Evolutionary approaches to study the natural history of
Yersinia pestis
Author: Carien Hilvering, master’s student Infection & Immunity, Graduate school of Life Sciences,
Utrecht University
Supervisor: Dr. Willem van Schaik, Assistant professor at the department of Medical
Microbiology, University Medical Center Utrecht, The Netherlands
Abstract
The contribution of evolutionary theory to microbiology is uncontestable. Evolutionary models help
us to study the phylogenetics of highly dangerous pathogens such as Yersinia pestis – the cause of
plague - from its early history until now. Besides reconstructing history, attempts have been made to
predict how Y. pestis will evolve in future, because it is still recognized as global health threat and as
a potential biological weapon. However, evolutionary theory allows for multiple explanations and
predictions. Is it realistic to expect this theory to have predictive value or to lead to unambiguous
outcomes? Should this not be the case, what is the added value of evolutionary theory precisely? In
this review, evolutionary approaches used to study evolution of Y. pestis are discussed, with the aim
to define how evolutionary theory has contributed to knowledge about this pathogen.
Contents
Abstract ................................................................................................................................................... 1
Evolutionary theory and its application .................................................................................................. 2
Yersinia pestis .......................................................................................................................................... 3
Bacterial heredity and variation .............................................................................................................. 4
Transmission and population dynamics of Y. pestis ................................................................................ 7
Evolution and genomics of Y. pestis ........................................................................................................ 9
The contribution of evolutionary theory to knowledge about Y. pestis ............................................... 13
References ............................................................................................................................................. 15
1
Evolutionary theory and its application
Evolution is the change in inheritable traits of an entity or population over successive generations.
Change is possible, because of the random mutations, recombinations and exchange occurring in
genetic material, which leads to diversification of organisms. Furthermore, evolutionary pressure
determines whether changes are successively passed on to later generations. Specifically,
evolutionary pressure is anything that reduces the reproductive success of an entity. On the other
hand, traits that improve reproductive success are selected by a process called natural selection. The
theory of evolution is often simplified by the term ‘survival of the fittest’(1-4).
Evolutionary theory provides an explanation for biological changes. Furthermore, it introduces
concepts and processes that may influence biological changes. How can this be used to gain
knowledge about microbial pathogens and what kind of questions can it answer?
Darwin himself already tried to extract laws from his theory of evolution and base predictions on
them(3). These attempts were not very satisfactory, because fitness of an organism depends on the
environment in the broadest possible sense, which thwarts the ability to predict survival(3). The role
of the environment in survival and fitness can be divided in direct effects and indirect effects (figure
1).
Figure 1 Schematic overview of influences on the survival of organisms. Direct and indirect environmental
changes influence the survival of organisms. Direct environmental changes affect survival independent of the
organism’s characteristics. Indirect environmental changes affect the organism’s fitness, which thereby affects
survival. Mutation influences fitness of an organism as well.
Direct environmental effects are changes in the environment that influence the organism’s survival
directly, which means that they are totally unconnected to the organism’s characteristics. In contrast,
indirect environmental effects are environmental changes that influence the fitness of an organism,
thereby indirectly affecting survival of the organism. For instance, a meteorite crash belongs to
environmental changes that directly affect survival, because survival is dependent on fortune and not
2
on fitness (e.g. one of an identical twin may survive while the other may not)(3). Direct effects are
mostly accidental and sudden environmental changes. Examples of environmental changes that
indirectly affect survival are changes in temperature and availability of energy sources. In these
cases, survival of the organism depends on its capacity to adapt to the changes. Indirect
environmental changes are mostly slow and continuous changes. The influence of the environment
implies that we can only predict survival of species when we are able to predict environmental
changes(3).
