Comparative genomics of gene-family size in closely related bacteria.

BACKGROUND: The wealth of genomic data in bacteria is helping microbiologists understand the factors involved in gene innovation. Among these, the expansion and reduction of gene families appears to have a fundamental role in this, but the factors influencing gene family size are unclear.
RESULTS: The relative content of paralogous genes in bacterial genomes increases with genome size, largely due to the expansion of gene family size in large genomes. Bacteria undergoing genome reduction display a parallel process of redundancy elimination, by which gene families are reduced to one or a few members. Gene family size is also influenced by sequence divergence and physiological function. Large gene families show wider sequence divergence, suggesting they are probably older, and certain functions (such as metabolite transport mechanisms) are overrepresented in large families. The size of a given gene family is remarkably similar in strains of the same species and in closely related species, suggesting that homologous gene families are vertically transmitted and depend little on horizontal gene transfer (HGT).
CONCLUSIONS: The remarkable preservation of copy numbers in widely different ecotypes indicates a functional role for the different copies rather than simply a back-up role. When different genera are compared, the increase in phylogenetic distance and/or ecological specialization disrupts this preservation, albeit in a gradual manner and maintaining an overall similarity, which also supports this view. HGT can have an important role, however, in nonhomologous gene families, as exemplified by a comparison between saprophytic and enterohemorrhagic strains of Escherichia coli.

Background

One of the unexpected revelations of prokaryotic genomes has been the existence of significant gene redundancy. The existence of multiple gene copies in eukaryotes has been known for a long time and is considered an important element in their molecular evolution [1,2]. In pre-genomic times, however, bacteria were considered to be streamlined cells that carried very little, if any, redundant information in their genomes. It therefore came as a surprise when the genome of Escherichia coli K12 showed that nearly 30% of the coding sequences could be grouped into gene families that were similar enough to be assigned similar functions [3,4]. They were described as 'paralog' gene families, with the implicit assumption that their similarity reflected similar evolutionary descent, but actual or potential functional divergence. Since then, the presence of gene families typically containing between two and 30 copies has been described for nearly every prokaryotic genome sequenced. The number of paralogous genes and families appears to correlate well with an increase in genome size [5,6]. The relative contribution of these genes in each genome seems to be independent of phylogenetic affiliation and, for a limited dataset, appears to depend on genome size [7].

These gene families of diverse size and degree of similarity remain an important and little explored feature of prokaryotes. In eukaryotic genomes they are generally taken as the result of gene duplication. This would either supply the required gene dosage or the raw material for adaptation by mutation and selection acting on one of the copies that diverges in properties or function [1,8]. In E. coli, a model organism in which traditional genetics and physiology have already allowed the unequivocal identification of more than half of the coding genes, the role of paralog families (whatever their origin) seems much more operational than in eukaryotes [4]. For example, the different members of a gene family contribute the proper gene dosage or, most often, provide different specificities for similar chemical reactions or for other processes such as transport of different molecules. Regarding origin, duplication is not necessarily the only source for new members of a gene family in prokaryotes. The gene pools are known to vary enormously from one strain to another [9,10], and horizontal gene transfer (HGT) acts as a powerful source of innovation [11]. Therefore, HGT could provide gene families with members already divergent in sequence and function [12]. In prokaryotes, gene families could be the result of incomplete xenologous gene replacement by which a gene from another genome gets incorporated into a gene family with which it shares some sequence similarity. This process would provide additional physiological plasticity, and studies on the DNA composition of paralogous genes suggest that its contribution might be substantial [13]. The divergence of some of the members of the gene families or their DNA composition could be taken as evidence for a HGT origin [4]. It is unclear at the moment the extent to which each of these genomic forces (gene duplication and HGT) contributes to genome expansion and variability [5,14-16].

To address these issues we have compared the size of gene families across bacterial taxa. To try to shed light on the evolutionary origin of these initially redundant genes we have studied the distribution of gene family size among completed genomes of strains within the same bacterial species and over larger taxonomic distances. If the different family members were acquired by HGT their numbers will vary widely among different strains, as already detected for single genes in adaptive islands [17] or for whole families predicted to have been transferred as a whole [18]. On the other hand, if the family numbers are similar in different strains, vertical descent or a very old HGT will be a more likely origin. We have also determined the contribution of paralogous families to genome size for all 127 available eubacterial genomes, updating earlier work on a more limited dataset [7]. We have also tried to identify other factors affecting the number of members in a family, besides genome size, particularly sequence divergence, gene function and species lifestyle.

