PAGE | 20 WHY MIGHT BACTERIAL PATHOGENS HAVE SMALL

A CLASSIFICATION OF THE KINDS OF QUESTIONS YOU MIGHT
ACTIVITY 58 MODAL VERBS – POSSIBILITY SPECULATION MIGHT
ANTENATAL AND POSTNATAL VULNERABILITY FACTOR CHECKLIST STRESSES THAT MIGHT

‘CONCEPTUAL HISTORY EXPLORING NEW FIELDS HOW CONCEPTUAL HISTORY MIGHT
“GENUINE MINISTRY” JW SIMS CONTRARY TO WHAT MANY MIGHT
“ONE MIGHT THINK IF PHILOSOPHY SPEAKS OF THE USE

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Why might bacterial pathogens have small genomes?



Lucy A. Weinert1, John J. Welch2







  1. Corresponding author: Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK; [email protected]

  2. Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK; [email protected]

Abstract

Bacteria that cause serious disease often have smaller genomes, and fewer genes, than their non-pathogenic, or less pathogenic relatives. Here, we review evidence for the generality of this association, and summarise the various reasons why the association might hold. We focus on the population genetic processes that might lead to reductive genome evolution, and show how several of these could be connected to pathogenicity. We find some evidence for most of the processes having acted in bacterial pathogens, including several different modes of genome reduction acting in the same lineage. We argue that predictable processes of genome evolution might not reflect any common underlying process.

Keywords

reductive genome evolution; antivirulence genes; population genetics

Reductive genome evolution and pathogenicity (RGEP)

Bacterial pathogens are one of the most serious threats to human health worldwide, and are evolving resistance to antibiotics at an alarming rate [1]. In a “post-antibiotic era” creative approaches to combating infectious disease will become paramount.

One crucial research goal is to understand why and how bacteria become pathogens. Of course, every instance of pathogen emergence is unique in some way, and they could have little in common. But if we were able to identify any common features – whether they be repeated genomic changes or shared ecological contexts – this might allow us to forecast pathogen emergence, to develop preventative strategies, or improve treatments.

One intriguing observation is the tendency for pathogenicity to be associated with reductive genome evolution (an association henceforth called RGEP). It has been noted for many years that pathogenic bacteria very often have smaller genomes and fewer genes than their nearest non-pathogenic or less-pathogenic relatives [2-4]. And examples of RGEP are found over a diverse range of bacterial phyla [5-8], including some of the world’s most devastating pathogens, such as Shigella flexneri, Yersinia pestis, Salmonella typhi and Mycobacterium tuberculosis [6, 7, 9, 10].

Formal comparative studies are less common, and few have progressed beyond the remarkable illustrative plot of Moran [3] which used the 50 bacterial genomes then sequenced, and showed that the 15 obligate pathogens or symbionts in the dataset included the 14 smallest genomes. More recently, Georgiades and Raoult [11] used a phylogenetically independent approach to compare the genomes of the 12 most serious pandemic bacteria in humans, to close relatives that were not associated with major human pandemics. It was found that 11/12 of the “bad bugs” had the smaller genomes (see also [12]).

Clear examples of RGEP within single bacterial species are also rare, but the emerging zoonosis and pig pathogen, Streptococcus suis, can exist in non-pathogenic carriage forms, and as a respiratory pathogen, and also as a high virulence systemic pathogen [13]. In this species, the evolution of increased virulence has been repeatedly and independently associated with reductive genome evolution [5]. Replicated RGEP is also described in the Escherichia coli/Shigella species complex, where independent evolutionary transitions from non- or less-pathogenic E. coli to highly virulent Shigella spp. have been accompanied by net gene loss [9, 14, 15].

Cause, consequence or correlate?

One obvious question about RGEP is how or whether the association is causal. At first glance, the comparative evidence argues against a causal relationship. This is because the clearest and best-known examples of bacterial genome reduction include non-pathogens, such as the free-living marine bacteria Pelagibacter and Prochlorococcus [16, 17] and mutualist endosymbionts, such as Buchnera and Portiera [18, 19] (see also [20]). Furthermore, the symbionts have a host-restricted and intracellular ecology that is shared with many pathogens. But while this evidence is strong, it is not conclusive; not least because genome reduction might have many causes (can a single cause explain reduction in both Buchnera and Pelagibacter?). It is also possible that many commensal or mutualist symbionts evolved from pathogens, with secondary loss of pathogenicity associated with further gene loss [7]. A possible example is found in the genus Mycoplasma [21], where Mycoplasma flocculare inhabits the respiratory tract of pigs asymptomatically, but nests within a clade of pathogens [22]. All of these Mycoplasma have small genomes, typically around 1Mb, but M. flocculare is particularly small at less than 800kb.



