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Sensing Arboviruses : Dengue andChikungunya Virus
MindaugasPaužuolis
Degree project in biology, Master of science (2 years), 2017Examensarbete i biologi 45 hp till masterexamen, 2017Biology Education Centre, Uppsala University, and University Medical Centre Groningen,Experimental Virology,Antonius Deusinglaan 1, 9713 AV Groningen, The NetherlandsSupervisors: Dr. I. A. Rodenhuis-Zybert and Prof. Dr. J. M. SmitExternal opponent: Dr. R. Sirikharin
1
Contents Acknowledgments ............................................................................................................................... 4
Abstract ............................................................................................................................................... 5
General introduction ........................................................................................................................... 6
What are arboviruses? ..................................................................................................................... 6
Human pathogenic arboviruses and their distribution ................................................................... 7
References ........................................................................................................................................... 8
Introduction ....................................................................................................................................... 10
Dengue virus .................................................................................................................................. 10
DENV life cycle ............................................................................................................................... 10
Dengue fever .................................................................................................................................. 11
Dengue virus sensing and Toll-like receptors ................................................................................ 12
TLR2 and virus sensing ................................................................................................................... 12
HEK-Blue hTLR2 cell lines ............................................................................................................... 14
The purpose of the study .................................................................................................................. 15
Methods............................................................................................................................................. 16
Cell lines ......................................................................................................................................... 16
Compounds .................................................................................................................................... 16
Virus stocks .................................................................................................................................... 16
Secreted embryonic alkaline phosphatase assay (SEAP assay) ..................................................... 17
Viral RNA isolation ......................................................................................................................... 17
Quantitative PCR ............................................................................................................................ 17
Statistical analysis .......................................................................................................................... 19
Results................................................................................................................................................ 20
DENV activates NF-κB via TLR2 ...................................................................................................... 20
Endocytosis is important for NF-κB activation via TLR2 ................................................................ 21
Endocytosis inhibition reduces NF-κB activation via TLR2 ......................................................... 21
DENV produced in primary cells activate NF-κB ............................................................................ 21
Macrophage and Dendritic cell derived DENV activate NF-κB via TLR2 less than C6/36 derived
DENV ........................................................................................................................................... 21
Immature viral preparations activate NF-κB ................................................................................. 22
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Immature Pan-DENV activate NF-κB via TLR2 ............................................................................ 22
Multiple DENV strains activate NF-κB via TLR2 in serotype independent manner ....................... 23
NF-κB activation via TLR2 is DENV specific and not for WNV, ZIKV or CHIKV ............................... 25
Only DENV activates NF-κB via TLR2 out of tested viruses ........................................................ 25
Discussion .......................................................................................................................................... 26
Conclusion ......................................................................................................................................... 28
References ......................................................................................................................................... 29
Supplementary information .............................................................................................................. 32
Endocytosis inhibitor cell viability assay ........................................................................................ 32
Introduction ....................................................................................................................................... 36
Chikungunya virus and ................................................................................................................... 36
Chikungunya fever ......................................................................................................................... 36
CHIKV and arthralgia ...................................................................................................................... 37
Monocytes and immune response ................................................................................................ 37
Monocytes subsets during viral infections .................................................................................... 38
Chikungunya virus and monocytes ................................................................................................ 39
The purpose of the study .................................................................................................................. 40
Methods............................................................................................................................................. 41
Primary cell isolation ...................................................................................................................... 41
Compounds .................................................................................................................................... 41
Viral stocks ..................................................................................................................................... 41
In vitro cell infection ...................................................................................................................... 41
Flow cytometry .............................................................................................................................. 41
Statistical analysis .......................................................................................................................... 42
Results................................................................................................................................................ 43
CHIKV infection increase viability of PBMCs .................................................................................. 43
Effects of CHIKV infection on PBMCs and monocyte viability .................................................. 43
CHIKV infection increases PBMCs viability in comparison to TLRs agonists .............................. 43
Effect of CHIKV infection and TLR agonists stimulation on Monocyte subset distribution .......... 43
Effect of CHIKV infection on monocyte subset dynamics .......................................................... 43
CHIKV infection shifts monocyte population in different direction than TLRs agonists ............ 44
Quantification of PBMCs and monocyte susceptibility to CHIKV infection ................................... 45
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Effec of TLR agonist pre-treatment on CHIKV infection in PBMCs ................................................ 47
The effect of TLR4/7 agonists pretreatment on PBMCs viability and monocyte subset dynamics
.................................................................................................................................................... 47
Discussion .......................................................................................................................................... 49
Conclusion ......................................................................................................................................... 52
References ......................................................................................................................................... 53
Supplementary information .............................................................................................................. 57
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Acknowledgments
I would like to express my deepest gratitude to my supervisor Izabela A. Rodenhuis-Zybert for her
guidance and support throughout my research project. I am deeply grateful to Jose A. Aguilar
Briseño for his knowledge, advice, generosity and humour. Also I wish him all the lucky stars he
needs to finish his PhD successfully. I thank Heidi van der Ende-Metselaar for her help to master
protocols of ML3 laboratory and company during initial experiments in ML3. I am very grateful to
Jolanda M. Smit for providing me the opportunity to work in UMCG. I want thank all the member
of Experimental Virology group for encouragement, advice and outsider perspectives as well as
wonderful time in Groningen.
I feel deep gratitude to all people who helped me during this project and they helped to make this
a wonderful CHIKV challenge.
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Abstract
Arboviruses are one of the key health concerns over the past 20 years. There were several
outbreaks of arboviruses across the globe which brought attention to some forgotten pathogens.
Dengue (DENV) and Chikungunya (CHIKV) viruses are considered re-emerging pathogens as these
pathogens expand to new regions and caused large epidemics in South East Asian and Indian
Ocean islands. The response of innate immune system towards these viruses are not fully
understood especially virus sending by toll-like receptors (TLRs) and the effect of virus infection to
innate immune cells such as monocytes.
First project has been focused on the DENV interaction with TLRs to induce NF-κB activity. During
this research project, we elucidated the mechanism of DENV sensing by TLR2. We demonstrated
that DENV can activate NF-κB via TLR2 occurs in a replication independent manner. We also
showed that as well TLR2 mediated activation is initiated already at the cell surface. Interestingly,
while internalization of the TLR2 and/or the virus increases the level of NF-κB activation, occurs
irrespectively of the ability of the virus to fuse from within the endosomal compartment.
DENV/TLR2 axis activates NF-κB in a serotype-independent manner, but the ability to activate NF-
κB might not be shared between Flaviviruses.
Second project has been focused to determine the effect of CHIKV infection on PBMCs and
monocyte in context of PBMCs. CHIKV infection increases human peripheral blood mononuclear
cells (PBMCs) viability as well as cause the shift of monocyte subsets towards an intermediate
phenotype and this effect in not comparable to stimulation with TLR2/4/7 agonists. The effect of
CHIKV infection on PBMCs viability and monocyte subset distribution is abolished by pre-
treatment of PBMCS with TLR4/7 agonists. However, we were unable to quantify CHIKV infection
in PBMCs.
In conclusion, we presented a novel role of TLR2 during early host cell interactions with DENV and
presented elucidate how CHIKV shapes immune systems response. The obtained knowledge will
contribute to the understanding the mechanism of virus induced immunopathology.
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General introduction
What are arboviruses?
Arthropod-borne viruses (Arboviruses) are a group of viruses that are transmitted between their
hosts by hematophagous insects like mosquitoes, ticks, or flies. The three virus families that form
the core of arbovirus ecological group are Bunyaviridae, Flaviviridae and Togaviridae (Anez et al.
2012). Arboviruses are adapted to replicate in invertebrate cells of their vectors and vertebrate
cells as during their life cycle viruses replicate in vector and host cells (see fig. 1). Viruses from
Flaviviridae and Togaviridae families are transmitted by mosquitos from Aedes family for example
Aedes aegypti, Aedes albopictus and Aedes polynesiensis. Arboviruses transmission occurs during a
blood meal of a vector. Ae.albopictus as well as Ae. Aegypti are key vectors for human pathogens
from Flaviviridae and Togaviridae such as Dengue (DENV), Chikungunya (CHIKV), Zika (ZIKV) and
Yellow Fever virus (Weaver and Reisen, 2010). Ae. Aegypti prefer replicate in all available location
containing water close to humans as its developmental cycle relies on energy acquired from
human blood (Harrington et al., 2001). In recent years, Ae. albopictus mosquito has contributed
significantly to the spread of ZIKV and CHIKV as this species is more adapted to milder climates
(Vasilakis & Weaver 2017). Humans are major hosts for arboviruses such as DENV, CHIKV and
ZIKV there virus can induce high viremia and infect vector. But for some other viruses like Rift
Valley fever virus humans are dead end host, because virus cannot replicate efficiently and
produce large number of viral particles necessary for transmission (Kuno & Chang 2005).
Figure 1 Arboviruses transmission. A, General arbovirus transmission cycle in nature. Arboviruses circulate in enzootic cycle between vector (mosquito, tick or flies) and host (birds, mammals and primates). B, Transmission cycles of Dengue, Yellow fever and Chikungunya viruses. Sylvatic cycle of transmission relies on non-human hosts in forests or rural areas. However, in some cases pathogens can spill over and cause localized outbreaks. Dengue and Chikungunya have adapted to urban cycle of transmission which relies on humans as main reservoir by causing infections with high viremia. Viruses from humans can infect vectors and maintain the cycle. Adapted from Anez et al. 2012.
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Human pathogenic arboviruses and their distribution
As arboviruses are adapted to a range of vectors such as mosquitos, ticks and flies, a number of
human pathogenic arboviruses have been observed across the globe (see fig. 2). Due to increasing
urbanization, disruptions of vector control programs and virus adaptation to new vectors have led
to reemergence of old pathogens. Pathogens like Yellow fever virus, CHIKV and ZIKV recently
caused large outbreaks in South America, Africa and the Indian Ocean as viruses entered
vulnerable populations or adapted to novel vectors (WHO 2017a; WHO 2017b; WHO 2017c). One
of the most prevalent arboviruses is DENV, a flavivirus that infects around 80 million people yearly.
DENV infection takes away 20,000 deaths per year and it is associated with extreme economical
costs for communities (Devaux 2012; Fredericks & Fernandez-Sesma 2014). CHIKV is another
arbovirus of the Togaviridae family that circulates in same geographical regions as DENV and is a
serious public health concern in many tropical countries.
Figure 2 Spread of arboviruses across the globe. Adapted from Anez et al. 2012.
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References
Anez, G. et al., 2012. Dengue virus and other arboviruses: a global view of risks. ISBT Science
Series, 7(1), pp.274–282.
Devaux, C.A., 2012. Emerging and re-emerging viruses: A global challenge illustrated by
Chikungunya virus outbreaks. World Journal of Virology, 1(1), p.11.
Fredericks, A.C. & Fernandez-Sesma, A., 2014. The Burden of Dengue and Chikungunya
Worldwide: Implications for the Southern United States and California. Annals of Global Health,
80(6), pp.466–475.
Harrington, L.C., Edman, J.D. & Scott, T.W., 2001. Why do female Aedes aegypti (Diptera:
Culicidae) feed preferentially and frequently on human blood? Journal of medical entomology,
38(3), pp.411–22.
