Variable transformation rates across Acinetobacter and Legionella strains

We analysed 496 draft genomes of Ab and 786 of Lp. The species pangenomes included 31,103 (Ab) and 11,932 (Lp) gene families. Ab genomes had on average 3,598 genes with a coefficient of variation of 5.0%, whereas genomes of Lp had on average 3,091 genes with a coefficient of variation of 3.3%. We used the gene families present in more than 95% of the genomes (persistent gene families: 2,325 in Lp and 2,629 in Ab) to build 2 types of recombination-aware phylogenetic trees for each species by maximum likelihood (see Methods, S1 Data). The phylogenetic reconstructions and most analyses in this study were done on 3 pairs of phylogenetic trees (ignoring recombination, using random positioning of genes, and using the most likely organization). The qualitative results of all major analyses were similar in all the cases. Hence, only the recombination-aware method using the consensus genome organization is presented in the text. The phylogenetic trees of the 2 species have approximately similar average root-to-tip distances (Ab: 0.031 subst^-1; Lp: 0.047 subst^-1), but the Lp tree has many more small terminal branches than Ab. Altogether, the Ab data set is more variable in terms of gene repertoires, but the species are of comparable age (distance to the species’ last common ancestor).

We used a luminescence-based assay to quantify the ability of each strain to acquire DNA by natural transformation (see Methods). Bacteria are provided with a linear transforming DNA consisting of the Nluc gene flanked by sequences homologous to the chromosome. Integration by natural transformation results in Nluc expression which is detected following addition of furimazine. Transformation assays were performed multiple times and revealed good concordance (S7 Data). Strains known to be non-transformable (Ab ΔcomEC, Lp Lens) were used to define the minimal detection limit of the method (S2 Data and S2 Fig), which is of the same magnitude in the 2 species: 300 for Lp and 400 for Ab. The distributions of the average values of transformation extend below the detection limit of transformation and have a long tail of higher values spanning several orders of magnitude (Fig 1B). As a result, 52% of the strains were deemed transformable in Lp and 64% in Ab. The range of transformation rates is higher in Ab (6 orders of magnitude) than in Lp (4). This might be due to the more diversified set of strains Ab collection, notably encompassing environmental strains (absent in the Lp data set). Accordingly, Ab environmental strains were associated with greater transformation rates than the clinical ones (phyloglm T2, p = 1.08 × 10⁻⁴).

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Fig 1. Distribution of the transformation phenotype.

(A) Distribution of the log10-transformed average transformation rates and of the binary trait across the recombination-free rooted phylogenetic trees of A. baumannii (left) and L. pneumophila (right). sncRNA rocRp presence (dark pink) distribution is also represented across L. pneumophila phylogenetic tree. The dashed line in the log10-transformed average transformation rates distribution corresponds to the transformation rate threshold that separates transformable from non-transformable strains. Tree scale is in substitution per site. (B) Distribution of the log10-transformed average transformation rates in A. baumannii (left) and L. pneumophila (right). The red vertical line stands for the threshold between transformable and non-transformable strains. ΔcomEC Ab strain and Lp Lens strain are known non-transformable strains. AB5075 Ab strain and Lp Paris strain are among the most transformable known strains. The data underlying this figure can be found in S8 Data.

https://doi.org/10.1371/journal.pbio.3002814.g001

Variations in transformation rates could be caused by differences between the focal strains and the template sequence used to produce linear transforming DNA (reference Ab A118 and Lp Paris strains). Instead, we found that transformation rates are highly variable across the phylogenetic trees (Fig 1A). The rooted recombination-free phylogeny of Lp is divided into 2 large phylogroups. One of them composed of 78 strains was more often associated with non-transformability than the other strains of Lp (χ² = 41.22, p = 1.36 × 10⁻¹⁰). This phylogroup is considered to belong to the subspecies raphaeli [27]. In Ab, the variations in transformation rates did not correlate with the strains’ phylogenetic distance (Ab: Spearman correlation ρ = 0.0034, p = 0.22) even between closely related strains at a patristic distance of less than 0.02 nucleotide substitutions per site (Ab: ρ = 0.029, p = 0.18) (S3 Fig). In Lp, the correlation was significant but its effect size as measured by the coefficient of association was extremely low in both cases (all patristic distances: ρ = −0.046, p < 2.2 × 10⁻¹⁶; patristic distance less than 0.02 nucleotide substitutions/site: ρ = −0.034, p < 2.2 × 10⁻¹⁶) (S3 Fig). The phylogenetic distance between the focal and reference strains are not correlated to the differences in transformation rates for the most distant strains (patristic distances larger than 0.04 nucleotide substitutions per site) (Ab: ρ = −0.011, p = 0.81; Lp: ρ = 0.029, p = 0.80, S4 Fig). When focusing on the actual homology arms, all strains presented homology arms with more than 99.9% identity with the donor plasmid in Ab and in Lp. In Ab, transformation rate was not correlated with the divergence observed on the homology arms (S5 Fig, ρ = −0.026, p = 0.54). In Lp, there was a significant correlation between transformation rate and divergence at the homology arms (S5 Fig, ρ = 0.50, p < 2.2 × 10⁻¹⁶) even when focusing on the closest strains to the plasmid donor strain (ρ = 0.48, p < 2.2 × 10⁻¹⁶). This effect size is not negligible but the important variance we observed in the transformation rates for any percentage of identity of homology arms prevented us from finding a form of normalization that would correct for this association. However, transformation rates are known to be robust to sequence divergence of 1% to 5% [28,29] which is much higher than what we observed here (>99.9%), suggesting that the correlation observed in Lp between sequence divergence and transformation rates might be indirect. In conclusion, transformation rates exhibit high variability across the species which is not explained by the phylogenetic distance between the donor and the recipient bacteria. The sequence divergence between the recipient chromosome and the homology arms of their transforming plasmid is not affecting the patterns of transformation rates in Ab but could explain a part of the variation in Lp.

Transformation rates evolve according to a jump process

The analysis of transformation frequencies in the light of the phylogeny of the species revealed high variability. We wished to study this variability and how it unfolds in time to understand the evolutionary processes at play. The simplest model of evolution is when the trait follows a Brownian motion(BM)-like process, i.e., when it endures incremental changes drawn from a random distribution with zero mean and finite constant variance. We used the classical Pagel’s λ and Blomberg’s K indices to assess this model [30–32], but they provided inconsistent results (S3 Data). According to Pagel’s λ, the trait might evolve according to a Brownian motion model (S6 Fig), but Blomberg’s K could not rule out the hypothesis that the trait was randomly distributed across the phylogeny. To understand these contradictory results, we used the Fritz and Purvis’s D statistic to model the binary trait and quantify the strength of the phylogenetic signal on it [33]. This statistic indicated that closely related strains had a less similar phenotype than expected under a Brownian model but more than if it was random (Lp: D = 0.52; Ab: D = 0.58). This means that the Brownian model alone is not sufficient to explain the evolutionary dynamics of natural transformation.