If the comparative evidence remains unclear, a more direct approach is to examine particular examples of gene gain or loss, and ask how they affect virulence. Most such examples involve the gain of virulence factors, often via mobile elements. Putative virulence genes are even over-represented in pathogenic S. suis, which have otherwise undergone massive gene loss [5] (Box 1). However, there are also many other examples of virulence caused by gene loss or inactivation [23-28]. For example, cadA in E. coli encodes a lysine decarboxylase enzyme, and has been repeatedly lost in enteroinvasive E. coli and Shigella [29]. The enzyme’s product, cadavarine, prevents transepithelial migration, which inhibits the virulence phenotype [30]; cadA is therefore an “anti-virulence gene” in the most straightforward sense [31]. However, losses of many types of element might increase virulence, as evidenced by loss-of-function mutations being associated with higher virulence in Staphylococcus aureus [32-34] and Mycobacterium tuberculosis [35] (see also [36] for an analogous phenomenon in human mitochondria).

Of course, both gains and losses might contribute to individual transitions to pathogenicity (Box 1), and their relative contributions are difficult to assess, even with well-developed methods of comparative genomics (see Box 2 for details).

The population genetics of genome reduction

A distinct approach to understanding RGEP is to consider the population genetic processes that might lead to bacterial genome reduction (Table 1). This arrangement allows us to draw connections to other examples of reductive genome evolution [3, 20, 37]. Here we ask whether any of the processes has a consistent association with pathogenicity, whether as cause, consequence or correlate.

Changes in selection pressures

The evolution of pathogenicity or increased virulence could be preceded by changes in the form or strength of natural selection acting on bacterial populations, due either to the colonisation of new hosts or tissues; or to changes in an existing host, such as impairment of immune response, or drug treatment [23, 38, 39]. Adoption of a pathogenic lifestyle can also lead to recurrent changes in selection pressures, due to the host adaptive immune response or host-parasite coevolution, leading to “arms-race” or “red-queen” dynamics [40, 41]. Any change in selection pressure might lead to gene loss, and do so in several ways (Table 1i-iii; [4, 25, 28]).

First, as is clear from eukaryotes, where genome size is uncoupled from gene number [42], genome size might itself influence many phenotypes, including cell size, or the costs and speed of replication [43]. Of these, faster within-host replication is most plausibly linked to virulence [44]. Selection for a smaller genome (Table 1i), is most likely to result in deletions that leave the bacterium’s functional repertoire more-or-less intact, such as loss of redundancy in biochemical pathways [10, 37].

This “streamlining” hypothesis has mixed support from experimental and comparative studies. Selection for rapid growth repeatedly favoured large deletions in the non-pathogen Methylobacterium extorquens [45], and deletion mutants have been shown to exhibit faster growth in pathogens including Salmonella enterica, Pseudomonas putida and Mycoplasma genitalium [46-48]. However, E. coli has yielded mixed results [49-52], and at broad taxonomic scales, there is little correlation between doubling time and genome size [53-55], perhaps because bacterial replication can be initiated continuously [56].

An alternative scenario (Table 1ii) is that some subset of genes could become deleterious for the bacteria, because of their sequence, as opposed to their length. This scenario would link to pathogenicity if the genes were immune targets, whose loss keeps the bacteria hidden from the host [28]. Evidence of this process comes from pathogenic Bordetella pertussis and Shigella, which have lost genes involved in flagella synthesis [28, 57]. Mammalian hosts can detect the conserved domains of flagellin monomers through the Toll-like receptor 5 (TLR-5), which triggers pro-inflammatory and adaptive immune responses [58]. Similarly, pathogenic Yersinia pestis has lost lpxL, which encodes an acyltransferase that modifies bacterial lipopolysaccharide (LPS) and stimulates host TLR-4. When a cloned lpXL gene from E. coli was introduced in to Y. pestis, infection was effectively cleared from mice, rendering the Y. pestis mutant avirulent [59].

Finally (Table 1iii), a relatively benign or homogeneous host environment can lead to some genes becoming less necessary to the bacterium [6, 10, 60-62]. This relaxation of purifying selection could lead to genome reduction if combined with a mutational bias towards deletions – which does seem to be present in bacteria [43, 53].

This last scenario applies to any host-restricted bacteria, since loss of the ability to grow outside of host tissues implies a net reduction in metabolic capability, and it has been proposed to explain genome reduction in host-restricted commensals and mutualists. However, a direct link to pathogenicity would apply when virulence involves the use of host resources with deleterious consequences for the host, e.g. bacteria producing siderophores that sequester iron from red blood cells [63-65]. The resources available can also differ in critically ill hosts, since energy production can shift from mitochondrial oxidation to glycolysis, resulting in an abundance of simple carbohydrates becoming available for the bacteria [62, 66].

