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. 2020 Dec 12;38(4):1570–1579. doi: 10.1093/molbev/msaa323

Genome Reduction Is Associated with Bacterial Pathogenicity across Different Scales of Temporal and Ecological Divergence

Gemma G R Murray 1,✉,#, Jane Charlesworth 2,3,5,#,✉, Eric L Miller 6,1, Michael J Casey 7,2, Catrin T Lloyd 2, Marcelo Gottschalk 4, Alexander W (Dan) Tucker 1, John J Welch 2, Lucy A Weinert 1
Editor: Daniel Falush
PMCID: PMC8042751  PMID: 33313861

Abstract

Emerging bacterial pathogens threaten global health and food security, and so it is important to ask whether these transitions to pathogenicity have any common features. We present a systematic study of the claim that pathogenicity is associated with genome reduction and gene loss. We compare broad-scale patterns across all bacteria, with detailed analyses of Streptococcus suis, an emerging zoonotic pathogen of pigs, which has undergone multiple transitions between disease and carriage forms. We find that pathogenicity is consistently associated with reduced genome size across three scales of divergence (between species within genera, and between and within genetic clusters of S. suis). Although genome reduction is also found in mutualist and commensal bacterial endosymbionts, genome reduction in pathogens cannot be solely attributed to the features of their ecology that they share with these species, that is, host restriction or intracellularity. Moreover, other typical correlates of genome reduction in endosymbionts (reduced metabolic capacity, reduced GC content, and the transient expansion of nonfunctional elements) are not consistently observed in pathogens. Together, our results indicate that genome reduction is a consistent correlate of pathogenicity in bacteria.

Keywords: reductive genome evolution, pathogenicity, bacterial evolution, endosymbionts

Introduction

The emergence of new bacterial pathogens is a major threat to human health and food security across the globe (Vouga and Greub 2016). Although every instance of pathogen emergence will be unique in some way, identifying common features could help us to understand, predict, and ultimately prevent these transitions to pathogenicity. One intriguing observation is that some of the most serious human pathogens have smaller genomes and fewer genes than their closest nonpathogenic or less pathogenic relatives (Pupo et al. 2000; Ochman and Moran 2001; Moran 2002; Stinear et al. 2008; Toft and Andersson 2010; Georgiades and Raoult 2011; Langridge et al. 2015; Weinert and Welch 2017). Nevertheless, without formal comparative studies, it is difficult to know whether these are isolated instances of genome reduction, or part of a broader trend (Weinert and Welch 2017).

There are also doubts about whether genome reduction has anything to do with pathogenicity per se (Weinert and Welch 2017). Most notably, similar patterns of genome reduction are found in mutualist or commensal bacteria that have adopted a host-restricted or intracellular lifestyle. In these endosymbiotic bacteria, genome reduction appears as part of a process that often includes a decreased proportion of G/C relative to A/T bases, a preferential loss of genes in metabolic pathways, and a transient expansion in the proportion of the genome that is nonfunctional (because of pseudogenization or the proliferation of selfish elements). This “endosymbiont syndrome” is a plausible outcome of evolution in small isolated populations, where natural selection is less effective and opportunities for gene exchange and homologous recombination are reduced; coupled with a relaxation of some selection pressures, due to a dependency on the host (Mira et al. 2001; Moran 2002; Newton and Bordenstein 2011; McCutcheon and Moran 2012; Rao et al. 2015; Bobay and Ochman 2018). As such, it has been suggested that the genome reduction observed in pathogens may be a consequence of the intracellular or host-restricted lifestyle that they share with endosymbionts, rather than their pathogenicity (Moran 2002).

Here, we present a systematic study of genome reduction and pathogenicity in bacteria across multiple scales of ecological and temporal divergence (fig. 1). First, at the broadest scale, we compare pairs of bacterial species, where a known vertebrate pathogen has a nonpathogenic relative in the same genus. These data span six distinct phyla and include a wide range of pathogen ecologies with variable degrees of host restriction (fig. 1a, table 1). Second, we focus on Streptococcus suis, an opportunistic pathogen of pigs, whose pathogenicity has previously been associated with genome reduction (Weinert et al. 2015; Vötsch et al. 2018). This bacterium is of particular interest because it has undergone multiple independent transitions between carriage and disease forms, yet both forms are generally extracellular, and have equal levels of host restriction. As such, we can observe “replicated” changes in pathogenicity that are not accompanied by changes in the broader ecology (see below). Furthermore, the species includes multiple genetic clusters of closely related isolates that vary in their association with disease. By comparing patterns between clusters (fig. 1b), and between isolates within clusters (fig. 1c), we can contrast long- and short-term changes.

Fig. 1.

