arbovirus infection dynamics in the mosquito …

The Pennsylvania State University

The Graduate School

ARBOVIRUS INFECTION DYNAMICS IN THE MOSQUITO AEDES AEGYPTI

A Dissertation in

Entomology

by

Mario De Jesus Novelo Canto

© 2021 Mario De Jesus Novelo Canto

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2021

ii

The dissertation of Mario De Jesus Novelo Canto was reviewed and approved by the

following:

Elizabeth A. McGraw

Professor and Huck Scholar in Entomology

Head of the Department of Biology

Dissertation Adviser

Chair of Committee

Jason L. Rasgon

Professor of Entomology and Disease Epidemiology

Margarita López-Uribe

Lorenzo L. Langstroth Early Career Professor

Assistant Professor of Entomology

David Kennedy

Assistant Professor of Biology

Gary W. Felton

Professor of Entomology

Head of the Department of Entomology

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ABSTRACT

Arboviral diseases account for more than 17% of all infectious diseases globally,

among the most prevalent are dengue fever and chikungunya, with over half of world’s

population at risk of infection. Although mortality is generally low, the morbidity caused

by these pathogens is associated with a substantial socioeconomic burden. Occasionally,

infection with dengue virus (DENV) can lead to death due to dengue shock syndrome in

all ages and chikungunya virus (CHIKV) infection can be severe for infants, with nearly

half of infected newborns experiencing encephalopathy and multiple organ dysfunction.

Both DENV and CHIKV are RNA viruses transmitted through the bite of the mosquito

Aedes aegypti. Arboviral infection in the mosquito is a complex process, as the dynamics

of infection can be influenced by the genetic variation of the virus altering the infection

rates and transmission potential in mosquitoes. Competition between strains or serotypes

can also affect viral population dynamics within the vector, thus modulating the

transmission potential. This happens in nature when mosquitoes take multiple blood meals

from several different hosts, each infected with a different or multiple virus. How different

viruses interact and compete inside the mosquito during the stepwise process of infection

or how the vector immune response changes in response to these different viruses is an

area of vital research.

Due to the lack of an effective vaccine and antiviral drugs, vector control is the

most efficient strategy in reducing arbovirus transmission. Therefore, understanding the

basic processes shaping vector-virus interactions is necessary for both implementation of

more effective control strategies and the development of genetic modifications (GMO) and

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biocontrol in mosquitoes to limit transmission. Current example of such technologies

includes clustered regularly interspaced short palindromic repeats (CRISPR) and the use

of the endosymbiont Wolbachia. The three major aims of my dissertation are to investigate

the 1) infection dynamics of DENV in the mosquito Ae. aegypti; 2) competitive interactions

between co-infecting DENV serotypes in both wild type and Wolbachia – infected

mosquitoes and 3) genetic basis of Ae. aegypti immune response to DENV and CHIKV

infection.

In chapter 1 1 I reviewed the history and role of Ae. aegypti as a disease vector and

the importance of my research by studying the process of infection with different

arboviruses inside the vector. In chapter 2 I examined the infection dynamics of DENV

in the mosquito Ae. aegypti, specifically, the effect of infectious dose and serotype on the

growth kinetics of virus in the tissues define the stepwise process of infection in the

mosquito body. In chapter 3 I studied the competitive interactions between co-infecting

DENV serotypes both in wild type mosquitoes and in Wolbachia-infected Ae. aegypti.

Chapter 4 examines the comparative genetic basis of Ae. aegypti response to DENV and

CHIKV infection using a family-breeding design. Finally, chapter 5 synthesizes the

findings of the dissertation, including future directions, as well as discussing unanswered

questions in this research area.

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TABLE OF CONTENTS

LIST OF FIGURES ................................................................................................................. viii

LIST OF TABLES ................................................................................................................... ix

ACKNOWLEDGEMENTS ..................................................................................................... x

Chapter 1 Introduction ............................................................................................................ 1

1.1 Aedes aegypti ............................................................................................................ 1 1.2 Dengue and chikungunya .......................................................................................... 4 1.3 Vector – virus dynamics ............................................................................................ 5 1.4 Vector control ............................................................................................................ 8 1.5 References ................................................................................................................. 12

Chapter 2 Intra-host growth kinetics of dengue virus in the mosquito Aedes aegypti ............. 23

2.1 Abstract ..................................................................................................................... 23 2.2 Author summary ........................................................................................................ 24 2.3 Introduction ............................................................................................................... 25 2.4 Methods ...................................................................................................................... 28

2.4.1 Ae. aegypti rearing and virus preparation ........................................................ 28

2.4.2 Mosquito infections ......................................................................................... 29

2.4.3 Mosquito dissection and RNA extraction ....................................................... 29

2.4.4 DENV absolute quantification via RT-qPCR ................................................. 30

2.4.5 Survival assay .................................................................................................. 31

2.4.6 Data analysis ................................................................................................... 31

2.5 Results ....................................................................................................................... 32

2.5.1 The proportion of DENV – infected mosquitoes is affected by both

infectious dose and DPI .................................................................................... 33