Kuno, G. & Chang, G.-J.J., 2005. Biological Transmission of Arboviruses: Reexamination of and New
Insights into Components, Mechanisms, and Unique Traits as Well as Their Evolutionary Trends.
Clinical Microbiology Reviews, 18(4), pp.608–637.
Vasilakis, N. & Weaver, S.C., 2017. Flavivirus transmission focusing on Zika. Current Opinion in
Virology, 22, pp.30–35.
Weaver, S.C. & Reisen, W.K., 2010. Present and future arboviral threats. Antiviral research, 85(2), pp.328–45.
WHO, 2017a. WHO, Chikungunya. (online) Available at: <
http://www.who.int/denguecontrol/arbo-viral/other_arboviral_chikungunya/en/> [Accessed 10
July 2017].
WHO, 2017b. WHO, Yellow fever outbreak Angola, Democratic Republic of the Congo and Uganda
2016-2017. (online) Available at: <http://www.who.int/emergencies/yellow-fever/en/> [Accessed
12 July 2017].
WHO, 2017c. WHO, Zika situation report. (online) Available at:
<http://www.who.int/emergencies/zika-virus/situation-report/10-march-2017/en/> [Accessed 12
July 2017].
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EARLY HOST CELL - DENGUE VIRUS
INTERACTIONS: ROLE OF TLR2
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Introduction
Dengue virus
The virus particle of DENV has a spherical structure and is surrounded by a lipid envelope. Its
genome is about 11 kilobases of positive sense single-stranded RNA and it encodes three
structural proteins (C, E and prM) and seven nonstructural proteins (NS1, NS2A NS2B NS3 NS4A,
NS4B and NS5) that form the viral replication complex (Fields et al. 2013). DENV circulates as four
different serotypes (1-4). Serotypes have 60 to 70 % sequence similarity to at the genome and
amino acid levels (Fields et al. 2013). Infection by DENV leads to lifelong immunity in patients, but
due to antigenic variation immunity is acquired only for the infected stain. Virus transmission is
usually carried out between suitable hosts by Ae. albopictus or Ae. aegypti mosquitos (Chen &
Wilson 2010). The first isolates of each serotype of the virus have been recorded in Asia during
1940s and 1950s, but over time virus managed spread to the Americas, Australia and Africa
containing suitable vectors (see fig. 1). Today, DENV is endemic in Asia, Central and South America
and DENV has been reported in several European countries such as France, Croatia and Portugal.
Multiple virus serotypes also co-circulate in endemic areas around the world (Messina et al. 2014).
Figure 1 Spread of DENV serotypes across the globe during 1943 – 2013. Adapted from Messina et al. 2014.
DENV life cycle
DENV can enter cells via clathrin-mediated endocytosis or by a clathrin independent pathway,
depending on the host cell. Clathrin-mediated endocytosis is one of the most wells studied entry
pathways for DENV (Cruz-Oliveira et al., 2015). The virus attaches to a surface molecule on the cell
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and induces clathrin pit formation. Virus binding to the clathrin pit leads to vesicle formation and
subsequent fusion with endosomes. The fusion causes a decrease in pH which induces a
conformational change in the E protein. This conformational change enables the E protein to
interact with a receptor on the vesicle membrane and fuse with the cell (Cruz-Oliveira et al., 2015).
Following fusion, the viral genome is released and polyprotein synthesis initiates. The viral
polyprotein is cleaved by viral and host proteases. The initial round of viral genome translation is
followed by transcription of negative strand RNA that acts as a template for transcription of the
positive strand genome (Bäck and Lundkvist, 2013). After many rounds of translation, viral
structural proteins initiate virion assembly. First, capsid proteins interact with a positive sense RNA
genome and migrate to the endoplasmic reticulum to acquire a lipid membrane with E and prM
proteins (Bäck and Lundkvist, 2013). After the assembly of immature virions, particles are
transported to the Golgi for final posttranslational modifications. During processing in Golgi
complex, furin cleaves the prM protein resulting in mature M protein and propeptide. The
propeptide remains attached to M protein due to low pH, but it dissociates after viral particle is
released from a cell (see fig. 2) (Rodenhuis-Zybert et al, 2010). During the replication cycle,
infected cells produce three types of virions that differ in maturation state as well as structural
conformation. Fully mature particles - containing only cleaved M are smallest in the diameter, fully
immature particles, which only has prM on their surface, have the largest diameter, and partially
mature particles containing heterogeneous mix of mature M and prM are intermediate in size
(Lok, 2016).
Figure 2 DENV particle structure during the maturation process. During maturation process, immature DENV structure changes at neutral and low pH. After the loss of pr peptide, viral particles undergo structural changes to acquire different morphology. E protein is colored gray and pr propeptide is cyan. Adapted from Zhang et al. 2012.
Dengue fever
DENV infection can result in several outcomes. Half of DENV infections are asymptomatic (CDC,
2014). In the other half, symptoms of infection include rash, fever, headache, as well as muscle
and joint pains (Sigfrid et al. 2017). In a limited number of cases, symptoms can progress to severe
fever leading to extensive fluid loss, hemorrhage, and organ failure (Leong et al. 2007).After the
infection patients develop immunity to DENV. However, due to limited antigenic similarity
between the four serotypes of DENV long term-immunity only develops to the inoculated
serotype. Moreover the infection with different DENV serotype can lead to more severe disease.
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The immunological phenomenon called “original antigenic sin” that suggest that pre-existing
immunity could lead to more severe disease after infection with different DENV serotype
(Rothman 2011). “Original antigenic sin” refers to multiple cross reactive T cells acquired during
previous infections with virus and these cells are low specificity to DENV antigen. But these CD8+ T
cells can induce low antiviral response and leads to immune cell activation. Cross reactive T cells
are activated by infected dendritic cells and other antigen presenting cells. After virus recognition
by toll-like receptors or other RNA sensing proteins, infected cells initiate secretion of TNF-α and
type I interferon that induce T cell activity (Rodenhuis-Zybert et al, 2010; Pandey et al., 2014)
Dengue virus sensing and Toll-like receptors
Toll-like receptors (TLRs) are cellular receptors that recognize a variety of intracellular and
extracellular pathogen associated molecules known as pathogen-associated molecular patterns
(PAMPs). Ten TLRs have been identified in humans so far, and each of them recognizes a specific
group of PAMPs (O’Neill et al. 2013). The association of TLR with PAMPs induces an immune
response by activation of a complex signaling cascade. The signaling cascade starts after binding of
a TLR receptor ligand induces dimerization of the receptor, leading to binding of an adaptor
protein, to the intracellular domain of the TLR. Adaptor proteins include: myeloid differentiation
primary response protein 88 (MyD88), Toll/IL-1 receptor domain containing adaptor inducing
interferon-beta (TRIF) or TRIF related adaptor molecule (TRAM). Additional factors also participate
in the cascade, including IL-1R associated protein (IRAK) and tumor necrosis factor receptor-
associated factor 6 (TRFA6), which is a ubiquitin ligase E. TRAF6 activates the transforming growth
factor β-activated kinase 1 (TAK1) complex, which subsequently induces activation of NF-κB
essential modulator (NEMO). Following its activation, NEMO activates inhibitor kappa B (IκB) and
inhibitor kappa B kinase complex (IκBK). This leads to degradation of phosphorylated IκB6, which
ends up activating nuclear factor kappa B (NF-κB) and interferon regulatory factors. Activated NF-
κB is translocated into the nucleus, where it induces chemokine, cytokine and other antiviral gene
expression (see fig. 3) (Kawai & Akira 2006). Individual TLRs signal using different proteins in the
pathways. TLR7 uses MyD88 adaptor protein while TLR3 employs TRIF. Despite this, any pathway
TLR induced signal results in activation of inflammatory or antiviral response (Brown et al. 2011).
TLRs that typically recognize viral PAMPs include TLR3, TLR7/8 and TLR9. TLR3 recognizes double
stranded viral RNA, TLR7/8 recognizes single stranded viral RNA and TLR9 recognizes viral DNA
(Barton, 2007). Still, there are reports suggesting that TLR2 and TLR4 are capable of recognizing
not only bacterial ligands, but also viral proteins (Zhou et al. 2005; Georgel et al. 2007).
TLR2 and viral sensing
The most conventional role of TLR2 is to recognize triacyl or diacyl lipopeptides of bacterial origin
by forming a complex with TLR1 or TLR6. These complexes can then induce the signaling cascade
described culminating in NF-kB activation and subsequent transcription of antiviral genes involved
in production of interferon, proinflammatory cytokines and reactive oxygen/nitrogen species
(Barbalat et al. 2009). Although TLR2 sensing was originally thought to be restricted to molecules
of bacterial origin, an increasing body of evidence suggests that TLR2 recognizes a wider range of
ligands. TLR2 has been shown to trigger NF-kB activation upon sensing of measles (MV), human
13
cytomegalovirus, hepatitis C virus (HCV) and herpes simplex virus 1 (Bieback et al. 2002; Compton
et al. 2003; Chang et al. 2007; Kim et al. 2012). Furthermore, there have been reports that TLR2
and TLR4 are activated by the NS1 protein of DENV, and this activation leads to an immune
response (Chen et al. 2015; Modhiran et al. 2015).
Figure 3 The TLR signalling cascade. The TLR signalling pathway is initiated by ligand binding to the receptor and leading to a dimer formation to the receptor. Following this, adaptor proteins (myeloid differentiation primary-response protein 88 (MYD88), TIR domain-containing adaptor protein inducing IFNβ (TRIF) and TRIF-related adaptor molecule (TRAM)) binds the
intracellular domain of TLR (Toll–IL‑1‑resistance (TIR)). The interaction between TLRs and adaptor proteins triggers
downstream signalling cascades that induce complex formation between IL‑1R‑associated kinases (IRAKs) and the adaptor proteins TNF receptor-associated factors (TRAFs), which activates mitogen-activated protein kinases (MAPKs) and activation of
transcription factors such as nuclear factor‑κB (NF‑κB) and activator protein 1 (AP1). TLR signalling induces production of pro-inflammatory cytokines, and activates type I interferon response. Adapted from O’Neill et al. 2013.
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HEK-Blue hTLR2 cell lines
During this project we used commercially available HEK-Blue hTLR2 cells, Null2 and HEK-Blue TLR4
cell lines from Invivogen. The TLR cell lines overexpress TLR1/2/4/6 and CD14 as well as contain
reporter plasmid with secreted embryonic alkaline phosphatase (SEAP) under the control of an NF-
kB promoter (see fig. 4). Parental cell line HEK-Blue Null2 was used as a control because in has
endogenous levels of TLRs expression and contains the same SEAP reporter plasmid. HEK-Blue
cells lines are good model to study TLR activity as activation of TLRs leads to induction of NF-kB,
which activates transcription of SEAP reporter gene. SEAP levels in the supernatant correlates with
NF-kB activity and can therefore be used as an indirect measurement of transcription factor
activity, on a calorimetric assay. Mixing supernatant from HEK-Blue cells with Quanti-Blue media
leads to an enzymatic reaction catalyzed by SEAP, and induces a color change which can be
quantified using microplate reader (Invivogen, 2016).
.
Figure 4 Mechanism of SEAP assay. HEK293 cells were co-transfected with hTLRs and SEAP genes. Stimulation of TLR by a ligand causes induction of signalling cascade that results in activation of NF-κB transcription factor which activates SEAP transcription. The level of NF-κB activity can be quantified by using QUANTI-Blue media, which changes colour at presence of SEAP. Adapted from Invivogen 2016.