Fig. 1.

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The evolution of pathogenicity over three evolutionary scales: (a) between species of bacteria, (b) between clusters of Streptococcus suis, and (c) between isolates of S. suis within clusters. (a) A cladogram of our 31 pairs of congeneric species, comprising a pathogen (red) and a nonpathogen (gray). Numbers refer to table 1 and suprageneric relationships are from (Zhu et al. 2019). (b) A core genome phylogeny of our 1,079 isolates of S. suis. Individual disease (red) and carriage (gray) isolates are indicated in the inner strip. The outer strip describes the 34 genetic clusters, with the 11 “mixed clusters” that include multiple disease and carriage isolates numbered. (c) An illustrative phylogeny of our largest and most pathogenic cluster, constructed from a recombination-stripped local core genome alignment. Individual disease (red) and carriage (gray) isolates are again indicated on the strip.

Table 1.

Between-Species Data Set.

Phylum Pair Genus Pathogen sp. (#) Nonpathogen sp. (#)
Firmicutes 1 Clostridium tetani (2) carboxidivorans (1)
2* Bacillus anthracis (31) subtilis (39)
3* Streptococcus pneumoniae (28) thermophilus (8)
4 Streptococcus suis (19) oligofermentans (1)
Tenericutes 5 Mycoplasma putrefaciens (2) yeatsii (1)
6 Mycoplasma hyopneumoniae (2) flocculare (1)
Actinobacteria 7* Rhodococcus equi (1) pyridinivorans (1)
8§ Corynebacterium diphtheriae (13) efficiens (1)
9* Mycobacterium abscessus (28) smegmatis (7)
10*§ Mycobacterium leprae (1) indicus (1)
Spirochaetes 11 Treponema pallidum (2) primitia (1)
12* Leptospira interrogans (8) biflexa (2)
13 Brachyspira hyodysenteriae (2) murdochii (1)
Bacteroidetes 14 Bacteroides helcogenes (1) vulgatus (1)
15 Flavobacterium branchiophilum (1) johnsoniae (1)
16 Flavobacterium columnare (4) indicum (1)
Proteobacteria 17§ Brucella suis (15) ceti (1)
18 Rickettsia prowazekii (9) e. I. scapularis (1)
19 Rickettsia rickettsii (9) peacockii (1)
20 Neisseria gonorrhoeae (9) lactamica (1)
21 Burkholderia pseudomallei (46) thailandensis (11)
22 Taylorella equigenitalis (2) asinigenitalis (1)
23*§ Bordetella pertussis (51) hinzi (2)
24§ Francisella noatunensis (6) philomiragia (7)
25 Legionella pneumophila (21) fallonii (1)
26 Aeromonas salmonicida (1) media (1)
27§ Actinobacillus pleuropneumoniae (4) succinogenes (1)
28 Haemophilus influenzae (16) parainfluenzae (1)
29§ Citrobacter rodentium (1) amalonaticus (3)
30 Yersinia enterocolitica (9) rohdei (1)
31 Yersinia pestis (32) similis (1)
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Note.—Pairs with a known difference in levels of intracellularity (*) and/or host-restriction (§) between pathogen and nonpathogen.

Results and Discussion

Data Sets

For our broadest-scale between-species data set (fig. 1a), we carried out a systematic search for phylogenetically independent species pairs, comprising a vertebrate pathogen, a congeneric nonpathogen, and an outgroup, with publicly available whole-genome data. Our choice of pairs followed a strict protocol to minimize the influence of subjective choice (see Materials and Methods for full details). Following this protocol, we obtained 31 species pairs (table 1, supplementary table S1, Supplementary Material online) represented by 478 ingroup genomes (supplementary table S2, Supplementary Material online). These pairs spanned all six of the bacterial phyla that contain known pathogen species. They include obligate (e.g., Mycobacterium leprae) and opportunistic pathogens (e.g., Streptococcus pneumoniae); pathogens with different primary hosts (e.g., Legionella pneumophila); and pathogens with several (e.g., Yersinia pestis), and no (e.g., Taylorella equigenitalis) close pathogenic relatives. These data therefore represent a breadth of pathogenic ecologies and evolutionary histories. Because the species pairs differed both in their sampling densities (i.e., the number of available genomes), and in the evolutionary distances between the species, we developed a phylogenetic comparative method to properly account for these differences.