Results and discussion

Gene family size in bacterial genomes

Previous work on a more reduced set of sequenced genomes had determined that large genomes contain more paralogs and more gene families than smaller genomes [7]. Jordan and collaborators also found a correlation between the fraction of the genome occupied by gene families and the genome size; that is, larger genomes had a larger proportion of redundant genes. However, at the time of that analysis, the sequences of genomes larger than 5 million base pairs (5 Mbp) were not available. Now, the inclusion of genomes nearly twice as large confirms both trends (Figure 1): for example, nearly 50% of the genome is occupied by paralogous genes in Streptomyces coelicolor. A closer look at these data shows that larger genomes have larger gene families, as the average family size also increases with genome size (Figure 1, inset). Thus, the higher percentage of paralogs in large genomes is partly due to the expansion of existing gene families, together with a larger number of new families. The large-genomed species at one end of the distribution, such as Streptomyces, have gene families of up to 85 members, whereas the largest gene families in middle-sized genomes such as those of E. coli or Salmonella have more moderate numbers (40-45). This is reminiscent of the situation in eukaryotes, where the number of gene families increases with the number of genes in the genome at a lower rate than in prokaryotes [6], indicating that gene families have many more members in the larger eukaryotic genomes. Also consistent with this trend, some reconstructions of prokaryotic genome evolution based on gene content conclude that gene duplication has a critical role in the expansion of genome size [15].

Figure 1

Relationship between percentage of genes belonging to paralogous families plotted versus genome size in 127 eubacterial genomes. Inset shows the average gene family size versus genome size for the same genomes, except Shigella flexneri, Bordetella pertusis, B. parapertussis and B. bronchiseptica, which contain a high number of IS elements. Some genomes with atypical values are identified: Mpn, Mycoplasma pneumoniae; Mpt, Mycoplasma penetrans; Mga, Mycoplasma gallisepticum; Mlp, Mycobacterium leprae; Pir, Pirellula sp.

Exceptions to the linear correlation in this graph are interesting to consider. On one hand, Pirellula (marked as Pir in Figure 1) has an enormous genome with a surprisingly low relative number of paralogs. This is due to an overrepresentation of small gene families and the absence of large ones (the largest gene family contains 57 members; see Additional data file 1). Pirellula is a marine bacterium and the reason for the reduced gene family size might be the homogeneity of the marine environment, in contrast to other large-genomed bacteria included in the graph which have the ability to survive in many different niches or in much more heterogeneous habitats, such as soil. In agreement with this, Pirellula has a greatly reduced number of transcriptional regulators, which again might reflect a relatively constant environment [19]. At the other end of the distribution, exceptions occur for three species that have small genomes with a larger-than-expected percentage of paralogs. All these species are mycoplasmas, and the high percentage of paralogs is due to a few gene families that are greatly expanded, including more than 25 members. In Mycoplasma penetrans, for example, these families include surface-exposed lipoproteins involved in antigenic variation [20], which are critical to the success of microbes exposed to the immune system of their hosts. On the other hand, the small genomes of other pathogenic bacteria correspond to intracellular parasites that do not need to evade the immune system [21], and these species show the smallest portion of paralogs. Finally, the largest gene families that we detected were those involving mobile genetic elements such as the IS elements of Shigella flexneri, where families surpassed 100 members (not included in the inset of Figure 1).

The data in Figure 1 cannot be viewed as a continuum, because small genomes are not ancestral to bigger ones. Instead, small genomes have been shown to be the result of reductive evolution, a process by which a larger-sized ancestor changes niche and undergoes a dramatic loss of DNA [22,23]. Both small and large genome fragments can be eliminated but the outcome of this process for gene families has not been documented. We have compared the number of members per gene family in two genomes that are undergoing rapid reductive evolution - Shigella flexneri 2a and Mycobacterium leprae TN - with larger-genomed close relatives (Figure 2). Shigella is a close relative of E. coli that has specialized in living as a human pathogen [24,25]. As a result of the expansion of the human population from Neolithic times a number of more generalistic or opportunistic pathogens found a new niche; Salmonella typhi might be a similar example [26]. In both cases there is a clear tendency to genome reduction accompanied by expansion of IS families (314 and 46 IS elements, respectively).

Figure 2

Gene family sizes in genomes undergoing reductive evolution compared to a phylogenetically related larger sequenced genome. (a) Mycobacterium leprae (reductive) vs Mycobacterium tuberculosis H37Rv; (b) Shigella flexneri (reductive) vs Escherichia coli K12. Orthologous genes in the genome pairs (identified by amino-acid sequence similarity) are displayed in arbitrary order and plotted against the number of homologs in their own genome (that is, paralogs). Only protein-coding genes are included. IS elements from S. flexneri 2a are excluded.

