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Replication Vesicles are Load- and Choke-Points in the
Hepatitis C Virus Lifecycle
Marco Binder
1"
, Nurgazy Sulaimanov
2,3"
, Diana Clausznitzer
2
, Manuel Schulze
2
, Christian M. Hu
¨ber
1¤
,
Simon M. Lenz
4
, Johannes P. Schlo
¨der
4
, Martin Trippler
5
, Ralf Bartenschlager
1
, Volker Lohmann
1
,
Lars Kaderali
2,3
*
1Heidelberg University, Medical Faculty, Department of Infectious Diseases, Molecular Virology, Heidelberg, Germany, 2Technische Universita
¨t Dresden, Institute for
Medical Informatics and Biometry, Dresden, Germany, 3Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Heidelberg, Germany, 4Heidelberg
University, Interdisciplinary Center for Scientific Computing (IWR), Simulation and Optimization Group, Heidelberg, Germany, 5University Hospital of Essen, Department of
Gastroenterology and Hepatology, Essen, Germany
Abstract
Hepatitis C virus (HCV) infection develops into chronicity in 80% of all patients, characterized by persistent low-level
replication. To understand how the virus establishes its tightly controlled intracellular RNA replication cycle, we developed
the first detailed mathematical model of the initial dynamic phase of the intracellular HCV RNA replication. We therefore
quantitatively measured viral RNA and protein translation upon synchronous delivery of viral genomes to host cells, and
thoroughly validated the model using additional, independent experiments. Model analysis was used to predict the efficacy
of different classes of inhibitors and identified sensitive substeps of replication that could be targeted by current and future
therapeutics. A protective replication compartment proved to be essential for sustained RNA replication, balancing
translation versus replication and thus effectively limiting RNA amplification. The model predicts that host factors involved
in the formation of this compartment determine cellular permissiveness to HCV replication. In gene expression profiling, we
identified several key processes potentially determining cellular HCV replication efficiency.
Citation: Binder M, Sulaimanov N, Clausznitzer D, Schulze M, Hu
¨ber CM, et al. (2013) Replication Vesicles are Load- and Choke-Points in the Hepatitis C Virus
Lifecycle. PLoS Pathog 9(8): e1003561. doi:10.1371/journal.ppat.1003561
Editor: Claus O. Wilke, University of Texas at Austin, United States of America
Received December 21, 2012; Accepted July 2, 2013; Published August 22, 2013
Copyright: ß2013 Binder et al. 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: The authors acknowledge funding from the German Ministry for Education and Research (BMBF), grant 0313923 (ForSys/ViroQuant), the European
Union’s Seventh Framework Program (FP7/2007–2013), grant 267429 (SysPatho), and the Helmholtz Alliance on Systems Biology (SBCancer). The fundershadno
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: lars.kaderali@tu-dresden.de
¤ Current address: Department of Obstetrics and Gynaecology, University of Cambridge Clinical School, The Rosie Hospital, Cambridge, United Kingdom.
"MB and NS are equally contributing first authors.
Introduction
Hepatitis C virus (HCV) infection is a major global health
problem, with approximately 170 million chronically infected
individuals worldwide and 3 to 4 million new infections occurring
each year [1]. Acute infection is mostly asymptomatic, however, it
develops into a chronic infection in about 80% of patients, and
then is a leading cause of liver cirrhosis, hepatocellular carcinoma
and subsequent liver transplantation [2,3,4]. A significant fraction
of patients cannot be cured even with modern combination
therapies, partially due to ab initio non-responsiveness, but also due
to the emergence of drug-resistant HCV quasispecies.
HCV is an enveloped plus-strand RNA virus and belongs to the
Flaviviridae family. Upon entry into the host cell, its 9.6 kb genome
is translated by a cap-independent, internal ribosomal entry site
(IRES) mediated mechanism into a single large polyprotein. Viral
and cellular proteases co- and post-translationally cleave this
precursor into ten mature viral proteins, comprising three
structural proteins (core, E1, E2), the ion channel p7 as well as
the six non-structural (NS) proteins NS2, 3, 4A, 4B, 5A and 5B [5].
The five ‘‘replicase’’ proteins NS3 to NS5B are essential and
sufficient for intracellular genome replication. NS3 comprises an
RNA helicase and a protease domain, the latter of which, together
with the co-factor NS4A, forms the major viral protease NS3/4A,
liberating itself and all other replicase proteins from the
polyprotein precursor. NS4B, together with other NS proteins,
induces membrane alterations, observable as convoluted, vesicular
membrane structures known as the membranous web and believed
to act as the sites of RNA replication [6,7]. The exact architecture
and topology of these structures, and particularly their structure-
function-relationship, is not fully understood yet. However, for
Dengue virus (DV), a related flavivirus, the three-dimensional
makeup of the membrane rearrangements has been solved
recently [8]. There, numerous small, vesicular invaginations into
the rough endoplasmic reticulum (ER) serve as a protected
environment for genome replication. NS5A is a phosphoprotein
important both in RNA replication and particle assembly and/or
release. NS5B, the RNA-dependent RNA polymerase (RdRP), is
the core enzyme of the replicase complex. In order to amplify the
viral RNA, NS5B first synthesizes a complementary (i.e. negatively
oriented) strand from the plus stranded genome, putatively
resulting in a double-stranded (ds) intermediate [9]. From this
negative strand template, NS5B then transcribes progeny plus
strands. Given the ,10-fold higher number of plus strands over
PLOS Pathogens | www.plospathogens.org 1 August 2013 | Volume 9 | Issue 8 | e1003561
minus strands within the host cell, this most likely occurs in a
repetitive manner [10]. Newly synthesized plus strands are then
released by an unknown mechanism from the replicative
compartment and can then either be directed to encapsidation
into assembling virions, or re-enter the replicative cycle by serving
as templates for further translation and subsequent incorporation
into a new replication complex.
It is interesting to note that although HCV establishes a
persistent infection, it does not have a latent phase; throughout the
course of the infection, which can be decades long in many
patients, there is constant production of viral RNA, proteins and
infectious particles. In most viral infections, presence of non-self
structures, such as dsRNA or viral proteins, is readily detected by
sensors of the immune system, leading to the production of type I
interferon (IFN) and activation of the adaptive immune response
[11]. Also in case of HCV, innate as well as adaptive immune
responses are elicited, however, by means of various complex
interactions with cellular processes, the virus is capable to blunt
these defense mechanisms and thus is able to persist [12]. This
ability of HCV to maintain low profile persistence is most likely
intimately linked to its tightly controlled RNA replication; for the
closely related bovine diarrhea virus (BVDV), which can be
converted from a persistently to an acutely replicating form, a
direct correlation between excessive RNA replication and the
induction of cytopathic effects has been described [13]. To
comprehensively study these complex and highly dynamic
processes that can only inappropriately be addressed by individual
experiments, an eminent approach is mathematical modeling.
Consequently, a basic model of HCV infection dynamics was
published almost 15 years ago [14] and has since led to the
development of several related models of HCV infection and
therapy dynamics [15,16,17,18,19,20,21]. However, all of these
models described the long-term dynamics at the level of cell
populations, organs and even organisms (patients), and did not
take intracellular processes such as genome translation and the
actual RNA replication into account. With the development of
subgenomic HCV replicons, detailed studies of intracellular RNA
replication became possible [22,23]. A thorough quantitative
analysis of persistent subgenomic replicons in Huh-7 cells [10] led
to the development of a first mathematical model of intracellular
steady state RNA replication [24]. Further models addressed the
effect of potential drugs on viral replication [25] or included the
production of virus particles [26,27]. However, all published
models were solely based on measurements of steady state
replication. In contrast, to understand how the virus on the one
hand manages to efficiently (and quickly) establish itself within a
host cell before the cell is able to mount an antiviral response,
while on the other hand, it is strictly limiting its own amplification,
static (steady state) data is not sufficient but needs to be
complemented by information about the dynamic aspects of
replication. Previous studies on replication kinetics in cell culture
in fact point to a highly dynamic initial phase of RNA replication
in the first few hours after genome transfection or infection, which
then reaches a steady state within 24–72 hours [22,28,29]. Actual
amplification kinetics and the absolute levels attained in the steady
state vary largely between different experimental systems and are
mainly determined by the permissiveness of the employed host cell
[30,31,32] and by the viral isolate [30,32,33].
Therefore, in our present study we quantitatively followed the
onset of intracellular RNA replication within the first couple of
hours upon introduction of HCV genomes into the host cells.
Based on these data we developed a comprehensive mathematical
model capable of precisely describing both, the dynamic and the
steady state phases of viral replication. We then used this model to
study aspects of the viral replication cycle that cannot directly be
accessed experimentally.
Results
To assess the dynamics of HCV RNA replication, we performed
quantitative, time resolved measurements of strand specific viral
RNA and polyprotein concentrations over 72 h after viral RNA
transfection into Huh7 cells. To achieve sufficiently strong
replication that can be measured reliably, we used subgenomic
reporter replicons carrying the firefly luciferase gene in front of the
viral proteins [28] (figure 1A), and we synchronized the onset of
replication to the largest feasible extent by using electroporation to
instantaneously introduce in vitro transcribed replicon RNA into
the cells. As host cellular factors play a critical role in determining
the efficiency of viral replication [30,31], we used two different cell
lines: Huh7-Lunet is a clonal cell line of exceptionally high
permissiveness for HCV RNA replication [34], whereas a low
passage of standard Huh-7 cells (Huh-7 lp) replicates HCV RNA
to significantly lower levels, as has been described previously [30].
Over the course of 72 hours we then followed HCV replication,
measuring plus-strand and minus-strand RNA by strand specific
quantitative Northern blotting and firefly luciferase activity as a
highly sensitive surrogate marker of viral protein translation, since
luciferase expression was under the control of the HCV IRES (see
figure 1A). Of note, luciferase activity correlates with the amount
of viral protein translated, but does not allow discrimination
between cytoplasmic NS proteins and proteins inside the RC.
Upon transfection of replicon RNA into Huh7-Lunet cells, the
RNA was instantly translated into protein and at the same time
was rapidly degraded (figure 1B). Consequently, after a first peak,
translation also leveled off or even decreased slightly, while
negative strand RNA first became detectable at 4–8 hours post
transfection. From around 8 hours on, synthesis of new positive
strand RNA then exceeded its degradation, and levels of both,
positive and negative strand RNA as well as of viral protein started
to increase rapidly (note the logarithmic scale in figure 1B). A
steady state was finally reached at around 30 hours post
Author Summary
Hepatitis C is a severe disease and a prime cause for liver
transplantation. Up to 3% of the world’s population are
chronically infected with its causative agent, the Hepatitis
C virus (HCV). This capacity to establish long (decades)
lasting persistent infection sets HCV apart from other plus-
strand RNA viruses typically causing acute, self-limiting
infections. A prerequisite for its capacity to persist is HCV’s
complex and tightly regulated intracellular replication
strategy. In this study, we therefore wanted to develop a
comprehensive understanding of the molecular processes
governing HCV RNA replication in order to pinpoint the
most vulnerable substeps in the viral life cycle. For that
purpose, we used a combination of biological experiments
and mathematical modeling. Using the model to study
HCV’s replication strategy, we recognized diverse but
crucial roles for the membraneous replication compart-
ment of HCV in regulating RNA amplification. We further
predict the existence of an essential limiting host factor (or
function) required for establishing active RNA replication
and thereby determining cellular permissiveness for HCV.
Our model also proved valuable to understand and predict
the effects of pharmacological inhibitors of HCV and might
be a solid basis for the development of similar models for
other plus-strand RNA viruses.
Replication Vesicles in the HCV Lifecycle
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transfection (in Huh7-Lunet), which was stable until the end of the
observation at 72 hours.
