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The challenges of modelling antibody
repertoire dynamics in HIV infection
rstb.royalsocietypublishing.org
Shishi Luo1,2 and Alan S. Perelson3
1
Review
Cite this article: Luo S, Perelson AS. 2015
The challenges of modelling antibody
repertoire dynamics in HIV infection. Phil.
Trans. R. Soc. B 370: 20140247.
http://dx.doi.org/10.1098/rstb.2014.0247
Accepted: 1 May 2015
One contribution of 13 to a theme issue
‘The dynamics of antibody repertoires’.
Subject Areas:
theoretical biology, immunology, evolution,
microbiology
Keywords:
HIV, evolution, immunology
Author for correspondence:
Alan S. Perelson
e-mail: [email protected]
Department of Electrical Engineering and Computer Science, and 2Department of Statistics, UC Berkeley,
Berkeley, CA 94110, USA
3
Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, NM 87545, USA
Antibody affinity maturation by somatic hypermutation of B-cell immunoglobulin variable region genes has been studied for decades in various
model systems using well-defined antigens. While much is known about the
molecular details of the process, our understanding of the selective forces
that generate affinity maturation are less well developed, particularly in the
case of a co-evolving pathogen such as HIV. Despite this gap in understanding,
high-throughput antibody sequence data are increasingly being collected to
investigate the evolutionary trajectories of antibody lineages in HIV-infected
individuals. Here, we review what is known in controlled experimental systems about the mechanisms underlying antibody selection and compare this
to the observed temporal patterns of antibody evolution in HIV infection.
We describe how our current understanding of antibody selection mechanisms
leaves questions about antibody dynamics in HIV infection unanswered.
Without a mechanistic understanding of antibody selection in the context of
a co-evolving viral population, modelling and analysis of antibody sequences in HIV-infected individuals will be limited in their interpretation and
predictive ability.
1. Introduction
The evolution of the antibody repertoire in response to HIV infection, where the
viral population co-evolves with the antibody response, is not well understood.
The use of high-throughput antibody sequence data in characterizing antibody
repertoire dynamics is also in its infancy, with methods still being standardized
and basic questions investigated [1]. Notwithstanding, antibody sequence data
generated from HIV patients are being used to study the adaptive immune
response to HIV infection. In particular, spurred by the discovery of potent antibodies that neutralize a broad panel of HIV strains, large antibody repertoire
datasets are being collected from HIV-infected individuals (e.g. [2]). One goal
of this work is to identify antibody affinity maturation pathways that might
be mimicked by vaccine protocols [3].
With the collection of data in these areas racing ahead, it is important to
understand the complexities of antibody dynamics where the viral population
evolves throughout the course of infection, as in the case of HIV (shown schematically in figure 1). To illustrate these complexities, we first review the mechanisms
that are known to underlie antibody selection in the presence of a single antigen
[5,6]. We then review empirically observed temporal dynamics of the antibody
response in HIV-infected patients. We argue that the mechanisms determined
from single-antigen studies are insufficient to guide the analysis and modelling of
antibody evolution during HIV infection. In particular, understanding antibody–
virus co-evolutionary dynamics requires knowledge of the B-cell response when
an antigen changes over time and of B-cell competition in the presence of multiple
co-circulating antigens. Neither of these processes can be deduced from singleantigen studies. In the absence of experimental evidence, any analysis or modelling
of the antibody response to HIV infection will require making assumptions about
these processes. The analysis and interpretation of antibody sequence data will
therefore be limited by the validity of these assumptions.
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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acute
infection
clinical latency/
chronic infection
weeks
antibody
evolution
homogeneous
increasing diversity
immune escape
increasing
strainnonneutralizing specific
cross-neutralization
Figure 1. Schematic of the viral population dynamics (top) and evolutionary
dynamics of viral and antibody populations (bottom) during HIV infection.
General patterns are shown; specific quantities vary substantially among individuals. Patterns of viral evolution are based on Shankarappa et al. [4].
Patterns of antibody evolution are described in §§3 – 5.
2. Antibody selection in germinal centres
Selection of B cells, and their expressed antibodies, chiefly
occurs in sites called germinal centres (reviewed in [5]). Germinal centres are dynamic structures that form approximately
one week after infection when antigen-activated B cells migrate
to B-cell follicles in lymph nodes and the spleen. Once inside
germinal centres, B cells undergo proliferation and mutation
in a region of the germinal centre, known as the dark zone,
and selection in another region, called the light zone. Replication has been measured to occur every 6–14 h [5] and
somatic mutations accumulate with each replication. In mice
infected with influenza, this mutation occurs at a rate of 1023
per base per generation [7].
