Phylodynamics of HIV-1 Subtype C Epidemic in East
Edson Oliveira Delatorre, Gonzalo Bello*
Laborato ´rio de AIDS & Imunologia Molecular, Instituto Oswaldo Cruz, Rio de Janeiro, Brazil
The HIV-1 subtype C accounts for an important fraction of HIV infections in east Africa, but little is known about the genetic
characteristics and evolutionary history of this epidemic. Here we reconstruct the origin and spatiotemporal dynamics of the
major HIV-1 subtype C clades circulating in east Africa. A large number (n=1,981) of subtype C pol sequences were retrieved
from public databases to explore relationships between strains from the east, southern and central African regions.
Maximum-likelihood phylogenetic analysis of those sequences revealed that most (.70%) strains from east Africa
segregated in a single regional-specific monophyletic group, here called CEA. A second major Ethiopian subtype C lineage
and a large collection of minor Kenyan and Tanzanian subtype C clades of southern African origin were also detected. A
Bayesian coalescent-based method was then used to reconstruct evolutionary parameters and migration pathways of the
CEAAfrican lineage. This analysis indicates that the CEAclade most probably originated in Burundi around the early 1960s,
and later spread to Ethiopia, Kenya, Tanzania and Uganda, giving rise to major country-specific monophyletic sub-clusters
between the early 1970s and early 1980s. The results presented here demonstrate that a substantial proportion of subtype
C infections in east Africa resulted from dissemination of a single HIV local variant, probably originated in Burundi during
the 1960s. Burundi was the most important hub of dissemination of that subtype C clade in east Africa, fueling the origin of
new local epidemics in Ethiopia, Kenya, Tanzania and Uganda. Subtype C lineages of southern African origin have also been
introduced in east Africa, but seem to have had a much more restricted spread.
Citation: Delatorre EO, Bello G (2012) Phylodynamics of HIV-1 Subtype C Epidemic in East Africa. PLoS ONE 7(7): e41904. doi:10.1371/journal.pone.0041904
Editor: Marco Salemi, University of Florida, United States of America
Received April 23, 2012; Accepted June 27, 2012; Published July 27, 2012
Copyright: ? 2012 Delatorre, Bello. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by Fundac ¸a ˜o Carlos Chagas Filho de Amparo a ` Pesquisa do Estado do Rio de Janeiro and Conselho Nacional de
Desenvolvimento Cientı ´fico e Tecnolo ´gico grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Human immunodeficiency virus type 1 (HIV-1) sequences
belonging to the pandemic group M are classified into nine
subtypes (A–D, F–H, J, and K), six sub-subtypes (A1–A4, and F1–
F2), and a variety of inter-subtype recombinant forms (Los Alamos
HIV sequence database: http://hiv-web.lanl.gov/). Subtype C is
the most prevalent variant, accounting for nearly half (48%) of all
global infections . This high prevalence is due to the
predominance of subtype C in southern Africa, east Africa and
India, with further infections in central Africa and Brazil.
Subtype C accounts for .95% of HIV infections in all southern
African countries . Several studies showed that subtype C
sequences from neighboring southern African nations display a
great degree of phylogenetic intermixing with no evidence of
significant geographical clustering [2,3,4,5,6,7], indicating a
largely unrestricted viral movement across the entire subcontinent.
A more recent phylogenetic study revealed that after sequential
pruning of ambiguously positioned taxa 10 strongly supported
subtype C clusters becomes apparent in southern Africa, showing
that the geographic subdivision of subtype C viruses circulating in
this region is higher than expected by chance . Most subtype C
clusters identified, however, circulate in more than one southern
African country and all four countries analyzed (Botswana,
Malawi, South Africa and Zambia) comprise strains from multiple
clusters. Thus, HIV epidemics in southern African countries are
probably the result of the introduction and circulation of multiple
subtype C strains with a variable level of local and regional
In contrast to the southern African region, the prevalence of
HIV-1 subtype C clade displays a great variation among eastern
African countries. Subtype C reaches high prevalence in Burundi
(.80%) [9,10], Djibouti (.70%)  and Ethiopia (.95%)
[16,17,18,19,20], and relatively low prevalence in Rwanda
(14%)  and Uganda (,5%) [22,23,24,25,26,27,28]. Subtype
C also accounts for a minor fraction (,15%) of HIV infections in
[28,35,36,37] regions of Kenya; but displays a much higher
frequency (25–50%) in some cities of the northern region that
borders Ethiopia [38,39].