2.5.2 EIP is affected by the serotype strain and initial infectious dose .................... 34

2.5.3 Intra-host DENV kinetics are influenced by initial infectious dose and

strain ................................................................................................................. 37

2.5.4 Some treatments do not show substantial DENV replication and others

exhibit subpopulation structure of DENV load in infected mosquitoes ........... 37

2.5.5 DENV load is always higher in the midgut than other tissues for high

infectious dose .................................................................................................. 42

2.5.6 Tissue-specific DENV kinetics vary by strain in the high infectious dose .... 43

2.5.7 The DENV-2 strain has a higher maximum DENV load than the DENV-1

strain in the low infectious dose ....................................................................... 44

2.6 Discussion ................................................................................................................. 46

2.7 Acknowledgments ...................................................................................................... 50

2.8 References ................................................................................................................. 51

Chapter 3 The effects of DENV serotype competition and co-infection on viral kinetics in

Wolbachia-infected and uninfected Aedes aegypti mosquitoes ............................................... 57

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3.1 Abstract ..................................................................................................................... 57

3.2 Introduction ............................................................................................................... 59

3.3 Methods ..................................................................................................................... 62

3.3.1 Mosquito lines and rearing ............................................................................. 62

3.3.2 Virus culture and titration .............................................................................. 63


3.3.4 DENV absolute quantification via RT-qPCR ................................................ 65

3.3.5 Statistical analysis .......................................................................................... 66

3.4 Results ....................................................................................................................... 67

3.4.1 DENV serotype competition in co-infection ................................................... 67

3.4.2 Co-infection alters infection dynamics of DENV serotypes in wild type

mosquitoes ........................................................................................................ 72

3.4.3 Tissue specific differences in viral dynamics .................................................. 72

3.5 Discussion ................................................................................................................. 74

3.6 Conclusion ................................................................................................................. 78

3.7 Acknowledgements .................................................................................................... 79

3.8 References ................................................................................................................. 80

Chapter 4 Family level variation in the mosquito Aedes aegypti susceptibility and

immune response to dengue and chikungunya viral infection ........................................ 85

4.1 Abstract ..................................................................................................................... 85

4.2 Introduction ............................................................................................................... 86

4.3 Methods ...................................................................................................................... 91

4.3.1 Mosquito line and stock practices ................................................................... 91

4.3.2 Breeding design .............................................................................................. 92

4.3.3 Virus culture ................................................................................................... 92


4.3.5 Viral quantification ......................................................................................... 93

4.3.6 Selection of immune genes for expression analysis ....................................... 94

4.3.7 Immune gene expression ................................................................................. 95

4.3.8 Analysis of genetic variance ........................................................................... 96

4.4 Results ....................................................................................................................... 97

4.4.1 Family level variation in the heritability of DENV and CHIKV viral loads ... 97

4.4.2 Genetic correlation between DENV and CHIKV across mosquito families ... 99

4.4.3 Phenotypic extremes of viral loads for both DENV and CHIKV infection

in the mosquito families ................................................................................... 99

4.4.4 Immune genes relative expression .................................................................. 101

4.5 Discussion ................................................................................................................. 105

4.6 References ................................................................................................................. 112

Chapter 5 Synthesis and General Discussion .......................................................................... 120

5.1 References .................................................................................................................. 125

Appendices .............................................................................................................................. 127

Appendix A Supplementary materials from Chapter 2 .................................................... 127

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Appendix B Supplementary materials from Chapter 3 .................................................... 135

Appendix C Supplementary materials from Chapter 4 .................................................... 136

viii

LIST OF FIGURES

Figure 2.1: Susceptibility of DENV strains by DPI, tissue and infectious dose ...................... 35

Figure 2.2: DENV kinetics by strain, tissue and infectious dose ............................................. 39

Figure 2.3: “Trickledown” or declining viral load with tissue progression ............................. 43

Figure 3.1: Mosquito susceptibility to DENV in co-infections by DPI, tissue, and line ......... 69

Figure 3.2: DENV load in co-infection by DPI, tissue, and mosquito line .............................. 70

Figure 3.3: Co-infection serotype ratio by DPI and tissue ....................................................... 71

Figure 3.4: WT line susceptibility to DENV in co- and mono-infections by DPI, tissue,

and serotype ..................................................................................................................... 73

Figure 3.5: WT line DENV viral RNA load in co- and mono-infection by DPI, tissue, and

serotype ............................................................................................................................ 74

Figure 4.1:Ranked families by viral load ................................................................................ 97

Figure 4.2: Scatter plot showing the relationship between mean DENV and CHIKV loads

from individual families ........................................................................................................... 98

Figure 4.3:Whole mosquito viral loads per family representing the extremes of the

distribution ....................................................................................................................... 99

Figure 4.4: AGO2 and DCR2 expression relative to the housekeeping gene RPS17 in

family groups classified as High and Low Viral loads .................................................... 102

Figure 4.5: TEP3 and CECH expression relative to the housekeeping gene RPS17 in

family groups classified as High and Low Viral loads .................................................... 103

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LIST OF TABLES

Table 2.1: The DENV strains used in this study ...................................................................... 29

Table 2.2: Susceptibility by Strain, Tissue, Dose and DPI ...................................................... 36

Table 2.3: DRC parameters for successful infections by tissue, infectious dose and strain .... 40

Table 2.4: DRC parameter contrasts for successful infections between strains, infectious

dose and subpopulations ................................................................................................. 41

Table 2.5: Linear regression model for SG ............................................................................ 45

Table 2.6: Serotype contrasts for growth rate in SG ................................................................ 45

Table 3.1: Dengue serotypes and strains use ........................................................................... 64

Table 3.2: Viral titers ............................................................................................................... 64

Table 4.1: Primers and probes for qPCR gene expression and viral quantification ................ 95

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ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to my Adviser, Professor Elizabeth A.

