Hazardous Loss of Genetic Diversity through Selective Sweeps in Asexual Populations.

AbstractWith the twofold cost of sex, derived asexual organisms have an immediate reproductive advantage over their sexual sisters. Yet the "twiggy" phylogenetic distribution of asexual lineages implies that they become extinct relatively quickly over evolutionary time. Meanwhile, bacteria and archaea have persisted for billions of years without requiring sexual reproduction. A simple explanation for this difference is that prokaryotes have very large population sizes that are not subject to the accumulation of deleterious mutations, but this implies that drift and mutational meltdown dominate derived asexual populations. Here, we explore a different hazard, quantifying the degree to which genetic variation is lost in asexual populations experiencing selective sweeps. Even though large populations generate diversity by mutation during sweeps, we find that populations that are safe from mutational meltdown may still be reduced to dangerous effective population sizes by sweeps. Thus, ironically, adaptation itself reduces further adaptive potential and may predispose asexual populations to extinction. We derive a simple approximation for the effective population size after a hard sweep and explore the impact of recent sweeps on evolutionary rescue. These factors may help to explain the phylogenetic twigginess of asexuals, the maintenance of sex and recombination, and the evolutionary persistence of prokaryotes.

Surviving environmental change: when increasing population size can increase extinction risk.

Populations threatened by an abrupt environmental change-due to rapid climate change, pathogens or invasive competitors-may survive if they possess or generate genetic combinations adapted to the novel, challenging condition. If these genotypes are initially rare or non-existent, the emergence of lineages that allow a declining population to survive is known as 'evolutionary rescue'. By contrast, the genotypes required for survival could, by chance, be common before the environmental change. Here, considering both of these possibilities, we find that the risk of extinction can be lower in very small or very large populations, but peaks at intermediate population sizes. This pattern occurs when the survival genotype has a small deleterious effect before the environmental change. Since mildly deleterious mutations constitute a large fraction of empirically measured fitness effects, we suggest that this unexpected result-an intermediate size that puts a population at a greater risk of extinction-may not be unusual in the face of environmental change.

The role of a programmatic immune response on the evolution of pathogen traits.

In modelling pathogen evolution during epidemics, it is important to understand the interactions between within-host infection dynamics and between-host pathogen transmission. Multiscale models often assume an immune response that is highly responsive to pathogen dynamics. Empirical evidence, however, suggests that the immune response in acute infections is triggered and programmatic. This leads to somewhat more predictable infection trajectories where transition times and, consequently, the infectious window are non-exponentially distributed. Here, we develop a within-host model where the immune response is triggered by pathogen growth but otherwise develops independently, and use this to obtain analytic expressions for the infectious period and peak pathogen load. This allows us to model the basic reproductive number in terms of explicit functional relationships among within-host traits including the growth rate of the pathogen. We find that the dependence of pathogen load and the infectious window on within-host parameters constrains the evolution of the pathogen growth rate. At low growth rate, selection favours a higher pathogen load and therefore increasing pathogen growth rate. At high growth rates, selection for a longer infectious window trades off against selection against the effects of virulence. At intermediate growth rates the basic reproductive number is relatively insensitive to changes in the growth rate. The resulting "flat" region of the pathogen fitness landscape is due to the stability of the programmatic immune response in clearing the infection.

Spatial dynamics of inflammation-causing and commensal bacteria in the gastrointestinal tract.

In recent years, new research programmes have been initiated to understand the role of gut bacteria in health and disease, enabled in large part by the emergence of high-throughput sequencing. As new genomic and other data emerge it will become important to explain observations in terms of underlying population mechanisms; for instance, it is of interest to understand how resident bacteria interact with their hosts and pathogens, and how they play a protective role. Connecting underlying processes with observed patterns is aided by the development of mathematical models. Here, we develop a spatial model of microbial populations in the gastrointestinal tract to explore conditions under which inflammation-causing bacteria can invade the gut and under which such pathogens become persistent. We find that pathogens invade both small and large intestine from even a relatively small inoculum size but are usually eliminated by the host response. When the immune response is weak, the pathogen is able to persist for a long period. Spatial structure affects these dynamics by creating moving refugia which facilitate bouts of pathogen resurgence and inflammation in persistent infections. Space also plays a role in repopulation by commensals after infection. We further find that the rate of decay of inflammation has a stronger effect on outcomes than the initiation of inflammation or other parameters. Finally, we explore the impact of partially inflammation-resistant commensals on these dynamics.