Little is known about the genetic characteristics of HIV-1
subtype C strains circulating in east Africa. Previous studies
showed that two genetically different subtype C strains designated
C and C9, have been co-circulating in roughly similar prevalence
and among the same risk groups and geographical areas in
Ethiopia [13,15,40]. A recent study of Thomson and Ferna ´ndez-
Garcı ´a  revealed that the Ethiopian-C clade corresponds to one
subtype C cluster also found in other east African countries
including Burundi, Djibouti, Kenya, and Uganda; while the
Ethiopian-C9 clade was assigned to an independent cluster
in Tanzania (20–40%)
[28,32,33,34] and central
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associated to southern Africa. Other studies performed in Kenya
showed that subtype C samples from this country are not
concentrated in a single cluster, but distributed in several
independent lineages associated to sequences from both east and
southern Africa [34,39]. Despite these previous studies, we still
have an incomplete understanding of the number, onset date, and
migration pattern of the distinct HIV-1 subtype C lineages
circulating in the eastern African region.
To obtain a more comprehensive picture of the spatiotemporal
dynamics of the HIV-1 subtype C epidemic in east Africa, we
analyzed a large number of subtype C pol sequences sampled from
the east (Burundi, Ethiopia, Kenya, Tanzania and Uganda),
southern (Botswana, Malawi, Mozambique, South Africa, Zambia
and Zimbabwe) and central (Angola and Democratic Republic of
Congo) African regions over a time period of 25 years (1986–
Materials and Methods
HIV-1 subtype C pol sequences from east, southern, and central
African countries, that matched the selected genomic region (nt
2253–3272 relative to HXB2 clone) were retrieved from the Los
Alamos HIV Database (http://hiv.lanl.gov). Countries were
grouped in geographical regions according to the classification
proposed by Hemelaar et al . In order to improve the accuracy
of phylogenetic inference only sequences from antiretroviral
therapy naı ¨ve individuals were selected. The subtype assignment
of all sequences was confirmed by the REGA HIV subtyping tool
v.2  and by maximum likelihood (ML) phylogenetic analysis
(see below) with HIV-1 subtype reference sequences. Those
sequences with incorrect subtype C classification, sequences
containing frame-shift mutations or deletions, multiple sequences
from the same individual and those sequences from countries
poorly represented (,5 sequences) were removed. This resulted in
a final dataset of 1,981 HIV-1 subtype C pol sequences sampled
from 12 different African countries (Table 1). Sequences were
aligned using the CLUSTAL X program  and alignment is
available from the authors upon request.
Substitution saturation and likelihood mapping analyses
Substitution saturation was evaluated by plotting the estimated
number of transitions and transversions against genetic distance
for each pairwise comparison in our alignment of 1,981 HIV-1
subtype C pol sequences using DAMBE program . The
phylogenetic signal in the pol dataset was investigated with the
likelihood mapping method  by analyzing 10,000 random
quartets. Likelihood mapping was performed with TREE-PUZ-
ZLE program  using the online web platform Phylemon 2.0
ML phylogenetic trees were inferred under the GTR+I+C4
nucleotide substitution model, selected using the jModeltest
program . ML tree was reconstructed with PhyML program
 using an online web server . Heuristic tree search was
performed using the SPR branch-swapping algorithm and the
reliability of the obtained topology was estimated with the
approximate likelihood-ratio test (aLRT)  based on the
Shimodaira-Hasegawa-like procedure. The ML trees were visual-
ized using the FigTree v1.3.1 program .