McGraw, for giving me the opportunity to be part of her lab, for her guidance, advice and

most of importantly, for her unwavering support in making me a better scientist. And also,

if I’m honest, for having the patience to deal with countless manuscript drafts filled with

grammatical errors that I send her way. I would also like to thank the members of my

committee, Professor Jason L. Rasgon, Assistant Professors Margarita López -Uribe and

David Kennedy for their insights and advice.

I want to acknowledge the past and present McGraw lab members and friends from

Melbourne and State College, I will be forever grateful for their company, generosity, for

the laughs, drinks and the good times. Cass, Gerard, Michelle, Suzie, Jim, Helena, Tent,

Heather, Toby and Louise, miss you guys. A very special thanks to Emily Kerton from our

time in Monash, for being a superstar, and helping me carry out two of my three research

projects. I want to thank my PSU lab mates for making me feel welcome in a new place:

Fhallon, Leah, Heverton, Austin and Matt.

To my family and friends from back home, for their love and support. To my

parents, Mario and Carolina, and my sisters Marinha, Cari and Paz for their unconditional

love and strength. Como decia Jorge Negrete: “México lindo y querido, si muero lejos de

ti que digan que estoy dormido y que me traigan aquí”.

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Finally, to my loving partner Staci Cibotti, for being the most awesome human

being. To be honest I don’t think you contribute at all to me finishing my PhD, but you

sure make my life happier. To Jack the cat.

This work was conducted under the Institutional Biosafety Committee Protocol

47902, with funding from the Australian Research Council (DP160100588), the Australian

Research Council Laureate Fellowship (FL170100022), the National Health and Medical

Research Council Grant (APP1103804), the National Institute of Allergy and Infectious

Diseases (1R01 AI143758), start-up funds from the PSU College of Agriculture and the

Department of Entomology.

The findings and conclusions in this dissertation do not reflect the view of the

funding agencies.

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“Try not. Do or do not, there is no try” – Master Yoda.

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Chapter 1

Introduction

1.1 Aedes aegypti

The yellow fever mosquito, Ae. aegypti, is one of the most feared and important

dipterans around the world (McGregor & Connelly, 2020). The reason behind its notoriety

is because no other mosquito species has had such a dramatic impact on human history

(Jeffrey R. Powell & Tabachnick, 2013). Often called, “the most dangerous animal in the

planet”, Ae. aegypti alone is responsible for the transmission of at least half a dozen

medically important pathogens to humans (Jeffrey R. Powell, 2016), with the earliest

recorded mosquito-borne virus outbreak of yellow fever occurring in the XV century

(Staples & Monath, 2008). Several of the pathogens transmitted by Ae. aegypti are

responsible for substantial global socioeconomic losses. Chiefly among them are: yellow

fever (YFV), Zika (ZIKV), chikungunya (CHIKV) and dengue (DENV) viruses (Leta et

al., 2018). Globally, DENV is the most important vector-borne viral disease, with estimates

of 3.6 billion people living in areas of high transmission risk, and up to $20 billion in

economic losses linked to DENV infection . Additionally, newly emerging pathogens like

CHIKV have caused several major epidemics in the past 20 years, and although mortality

remains low, CHIKV infection can be severe in the young and cause ongoing arthritis like

complications. Congenital infection rates are upwards of 50% and nearly half of infected

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newborns will experience central nervous system disease (encephalopathy) (Morrison,

2014).

The Ae. aegypti life cycle occurs in four defined stages: 1) female mosquitoes lay

eggs on wet walls of water containers above the waterline, preferably in artificial containers

around human habitation, 2) larvae hatch once the water rises enough to cover the eggs, 3)

pupae develop until the adult mosquito is ready to emerge and, 4) after emergence, male

mosquitoes feed on nectar and females feed almost exclusively on human blood to produce

eggs (Christophers, 1960). Because of this, the natural history of Ae. aegypti is deeply

intertwined with human history (McNeill, 2010). Native and once confined to the African

continent, where ancestral populations exhibit sylvatic cycles, Ae. aegypti territorial

expansion around the globe coincides with the transatlantic slave trade by European

colonialists (Lounibos, 1981; Tabachnick, 1991). “Domestication” of the mosquito is

thought to have occurred in two ways; 1) with the transition from nomadic to sedentary

lifestyles, humans began living in villages in close proximity and storing water year around,

creating an ideal niche for the mosquito to exploit, and 2) during slave or trade voyages,

where ships had optimal breeding conditions such as crowding and long-term water

storages (Jeffrey R. Powell & Tabachnick, 2013; Wilke et al., 2020). Regardless of the true

domestication origin of Ae. aegypti, its current preference for both human habitat and

blood, made this species and efficient disease vector (J. R. Powell, 2018; Scott A Ritchie,

2014).

