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  • Research article
  • Open Access

Micro-evolution of three Streptococcus species: selection, antigenic variation, and horizontal gene inflow

Contributed equally
BMC Evolutionary Biology201919:83

https://doi.org/10.1186/s12862-019-1403-6

  • Received: 22 December 2017
  • Accepted: 25 February 2019
  • Published:

Abstract

Background

The genus Streptococcus comprises pathogens that strongly influence the health of humans and animals. Genome sequencing of multiple Streptococcus strains demonstrated high variability in gene content and order even in closely related strains of the same species and created a newly emerged object for genomic analysis, the pan-genome. Here we analysed the genome evolution of 25 strains of Streptococcus suis, 50 strains of Streptococcus pyogenes and 28 strains of Streptococcus pneumoniae.

Results

Fractions of the pan-genome, unique, periphery, and universal genes differ in size, functional composition, the level of nucleotide substitutions, and predisposition to horizontal gene transfer and genomic rearrangements. The density of substitutions in intergenic regions appears to be correlated with selection acting on adjacent genes, implying that more conserved genes tend to have more conserved regulatory regions.

The total pan-genome of the genus is open, but only due to strain-specific genes, whereas other pan-genome fractions reach saturation. We have identified the set of genes with phylogenies inconsistent with species and non-conserved location in the chromosome; these genes are rare in at least one species and have likely experienced recent horizontal transfer between species. The strain-specific fraction is enriched with mobile elements and hypothetical proteins, but also contains a number of candidate virulence-related genes, so it may have a strong impact on adaptability and pathogenicity.

Mapping the rearrangements to the phylogenetic tree revealed large parallel inversions in all species. A parallel inversion of length 15 kB with breakpoints formed by genes encoding surface antigen proteins PhtD and PhtB in S. pneumoniae leads to replacement of gene fragments that likely indicates the action of an antigen variation mechanism.

Conclusions

Members of genus Streptococcus have a highly dynamic, open pan-genome, that potentially confers them with the ability to adapt to changing environmental conditions, i.e. antibiotic resistance or transmission between different hosts. Hence, integrated analysis of all aspects of genome evolution is important for the identification of potential pathogens and design of drugs and vaccines.

Keywords

  • Streptococcus
  • Pan-genome
  • Genomic rearrangements
  • Antigen variation
  • Gene inflow
  • Selection in upstream regions

Background

The genus Streptococcus are Gram-positive bacteria that exert strong influence on the health of humans and animals. In particular, Streptococcus pneumoniae, normally a commensal from the nasopharynx microflora, at the same time is responsible for most pneumonia cases and is second only to Mycobacterium tuberculosis as a cause of mortality from bacterial infection worldwide [1]. Streptococcus pyogenes is among the top ten of bacterial causes of human mortality worldwide [2, 3], and due to the molecular mimicry with heart and brain cells causes severe autoimmune sequelae like rheumatic fever [4] and, possibly, autoimmune neuropsychiatric disorders [5]. Streptococcus suis rarely causes disease in human, but is one of the most important swine pathogens [6].

Sequencing of multiple strains of one species has demonstrated that the genome of any single strain does not reflect the genetic variability of the species, as two strains may differ by 20–35% of the gene content [7]. The concept of pan-genome was introduced to represent the total set of genes observed in genomes of strains assigned to a given species [79]. The pan-genome consists of core genes, present in all sequenced strains, dispensable, or periphery, genes, present in a subset of strains, and unique, strain-specific genes. The pan-genome is said to be open if upon addition of new strains its size continues to grow, or closed, if at some point it saturates [7].

Fractions of the pan-genome may differ not only in size, but also in the functional composition [10]. In general, core genes encode housekeeping functions, while dispensable and unique genes confer selective advantages such as adaptation to particular niches, e.g. colonization of different hosts for pathogens, or antibiotic resistance [11]. So one may expect that genes from different fractions of the pan-genome evolve in different modes, including gene gain/loss rate, frequency of horizontal gene transfer, and selective pressure [12, 13].

A consequence of the highly dynamic nature of bacterial genomes is frequent genomic rearrangements. Large inversions across the replication axis, deletions and insertions have been observed in S. pneumoniae [14, 15], S. suis [16, 17] and S. pyogenes [18]. The inversions have been suggested to rebalance the chromosomal architecture affected by insertions of large DNA segments [14]. The majority of these rearrangements occur at genome areas encoding transposases. Other genomic rearrangements occur at rRNA operons or sites encoding phage integrases and/or phage-related proteins.

Genome arrangement may have profound effects on a bacterial phenotype. Rearrangements can disrupt genes, create new genes by fusion of gene parts, or change gene expression. One example of such inversions is truncation of the so-called srtF pilus island in S. pneumoniae NSUI060 [19]. In S. pyogenes M23ND, genomic rearrangements resulted in re-clustering of a broad set of CovRS-regulated, actively transcribed genes, including virulence factors and metabolic genes, to the same leading strand. This may provide a potential advantage by creating spatial proximity to the transcription complexes, which may contain the global transcriptional regulator, CovRS, and RNA polymerases, in turn allowing for efficient transcription of the genes required for growth, virulence, and persistence [20].

Here we describe a comprehensive pan-genomic analysis of S. pneumoniae, S. pyogenes, and S. suis strains with integrated analysis of their genome evolution. The paper is organized as follows. First, we describe and functionally characterize the pan-genome and then use the results of this analysis to detect variations in selection regimes for genes and intergenic regions from different pan-genome fractions. Next, we focus on genome rearrangements revealing large parallel inversions in all studied species and make a prediction of the antigenic variation of histidine triad protein PhtD in S. pneumoniae. Finally, we use the gene order data to identify and functionally characterize the fraction of genes horizontally transferred after the divergence of the studied species and further spreading between the strains.

Methods

Genome sequences

The selection of the species was based on the number of available strain genomes. We analyzed 25 strains of Streptococcus suis, 50 strains of Streptococcus pyogenes, and 28 strains of Streptococcus pneumoniae, all available complete genomes as of June 2016 (Additional files 1: Table S1 and 2: Figure S1). The complete genomes were downloaded from the GenBank [21]. For all but two genomes (Streptococcus pyogenes STAB901 and Streptococcus pyogenes MTB313) the GenBank annotation coincides with that of the NCBI Refseq database.

Construction of orthologous groups (OGs)

We constructed orthologous groups using Proteinortho v5.13 with the default parameters [22]. Each gene was thus assigned to an orthologous group or labeled as a singleton. The size of a pan-genome was estimated with the Chao algorithm from the Micropan R-package [23].

Assignment of Gene Ontology (GO) terms to orthologous groups

To assign GO terms to genes, we used Interproscan [24]. A GO term was assigned to an orthologous group, if it was assigned to at least 90% of genes in this group. To determine overrepresented functional categories, we used GOstat [25]. The fit by theoretical models was estimated using the Akaike information criterion (AIC) [26].

Assignment of KEGG Orthology (KO) categories to orthologous groups

Initially, we assigned KO categories to genes with GhostKOALA [27]. Then a KO category was assigned to an orthologous group, if it was assigned to at least 90% of genes in this group. KO terms were divided into supercategories “Genetic Information Processing”, “Metabolism”, “Cellular Processes”, “Environmental Information Processing”, and “other” based on the KEGG hierarchy classification.

