- Research article
- Open Access
Genomic factors related to tissue tropism in Chlamydia pneumoniae infection
BMC Genomics volume 16, Article number: 268 (2015)
Chlamydia pneumoniae (Cpn) are obligate intracellular bacteria that cause acute infections of the upper and lower respiratory tract and have been implicated in chronic inflammatory diseases. Although of significant clinical relevance, complete genome sequences of only four clinical Cpn strains have been obtained. All of them were isolated from the respiratory tract and shared more than 99% sequence identity. Here we investigate genetic differences on the whole-genome level that are related to Cpn tissue tropism and pathogenicity.
We have sequenced the genomes of 18 clinical isolates from different anatomical sites (e.g. lung, blood, coronary arteries) of diseased patients, and one animal isolate. In total 1,363 SNP loci and 184 InDels have been identified in the genomes of all clinical Cpn isolates. These are distributed throughout the whole chlamydial genome and enriched in highly variable regions. The genomes show clear evidence of recombination in at least one potential region but no phage insertions. The tyrP gene was always encoded as single copy in all vascular isolates. Phylogenetic reconstruction revealed distinct evolutionary lineages containing primarily non-respiratory Cpn isolates. In one of these, clinical isolates from coronary arteries and blood monocytes were closely grouped together. They could be distinguished from all other isolates by characteristic nsSNPs in genes involved in RB to EB transition, inclusion membrane formation, bacterial stress response and metabolism.
This study substantially expands the genomic data of Cpn and elucidates its evolutionary history. The translation of the observed Cpn genetic differences into biological functions and the prediction of novel pathogen-oriented diagnostic strategies have to be further explored.
Chlamydia are obligate intracellular bacteria known to infect not only humans, but also reptiles, amoebae, fish and mammals . Beside C. trachomatis and C. psittaci, C. pneumoniae (Cpn) is the third chlamydial species with human pathogenic potential, showing the highest infection and seropositivity rates (>70%) amongst them at the age of 65 years . While acute infections mainly affect the upper and lower respiratory tract and clinically impress as pharyngitis or pneumonia, chronic Cpn infections have been implicated in the pathogenesis of atherosclerosis and chronic obstructive pulmonary disease (COPD). Both are amongst the most widespread diseases of the elderly and are the first and fourth leading causes of death among the elderly in the industrialized world .
Genomic signatures of Cpn can be reliably found in atherosclerotic plaques, but also in circulating blood monocytes from patients suffering from acute myocardial infarction [4,5]. Although short-term antimicrobial treatment does not ameliorate cardiovascular disease progression in humans, data from numerous animal studies give supporting evidence that Cpn can disseminate from the lung and infect the vasculature using peripheral blood monocytes (PBMC) [6,7]. Only a few studies focusing on genomic differences between respiratory and vascular isolates have been published, none of them was using whole genome analysis of clinical isolates. Thus, it was suggested that respiratory strains contain multiple copies of the tyrP gene; whereas, vascular strains contain only a single copy . In contrast, Cochrane et al. later described multiple Cpn genotypes in atherosclerotic plaques of carotid endarterectomy specimens by sequencing the VD4 region of the ompA gene and a 568 nt fragment spanning the ygeD and urk genes .
It was shown recently by Harris et al., that whole-genome sequencing provides a more accurate scenario of the diversity of clinical C. trachomatis isolates than genotyping of single genes that are subject to recombination between different chlamydial biovars . Although most Cpn genes are supposed to be less variable, specific regions in the genome baring the potential to vary by homologous recombination and slipped-mispair have been described [11-13]. At that time, however, the available sequence data were not yet sufficient to determine the actual contribution of recombination events to the evolution of Cpn strains.
So far, the genomes of only four human pathogenic Cpn have been sequenced [14-16]. All of them have been isolated from the respiratory tract. They show a high level of identity in their genome sequence and only few insertions and deletions (InDels) between isolates. We followed the strategy to perform whole-genome sequencing of selected respiratory and non-respiratory Cpn isolates to reliably determine the occurrence of single nucleotide polymorphisms (SNPs), InDels and copy number variations (CNVs) in comparison to the already published Cpn genomes. We have selected isolates with widespread geographical distribution and sampled over a long period for sequencing, in order to define genomic markers that are most related to the anatomical origin of the respective Cpn isolate.
Results and discussion
Genome sequencing and assembly
We selected Cpn clinical isolates originating from different tissues and spanning a broad range of sampling times and locations as well as an animal isolate as a phylogenetic out-group. From 24 isolates, which were cultivated on HEp-2 cells and sequenced in multiple runs on an Illumina GA II sequencer, 19 yielded a sufficient amount of reads for genome assembly (Table 1). The fraction of chlamydial sequences in the total DNA ranged from 4% (CWL011) to 32% (PB1). Only the GiD isolate DNA could be enriched to 99% chlamydial sequences.
Mapping the non-human reads from each isolate against the reference isolate CWL029 resulted in 93.7%-99.9% coverage of the reference genome (Table 2). Compared to CWL029, therefore ~1.2 Kb to 77.5 Kb of the newly sequenced genomes consist of polymorphic regions in which no reads could be matched against the reference. In order to further resolve these polymorphic regions, a reference-aided comparative assembly approach was applied.
