PUBLIC HEALTH RISKS ASSOCIATED WITH INFLUENZA D, CHIKUNGUNYA, AND ZIKA VIRUS INFECTIONS: VIROLOGY IN THE CONTEXT OF ONE HEALTH
By
SARAH KELLER WHITE
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2017
© 2017 Sarah Keller White
To Brenton for inspiring me to persevere, being supportive and learning along with me
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ACKNOWLEDGMENTS
I thank my mom and dad, husband, family, and friends who have supported me
throughout this process - your love and encouragement are deeply appreciated. I also
thank my mentors, collaborators, and colleagues who have helped guide me through
my project(s) and forge new paths. Thank you for the opportunity to do this work and
contribute to a field in which I am so passionate.
5
TABLE OF CONTENTS Page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
CHAPTER
1 INTRODUCTION .................................................................................................... 13
One Health and Virology ......................................................................................... 13 Viral Serology ......................................................................................................... 15
Virus Detection, Isolation, and Characterization ..................................................... 17 Summary ................................................................................................................ 20
2 INFLUENZA D VIRUS SEROLOGY ....................................................................... 21
Background ............................................................................................................. 21 Objectives ............................................................................................................... 22
Study Design .......................................................................................................... 22
Results .................................................................................................................... 24
Discussion .............................................................................................................. 25
3 GENETIC CHARACTERIZATION OF CHIKUNGUNYA VIRUS FROM A 2014 OUTBREAK IN HAITI AND IDENTIFICATION OF CO-INFECTING ARBOVIRUSES ...................................................................................................... 28
Background ............................................................................................................. 28
Objectives ............................................................................................................... 29 Methods .................................................................................................................. 30
Sample Collection ............................................................................................ 30
Sample Culture ................................................................................................. 30
Molecular Detection .......................................................................................... 32 Sequencing ...................................................................................................... 32
Results .................................................................................................................... 33
Samples ........................................................................................................... 33 Sequencing ...................................................................................................... 34
Discussion .............................................................................................................. 35
4 ZIKA VIRUS DETECTION AND ISOLATION FROM PATIENTS WITH DOMESTIC OR TRAVEL-ACQUIRED CASES OF ZIKA FEVER ........................... 44
6
Background ............................................................................................................. 44 Objectives ............................................................................................................... 46 Methods .................................................................................................................. 46
Cell Lines .......................................................................................................... 46 Zika Virus Propagation and Quantification in Cultured Cells ............................ 47 Patient Samples ............................................................................................... 49 Sequencing ...................................................................................................... 51
Results .................................................................................................................... 52
ZIKV Lab Strains .............................................................................................. 52 Patient Samples ............................................................................................... 52 Sequencing ...................................................................................................... 56
Discussion .............................................................................................................. 56
5 CONCLUSIONS ..................................................................................................... 64
APPENDIX: SEQUENCING PRIMERS ........................................................................ 66
LIST OF REFERENCES ............................................................................................... 71
BIOGRAPHICAL SKETCH ............................................................................................ 81
7
LIST OF TABLES
Table page 2-1 Influenza D virus HI results obtained with chicken or turkey red blood cells ....... 26
2-2 Demographic characteristics of participants with and without cattle-exposure, Florida 2011........................................................................................................ 26
2-3 Serology results from hemagglutination inhibition and micronuetralization assays on serum from participants with and without cattle exposure, and exposure to both cattle and swine, Florida 2011 ................................................ 27
2-4 Cross-reactivity results between anti-IDV antibodies and influenza A H1N1, and influenza C virus strains via HI assay. ......................................................... 27
3-1 Molecular detection results of arbovirus co-infections identified in human plasma samples, Haiti 2014................................................................................ 38
3-2 Whole genome sequences of Chikungunya virus isolates from Haiti, 2014. ...... 38
4-1 Zika virus strains ................................................................................................. 59
4-2 Zika virus titration results .................................................................................... 59
4-3 Sample collection ............................................................................................... 59
4-4 Zika virus molecular detection results ................................................................. 60
A-1 Sequencing primers for Chikungunya virus ........................................................ 66
A-2 Dengue virus type 2 sequencing primers ........................................................... 68
A-3 Zika virus sequencing primers ............................................................................ 70
8
LIST OF FIGURES
Figure page 3-1 Map of Haiti featuring location of Gressier/Leogane areas and collection
locations. ............................................................................................................ 39
3-2 Isolation of Chikungunya virus from plasma samples in cell culture ................... 40
3-3 Co-infected plasma samples in cell culture ........................................................ 41
3-4 CHIKV tree generated by Neighbor Joining Method, NCBI BLAST .................... 42
3-5 ZIKV tree generated by Neighbor Joining Method, NCBI BLAST ....................... 43
4-1 Isolation of lab strain Zika virus .......................................................................... 61
4-2 Isolation of Zika virus from saliva and urine samples ......................................... 62
4-3 Mixed virus CPE revealed by plaque assay ....................................................... 62
4-4 Detection of genomic RNA of Zika virus in urine and saliva samples by RT-PCR analysis ...................................................................................................... 63
9
LIST OF ABBREVIATIONS
ATCC American Type Culture Collection
BLAST Basic Local Alignment Search Tool
CDC Centers for Disease Control and Prevention
CHIKV Chikungunya virus
DENV Dengue virus
aDMEM Advanced Dulbecco’s modified essential medium
dpi Days post-infection
ECSA East/Central/South African lineage
EMEM Eagle’s minimum essential medium
FBS Fetal bovine serum
GBS Guillain-Barré syndrome
HAU Hemagglutination units
HI Hemagglutination inhibition
HSD Health Sciences Department
ICV Influenza C virus
IDV Influenza D virus
MDCK Madin-Darby canine kidney cells
MN Microneutralization
NCBI National Center for Biotechnology Information
NGS Next generation sequencing
PBS Phosphate buffered saline
PSN Penicillin/streptomycin/neomycin
RBC Red blood cells
RT-PCR Reverse transcription polymerase chain reaction
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ST Swine testis cells
TCID50 50% tissue culture infectious dose
UF University of Florida
WHO World Health Organization
ZF Zika Fever
ZIKV Zika virus
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
PUBLIC HEALTH RISKS ASSOCIATED WITH INFLUENZA D, CHIKUNGUNYA, AND
ZIKA VIRUS INFECTIONS: VIROLOGY IN THE CONTEXT OF ONE HEALTH
By
Sarah Keller White
May 2017
Chair: John A. Lednicky Major: Public Health
Influenza, Chikungunya, and Zika viruses all pose current or emerging threats to
public health. The project work was designed to provide a breadth of experience and
knowledge of virology in terms of public health. There were three objectives: (a) to apply
serology and virology techniques to determine whether influenza D virus-specific
antibodies are present in an at-risk population, (b) to identify and sequence viruses
isolated from the plasma of patients with undifferentiated febrile illnesses during an
arbovirus outbreak, and (c) to detect/identify arboviruses in travelers to Central
American and Caribbean countries with suspected arbovirus illnesses.
Influenza D virus (IDV) is a newly emerging virus of cattle and swine, and recent
studies suggest it also affects goats and sheep. A cross-sectional serosurvey of 35
cattle workers and 11 non-cattle exposed controls revealed an IDV antibody response in
94% of individuals with occupational exposure to cattle.
An outbreak of Chikungunya Fever occurred in Haiti during May to August 2014,
followed by Dengue virus (DENV) type 1 and DENV type 4 outbreaks. During this
period, 100 clinical plasma samples tested positive for Chikungunya virus (CHIKV), of
12
which eight were co-infected with either DENV type 2 (n=1), Mayaro virus (n=1), or Zika
virus (ZIKV) (n=6). Genetic characterization of the circulating CHIKV strains revealed
high similarity within Haiti (>99%) and among strains from Caribbean countries (>97%).
Zika virus emerged in the Americas in 2015, leading to numerous travel warnings
issued by the Centers for Disease Control and Prevention. During the first six months of
2016, four individuals returned from either Haiti (n=3) or Colombia (n=1) with symptoms
of arbovirus infection. Specimens were collected and tested for the presence of ZIKV,
DENV, or CHIKV by molecular and virus isolation methods. Three travelers tested
positive by RT-PCR for ZIKV, and two of the three individuals had co-infections, one
each with CHIKV and DENV type 2.
Information gathered through these studies furthers our scientific understanding
of IDV serology and the zoonotic risks IDV may pose to humans, and the ability to
detect and isolate viruses from different types of specimens collected from humans with
suspected arbovirus diseases.
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CHAPTER 1 INTRODUCTION
One Health and Virology
An approach toward a more encompassing view of health – One Health, is
increasingly being accepted in many health fields. One Health considers a multitude of
aspects to combat and solve challenges in a unique way by uniting skillsets of human
and animal health and environmental sciences. Traditionally, medical and veterinary
practices have functioned independently of each other, as have various scientific fields.
However, climate change, globalization, emerging infectious disease, and
environmental stressors are some of the topics these fields are collaborating on using
the One Health approach. As examples, One Health research has shown that immense
pathogen transfer occurs at the human-animal interface, and has highlighted the risks of
zoonotic and reverse zoonotic outbreaks, as observed in many influenza virus
outbreaks (1-5). Also, climate change is having a profound effect on resource
availability, and the ability of an array of vectors (animal and arthropod) to spread
disease (6-9). Apart from climate, intense globalization of our society and industries
allows for the rapid, fluid movement of people, ideas, goods, and even pathogens (10).
Application of the One Health approach began primarily in the medical and
veterinary fields with a focus on zoonotic disease(11). However, another appropriate
application of the One Health approach is in investigations of vector-borne diseases.
Vector-borne diseases are unique in that ongoing transmission requires a vector
(typically an arthropod), and that vector is influenced greatly by the environment
(climate, weather) and other human-influenced environmental factors that are required
for survival. Scientists that specialize in ecology, entomology, virology, immunology, and
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human and veterinary medicine, to name a few, play a role in such studies. In terms of
virology – how a virus can be successfully transmitted from an arthropod and infect
mammals, is a unique research opportunity for multiple disciplines to work together to
answer a research question. Virology plays a large role in public health research,
considering the mechanisms of action utilized by viruses to replicate or infect two very
different systems – vertebrate and invertebrate, and virus isolation and sequencing from
both host and vector can shed light on virus adaptation or immune evasion. Specialists
from other fields are vital for the correct identification of the vector species,
understanding the vector biology, interpreting the role of changes in climate and the
environment on the presence of the vector and the transmissibility of the virus,
mechanisms of infection and host immune system activation and response, and
treatment or prevention of disease. All of the knowledge contributed by these specialists
complete a larger picture of vector-borne disease emergence and spread at a time
when the Western Hemisphere is experiencing epidemics caused by CHIKV and ZIKV,
as well as mosquito species invading naïve environments (12-16).
By incorporating the ideology of the One Health approach with the science and
technical skills of virology, the goals of this investigation were to: (a) apply serology and
virology techniques to determine whether IDV-specific antibodies are present in an at-
risk human population, (b) identify and sequence virus strains and identify co-infections
from the plasma of patients with undifferentiated febrile illnesses during a CHIKV
outbreak, and (c) detect/identify arboviruses in travelers to Central American and
Caribbean countries with suspected arbovirus illnesses. Information arising from the
body of work presented here advances our scientific understanding of: (a) the value and
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limitations of serology in the retrospective analysis of IDV exposure in humans, and (b)
the relevance of viral gene sequence analyses and phylogenetics during an
investigation of the dynamics of an arbovirus outbreak in humans, and underscores the
clinical diagnostics importance of detecting virus co-infections during arbovirus
outbreaks.