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The purpose of the study
Based on previous research about viral PAMPs sensing by TLR2, we sought to examine the role of
TLR2 in DENV sensing and antiviral response. My project focused on the analysis of DENV2
interaction with TLR2 and NF-κB activation. Specifically, I sought to address the following issues:
Role of endocytosis in NF-κB activation via TLR2
Impact of virion structure on the NF-κB activation via TLR2 signaling
How virus preparation origin affects TLR2 signaling to activate NF-κB
The second part of the study sought to determine whether TLR2 interaction is DENV2 specific. This
part of the project was focused on the following research questions
Is NF-κB activation via TLR2 DENV2 specific?
Is NF-κB activation via TLR2 unique to DENV or this is common between Flaviviruses?
16
Methods
Cell lines
Human embryonic kidney (HEK)-Blue™ TLR2 (InvivoGen, San Diego, CA) cell line was cultured in
Dulbecco's Modified Eagle's Medium (DMEM) (Life Technologies) (+glucose, sodium pyruvate,-
glutamine) with HEK-Blue™ Selection (1X), normocin™ (50ng/ml), 10% of fetal bovine serum (FBS),
mix of penicillin/streptomycin (P/S) (50 U/ml). HEK-Blue™ Null2 cell line was cultured in the same
media as HEK-Blue™ TLR2 cells but HEK-Blue™ selection was replaced with zeosin (100 µg/ml). Cell
lines were maintained according to manufacturer’s instructions and cultured at 37°C and 5% CO2.
Baby hamster kidney cell line was cultured in DMEM with glucose and glutamine, 10% FBS, 1%
HEPES, 1% P/S and 1% non-essential amino acids at 37°C and 5% CO2.
Compounds
Chlorpromazine hydrochloride (Sigma-Aldrich), dynasore hydrate (Sigma-Aldrich), pitstop (PS) 2
(Sigma-Aldrich), chloroquine (Sigma-Aldrich), wortmannin (W) (Sigma-Aldrich) and ammonium
chloride (NH4Cl) (Merck) stocks were diluted using FBS-free DMEM (Life Technologies) and used to
pretreat HEK-Blue™ TLR2 cells before infection with DENV. Cells were pre-treated with PS (60
µM), W (2µM) and NH4Cl (50 mM). As positive control, TLR2 agonists (PAM3CSK4 (PAM3) 25
ng/ml) and PAM2CSK4 (PAM2) 3 ng/ml) were used to stimulate HEK-Blue TLR2 cells to activate NF-
κB during experiments unless specified differently. HEK-Blue Null2 cells were stimulated with TNF-
α (50 ng/ml) during experiments unless specified differently. HEK-Blue-TLR4 cells were stimulated
with LPS (100 ng/ml) during DENV experiments. All the stimulants of HEK-Blue cells were bought
from Invivogen (San Diego, CA).
Virus stocks
During the experiments a panel of DENV were tested (see table 1). The majority of experiments
were carried out using DENV2 strain 16681. Also, purified immature virions from serotypes 1, 2
and 4 were investigated for ability to activate NF-κB 9 (see table 2).
Table 1 List of seed DENV serotypes and strains used during the study.
Serotype Strain DENV1 16007
Hawaii DENV2 16681
NGC TSVO1
DENV3 16562 H87
DENV4 1036 H241
17
Table 2 List of purified immature virions of DENV used during the study
Serotype Strain DENV1 16007 DENV2 16681 DENV4 1036
Table 3 List of Flaviviruses used during the study
Virus Strain West Nile (WNV) New York 99 Zika (ZIKV) French Polynesia
Secreted embryonic alkaline phosphatase assay (SEAP assay)
HEK-Blue™ TLR2 or HEK-Blue™ Null2 cells were seeded (50,000 cells/well) in 100 µl of media in 96
well flat bottom plates with DMEM media as described before. DENV stocks were diluted in FBS-
free DMEM. The total volume of the assay was 200 µl/well. HEK-Blue™ TLR2 and HEK-Blue™ Null2
cells were incubated at 37°C, 5% CO2 for 24 h.
Supernatants (50 µl) from stimulated cells were collected and mixed with Quanti-Blue™ medium
(150 µl) and incubated at 37° C 45 min and under UV-lamp for 15 min. OD was measured at 630
nm using Synergy HT plate reader (Biotek, Winooski, VT)
Viral RNA isolation
DENV RNA from infected cells supernatants was isolated using QIAamp Viral RNA Mini Kit (Qiagen,
Germany) according to supplier’s instructions.
Quantitative PCR
Isolated RNA from cell supernatants were used to make cDNA using Omniscript RT Kit (Qiagen,
Germany) using reagents at concentrations listed in table 1 and primers listed in table 2. cDNA was
amplified following these settings (25°C for 10 min, 37°C for 60 min and 93°C for 5 min). After
amplifications step cDNA was stored at -20° C before used for qPCR.
Table 4 Reagents and concentrations used for DENV cDNA production
Solution Stock concentration Concentration in master
mix
Reverse Transcriptase buffer 10x 1x
dNTP mix 5 mM (each) 0,5 mM (each)
Anti-sense primer 25 5 µM 0,5 µM
18
Omniscript RT 200 Units 10 Units
Water
RNA
Total
Table 2 Primers used for cDNA and qPCR
Primer Sequence DENV strain
25 (Reverse) TGC AGC AAC ACC ATC TCA TTG DENV2 16681 strain, PR159 strain, NGC strain
24 (Forward) ACA GGT TAT GGC ACT GTC ACA AT DENV2 16681
cDNA was used quantitate PCR to determine viral particle titers using duplicates using 96 well
plate. Viral titers were determined using individual standard curve using serial dilution of DENV
transcript. The limit of detection was set at 250 DNA/ml. The master mix for qPCR was prepared as
listed below (see Table 3) using HotStarTaq (Qiagen, Germany). StepOnePlus™ Real-Time PCR
System (Applied Biosystems, Foster City, CA) was used to carry out qPCR using following settings -
60°C for 120 sec, 95°C for 900sec and 40 cycles of 95°C for 15sec and 60°C for 60sec.
Table 3 Reagents and concentrations used for qPCR.
Solution Stock concentration Concentration in master
mix
cDNA
Fluorogenic probe
(DEN16681/NS5prb)
5 µM
200 nM
Sense primer (primer 24) 5 µM 900 nM
Anti-sense primer (primer 25) 5 µM 900 nM
dNTPs 10 mM 0,2 mM
Hot star Taq 5 Units / µl 0,5 U
10* reaction buffer 1*
MQ
Total
19
Statistical analysis
The statistical analysis of was carried out using GraphPad Prism 5 (San Diego, CA). One-way Anova
and T test statistical tests were utilized for the analysis of experimental data. A p-value of 0.05 was
considered to be statistically significant. Stars in the figures indicate following *=p<0.05,
**=p<0.01 and ***=p<0.001
20
Results
DENV activates NF-κB via TLR2
Purified, UV-inactivated and standard preparation of DENV2 from C6/36 were tested for NF-κB
activation HEK-Blue TLR2 cells. Purified virus and UV-inactivated virus preparations were used to
establish that virion is interacting with TLR and not viral proteins. It was observed that DENV2
samples induced significantly higher NF-κB activity via TLR2 than unstimulated cells (p<0.001). The
samples of the virus induced similar NF-κB activity as it was observed after stimulation with TLR2
agonist. Also NF-κB activation was replication independent as UV-inactivated non-replicating virus
induced identical activity as live DENV (see fig. 4A). In order to ensure that DENV activates NF-κB
via TLR2, TLR2 was blocked. The blockage caused a severe decrease in NF-κB activity after virus
stimulation (p<0.001) (see fig. 4B). As TLR2 forms heterodimers with TLR1 or TLR6 as well as
interacts with CD14 to induce downstream signalling, TLR1/6 and CD14 were blocked in HEK-Blue
TLR2 cells. It was observed that blockage of TLR6 and CD14 caused most pronounced decrease in
NF-kB activation after DENV2 infection (p<0.001), while blockage of TLR1 did not interfere with
transcription factor activation after the virus addition (see fig. 4C). TLR1 and CD14 blockage
induced most significant decrease in NF-κB for PAM3 stimulated HEK-Blue TLR2 cells and TLR6
blockage had the most significant effect on NF-κB activity in PAM2 stimulated HEK-Blue TLR2 cells
(see fig. 4C).
Figure 4. Role of TLR2 and TLR2-mediated NF-κB activation in DENV-2 infection. HekBlueTLR2™ cells (Invivogen) were exposed to TLR2 agonists (PAM3, PAM2) or DENV at a multiplicity of genome equivalent of 1000 or its equivalent MOI of 30) in the presence or absence of TLR2/1/6 and CD14 blocking antibodies. The NF-κB activation was measured at 24hpi. CC= cellular control(non-treated cells), UV-DENV-2= UV-inactivated, pDENV-2 = purified DENV particles. Graphs represent mean with SD of three independent experiments and data was analysed using One-way Anova and T-test.
21
Endocytosis is important for NF-κB activation via TLR2
Endocytosis inhibition reduces NF-κB activation via TLR2
As TLR2 can initiate signaling cascade from cell surface or endosome after internalization, we
wanted to examine where the signal is coming from to activate NF-κB transcription factor. HEK-
Blue TLR2 cells were treated with endocytosis inhibitors (PS, W and NH4Cl) before stimulation
with DENV. Endocytosis inhibitors were selected after a cytotoxicity assay and previous experience
in the laboratory (see supplementary information fig. 1). The pretreatment with PS and NH4Cl
have reduced level of NF-κB activity after stimulation with DENV2 (p<0.01 and p<0.001
respectively). Pretreatment with W had no effect as it was expected because W interferes with
actin remodeling, which is not necessary for TLR2 internalization (see fig 5A). However,
endocytosis inhibitor treatment did not affect DENV2 replication as viral titers were almost
identical (see fig. 5B). Also, the rate of DENV infection in HEK-Blue TLR2 cells was very low and it
was difficult to assess the effect of endocytosis inhibitors (supplementary information fig. 2).
Figure 5. NF-κB activation via TLR2 by DENV after treatment with endocytosis. A and B HEK-Blue TLR2 cells were pretreated with endocytosis inhibitors (PS (60 µM), W (2µM) and NH4Cl (50 mM) before the exposure to DENV2 (strain 16681 MOG 3500). A, NF-κB activity was measured by SEAP assay after 24 h B, Viral RNA were extracted from supernatants of DENV infected HEK-Blue TLR2 cells and viral titers were quantified using two-step quantitative RT-PCR. Results are presented as a mean ± SEM.A N=3 and B N=2. Statistical analysis – One-way Anova.
DENV produced in primary cells activate NF-κB
Macrophage and Dendritic cell derived DENV activate NF-κB via TLR2 less than C6/36 derived
DENV
In order to examine the effect of virus origin to activation of NF-κB via TLR2 DENV2 grown in
primary macrophage (MQ) and dendritic cell (DC) cultures was tested in HEK-Blue TLR2 cells.
DENV2 grown in DC cells induced activation of NF-κB via in HEK Blue TLR2 cells (p<0.001).