In Shigella there is a clear reduction in gene family copy number (Figure 2), which seems to be higher than would be expected from the random location of IS elements, suggesting that they might insert preferentially in gene family members. Something similar is found in the case of M. leprae (Figure 3), although in this case the main mechanism for gene inactivation is the generation of pseudogenes by mutation [27]. M. leprae is closely related to M. tuberculosis, with which it shares many homologous sequences. However, most gene families have been simplified in the short time period in which the leprosy bacillus has adopted its mainly intracellular lifestyle. This also illustrates the fact that, as described above, an early step in genome reduction allowed by intracellular parasitism or a narrower range of hosts is the shrinkage of gene families. It shows that the smaller percentage of paralogs in reduced genomes is probably due to simplification of existing gene families. A similar pattern was found in the small-genomed intracellular species Rickettsia and Buchnera when compared with free-living species of the same taxonomic group (see Additional data file 2). Thus, both genome expansion and reduction can be partly explained by the parallel growth or simplification, respectively, of gene families.

Figure 3

The number of members in E. coli K12 gene families plotted versus mean sequence identity of pairwise comparisons among the members of each family.

Another feature we could detect in the evolution of gene families was that large families were more divergent (Figure 3). This could partly be due to a side-effect of the higher variability of a larger sample size or to misidentification of family members at low sequence identity levels. However, given the observed similarity of functions in these large families ([4,28] and R.P., A.M. and F.R-V., unpublished results), a substantial proportion must be true paralogous genes. Thus, this relationship can be interpreted as older (more divergent) families containing more members. Smaller families range from those with very similar members to those in which the members are very different. The latter probably represent either old families in which new members have not evolved because new duplications do not confer a selective advantage, or more recent incomplete xenologous replacements.

Gene family size in intraspecific and interspecific comparisons

The sequencing of several strains of a single species is now common in bacterial genomics. One of the most remarkable findings has been the different gene pools carried by strains that are highly similar if their housekeeping genes only are compared. For example, different virotypes of E. coli were shown to contain very different gene complements, with large pools of genes characteristic of each virotype [10]. Obvious candidates to vary would be multigene families. Thus, the comparison of the numbers of members within a single species might shed light in their origin. If the members of a gene family are frequently acquired by HGT from outside, the numbers should be expected to vary broadly in different lineages of the species (as a result of different acquisitions). On the other hand, if the numbers are similar, that would indicate that the families were already present in the common ancestor and represent a relatively stable feature of the genome.

We selected distinct prokaryotic taxa in which three or more strains have been fully sequenced (Escherichia coli, Streptococcus pyogenes, Staphylococcus aureus and Chlamydophila pneumoniae) and for each taxon established a list of homologous genes common to all strains. The gene family to which each homolog belonged was determined for each strain, and the number of family members compared for equivalent families (Figure 4). In all four species considered, the different strains showed a remarkably similar pattern of gene family size distributions: large gene families in one strain were also expanded in the others; small families were small, regardless of strain or virotype. Caution has to be exercised when examining these plots, as a gene can be a member of more than one gene family. However, although some of the gene families in Figure 4 are redundant, the parallel size pattern of gene families across strains is remarkably clear and seems to reflect a stable feature of the genome. Thus, the majority of gene families were most likely to have been formed by ancestral gene duplications or ancient gene transfers common to all strains. In addition, the preservation of gene family size in different strains strongly suggests that most family members have a high value for survival; redundant copies would otherwise be quickly eliminated.

Figure 4

Gene family sizes for homologous genes in groups of strains belonging to the same species, represented as in Figure 2. (a) Chlamydophila pneumoniae strains; (b) Streptococcus pyogenes strains; (c) Escherichia coli strains; (d) Staphylococcus aureus strains. Strain denomination and graph code displayed in the top right-hand corner. Only protein-coding genes are included. Zero on the y-axis indicates single-copy genes; 1 indicates a gene family formed of two members.