Establishing of a base model to describe initial HCV RNA
replication dynamics
In order to comprehensively understand the observed HCV
replication dynamics and its underlying molecular processes, we
set up a mathematical model of the intracellular HCV RNA
replication. Dahari and colleagues developed a similar model
previously, upon which we could build here [24]. Briefly, our
model comprises all relevant molecular species (RNA, proteins,
ribosomes, etc.), and describes each step in the RNA replication
cycle, such as translation, protein maturation and the formation of
the actual RNA replication complex, as reactions of the involved
molecules using differential equations based on standard mass
action kinetics. Of note, the establishment of a vesicular replication
compartment (RC) by viral proteins (in concert with cellular
functions) within which RNA replication takes place is reflected in
the model by partitioning of the respective molecular species into
distinct ‘‘cytoplasmic’’ and ‘‘replication compartment’’ pools; e.g.
only cytoplasmic HCV RNA (R
Pcyt
) can be translated by
ribosomes, but not HCV RNA within the replication compart-
ment (R
P
). Model equations (eq.) of our final model are given in the
materials and methods section and a schematic illustration is
shown in figure 2C. The original model of Dahari was solely based
on steady state measurements of viral RNA and protein
concentrations in a stable replicon cell line [10], and accordingly
was not capable of explaining the dynamic phase during the
establishing of replication as observed in our experimental data,
even after re-fitting all model parameters (high permissive cell line;
total sum of squared residuals x
2
= 8.69, compare supplementary
figure S1). From this finding it was evident, that modifications to
the model were required in order to accurately capture the initial
dynamics of HCV RNA replication, as it can be observed upon
transfection of viral genomes into ‘‘naı
¨ve’’ cells.
Based on biological reasoning, we extended and modified
Dahari’s original model at two steps of the replication cycle. For
one, to account for ab initio replication in our setting (in contrast to
pre-formed steady-state replication), we introduced one additional
RNA species R
punp
, representing the transfected ‘‘naked’’ replicon
RNA and an according processing step (rate k
0
), subsuming any
re-folding, association with RNA-binding proteins and other
Figure 1. Measurement of HCV replication dynamics. (A) Schematic representation of the subgenomic HCV luciferase reporter replicon used
for the study. The 59-non-translated region (NTR) contains the HCV internal ribosome entry site (IRES), controlling translation of the firefly luciferase
gene (Luc). The non-structural proteins of the HCV isolate JFH1 are under control of the encephalomyocarditis (EMCV) virus IRES, and are followed by
the orthologous 39- NTR of JFH1. (B&C) Quantitative assessment of the HCV replication dynamics upon instantaneous (t = 0 h) electro-transfection
into (B) high permissive Huh7-Lunet cells or (C) low permissive Huh-7 low passage cells. The top panel shows a Northern blot analysis of the viral
plus- and minus-strand RNA. The lower panel shows a graph of the Northern blot signals quantified by phosphor imaging (plus-strand RNA: blue
lines; minus-strand RNA: red lines), as well as the corresponding luciferase activity (RLU, yellow lines). Luciferase activity and plus-strand RNA are
normalized to the input values (2 h and 0 h, respectively; one representative experiment is shown. Lines in the plots are for illustrative purposes and
connect data points, but are not results of mathematical modeling.
doi:10.1371/journal.ppat.1003561.g001
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Figure 2. Model development and model selection. (A) Model simulation of our calibrated base model (comprising the model by Dahari et al.
[24], with an added initial processing step for transfected RNA and cis-triggered formation of the replication compartment,) compared to
experimental data for high-permissive Huh 7-Lunet cells. Black: Polyprotein, red: plus-strand RNA, blue: minus-strand RNA. (B) Different hypotheses
for the involvement of a host function at all feasible steps in the viral lifecycle were assessed to explain differences observed in the replication
dynamics of Huh7-Lunet and Huh-7 lp cells. For each hypotheses, the base model was calibrated simultaneously to data from high- and low-
permissive cell lines, allowing only parameters to differ between the two cell lines that are involved in the respective process. The table shows
resulting residual squared errors (x
2
) and computed values of the Akaike Information Criterion, a measure that balances goodness-of-fit with the
degrees of freedom of a model. Time courses for the individual fits are shown in supplementary figure S1. (C) Graphical illustration of the final model.
The main steps are: (1) viral RNA enters the cell, e.g. via transfection (in our subgenomic replicon experiments) or via receptor mediated endocytosis
(in a natural infection setting). RNA then undergoes some structural preprocessing (eq. 1), leading to an increased stability and availability to the
translation machinery (as R
pcyt
, eq. 2). (2) Ribosomes bind the viral RNA, forming translation complexes (T
c
, eq. 3) and translate it into a polyprotein (P,
eq. 4); (3) the polyprotein is subsequently cleaved into the mature viral proteins (E
cyt
) with rate k
c
(eq. 5); (4) viral proteins then induce the formation
of a membranous replication compartment (RC), into which actively translated plus-strand RNA (T
c
), viral NS proteins (E
cyt
) and one or more host
factors (subsumed as HF) enter with rate k
Pin
, forming the plus-strand replication initiation complex (R
ip
, eq. 7); (5) complementary minus-strand is
then transcribed with rate k
4m
, and the complex dissociates into dsRNA (R
ds
, eq. 8) and viral polymerase (E, eq. 9); (6) dsRNA and polymerase can then
re-associate (R
Ids
, eq. 10) with rate k
5
and synthesize progeny plus-strand genomes (R
p
) at rate k
4p
(eq. 11); (7) eventually, new positive strand RNA (R
p
)
is liberated from the replication vesicles into the cytoplasm at rate k
pout
(eq. 11 and 2) or, alternatively, can remain within the vesicles for further
genome replication (initiating at rate k
3
), and is ultimately degraded.
doi:10.1371/journal.ppat.1003561.g002
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processes that might take place and be required before in vitro
transcribed RNA assumes a translation-competent state (eq. 1 and
2). This processing corresponds to viral genomic RNA being
released into the cytoplasm upon actual infection. We furthermore
allowed RNA degradation to be different (presumably higher) for
the ‘‘unprocessed’’ transfected RNA (m
pUnp
) as compared to
‘‘processed’’ or cell-derived RNA (m
pcyt
). The second step that we
updated to reflect the current biological understanding of the
molecular processes was the initiation of minus strand RNA
synthesis (which in the model is assumed to correspond to the
formation of the replicative compartment, as discussed later). It
has been described for HCV, but also for other viruses
[35,36,37,38], that the formation of a productive replicase
complex requires the viral polymerase (NS5B) and possibly other
NS-proteins to be supplied in cis. This means that for reasons not
yet fully elucidated, NS5B cannot initiate RNA synthesis from a
free, cytosolic RNA genome, but only from the very RNA that it
has been translated from. This implies a tight spatio-temporal
coupling of (poly)protein production and initiation of RNA
replication, i.e. initiation can only occur immediately after
translation/polyprotein processing and therefore in close proxim-
ity to the translation complex (T
C
). As our model does not account
for spatial effects (such as diffusion), we approximated this cis-
process by requiring an active translation complex instead of free,
non-translating RNA (R
Pcyt
) for the initiation of minus strand RNA
synthesis (R
IP
, eq. 7). This cis-triggered formation of the replicative
compartment consequently is the only route for uptake of viral
genomes and also NS proteins (E
cyt
) into replication vesicles. This is
a major change to Dahari’s original model, in which cytosolic
RNA (R
pcyt
) and NS proteins (E
cyt
) could freely and independently
enter the compartment. This model, comprising Dahari’s original
model with the described extensions, we then considered our base
model.
We then tested, whether our base model would be capable of
explaining the measured replication dynamics. We therefore fitted
the model to the experimental data from the high permissive
Huh7-Lunet cells. In fact, this resulted in a significantly better fit as
compared to the original model (Dahari: x
2
= 8.69, base model:
x
2
= 2.12) and was capable of adequately describing both, the
highly dynamic initial phase as well as the ensuing steady state of
viral RNA replication (figure 2A).
Host factor involved in formation of replication vesicles is
sufficient to explain replication dynamics in differently
permissive cells
Having established a base model for HCV replication, we next
wanted to assess which factors could explain differences observed
between high and low permissive cell lines. In our experimental
measurements for two differently permissive cell lines, Huh7-
Lunet (high permissive) and Huh-7 lp (low permissive), replication
reached a steady-state within the period of observation (72 h),
however, plateau levels of viral protein, plus-strand RNA and
minus-strand RNA differed by approximately one order of
magnitude; further, the onset of the net increase of plus-strand
RNA was delayed significantly in the low permissive cells and also
the minimum concentration of plus-strand RNA reached during
net degradation in the first hours after transfection were
significantly lower in low permissive cells (compare figure 1B
and C). As both, Huh7-Lunet and Huh-7 lp cells, were transfected
with the same subgenomic HCV replicon, these differences must
be due to differences between the host cells. In order to reflect this
host influence also in our model, we tested different steps in the
HCV RNA replication cycle that do or could feasibly depend on a
host process: (A) efficiency of RNA entry or initial RNA
processing; (B) the number of ribosomes available for RNA
translation; (C) RNA degradation in the cytoplasm (possibly
including antiviral processes such as activation of RNaseL); (D)
polyprotein translation or maturation (i.e. cleavage); (E) the
formation of the replicative compartment/initiation of minus-
strand synthesis; (F) RNA synthesis or (G) RNA degradation inside
the replication compartment; or (H) the export of newly
synthesized RNA into the cytoplasm. To evaluate these alterna-
tives for their capacity to explain the differences in dynamics and
steady-state levels between the two cell lines, we fitted our base
model simultaneously to the experimental data from both cell
lines, leaving only the parameters free to differ between high and
low permissive cells that, in the respective hypothesis (A) to (H),
depend on the corresponding host factor; all other parameters
were constrained to be identical between the two cell lines. We
found that hypotheses (A), (B), (C), (D), (F), (G) and (H) could not
explain the above described qualitative difference in replication
dynamics: while (C) and (H) did lead to a steady-state but could
not reproduce the lower plateau RNA levels in Huh-7 lp,
hypotheses (A), (B), (F) and (G) altogether failed to establish a
steady-state in low permissive cells in the course of the simulated
time period of 80 h (supplementary figure S2). In order to identify
the best fitting hypothesis, we also quantitatively assessed the
capability of each hypothesis to fit both data sets by calculating x
2
over all data points from the two time series, as well as Akaike’s
information criterion (AIC), which additionally takes into account
the number of unconstrained parameters (figure 2B). While
parameter differences in the RNA synthesis inside the RC, i.e.
hypothesis (F), led to the lowest overall x
2
value, hypothesis (E)–
assuming a difference in the formation of the RC and initiation of
RNA synthesis– led to a slightly larger x
2
(5.84 vs. 5.60) but a
significantly lower AIC (221.31 vs. 20.68). Moreover, hypothesis
(E) reached a steady-state within 80 h, while (F) did not. This
comparison therefore identified the initiation of minus strand
RNA synthesis (i.e. the formation of the RC) as the step in the
model, at which alteration of a single reaction rate suffices to
optimally transform replication dynamics from high permissive
cells into the dynamics found in low permissive cells.
Biologically, this step is highly complex and not thoroughly
understood yet. After translation and polyprotein processing,
reorganization of host cell endomembranes is triggered by viral NS
proteins such as NS4B, which has been shown to be a key player in
the formation of membrane convolutions at the rough endoplas-
mic reticulum. These vesicular membrane structures, dubbed the
membranous web, have been reported to be the site of HCV RNA
replication [7], providing a distinct replicative compartment for
the viral replicase machinery. However, the molecular mecha-
nisms leading to the formation of productive replication vesicles
are not known. Nonetheless, it is clear that host factors must be
required in this complex process, for example proteins involved in
membrane biogenesis and reorganization, as well as signal
transducers and regulatory molecules; and also the initiation of
minus strand RNA synthesis might require a cellular co-factor. It
appears plausible that limited abundance of one of these factors in
some cells might be responsible for their lower permissiveness for
HCV replication. Therefore, we next wanted to include this host
factor as an explicit species in our model, which is required for RC
formation/minus strand initiation. For that purpose, we subsumed
all these possible host determinants by one unspecified host factor
HF (see figure 2C), which we assumed to interact with viral NS
proteins (E
cyt
, e.g. NS4B or NS5A) and with actively translated
HCV RNA (T
C
) to create replication vesicles and to allow for
initiation of minus-strand RNA synthesis (being part of the minus-
strand initiation complex R
IP
, see eq. 7 and figure 2C). In addition,
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we made the (non-crucial, see supplementary figure S3 and
supplementary table S5) assumption that HF is only catalyzing the
reaction without being consumed.
