1. Host switches in malaria: evolutionary
guesses and functional clues
John Powers
BIOL 526H
12/11/2014

2. Abstract
Five malaria parasites infect human hosts, but hundreds more parasitize such diverse
vertebrate lineages as rodents, lizards, and birds. An understanding of how this diversity in host
association arose is key to predicting future cross-species transfers. Specifically, the previously
accepted evolutionary relationships within malaria parasites (Haemosporida) have recently
been overthrown by molecular techniques. This study provides a review of the current
controversies in malarial phylogenetics and directions for future research.
Growing an evolutionary tree
Old schools of phylogenetic classification
Part of the confusion over malaria’s phylogeny stems from antique classification schemes that
assumed a parsimonious tree (Figure 1A) to explain observed patterns of two visible traits (the
presence of an asexual reproductive phase called merogony and a characteristic malaria
pigment), which were assumed to have evolved exactly once and never lost from daughter
species. Since these traits were used to build putative trees before the advent of molecular
methods it is tautological (a self-referential logical fallacy) to use those trees to pinpoint where
these adaptations were gained or lost (Rich and Xu 2011). Phylogenies incorporating genomic
sequence data now show multiple gain and loss events of these traits (Figure 1B-C). In fact, the
outgroup taxon for many recent analyses (Leucocytozoon) was chosen based on the lack of
these traits on the basis that they define a malaria parasite (for example Martinsen et al. 2008).
Besides rooting the tree, choice of outgroup can have a profound effect on phylogenetic
analyses. If it is too closely related to the ingroup, there is a chance that it belongs to the
ingroup, and can therefore skew the tree. Outlaw and Ricklefs (2011) reanalyzed the data used
to construct the tree in Figure 1C with an outgroup-free method described by Huelsenbeck et
al. (2002) to show that Leucocytozoon properly belongs in the ingroup (Figure 1D). In contrast,
if the outgroup is too distantly related and the rate of substitution is high, it causes “long-
branch attraction”, where highly divergent ingroup species are incorrectly clustered together
(Outlaw and Ricklefs 2011).

3. Figure 1. Trees adopted from Outlaw and Ricklefs 2011. (A) classical (B) Perkins and Schall 2002 based on cytb only
(C) Martinsen et al. 2008 based on four genes (D) Martinsen’s data reanalyzed without fixed outgroup. Shapes
show gain and loss events of two key traits. LEU, Leucocytozoon; PLA, Plasmodium; HEP,Hepatocystis;
HAE, Haemoproteus; PAR, Parahaemoproteus; POL, Polychromophilus. Mammilian, black; avian or reptilian, blue.
The trees predicted from molecular methods usurped those based on external characteristics
such as life-history stages, morphological traits, or symptoms expressed in the host. Perkins and
Schall (2002) showed that some of these traits are not predictive of phylogeny and instead
could have been produced by convergent evolution (homoplasy).
Taxon bias
Another systematic bias present in many analyses is taxon bias. For example, if a study includes
mostly primate parasites, there will be incorrect clustering of the dissimilar species when
neighbor-joining. Since P. falciparum is distantly related to other primate malarias, this has led
to confusion whether it is most closely related to the avian, reptile, or primate lineages. Since
both avian malarias and P. falciparum are distantly related to other primate malarias, early
phylogenetic studies hypothesized a clustering of the two and therefore a host switch from
birds to humans. Analyses that incorporated more ingroup taxa and a closer outgroup showed
that it was more closely related to the chimpanzee malaria P. rechinowi (Perkins and Schall
2002).

4. Suitable genes for analysis
Malaria parasites hold genetic material in the nucleus, the three remaining genes of the
dependent mitochondrion, and the apicoplast, a non-photosynthetic plastid. The genetic
information contained in each is not equivalent: the saturation level (prevalence of sites where
more than one nucleotide change has occurred between species), substitution rate, and base
composition vary between genes in each (Bensch et al. 2013), which in turn affect phylogenetic
reconstructions. Neighbor-joining methods that simply concatenate sequences from each are
biased toward the fastest changing genes, but Bayesian methods are able to partition the genes
during the analysis to correct for variable rates of substitution. Dávalos and Perkins (2008) also
suggest models that partition rates of change by codon position to preserve the phylogenetic
signal when it is covered by saturation and skewed base composition (neighbor-joining and
other distance algorithms stumble with the AT-rich genome). Inclusion of multiple genes rather
than a single one should improve the resolution and statistical confidence (posterior
probability) of nodes on the tree, as shown in Martinsen et al. 2008, which used genes from all
three sources instead of the single mitochondrial gene (cytb) used for Perkins and Schall 2002.
Using whole-genome sequences, Silva et al (2011) identified 45 orthologous genes by BLAST
comparison of their exons. While increasing statistical power, an unfortunate consequence of
this method was that it chose genes with high sequence similarity, adding to the problem of
amino acid sequence convergence they observed.
Not all genes are good candidates for phylogenetic analysis. The earliest molecular studies used
the gene that encodes the parasite’s 18S rRNA. However, there are multiple copies of this gene
(paralogs) that evolve independently and are expressed at different points during the malarial
life cycle (Martinsen 2008). Other studies used the gene for circumsporozoite protein, secreted
during the sporozoite phase. Phylogenetic algorithms assume that loci experience neutral
selection, acting as a molecular clock that accumulates mutations randomly. However,
circumsporozoite protein plays a role in interaction with the host, so is under strong selection
by the host immune system. This was demonstrated for a suite of cell-surface protein genes by