For our within-species comparisons (fig. 1b and c), we collated 1,079 whole genomes of S. suis, collected across three continents (table 2, supplementary table S3, Supplementary Material online). Data were associated with clinical information, and include “carriage isolates” from the tonsils of pigs without S. suis associated disease, and “disease isolates” from the site of infection in pigs with S. suis associated disease (divided into respiratory or systemic infections). We also included zoonotic disease isolates from humans with systemic disease in Vietnam, which a previous study found to be indistinguishable from isolates associated with systemic disease in pigs (Weinert et al. 2015). A core genome alignment was used to build a consensus phylogeny; but as S. suis is highly recombining, we inferred genetic clusters using an approach that does not assume a single evolutionary history across the genome (Corander et al. 2008; Tonkin-Hill et al. 2018). We identified 34 clusters, of which 33 contained isolates that could be unambiguously categorized as carriage or disease (fig. 1b, supplementary fig. S1a and table S4, Supplementary Material online). These clusters have variable levels of genetic diversity and include some with recent origins; for example, a previous study dated the origin of our largest and most pathogenic cluster (cluster 1, fig. 1c) to the 1920s (Weinert et al. 2015). There were marked differences between clusters in the proportion of disease isolates, and this correlated with the presence of virulence genes and serotypes with known disease associations (supplementary fig. S2, Supplementary Material online). Allowing for comparisons at the finest scale, 11/34 were “mixed clusters,” containing both multiple carriage isolates and multiple disease isolates, and these are numbered in figure 1b.

Table 2.

Streptococcus suis Data Set.

Origin # Isolates: Total (SP, RP, C) Genome Size Range (Mb) Genetic Cluster(s) Dates of Collection Reference(s)
UK 440 (43, 52, 205) 1.95–2.56 1–11, 13–14, 16–18, 20, 22–25, 27–28, 30–33 2009–2015 Weinert et al. (2015); Wileman et al. (2019)
Canada 197 (36, 31, 56) 1.92–2.49 1–3, 5–18, 20, 22–26, 29–30 1983–2016 Hadjirin et al. (2020)
China 197 (0, 0, 197) 2.06–2.54 1, 3, 5–12, 18–22, 27–30, 32, 34 2013–2014 Zou et al. (2018)
Vietnam 190 (149, 0, 32) 1.97–2.19 1 2000–2010 Weinert et al. (2015)
USA 16 (3, 4, 0) 2.02–2.46 3, 5, 8–10, 26 2016 Hadjirin et al. (2020)
Spain 10 (7, 0, 0) 2.03–2.42 1, 4, 8, 22 2016 Hadjirin et al. (2020)
Reference Collection 29 (14, 2, 3) 1.98–2.30 1–4, 6, 8, 13–14, 18–19 — NCBI GenBank, http://www.ncbi.nlm.nih.gov/genbank (last accessed 2016)
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Note.—SP, systemic pathogen; RP, respiratory pathogen; C, carriage.

Pathogenicity Is Associated with Genome Reduction at All Divergence Scales

Across all three scales of divergence, our data sets showed substantial variation in genome size, and in each case, we found a strong and negative association with pathogenicity. This is shown in figure 2. At the broadest scale, pathogenic species had smaller genomes than their nonpathogenic relatives more often than expected by chance (fig. 2a; see also supplementary table S5 and fig. S3a, Supplementary Material online for robustness analyses). Within S. suis, genetic clusters had smaller genomes when they contained a higher proportion of disease isolates (fig. 2b; see also supplementary table S6, Supplementary Material online). The same pattern held within clusters; in 11/11 mixed clusters, disease isolates had smaller genomes on average, than carriage isolates from the same cluster (fig. 2c).

Fig. 2.

Fig. 2.

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Pathogenicity is associated with smaller genomes. (a) Each point represents a phylogenetically independent species pair, with numbers and colors from figure 1a. Standardized contrast values are ordered such that negative values imply smaller genomes in the pathogenic species. P-value is from a permutation test of the null hypothesis of no difference in the mean contrast value (as indicated by the dotted line). (b) Each point represents a cluster of Streptococcus suis isolates, with colors from figure 1b, and sizes indicating the number of isolates. A sample-size weighted regression shows that clusters containing a larger proportion of pathogenic isolates have a smaller average genome size. (c) Each point shows the difference in mean genome size between disease and carriage isolates in a cluster of S. suis isolates, such that negative values imply smaller genomes in the pathogens. Each point corresponds to a “mixed cluster” (containing multiple disease and carriage isolates), with colors and numbers matching figure 1b. The P-value is from a permutation test as in (a).

Two further lines of evidence suggest that changing genome size is a cumulative process that persists for long periods of time. First, in our between-species data set, a Brownian motion model of genome size evolution provides a good fit, which demonstrates that more distantly related pairs have larger differences in genome size. This is shown in supplementary figure S4, Supplementary Material online. Second, in S. suis, between-cluster differences in genome size remain apparent when we consider the carriage isolates alone: carriage isolates have smaller genomes when they are found in clusters containing a higher proportion of disease isolates (“Data Set C” in supplementary table S6, Supplementary Material online). Finally, in S. suis, there is an association between genome size, and disease severity. We see the smallest genomes in isolates associated with more invasive systemic disease, with less severe respiratory disease isolates tending to have intermediate genome size. This is shown in supplementary figure S5, Supplementary Material online.