Viral Serology
Viral serology is a sub-specialty of immunology that is used to monitor the
immune system’s response to viral antigen exposure resulting from virus infection or
immunization. A serological diagnosis can be made by the detection of an increase in
titers of virus-specific antibodies that occur as a virus-induced illness progresses. There
are multiple serology approaches, each designed to address specific questions, relative
to either the timing of exposure or the persistence of vaccine-induced antibodies. In
general, a serum IgM response occurs first, during the early stages of an acute
infection, followed later by a serum IgG response during or after the convalescent stage
of infection. For viral diagnostics, acute and convalescent sera (referred to as “paired
sera”) are drawn when possible, and a four-fold increase in titer against a specific
antigen is taken as “proof” of infection by the virus. Unfortunately, paired sera are rarely
available for retrospective studies, and are even challenging to acquire in some
prospective studies (17, 18). There are some limitations on the use of serology in viral
diagnostics. An important one is that antibodies may cross-react with antigens of
multiple related viruses; whereas cross-reactivity may be beneficial for the host, it
creates a challenge for serology-based diagnostics, as it complicates discerning the
causative agent. For example, among the alphaviruses, which contain eight antigenic
serogroups, anti-Chikungunya virus (CHIKV) antibodies readily neutralize O’nyong
16
nyong virus (19-21). This cross-neutralization activity occurs in both hemagglutination
inhibition and plaque reduction neutralization assays (20). Recent development of
commercially available species-specific IgM and IgG antibodies have improved
arbovirus serology diagnostics (22). One is able to test sera against cross-reactive
antigens and suggest the antigen with the highest titer is the most likely causative agent
(23, 24). In general, when utilizing serologic assays to screen serum samples for
antibodies, it is important to take a conservative approach to reporting results, as the
assay may detect non-specific virus neutralization from innate immune components
present in samples. Despite the challenges and limitations, viral serology is useful for
identifying past viral infection or exposure, and if used in a longitudinal study, can shed
light on the timing of infection and fluctuation of present antibodies.
Viral serology has been very useful in influenza diagnostics as protein-specific
antibodies for influenza A H1N1 generally do not cross-react with influenza B or C
viruses, but multiple assays such as hemagglutination inhibition and virus neutralization
must be performed to provide confirmation of the result (25). The hemagglutination
activity of influenza and other viruses resulted in hemagglutination inhibition (HI) assays
being valuable in serological studies, but inter-laboratory variability and subjective
interpretation of results opened the doors for additional assays to be employed (26).
These assays look at the interaction between viral antigen, red blood cell, and antibody,
which is less sensitive and, as mentioned previously, non-specific agglutination activity
can skew results. Virus neutralization and microneutralization (MN) assays test the
neutralization abilities of antibodies present in a sample when exposed to virus in cell
culture. With hemagglutination inhibition, a common assay employed in influenza virus
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serology research, and microneutralization assays considered a gold standard assay,
both have a function in serology studies.
This serology study serves as the pioneer for IDV research in at-risk humans;
IDV serology studies have focused on cows (the suspected natural reservoirs) and pigs
(the first IDV isolate was from a pig), and others have screened for antibodies in the
general human population. Results of this work have the potential to increase
opportunities for future funding and spark interest in IDV as an emerging zoonotic
pathogen. The results of this study raise IDV awareness in the human and animal
medical fields, and may motivate development of IDV vaccines or antivirals for
veterinary use. By knowing that IDV may be a human pathogen, we have better
justification to include IDV as a suspected pathogen in a human with undiagnosed
respiratory illness.
Virus Detection, Isolation, and Characterization
While viral serology is one sub-specialty that provides insight on antigen
exposure or antibody persistence, isolation of infectious virus or molecular detection of
viral genetic material allows for more in-depth analyses of phylogenetics and viral
evolution, and focuses on the pathogen itself rather than the host response. Use of cell
culture techniques to isolate viruses from clinical specimens, then replicate the virus, is
crucial, as viruses in clinical samples may be present in limited numbers. With additional
virus produced in cell cultures, one can uncover more information about the virus itself,
and this improves clinical diagnostics, as some viruses are present at levels below the
threshold of detection in the specimen by even the most sensitive molecular detection
methods (27). While it is more resource-intensive to isolate and propagate (“culture”)
virus from a specimen, the “amplification” of virus quantities over that originally present
18
provides an opportunity to gather more information about the virus and the infection.
Noteworthy, in the case of viral co-infections, virus isolation in cell culture can lead to
detection of more than one type of virus. Molecular tests are only able to detect what
they are designed for, in which case viruses present in a mixed infection may be
missed. Also from a clinical perspective, when one pathogen is identified, no further
investigation for additional pathogens is performed, again missing out on mixed
infections. However, isolation in cell culture (especially with the use of multiple cell lines
that allow different viruses to grow) may lead to growth of multiple viruses present and
possible detection of different cytopathic effects.
Coupling culture and molecular detection methods increases the likelihood of
virus detection from a specimen. However, there are some limitations to virus culture.
As in nature, viruses in cell cultures experience a bottleneck event that can hinder the
detection of a viral quasi-species (28, 29). A viral quasi-species is a group of variant
viruses (such as in a host) that are linked through mutation, all of which contribute to the
population characteristics. Isolation of virus in cell cultures also does not guarantee that
the predominant viruses that have been isolated possess a genome identical to the
consensus genomic sequence derived directly from virus in a specimen. There are
many reasons for this, including the practice of isolated viruses in cells from a species
different from the specimen’s source (e.g. isolation of a virus from a human in canine
cells). Moreover, RNA viruses also have a relatively high rate of mutation – around 10-4
to 10-6 mutations per base pair per generation (29). Considering these factors during
molecular and genetic analyses, and appropriate cell lines and permissive temperatures
are utilized, virus isolation methods remain crucial for diagnostics and research.
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The identification of co-infections is not common because of the cost associated
with sample processing and the time it takes to detect them. Typical commercial
detection kits or molecular methods are not designed, or are not sensitive enough, to
detect more than one agent, and virus isolation in cultured cells or animals is generally
not feasible in a clinical or surveillance setting. Multiplex platforms that succeed in
detecting multiple agents in a sample tend to be resource-intensive (either labor or
financially). When the suspected causative agent is identified, further testing for
additional viruses is rarely performed, thus most co-infection or dual-infection studies
published are case studies (30-32). Few studies have attempted to report the rates of
co-infections in arbovirus outbreaks, estimated to be about 10% of infections (33) but
these data are limited to one outbreak or one location, not providing generalizable
results (34). It is disputed whether co-infections produce more severe outcomes (35),
but the implications of misdiagnosis have been documented (36).
This investigation of the 2014 CHIKV outbreak in Haiti will be the first genetic
analysis during the CHIKV Caribbean epidemic that only looks at child cases. Unlike
most other studies, cell cultures are used to search in specimens for co-infecting viruses
that are not detected by the diagnostic PCR-based methods. Behaviors of children, (ie.
time spent outdoors, hygiene patterns, and immune status) and other characteristics
classify children as a vulnerable population in terms of infectious disease morbidity and
mortality. Additional factors that play a role in labeling children a vulnerable population
for arboviruses include national infrastructure, access to care, and maternal health (37,
38). Extensive reports of a clear distinction between infectious disease risk in
developing versus developed countries exist (37, 38). In terms of arboviruses such as
20
DENV and ZIKV, which are transmitted sexually and by trans-placental diffusion, access
to prenatal and postnatal care, and maternal health education and awareness impact
outcomes of infection, potentially increasing risk of adverse outcomes to the child (37,
38). The burden of disease greatly affects children under the age of 10, with years of life
lost estimated to be 32.2 per 100,000 person-years, and disability-adjusted life-years to
be 40.67 per 100,000 person-years (37). Studies on DENV show that children are at
higher risk of adverse outcomes related to infection (39). However, to date, no study of
a CHIKV outbreak has focused solely on children.
Summary
Virology is a branch of science that incorporates multiple detection and
diagnostic techniques including serology, culture, molecular detection, and genetic
analysis to identify and describe viruses. Viruses can infect a wide range of organisms,
and are very diverse themselves. Many have the ability to infect multiple species and
some are transmitted by viral vectors such as arthropods. In terms of One Health,
viruses play a role in cross-species transmission, vector transmission, and
environmental persistence. Here, virology is applied to the serologic study of a potential
novel zoonotic virus, the genetic analysis of viruses that emerged during an outbreak
and the identification of co-infections, and refined methods for arbovirus isolation and
the detection of co-infecting viruses.
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CHAPTER 2
INFLUENZA D VIRUS SEROLOGY
Background
Influenza D virus (IDV) is a novel influenza virus with proposed classification:
family Orthomyxoviridae, genus Influenzavirus D, species Influenza D virus, which was
first isolated from swine in 2011 (40), and has been associated with influenza-like illness
in cattle and swine of different countries (41-45). IDV is not closely related to Influenza
A or B viruses; and while both IDV and Influenza C virus (ICV) have seven genomic
segments, genetic reassortment leading to the production of viable progeny does not
occur, there is no cross-reactivity by polyclonal antibody, and the nucleotide identities
are approximately 50%, indicating they are genetically and antigenically distinct (42).
The estimated seroprevalence of IDV among US cattle herds is 88% (41, 46), and 2% in
farmed American swine (42). The detection of high antibody titers and the relative ease
of virus isolation from cattle have led to the thesis that cattle may be the natural IDV
reservoir (41-45, 47). Based on those assertions, serology studies have focused on
cattle (41-43, 45, 47). A serology study of animals in calving operations in Mississippi
found 94% seroprevalence among neonatal beef calves (48). Another study detected
IDV antibodies in goats and sheep, but not in chickens (49). The numerous animal
serosurveys and studies have shown no cross-reactivity with other influenza viruses
(50). In vivo studies in ferrets and guinea pigs, both models for human influenza virus
studies, reveal that IDV successfully infects and replicates in upper and lower
respiratory tracts and is capable of being transmitted to close contacts (42, 51). The
Reprinted from Journal of Clinical Virology, 81, White SK, Ma W, McDaniel CJ, Gray GC, and Lednicky JA, Serologic evidence of exposure to influenza D virus among persons with occupational contact with cattle, 31-33, 2016, with permission from Elsevier.
22
postulated cell receptor binding protein structure of IDV is similar to that of the human
pathogen ICV (40). Taken together, these observations suggest IDV has the potential to
infect humans, and if true, IDV may be a disease agent of humans (42, 46, 51). While
the IDV seroprelavence in the general human population was estimated to be 1.3%
(40), a recent study was unsuccessful at identifying IDV from archived human
respiratory samples (52). To date, there have been no reports of IDV seroprevalence
among individuals who have frequent contact with cattle (or swine).
Objectives
I hypothesized cattle workers were at increased IDV exposure risk, and utilized a
cross-sectional serological study to gain insights on the zoonotic potential of IDV to
human adults with occupational exposure to cattle in north-central Florida.
Study Design
Studies were approved by the University of Florida (UF) Institutional Review
Board and all participants provided informed consent. During 2011-2012, beef and dairy
cattle farms in north-central Florida were phoned and healthy workers >18 years of age
were asked to participate, and fliers were used to enroll healthy non-cattle-exposed
adults from the UF Health Sciences Department (HSD). Cattle-exposed participants
reported a minimum of weekly contact with cattle for six months prior to enrollment;
individuals with no cattle exposure in the previous ten years were also enrolled. All
participants permitted phlebotomy and completed a questionnaire (available in English
and Spanish) documenting demographics, medical history, and animal (cows, pigs,
horses, dogs, and cats) and occupational exposures.
Serum samples were tested for reactivity against Influenza D/Bovine/Kansas/1-
35/2010 virus (D/Bovine/KS) using hemagglutination inhibition (HI) and
23
microneutralization (MN) assays (46). Positive control serum (rabbit anti-
D/swine/Oklahoma/1334/2011 (anti-D/Swine/OK)) (46) and negative virus controls
(negative serum and phosphate buffered saline, PBS) were tested by both assays. Anti-
D/Swine/OK serum was used due to the non-availability of anti-D/Bovine sera, which
was previously shown to be equally effective for use as a positive control reagent in HI
assays with D/Bovine/KS as anti-D/Bovine sera (49). D/Bovine/KS was propagated in
swine testicle cells (ST) (CRL-1746, ATCC, Manassas, VA, USA) at 33°C in serum-free
aDMEM lacking TPCK-trypsin. These are considered standard methods for propagation
of IDV, for which an optimal incubation temperature has yet to be determined (40, 46).
Virus yields were quantified 3dpi in ST cells at both 33°C and 37°C via 50% tissue
culture infectious dose (TCID50) assays (53). Spent media and cell lysate were also
tested via molecular methods using real-time RT-PCR (rt-RT-PCR) using published
primers specific to the PB 1 gene (54) with iScript One Step RT-PCR kit with SYBR
Green (Bio-Rad Laboratories, Hercules, CA, USA) per manufacturers’ protocol.