Besides the environment, mutations influence fitness of organisms as well. Stochasticity –
randomness- in the occurrence of mutations and recombinations makes predictions of evolution
even harder(3, 5). Additionally, the precise effect of the environment on mutations is not clear
either. We know that environmental factors such as UV light and chemicals cause mutations in
genetic material, but we are not able to predict where in the genome this will be and what the effect
on fitness of organisms is. Whether the incapacity of evolutionary theory to be predictive is just due
to a lack of data or inherent to the theory, is still ongoing(5). However, predictive value is not the
only value that contributes to scientific knowledge. In this review we will investigate how
evolutionary theory is used to obtain insights into the natural history of the bacterium Yersinia
pestis, the cause of plague.
Yersinia pestis
Yersinia pestis is a zoonotic gram-negative bacterium, which belongs to the Enterobacteriaceae
family. Y. pestis causes plague in humans and is fatal without antimicrobial treatment(6-8). Three
types of plague are distinguished based on the infected tissue. In bubonic plague, lymph nodes are
swollen due to infection and these swellings are called buboes. The second form of plague is
septicemic plague, in which high numbers of bacteria are found in the bloodstream. Blood clotting
during infection lead to the typical subcutaneous bleedings, which turn black and hence the
alternative name for plague: ‘Black Death’(6, 7). The third tissue that can be infected with Y. pestis
are the lungs during pneumonic plague. This form of plague can spread between humans without the
need for animal vectors and is therefore highly dangerous concerning epidemic spread(6, 9).
Y. pestis is thought to have caused three pandemics: The Plague of Justinian (541-750), Black Death
(14-19th centuries) and the Third Plague Pandemic (19th and 20th centuries)(8-10). The pandemic
outbreak of Black Death is a good example of a rapid emerging epidemic and is thought to be
responsible for the observed genetic divergence that has occurred in Y. pestis around that time(8).
The Black Death started in Northeastern Europe in 1347 and spread via the Mediterranean countries
3
to Northwestern Europe(11). In a period of only five years, 30-50% of the European population was
perished as a result of plague(11). The total number of victims caused by Y. pestis is estimated
around 160 million(12). Currently, 1000-3000 cases of plague are reported per year worldwide(13).
Bacterial heredity and variation
Four systems of inheritance are thought to influence the evolution of biological organisms(2): genetic
inheritance, epigenetic inheritance, a behavioral system of inheritance and a symbolic one. In this
review we will focus mainly on genetic inheritance, because genome-wide information on epigenetic
evolution of Yersinia pestis is not available and behavioral and symbolic inheritance do not apply to
bacteria.
The field of population genetics studies inheritable traits that are passed on by genetic material by
determining the frequency by which genetic patterns (alleles, genes, mobile elements) occur and
how these change over time(1, 14). This field of research has been boosted by the development of
high-throughput sequencing techniques since the mid-1990s. The possibility to sequence whole
genomes together with the relatively quick adaptive capacity of bacteria has given us much insight in
evolutionary processes at the genetic level.
Bacteria reproduce asexually by dividing the mother cell into two daughter cells in a process called
binary fission. This leads to a clonal expansion of bacterial populations, in which chromosomes of
daughter cells are identical to the initial clone(1, 4). Diversification of traits is established by de novo
mutations in chromosomal DNA, which are passed on ‘vertically’ to descendants of the cell that
acquired the mutations (box 1 SNPs and substitutions). This acquisition of random alterations in
genetic material that is passed on to next generations is called genetic drift(1, 4). Eventually, genetic
drift results in the emergence of new lineages and this can be depicted in a phylogenetic tree (box 2
a primer on phylogenetic trees). The tree structure enables us to infer how closely related species are
and define the most recent common ancestor (MRCA) that is shared by multiple species.
Furthermore, estimations about the moment in history when a bifurcation took place can be made,
based on mutations rates of bacteria(1, 4).
However, genetic drift is not the only process that alters genetic information in bacteria. Gene flow is
the ‘horizontal’ transmission of chromosomal DNA between lineages of the same species, but also
between distinct species(1). Gene flow takes place in bacteria as well by a process called horizontal
gene transfer (HGT). During HGT, DNA is exchanged by parasexual mechanisms called conjugation,
transformation and transduction(1). Horizontal exchange is likely to happen more often in the same
or closely related species, since the exchange of chromosomal DNA is mostly dependent on
4
homologous recombination. This ability of horizontal exchange has consequences for genetic
diversity, because it spreads mutations independently of lineages and can either increase or reduce
diversity(4). Furthermore, HGT affects clonality of bacterial populations, because it enables
convergence of separate lineages. The occurrence of homology due to convergent evolution is called
homoplasy(14). Consequently, the structure of a tree may not suffice when depicting population
genetics and this has resulted in the use of network or mosaic schemes of evolution(4).