Modelling the effect of birth and feeding modes on the development of human gut microbiota.

The human gut microbiota is transmitted from mother to infant through vaginal birth and breastfeeding. Bifidobacterium, a genus that dominates the infants' gut, is adapted to breast milk in its ability to metabolize human milk oligosaccharides; it is regarded as a mutualist owing to its involvement in the development of the immune system. The composition of microbiota, including the abundance of Bifidobacteria, is highly variable between individuals and some microbial profiles are associated with diseases. However, whether and how birth and feeding practices contribute to such variation remains unclear. To understand how early events affect the establishment of microbiota, we develop a mathematical model of two types of Bifidobacteria and a generic compartment of commensal competitors. We show how early events affect competition between mutualists and commensals and microbe-host-immune interactions to cause long-term alterations in gut microbial profiles. Bifidobacteria associated with breast milk can trigger immune responses with lasting effects on the microbial community structure. Our model shows that, in response to a change in birth environment, competition alone can produce two distinct microbial profiles post-weaning. Adding immune regulation to our competition model allows for variations in microbial profiles in response to different feeding practices. This analysis highlights the importance of microbe-microbe and microbe-host interactions in shaping the gut populations following different birth and feeding modes.

Evolution of the Stator Elements of Rotary Prokaryote Motors.

The bacterial flagellar motor is driven by an ion flux that is converted to torque by motor-attendant complexes known as stators. The dynamics of stator assembly around the motor in response to external stimuli have been the subject of much recent research, but less is known about the evolutionary origins of stator complexes and how they select for specific ions. Here, we review the latest structural and biochemical data for the stator complexes and compare these with other ion transporters and microbial motors to examine possible evolutionary origins of the stator complex.

Nonsynonymous Polymorphism Counts in Bacterial Genomes: a Comparative Examination.

Genomic data reveal single-nucleotide polymorphisms (SNPs) that may carry information about the evolutionary history of bacteria. However, it remains unclear what inferences about selection can be made from genomic SNP data. Bacterial species are often sampled during epidemic outbreaks or within hosts during the course of chronic infections. SNPs obtained from genomic analysis of these data are not necessarily fixed. Treating them as fixed during analysis by using measures such as the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS) may lead to incorrect inferences about the strength and direction of selection. In this study, we consider data from a range of whole-genome sequencing studies of bacterial pathogens and explore patterns of nonsynonymous variation to assess whether evidence of selection can be identified by investigating SNP counts alone across multiple WGS studies. We visualize these SNP data in ways that highlight their relationship to neutral baseline expectations. These neutral expectations are based on a simple model of mutation, from which we simulate SNP accumulation to investigate how SNP counts are distributed under alternative assumptions about positive and negative selection. We compare these patterns with empirical SNP data and illustrate the general difficulty of detecting positive selection from SNP data. Finally, we consider whether SNP counts observed at the between-host population level differ from those observed at the within-host level and find some evidence that suggests that dynamics across these two scales are driven by different underlying processes.IMPORTANCE Identifying selection from SNP data obtained from whole-genome sequencing studies is challenging. Some current measures used to identify and quantify selection acting on genomes rely on fixed differences; thus, these are inappropriate for SNP data where variants are not fixed. With the increase in whole-genome sequencing studies, it is important to consider SNP data in the context of evolutionary processes. How SNPs are counted and analyzed can help in understanding mutation accumulation and trajectories of strains. We developed a tool for identifying possible evidence of selection and for comparative analysis with other SNP data. We propose a model that provides a rule-of-thumb guideline and two new visualization techniques that can be used to interpret and compare SNP data. We quantify the expected proportion of nonsynonymous SNPs in coding regions under neutrality and demonstrate its use in identifying evidence of positive and negative selection from simulations and empirical data.