The comparative assembly of the non-human reads of the sequenced isolates against the closest available complete reference sequence among the publicly available genomes of Cpn produced between 1 and 147 contigs. At least 97.5% of the corresponding reference genome was covered in all of the isolates (Table 2). Compared to the widely used strategy of mapping, comparative assembly also allows reconstructing highly variable regions of the genome, longer insertions and deletions as well as genome rearrangements. In order to examine the correctness of the assembly process, we compared the SNPs derived from our assemblies to results from an earlier study from Rattei et al. , in which selected chromosomal regions of the same Cpn isolates have already been sequenced. The comparison indicates that 99.5% of the SNPs identified in the previous study were successfully recovered in our current whole-genome assemblies (Additional file 1: Table S1). A test for heterozygous SNPs, which would have indicated different subpopulations, yielded no evidence for the presence of multiple genotypes in any of the isolates (data not shown). Along with these newly sequenced genomes we have also included the publicly available genomes of four clinical Cpn isolates (CWL029, AR39, J138 and TW183) and one Koala isolate (LPCoLN) into all subsequent comparative analyses.
Genomic variation of Cpn clinical isolates
A multiple whole genome alignment of all Cpn clinical isolates served as backbone for comparative genomics. We determined a total of 1,363 SNP and 184 InDel loci from the alignment (Figure 1; Additional file 1: Tables S2 and S3 and Additional file 2). These findings indicate very low overall sequence diversity of all sequenced Cpn clinical isolates and are in very good agreement with previous studies of selected genomic regions [12-15]. The strategy of whole genome sequencing and comparative assembly, however, revealed a more unequal distribution of variations in the genome. The genes with the highest density of SNPs and InDels mostly encode ‘hypothetical proteins’ and in many cases contain both SNPs and InDels (e.g. CPn_0012, CPn_0010, CPn_0010.1, CPn_0013, CPn_1054) (Additional file 1: Tables S4a and S5a). Variation frequency is generally highest in the intergenic regions (Additional file 1: Tables S4b and S5b).
Taking the annotation of the Cpn strain CWL029 as a reference, 47% of all SNP loci (637) have at least one non-synonymous (ns) exchange, 36% (491) are only synonymous (s), 14% (191) are located in intergenic regions and 3% (44) are in noncoding genes. Approximately 75% (137) of the InDels are located in coding sequences. Already in previous studies an unexpectedly high number of non-synonymous SNPs (nsSNPs) were particularly observed when human isolates were compared to non-human isolates [12,13]. It was hypothesized that zoonosis events would temporarily favor positive selection and a higher rate of non-synonymous substitutions, whereas negative selective pressure dominates after successful adaptation. To test this assumption, we calculated the Ka/KS ratio of non-synonymous versus synonymous substitutions per gene. Ka/KS ratios of the clinical isolates compared to the animal outgroup DC9 (Additional file 1: Table S6a) were on average higher than the ratios among the clinical isolates (Additional file 1: Table S6b). Ka/KS ratios > 1 for 54 genes in the comparison to DC9 indicate that these genes were under positive selection during the adaptation to the human host. Comparing only the genomes of clinical isolates, most genes have the expected ratios << 1 of negative selective pressure. Only four genes have Ka/KS ratios > 1, out of which three genes are ‘putative uncharacterized proteins’ (CPn_0456, CPn_0809 and CPn_1027) while CPn_0598 is a ‘dipeptide transport system permease protein’. However, there are no indications that these genes are pseudogenes.
Genome recombination has been proposed but could not be shown in Cpn so far [11-13]. As our study significantly expands the number of Cpn genome sequences, it is now feasible to address the question of recombination. From a multiple genome alignment also containing DC9 as closest representative of non-human isolates, we determined a total number of 4,690 SNP loci. A statistical Phi test and a Bayesian inference approach yielded significant evidence that recombination has occurred between the isolates (p-value < 1e-05). Recombination was observed between human, but not between human and the non-human isolate (Figure 2 and Additional file 3). Testing sliding windows along the genome alignment suggests two regions of recombination, one between the genomic positions 10 Kb and 40 Kb and the second between 1 Mb and 1.07 Mb (Additional file 1: Table S7a and Additional file 4). The areas adjacent to the two regions of recombination did not show evidence for recombination. This suggests an exchange of DNA within these two relatively short genomic regions. Due to the limited number of SNPs the actual recombination sites cannot be determined more precisely. The first region contains 7 pmp-genes of which 6 are annotated as pseudogenes (Additional file 1: Table S7b). The second region is enclosed by the operon of ribosomal RNAs and three ribosomal proteins whereas the genes in between encode various different enzymes (Additional file 1: Table S7c).
Evolution of the Cpn clinical isolates
The newly sequenced genomes of 19 Cpn isolates as well as the five publicly available Cpn genomes represent the most comprehensive dataset for the reconstruction of the evolutionary history of human pathogenic Cpn. We calculated a multiple genome alignment based on the 22 human Cpn isolates (Table 1). The two non-human isolates (DC9 and LPCoLN) were integrated as outgroups. From this alignment, we derived a total number of 13,324 SNP loci.
The most stable tree was obtained from all SNP positions, including non-synonymous, synonymous, non-coding and intergenic loci (Figure 3). The tree shows a clear separation between outgroups, DC9 from frog and the koala isolate LPCoLN, and the human isolates. The human isolates split up into three clusters, all supported by high bootstrap values. TW183 and UZG1 form one cluster that branches deeper in the tree than the other two human clusters. The isolates YK41, AR39, GiD and J138 are contained in another cluster together with CM1, which is located in a sister branch within this cluster. The long sub-branch of J138 can be mainly ascribed to a region between genomic positions 1,204,000 and 1,205,000, showing around 110 SNPs that are only present in J138. In J138 no gene is annotated in this region, whereas it is marked as pseudogene CpB1096 in TW138 and contains an annotated gene (CPn_1054) in CWL029. The third cluster contains three sub-groups, all of which are well supported by bootstrap values. The first group consists of H12, Panola and K7; the isolates U1271, CWL011, CWL029 and CWL029c form the second group. The largest sub-group in the branch consists of the isolates Wien2, Wien3, MUL2216, CV15, Wien1, CV14, PB1 and PB2. Inside this sub-group there are again three small groups, the first containing Wien2 and Wien3, the second containing MUL2216 and CV15 and the third consisting of Wien1, CV14, PB1 and PB2. All of these three groups have good bootstrap support. Overall, there is a remarkable congruence between the phylogenetic tree presented in this study and the phylogeny calculated from a set of selected SNP positions reported in an earlier study from Rattei et al. , with our new phylogeny providing a much higher resolution of the CWL029 containing cluster. The topology of this tree, based on all SNPs, is also supported by trees based on subsets of SNPs, such as from 31 phylogenetic marker genes (Additional file 5 A) and from 545 genes that represent the chlamydial pan-genome (Additional file 5 B).