The wide variation of the transformation trait across the phylogenetic trees led us to consider complex models that extend the classical Brownian motion model by accounting for jump processes, i.e., that account for evolutionary processes where the trait may occasionally change abruptly [34]. We assessed how 9 models with diverse evolutionary dynamics fitted the evolution of the transformation rates (log transformed, S4 Data). Six of them use Lévy processes with 2 components: a Brownian motion and a pure jump process (S6 Fig). In a jump process, the trait has abrupt changes that are punctual or occur over a short period of time. As expected, the Brownian motion model did not fit the data very well (Ab: AIC_c = 3,521; Lp: AIC_c = 2,198). The best fitting models in both species were all jump processes (Jump Node, Variance Gamma, Brownian Model + Normal Inverse Gaussian, and Brownian Model + Variance Gamma, S4 Data), which had all approximately similar fit with the data (similar AIC_c) because they only differ in the type of random distribution defining the frequency and the amplitude of the jump (S6 Fig). The good fit of these models suggests that natural transformation evolves as the result of 2 processes. First, a gradual process of divergence that is captured by the Brownian model and could result from mutations of small effect on the trait, e.g., point mutations with effect on gene expression of the machinery of transformation. Second, the Brownian process is complemented by frequent sudden changes that separate periods of relative stability and which are better captured by the jump process. Such sudden changes could be explained by the acquisition or loss of genetic determinants of transformation, i.e., they are compatible with the well-known impact of horizontal gene transfer in bacterial evolution which can lead to large sudden phenotypic changes [35].

Loss of transformation is counter-selected

The previous results suggest sudden transition between transformability and non-transformability. If transitions result from intragenomic conflicts, the latter must result from the deleterious impact of the loss of transformation for bacterial fitness. To test this hypothesis, we inferred the ancestral states of transformation in the phylogenetic tree and searched for recent transitions, i.e., those occurring in terminal branches. More than a fifth of the terminal branches had a phenotype transition (Lp: 20%; Ab: 20%), and there was a very large excess of transitions to non-transformability relative to those towards transformability (Lp: 79% of all events, χ², p < 2.2 × 10⁻¹⁶; Ab: 81%, χ², p < 2.2 × 10⁻¹⁶). If the process was at equilibrium, there should be an equal number of transitions in both directions. Excess of transitions towards one of the states is usually a sign of purifying selection, i.e., a process where genetic changes enrich populations in a state (here, non-transformability) that is deleterious and therefore gradually purged by natural selection [36].

If the loss of transformation is deleterious for bacteria, then it should impact the evolutionary trajectories of bacterial lineages. Notably, if lineages of non-transformable strains are less fit then they are expected to be shorter on average than the others because the lineage is more rapidly lost by natural selection (or reverts to the other phenotype). We took all terminal branches where there is no change in the phenotype and analysed their lengths. This revealed much shorter branches in non-transformable strains in Ab (90× shorter, Wilcoxon, p = 1.14 × 10⁻⁰⁹) but slightly longer ones in Lp (0.4× shorter, Wilcoxon, p = 0.03). Again, this suggests that non-transformable strains, at least in Ab, tend to be removed from the population by natural selection. It should be noted here that the reasons for the counter-selection of non-transformable strains can be varied and depend on the reason of existence of natural transformation. If transformation is adaptive mostly because it removes MGEs from genomes, then the cost of its loss is the accumulation of costly MGEs in lineages (as shown below).

Transformation impacts recombination rates and genetic linkage

Some of the proposed causes of selection for transformability, e.g., DNA as a source of nutrient or for repair, cannot be tested with our data. But the hypothesis that natural transformation facilitates adaptation by favoring allelic exchanges can be tested. We identified recombination tracts in the terminal branches of the species trees which covered 2,628 persistent gene families in Ab and 2,325 in Lp (see Methods). When considering the phylogeny, transformable strains exhibited a higher recombination rate than the non-transformable ones (phyloglm T17, Lp: p = 1.29 × 10⁻¹⁵; Ab: p = 1.36 × 10⁻³). Hence, we can identify the expected association between recombination rates and transformation. To better understand the relation between recombination and transformation, we analysed strains whose transformation phenotype changed recently. We also observed greater cumulated lengths of recombination tracts when the strain became transformable in the extant branch than when it became non-transformable, but the result was significant only in Ab (Fig 2A and S5 Data). This suggests that the gain of transformation is responsible for an increase in recombination rates but only in Ab.

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Fig 2. Association of the transformation phenotype on recombination.

(A) Distribution of CLRTs (Log10-transformed) in TBs. The CLRT was normalized by the length of the terminal branches in relation to the inference of changes in the phenotype for these groups to be comparable. Wilcoxon test, *: p < 0.05, **: p < 0.01, ***: p < 0.001, ****: p < 0.0001. (B) Distribution of Δr² values in the [−0.1;0.1] interval computed in 500 nt screened windows where r² was not null in both T and NT populations. The full span of the distributions is presented in S7 Fig. Δr² was calculated as r²_mean(NT)-r²_mean(T). The data underlying Fig 2A can be found in S9 Data and Fig 2B in S10 Data. CLRT, cumulated length of recombination tract; TB, terminal branch.

https://doi.org/10.1371/journal.pbio.3002814.g002

Since transformation favors allelic exchanges, it could have an important impact in breaking genetic linkage. This hypothesis can be tested by analyzing the patterns of linkage disequilibrium. We calculated the squared correlation (r²) between bi-allelic values at 2 loci in windows of 500 nt along the genome of transformable and non-transformable strains. The difference between the 2 (Δr²) was significant (Lp: average 0.018, Wilcoxon, p < 2.2 × 10⁻¹⁶, Ab: average 0.0092, Wilcoxon, p < 2.2 × 10⁻¹⁶) (Figs 2B and S4), indicating higher correlation, hence higher linkage disequilibrium, in non-transformable strains. This agrees with the above hypothesis and suggests that transformation may break deleterious allele combinations or create novel adaptive ones.

Transformation rate variations rarely depend on its molecular pathway

To understand the causes of the loss of transformation, we focused initially on the most obvious candidate genes: the ones directly involved in the molecular process (Fig 3). They are the most susceptible to influence the transformation phenotype. We made a survey of the literature to list genes associated with transformation and searched for their presence in every genome (S11 and S12 Data). Very few of these genes are ever missing in Lp, suggesting that inactivation of the transformation genes is rarely the cause of variations in this species. The notable exception is the gene gspH/fimT which was missing in some Lp strains, most often in the non-transformable ones. It was previously shown that this gene is dispensable for transformation in Lp [37], which is consistent with the observation that it is lacking in some transformable strains. Our results suggest that its loss may be associated with lower frequency of transformation (on average 0.8-fold less transformable) that in some cases drops below the detection level. The gene pilQ was interrupted by a phage in 1 Ab strain and pseudogenized in 3 Lp (all 4 strains are non-transformable). In Ab some genes for minor pilins could not be identified in a few genomes of both transformable and non-transformable strains (PilEVX). Knock-out of minor pilins was enough to block transformation in Ab W068 [38]. Their absence in transformable strains may be explained by the minor pilins having a more diverging sequence than our search constraints would allow. Pilins evolve quickly by point mutation, HGT and duplication processes, complicating their detection in draft genomes. More importantly, the gene comM, encoding a helicase involved in the homologous recombination of transforming DNA into the chromosome, was often pseudogenized. This was significantly more frequent in non-transformable than in transformable strains (phyloglm T20, p = 9.45 × 10⁻⁹) (Fig 3) and in clinical strains relative to environmental ones (phyloglm T21, p = 6.47 × 10⁻⁸). The inactivation of this gene was most often caused by its interruption in the MgCh domain by the integration of MGEs. This has been described in clinical strains, where AbaR and AbGRI resistance islands integrate in this region. But contrary to previous analyses our data set has only a small number of clinical strains. Our detailed analysis of this locus revealed AbaR islands in more than half of comM inactivations. In the other cases, we found in these regions MGEs with a plethora of insertion sequences, integrons, and defense systems (S8 Fig). For example, the gene was interrupted by a locus encoding CBASS (40% of the times) and Zorya (6%). The latter interruption was only observed in environmental strains. This is consistent with the existence of genetic conflicts between MGEs and the host regarding natural transformation beyond the selection for the spread of antibiotic resistance genes in clinical strains.