A standard HI assay using 0.5% (v/v) (“packed”) turkey red blood cells (RBCs)
(Lampire Biological Laboratories, Pipersville, PA, USA) was first used to assess serum
antibody levels to IDV (46, 55). Human sera were treated with receptor-destroying
enzyme (Denka Seiken USA Inc., California, USA) overnight at 37°C, heat-inactivated
at 56°C for 1 hr, diluted 1:10 with PBS, and hemadsorbed with RBCs. Serum samples
were tested in duplicate by performing two-fold dilutions. A subset of 22 singlet serum
samples were also screened using 0.5% packed chicken RBCs (Lampire Biological
Laboratories, Pipersville, PA, USA) to examine sensitivity and specificity; the HI assay
results matched those obtained with turkey RBC (Table 2-1) (45). For each batch, a
24
virus back-titration was set up to ensure 16 HAU/100µL of virus was used. The positive
control had a titer of 1:640 and the negative control had a titer of <1:10: titers ≥1:40
were considered positive.
Microneutralization assays were performed using Madin-Darby Canine Kidney
(MDCK) epithelial cells (55) as a second measure of antibody production against IDV
relative to the HI results; MDCK cells were used for consistency with the work of others
(40, 46, 47). Serum samples were tested in duplicate using two-fold dilutions, incubated
with IDV at 200 TCID50/100µL for two hours, followed by the addition of 1x106 MDCK
cells per well. Plates were read after 3 days’ incubation at 33°C in 5% CO2. Wells that
were completely protected from IDV-induced cytopathic effects were used to establish a
cut-off point with a positive titer of ≥1:40.
Results
From a total of 46 enrolled participants, 35 (76%) reported occupational exposure
to cattle and 11 (24%) reported no cattle exposure. Of the 35 cattle-exposed, 20 (57%)
reported having swine exposure also. Similar demographics were seen in the cattle-
exposed and non-cattle-exposed groups (Table 2-2). Of the cattle-exposed, 31 (89%)
reported exposure to calves one year old or younger. Thirty-nine samples (35 cattle-
exposed and four non-cattle-exposed) were tested using HI assays, with 32 (91%)
positive among the cattle-exposed (maximum titer 1:160) and 3 (75%) among non-
cattle-exposed individuals (maximum titer 1:80) (Table 2-1). Due to limited resources
and amounts of serum samples, only 39 (85%) could be tested by both HI and MN
assays (35 cattle-exposed and 4 non-cattle-exposed). All serum samples were tested
by MN assay; 34 (97%) sera from cattle-exposed (maximum titer 1:320) and two (18%)
non-cattle-exposed persons were positive. One non-cattle-exposed individual with an
25
MN titer of 1:80 reported non-occupational contact with swine, and contact with cattle
>10 years prior to serum draw. Overall, 94% of the cattle-exposed persons had a
positive HI and MN titer. Characteristics of the seropositive cattle-exposed persons
included: 41% worked with sick cattle, 4% worked with swine diagnosed with respiratory
illness, and 41% self-reported febrile illness in the previous year.
Virus yields in ST cells are observed ten-fold higher when grown at 33°C versus
37°C, and virus yields are not affected by the presence of TPCK-trypsin. Detection of
IDV using rt RT-PCR roughly corresponded to viral titers. No antigenic cross-reactivity
between D/Bovine/KS and ICV (strain C/Taylor/1233/47) was observed in our assays
(Table 2-4). Similar findings were obtained in other antigenic cross-reactivity studies
between D/Bovine/KS, and circulating influenza A, B, and C viruses (40, 47).
Discussion
Given recent findings of greater detection of active IDV infection and a 94%
seroprevalence among calves in Mississippi (48), it is plausible that cow-calf operations
(which predominate in Florida’s cattle industry) pose a greater risk of IDV transmission
to humans than operations housing adolescent and adult cattle (56). The 18%
seroprevalence among our 11 non-cattle-exposed individuals detected by MN is likely
attributed to a small sample size, compared to 1.3% seroprevalence previously reported
among the general population (40). The questionnaire did not include goat and sheep
exposure, which could attribute to IDV antibody detection to if these animals are able to
be infected and transmit IDV (49). These results indicate that cattle-exposed individuals
may have been infected with IDV through occupational zoonotic transmission.
26
Table 2-1. Influenza D virus HI results obtained with chicken or turkey red blood cells
Titer Chicken RBCs Turkey RBCs
<1:40 3 3 1:40 2 2 1:80 6 6
1:160 0 0 1:320 1 1 1:640 0 0
HI: Hemagglutination Inhibition; RBCs: red blood cells Table 2-2. Demographic characteristics of participants with and without cattle-exposure,
Florida 2011
Demographic Characteristics
Cattle-exposed, n (%), n=35 (76)
Non-cattle exposed, n (%), n=11 (24)
Gender M 23 (66) 7 (64) F 12 (34) 4 (36)
Age 18-25 years 11 (31) 2 (18) 26-45 years 11 (31) 5 (46) 46-65 years 7 (20) 3 (27)
≥ 66 years 6 (18) 1 (9) Exposure to calves 31 (89) 0 (0) Exposure to swine 20 (57) 1 (9)
27
Table 2-3. Serology results from hemagglutination inhibition and micronuetralization assays on serum from participants with and without cattle exposure, and exposure to both cattle and swine, Florida 2011
Assay Titer Cattle exposure, n
n=35 No cattle exposure,
n=11 Assay Controls
HI
<1:40 3 1 PBS + Negative
serum 1:40 1 2 1:80 20 1 1:160 9 0 1:320 2 0 1:640 0 0 Anti-D/Swine/OK
Not
Tested 0 7
MN
<1:40 1 9 PBS + Negative
serum 1:40 2 1 1:80 18 1 1:160 11 0 1:320 3 0 Anti-D/Swine/OK
HI: Hemagglutination inhibition assay; MN: Microneutralization assay; PBS: Phosphate buffered saline Table 2-4. Cross-reactivity results between anti-IDV antibodies and influenza A H1N1,
and influenza C virus strains via HI assay.
Influenza virus strain name
Rabbit anti-D/swine/OK/1334/2011
Rabbit anti-C/Taylor/1233/47
Mouse anti-A/Mexico/4108/2009
D/Bovine/Kansas/1-35/2010
1:320 <1:10 <1:10
C/Taylor/1233/47 <1.:10 1:640 <1:10 A/Mexico/4108/2009
(H1N1) <1:10 <1:10 1:320
28
CHAPTER 3 GENETIC CHARACTERIZATION OF CHIKUNGUNYA VIRUS FROM A 2014
OUTBREAK IN HAITI AND IDENTIFICATION OF CO-INFECTING ARBOVIRUSES
Background
Historically, Chikungunya virus (CHIKV) (family Togaviridae, genus Alphavirus)
outbreaks and epidemics have been isolated to regions in Asia, Africa and the Pacific
Islands, but emerged in the Americas with sustained autochthonous transmission for the
first time in 2013 (57, 58). First isolation of CHIKV dates to 1952 in present-day
Tanzania, but remained relatively unstudied until a large outbreak in the Indian Ocean
Islands in 2006 (22, 59). Chikungunya virus is an alphavirus that causes similar acute
symptoms to flaviviruses, such as dengue virus (DENV), but also can cause persistent
arthralgia (up to two years) in some cases (22). Upwards of 90% of infected individuals
are symptomatic (60). Because acute symptoms are similar– fever, headache, myalgia,
posing a challenge for differential diagnosis, can lead to over-/under-reporting of CHIKV
cases; either overreporting of other arbovirus infections as CHIKV or underreporting of
CHIKV by misdiagnosing as another arbovirus (22). CHIKV is considered a risk-group 3
agent and work must be performed in a BSL-3 facility.
Two distinct CHIKV lineages have been well-characterized, the Asian lineage
and the East/Central/South African (ECSA) lineage (59), with recently described third
and fourth lineages, originating from West Africa and the Indian Ocean epidemics,
respectively (61, 62). Strains causing outbreaks in China, Indian Ocean Islands, and
Italy in the early- to mid- 2000s have belonged to the ECSA lineage, but a large
epidemic in India had sequences that fell into both Asian and ECSA lineages (63-65).
The recent epidemic in the Americas and Caribbean is suspected of Asian lineage, but
isolates from Brazil have aligned more closely with the ECSA lineage (57, 66). Another
29
genetic characteristic, which has been associated with severe CHIKV outbreaks, is the
presence of an E1 surface glycoprotein mutation, A226V, first characterized in the
Indian Ocean Islands outbreak (59). This mutation, along with another identified
mutation, L210Q, in the E2 surface glycoprotein have allowed for viral adaptation to the
Aedes albopictus vector (67). The identification of these mutations leads to increased
threat of CHIKV spread to areas where the primary vector Ae. aegypti are rare, but Ae.
albopictus are more common; such as areas in the United States and South America (2,
67). And perhaps there are additional mutations which functions have yet to be
identified that are contributing to the ongoing transmission of CHIKV in the Americas.
To date, there is little CHIKV information from Haiti, with sparse information from
other countries involved in the epidemic in the Americas and Caribbean that began in
2013 (68, 69). While the weekly case count has declined, there are still thousands of
suspected cases being reported to the World Health Organization (WHO) (69). Haiti has
not reported any CHIKV case statistics to WHO since 2014, and of the suspected
65,000 cases, there were 14 confirmed (68); still WHO and other organizations
including the US Centers for Disease Control and Prevention (CDC) recognize the
continued risk for CHIKV transmission in Haiti.
Objectives
By characterizing a CHIKV outbreak in Haiti during 2014 as a probable subset of
the Caribbean outbreak, and adding genetic information to the existing body of
sequences, a more complete picture can be created of how CHIKV is spreading through
the Americas. Findings from the 2014 CHIKV outbreak in Haiti could provide a link to
subsequent CHIKV outbreaks in the region, through cases imported to or exported from
Haiti. Additionally, testing samples and cultures for co-infections will add to the limited
30
existing body of literature regarding arboviral co-infections and rate of co-infection. We
hypothesize the genome analyses will reveal nearly identical CHIKV strains circulating
in Haiti as in surrounding Caribbean countries, and that the rate of co-infections will be
about 10%, similar to previous reports (33).
Methods
Sample Collection
Blood samples were collected in K2EDTA tubes (BD Vacutainer, Becton,
Dickinson and Company, Franklin Lakes, NJ, USA) from schoolchildren with acute
febrile illness between May and August 2014. This was conducted in collaboration with
an ongoing project with the Christianville Foundation, which operates four schools in the
Gressier/Leogane region (Figure 3-1) (70). Initially, the samples were screened for
CHIKV and DENV using molecular methods. Whole blood samples were centrifuged
and plasma stored at -70°C until further testing. The University of Florida IRB and the
Haitian National IRB approved all protocols. Written informed consent was obtained
from parents or guardians of all study participants. All virus isolation and RNA extract
work was performed in a BSL-3 facility at the University of Florida Emerging Pathogens
Institute. Specific disposable personal-protective equipment was worn, including a
Powered Air Purifying Respirator (PAPR), gown, two pairs of gloves, and two layers of
shoe covers.