Since the first confirmed outbreak of YFV in Yucatán, México, in 1614 (Staples &

Monath, 2008) and the first time Ae. aegypti was identified as an arboviral vector in Cuba

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in 1906 (Bancroft, 1906; Reed & Carroll, 1901), there has been a search for mosquito

abatement methods, eventually leading to the current concept of vector control. From an

epidemiological point of view, vector control methods can only be effective if they are

contrasted with a mosquito efficacy in transmitting a pathogen (Roiz et al., 2018; David L.

Smith et al., 2012). This efficacy has been described as the vectorial capacity (VC), a

mathematical link between the mosquito’s natural history and pathogen transmission

(Macdonald, 1957). VC defines, within a vector population, the pathogen transmission

potential by using mosquito metrics such as: daily survival rate, biting rate and the extrinsic

incubation period (EIP) (Macdonald, 1957; David L. Smith et al., 2012; D. L. Smith et al.,

2014; Souza-Neto, Powell, & Bonizzoni, 2019). The EIP has been defined as the time it

takes from pathogen acquisition to transmission by a vector, to a susceptible vertebrate

host, and it represents one of the most influential parameters in modeling mosquito-borne

pathogen transmission (Ohm et al., 2018; Claudia Rückert & Ebel, 2018; Tjaden, Thomas,

Fischer, & Beierkuhnlein, 2013). Several factors can influence the length of the EIP, such

as temperature, vector microbiome, initial pathogen dose, and both the mosquito and viral

genotype (Armstrong & Rico-Hesse, 2003; Carrington & Simmons, 2014; Fontaine et al.,

2018; A. Gloria-Soria, Armstrong, Powell, & Turner, 2017; Gould & Higgs, 2009;

Kauffman & Kramer, 2017; L. Lambrechts et al., 2009; Le Flohic, Porphyre, Barbazan, &

Gonzalez, 2013). The interactions between any of these factors in shaping the transmission

dynamics of a pathogen represents vital research area. Specifically, the vector - pathogen

interactions are the main focus of this dissertation, using Ae. aegypti and DENV and

CHIKV viruses.

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1.2 Dengue and chikungunya

Dengue fever is one of the most prevalent arboviral diseases globally (Murray,

Quam, & Wilder-Smith, 2013), with estimates of more than 390 million infections per year

(Bhatt et al., 2013) and over half of the world’s population at risk (Brady et al., 2012).

Although mortality is generally low, the morbidity caused by dengue fever is associated

with a substantial socioeconomic burden (Duane J. Gubler, 2011). Dengue fever is caused

by four different serotypes of virus (DENV 1–4), belonging to the Flaviviridae family,

(Deen et al., 2006) that are 65–70% similar at the DNA sequence level across their ~11-kb

genomes (Perera & Kuhn, 2008), while also exhibiting a high-average within-serotype

diversity of ~3% at the amino acid level (Azhar et al., 2015).

Compared to DENV, CHIKV was relatively unknown until it started disseminating

across the globe in 2004, causing large outbreaks in Latin America, Asia and Africa

(Petersen & Powers, 2016). Chikungunya fever is caused by a single CHIKV serotype that

belongs to the Alphavirus genus and it has an ~11 kb single stranded positive-sense RNA

genome (Tanabe et al., 2018). The more substantial impact of the disease, is in long term

morbidity (Silva & Dermody, 2017). While adults tend to experience a syndrome involving

rash, fever and arthritis, many go on to have long-term complications and up to 50% of

infected newborns will develop encephalopathy. Additionally, recent studies have

determined that 48% of infected individuals develop chronic arthritis within 20 months of

infection (Yactayo, Staples, Millot, Cibrelus, & Ramon-Pardo, 2016).

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1.3 Vector-virus dynamics

Arboviral infection in the mosquito is a complex process (Lequime, Fontaine, Ar

Gouilh, Moltini-Conclois, & Lambrechts, 2016). Viruses must infect several tissues,

including the midgut and salivary glands, in a stepwise fashion (Franz, Kantor, Passarelli,

& Clem, 2015). Following ingestion, the virus enters the midgut epithelial cells, where it

replicates. Subsequently, it disseminates and infects secondary tissues, ultimately reaching

the salivary glands (Khoo, Doty, Held, Olson, & Franz, 2013). The midgut is thought to

represent a key barrier in the infection process, preventing many mosquitoes from reaching

a disseminated infection stage (Cox, Brown, & Rico-Hesse, 2011). Once the mosquito is

infected with a virus, the infection can persist in multiple tissues and the vector has the

potential of constantly spreading the pathogen throughout its life (Miller, Mitchell, &

Ballinger, 1989). A key determinant in the kinetics of infection is the antiviral immunity

of the mosquito, that ultimately affects the EIP and the mosquito VC. Several innate

immune pathways have been reported to control or modulate viral infection inside the

mosquito in both systemic and tissue specific ways. Some of the main pathways are, RNAi,

Toll, Janus Kinase - signal transduction and activators of transcription (JAK-STAT), the

Immune deficiency factor (IMD) and antimicrobial peptides (AMP’s) (Bartholomay et al.,

2010; Blair & Olson, 2015; Cheng, Liu, Wang, & Xiao, 2016; R. Zhang et al., 2017).