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Fig 3.

Presence of genes involved in natural transformation in A. baumannii (left) and L. pneumophila (right). The genes were divided regarding their function: DNA uptake, recombination, and regulation (Rg) of transformation. The data underlying this figure can be found in S12 Data.

https://doi.org/10.1371/journal.pbio.3002814.g003

Interrupted transformation genes might be recovered by recombination and allow re-acquisition of the transformation phenotype. To test this possibility, we analysed the patterns of recombination on the locus in the 386 genomes of Ab with a complete comM. We found that comM was covered by a recent recombination tract in 35 samples. This raises the possibility that inactivated comM genes may become functional again by recombination with homologous DNA arriving from other strains. This process is easier to achieve for genes like comM whose inactivation does not completely block recombination. Yet, recombination salvaging comM does not happen more often than expected by chance (χ², p = 0.72). With the exception of comM in Ab, variations in transformation rates were rarely explained by the inactivation of transformation-related genes.

Ab can be capsulated (unlike Lp) and it has been recently suggested that its capsule may affect transformation rates [39]. We searched for capsule loci in all Ab strains using Kaptive [40]. All genomes presented one presumably complete locus suggesting that capsule inactivation was not causing the emergence of transformable strains. Future work will be needed to test if changes in the expression of the capsule could contribute to the variation in transformation rates in Ab.

MGEs shape transformation rate variations

To identify the genetic determinants responsible for shifts between transformability and non-transformability, we performed a unitig-based Genome-Wide Association Study (GWAS_U) on the binary transformation phenotype (). We used a linear mixed model (LMM) to correct for population structure using the recombination-aware phylogenetic trees. We applied a Benjamini–Hochberg adjustment for multiple tests at 0.05 (S9 Fig), because Bonferroni corrections are known to be excessively conservative [41]. Indeed the latter led to loss of hits that were experimentally verified to affect transformation rates (ComM, see Methods). The relevant genetic variants were mapped to the gene families. In Lp, 378 gene families including 1 sncRNA gene were associated with the inhibition of transformation and 271 were associated with increased transformability (Fig 4A and S6 Data). In Ab, 836 gene families were associated with the inhibition of transformation and 426 with increased transformability (Fig 4A and S6 Data). In both species, it is thus less than 5% of the pangenome that could explain the non-transformable phenotype. Given the linkage between genes, this proportion might be overestimated. These gene families did not include any capsule loci genes. Among the genes directly involved in natural transformation only unitigs mapping comEA, rnr, recQ in Lp and pilP, pilT in Ab were associated with non-transformability. Since these genes are part of the persistent genome, this suggests that some variants may lower transformation rates. Only the unitigs mapping comM were positively associated with transformability, a consequence of the abovementioned frequent inactivation of this gene. This confirms the impact of comM inactivations on transformation and suggests that natural sequence variants of the other genes may affect transformation rates. Overall, the genes directly implicated in the pathway of natural transformation are a very small fraction of all genes identified by the .

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Fig 4. Volcano plots showing average effect sizes and significance of the association of the gene families with the transformation phenotype according to in A. baumannii and L. pneumophila.

Each circle stands for a gene family. The size of the circle depends on the number of unitigs that mapped the gene in all the samples. The value on the x-axis corresponds to the average effect size of all the unitigs mapping the gene. The y-axis indicates how significant this effect can be by representing the maximal -log10-transformed p-value adjusted for population structure (lrt-pvalue) of all the unitigs of this gene. The BH threshold (dark red line) of 0.05 is set at the lrt-pvalue that once corrected by BH is equal to 0.05. Significantly associated gene families are above the Benjamini–Hochberg (BH) threshold (red dashed line). The lower graphs (B) are similar to the ones on top (A), but gene families were colored in respect to their MGE. Only gene families whose annotation was known and that had either a strong effect size or were very significant were labeled for readability. The whole set of gene families, their p-values, effect size, and frequency is listed in S6 Data. The data underlying this figure can be found in S6 Data. GWAS, genome-wide association study; MGE, mobile genetic element.

https://doi.org/10.1371/journal.pbio.3002814.g004

In both species, recognizable MGEs are important genetic determinants of transformation inhibition. We classed them in ICEs, plasmids, phages, and transposable elements (Fig 4B). Beyond the genes that were individually significantly associated with low transformation, many of the other MGE genes were collectively negatively associated with the loss of transformation, even when each individual effect was not significant (Fig 4B). For example, many Insertion Sequences, sometimes part of larger elements like plasmids and ICEs, are among transformation-inhibiting candidates in both Ab and Lp (Fig 4B).

Several prophage genes are negatively associated with transformation in Ab. Some of these functions can also be found in other elements, but others are very specific to phages as revealed by the analysis of their viral quotients (see Methods). Out of the 836 Ab transformation-inhibiting gene families from , 24 matched proteins with very high viral quotient (higher than 0.9, see Methods). Among them, the most significantly transformation-inhibiting gene seems to be part of a prophage anti-defense system. We predicted its protein structure with AlphaFold and aligned it against a large data set of protein structures using Foldseek. This analysis revealed structural homology to DarA, a protein that is involved in capsid morphogenesis and is a component of an anti-restriction system (found in A. baumannii ACICU, S10 Fig) [42]. However, the homology is restricted to the N-terminal domain and this gene stands alone in the GWAS (the Dar system being composed of many proteins). Further work will be necessary to disentangle the function of this protein. In brief, the abundance of prophage genes in the GWAS suggests these elements have an important role in the inhibition of transformation in Ab.

We could not identify intact prophages in the Lp strains. Instead, the transformation-inhibiting candidates in this species were often associated with functions found on plasmids, including conjugation and the plasmid-encoded sncRNA RocRp (Fig 4). A little more than 15% of the Lp transformation-inhibiting genes identified in (62/378) were carried by plasmids. It was previously shown that RocRp is plasmid-encoded and inhibits transformation [21]. Our results show that similar plasmids lacking RocRp do exist (S11 Fig). Importantly, none of the RocRp copies could be identified in the chromosomal contigs. RocRp plasmids also lacked serine/tyrosine recombinases as well as dif sites or XerL homologs that could lead to Xer-mediated integration in the chromosome. To confirm that contig assignment was not affecting our results, we searched 113 complete Lp genomes from RefSeq where all 5 occurrences of RocRp were also in plasmids that had high similarity in gene repertoires (S11 Fig). Of note, some plasmids lacking RocRp had moderately high wGRR (>0.6) with the RocRp plasmids. Overall, 32% of the non-transformable strains encoded RocRp plasmids. This raised the following question: Are the plasmid-associated genes in the found because of their genetic linkage with rocRp or because they are independently associated with the inhibition of transformation? To answer this question, we built an LMM where the presence of rocRp in the strain is a covariate of (-cov). This model retained some significant associations between plasmid-associated genes and transformation in Lp (BH adjusted p-value <0.05, S12 Fig), but the overall p-values of these genes were much smaller. In fact, most of these genes were conjugation genes that also exist in ICEs suggesting their association might be retained because of some other transformation determinants that ICEs carry. Hence, the negative association between plasmids and transformation is largely driven by genetic linkage with the transformation-inhibiting sncRNA rocRp, albeit some other genes present in certain ICEs may be good candidates for secondary modulators of transformation rates.