Sample Culture
Utilizing epithelioid cells derived from African Green Monkey kidneys (Vero E6,
CRL-1586), obtained from ATCC (Manassas, VA, USA) samples were cultured for the
isolation of CHIKV and other viruses present. This cell line was employed because the
cells support most arbovirus growth, CHIKV grows quickly and the cells do not produce
31
interferon (71). Plasma samples were filtered through 0.45µM sterile filters due to the
presence of environmental bacteria (mainly Staphylococcus aureus) due to collection
practices where tubes were handled outside of a sterile biosafety cabinet in a laboratory
also conducting bacteriology work. Cells were maintained in cell culture medium
comprised of aDMEM (advanced Dulbecco’s modified essential medium) supplemented
with 10% low antibody, heat-inactivated, gamma-irradiated FBS (fetal bovine serum),
GlutaMAX (L-alanine and L-glutamine supplement that is more stable in solution than L-
glutamine, Invitrogen, Carlsbad, CA, USA), and PSN antibiotics (50 µg/ml penicillin,
50µg/ml streptomycin, 100µg/ml neomycin) at 37°C with 5% CO2. Confluent cell
cultures were split into 25cm2 flasks 24 hours prior to infecting so that cell monolayers
were 60% confluent. Culture medium was removed and inoculum containing 100µl
filtered serum sample and 400µL aDMEM with 5% low antibody, heat-inactivated,
gamma-irradiated FBS, GlutaMAX, and PSN was added to the monolayer and rocked
every 15 minutes for 1 hour at 37°C with 5% CO2. A negative control (non-infected)
flask was inoculated with 500ul of DMEM without virus or plasma for 1 hour. After
allowing for virus adsorption for the hour, inoculum was removed and replaced with 3ml
DMEM with 10% low antibody, heat-inactivated, gamma-irradiated FBS, GlutaMAX, and
PSN and returned to incubate at 37°C with 5% CO2. Cultures were refed every 3 days
by the removal of 1.0ml of spent media and replacement with 1.0ml of DMEM with 5%
low antibody, heat-inactivated, gamma-irradiated FBS for up to 15 days’ post-
inoculation, or until visual observation of CHIKV-induced cytopathic effects (CPE).
Spent media (1ml) was collected at initial observation of CPE, then the cells refed with
1ml of DMEM with 5% low antibody, heat-inactivated, gamma-irradiated FBS,
32
GlutaMAX, and PSN, and when CPE was observed throughout 80% of the monolayer a
final collection of 2ml spent media, followed by manual scraping of the cell monolayer
and 1ml of cells was harvested.
Molecular Detection
RNA was extracted from both unprocessed plasma samples, lysate (spent media
containing free virus particles) and lysed cells using the QIAamp viral RNA mini kit per
manufacturer’s protocol (QIAGEN Inc, Valencia, CA, USA). To verify that CHIKV had
been inactivated by the RNA extraction process, 10% (7uL) of the extracted viral RNA
(vRNA) was inoculated on Vero E6 monolayers in a T25 flask (growth surface = 25cm2)
at 60% confluency; the cells were observed for 10 days to ensure no CHIKV-specific
CPE occurred. Following confirmation of CHIKV inactivation, vRNA was tested for
CHIKV (72), DENV serotypes 1-4 (73), and ZIKV (74) by real-time RT-PCR (rtRT-PCR)
following published protocols. Sample cultures displaying non-CHIKV CPE and that
were negative for DENV and ZIKV by rtRT-PCR were screened with a duplex RT-PCR
for other alphaviruses and flaviviruses (75).
Sequencing
To capture the span of the outbreak, three CHIKV isolates were selected from
May, July, and August for full genome sequencing. Additional isolates that had co-
infections were also sequenced for CHIKV using a primer walking method (Table A-1).
Utilizing sequencing primers that spanned the whole genome in 800bp overlapping
segments, amplification of each segment was performed using Accuscript high fidelity
first strand cDNA synthesis kit (Agilent Technologies, Santa Clara, California) followed
by PCR with Phusion polymerase (New England Biolabs, Ipswich, MA, USA). The 5’
and 3’ ends of the viral genomes were obtained using RNA-ligase mediated (RLM)
33
systems for 5’ and 3’ Rapid Amplification of cDNA Ends (RACE) per the manufacturer’s
protocols (Life Technologies, Carlsbad, CA, USA). This method of sequencing allows
for complete ‘nose-to-tail’ sequencing of cDNA comprised of both the translated and
untranslated regions of the genome. Amplicons were purified, sequenced bidirectionally
using Sanger Sequencing, and assembled with the use of Sequencher DNA sequence
analysis software v2.1 (Gene Codes, Ann Arbor, MI, USA).
Available CHIKV, ZIKV, and DENV type 2 sequences were obtained from NCBI
(www.ncbi.nlm.nih.gov) to compare to our Sanger sequencing results. Direct
comparisons of nucleotide substitutions were made between CHIKV whole genome
sequences from Haiti. For analyses, only complete genome sequences and the
sequences obtained in this study were considered. The nucleotide sequences were
aligned using ClustalW (76). Two basic phylogenetic trees were obtained through NCBI
using Neighbor-Joining and Minimum Evolution methods. While these methods are very
basic means of describing the sequences’ evolution and relationships, they are quick
and have relatively few assumptions (77). The trees generated are not likely to be the
most accurate phylogenetic trees, as other methods will take into account a molecular
clock and evolutionary time scale, but this provides a foundation for a more advanced
phylogenetic analysis.
Results
Samples
Among the 305 plasma samples taken from schoolchildren during the 2014
CHIKV outbreak in Haiti, 100 tested positive via real-time RT-PCR for CHIKV (72). Of
the 100 CHIKV-positive samples, attempts were made to isolate virus from 72, with the
remaining samples not inoculated onto cell cultures due to insufficient sample volume.
34
CHIKV-induced CPE, consisting of cell membrane blebbing, cell lysis and apoptosis,
were observed on average 5 dpi, with some samples displaying advanced CPE as early
as 2pi and others not until 20dpi (Fig. 3-2). Eleven samples (15%) did not display
expected CHIKV CPE, perhaps due to non-viable virus in the plasma, or due to the
presence of bacteria which killed the cells before the virus could replicate. Eight other
samples (Table 3-1) did not display expected CHIKV-induced CPE (Fig. 3-3), which
were instead caused by another virus: ZIKV (n=6), DENV type 2 (n=1), and Mayaro
virus (n=1) as identified by RT-PCR (73-75).
Sequencing
A total of 10 CHIKV isolates were fully sequenced with an additional five CHIKV
isolates were deposited to the World Reference Center for Emerging Viruses and
Arboviruses (University of Texas Medical Branch, Galveston, TX, USA) to be fully
sequenced at a future date (Table 3-2). Among the 10 full genome sequences, over
99.9% similarity was observed. One synonymous substitution was observed between
virus isolate CHIKV/Haiti-1/2014 and the other isolates at nt position 2440. The other
CHIKV sequences from Haiti are also highly similar, suggesting only one introduction of
CHIKV into Haiti in 2014 (Fig 3-4).
The six ZIKV sequences are highly similar (99.9%) to each other, but surprisingly
differ more from the December 2014 ZIKV isolate sequence (99.6%). There were 32 nt
differences observed from the summer 2014 ZIKV sequences to the December 2014
sequence, two of those were missense mutations (position 19 and 10,301). While both
groups (May-June 2014 and December 2014) fall into the Asian lineage (98% similar
compared to 89% similar to the African lineage), the difference in sequences between
May and December 2014 suggest multiple introductions of ZIKV into Haiti (Fig 3-5).
35
The co-infection with CHIKV and DENV-2 was sampled on June 11, 2014 and
the sequence was highly similar to the DENV-2 isolates from French Guiana and Peru
in 2006 and 2010, respectively. The Haiti DENV-2 isolate is also similar to the isolate
from Haiti in 2016 with only one synonymous mutation. The genome of DENV-2 virus
appears to be stable throughout the Americas and the Caribbean, as few outbreaks
have been noted in the past decade; however low level sustained transmission has
been observed.
The MAYV sequencing results from the co-infection with CHIKV and MAYV in
August 2014 are pending. This is the earliest documented case of MAYV in Haiti to
date.
Discussion
Chikungunya virus outbreaks commonly last one season, up to one year; CHIKV
has been circulating throughout the Caribbean countries for years now, with low level
transmission starting to reemerge again in countries affected in 2014. Haiti experienced
a large outbreak during 2014, but the number of positive cases quickly declined. Based
on our study, the genome did not change during the outbreak, indicative of one
introduction leading to the outbreak. Next generation sequencing (NGS) would provide
information on quasi-species present in serum; however, for the cultured samples and
for specimens collected within a short timeframe, the consensus sequences provide
sufficient information. Additionally, having sequenced the noncoding region of the virus
genome can add to further analyses of secondary structural changes or changes in
replication efficiency (78, 79). The sequences did not contain the A226V or L 210Q
mutations, which are historically associated with more adverse outcomes and change in
vector competency (67, 80, 81). While we do not know for sure, the primary vector was
36
Ae. aegypti future studies that include vector surveillance could shed light on
transmissibility of disease or change in vector competency before an outbreak occurs.
Additional inclusion of serology studies on the CHIKV-positive individuals would also
provide a more complete picture on the recurrence of CHIV infection, which is sparse in
the literature (82).
Identification of co-infections in this study fell close to the published expected co-
infection rate (8% vs. 10%, respectively). What remains to be determined is if there is
the potential for one mosquito to transmit multiple pathogens to one individual, and if
there are more (or less) severe outcomes associated with co-infections. In culture,
alphaviruses and flaviviruses utilize the same cellular machinery for replication, so they
(or two viruses of the same family) can hinder each other’s growth. One questions if this
happens in vivo, and if there is a protective factor provided by a co-infection with lower
viremia. Our molecular data suggest that a co-infection is antagonistic, in that the Ct
values of co-infected plasma are generally higher than in mono-infections of CHIKV.
Limitations of this study include plasma sample contamination, storage
conditions, and the use of sequence-specific primers. A number of plasma samples had
bacterial contamination, from the collection environment or otherwise, which in culture
overwhelmed cells before virus infection could be detected. In addition, samples were
stored for prolonged periods at -80°C under suboptimal conditions for two years before
additional molecular and culture methods were employed. During this time, it is possible
viral degradation occurred and pathogens went undetected. The molecular techniques
employed only identified the viruses we targeted – CHIKV, DENV1-4, and ZIKV. The
MAYV-positive co-infection was suspected by observation of different CPE and
37
confirmed by MAYV nested primers (75). More resources would be needed to deep
sequence samples for additional pathogens.
38
Table 3-1. Molecular detection results of arbovirus co-infections identified in human plasma samples, Haiti 2014.
Accession # Date
Collected
Real-time RT-PCR in
Haiti CHIKV*
Real-time RT-PCR @ UF*
DENV1 DENV2 DENV3 DENV4 ZIKV
14-1-0029 29-May-14 17 - - - - 40
14-1-0033 29-May-14 18 - - - - 37
14-1-0036 2-Jun-14 25 - - - - 37
14-1-0054 5-Jun-14 36 - - - - 36
14-1-0074 10-Jun-14 16 - - - - 37
14-1-0078 11-Jun-14 39 - 38 - - -
14-1-0097 24-Jun-14 30 - - - - 35
* Values reported are cycle threshold values
Table 3-2. Whole genome sequences of Chikungunya virus isolates from Haiti, 2014.
Sequence designation Date collected GenBank Accession
Haiti-1/2014 02-June-2014 KX702401 Haiti-2/2014 09-June-2014 KX702402 Haiti-3/2014 29-May-2014 KY415978 Haiti-4/2014 29-May-2014 KY415979 Haiti-5/2014 05-June-2014 KY415980 Haiti-6/2014 10-June-2014 KY415981 Haiti-7/2014 11-June-2014 KY415982 Haiti-8/2014 02-June-2014 KY415983 Haiti-9/2014 24-June-2014 KY415984
Haiti-10/2014 13-August-2014 KY415985 Haiti-11/2014* 23-May-2014 Nd Haiti-12/2014* 03-June-2014 Nd Haiti-13/2014* 05-June-2014 Nd Haiti-14/2014* 06-June-2014 Nd Haiti-15/2014* 10-June-2014 Nd
* Denotes isolate submitted to World Reference Center for Emerging Viruses and Arboviruses; Nd = not deposited to GenBank as of February 2017
39
Figure 3-1. Map of Haiti featuring location of Gressier/Leogane areas and collection locations.
40
A. Non-infected, Vero E6 B. Non-infected, Vero E6
C. Plasma, Vero E6 8dpi D. Plasma, Vero E6 6dpi E. Plasma, Vero E6 8dpi
400X
400X
200X
200X 200X
Figure 3-2. Isolation of Chikungunya virus from plasma samples in cell culture. (A) and (B) Non-infected Vero E6 cells (C) through (E) filtered plasma samples
41
400X 200X
200X 400X
A
C
B
D
Figure 3-3. Co-infected plasma samples in cell culture. (A) and (B) non-infected Vero E6 cells (C) and (D) filtered plasma samples, collected 6dpi and 8dpi respectively.