Distances in the new phylogenetic tree are generally in agreement with the number of SNPs between the different isolates but also with the number of InDels between the isolates (Additional file 1: Tables S8 and S9). Whereas the SNPs were used to calculate the phylogenetic tree and therefore are expected to represent the topology, the InDels are independent from the tree bust still show a similar distance pattern. In some cases, not only the numbers of InDels, but also the contained sequences, show a pattern related to the SNP phylogeny. E.g., the five vascular isolates PB1, PB2, CV14, CV15 and Wien1 have a deletion of ~420 nt in the pmp_20 gene, which is not present in any other isolate (Figure 4). In the phylogenetic tree, these five isolates form one sub-group in the branch of vascular isolates.
Although the low number of SNPs limits the phylogenetic reconstruction of specific genomic regions, we calculated phylogenetic trees from SNPs in all 2 Kb regions in the recombination areas determined before. Only a short 2 Kb region between the genomic positions 14 Kb and 16 Kb revealed clear phylogenetic evidence for recombination between the isolates TW183 and UZG1 and the group of vascular isolates. Additional file 4 shows the maximum likelihood phylogenetic tree based on SNPs derived from this 2 Kb region and an additional tree derived from the surrounding region. The isolates TW183 and UZG1 and the vascular isolates are located on the same branch of the tree based on the 2 Kb region, but are clearly separated in the tree of the surrounding region, resembling the topology of the whole-genome based tree.
Pathogen-related differences with respect to Cpn tissue tropism
An earlier study indicated that respiratory strains of Cpn clinical isolates contain multiple copies of the tyrP gene, whereas vascular strains contain a single copy . In order to determine the tyrP copy number in the 18 newly sequenced genomes of human Cpn isolates we mapped the sequenced reads specifically against the entire tyrP locus of CWL029, containing two tyrP gene copies (Additional file 1: Table S10). We determined the tyrP copy number in each genome from the average sequencing depth of the tyrP locus compared to the upstream and downstream regions. One copy of the tyrP gene was observed in all vascular isolates, whereas the respiratory isolates vary between one (GiD, K7, Panola, MUL2216, J138, CWL029c) and two copies (CM1, H12, CWL011, U1271, UZG1, YK41, CWL029, AR39) (Figure 3D, Table 3). The varying tyrP copy numbers between several closely related isolates, e.g. in CWL029c and CWL029, might represent intra-strain polymorphism which has been reported previously by Gieffers et al. . Mitchell et al. already described in 2010 that the respiratory isolate J138 and the conjunctival isolate TW183 contain only one tyrP copy and questioned the hypothesis that the tyrP copy number determines tissue tropism (Mitchell, Hovis, et al. 2010). We have found further non-vascular isolates having only a single tyrP gene (CWL029c, K7, GiD). The homogenous presence of a single tyrP gene in vascular isolates therefore indicates the close phylogenetic relatedness between these isolates and might contribute to the tissue tropism in the connection with other predisposing genetic factors.
The presence of phages in various Chlamydia genomes has been shown to be related with chlamydial infection and pathogenicity. Hoestgaard-Jensen et al. could show that ϕCPAR39 expression resulted in T3S disruption and IncA accumulation in the morphological abnormal chlamydial reticulate bodies (RBs) . Searching the newly sequenced genomes yielded no evidence for phage-like sequences in any of the isolates of this study. Neither mapping of the sequenced reads to known Chlamydia phages, nor re-assembly of the non-human and non-chlamydia reads, nor the search for phage-related sequences in the InDels, indicated the presence of phages. Thus, we did not find any support that phage-related determinants are among the primary factors for tissue tropism. However, we cannot completely rule out that we missed phages in one or the other isolates as our genome sequencing methodology was not optimized for phage detection .
High genomic similarity of isolates from same tissues was found earlier [12,19] and points to specific genotypes that may determine Cpn tissue tropism. We therefore assigned attributes characterizing the isolates to the corresponding leaf in the phylogenetic tree (Figure 3A-C). Table 1 presents: the type of tissue from which the isolate was sampled and is used as an identification mark for tropism in this study; the geographical location of the sampling; and the year of sampling. Among these three attributes “tissue” mostly corresponds to the topology of the tree, whereas the others show weak (“location”) or no (“year”) correlation to the genotype. All vascular isolates (Wien2, Wien3, CV15, Wien1, CV14, PB1 and PB2) are located in one, monophyletic and short-branching part of the tree with only one respiratory isolate, MUL2216, in between. It can, however, not be excluded that MUL2216 is potentially a vascular isolate that was just isolated in an early stage of infection. Contrary to that, the attributes “location” and “year” do not show comparable agreement with the genotype. Although vascular isolates were only available from two different countries, the sampling occurred in different years in which respiratory isolates also were collected. The remarkable agreement between the clusters formed by the attribute “tissue” and the topology of the tree suggest genotypic factors to be involved in the Cpn tissue tropism.