With this additional modification to the mathematical descrip-
tion of the formation of replication compartments, and calibration
of the model to the experimental data from both cell lines
(constraining parameters and initial values to biologically mean-
ingful ranges taken from measurements or literature wherever
possible), excellent agreement between the model and experimen-
tal data was achieved, both, for high and low permissive cells with
an overall x
2
of 2.01 and AIC of 2112.31(figure 3A and B). We
therefore considered this our final working model, illustrated in
detail in figure 2C. Briefly, the model comprises 13 molecular
species in two distinct compartments, the cytoplasm and a
replicative compartment (RC), and is parameterized with 16
parameters, corresponding to reaction rates, as well as three non-
zero initial values: the initial concentration of HCV RNA (R
punp
),
the initial concentration of the host factor (HF), as well as the total
number of ribosomes available for viral RNA translation (R
ibotot
).
The full system of differential equations and detail on the modeling
procedure can be found in the Materials & Methods section; more
detail on parameter optimization and analysis are given in
supplementary text S1.
Interestingly, analysis of the fitted parameters showed that the
concentration of the host factor was more than 10 fold higher in
highly permissive Huh7-Lunet cells than in low permissive Huh-7
lp cells. This difference led to slower formation of the replication
compartment in Huh-7 lp cells, which in turn resulted in the
observed delay in early viral replication and in decreased steady
state levels in these cells. Based on our model and computational
analysis, we therefore propose that a host process is critically
involved in the formation of replication vesicles and/or the
initiation of minus-strand RNA synthesis, turning this into the
rate-limiting step for HCV RNA replication in low permissive
cells.
Model validation by targeted intervention
While the model could be very well fitted to the original
replication data, we then wanted to corroborate its applicability for
predicting replication dynamics also under distinct conditions that
were not part of the calibration process. For this purpose, we
performed additional, independent experiments using mutant
HCV replicons with defects at defined stages of the replication
cycle. We predicted the impact of such defects on viral replication
a priori using the model, and retrospectively compared the results
with the experimental data in order to assess the validity of model
predictions. This approach of introducing targeted mutations into
the HCV genome interfering with distinct functions in the viral
RNA replication cycle allows validation of individual steps in the
model, thus step-wise reconfirming model assumptions and
parameters.
As a test of the translation phase of the model, we measured
viral plus-strand RNA and protein expression using a replication
deficient replicon harboring a deletion of the catalytic triad (GDD
motif) of the NS5B polymerase. The measured RNA and protein
data thus reflect only the effect of translation and degradation in
the cytoplasm, with no RNA replication occurring. We predicted
the impact of this intervention with our model by setting the
formation rate k
Pin
of the plus strand replication initiation complex
R
Ip
to zero (eq. 3, 5 and 7), thus completely switching off
polymerase activity at the earliest possible point, while leaving all
other model parameters unchanged. Notably, our model predic-
tions of this intervention matched the experimental data from
both, Huh7-Lunet and Huh-7 lp cells, validating our model of
cytoplasmic RNA degradation and translation (Figures 4A and B).
The fact that the experimental measurements showed almost
identical RNA decay dynamics and viral protein (luciferase) levels
in high and low permissive cells is also direct experimental
confirmation of our modeling based assessment above, that
differences in permissiveness cannot be related to RNA ‘‘process-
ing’’ or degradation, or to ribosome availability or protein
translation in the cytoplasm (hypotheses (A), (B), (C) and (D)
tested above).
We next focused on validating the RNA replication steps of our
model. For this purpose, we utilized chimeric replicons with
heterologous 59-or39-NTRs derived from a different genotype
[22]. We previously showed that these chimeric replicons exhibit
decreased replication efficiency due to inefficient initiation of plus-
strand synthesis (in case of the 59-NTR exchange) or minus-strand
synthesis (in case of the 39-NTR exchange) [22]. We predicted the
Figure 3. Experimentally measured and model predicted time courses of viral RNA replication. Experimental data (symbols) and results
of model simulation (lines) over 80 hours, showing the dynamics of viral replication in (A) high permissive Huh7-Lunet and (B) low permissive Huh-7
lp cells. Solid blue lines and symbols: viral plus-strand RNA; dashed red lines and symbols: viral minus-strand RNA; dotted black lines and symbols:
rescaled luciferase activity (i.e. polyprotein molecule numbers). Experimental data represent mean values +/2two standard deviations from three
independent replicates. Note the logarithmic scale of the y-axes. Model predictions were obtained after calibration of model parameters to the data.
doi:10.1371/journal.ppat.1003561.g003
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effect of these interventions with the fitted model by decreasing the
parameters k
pin
and k
4m
for the 39-NTR exchange (eq. 3, 5, 7, 8
and 9), and k
5
and k
4p
for the 59-NTR exchange (eq. 7, 8 and 9),
corresponding to the rates of the minus- and plus-strand initiation
and synthesis, respectively (for reference, see figure 2C). Compar-
ison of our prediction with experimental measurements demon-
strated that in both cases the model qualitatively agreed with the
experimental data. Consequently, upon refitting of these param-
eters to the new data, the model was capable of quantitatively
describing the perturbed replication kinetics (figure 4C). Further-
more, the model correctly predicted the impact of the respective
NTR-exchanges onto the ratio of plus- to minus-strand RNA at
the steady state (figure 4D). Predictions for both NTR-exchanges
were in close quantitative agreement with our previously published
experimental observations, which showed an 8.7:1 (simulation
9.0:1) ratio between plus- and minus-strand for the wildtype,
16.1:1 (11:1) for the 39-NTR-chimera, and 4.7:1 (4.8:1) for the 59-
chimera [22].
Taken together, our model was able to correctly infer the effects
of targeted interventions at different steps of the replication
process, including complete replication deficiency, as well as
specific inhibition of plus- or minus-strand RNA synthesis,
respectively. We therefore conclude that our model provides a
realistic description of HCV RNA replication dynamics, and thus
can be confidently used to further study such processes in silico that
are difficult or impossible to address experimentally.
Replication vesicles are load and choke points of viral
replication
Having such a comprehensive and accurate model at hand,
we proceeded by applying it to concrete problems in the field of
HCV research. The first question we addressed was which sub-
steps of HCV RNA replication would be most susceptible to
targeted interference. Such processes are potentially attractive
targets for the design of new DAAs against HCV. To find out
which step in the replication cycle has the biggest impact on the
resulting RNA and protein levels, we assessed the relative
sensitivity of replication towards alterations of reaction rates in
the model. To account for the two clearly discernable phases of
replication – the highly dynamic establishing phase and the
steady-state phase – we performed a global sensitivity analysis
using the extended Fourier Amplitude Sensitivity Test (eFAST)
[39,40] at an early (4 h) and at a late (72 h) time point. We
separately assessed the sensitivities of plus-strand RNA, minus-
strand RNA as well as protein levels towards individual and
Figure 4. Validation of model predictions. (A+B) Model validation using replication deficient HCV RNA (NS5BDGDD) in high- and low permissive
cells. (A) Plus-strand RNA concentration and (B) protein translation (luciferase activity) were measured. Solid lines indicate model predictions.(C+D)
Model validation using chimeric NTR HCV replicons. Exchange of 59-NTR (green symbols) specifically inhibits initiation of plus-strand synthesis, 39-NTR
exchange (brown symbols) inhibits initiation of minus-strand synthesis. Luciferase measurements are shown as means +/2two standard deviations
of two independent experiments. Lines represent model predictions. (D) Comparison of model prediction and literature data [22] for resulting plus-
to minus-strand RNA ratios.
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simultaneous changes of 16 rate constants and the three initial
values (figure 5 and supplementary figure S4).
For the establishing phase of replication, this analysis showed
that the most influential processes are the polyprotein translation
(rate k
2
), the export rate of RNA into the cytoplasm (rate k
Pout
) and
the efficiency of plus- (rate k
4p
) and minus- (rate k
4m
) strand RNA
synthesis inside the replication compartment, respectively
(figure 5A). As one would expect, alterations in k
2
mainly influence
the amount of viral protein (eq. 4 and 6) and only to a lesser degree
viral RNA (eq. 2 and 3), whereas k
4m
mainly affect RNA species
(eq. 7, 8 and 9). k
4p
and k
Pout
in contrast strongly influence RNA
and protein concentrations (eq. 8, 9, 10 and 11). Further
important steps are the initial ‘‘processing’’ of the transfected
RNA (rate k
0
), since this determines at what time and to what
extent RNA is available for translation, as well as the RNA
degradation rate m
RC
inside the replication compartment. The
availability of viral RNA for rapid genome replication and the
replication process inside the membranous web itself are therefore
key determinants of the initial replication dynamics and thus the
efficiency of infection, and consequently constitute a very
attractive target for anti-viral drugs. Interestingly, the rate of
polyprotein translation (eq. 4) naturally has a big impact on viral
protein concentration, but only a fairly restricted influence on
RNA levels. Furthermore, the cleavage rate of nascent viral
polyprotein (eq. 4 and 5, rate k
c
) only very mildly impacts
replication dynamics.
A profoundly different pattern can be observed for the steady
state phase. The single most influential parameter determining
viral RNA and protein levels was found to be the degradation rate
of viral RNA inside the replication vesicles m
RC
(eq. 7 to 11), while
most other parameters showed no significant sensitivities (figure 5B
and supplementary figure S4). However, it is virtually impossible
to influence this parameter by cellular (e.g. innate immune) or
pharmacological intervention (except by physical destruction of
the membranous structures), therefore making inhibition of viral
replication particularly cumbersome once the steady state has been
established. Taken together with the results from the early phase,
these analyses suggest a key role of the replicative compartment for
a successful establishment and maintenance of infection.
Replication vesicles attenuate exponential RNA
replication and balance protein translation and RNA
replication
In the light of the above findings, pointing to a central role of
the membranous web within the RNA replication cycle, we further
studied the underlying molecular functions of this compartment.
For one, we assessed the importance of its protective character
onto the dynamics of viral genome replication. Model fitting led to
a more than 4-fold lower RNA degradation rate within the
replication compartment (m
RC
) as compared to RNA degradation
in the cytoplasm (m
pcyt
, see table 1). To simulate the effect of less
stringent protection of the RNA inside the RC, we then
deliberately increased its degradation rate (m
RC
) and calculated
the resulting levels of plus strand RNA over time (figure 6A). This
analysis showed that the degradation rate inside the replicative
compartment inversely correlated with the amount of RNA
produced at any given time. Interestingly, this correlation was not
continuous, exhibiting a threshold of productive RNA replication,
constituting a ‘‘cliff’’, crossing of which prevented the establishing
of a (non-zero) steady-state and effectively killing off viral
replication (figure 6A, dark blue area, see also supplementary
figure S5). This highly instable region with very low (or zero) RNA
copy numbers, strikingly, was reached once degradation inside the
RC (m
RC
) was approximately equal to the degradation rate in the
cytoplasm (m
pcyt
). Our model therefore predicts that the viral RNA
must be protected from active degradation during replication in
order for HCV to maintain robust persistent replication. While it
is virtually impossible to reproduce the above findings in a
biological experiment (i.e. increasing RNA degradation inside the
replicative compartment), previous in vitro data actually showed
that viral RNA in the cell, particularly the minus-strand, is highly
resistant to nuclease treatment [10], implying that indeed
Figure 5. Global sensitivity analysis of the replication model. Sensitivity analysis was performed using the extended Fourier Amplitude Test
(eFAST) at (A) 4 hours and (B) 72 hours. Shown are eFAST total order sensitivity indices for plus strand RNA; sensitivities for minus strand RNA and
viral protein can be found in supplementary figure S4. The dotted blue line indicates the level of a negative control parameter that does not occur in
any of the equations. Sensitivities lower or equal to this negative control should not be considered significantly different from zero [39].
doi:10.1371/journal.ppat.1003561.g005
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degrading enzymes cannot enter the replication vesicles. More-
over, in inhibitor studies, ongoing HCV replication was blocked by
interferon or a pharmacologic NS3/4A inhibitor, leading to rather
slow decrease of RNA with a half-life of 12–20 h [41,42], most
likely representing a slow degradation of replication vesicles. In
good agreement with these studies, our model predicts a half-life
for RNA inside the replicative compartment of 12 h (rate
m
RC
= 0.08 h
21
), whereas RNA transfected into the cytoplasm
decayed with a half-life of approximately two hours in the
experiments using a replication-defective replicon (see figure 4A).