There are now many examples of hosts or their microbiomes provisioning auxotrophic pathogens [67], and of gene loss that is plausibly due to such provisioning [6]. For example, isolates of Pseudomonas aeruginosa derived from the lungs of cystic fibrosis patients often lose genes involved in carbohydrate metabolism, because they are able to utilise several amino acids found within sputum [60, 61]. Similarly, genes involved in anaerobic metabolism have been lost in more virulent extra-intestinal Salmonella pathovars [10, 62]. Nevertheless, a reduction in metabolic capability need not be caused by host provisioning, and in most cases, we know little about host growth environments in vivo.

Changes in the population genetic environment

The efficacy of natural selection depends crucially on parameters such as the population size and mutation rate. A second set of scenarios (Table 1iv-vi) invokes changes in these parameters, rather than changes in the selection pressures. Again, these changes might result from the evolution of pathogenicity, or help to cause it; for example, by pushing the bacterial population to the vicinity of a new and pathogenic fitness peak.

The first scenario of this kind involves changes in the strength of genetic drift, as quantified by the effective population size (Table 1iv). Drift can lead to maladaptive genome degradation, since natural selection becomes ineffective when drift is too strong [43, 68]. This is often invoked to explain genome reduction in intracellular symbionts [20].

Even bacteria with huge census sizes could experience high levels of drift, due either to demographic processes, or to high levels of clonality, which leads to purifying selection amplifying drift. Each of these factors could be important in pathogenic bacteria. Regarding demography, host restriction could create high levels of population structure; infection of new host individuals could involve severe bottlenecks, in which a few founding cells replicate and undergo clonal expansion [39, 69, 70]; and host immune response could decimate, but fail to eliminate, the bacterial colonists. Regarding clonality, there is no obvious association with pathogenicity per se, but many obligate pathogens do have low recombination rates [71], and in Streptococcus suis, strains associated with systemic infection show lower recombination rates than non-clinical strains [5].

Whatever causes the drift, there is some evidence of maladaptive genome degradation acting in bacterial pathogens. Direct experimental evidence comes from Salmonella typhimurium isolate LT2 [68]; subjected to repeated single-cell bottlenecks, it lost of up to 5% of its chromosome within a few thousand generations. All of the reduced genomes had lower fitness than their wild-type progenitor [68].


Less directly, there are widespread correlations between small genome size, and indications that purifying selection has not been effective, including high rates of protein evolution (i.e. high dN/dS ratios) [43, 72], and low GC content [3], which sometimes implies low levels of adaptive codon usage [73]. Both higher rates of GC-to-AT mutation, and increased rates of protein evolution characterise pathogenic Shigella when compared to less or non-pathogenic E. coli [74]. However, a broad-scale comparative analysis [75], found that pathogens had stronger codon usage bias than non-pathogens, which is the opposite of the predicted effect. Furthermore, the signatures can be difficult to interpret: accelerated protein evolution can arise from positive selection [37, 76], and if traits such as genome size or codon usage are under stabilising selection, rather than directional selection, then no clear relationship with the effective population size is predicted [77].

Two other scenarios invoke changes in the rate or spectrum of mutations, rather than drift (Table 1v-vi). First, transitions to pathogenicity might result in reduced opportunities for gene gain via horizontal gene transfer (Table 1v; [78-80]). This is a plausible corollary of intracellularity, and there is evidence that intracellular bacteria harbour less mobile DNA (phage, plasmid, and transposon, integrons and ICEs) than extracellular bacteria [81]. Reduced opportunities might also be caused by other features of the genomes [80], including low GC content, or, less speculatively, prior deletion events. For example, in the opportunistic periodontal pathogen Aggregatibacter actinomycetemcomitans, many strains have lost competence, and this is associated with smaller genome size, and loss of CRISPR function [82].

A second possibility (Table 1vi) is that pathogens might experience higher rates of deletion mutations [43, 53]. As with rates of gene exchange, changes in mutation rates could be either environmental, or genomic, or some combination of the two [83].

Speculative environmental sources include host-induced mutagenesis, as is known to occur as an antiviral defence [84], or mutagenic microniches, such as the gastric niche of Helicobacter pylori [85]. Increased levels of stress might also be mutagenic; heat shock proteins and chaperonins are often up-regulated in bacteria during infection [86], and chaperonins are more functionally divergent in pathogens, than in other intracellular bacteria [87].