Since the phylogenetic tree showed a clear separation of respiratory and conjunctival from vascular isolates we determined the SNP positions distinguishing the two groups. In total, nine locations that are characteristic for the type of the isolate (respiratory/conjunctival or vascular) were found (Table 4). These SNPs are all non-synonymous and located in genes involved in RB to EB transition, inclusion membrane formation, bacterial stress response and metabolism. For several of the genes, biological functions have already been elucidated that could have a direct impact on implementation of Cpn infections under the prerequisite of different environmental conditions of the lung and the vasculature. For example, CPn_0289 regulates the uptake of glutamate that has been shown to stimulate growth of Cpn but not C. trachomatis . The glutamyl tRNA reductase (CPn_0714) mediates the metabolism of phenylalanine that is involved in the extracellular protein synthesis of the transcription termination factor Rho and the Nudix phosphohydrolase in C. trachomatis, thereby mitigating cellular stress responses . Special focus should be given to the regulatory and stress response genes CPn_0081 (RNA polymerase beta) and CPn_0793 (pp2c phosphatase) to look for bacterial adaption mechanisms in different microenvironments. In terms of pathogenicity and intracellular host-pathogen interactions, further investigations on the transcriptional relevance of the nsSNPs in the incC gene (CPn_0292) and the acyl-carrier UDP-GlcNAc O-acyltransferase (Cpn_0650) are needed. To substantiate our findings, we checked these characteristic SNPs in additional two respiratory isolates from the US (W5 and W6) and one additional respiratory (MUL2076) and vascular (CV6) isolate from Germany. We sequenced the respective regions harboring the characteristic sSNPs in CPn_0081, CPn_0650 and CPn_0920, the isolates grouped within the newly defined vascular and respiratory cluster, respectively (Additional file 1: Table S11).
While acute Cpn infections of the upper and lower respiratory tract are frequently seen in clinics and seem to emerge in small endemic episodes, the relevance of Cpn infections in the pathogenesis of atherosclerotic vascular diseases is less clear. Although experimental models support the hypothesis that Cpn contributes to the development of atherosclerotic lesions by direct or indirect mechanisms , clinical evidence is still missing. We were interested in the genetic background of clinical isolates from Cpn that were derived from different anatomical origins. Selection of isolates was motivated by the availability of re-cultivable strain preparations, clear documentation of the isolation source and the endeavor to obtain as large geographical distribution as possible. The only vascular strain A03  from the US we had access to was severely contaminated with Mycoplasma and could not be re-cultivated. Previous studies determined the genetic variation in Cpn from selected SNPs  or gene sequences  rather than complete genomes due to the high sequencing costs at that time, which are particularly exacerbated by an unavoidable proportion of human DNA in chlamydial samples. The recent progress in DNA sequencing techniques makes it feasible to sequence a sufficient amount of DNA to reconstruct entire genomes at low costs. In this study, we therefore applied the Illumina technique to 19 Cpn isolates. Independent of our study, Harris et al. recently pursued a similar approach to sequence 36 C. trachomatis genomes , also based on next-generation sequencing. Due to the shortness of the reads (36nt, 54nt and 76 nt) and for some isolates the limited amount of sequence data, neither mapping to the CWL029 genome as a reference, nor de novo assembly allowed us to assemble complete genomes. To overcome this limitation, and in difference to the manual scaffolding used for C. trachomatis , we applied a comparative assembly approach. It automatically combines mapping against a closely related reference genome and de novo assembly of regions that cannot be mapped to the reference. Comparative assembly works the better the closer the reference genome fits the target genome. Therefore, we selected the closest reference genome for each of our isolates individually. This special approach allowed us to assemble the complete genomes of six out of 19 isolates as well as genomes consisting of less than 10 contigs for a further nine isolates. The number of contigs for the four remaining isolates ranges between 13 and 147. The quality of these assemblies was verified by comparison with previous genotyping, which indicated a very high accuracy of the obtained genome sequences. High quality of the detected variants was ensured by requiring a sufficient quality (Phred score > = 20) and read coverage (> = 3). A comparison to the genome of the Cpn reference strain CWL029 yielded 6 SNP loci as well as 6 InDels, where all isolates have the same genotype except for CWL029 (Additional file 1: Tables S2 and S3). These positions represent likely errors in the CWL029 reference genome and were not observed in the CWL029-labstrain CWL029c formerly obtained from ATCC. Our findings were additionally validated by Sanger sequencing of the respective genomic loci.
We detected very low sequence diversity of the human Cpn isolates and a much higher evolutionary distance of the animal isolates confirming results from previous studies [12,19]. However, the unmatched number of 24 complete Cpn genome sequences allowed us to investigate the evolutionary relationships between Cpn isolates at a novel resolution level. High bootstrap support in most parts of the phylogenetic tree underpins the statistical significance of our results. We found significant evidence of recombination between human Cpn isolates and identified a region of 2 Kb around the putative IncA pseudogene Cpn_0010.1 that recombined between the CWL029 group, TW183 and UZG1. Thus, Cpn shows in principle similar evolutionary mechanisms as C. trachomatis, in which recombination occurs to an even greater extent . Also, considering the overall number of SNPs as well as the SNP distribution, we found that human Cpn isolates show much less variation than human C. trachomatis isolates. Earlier studies already pointed to an unexpectedly high nsSNP/sSNP ratio, suggesting that also sSNPs underlie selection in Cpn genome evolution . Our genome-wide dataset allowed an unbiased, gene-wise determination of the selective pressure. Remarkably, positive selective pressure (KaKs ratio > 1) was found for four genes and suggests a relationship of these genes to host pathogenicity which requires fast adaptation. However, only for one of them a putative function (Dipeptide transport system permease protein) could be predicted. The variations in plasticity zones suggested by earlier studies [15,19] were confirmed by our data; however, these are not in regions of particular high variability in our multi-genome alignments. Although we recovered other candidates for highly variable regions in the Cpn genomes, the existence of plasticity zones as observed in C. trachomatis  remains questionable.