5. showing that there was a high ratio of non-synonymous (amino-acid altering) mutations to
nonsynonymous mutations (Hughes and Hughes 1995).
Case study: Origin of P. falciparum
The human-chimpanzee divergence 5-7 My ago was assumed to coincide with the P. falciparum
– P. reichenowi split based on the codivergence hypothesis (the malaria species, which make up
the subgenus Laverania, are now specific to humans and chimpanzees, respectively). However,
recent work showed that P. falciparum exists within a clade of previously unknown gorilla
malarias, indicating a recent host switch from gorillas to humans after humans diverged from
chimpanzees (Liu et al. 2010). According to Liu et al., this “malarial Eve” event accounts for the
low genetic diversity of P. falciparum in humans, its unexpectedly high virulence (associated
with a recent host switch), and the incomplete attack on protective human polymorphism like
hemoglobin C. (Another explanation for this low genetic diversity is a recent “selective sweep”
by anti-mitochondrial drugs that erases polymorphism, which Liu rejects. Yet another is a
population bottleneck in strains that accompanied humans out of Africa via ancient migration
or the American slave trade.) Rich et al. (2009) placed this Eve event as late as 10,000 years ago
by arguing that P. falciparum falls within the range of P. reichenowi diversity, so the species
only diverged recently. This timing coincides with the advent of human agricultural societies
and population density, thought to increase the probability of cross-species infection.
Silva et al. (2011) counters that Liu et al. did not rule out the opposite host switch, a recent
transfer from humans to gorillas. Silva et al. makes a second important criticism of the recent
host switch: it means that Homo would have had no other Laverania parasites beforehand,
even though Homo was in close contact with chimpanzee parasites during its evolution. Hughes
and Verra (2010) argue that the sequence divergence between the two species is too great to
support the recent divergence hypothesis (the substitution rate would be too high). Further
support of the cospeciation hypothesis comes from comparing genetic differences (non-
synonymous polymorphisms) within P. falciparum to its differences with P. reichenowi to
determine the divergence time, which only gave reasonable substitution rates in the
hypothesized 5-7 Mya range.

6. This controversy may not be solved without further sampling of ape malaria samples. Following
Liu et al., this should be done by single-genome amplification of fecal DNA from wild apes as
bulk PCR resulted in DNA from simultaneous infections confusingly recombining in vitro. To tell
whether P. falciparum switched from apes to humans or vice versa, researchers should screen
for drug-resistance alleles, which can only come from human malaria populations (as was the
case with recent bonobo infections, Silva et al. 2011). If apes do indeed represent a reservoir of
P. falciparum as suggested by Duval et al. (2010), it may hinder efforts to eliminate the disease
in humans.
Molecular clocks
Intertwined with competing models of evolutionary relationships between the malarias is
controversy in the timing of parasite species divergence. Accurate estimates of these timings
would resolve whether cospeciation occurred or if parasites colonized vertebrate and insect
hosts long after their radiation. Assuming a fixed molecular clock (one where the rate of
nucleotide substitutions is static), a single reliable date could be used to find the clock rate,
scale a phylogeny back in time, infer dates of other malaria species divergences, and check if
they coincide with host species divergence times. If they do not, this could indicate a more
recent host switch.
Unfortunately, fossil evidence is scanty at two amber samples, and neither fossil can be
confirmed as a direct ancestor to extant malaria species or placed on current phylogenies. In
addition, while an average clock rate of 2% per million years (My) has been determined for
vertebrate (and plant) taxa, it is not directly applicable to malaria parasites, which have
different generation times, metabolism, and mismatch repair mechanisms (Bensch 2013). The
mitochondrial genes are thought to have a slower clock than usual since they exist as multiple
copies that could display concerted evolution (Bensch 2013).
Another strategy is to use a known a codivergence date to calibrate the tree. The hypothesized
cospeciation event of P. falciparum-P. reichenowi and chimpanzee-human initially showed
promise, but recent findings call this date into question (see above). The codivergence of
malaria parasites at the Asian macaque-African mandrill split would be useful if the latter’s date