Box 1: SNPs and substitutions
Single nucleotide polymorphisms (SNPs) are places in the genome where organisms differ in
one nucleotide change(10, 15, 16). SNPs can be identified by comparing the sequences of
different genomes. The number of SNPs is influenced by the mutation rate. Substitutions are
changes in the genome inherited by virtually all individual descendants of a species(15).
Therefore, knowledge about the genealogy of compared species is needed, to determine
whether the observed SNPs are substitutions. The substitution rate reflects the mutation rate
and this rate differs between species and over time(15).
In genetics, non-synonymous mutations and synonymous mutations are distinguished. A
synonymous mutation is a mutation in a protein-coding DNA sequence, which does not change
the encoded amino-acid. On the contrary, a non-synonymous mutation changes the encoded
amino-acid. One can imagine that genetic drift – the occurrence of random mutations- affects
synonymous and non-synonymous mutations equally(14, 15). On the other hand, natural
selection is suspected to have more influence on non-synonymous than synonymous
mutations, because they have larger effect on the phenotype. The effect of non-synonymous
mutations can be deleterious, but may also be strongly advantageous and therefore the ratio
of non-synonymous mutations (dN) to synonymous mutations (dS) will be affected by natural
selection. During ‘neutral evolution’, dN/dS =1. When non-synonymous mutations are
advantageous dN/dS will be >1. When non-synonymous mutations are deleterious, dN/dS will
be <1. This ratio is often used to determine the dominating evolutionary processes that take
place in a gene or in small parts of the genome(14, 15).
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Box 2: A primer on phylogenetic trees
Phylogenetic trees are essentially graphic representations of the inferred evolutionary relationships
between entities/characters (species, genomes, short sequences). The distance between two characters in
a phylogenetic tree resembles the evolutionary distance between those two characters. The construction
of the network or tree can be performed using different mathematical approaches and in order to depict
an accurate phylogeny a suitable approach has to be followed(17). Two main approaches are
distinguished: (1) a character-based approach or (2) a distance-based approach. In a distance based
approach, the distance between characters are calculated pairwise (figure box 2). After this calculation,
the tree is reconstructed with these distances and characters are ignored. In a character-based approach,
the model of reconstruction makes use of the characters on themselves.
Figure box 2 A distance matrix of five characters.
The numbers in the blue boxes are the characters 1
to 5 and they are plotted against each other. The
numbers in white boxes are the distances between
the plotted characters.
Evolutionary relationships can be inferred from differences between or within species, for instance in
appearance (morphovars), biochemical or physiological features (biovars), antigenic properties
(serovars)(10). However, the determination of the genetic distance between bacteria will provide the
most accurate description of the evolutionary trajectory of a bacterial species and, consequently, modern
phylogenetic trees are almost exclusively based on genetic information.
To choose the proper approach for an accurate phylogenetic tree, one should know what drives the
genetic variation in the organism: mutation or recombination. Namely, the level of genetic drift and gene
flow has consequences for population dynamics(17). Diversity in fully clonal populations is pushed by
genetic drift, while diversity in totally panmictic – where random mating occurs -populations exists due to
recombination(17). Most bacterial populations are not completely clonal or panmictic and both mutation
and recombination occur, or have occurred in the history of a species.
The trees that are generally used in phylogenetics are trees based on optimality criterions. These
optimality criterions make it possible to choose the best approach that fits a dataset depending on how
the criteria are optimized. Examples of phylogenetic trees that make use of optimality criterions are
maximum likelihood trees, maximum parsimony trees and minimum spanning trees(17).