Only a few studies directly addressed the question of genetic factors that are related to tissue tropism in Cpn infections to date. The most extensive showed minor polymorphisms present in the variable domain 4 (VD4) region of the outer-membrane protein-A (ompA) gene and the intergenic region between the ygeD and uridine kinase (ygeD–urk) of Cpn infecting human atherosclerotic carotid plaques . The polymorphic outer membrane protein (pmp) genes have been suggested previously as a marker for intra-species variation of Cpn, differentiating isolates by the size of their PCR product . We observed not only a high number of recombination in the pmp genes in general but also a deletion of ~420 nt in pmp20 in five of the vascular isolates, suggesting a central functional role of this highly variable region in chlamydial diseases . For Cpn it was shown that pmp20 acts as adhesin, is required for efficient intracellular infection and promotes NFκB- mediated pro- inflammatory signaling cascades in vascular endothelial cells [24,25], linking pmp20 to the vascular pathogenesis of Chlamydia.
Our data indicate that multiple copies of tyrP are not a prerequisite for respiratory isolates, which is in contrast to previous publications . On the other hand, all vascular isolates harbored only one single copy that could be a consequence of their close relatedness. A functional relevance, however, might exist in the context of persistence development. In this regard the isolate MUL2216, that has been isolated from bronchoalveolar-lavage fluid (BAL) of a 43 year old male patient suffering from COPD and chronic, non-productive cough, is of particular interest, as it expresses five out of the nine nsSNPs classified as “vascular genotype” and contains only one tyrP copy. To test the hypothesis whether polyclonal infections occur in vivo, it would require to subsequently genotype Cpn from DNA samples derived from respiratory samples and whole blood within short time after respiratory resolution of the respiratory symptoms  like it was done before for C. trachomatis isolates from clinical swabs . A particular focus should be given in further studies to the non-respiratory, non-vascular human pathogenic isolates. Dean et al. could show in a trachoma-endemic area that infections with Cpn were significantly associated with severe conjunctival inflammation overlapping with the clinical appearance of C. trachomatis induced trachoma . However, only few cases of Cpn induced conjunctivitis were reported so far  so that re-cultivable isolates were not adequately represented in our study.
Taken together, our data highlight for the first time genetic differences of clinical Cpn isolates that are connected to the anatomical origin of cultivation. With novel technologies for genetic modification of Chlamydia at hand, a detailed functional characterization of the different clinical isolates with respect to growth, extracellular survival, stress responsiveness and persistence induction is required. In addition, a clinical evaluation of atherosclerotic patients that is not based on the presence of anti-chlamydial antibodies but on the genetic background of the respective clinical isolate could foster our understanding of the pathophysiological relevance of Cpn infections in these patients and help to optimize diagnostic tools for the identification of vascular infections.
Selection of the clinical samples, sample preparation and sequencing
Table 1 lists all clinical Cpn isolates that were used in this study. In total 24 isolates were selected from a pool of 38 isolates that were used in the study from Rattei et al.  according to three stringent characteristics: 1) highest variety in geographical origin; 2) highest variety in anatomical origin; and 3) possibility to purely cultivate the pathogen. Some isolates that would have met the criteria like AL-1, T-45 (respiratory tract, Umea/Sweden) or A03 (coronary artery, Louisville/USA) had to be excluded because the isolate could not be re-cultivated or was contaminated with other bacteria, respectively. The CWL029 isolate that was propagated over several cultivation passages is designated CWL029c. Chlamydia were cultivated on HEp-2 cells as described previously , purified by centrifugation on 30% Urografin (Schering, Berlin, Germany) and chlamydial DNA was extracted using the Nucleo Spin Tissue kit (MacheryNagel, Dueren, Germany) and Proteinase K digestion. Clinical isolates were cultivated in strict separation to avoid cross-contamination. Sequencing was performed on the Illumina Genome Analyzer II using 36 bp, 54 bp or 76 bp paired-end runs.
Filtering and mapping of the reads
We determined the optimal criteria for minimal read length, minimal quality and requirement of mate-pair reads for each isolate based on the different dataset characteristics using FastQC v0.10.0 . Length and quality criteria were used to filter the reads using fastq_quality_trimmer from the FASTX Toolkit 0.0.13 . Reads without a mate were filtered out using an in-house python script. Reads retained from the filtering were mapped against a combined reference genome consisting of Cpn CWL029 (NC_000922.1) and human (NCBI Build 37.3, accession GCF_000001405.17) using BWA (version 0.5.9)  and samtools 0.1.12a . Reads mapping to human were identified using samtools (command “samtools view” with additional parameter “-f 0x0002” for reads mapped in a proper pair) and subtracted from the filtered reads using an in-house python script.
Check for polyclonal isolates
The mapping was used to derive a consensus pileup file (command “samtools pileup -c”). We then performed SNP calling and filtered out error-prone variant calls having a low SNP quality according to the samtools documentation (command “samtools.pl varFilter < consensus pileup file > | awk '$6 > = 20'”). The SNP calling gives either the nucleotide of the homozygous SNP or an IUPAC ambiguity code if it is a heterozygous SNP. A significant amount of heterozygous SNPs would indicate the presence of polyclonal isolates. We determined the numbers of homozygous and heterozygous SNPs for each isolate.