The kinetics of infection are equally influenced by genetic variation in the virus.

For DENV, comparisons between strains within single serotypes, have revealed variation

in both infection rates and EIP in mosquitoes (Anderson & Rico-Hesse, 2006; Armstrong

& Rico-Hesse, 2003), potentially due to differences in viral replication rates (Cox et al.,

6

2011). In humans, genetic variation within and between serotypes also determines relative

viral fitness (replication rate) (Louis Lambrechts et al., 2012; Ty Hang et al., 2010), with

particular strains linked to more severe disease outcome. The intra-host/vector diversity of

DENV can also play a role in transmission, such as variants with a replicative advantage

can spread more rapidly overall, eventually displacing those lower replication rates.

(Guzman & Harris, 2015). For example, an uncharacteristically large outbreak of dengue

in Cairns, Australia in 2008/2009 was attributed to the very short EIP of the DENV-3 strain

in the mosquito (S. A. Ritchie et al., 2013). DENV-2 strains from the American and South

East Asian genotypes differ in their EIP lengths, with the South East Asian genotypes

having shorter EIPs. This shorter EIP was thought, in part, to explain the displacement of

the American DENV strains in South America by the Asian lineage (Anderson & Rico-

Hesse, 2006). Surprisingly, little is known about DENV kinetics in the mosquito and how

the virus interacts with individual tissues during infection. Given that the virus moves in a

stepwise fashion, selective pressures in initial tissues might have cumulative effects on the

viral kinetics in downstream tissues (Hall, Bento, & Ebert, 2017).

Competition between strains or serotypes can also affect viral population dynamics

within the vector thus modulating the transmission potential (Louis Lambrechts et al.,

2012; Vogels et al., 2019). This happens in nature when mosquitoes take multiple blood

meals from several different hosts, each infected with a different or multiple DENV

serotypes (Shrivastava et al., 2018). In controlled laboratory experiments, using field

derived mosquito populations, there are no differences in dissemination and transmission

rates between DENV-1 and DENV-4 mono-infections in Ae. aegypti, but during co-

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infection, DENV-4 has a much higher dissemination rate, leading to the exclusive presence

of DENV-4 in the saliva (Vazeille, Gaborit, Mousson, Girod, & Failloux, 2016).

Furthermore, differential replication between DENV-2 and DENV-3 have been shown,

with DENV-2 exhibiting a much higher replication efficiency in both in vitro and in vivo

during co-infection (Quintero-Gil, Ospina, Osorio-Benitez, & Martinez-Gutierrez, 2014).

Additionally, the impact of co-infection with different families of arboviruses in vector

competence has just been recently studied. For example, mosquitoes exposed to double or

triple CHIKV, ZIKV and DENV viruses were capable of transmitting all pathogens

concurrently, without noticeable changes to mosquito infection and dissemination rates (C.

Rückert et al., 2017). Co-infection studies may shed light on the outcome of competitive

processes in field mosquitoes, even if rare, but also importantly allow for direct

comparisons of the transmissibility between viruses.

Compared to other viruses, CHIKV is much less studied. This is in part due to its

very recent re-emergence (Wahid, Ali, Rafique, & Idrees, 2017), but also the requirement

that all in vitro and in vivo CHIKV research be carried out under BSL-3 conditions. To

date, there are only a handful of studies in the literature that examine the basis of the

mosquito’s antiviral response to CHIKV infection (Chowdhury et al., 2020; McFarlane et

al., 2014; Zhao, Alto, Jiang, Yu, & Zhang, 2019). Interestingly, the traditional mosquito

innate immune pathways (Toll, JAK/STAT, RNAi) play little or no role in limiting CHIKV

infection (McFarlane et al., 2014; Shrinet, Srivastava, & Sunil, 2017). Not surprisingly

then, the transcriptional response of mosquitoes infected with CHIKV and DENV also

share little overlap (Shrinet et al., 2017). Additionally, there have been several recent

8

discoveries of novel non-canonical antiviral genes in Drosophila (Kounatidis et al., 2017;

L. Zhang et al., 2020) and in Ae. aegypti (Sigle & McGraw, 2019). The antiviral action of

each of these genes was revealed only by examining population level genetic variation in

the antiviral response. Quantitative genetic studies have also revealed differences in the

transmission potential between arboviruses (Sanchez-Vargas, Olson, & Black, 2021) and

helped understand the genetic basis of Wolbachia mediated pathogen blocking in in Ae.

aegypti (Terradas, Allen, Chenoweth, & McGraw, 2017).