6
(Box 2 continued)
Maximum likelihood trees (ML trees) use probability as an optimality criterion. When calculating ML trees
the probability of each tree that can be formed with a given dataset is calculated. The probability is based
on evolutionary models and the outcome therefore depends on the model used(17). This brings along the
assumption that maximum likelihood indeed reflects reality best. For instance, one can assume that each
nucleotide substitution has the same probability to occur. Another probability model assumes that
transversions (pyrimidine to a purine or vice versa) are less likely to occur than transitions (pyrimidine to
pyrimidine, or purine to purine)(15).
The construction of maximum parsimony trees (MP trees) is a character-based approach and uses the
assumption that the simplest model is the most accurate model. This means that a tree with the least
evolutionary changes to explain a dataset of characters is preferred(17).
The construction of minimum spanning trees (MS trees) is a distance-based approach and uses the
optimality criterion of minimum distance. The optimal tree has a minimal total distance of branches
compared to other trees that explain the dataset(17). Highly clonal populations can be accurately depicted
by MS trees on the condition that homoplasies are not frequent, as is the case in Y. pestis(8, 14, 18).
However, a character-based approach such as maximum likelihood is favoured as well in these cases.
Phylogenetic trees that show evolutionary interrelationships between Y. pestis strains are often based on
single nucleotide polymorphisms (SNPs), because the genetic variation between different samples is little
and unrefined genetic approaches for phylogenetic analyses do therefore not suffice(18).
Transmission and population dynamics of Y. pestis
Despite the important role of Y. pestis in human history, rodents and fleas are the primary hosts of Y.
pestis during endemic or enzootic periods, but epidemic expansions can lead to infection of almost
all mammals(9, 10). Transmission among mammals occurs via fleabites, respiratory droplets or
physical contact(8, 9, 19).
The enzootic cycle exists of transmission between fleas and maintenance hosts. These maintenance
hosts are partially resistant and therefore enzootic populations show less mortality than epidemic
populations(9). Sometimes, Y. pestis spreads from maintenance hosts to more susceptible hosts,
called epizootic hosts. What exactly determines the shift from enzootic to epizootic transmission
7
cycles is not fully understood(20, 21). However, it is clear that humans are more prone to infection
during high die-off numbers of rodents(20). This increased susceptibility could be due to increased
exposure to infectious fleas and dead animals. Furthermore, the assembly of spatially distinct groups
of hosts may also determine the shift from enzootic to epizootic transmission cycles. Fleas may serve
as a connection between such groups and the importance of flea transmission dynamics in the
population dynamics of Y. pestis became apparent recently(20). The proposed models show that flea
characteristics that give rise to the turnover rate and strength of flea infection cycles strongly affect
epizootic behavior of Y. pestis(20).
When fleas ingest a blood meal from an
infected host, bacteria enter their
digestive tract. The digestive tract of a
flea consists of a foregut and a midgut
(figure 2). A valve in the foregut called
proventriculus prevents regurgitation
from blood from the midgut to the
foregut. The midgut is one single
structure that is responsible for bloodmeal storage, digestion and
adsorption(19, 22). Half of the infected
fleas clear a Y. pestis infection, because
the bacteria do not adhere to the
midgut epithelium and infection is
therefore dependent on ingestion of
high amounts of bacteria. When
bacterial density is high enough,
bacterial aggregates are formed that are
too large to be excreted through feces.
These aggregates grow until fleas are
colonized with 105 to >106 bacteria after
two weeks of infection(19, 22).
Figure 2 Digestive tract of Xenopsylla cheopis. (a) The flea’s digestive tract
filled with blood immediately after consuming blood (b) Dissection picture
of the flea’s proventriculus (PV) (c) Dissection picture of the flea’s digestive
tract consisting of the esophagus (E), proventriculus (PV) and midgut (MG).
(d) Dissection picture of the flea’s digestive tract containing a biofim.
Figures adapted from(19) and (23).