Determination of tyrP copy number
We determined the tyrP copy number from the sequencing depth of the tyrP locus (CWL029-based genomic position 1,111,812 to 1,115,418) relative to the 3 Kb upstream and 3 Kb downstream region for each isolate in the mapping against the reference CWL029. Therefore, we measured the average coverage in each of these three regions based on the mapping described above. The coverage of the tyrP locus compared to the neighboring regions indicates the copy number of the tyrP gene: if the coverage of the locus and the surrounding regions are similar, the isolate most likely contains two copies of the tyrP gene; a coverage of the tyrP locus which is half as high as the coverage of the surrounding region points to only one copy of the tyrP gene in the isolate. Three or more copies of the tyrP gene would be indicated by coverage of the tyrP locus that is significantly higher than the coverage of the surrounding region.
Genome assembly and annotation
We used the comparative assembler AMOScmp-shortReads-alignmentTrimmed from the AMOS framework 3.1.0  with default parameters for the combined mapping and de novo assembly of the remaining non-human reads. The reference genome with the smallest distance in the phylogenetic tree presented in the study from Rattei et al. , was used as reference in the comparative assembly for the corresponding isolate. The non-human reads were mapped to the newly assembled genomes using BWA  (default parameters) and the coverage per nucleotide was determined with samtools . The read coverage per isolate was visualized using R .
The assembled genomes were annotated using a house-internal workflow that integrates ab initio predictions from Glimmer , Genemark , Prodigal  and Critica  with homology information derived from a BLAST  search against NCBI NR. Noncoding RNAs were identified by tRNAscanSE , RNAmmer , and the Rfam  database and functional annotation of coding sequences was based on interproScan  and homology searches against the databases Swissprot and trEMBL .
Multiple genome alignment and variant detection
A multiple genome alignment of the assembled contigs of the 18 human isolates as well as the publicly available genomes of human isolates was calculated using the progressiveMauve algorithm implemented in the program Mauve (version 2.3.1)  and SNPs were identified by Mauve. InDels were inferred from gaps in the Mauve alignment using in-house perl scripts. Contig boundaries (defined as first and last 10 nucleotides in a contig) were removed to prevent spurious SNPs and InDels.
Polymorphic regions were identified using a sliding window approach looking for regions with at least 5% variation in a 100-nucleotide window. Customized python scripts were used to compare SNP positions and variants identified in the current study to the data of the previous study by Rattei et al. . SNPs were classified as synonymous, non-synonymous and noncoding based on the CWL029 GenBank annotation and SNPs, InDels and the CWL029 GenBank annotation were visualized in a genome plot using DNAPlotter .
For phylogenetic reconstruction, we calculated (as described above) a multiple genome alignment based on the 18 human isolates and the four publicly available genomes of Cpn clinical isolates. The two non-human isolates DC9 and the publicly available Cpn koala strain LPCoLN were included as outgroups. SNPs were derived from the multiple genome alignment as described above and used as input for phylogenetic analysis.
A third multiple genome alignment, (calculated as described above) including all newly sequenced isolates and the publicly available Cpn isolates except the koala isolate LPCoLN, accounts for the relatively high dissimilarity of LPCoLN to both DC9 and the human isolates. This third multiple genome alignment was the basis for recombination and KaKS analyses.
A maximum likelihood phylogenetic tree based on an alignment of the concatenated SNP variants of each isolate was calculated using RaxML  v7.2.6 (parameters “-m GTRGAMMA -x 12345 -N 1000 -f a”). The tree topology was manually compared to phylogenetic trees from bayesian inference calculated by MrBayes  (v3.1.2, parameters: “lset parsmodel = yes; mcmc ngen = 100000 samplefreq = 100; sump burnin = 250; sumt burnin = 250;”). The maximum likelihood phylogenetic tree was visualized using iTOL .
AMPHORA2  was applied to the reference genome CWL029 to identify the coordinates of phylogenetic marker genes within the genome. SNP variants within these genes were concatenated and a maximum likelihood phylogenetic tree was calculated as described above. A third maximum likelihood tree was calculated from SNPs located in the 545 genes that are members of the Chlamydiae non-supervised orthologous groups ChlaNOGs in the Eggnog-4.0 database  and represent the chlamydial pan-genome.
Determination of the Ka/Ks ratio
For calculating the ratio of non-synonymous (Ka) and synonymous substitution rates (KS), orthologous proteins were identified using Mauve. Alignments of protein-coding DNA sequences for all possible ortholog-pairs were created using ParaAT . The Ka/KS ratio for each ortholog-pair was calculated based on the alignment using the program KaKs_Calculator_2.0  with default parameters.
Analysis of genomic recombination
Recombination between the different isolates was analyzed using the Phi test for recombination implemented in the tool SplitsTree4 . Therefore, the concatenated SNP variants of each isolate were aligned and a neighbor-joining phylogenetic tree was calculated using the program readAL . The phylogenetic tree was used as input for SplitsTree4. In order to identify recombination hotspots, the analysis was repeated using fragments of the genome. Additionally, ClonalFrame , another program to test for recombination, was applied to the multiple genome alignment produced by MAUVE.
Horn M, Collingro A, Schmitz-Esser S, Beier CL, Purkhold U, Fartmann B, et al. Illuminating the evolutionary history of chlamydiae. Science. 2004;304:728–30.
Grayston JT. Background and current knowledge of Chlamydia pneumoniae and atherosclerosis. J Infect Dis. 2000;181 Suppl 3:S402–10.
