 microplate reader (Molecular Devices, CA, USA) every 15 min for 33 h. The experiment was performed at 25 °C, the same temperature used for the worm assays, and the plates were shaken for 5 s before the measurements by the plate reader to allow aeration. The Softmax Pro 6.2.1 (Molecular devices, CA, USA) software was used to analyze the data. Specific growth rates (μ) were calculated based on the exponential phase of the growth curves. The μ values were calculated using the following formula: OD = N eμt where OD is the measured optical density, N the initial optical density, and t the time.
Genomic analysis of P. aeruginosa strains
A full list of P. aeruginosa species, consisting of 1734 strains, was downloaded from RefSeq database [50] (on December 2016). In addition, the corresponding annotation files that include (1) genomic sequences, (2) nucleotide and (3) protein sequences for coding genes, and (4) feature tables were downloaded from the RefSeq database as well. Next, several filtration steps were applied to remove strains that (1) had no proper 16S rRNA annotations (missing sequence, or sequence that is shorter than 1000 nts, or sequence that showed less than 80% identity to PA14 16S rRNA) and (2) contained more than 100 core genes with multiple members or were missing more than 15% of the core genes. The second filter was applied after one round of clustering with CD-HIT [14] and identification of core genes (see details below). This process resulted in a final set of 1488 strains (Additional file 8: Table S7).
Clustering analysis of P. aeruginosa coding sequences
The protein sequences of 1488 strains (obtained from the RefSeq database) were clustered using CD-HIT (v4.6.5), with the following settings -c 0.70 -n 5 -g 1 -p 1. The procedure yielded 23,793 clusters of homologous genes. The output of the clustering analysis was post-processed to generate a statistical report that lists for each cluster (i.e., each homologous gene) the representative sequence, its function, the total number of occurrences of the gene across the full set of 1488 strains, and the number of strains that contain at least one copy of the gene. A presence/absence matrix for each gene across 1488 strains was generated. In addition to the full matrix, a presence/absence matrix for the collection of 52 experimentally studied strains was extracted. Gene clusters that had no representatives in these 52 strains were removed, resulting in a matrix with 11,731 genes (Additional file 9: Table S8).
Phylogenetic analysis
Core-genes across the 1488 strains were defined as genes present in more than 90% of the strains in a single copy only (resulted in 3494 core-genes). For each cluster representing a core gene, the following steps were applied: the corresponding DNA sequences were aligned using MAFFT default parameters (version 7.273) [51]; gblocks (ver 0.91b) [52] was applied on the alignment to remove poorly aligned positions (with parameters -t = d -b5 = a); an in-house code was used to remove all the invariant positions (excluding gaps); the alignments were padded with gaps for strains in which the core gene was missing. All the alignments were then concatenated to a final alignment of 523,361 nucleotides. The program FastTree [53], version 2.1, with settings: -gtr, was then used to generate the phylogenetic tree of the 1488 strains. The recombination-aware approach, ClonalFrameML [54], was used to reconstruct the phylogenetic tree with corrected branch lengths. The input to the method was the tree generated by the FastTree program and the multi-fasta alignments of 3494 core-genes. The interactive Tree of Life web-based tool [55] was used for visualization of the resulting phylogenetic tree. Information about MLST, source (clinical/environmental), and strains that are part of the experimental collection was incorporated into the tree view. A phylogenetic tree of the 52 experimentally studied strains was extracted from the ClonalFrameML phylogenetic tree of the 1488 strains using the “ape” package in R.
Statistical test for association of genetic elements (coding/non-coding genes) with virulence
The Mann-Whitney (MW) ranking test and linear-regression (LR) analysis were applied to every gene to test the association of the presence/absence pattern with virulence. Genes were considered associated if both tests yielded a p value lower than 0.05, and at least one of the tests yielded a p value smaller than 0.01. Among the virulence-associated genes, genes with negative slope (based on linear regression) were associated with low survival/high virulence (referred to as high-virulence associated or HVA), while genes with positive slope were associated with high survival/low virulence (referred to as low virulence associated or LVA). All the p values are shown in log10 scale as absolute values. The control for multiple hypothesis testing was performed using a permutation test as described below.