Characterization of intrasubtype C/C9 recombinant
Putative intrasubtype C/C9 recombinant sequences in Ethiopia
were identified by Bootscanning using Simplot version 3.5.1 ,
following the same procedure described by Pollakis et al .
Bootstrap values supporting branching with reference sequences
were determined in Neighbor-Joining (NJ) trees constructed using
the K2-P nucleotide substitution model, based on 100 re-
samplings, with a 300 bp sliding window moving in steps of 10
Analysis of spatiotemporal dispersion pattern
The evolutionary rate (m, units are nucleotide substitutions per
site per year, subst./site/year), the age of the most recent common
ancestor (Tmrca, years), and the spatial dynamics of major subtype
C clades from east Africa were jointly estimated using the Bayesian
Markov Chain Monte Carlo (MCMC) approach implemented in
the BEAST software package v1.6.2 [53,54]. Analyses were
performed using the GTR+I+C4nucleotide substitution model, an
uncorrelated Lognormal relaxed molecular clock model , a
Bayesian Skyline coalescent tree prior , and a discrete
phylogeographic model in which all possible reversible exchange
rates between locations were equally likely . Two separate
MCMC chains were run for 46108generations and adequate
chain mixing was checked by calculating the effective sample size
(ESS) after excluding an initial 10% for each run using program
TRACER v1.4 . MCMC runs converged to almost identical
values and combined estimates showed ESS values .200.
Maximum clade credibility (MCC) trees were summarized from
the posterior distribution of trees with TreeAnnotator and
visualized with FigTree v1.3.1. Migratory events were summarized
using the cross-platform SPREAD application .
A large dataset of HIV-1 subtype C pol sequences (n=1,981)
downloaded from the Los Alamos HIV Database (http://hiv.lanl.
gov) was used to characterize the relationship between subtype C
sequences sampled from east, central and southern African
countries. The transition/transversion vs divergence graphics
Table 1. HIV-1 subtype C sequences.
African regionCountryN Sampling date
CentralAngola 31 2001–2010
Democratic Republic of Congo222002–2007
Southern Botswana70 2001
South Africa 1,0311999–2009
Zambia 150 1998–2008
Tanzania 81 1997–2009
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showed that both type of nucleotide substitutions increase linearly
with the genetic distance, with transitions being higher than
transversions (Figure S1a), thus indicating no substitution
saturation in our alignment. While, the likelihood-mapping
analysis showed that most (90%) of the randomly chosen quartets
from the HIV-1 subtype C alignment were equally distributed in
the three corners of the likelihood map (Figure S1b), indicating a
strong tree-like phylogenetic signal in the data. Both analyses
indicate that the HIV-1 subtype C pol dataset used in this study
contains enough evolutionary information for reliable phylogenet-
ic and molecular clock inferences.
The ML phylogenetic analysis revealed that most (73%) subtype
C sequences from east Africa branched within a highly supported
(aLRT=0.93) monophyletic cluster, here called CEA, that contains
sequences from all five east African countries analyzed (Figure 1).
Notably, the CEAclade comprises a minor proportion (9%) of the
54 sequences from central Africa, but none of the 1,576 sequences
from southern Africa here included. A minor fraction (11%) of
subtype C sequences from east Africa branched in a second well
supported (aLRT=0.94) monophyletic cluster that comprises
sequences from Ethiopia, and corresponds to the so called
Ethiopian-C9 (C9ET) clade (Figure 1). The remaining subtype C
east African sequences (16%) were distributed in several indepen-
dent lineages of small size (n#5 sequences) that were intermixed
among strains from southern African countries (Figure 1).