8
The formation of aggregates in the flea midgut is dependent on the Yersinia murine toxin (Ymt) gene,
which encodes a phospholipase D (PLD) and is present on a Y. pestis specific plasmid called pMT(19,
22). The expression of Ymt is enhanced when temperature drops, which is the case when Y. pestis
enters a flea from the mammalian host. Without PLD protein production, fleas clear a Y. pestis
infection within one day from the flea midgut(19, 22). When the number of Y. pestis bacteria in the
midgut rises, this may lead to biofilm formation on the flea’s proventriculus (figure 2d). This biofilm
hinders function of the proventriculus - the prevention of regurgitation - and therefore increases the
chances on bacterial transmission from the flea into new hosts(19). Biofilm formation depends on
hmsHFRS genes in chromosomal DNA that produce polysaccharide extracellular matrix (ECM) (19,
24). This biofilm can block the ingestion route partially or completely and in both cases the flea will
starve leading to an increase in feeding attempts, thereby enhancing transmission possibilities.
Complete blockage of the digestive tract eventually leads to the flea’s death, but partial blockage
does not lead to increased mortality and these partially blocked fleas can therefore infect many hosts
throughout their lifetime(19).
For a long time biofilm formation in fleas (‘blocked’ fleas) has been thought to be required for
transmission into new hosts. However, the occurrence of early-phase transmission (≤ 4 days post
infection) in unblocked fleas has been shown by Eisen et al. in 2006(25). The existence and relevance
of early-phase transmission in (epidemic) spread of Y. pestis is not fully accepted in the field(21).
However, evidence has accumulated in the past years that support the existence of early-phase
transmission and some attempts have been made to investigate how it influences population
dynamics of Y. pestis(20, 25-28). The possibility of early-phase transmission allows for the existence
of a short-term reservoir (2-3 weeks) required to explain the fast rates of epidemic spread, which
could not be explained by blocked flea transmission(20, 25, 29).
Evolution and genomics of Y. pestis
The Yersinia lineage diverged approximately 200 million years ago into a Yersinia enterocolitica and a
Yersinia pseudotuberculosis lineage(30). Both carry functional variants of a low calcium response
virulence plasmid (LCR), often called pCD1 or pYV (Yersinia virulence(30)). Y. pestis evolved 28.0001500 years ago from the enteropathogen Y. pseudotuberculosis, which causes mild food- and waterborne diseases of the gastrointestinal tract(8-10). This is in contract to the high virulence of Y. pestis,
which causes fatal disease without antibiotic treatment and is transmissible via flea vectors.
Furthermore, Y. pestis is not an enteropathogen, but makes use of a dermal portal of entry(13).
9
The chromosomes of both Y. pestis and Y. pseudotuberculosis are closely related. 75% of the
chromosomal genes in Y. pestis show ≥97% homology to genes of Y. pseudotuberculosis(31). Only 32
chromosal genes and two plasmids (PCP1 and PMT1) have been introduced in Y. pestis since the
divergence from Y. pseudotuberculosis(31). The introduced genes in Y. pestis encode for membrane
proteins, esterases, lipoproteins and DNA-binding proteins(31). Whether the newly acquired
chromosomal genes provide an explanation for the high virulence of Y. pestis is not clear yet.
Furthermore, the newly acquired plasmids cannot fully account for high virulence, since the
introduction of these plasmids in Y. pseudotuberculosis did not yield as high virulence as is seen in Y.
pestis(31). The introduced plasmid PCP1 is responsible for tissue invasion and capsule formation,
while pMT1 is responsible for flea infection(31). The source of these plasmids remains under
discussion(16), but half of the DNA sequences of pMT1 show similarity to DNA sequences on a
Salmonella enterica plasmid(9, 10, 16). Surprisingly, the plasmid (pYV) that encodes for a type III
secretion system, which is capable of suppressing phagocytosis and other innate immune responses,
is shared by both Y. pestis and Y. pseudotuberculosis and is therefore not able to explain the
difference in virulence(31).