DENV
DENV +
PS
DENV +
W
DENV +
NH4C
l
0
20
40
60
80
100
NF
- B
acti
vit
y /
%
** ***
DENV
DENV +
PS
DENV +
W
DENV +
NH4C
l
0
2.0100 9
4.0100 9
6.0100 9
8.0100 9
1.0101 0
Gen
om
e c
on
tain
ing
part
icle
s /
ml
A B
22
However, DC origin DENV2 induced considerably lower NF-κB activity than C6/36 origin DENV
(p<0.001) (see fig. 6A).
To ensure that NF-κB activity was induced by DENV2 interaction with TLR2 and not a soluble factor
in the viral preparation, MQ and DC origin viruses were tested in HEK-Blue Null2 cells for NF-κB
activation. DENV2, grown in DC and MQ cell cultures, caused a significantly lower increase in NF-
κB activity in comparison to TNF-α treated cells (p<0.001). Furthermore cells treated with DENV
from DC and MQ cells had higher NF-κB activity than non-treated cells (see fig. 6B).
Figure 6. NF-κB activation by DENV grown in primary cells. HEK-Blue TLR2 (A) HEK-Blue Null2 (B) cells were treated with DENV2 produced in macrophages (strain 16681, genome multiplicity factor (MOG) 1000) and dendritic cells (strain 16681 MOG 1000). NF-κB activity was quantified using SEAP assay 24hpi. Bars represent mean ± SEM. N=3. NF-κB activity was normalized to PAM2 (A) and TNF-α (B) treated cells. Statistical analysis One-way Anova.
Immature viral preparations activate NF-κB
Immature Pan-DENV activate NF-κB via TLR2
During an infection, viral particles can differ in their maturity state and we tested purified
immature DENV particles from serotypes 1, 2 and 4 for activation of NF-κB via TLR2. Immature
viral particles activated NF-κB via TLR2 to notable higher level than untreated cells (p<0.001).
Immature virions from all three serotypes activated NF-κB to similar extent (see fig. 7A). The
similar effect on the transcription factor was surprising because due to low DENV1 immature
particle titers a smaller amount of DENV1 viral particles was used (MOG 250 and MOG 125).
DENV1/2/4 immature particles induced similar NF-κB activity to standard DENV2 virus preparation
(see fig. 7A) We also tested lower ratios of DENV2 immature particles for NF-κB activation and we
observed decreasing activity of NF-κB as cell cells were treated with lower MOGs (see
supplementary information fig. 4).
TLR1/2/6 and CD14 were blocked to examine essential receptors for immature virions to induce
NF-κB activation in HEK-Blue TLR2 cells. Data collected showed that blockage of TLR2 caused most
PAM
2CC
DENV2
C6/
36
DENV2
DC
DENV2
MQ
0
20
40
60
80
100
HEK-Blue TLR2
***
***
***
NF
- B
acti
vati
on
/ %
TNF- C
C
DENV2
DC
DENV2
MQ
0
20
40
60
80
100
HEK-Blue Null2
***
NF
- B
acti
vati
on
/ %
**
A B
23
pronounced reduction of NF-κB activity in comparison to untreated cells (p<0.001) (see fig. 7B).
Also the blockage of TLR6 and CD14 caused a considerable decrease in the NF-κB activity after
virions treatment (p<0.001) (see fig. 7B).
Figure 7. NF-κB activation by purified immature DENV particles. HEK-Blue TLR2 cells were treated with purified immature DENV1 (genome multiplicity factor (MOG) 250, strain 16007), DENV2 (MOG 1000, strain 16681) and DENV4 (MOG 1000, strain 1036) (A). HEK-Blue TLR2 cells were treated with purified immature DENV2 (MOG 1000, strain 16681)or at presence of TLR2/1/6 and CD14 blocking antibodies (B). NF-κB activity was quantified with SEAP assay 24 post treatments. Graphs show mean ± SEM. N=3. Data has been normalized to PAM2 (A) and PAM3 (B) treated cells. Statistical analysis One-way Anova.
Multiple DENV strains activate NF-κB via TLR2 in serotype independent manner
The panel of viruses representing all four serotypes of DENV were assembled and tested for their
ability to activate NF-κB via TLR2 in HEK-Blue TLR2 cells. At least two different strains for each
DENV serotype were tested. It was observed that all DENV serotypes can activate NF-κB via TLR2
to a variable degree. Highest levels of NF-κB activity were induced by DENV1, DENV2 and DENV3
strains (p<0.001) (see fig. 8A). Virus from DENV serotype 4 induced lower a lower activity of NF-κB
in comparison to other serotypes, but still NF-κB activity was higher than untreated cells (p<0.01)
(see fig. 8A). In summary, NF-κB was activated by one strain tested from DENV1/3/4 and two
strains from DENV2.
In order to confirm that NF-κB activity in HEK TLR2 cells was induced by virus interaction with
TLR2, we selected DENV from all four serotypes for virus ability to induce NF-κB activity by other
nonrelated pathways to TLR2 signaling cascade. Viruses were tested in HEK-Blue Null2 and HEK-
Blue TLR4 cell lines. All four viruses induced significantly lower NF-κB activity than in TNF-α treated
HEK-Blue Null2 cells (p<0.001). Moreover, NF-κB activity was indistinguishable between DENV
exposed cells and non-treated HEK-Blue Null2 cells (see fig. 8B). Similarly, NF-κB activity induced
PAM
2CC
DENV1
prM
DENV2
stan
dard p
repar
atio
n
DENV2
prM
DENV4
prM
0
20
40
60
80
100HEK-Blue TLR2
NF
- B
acti
vati
on
/ %
***
***
******
***
PAM
3CC
DENV2
prM
Anti
TLR1
Anti
TLR
2
Anti
TLR6
Anti
CD14
0
20
40
60
80
100HEK-Blue TLR2
NF
- B
acti
vati
on
/ %
**
***
***
***
A B
24
by DENV was substantially lower than in LPS treated HEK-Blue TLR4 cells (p<0.001). In addition,
NF-κB activity in DENV exposed cells was equivalent to NF-κB baseline observed in non-treated
HEK-Blue TLR4 cells (see fig. 8C).
Figure 8. NF-κB activation by DENV via TLR2, but not TLR4. HEK-Blue TLR2 (A) cells were exposed to DENV (DENV1 16007 genome multiplicity factor (MOG) 3500, DENV1 Hawaii infectious multiplicity factor (MOI) 0,06, DENV2 16681 MOG 3500 , DENV2 NGC MOI 50, DENV2 TSVO1 MOI 2, DENV3 16562 100 µl of virus preparation, DENV3 H87 MOG 1100, DENV4 1036 MOG 3500, DENV4 H241 MOI 0,65. NF-κB activity was measured using SEAP assay 24 hpi. Bars show mean ± SD. N=3 (DENV1 16007, DENV2 16681, DENV4 1036) and N=2 (DENV1 Hawaii, DENV2 NGC, DENV2 TSVO1, DENV3 16562, DENV3 H87, DENV4 H241). Statistical analysis One-way Anova. HEK- Blue Null2 (B) and HEK-Blue TLR4 (C) cells were exposed with DENV (DENV1 16007 MOG 3500, DENV2 16681 MOG 3500, DENV3 H87 MOG 2500, DENV4 1036 MOG 3500). Bars represent mean ± SEM. N=3. Statistical analysis One-way Anova. NF-κB response was normalized to PAM3 (A), TNF-α (B), LPS (C) cells.
TNF- C
C
DENV1
DENV2
DENV3
DENV4
0
20
40
60
80
100HEK-Blue Null2
*** *** *** ***
NF
- B
acti
vit
y /
%
LPS
CC
DENV1
DENV2
DENV4
0
20
40
60
80
100HEK-Blue TLR4
*** *** ***
NF
- B
acti
vit
y /
%
A B
C
HEK-Blue TLR2
PAM
3CC
DEN
V1
1600
7
DEN
V1
Haw
aii
DEN
V2
1668
1
DEN
V2
TSVO1
DEN
V2
NGC
DEN
V3
1656
2
DEN
V3
H87
DEN
V4
1036
DEN
V4
H24
1
0
20
40
60
80
100*** *** *** *** ***
**NF
- B
acti
vit
y /
%
25
NF-κB activation via TLR2 is DENV specific and not for WNV, ZIKV
Only DENV activates NF-κB via TLR2 out of tested viruses
To investigate if other flaviviruses can activate NF-κB via TLR2 we tested DENV together with ZIKV
and West Nile virus (WNV) in HEK-Blue TLR2 cells. Only DENV induced considerable activation of
NF-κB via TLR2 (p<0.001) (see fig. 9A). ZIKV and WNV induced level of NF-κB activation was similar
to activity of the transcription factor observed in non-treated cells.
We concluded that the induction of NF-κB was mediated by TLR2. As HEK-Blue Null2 cells treated
with DENV, ZIKV and WNV had considerably lower NF-κB activity than it was observed in TNF-α
treated HEK-Blue Null2 cells (p<0.001) (see fig. 9B).
Figure 9. ZIKV and WNV did not activate NF-κB via TLR2. HEK-Blue TLR2 (A) and HEK-Blue Null2 (B) cells were infected with DENV2 (16681, infectivity multiplicity factor (MOI) 10), ZIKV (French Polynesia, MOI 10) and WNV (New York 99, MOI 10). NF-κB activity was quantified using SEAP assay 24 hpi. Bars show mean ± SD. N=3. Statistical analysis One-way Anova. Data was normalized to PAM3(A) and TNF-α (B) stimulated cells.
PAM
3 CC
DENV
ZIKV
WNV
0
20
40
60
80
100
HEK-Blue TLR2
***
***
NF
- B
acti
vit
y /
%
TNF- C
C
DENV
ZIKV
WNV
0
20
40
60
80
100
HEK-Blue Null2
*** *** ***
NF
- B
acti
vit
y /
%
A B
26
Discussion
A goal of this project was to on elucidate viral and host factors essential for NF-κB activation via
TLR2. Our results showed that multiple strains of DENV virus activate NF-κB via TLR2, and that
CD14 and TLR6 play partial roles in NF-κB activation. We showed that NF-κB activation might
depend on the cell type in which virus was grown and that virion structure might impact the
activation of NF-κB. Finally, we showed that ZIKV or WNV did not activate NF-κB.
Our results showed that DENV2 virus activates NF-κB via TLR2 in a replication-independent
manner. For most of our assays, non-purified standard DENV preparations were used and it could
be assumed that these preparations contained secreted viral proteins such as NS1. Furthermore,
we found that incubation of DENV1, 2 and 4 viruses with HEK-Blue TLR4 cells did not lead to any
activation of NF-κB. Our data contradicts to several publications that showed DENV NS1 protein
can activate TLRs. Using human peripheral blood mononuclear cells (PBMCs) and knock-out mouse
models, Chen and colleagues showed that DENV NS1 protein can be recognized by TLR2/6
heterodimer and TLR6 was proposed to be the key recognition receptor (Chen et al. 2015). During
this study, Chen and colleagues treated PBMCs with individual DENV proteins and analyzed levels
of secreted IL-6 and TNF-α as markers for NF-κB activation. After blockage experiments and in vitro
study in HEK 293 transfected cell lines, they concluded that NS1 protein is inducing NF-κB via
TLR2/6 heterodimers (Chen et al. 2015). In another study, Modhiran and collaborators showed
that interactions between and TLR4 activated transcription of pro-inflammatory genes mouse
macrophages and human PBMCs, and lead to disruption of the endothelial monolayer in vitro.