7. range was better known. If parasites are instead hypothesized to have diverged in lockstep with
ancient vector radiation (see above), the clock rate is implausibly slow at 0.1% / My. Therefore
specific associations with vectors may not be very strict. The existence of host switching calls
into question the use of codivergence times to calibrate the clock.
A clever method devised by Ricklefs and Outlaw (2010) estimated the bird-parasitizing
Haemosporidian clock rate by calculating the ratio of genetic differences between an endemic
bird host and its sister taxon and an endemic malaria parasite and its sister taxon. Since the
birds were colonized by the parasites sometime after their divergence from their sister, the
substitution rate for the parasites can be calculated from the known substitution rate for the
birds, giving an estimate of 1% / My. Three important caveats with this method are that the
host colonization time is assumed to be uniformly distributed, no parasite extinction is allowed,
and genetic saturation is assumed low, which may not be the case (Silva et al 2011). Employing
this clock rate predicts a scenario where malarial parasites diversified through the vertebrates
within the last 20 million years (Outlaw and Ricklefs 2011, Bensch et al 2013). They could do
this without a high frequency of unfavorable host shifts by infrequently shifting across large
host taxonomic divides and then diversifying within closely related hosts. Another ingenious
timing method involves the simultaneous colonization of Madagascar by and parallel
divergence of lemurs and malaria 20 Mya (a geologic date), which gives a useful external
validation point (Pachecho et al. 2011).
Statistical techniques: the tanglegram jungle
A useful application of a parasite phylogeny once it has been created is deduction of the
evolutionary history of host-parasite association by overlay with a corresponding host
phylogeny and lines indicating extant relationships. Such an assemblage is called a tanglegram
(Figure 2). The algorithmic problem of this reconstruction is to enumerate all possible
cophylogenies and find the most likely overlap configuration of the two trees. While the
enumeration task is computationally infeasible (occurring in exponential time), methods exist
to “evolve” a population of cophylogenies to a state of highest fitness, or lowest cost, through
iterations of selection, “mating”, and recombination of the information of each parent into

8. offspring (Pevzner and Shamir 2011). The following allowed events are assigned costs inversely
proportional to their likelihood:
1. co-divergence/co-speciation of parasite and host simultaneously
2. duplication: parasite speciates independently of host
3. extinction/lineage sorting: parasite fails to diverge when host speciates
4. horizontal transfer / host switch: duplication where parasite moves to new host
In general, host switches are assigned a high cost since it is evolutionarily unlikely that a
parasite will be able to colonize a new host without suffering a fitness reduction. One can
quantify the contribution of each event by assigning high costs to each in turn, thereby
eliminating it from the model (Garamzegi 2009). Some weaknesses of these reconstructions are
that extant associations between host and parasite phylogeny can be caused by extinction and
subsequent recolonization events in the past that are disregarded based on cost. Also, having
no evidence for malaria parasitism in a host may reflect imperfect sampling, not actual lack of
parasitism.
Figure 2. A tanglegram of malaria species and primate genera (species not shown for clarity), reproduced from
Garamszegi et al. (2009). Line weight indicates significance of tendency for co-speciation, tested for each host-
parasite linkage by the software package ParaFit against a background of randomized incidences.

9. Another way to approach the problem is to estimate the ancestral state of parasite associations
with hosts at each node with a Markov chain Monte Carlo model (Figure 3), which uses a
Bayesian sample of phylogenetic tree hypotheses.
Figure 3. Estimated ancestral states reproduced from Garamszegi et al. (2009). Pie charts indicate posterior
densities of host identity, and triangles indicate host switches.
Garamszegi et. al also (2009) also tested whetheter the probability of extant parasite
associations with their hosts was due to random host choice (null hypothesis) or if host choice
was constrained by the host taxon. This effectively tests the earlier assertion that parasites
prefer to colonize similarly related hosts. They found that primate malarias do not link tightly
enough with host genus to be significant, but that they do link tightly with host family,
indicating some dependence on the phylogenetic history of their human hosts. However, some
parasite lineages, including those infecting humans, showed much more freedom of
association, supporting the hypothesis of frequent host switching across large distances in the
host phylogeny. This has important consequences for the potential cross-species transfer of

10. another malaria to humans, since we can no longer exclude transfers from distantly related
hosts, such as rodents or birds.
Importance of phylogeny
Diverse malaria parasites have drastic effects on human and wildlife populations, with potential
for cross-species transfer to spark the next epidemic. The probability of such a switch must be
known. In addition, developing vaccines and treatments for the disease relies on model
malarias that must be evolutionarily close to ensure applicability to human malarias. Finally,
malaria parasites provide a worldwide proving ground for theories in ecology and evolution,
which rely on robust phylogenies.

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