Background

DNA strands in most prokaryotic genomes often experience strand-biased spontaneous mutations, especially in protein coding regions, which occur preferentially in the leading strand during DNA replication [http://www.gem.re.kr/.

2.3 Genomic and evolutionary distances

The genomic distances between GIs and sGCSs were calculated using their genomic coordinates. For each GI, the distance to the sGCSs was determined by the nearest sGCS. To compare genomic distances between different species, instead of using physical distances, we obtained relative distances by dividing them with the length of each genome. This way, relative distances in different genomes are on the same scale (0 to 1) and are thus mutually comparable. GI homologues were obtained by searching evolutionarily highly-correlated bacterial genomes. GIs found in at least two strains were selected for analysis. For each pair, the BLASTN algorithm was used to evaluate their similarity. GIs with ≥ 80% overlap to each other were considered pairs of homologues. Evolutionary distance between each pair was obtained by the sequence similarity distance in the HKY85 model using PAUP [23, 24]. The matrix of distances was parsed to obtain a list of evolutionary distances. Next, correlations between evolutionary distances between homologous GIs and their corresponding genomic distances were calculated with R. A phylogenic tree was also constructed via the neighbor joining method using PAUP.

Results

3.1 Identifying special features in bacterial genomes: switch signals of GC skews and GIs

The dataset used for this study includes 1090 bacterial chromosomes (from 1009 bacterial species) as samples and 83 chromosomes (from 79 archaeal species) as controls. In a previous study, sGCSs were used to predict origins (ori) or termini (ter) of replication, as well as gene density and purine excess (C+T-G-A) [5]. It was hypothesized that sGCSs may be important signals for genome bias. In this study, we investigated sGCSs for specific GC content-related genomic features, using 2-kb sliding windows with 1-kb steps along the various genomes. We found that most of the bacteria, such as Firmicutes, Proteobacteria, and Bacteroidetes, contain much fewer sGCSs in their genomes compared to archaea (Table 1). For further comparison, we counted the number of bacteria and Archaea with different numbers of sGCSs (i.e., 2, 4-8, and ≥ 10, Table 1). In the bacteria group, most genomes contain less than eight sGCSs and show a simplified switch model of compositional bias (e.g., Bacteroidetes (24/25, 96%) and Firmicutes (188/188, 100%)) (Table 1). However, in ancient bacterial genomes, the number of sGCSs is seldom fewer than eight. For example, six of seven Aquificae strains have more than eight sGCSs, while 53% of Actinobacteria and 44% of Cyanobacteria have more than eight (see Table 1).

Table 1 Distribution of sGCSs in different phyla.
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The acquisition of foreign DNA may modify compositional bias, and GC content change is a predominant outcome of this process. Another outcome of foreign DNA insertion is the appearance of GIs, which may change the virulence or function of the host strain (Figure 1D). In this study, we calculated GC content deviations for all the bacterial genomes. Then, we searched the genomic sequence for GIs by identifying the genomic segments with GC contents significantly different from the mean value of the genome (i.e., greater than three times the standard deviation). From all of the genomes analyzed, 20,541 GIs were detected, according to the above criteria, with lengths from 2 to 80 kb, depending on the size of the sliding window used.

3.2 GIs are located next to sGCSs

Bacterial genomes exhibit strong sGCSs signals, which is easy to understand because the genomes of different strains often share one replicon (Figure 2 AB). For a better comparison, we aligned all the genomes at the ori, and calculated relative genomic positions by dividing them with the length of each genome. sGCSs and pGIs were then plotted according to their relative genomic positions. When aligned at the origin and marked with relative distances, the genomes had an overrepresentation of sGCSs at 1/3, 1/2, and 3/4 marks. (Figure 2 AB). Furthermore, we found that aside from their special distribution (Figure 2 A), sGCSs are closely correlated with GIs. These GIs are thought to have come from lateral gene transfer (LGT) events between different species but not from vertical inheritance due to their different genomic features. Based on the correlation between sGCSs and GIs, we suspect that sGCS regions are hotspots for horizontal DNA transfer in bacterial genomes,