The analysis of sequence distribution among clades by country
of origin revealed three different patterns within east Africa
represented by Burundi/Uganda, Ethiopia and Kenya/Tanzania
(Figure 2a). All or most subtype C strains circulating in Burundi
and Uganda belong to the major clade CEA. Subtype C strains
from Ethiopia, by contrast, were mainly distributed into clades
CEA(61%) and C9ET(37%). Finally, about 64% of subtype C
sequences from Kenya and 49% from Tanzania branched within
the major clade CEA, while the remaining sequences were
distributed in the multiple minor clades of southern African
origin. Such geographical variation in the prevalence of different
subtype C clades could be also observed at a more local scale in
Tanzania (Fig. 2b). In the Kagera and Mwanza regions (north),
most (.70%) subtype C strains belong to the CEAclade. In the
Kilimanjaro region (northeast), sequences from both the CEAand
‘‘southern African’’ clades reach a roughly similar prevalence. In
the Mbeya region (southwest), only ‘‘southern African’’ clades
Migration pattern of HIV-1 CEAclade
A closer inspection of the HIV-1 CEA clade showed that
sequences from Burundi occupies the most basal position in the
clade (Figure S2), thus suggesting that Burundi was the most
probable epicenter of dissemination of this subtype C lineage. The
migration pattern of the CEAlineage was reconstructed using a
Bayesian statistical framework that allows ancestral reconstruction
of the locations at the interior nodes of Bayesian tree while
accommodating phylogenetic uncertainty. Sequences with no
information about sampling date (n=2), sequences with unex-
pectedly long branches in the phylogenetic analysis (n=10), and
Ethiopian sequences with evidence of intra-subtype recombination
(n=8, see below) were excluded from this analysis. This resulted in
a final dataset of 236 sequences (Burundi=92, Ethiopia=47,
Kenya=24, Tanzania=40, and Uganda=33) sampled between
1990 and 2010.
The Bayesian MCC tree supports the hypothesis that the CEA
clade originated in Burundi (PP=1) and was later exported to the
other east African countries where it further spread, establishing
new local epidemics (Figures 3 and 4). Estimation of viral
movement among countries, obtained by counting the state
changes along the tree nodes, points to the role of Burundi as the
most important hub of dissemination of this subtype C lineage in
east Africa, followed by Tanzania (Table 2). Several migration
events of the lineage CEAfrom Burundi to Ethiopia (n=4), Kenya
(n=5), Tanzania (n=8) and Uganda (n=8) were detected, as well
as from Tanzania to both Kenya (n=3) and Uganda (n=7).
Importation of the CEA lineage into Burundi from other east
African countries, and viral exchanges between Ethiopia, Kenya
and Uganda were seldom detected in our dataset.
The Bayesian analysis also supports an important phylogeo-
graphic subdivision within the CEAlineage. Consistent with the
ML topology (Figure S2), most subtype C sequences from
Ethiopia, Kenya, Tanzania and Uganda branched in country-
specific monophyletic sub-clusters that most probably (PP$0.93)
had a Burundian origin (Fig. 3). The CET1and CET2lineages, that
correspond to the so called Ethiopian-C clade, comprise 44% of all
Ethiopian sequences here included and were almost exclusively
composed by sequences from this country. The CKEand CUG
lineages comprise 33% and 37% of all sequences from Kenya and
Uganda, respectively, and their circulation seems to be mainly
restricted to those countries. Finally, the CTZlineage comprises
39% of all Tanzanian sequences analyzed and has also been
disseminated to Kenya and Uganda. Both ML and Bayesian
analyses further suggest that the CEAclade branched in two major
sub-clades: one composed by sequences from Burundi and lineages
CET1, CET2and CUG;the other one composed by sequences from
Burundi and lineages CKEand CTZ. The statistical support of such
major sub-clades in Bayesian analysis, however, was not significant
(PP,0.50) and this observation should be interpreted with caution.
Time-scale of the HIV-1 CEAclade
The median estimated evolutionary rate for the pol region of the
CEAclade was 1.861023(95% highest posterior density [HPD]:
1.161023–2.461023) subst/site/year, similar to that previously
estimated for HIV-1 subtype C lineages circulating in South
America  and southern Africa . Importantly, the coefficient
of rate variation was higher than zero (0.26 [95% HPD: 0.21–
0.31]), thus demonstrating a significant variation of substitution
rate among branches in the CEAclade and supporting the use of a
relaxed molecular clock model to reconstruct the time-scale of this
lineage. According to this analysis the CEAclade started to spread
in Burundi at 1962 (95% HPD: 1942–1975), while major sub-
clades CET1/CET2, CKE, CTZ and CUG began to expand in
Ethiopia, Kenya, Tanzania and Uganda, respectively, by the early
1970s (Figure 3).