42
Figure 3-4. CHIKV tree generated by Neighbor Joining Method, NCBI BLAST. Strains isolated in Haiti, 2014 are denoted in red. Collapsed leaves for tree clarity are labeled with number of strains in that cluster and the country(ies) of origin. Red arrow denotes only other sequence from Haiti outside of the 12 clustered sequences.
43
Figure 3-5. ZIKV tree generated by Neighbor Joining Method, NCBI BLAST. Strains isolated in Haiti, 2014 are denoted in red, with Zika virus strain Haiti/1225/2014 being the isolate from December 2014, and the remaining 6 isolated in May and June 2014 cluster collapsed and labeled ZIKV, Haiti May-June 2014.
44
CHAPTER 4 ZIKA VIRUS DETECTION AND ISOLATION FROM PATIENTS WITH DOMESTIC OR
TRAVEL-ACQUIRED CASES OF ZIKA FEVER
Background
In addition to the emergence of CHIKV in the Americas in 2013, Zika virus (ZIKV)
has also emerged, raising more concerns of the spread and the threat of arboviruses
and their vectors into naïve environments. Arbovirus infections present as acute febrile
illnesses, making them difficult to diagnose based on medical evaluation alone. With
ZIKV, there is a high (80%) likelihood of an infected person being an asymptomatic
carrier, and a very low risk of ZIKV-induced mortality (<1%) (83). Symptoms more
specifically seen with ZIKV infections are conjunctivitis, maculopapular skin rash, and
muscle or joint pain in addition to mild fever, headache, malaise, and swelling (edema)
of the ankles all of which generally subside in a week (13, 84, 85).
Zika virus (family Flaviviridae, genus Flavivirus) is a mosquito-borne RNA virus
(86, 87) that beginning in 2015 caused outbreaks and epidemics of Zika Fever (ZF) in
the Americas, Caribbean, Cape Verde, and the Pacific Islands (88-91). The first
isolation of ZIKV was in 1947, from a febrile sentinel Rhesus monkey in the Zika Forest
of Uganda (92). In Africa and Asia, a sylvatic cycle maintains ZIKV transmission that
involves mosquitos and non-human primates. But antibodies to ZIKV have been
detected in large mammals and in rodents, though the role of these animals as
reservoirs, if any, remains unknown (93). Prior to 2005, sporadic cases of ZF were
reported solely in Africa and Asia (94). The first well-described epidemic outside these
geographic regions occurred in Yap State, Micronesia, during 2007 where the majority
of the population developed ZF (83). For that outbreak, the local mosquito vector went
undetermined, as no ZIKV was isolated or detected by molecular methods from
45
mosquitoes (83). A second well-documented ZF epidemic (again outside of endemic
regions) began in October 2013 in French Polynesia, from which it spread to New
Caledonia, Cook Islands, and Easter Island by 2014 (95, 96). In those outbreaks, about
80% of ZF cases were asymptomatic (83, 95). During this outbreak, Guillain-Barré
syndrome (GBS) was reported in association with ZIKV infection for the first time (97).
In December 2014, the first autochthonous cases in the Caribbean were identified in
Haiti (98), and by April 2015, in Brazil (99). In February 2016, the Brazilian Ministry of
Health estimated an incidence of more than one million cases of ZF, during which a
significant increase of GBS and microcephaly cases were detected (84, 100). Between
October 2015 and March 2016, 6,158 cases of microcephaly and/or central nervous
system malformation were documented in contrast to the estimated 163 average annual
cases (101). Additional neurological and congenital abnormalities that may stem from
ZF are being studied (84).
Although ZIKV was identified nearly 70 years ago, significant gaps in knowledge
still exist. An underlying unknown is vector competency and host risks associated with
an evolving viral genome – arboviruses face a unique challenge of fitness in that both
vector and host competency must be achieved in order for the virus to propagate (2,
28). The predominant vectors for both CHIKV and ZIKV are Aedes albopictus and Ae.
aegypti, with laboratory studies showing vector competency. However, in the
environment, it is not well understood if other vectors transmit these viruses (6, 12). In
addition, little research has been conducted to detect these circulating arboviruses in
field-caught mosquitos in areas where human cases are present, or where the threat of
human infection is likely (15, 33, 102-105). While research exists on DENV antibody-
46
dependent enhancement, a hypothesis that suggests antibodies from an initial DENV
infection cause increased severity in subsequent infections by the presence of
antibodies facilitating increased viral loads (106); recent research suggests this can
occur with ZF following a DENV infection (107). This could have severe implications on
vaccine development. It is still not understood what, if any, viral genetic mutations have
led to congenital malformations or GBS outcomes.
Objectives
With so many questions surrounding ZIKV, establishing successful protocols and
procedures to optimize detection and isolation of the virus from patients are essential.
Successful isolation of infectious ZIKV virions from blood, saliva, semen, vaginal
secretions, and urine hint at the broad dissemination of the virus in the human host (38,
88, 108, 109). However, optimal timing of specimen collection after onset of symptoms,
and presence of the virus in different specimens (such as urine or saliva) after acute
infection, are not well understood (108, 110-112). This study describes ZIKV
propagation and detection procedures, as well as methods for specimen collection from
cases with a suspected ZIKV infection, and the processing and testing thereof.
Methods
Cell Lines
Two cell lines, Vero E6 (CRL-1586; African green monkey kidney epithelial cells)
and LLC-MK2 (CCL-7; Rhesus monkey kidney epithelial cells), were obtained from
American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured in aDMEM
with 10% low antibody, gamma-irradiated, heat inactivated FBS, GlutaMAX, and PSN at
37°C with 5% CO2. These two cell lines were utilized for their ability to support the
growth of most known arboviruses (including ZIKV), and lack of IFN production (113).
47
Zika Virus Propagation and Quantification in Cultured Cells
Six ZIKV strains were obtained from Biodefense and Emerging Infections
Research Resources Repository (BEI Resources, Manassas, VA, USA), with five
strains representative of virus in circulation in the Americas and the Caribbean (Asian
lineage), and one from the old African lineage - the original isolate, ZIKV MR-776, which
is considered the reference strain (Table 4-1). These strains were used to refine ZIKV
quantification and detection methods, as well as serve as positive controls for molecular
diagnostics.
Each of the six ZIKV strains were inoculated into separate T25 (25cm2 growth
surface) flasks with either LLC-MK2 or Vero E6 cells at 60% confluency for observation
of ZIKV-induced CPE. Culture medium was removed and inoculum containing 25µL
stock virus and 475µl aDMEM with 3% low antibody, heat-inactivated, gamma-irradiated
FBS, GlutaMAX, and PSN was added to the monolayer and gently rocked once every
15 minutes at 37°C with 5% CO2 for 1 hour. A negative control (non-infected) flask for
each cell line was inoculated with 500µl of aDMEM without virus. After allowing for virus
adsorption for 1 hour, an additional 2ml of aDMEM with 10% low antibody, heat-
inactivated, gamma-irradiated FBS, GlutaMAX, and PSN was added to each flask,
which were then incubated at 37°C with 5% CO2. Cultures were maintained with the
replacement of 1ml of aDMEM every 3 days for up to 10 days’ post-inoculation, or until
visual observation of ZIKV-induced cytopathic effects (CPE). Expected ZIKV-induced
CPE were perinuclear vacuolation followed by apoptosis. Spent media (2ml) was
collected at initial observation of CPE, cells were refed with 2ml of DMEM, and final
collections of spent media and lysed cells was taken when CPE were observed in 50%
of the monolayer.
48
Virus yields were quantified in either Vero E6 or LLC-MK2 cells via 50% tissue
culture infectious dose (TCID50) assays (53). Vero E6 or LLC-MK2 cells were plated on
96-well plates to reach confluency in 24 hours, then 10-fold serial dilutions of virus
cultures were added to wells in replicates of eight. Plates were incubated for 7 days at
37°C with 5% CO2 and then titers determined.
For comparison, plaque assays were also used to quantitate ZIKV and obtain
viral titers. Plaque assays are more difficult and costly to perform but generate a direct
count of infectious particles in a given volume, whereas TCID50 assays yield a statistical
measure of virus quantity and concentration. For many viruses, somewhat higher titer
values are obtained using TCID50 determinations, so plaque assays were also
performed to gauge the accuracy of the TCID50 values. Plaque assays are also useful in
virus purification as individual plaques can be picked and the virus particles therein
propagated to generate virus stocks. Plaque assays were performed with newly
confluent Vero E6 or LLC-MK2 cells in 6-well plates, to which 250µl of virus dilutions of
10-2, 10-3, 10-4, 10-5, and 10-6 in aDMEM were added to each well. After inoculation, the
6-well plates were rocked every 15 minutes for 1 hour at 37°C with 5% CO2. Inocula
were then removed, the monolayer washed twice with serum-free Eagle’s minimum
essential medium (EMEM) and the cells overlayed with 3ml of 1:1 solution of 1.6%
melted agarose with EMEM and 3% low antibody, heat-inactivated, gamma-irradiated
FBS with PSN. After the agarose solidified, the plates were incubated upside down for 5
days at 37°C with 5% CO2, after which an additional 2ml overlay with 1:1 solution of
1.6% agarose and serum-free EMEM with 0.14mg/ml neutral red was added. Plaque
morphology was observed and plaques counted at 7 dpi.
49
Patient Samples
Individuals who presented to the infectious disease clinician at UF Health with
symptoms of ZF and travel history to countries with ongoing ZIKV transmission (per
CDC travel recommendations) between January and May 2015, provided informed
consent and blood, saliva, and/or urine samples. As the specimens were collected at
various stages of the project, methods were not yet harmonized. Whole blood was
collected into acid citrate dextrose tubes (ACD Vacutainer blood collection tube, Becton
Dickenson and Company, Franklin Lakes, NJ, USA). Plasma aliquots were provided in
sterile, noncytotoxic cyrogenic tubes. Saliva samples were collected by swabbing the
patients’ salivary glands with a Copan flocked nylon swab, then inserting the swab into
a Copan transport tube containing universal virus transport media (VTM). The Copan
VTM is a room temperature stable modified Hank’s balanced salt solution supplemented
with bovine serum albumin, antibiotics, and sucrose (a cryoprotectant), and glass beads
(Copan Diagnostics, Inc., Murrieta, CA, USA). Urine was collected aseptically in a 50ml
sterile polypropylene tube. All specimens were immediately transported to the
laboratory following collection, and aliquoted and stored at -80°C until further testing.
All whole blood samples were aliquoted into 500µl volumes to prevent multiple
freeze-thaw cycles of the sample. Each saliva swab was swirled against the sides of the
tube to extrude material, removed, and the tube containing the glass beads and VTM
was vortexed to aid in lysis of infected cells and release of viable virus into the VTM,
centrifuged at low speed to pellet cellular debris, and the supernatant aliquoted. Urine
from the first patient was immediately aliquoted following collection (without separation)
to prevent multiple freeze-thaw cycles of the sample. For subsequent patients, urine
was centrifuged and only supernatants were aliquoted so that sediments (including
50
casts, red blood cells, white blood cells, bacteria, and yeast) from the urine would not be
added to the cell cultures.
Specimens were also inoculated onto cell cultures, utilizing 6-well plates of 60%
confluent LLC-MK2 or Vero E6 cells, or 80% confluent MRC-5 cells. Culture medium
was removed and inoculum containing 100µl specimen and 150µl aDMEM with 3% low
antibody, heat-inactivated, gamma-irradiated FBS, GlutaMAX, and PSN was added to
the monolayer and gently rocked once every 15 minutes at 37°C with 5% CO2 for 1
hour. A negative control (non-infected) flask for each cell line was inoculated with 500µl
of aDMEM without virus. After allowing for virus adsorption for 1 hour, an additional 2ml
of aDMEM with 10% low antibody, heat-inactivated, gamma-irradiated FBS, GlutaMAX,
and PSN was added to each flask, which were then incubated at 37°C with 5% CO2.
Cultures were maintained with the replacement of 1ml of aDMEM every 3 days for up to
31 days’ post-inoculation, or until visual observation of ZIKV-induced cytopathic effects
(CPE). Spent media (2ml) was collected at initial observation of CPE, the cells refed
with 2ml of DMEM, and a final collection of spent media and lysed cells was taken when
50% of the monolayer displayed CPE.
