertion that had characteristics of an MGE, the element was manually annotated. In some cases, genomes had to be reassembled as described for the references, then organised into scaffolds with SSPACE2 [151] as described previously [138], in order to extract the relevant MGE sequence. This also allowed their insertion sites to be ascertained and classified as described previously [57]. These annotated MGEs have been submitted to Genbank with the accession codes listed in S2 Table. To ensure they represented genuine pneumococcal prophages, they were included in a hierarchical clustering of known pneumococcal prophages, constructed based on CDS content as described previously [134]. Sequence reads were then mapped against these prophages using BWA with a requirement for absolute sequence identity for alignment, to minimise mapping of sequence reads originating from other prophages not in the reference set. These read alignments were then used to generate the heatmaps shown in Figs 6, 8, S5, S6 and S8.

Distribution of Transformation and MGEs between Streptococcal Species

The 143 complete or high-quality draft streptococcal genomes listed in S4 Table were scanned for prophages using Phage_Finder v2.1 [152]. Naturally transformable species within the streptococcal genus were identified based on past reviews and recent experimental work [28,32,84,121–123]. The comparison of these raw data found naturally transformable isolates to have significantly fewer prophages than nontransformable isolates (mean number of prophages per transformable genome: 0.74; mean number of prophages per nontransformable genome: 1.77; Wilcoxon rank sum test: W = 3316, p = 0.00089). To ensure this result was not a consequence of biased sampling, three genomes from unnamed species were excluded (Streptococcus sp. I and Streptococcus sp. VT), as their competence for transformation could not be established. Pneumococcal isolates that represented duplicate samples of very closely related genotypes were also removed (S. pneumoniae R6, a duplicate of D39 [153], and S. pneumoniae 03–4156, 03–4183, 99–4038, and 99–4039, which all share a prophage insertion with the closely related isolate OXC141 [134]). The results described in the Discussion confirm that the observed association persists in this curated dataset.