Prediction of virulence-related orthologous groups

We found virulence-related genes with MP3 (threshold 0.2) [28] that combined a support vector machine classifier trained on virulence factors from MvirDB [29] and a hidden Markov model classifier based on Pfam domains present in virulence factors. Orthologous group was considered virulence-related if at least 10% of its members were predicted to be virulence-related. To predict potential prophages, we used web server PHAST [30].

p N/p S calculation

To estimate the number of synonymous (pS) and nonsynonymous (pN) polymorphisms, we aligned amino acid sequences of proteins using MUSCLE [31] and then reconstituted the corresponding nucleotide alignment. Then we calculated pN and pS using the KaKs-Calculator Toolbox v2.0 with the Modified version of the Yang-Nielsen (MYN) method [32]. Multiple substitutions were accounted for using the Jukes-Cantor correction [33]. For these calculations, we considered different Streptococcus species separately. While homologous recombination clearly is important for the Streptococcus evolution, in case of pairs of very close genomes, homologous recombination would affect synonymous and non-synonymous substitution at the same degree. For each species and each orthologous group not containing paralogos, we performed pairwise comparisons of all strains and assigned the median pN/pS ratio to this group.

Selection in intergenic regions

We extracted intergenic regions from.gbk files downloaded from the NCBI Genome database. We removed intergenic regions shorter then 50 bp. Out of the remaining intergenic regions we constructed the sample of upstream fragments in the following way. We extracted 100 bp upstream fragments for all intergenic regions longer than 100 bp [34, 35]. For intergenic regions shorter than 100 bp its complete sequence was considered as an upstream fragment.

We estimated the fraction of positions under negative selection in two ways. To assess the correlation between the level of conservation in intergenic region and universality of the respective genes, we simply calculated substitutions in upstream fragments. Specifically, we considered all pairs of strains from one species, extracted aligned upstream fragments of orthologous genes from the multiple genome alignment, and counted nucleotide substitutions with the Jukes-Cantor correction. The same approach was used to compare the conservation level in univeral regions and regions deleted in some strains.

To estimate the overall selection pressure in intergenic regions, we applied the method from [36] to calculate the fraction of positions under negative selection by comparing conservation statistics of multiple sequence alignments of orthologous upstream fragments from strains of two closely related species.

Detection and analysis of large insertions/deletions (indels)

In orthologous upstream fragments, we considered all indels of length at least six nucleotides, observed in at least two strains, and not located at the alignments termini (to reduce the bias from misalignment of fragment termini and varying length of upstream regions).

Identification of candidate transcription-factor binding sites

We scanned for candidate binding sites in upstream fragments with FIMO [37], using positional weight matrices downloaded from PRODORIC [38]. Candidate binding sites were filtered using the FDR correction for multiple testing (q<0.05).

Gene composition of the leading and lagging strands

We identified origin (OriC) and terminus (Ter) of replication analyzing GC-skew plots. Based on the OriC and Ter locations, we determined the strands for genes from different fractions of the pan-genome. To test the statistical significance of differences between the pan-genome fractions, we performed a permutation test by shuffling genes between pan-genome fractions (retaining the fractions sizes) 250 times, thus obtaining the distribution of differences between the fractions under the random null model, and compared the observed differences with this distribution. Calculated differences with p-value satisfying the threshold with the Bonferroni correction for multiple testing were considered as statistically significant.

Statistical significance of over-representation of inter-replichore inversions was calculated as the probability of a given number of inter-replichore inversions in the set of inversions with given lengths. The probability of occurrence of the origin or the terminator of replication within the inversion was calculated as the ratio of the inversion length to the replichore length.

Construction of phylogenetic trees

For construction of phylogenetic trees we used concatenated aligned amino acid sequences of all core genes reverse translated to nucleotide alignment. Then maximum likelihood trees were constructed by RAxML [39] with default parameters.

Synteny blocks and rearrangements history

Synteny blocks were constructed using the Sibelia algorithm [40] with default parameters for whole-genome nucleotide alignments. Blocks observed in a genome more than once were filtered out. The history of inversions was reconstructed using the MGRA algorithm [41].

Detection of gene inflow

To detect genes horizontally transferred into species, we used the following model. If a gene with a mosaic phyletic pattern has been inherited vertically from the common ancestor and lost by several genomes, we expect to find it at the same syntenic region in the remaining strains. Genes not satisfying this condition are candidates for having been obtained horizontally. For this analysis, we excluded genes whose universal neighbours were affected by the reconstructed rearrangements, that is, genes located at or near boundaries of synteny blocks.

Results and discussion

Pan-genome and its fractions

We constructed 5742 OGs comprising 192782 genes. The number of genes in a genome assigned to OGs was 1872±178 with the median 1857 (Additional files 1: Tables S1 and 3: Table S2); the number of singletons was 48±53, median=22.

For strains of each species and for combinations of species we performed the standard pan-genome analysis to characterize the distribution of OGs by strains and to estimate the sizes of core and pan-genomes. The distribution of OGs by strains had a typical U-shape [9, 10, 42] (Fig. 1a), that could be fitted by a sum of three exponents (as in [42]), describing the common core, periphery genes, and unique genes, or by a sum of two power law functions (as in [10]), that divide the pan-genome into almost universal and almost unique genes (Fig. 1b). These two fits had almost equal R-squared values, but based on the AIC, the sum of three exponents was slightly more significant. When all three species were combined, the U-curve had additional minor peaks reflecting species-specific genes (Fig. 1a).
Fig. 1
Fig. 1

Distribution of orthologous groups (OGs) by the number of strains in which they are present. a For all analyzed strains (25 S. suis, 28 S. pneumoniae, and 50 S. pyogenes). b For 25 strains of S. suis with fitting by the sum of three exponential functions and by the sum of two power functions

The core genome of the three species converged to 458 genes (core and pan-genomes of the species are described in Table 1). The pan-genome was open, exceeding 10300 genes (Fig. 2a). Species-specific pan-genomes were also open, with core genomes accounting for approximately half of genes in any given genome. The Chao approximation of the total pan-genome size was 23217. The fraction of unique genes in a genome was less than 4%, with the highest fraction of unique genes in S. pneumoniae, and the lowest one in S. pyogenes (Additional file 4: Figure S2). The latter observation is partially explained by the presence of some very close strains in the analyzed genome set.
Fig. 2
Fig. 2

Sizes of the pan-genome fractions. a Sizes of the core genome, the pan-genome without singletons, and the total pan-genome with singletons as functions of the number of the analyzed strains. b Number of orthologous groups (OGs) are present in a given fraction of strains. Core OG present in all strains, singletons are present in only one strain each. All fractions reach saturation, while the pan-genome continues to grow due to singletons

Table 1

Size of pan-genome fractions

Species

Core genes

>90%

>50%

>10%

Not specie-specific

Total pan-genome

Chao estimation

All

458

999

1361

3339

5742

10326

23217

S. suis

1164

1486

1803

2582

3264

4672

8562

S. pneumoniae

1243

1480

1838

2768

3410

5672

13680

S. pyogenes

800 (1017*)

1403

1588

2258

2843

3757

6477

* - without S. pyogenes STAB901 and S. pyogenes MTB313 which were excluded from the RefSeq database

As in [10], we split the pan-genome into percentile fractions by considering OGs present in at least a given fraction of strains. All such pan-genome fractions reach saturation after addition of the first few strains, an exception being the core genome, that continues shrinking, although at a decreasing rate, and the total pan-genome that grows, mostly due to strain-specific, unique genes. If unique genes are excluded, the total pan-genome becomes closed and converges to about 5750 genes (Fig. 2b and Table 1).