Molecular tests of specimens and spent media or cell lysates were performed by
a published conventional RT-PCR protocol, and secondly, a real-time RT-PCR protocol
to confirm positive results (74, 114, 115). The QIAamp viral RNA mini kit (QIAGEN Inc,
Valencia, CA, USA) was used to extract viral RNA (vRNA) from specimens and spent
cell media or cell lysates. Conventional RT-PCR (114) was performed with Omniscript
RT (QIAGEN Inc, Valencia, CA, USA) using the reverse primer and 5μl extracted vRNA
per manufacturer’s directions, followed by OneTaq PCR (New England Biolabs,
51
Ipswich, MA, USA) with 3μl cDNA, and final concentrations of 0.5μM forward primer and
0.8μl reverse primer (114). RT-PCR results were visualized using gel electrophoresis
with a 2% gel stained with 0.1 μg/μl ethidium bromide; the target amplicon was 192bp
(114). Real-time RT-PCR was conducted using Superscript III One-Step RT-PCR
System with Platinum Taq (ThermoFisher Scientific, Waltham, MA, USA), and published
primer and probe concentrations and conditions (74, 115). The two primer/probe sets
were utilized to confirm detected and suspected positives.
Sequencing
The CHIKV, DENV-2, and ZIKV genomes were fully sequenced using a genome
walking method with primers that spanned the whole genome in ~800bp overlapping
segments (Table A-1, Table A-2, and Table A-3). Amplification of each segment was
accomplished using Accuscript high fidelity first strand cDNA synthesis kit (Agilent
Technologies, Santa Clara, CA, USA) followed by PCR with Phusion polymerase (New
England Biolabs, Ipswich, MA, USA). To obtain the 5’ and 3’ ends of the viral genomes,
systems for 5’ and 3’ Rapid Amplification of cDNA Ends (RACE) were used per the
manufacturer’s protocols (Life Technologies, Carlsbad, CA, USA). This method of
sequencing allows for complete ‘nose-to-tail’ sequencing of cDNA comprised of both the
translated and untranslated regions of the genome. Amplicons were purified, sequenced
bidirectionally using Sanger Sequencing, and assembled with the use of Sequencher
DNA sequence analysis software v2.1 (Gene Codes, Ann Arbor, MI, USA). Comparative
analyses against other full-genome sequences available in GenBank were conducted
using NCBI BLAST (National Center for Biotechnology Information, Bethesda, MD,
USA).
52
Results
ZIKV Lab Strains
On average, ZIKV lab strains began displaying CPE at 2dpi with advanced CPE
4-8dpi (Fig. 4-1). CPE was more noticeable earlier in LLC-MK2 than Vero E6, however,
virus yields for the two cell lines were similar.
ZIKV titers were observed 5dpi, and ranged from 5x105 to 5x107 TCID50/ml
(Table 4-2). No definite differences were found in titers taken from LLC-MK2 or Vero E6
cells. These titers were used for ZIKV concentrations in the plaque assay and standard
curve generation.
Plaque assay was performed on all ZIKV lab strains. Similar to the TCID50
results, there were no differences between the number of plaque forming units observed
in LLC-MK2 and Vero E6 within each strain. The quantification of pfu/ml corresponded
with the TCID50/ml (Table 4-2).
Patient Samples
Five patients (identified as sample1 through sample5) presented to the UF
Health infectious disease clinician with ZIKV symptoms between January and May
2016. Three patients (sample2, sample4, sample5) provided plasma, saliva, and urine,
and the remaining two provided whole blood, not plasma. Only one patient (sample4)
reported no travel in the previous 90 days, while three patients (sample1, sample3, and
sample5) traveled to Haiti, and one (sample2) had travel history to Colombia. An
additional individual (sample6) did not have ZIKV symptoms and provided saliva and
urine samples as a negative-specimen control. Sample4 tested negative for ZIKV by
molecular methods but positive for DENV type 2 (by both molecular and culture
53
methods), and sample5 and sample6 was negative for all CHIKV, DENV, and ZIKV
(Table 4-3).
All patient samples were grown in LLC-MK2, MRC-5, and Vero E6 cells. While
the lab strain ZIKV was only grown in two of these cell lines, MRC-5 were also
employed for patient samples as DENV strains from the Americas tend to produce
easily discernable CPE quickly in those cells, and also produce higher virus yields than
in LLC-MK2 or Vero E6 cells. There were CPE observed in at least one cell line for four
of the six patient samples, with no CPE observed from sample5 plasma, and sample5
and sample6 saliva and urine inoculations for 32dpi.
Cells inoculated with sample1 saliva and urine displayed early CPE by 2dpi, with
wide-spread CPE present by 9dpi, including perinuclear vacuolation in both LLC-MK2
and Vero E6 cultures (Fig. 4-2). Apoptosis was not apparent until 16dpi, at which point
spent media and cell lysate were collected. No noticeable CPE were observed in MRC-
5 cultures inoculated with saliva or urine, but the cells were scraped and collected
together with spent media 16dpi. Cells inoculated with sample1 blood did not form CPE
throughout a 32-day observation period. The patient provided a follow-up sample1.1
saliva and urine were cultured and began showing CPE 12dpi, even though the saliva
specimen tested negative for ZIKV by RT-PCR initially. Both sample1.2 follow-up saliva
and urine specimens were negative, even after culture for 21dpi.
Cells inoculated with sample2 blood, saliva and urine displayed wide-spread
CPE in LLC-MK2 and Vero E6 at 10dpi. Again, no CPE were observed in MRC-5
cultures over a 31-day observation period. This patient did not provide follow-up
specimens.
54
Cells inoculated with sample3 saliva and urine caused apoptosis in LLC-MK2
and Vero E6 16dpi, and MRC-5 cells were collected 3 days later at 19dpi when
cytoplasmic blebbing was observed. Sample3 blood did not display CPE in any cultures
throughout a 31-day observation period. This patient provided a follow-up saliva swab
and urine sample 36 days after symptom onset, these specimens were maintained in
culture for 35 days and no CPE were observed.
Sample4 serum, saliva, and urine were inoculated in cells for 21 days before
CPE were observed; although this did not appear as expected ZIKV CPE. No
perinuclear vacuoles were visible, but rather diffuse CPE in Vero E6 cells, and in MRC-
5 cytoplasmic blebbing and apoptosis were observed. This CPE is more consistent with
CPE observed in DENV-infected cells. This patient also provided follow-up serum and
urine samples and saliva swabs 9 days and 14 days after symptom onset. The first
follow-up, sample4.1 serum and urine again produced CPE consistent with DENV-
infected cells at 20dpi. No CPE were observed in the saliva culture nor the sample4.2
serum, saliva, or urine cultures maintained for 35 days.
Cells inoculated with sample5 blood, saliva, and urine, did not show CPE
throughout the 32-day observation period. This patient also provided two sets of follow-
up blood, saliva, and urine specimens (12 days’ and 14 days’ post-symptom onset),
none of which produced CPE throughout the 32-day observation period.
The specimen controls (sample6) did not produce CPE in any cell line for up to
32dpi as well.
The patient that traveled to Colombia (sample2) returned to UF Health 2 months
after the initial specimen collection with persistent arthralgia. Sample2 saliva and urine
55
were further analyzed for additional viruses. A plaque assay was performed on sample2
urine, grown in Vero E6 cells, collected 10dpi. Five days’ post-inoculation, the sample
produced two visually different CPE-forming plaques (Fig 4-3). This sample was co-
infected with CHIKV, and was confirmed by rtRT-PCR.
All patient specimens were first screened by RT-PCR, then RT-PCR was used to
screen their corresponding cell culture spent media and cell lysates (Table 4-4). Real-
time RT-PCR was also employed to confirm positive results. Cultured sample1 and
sample2 were ZIKV-positive in multiple cell lines (Fig. 4-4); moreover, the results of
rtRT-PCR and conventional RT-PCR were concordant, with the exception of detection
of ZIKV in sample1.1 urine and saliva and in sample2 blood and saliva cultured on LLC-
MK2 cells (Table 4-4). Sample1.2 urine initially tested negative by RT-PCR, but
following culture was positive, which indicates the amount of virus present could have
been below the detectable threshold of the RT-PCR methods. Sample2 blood had
higher Ct values in culture than in the original specimen; it is possible the cell lines for
virus isolation attempts were suboptimal for the virus strain. Sample3 blood was
negative, even after culture, but saliva and urine were positive. Sample3.1 saliva and
urine were negative, even after culture. Sample4 was DENV type 2 positive in urine
cultured in MRC-5 cells 20dpi (Ct value=26), and equivocal for DENV type 2 in
sample4.1 blood in Vero E6 cells 16dpi (Ct value=41). Sample5 and both follow-ups
were negative for ZIKV as well as DENV1-4 and CHIKV, perhaps due to later sampling
times and clearing of viremia, or another virus that was not tested for was the causative
agent. These results shed light on the value of attempting virus isolation from
56
specimens in multiple cell lines to best capture the information about what viruses are
present in a given sample.
Sequencing
The genomic sequences of the ZIKV and CHIKV from the co-infected patient with
recent travel to Colombia (sample2) were found to be highly similar to respective ZIKV
and CHIKV strains circulating in Colombia in 2015 and 2016 (both 99.9% similar) (116).
The ZIKV sequence from the patient with travel to Haiti (sample3) that was also
infected with DENV-2 showed high similarity with other ZIKV sequences in Haiti, but
also a 2015 Brazilian strain and 2016 Venezuelan strain (117). As noted previously,
there is suspected movement of ZIKV between Haiti and other countries affected by the
recent epidemic. Similar to the 2014 Haiti DENV-2 strain, this DENV-2 strain is highly
similar (99.9%) suggesting stability in the genome, as few outbreaks have been noted in
the past decade; however, low level sustained transmission has been observed.
The DENV-2 strain that was from a patient with no travel in the previous 90 days
was, again, very similar to the 2010 Peru strain (99.6%). While the timing of this DENV-
2 was within two months of the isolation of DENV-2 and ZIKV vo-infection from an
individual that traveled to Haiti, the similarity between the two strains was lower
(99.3%). There are little sequence data available in GenBank between 2011 and 2016,
so generation of a phylogenetic tree is much more difficult.
The ZIKV sequencing results from sample1 are pending.
Discussion
Zika virus remains a puzzling virus that can induce a number of long-term effects,
either in neonates or in adults. The ZIKV-positive specimens were collected soon after
onset of symptoms, or as follow-up samples from known ZIKV-positive patients. While
57
sample5 was ZIKV-negative even soon after symptom onset, and was CHIKV and
DENV-negative, they could have had another virus causing similar febrile symptoms.
Conflicting data have been published on the presence of ZIKV in different specimens
(urine, saliva, vaginal secretions, or semen) for prolonged periods of time after
symptoms subside, but here we detected virus in urine 10 days after symptom onset in
one patient, but did not detect ZIKV in another patient 10 days after symptom onset.
DENV was also detected in urine after 9 days passed symptom onset, indicating viral
shedding of flaviviruses occurs through the urinary tract, as other research supports
(118, 119). This study also supports culture methods to be vital to detection, especially
in specimens collected long after symptoms have subsided, when viral RNA may be
present below the threshold of molecular detection.
Past studies have used Vero E6 cells for ZIKV growth and detection but here we
find ZIKV producing more CPE in LLC-MK2. This does not necessarily coincide with the
virus growing better, but visual identification of ZIKV propagation seems to be better in
LLC-MK2 cells. Additionally, virus titration and plaque assay data coincide, and there
was no difference between LLC-MK2 or Vero E6 titer and plaque assay results. This
suggests that at high concentration of viable virus, both LLC-MK2 and Vero E6 cells are
sufficient for identification and quantification of ZIKV.
The use of plaque assays to discern co-infections was proven in this study. The
only reason a plaque assay was performed for sample3 was because of persistent
arthralgia reported to the physician. CHIKV would not have been identified without the
use of plaque assay techniques. Special note of persistent arthralgia should be taken
58
for potential CHIKV (or MAYV) infections, and if seen in patients diagnosed with another
pathogen, plaque assay should be employed.