Time-scale of the HIV-1 subtype C Ethiopian clades
The time-scale of the two major Ethiopian clades (CETand
C9ET) was also estimated by combining all sequences from this
country in a single dataset and incorporating the posterior
distribution of the substitution rate previously estimated for the
CEAlineage as an informative prior. This analysis resulted in a
Bayesian MCC tree in which clades CETand C9ETwere poorly
supported (PP,0.5) and several strains branched outside those
major clades (Figure S3). A careful exploration of Ethiopian
sequences, revealed that some strains initially classified within
clades CET(n=8) or C9ET(n=10) and those strains that branched
outside major Ethiopian clades (n=2) are putative C/C9
intrasubtype recombinant viruses (Figure S3). When those viruses
were excluded, the clades CETand C9ETsegregate in two highly
supported (PP.0.9) reciprocally monophyletic groups (Figure 5).
According to this new Bayesian MCC tree, the median Tmrcawas
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estimated at 1978 for clade CET, 1981 for sub-clade CET1, 1984
for sub-clade CET2, and 1981 for clade C9ET(Figure 5).
This study demonstrates a significant phylogeographic subdivi-
sion of HIV-1 subtype C strains circulating in the east respect to
those circulating in the central and southern African regions,
consistent with a recent study . Most (73%) subtype C
sequences from east Africa analyzed in this study branched within
a highly supported monophyletic clade, here called CEA, that
comprise 100% of subtype C sequences from Burundi, 97% from
Uganda, 64% from Kenya, 61% from Ethiopia, and 49% from
Tanzania. This major east African clade also comprises a minor
proportion (,10%) of sequences from central Africa, but no
sequence from southern Africa, thus indicating that its circulation
is mainly restricted to the east African region. Of note, the
genealogies previously inferred for HIV-1 subtypes A and D also
support a model of limited introduction of each subtype into east
Africa, followed by a subsequent local expansion .
Our phylogeographic study suggests that the CEAclade most
probably originated in Burundi and after a period of local
expansion, this viral lineage was disseminated at multiple times to
Ethiopia, Kenya, Tanzania and Uganda, where it generated new
local epidemics. Several introductions of the CEAlineage from
Tanzania into both Kenya and Uganda were also detected, while
viral exchanges between Ethiopia, Kenya and Uganda were less
frequent. Five major country-specific monophyletic sub-clusters
were detected within the CEAclade that comprise 44%, 33%,
37%, and 39% of all sequences from Ethiopia, Kenya, Uganda
and Tanzania here included, respectively. Thus, despite frequent
viral movement among east African countries, a significant
proportion of subtype C infections in Ethiopia, Kenya, Tanzania
and Uganda most likely resulted from the expansion of a few
It has been suggested that interconnectivity between population
centers was a critical factor in the spread of HIV-1 subtypes A and
D across Africa . The restricted circulation of the CEAlineage
in southern African countries is consistent with this model,
considering the relative inaccessibility between the principal
population centers of eastern and southern African regions. This
model, however, is not consistent with the proposed role of
Burundi as the main hub of dissemination of the CEAclade in the
region, as this small country is poorly interconnected to other east
African countries. Previous studies have also shown a strongly
supported phylogenetic relationship between subtype C sequences
Figure 1. Maximum likelihood phylogenetic tree based on 1,981 HIV-1 subtype C pol (, ,1,000 pb) sequences. Sequences were sampled
at different countries from the east (n=352), central (n=53) and southern (n=1,576) African regions shown in Table 1. The color of branches
represents the geographic region from where the subtype C sequences originated, according to the map given in the figure. The boxes highlight the
position of the major east African subtype C lineages. The tree was rooted using HIV-1 subtype A1 and D reference sequences (black branches).