Experimentally very hard to address, however, is the degradation
rate m
pcyt
of cytoplasmic HCV RNA generated through replication
that might exhibit a different folding or be bound by other proteins
as compared to transfected RNA. Yet, it appears highly likely that
this degradation rate would more closely match the rate of
degradation of transfected, cytoplasmic RNA rather than that of
RNA within the membranous replicative environment. In keeping
with this plausible assumption, our model predicts a half-life for
newly synthesized cytoplasmic RNA of approximately 165 min
(m
pcyt
= 0.363 h
21
). Although model estimations for both, m
pcyt
and
m
RC
, exhibit a rather broad confidence interval, simultaneous
modification of both parameters shows that m
RC
needs to be
substantially lower than m
pcyt
in order to explain the observed
kinetics (figure 6B, dark blue area). In terms of viral protein,
Quinkert and colleagues showed that in contrast to RNA, only a
small fraction (,5%) of NS5B molecules is protease resistant [10].
In line with these observations, our model predicts that the vast
majority of viral protein remains in the cytoplasm.
Another important question, which can hardly be addressed
experimentally, is the possibility of re-initiation of minus-strand
synthesis inside the replication vesicle. While theoretically it is
feasible that the replicative machinery re-initiates minus-strand
synthesis on newly produced plus-strands inside the replication
compartment (eq. 7, second to last term), the alternative
hypothesis is that such an initiation event can only happen in cis
upon translation in the cytoplasm (see also section on model
development above). In fact, when analyzing the calibrated model,
we found that the rate constant for this reaction (k
3
in eq. 7, see
figure 2C for reference) needed to be close to zero
(,10
24
h
21
*molecules
21
) to fit the experimental data, and the
concentration of ‘‘active’’ polymerase (E) was severely limiting the
rate of RNA synthesis during the initial dynamic phase. This
resulted in an extremely low efficiency of internal re-initiation,
implying that most or all of the newly synthesized viral plus-strand
RNA is exported to the cytoplasm, from where it must be re-
imported for further rounds of RNA replication to occur. Hence,
our model supports the notion that negative-strand initiation is
very different from plus-strand initiation in that it most likely
depends on actively translated RNA with the required NS
proteins, mainly NS5B, being supplied in cis.
The observed relative shortage of active polymerase within the
replication vesicles and the lack of internal re-initiation conse-
Table 1. Parameter estimates obtained from model calibration.
Rate constant Definition Rate constant 90% confidence interval Reference
k
0
Processing rate of transfected positive-strand RNA 0.00415 h
21
(1.07e-3, 1.61e-2)
k
1
Formation rate of translation complex 1 h
21
molecule
21
Fixed after sensitivity/identifiability analysis
k
2
Polyprotein translation rate 100 h
21
Experimentally observed [24]
k
c
Polyprotein cleavage rate 1 h
21
Fixed after sensitivity/identifiability analysis
k
Pin
Formation rate of the plus-strand replicative
intermediate complex
9.04e-6 h
21
molecule
22
(3.85e-7, 2.12e-4)
k
Pout
Transport rate of nascent plus-strand RNA into
cytoplasm
0.307 h
21
(0.167, 0.538)
k
3
Formation rate of the plus-strand replicative
intermediate complex from within the RC
10
24
h
21
molecule
21
Fixed after sensitivity/identifiability analysis
k
4m
Minus-strand RNA synthesis rate 1.7 h
21
Experimentally observed [23,77,78]
k
4p
Plus-strand RNA synthesis rate
k
5
Formation rate of the minus-strand replicative
intermediate complex
10 h
21
molecule
21
Fixed after sensitivity/identifiability analysis
m
Punp
Degradation rate of unprocessed plus-strand RNA 0.754 h
21
(0.510, 1.11)
m
Pcyt
Degradation rate of processed plus-strand RNA 0.363 h
21
(0.168, 0.783)
m
Tc
Degradation rate of translation complex 0.181 h
21
(0.0841, 0.392)
m
Ecyt
Degradation rate of NS5B protein 0.06 h
21
Experimentally observed [76,81,82]
m
RC
Degradation rate of RNA and E in the replication
compartment
0.0842 h
21
(0.0193, 0.366)
m
L
Degradation rate of luciferase 0.35 h
21
Experimentally observed [79,80]
HF
high
(0) Initial values for activated host factor in high
permissive cells
48 molecules (11, 215)
HF
low
(0) Initial values for activated host factor in low
permissive cells
4 molecules (1, 14)
R
ibo
(0) Total ribosome complexes 628 molecules (68, 5810)
fScale
Scaling factor for Luciferase polyprotein marker 2160 (474, 9870)
Parameter estimates and confidence bands were obtained using multiple shooting, simultaneously fitting the model to the data from Huh7-Lunet and Huh-7 lp cell
lines.
doi:10.1371/journal.ppat.1003561.t001
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quently prevents an exponential amplification of the viral RNA
within the replicative compartment. Replication vesicles thus
attenuate the rate of viral replication by limiting the availability of
the factors required for minus-strand initiation. At the same time,
depending on the export rate of newly synthesized plus-strand
RNA from the replication vesicles (k
pout
), they can also exert tight
control over protein translation. Newly synthesized RNA can
either be exported to the cytoplasm where it can be used for
another round of protein translation (or, in an actual infection
setting, the assembly of new viral particles), or it accumulates
within the replication vesicles; there, however, it cannot be used as
a template for minus strand synthesis due to the above described
reasons.
Taken together, the development of a membranous replication
compartment, by physically separating production of new protein
(translation) and the generation of new RNA (replication),
therefore constitutes an important additional level of control over
the virus’ replication kinetics. This high degree of controllability
might be one reason for the evolutionary success of membranous
replicative structures, as basically formed by all positive strand
RNA viruses. In case of HCV, it allows for sustained low-level
replication as is required for the establishment of persistence,
mainly by restricting availability of the required proteins within
the replicative compartment.
Different processes are limiting RNA replication in high
and low permissive cells
Particularly for a persistent virus, tight control over its own
replication is essential in order to not overwhelm its host cell and
thereby kill it [13]. As we have learned above, the distinct
replication compartment plays a central role in this self-limitation.
Consequently, we therefore studied, which processes in turn
regulate the formation of replication vesicles and eventually lead to
the establishment of a steady state. The host factor (HF) in our
model has been found to be a requisite for the attainment of a
steady state and its concentration was a determinant regulating
plateau levels of viral RNA and protein between the two
differently permissive cell lines. For that reason, we now
systematically assessed the impact of different availabilities of HF
onto steady-state levels of viral RNA and protein. For HCV RNA
levels, this analysis showed a linear correlation with HF
concentrations in the range tested: the more abundant HF was,
the more RNA replication took place. Interestingly, however,
polyprotein levels exhibited a saturation behavior, reaching a
plateau for HF concentrations above 20 ‘‘molecules’’ (note that HF
is a virtual species, so ‘‘molecules’’ is an arbitrary unit) (figure 6C).
To understand this nonlinear dependence of viral protein on HF
Figure 6. Analysis of the importance of a distinct replicative
compartment (RC). (A) Protective effect of replication vesicles:
replication dynamics (plus strand RNA shown) at different degradation
rates (m
RC
) of viral RNA inside of the replicative compartment (RC).
Actual values for m
RC
and m
Pcyt
obtained from model calibration are
marked in the figure. Different degradation rates are depicted on the y-
axis, resulting time courses for positive strand RNA molecules are color-
coded along the x-axis. At m
RC
=m
Pcyt
, viral RNA replication becomes
unstable, and efficient replication cannot be sustained. (B) The plot
shows the resulting sum of residual squared errors (x
2
)when
simultaneously varying the degradation rates m
RC
and m
Pcyt
. The plot
shows that x
2
increases over five-fold when m
RC
and m
Pcyt
attain similar
magnitudes. (C) Effect of host factor (HF) expression levels on the steady
state levels of viral RNA and protein. Plus-strand RNA steady state levels
(red line) respond linearly to concentration changes of HF in the range
of 1–100 HF ‘‘molecules’’. Viral polyprotein levels (blue line) show a bi-
phasic steady state behavior with an exponential response for up to
approx. 20 HF ‘‘molecules’’, showing saturation thereafter. Note that HF
is a hypothetical species likely comprising different host cellular
proteins and/or processes; ‘‘molecules’’ therefore does not reflect
physical molecule numbers.
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levels, we analyzed the model under conditions of varying HF
amounts and found that this saturation stems from different factors
being limiting for increasing HF levels: in low permissive cells
(featuring low HF concentrations of around 4 ‘‘molecules’’), HF
availability is limiting the formation of replication vesicles (eq. 7).
Therefore, overall RNA concentrations remain relatively low,
leaving polyprotein production at a low but steady level; here,
RNA in the cytoplasm is the rate limiting factor for protein
translation. In high permissive cells (featuring high HF levels of
around 50 ‘‘molecules’’), in contrast, rapid formation of replication
vesicles occurs with an associated rapid increase in viral RNA
levels. However, ribosome availability (R
ibo
) then becomes limiting
for protein translation (eq. 3), explaining the plateau seen for viral
protein concentrations (figure 6C). Accordingly, the ratio between
viral protein (i.e. luciferase) and plus-strand RNA is lower in the
steady state in high permissive cells. This is well in line with the
experimental data (figure 1, compare B and C).
Interestingly, these findings suggested that the actual mecha-
nisms governing the establishing of the steady state in low
permissive and high permissive cells are different. While in low
permissive cells the formation of replication vesicles is the limiting
step due to a lack of host factor HF, surprisingly the host
translation machinery is the bottleneck in high permissive cells.
Transcriptional profiling of different host cells identifies
genes correlating with cellular permissiveness for HCV
RNA replication
As differential abundance of the host factor (or host process) HF
in our model sufficed to explain the observed difference in HCV
replication dynamics between high and low permissive cells, it was
intriguing to identify the biological nature of this factor. For that
purpose, we set out to compare gene expression profiles of Huh-7
cells of different passage number or clonal origin that we had
found to exhibit substantially different permissiveness for HCV
RNA replication [22,30] (figure 7A). We performed full-genomic
cDNA microarray (Affymetrix HGU133plus 2.0) analysis in eight
such Huh-7 derived cell lines, including the above used Huh7-
Lunet and low passage (lp) Huh-7 cells. Figure 7B shows a
scatterplot of the normalized gene expression values for these two
cell lines. Assuming a direct correlation between permissiveness
and the expression of the host factor HF as suggested by the above
analysis (compare figure 6B), we fitted a linear model of each
gene’s expression level to the observed replication efficiencies in all
eight cell lines. By this, we could assess how well each individual
gene predicts replication efficiency over the full set of cells. On
these data, we then carried out an analysis of variance (ANOVA)
to identify genes whose expression profiles correlated significantly
with replication efficiency. Figure 7C shows the resulting p-values
over the degree of differential expression (as log fold-change)
between Huh7-Lunet and Huh-7 lp (see also supplementary table
S1). We could identify 355 genes, whose expression levels
correlated with permissiveness (p,0.2) and which exhibited a
difference in expression levels of more than 23% (log fold-change
.0.3 or ,20.3) (figure 7C and supplementary table S2). We then
subjected these potential HF candidates to bioinformatics analyses
in order to identify host cellular processes or pathways, which are
over-represented among those genes (supplementary tables S3 and
S4). These analyses mainly identified metabolic processes such as
lipid metabolism and cell growth and proliferation, which is in line
with the notion of HCV RNA replication requiring proliferating
cells for efficient replication, at least in Huh-7 cells [43], and
numerous reports on its requirement on lipid biosynthesis
(reviewed in [44]).