We also considered the OG distribution in all three species simultaneously as a plot in three dimensions (Fig. 3, Additional file 5: Figure S3). Excluding singletons, the largest group of OGs was formed by species-specific periphery (1136 in S. pneumoniae, 891 in S. suis, 922 in S. pyogenes), then OGs from the common core of the three species (458 OGs; or 825 OGs for a more relaxed definition with OG allowed to be absent in one strain in each species), then OGs belonging to the inner space of the plotted cube, i.e. to the common periphery of all three species (270 OGs), species-core OGs (114 in S. pneumoniae, 126 in S. suis, 87 in S. pyogenes), and, finally, some OGs formed common cores of species pairs to the exclusion of the third species (93 for S. pneumoniae and S. suis, 30 for S. suis and S. pyogenes, 12 for S. pneumoniae and S. pyogenes, reflecting closer phylogenetic relationships between S. pneumoniae and S. suis).
Fig. 3
Fig. 3

Distribution of orthologous groups (OGs) by the number of strains in which they are present as a plot in three dimensions. Axes correspond to species. Size of dots reflects the number of OGs. Red dots marks singletons OGs. Most dots reside at edges or in corners of the cube plot; Additional file 5: Figure S3 contains two-dimensional projections corresponding to pairwise comparisons

Distribution of GO terms across pan-genome fractions

Interproscan [24] provided at least one GO term to 127672 genes. These assignments are largely consistent, as members of an orthologous group tend to be assigned the same GO term (Additional file 6: Figure S4). Requiring that at least 90% of proteins from an OG share the GO term, we assigned GO terms to 2969 orthologous groups.

The distribution of orthologous groups with determined GO terms across the pan-genome, given in Fig. 4a, shows that core-genome groups tend to be more often assigned GO terms than genes from the unique fraction of the pan-genome. Indeed, the unique genes mainly had no GO terms (Fig. 4a, “Strain-specific OGs”) or KEGG KO terms (Fig. 4b, “Strain-specific OGs”, Additional file 7: Figure S5) and were annotated as “hypothetical proteins”, hence likely encoding mobile elements and phage-related proteins or simply resulting from genome misannotation (Additional file 8: Figure S6). However, some important gene groups also fell in this category, as 15% of unique genes were predicted to be virulence-related (Additional file 9: Figure S7). The exact fraction of functionally relevant genes in this group is hard to estimate, as the absence of homologs makes functional annotation almost impossible (although gene calling artifacts in some cases may be recognized by the comparison of strains).
Fig. 4
Fig. 4

Distributions of orthologous groups (OGs) (a) with or without a GO term and (b) with known or unknown high-level KEEG KO category across the pan-genome fractions. Pan-genome fractions: Common core - OGs present in all strains, Common broad core - OGs missing at most in one strain of each species, S. pyogenes core - OGs present in all S. pyogenes strains, S. suis core - OGs present in all S. suis strains, S. pneumoniae core - OGs present in all S. pneumoniae strains, S. pyogenes specific core - OGs present in all S. pyogenes strains and absent in other species, S. suis specific core - OGs present in all S. suis strains and absent in other species, S. pneumoniae specific core - OGs present in all S. pneumoniae strains and absent in other species, S. pyogenes specific periphery - OGs present in some S. pyogenes strains and absent in other species, S. suis specific periphery - OGs present in some S. suis strains and absent in other species, S. pneumoniae specific periphery - OGs present in some S. pneumoniae strains and absent in other species, common periphery - OGs present in some but not all strains of each species, not strain-specific OGs - all OGs excluding singleton OGs, strain-specific OGs - singleton OGs, S. pyogenes periphery - OGs present in some S. pyogenes strains, periphery of S. suis and S. pyogenes - OGs present in some S. suis and S. pyogenes strains,S. suis periphery - OGs present in some S. suis strains, S. pneumoniae periphery - OGs present in some S. pneumoniae strains, periphery of S. pneumoniae and S. suis - OGs present in some S. pneumoniae and S. suis strains, specific core of S. pneumoniae and S. pyogenes - OGs present in all S. pneumoniae and S. pyogenes strains and absent in S. suis strains, periphery of S. pneumoniae and S. pyogenes - OGs present in some S. pneumoniae and S. pyogenes strains, specific core of S. suis and S. pneumoniae - OGs present in all S. suis and S. pneumoniae strains and absent in S. pyogenes strains, specific core of S. suis and S. pyogenes - OGs present in all S. suis and S. pyogenes strains and absent in S. pneumoniae strains

Overrepresented functional categories in different fractions of the pan-genome with regards to the described cube representation are shown in Additional file 3: Table S2. The common core genome and weakly species-specific cores, that is genes observed in all strains of one species and some strains of the remaining species, are enriched with GOs involved in information processing, such as translation, ribosome, gene expression, RNA, and all kinds of metabolic processes. The periphery is enriched in a small set of functions, including response to other organisms and pathogenesis (this fraction features the highest percent of predicted virulence-related genes, Additional file 9: Figure S7), in particular, sialidase activity (S. pyogenes, S. pneumonie), DNA binding and some carbohydrate-related functions (S. pneumoniae, S. suis), as well as transcription factors (S. pyogenes). Strain-specific genes are mainly enriched in transposase activity, DNA recombination, and DNA integration, consistent with the origin of strain-specific genes from mobile elements [13]; in addition, these categories are enriched in orthologous groups from the common periphery, that is, among genes present in a fraction of strains from all three species. Species-specific cores are enriched in vitamin biosynthesis (S. pneumonie), transport, histidine and lactose metabolism, and response to oxidative stress (S. pyogenes), and iron transport, amino acid metabolic processes, and regulation of transcription (S. suis).

The distribution of KEGG KO categories across the pan-genome is shown in Fig. 4b. The fraction of orthologous groups assigned with a KO category decreases when moving from the core genome to the periphery and then to strain-specific genes. Most orthologous groups related to “Genetic Information Processing”, that can be considered as most essential groups, correspond to the common core, followed by the periphery and then strain-specific genes; no such orthologous groups were found among species-specific cores.

Hence, the functional distribution agrees with the pan-genome model in which the core is responsible for information and most metabolic processes, the periphery performs fine-tuning of bacteria to specific ecological niches, and strain-specific genome fraction is comprised mainly of mobile elements-related genes [43].