We have obviously not excluded the possibility that nonhomologous gene families add to the differences among the compared genomes. For example, in a pairwise comparison between E. coli K12 and E. coli O157:H7, 186 genes belonging to paralog families were unique to K12 and 788 to O157:H7, versus 403 singletons (single-copy genes not belonging to families) unique to K12 and 883 to O157:H7. Thus, K12 keeps the same standard proportion of 30% paralogs for the differential gene pool. In O157:H7, on the other hand, paralogs account for 47% of the set of unique genes. The interpretation might be that the large islands that characterize the genome of the enterohemorrhagic virotype tend to carry a bigger proportion of families than the rest of the genome. Thus, it is possible that in some strains, HGT may contribute to expand and generate gene families that do not appear as homologs in closely related genomes. For example, 146 genes belonging to families of 10 or more members were detected in the O157:H7 differential pool, including three whole families of 14, 17 and 20 members with a G+C content of 57, 54 and 53%, respectively (the average G+C content in E. coli O157:H7 is 50.6%). The largest differential family in K12 had 11 members, which were not present in the enterohemorrhagic strain, and had a G+C content of 54.1% (the average G+C content of E. coli K12 is 50.5%).

To investigate whether the conservation in the size of homologous families was maintained across more divergent genomes, gene family plots were performed between species. A representative case for a Gram-negative (Pseudomonas) and a Gram-positive (Bacillus) comparison is illustrated in Figure 5. The preservation of family size was still remarkable, although, in the case of Pseudomonas, the number of orthologous genes is considerable smaller. The overall pattern of family sizes is preserved across these species. The two Bacillus species considered have the same genome size and one species contains larger numbers in some families but fewer in others (Figure 5b). The same trend was found in comparisons between species of Staphylococcus, Streptococcus, Salmonella and Mycoplasma (data not shown). It is also interesting to analyze the variation detected. Part of it can be attributed to differences in genome size. Pseudomonas syringae is approximately 200 kb larger than its other sequenced partners, which have mostly smaller gene families. However, part of the variability is also due to intrinsic differences between the species. For example, P. syringae contains some large gene families involved in invasion of the plant host and in pathogenesis [29]. One way to examine whether this variation can underlie the phenotypic/ecological characteristics of a given species is to visualize the size difference of each paralog group for some representative cases.

Figure 5

Gene family sizes for homologous protein-coding genes in different species of the same genus. (a) Pseudomonas spp; (b) Bacillus spp. (c) Difference in the size of equivalent gene families between E. coli K12 and S. typhimurium LT2. Positive values indicate larger families in E. coli; negative values indicate larger families in S. typhymurium. The potG gene family is indicated.

Figure 5c shows the difference in gene family size in the interspecific comparison of E. coli K12 and S. typhimurium LT2. Both strains have similarly sized genomes (S. typhimurium is 218 kb larger) and a relatively high level of homology (3,026 orthologous genes). Of these, there are 572 homologs belonging to families that differ in size between the two genomes, and 435 belonging to families having the same number of members in both species. The rest are single-copy genes in both genomes. Forty-eight families were significantly larger (two or more extra copies) in E. coli, while 53 were larger in Salmonella. These differences can be taken as an example of the evolution of gene families in two diverging groups. Although the natural history of these model bacteria is not as well known as might be expected, it is generally believed that both Salmonella and Escherichia are mostly saprophytic facultative anaerobes that inhabit the intestine of vertebrates. The divergence between these two microbes arose after the origin of mammals around 120 million years ago. E. coli specialized as a commensal and an opportunistic pathogen of mammals, as witnessed, for example, by its ability to degrade lactose. On the other hand, Salmonella remains as a commensal in reptiles, with some serotypes colonizing mammals, but as a pathogen rather than a commensal and after developing strategies for intracellular invasion of the host [30,31]. Accepting this scenario, the fact that many gene families (and the number of members of each family) are preserved reflects a significant involvement in the saprophytic intestinal lifestyle, preserved over many millions of years. On the other hand, significant differences are starting to arise between the two species, perhaps reflecting their specialization in different hosts and lifestyles [32]. A dramatic example is the potG gene family, which has 13 more members in S. typhimurium than in E. coli (Figure 5). This is an ATP-binding component of spermidine/putrescine transport and for some reason its amplification has been selected in this species. Proteins involved in the transport of spermidine and putrescine have been shown to be involved in attachment to host cells and virulence [33]. Therefore, the size of this gene family might reflect the more pathogenic lifestyle of Salmonella.