Given the importance of MGEs in shaping transformation rates, anti-MGE defense systems could directly lower transformation rates by targeting incoming DNA [43] or indirectly increase transformation rates by preventing the acquisition of MGEs encoding transformation-inhibiting genes. We separated innate (e.g., restriction-modification systems) from adaptive (CRISPR-Cas) defense systems because the former may block MGEs and bacterial DNA arriving by transformation, whereas the latter are only expected to block MGEs (since they provide a specific defense). We detected a negative association between the number of putatively innate defense systems of a strain and its transformability in Lp and in Ab (phyloglm T12, Lp: p = 1.03 × 10⁻¹³; Ab: p = 3.61 × 10⁻⁹) and more specifically when the defense system was a restriction-modification system (phyloglm T13, Lp: p = 4.34 × 10⁻⁸; Ab: p = 1.30 × 10⁻⁶). Of note, these unitigs did not map the R-M systems found in Vesel and colleagues [43], showing that other R-M systems also contribute to diminish transformation rates. In contrast, there was a positive association between the number of CRISPR-Cas systems carried by a strain and its transformability in both species (phyloglm T14, Lp: p = 7.81 × 10⁻⁹; Ab: p = 0.018). These results suggest that defense systems impact transformation rates in ways depending on their ability to specifically target MGEs.

Since both MGEs and innate defense systems, especially restriction-modification systems, are associated with lower transformation rates, we quantified how much variation in transformation rates could be explained by each of them. We performed a phylogenetic logistic regression expressing the binary transformation phenotype as a function of the number of mobile genetic elements inhibiting transformation (phages in Ab and conjugative systems in Lp) and of the number of restriction-modification system a strain carries. As expected, both were significatively and negatively associated with transformation, but phages in Ab (phyloglm T22, effect = −0.97 [−1.038; −0.905], p = 4.3 × 10⁻³) and conjugative systems in Lp (phyloglm T22, effect = −0.76 [−0.779; −0.735], p = 1.4 × 10⁻⁴) had a stronger inhibitory effect than restriction modification systems (phyloglm T22, Ab: effect = −0.61 [−0.634; −0.590], p = 3.55 × 10⁻⁸; Lp: effect = −0.41 [−0.441; −0.398], p = 1.09 × 10⁻⁹). This suggests that variations in transformation rates are more impacted by MGEs inhibitory effects than by the action of restriction-modification systems.

Transformation is associated with the loss of MGEs

The chromosome curing model suggests that genetic conflict between MGEs and the host arise because transformation cleans the bacterial chromosome from its MGEs. One would thus expect to find fewer MGEs in transformable strains than in the others. We analyzed the frequency of MGEs in relation to the transformation phenotype and the phylogenetic structure using a phylogenetic logistic regression [44]. Even though some prophages are negatively associated with transformation (see above), the number of prophages in Ab does not depend on the strain transformation phenotype (p > 0.05; no prophages in Lp). When compared to the others, the chromosomes of transformable strains carry fewer Insertion Sequences (Fig 5; phyloglm T19, Lp: p = 5.97 × 10⁻⁵; Ab: p = 0.0016) and fewer conjugative systems (phyloglm T7, Lp: p < 2.2 × 10⁻¹⁶; Ab: p = 1.21 × 10⁻⁷). Of note, conjugative plasmids were significantly rarer in transformable strains in both species (phyloglm T9, Lp: p < 2.2 × 10⁻¹⁶; Ab: p = 1.38 × 10⁻⁸). Hence, many types of MGEs are less abundant in transformable strains.

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Fig 5.

Distribution of the number of Insertion Sequences in the bacterial chromosome per isolate in transformable and non-transformable strains in A. baumannii (left) and L. pneumophila (right). The data underlying this figure can be found in S14 Data.

https://doi.org/10.1371/journal.pbio.3002814.g005

If recombination cures the chromosome from MGEs, one expects to find an excess of homologous recombination targeting persistent genes that flank MGEs. For each persistent gene, we used the number of times it was covered by a recombination tract in our collection as a proxy for its recombination rate. Based on the MGE flanking persistent genes previously identified in the whole collection, we were able to evaluate for each persistent gene if it had a MGE in its direct neighborhood in at least 1 genome. We observed that among persistent genes, those in the direct neighborhood of an MGE had higher recombination rates in Ab: conjugative systems (Ab: Wilcoxon, p < 2.2 × 10⁻¹⁶), and insertion sequences (Ab: Wilcoxon, p < 2.2 × 10⁻¹⁶), but not for phages (Ab: Wilcoxon, p = 0.08). In Lp the association was significant when the MGE was an insertion sequence (Lp: Wilcoxon, p = 1.7 × 10⁻⁵). Interestingly, recombination also targets at high frequency the persistent genes neighboring defense systems (Ab: Wilcoxon, p < 2.2 × 10⁻¹⁶), which fits previous observations that these systems evolve rapidly and are often within MGEs [45]. In conclusion, core genes flanking MGEs show an excess of recombination tracts, as expected if transformation removes neighboring MGEs by recombination. It is worth noting that such events of recombination may also occur by incoming DNA arising directly from the action of MGEs, e.g., by HFR-like conjugation [46] or lateral transduction [47]. These removals may become fixed in the population when they increase bacterial fitness.

Bacterial strains, origin and typing

We analyzed draft assemblies of 830 Lp clinical strains from the Centre National de Référence pour Legionella (CNRL) collection and of 510 Ab environmental and clinical strains. We sequence typed Ab collection with mlst v.2.19.0 (https://github.com/tseemann/mlst). The Lp collection was assembled from clinical isolates collected in France from 2018 to 2020 and was sequence typed by the CNRL. This publication made use of the PubMLST website (https://pubmlst.org/) developed by Keith Jolley [68] at the University of Oxford. We also typed Ab capsules with Kaptive v.2.0.3 [40,69]. The search for antimicrobial resistance genes was done with ABRicate (https://github.com/tseemann/abricate) which used the following databases ARG-ANNOT [70], CARD [71], MEGARes [72], and VFDB [73].

Plasmid constructions

Plasmid pJET.Lp-pilMNOPQ::nLuc was constructed by cloning the nanoluc gene (Nluc) along with flanking arms of 2,000 bp from the pilMNOPQ locus obtained from the genomic DNA of Legionella pneumophila strain Paris. Plasmid pJET.Ab-pilMNOPQ::nLuc was constructed by cloning the nanoluc gene along with flanking arms of 2,000 bp from the pilMNOPQ locus obtained from the genomic DNA of Acinetobacter baumannii strain A118. The gene NLuc is expressed from a synthetic promoter, Ptac. This promoter is normally repressed by lacI, but since both tested species lack the lac operon, the promoter is strong and constitutive.

Plasmids are used uncut because they cannot replicate in the tested species, thus transformants can only be obtained through recombination with the chromosome. The initial step of transformation is fragmentation of the transforming DNA, this has higher chance to occur outside of the cassette when cloned on a plasmid, lowering the chance of cleavage happening within the NLuc gene or within the homology arms.