ZIKV still poses numerous questions to researchers across diverse fields. A
collaborative approach to pathogenesis, vector/environmental implications, immunology,
and ecology could help shed light on what remains unknown about ZIKV. More in vitro
studies can deduce changes in ZIKV genome that has caused more severe congenital
and neurodegenerative outcomes. Additionally, field studies of vectors carrying ZIKV
should be conducted, especially in areas with active ZIKV infections. Immunological
impacts of ZIKV infections, whether reinfection is possible (or more severe), and
implications on DENV infection remain unknown. This study provides groundwork to
continue effective ZIKV surveillance for individuals with symptoms and for in vitro
studies, the use of LLC-MK2 cells could be beneficial in addition to or instead of Vero
E6.
59
Table 4-1. Zika virus strains
Lineage Strain Source, Location
Asian
FLR Human blood, Colombia H/PAN/2015/CDC-259249 Human serum, Panama H/PAN/2015/CDC-259359 Human serum, Panama MEX 2-81 Ae. aegypti, Mexico
PRVABC59 Human blood, Puerto Rico
African MR 766 Rhesus monkey blood,
Uganda
Table 4-2. Zika virus titration results
Zika virus strain Culture cell line TCID50/ml PFU/ml
FLR LLC-MK2 1x106 6x105
FLR Vero E6 1x106 5x105 H/PAN/2015/CDC-259249 LLC-MK2 5x107 9x106 H/PAN/2015/CDC-259249 Vero E6 1x107 4x106 H/PAN/2015/CDC-259359 LLC-MK2 3x107 9x106 H/PAN/2015/CDC-259359 Vero E6 1x107 6x106 MEX 2-81 LLC-MK2 8x105 7x105 MEX 2-81 Vero E6 5x105 4x105 PRVABC59 LLC-MK2 1x107 6x106 PRVABC59 Vero E6 1x107 3x106 MR 766 LLC-MK2 3x107 6x106 MR 766 Vero E6 1x107 2x106
Table 4-3. Sample collection
Patient ID Sample# Collection (days post-symptom onset)
Specimens collected Pathogen(s) identified
Haiti 1
1 2 days Whole blood, Saliva, Urine ZIKV
1.1 10 days Saliva, Urine ZIKV
1.2 16 days Saliva, Urine None
Colombia 1 2 3 days Serum, Saliva, Urine ZIKV + CHIKV
Haiti 2 3 8 days Whole blood, Saliva, Urine ZIKV + DENV2
3.1 36 days Saliva, Urine None
Local 1
4 6 days Serum, Saliva, Urine DENV2
4.1 9 days Serum, Saliva, Urine DENV2
4.2 14 days Serum, Saliva, Urine None
Haiti 3
5 10 days Whole blood, Saliva, Urine None
5.1 12 days Whole blood, Saliva, Urine None
5.2 14 days Whole blood, Saliva, Urine None
Control 6 NA Saliva, Urine None
60
Table 4-4. Zika virus molecular detection results
Sample RT-PCR Results Ct Value
Sample1 Blood Positive nt Sample1 Saliva Positive nt Sample1 Saliva, LLC-MK2 16dpi Positive 4 Sample1 Saliva, MRC-5 16dpi Positive 13 Sample1 Urine Positive nt Sample1 Urine, LLC-MK2 16dpi Positive 8 Sample1 Urine, MRC-5 16dpi Positive 13 Sample1.1 Saliva Negative 37 Sample1.1 Saliva, LLC-MK2 12dpi Positive 30 Sample1.1 Urine Positive 29 Sample1.1 Urine, LC-MK2 12dpi Positive 18 Sample1.2 Saliva Negative Nt Sample1.2 Urine Negative Nt Sample1.2 Saliva, LLC-MK2 15dpi Negative 0 Sample1.2 Urine, LLC-MK2 20dpi Positive 37 Sample2 Blood Positive 8 Sample2 Blood, LLC-MK2 10dpi Negative 20 Sample2 Blood, Vero E6 10dpi Positive 36 Sample2 Saliva Positive 12 Sample2 Saliva, LLC-MK2 10dpi Negative 13 Sample2 Saliva, Vero E6 10dpi Positive 33 Sample2 Urine Positive 16 Sample2 Urine, LLC-MK2 10dpi Negative 24 Sample2 Urine, Vero E6 10dpi Positive 18 Sample3 Blood Negative 0 Sample3 Blood, LLC-MK2 13dpi Negative 0 Sample3 Saliva Positive Nt Sample3 Saliva, MRC-5 19 dpi Positive 18 Sample3.1 Saliva Negative 0 Sample3 Urine Positive Nt Sample3 Urine, LLC-MK2 16dpi Positive 12 Sample3 Urine, MRC-5 19dpi Positive 20 Sample3.1 Urine Negative 0 Sample3.1 Urine, LLC-MK2 12dpi Negative Nt Sample4 Plasma1 Negative 0 Sample4 Saliva1 Negative 0 Sample4 Urine1 Negative 0 Sample5 Blood Negative 0 Sample5 Saliva Positive 0 Sample5 Urine Positive 0 Sample5.1 Blood Negative 0 Sample5.1 Saliva Negative 0 Sample5.1 Urine Negative 0
61
Table 4-4 continued
Sample RT-PCR Results Ct Value
Sample5.2 Blood Negative 0 Sample5.2 Saliva Negative 0 Sample5.2 Urine Negative 0 Sample6 Saliva Negative 0 Sample6 Urine Negative 0
1. Samples 4.1 and 4.2 are not included in the table due to initial sample being ZIKV negative, although these samples were tested for ZIKV vRNA and remained negative. nt: Not tested due to insufficient sample volume.
Figure 4-1. Isolation of lab strain Zika virus. A: Non-infected LLC-MK2 cells 3 days post-infection. B: Non-infected Vero E6 cells 3 days post-infection. C: ZIKV strain MR766, LLC-MK2 cells 2 days post-infection. D: ZIKV strain MR766, Vero E6 cells 2 days post-infection. E: ZIKV strain MEX 2-81, LLC-MK2 cells 3 days post-infection. F: ZIKV strain MEX 2-81, Vero E6 cells 3 days post-infection.
A B
D C
E F
62
Figure 4-2. Isolation of Zika virus from saliva and urine samples. A: Non-infected LLC-
MK2 cells 9 days post-infection. B: Sample1 saliva specimen, LLC-MK2 cells, 9dpi C: Sample1 urine specimen, LLC-MK2 cells, 9dpi.
Figure 4-3. Mixed virus CPE revealed by plaque assay. A: Non-infected LLC-MK2 cells.
B: Virus infected cells; the cells are darker, and ZIKV-infected cells (small arrows) are vacuolated, whereas a plaque formed by CHIKV is easily distinguishable (large arrow).
Urine, LLC-MK2 9dpi
A B C
A B
63
Figure 4-4. Detection of genomic RNA of Zika virus in urine and saliva samples by RT-
PCR analysis. 1) Sample1 saliva specimen, MRC-5 16dpi. 2) Sample1 urine specimen, MRC-5 16dpi. 3) Sample1 saliva specimen, LLC-MK2 16dpi. 4) Sample1 urine specimen, LLC-MK2 16dpi. 5) Sample2 saliva specimen, C6/36, 10dpi. 6) Sample2 urine specimen, C6/36, 10dpi. 7) Sample2 blood specimen, C6/36, 10dpi. 8) Sample2 saliva specimen, Vero E6, 10dpi. 9) Sample2 urine specimen, Vero E6, 10dpi. 10) Sample2 blood specimen, Vero E6, 10dpi. 11) Sample2 saliva specimen, LLC-MK2, 10dpi. 12) Sample2 urine specimen, LLC-MK2, 10dpi. 13) Sample2 blood specimen, LLC-MK2, 10dpi. +) Zika virus strain H/PAN/2015/CDC-259359.
64
CHAPTER 5 CONCLUSIONS
Serology can be a useful tool in public health surveillance by identifying past
exposures to viruses, but this technique does not allow conclusions to be drawn on
when an infection took place, or the severity of the infection. Some antibodies persist for
a very long time, while others wane; and differences in immune systems of individuals
play a role in antibody development and maintenance. The generation of antibodies
does not necessarily require a severe symptomatic infection; asymptomatic infections
can be seropositive (120, 121). In the case of IDV, in which cattle are considered the
primary reservoir, do not develop severe disease, and are likely to have IDV-specific
antibodies, it remains to be determined if a symptomatic infection develops in humans.
It is biologically plausible that humans can be infected with IDV, as shown through
animal modeling; however, this study is the first to show an immune response in
humans likely to be exposed to IDV. Consideration of IDV as a cause for acute upper
respiratory infections among persons with cattle contact and subsequent studies for
isolation of IDV from individuals with respiratory infection should be conducted.
When identifying the cause of an active infection, a most probable cause will be
determined or the diagnosis may remain unresolved. The task to identify the causative
agent is further complicated by similar clinical manifestations or confounding serology,
as is the case with arboviruses. With cyclical patterns of outbreaks of different
arboviruses, the predominating virus at a given time will be the most common diagnosis.
During the 2014 CHIKV outbreak in Haiti, an 8% arbovirus co-infection rate was
observed, which only adds to the complication of diagnosis. While there was little
genetic diversity among CHIKV isolates during that time, the characterization of ZIKV
65
revealed multiple introductions into Haiti, and prolonged circulation of the virus through
the population between 2014 and 2016. By expanding the body of full genome
sequence data, further phylogenetic studies can discern these introductions from one or
multiple countries. Inclusion of ZIKV infection reports and genome sequences from the
Dominican Republic, a country that has close socioeconomic ties to Haiti, should be
studied as an export and import site of arboviruses among the region. This new
information raises further questions about the immune status of the Haitian population
against CHIKV and ZIKV. This also suggests that multiple arboviruses are in circulation
simultaneously, increasing the risk for co-infections. And with limited epidemiology
studies on co-infection risks and outcomes, there is an undefined risk of adverse
outcomes in co-infected cases.
With the ZIKV outbreak that emerged in 2015 in the Western Hemisphere, the
public health sector and researchers sprang into action. With travel-acquired cases
cropping up worldwide, and the risk of geographic spread to areas with competent
vectors, this virus was considered a serious threat. Reports surfaced of microcephaly
related to ZIKV infection, and virologists were able to detect and isolate virus from a
variety of patient specimens, some for long periods well after symptoms resolved. But
no comparative analyses of sample types or timing of sample collections have been
made. Additionally, sensitivity limitations of molecular detection may misdiagnose due to
low viremia in a specimen or suboptimal collection timing. Virus isolation in cell culture
amplifies viable virus present in a sample which increases the ability of molecular
detection. While correlations between quantitative molecular techniques cannot be
drawn with viremia, there is less likelihood of a false negative diagnosis.