Genes from the common core show a weak preference to the leading strand, whereas periphery and strain-specific genes tend to be located at the lagging strand (Table 2). The leading strand preference of the core genes may be associated with their higher transcription level and/or with essentiality of these genes [44]. However, this difference is not very strong, as Streptococcus feature a general, strong bias with about 80% of genes located at the leading strand.
Table 2

Strand preference of genes from pan-genome fractions

Pan-genome fraction (number of OGs)

Percent of genes on leading strand

Standart deviation

All genes (5742 OGs + 4584 singletons)

79.6

1.4

Non strain-specific OGs (5742)

79.8 *

1.4

Common core (458)

83.9 *

0.6

S.pneumoniae specific core (114)

79.6

0.9

S.pyogenes specific core (87)

73.5 *

0.2

S.suis specific core (126)

78.8

1.6

S.pneumoniae periphery (1136)

75.2 *

5.2

S.pyogenes periphery (922)

75.5 *

3.6

S.suis periphery (891)

78.3 *

3.3

Common periphery (270)

77.8 *

6

Strain-specific OGs (4584)

70.1 *

15.2

Here, periphery is defined as genes present in some strains of a given species and absent in other species. * - statistically significant

Selection regime in the pan-genome fractions

Genes from the core genome encoding essential, housekeeping functions should evolve under higher purifying selection [45] yielding lower pN/pS ratio, compared to dispensable genes from the periphery genome. Indeed, as shown in Fig 5a and Additional file 10: Figure S8a, the pN/pS ratio is the smallest for the core genome (Mann–Whitney, p <0.01 for comparisons of core and periphery fractions within the same species).
Fig. 5
Fig. 5

(a) Median value of the pN/pS ratio with the Jukes-Cantor correction for genes and (b) median number of nucleotide substitutions in upstream regions (dD). Significance level is indicated with stars (p-value <0.001)

In addition to protein-coding genes, purifying selection acts on regulatory elements in intergenic regions. In this and the next sections, we attempt to quantify this selection by determining the fraction of intergenic nucleotide positions evolving under negative selection and by comparing regions that are deleted in some strains with universal intergenic regions.

The median fraction of nucleotide substitutions (with the Jukes-Cantor correction) in intra-species alignments of orthologous upstream regions was 5.6%. The distribution of the number of nucleotide substitutions with the Jukes-Cantor correction, dD, is shown in Fig. 5b and Additional file 10: Figure S8b. The fraction of the pan-genome with the lowest number of substitutions is the core genome. Hence, not only the core genes, but their expression level and regulation are likely to be conserved.

In inter-species alignments, conserved columns may indicate functional conservation or simply insufficient time after speciation to accumulate mutations in all non-essential positions. To estimate the number of hidden non-conserved positions we used the method from [36]. We have calculated that only 10-20% of positions in the upstream regions evolve under purifying selection (Additional file 11: Figure S9). However, this may be an underestimate due to the large distance between the analysed species and the low number of conserved positions.

Inserted and deleted fragments in intergenic regions are not neutral

Many alignments of orthologous upstream regions contained extended insertions and/or deletions (indels). One might expect that sequences within indels are selectively neutral. However, the indel fragments demonstrate strong sequence conservation (in the remaining genomes), and the level of conservation increases with the indel length (Fig. 6, Additional file 12: Figure S10). One possible explanation could be horizontal transfer of regulatory sequences enabling fast change of the level and mode of expression for the adjacent gene(s) [46]. However, computational scanning for candidate binding sites of transcription factors (see Methods) in the indel fragments has not produced an excess of candidate sites compared with control, random fragments from the same upstream regions, controlled for length. This might be due to low recall of the recognition rules, noise in predictions, and the fact that evolving intergenic regions may contain genes for regulatory RNAs [47, 48].
Fig. 6
Fig. 6

Dependence of the mean conservation level of nucleotides in indels in upstream regions in genome of S. pyogenes on the indel size. Blue dots correspond to the mean conservation level, green dots correspond to the number of indels of this size

Genomic rearrangements and antigenic variation of histidine triad protein PhtD

An important mode of genome evolution is rearrangements of chromosome fragments. In prokaryotes with single chromosomes the prevalent type of rearrangements are symmetrical inversions around the origin of replication [4952]. While several inversions in some Streptococcus strains had been described [53], the increased phylogenetic coverage allowed us to actually map the events to the phylogenetic tree.

Synteny blocks were obtained using whole-genome alignments for each species. Only blocks present in all strains were used for the reconstruction of inversions. As a result, 13 inversions for S. pneumoniae, 21 inversions for S. suis, and 26 inversions for S. pyogenes were identified. Mapping these events to phylogenetic trees (Additional file 13: Figure S11) revealed cases of parallel inversions in all three species.

The observed parallel inversions could be explained by homologous recombination (horizontal transfer between strains) involving a segment containing the inverted fragments. If this were the case, sequence trees constructed using the genes from the inverted fragments would cluster together strains with the parallel inversions. However, such trees for all inversions are consistent (Additional file 14: Figure S12) with the benchmark tree constructed using the alignments of all core gene (Additional file 2: Figure S1) confirming the independent origin of these inversions.

Previously, inversions in Streptococcus spp. were explained by selection to rebalance the replichore architecture affected by insertion of prophages [14]. To check this hypothesis, we compared lengths of prophage regions in strains that contained the same inversion, and vice versa the number of inversions in strains with the same rate of prophage insertions (Additional file 1: Table S1). No correlation between the rates of prophage insertions and inversions was observed.

All inversions were bounded by mobile elements or clusters of rRNA except one event in the S. pneumoniae subtree. This inversion of length 15 kB was found in four separate branches and breakpoints were formed by genes encoding the surface antigen proteins PhtB and PhtD from a family characterized by the presence of several histidine triad (HxxHxH) motifs. PhtD and PhtB are relatively large proteins with lengths about 850 amino acids thought to be involved in multiple functions, including metal ion homeostasis, evasion of complement deposition, and adherence of bacteria to host cells [54]. In pairs of strains with and without the inversion, these proteins are composed of two independent parts with the inversion breakpoint in the middle of the gene that might indicate the action of an antigen variation mechanisms (Fig. 7).
Fig. 7
Fig. 7

Number of mismatches in alignments of the histidine-triad proteins in S. pneumoniae ST556 and S. pneumoniae TCH8431/19A. The red points and blue points show the local dissimilarity level of PhtB from S. pneumoniae ST556 vs PhtB in S. pneumoniae TCH8431/19A and PhtD from S. pneumoniae TCH8431/19A, respectively

As more than 80% core genes in Streptococcus spp. are found on the leading strand (see above), one would expect strong selection against intra-replichore inversions, as they do switch genes between leading/lading strands. Indeed, inter-replichore inversions are overrepresented (57 events of 62, the p-value = 9×10−14) (Fig. 8).
Fig. 8
Fig. 8

Distribution of inversion lengths. Red color corresponds to intra-replichore inversions; blue color, inter-replichore inversions

Detection of gene inflow

To identify genes horizontally transferred after the divergence of the studied Streptococcus spp. and further spreading between the strains, we selected genes that were not unique and that were not common for at least one Streptococcus species (referred to periphery genes). Positions of single-copy, universal genes were analyzed to construct syntenic regions for all strains and to compile a set of periphery genes occurring in different syntenic regions and, therefore, likely being spread by horizontal gene transfer. The set comprised 277 orthologous groups that is about 7% of single-copy periphery genes. To confirm horizontal gene transfer, we constructed phylogenetic gene trees for all orthologous groups that were present at least in two Streptococcus species and at least in two strains in each species and checked whether each species were monophyletic, that is, formed a separate branch in these trees (Table 3). Most groups (88%) yielded trees with monophyletic species (consistent trees) and had conserved location in all genomes, indicating vertical inheritance from a common ancestor. About a half of groups with inconsistent trees had conserved genome positions that may be explained by homologous recombination; the remaining half had non-conserved location. The set of 48 orthologous groups with inconsistent trees and non-conserved positions are candidates for horizontal transfer between species. More than a half of these genes are rare in all three Streptococcus species; others are rare in at least one species (Fig. 9).
Fig. 9
Fig. 9