Natural transformability assay for L. pneumophila

L. pneumophila strains from frozen stock cultures were thawed, 5 μl of the cells were spotted onto CYE solid medium plates and incubated for 3 days at 37°C. Cells from the plates were subsequently resuspended into 100 μl liquid AYE medium in 96-well plates using a 96-well Scienceware replicator and allowed to grow for 3 days at 37°C in a shaking incubator. Next, 2 μl of this culture was transferred to 100 μl of fresh AYE medium in 96-well plates containing 20 ng/μl of pJET.Lp-pilMNOPQ::nLuc plasmid DNA and allowed to grow for 3 days at 30°C in a shaking incubator. As this plasmid is non-replicative in L. pneumophila, DNA molecules which are internalized undergo a double recombination event allowing the insertion of nanoluc gene in the pilMNOPQ locus and subsequent expression of the NanoLuc Luciferase enzyme. Subsequently, 80 μl of cells were mixed with 20 μl of the Nano-Glo Luciferase Assay Substrate and Nano-Glo Luciferase Assay Buffer followed by incubation at room temperature for 10 min. The expressed NanoLuc Luciferase enzyme was reported as luminescence units (LUs) on a Promega GloMax Navigator plate reader. The optical density of the cell suspension at 600 nm was detected on a Tecan plate reader. Relative luminescence units (RLUs) were calculated by dividing the luminescence values by the optical density values. RLU values were used as a proxy for transformability of the strains. The assay was repeated independently for each strain in the collection between 3 to 6 times. In Lp, competence occurs at the transition between exponential and stationary [61]. It is possible that some strains display different kinetic of expression. To limit this effect, transformation experiments were conducted with transforming DNA present during the entire growth experiment.

Natural transformability assay for A. baumannii

Natural transformation in A. baumannii requires induction by agarose. Agarose soluble extract media (ASEM) was prepared by adding 2 g of Agarose D3 (Euromedex) to a 5 g/L solution of Tryptone media (Bacto). This suspension was vortexed for 5 min and subsequently centrifuged to sediment the insoluble agarose particles. The supernatant was collected and filtered using a 0.22 μm filter. A. baumannii strains from frozen stock cultures were thawed, subsequently 2 μl of cells were transferred to LB medium (Lennox formulation) and incubated overnight at 37°C. The following day, 2 μl of cells were transferred to ASEM containing 2 ng/μl of pJET.Ab-pilMNOPQ::nLuc plasmid DNA using a 96-well Scienceware replicator and allowed to grow overnight at 37°C. As this plasmid is non-replicative in A. baumannii, DNA molecules which are internalized undergo a double recombination event allowing the insertion of nanoluc gene in the pilMNOPQ locus and subsequent expression of the NanoLuc Luciferase enzyme. Subsequently, RLU values were calculated similarly as described above for L. pneumophila. The assay was repeated independently for each strain in the collection between 4 to 6 times. In A. baumannii, competence is expressed in exponential phase [74]. It is possible that some strains display different kinetic of expression. To limit this effect, transformation experiments were conducted with transforming DNA present during the entire growth experiment.

Recently, an R-M system was discovered that restricts transformation of replicating plasmids but has a lesser effect on transformation of DNA recombining with the chromosome (which is what is tested in our assay) [43]. It should be noted that the transforming DNA used in our assay is extracted from an E. coli strain whose methylation system (dam) protects from this A. baumannii R-M.

Validation of the natural transformability assay

We, first, verified that the number of transformants had a linear relationship with the measured luminescence. To do so, we measured the luminescence values of serial dilutions of transformed bacteria. We isolated 3 transformants of the A. nosocomialis M2 strain. They were grown to obtain cultures whose titers were determined (colony-forming unit, CFU, per 100 μl). Then, 10-fold serial dilutions were made and the luminescence of 100 μl suspension was determined. This results in a plot (S13 Fig) in which we can see a linear relationship between CFU count (equivalent to the number of transformants) and luminescence (adjusted r² = 0.99, p < 2.2 × 10⁻¹⁶). This is true down to 2 × 10² CFU, which means that we can detect 20 transformants in a well.

We, then, benchmarked our assay by comparing the transformation frequencies determined by selection with a genetic marker and by luminescence values. A. nosocomialis M2 strain was transformed with varying amounts of the transforming plasmid, which conveniently carries Nluc but also a genetic marker, kanamycin. After transformation, the culture was used to determine the transformation frequencies, defined here as the ratio of the determined CFU count of transformants (on selective plates with kanamycin) and the total CFU count (non-selective plate). The luminescence of the same culture was measured. Luminescence measurements and transformation frequencies had a good fit using linear regression (adjusted r² = 0.95, p = 1.25 × 10⁻⁴, S14 Fig).

To ensure the variations in luminescence were not simply the results of variations in expression of the luciferase marker, we transformed a random set of 60 isolates with the nanoluc marker (which also confers resistance to kanamycin). Because not all isolates are kanamycin-sensitive, we ended up with 15 transformed isolates. We cultured them and measured the luminescence level. These strains show 100-fold variations in transformation frequencies determined by nanoluc, yet quantification of the amount of Nanoluc they produce shows only a 3-fold difference between the minimum and maximum values (S11 Data). Thus, variations in transformation frequencies could not be attributed to variations in expression of nanoluc between strains.

To further validate our assay, we tested it with well-documented strains, including mutants whose transformation defects were well known. All these strains and their RLU measurements are to be found in S15 Fig. These results confirm that our system is able to recapitulate published data. Notably, that comM inactivation decreases transformation by more than one order of magnitude and that comEC inactivation decreases RLU levels to below the threshold we used to class strains as non-transformable. Finally, it shows clear differences between the transformable and non-transformable wild-type strains in terms of RLU.

Assessment of the transformability phenotype

Since the number of Ab or Lp transformants is linearly correlated with the luminescence signal (S13 Fig), each strain transformation rate was then calculated as the average of the log10-transformed OD-corrected luminescence value on all the replicates. We used the average on all replicates because the assay gave reproducible results between replicates: Spearman correlation coefficients were on average 0.72 in Lp and 0.90 in Ab (S7 Data and S16 Fig). The log10-transformation of the OD-corrected raw luminescence values reduced the skewness of the different replicates and allowed us to compare them. The threshold between transformable and non-transformable strains (Lp: LumR/OD = 300, Ab: LumR/OD = 400) was set based on the maximum of the transformation rates of a non-transformable strain: the Lens strain for Lp (120 replicates) and an engineered ΔcomEC strain for Ab (28 replicates). We chose to use the maximum as a threshold to ensure that any strain we classified as transformable with a transformation rate beyond this threshold could not actually be non-transformable.

Construction of pangenomes

We removed poor quality draft assemblies with PanACoTA v.1.2.0 [75] keeping drafts with less than 100 contigs when the sum of the 100 largest contigs was at least 90% of the genome (L90 ≤ 100). We excluded strains that were too similar to already included strains or too distant from the other strains in terms of mash distance to be part of the species (Lp = [10⁻⁶;0.1], Ab = [10⁻⁶; 0.06]). After filtering, we were left with 786 Lp isolates and 496 Ab isolates, all listed in S8 Data.

We annotated the draft assemblies (prokka from PanACoTA v.1.2.0 completed with eggnog-mapper 2.1.9) and built the pangenome of each collection using single-linkage clustering to form families of proteins with at least 80% identity (using mmseqs2 v.12-113e3 within PanACoTA v.1.2.0). We defined the persistent genomes of each species. A pangenome family was considered persistent if at least 95% of the genomes had a unique member of this family (Lp: 11,932 pangenome families and 2,326 strict-persistent pangenome families, Ab: 31,103 pangenome families and 2,629 strict-persistent pangenome families).

Competence genes presence/pseudogenization

We gathered from the literature a list of competence genes and genetic elements (protein coding genes, sncRNA) involved in the transformation process (S13 Data). We checked in all our strains for the presence of these elements. We retrieved their protein sequences from Paris and Philadelphia strains for Lp and from A. pittii PHEA-2 and A. baumannii D1279779 for Ab, respectively. We searched for homologous regions to proteins involved in transformation using tblastn v.2.12.0 (-evalue 0.001, -seg no) in the genomes in our collection [76]. We deemed a gene present if the alignment given by tblastn had more than 80% identity with the query protein and covered more than 80% of it. sncRNA genes were searched with blastn v.2.12.0 (-evalue 1e-10). A sncRNA gene was present if the alignment with the query gene had 100% identity and covered more than 90% of it.