They also showed that TLR4 antagonists could minimize capillary leakage in vivo during DENV
infection. These results led them to conclude that DENV2 NS1 can by sensed by TLR4 (Modhiran et
al. 2015).
However, both studies used only purified NS1, and did not use live DENV or inactivated virus for
direct NF-κB activation assays. Furthermore, both groups used HEK cells transfected with TLRs for
in vitro assays. Modhiran and collaborators used HEK 293 cells transfected with TLR4 and TLR2
expressing plasmids to confirm NS1 recognition by TLR4. For TLR2 experiments, cells were
transfected with TLR2 alone, whereas TLR2 induces downstream signalling only after dimerization
with with TLR1 or TLR6 (Bagheri et al. 2014). Importantly, HEK 293 cell lines express low levels of
TLR6 and TLR1 (Buwitt-Beckmann et al. 2006). Therefore, it could be assumed that lack of IL-8
transcription could be caused by insufficient levels of TLR2 co-receptors.
To analyse the effect of DENV on NF-κB via TLR2 we used a variety of DENV grown in different cell
types. We have shown that virions produced in dendritic cells (DCs) can induce NF-κB activity, but
to a lower extent than standard virus preparation from C6/36 cells. The difference in NF-κB activity
could be a result of variation in virus grown in mosquito cells and virus grown in primary human
cells. Several studies have shown that viral preparations from mosquito cells contain high levels of
high immature viral particles (Junjhon et al. 2008; Zybert et al. 2008; Junjhon et al. 2010). On the
other hand, virus preparations from DC cells had been shown to possess low immature DENV
27
virion content. DC viral preparations have also been observed to be less efficient to infect mature
DCs than their counterparts grown in mosquito cells or mammalian cells lines (Dejnirattisai et al.
2011).
Based on these reports, purified immature DENV1, 2 and 4 viral particle preparations were tested
for induction of NF-κB activity via TLR2. We have observed similar NF-κB activity for all three
serotypes as in standard viral preparation. As mentioned in the introduction, immature DENV
particles have distinct morphology in comparison to mature particles. This could therefore suggest
that viral particle fusion might not be necessary to initiate the signalling cascade for NF-κB
activation. As it has been shown that immature viral particles were incapable to fuse with target
cells and had drastically reduced infectivity (Zybert et al. 2008). Immature viral particles could
most likely induce NF-κB activity by interacting with TLR2 on the cell surface. As mature and
immature viral particles differ in size and morphology, more studies are required to investigate the
mechanism of DENV recognition by TLR2.
Our experiments with immature viral particles showed that NF-κB can be activated by four
serotypes DENV. We have observed that at least one of the strains tested from DENV serotypes 1,
3 and 4 activated NF-κB via TLR2. It is interesting to highlight that DENV serotypes have only 65 –
70 % sequence similarly across the genomes and surface proteins (Fields et al. 2013). This suggests
that the motif recognized by TLR2 is not sequence-specific, but it has a distinct structure possibly
similar to conventional TLR2 ligands. Further supporting this hypothesis, several distantly related
RNA and DNA viruses have been shown to activate inflammatory immune responses via TLR2
(Bieback et al. 2002; Compton et al. 2003; Chang et al. 2007; Kim et al. 2012). So far it has been
suggested that TLR2 can recognize viral glycoproteins. In case of measles virus (MV), hemagglutin
glycoprotein (H protein) has been shown to interact with TLR2, and a mutation at position 481 of
H protein abolished the interaction with TLR2 which abolished immune response activation and
secretion of IL-6 (Bieback et al. 2002). It has also been shown that HCV core and NS3 proteins were
able to interact with TLR2 and activate NF-κB. Researchers determined that the interaction was
conformation dependent, and that amino acids 2 to 122 of core protein, and 1450 to 1643 in NS3
protein were critical motifs for TLR2 interaction (Dolganiuc et al. 2004). The regions of the HCV
proteins highlighted in Dolganiuc study were responsible for RNA binding (core protein) and one of
the subdomains of RNA helicase/NTPase domain (NS3 protein) (Moradpour & Penin 2013). Our
data suggested that immature particles or prM protein on the surface of DENV virions could
interact with TLR2 and activate NF-κB.
We also assessed the potential of ZIKV and WNV to induce NF-κB via TLR2. We found that the
strains of ZIKV and WNV we tested did not activate NF-κB via TLR2. As immature DENV virion role
in NF-κB activation could be speculated, researchers have shown that WNV infected cells produce
immature particles and these particles differ in size to mature particles (Zhang et al 2007). This
could hint that other flaviviruses could activate NF-κB via TLR2 depending on immature viral
particle content in viral preparation.
28
Conclusion
As DENV has spread across the globe and infects large numbers of populations in South America
and South East Asia, DENV is becoming a major health concern. During my project I have studied
factors of DENV that were important for NF-κB activation via TLR. In summary, we showed that
DENV virion can activate NF-κB via TLR2, in contrast to previously published studies. We also
showed that NF-κB activation is replication and virion origin independent. Furthermore, we
observed that NF-κB was activated in serotype independent manner by multiple strains. However,
we did not observe NF-κB activation by other Flaviviruses, but we do not exclude possibility that
they could activate NF-κB. We could also show that endocytosis of TLR2 participate to limited
extent in NF-κB activation. A better understanding of the virus interaction with host innate
immune system will provide better insight on mechanisms of virus induced pathology.
29
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32
Supplementary information
Endocytosis inhibitor cell viability assay
Figure 1 HEK-Blue TLR2 cell viability after 24h treatment with endocytosis inhibitors. HEK-Blue TLR2 cells were treated with endocytosis inhibitors (chloropromazine (CPZ) (20 µg/ml, 10µg/ml, 5 µg/ml), pitstop (PS) (30µM, 20µM, 10 µM), ammonium chloride (NH4Cl) (30µM, 10µM, 1µM), dynasore (DYN) (80µM, 40µM, 20µM) and chloroquine (CHQ) (50 µg/ml, 20µg/ml, 5µg/ml)). Cell viability was measured with tryphan blue stain after 24h. Bars represent mean ± SEM. N=2. Data has been normalized to untreated cells.
33
Figure 2 DENV infection in HEK-Blue TLR2 cells. HEK-Blue TLR2 cells were pretreated with endocytosis inhibitors PS (60 µM), W (2µM) and NH4Cl (50 mM). Cells were infected with DENV2 (strain 16681) (MOG 3500) and intracellular DENV2 antigen was quantified using FACS 24 hpi. Dot plots represent rate of infection (frequency of 4G2 positive cells). N=1.
Mock DENV
Side Scatter
Mock
Pitstop
Wortmannin
NH4Cl
34
Figure 3 NF-κB activity after pretreatment with endocytosis inhibitors. HEK-Blue TLR2 cells were pretreated with endocytosis inhibitors PS (60 µM), W (2µM) and NH4Cl (50 mM) and stimulated with PAM3 (A, 25 ng/ml), PAM 2 (0.5 ng/ml B) (3 ng/ml C). NF-κB activity was measured using SEAP assay after 24h. Bars represent mean ± SEM. N=3 (A and B) N=1 (C). Statistical analysis One-way Anova.
Figure 4. NF-κB activation via TLR2 by purified DENV2 immature virus preparation Immature. HEK-Blue TLR2 cells were treated with PAM3CSK4 and immature DENV2 virions (genome multiplicity factors (MOG) 100, 250 and 100). NF-κB activity was measured using SEAP assay after 24h. Bars represents mean ± SEM. Results were normalized to PAM3CSK4 treated cells. Statistical analysis One-way Anova.
PAM
3CSK4
PAM
3 +
PS
PAM
3 +
W
PAM
3 +
NH4C
l
0
20
40
60
80
100
NF
-kB
acti
vati
on
/ %
of
co
ntr
ol
*
PAM
2CSK4
PAM
2 +
PS
PAM
2 +
W
PAM
2 +
NH4C
l
0
20
40
60
80
100
NF
-kB
acti
vati
on
/ %
of
co
ntr
ol
PAM
2
PAM
2 +
PS
PAM
2 +
W
PAM
2 +
NH4C
l
0
20
40
60
80
100
NF
- B
acti
vati
on
/ %
*** ***
A B
C
CC
PAM
3
MOG 1
000
MOG 2
50
MOG 1
00
0
20
40
60
80
100
n=1
NF
- B
acti
vati
on
/ %
***
***
***
***
35
EFFECT OF CHIKUNGUNYA
INFECTION ON MONOCYTE
SUBSETS DYNAMICS
36
Introduction
Chikungunya virus
Chikungunya virus (CHIKV) is an enveloped, positive sense single stranded RNA virus from genus
Alphavirus in the Togaviridae family. Its viral genome, which is almost 12 kilobases in length,
encodes two viral polyproteins. The first polyprotein encodes four nonstructural proteins (nsP1,
nsP2, nsP3 and nsP4) and the second encodes five structural proteins (C, E1, E2, E3 and 6K) (CHIKV
information, 2017). CHIKV E2 and E1 glycoproteins form dimers that mediate virus attachment
onto a cell. Following attachment, the virus enters the cell via clathrin mediated endocytosis. Entry
is followed by the uncoating of the virus genome and viral protein translation. Viral nonstructural
proteins and host cell proteins initiate transcription of negative strand RNA molecule (Solignat et
al., 2009). The negative strand RNA molecule acts as a template for subgenomic RNA that is used
for structural protein translation. Viral glycoproteins are translated by membrane-associated
ribosomes and post-translationally modified by host factors. The viral particles then assemble at
the plasma membrane of the infected cell, where the new virions bud off (Burt et al., 2017).
Based on the geographical distribution, CHIKV has been grouped with Old World alphaviruses such
as Sindbis virus and Semliki Forest virus (Zuckerman et al. 2008). CHIKV is an arthropod-borne
virus which infects arthropods as well as mammals. For a long time, Aedes aegypti mosquitos were
the sole arthropod vector for CHIKV, but recently the virus has adapted to Aedes albopictus
mosquitos (Simon et al. 2011). Adaptation to the new vector has enabled the virus to spread from
Africa to Indian Ocean countries, South Pacific islands, and the Caribbean causing epidemics in
more than 40 countries (see fig. 1) (Burt et al. 2012; Dupont-Rouzeyrol et al. 2012; Van Bortel et
al. 2014). There were even CHIKV outbreaks in France and Italy caused by virus imported from
endemic regions which was picked up by local mosquito populations (Rezza et al. 2007; Gould et
al. 2010). A 2006 epidemic in Reunion Island, France’s overseas department in the Indian Ocean
was one of the largest endemic outbreaks affecting a third of the entire island population
(Schwartz & Albert 2010). A few years later, the virus was reintroduced in the island and caused
small outbreak in western part of the island (D’Ortenzio et al. 2011).
Chikungunya fever
CHIKV infection presents itself with as a rash, fever and polyarthritis symptoms at the acute stage.
Arthralgia caused by the virus affects small joint in extremities, but it also can affect larger joints.
In most cases, the acute stage symptoms disappear after 10 days post-infections (Jaffar-Bandjee et
al. 2010). Several studies were carried out to study effects of CHIKV infection and development of
chronic infection after the outbreaks in Reunion island, especially with a focus on rheumatic
disorders (Fritel et al. 2010; Marimoutou et al. 2012; Gérardin et al. 2013; Schilte et al. 2013;
Gérardin et al. 2014; Ramful et al. 2014; Javelle et al. 2015; Marimoutou et al. 2015; Oliveira et al.