All of these results apply to total genome size. However, we observed the same pattern when we used genome annotations to consider only known functional elements. Across all divergence scales, pathogenicity was associated with fewer genes, and smaller coding length, as well as smaller genome size (supplementary fig. S3a–i, Supplementary Material online). In S. suis, a large fraction of genome reduction is due to the loss of mobile genetic elements (see supplementary figs. S7a and S8a–c, Supplementary Material online). However, this cannot account for all genome size variation, as genome reduction is also observed in the remainder of the genome, with mobile elements excluded (supplementary fig. S8b and c, Supplementary Material online).

Pathogenicity Is Not Consistently Associated with an Endosymbiont Syndrome

In endosymbionts, genome reduction is frequently associated with a preferential loss of metabolic genes, a transient proliferation of nonfunctional DNA, and a reduction in GC content. Below, we ask whether these signatures also accompany the genome reduction observed in pathogens.

Metabolic Genes

Metabolic genes are often lost in obligate endosymbionts, because they can instead rely on the host for certain nutrients, therefore reducing selective constraint on their own genes. This is especially true of intracellular symbionts that live in the nutrient-rich host cytoplasm (Moran 2002; Zientz, Dandekar et al. 2004). Although this process is expected to be especially prominent in mutualists, which might be actively provisioned by the host, pathogens might also acquire products from their hosts. However, we found no evidence to support this hypothesis. Having identified genes with metabolic function using COG categorization (see Materials and Methods), we found that pathogens did contain fewer metabolic genes over all three timescales (supplementary fig. S3m–o and table S5, Supplementary Material online). Nevertheless, as shown in figure 3a–c, these trends were no greater than would be expected, given the overall pattern of gene loss that we demonstrated above. In particular, between species, the proportion of genes with a metabolic function evolved gradually over time (supplementary fig. S6j and k, Supplementary Material online), but does not differ significantly between pathogenic and nonpathogenic congeners (fig. 3a, supplementary table S5, Supplementary Material online); whereas within S. suis, the proportion of metabolic genes is actually higher in pathogens, both between and within clusters (fig. 3b and c), suggesting that they are lost less rapidly than randomly chosen genes. This is the exact opposite of the predicted pattern. These analyses considered all metabolic genes as a single category. However, finer grained analyses, using individual COG categories associated with metabolism, also revealed no consistent pattern of preferential gene loss (supplementary table S7, Supplementary Material online). Between-cluster analysis in S. suis did show preferential loss of genes in two of the eight categories: energy production and conversion, and amino acid transport and metabolism. But neither result would survive correction for multiple testing; and for both categories, trends in the opposite direction were found within clusters of S. suis, and between species (supplementary table S7, Supplementary Material online). Together, results suggest that preferential loss of genes with a metabolic function is not a consistent correlate of pathogenicity. In fact, as shown in supplementary figure S9, Supplementary Material online, genome reduction in S. suis does not appear to involve the loss of any consistent set of genes. The most striking pattern is that putative virulence genes are more consistently present in pathogens, bucking the overall trend for gene loss (Weinert et al. 2015).

Fig. 3.

Fig. 3.

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Pathogenicity is not consistently associated with the “endosymbiont syndrome”. Each panel tests for an association between pathogenicity and another signature of the endosymbiont syndrome, (a–c): the proportion of protein-coding genes with metabolic function, (d–f): the proportion of the genome with known function, (g–i): the proportion of the genome comprising GC base pairs. Negative contrasts (a, c, d, f, g, i) or negative regression slopes (b, e, h) are consistent with an endosymbiont syndrome. All other details match figure 2.

Nonfunctional Elements

Although very old endosymbionts are often gene dense (e.g., Nakabachi et al. 2006; McCutcheon and Moran 2007; Kuo et al. 2009), more recently established endosymbionts often show increased numbers of pseudogenes and/or an expansion of nonfunctional selfish elements. This results in a lower proportion of their genome being functional, suggesting a reduction in either the strength or efficacy of negative selection. To test whether this pattern also held in pathogens, we estimated the proportion of each of our genomes that contained functional sequence (see Materials and Methods). Results, shown in figure 3d–f, show no evidence of the predicted pattern on any of our three timescales. Between species, we found no consistent difference in the proportion between pathogens and nonpathogens (fig. 3d, supplementary table S5, Supplementary Material online). The evolution of this proportion also failed to fit a Brownian motion model of evolution (supplementary fig. S6d and e, Supplementary Material online), confirming that the accumulation of nonfunctional DNA is a transient process. Results in S. suis were again the opposite of the prediction. The proportion of the genome that was functional was consistently higher in pathogens (fig. 3e and f). As such, the within-species transitions to pathogenicity differed from the more recent transitions to symbiosis.