When the protein coding gene was not present, we searched for pseudogenes. We considered that we identified a pseudogene of a competence gene when we identified a gene/pseudogene more than 80% identical to the query protein and with an alignment covering between 20% and 80% of the protein (using the output of tblastn as described above). Some genes may look like disrupted simply because they are at the border of a contig. To account for contig borders, we considered that if in addition the alignment was located at less than 50 nucleotides from the end of the contig, we could not classify it as present, missing or pseudogene and called them unascertainable. Some genes marked as pseudogenes are split on 2 different contigs. They may be interrupted or not in the actual genome. If both alignments are at the border of the contigs (less than 50 nucleotides from the end of the contig), this is consistent with the 2 hypotheses, and we marked them as unascertainable. If they are not both at the border, we marked them as pseudogenes.

Pseudogenes with large nucleotide insertions were further characterized to assess if the disruption was due to MGE and/or AMR genes. A supplementary step to assess the nature of the interruption was necessary when these pseudogenes were split on 2 contigs. We took the 2 alignments of the gene (one in each contig) and ordered them in relation to the known full gene sequence. This allowed to identify the sequence interrupting the gene. This was only the case for ComM protein, which has 3 domains organized in the following order: chlI (PF13541.9), Mgch (PF01078.24), and MgchC (PF13335.9). We relied on the coordinates and order of the domains in our alignments to identify the correct region of interruption in which we would search for MGEs and AMR genes. To specifically identify if an AbaR was interrupting comM, we searched for the conserved sequences any AbaR contain in its terminal region: the left-end conserved sequence (CS_L) and the right-end conserved sequence (CS_R) that corresponds to tniC and tniA presence and orf4 presence, respectively [77]. To do so, we searched for homologous regions to TniC (WP_000736404.1), TniA (WP_000573062.1), and Orf4 (WP_001144958.1) using tblastn (blast+/2.10.1) in comM region of interruption. Each gene was deemed present if the alignment had more than 80% identity with the query protein and covered more than 80% of it. We deemed an AbaR being present in the genome when at least 1 conserved sequence out of CS_L and CS_R was identified. We did not observe any interruption of comM in Lp. We nevertheless searched for the Tn7-like AbaR backbone (CS_L and CS_R) in Lp genomes with the same method but relaxed the filtering constraints (-evalue 1e-03). No Tn7-like elements were found in Lp.

In Acinetobacter baumannii, we had to devise a specific method to search for pilA because of the high variability of its sequence across the species [78]. We searched the most conserved region of pilA, the pilin domain (PF00114) in the representative sequence of each gene family with hmmsearch (hmmer/3.3.2). We considered that the pilin was present when the sequence score of the alignment was above the gathering threshold score cutoff (—cut_ga). Every gene family (34) presenting this domain was considered encoding PilA and every strain having this gene family was deemed having pilA. We checked if the strains lacking these gene families could actually have an even more diverging sequence for pilA. We searched for homologous regions of 4 PilA proteins (QNT88830.1, WP_000993715.1, WP_031953428.1, WP_000993729.1) previously studied in A. baumannii [78] using tblastn v.2.12.0 (-evalue 0.001, -seg no) in the genomes of those strains. We relaxed the constraints compared to other genes and considered the gene present if the alignment with the query had more than 40% identity, 20% coverage, and a total length between 100 and 200 amino acids.

We also searched for the presence of the rocRp gene in the complete genomes of Lp from RefSeq downloaded on May 2023 using blastn v.2.12.0. We deemed it present when the same criteria as above were met (100% identity, 90% coverage). To check that chromosomes do lack rocRp, we made a complementary analysis using a lower threshold of identity (90%). This analysis also failed to reveal chromosomal versions of the gene.

Variant calling and estimation of genetic linkage

We identified single-nucleotide polymorphisms (SNPs) and small insertions and deletion (indels) in our strains using as a reference genome the Paris strain for Lp and the AB5075 strain for Ab. The identification was done using snippy v.4.6.0 (https://github.com/tseemann/snippy). We annotated them and predicted their functional effects with snpEff v.4.3 [79].

From the previous variant calling, we only kept biallelic SNPs that were polymorphic in transformable strains and in non-transformable ones. We defined nonoverlapping windows of 500 bp that scanned the whole genome. We considered pairs of SNPs (A, B). In each pair, we calculated p_r the frequency of the reference allele of SNP A (A_ref) and q_r the frequency of the reference allele of SNP B (B_ref). We also calculated x_rr the allelic frequencies of each pair of SNPs (A_ref, B_ref). We were then able to compute the D measure of linkage disequilibrium, its normalized value D’ and the r², the square of the correlation coefficient of each pair of SNPs as follows [80]. with We compared the distribution of r² in each genomic window between transformable and non-transformable populations. To assess if there was any difference in genetic linkage along the genome between transformable and non-transformable strains, we computed an average r² for each window in both populations and tested with a Wilcoxon rank-sum test if the difference between the two, Δr², was positive, i.e., if we rejected the null hypothesis H₀, Δr²≤0.

Phylogenetic inference and analysis of recombination

We took the gene families that were regarded as persistent in Ab and Lp. In each species, we aligned each gene family with mafft v.7.467 [81] within PanACoTA v.1.2.0 (default parameters) [75] at the protein level. We back translated protein alignments to nucleotide ones (i.e., we replaced each amino acid by its original codon) because the latter provide more signal when one studies polymorphisms at the species level. Alignments at nucleotide level were concatenated to make 2 matrices of alignments of 2,629 strict-persistent genes in Ab and 2,325 strict-persistent genes in Lp ordered according to the persistent genes order and orientation of a complete genome of the collection (Lp: Paris strain; Ab: AB5075 strain). If a persistent gene was missing from the complete genome, its position and orientation was inferred from the most often frequent position and orientation it had in the collection. These matrices were then used as an input to IQTree v.1.6.12 modelfinder [82–84] to build the phylogenetic tree of each species.

We made a subsequent step of phylogenetic inference to account for the presence of recombination. We took the previous tree and used it as a starting point for Gubbins v.2.4.1 [85]. Gubbins v.2.4.1 allowed us to mask the regions in the alignment whose polymorphism was due to recombination. We finally built the phylogenetic trees based on the recombination region-free alignment with IQTree v.1.6.12 modelfinder according to which the best-fit model based on BIC was TVM+F+I+G4. To ensure of the branch robustness, we performed a 1,000 ultrafast bootstrap.

Trees were rooted based on outgroups: L. longbeachae for L. pneumophila and A. baylyi ADP1 for A. baumannii. We added each outgroup to their respective collection. We built their pangenomes, defined their strict-persistent genomes, and aligned them in the same manner we did for the initial collections and with the same parameters. We finally built the phylogenetic tree based on this alignment with IQTree v.1.6.12 modelfinder (best-fit model: TVM+F+I+G4; 1,000 ultrafast bootstraps) so as to determine the position of the root in the recombination-free phylogenetic tree. All trees are listed in S1 Data.

We compared the changes in topology and branch lengths between the recombination-unaware and the recombination-free phylogenetic tree by measuring their weighted Robinson–Fould (wRF) metric (phangorn R package: wRF.dist function). This metric counts the minimum number of branch rearrangements needed to transform one tree into another and weights each branch by its length.

The average root-to-tip distance of the phylogenetic tree was calculated by averaging the distance to the root of each tip (adephylo R package: distRoot function).