The mechanisms of B-cell selection in germinal centres are
becoming increasingly understood in murine model systems in
which the immune system is exposed to a single known antigen. Recent work by Gitlin et al. [6] has experimentally
demonstrated that the length of time a B cell spends replicating
and mutating in the dark zone of the germinal centre is correlated with the affinity between its expressed antibody and the
invading antigen. Affinity-based selection of B cells occurs
in the light zone of the germinal centre, where B cells collect
antigen displayed on follicular dendritic cells. B cells expressing receptors with higher affinity to the antigens will collect
more antigen from follicular dendritic cells than B cells with
low-affinity receptors. The antigen collected by B cells are internalized and then presented as peptides to follicular helper
T cells (TFH cells). B cells that present more peptides to TFH
cells survive longer and spend more time in the dark zone
and undergo more replication and mutation.
These experimental studies of germinal centre reactions
have allowed explicit computational modelling of antibody
selection in the case where there is a single type of antigen
[8,9]. However, equally detailed modelling of antibody selection
in the case of a co-evolving viral population is currently difficult.
Indeed, recent models, such as those by Meyer-Hermann et al.
[8] and Wang et al. [9] do not include viral evolution, although
the latter model uses multiple non-evolving viral strains to
3. Initial stages of HIV infection
Early in HIV infection the viral population expands and
then settles to an approximate steady-state size, called the
set-point viral load (figure 1). The virus in the acute stage
of an infection caused by sexual transmission is largely
homogeneous and the mutations that occur in the viral population in these early stages appear to be random [14,15].
Indeed, there is little selection pressure exerted on the
virus, as the earliest HIV-specific antibodies are not detected
until around two to three weeks after infection [16], or
13 days after detectable plasma viraemia [17]. These
antibodies predominantly target epitopes on the HIV envelope gp41, which is not exposed on functional HIV envelope
and are therefore non-neutralizing [16,17].
It is puzzling that in the same time that it takes the
adaptive immune system to mount a neutralizing antibody response to a typical infection [18], it only mounts a
non-neutralizing response against HIV. One possible reason
is that the contact region between gp41 and gp120 on
HIV-1 envelope is immunogenic and often exposed on
disassembled gp41– gp120 complexes that are abundant
throughout HIV infection [16]. Viral particles that display
this immunogenic region may therefore also be abundant
Phil. Trans. R. Soc. B 370: 20140247
viral
evolution
years
2
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viral
population
dynamics
simulate vaccination protocols. The difficulty of constructing
models of co-evolving virus and antibody populations lies in
the lack of experimental understanding about germinal centre
reactions during chronic infection. Critical quantities such as
the frequency with which B cells cycle through germinal centres
over the course of infection, how long it takes mutant viral
strains in other parts of the body to appear in germinal centres,
and what fraction of B cells leaving germinal centres are plasma
cells versus memory cells, are unknown. In addition to these
unknown quantities in chronic infection, HIV poses a further
complication in that TFH cells are CD4þ cells and hence targets
of HIV infection. Because CD8þ T cells are generally excluded
from B-cell follicles, infected CD4þ TFH are substantially
shielded from CD8-mediated suppression [10]. The effects of
HIV infection on TFH cell function in germinal centre reactions
are currently being explored, but impairments in TFH function
have been suggested to contribute to the relative inefficiency
of broadly neutralizing antibody generation in HIV-infected
individuals [11,12].
In the absence of this information, one study that models
both antibody and viral evolutionary dynamics avoids explicitly modelling germinal centre reactions altogether [13].
Instead, virus –antibody co-evolution is modelled phenomenologically, with antibodies and viral epitopes modelled as
character strings that replicate and mutate. Antibody affinity
is evaluated by a string-matching procedure. Both antibodies
with relatively high affinity and viral strains escaping most
antibodies have high relative fitness and tend to persist and
evolve further.
It would be preferable if the analysis of HIV antibody
sequence data was based on more mechanistically explicit
models of antibody selection with a co-evolving viral population. To this end, we describe observed temporal patterns
of antibody evolution in HIV infection with which such
models would need to be consistent. These temporal patterns
further highlight properties of antibody selection in HIV
infection that current experimental results cannot explain.