On the contrary, 317 genes of Y. pseudotuberculosis are absent in Y. pestis and 147 genes have
mutated into pseudogenes, suggesting that loss of genes may contribute to pathogenesis as well. The
function of identified pseudogenes are largely known and they are found to be important in
metabolism, cell-cell contact and pathogenesis(13, 31). For instance, some toxins are turned into
pseudogenes, which may enable Y. pestis to use fleas as a vector(13). Furthermore, pathoadaptive
mutations – mutations that increase virulence by the inactivation of a gene- may also account for the
increased virulence(13).
Besides introduction and loss of genes and plasmids, an increase in insertion (IS) elements and
regions with a high GC-content is seen in Y. pestis(13, 31). The increased IS elements indicate that the
genome of Y. pestis underwent more insertion events than Y. pseudotuberculosis. Genome
rearrangements such as insertions and recombination events most likely account for the increase in
GC regions. Comparison of three Y. pestis strains indicates that genomic rearrangement may be more
important than acquisition of new genes, because many differences in rearrangement events can be
distinguished between the three strains(13, 31). However, the exact contribution of genomic
rearrangement to virulence has to be revealed by further research.
Before DNA sequencing techniques were easily accessible, Y. pestis samples were characterized by
their ability to reduce nitrate and ferment sugars(10). The biovars Orientalis, Medievalis, Antiqua and
Pestoides were distinguished in this way(10) and the abbreviations ORI, MED, ANT and PE are still
10
used, respectively. However, genomics is now used to identify new strains and compare them to
known strains.
The genome of Y. pestis can be divided into a core-genome and an accessory genome, which both
form the pan-genome of the bacterium(8). The core-genome is defined as the genomic DNA that is
present in all Y. pestis strains, while the accessory genome is variably present in strains. The coregenome is 3.53 Mb in length and the pan-genome is 5.46 Mb in length, based on the genomes of 133
strains(8). Only 2298 single nucleotide polymorphisms (SNPs) have arisen in the core-genome since
the emergence of Y. pestis, which makes it a highly monomorphic pathogen(8). Furthermore, almost
all of the acquired SNPs have occurred only once, which makes the reconstruction of the
phylogenetic tree reliable(8, 10). Additionally, the sequenced genomes have been aligned to a wellannotated genome to filter repetitive SNPs, which may have emerged due to recombination
events(8, 10).
Despite the monomorphic genetics of Y. pestis, multiple bursts of diversification called polytomies
are present in the phylogenetic tree (figure 3)(8). Polytomies are nodes where more than two
branches orginate from. These polytomies occur in an uneven pace along the branches, which
implies that the substitution rate varies over time(8). These periods of high substitution rates could
be explained by multiple diversifying events rapidly followed by natural selection and thereby
fixation of mutations in a lineage. However, the observed ratio of non-synonymous mutations to
synonymous mutations (dN/dS ) is nearly neutral. Furthermore, no signs of homoplasies have been
observed(8). Therefore, it is now believed that high mutation rates are generated by high replication
rates due to a shift from the endemic, sylvatic phase of the bacterium to an epidemic spread among
variant host species(8).
11
Figure 3 Minimum spanning tree of 133 genomes, based on 2298 SNPs in the core-genome of Y. pestis. An Y.
pseudotuberculosis is used as an out group and is depicted as the most recent common ancestor (MRCA). Five branches are
recognized, numbered from 0 to 4. Colours distinguish populations within branches. Biovars are abbreviated as follows:
Medievalis (MED), Orientalis (ORI), Antiqua (ANT), Pestoides (PE), Intermediate strains between ORI and ANT (IN). Branch
lengths are proportional to the natural logarithm of the amount of SNPs between nodes, or between nodes and endpoints.
Figure reproduced from Cui et al. (8).
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The contribution of evolutionary theory to knowledge about Y. pestis
Despite its inability to predict evolution of microbes, evolutionary theory still contributes to
knowledge about microbial evolution, because the value of a scientific theory not only depends on its
predictive power, but also on its explanatory power(3). In this part, we will touch on the explanatory
power of evolutionary theory in understanding the evolution of Y. pestis. Cui et al. recently published
a comprehensive and detailed paper on the phylogenetics of Y. pestis and they have used
evolutionary theory as a tool to search for new data(8). I will review their approach, claims and
hypotheses to demonstrate the use of evolutionary theory.