1.4 Vector control

One of the most important outcomes of researching mosquito-pathogen interactions

is the development and optimization of vector control methods. Ae. aegypti natural history

has allowed this species to encroach and establish in new geographic areas (McGregor &

Connelly, 2020). Classic vector control methods, used to this day, include the removal of

breeding containers and areas of stagnant water (D. J. Gubler & Clark, 1996; Jones, Ant,

Cameron, & Logan, 2021; Lobo, Achee, Greico, & Collins, 2018). However, because of

the mosquito’s preference for small and often cryptic breeding grounds, complete removal

its challenging, even for the most prepare abatement programs particularly in areas of high

annual rainfall (Cheong, 1967; McGregor & Connelly, 2020). Additionally, Ae. aegypti

has been shown to breed in septic tanks, sewage plants, cesspits and stormwater drains,

making the task of monitoring mosquito populations even more cumbersome (Arana-

Guardia et al., 2014; Babu, Panicker, & Das, 1983; Barrera et al., 2008). Insecticide-based

approaches offer the next line of suppression against mosquito populations. This method

can target both larvae and adult mosquitoes and it has been responsible for the eradication

9

of diseases like typhus, malaria, yellow fever and even dengue fever in many countries

(Achee et al., 2015; Keatinge, 1949; Rogan & Chen, 2005). The two main problems with

the use of insecticides are that 1) they can cause widespread negative health effects in non-

targets, such as humans, animals or beneficial insects and 2) target insects can develop

resistance, driving the use of higher insecticides concentrations and increasing the negative

effects (Aktar, Sengupta, & Chowdhury, 2009; Calvo-Agudo et al., 2019; Rivero, Vézilier,

Weill, Read, & Gandon, 2010). For Ae. aegypti, many studies have shown low efficiency

of insecticides in wild populations (Maciel-de-Freitas et al., 2014; Marcombe et al., 2011;

Moyes et al., 2017; Plernsub et al., 2016) particularly due to point mutations in the voltage

sodium channel, the main target of several classes of insecticides (Brito et al., 2013; Moyes

et al., 2017).

Novel biotechnological vector control strategies that could work in tandem with the

classic methods are currently being developed (Barrera et al., 2008; Jones et al., 2021; Roiz

et al., 2018; Wilson et al., 2020). Some of the new approaches for reducing arthropod borne

diseases include the use of gene editing technologies like RNA interference (RNAi) and

CRISPR to induce pathogen resistant phenotypes or to kill mosquitoes at early

developmental stages (Chaverra-Rodriguez et al., 2018; Macias, Ohm, & Rasgon, 2017).

Other strategies are the use of sterile insect technique (SIT) involving mass rearing of the

targeted mosquito, followed by sterilization and release into the wild to suppress the

population reproductive potential (Alphey et al., 2010; Kandul et al., 2019; Zheng et al.,

2019). Similarly, the release of insects carrying a dominant lethal (RIDL) uses dominant

lethal transgenes that are suppress during mass laboratory rearing, but once the mosquitoes

10

are release in the wild their offspring will die due to the lack of lethal transgene suppression

in the field (Lin & Wang, 2015; Phuc et al., 2007; Watkinson-Powell & Alphey, 2017).

Finally, the bacteria Wolbachia pipientis is currently being trialed and used as a vector

control method (John H. Werren, Baldo, & Clark, 2008; P.-S. Yen & Failloux, 2020).

Wolbachia is an intracellular Gram-negative bacterium that is maternally transmitted and

naturally occurs in more than 60% of insect species but not in Ae. aegypti (Andrea Gloria-

Soria, Chiodo, & Powell, 2018; Hertig & Wolbach, 1924; Hilgenboecker, Hammerstein,

Schlattmann, Telschow, & Werren, 2008; J. H. Werren, 1997). Wolbachia’s ability to

manipulate the host reproductive biology has been shown to cause a several phenotypes in

its host, including sex ratio distortions such as male-killing, feminization, parthenogenesis

and cytoplasmic incompatibility (CI). In the case of CI, Wolbachia-infected males mate

with uninfected females and prevent them from producing viable offspring, whereas all

other possible crosses are successful. Because Wolbachia is vertically-inherited, infected

females have a reproductive advantage by default, and the infection tends to spread in

populations (Asgharian, Chang, Mazzoglio, & Negri, 2014; McGraw & O'Neill, 1999;

Weeks & Breeuwer, 2001; J. H. Yen & Barr, 1971; Zabalou et al., 2004). After Wolbachia

was introduced into Ae. aegypti, where it formed stably inherited infections, it was also

shown to limit the replication of several pathogens during coinfection in the mosquito

including; ZIKV, YFV, CHIKV and DENV (Hoffmann et al., 2014; Hurk et al., 2012;

Moreira et al.; Walker et al., 2011). Subsequently, Wolbachia has been tested in large scale

field release programs in numerous locations around the globe, where the bacterium has

spread to near fixation and lead to overall reductions in human disease incidence,

11

measuring up to 77% for dengue virus and 60% for chikungunya virus (Durovni et al.,

2019; Indriani et al., 2020; Pinto et al., 2021).