Distribution of genes with non-conserved genome positions and non-consistent phylogenetic trees. One column corresponds to one ortholog group. Bars show the fraction of genomes where the orthologs are present

Table 3

Statistics of periphery genes

 

Conserved location

Non-conserved location

All

Consistent tree

1181

128

1309

Inconsistent tree

44

48

92

All

1225

176

1401

An analysis of overrepresented GO terms in the set of orthologous groups with non-conserved positions, compared with all non-core groups (Table 4), yielded many functions involved in interaction with DNA (DNA binding, nucleic acid binding, sequence-specific DNA binding), that may be linked to regulation, but also to mobile elements (DNA integration). Other overrepresented functions such as ATP hydrolysis coupled proton transport, energy coupled proton transport against electrochemical gradient, proton-transporting V-type ATPase complex, etc., all are likely linked to the V-type ATPase, that is thought to be horizontally transfered from archaea [55].
Table 4

Overrepresented GO terms in genes with non-conserved location, compared with all non-core genes

GO term

Genes with non-conserved location

Total number of genes with this GO term

P-value

Description

GO:0003677

51

603

1.5×10−3

DNA binding

GO:0015991

4

5

2.2×10−3

ATP hydrolysis coupled proton transport

GO:0015988

4

5

2.2×10−3

Energy coupled proton transport, against electrochemical gradient

GO:0003676

58

762

3.3×10−3

Nucleic acid binding

GO:0016469

4

6

3.8×10−3

Proton-transporting two-sector ATPase complex

GO:0006818

4

7

6.2×10−3

Hydrogen transport

GO:0015992

4

7

6.2×10−3

Proton transport

GO:0043565

14

98

6.8×10−3

Sequence-specific DNA binding

GO:0015074

13

100

2.6×10−2

DNA integration

GO:0033176

2

2

5.5×10−2

Proton-transporting V-type ATPase complex

GO:0033179

2

2

5.5×10−2

Proton-transporting V-type ATPase, V0 domain

Discussion

The pan-genome of many bacterial species including Streptococcus was shown to be open [56, 57]. In agreement with previous observations, the pan-genome of studied Streptococcus species is also open but it is mainly due to strain-specific genes. The pan-genome size exceeds 10300 genes, but if unique genes are excluded, the total pan-genome becomes closed and converges to about 5750 genes. Splitting the pan-genome into percentile fractions by considering OGs present in at least a given fraction of strains revealed the saturation of all such fractions after addition of the first few strains except the core genome and unique genes.

In a typical genome for studied Streptococcus species, one quarter of genes belong to the genus core genome; one quarter, to the species-specific core; most other genes are periphery ones, and a minority are strain-specific. The core genome of studied Streptococcus species is enriched with information-process and main metabolic functions and depleted with mobile elements and phage-related genes; the periphery fraction is enriched with niche-specific metabolic functions, including pathogenesis-related ones; and strain-specific genes are enriched with hypothetical genes and mobile elements, but also contain many virulence-related genes. At that, Streptococcus has a broad periphery and a huge repertoire of strain-specific genes. A large periphery fraction of pan-genome is thought to be a characteristic of organisms with large long-term effective population sizes and an ability to fill a variety of new niches [58].

Variation of selection regimes for genes and their upstream regions is consistent with the suggested evolutionary role of pan-genome fractions [12, 59]. Specifically, the core genes demonstrate a lower level of substitutions than periphery and unique genes and this tendency holds both for protein-coding sequences and for upstream regions [6062]. More generally, while it is known that intergenic regions in bacteria experience purifying selection [63, 64], its strength appears to be different between pan-genome fractions. The fact that upstream regions of core genes have fewer substitutions might reflect stronger conservation of their regulation or more complex regulation, yielding a larger density of transcription-factor binding sites and other regulatory structures. On the other hand, fragments of intergenic regions that are deleted (or inserted) in some strains, are not less conserved than the surrounding regions, which might be a sign of newly evolving regulatory interactions or of ‘horizontal regulatory transfer’ [46, 65]. Evolution of intergenic regions in prokaryotes is a sparsely studied area, and new tools such as PIGGY [66] should accelerate the progress in this direction, specifically, by allowing for rapid analysis of additional, diverse species and genera.

In many bacteria, including Streptococcus, within-replichore inversions, that is, inversions with endpoints in the same replichore, have been shown to be relatively rare and significantly shorter than inter-replichore inversions [6771]. Both non-random mutational processes and selection have been suggested as potential drivers of biased inversion landscapes [67, 69, 72, 73]. In more recent papers it was shown that symmetric inversion bias is not a universal feature of prokaryotic genome evolution but varies considerably across clades and the magnitude of the symmetric inversion bias is associated with various features of adaptive genome architecture, including the distance of essential genes to the origin of replication and the preferential localization of genes on the leading strand [74].

The pattern of inversions reconstructed in the studied Streptococcus species revealed a strong selection against intra-replichore inversions that, in agreement with previous observations, might be caused by strong preferential localization of genes on the leading strand (more than 80% of core genes). Despite low frequency of inversions, parallel inversions were observed in all three studied species. Most inversions were bound by mobile elements or clusters of rRNA, so most parallel events were likely to be caused by intragenome recombination linked to a limited number of repeated elements. The exception was the inversion in the S. pneumoniae subtree with breakpoints formed by genes encoding surface antigen proteins phtB and phtD. As the inversion was shown to exchange gene fragments, it is likely to indicate the action of antigen variation.

Phase variation is known to be an important mechanism that leads to phenotype diversification via intra-genomic recombination. Antigenic variation via inversions of short genomic fragments was shown to play a significant role for the S. pneumoniae infection influencing its pathogenicity [75]. While this paper was under review, antigen variation by the observed large parallel inversion between the phtD and phtB genes in S. pneumoniae had been confirmed [76]. The practical relevance of this observation comes from the fact that this protein is a candidate for a next-generation pneumococcal vaccine [77]. This shows that evolutionary and functional analysis of predicted parallel rearrangements with direct confirmation of this mechanism may identify possible cases of phase variation by inversions in human pathogens.

In the studied Streptococcus species, about 7% single-copy periphery genes occur in multiple syntenic regions. The genes with inconsistent trees and non-conserved genome position are rare in at least one species and have likely experienced horizontal transfer between species. Hence, a large periphery in the Streptococcus pan-genome is likely to be explained by horizontal gene transfer, that is known to be one of the major drivers of genome evolution [78, 79]. Horizontal gene transfer in Streptococcus is facilitated by the competence system and is associated with immune system [80]. Moreover, the early proof that DNA carries genetic information was provided by experiments with pneumococcus [81, 82]. This emphasizes the importance of pan-genome studies of medically relevant bacteria, as their pathogenicity may be affected by rare periphery or even strain-specific genes.