Of note, Gubbins was initially intended to be used for the analysis of very closely related strains to identify recent events of recombination [85]. In this study, we used it mostly to characterize recent events of recombination. FastGear is a software specifically developed to identify ancient events of recombination [86]. Its publication study shows that for recent events of recombination there is no significant advantage in using FastGear or Gubbins. Since FastGear is not adapted to the study of such large-scale genome data, we used Gubbins throughout our work. To assess the robustness of our key findings we compared our results when using the recombination-corrected phylogeny (the standard approach used everywhere except when explicitly stated across the main text), the tree without correction for recombination, and a third analysis where recombination was inferred with Gubbins from randomly ordered multiple alignments (in Ab). These different methods seem to have relatively little impact on the results (S17 Fig).

We also used the information on recombination tracts covering the persistent genes to assess the likelihood that neighboring non-persistent genes arose or were affected by recombination.

Phylogenetic signal

We looked for phylogenetic inertia of the transformation trait in the rooted recombination-free phylogenetic trees. We calculated the phylogenetic signal on the log10-transformed transformation rates with Pagel’s λ and Blomberg’s K (phytools R package: phylosig function with test = TRUE to conduct a hypothesis test of K or λ) [87] and on the binary transformation phenotype with Fritz and Purvis’ D statistic (caper R package: phylo.d function and its default parameters) [33].

Model of evolution for the trait

We assessed the models of the quantitative evolution of the trait. For this we used the log10-transformed transformation rates across the rooted recombination-free phylogenetic trees. We tested all the models mentioned in S4 Data for the trait evolution with fitContinuous function from geiger R package [88] and with fit_reml_levy function from pulsR R package [34]. We used the AIC_c as the selective criterion for the quality of model fit.

Ancestral reconstruction of the transformation trait

We reconstructed the ancestral states of the binary transformation trait along the recombination-free rooted phylogenetic tree using the MAP prediction method and the F81 evolutionary model of PastML v.1.9.34 [89]. We only kept nodes for which one of the possible reconstructed states had a marginal posterior probability superior to 0.6. We then assigned to the node the transformation state with the highest marginal posterior probability.

Plasmid identification in draft assemblies

We classified contigs as plasmid by calculating the weighted gene repertoire relatedness (wGRR) of each contig against each RefSeq plasmid genome. We first searched for sequence similarity between all of their proteins using blastp (blast+ v.2.12.0). For each pair of contig/plasmid, the wGRR takes into account their number of bidirectional best hits and their sequence identities and gives an assessment of their gene repertoires similarity with the following formula: For a pair of elements A and B, id(A_i, B_i) is the sequence identity of the i-th pair of homologous proteins. Min(A,B) is the number of proteins in the smaller element. The wGRR of 2 elements is thus defined as the sum of the identity for all pairs of homologous proteins normalized to the number of proteins found in the smallest element.

Contigs with a wGRR superior to 0.9 were kept and preliminarily assigned to the corresponding plasmid. These contigs could either be from a plasmid or be a part of the chromosome carrying a MGE similar to the plasmid. Contigs respecting the wGRR criterion, shorter than 500 kb (the maximum size of Ab and Lp RefSeq plasmids being around 300 kb) and carrying less than 50% of persistent genes were deemed part of a plasmid (S18 Fig). In Lp (Ab), 97.4% (43.3%) of the plasmids had no persistent gene at all. If the total length of the contigs assigned to the plasmid in a sample was longer than 40% of the actual plasmid length, we took this as indication of the presence of the whole plasmid in the sample. Contigs longer than 500 kb and with at least twice the number of proteins than the plasmid it was assigned to were deemed to be part of the bacterial chromosome.

We encountered in Ab 2 plasmids, Ab TG29392 plasmid pTG29392_1 from Ab TG29392 strain and plasmid pVB2107_2 from Ab VB2107 strain, which were completely integrated into chromosomal contigs (hits covering more than 50% of the plasmid). pTG29392_1 was integrated in one contig in 14% of the samples and pVB2107_2 in one contig in 3% of the samples. We classed these contigs as chromosomal contigs.

Identification of MGEs and defense systems

We characterized the different types of mobile genetic elements. Conjugative elements were searched using Macsyfinder 20221213.dev [90,91] and the CONJScan/Chromosome model (https://github.com/macsy-models). We detected conjugative systems (CONJ) which are complete conjugative systems formed by a T4SS, a type IV coupling protein (t4cp) and a relaxase, mobilizable systems (MOB) which are systems that have a relaxase that is either alone or co-localizing with a CONJ component but not enough of the latter to make a functional conjugative system. We checked if 2 contigs of a sample presented complementary incomplete CONJ on their extremities. If so, we included them with the other conjugative systems.

Available methods cannot precisely delimit the ICEs. Hence, we defined them as regions at a distance of less than 10 kb from a conjugative system on the contigs that we did not identify as plasmid-like (cf. Plasmid identification in draft assemblies). This is a conservative estimate, given the average size of ICEs of Proteobacteria: 58 kb for MPF_T, 84 kb for MPF_G, and 87kb for MPF_F [92].

Insertion sequences were searched using ISEScan v.1.7.2.3 [93]. We used the option—remove-ShortIS to remove incomplete IS elements that is to say IS elements of length inferior to 400 bp or single copy IS element without perfect terminal inverted repeat.

Putative prophages were initially searched using VirSorter v.2.2.3 (docker://jiarong/virsorter:latest) [94]. We further analyzed the resulting elements with CheckV v.0.7.0 [95] and kept the ones deemed of high and medium quality. These putative viral regions were then annotated using PHROG v.4 [96]. We built the hmm profiles of each PHROG based on their multiple alignments with hmmbuild (hmmer/3.3.2 default parameters). We searched for those profiles in the previous viral regions with hmmsearch (hmmer/3.3.2 default parameters). We also calculated a viral quotient for each PHROG annotation with the following formula: Gene families with a viral quotient superior to 0.9 were specific to phages and were as such labeled as phage-specific genes. The final list of prophages was obtained from the preliminary list by including information on the core genes (that should not be within prophages) and on the PHROG hits (which should be in prophages). Each putative prophage comprised between 2 persistent genes in a putative prophage containing at least 5 genes among which one had a PHROG annotation and whose average viral quotient is more than 0.5 was deemed to be a prophage.

Integrons were searched for using IntegronFinder v.2.0.2 [97–100].

We also characterized defense systems. CRISPR-Cas systems were searched using CRISPRCasFinder v. 4.2.20 [90,91,101,102]. We retained the ones verifying the following criteria: the presence of a Cas gene in the corresponding genome, the presence of more than 2 spacers in the array, and imposing a mean size for spacers of at least 15 bp. We identified the targets of these spacers by running blastn and its task option of blastn-short (blast+ v.2.12.0) on our collection of genomes, available RefSeq plasmids and GenBank phages setting the same parameters as CRISPRTarget (CRISPRSuite) [103]. We searched for other potential defense systems with the DefenseFinder models v.0.0.3 [62] using Macsyfinder 20221213.dev [91]. This version searched for 109 defense systems.

A summary of the amount of MGEs each strain carries can be found in S14 Data.

Presence of MGEs in recombination regions

The analysis of Gubbins to detect recombination was made on the persistent genes (present in most genomes, see above). To know if recombined regions potentially encompass MGEs (which are never persistent genes), we had to devise a different method. First, we counted for each persistent gene how many times it was part of a recombination tract, thus obtaining a measure of their recombination rate. In parallel, we identified the closest upstream and downstream persistent genes for every characterized MGE. Of note, we could not determine the flanking persistent genes of 39.1% (30.0%) of the MGEs because none was present in one of the directions in the contig. Among these, 9.5% (24.2%) were on contigs classified as plasmids in Ab (Lp). These elements were excluded from the analyses of genomic neighborhood. We then separated the persistent genes flanking MGEs from the others. Finally, we compared the frequency of recombination of persistent genes of these 2 groups using a Wilcoxon test. We classed the loci of MGE integration into several categories: conjugative systems, phages, ISs, and defense systems hotspots.