WHO Media Center. WHO 2011 factsheet. 2011. http://who.int/mediacentre/factsheets/fs310/en/.
Maass M, Jahn J, Gieffers J, Dalhoff K, Katus HA, Solbach W. Detection of Chlamydia pneumoniae within peripheral blood monocytes of patients with unstable angina or myocardial infarction. J Infect Dis. 2000;181 Suppl 3:S449–51.
Borel N, Summersgill JT, Mukhopadhyay S, Miller RD, Ramirez JA, Pospischil A. Evidence for persistent Chlamydia pneumoniae infection of human coronary atheromas. Atherosclerosis. 2008;199:154–61.
Gieffers J, van Zandbergen G, Rupp J, Sayk F, Krüger S, Ehlers S, et al. Phagocytes transmit Chlamydia pneumoniae from the lungs to the vasculature. Eur Respir J. 2004;23:506–10.
De Kruif MD, van Gorp ECM, Keller TT, Ossewaarde JM, ten Cate H. Chlamydia pneumoniae infections in mouse models: relevance for atherosclerosis research. Cardiovasc Res. 2005;65:317–27.
Gieffers J, Durling L, Ouellette SP, Rupp J, Maass M, Byrne GI, et al. Genotypic differences in the Chlamydia pneumoniae tyrP locus related to vascular tropism and pathogenicity. J Infect Dis. 2003;188:1085–93.
Cochrane M, Walker P, Gibbs H, Timms P. Multiple genotypes of Chlamydia pneumoniae identified in human carotid plaque. Microbiol Read Engl. 2005;151(Pt 7):2285–90.
Harris SR, Clarke IN, Seth-Smith HMB, Solomon AW, Cutcliffe LT, Marsh P, et al. Whole-genome analysis of diverse Chlamydia trachomatis strains identifies phylogenetic relationships masked by current clinical typing. Nat Genet. 2012;44:413–9.
Rocha EPC, Pradillon O, Bui H, Sayada C, Denamur E. A new family of highly variable proteins in the Chlamydophila pneumoniae genome. Nucleic Acids Res. 2002;30:4351–60.
Rattei T, Ott S, Gutacker M, Rupp J, Maass M, Schreiber S, et al. Genetic diversity of the obligate intracellular bacterium Chlamydophila pneumoniae by genome-wide analysis of single nucleotide polymorphisms: evidence for highly clonal population structure. BMC Genomics. 2007;8:355.
Mitchell CM, Hutton S, Myers GSA, Brunham R, Timms P. Chlamydia pneumoniae is genetically diverse in animals and appears to have crossed the host barrier to humans on (at least) two occasions. PLoS Pathog. 2010;6:e1000903.
Shirai M, Hirakawa H, Kimoto M, Tabuchi M, Kishi F, Ouchi K, et al. Comparison of whole genome sequences of Chlamydia pneumoniae J138 from Japan and CWL029 from USA. Nucleic Acids Res. 2000;28:2311–4.
Read TD, Brunham RC, Shen C, Gill SR, Heidelberg JF, White O, et al. Genome sequences of Chlamydia trachomatis MoPn and Chlamydia pneumoniae AR39. Nucleic Acids Res. 2000;28:1397–406.
Kalman S, Mitchell W, Marathe R, Lammel C, Fan J, Hyman RW, et al. Comparative genomes of Chlamydia pneumoniae and C. trachomatis. Nat Genet. 1999;21:385–9.
Hoestgaard-Jensen K, Christiansen G, Honoré B, Birkelund S. Influence of the Chlamydia pneumoniae AR39 bacteriophage ϕCPAR39 on chlamydial inclusion morphology. FEMS Immunol Med Microbiol. 2011;62:148–56.
Klumpp J, Fouts DE, Sozhamannan S. Next generation sequencing technologies and the changing landscape of phage genomics. Bacteriophage. 2012;2:190–9.
Mitchell CM, Hovis KM, Bavoil PM, Myers GSA, Carrasco JA, Timms P. Comparison of koala LPCoLN and human strains of Chlamydia pneumoniae highlights extended genetic diversity in the species. BMC Genomics. 2010;11:442.
Al-Younes HM, Gussmann J, Braun PR, Brinkmann V, Meyer TF. Naturally occurring amino acids differentially influence the development of Chlamydia trachomatis and Chlamydia (Chlamydophila) pneumoniae. J Med Microbiol. 2006;55(Pt 7):879–86.
Haider S, Wagner M, Schmid MC, Sixt BS, Christian JG, Häcker G, et al. Raman microspectroscopy reveals long-term extracellular activity of Chlamydiae. Mol Microbiol. 2010;77:687–700.
Rosenfeld ME, Campbell LA. Pathogens and atherosclerosis: update on the potential contribution of multiple infectious organisms to the pathogenesis of atherosclerosis. Thromb Haemost. 2011;106:858–67.
Tondella MLC, Talkington DF, Holloway BP, Dowell SF, Cowley K, Soriano-Gabarro M, et al. Development and evaluation of real-time PCR-based fluorescence assays for detection of Chlamydia pneumoniae. J Clin Microbiol. 2002;40:575–83.
Mölleken K, Schmidt E, Hegemann JH. Members of the Pmp protein family of Chlamydia pneumoniae mediate adhesion to human cells via short repetitive peptide motifs. Mol Microbiol. 2010;78:1004–17.
Niessner A, Kaun C, Zorn G, Speidl W, Türel Z, Christiansen G, et al. Polymorphic membrane protein (PMP) 20 and PMP 21 of Chlamydia pneumoniae induce proinflammatory mediators in human endothelial cells in vitro by activation of the nuclear factor-kappaB pathway. J Infect Dis. 2003;188:108–13.