GC Content

The genomes of endosymbiotic bacteria often have lower GC contents than those of their free-living counterparts (Moran 2002). This could reflect metabolic adaptation to the host environment (Rocha and Danchin 2002; Dietel 2019), or a reduction in GC-biased gene conversion due to lower rates of recombination (Lassalle et al. 2015). But it is usually assumed to be a consequence of a reduced efficacy of selection, coupled with a bias towards GC-to-AT mutations (Hershberg and Petrov 2010; Hildebrand et al. 2010; McCutcheon and Moran 2012). Results shown in figure 3g–i, show that patterns of GC content in pathogens are more complicated than predicted. Between species, we did observe the predicted tendency for pathogens to be GC-poor (fig. 3g). However, further analysis showed that this result was attributable to a subset of pairs that had undergone a large-scale shift in ecology. In 12/31 pairs, the pathogenic species showed a greater degree of host restriction and/or intracellularity than its nonpathogenic congener (table 1), and the relationship between pathogenicity and GC held only for these pairs, and not for the remainder of the data (supplementary table S5, Supplementary Material online). Results for genome size showed the exact opposite pattern: the association between genome reduction and pathogenicity was driven by 19/31 pairs with similar ecologies (supplementary table S5, Supplementary Material online). (Results for metabolic genes and nonfunctional DNA showed no differences between either division of the data: supplementary table S5, Supplementary Material online.) As such, between species, the AT-richness of some pathogens might be explained by aspects of their ecology that they share with endosymbionts, but their genome reduction cannot be explained in this way.

Within S. suis, the raw results for GC showed no clear patterns (fig. 3h and i, supplementary fig. S1c, Supplementary Material online). However, this is attributable to divergent groups of S. suis with unusual GC content (Baig et al. 2015; see supplementary figs. S1a, c, and S7b, Supplementary Material online for details). Once these groups are removed, results suggest a balance of opposing forces relating to the mobile accessory genome, and to the core genome. We find that mobile genetic elements tend to have lower GC content (supplementary fig. S8d, Supplementary Material online). As such, their absence from isolates with smaller genomes tends to increase genome-wide GC (supplementary fig. S7c, Supplementary Material online). By contrast, the core genome shows the reverse pattern, with isolates with smaller genomes being less GC rich (supplementary fig. S7d, Supplementary Material online). But this is only evident between genetic clusters, suggesting that accumulation of GC-to-AT mutations in the core genome is a slow process. The result is that the predicted association between pathogenicity and low GC is observed in S. suis, but only between clusters, and only in the core genome. This is shown in supplementary figure S10, Supplementary Material online.

The decreased GC content in more pathogenic clusters of S. suis might be caused by reduction in the efficacy of selection, or fewer opportunities for GC-biased gene conversion. Consistent with these hypotheses, we find that more pathogenic cluster of S. suis have shorter terminal branches (supplementary fig. S11, Supplementary Material online), as well as evidence of faster rates of protein evolution, and lower rates of homologous recombination (supplementary fig. S12, Supplementary Material online; see also Weinert et al. 2015). We note that these changes cannot be due to increased host restriction or intracellularity, such as we have inferred in the between-species data. Another possibility is that they stem from bottlenecks associated with transmission between hosts (Kono et al. 2016; Sheppard et al. 2018). There is evidence of high rates of transmission in pathogenic S. suis in the broad geographic spread of the more pathogenic clusters, despite their relatively recent origin. For example, cluster 1 includes isolates from China, Vietnam, the UK, Spain and Canada (supplementary table S4, Supplementary Material online).

Conclusion

We have demonstrated a statistical association between pathogenicity and genome reduction in bacteria. This association applies across bacterial phyla, across a wide range of pathogenic ecologies, and across different scales of divergence. We have also demonstrated that genome reduction in pathogens can occur without the correlates that are often observed in bacterial endosymbionts. In particular, we find no evidence of the preferential loss of genes with metabolic functions, which is predicted if genome reduction is driven by increased dependence on, or exploitation of, the host. We also find little evidence of maladaptive genome evolution involving the accumulation of nonfunctional elements. In fact, both trends go in the opposite direction in S. suis. We do find that genome reduction is sometimes associated with a reduction in GC content, but this signature is patchy. In the between-species data, it is only present when the pathogenic species has become more host-restricted or intracellular, and in S. suis, it is observed only over longer time periods and only in the core genome; perhaps due to bottlenecks associated with increased transmission. Genome reduction in bacterial pathogens is therefore distinct from that in endosymbionts, and cannot be solely attributed to features, such as host restriction or intracellularity. Consequently, genome reduction could prove a useful marker of emerging and increasing pathogenicity in bacteria.