Functional classification of gene families

Do certain functions predispose genes to form families? Do single genes that do not form families belong to a different category? To address these questions, extended gene families were identified, where a gene was not allowed to belong to more than one family. Thus, if gene A matched gene B, and gene B matched C, but A did not match C, all three were considered part of the same family, as it is likely that they are all evolutionarily derived from each other [34]. This method of transitive assembly of paralogs has been confirmed to include, in most cases, genes with related functions [4]. We found that, for all 127 sequenced species, singletons (genes in a single copy in a given genome) were massively overrepresented by genes with an unknown or hypothetical function. When only genes with a known or predicted function were included, these single genes without paralogs appeared equally distributed among the different functional categories. However, when genes belonging to families, especially large ones, were considered, a significant fraction had particular functions, such as transport of metabolites (data not shown). These data are, however, probably unrealistic because they represent the distribution of genes in sequenced genomes only, and certain species are overrepresented. In addition, larger genomes will also weigh more in this comparison than small genomes, as will species with several sequenced strains. We did, however, find relatively uniform results for individual genomes. Figure 6 shows such a distribution for two species, one Gram-negative (E. coli K12) and the other Gram-positive (B. subtilis). For E. coli, in which a large proportion of genes has been allocated a function, families with more than five members contain fewer unknown or hypothetical genes than do smaller families, and the distribution of functions among categories is unequal, with certain categories being overrepresented. Among these, genes involved in transport of different metabolites predominate (39% of the total), followed by those with transcription and replication/repair functions. In genes that do not belong to a family, however, most functional categories are equally represented and a large proportion of these singletons have an unknown function. The overrepresentation of unknown or hypothetical open reading frames (ORFs) could, in part, be due to many of these singletons not being real genes, as supported by their shorter length when compared to genes belonging to families. In the gamma-proteobacteria, for example, average singleton length is 127 nucleotides less than in genes belonging to families. It is also interesting to note that the phylogenetic distribution of these unknown singletons is not different from that of unknown paralogs (see Additional data file 4). In conclusion, some functions do appear to be more prone to develop families, although the functions overrepresented in a particular species may depend on its lifestyle.

Figure 6

Proportions of assigned functions among genes belonging to families and singletons in B. subtilis and E. coli K12. Gene functions were assigned according to the Cluster of Orthologous Genes (COGs) classification [41]. Extended gene families are considered, in which a gene belongs to a single family only (see Materials and methods).

Conclusions

In eukaryotic genomes, a cornerstone of gene creation is extension of paralogous families by gene duplication [2]. This is reflected in the slow increase of new gene families with genome size, which does correlate with an increase in the size of the families [6]. The importance of DNA duplication in eukaryotes is probably also favored by the limitations of HGT in this group [35]. Despite the pervasiveness of HGT in prokaryotes, the increase in gene families with genome size is also robust (Figure 1). One obvious fact contributing to this situation might be that the pool of essential genes that have to be present for basic cell biology represents a larger percentage of a smaller genome, restricting the contribution of redundant genes with related functions and thus more expendable. However, this does not explain the high level of correlation maintained at the larger end of the range.

Of course, with the number of genomes available presently there is a certain representation bias, with a large input from human pathogens. Among these, small genomes often correspond to intracellular forms that are protected from the immune system of the host. Variability of antigen specificity is one paradigmatic case that justifies gene familes in extracellular pathogens of vertebrates, for example the PPE genes of Mycobacterium [28] and the Pap adhesins in E. coli [36]. The exceptional case of the mycoplasmas points in this direction as they possess small genomes but are extracellular mucosa-associated pathogens, and hence subjected to the host immune system [37]. At the other end of the genome size range there are many more free-living, saprophytic or opportunistic pathogens, a lifestyle that requires a highly versatile gene complement in order to survive, for example, both inside and outside a host. Again, the one exception is a single large-genome species from a relatively stable environment (Pirellula, which lives in the open ocean). Here, the possibility to carry out many different physiological activities is probably more advantageous than the ability to adapt the same activity to a wider range of conditions. Thus, as with other aspects of biology, the genomic properties of bacteria appear to be greatly conditioned by their specialist or generalist lifestyle.

The comparison of gene family size among strains from a single species shows a remarkable level of conservation, even when genome sizes are very different. This conservation indicates that gene family size is probably an ancestral feature rather than reflecting the acquisition of paralogs by HGT. This is consistent with evolutionary models based on bacterial gene content, which concluded that most protein gene families are transmitted by vertical inheritance [16]. The conservation that is detected even among more distantly related taxa strengthens this view, as in mostly free-living and very niche-diversified species such as Pseudomonas, there is a remarkable degree of conservation. This might reflect involvement of the gene families in more fundamental (less environment-dependent) processes of cell biology.