Conclusions

Members of the genus Streptococcus have a highly dynamic, open pan-genome, that potentially confers them with the ability to adapt to changing environmental conditions, i.e. antibiotic resistance or transmission between different hosts. Streptococcus genome plasticity is shaped by a dynamic interaction of major evolutionary forces such as horizontal gene transfer, genome rearrangements, and propagation of mobile elements reflecting the ecological niche and the lifestyle. Hence, integrated analysis of all aspects of genome evolution is important for the identification of potential pathogens and design of drugs and vaccines.

Notes

Declarations

Acknowledgements

We are grateful to Marat Kazanov for sharing preliminary data.

Funding

The study was supported by the Russian Foundation of Basic Research under grant 16-54-21004 and program “Molecular and Cellular Biology” of the Russian Academy of Sciences.

Availability of data and materials

All sequences analyzed in this study were taken from GenBank. Accession numbers and details are available in Additional file 1: Table S1. Orthologous groups composition described in Additional file 15: Table S3 and GO term annotations are available in Additional file 3: Table S2.

Authors’ contributions

PVS, OOB and MSG conceived and designed the study; PVS, OOB and AAK analyzed the data; PVS, OOB and MSG wrote the paper. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Authors’ Affiliations

(1)
Vavilov Institute of General Genetics Russian Academy of Sciences, Gubkina str. 3, Moscow, 119991, Russia
(2)
Kharkevich Institute for Information Transmission Problems, 19, Bolshoy Karetny per., Moscow, 127051, Russia
(3)
Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
(4)
Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
(5)
Faculty of Computer Science, Higher School of Economics, Moscow, Russia