Materials and Methods

Data Sets

For the between-species data set, we aimed for consistency, and so chose our data from a single common source: the NCBI RefSeq database (O’Leary et al. 2016; release 76, apart from one Rickettsia peacockii genome, added from release 77). We began by identifying all eubacterial genera that were represented by multiple named species in RefSeq. We then used Bergey (Whitman 2015), and the wider literature, to classify all species in these genera as “pathogens”, “nonpathogens”, or “ambiguous/unknown”. Pathogenicity was defined with respect to vertebrates, not only because vertebrate pathogenicity is better studied, but because vertebrate adaptive immunity is implicated in some theories of genome reduction (Weinert and Welch 2017). We note that all such designations must contain an element of uncertainty and ambiguity, not least because of the ubiquity of opportunistic pathogenicity. For this reason, we restricted our definition of “pathogens” to species that have repeatedly been reported to cause disease in immuno-competent vertebrate hosts, and preferred species where there was evidence of long-term persistence as a pathogen. For example, we scored Staphylococcus aureus as “ambiguous,” because human infection commonly occurs from carriage forms (Huang et al. 2011), and because carriage status could not be inferred from metadata associated with the sequenced isolates. Similarly, “nonpathogens” were defined as species that were known to be free-living or commensals, even if there were isolated cases of secondary infections. For example, Aeromonas media was designated as a nonpathogen, despite a single reported case of isolation, together with pathogenic Yersinia enterocolitica in a patient recovering from infection with Aeromonas caviae (Rautelin et al. 1995).

After these assignments, we aimed to choose pairs without further subjectivity, or influence of prior knowledge. As such, we used the following process. First, for each genus containing at least one pathogen and one nonpathogen, we downloaded all available complete genomes (see supplementary table S2, Supplementary Material online). (Draft genomes were excluded because we observed that they varied substantially in length for a few species.) We then used Phylosift (Darling et al. 2014) to align 37 single copy orthologs identified as universal to all bacteria. Concatenated alignments of these loci were checked and corrected by eye (available on Dryad at https://doi.org/10.5061/dryad.nzs7h44qc), and we used MEGA7 (Kumar et al. 2016) to build neighbor-joining phylogenies using variation at synonymous sites. We used a modified Nei–Gojobori method using Jukes–Cantor and complete deletion of sites with missing data. These genus-level phylogenies were then midpoint rooted using the R package Phangorn (Schliep 2011). Then, using these trees, we identified all possible phylogenetically independent pairs consisting of a pathogen and nonpathogen species. This included checking that the genomes from both species were monophyletic with respect to each other, and all other species in the genus-level data set, based on the relationships of these 37 marker genes. When a nonpathogen was a sister group to multiple pathogenic species, we chose the best-sampled pathogen species with the largest number of available genomes. This process yielded the 31 pairs listed in table 1 and supplementary table S1, Supplementary Material online. We next noted a suitable outgroup for each pair, and re-estimated trees including only genomes from the pair and outgroup. These pair-level phylogenies were checked for consistency against the relevant whole-genus phylogenies and used when calculating the independent contrasts (see below).

Finally, we returned to the literature, to identify the subset of pairs with a qualitative difference in ecology between the pathogen and nonpathogen. In particular, we noted pairs where the nonpathogen was extracellular and the pathogen facultatively intracellular (pairs 2, 7, 9, 10, 12, and 23) and where the pathogen, but not the nonpathogen, replicated exclusively within their hosts in nature (pairs 3, 8, 10, 17, 23, 24, 27, and 29); these pairs are indicated in table 1.

For our S. suis data, we used isolates originating from six collections spanning six countries (table 2) with the same diagnostic criteria of pathogenicity status. The first collection includes isolates from the UK sampled between 2009 and 2011 (described in Weinert et al. 2015). The second includes isolates from pigs and human meningitis patients from Vietnam, sampled between 2000 and 2010 (described in Weinert et al. 2015). The third includes carriage isolates from pigs from five intensive farms in the UK, and from five intensive farms and five traditional farms in China from between 2013 and 2014 (described in Zou et al. 2018). The fourth includes disease isolates sampled from UK pigs (described in Wileman et al. 2019). The fifth includes isolates from North American and Spanish pigs, sampled between 1983 and 2016 (Hadjirin et al. 2020). The sixth includes 29 reference isolates downloaded from GenBank (NCBI). Full details of all genomes are in supplementary table S3, Supplementary Material online.