Genomic evolution simulations concluded that the amount of gene duplication is independent of HGT levels [15]. On the basis of these simulations, an upper limit of 20% was estimated for paralogs of xenologous origin. Assuming that the extra members of a gene family from our paralog plots represent an upper limit of HGT for established families, we calculate that gene transfer accounts for a maximum of 11% of a given family in E. coli (Figure 4c). However, this does not take into account families that are unique to a given strain and that may have a xenologous origin. The fact that these families are not included in the paralog plots (which display only homolog pairs between strains) suggests that they can represent transfers to a given strain. Thus, the paralog plots present a picture of stability and limited xenologous genes for already established families, but this is not inconsistent with the transfer of families that appear to be unique to a given strain or species. It could, theoretically, be more probable that gene families expand by horizontal transfers than by gene duplication [12]. This way, xenologous genes would already confer a functionally distinct role and would avoid the neutrality period in which redundant gene copies coexist and can be eliminated [38]. The results shown here suggest that the overrepresentation of duplications among transferred genes found by Hooper and Berg [13] might be a feature of these specific families but not of more ancient, homologous ones.

Materials and methods

The protein sequences of the 127 completely sequenced eubacterial genomes at the time this paper was submitted for publication were retrieved from the Genome division, Entrez retrieval system of the National Center for Biotechnology Information (NCBI; [39]). Table 1 shows a list with all the genomes used, with their genome size and accession numbers. To detect potentially homologous genes we started by carrying out an all-against-all BLASTP [40] search of every protein sequence in one genome against every protein sequence in all the other genomes. We then recorded the best reciprocal hit for each protein sequence with an E-value lower than 10-5 and sequence identity higher than 50% over more than 60% of the length. To validate the results, we performed some representative comparisons by studying the distribution of the ratio of bit score to the maximal bit score [41]. This method would separate probable homology from random similarity. We obtained almost identical results, with only a reduced set of the respective homologous genes being different in the two lists. For example, out of 3,026 homolog pairs between E. coli K12 and S. typhimurium detected by the reciprocal hit method, only one pair was found to differ with the bit score method. In addition, only three genes were detected with the reciprocal best-hit method that were not selected as homologs using the bit score method (using a cut-off value of 0.4). Finally, the bit-score ratio method identified 165 additional homologs that were not selected using reciprocal best-hits because they did not satisfy the length and/or sequence-identity requirements. Therefore, the list of homologous genes obtained by reciprocal best-hits was used for all the analyses.