2016; Bouquillard et al. 2017). One of the studies analyzed neurocognitive outcome in children
who had a perinatal mother-to-child CHIKV infection. Infected children had neurocognitive delays
and deficiencies and in some cases children developed microcephaly and cerebral palsy (Gérardin
et al. 2014).
37
Figure 1 CHIKV strain distribution across the globe. Adapted from Burt et al. 2017
CHIKV and arthralgia
After the acute stage, arthralgia can persist for an extended period of time and become chronic, as
Schilte and colleagues have observed. The authors showed that arthralgia can reoccur over a
period of three years with periods of recovery. They proposed that the condition was caused by
local inflammatory conditions rather than systemic one (Schilte et al. 2013) A study carried out by
Marimoutou and colleagues assessed quality of life and morbidity of 297 patients thirty months
after infection and they observed that patients who were infected with CHIKV had more severe
and intense rheumatic state than patients who were not infected with CHIKV (Marimoutou et al.
2012). A follow up analysis of the cohort, showed that CHIKV-induced musculoskeletal disorders
persisted up to six years after initial exposure to the virus (Marimoutou et al. 2015). Age, severity
of initial arthralgia and presence of CHIKV-specific antibodies were proposed as markers of CHIKV
infection induced rheumatism (Gérardin et al. 2013). Deeper clinical analysis of arthralgia patients’
post-CHIKV infection showed no presence of CHIKV in joint fluid or synovial tissue, but some
patients’ conditions closely resembled rheumatoid arthritis (Bouquillard et al. 2017). At the
molecular level, patients with chronic CHIKV-induced arthralgia had higher levels of IL-6 and
granulocyte macrophage colony-stimulating factors (GM-CSF) than patients who fully recovered
(Chow et al. 2011). IL-12 was also observed elevated in patients with chronic CHIKV infections as
well as extensive cell death and tissue lesions visible at synovial tissues (Hoarau et al. 2010). IL-6,
IL-12 and GM-CSF have been associated with macrophage activation and recruitment, and could
be essential for chronic stage CHIKV infection (Porcheray et al. 2005).
Monocytes and the immune response
As a part of the immune system, monocytes are recruited to the site of infection and directly
participate in antimicrobial action at the site of infection (Serbina et al. 2008). There are three
main monocyte subsets in humans: classical (CD14+CD16-), intermediate (CD14++CD16+) and non-
38
classical (CD14+CD16++) (see fig. 2). These subsets can also be identified using additional markers
which function as adhesion molecules or chemokine receptors. Classical monocytes also express
CCR2 and CD62L, intermediate monocyte possess HLA-DR and CD74, and non-classical monocytes
express Siglec10 and CD43 (Tacke & Randolph 2006; Wong et al. 2012). Additionally, monocyte
subgroups differ in their functions. Classical monocytes are more efficient in phagocytosis, while
intermediate monocytes are more potent ROS producers. Non-classical monocytes are unique in
their sentinel function (Wong et al. 2012) Studies have shown that monocyte subsets differ in
their potential to secrete IL-1β, IL-6 and TNF-α. Classical monocytes were the most potent
producers of the cytokines while non-classical monocytes were least efficient. During steady state
(non-inflammatory condition) monocytes act as a source of tissue for resident dendritic cells (DC)
and macrophages (MQ). Monocyte subsets differ in their potential for differentiation to dendritic
cells (DC) and macrophages (MQ). Classical and intermediate monocytes are able to differentiate
into DC, but classical monocytes are the main source of monocyte origin DCs. All three monocyte
subsets can develop to MQ (Boyette et al. 2017)
Figure 2 Features of human monocyte subsets. Classical monocytes (CD14++
CD16-) represent the main population of circulating
monocytes, that secrete immune suppressive IL-10 upon LPS stimulation. Intermediate (CD14++
CD16+) and non-classical
monocytes (CD14+CD16
++) comprise a minority of all monocytes and have unique additional surface markers. Intermediate and
non-classical monocytes produce a variety of inflammatory cytokines after stimulation with TLR4/7 agonists. FAC plot represents typical distribution of human monocyte subsets. Adapted from Zimmermann et al., 2012.
Monocytes subsets during viral infections
It was shown for several other viruses that the dynamic of monocyte population shifts upon virus
infection. A study carried out for influenza A virus showed a decrease in CD14 positive monocyte
population, while CD16 positive monocyte remained stable. The authors suggested that the
decrease in CD14 positive monocytes was caused by influenza virus induced apoptosis (Hoeve et
39
al. 2012). Monocyte populations might shift in a virus-specific manner, as it was observed that
HIV-1 induces expansion of non-classical (CD14+CD16++) monocyte expansion during in vitro
infections and in AIDS patients (Thieblemont et al. 1995). On the other hand, infection with DENV
induced expansion of the intermediate monocyte subset (CD14++CD16+) in dengue fever patients,
confirmed by in vitro infections of healthy donor PBMCs. The expansion of the intermediate
monocyte subset has been associated with early stage of DENV infection (Kwissa et al. 2014).
Chikungunya virus and monocytes
In the light of recent CHIKV epidemics in regions around the Indian Ocean, researchers have
attempted to determine tissue tropism of CHIKV strains that circulated during recent outbreaks. It
was observed that human epithelial and endothelial cells as well as primary fibroblasts and
monocyte derived macrophages were susceptible to CHIKV infection and were able to sustain virus
growth. The same study also showed that CHIKV had a cytopathic effect on infecting cells, but was
also sensitive to type I and II interferon response (Sourisseau et al. 2007). Studies carried out in
adult cynomolgus macaques showed that macrophages, dendritic cells and endothelial cells were
susceptible to CHIKV infection. Furthermore, CHIKV was able to establish a reservoir in
macrophages during later stages of infection (Labadie et al. 2010). Several other studies carried
out in human PBMCs showed CHIKV infection in monocytes using FACS (Her et al. 2010; Teng et al.
2012). However, as researchers used only CD14 as a marker to define monocyte populations, it
remained to be determined whether the virus infects all three monocyte subsets or was specific to
different subsets.
40
The purpose of the study
Previous studies showed that DENV and HIV infections affect monocyte population dynamics in
vivo. Furthermore, studies on about CHIKV infection in monocytes left unanswered questions
about the effect on monocyte subset polarization and overall viability of PBMCs. Unpublished data
from the laboratory suggested that CHIKV infection affects cell viability and monocyte subset
distribution. In order to explore these observations in more detail my project was focused to
answer the following research questions:
Does CHIKV infection modulates the viability if PBMCs and monocyte?
Does CHIKV infection modulate monocyte subsets’ distribution?
How the effect of CHIKV infection on monocyte subset shift compares to TLRs agonist
stimulation?
How susceptible are monocytes in context of PBMCs for CHIKV infection?
How TLRs agonist stimulation affect PBMCS viability and monocyte subset distribution after
CHIKV infection?
The listed questions were answered using healthy donor PBMCs, which were characterized using
FACS.
41
Methods
Primary cell isolation
PBMCs Buffy coat (Sanquin Bloodbank, Groningen, Netherlands) was transferred to Falcon tube
(50ml) and mixed at ratio of 1:3 with RPMI 1640 (+HEPES) (Gibco). The diluted buffy coat was
place on Ficoll (16 ml) in Falcon tube. The tubes with diluted buffy coat and Ficoll-Hypaque were
centrifuged at 1569 rpm and 21°C for 30 min. PBMCs layer was collected to new Falcon tube and
mixed with RPMI and centrifuged for 10 min at 250 g. After centrifugation, PBMCs ring was
removed and cells were washed with red blood cell lysis buffer (150 mM NH4Cl, 1 mM KHCO3, 0.1
mM Na2EDTA, pH 7.3, autoclaved) (5 ml) and incubated for 5 min before mixed with RPMI and
centrifuged for 10 min at 250g. After the wash cells were resuspended in RPMI (5% FBS) (5 ml) and
an aliquot of cell suspension was mixed with tryphan blue to count viable cells. Cells were
prepared for cryo-storage in vials (49x106cells/ml in 90% FBS, 10% DMSO) and store in CoolPack
overnight at -80°C before transfer to liquid nitrogen tank.
Compounds
TLR2 agonists (PAM3CSK4 and PAM2CSK4) were acquired from InvivoGen (San Diego, CA). TLR4
agonist (LPS from E. coli K12) was bought from InvivoGen (San Diego, CA). TLR7 agonist (R848) was
acquired from InvivoGen (San Diego, CA).
Viral stocks
CHIKV (La Reunion OPY1 strain) used in the study was produced by PhD students Ellen Bouma and
Mariana Ruiz. Virus inactivation was obtained by 105 min incubation of virus aliquots under UVS-
28 8 watt Lamp.
In vitro cell infection
PBMCs were infected with CHIKV at multiplicity factor of infection (MOI) 2.5 and 10 (to total
number of PBMCs). Virus live and UV-inactivated were diluted in RPMI (5% FBS) (417 µl) and
added to cell suspension (200 µl). Samples were incubated at 37°C and 5% CO2 for first 2 h out of
48 cells were agitated and after agitation FBS (25µl) was added to the cells. All TLR agonists were
diluted using FBS free RPMI and added to cell suspension. In order to induce monocyte subsets
shift PBMCs were stimulated with LPS (100 ng/ml), R-848 (2 µg/ml) and PAM3CSK4 (50 ng/ml).
During pretreatment experiments, PBMCs were stimulated with LPS (1µg/ml), R-848 (1µg/ml) and
PAM2CSK4 (200 ng/ml).
After incubation period, cell-free supernatants (centrifuged at 1550 rpm for 5 min) were stored for
future experiments. PBMCs cell pellet was resuspended in FACS buffer (1x PBS, 2% FBS, EDTA 5
mM, without Ca2+and Mg2+) and used for cell phenotyping with flow cytometry.
Flow cytometry
Initially phenotyping of monocyte population in PBMCs context and detection of CHIKV infection
were carried out in single step direct surface staining process, but in later stages CHIKV antigen
detection involved two-step indirect staining protocol. The antibodies (mAb) used in this study
42
targeted monocyte subsets surface markers as well as cell viability (see Table 4). At initial stages of
the project CHIKV detection was based on GFP reporter gene inserted into the genome of virus
used for in vitro infections. Later on mAb targeting CHIKV E2 protein stem region was used to
detect virus presence. The mAb (µg/ml) was diluted in FACS buffer and incubated for 30 min
followed by wash with FACS buffer (500 µl) and centrifugation for 5 min at 1550 rpm. Second
staining step used mAb targeting mouse antigen (µg/ml) and incubated for 30 min as well followed
by a wash and centrifugation as described before. Before surface staining, PBMCs were washed
twice with FACS buffer (500 µl) and centrifuged at 1550 rpm for 5 min followed by resuspension in
FACS buffer (50 µl). Cell phenotyping was carried out by incubating mAb ( µg/ml in 50 µl of FACS
buffer) for 30 min followed by wash with FACS buffer (500 µl) and 5 min centrifugation at 1550
rpm. Cells were fixed using 10% paraformaldehyde (100 µl) followed by wash with FACS buffer and
centrifugation as described before. Finally cells were resuspened in FACS buffer (250 µl) and
stored at 4°C until data acquisition with FACS Verse (Beckman Coulter, Indianapolis, IN) using
FACSuite software (Beckman Coulter, Indianapolis, IN). Based on available cell number, 10,000-
100,000 events were acquired for analysis excluding dead cells and aggregates based on FSC-
A/FSC-W and SSC-A/SSC-W gating. The data obtained from flow cytometry was analyzed using
Kaluza 1.3 software (Beckman Coulter, Indianapolis, IN).