Pathogenicity status was defined in the following way. Isolates were classified as associated with “disease”, if they were recovered from systemic sites in pigs or humans with clinical signs consistent with S. suis infection, including meningitis, septicemia and arthritis (“systemic disease” isolates), or were recovered from a pig’s lungs in the presence of lesions of pneumonia (“respiratory disease” isolates). Isolates recovered from the tonsils or tracheo-bronchus of healthy pigs or pigs without any typical signs of S. suis infection were classified as “carriage”. The remaining isolates remained unclassified, due to insufficient clinical information or ambiguity in the cause of disease.

For most of the collection, serotyping was performed using antisera to known S. suis serotypes by the Lancefield method (Weinert et al. 2015; Wileman et al. 2019). Isolates that could not be typed with known sera were classified as nontypeable. A subset of UK isolates and the Chinese isolates were serotyped in silico using capsule genes of known serotypes (described in Zou et al. 2018). Isolates that were not serotyped were excluded from comparisons.

Sequence data from all isolates were used to generate de novo assemblies using Spades v.3.10.1 (Bankevich et al. 2012), after first removing low quality reads using Sickle v1.33 (Joshi and Fass 2011). Measures were taken to ensure all assemblies were high-quality, as described in previous studies (Weinert et al. 2015; Zou et al. 2018; Wileman et al. 2019; Hadjirin et al. 2020). Briefly, Illumina reads were mapped back to the de novo assembly to investigate polymorphic reads in the samples (indicative of mixed cultures) using BWA v.0.7.16a (Li and Durbin 2009), and genomes that exhibited poor sequencing quality (i.e., poor assembly as indicated by a large number of contigs, low N50 values or a high number of polymorphic reads) or that which were inconsistent with an S. suis species assignment were excluded from the analysis. Altogether this left 1,079 genomes.

To identify genetic structure in our S. suis isolates we identified 429 low-diversity core genes in our data set, aligned them using DECIPHER (Wright 2015), and stripped regions that could not be aligned unambiguously due to high divergence, indels or missing data (available on Dryad at https://doi.org/10.5061/dryad.nzs7h44qc). This conserved region of the core genome was first used to construct a consensus neighbor-joining tree using the ape package in R and a K80 model (Paradis et al. 2004). This tree is shown in figure 1b and supplementary figure S1, Supplementary Material online, and was used to generate the covariance matrices used in the phylogenetically corrected regressions (supplementary table S6 and fig. S11, Supplementary Material online). The same data were used to identify genetic clusters, using the hierBAPS package in R (Corander et al. 2008; Tonkin-Hill et al. 2018). Initial analysis identified 35 clusters. To evaluate this clustering we mapped the clusters onto the core gene phylogeny, and following the definition in Hudson et al. (1992), estimated FST between clusters from pairwise nucleotide distances in the core gene alignment. We identified a pair of clusters with very low FST (<0.02) that were also monophyletic in the tree, and these clusters were combined to form cluster 3 (supplementary table S1, Supplementary Material online). Full details of all clusters are found in supplementary table S4, Supplementary Material online.

The illustrative genealogy shown in figure 1c involved mapping to the reference genome BM407 (see supplementary table S3, Supplementary Material online) using Bowtie2 (Langmead and Salzberg 2012), recombination-stripping in Gubbins (Croucher et al. 2015), and tree construction with MrBayes (Huelsenbeck and Ronquist 2001) with default parameters and the HKY + Γ substitution model.

Genome Annotation

For between-species data, we used the RefSeq annotations. We also carried out re-annotations using Prokka (v2.8.2) (Seemann 2014). Although Prokka and RefSeq annotations were generally congruent, Prokka does not explicitly annotate pseudogenes and thus high levels of pseudogenization in a handful of species (e.g., Rickettsia prowazekii), led to erratic results, so we preferred RefSeq annotations. We also excluded plasmids because these can be lost during culture and sequencing. However, the main results concerning genome size are robust to their inclusion (supplementary table S5, Supplementary Material online).

The draft S. suis genomes were also annotated using Prokka (v2.8.2) (Seemann 2014). Orthologous genes were initially identified using Roary (Page et al. 2015), with the recommended parameter values. We then manually curated these orthology groups, in order to identify orthologous genes that had been wrongly placed in distinct orthology groups either due to high levels of divergence or incomplete assemblies. We also checked all instances of gene absence in each orthology group, since these might have resulted from incomplete genome assemblies. This was undertaken using all-against-all gene group nucleotide BLAST search (BLASTN), and BLASTN search of all orthology groups against all of the genomes in which that group was described as absent (Camacho et al. 2013). The final set of orthology groups were used to define the core genome (supplementary figs. S7 and S8, Supplementary Material online).