Figure 2
figure 2

Distribution of GI, sGCS, and PAIs in the genome. (A) Scatter plot of the positions of GIs vs. sGCSs. For each genome, we coupled the positions of sGCSs and GIs. (B) Distribution of sGCSs, GIs, and PAIs in the genome. (C) Frequency of Ds along the genome with different sGCSs groups. (D) Gene classification according to COG functions in GIs (red) and all of the genomes. For each category: A, RNA processing and modification; B, Chromatin structure and dynamics; C, Energy production and conversion; D, Cell cycle control, cell division, chromosome partitioning; E, Amino acid transport and metabolism; F, Nucleotide transport and metabolism; G, Carbohydrate transport and metabolism; H, Coenzyme transport and metabolism; I, Lipid transport and metabolism; J, Translation, ribosomal structure, and biogenesis; K, Transcription; L, Replication, recombination, and repair; M, Cell wall/membrane/envelope biogenesis; N, Cell motility; O, Posttranslational modification, protein turnover, chaperones; P, Inorganic ion transport and metabolism; Q, Secondary metabolites biosynthesis, transport and catabolism; R, General function prediction only; S, Function unknown; T, Signal transduction mechanisms; U, Intracellular trafficking, secretion, and vesicular transport; V, Defense mechanisms; and W, Cytoskeleton.

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The diversified frequency of sGCSs and variation of GC skews in different genomes usually indicate different replication mechanisms. To investigate the relationship between sGCSs frequency and replication mechanisms, we separated the genomes in the study into several groups according to their sGCS numbers. For example, in most typical Firmicutes (i.e., gram-positive bacteria), such as S. suis, replicons often display specific patterns and can therefore be easily detected in the genome. Firmicutes' sGCSs are most often located at the replication ori/ter and the middle of the genomes. Therefore, the number of sGCSs is usually two. In some strains used in industry, such as Streptomyces avermitilis, the number of sGCSs is often greater than two because these strains employ different replication mechanisms. Furthermore, in bacteria such as Yersinia pestis KIM and Y. pestis 91001, sGCS distributions vary significantly due to large scale genome rearrangements, duplications, and insertions. Notably, we found that the appearance of GIs near sGCSs is not impacted by these replication mechanisms and rearrangements. After categorizing the genomes according to their sGCS numbers, we found that for all categories, GIs are highly enriched in the sGCS flanking regions (Figure 2C).

Recently acquired GIs were found in a significant number of pathogen isolates [21, 25]. Example of such PAIs are VSP I and II in V. cholerae, which are only found in the Vibrio seventh pandemic. LEE, a well-known GI in Escherichia coli O157, encodes structural, accessory, effector, and regulatory molecules and is located near to ter sites [25]. An additional 87-kb O island 48 (OI-48) is found in O157:H7 strains, EDL933, and Sakai, which is associated with tellurite-resistance. Our analysis successfully identified these GIs, demonstrating the validity of our approach. Another example of this type of recently acquired island is a 89-kb genome fragment in S. suis that contains zeta-toxin, a two-component signal transduction system, and three ABC transporter cassettes [21]. Again, these islands with genes related to the toxins and infectivity of pathogens are all located near sGCSs, indicating the correlations between GIs and sGCSs.

3.3 Based on a phylogenetic analysis, newer GIs are more likely to occur closer to sGCSs

To identify the origins of the GIs examined, we clustered the 14,921 pGIs from the 1009 bacterial genomes into 158 groups and then conducted a large-scale phylogenetic analysis. Our analysis revealed that the evolutionary distances of GIs are highly correlated with their genomic positions. Two distances, the physical distance between a pGI to the closest sGCS (Ds) and the evolutionary distance (D e ) between two homologus pGI, were calculated. For each homologue group, we plotted these two distances. To study the correlation between Ds and D e , we performed regression analysis on the two distances (Figure 3). For the genomes with two sGCSs, we saw a clear pattern. The plot of Ds vs. De reveals a positive correlation (correlation = 0.818) in 0-25% genomic regions and a negative correlation (correlation = -0.762) 25-50% regions (Figure 3). These results show that for the pGIs near sGCSs (0-50%), the correlation is statistically significant. The results agree with recent acquisitions of these genomic islands, which were horizontally transferred into the susceptible regions of the genomes recently and are therefore closer to sGCSs. However, when the distance of a pGI to the nearest sGCS is greater than 25% of the distance in the genomes with two sGCSs, the correlation is reversed, (i.e., the evolutionary distance is reduced with the increasing of the physical distance from the sGCS). This observation indicates that when GIs were inserted in genomic regions far from sGCSs, positive correlations between physical distances and evolutionary distances no longer hold. However, we did not find clear patterns for genomes with more than two sGCSs.