While the number of potential HF candidate genes was too large
to be functionally validated individually within this study, we
surveyed previously published data on HCV host factors,
including a manually curated database of HCV-host interactions
(VirHostNet [45]) as well as large-scale siRNA-based screens
[46,47,48,49]. Whereas such high-throughput approaches exhibit
very high false-negative rates [50] and therefore are not suited to
exclude HF candidates from our analysis, their false-positive rate is
very well controlled and the identified hit genes are highly reliable.
Using these data, we could in fact identify 17 of our HF candidates
to be implicated with HCV (table 1; marked in red in figure 7C).
Six of these genes (JAK1, LHX2, PIP5K1A, RPS27A, PPTC7 and
COPA) were found in siRNA-mediated approaches to directly
influence HCV replication, as would be expected for a limiting
host factor. Five genes (TF, VCAN, TRIM23, SORBS2 and
MOBK1B) were identified in a large-scale yeast-two-hybrid based
interaction screening [51] to interact with at least one HCV
protein (interaction partner listed in table 1). This, however, does
not necessarily indicate that the interaction is essential for RNA
replication. On similar lines, six further genes (MCL1, SERP-
ING1, CASP8, PIK3CB, GAB1 and APOB) were previously
reported to interact with specific HCV proteins in individual
studies. Interestingly, most of them (MCL1, CASP8, PIK3CB and
GAB1) were implicated with a modulation of apoptosis and cell
survival/proliferation, supporting our above analysis, in which
‘‘cell growth and proliferation’’ was found to be an enriched
function among the differentially expressed genes (supplementary
tables S3 and S4).
Based on our model prediction of a limiting host factor/process
involved in the formation of functional replication compartments
and utilizing our transcriptomic analysis of differently permissive
cells, further studies should be devised aiming to delineate the
exact nature of this factor or process. Identification of a cellular
function that is essential for HCV replication but naturally limiting
in certain cell lines would be very intriguing in terms of
pinpointing novel targets for anti-HCV therapy. Such a factor
would promise to be inhibitable without critically affecting host
cell viability, while severely compromising HCV replication
efficiency.
Discussion
Extended mathematical model precisely predicts HCV
RNA replication dynamics in different cells
In the present study, we have developed a mathematical model
of the intracellular steps of HCV replication. In contrast to
previous models [24,25,26] we were not only interested in studying
steady state replication in stable replicon cell lines, but specifically
addressed the highly dynamic initial phase after RNA genome
delivery into the host cell. We therefore performed quantitative,
time-resolved measurements of viral protein translation as well as
strand-specific viral RNA concentrations in two distinct Huh-7
derived cell lines, exhibiting a vastly different permissiveness for
HCV RNA replication [32]. With this data, we tried to re-
calibrate the most comprehensive HCV replication model
available to date [24], but found that the model was not capable
of explaining the observed dynamics and ensuing steady state
simultaneously. We therefore modified and extended that model
by accounting for the ‘‘naked’’, unprotected nature of the initially
transfected in vitro transcribed RNA and by updating of the
formation step of the RC and the initiation of negative strand
RNA synthesis to the current biological understanding of this
process. Under steady state conditions, as studied by previous
models, equilibrium of the viral replication machinery with static
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ratios between cytosolic viral RNA and NS proteins has been
achieved. Therefore, in the model by Dahari and colleagues [24],
uptake of viral RNA and protein into the replicative compartment
could be described by simple first order import reactions. In our
setting, however, concentrations for replication competent viral
RNA and NS proteins start from zero and grow dynamically in the
course of the experiment. Hence, simple first order import
reactions do not suffice if the uptake depends on the abundance
of more than one species, which is highly likely given biological
evidence. Accounting for the above described cis-requirement for
initiation of productive replication complexes [35,36,37], which
means that an RNA molecule can be used as a template for
replication only by an NS5B molecule having been translated from
that very RNA, we solely allowed a complex of actively translated
plus-strand RNA (i.e. translation complexes T
C
) and cytosolic NS
proteins (E
cyt
) to be taken up into the RC.
While these model extensions greatly enhanced the fitting
quality to the data of a single cell line, we then identified that step
in the model, at which an altered kinetic rate could explain the
dynamics found in the second cell line as well. For this purpose, we
tested a series of hypotheses, fitting the model simultaneously to
the two differently permissive cell lines and allowing only those
parameters to differ that would be influenced by the host cell in the
respective hypothesis. By this approach, we could exclude various
processes, e.g. differences in translation efficiency, altered cyto-
plasmic RNA degradation or different RNA synthesis rates within
replication complexes. It is also biologically plausible, that these
processes do not differ between the two examined Huh-7 cells lines
and therefore cannot explain the observed differences in permis-
siveness; both, translation and RNA degradation have been shown
before to be comparable across different Huh-7 cells [30], and the
polymerization rate of the HCV RdRP NS5B is unlikely to depend
on host factors (other than ribonucleotides). In principle, a
combination of several such processes might be able to explain
the observed behavior; however, following Occam’s razor, we
considered the simplest solution to be the most likely one.
Eventually, we identified the formation process of replicative
vesicles to be the best suited step in the model, altering the rate of
which sufficed to fit the model to measured data from either cell
line. We then introduced a host factor (HF) as a new species in our
model, and required viral RNA (in the form T
C
) and NS protein
(E
cyt
) to form a complex with it in order to allow for the initiation of
negative strand RNA synthesis and the formation of the RC.
Assumption of different concentrations of this host factor then was
sufficient to very accurately explain the differences in RNA
replication permissiveness in the two cell lines. This final model
therefore completely satisfied all experimental observations and
Figure 7. Gene expression profiling of differently permissive
Huh-7 cells. (A) Relative permissiveness for HCV replication of eight
different Huh7 derived cell lines. Permissiveness was normalized to
Huh-7 p28 cells. (B) Scatterplot of host gene expression in high
permissive Huh7-Lunet versus low permissive Huh-7 lp cells. Off-
diagonal elements are differentially expressed and are potential
candidates underlying the difference in replication efficiency. Colors
encode the distance from the diagonal. A selection of strongly
differentially expressed genes is labeled with gene symbols. (C) Eight
different cell lines with different replication permissiveness (see panel
A) were used, and replication efficiency was correlated with host gene
expression. A linear model was fitted to predict replication permissive-
ness from gene expression data, and goodness of fit assessed using
ANOVA. Shown are resulting p-values, plotted over the log- fold-change
of expression between Huh7-Lunet and Huh-7 lp cells. Shown are genes
with p-values,0.2 and a log-fold-change of more than 0.3 or less than
20.3. Seventeen genes that were previously shown to be functionally
linked to HCV replication or to directly interact with viral proteins are
highlighted in red and labeled.
doi:10.1371/journal.ppat.1003561.g007
Replication Vesicles in the HCV Lifecycle
PLOS Pathogens | www.plospathogens.org 12 August 2013 | Volume 9 | Issue 8 | e1003561
could also correctly predict the effects of targeted perturbations
during extensive validation experiments.
Mathematical HCV replication model defines optimal
targets for pharmacologic intervention
We then used the calibrated and validated model to further
study individual steps of the viral lifecycle. Sensitivity analysis was
applied to pinpoint the most influential steps, perturbation of
which would lead to the greatest impact on replication dynamics
and yield. A very interesting first finding was that once steady state
replication has been reached, the system proved to be relatively
robust towards perturbation of individual sub-steps of replication.
The degradation rate of RNA inside the RC was the most sensitive
parameter under these conditions, and had a significantly higher
influence than all other rates. This parameter, however, can
hardly be influenced biologically or therapeutically. Very likely,
this robustness is key to HCV’s prevailing in the face of cellular
stress- and innate immune responses [52,53,54,55]. The actual
mechanistic basis of this remarkable robustness so far remains
elusive.
In contrast, at an early time point after introduction of HCV
genomes into the cell, the system was found to be substantially
more fragile with respect to the number of sensitive parameters.
This suggests that therapeutic intervention with HCV replication
by DAAs would be most efficient in newly infected cells,
emphasizing the potential of such drugs for the prevention of
reinfection upon liver transplantation. The processes found to be
most sensitive during the early phase of replication were
polyprotein translation as well as the RNA polymerization rate
of NS5B. Of note, polyprotein cleavage by the viral NS3/4A
protease was surprisingly little influential. This, however, has been
described before, e.g. in a study examining the role of cyclophilin
A for HCV replication [56]. In that study, viral mutations
conferring resistance to the cyclophilin A inhibitor Alisporivir
(Debio-025) were shown to significantly affect the efficiency of
polyprotein cleavage without notably affecting RNA replication of
the replicon [56]. This could raise some concern about the first
(very recently) approved direct acting antivirals for HCV, the
NS3/4A inhibitors Telaprevir and Boceprevir [57]: on the one
hand, they need to exhibit an extremely high potency of inhibition
in order to suppress HCV RNA replication efficiently. On the
other hand, there should be comparatively little restrictions to the
development of escape mutations rendering NS3/4A resistant to
the compounds, owing to the relatively small effect on replication
dynamics even in a case where the mutation functionally lowers
protease activity as it is predicted by our model. Simply put, the
virus can effectively buy itself out of pharmacologic inhibition at
only modest fitness costs, and in fact, at least for the first
generation of protease inhibitors, this is indeed the case [58,59]. In
contrast, according to our model analysis, HCV should be far
more sensitive towards inhibition of the NS5B polymerase activity.
In line with this prediction, an NS5B inhibitor (HCV-796) yielded
a significantly faster and stronger response when directly
compared to a very potent protease inhibitor (BILN 2061), both
dosed at the same multiples of their respective EC50s [60]. This
difference in efficaciousness could even get potentiated when
considering the development of escape mutations. Particularly for
nucleoside/nucleotide analogues, which target the catalytically
active center of NS5B, all so far observed resistance mutations
have a negative influence on its polymerase activity [61]. Based on
our model, however, lowering NS5B activity is predicted to have a
pronounced impact on overall replication efficiency, thereby
substantially increasing the fitness costs for such escape mutations.
In fact, despite being ‘‘genetically easy’’ (i.e. single nucleotide
exchanges suffice) such resistance mutations against nucleotidic
inhibitors have been shown to be of negligible clinical relevance
due to their extraordinarily strong impact on replication efficiency
[62]. In general, we want to note that a modeling approach as ours
can help in estimating and understanding the sensitivity of HCV
replication upon (e.g. pharmacologic) inhibition of a particular
step in the life-cycle. It cannot, however, generally predict the
development of resistance mutations, as the actual number and
position of nucleotide/amino acid exchanges required for resis-
tance eventually determine the likelihood of their occurrence and
their fitness-cost, respectively.
Steps of RNA replication and involvement of host factors
One simplification that we accepted in developing the model is
that the formation of the membranous vesicles is modeled as one
step (eq. 7) together with the formation of the actual replicase
complexes (i.e. the initiation of negative strand RNA synthesis).
This is owing to a lack of an experimental handle for the
discrimination of ‘‘productive’’ from empty or non-functional
vesicles. In fact, it has been shown that the vesicular membrane
structures are formed by viral NS protein also in the absence of
RNA replication [6,63]. Therefore it seems likely that initiation of
RNA synthesis will depend on the formation of membrane
alterations, but still represents a distinct step in the formation of an
active replication site. However, in this two-step scenario,
membranous vesicles would form based on the concentration of
cytosolic NS proteins (E
cyt
) and a host factor (HF), and replication
complexes (R
ip
) would mainly depend on T
c
(and possibly E
cyt
and
HF) and the availability of vesicles. In effect, formation of
productive replicative vesicles would again depend on those three
species, T
C
,E
cyt
and HF and should in principle be compatible
with our simplified one-step model.