Table 5 Antibodies used in the study
Antibody Fluorophore Clone Manufacturer
Fixable viability dye eFluor 780 eBioscience Anti-human CD14 eFluor 450 61D3 eBioscience Anti-human CD16 PE B73.1 eBioscience Mouse IgG2a K isotype control
PE eBM2a eBioscience
Mouse IgG1 K isotype control
eFluor 450 P3.6.2.8.1 eBioscience
Anti-human CD16 APC eBioCB16 eBioscience
Rabbit anti CHIKV E2 - - Gift from G.Pijlmann
Chicken anti Rabbit AF 647 A-21443 LifeTechnologies
Statistical analysis
The statistical analysis of was carried out using GraphPad Prism 5 (San Diego, CA). Mann-Whitney
statistical test were utilize to analyze data from FACS analysis. A p-value of 0.05 was considered to
be statistically significant.
43
Results
CHIKV infection increase viability of PBMCs
Effects of CHIKV infection on PBMCs and monocyte viability
The effect of CHIKV infection on the viability of PBMCs and monocyte were assessed using FACS. It
was observed that infection with live CHIKV increases the viability of PBMCs and the effect is not
observed after PBMCs exposure to UV-inactivated virus (p<0.01) (see fig. 3A). The analysis of
monocyte viability in context of PBMCs after infection with CHIKV or treatment with UV-I virus
were similar to viability of mock infected cells (see fig. 3C).
CHIKV infection increases PBMCs viability in comparison to TLRs agonists
The viability of PBMCs after CHIKV infection was compared with the viability of TLR agonists
treated PBMCs. PBMCs were stimulated with PAM2/3 as TLR2 agonists, LPS as TLR4 and R848 as
TLR7 agonist. The treatment with TLR agonist did not increase cell viability and PBMCs viability was
considerably lower than CHIKV infected cells (see fig. 3B). Viability of CHIKV infected PBMC was
higher than LPS (p<0.01), R848 (p<0.001) and PAM2/3 treated cells. Monocyte viability in context
of PBMCs remained similar after treatment with TLR agonists in comparison to CHIKV infected
cells and mock treated cells (see fig. 3D).
Effect of CHIKV infection and TLR agonists stimulation on Monocyte subset
distribution
Effect of CHIKV infection on monocyte subset dynamics
In order to understand monocyte subsets dynamics during the acute stage of CHIKV infection, we
infected human PBMCs with CHIKV at MOI 10 and 2.5 and 48hpi monocyte populations were
characterized using FACS. CD14 and CD16 were selected to define monocyte populations in
PBMCs. During experiments, we observed that CHIKV infection shifts monocyte population
towards intermediate phenotype (CD14++CD16+) in comparison to mock infected cells (p<0.001)
(see fig. 4A). The monocyte shift towards intermediate phenotype was replication dependent as it
monocytes maintained classical phenotype (CD14++CD16-) after treatment with UV-inactivated
CHIKV (p<0.001) (see fig. 4A).
44
Figure 3 PBMCs viability after CHIKV infection and TLRs agonists’ treatment. PBMCs and monocytes in context of PBMCs were infected with CHIKV (MOI 10), treated with UV-inactivated, non-replication CHIKV (UV-I CHIKV) (equivalent of MOI 10) and TLRs agonists (LPS (100 ng/ml), R848 (2µg/ml) and PAM2/3 (50 ng/ml)). Cell viability was assessed with FACS after 48 hpi. Graphs represent mean ± SEM. N=3. Statistical analysis One-way Anova.
CHIKV infection shifts monocyte population in different direction than TLRs agonists
During our experiments, CHIKV infection was compared with TLRs agonists’ stimulation and their
effect on monocyte population shift. LPS (TLR4 agonist), PAM2 and PAM3 (TLR2 agonists) and
R848 (TLR7 agonist) were used to stimulate PBMCs. It was observed that treatment with TLR
agonists maintained CD14++CD16- monocyte phenotype. Furthermore, LPS and R848 induced
extensive enrichment of CD14++CD16- monocytes in comparison to mock treated cells (p<0.001
and p<0.01 respectively). CHIKV infection caused increase in CD14++CD16+ monocyte population
(p<0.001) (see fig. 4B).
MOCK
CHIK
V
UV-I
CHIK
V
60
70
80
90
100 **
PBMCs
Via
bili
ty /
%
MOCK
CHIK
VLP
S
R84
8
PAM
2/3
50
60
70
80
90
100
**
***
*
PBMCs
Via
bili
ty /
%
A B
MOCK
CHIK
V
UV-I
CHIK
V
60
70
80
90
100
Monocytes
Via
bili
ty /
%
MOCK
CHIK
VLP
S
R84
8
PAM
2/3
0
20
40
60
80
100
Monocytes
Via
bili
ty /
%
C D
45
Figure 4. Monocyte subset distribution after CHIKV infection and TLRs agonists treatments. A, PBMCS infected with CHIKV (MOI 10) or treated with UV-inactivated, non-replicating CHIKV (equivalent of MOI 10). Monocyte subsets were characterized using FACS 48 hpi. B, PBMCS were infected with CHIKV (MOI 10) and treated TLR2/4/7 agonist (LPS (100 ng/ml), R848 (2µg/ml) and PAM2/3 (50 ng/ml)). Monocyte subsets characterized using FACS 48 hpi. Bars represent mean ± SD, N=3. Statistical analysis Two-way Anova.
Quantification of PBMCs and monocyte susceptibility to CHIKV infection
During this project, CHIKV infection in PBMCs was quantified using several different FACS based
methods. Firstly, GPF reporter containing virus was used and a very low level of infection was
observed. The GFP signal was highest in CHIKV infected PBMCs in comparison to mock infected
cells, UV-CHIKV treated cells and TLRs agonists treated PBMCs (p<0.01). However, there was
background GFP signal in all samples (see fig. 5A). At monocyte level, GFP signal from CHIKV
infected cells was very small, but higher than mock treated cells (p<0.01). Nevertheless, GFP signal
from CHIKV-UV or TLRs agonist treated cells were very similar to GFP signal in CHIKV infected cells
(see fig. 5B). Also mean fluorescent intensity (MFI) of GFP was very similar between the UV-CHIKV
treated cells and CHIKV infected cells (see supplementary information fig. 1A)
During following experiments, surface staining for CHIKV E2 glycoprotein was used to quantify the
virus infection in PBMCs. However we observed high background signal in monocyte fraction of
PBMCs (see fig. 4C). As UV-inactivated virus was used as a control to live CHIKV, there was very
small difference between the two. Due to high background signal and very small difference
between live and inactivated virus it was not possible confidently quantify CHIKV infection in
PBMCs (see fig. 4C). Also MFI of E2 was slightly distinctive between CHIKV infected and UV-CHIKV
treated cells (see supplementary information fig. 1B).
Intracellular staining for CHIKV E2 protein was also used to quantitate CHIKV infection in PBMCs.
PBMCs from two different donors were used and a low shift of CHIKV E2 signal was observed for
one donor (see supplementary information fig. 2). However, MFI of CHIKV infected cells was very
similar to E2 signal from CHIKV-UV treated cells for both donors.
CD14
++CD16
-
CD14
++CD16
+
CD14
+CD16
++
0
20
40
60
80
100MOCK
CHIKV
UV-I CHIKV
***
***
Perc
en
tag
e o
f liv
e m
on
ocyte
s /
%
CD14
++CD16
-
CD14
++CD16
+
CD14
+CD16
++
0
20
40
60
80
100MOCK
CHIKV
LPS
R848
PAM2/3
***
***
Perc
en
tag
e o
f liv
e m
on
ocyte
s /
% ***
***
**
**
A B
46
Co
un
ts
Mock CHIKV live CHIKV UV-I
CHIKV E2
C
MOCK
CHIK
V
UV-I
CHIK
VLP
S
R84
8
PAM
2/3
0
2
4
6
8
10
**
**
****
GF
P p
os
itiv
e c
ell
s /
%
MOCK
CHIK
V
UV-I
CHIK
VLP
S
R84
8
PAM
2/3
0
2
4
6
8
10
**
GF
P p
osit
ive
mo
no
cyte
s /
%
A B
Figure 5. CHIKV infection in PBMCS 48hpi. PBCMs were infected with CHIKV (MOI 10) or treatment with TLRs agonists (LPS (100 ng/ml), R848 (2µg/ml) and PAM2/3 (50 ng/ml)). GFP signal was quantified in PBMCs (A) and monocytes (B) using FACS 48hpi or 48 h post-treatment. Graph represents mean ± SD, N=3. C, PBMCs were infected with CHIKV (MOI 2.5) and treated with UV-inactivated, non-replicating CHIKV (equivalent of MOI 2.5). Surface CHIKV E2 positive cells were quantified using FACS 48 hpi. Histogram represents total cell population and small dot plot shows CD14/CD16 positive cells. Representative experiment.
47
Effec of TLR agonist pre-treatment on CHIKV infection in PBMCs
The effect of TLR4/7 agonists pretreatment on PBMCs viability and monocyte subset dynamics
Another research question of this project was focused to evaluate how pretreatment with LPS
(TLR4 agonist) and R848 (TLR7 agonist), which skewed monocytes subsets towards the classical
phenotype would affect the scenario following CHIKV infection. During experiments, data analysis
suggested that the pretreatment with TLR7 agonist did not induce pronounce decrease in PBMCs
viability in comparison to infected cells that were not pretreated for both donors (see fig. 6A). In
case of LPS a small decrease in PBMCs viability was observed in LPS pretreated cells after CHIKV
infection for donor 1 (p<0.01) (see fig. 6A). Interestingly, as shown in Fig.6B, CHIKV did not induce
large expansion of CD14++CD16+ monocytes following the infection of non-treated cells (see fig.6B
non-treated cells). Our data shows that R848 pretreatment affected monocyte shift but there was
not drastic change for both donors (see fig. 6B). Subsequently to LPS pretreatment, CHIKV
infection do not induce increase of CD14++CD16+ monocyte subset, but lead to increase in
CD14++CD16- monocyte subset (p<0.05) (see fig. 6B donor1) and majority of monocyte population
maintains CD14++CD16- phenotype (see fig. 6B).
48
Figure 6 Effect of LPS and R848 pre-treatment on PBMCs viability and monocyte subsets. PBMCs were treated with LPS (1 µg/ml) and R848 (1 µg/ml) for 24 h. After 24 h treatment cells were infected with CHIKV (MOI 10). Cell viability (A) and monocyte subset distribution (B) were quantified using FACS. Bars represent mean ±SD. A N=2, B N=1.Statistical analysis T test (A) and Two-way Anova (B).