Multilevel genome typing: genomics-guided scalable resolution typing of microbial pathogens.

BackgroundBoth long- and short-term epidemiology are fundamental to disease control and require accurate bacterial typing. Genomic data resulting from implementation of whole genome sequencing in many public health laboratories can potentially provide highly sensitive and accurate descriptions of strain relatedness. Previous typing efforts using these data have mainly focussed on outbreak detection.AimWe aimed to develop multilevel genome typing (MGT), using consecutive multilocus sequence typing (MLST) schemes of increasing sizes, stepping up from seven-gene MLST to core genome MLST, to allow examination of genetic relatedness at multiple resolution levels.MethodsThe system was applied to Salmonellaenterica serovar Typhimurium. The MLST scheme used at each step (MGT level), defined a given MGT-level specific sequence type (ST). The list of STs generated from all of these increasing MGT levels, was named a genome type (GT). Using MGT, we typed 9,096 previously characterised isolates with publicly available data.ResultsOur approach could identify previously described S. Typhimurium populations, such as the DT104 multidrug resistance lineage (GT 19-2-11) and two invasive lineages of African isolates (GT 313-2-3 and 313-2-752). Further, we showed that MGT-derived clusters can accurately distinguish five outbreaks from each other and five background isolates.ConclusionMGT provides a universal and stable nomenclature at multiple resolutions for S. Typhimurium strains and could be implemented as an internationally standardised strain identification system. While established so far only for S. Typhimurium, the results here suggest that MGT could form the basis for typing systems in other similar microorganisms.

Controlled fire use in early humans might have triggered the evolutionary emergence of tuberculosis.

Tuberculosis (TB) is caused by the Mycobacterium tuberculosis complex (MTBC), a wildly successful group of organisms and the leading cause of death resulting from a single bacterial pathogen worldwide. It is generally accepted that MTBC established itself in human populations in Africa and that animal-infecting strains diverged from human strains. However, the precise causal factors of TB emergence remain unknown. Here, we propose that the advent of controlled fire use in early humans created the ideal conditions for the emergence of TB as a transmissible disease. This hypothesis is supported by mathematical modeling together with a synthesis of evidence from epidemiology, evolutionary genetics, and paleoanthropology.

The Role of Pleiotropy in the Evolutionary Maintenance of Positive Niche Construction.

Organisms often modify their environments to their advantage through a process of niche construction. Environments that are improved through positive niche construction can be viewed as a public good. If free riders appear that do not contribute to the shared resource and therefore do not incur any associated costs, the constructed niche may become degraded, resulting in a tragedy of the commons and the extinction of niche constructors. Niche construction can persist if free riders are excluded, for example, if niche constructors monopolize the resource they produce to a sufficient degree. We suggest, however, that the problem of free riders remains because it is possible that nonniche constructors with an enhanced ability to access the resource appear and invade a population of constructors. Using mathematical models we show that positive niche construction can be maintained if it is inextricably linked to a mechanism that makes free riding costly, such as a trait that confers a benefit to only niche constructors. We discuss this finding in terms of genetic interactions and illustrate the principle with a two-locus model. We conclude that positive niche construction can both evolve and be maintained when it has other beneficial effects via pleiotropy. This situation may apply generally to the evolutionary maintenance of cooperation.

Maximum likelihood estimates of pairwise rearrangement distances.

Accurate estimation of evolutionary distances between taxa is important for many phylogenetic reconstruction methods. Distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements. Corresponding corrections for genome rearrangement distances fall into 3 categories: Empirical computational studies, Bayesian/MCMC approaches, and combinatorial approaches. Here, we introduce a maximum likelihood estimator for the inversion distance between a pair of genomes, using a group-theoretic approach to modelling inversions introduced recently. This MLE functions as a corrected distance: in particular, we show that because of the way sequences of inversions interact with each other, it is quite possible for minimal distance and MLE distance to differently order the distances of two genomes from a third. The second aspect tackles the problem of accounting for the symmetries of circular arrangements. While, generally, a frame of reference is locked, and all computation made accordingly, this work incorporates the action of the dihedral group so that distance estimates are free from any a priori frame of reference. The philosophy of accounting for symmetries can be applied to any existing correction method, for which examples are offered.