In figure 3 and related analysis, we defined the “functional” proportion of the genome as any region annotated as a protein- or RNA-coding locus. For both data sets, all protein-coding genes were assigned a COG category, and categories C, E, F, G, H, I, P, and Q defined as “metabolic genes”. For the S. suis data set, we also identified genes that were annotated as transposases or integrases in the Prokka annotations. For the 29 S. suis isolates with complete assemblies we identified mobile genetic elements using IslandViewer 4 (Bertelli et al. 2017).

Statistical Analyses

For the between-species data, each comparison pair differed in the number of genomes sampled, and the amount of evolutionary change between the species. For this reason, we standardized the weightings using a method of independent contrasts. In brief, each comparison point was equivalent to the difference between the ancestral trait values for the sampled genomes from the pathogenic and nonpathogenic species that would be inferred from a Brownian motion model of trait evolution (and using the tip value in the case of a single genome). The contrast for each pair was then standardized by its associated standard deviation. The method used the pic and ace functions in the ape package in R (Felsenstein 1985; Paradis et al. 2004), and for each pair, we used the genealogies constructed from the 37 “universal genes”, described above, so that amounts of molecular evolution were comparable across the entire data set. We also added a fixed constant of 1/length(alignment) to deal with zero-length branches in some of the genealogies (reducing this constant by a factor of 10 had no appreciable effect on our results). For each variable, we then applied a standard transformation. We chose a logarithmic transformation for data that are positively valued but unbounded above (such as genome size and gene number), so that doublings in value were always represented as changes of the same size. For data that are proportions, such as GC content, and are therefore bounded at 0 and 1, we used a logit transformation, log(x/(1–x)). To test the suitability of these transformations, and to understand the general patterns of evolution in each variable, we tested the validity of the Brownian motion model following the recommendations of (Freckleton 2000). As shown in supplementary figures S4 and S6, Supplementary Material online, for most traits, the model provided a good fit. The sole exception was the proportion of genomes with known function (supplementary fig. S6d–f, Supplementary Material online), which is consistent with the rapid loss of nonfunctional elements, as discussed in the main text. Even after standardizing the variances, the set of contrasts was usually highly nonnormal (e.g., fig. 2a), and so we tested the null hypothesis of a vanishing mean (i.e., no consistent trait difference between pathogenic and nonpathogenic species) by randomly permuting the labels “pathogen” and “nonpathogen” within each pair (i.e., randomly choosing the sign of each of the 31 contrasts). The test statistic was the mean contrast value (using the true signs), and 106 random permutations were used to construct its null distribution. We also repeated results after removing outliers, identified by eye (supplementary table S5, Supplementary Material online). The same permutation approach was used for the within-cluster data set, although here, the disease and carriage isolates were interspersed in the genealogy, and so we used the raw means of the trait values for each class of isolate.

For the between-cluster analyses, tests also had to account for the differences in cluster size. For this reason, most results used weighted linear regression (using the square root of the number of isolates in each cluster as weights). Because these analyses ignored possible covariances between clusters, due to their shared ancestry, we also used phylogenetically corrected regressions, retaining only the larger clusters (containing at least 20 isolates). These analyses, shown in supplementary table S6, Supplementary Material online data set “D,” used the gls function in the nlme package in R (Pinheiro et al. 2015), and Pagel’s “lambda correlation structure” (corPagel in the ape package, Pagel 1999; Paradis et al. 2004).

Supplementary Material

Supplementary data are available at Molecular Biology and Evolution online.

Supplementary Material

msaa323_Supplementary_Data
Click here for additional data file. (3.3MB, zip)

Acknowledgments

We wish to acknowledge Nick Goldman, Marta Matuszewska, and Mukarram Hossain for helpful discussions. L.A.W., G.G.R.M., and E.L.M. were supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (109385/Z/15/Z). G.G.R.M. was also supported by a ZELS BBSRC award (BB/L018934/1) and a Research Fellowship at Newnham College. J.C. was supported by an EBPOD fellowship, jointly funded by EBI and the University of Cambridge.

Data Availability

Concatenated alignments of the 37 single copy orthologs identified for the between-species data set, and a core gene alignment of our S. suis data set are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.nzs7h44qc.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

msaa323_Supplementary_Data
Click here for additional data file. (3.3MB, zip)

Data Availability Statement

Concatenated alignments of the 37 single copy orthologs identified for the between-species data set, and a core gene alignment of our S. suis data set are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.nzs7h44qc.

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