First of all, the monomorphic nature of Y. pestis is affirmed by the comparison of 133 genomes that
are sampled at different locations and time points. The sampled bacteria are obtained from humans,
a variety of rodent genera, other non-primate mammals and human fleas. The isolates were sampled
in China, the former Soviet Union, Madagascar, Mongolia and Myanmar. The search for geographical
and host diversity in sampling is based on the understanding that this way of sampling will probably
lead to large evolutionary distances and these are needed for an accurate phylogenetic analysis.
Secondly, the usage of Y. pseudotuberculosis as an outgroup for the phylogenetic analysis in this
study and earlier studies(31) revealed that Y. pestis diverged from Y.pseudotuberculosis. The concept
of Darwin’s tree of life and the existence of a common ancestor, makes the comparison of these
genomes understandable. Furthermore, the use of a Maximum Likelihood approach to reconstruct Y.
pestis phylogeny is based on appropriate evolutionary models as well.
Only 2298 SNPs were present in the core-genome of 133 Y. pestis genomes(8). This was not due to
convergent evolution, because there were no or little traces of recombination events. Furthermore,
the genetic variation was mainly caused by neutral evolution, because dN/dS was 0.91. By using
evolutionary theory as a tool and deduce from it the effect of natural selection on dN/dS ratios,
knowledge is yielded about the cause of genetic variation in Y. pestis, which was concluded to be
mainly caused by genetic drift.
The results show correlation of a polytomy (‘starburst genealogy’) with the spread of plague in the
Middle Ages. Furthermore, they show that mutations arise at uneven pace during the evolution of Y.
pestis. Evolutionary approaches imply a few possibilities to explain these facts, namely: (1) the
existence of a mutator strain, (2) rapid fixation of favourable SNPs due to diversifying selection or (3)
simply the effect of increased replication due to epidemic spread. Cui et al. investigated these
possibilities and they were able to refute the first two options. In earlier studies, they already
concluded that plague had spread from China or somewhere near China(10). This also hinted
towards the demographic changes in history of the bacterium. They compared ancient Chinese trade
13
routes with the sampling locations of the study’s Chinese samples and indeed, there is a correlation
between the trade routes and the sampling locations. Furthermore, samples of the deepest
branching lineages were only found on the Qinghai-Tibet plateau and this may be the origin of Y.
pestis spread. The samples obtained in this region also show the highest diversion, which can also
suggest that it is the original source of spread(8). This made them suggest that the changes between
endemic and epidemic periods have caused the changes in clock rate variation(8).
Figure 4 The correlation of Y. pestis samples and ancient trade routes (grey lines). The circle depicts the Qinghai-Tibet
Plateau. The colours and figures of samples are shown in the legend (supplemental information can be found in figure 3).
Figure reproduced from Cui et al (8).
Finally, reports showed that Black Death has most likely been caused by Y. pestis, because antigens
and DNA of Y. pestis has been found in skeletons of Black Death patients(11, 32, 33). Additionally,
14
reconstructions of Y. pestis genomes have been made with samples of skeletons buried in Londen
and Germany, shortly after Black Death was reported to have reached Europe(11, 34). Furthermore,
recent findings on skeletons between the 5th and 7th centuries, support the hypothesis that the
Justinian Pandemic was also caused by plague(35-37).
The search for data that can either confirm or refute the hypotheses based on evolutionary models is
a perfect example of the value of evolutionary theory in gaining knowledge on microbial evolution.
This shows that evolutionary theory is used as heuristic method to formulate new research
questions. Furthermore, the enormous explanatory power of evolutionary theory help us to
understand natural processes occurring during the evolution of microbes. The incapacity of
evolutionary theory to have predictive value –at this moment – does not set back the value of
evolutionary theory. To conclude with a phrase of Michael Scriven (1959)(3): ‘Satisfactory
explanation of the past is possible even when prediction of the future is impossible.’
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