References

  1. Krzyściak W, Pluskwa K, Jurczak A, Kościelniak D. The pathogenicity of the Streptococcus genus. Eur J Clin Microbiol Infect Dis. 2013; 32(11):1361–76.PubMedPubMed CentralView ArticleGoogle Scholar
  2. Brown JS, Gilliland SM, Holden DW. A Streptococcus pneumoniae pathogenicity island encoding an ABC transporter involved in iron uptake and virulence. Mol Microbiol. 2001; 40(3):572–85.PubMedView ArticleGoogle Scholar
  3. Richards VP, Palmer SR, Pavinski Bitar PD, Qin X, Weinstock GM, Highlander SK, Town CD, Burne RA, Stanhope MJ. Phylogenomics and the dynamic genome evolution of the genus Streptococcus. Genome Biol Evol. 2014; 6(4):741–53.PubMedPubMed CentralView ArticleGoogle Scholar
  4. Cunningham MW. Post-Streptococcal Autoimmune Sequelae: Rheumatic Fever and Beyond In: Ferretti JJ, Stevens DL, Fischetti VA, editors. Streptococcus pyogenes: Basic Biology to Clinical Manifestations. Oklahoma: University of Oklahoma Health Sciences Center: 2016.Google Scholar
  5. Mullen S. Review of pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections. Ment Health Clin. 2015; 5(4):184–8.View ArticleGoogle Scholar
  6. Gottschalk M, Segura M. The pathogenesis of the meningitis caused by Streptococcus suis: the unresolved questions. Vet Microbiol. 2000; 76(3):259–72.PubMedView ArticleGoogle Scholar
  7. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005; 15(6):589–94.PubMedView ArticleGoogle Scholar
  8. Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci U S A. 2005; 102(39):13950–5.PubMedPubMed CentralView ArticleGoogle Scholar
  9. Hogg JS, Hu FZ, Janto B, Boissy R, Hayes J, Keefe R, Post JC, Ehrlich GD. Characterization and modeling of the Haemophilus influenzae core and supragenomes based on the complete genomic sequences of rd and 12 clinical nontypeable strains. Genome Biol. 2007; 8(6):103.View ArticleGoogle Scholar
  10. Gordienko EN, Kazanov MD, Gelfand MS. Evolution of pan-genomes of Escherichia coli, Shigella spp., and Salmonella enterica. J Bacteriol. 2013; 195(12):2786–92.PubMedPubMed CentralView ArticleGoogle Scholar
  11. Muzzi A, Masignani V, Rappuoli R. The pan-genome: towards a knowledge-based discovery of novel targets for vaccines and antibacterials. Drug Discov Today. 2007; 12(11):429–39.PubMedView ArticleGoogle Scholar
  12. Sarkar SF, Guttman DS. Evolution of the core genome of Pseudomonas syringae, a highly clonal, endemic plant pathogen. Appl Environ Microbiol. 2004; 70(4):1999–2012.PubMedPubMed CentralView ArticleGoogle Scholar
  13. Wolf YI, Makarova KS, Lobkovsky AE, Koonin EV. Two fundamentally different classes of microbial genes. Nat Microbiol. 2016; 2:16208.PubMedView ArticleGoogle Scholar
  14. Camilli R, Bonnal R, Del Grosso M, Iacono M, Corti G, Rizzi E, Marchetti M, Mulas L, Iannelli F, Superti F, Oggioni M, De Bellis G, Pantosti A. Complete genome sequence of a serotype 11A, ST62 Streptococcus pneumoniae invasive isolate. BMC Microbiol. 2011; 11(25).Google Scholar
  15. Williams T, Loman N, Ebruke C, Musher D, Adegbola R, Pallen M, Weinstock G, Antonio M. Genome analysis of a highly virulent serotype 1 strain of Streptococcus pneumoniae from West Africa. PLoS ONE. 2012; 7(10):26742.View ArticleGoogle Scholar
  16. Yao X, Li M, Wang J, Wang C, Hu D, Zheng F, Pan X, Tan Y, Zhao Y, Hu L, Tang J, Hu F. Isolation and characterization of a native avirulent strain of Streptococcus suis serotype 2: a perspective for vaccine development. Sci Rep. 2015; 5:9835.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Athey T, Auger J, Teatero S, Dumesnil A, Takamatsu D, Wasserscheid J, Dewar K, Gottschalk M, Fittipaldi N. Complex population structure and virulence differences among serotype 2 Streptococcus suis strains belonging to sequence type 28. PLoS ONE. 2015; 10(9):0137760.View ArticleGoogle Scholar
  18. Hamada S, Kawabata S, Nakagawa I. Molecular and genomic characterization of pathogenic traits of group a Streptococcus pyogenes. Proc Jpn Acad Ser B Phys Biol Sci. 2015; 91(10):539–59.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Athey T, Teatero S, Takamatsu D, Wasserscheid J, Dewar K, Gottschalk M, Fittipaldi N. Population structure and antimicrobial resistance profiles of Streptococcus suis serotype 2 sequence type 25 strains. PLoS ONE. 2016; 11(3):0150908.View ArticleGoogle Scholar
  20. Bao Y, Liang Z, Mayfield J, McShan W, Lee S, Ploplis V, Castellino F. Novel genomic rearrangements mediated by multiple genetic elements in Streptococcus pyogenes M23ND confer potential for evolutionary persistence. Microbiology. 2016; 162(8):1346–59.PubMedPubMed CentralView ArticleGoogle Scholar
  21. NCBI RC. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2017; 45(D1):12.View ArticleGoogle Scholar
  22. Lechner M, Findeiß S, Steiner L, Marz M, Stadler PF, Prohaska SJ. Proteinortho: detection of (co-) orthologs in large-scale analysis. BMC Bioinformatics. 2011; 12(1):124.PubMedPubMed CentralView ArticleGoogle Scholar
  23. Snipen L, Liland KH. Micropan: An R-package for microbial pan-genomics. BMC Bioinformatics. 2015; 16(1):79.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, McWilliam H, Maslen J, Mitchell A, Nuka G, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014; 30(9):1236–40.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Beißbarth T, Speed TP. GOstat: find statistically overrepresented gene ontologies within a group of genes. Bioinformatics. 2004; 20(9):1464–5.PubMedView ArticleGoogle Scholar
  26. Hurvich CM, Tsai C-L. Regression and time series model selection in small samples. Biometrika. 1989; 76:297–307.View ArticleGoogle Scholar
  27. Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016; 428(4):726–31.PubMedView ArticleGoogle Scholar
  28. Gupta A, Kapil R, Dhakan DB, Sharma VK. MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. PloS ONE. 2014; 9(4):93907.View ArticleGoogle Scholar
  29. Zhou C, Smith J, Lam M, Zemla A, Dyer MD, Slezak T. MvirDB—a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Res. 2006; 35(suppl 1):391–4.Google Scholar
  30. Zhou Y, Liang Y, Lynch KH, Dennis JJ, Wishart DS. PHAST: a fast phage search tool. Nucleic Acids Res. 2011; 39(suppl 2):347–52.View ArticleGoogle Scholar
  31. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004; 32(5):1792–7.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Zhang Z, Li J, Zhao X-Q, Wang J, Wong GK-S, Yu J. KaKs_Calculator: calculating Ka and Ks through model selection and model averaging. Genom Proteomics Bioinforma. 2006; 4(4):259–63.View ArticleGoogle Scholar
  33. Jukes TH, Cantor CR. Evolution of protein molecules. Mammal Protein Metab. 1969; 3(21):132.Google Scholar
  34. Gordon JJ, Towsey MW, Hogan JM, Mathews SA, Timms P. Improved prediction of bacterial transcription start sites. Bioinformatics. 2005; 22(2):142–8.PubMedView ArticleGoogle Scholar
  35. Burden S, Lin Y-X, Zhang R. Improving promoter prediction improving promoter prediction for the nnpp2. 2 algorithm: a case study using Escherichia coli DNA sequences. Bioinformatics. 2004; 21(5):601–7.PubMedView ArticleGoogle Scholar
  36. Tsoy OV, Pyatnitskiy MA, Kazanov MD, Gelfand MS. Evolution of transcriptional regulation in closely related bacteria. BMC Evol Biol. 2012; 12(1):200.PubMedPubMed CentralView ArticleGoogle Scholar
  37. Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011; 27(7):1017–8.PubMedPubMed CentralView ArticleGoogle Scholar
  38. Münch R, Hiller K, Barg H, Heldt D, Linz S, Wingender E, Jahn D. PRODORIC: prokaryotic database of gene regulation. Nucleic Acids Res. 2003; 31(1):266–9.PubMedPubMed CentralView ArticleGoogle Scholar
  39. Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006; 22(21):2688–90.PubMedPubMed CentralView ArticleGoogle Scholar
  40. Minkin I, Patel A, Kolmogorov M, Vyahhi N, Pham S. Sibelia: A Scalable and Comprehensive Synteny Block Generation Tool for Closely Related Microbial Genomes In: Darling A, Stoye J, editors. Algorithms in Bioinformatics. WABI 2013. Lecture Notes in Computer Science, vol 8126. Berlin: Springer: 2013.Google Scholar
  41. Avdeyev P, Jiang S, Aganezov S, Hu F, Alekseyev MA. Reconstruction of ancestral genomes in presence of gene gain and loss. J Comput Biol. 2016; 23(3):150–64.PubMedView ArticleGoogle Scholar
  42. Koonin EV, Wolf YI. Genomics of bacteria and archaea: the emerging dynamic view of the prokaryotic world. Nucleic Acids Res. 2008; 36(21):6688–719.PubMedPubMed CentralView ArticleGoogle Scholar
