Nature Genetics: doi: /ng Supplementary Figure 1. Icm/Dot secretion system region I in 41 Legionella species.

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1 Supplementary Figure 1 Icm/Dot secretion system region I in 41 Legionella species. Homologs of the effector-coding gene lega15 (orange) were found within Icm/Dot region I in 13 Legionella species. In four genomes, other putative effectors (bright yellow) were also found in this region. Non-effector genes that were found between the two parts of the region are in gray. In nine genomes, there are no genes other than Icm/Dot-coding genes in the region. Gene symbols: V, icmv; W, icmw; X, icmx; A, dota; B, dotb; C, dotc; D, dotd.

2 Supplementary Figure 2 Workflow overview. A graphical summary presenting the main analyses performed in this study and the output at each stage.

3 Supplementary Figure 3 Testing for bias in effector detection. (a) Comparison of the number of putative effectors and ORFs with Icm/Dot C-terminal translocation signal per species. The x axis represents the percentage of the total set of ORFs of interest that was detected in each species (for example, the bottom bar represents the percentage of putative effectors in L. parisiensis out of the total number of putative effectors in all the genomes combined). The species tree is presented on the left. (b) Correlation between the number of putative effectors and the number of ORFs with C-terminal Icm/Dot translocation signal. A strong (R 2 = 0.762) and significant correlation (P value = , Pearson correlation test) was found between the two. Both the number of effectors and the number of ORFs with translocation signal per species did not significantly deviate from the normal distribution (Shapiro-Wilk normality test P values = 0.43 and 0.68, respectively). (c) Correlation between the evolutionary distance from L. pneumophila and the ratio of putative effectors to ORFs with Icm/Dot C-terminal translocation signal. No significant correlation was detected (P value = 0.14, Spearman correlation test), suggesting that there is probably no significant preferential effector detection in species that are evolutionarily close to L. pneumophila. Spearman correlation was used because both distributions deviated from normality (P value = and 0.01, respectively, Shapiro-Wilk test).

4 Supplementary Figure 4 The evolution of the seven core effectors. Maximum-likelihood phylogenies are presented for each core effector along with the species tree. Numbers next to inner nodes denote the bootstrap support values.

5 Supplementary Figure 5 Mutants lacking each of the two core effectors exhibit an intracellular growth phenotype. Intracellular growth in A. castellanii of deletion mutates of (a) lega3 and (b) mavn as compared to wild type and plasmid complementation indicate that these two core effectors affect intracellular growth. The five other core effectors did not display an intracellular growth phenotype in A. castellanii. The growth curves represent the averages of three repetitions of the experiments. Bar heights represent standard deviation.

6 Supplementary Figure 6 Legionella effector gain and loss dynamics. The number of effector gain events and the number of effector loss events were computed on the basis of phyletic patterns of effector presence/absence using a maximum-likelihood algorithm 62. Pie size is proportional to the rate of events (number of events normalized to branch length). Each pie presents the ratio between gain (green) and loss (red) of effectors. The results suggest that the L. pneumophila clade (species marked in dark red) is much more prone to gain and loss of effectors than the L. micdadei clade (marked in dark green). The numbers on the right refer to the cluster of effector repertoires in Figure 4.

7 Supplementary Figure 7 Comparison between the Legionella species tree and the gene trees of effectors that underwent HGT. The gene trees for each effector that underwent at least two gain events, had no paralogs and was present in at least four species was reconstructed and compared to an appropriate pruned version of the species tree using the AU test. q values were calculated by adjusting resulting P values using FDR 61 to account for multiple testing. The plot displays the distribution of log(q values) over 96 effector trees as compared to the species tree. The blue dashed line denotes the 0.01 cutoff. Most of the effector trees agree with the pruned version of the species tree, suggesting that there is preferential horizontal gene transfer of effectors among evolutionarily close species.

8 Supplementary Figure 8 Putative effectors coding for the PI4P-binding domain or LED022. All the putative effectors harboring a PI4P-binding domain or the uncharacterized LED022 domain mapped onto the Legionella species tree.

9 Supplementary Figure 9 Putative effectors coding for the ankyrin domain. All the putative effectors with ankyrin repeats mapped onto the Legionella species tree.