Genome-wide association study

We performed a genome-wide association study (GWAS) to identify genetic determinants of transformation. We used a unitig-based approach (GWAS_U). A unitig is an unambiguous combination of k-mers that represents the sequences without branch in the assembly graph. This approach allows to encompass information: on SNPs and large and small indels. We can thus test all these different levels of genetic variants all at once in the GWAS_U. This was done on the binary transformation phenotype (TF^bin) using respectively 786 L. pneumophila genomes and 496 A. baumannii genomes with pyseer v.1.3.9 [104]. The unitigs were called on our collection of draft assemblies with unitig-caller (from conda installation of pyseer). The association between the unitig or the gene presence/absence and the transformation phenotype was assessed with a LMM. The recombination-free phylogenetic tree allowed us to generate a distance and a kinship matrix using the scripts coming with pyseer, which were used in the LMM to control for population structure. We generated the QQ-plots of this analysis (S9 Fig) with qq_plot.py script provided by pyseer to verify if it was properly corrected notably for population structure. To address the problem of multiple comparisons in our analysis, we applied a Benjamini–Hochberg adjustment on the p-value of association already adjusted for population structure [105]. We deemed the association between the presence of a unitig (or a gene) and the transformation phenotype significant when the adjusted p-value was inferior to 0.05. As a matter of comparison, we also computed the Bonferroni threshold of significancy with scripts provided by pyseer. This Bonferroni correction is made with the number of unique variant patterns as the number of multiple tests. The use of Bonferroni correction with the number of unique variant patterns (Lp: 406151; Ab: 357728) gave very stringent significancy thresholds (Lp: 1.23 × 10⁻⁷, Ab: 1.4 × 10⁻⁷). With this correction only 1 gene family association, which was negative, remained significant in Ab (and not ComM which was shown to modulate transformation rates experimentally and is correctly identified by the FDR analysis) while in Lp 65 negative and 6 positive associations stayed significant.

To study the functions of genes with unitigs that were deemed significantly associated with changes in transformation rates, we first identified the genes having the unitigs with the annotate_hits_pyseer.py script provided by pyseer. To get a clearer picture of the associations, we summarized the characteristics of each gene association over all the unitigs mapping the gene, notably its statistical significance and its positive or negative effect on the phenotype. We only consider genes that are mapped by at least 1 significant unitig. These genes can be mapped by unitigs with significant effect of different signs (positively and negatively associated with transformation), in which case we exclude them from the main analysis and study them separately (see below). We are then left with genes mapped by unitigs (often many) for which those significantly associated with transformation are all of the same sign.

Following pyseer summarize_annotations.py, we summarized the positive or negative association between these gene families containing the unitigs and the phenotype, its effect size (by averaging it over all the unitigs of the significant effect sign that mapped the gene), its significance (by assigning the p-value of the most-significantly associated unitig with the gene), its minimum allele frequency and the number of unitigs mapping them. We added on top of the functional annotation the information on the presence of the gene family in an MGE or a secretion system (as defined above). In the case of Lp, we also studied the unitigs in the light of the presence of sncRNA.

For the unitigs that were annotated as carrying a RM system in Ab and significantly associated with the transformation phenotype, we additionally verified if this was the system described by Vesel and colleagues [43] in A118 strain. To do so, we extracted the unitigs that mapped to the restriction modification systems. We then performed a nucleotidic alignment of these unitigs to the coding sequences composing this specific RM system, that is to say H0N27 10820 (methylase), H0N27 10825 (helix-turn helix transcriptional regulator), and H0N27 10830 (restriction endonuclease), with blastn (—task blastn, blast+/2.10.1). There was no identity between these unitigs and the previously listed components. This lack of identity suggests that we are not looking at the same RM-systems. However, we cannot exclude that the RM systems pointed out by the unitigs may have a similar mode of action of this RM system. Indeed, the mode of action of Vesel and colleagues’ RM system does not rely on the methylation marks of the non-self DNA but rather consists in adding a methylation mark to the self-DNA and protect it from the endonuclease action while the non-self DNA without the mark can be degraded.

Detection of genes with variants of opposite effect on transformation

Some gene families were associated in an ambiguous way with the transformation phenotype. In Ab (Lp) 12% (43%) of the genes with 1 unitig significantly associated with variation in transformation rates also had unitigs whose effect was significant and of opposite sign. We treated those genes separately. Indeed, such a situation could be explained by unitigs carrying variants of the same gene (or same type of MGE) differently associated with the phenotype. So, averaging the effect size as we did before over all the unitigs of the gene would give misleading information on the association (e.g., if 2 unitigs have effects of symmetrical amplitude the average effect is zero, like for genes lacking unitigs altogether). We mapped the unitigs on the genes and called the variants with snippy v.4.6.0 (https://github.com/tseemann/snippy). We reduced the regions of interest to the regions that overlapped between unitigs of opposite association when this was possible. We summarized the positive or negative association to the phenotype, the effect size, the significance, the minimum allele frequency, and the number of unitigs mapping to each variant. This procedure identified 52% (out of the 12%) of the genes in Ab and 66% (out of the 43%) in Lp. These genes with overlapping unitigs of opposite sense will be candidates for future analyses. Given their abundance, we further enquired on the regions with such unitigs in Lp. In Lp, half of them were in gene families of ICEs.

Strains specificities affecting/affected by transformation rates

To characterize the association between traits across the tree, we need to take into account the phylogenetic structure of the bacterial population. We used phylogenetic logistic regression (phyloglm function from phylolm R package) [44] to estimate the effect diverse factors from our data could have on the transformation phenotype. For the phylogenetic correlations to take into account the phylogeny, we provided the recombination-free rooted phylogenetic tree and fitted our data using the “logistic_MPLE” method with the following parameters: btol = 10 and boot = 100. Confidence intervals (alpha = 0.05) were computed from the estimates of the stderr obtained given by phyloglm. All the test results can be found in S15 Data.

Search for serine and tyrosine recombinase in RocRp-carrying plasmids

We searched for tyrosine (PF00589) and serine recombinases (presence of both PF00239 and PF07508) with hmmsearch (hmmer/3.3.2 —cut_ga). No tyrosine recombinase nor serine recombinase were found.

Search for Xer-mediated recombination in RocRp-carrying plasmids

We searched for homologous regions to the 29-nucleotide conserved sequence of the Lp dif site [106] in rocRp-carrying plasmids using blastn (—task blastn-short, blast+/2.10.1). We found no hits with more than 80% identity and more than 80% coverage of the queried sequence). We also searched for XerL orthologs in rocRp-carrying plasmids. We searched for the XerL sequence from Lp strain Lp02 [106] in the plasmid genomes using tblastn (blast+/2.10.1 -evalue 1e-03). We found no hits to this sequence in the RocRp-carrying plasmids.

Structural prediction of hypothetical proteins of interest

To get insights into the functional role of proteins of unknown function, we characterized their protein domains. We first predicted their protein structure with ColabFold v.1.5. [107]. The output protein structure was then given to Foldseek server [108] which compared it to a large collection of protein structures (6 databases: AlphaFold/Proteome, AlphaFold/Swiss-Prot, AlphaFold/UniProt50, GMGCL, MGnify-ESM30, PDB100) in 3Di/AA mode.

Graphical representation of GWAS output

All the graphs were done with R v.4.1.0.