Mathematical Models for the Epidemiology and Evolution of Mycobacterium tuberculosis.

This chapter reviews the use of mathematical and computational models to facilitate understanding of the epidemiology and evolution of Mycobacterium tuberculosis. First, we introduce general epidemiological models, and describe their use with respect to epidemiological dynamics of a single strain and of multiple strains of M. tuberculosis. In particular, we discuss multi-strain models that include drug sensitivity and drug resistance. Second, we describe models for the evolution of M. tuberculosis within and between hosts, and how the resulting diversity of strains can be assessed by considering the evolutionary relationships among different strains. Third, we discuss developments in integrating evolutionary and epidemiological models to analyse M. tuberculosis genetic sequencing data. We conclude the chapter with a discussion of the practical implications of modelling - particularly modelling strain diversity - for controlling the spread of tuberculosis, and future directions for research in this area.

The emergence of latent infection in the early evolution of Mycobacterium tuberculosis.

Mycobacterium tuberculosis has an unusual natural history in that the vast majority of its human hosts enter a latent state that is both non-infectious and devoid of any symptoms of disease. From the pathogen perspective, it seems counterproductive to relinquish reproductive opportunities to achieve a détente with the host immune response. However, a small fraction of latent infections reactivate to the disease state. Thus, latency has been argued to provide a safe harbour for future infections which optimizes the persistence of M. tuberculosis in human populations. Yet, if a pathogen begins interactions with humans as an active disease without latency, how could it begin to evolve latency properties without incurring an immediate reproductive disadvantage? We address this question with a mathematical model. Results suggest that the emergence of tuberculosis latency may have been enabled by a mechanism akin to cryptic genetic variation in that detrimental latency properties were hidden from natural selection until their expression became evolutionarily favoured.

Modelling the role of immunity in reversion of viral antigenic sites.

Antigenic sites in viral pathogens exhibit distinctive evolutionary dynamics due to their role in evading recognition by host immunity. Antigenic selection is known to drive higher rates of non-synonymous substitution; less well understood is why differences are observed between viruses in their propensity to mutate to a novel or previously encountered amino acid. Here, we present a model to explain patterns of antigenic reversion and forward substitution in terms of the epidemiological and molecular processes of the viral population. We develop an analytical three-strain model and extend the analysis to a multi-site model to predict characteristics of observed sequence samples. Our model provides insight into how the balance between selection to escape immunity and to maintain viability is affected by the rate of mutational input. We also show that while low probabilities of reversion may be due to either a low cost of immune escape or slowly decaying host immunity, these two scenarios can be differentiated by the frequency patterns at antigenic sites. Comparison between frequency patterns of human influenza A (H3N2) and human RSV-A suggests that the increased rates of antigenic reversion in RSV-A is due to faster decaying immunity and not higher costs of escape.

The genome-wide DNA sequence specificity of the anti-tumour drug bleomycin in human cells.

The cancer chemotherapeutic agent, bleomycin, cleaves DNA at specific sites. For the first time, the genome-wide DNA sequence specificity of bleomycin breakage was determined in human cells. Utilising Illumina next-generation DNA sequencing techniques, over 200 million bleomycin cleavage sites were examined to elucidate the bleomycin genome-wide DNA selectivity. The genome-wide bleomycin cleavage data were analysed by four different methods to determine the cellular DNA sequence specificity of bleomycin strand breakage. For the most highly cleaved DNA sequences, the preferred site of bleomycin breakage was at 5'-GT* dinucleotide sequences (where the asterisk indicates the bleomycin cleavage site), with lesser cleavage at 5'-GC* dinucleotides. This investigation also determined longer bleomycin cleavage sequences, with preferred cleavage at 5'-GT*A and 5'- TGT* trinucleotide sequences, and 5'-TGT*A tetranucleotides. For cellular DNA, the hexanucleotide DNA sequence 5'-RTGT*AY (where R is a purine and Y is a pyrimidine) was the most highly cleaved DNA sequence. It was striking that alternating purine-pyrimidine sequences were highly cleaved by bleomycin. The highest intensity cleavage sites in cellular and purified DNA were very similar although there were some minor differences. Statistical nucleotide frequency analysis indicated a G nucleotide was present at the -3 position (relative to the cleavage site) in cellular DNA but was absent in purified DNA.