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Neutralizing antibodies specific to the transmitted HIV strain,
called autologous antibodies, eventually arise, but are typically
not detected until months after infection, with some patients
taking more than a year [2,16,20,21]. The epitopes targeted
by these neutralizing antibodies tend to be in variable regions,
e.g. the V3 loop, which, owing to being prominently exposed
on the HIV envelope, may be more immunogenic than other
neutralizing targets [16,22].
Autologous neutralizing ability is demonstrated in vitro
by assaying virus sampled from a patient early in infection
against plasma collected later in infection (e.g. [21,23]). However, when the same assay is carried out for the viral
population contemporaneous with this same neutralizing
plasma, the contemporaneous virus population is not inhibited. In other words, by the time a neutralizing antibody
response against a virus strain is detectable in the blood, the
viral population has already mutated to escape it. Figure 2
shows the lag in response time of neutralizing plasma against
the viral population from four patients [21]. The lag, which
varies over time and among patients, ranges from about six
to 16 months.
Because viral escape occurs, the initial neutralizing antibodies do not clear the infection. In fact, whether these
antibodies play any role in controlling the viral population
is unclear [24]. This is slightly counterintuitive because for
immune escape variants to replace the infecting strain, the
viral population would need to have been under selective
pressure. Yet there are no obvious clinical manifestations of
this selection, such as a notable decrease in the viral population. This could be due to plasma and viral samples being
taken too infrequently to detect such changes, or that
the effects of the antibody response manifest themselves in
tissues rather than peripheral blood.
5. Co-evolution in later stages of infection
Time-series antibody and viral sequence data for later stages of
infection are most detailed in patients who develop broadly
neutralizing antibodies. The pattern common to the cases
studied to date is a repeating cycle of immune escape by the
virus and increased antibody neutralization breadth, with
the antibody response lagging behind the viral population.
Moore et al. [26] studied two HIV-1 patients who developed
broad antibodies, where breadth in both patients was based on
binding to a glycan on the envelope protein. The glycan target
was not on the transmitted strain, and glycan shielding is
a known HIV immune escape mechanism [20]. Thus, the
appearance of the glycan in the viral population at around
six months after infection was hypothesized to be an escape
mutation in response to an initial autologous antibody
response. At around 12–15 months post infection, antibodies
that target the glycan were detected in the individual’s sera.
This suggests that selection for antibodies against the escape
variant occurred, albeit with a lag of more than six months.
Finally, at 2 years post infection, the glycan disappeared from
the viral population, suggesting that the virus again escaped
the antibody response.
A similar chain of events was observed by Wibmer et al.
[27], where three waves of increasingly broad neutralizing
antibody responses occurred in a single individual. The first
wave of antibodies emerged at around 16 months and targeted the V2 loop, another region known to be variable
and immunogenic [22]. Escape from this first wave occurred
via deletion of a glycan on which the first-wave antibody
depended. This deletion simultaneously exposed an epitope
on the CD4 binding site, leading to a second wave of broadly
neutralizing antibodies at around 28 months that relied on
CD4 binding site neutralization. Immune escape and a subsequent third wave of broadly neutralizing antibodies
followed, though the location of the escape mutations
and the targets of the antibodies were not identified in
the study.
Perhaps the most detailed study to date, with resolution at
the level of antibody clonal lineages, comes from [28] in the
case of patient CH505 [2]. Gao and colleagues identified a
3
Phil. Trans. R. Soc. B 370: 20140247
4. Antibody neutralization of the infecting strain
and rapid viral escape
In any case, the above suggests that while it takes substantial time for the antibody population to mount a neutralizing
response, the viral population can rapidly escape neutralizing
antibodies. This gives the impression (figure 2) that the antibody response is consistently targeting past viral strains, not
current ones. Therefore, another challenge to studying antibody repertoire dynamics in HIV infection is a lack of
knowledge of how a change in the viral strain is reflected in
the antibody selection process. A recent study demonstrated
how follicular dendritic cells retain intact antigen for long
periods of time [25]. It is possible, then, that viral particles displayed on follicular dendritic cells during HIV infection are
older relative to the viral strains abundant in the rest of the
body, forcing affinity maturation towards older strains. However, until experiments on germinal centre reactions with
temporally changing antigens are conducted, it is unclear
whether to model the delay in a neutralizing response as the
result of restrictive properties of follicular dendritic cells or as
the result of inherently slower rates of mutation and selection
during antibody affinity maturation.
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on follicular dendritic cells, leading to selection for B cells that
recognize this non-functional region.
Another possibility is that gp41-specific antibodies arise
from memory B-cell populations primed by gut bacteria.