Permutation test to control for multiple hypothesis testing
Ten thousand permutations of the virulence values and their assignment to strains were generated (i.e., median worm survival values), and the MW and LR association tests were repeated for each permutation. Then, for each gene, the number of times that it received a better p value using the shuffled virulence data compared to the original one was recorded, separately for MW and LR. The above count was divided by 10,000 to obtain the permutation corrected p value for the MW and LR tests. The MW and LR p values were considered significant if their corresponding corrected p value was lower than 0.05.
Assessment of confounding effects due to population structure
The phylogenetic method reported by Collins and Didelot [25], known as treeWas, was used to address the potential influence of population structure in the statistical association between accessory genes and virulence. The method was applied on the input consisting of (1) 11,731 gene clusters presence/absence matrix, (2) median survival vector, and (3) ClonalFrameML phylogenetic tree of the 52 strains. The method returns as output three types of scores and their corresponding p values for every gene cluster: (1) “Terminal Score” which measures sample-wide association between genotype (gene presence) and phenotype (median survival), without relying on the phylogenetic tree; (2) “Simultaneous Score” which measures the degree of simultaneous change in the phenotype and genotype across branches of the phylogeny; and (3) “Subsequent Score” which measures the proportion of the tree in which genotype and phenotype co-exist. The computed scores were considered significant if their p values < 0.05 (Additional file 3: Table S2).
Collection of known non-coding RNA (ncRNA) in P. aeruginosa
The collection of ncRNAs (excluding rRNAs and tRNAs) in P. aeruginosa was constructed using two resources: RFAM 12.2 [56] and RefSeq annotations [50]. First, 75 non-coding RNA families were extracted from RFAM, with a total of 1363 sequences across P. aeruginosa strains. To get the representative sequences (there could be more than one) for each family, the sequences of each family were clustered using CD-HIT-est (with 80% identity). This analysis resulted in 115 sequences (representing 75 different ncRNA families). Second, using RefSeq annotations of the 1488 strains, 2549 ncRNA sequences were extracted. Altogether, our collection comprised of 83 ncRNA families, represented by 123 sequences. Finally, the collection of the 123 sequences was blasted against the 1488 genomic sequences, and a presence/absence matrix for each of the sequences in all the strains was generated. Rows that represent sequence members from the same family were collapsed, resulting in matrix with 83 rows.
Collection of previously identified virulence genes in P. aeruginosa
A list of virulence genes, in either PA14 or PAO1, was downloaded from [57]. The list was filtered to contain only genes that were reported to contribute to P. aeruginosa virulence towards C. elegans, resulting in 56 genes. Another four genes were added based on the publication [30]. The homologous gene clusters that contained the above genes were marked as virulence genes. The full list of 60 virulence genes is found in Additional file 4: Table S3.
Analysis of CRISPR-Cas systems
Identification of CRISPR-Cas systems
The presence of CRISPR-Cas systems in the genomes of our P. aeruginosa collection was determined by identifying the gene clusters that encode for Cas proteins.
Identification of anti-CRISPR genes
The most up to date collection of anti-CRISPR genes was downloaded from [58], consisting of 41 sequences (). Annotations (e.g., CRISPR-Cas subtype inhibited) for each sequence were maintained. The representative sequences of the clusters of homologous genes (see CD-HIT clustering above) were blasted against the anti-CRISPR sequences using blastp [59] and e-value threshold of e−10. A coverage of more than 35% of the anti-CRISPR sequence was considered a hit.
Determination of active/inactive systems
The annotation on the type of CRISPR-Cas system(s) that is inhibited by each anti-CRISPR protein was used to define CRISPR-Cas activity. The type(s) of CRISPR-Cas systems of every strain were matched to the type(s) inhibited by the anti-CRISPR genes present in the same genome. Strains where all present CRISPR-Cas system(s) are inhibited by type-matching anti-CRISPR proteins were considered inactive.
A second approach to determine active/inactive systems was compared to the method above. The presence in the same genome of