Dynamics of bacterial insertion sequences: can transposition bursts help the elements persist?

BACKGROUND: Currently there is no satisfactory explanation for why bacterial insertion sequences (ISs) widely occur across prokaryotes despite being mostly harmful to their host genomes. Rates of horizontal gene transfer are likely to be too low to maintain ISs within a population. IS-induced beneficial mutations may be important for both prevalence of ISs and microbial adaptation to changing environments but may be too rare to sustain IS elements in the long run. Environmental stress can induce elevated rates of IS transposition activities; such episodes are known as 'transposition bursts'. By examining how selective forces and transposition events interact to influence IS dynamics, this study asks whether transposition bursts can lead to IS persistence. RESULTS: We show through a simulation model that ISs are gradually eliminated from a population even if IS transpositions occasionally cause advantageous mutations. With beneficial mutations, transposition bursts create variation in IS copy numbers and improve cell fitness on average. However, these benefits are not usually sufficient to overcome the negative selection against the elements, and transposition bursts amplify the mean fitness effect which, if negative, simply accelerates the extinction of ISs. If down regulation of transposition occurs, IS extinctions are reduced while ISs still generate variation amongst bacterial genomes. CONCLUSIONS: Transposition bursts do not help ISs persist in a bacterial population in the long run because most burst-induced mutations are deleterious and therefore not favoured by natural selection. However, bursts do create more genetic variation through which occasional advantageous mutations can help organisms adapt. Regulation of IS transposition bursts and stronger positive selection of the elements interact to slow down the burst-induced extinction of ISs.

Delineating community outbreaks of Salmonella enterica serovar Typhimurium by use of whole-genome sequencing: insights into genomic variability within an outbreak.

Whole-genome next-generation sequencing (NGS) was used to retrospectively examine 57 isolates from five epidemiologically confirmed community outbreaks (numbered 1 to 5) caused by Salmonella enterica serovar Typhimurium phage type DT170. Most of the human and environmental isolates confirmed epidemiologically to be involved in the outbreaks were either genomically identical or differed by one or two single nucleotide polymorphisms (SNPs), with the exception of those in outbreak 1. The isolates from outbreak 1 differed by up to 12 SNPs, which suggests that the food source of the outbreak was contaminated with more than one strain while each of the other four outbreaks was caused by a single strain. In addition, NGS analysis ruled in isolates that were initially not considered to be linked with the outbreak, which increased the total outbreak size by 107%. The mutation process was modeled by using known mutation rates to derive a cutoff value for the number of SNP difference to determine whether or not a case was part of an outbreak. For an outbreak with less than 1 month of ex vivo/in vivo evolution time, the maximum number of SNP differences between isolates is two or four using the lowest or highest mutation rate, respectively. NGS of S. Typhimurium significantly increases the resolution of investigations of community outbreaks. It can also inform a more targeted public health response by providing important supplementary evidence that cases of disease are or are not associated with food-borne outbreaks of S. Typhimurium.

Defining the Core Genome of Salmonella enterica Serovar Typhimurium for Genomic Surveillance and Epidemiological Typing.