10 SUPPLEMENTARY NOTE Gene insertion in Icm/Dot region-i Thirteen species contained an effector-encoding gene (a lega15 homolog, marked in orange in Supplementary Fig. 1) between the two sub-regions of region-i. These species also form a monophyletic clade together with L. gormanii, in which this effector is missing, indicating that the LegA15 homolog was probably acquired once in the common ancestor of the species in this clade, and subsequently lost in the lineage leading to L. gormanii. Analysis of possible bias in effector identification The training set used to identify effectors was based almost exclusively on L. pneumophila effectors. Thus, a certain bias towards effectors with characteristics similar to L. pneumophila s effectors can be expected. To assess the strength of such a bias, we compared the number of predicted effectors to the number of proteins with Icm/Dot translocation signal, which was shown to be present in effectors from different Legionella species, and even in C. burnetii effectors 1. The results show a strong and significant correlation between the number of effectors and the number of proteins bearing a translocation signal across all the species (p-value , R 2 = 0.76, Pearson correlation, see Supplementary Fig. 3). Further, no significant correlation was found between the evolutionary distance from L. pneumophila and the ratio between predicted effectors and proteins with translocation signal (Supplementary Fig. 3c). We conclude that no bias towards preferential effector identification in species that are evolutionarily close to L. pneumophila could be detected. Evidence supporting the identification of unique effectors We identified 70 unique putative effectors that show no similarity (BLAST e-value < ) to any other Legionella protein, out of these 23 contain regulatory elements highly similar to binding sites of transcription factors associated with pathogenesis (CpxR and PmrA) 2,3, and 28 had C-terminal amino-acid profile indicative of Icm/Dot secretion signal 4. Further, for 11 unique effectors we identified domains known to be encoded by effectors, and 53 had at least one additional effector encoded in their genomic

11 vicinity (Supplementary Table 6). In total, for 62 of the 70 unique effectors we found sequenced-based evidence supporting the prediction they function as effectors Preferential HGT among closely related Legionella species The numerous HGT events discovered in the effector gain-loss analysis, led us to search for patterns, other than the phylogenetic relationships, that would explain the variable effector repertoires in the different Legionella species. To address this, we compared the effector gene repertoires by calculating the fraction of shared effectors between each two species, and then clustered species based on the similarities in their effector pool (Fig. 4). The emergent clusters strongly agree with the phylogenetic relationships: we could match these effector-based clusters to monophyletic clades of the Legionella species tree (marked by number on the right of Fig. 4 and Supplementary Fig. 6). Further, the organization among these clusters mostly agreed with the phylogeny as well. This pattern could arise either by HGT events with no consistent directionality among Legionella species, or by preferential transfer between closely related species. To test which of these is more dominant we reconstructed the phylogenies of 96 effectors that underwent HGT and compared them to the species tree using the AU-test (see Methods). In the majority of cases (62.5%), there was no significant difference between the placement of the genes in the effector tree and the placement of the species bearing them in the species tree (Supplementary Fig. 7). These results suggest that the HGT events that these effectors underwent were preferentially among closely related species, which explains, to a large extent, the observed agreement between effector sets and the phylogenetic clustering of the species. SUPPLEMENTARY NOTE REFERENCES 1. Lifshitz, Z. et al. Identification of novel Coxiella burnetii Icm/Dot effectors and genetic analysis of their involvement in modulating a mitogen-activated protein kinase pathway. Infect. Immun. 82, (2014). 2. Altman, E. & Segal, G. The response regulator CpxR directly regulates txpression of several Legionella pneumophila icm/dot components as well as new translocated substrates. J. Bacteriol. 190, (2008).

12 3. Zusman, T. et al. The response regulator PmrA is a major regulator of the icm/dot type IV secretion system in Legionella pneumophila and Coxiella burnetii. Mol. Microbiol. 63, (2007). 4. Lifshitz, Z. et al. Computational modeling and experimental validation of the Legionella and Coxiella virulence-related type-ivb secretion signal. Proc. Natl. Acad. Sci. 110, E707 E715 (2013).

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