Donor 2
Non-tr
eate
dLP
S
0
20
40
60
80
100
Pretreatment
Via
bilit
y o
f P
BM
Cs /
%
Donor 2
Non-tr
eate
d
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8
0
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100
Pretreatment
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bilit
y o
f P
BM
Cs /
%
Donor 1
Non-tr
eate
dLP
S
0
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40
60
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100
*
Pretreatment
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y o
f P
BM
Cs /
%
Donor 1
Non-t
reat
ed
R84
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Pretreatment
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f P
BM
Cs /
%
A
BDonor 2
CD14
++CD16
-
CD14
++CD16
+
CD14
+CD16
++
0
20
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100Non-treated
LPS
R848
Perc
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s /
%
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CD14
++CD16
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++CD16
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CD14
+CD16
++
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60
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100Non-treated
LPS
R848
Perc
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e o
f m
on
ocyte
s /
% *
*
49
Discussion
This part of the project aimed to elucidate the effect of CHIKV infection on human monocyte
subset polarization in the context of PBMCs. My data shows that in replication dependent manner,
CHIKV directed monocyte polarization towards an intermediate phenotype and increased PBMCs
viability. However, we encountered difficulties quantifying CHIKV infection in PBMC and
determining which subset of monocytes are infected by CHIKV. Interestingly, CHIKV infection
directed monocyte subset towards an intermediate phenotype, while stimulation with TLR2/4/7
agonists maintained a classical monocyte phenotype. Moreover, in contrast to TLR agonist
treatment, CHIKV infection increased PBMC viability.
We observed that CHIKV infection increased the viability of the total PBMC population, but not
that of monocytes. This observation could be due to interplay between two pathways. It has been
established that CHIKV induces a type I IFN response, and that this pathway is essential for virus
containment (Couderc et al. 2008; Schilte et al. 2010. CHIKV infection may increase PBMC survival
by inducing immune activation and secretion of stimulatory factors such as type I IFN and GM-CSF
as these factors were observed to increase cell viability after virus infection in PBMCs (Achour et
al. 2009; Fu et al. 2011). Plasmacytoid (pDC) and conventional DCs could produce high levels of
type I IFN, induced by recognition of double stranded RNA, which could only be present after
infection with a live virus and not UV-inactivated CHIKV (Siegal et al. 1999; Diebold et al. 2003).
Although, I could not confirm that these cell types were infected and activated, researchers have
reported that pDCs can be activated to produce type I IFN without direct infection by a virus
(Dalod et al. 2003; Ruiz Silva et al 2017). Nevertheless, the presence and amounts of type I IFNs
and GM-CSF in supernatants from the infected cells should be confirmed, and additional
experiments carried out to confirm this hypothesis.
Increased PBMC survival after CHIKV infection may also rely on CHIKV induced autophagy which
delays apoptosis. During experiments with CHIKV in mouse embryonic fibroblasts cell line,
autophagy and apoptosis were mutually exclusive, but dependent on the level of virus replication
(Joubert et al. 2012). In our case data could suggest that autophagy was protecting PBMC as virus
infection was not that efficient and it is possible that virus replication did not induce apoptosis
efficiently enough to overcome the autophagy signal. Nevertheless, it is important to analyse
CHIKV-induced autophagy in PBMCs to dissect the interactions between autophagy and apoptosis
pathways in human PBMCs, as Joubert et al. only carried out experiments in cell lines and murine
model systems.
We have observed that CHIKV infection promoted expansion of intermediate monocytes
(CD14++CD16+). This observation corroborates observations from a study with Dengue virus
(Kwissa et al., 2014). We observed that LPS stimulation caused a significant increase in classical
(CD14++CD16-) monocytes and our observation was similar to data published by Belge et al.
However, the authors included HLA-DR as a secondary marker for intermediate (CD14++CD16+)
monocytes and authors discovered that prolonged stimulation of PBMCs with LPS caused
50
downregulation of CD16 on the surface of monocytes (Belge et al. 2002). The decline in
intermediate and non-classical monocyte subset frequencies that we observed might not be
caused by a loss of monocytes, but by a decrease in CD16 expression. In future experiments,
addition of secondary markers for all three monocyte subsets would resolve this ambiguity. During
our study we were not able to confirm observations about TLR4/7 agonist activity on monocyte
populations shown by Kwissa and collaborators. Kwissa et al showed that a treatment of R848, a
TLR7 agonist, induced a monocyte population shift towards an intermediate phenotype. The
authors also observed that only half of the monocyte population maintained a classical phenotype
after LPS treatment (Kwissa et al. 2014). However, we observed that monocytes treated with LPS
or R-848 maintained a classical phenotype, even though we used the same or higher
concentrations as Kwissa and colleagues.
Our experiments to quantify CHIKV infection in PBMCs showed that PBMCs treated with TLR
agonists were GFP positive. Autofluorescence observed in the GFP channel was most likely caused
by metabolic co-factors such as NADH, riboflavins, and flavin coenzymes. The signal increase may
also have been caused by an upregulation in metabolism after TLR activation (Mosiman et al.,
1997; Pearce & Pearce 2013). At the later stages of the study, surface expression of CHIKV E2
protein was analyzed to quantify virus infection in PBMCs. E2 surface expression was analysed
based on previous reports showing that E2 was expressed on the surface of CHIKV-infected PBMCs
(Garoff et al. 2004; Soonsawad et al. 2010). Moreover, previous study with influenza virus has
used cell surface expression of viral proteins wot quantify virus infections (Hoeve et al. 2012).
Interestingly, a large background signal was observed in mock treated samples after surface-
staining for CHIKV E2 protein. The background population constituted mainly monocytes. This
background signal was not observed in our control samples for secondary antibody. This could
suggest that primary CHIKV E2 directed antibody was binding to a different surface marker on
monocytes. CHIKV E1 protein has been suggested to have sequence similarity to human HLA-B27
protein, and this could be implicated in the development of chronic arthralgia (Reddy et al., 2017).
Our analysis of CHIKV E2 sequence alignments did not showed similarity to known monocyte
surface markers, but more extensive analysis might be required.
The issues I have encountered to quantify CHIKV infection in human PBMCs were not expected to
be that complex as it has been shown that CHIKV can infect PBMCs. In 2010, Her and colleagues
showed that CHIKV can be detected in PBMCs from infected patients during the acute stage of
infection by quantifying viral antigens and RNA. Researchers observed pronounced differences in
monocyte populations of infected patients compared to healthy controls. An assessment of a 13
patient cohort revealed that viral RNA levels in blood were associated with a CHIKV signal from
infected monocytes. These observations were confirmed by in vitro infections of healthy donor
PBMCs and enriched monocytes. During in vitro experiments in PBMCs, researchers showed a low
infection of B cell and plasmacytoid DC. Furthermore, CHIKV infection of enriched monocyte
population reached infection rate of 40% at 12hpi. The findings suggested that monocytes could
be considered the primal targets of CHIKV infection, and they might have a role in virus
dissemination to muscles and joints, and play role in immunopathological conditions induced by
51
CHIKV infection (Her et al. 2010).These observations were confirmed by Teng and colleagues in in
vitro CHIKV infections in monocytes and B cells using FACS. On the RNA level, the observations
were similar, as most of CHIKV replication was in monocytes and B cells. Interestingly, the virus
induces different interferon (IFN) genes in monocytes and B cells. IFNA gene was induced only in
monocytes while IFNB gene was induced in monocytes and B cells. Furthermore, CHIKV infection
in monocytes highly activated Viperin, type I IFN activated protein, which was essential for virus
control in vitro as well as in vivo (Teng et al. 2012).
Difficulties in quantifying CHIKV infection during my project were likely related to the methods I
used for infection. There are several differences between the Her et al methods and the protocol
employed during our study. The essential difference was that for in vitro CHIKV infection we used
isolated PBMCs, while Her and colleagues used whole blood from healthy donors. Furthermore,
their infections were done at an MOI of 50, five times higher than what I used. There were several
minor differences in infection protocols between our methodology and Her et al. We infected our
cells for 48 h while Her and colleagues infected cells for 24 h. While we targeted CHIKV E protein
to detect viral infection, Her at al. had access to primary antibody which presumably targeted non-
structural CHIKV proteins. (Her et al. 2010). These differences in protocol are likely to have
influenced our assessment of CHIKV infection in monocytes and PBMCs.
Moreover, Sourisseau with colleagues did not observe CHIKV infection in cultured monocytes or
monocyte derived dendritic cells even though they used high MOIs (50 and 10), suggesting that
infection outcomes are different depending on the use of whole blood or PBMCs (Sourisseau et al.
2007).
52
Conclusion
In the past decades, there has been a surge in the number of CHIKV cases across the globe, which
has caused a great strain on the limited health care infrastructure in developing countries.
Therefore it is important to study CHIKV infection mechanism for the development of better
therapeutics tools. This project has been focused on the effect of CHIKV infection to PBMC and
monocyte in context of PBMCs. In conclusion, it was found out that CHIKV infection increased
viability of PBMCs, but not monocytes. Also it was shown that CHIKV induce monocyte subset shift
towards an intermediate phenotype in a replication dependent manner. Furthermore, monocyte
shift towards an intermediate phenotype was only observed after CHIKV infection and not after
stimulation with TLR2/4/7 agonists. However, active CHIKV infection in PBMCS using GFP-labelled
virus and GFP surface staining was not observed. PBMCs pre-treatment with TLR4/7 agonists was
able to abolish CHIKV induced increase in PBMCs viability and monocyte shift towards an
intermediate phenotype. Further research will be necessary to understand the role of monocytes
in CHIKV-induced pathology.
53
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Supplementary information
Figure 1 Representative experiment of CHIKV infection in PBMCs at 48hpi. PBMCs were infected with CHIKV (MOI 10), treated with CHIKV-UV (equivalent of MOI 10). GFP (A) and surface CHIKV E2 (B) were quantified using FACS 48hpi. CHIKV UV- UV- inactivated CHIKV, non-replicating. Histograms represent frequency of CHIKV E2 positive cells.
Figure 2 CHIKV infection in PBMCs at 48 hpi. PBMCs were infected with CHIKV (MOI 10), treated with CHIKV-UV (equivalent of MOI 10). Intracellular CHIKV E2 staining was quantified using FACS 48hpi for two donors. CHIKV UV- UV- inactivated CHIKV, non-replicating. Histograms represent frequency of CHIKV E2 positive cells N=1.
Donor 1 Donor 2
58
Figure 3 PBMCs, monocyte viability and monocyte subsets at 0 h. PBMCs were thawed and seeded for the experiments. Cell viability (A) and monocyte subset distribution (B) were assessed using FACS at 0 h. Mock – no treatment, CD14++CD16- -classical monocytes, CD14++CD16+-intermediate monocytes, CD14+CD16++- non-classical monocytes. Bars represent mean ± SD, pie plots represent mean. N=3.
Total l
ive
cells
Live
monocy
tes
70
80
90
100
Via
bilit
y /
%
A
B
59
Figure 4 PBMCs, monocyte viability and monocyte subsets after 24 h. PBMCs were treated with LPS (1µg/ml) or R848 (1µg/ml) for 24h. Cell viability (A) and monocyte subset distribution (B, C, D) were assessed using FACS after 24 h incubation. Mock – no treatment, CD14
++CD16
- -classical monocytes, CD14
++CD16
+-intermediate monocytes, CD14
+CD16
++- non-classical monocytes.
Bars represent mean ± SD, pie plots represent means N=3.
Total c
ells
Monocy
tes
0
20
40
60
80
100MOCK
LPS
R848V
iab
ility
/ %
A
B C
D