Evidence for increases in mutation rate due to changes in the genome, include mutator strains found in clinical samples of many pathogens, including Escherichia coli, Salmonella, Pseudomonas aeruginosa, Helicobacter pylori, Staphylococcus aureus, Mycobacterium tuberculosis, Haemophilus influenzae, Klebsiella pneumoniae, and Stenotrophomonas maltophilia [88, 89]. But not all mutators are pathogens [90], and despite a suggestive link to epidemicity in Neisseria meningitidis [91], it remains unclear whether hypermutators are genuinely overrepresented in virulent strains [83, 89, 90, 92]. There are also virulent pathogens with highly reduced genomes that have lost genes in DNA repair pathways (e.g. Rickettsia prowazekii and Rickettsia typhi [93], Mycoplasma spp. [94], Mycobacterium tuberculosis and Mycobacterium leprae [95, 96]). Again, however, this is not limited to pathogens [97, 98], and does not always result in a net increase in mutation rate [16] (though see [68]). Finally, and most directly, higher background mutation rates, though lower rates of stress-induced mutation, have been found in multiple independently-derived pathogenic strains of E. coli, when compared to their commensal relatives [83].

Byproducts of adaptive substitution

A third set of scenarios combines elements of the other two (Table 1vii-viii); they invoke changes to the population genetic environment, which are themselves caused by high rates of adaptive substitution. This high rate might result from adaptation to pathogenicity, or ongoing arms-race dynamics following the adoption of a pathogenic niche.

The first such scenario (Table 1vii) invokes the hitchhiking to fixation of deletions that are not themselves adaptive. This is really a special case of scenario iv (reduced effective population size), in which natural selection acting on some sites causes genetic drift at other sites. The drift caused by hitchhiking will only have a substantial effect when homologous recombination is low (see [99] for an experimental demonstration in yeast), and have especially long-lasting effects for deletion mutations, because of their highly asymmetric mutation rates [43]. Low rates of insertion back mutations could prevent restoration of deleted regions, even when these are selectively favoured.

A final scenario (Table 1viii) involves the hitchhiking of mutator alleles (or facultative mutators), which then cause deletions [37], as opposed to hitchhiking of the deletions themselves. Intuitive arguments about such “second-order” effects can be misleading, but modelling and experiments agree that when rates of homologous recombination are low, bacterial mutators can increase in frequency in environments where many beneficial alleles are possible, despite their also generating larger numbers of deleterious mutations [37, 100]. This hypothesis was invoked to explain genome reduction in free-living bacteria, which have very large population sizes [37], but it might also apply to pathogens with prevalent mutator strains, or lacking DNA repair pathways (see above). Asymmetrical mutation or ongoing arms-races might explain why there is no reversion to lower mutation rates.

Concluding remarks

This article has reviewed the possible association between reductive genome evolution and bacterial pathogenicity (RGEP). One major conclusion is how little we know about this association. Major questions remain about its generality, and how, if at all, the association is causal.

We have arranged this review according to a third consideration: the population genetic processes that might be involved in genome reduction.

This arrangement cuts across the factors that are most relevant to the patient and clinician (Is the infection systemic or localised, mild or life-threatening?), and to the evolutionary success of the bacterium (Is the virulence adaptive or maladaptive?). The latter approach is the province of optimal virulence theory, and is discussed in Box 3. Our focus on population genetics, has allowed us to draw connections to better-studied examples of genome reduction, in endosymbionts and free-living marine bacteria.

One surprising, if tentative conclusion to emerge from this review is that a replicated and predictable pattern of genome evolution (RGEP) need not have a single underlying cause. Most of the scenarios listed in Table 1 have a plausible link to pathogenicity, and there is evidence (experimental, comparative or both) for most of them acting in one or more bacterial pathogens. There is even evidence for multiple processes acting in a single lineage. For example, in the Shigella/Escherichia coli complex, where more virulent Shigella genomes are smaller, we see evidence within Shigella of the plausibly selective loss of antivirulence genes [30], of host provisioning leading to some genes becoming unnecessary [15] and of ineffective purifying selection [14, 74]. In E. coli, we see evidence of deletions linked to a growth advantage [51, 52], and of mutators increasing the rate of adaptation to novel environments [100], and appearing at higher frequency in clinical isolates [83]. Perhaps, then, we should not view the scenarios in Table 1 as clearly distinct. This was already evident from our scenarios vii-viii, which involve hitchhiking, where positive selection amplifies the action of genetic drift. But there is a much wider range of potential feedbacks and interactions between the scenarios, and some of these are described in Box 4.