Salmonella enterica serovar Typhimurium is the most common Salmonella serovar causing foodborne infections in Australia and many other countries. Twenty-one S. Typhimurium strains from Salmonella reference collection A (SARA) were analyzed using Illumina high-throughput genome sequencing. Single nucleotide polymorphisms (SNPs) in 21 SARA strains ranged from 46 to 11,916 SNPs, with an average of 1,577 SNPs per strain. Together with 47 strains selected from publicly available S. Typhimurium genomes, the S. Typhimurium core genes (STCG) were determined. The STCG consist of 3,846 genes, a set that is much larger than that of the 2,882 Salmonella core genes (SCG) found previously. The STCG together with 1,576 core intergenic regions (IGRs) were defined as the S. Typhimurium core genome. Using 93 S. Typhimurium genomes from 13 epidemiologically confirmed community outbreaks, we demonstrated that typing based on the S. Typhimurium core genome (STCG plus core IGRs) provides superior resolution and higher discriminatory power than that based on SCG for outbreak investigation and molecular epidemiology of S. Typhimurium. STCG and STCG plus core IGR typing achieved 100% separation of all outbreaks compared to that of SCG typing, which failed to separate isolates from two outbreaks from background isolates. Defining the S. Typhimurium core genome allows standardization of genes/regions to be used for high-resolution epidemiological typing and genomic surveillance of S. Typhimurium.

Genomic Variability of Serial Human Isolates of Salmonella enterica Serovar Typhimurium Associated with Prolonged Carriage.

Salmonella enterica serovar Typhimurium is an important foodborne human pathogen that often causes self-limiting but severe gastroenteritis. Prolonged excretion of S. Typhimurium after the infection can lead to secondary transmissions. However, little is known about within-host genomic variation in bacteria associated with asymptomatic shedding. Genomes of 35 longitudinal isolates of S. Typhimurium recovered from 11 patients (children and adults) with culture-confirmed gastroenteritis were sequenced. There were three or four isolates obtained from each patient. Single nucleotide polymorphisms (SNPs) were analyzed in these isolates, which were recovered between 1 and 279 days after the initial diagnosis. Limited genomic variation (5 SNPs or fewer) was associated with short- and long-term carriage of S. Typhimurium. None of the isolates was shown to be due to reinfection. SNPs occurred randomly, and the majority of the SNPs were nonsynonymous. Two nonsense mutations were observed. A nonsense mutation in flhC rendered the isolate nonmotile, whereas the significance of a nonsense mutation in yihV is unknown. The estimated mutation rate is 1.49 × 10(-6) substitution per site per year. S. Typhimurium isolates excreted in stools following acute gastroenteritis in children and adults demonstrated limited genomic variability over time, regardless of the duration of carriage. These findings have important implications for the detection of possible transmission events suspected by public health genomic surveillance of S. Typhimurium infections.

Recombination within the pandemic norovirus GII.4 lineage.

Norovirus (NoV) is the leading cause of viral gastroenteritis globally. Since 1996, NoV variants of a single genetic lineage, GII.4, have been associated with at least six pandemics of acute gastroenteritis and caused between 62 and 80% of all NoV outbreaks. The emergence of these novel GII.4 variants has been attributed to rapid evolution and antigenic variation in response to herd immunity; however, the contribution of recombination as a mechanism facilitating emergence is increasingly evident. In this study, we sought to examine the role that intragenotype recombination has played in the emergence of GII.4 variants. Using a genome-wide approach including 25 complete genome sequences generated as part of this study, 11 breakpoints were identified within the NoV GII.4 lineage. The breakpoints were located at three recombination hot spots: near the open reading frame 1/2 (ORF1/2) and ORF2/3 overlaps, as well as within ORF2, which encodes the viral capsid, at the junction of the shell and protruding domains. Importantly, we show that recombination contributed to the emergence of the recent pandemic GII.4 variant, New Orleans 2009, and a newly identified GII.4 variant, termed Sydney 2012. Reconstructing the evolutionary history of the GII.4 lineage reveals the widespread impact of both inter- and intragenotype recombination on the emergence of many GII.4 variants. Lastly, this study highlights the many challenges in the identification of true recombination events and proposes that guidelines be applied for identifying NoV recombinants.