Table 1

Species used in the current work and their accession numbers

Species	Accession number	Genome size (bp)
Agrobacterium tumefaciens str. C58 (Cereon)	NC_003062	2,841,581
Agrobacterium tumefaciens str. C58 (U. Washington)	NC_003304	2,841,490
Aquifex aeolicus VF5	NC_000918	1,551,335
Bacillus anthracis str. Ames	NC_003997	5,227,293
Bacillus cereus ATCC 14579	NC_004722	5,411,809
Bacillus halodurans	NC_002570	4,202,353
Bacillus subtilis subsp. subtilis str. 168	NC_000964	4,214,814
Bacteroides thetaiotaomicron VPI-5482	NC_004663	6,260,361
Bifidobacterium longum NCC2705	NC_004307	2,256,646
Bordetella bronchiseptica	NC_002927	5,339,179
Bordetella parapertussis	NC_002928	4,773,551
Bordetella pertussis	NC_002929	4,086,189
Borrelia burgdorferi B31	NC_001318	910,724
Bradyrhizobium japonicum USDA 110	NC_004463	9,105,828
Brucella melitensis 16M	NC_003317	2,117,144
Brucella suis 1330	NC_004310	2,107,792
Buchnera aphidicola str. APS (Acyrthosiphon pisum)	NC_002528	640,681
Buchnera aphidicola str. Bp (Baizongia pistaciae)	NC_004545	615,980
Buchnera aphidicola str. Sg (Schizaphis graminum)	NC_004061	641,454
Campylobacter jejuni subsp. jejuni NCTC 11168	NC_002163	1,641,481
Candidatus Blochmannia floridanus	NC_005061	705,557
Caulobacter crescentus CB15	NC_002696	4,016,947
Chlamydia muridarum	NC_002620	1,072,950
Chlamydia trachomatis	NC_000117	1,042,519
Chlamydophila caviae GPIC	NC_003361	1,173,390
Chlamydophila pneumoniae AR39	NC_002179	1,229,858
Chlamydophila pneumoniae CWL029	NC_000922	1,230,230
Chlamydophila pneumoniae J138	NC_002491	1,226,565
Chlamydophila pneumoniae TW-183	NC_005043	1,225,935
Chlorobium tepidum TLS	NC_002932	2,154,946
Chromobacterium violaceum ATCC 12472	NC_005085	4,751,080
Clostridium acetobutylicum	NC_003030	3,940,880
Clostridium perfringens str. 13	NC_003366	3,031,430
Clostridium tetani E88	NC_004557	2,799,251
Corynebacterium diphtheriae	NC_002935	2,488,635
Corynebacterium efficiens YS-314	NC_004369	3,147,090
Corynebacterium glutamicum ATCC 13032	NC_003450	3,309,401
Coxiella burnetii RSA 493	NC_002971	1,995,275
Deinococcus radiodurans	NC_001263	2,648,638
Enterococcus faecalis V583	NC_004668	3,218,031
Escherichia coli CFT073	NC_004431	5,231,428
Escherichia coli K12	NC_000913	4,639,221
Escherichia coli O157:H7	NC_002695	5,498,450
Escherichia coli O157:H7 EDL933	NC_002655	5,528,445
Fusobacterium nucleatum subsp. nucleatum ATCC 25586	NC_003454	2,174,500
Gloeobacter violaceus	NC_005125	4,659,019
Haemophilus ducreyi 35000HP	NC_002940	1,698,955
Haemophilus influenzae Rd	NC_000907	1,830,138
Helicobacter hepaticus ATCC 51449	NC_004917	1,799,146
Helicobacter pylori 26695	NC_000915	1,667,867
Helicobacter pylori J99	NC_000921	1,643,831
Lactobacillus plantarum WCFS1	NC_004567	3,308,274
Lactococcus lactis subsp. lactis	NC_002662	2,365,589
Leptospira interrogans serovar lai str. 56601	NC_004342	4,332,241
Listeria innocua	NC_003212	3,011,208
Listeria monocytogenes EGD-e	NC_003210	2,944,528
Mesorhizobium loti	NC_002678	7,036,074
Mycobacterium bovis subsp. bovis AF2122/97	NC_002945	4,345,492
Mycobacterium leprae	NC_002677	3,268,203
Mycobacterium tuberculosis CDC1551	NC_002755	4,403,836
Mycobacterium tuberculosis H37Rv	NC_000962	4,411,529
Mycoplasma gallisepticum R	NC_004829	996,422
Mycoplasma genitalium	NC_000908	580,074
Mycoplasma penetrans	NC_004432	1,358,633
Mycoplasma pneumoniae	NC_000912	816,394
Mycoplasma pulmonis	NC_002771	963,879
Neisseria meningitidis MC58	NC_003112	2,272,351
Neisseria meningitidis Z2491	NC_003116	2,184,406
Nitrosomonas europaea ATCC 19718	NC_004757	2,812,094
Nostoc sp. PCC 7120	NC_003272	6,413,771
Oceanobacillus iheyensis HTE831	NC_004193	3,630,528
Pasteurella multocida	NC_002663	2,257,487
Photorhabdus luminescens subsp. laumondii TTO1	NC_005126	5,688,987
Pirellula sp.	NC_005027	7,145,576
Porphyromonas gingivalis W83	NC_002950	2,343,476
Prochlorococcus marinus str. MIT 9313	NC_005071	2,410,873
Prochlorococcus marinus subsp. marinus str. CCMP1375	NC_005042	1,751,080
Prochlorococcus marinus subsp. pastoris str. CCMP1378	NC_005072	1,657,990
Pseudomonas aeruginosa PA01	NC_002516	6,264,403
Pseudomonas putida KT2440	NC_002947	6,181,863
Pseudomonas syringae pv. tomato str. DC3000	NC_004578	6,397,126
Ralstonia solanacearum	NC_003295	3,716,413
Rickettsia conorii	NC_003103	1,268,755
Rickettsia prowazekii	NC_000963	1,111,523
Salmonella enterica subsp. enterica serovar Typhi	NC_003198	4,809,037
Salmonella enterica subsp. enterica serovar Typhi Ty2	NC_004631	4,791,961
Salmonella typhimurium LT2	NC_003197	4,857,432
Shewanella oneidensis MR-1	NC_004347	4,969,803
Shigella flexneri 2a str. 2457T	NC_004741	4,599,354
Shigella flexneri 2a str. 301	NC_004337	4,607,203
Sinorhizobium meliloti	NC_003047	3,654,135
Staphylococcus aureus subsp. aureus MW2	NC_003923	2,820,462
Staphylococcus aureus subsp. aureus Mu50	NC_002758	2,878,040
Staphylococcus aureus subsp. aureus N315	NC_002745	2,814,816
Staphylococcus epidermidis ATCC 12228	NC_004461	2,499,279
Streptococcus agalactiae 2603V/R	NC_004116	2,160,267
Streptococcus agalactiae NEM316	NC_004368	211,485
Streptococcus mutans UA159	NC_004350	203,0921
Streptococcus pneumoniae R6	NC_003098	2,038,615
Streptococcus pneumoniae TIGR4	NC_003028	2,160,837
Streptococcus pyogenes M1 GAS	NC_002737	1,852,441
Streptococcus pyogenes MGAS315	NC_004070	1,900,521
Streptococcus pyogenes MGAS8232	NC_003485	1,895,017
Streptococcus pyogenes SSI-1	NC_004606	1,894,275
Streptomyces avermitilis MA-4680	NC_003155	9,025,608
Streptomyces coelicolor A3(2)	NC_003888	8,667,507
Synechococcus sp. WH 8102	NC_005070	2,434,428
Synechocystis sp. PCC 6803	NC_000911	3,573,470
Thermoanaerobacter tengcongensis	NC_003869	2,689,445
Thermosynechococcus elongates BP-1	NC_004113	2,593,857
Thermotoga maritima	NC_000853	1,860,725
Treponema pallidum	NC_000919	1,138,011
Tropheryma whipplei TW08/27	NC_004551	925,938
Tropheryma whipplei str. Twist	NC_004572	927,303
Ureaplasma urealyticum	NC_002162	751,719
Vibrio cholerae	NC_002505	2,961,149
Vibrio parahaemolyticus RIMD 2210633	NC_004603	3,288,558
Vibrio vulnificus CMCP6	NC_004459	3,281,945
Vibrio vulnificus YJ016	NC_005139	3,354,505
Wigglesworthia glossinidia (from Glossina brevipalpis)	NC_004344	697,724
Wolinella succinogenes	NC_005090	2,110,355
Xanthomonas axonopodis pv. citri str. 306	NC_003919	5,175,554
Xanthomonas campestris pv. campestris str. ATCC 33913	NC_003902	5,076,188
Xylella fastidiosa 9a5c	NC_002488	2,679,306
Xylella fastidiosa Temecula1	NC_004556	2,519,802
Yersinia pestis CO92	NC_003143	4,653,728
Yersinia pestis KIM	NC_004088	4,600,755