Regardless of the vector control strategy, the effectiveness of these methods can be

shaped by vector-pathogen interactions. Therefore, any insights into vector x virus

interactions will inform the development and successful implementation of control

practices. My research will provide a better understanding of virus-vector dynamics, which

will assist in developing intervention points for genetic modification. My findings will

help expand our model of vectorial capacity by adding components such as dose and

serotype, and our understanding of DENV epidemiology, such as the effect of serotype

competition in both transmission and Wolbachia mediated pathogen blocking.

Additionally, my work will reveal mosquito genes involved with mosquito immunity to

CHIKV, that is less well studied than DENV, offering new potential targets for control.

12

1.5 References

Achee, N. L., Gould, F., Perkins, T. A., Reiner, R. C., Jr., Morrison, A. C., Ritchie, S. A.,

Gubler, D. J., Teyssou, R., & Scott, T. W. (2015). A critical assessment of vector control for

dengue prevention. PLoS Neglected Tropical Diseases, 9(5), e0003655-e0003655.

doi:10.1371/journal.pntd.0003655

Aktar, M. W., Sengupta, D., & Chowdhury, A. (2009). Impact of pesticides use in

agriculture: their benefits and hazards. Interdisciplinary toxicology, 2(1), 1-12.

doi:10.2478/v10102-009-0001-7

Alphey, L., Benedict, M., Bellini, R., Clark, G. G., Dame, D. A., Service, M. W., &

Dobson, S. L. (2010). Sterile-insect methods for control of mosquito-borne diseases: an analysis.

Vector borne and zoonotic diseases (Larchmont, N.Y.), 10(3), 295-311.

doi:10.1089/vbz.2009.0014

Anderson, J. R., & Rico-Hesse, R. (2006). Aedes aegypti vectorial capacity is determined

by the infecting genotype of dengue virus. American Journal of Tropical Medicine and Hygiene,

75(5), 886-892.

Arana-Guardia, R., Baak-Baak, C. M., Loroño-Pino, M. A., Machain-Williams, C.,

Beaty, B. J., Eisen, L., & García-Rejón, J. E. (2014). Stormwater drains and catch basins as

sources for production of Aedes aegypti and Culex quinquefasciatus. Acta Tropicaica, 134, 33-42.

doi:10.1016/j.actatropica.2014.01.011

Armstrong, P. M., & Rico-Hesse, R. (2003). Efficiency of dengue serotype 2 virus strains

to infect and disseminate in Aedes aegypti. The American Society of Tropical Medicine and

Hygiene, 68.

Asgharian, H., Chang, P. L., Mazzoglio, P. J., & Negri, I. (2014). Wolbachia is not all

about sex: male-feminizing Wolbachia alters the leafhopper Zyginidia pullula transcriptome in a

mainly sex-independent manner. Frontiers in microbiology, 5, 430-430. Do i:1 0.3 389 /fmi cb.2

014.00430

Azhar, E. I., Hashem, A. M., El-Kafrawy, S. A., Abol-Ela, S., Abd-Alla, A. M., &

Sohrab, S. S. (2015). Complete genome sequencing and phylogenetic analysis of dengue type 1

virus isolated from Jeddah, Saudi Arabia. Virology Journal, 12. doi:10.1186/s12985-014-0235-7

Babu, C. J., Panicker, K. N., & Das, P. K. (1983). Breeding of Aedes aegypti in closed

septic tanks. Indian Journal of Medical Research, 77, 637.

Bancroft, T. L. (1906). On the aetiology of dengue fever. Australian Medical Gazette.,

25, 17-18.

Barrera, R., Amador, M., Diaz, A., Smith, J., Munoz-Jordan, J. L., & Rosario, Y. (2008).

Unusual productivity of Aedes aegypti in septic tanks and its implications for dengue control.

Medical and Veterinary Entomology, 22(1), 62-69. doi:10.1111/j.1365-2915.2008.00720.x

13

Bartholomay, L. C., Waterhouse, R. M., Mayhew, G. F., Campbell, C. L., Michel, K.,

Zou, Z., Ramirez, J. L., Das, S., Alvarez, K., Arensburger, P., Bryant, B., Chapman, S. B., Dong,

Y., Erickson, S. M., Karunaratne, S. H. P. P., Kokoza, V., Kodira, C. D., Pignatelli, P., Shin, S.

W., Vanlandingham, D. L., Atkinson, P. W., Birren, B., Christophides, G. K., Clem, R. J.,

Hemingway, J., Higgs, S., Megy, K., Ranson, H., Zdobnov, E. M., Raikhel, A. S., Christensen, B.

M., Dimopoulos, G., & Muskavitch, M. A. T. (2010). Pathogenomics of Culex quinquefasciatus

and meta-analysis of infection responses to diverse pathogens. Science (New York, N.Y.),

330(6000), 88-90. doi:10.1126/science.1193162

Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L.,

Drake, J. M., Brownstein, J. S., Hoen, A. G., Sankoh, O., Myers, M. F., George, D. B., Jaenisch,

T., Wint, G. R. W., Simmons, C. P., Scott, T. W., Farrar, J. J., & Hay, S. I. (2013). The global

distribution and burden of dengue. Nature, 496(7446), 504-507. doi:10.1038/nature12060 http:

//www.nature.com/nature/journal/v496/n7446/abs/nature12060.