On similar lines, for reasons of simplicity, our model considers
only one single, large replication compartment. This assumption is
clearly not correct, as numerous sites of virus induced convoluted
membrane structures have been observed in HCV replicating cells
[7] and each cell holds approximately 100 negative strand RNAs
(i.e. markers for productive replication complexes) on average
[10]. However, the approximation with a single large replicative
compartment should be adequate provided the real number of
vesicles is large enough for formation or loss of individual vesicles
not to lead to significant sudden changes of viral RNA and protein
availability in the cytoplasm. As measurements of replication are
technically limited to bulk assessments and cannot probe
individual vesicles, for the time being this point cannot be
addressed more adequately. Similarly, there might also be (and
likely is) heterogeneity among cells in terms of kinetics and
absolute numbers. Also here, probing individual cells for plus and
minus strand RNA as well as for polyprotein production is almost
impossible with today’s technology, and consequently, our model
represents an approximation of the average cellular behavior in a
larger population of cells.
Curiously, a central result of our study was the conclusion that
the assumption of a key host factor was essential to fit our model to
the dynamics of RNA replication. This factor was important to
explain RNA replication in Huh-7 cells, but might not be as
limiting in other HCV permissive cells, e.g. primary human
hepatocytes. Moreover, in a physiological setting, restrictions in
other steps of the viral life cycle, e.g. sub-threshold receptor levels
during entry [64,65] or a limitation in the apolipoprotein system
required for particle secretion [66] might play critical roles as well.
Importantly, also the innate immune response (and on a larger
time-scale also the adaptive one) poses severe restrictions on viral
replication via effector genes, whose molecular identity and
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functions have only recently begun to be identified [67,68]. These
influences would need to be included in a future, fully
comprehensive model of HCV replication. For our present model,
based on Huh-7 cells, however, we have so far neglected any
impact by the innate immune system, as we could previously
demonstrate that presence or absence of functional immune
recognition of HCV by the (Huh-7 derived) host cell does not have
a measurable effect on its permissiveness [32].
Still, for RNA replication in this single most important cell
culture system for HCV, we found a limiting host function
involved in the formation of the replication compartment to be
crucial to explain the observed replication kinetics. The molecular
function of this host factor is still unclear; one or more cellular
proteins could be involved, taking part in the formation of the
membrane alterations or in the initiation of RNA synthesis. Even a
more general condition such as stress tolerance could serve as the
host requirement proposed by our model. Since this host factor(s)/
condition(s) HF was sufficient to model the varying RNA
replication efficiencies in different Huh-7 populations, we
performed gene expression profiling to identify genes potentially
defining permissiveness. While our analysis identified 355 genes,
whose expression correlated with the degree of permissiveness of
the respective cell line, there were no single factors or well-defined
pathways that stood out significantly. In order to test the limiting
nature of these identified factors for HCV RNA replication, one
would have to individually overexpress those genes in low
permissive cells and assay for an enhancement in HCV
replication. Whereas this was beyond the capacity of our current
study, we made use of extensive publicly available data on cellular
interaction partners of HCV (VirHostNet [45]) and high-
throughput RNAi-based knock-down studies [46,47,48,49] in
order to recognize genes that had been implicated with HCV
before. This approach identified 17 cellular genes whose
expression levels on the one hand correlated well with permis-
siveness for HCV replication, and that, on the other hand, were
either reported to at least interact with an HCV protein, or were
shown to have a direct impact on HCV replication upon knock-
down (table 2). While for this small sub-set of genes a reliable
functional link to HCV could therefore be established, we cannot
exclude any of the remaining differentially expressed genes as
potentially crucial host factors for HCV; this is true even in spite of
a virtually genome-wide coverage of the published screening
studies, as such approaches are characterized by extremely high
false-negative rates [50]. Therefore, comprehensive future studies
need to exploit the information contained in our transcriptomic
analysis, systematically testing those host factors for an impact on
HCV replication that most significantly correlated with permis-
siveness.
The role of membrane alterations in regulating RNA
replication
Already during model development, but also throughout our
model analyses, the formation and function of the membranous
replication compartment was found to be crucial for successful
viral HCV replication. Previous literature as well as our model
analysis imply that membrane alterations serve at least three
distinct purposes. For one, they provide a protected environment
for RNA replication, shielding this very sensitive process from the
host cell degradative machinery as also shown experimentally
before [10]. Without this protection, the viral RNA would quickly
be degraded, and replication, according to our model, would
become highly vulnerable to stochastic effects due to very low
molecule numbers. In fact, should cytoplasmic RNA degradation
be only slightly stronger than our mean estimate for m
pcyt
(but well
within its confidence interval), e.g. upon stress or under conditions
of an activated immune response, the system would cross a
threshold and replication would die off inevitably. Therefore, to
compensate for such a lack of protection of the replication
machinery, HCV would have to develop a completely different
amplification strategy, most likely involving a much higher rate of
RNA synthesis in order to maintain sustained replication. This,
very likely, would not be compatible with low-level, low profile
replication as required for persistence [13]. Secondly, sequestra-
tion of viral replicative intermediates, such as double-stranded
RNA, into membranous compartments also shields them from
recognition by ubiquitous pattern recognition receptors of the
intrinsic innate immunity (which, as described above, is neglected
by our current model). A third important aspect, however, is the
fact that this strict compartmentalization allows for a tight control
of viral RNA replication versus protein translation. By limiting the
amount of viral and/or host protein inside, the replicative
compartment not only protects, but paradoxically also attenuates
RNA replication. Presumably, this serves to limit replication to
levels sustainable by the cell and permitting low-level persistent
replication over a long period of time with very limited detection
by the immune system. At the same time, by controlling the
amount of newly synthesized RNA released into the cytoplasm, the
vesicles indirectly control the amount of protein translation and, in
an in vivo situation, particle formation, as was also suggested by
another modeling approach [26].
We provide the first comprehensive modeling of the entire RNA
replication cycle of a positive strand RNA virus, from the onset of
RNA replication to steady state levels. However, membranous
replication sites are a hallmark of all positive strand RNA viruses
with very different replication strategies. In case of HCV the
membranous replication compartment seems to have a rather
limiting role in virus RNA replication, probably contributing to
viral persistence and chronic disease. In contrast, most positive
RNA viruses replicate fast, cause acute diseases and are cleared by
the immune system (e.g. the closely related flaviviruses such as
Dengue or West Nile virus). Interestingly, in the related group of
pestiviruses, pairs of viral isolates have been found, replicating
either in a non-cytopathic/persistent or in a cytopathic/acute
manner [69]. Upon integration of cellular mRNA sequences into
their genomes, dramatically enhancing the efficiency of viral RNA
replication, these biotypes switch from well-controlled, persistent
infection to an aggressively replicating, cytophatogenic phenotype
[70]. Also in case of Sindbis virus, cytopathic replication can be
switched to persistence by a single point mutation [71]. Both
examples demonstrate a tremendous flexibility to adapt the
concept of membranous replication compartments to various
replication strategies. It would therefore be highly interesting to
use our model as a blueprint for modeling replication kinetics of
closely related positive strand RNA viruses following a lytic/acute
replication strategy, e.g. Dengue virus or West-Nile-virus. Com-
paring the principles governing replication of such a virus to the
here described strategy of HCV could offer a completely new
approach to examining– and eventually comprehending– the
general requirements allowing viruses to establish chronicity.
Extending mathematical modeling towards the whole
viral replication cycle and systemic spread
Another obvious yet intriguing direction into which our
presented modeling approach could be developed, is extending it
to comprise the full infectious virus life cycle, including particle
production and secretion, receptor binding and cell entry. In fact,
two very recent publications studied RNA replication kinetics
upon HCV infection [6,29] and found a dynamic behavior
Replication Vesicles in the HCV Lifecycle
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extremely reminiscent of what we describe here for subgenomic
replicons: the initially present RNA is rapidly degraded early upon
infection and then starts to replicate exponentially at around 6 to
8 hours post infection, which is reflected in both, plus- and minus-
strand RNA signals. This similarity to the kinetics observed in our
experiments is remarkable, as initial RNA concentrations are
about two to three orders of magnitude less in the infection
(roughly 1–50 genomes per cell) as compared to our transfections
(,4.000 genomes per cell). The single major difference to the here
described situation in a replicon setting is the increasing excess of
plus-strand RNA over the minus-strand for late time points (e.g.
50-fold excess at 72 h) which seems to be due to decreasing minus-
strand levels, while plus-strand RNA basically maintains a steady-
state [29]. It is intriguing to speculate that this phenomenon might
reflect partitioning of the plus-strand RNA into translation/
replication on the one hand, and particle assembly/genome
encapsidation on the other hand. As encapsidated genomes would
no longer be available for initiation of new replication complexes,
minus-strand RNA levels should consequently decrease over time.
In order to adapt our model to an actual infection setting,
however, we will need to switch to a stochastic model to deal with
extremely low copy numbers of RNA per cell. Such situations can
be addressed mathematically using the Gillespie algorithm,
provided appropriate single cell measurements are available.
The model could then also be extended to describe the
extracellular steps of the viral life cycle, up to receptor binding
and cell entry, which could finally allow for very precise simulation
of viral spread through a population of naı
¨ve cells. Such a
comprehensive model would be highly valuable to examine and
predict the effects of therapeutic intervention with viral entry or
release as compared to inhibition of intracellular steps of
replication. Even more importantly, it could be suited to finally
link our fine-grained molecular model of HCV replication to the
very interesting patient-level models of HCV infection and therapy
dynamics [14,72], and thereby open up new avenues to rationally
designing novel therapeutic strategies, but also to understanding
the effects of molecule-scale events onto the progression of a
complex disease.
Materials and Methods
Cells and cell culture
All cells were maintained in supplemented Dulbecco’s modified
Eagle medium (DMEM) as described previously [10]. Huh-7 low
passage refers to naı
¨ve Huh-7 cells, passaged less than 30 times in
our laboratory, see also Binder et al. [32]. Huh7-Lunet and Huh-
7/5-2 are highly permissive clonal cell lines [32]. Huh7-Lunet NP
(unpublished) refers to a derivative of Huh7-Lunet, which is
significantly less permissive than its parental cell line.
HCV constructs and in vitro transcription
For kinetic analyses of HCV RNA replication, the genotype 2a
(JFH1 isolate) constructs pFKi389LucNS3-39_dg_JFH (wild-type)
and pFKi389LucNS3-39_dg_JFH/DGDD (replication deficient)
[73] were used, as well as the NTR-chimeric constructs pFK-
I
341
PI-Luc/NS3-39/JFH1/59Con (59-NTR exchange) and pFK-
Table 2. Established HCV host factors identified in transcriptomic analysis.
Gene Symbol Gene ID Gene Name
Previous Hit
(Reference)
Interaction
partner
log2 fold
change HP/LP p-value
MCL1 4170 Myeloid cell factor 1 [45,90] Core 20.4980 0.1632
TF 7018 transferrin [45,51] E2 20.3918 0.0514
VCAN 1462 versican [45,51] NS3 0.5329 0.0315
TRIM23 373 tripartite motif-containing 23 [45,51] NS3 20.3551 0.1132
SERPING1 710 serpin peptidase inhibitor, clade G
(C1 inhibitor), member 1
[45,91] NS3 0.3157 0.1285
CASP8 841 Caspase 8 [45,92] NS3 20.3883 0.0835
PIK3CB 5291 phosphoinositide-3-kinase, catalytic,
beta polypeptide
[45,93] NS5A 20.5875 0.0743
SORBS2 8470 sorbin and SH3 domain containing 2 [45,51] NS5A 20.3349 0.0646
GAB1 2549 GRB2-associated binding protein 1 [45,94] NS5A 20.3282 0.0895
APOB 338 Apolipoprotein B [45,95] NS5A 20.3219 0.0175
MOBK1B 55233 MOB1, Mps One Binder kinase
activator-like 1B (yeast)
[45,51] NS5A, NS5B 20.4355 0.0133
COPA 1314 coatomer protein complex, subunit alpha [47] 20.4471 0.1227
PPTC7 160760 PTC7 protein phosphatase homolog (S.
cerevisiae)
[48] 20.3196 0.1982
RPS27A 6233 ribosomal protein S27a [46] 20.3388 0.0131
PIP5K1A 8394 phosphatidylinositol-4-phosphate 5-kinase,
type I, alpha
[46] 20.4684 0.0562
LHX2 9355 LIM homeobox 2 [47] 0.4467 0.0708
JAK1 3716 Janus kinase 1 [49,51] Core, NS5A 20.3653 0.1826
Analysis of genes differentially expressed between high and low permissive Huh-7 based cell lines (log-fold change .0.3 or ,20.3 between high and low permissive
cells) and correlated with replication permissiveness of 8 cell lines (p-value,0.2). Resulting genes were intersected with published RNAi screening [46,47,48,49] and
virus-host protein interaction [45] data as described, yielding a list of 17 host factors that are differentially expressed between the high and low permissive cells, that
correlate with replication permissiveness in the eight cell lines used, and that have previously been shown to be associated with HCV infection or replication.
doi:10.1371/journal.ppat.1003561.t002
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PLOS Pathogens | www.plospathogens.org 15 August 2013 | Volume 9 | Issue 8 | e1003561
I
341
PI-Luc/NS3-39/JFH1/XCon (39-NTR exchange) [22]. Per-
missiveness of cell lines was assessed using a genotype 1b (con1)
replicon, using the plasmid pFK-I
341
PI-Luc/NS3-39/Con1/ET/
hg. In vitro transcription of HCV replicons was performed as
described previously [22,30]. Briefly, plasmid DNA was purified
by phenol/chloroform extraction and transcribed with 0.9 U/ml
T7 RNA polymerase (Promega). RNA was then purified by DNase
(Promega) digestion, extraction with acidic phenol and chloroform
and room temperature isopropanol precipitation. RNA concen-
tration was determined spectrophotometrically and integrity was
confirmed by agarose gel electrophoresis.