66
APPENDIX SEQUENCING PRIMERS
Table A-1. Sequencing primers for Chikungunya virus Primer Sequence
5’RACEroligo rArGrCrArUrCrGrArGrUrCrGrGrCrCrUrUrGrUrUrGrGrCrCrUrArCrUrGrG
5’RACE DNA AGCATCGAGTCGGCCTTGTTGGCCTACTGG
5’ RACE R GGCGCACTACCTATATCCAGGATGG
Primer 1 F ATGGCTGCGTGAGACACACGTAG
Primer 2 F CCCATCATGGATTCTGTGTAC
Primer 1 R GTAGCATGCGGCTTTCCGGGTAAAGC
Primer 2 R GTTGAACATAATCCTATGTTCTTAG
Primer 3 F GACCTGACAGAAGGTAGACGAGG
Primer 4 F GTGCGACCGTGTGCTGTTCTCAG
Primer 3 R GCATCCTGGGCTTCTTGGGCATTTCCG
Primer 4 R CCTTGCGTAACAGCCACTTGATTC
Primer 5 F GTGGTCGTCCGGGTTGTCAATCCCG
Primer 6F GCCGAAAGCAGACCTGATCCCATATAG
PRIMER 5 R GTTTTCTTTCTTTCCGCTAGTCACCAG
PRIMER 6 R GATAATGGCTGACTTACCAGATCCTG
PRIMER 7 F GTCATAGGAGTCTTCGGGGTACCAGG
PRIMER 8 F CCAGGCAAGACCTGGTGACTAGC
PRIMER 7 R CGCCATTATCGATGCGTGCTC
PRIMER 8 R GTCACCAGAGAGTGTCTTCCATACCAG
PRIMER 9 F GG AAGACACTCTCTGGTGACCCG
PRIMER 10F GGAAACTTCAAGGCAACTATTAAGG
PRIMER 9R GCACTGTTGGTAATGGTGTATGCG
PRIMER 10R CTACCAAGTGTTGCCGGTAGAC
PRIMER 11F CGGAGCGGACTATACATACAACC
PRIMER 12F CCTAGTGGTCATAAACATCCACAC
PRIMER 11R GTTAGCCTGTCTTTCCCTCCTGAG
PRIMER 12R GATGACCACGTCTGCATCCGTCG
PRIMER 13F CGTAGCTATACCTCTCCTCTC
PRIMER 14F CCAGTCACTGAACCACCTCTTTAC
PRIMER 13R GGTCGTATGTATCGCCCCG
PRIMER 14R GCATCCAGGTCTGACGGGACGG
PRIMER 15F CAGTTTGATCTAAGCGCCGATGGCGAGAC
PRIMER 16F GACGCCCCAGCCCTAGAACCGG
PRIMER 15R CGGGGTCTCTGCCATTAAATAC
PRIMER 16R GTTTACAGCCTCTCTTTAGTCTCTG
PRIMER 17F GTCCACGGCCAATAGAAGCAGG
PRIMER 18F CATCCAGAGACTAAAGAGAGGC
PRIMER 17R GCGTTCAATCTCCTAACCAATTCTCTG
PRIMER 18R CAGCCGCCTGTATAACCTGCACC
PRIMER 19F CAAAGCATACAGAGGAAAGGCC
PRIMER 20F CTGAACCCTTGGCAACAGCGTACC
PRIMER 19R CTTCGTACCTAGAGTACACCGC
PRIMER 20R CTGATTACTTCATCAGCCAGCGC
PRIMER 21F GCGGCAGGTGACGAACAAGACGAAG
PRIMER 22F GGCAACGAACAGGGCTAATAG
PRIMER 21R GTCGTACTTAGATGACCGCTTGAAG
PRIMER 22R GTGAACTTCGAAGCGTCGGACTTCATG
PRIMER 23F CCATCGATAACGCGGACCTGGCC
67
Table A-1 Continued
Primer Sequences
PRIMER 24F GAATGCGCGCAGATACCCGTGCA
PRIMER 23R GGCATGTGATTGTCCATATAACG
PRIMER 24R CATTGTTCCAGTAATCGTGCAC
PRIMER 25F CGGATGATAGCCATGATTGGACC
PRIMER 26F GCAGAGCGGGCCGGGCTATTTGTAAG
PRIMER 25R CTTCTTATGCGTCACCCACTCTTC
PRIMER 26R CTTGTACGGCTCATTGTTACCC
PRIMER 27F GAACCAAACTATCAAGAAGAGTGG
PRIMER 28F GGTTAACCGTGCCGACTGAGGG
PRIMER 27R GTAGGCGCCGCCCCACATGAATGG
PRIMER 28R GGCTCTTGTCCTTACACTCTGCTG
PRIMER 29F GTTATCCCGTCTCCGTACGTGAAATG
PRIMER 30F CCTGATTACAGCTGTAAGGTCTTCAC
PRIMER 29R CCCAAAGTCTGAGGAATGGGTGC
PRIMER 30R CATCGAGTGCACTGCACACTTGCC
PRIMER 31F GGACATGTCGTGTGAGGTATCAG
PRIMER 32F GGCGTAGCCATCATTAAATATGCAG
PRIMER 31R CTCTTCCGATTGCCAATTATGGTATTC
PRIMER 32R GCCTACATCTCAAAGCGAGTTCGG
PRIMER 33F GTAACAAAATATAAAACTAATAAAAATCA
PRIMER 34F GTAGGTACTTAAGCTTCTTAAAAGCAGC
PRIMER 33R AATATTAAAAACAAAATAACATCTCCTACG
PRIMER 34R CGGAGAATTGTGGAAGAGTTCGGTATGC
3’RACE F CCGAACTCTTCCATAATTCTCC
T25G TTTTTTTTTTTTTTTTTTTTTTTTTG
68
Table A-2. Dengue virus type 2 sequencing primersa Primer Sequence
d2a6 CATGGTAWGCCCAYGTTTTGT
d2a10 TACGCCCTTCCRCCTGCTTCA
d2a14 GCCGTGATTGGTATTGATACAGGA
d2a17 CCGCTGACATGAGTTTTGAGTC
d2a5B TTGTCGGTCTGGGGGGGTATAGAACCTGTTGATTCAACAG
d2s1C GATGAGGGAAGATGGGGAGTTGTTAGTCTACGTGGAC
d2s4 GCGAAGAAACAGGATGTTGTTG
d2s9 GCATTTTRGCCAGTTCTCTCCTA
d2s13 GCAGACAGAAGGTGGTGTTTT
d2s16 CAGGAAGTGGATAGAACCTTAGCA
d2a22 TGTGGTTCTCCGTTACGTGT
d2sP1 GTMGGAAATGACACAGGAAA
d2a21 CTGAAACCCCTTCTACAAAGTCTC
d2aP2 CATCCATTTCCCCATCCTCT
d2a20 GCCATARCCTGTCARTTCTGC
d2a19 GGCGRCCTAAGACATRTCTTTT
d2aP4 TTTCCTGATGACATYTGGATTTC
d2a18 CCACTGCCACATTTCAGTTC
d2s5 GGTGACACAGCCTGGGATTT
d2a18 CCACTGCCACATTTCAGTTC
d2s6 YATGACAGGAGACATCAAAGGA
d2s7 WCAACACAACTAYAGACCAGGCT
d2a16 CGGCTGTGACCAAGGAGTT
d2s8 TGGGCGTGACTTATCTTGC
d2a15 GTGCAACTCACTTTCCATGC
d2a14 GCCGTGATTGGTATTGATACAGGA
d2s9 GCATTTTRGCCAGTTCTCTCCTA
d2s10 GYGCTGTYCTAATGCATAAAGG
d2sP6 TGACTGGTATGAGTARTCTTG
d2aP7 ACTATTGCYGGAAGGTATCT
d2a12 ATGGRTCTCTRCTTCCCGG
d2s12 GGAAGACYTTTGATTCTGAGTATGT
d2a11 CCAGTGTGCACAGTCTTCATCAT
d2s13 GCAGACAGAAGGTGGTGTTTT
d2a10 TACGCCCTTCCRCCTGCTTCA
d2s14 CCACACTGGATAGCAGCTTCAATA
d2a9 CAATGCTATGTCTCARCATTGGTGT
d2s15 GACTYCAAGCAAAAGCAACC
d2a8 TGACACYGCAATGGTAGTGTT
d2a7 TTCTGGCGGRRTGAAGAA
d2sP10 GAGCATGAAACATCATGGCACT
d2a6 CATGGTAWGCCCAYGTTTTGT
d2s18 RGCAGAGTGGCTKTGGAAA
d2aP11 ATGGCTTCAGCAAGTTTCTTGTGT
d2s19 GGGACACAAGAATCACACTAGAAG
69
Table A-2 Continued
Primer Sequence
d2aP12 CCCTACCAATCAGTTCATCTTG
d2s20 GCCYTTYTGTTCACACCATTTCCA
d2a3 CCGTYGTCATCCATTCATG
d2s21 AGGAATACACAGATTACATGCCA
d2sP5 ATAATWGGGAAAAGAATAGA
d2aP8 CCTCTTGGTGTYGGTCTTTG
a. Christenbury JG, Aw PPK, Ong SH, Schreiber MJ, Chow A, Gubler DJ, Vasudevan SG, Ooi EE, and Hibberd ML. (2010). A method for full genome sequencing of all four serotypes of the dengue virus. Journal of Virological Methods. 169(1):202-206. Doi: 10.1016/jviromet.2010.06.01
70
Table A-3. Zika virus sequencing primers Primer Sequence (5’ -3’)
5’ UTR –R1 CATATTGACAATCCGGAATCCTCC
ZIKV-F1 ATGAAAAACCCAAAAAAGAAATCC
ZIKV-R1 CAAGCGATGGCAGCTGCTGCTAAC
ZIKV-F2 GAATACACAAAGCACTTGATTAGAGTC
ZIKV-R2 GAACCACTCCTTGTGAACCAACCAGTG
ZIKV-F3 CTTGATTGTGAACCGAGGACAGG
ZIKV-R3 TCCAAACAATGATTTGAAAGCTGCTC
ZIKV-F3A TGGAAGCCTAGGACTTGATTGTGAAC
ZIKV-F4 CTCATTGGGCAAGGGCATCCATC
ZIKV-R4 CCAGTAGCCTAGATCACTGTGTAC
ZIKV-F5 GGAACAGCTGTTAAGGGAAAGGAG
ZIKV-R5 CCAATTAGCTCTGAAGATGAAAGATAC
ZIKV-F6 CATTCAAAGTCAGACCAGCGTTGC
ZIKV-R6 GCACCACTCCTTTTTCCAGTCTTGA
ZIKV-F7 GCAGCTGGAGCGTGGTACGTATACG
ZIKV-R7 GAGTGGGTGACATTGACTGCTGTTG
ZIKV-F8 GCCCTTAGAGGGCTTCCAGTGCGTTATATG
ZIKV-R8 GAGGCCATCTTGGAGGTAAATATTG
ZIKV-F9 CACACTGGCTTGAAGCAAGAATGCT
ZIKV-R9 GCCATTTGGTTGTCCTGGGGAGATCTTTG
ZIKV-F10 GGTGGTGCTCATACCTGAGCCAG
ZIKV-R10 CCAAGTAACTTCCCCTAAAAATGTTACAC
ZIKV-F11 CTGGAACTCCTCTACAGCCACTTCAC
ZIKV-R11 GTGGTGGACACACTTTTTATGGTGTTG
ZIKV-F12 CCCGCAACTCTACACATGAGATGTAC
ZIKV-R12 CTAGCCACATATACCAGATGGCGC
ZIKV-F13 GAATTTGGAAAGGCCAAGGGCAG
ZIKV-R13 GGTGGCGGCAGGGAACCACAATG
ZIKV-F14 CTCCATCTCAAGGACGGGAGGTC
ZIKV-R14 GCGCGTGGGGTTTTTTGACTCAGTG
ZIKV-F15 CATGCTGCCTGTGAGCCCCTCAGAGGAC
ZIKV-R15 CCACTAGTCCCTCTTCTGGAGATCC
3’ UTR – F1 CTACCTATCCACCCAAGTTCGCTAC
3’ UTR – F2 GTGGCGACCTTCCCCACCCTTCAAT
71
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BIOGRAPHICAL SKETCH
Sarah completed her doctoral degree, majoring in public health with a one health
concentration in the spring of 2017. During her program, she worked for the Department
of Environmental and Global Health and at the Emerging Pathogens Institute where she
gained a variety of molecular biology and virology skills working with an array of viruses
ranging from Influenza virus, coronavirus, rotavirus, lentiviruses, and arboviruses,
including BSL-3 agents. She received national training in BSL-3 biosafety and
performed virus culture in cell lines and in embryonated chicken eggs in a BSL-3 facility.
She gained experience submitting grants and fellowships, writing project registrations,
Institutional Review Board and Institutional Animal Care and Use Committee
procedures, and USDA and CDC permit applications. Based on this work, she co-
authored 12 publications, presented five posters and won two poster awards, and four
oral presentations with one at an international conference. She has co-authored 35
genbank entries including influenza A virus, coronavirus NL63, enterovirus D68,
Chikungunya virus, Dengue virus, and Zika virus, of which 24 were whole genome
sequences. In addition, she deposited five Chikungunya virus and two Zika virus strains
in the World Reference Center for Emerging Viruses and Arboviruses repository.
Prior to the pursuit of her doctorate, Sarah received her Master of Public Health
degree in 2013 from Armstrong State University in Savannah, Georgia, where she was
an intern at a local hospital and proposed an updated campus-wide disinfectant
program. In 2011, she received her Bachelor of Science degree in animal sciences with
a business minor from Auburn University, where she was the recipient of the Hilmer L.
Jones agriculture scholarship four consecutive years.