A second conclusion is that examination of the population genetics, alone, tells us surprisingly little about the causal questions. All eight of the scenarios listed in Table 1 are consistent with genome reduction following as a consequence of pathogenicity, and 7/8 (excepting scenario v, reduced rates of gene gain) are consistent with genome reduction causing pathogenicity.

Finally, the approach does have some implications for the design of preventative or clinical interventions. The first, negative conclusion, familiar from the study of optimal virulence (Box 3), is that all such interventions can have unforeseen consequences. Second, if our goal is to identify common factors between independent origins of pathogenicity, this review makes clear that these common causal factors might be of quite different types.

For example, if reduced population size predictably leads to pathogenicity (as in scenario iv), then prophylactic treatments with incomplete bacterial suppression could be worse than no treatment at all; and this applies entirely regardless of the types of genomic change that cause pathogenicity. Conversely, if the losses of specific antivirulence genes are a common precondition for pathogenicity, we can synthesise their products as novel therapeutics; and this applies entirely regardless of the fitness effects of the deletions for the bacteria, and the population genetic processes that might lead to their loss.



Acknowledgements

We are very grateful to Jane Charlesworth, Eric Miller, Laurence Hurst and two anonymous reviewers for their helpful suggestions. LW is supported by a Dorothy Hodgkin Fellowship funded by the Royal Society (Grant Number DH140195) and a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 109385/Z/15/Z).



GLOSSARY

Antivirulence gene: In the narrow sense, a region of genome that functions to suppress the operation of a virulence gene. More broadly, any region whose removal or inactivation increases virulence.

Arms-race: the evolution of adaptations and counteradaptations during antagonistic coevolution (see also Red Queen).

Auxotrophic: unable to synthesise a compound required for growth.

dN/dS: In protein-coding sequences, the rate of substitutions that change the amino acid, divided by the rate of synonymous changes that leave the amino acid unchanged. If synonymous changes are under very weak or ineffective selection, the ratio tells us something about selection acting on the protein sequence.

Effective population size: A measure of genetic drift. Determines the efficacy, as opposed to the strength, of natural selection.

Fitness peak: Defined here as a genotype that is fitter than sequences differing by one or a few mutations.

Gene: Used here in the population genetics sense, to refer to any region of genome.

GWAS: Genome-wide association study. A set of techniques for associating allelic variants with phenotypic differences.

Hitchhiking: In the broadest sense, changes in allele frequency caused by natural selection on linked loci. An example of genetic drift caused by natural selection.

Host restriction: Bacteria are increasingly host restricted if they spend more time in a single host species, as opposed to alternative host species or free-living in the environment.

ICE: Integrative and conjugative elements, or conjugative transposons. A class of mobile genetic elements.

Opportunistic pathogen: An organism that causes disease only under certain circumstances: most characteristically, failures of host immune response, or integumentary barriers.

Pathogens: Organisms that cause disease.

Pathovar: a bacterial strain that differs in pathogenicity from other conspecific strains.

Red Queen: A type of antagonistic coevolution, which resembles arms races, but need not involve alleles reaching fixation, nor global fitness improvement, relative to the ancestral type.

SNP: Single-nucleotide polymorphism

Streamlining: Reductive genome evolution, whose phenotypic effect is due primarily to the change in genome size per se.

Systemic infection: Infection of the entire body.

Virulence: Defined here as the degree of harm caused to host. Virulence can be adaptive for the parasite or maladaptive, e.g., if death of the host reduces the parasite’s long-term reproductive success.

TABLES

Table 1: Possible population genetic scenarios that might explain an association between reductive genome evolution and pathogenicity

Population genetic scenario

Example of possible evidence


(1) Changes in selection pressures



(i)

Selection for smaller genome

Deletions linked to faster growth in Salmonella enterica [47]


(ii)

Some genes become actively deleterious

Loss of immune targets in Yersinia pestis [59]


(iii)

Some genes become less necessary

Loss of genes involved in carbohydrate metabolism in Pseudomonas aeruginosa [61]

(2) Changes in population genetic environment



(iv)

Reduced effective population size

Ineffective purifying selection in Shigella genomes [74]


(v)

Reduced rates of gene gain

Host-restricted bacteria harbour less mobile DNA [81]


(vi)

Increased rates of deletion

High prevalence of mutators in epidemic Neisseria meningitidis [91]

(3) Byproducts of adaptive substitution



(vii)

Hitchhiking of deletions

Asexual clones have more deletions in experimental populations of yeast [99]


(viii)

Hitchhiking of mutators

Mismatch repair mutants in Escherichia coli adapt more quickly to anaerobic environments [100]


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“POLKA WITH ALL YOUR MIGHT!” (1999) 1 LONG LIVE
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