Electroporation of HCV RNA and luciferase assay
Cells were transfected with in vitro transcribed HCV RNA by
electroporation as described previously [22]. For determination of
host cell permissiveness (figure 7), 5 mg of RNA were used for
electroporation and cells were seeded into 6-well plates (1/12
electroporation per well). Samples were lysed at 4, 24, 48 and 72 h
post transfection and stored at 280uC until measurement of
luciferase activity. For time resolved quantitation of HCV
replication, 4610
6
cells were transfected with 10 mg of HCV
RNA, corresponding samples were pooled and cells were seeded
into 6-well plates for luciferase assays as described above or into
10 cm cell-culture dishes at a density of 4610
6
cells per plate
(2610
6
cells/plate for time points 48 h and 72 h) for RNA
preparation and Northern blotting. For the 0 h RNA sample,
4610
6
cells were washed twice with DMEM directly after
electroporation, pelleted and lysed in guanidinium isothiocyanate.
Other samples were lysed at the indicated time points (2, 4, 8, 12,
18, 24, 48 and 72 h) and lysates were stored at 280uC until
further processing.
For determination of HCV replication by luciferase activity
measurement, all samples of one experiment were frozen at
280uC upon harvesting and thawed simultaneously prior to
luciferase detection. Measurements were performed as described
in Binder et al. [22], with all samples measured in duplicate.
Luciferase activity was normalized to the input activity assessed at
2 h (kinetic experiments) or 4 h (permissiveness determination)
post electroporation, to correct for transfection efficiency.
HCV RNA quantification by Northern blotting
RNA preparation and Northern blotting were performed
according to established procedures [22]. In essence, total cellular
RNA was isolated from guanidinium isothiocyanate lysates by a
phenol/chloroform based single-step protocol and denatured in
glyoxal. Samples were analyzed by denaturing agarose gel
electrophoresis and Northern hybridization. For strand specific
detection of HCV RNA, radioactively labeled riboprobes encom-
passing nucleotides 6273 to 9678 of the JFH1 sequence were
generated by T7- (minus-strand detection) or T3-polymerase (plus-
strand detection) mediated in vitro transcription of plasmid pBSK-
JFH1/6273-39[34]. Signals were recorded by phosphorimaging
using a Molecular Imager FX scanner (BioRad, Munich,
Germany) and quantified using the QuantityOne software
(BioRad). To determine absolute molecule numbers, signals were
quantified using serial dilutions of highly purified plus- and minus-
strand in vitro transcripts of known quantity, which were loaded
onto the same gel. Cross-hybridization of minus-strand probes
with the plus-strand standard was observed to a low extent and
corrected for.
Microarray data
Permissiveness of eight Huh-7 derived cell-lines was assessed
using a standard luciferase replication assay as described above.
Total cellular RNA of untransfected cells was then isolated by
Trizol extraction according to the manufacturer’s protocol
(Invitrogen, Karlsruhe, Germany), and gene expression was
measured using the Affymetrix Human Genome U133 Plus 2.0
platform. Data were normalized in R/Bioconductor using RMA
normalization. Genes were filtered using the variance-based (IQR)
filter in nsFilter, and log2 fold-changes between high and low
permissive cells were computed. Wethenfittedalinearmodelto
the data, predicting replication efficiency in the eight cell lines
from the corresponding gene expression values. ANOVA was
used to assess statistical significance of individual genes. Hit
selection was done using a relatively low threshold of 0.2 on the
p-value and a log fold-change of at least 0.3, corresponding to a
change in expression of approximately 25%. Resulting genes
were intersected with published RNAi screening [46,47,48,49]
and virus-host protein interaction [45] data as described,
yieldingalistof17hostfactorsthat are differentially expressed
between the high and low permissive cells, that correlate with
replication permissiveness in the eight cell lines used, and that
have previously been shown to be associated with HCV
infection or replication. Genes were then mapped to pathways
and annotated further using DAVID version 6.7 [74,75] and
IPA (Ingenuity Systems, www.ingenuity.com).
Mathematical model
We developed a mathematical model using ordinary differential
equations based on mass action kinetics. The model is subdivided
into two compartments: 1) initial RNA processing, translation into
the polyprotein and polyprotein processing (cleavage) occur in the
cytoplasm, and 2) viral genome replication takes place inside of the
replication compartment. A graphical summary of the model is
shown in Figure 2C. The following set of equations was used to
describe the processes in the two compartments:
Cytoplasm
dRunp
p
dt ~{k0Runp
p{munp
pRunp
pð1Þ
dRcyt
p
dt ~k0Runp
p{k1Rcyt
p(Ribotot {Tc)zk2Tc
zkPoutRp{mcyt
pRcyt
p
ð2Þ
dTc
dt ~k1Rcyt
p(Ribotot {Tc){k2Tc
{kPinTcEcyt (HF (0){RIp ){mTcTc
ð3Þ
dP
dt ~k2Tc{kcPð4Þ
dEcyt
dt ~kcP{kPinTcEcyt (HF (0){RIp ){mEcyt Ecyt ð5Þ
dL
dt ~k2Tc{mLLð6Þ
Here, R
punp
(eq. 1) represents the number of plus-strand RNA
molecules entering the cell upon transfection. This transfected
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PLOS Pathogens | www.plospathogens.org 16 August 2013 | Volume 9 | Issue 8 | e1003561
RNA is processed into translation competent R
pcyt
(eq. 2) at rate k
0
,
describing, for example, transport and structural re-folding
processes. The processed plus-strand RNA R
pcyt
interacts with
ribosomes R
ibo
at a constant rate k
1
to form translation complexes
T
c
(eq. 3), which degrade at rate m
Tc
. Ribosomes are recovered
when translation complexes T
c
degrade with rate m
Tc
. Note that, as
the total number of ribosomes in the cell (R
ibotot
) is assumed
constant, the number of ribosomes available for translation is given
by R
ibotot
–T
C
, and it is not necessary to introduce a separate
equation for ribosomes. Unprocessed and processed RNAs R
punp
and R
pcyt
degrade with rate constants m
punp
and m
pcyt
, respectively
(eq. 1 an 2). For simplicity, we assume that 10 ribosomes
simultaneously translate the same HCV RNA [76], therefore,
R
ibotot
represents complexes consisting of 10 ribosomes. Viral
polyprotein Pis formed from T
c
at an effective rate k
2
(eq. 4).
When the translation of polyprotein is complete, the translation
complex dissociates into plus-strand RNA and ribosomes at rate
k
2
. Newly produced polyprotein is cleaved with rate k
c
into the
mature viral nonstructural (NS) proteins E
cyt
(eq. 5). NS proteins
degrade at rate m
Ecyt
. Eventually, plus-strand RNA and NS
proteins, most notably the polymerase NS5B, interact in cis and
together with NS proteins in trans (E
cyt
) as well as a cellular factor
HF to form a replication complex within the induced vesicular
membrane structure. This cis interaction of R
pcyt
and translated NS
proteins is realized in the model by requiring active translation
complexes T
c
instead of free R
pcyt
for the formation of replication
complexes. The host factor HF catalyzes the formation of R
Ip
,at
the rate k
Pin
. Once R
Ip
is formed, ribosomes are freed again at rate
k
Pin
. This leads to the ternary reaction T
C
+E
Cyt
+HFRR
Ip
+R
Ibo
,
simultaneously describing formation of the replication compart-
ments and initiation of minus strand RNA synthesis, compare also
supplementary text S1 and supplementary figure S6. In turn, HF is
freed again when R
Ip
degrades or upon completion of minus strand
synthesis. As the total number of host factor molecules in the cell is
assumed constant, we can replace HF by HF(0) – RIp, where
HF(0) is the total number of HF molecules in the cell. Lastly, since
we use a luciferase readout to measure polyprotein concentration,
we furthermore include a luciferase marker Lin the model, which
is produced at the same rate as the polyprotein (k
2
), however does
not require further processing and degrades with rate m
L
(eq. 6).
Replication compartment
dRIp
dt ~kPinTcEcyt (HF (0){RIp ){k4mRIp
zk3RpE(HF(0){RIp){mRC RIp
ð7Þ
dRds
dt ~k4mRIp{k5Rds Ezk4pRIds{mRC Rds ð8Þ
dE
dt ~k4mRIp{k5Rds Ezk4pRIds
{k3RpE(HF(0){RIp){mRC E
ð9Þ
dRIds
dt ~k5RdsE{k4pRIds {mRC RIds ð10Þ
dRp
dt ~k4pRIds{k3RpE(HF (0){RIp ){kPout Rp{mRC Rpð11Þ
R
Ip
is the minus-strand RNA initiation complex (eq. 7), which
contains a plus-strand RNA serving as template for the synthesis of
minus-strand RNA. Minus strand RNA is synthesized from R
Ip
at
rate k
4m
, yielding double stranded RNA R
ds
(eq. 8). We assume
minus-strand RNA to be always bound to its complementary plus-
strand in a double-stranded replicative intermediate. When the
production of minus-strand RNA is complete, R
Ip
dissociates
into R
ds
,HF and viral NS protein E(eq. 9). Next, R
ds
interacts
again with Eto form a plus-strand RNA initiation complex, R
Ids
(eq. 10), to initiate the synthesis of new plus-strands, R
p
,witha
constant rate k
4p
, and dissociates into R
ds
and E.Newly
synthesized plus-strand RNA, R
p
(eq. 11), then leaves the
replication compartment at rate k
Pout
to participate in transla-
tion, or interacts with the polymerase Eand host factor HF to
again form the minus-strand RNA initiation complex R
Ip
at rate
k
3
. For simplicity, we assume that the RNA R
Ip
,R
ds
,R
Ids
and R
p
,
and proteins Eall degrade with rate m
RC
.
Model parameters and parameter estimation
Reaction rates in the model were taken from literature as far
as known, or estimated by fitting the model to the experimental
data. Following Dahari et al [24], we used a value of k
2
=100
polyproteins per hour per polysome for protein translation.
RNA replication was assumed to occur at a rate of
k
4m
=k
4p
= 1.7 viral RNA molecules per hour per replication
complex, assuming plus- and minus-strand synthesis to occur at
the same rate [23,77,78]. Based on an estimated half-life of
Luciferase of approximately 2 hours, we estimated the corre-
sponding degradation rate to be m
L
=0.35 h
21
[79,80]. We
furthermore estimated the NS protein half-life in the cytoplasm
to be around 12 hours, corresponding to a rate of m
E
-
cyt
=0.06 h
21
[76,81,82]. We observed from model calibration
that the optimization would yield values with m
Tc
.m
pcyt
,
violating the expectation that RNA in translation complexes
should be more stable than free RNA in the cytoplasm. We
henceaddedtheconstraintm
Tc
/m
pcyt
= 0.5, enforcing a 2-fold
higher stability of RNA that is actively translated. We
furthermore observed a low sensitivity of model output with
respect to parameters k
1
,k
c
,k
3
and k
5
, compare figure 5, and
hence fixed these parameters based on manual model analysis,
for details see supplementary text S1.