To detect potential paralogous genes, we carried out an all-against-all BLASTP [40] search of every protein sequence in a genome against every protein sequence in the same genome. We define paralogs as protein sequences satisfying an E-value threshold of 10-5 in BLASTP [40] search and having at least 30% sequence identity over more than 60% of their lengths [3].

When comparing paralogs between two species, a gene family was created for each homologous gene detected in both genomes. This gave rise to some redundant families but ensured that the comparison between species was done between equivalent gene families. To describe the functional assignment of paralogous genes, extended gene families were created [3] that contained all genes that were interrelated by hits among any of their members. This is based on the transitive nature of sequence homology [34] and is supported by the findings on well-studied genomes of species with a relatively well-known metabolism. In these cases, extended gene families seem to be formed by genes involved in similar functions [4]. To minimize the incorporation of multidomain proteins in a family together with unrelated members [2], length cut-offs were kept at 60%. The assignment of a function to a gene was based on the Clusters of Orthologous Groups (COGs) classification [42].

Additional data files

Additional data file 1 is a PDF file of a figure showing the number of paralogs and the percentage of paralogous genes in the different-sized gene families in Pirelulla sp. compared to other large-sized genomes. Additional data file 2 is a PDF file of a figure showing gene family sizes in intracellular genomes that have undergone reductive evolution compared to related free-living organisms. Additional data file 3 contains legends to the figures in Additional data files 1 and 2. Additional data file 4 is a zip file containing the data from which the figures in the manuscript were made. The files are ordered following the figures as they appear in the text, and a readme text file explains the content of each file.

Additional data file 1

A figure showing the number of paralogs and the percentage of paralogous genes in the different-sized gene families in Pirelulla sp. compared to other large-sized genomes

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Additional data file 2

A figure showing gene family sizes in intracellular genomes that have undergone reductive evolution compared to related free-living organisms

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Additional data file 3

The legends to the figures in Additional data files 1 and 2

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Additional data file 4

A zip file containing the data from which the figures in the manuscript were made

Click here for additional data file