Horizontal branch lengths are drawn to scale with the bar at the bottom indicating nucleotide substitutions per site.
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from Brazil, the UK, Burundi and Kenya; thus indicating that the
CEA clade has also been disseminated to South America and
Europe [60,62,63]. These evidences suggest that factors other than
accessibility may have shaped the dissemination of the CEAclade
at both local and global scale.
Burundi has known many violent ethnic conflicts mainly since
the 1960s that resulted in large migration flows. Two major civil
conflicts that took place in Burundi in 1972 and 1993 generated
especially large human movements with the former producing
around 300,000 refugees and the latter producing about 687,000
. Most refugees initially crossed the border of their country in
the east, fleeing to neighboring Tanzania, followed by movement
into other neighboring African countries and later to Europe and
North America. It has been estimated that there are about 200,000
Burundians currently living in Tanzania, 18,000 in the Demo-
cratic Republic of the Congo, 4,000 in Uganda, 10,000 in the
Figure 2. Geographic distribution of HIV-1 subtype C clades in east Africa. a) Map of Africa showing the frequency of distinct HIV-1 subtype
C clades across the five countries from the east region here studied (Burundi, Ethiopia, Kenya, Uganda and Tanzania). b) Map of Tanzania showing the
frequency of distinct HIV-1 subtype C clades across different country regions where patients included in the present study resided (Kagera, Mwanza,
Kilimanjaro and Mbeya). The legend for the colors on graphics is shown on the right.
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Figure 3. Time-scaled Bayesian MCC tree of the HIV-1 CEAlineage. Branches are colored according to the most probable location state of
their descendent nodes. The legend for the colors is shown on the left. The state posterior probability is indicated only at key nodes. The boxes
highlight the position of the major country-specific sub-clades detected in our study. The median age (with 95% HPD interval in parentheses) of
those country-specific sub-clades is shown. Horizontal branch lengths are drawn to scale with the bar at the bottom indicating years. The tree was
automatically rooted under the assumption of a relaxed molecular clock.
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European Union, and about 3,000 in the USA and Canada .
The molecular clock analysis clade traced the origin of the CEA
lineage in Burundi to the early 1960s, while the onset date of the
major sub-clades circulating in Ethiopia, Kenya, Tanzania and
Uganda was estimated at around the early 1970s, coinciding with
the first large Burundian migration flow. These analyses support
the notion that the Burundian migration flow occurring in 1972
may have played a fundamental role in the regional and
international dissemination of the CEAclade.
While subtype C epidemic in Burundi and Uganda is largely
dominated by the CEAclade, a second major subtype C lineage is
also circulating in Ethiopia. Our results showed that the two
Ethiopian lineages previously designated C and C9 , resulted
from independent founder strains originated in the eastern and
southern African regions, respectively, and further confirmed the
circulation of intra-subtype C/C9 recombinants in Ethiopia .
The prevalence of C/C9 recombinant viruses estimated in our
dataset (20%) was equal to the percentage found in the general
Ethiopian population . The onset date of Ethiopian clades C
and C9 was dated to between the early 1970s and the early 1980s;
consistent with previous estimations [65,66,67].
A large collection of minor subtype C lineages of southern
African origin were detected in Kenya and Tanzania, which
together represent 36% and 51% of sequences from those
countries here analyzed, respectively. These lineages seem to have
a more restricted expansion than the CEAclade, although they
were particularly prevalent (100%) in southwest Tanzania (Mbeya
region), close to Zambia and Malawi. The co-circulation of
subtype C sequences from both east and southern African origin in
Tanzania is consistent with its intermediate geographical position
between eastern and southern countries. It is unclear whether
subtype C clades of southern African origin detected in Kenya
were introduced from Tanzania and/or directly from southern
It is also unclear the relevance of these findings for HIV-1
vaccine design. Possible correlations of distinct HIV-1 subtype C
clades with differential susceptibility to neutralizing antibody and/
or cellular immune responses should be explored to justify the
selection of vaccines incorporating one or multiple immunogens
derived from major African subtype C clades . It is also
uncertain whether distinct subtype C lineages may possess
different biological properties that affect disease progression and
viral transmission. A recent study conducted in Ethiopia showed
that infection with clade CETis associated with initially lower
HIV-1 RNA plasma loads but more rapid onset of disease than
infections with clade C9ET. The authors proposed that the
clade CETmay be less efficiently transmitted than clade C9ET,
which is consistent with epidemiological evidence that show that
the strain C9EThas gained ground and surpassed the clade CET
over time [40,68]. New studies are necessary to determine if
subtype C lineages of east African origin are less transmissible than
those originated in southern Africa.