Estimation of the remaining 7 model parameters, 3 initial
values and a scale factor to convert luciferase measurements into
polyprotein molecule numbers was done using multiple shoot-
ing, as implemented in the PARFIT package [83,84,85]. We
simultaneously minimized the least squares prediction error on
the high and low permissive cells in log-concentration space,
using all individual measurements in the objective function. An
additional scaling factor was introduced in the optimization
problem to convert luciferase measurements for the viral
polyprotein to molecule numbers. All model species containing
viral plus-strand RNA or minus-strand RNA, respectively, were
added for comparison with the experimental data, yielding
R
ptot
=R
punp
+R
pcyt
+T
c
+R
Ip
+R
ds
+R
Ids
+R
p
for the total plus-strand
RNA and R
Mtot
=R
ds
+R
Ids
for the total negative strand RNA
concentrations. Ratios of RNA as reported in literature were
furthermore used to constrain the optimization [10]. As some
species attain very low values, we compared results of the
approximation using differential equations with a stochastic
solver (supplementary figure S7). For details of the parameter
estimation and objective function used see supplementary text
S1. Obtained model parameters and confidence intervals are
shown in table 1.
Replication Vesicles in the HCV Lifecycle
PLOS Pathogens | www.plospathogens.org 17 August 2013 | Volume 9 | Issue 8 | e1003561
Identifiability analysis
To test our model for structural identifiability, we performed a
local identifiability analysis at obtained optimal parameter values
using SensSB [86]. Results of this analysis are shown in
Supplementary Figure S8. High correlation between two param-
eters means that a change in the model output caused by a change
in one parameter can be compensated by an appropriate change
in the other parameter. This then prevents the parameters from
being uniquely identifiable despite the output being very sensitive
to changes in individual parameters. Parameters for which values
were known from literature or which were fixed were also included
in this identifiability analysis, to assess their effect on results. These
parameters are indicated in grey in the Figure; several of these
parameters are highly correlated with other parameters, thus
reiterating the importance of experimental measurements for
them. Importantly, the identifiability analysis indicates that most of
the parameters that had to be calibrated from data showed low
correlation to other parameters only, indicating an overall
satisfactory identifiability of the model and, in particular, no
indication of structural non-identifiability in the model with
correlation values close to 61.
We furthermore calculated confidence intervals on estimated
model parameters using the covariance matrix of the parameters,
as described in supplementary text S1. Most of the kinetic reaction
rates had reasonable standard errors and confidence bands, while
larger uncertainties were observed for the initial values, compare
table 1. This sloppiness is typical for models in systems biology
[87,88]. Based on our aim to develop a predictive model and not
uniquely identify individual reaction rates, our assessment was that
the model is sufficiently identifiable for our purpose.
Sensitivity analysis
Global sensitivity analysis was performed using the extended
Fourier Amplitude Sensitivity Test (eFAST) [39,40]. This algo-
rithm calculates the first and total-order sensitivity indices of each
parameter, and assesses the statistical significance of these
sensitivity indices by a method based on dummy parameters.
For details, we refer to Saltelli et al [89]. In brief, for a given model
y = f(x) with scalar y and input vector x=(x
1
,…,x
n
), the first order
sensitivity index with respect to x
i
is the expected amount of
variance that would be removed from the total output variance, if
we knew the true value of x
i
, divided by the total unconditional
variance:
Si~Var(Eyx
i
j
½)
Var(y):
S
i
is a measure of the relative importance of the individual variable
x
i
in driving the uncertainty in the output y. In contrast, the total
sensitivity index with respect to a variable x
i
measures the residual
output variance if only x
i
were left free to vary over its uncertainty
range, and all other parameters were known:
STi~E Var(yx
{i
j)½
Var(y):
S
Ti
is a measure of how important a parameter is in determining
the output variance, either singularly or in combination with other
parameters. To assess the significance of obtained indices, eFast
furthermore calculates the first and total order sensitivity index for
a dummy parameter that is not part of the model. Indices that are
not significantly larger than this dummy parameter index should
not be considered different from zero [39].
Figures 6 and S4 show the resulting eFAST total order
sensitivity indices of viral plus- and minus-strand RNA concen-
trations and viral polyprotein concentration with respect to the 16
model parameters and three initial values at two different time
points, early in the viral lifecycle and after attainment of the steady
state levels.
Supporting Information
Figure S1 Fit of the original model by Dahari et al. [24] to our
time-resolved measurements of positive strand RNA (blue),
negative strand RNA (red) and polyprotein (black).
(EPS)
Figure S2 Quantitative assessment of alternative models to
explain differences observed in HP and LP cells. Alternative
models were set up to explain observed data, assuming that cells
differ in (A) the initial RNA processing, (B) different numbers of
ribosomes available for RNA translation, (C) different RNA
degradation rates in the cytoplasm, (D) different polyprotein
translation rates, (E) different rates of formation of the replication
compartment, (F) different RNA synthesis rates inside the
replication vesicles, (G) different RNA degradation rates inside
the replication vesicles, and (H) different export rates of newly
synthesized RNA into the cytoplasm. Models were fitted to the
experimental data, and resulting x
2
and Akaike Information
Criterion (AIC) values compared. Line colors indicate polyprotein
(black), plus-strand RNA (red) and minus-strand RNA (blue).
(EPS)
Figure S3 Comparison of activatory with consumed host factor
(HF) model. The left plots show the model predictions in the high
permissive cell line, the right plot shows the predictions for the low
permissive cell line. Upper panels: activatory (enzymatic) HF
model, lower panels: consumed HF model.
(EPS)
Figure S4 Global sensitivity analysis of the replication model.
Sensitivity analysis was performed using the extended Fourier
Amplitude Test (eFAST) at (A, C, E) 4 hours and (B, D, F)
72 hours. Shown are eFast total order sensitivity indices for (A, B)
plus strand RNA, (C, D) minus strand RNA, and (E, F) viral
protein. These total sensitivity indices account for first and higher
order sensitivities involving each of the parameters indicated. The
dashed horizontal lines are sensitivities of a negative control
parameter that does not occur in any of the equations, and are
thus a measure of background variability of the sensitivity
estimation procedure. Sensitivities lower or equal to the dashed
line should not be considered as significantly different from zero.
(EPS)
Figure S5 The figure shows attained steady state levels of
positive strand RNA, for different values of the RNA degradation
rate m
RC
in the replication compartment. Note the transition at
m
RC
= 0.4, where a switch occurs from low-level persistent
replication to complete clearance of the infection.
(EPS)
Figure S6 The figure replaces the ternary interaction T
c
+
E
cyt
+HFRR
Ip
+R
ibo
by two binary reactions, assuming that T
c
and
E
cyt
bind first, forming an intermediate complex Cin a reversible
reaction with rates k
a
(forward reaction) and k
b
(backward
reaction), that then irreversibly reacts with HF to yield R
Ip
and
R
ibo
with rate k
c
. The figure in panel (A) was obtained by fixing
parameter k
a
to 3e-4, varying parameter k
b
between 1 and 200,
and then optimizing parameter k
c
to fit the experimental data. The
plot shows that increases in k
b
can be compensated by increases in
Replication Vesicles in the HCV Lifecycle
PLOS Pathogens | www.plospathogens.org 18 August 2013 | Volume 9 | Issue 8 | e1003561
k
c
, rendering the model practically non-identifiable. Panels (B) and
(C) show the obtained fits to the data for k
b
= 10 and k
b
= 200, with
associated values k
c
= 0.41 and k
c
= 7.64, respectively, and x
2
values
of 3.55 and 2.45, respectively. Blue line and points: (-) RNA, Red:
(-) RNA, Black: Viral Polyprotein (Luciferase).
(EPS)
Figure S7 Ten different runs for each high and low permissive
cells, using a stochastic solver (implicit tau method) to make
simulations with our calibrated replication model. Individual runs
show a very similar behavior to the deterministic ordinary
differential equation model, indicating that stochastic effects to
not play a major role in determining the overall dynamics of the
model.
(EPS)
Figure S8 Correlation between model parameters from iden-
tifiability analysis using SensSB [86]. High correlation between
two parameters means that a change in the model output caused
by a change in one parameter can be compensated by an
appropriate change in the other parameter. This then prevents the
parameters from being uniquely identifiable despite the output
being very sensitive to changes in individual parameters.
Parameters for which values were known from Literature or
which were fixed after identifiability analysis are indicated in grey
in the Figure. The Figure shows that most parameters are
identifiable at optimal values obtained from model fitting.
Parameters m
Ecyt
and m
L
, which are highly correlated, however,
the value for m
Ecyt
is known from literature and is not calibrated
using the data [76,81,82], rendering the second parameter m
L
identifiable. Similarly, the high correlation seen between R
ibotot
and
k
2
as well as the high correlation between HF
0
and k
4m
are
unproblematic, as parameters k
2
and k
4m
were set based on
literature data [23,24,77,78]. The correlation seen between k
Pout
und m
pcyt
, is unproblematic, as an additional constraint m
pcyt
=2m
Tc
on m
pcyt
is used in the parameter estimation.
(EPS)
Table S1 Differentially expressed genes between the eight cell
lines analyzed. The first column is the gene name, the second
column the corresponding Affymetrix ID. The logfc column is the
logarithm of the fold expression change between the high and low
permissive cells, whereas the p-value is computed from an analysis
of variance of the full panel of all eight cell lines.
(XLS)
Table S2 Differentially expressed genes between the eight cell
lines analyzed, showing the 355 genes with log-fold change .0.3
or ,20.3 between high and low permissive cells, and p-value,0.2
in correlation analysis with permissiveness over all 8 cell lines. The
table gives log fold-changes for all 8 cell lines, as well as p-value of
correlation for all genes.
(XLS)
Table S3 Annotation of 355 differentially expressed genes
correlating with permissiveness to cellular function categories.
Analysis was done using IP (Ingenuity Systems, www.ingenuity.
com). The functional analysis identified the biological functions
that were most significant to the data set. Molecules from the
dataset that met the p-value,0.2 and log fold-change .0.3 or
,20.3 criteria were associated with the biological functions in the
Ingenuity Knowledge Base. Right-tailed Fisher’s exact test was
used to calculate a p-value determining the probability that each
biological function assigned to the data set is due to chance alone.
Shown are annotations for category and cellular function, together
with p-value, number and names of respective molecules.
(XLS)
Table S4 Annotation of 255 differentially expressed genes
correlating with permissiveness to canonical pathways. Analysis
was done using IP (Ingenuity Systems, www.ingenuity.com).
Canonical pathway analysis identified the pathways from the
IPA library of canonical pathways that were most significant to the
data set. Molecules from the data set that met the p-value,0.2 and
log fold-change .0.3 or ,20.3 criteria and were associated with a
canonical pathway in the Ingenuity Knowledge Base were
considered for the analysis. The significance of the association
between the data set and the canonical pathway was measured in 2
ways: 1) A ratio of the number of molecules from the data set that
map to the pathway divided by the total number of molecules that
map to the canonical pathway is displayed. 2) Fisher’s exact test
was used to calculate a p-value determining the probability that
the association between the genes in the dataset and the canonical
pathway is explained by chance alone.
(XLS)
Table S5 Parameter estimates for the HCV replication model
(consumed HF).
(PDF)
Text S1 The supplementary text contains additional information
on gene expression analysis, model development and parameter
estimation, and model analysis.
(PDF)
Author Contributions
Conceived and designed the experiments: MB LK. Performed the
experiments: MB CMH MT VL RB. Analyzed the data: NS DC MS
SML JPS MB LK. Wrote the paper: NS MB VL LK. Developed the
models: NS MB LK. Performed multiple shooting optimization and
analysis: SML JPS.
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