Witte L, Droemann D, Dalhoff K, Rupp J. Chlamydia pneumoniae is frequently detected in the blood after acute lung infection. Eur Respir J. 2011;37:712–4.
Seth-Smith HMB, Harris SR, Skilton RJ, Radebe FM, Golparian D, Shipitsyna E, et al. Whole-genome sequences of Chlamydia trachomatis directly from clinical samples without culture. Genome Res. 2013;23:855–66.
Dean D, Kandel RP, Adhikari HK, Hessel T. Multiple Chlamydiaceae species in trachoma: implications for disease pathogenesis and control. PLoS Med. 2008;5:e14.
Krasny J, Tomasova-Borovanska J, Hruba D. The Relationship between chlamydia trachomatis and chlamydia pneumoniae as the cause of neonatal conjunctivitis (ophthalmia neonatorum). Ophthalmol J Int Ophtalmol Int J Ophthalmol Z Für Augenheilkd. 2005;219:232–6.
Maass M, Harig U. Evaluation of culture conditions used for isolation of Chlamydia pneumoniae. Am J Clin Pathol. 1995;103:141–8.
Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. [http://www.bioinformatics.babraham.ac.uk/projects/fastqc/]
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma Oxf Engl. 2009;25:1754–60.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/Map format and SAMtools. Bioinforma Oxf Engl. 2009;25:2078–9.
Treangen TJ, Sommer DD, Angly FE, Koren S, Pop M. Next generation sequence assembly with AMOS. Curr Protoc Bioinforma Ed Board Andreas Baxevanis Al. 2011;11:11.8.
R Core Team. R: A Language and Environment for Statistical Computing. 2014.
Aggarwal G, Ramaswamy R. Ab initio gene identification: prokaryote genome annotation with GeneScan and GLIMMER. J Biosci. 2002;27(1 Suppl 1):7–14.
Besemer J, Lomsadze A, Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29:2607–18.
Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.
Badger JH, Olsen GJ. CRITICA: coding region identification tool invoking comparative analysis. Mol Biol Evol. 1999;16:512–24.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.
Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35:3100–8.
Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005;33(Database issue):D121–4.
Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 2011.
Consortium U. The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res. 2009;37(Database issue):D169–74.
Darling AE, Mau B, Perna NT. ProgressiveMauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS One. 2010;5:e11147.
Carver T, Thomson N, Bleasby A, Berriman M, Parkhill J. DNAPlotter: circular and linear interactive genome visualization. Bioinforma Oxf Engl. 2009;25:119–20.
Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22:2688–90.
Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinforma Oxf Engl. 2003;19:1572–4.
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007;23:127–8.
Wu M, Scott AJ. Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2. Bioinforma Oxf Engl. 2012;28:1033–4.
Powell S, Forslund K, Szklarczyk D, Trachana K, Roth A, Huerta-Cepas J, et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 2014;42(Database):D231–9.
Zhang Z, Xiao J, Wu J, Zhang H, Liu G, Wang X, et al. ParaAT: A parallel tool for constructing multiple protein-coding DNA alignments. Biochem Biophys Res Commun. 2012;419:779–81.
Wang D, Zhang Y, Zhang Z, Zhu J, Yu J. KaKs_Calculator 2.0: a toolkit incorporating gamma-series methods and sliding window strategies. Genomics Proteomics Bioinforma Beijing Genomics Inst. 2010;8:77–80.
Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23:254–67.
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinforma Oxf Engl. 2009;25:1972–3.
Didelot X, Falush D. Inference of bacterial microevolution using multilocus sequence data. Genetics. 2007;175:1251–66.
First of all, we would like to thank all the colleagues who supported us by providing strains or giving additional references for the origin of the isolates. The work was funded by the ERA-Net PathoGenoMics “European Initiative to Fight Chlamydial Infections by Unbiased Genomics (ECIBUG)” and the DFG-Cluster of Excellence “Inflammation at Interfaces”. We specially thank Kristin Wischnat for competent technical assistance.
The authors declare that they have no competing interests.
TW and JH performed the computational analyses. SE and TS were responsible for the sequencing of the isolates. IK and KS performed the experimental work and contributed to drafting the manuscript. TR supervised the bioinformatics analysis, analyzed the data and wrote the manuscript. JR initiated the study, obtained the funding, analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.
Thomas Weinmaier and Jonathan Hoser contributed equally to this work.
Supplementary tables. Excel table containing all Tables S1-S11 that are referenced in the manuscript as separate worksheets.
Genome coverage per genome. PDF image showing the genome coverage per isolate.
ClonalFrame consensus phylogenetic tree. PNG image showing the consensus phylogenetic tree reconstructed by ClonalFrame.
Phylogenetic trees for recombined region CPn_0010.1. PNG image showing maximum likelihood phylogenetic trees for recombination-site 1; A: based on concatenated SNPs from genomic position 1 to 14 Kb and 16 Kb to 30 Kb; B: based on SNPs between genomic positions 14 Kb to 16 Kb.
Single nucleotide polymorphism phylogeny based on specific gene sets. PDF image showing maximum likelihood phylogenetic trees based on SNPs located in (A) 31 phylogenetic marker genes, (B) 545 genes that occur in all chlamydia.
About this article
Cite this article
Weinmaier, T., Hoser, J., Eck, S. et al. Genomic factors related to tissue tropism in Chlamydia pneumoniae infection. BMC Genomics 16, 268 (2015). https://doi.org/10.1186/s12864-015-1377-8
- Chlamydia pneumoniae
- Genome assembly
- Comparative genomics
- Tissue tropism