  43. Carlos Guimaraes L, Benevides de Jesus L, Vinicius Canario Viana M, Silva A, Thiago Juca Ramos R, de Castro Soares S, Azevedo V. Inside the pan-genome-methods and software overview. Curr Genom. 2015; 16(4):245–52.View ArticleGoogle Scholar
  44. Zheng W-X, Luo C-S, Deng Y-Y, Guo F-B. Essentiality drives the orientation bias of bacterial genes in a continuous manner. Sci Rep. 2015; 5:16431.PubMedPubMed CentralView ArticleGoogle Scholar
  45. Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, Waller A, Mende DR, Kultima JR, Martin J, Kota K, Sunyaev S, Weinstock G, Bork P. Genomic variation landscape of the human gut microbiome. Nature. 2013; 493(7430):45.PubMedView ArticleGoogle Scholar
  46. Oren Y, Smith MB, Johns NI, Zeevi MK, Biran D, Ron EZ, Corander J, Wang HH, Alm EJ, Pupko T. Transfer of noncoding DNA drives regulatory rewiring in bacteria. Proc Natl Acad Sci. 2014; 111(45):16112–7.PubMedView ArticleGoogle Scholar
  47. Čuklina J, Hahn J, Imakaev M, Omasits U, Förstner KU, Ljubimov N, Goebel M, Pessi G, Fischer H-M, Ahrens CH, Gelfand M, E E-H. Genome-wide transcription start site mapping of Bradyrhizobium japonicum grown free-living or in symbiosis – a rich resource to identify new transcripts, proteins and to study gene regulation. BMC Genomics. 2016; 17(1):302.PubMedPubMed CentralView ArticleGoogle Scholar
  48. Smirnov A, Schneider C, Hör J, Vogel J. Discovery of new RNA classes and global RNA-binding proteins. Curr Opin Microbiol. 2017; 39:152–60.PubMedView ArticleGoogle Scholar
  49. Bochkareva OO, Dranenko NO, Ocheredko ES, Kanevsky GM, Lozinsky YN, Khalaycheva VA, Artamonova II, Gelfand MS. Genome rearrangements and phylogeny reconstruction in Yersinia pestis. PeerJ. 2018; 6:4545.View ArticleGoogle Scholar
  50. Cossu M, Badel C, Catchpole R, Gadelle D, Marguet E, Barbe V, Forterre P, Oberto J. Flipping chromosomes in deep-sea archaea. PLoS Genet. 2017; 13(6):1006847.View ArticleGoogle Scholar
  51. Repar J, Supek F, Klanjscek T, Warnecke T, Zahradka K, Zahradka D. Elevated rate of genome rearrangements in radiation-resistant bacteria. Genetics. 2017; 205(4):1677–89.PubMedPubMed CentralView ArticleGoogle Scholar
  52. Wang D, Li S, Guo F, Ning K, Wang L.Core-genome scaffold comparison reveals the prevalence that inversion events are associated with pairs of inverted repeats. BMC Genomics. 2017; 18(1):268.PubMedPubMed CentralView ArticleGoogle Scholar
  53. Zhang A, Yang M, Hu P, Wu J, Chen B, Hua Y, Yu J, Chen H, Xiao J, Jin M. Comparative genomic analysis of Streptococcus suis reveals significant genomic diversity among different serotypes. BMC Genomics. 2011; 12:523.PubMedPubMed CentralView ArticleGoogle Scholar
  54. Plumptre C, Ogunniyi A, Paton J. Polyhistidine triad proteins of pathogenic streptococci. Trends Microbiol. 2012; 20(10):485–93.PubMedView ArticleGoogle Scholar
  55. Mulkidjanian AY, Makarova KS, Galperin MY, Koonin EV. Inventing the dynamo machine: the evolution of the F-type and V-type ATPases. Nat Rev Microbiol. 2007; 5(11):892.PubMedView ArticleGoogle Scholar
  56. Donati C, Hiller NL, Tettelin H, Muzzi A, Croucher NJ, Angiuoli SV, Oggioni M, Hotopp JCD, Hu FZ, Riley DR, et al. Structure and dynamics of the pan-genome of streptococcus pneumoniae and closely related species. Genome Biol. 2010; 11(10):107.View ArticleGoogle Scholar
  57. Vernikos G, Medini D, Riley DR, Tettelin H.Ten years of pan-genome analyses. Curr Opin Microbiol. 2015; 23:148–54.PubMedView ArticleGoogle Scholar
  58. McInerney JO, McNally A, O’Connell MJ. Why prokaryotes have pangenomes. Nat Microbiol. 2017; 2:17404.Google Scholar
  59. Losada L, Ronning CM, DeShazer D, Woods D, Fedorova N, Stanley Kim H, Shabalina SA, Pearson TR, Brinkac L, Tan P, et al. Continuing evolution of Burkholderia mallei through genome reduction and large-scale rearrangements. Genome Biol Evol. 2010; 2:102–16.PubMedPubMed CentralView ArticleGoogle Scholar
  60. Zhang J, Yang J-R. Determinants of the rate of protein sequence evolution. Nat Rev Genet. 2015; 16(7):409.PubMedPubMed CentralView ArticleGoogle Scholar
  61. Marek A., Tomala K.The contribution of purifying selection, linkage, and mutation bias to the negative correlation between gene expression and polymorphism density in yeast populations. Genome Biol Evol. 2018; 10(11):2986–96.PubMedPubMed CentralGoogle Scholar
  62. Koonin EV. Are there laws of genome evolution?. PLoS Comput Biol. 2011; 7(8):1002173.View ArticleGoogle Scholar
  63. Thorpe HA, Bayliss SC, Hurst LD, Feil EJ. Comparative analyses of selection operating on non-translated intergenic regions of diverse bacterial species. Genetics. 2017; 206(1):363–376.PubMedPubMed CentralView ArticleGoogle Scholar
  64. Molina N, Van Nimwegen E. Universal patterns of purifying selection at noncoding positions in bacteria. Genome Res. 2008; 18(1):148–60.PubMedPubMed CentralView ArticleGoogle Scholar
  65. Koonin EV. Horizontal transfer beyond genes. Proc Natl Acad Sci. 2014; 111(45):15865–6.PubMedView ArticleGoogle Scholar
  66. Thorpe HA, Bayliss SC, Sheppard SK, Feil EJ. Piggy: a rapid, large-scale pan-genome analysis tool for intergenic regions in bacteria. GigaScience. 2018; 7(4):015.View ArticleGoogle Scholar
  67. Eisen JA, Heidelberg J, White O, Salzberg S. Evidence for symmetric chromosomal inversions around the replication origin in bacteria. Genome Biol. 2000; 1:0011.View ArticleGoogle Scholar
  68. Suyama M, Bork P.Evolution of prokaryotic gene order: genome rearrangements in closely related species. Trend Genet. 2001; 17:10–3.View ArticleGoogle Scholar
  69. Tillier E, Collins R. Genome rearrangement by replication-directed translocation. Nat Genet. 2000; 26:195–7.PubMedView ArticleGoogle Scholar
  70. Darling AE, Miklós I, Ragan MA. Dynamics of genome rearrangement in bacterial populations. PLoS Genet. 2008; 4(7):1000128.View ArticleGoogle Scholar
  71. Nakagawa I, Kurokawa K, Yamashita A, Nakata M, Tomiyasu Y, Okahashi N, Kawabata S, Yamazaki K, Shiba T, Yasunaga T, Hayashi H, Hattori M, Hamada S. Genome sequence of an m3 strain of Streptococcus pyogenes reveals a large-scale genomic rearrangement in invasive strains and new insights into phage evolution. Genome Res. 2003; 13:1042–55.PubMedPubMed CentralView ArticleGoogle Scholar
  72. Makino S, Suzuki M. Bacterial genomic reorganization upon dna replication. Science. 2001; 292(5518):803.PubMedView ArticleGoogle Scholar
  73. Mackiewicz P, Mackiewicz D, Kowalczuk M, Cebrat S. Flip-flop around the origin and terminus of replication in prokaryotic genomes. Genome Biol. 2001; 2(12):1004.View ArticleGoogle Scholar
  74. Repar J, Warnecke T.Non-random inversion landscapes in prokaryotic genomes are shaped by heterogeneous selection pressures. Mol Biol Evol. 2017; 34(8):1902–11.PubMedPubMed CentralView ArticleGoogle Scholar
  75. Li J, Li J, Feng Z, Wang J, An H, Liu Y, Wang Y, Wang K, Zhang X, Miao Z, Liang W, Sebra R, Wang G, Wang W, Zhang J. Epigenetic switch driven by DNA inversions dictates phase variation in Streptococcus pneumoniae,. PLoS Pathog. 2016; 12(7):1005762.View ArticleGoogle Scholar
  76. Slager J, Aprianto R, Veening J-W.Deep genome annotation of the opportunistic human pathogen Streptococcus pneumoniae d39. Nucleic Acids Res. 2018; 46(19):9971–89.PubMedPubMed CentralGoogle Scholar
  77. Yun K, Lee H, Choi E, Lee H.Diversity of pneumolysin and pneumococcal histidine triad protein d of Streptococcus pneumoniae isolated from invasive diseases in korean children. PLoS ONE. 2015; 10(8):0134055.View ArticleGoogle Scholar
  78. Kunin V, Ouzounis C. The balance of driving forces during genome evolution in prokaryotes. Genome Res. 2003; 13(7):1589–94.PubMedPubMed CentralView ArticleGoogle Scholar
  79. Ochman H. Distinguishing the ORFs from the ELFs: short bacterial genes and the annotation of genomes. Trends Genet. 2002; 18(7):335–7.PubMedView ArticleGoogle Scholar
  80. Andam CP, Hanage WP. Mechanisms of genome evolution of Streptococcus. Infect Genet Evol. 2015; 33:334–42.PubMedView ArticleGoogle Scholar
  81. Griffith F.The significance of Pneumococcal types. J Hyg. 1928; 27(2):113–59.PubMedView ArticleGoogle Scholar
  82. Avery OT, MacLeod CM, McCarty M. Studies on the chemical nature of the substance inducing transformation of Pneumococcal types: Induction of transformation by a desoxyribonucleic acid fraction isolated from Pneumococcus type III. J Exp Med. 1944; 79(2):137–58.PubMedPubMed CentralView ArticleGoogle Scholar

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