Trama et al. [19] showed that gp41 antibodies cross-react
with gut commensal bacteria and found antibodies with similar characteristics to gp41-specific antibodies in HIV-negative
individuals. They suggested that since memory B cells
respond more rapidly than naive B cells, the non-neutralizing
antibody response to HIV could be due to the clonal expansion of B cells previously primed by gut bacteria, bacteria
that coincidentally share features with gp41.
Thus, one challenge in studying antibody repertoire
dynamics in HIV infection is that the part of the antibody repertoire that is initially elicited, or that responds most rapidly in
early infection, is non-neutralizing. Because determination of
antibody structure from sequence data is still a difficult and
low-throughput process, there is no straightforward way to
determine from sequence data alone what epitopes these
non-neutralizing antibodies are targeting. While these antibodies are being produced, what is happening to antibodies
that target neutralizing epitopes? Whether they are present
but at undetectable levels, or not present at all at this early
stage is an open question.
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Phil. Trans. R. Soc. B 370: 20140247
peak plasma inhibition (month)
20
10
CAP45
5
CAP88
CAP177
CAP210
0
2
4
6
8
virus (month)
10
12
14
Figure 2. Time viral population was isolated (months post infection) against the time that peak neutralizing plasma against the viral population was detected. Each
symbol represents a different patient from the study conducted in [21]. (Online version in colour.)
specific clonal lineage of B cells that selected for escape variants
of the founder HIV strain. These variants contained mutations
that enhanced reactivity towards a different B-cell lineage, one
that ultimately led to a broadly neutralizing antibody. Their
results imply that rather than a purely serial process of neutralization and escape, escape from one antibody lineage led to
selection of a concurrent antibody lineage that developed
broad neutralizing breadth.
The sequential selection scenarios in the first two studies
again raise the question of how affinity maturation occurs in
the context of temporally changing antigens. The results of
[28] raise a further complication: B cells specific to different
viral strains compete with each other in HIV infection. This
question has been investigated in HIV with a mouse model
by Forsell et al. [29]. They compared antibody responses in
mice vaccinated with a wild-type HIV strain with mice vaccinated with a modified strain. The modified strain had a
mutation that caused the V3 loop, a known immunodominant region, to be masked, i.e. glycosylated. Forsell and
colleagues were interested in knowing whether the B-cell
response to other epitopes would be increased as a result of
masking the more immunogenic V3 loop. They found that
while the antibody response to the V3 loop was indeed suppressed, there was no evidence of a concomitant increase in
the antibody response to other epitopes. The work of Forsell
et al. [29] thus suggests a specific mechanism of B-cell competition: one where the clonal expansion of a B-cell lineage is
determined by a target epitope’s absolute immunogenicity,
rather than its immunogenicity relative to other epitopes. This
result also raises questions regarding antibody competition
early in infection: if the targets of the initial non-neutralizing
antibodies are highly immunogenic, how are neutralizing antibodies to less immunogenic epitopes competing with them?
Also, there is the issue of competition between anti-Env B
cells and B cells specific for other HIV proteins that remains
to be quantitatively addressed.
6. Conclusion
High-resolution antibody sequence data are increasingly
being collected in studies of HIV and antibody co-evolution.
The current goal of inferring dynamics specific to each individual from these data is an important first step in
understanding the different paths that evolution might take
in generating antibody repertoires towards HIV. However,
to identify general characteristics of antibody –virus coevolution during HIV infection, we will need to construct
models that incorporate selective mechanisms that drive antibody repertoire dynamics. We have outlined here the gap
between what is known experimentally about antibody selection in single-strain systems and what has been observed
empirically for HIV infections. In particular, antibody selection in the presence of an evolving viral population and in
the presence of multiple distinct viral strains is not well
understood. Until the mechanisms of selection in these situations are determined experimentally, interpretation and
prediction based on high-throughput antibody sequence
data in HIV infection will be limited by the modelling
assumptions made about these mechanisms.
Authors’ contributions. S.L. and A.S.P. wrote the article.
Competing interests. We declare we have no competing interests.
Funding. This work was performed under the auspices of the US
Department of Energy under contract DE-AC52-06NA25396 and supported by the Center of Nonlinear Studies at Los Alamos National
Laboratory, NIH grant nos R01-AI028433, R01-OD011095 and the
Center for HIV AIDS Vaccine Immunology-Immunogen Design
(CHAVI-ID) grant UM1-AI100645.
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