In conclusion, the results presented here point to the existence
of a HIV-1 subtype C lineage characteristic of east Africa, which
accounts for .70% of subtype C infections in this African region.
This lineage probably emerged in Burundi in the 1960s and about
10 years later spread to Ethiopia, Kenya, Uganda and Tanzania,
where it disseminated establishing new local epidemics. The
subtype C epidemics in Ethiopia, Kenya and Tanzania also
resulted from the introduction and dissemination of additional
lineages of southern African origin. The explanation for the
pattern of spread of the HIV-1 subtype C epidemic in east Africa is
probably multifactorial and includes founder effects, massive
migration between countries as a consequence of ethnic conflicts
and geographical proximity.
Figure 4. Spatiotemporal dynamic of HIV-1 CEAclade dissem-
ination in east Africa. We provide snapshots of the dispersal pattern
for the years 1960, 1965, 1970, 1975, 1980, 1985, 1990 and 2000. Lines
between locations represent branches in the Bayesian MCC tree along
which location transition occurs. Location circle diameters are
proportional to square root of the number of Bayesian MCC branches
maintaining a particular location state at each time-point. The white-
green color gradient informs the relative age of the transitions (older-
recent). The maps are based on satellite pictures made available in
Table 2. Estimated number of migration events of HIV-1 CEA
clade among east African countries.
From/ToBurundi EthiopiaKenya TanzaniaUganda
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ping analyses. a) Transition (blue line) and transversion (green
line) versus divergence plot for the HIV-1 subtype C pol dataset. b)
Likelihood mapping of 10,000 random quarters selected from the
HIV-1 subtype C pol dataset. Distribution (left triangle) and
percentage (right triangle) of dots plotted in each region of the
map. Each dot represents the likelihoods of the three possible tree
topologies for a set of four sequences (quartets) selected randomly
from the dataset. The dots localized on the vertices, in the centre
and on the laterals represent the tree-like, the star-like and the
network-like phylogenetic signals, respectively.
Substitution saturation and likelihood map-
in Figure 1. The color of branches represents the country from
where the sequence originated, according to the legend shown on
the left. The boxes highlight the position of the major country-
specific sub-clades detected in our study. The aLRT support
values are indicated only at key nodes.
Close view of the HIV-1 CEAlineage despited
subtype C pol sequences from Ethiopia. Branches are
Time-scaled Bayesian MCC tree of HIV-1
colored according to the initial clade assignment of each sequence
based on ML analysis: CET(blue), C9ET(red), other clades (green).
The PP support is indicated only at key nodes. Positions of the
putative interclade C/C9 recombinant sequences are marked with
asterisks. Horizontal branch lengths are drawn to scale with the
scale at the bottom indicating years. The tree was automatically
rooted under the assumption of a relaxed molecular clock.
Representative bootscanning plots of some putative C/C9
intrasubtype recombinant sequences are depicted on the right.
Query sequences were compared to reference sequences of HIV-1
clades A1 (AB253429), D (AY371157), CET (AY242589), and
We wish to thank Dr. Vera Bongertz for critical review of the manuscript
and anonymous reviewers for comments.
Conceived and designed the experiments: GB. Performed the experiments:
GB EOD. Analyzed the data: GB EOD. Contributed reagents/materials/
analysis tools: GB EOD. Wrote the paper: GB EOD.
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