Figure 3
figure 3

Correlation between GI evolutionary distance and relative genomic distance. For each GI group, relative genomic distance and evolutionary distance were calculated. Along the relative genomic distance, average evolutionary distance were calculated. Average evolutionary distance was then plotted against relative genomic distance to reveal the correlation between relative genomic distance and evolutionary distance.

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The phylogenic analysis of all of the GI groups also suggests the correlation between Ds and De. For example, the well-known toxin co-regulated pilus (TCP) GI, found in four strains (N16961, MJ-1236, M66-2, and O395) is located at 43.40, 43.58, 44.64, and 49.07% in the genomes, respectively. We used N16961 as a standard for normalization and obtained evolutionary distances for the other three strains (0, 0, 0.00002, and 0.0003). Again, we observed a strong correlation between Ds and De, indicating that in highly conserved genomes, the physical distances of GIs to sGCSs are highly correlated with the evolutionary distances between them.

Discussion

Virulence properties of particular strains within a species are often associated with the presence of specific horizontally acquired genetic elements [21]. The Human Haplotype Project has identified the vast majority of conserved genome fragments, which separate the human genome into numerous blocks [26, 27]. Recently, a similar study on Y. pestis revealed that the mosaic structure of these blocks also exists in bacterial genomes [28]. The boundaries of the blocks are thought to be hotspots of recombination and insertion. For example, the major histocompatibility complex (MHC) is located between such blocks [29]. Our study sheds light on the hotspots in genomes for GI insertion using a large scale comparative genomic method. Our results suggest that GIs are likely to be inserted at the block boundaries of genomes of bacteria and other microbes, and sGCSs in these genomes are common separation spots for such blocks.

Via a phylogenetic analysis of each pGI and its homologues, we obtained the evolutionary distance for each pair of homologous pGIs. After studying the correlation between Ds and De, we found that they are positively correlated in regions closer to sGCSs (0-25%), while the correlation is reversed in more distal regions (25 - 50%). The turning point is near 25% region for geomes with two sGCSs. The mechanism underlying this phenomenon is currently unclear but may be caused by genomic rearrangements or deletions.

In human pathogens, many PAIs are found in GIs, such as VSP I and II in V. cholerae. However, generally speaking, PAIs and GIs refer to different genomic features. On the one hand, PAIs are sometimes evaluated by sequence similarity in other species, and these PAIs do not display abnormal GC content. Additionally, not all GIs are associated with pathogens. For example, in E. coli CTF073, none of the four abnormal GC content regions matches PAIs. These PAIs are different from typical PAIs due to special genomic rearrangement mechanisms. According to our observations, only laterally transferred GIs and newly acquired GIs are found near sGCSs. Notably, these types of horizontally transferred GIs were discovered in recent emerging infectious diseases and proven to enhance virulence or adaption of such strains [21, 30]. Therefore, GIs are of great importance in revealing the mechanisms of certain epidemic diseases. From the observation that GIs are likely to be inserted at genomic block boundaries, we propose that important virulence factors, which are associated with the outbreaks of many common diseases and/or enhanced virulence can be found near sGCSs.

Conclusion

In this study, in order to do a large scale study on the properties of genomic island, we used 1090 bacterial chromosomes (from 1009 bacterial species) as samples and 83 chromosomes (from 79 archaeal) as controls and separated them into three groups (sCGSs < = 2; 4 < = sCGSs < = 8; sCGSs > = 10) according to the number sCGSs. Interestingly, most of bacteria genomes contain less than 8 sCGSs, while archaeal genomes often contain more than 8 sCGSs. We then searched the genomic sequence for GIs by identifying the genomic segments with GC contents significantly different from the mean value of the genome and detected 20,541 GIs. We separated the GIs into different homolog groups and studied the correlation between relative genomic distance and evolution distance and found that sGCS regions are hotspots for horizontal DNA transfer in bacterial genomes. Since this is the first time for such an important property to be revealed by a large scale comparative genomic method, we believe our finding is of great importance for predicting both genomic island and their insertion sites.