Blair, C. D., & Olson, K. E. (2015). The role of RNA interference (RNAi) in arbovirus-

vector interactions. Viruses, 7(2), 820-843. doi:10.3390/v7020820

Brady, O. J., Gething, P. W., Bhatt, S., Messina, J. P., Brownstein, J. S., Hoen, A. G.,

Moyes, C. L., Farlow, A. W., Scott, T. W., & Hay, S. I. (2012). Refining the global spatial limits

of dengue virus transmission by evidence-based consensus. PLoS Neglected Tropical Diseases,

6(8), e1760. doi:10.1371/journal.pntd.0001760

Brito, L. P., Linss, J. G. B., Lima-Camara, T. N., Belinato, T. A., Peixoto, A. A., Lima, J.

B. P., Valle, D., & Martins, A. J. (2013). Assessing the effects of Aedes aegypti kdr mutations on

pyrethroid resistance and its fitness cost. PLoS ONE, 8(4), e60878. doi:10 .137 1/jour nal.p one.0

060878

Calvo-Agudo, M., González-Cabrera, J., Picó, Y., Calatayud-Vernich, P., Urbaneja, A.,

Dicke, M., & Tena, A. (2019). Neonicotinoids in excretion product of phloem-feeding insects kill

beneficial insects. Proceedings of the National Academy of Sciences, 116(34), 16817-16822.

doi:10.1073/pnas.1904298116

Carrington, L. B., & Simmons, C. P. (2014). Human to mosquito transmission of dengue

viruses. Frontiers in Immunology, 5, 290. doi:10.3389/fimmu.2014.00290

Chaverra-Rodriguez, D., Macias, V. M., Hughes, G. L., Pujhari, S., Suzuki, Y., Peterson,

D. R., Kim, D., McKeand, S., & Rasgon, J. L. (2018). Targeted delivery of CRISPR-Cas9

ribonucleoprotein into arthropod ovaries for heritable germline gene editing. Nature

Communications, 9(1), 3008. doi:10.1038/s41467-018-05425-9

Cheng, G., Liu, Y., Wang, P., & Xiao, X. (2016). Mosquito defense strategies against

viral infection. Trends in Parasitology, 32(3), 177-186. doi:10.1016/j.pt.2015.09.009

Cheong, W. H. (1967). Preferred Aedes aegypti larval habitats in urban areas. Bulletin of

the World Health Organization, 36(4), 586-589.

http://w/

http://w/

14

Chowdhury, A., Modahl, C. M., Tan, S. T., Wong Wei Xiang, B., Missé, D., Vial, T.,

Kini, R. M., & Pompon, J. F. (2020). JNK pathway restricts DENV2, ZIKV and CHIKV

infection by activating complement and apoptosis in mosquito salivary glands. PLoS Pathogens,

16(8), e1008754. doi:10.1371/journal.ppat.1008754

Christophers, S. (1960). Aedes aegypti (L.) the yellow fever mosquito: its life history,

bionomics and structure: London: The Syndics of the Cambridge University Press, Bentley

House, 200, Euston Road, N.W.I.

Cox, J., Brown, H. E., & Rico-Hesse, R. (2011). Variation in vector competence for

dengue viruses Does not depend on mosquito midgut binding affinity. PLoS Neglected Tropical

Diseases, 5(5), e1172. doi:10.1371/journal.pntd.0001172

Deen, J. L., Harris, E., Wills, B., Balmaseda, A., Hammond, S. N., Rocha, C., Dung, N.

M., Hung, N. T., Hien, T. T., & Farrar, J. J. (2006). The WHO dengue classification and case

definitions: time for a reassessment. The Lancet, 368(9530), 170-173. doi:10.1016/S0140-

6736(06)69006-5

Durovni, B., Saraceni, V., Eppinghaus, A., Riback, T. I. S., Moreira, L. A., Jewell, N. P.,

Dufault, S. M., O'Neill, S. L., Simmons, C. P., Tanamas, S. K., & Anders, K. L. (2019). The

impact of large-scale deployment of Wolbachia mosquitoes on dengue and other Aedes-borne

diseases in Rio de Janeiro and Niterói, Brazil: study protocol for a controlled interrupted time

series analysis using routine disease surveillance data. F1000Research, 8, 1328.

doi:10.12688/f1000research.19859.2

Fontaine, A., Lequime, S., Moltini-Conclois, I., Jiolle, D., Leparc-Goffart, I., Reiner, R.

C., Jr., & Lambrechts, L. (2018). Epidemiological significance of dengue virus genetic variation

in mosquito infection dynamics. PLoS Pathogens, 14(7), e1007187.

doi:10.1371/journal.ppat.1007187

Franz, A. W. E., Kantor, A. M., Passarelli, A. L., & Clem, R. J. (2015). Tissue Barriers to

Arbovirus Infection in Mosquitoes. Viruses, 7(7), 3741-3767. doi:10.3390/v7072795

Gloria-Soria, A., Armstrong, P. M., Powell, J. R., & Turner, P. E. (2017). Infection rate

of Aedes aegypti mosquitoes with dengue virus depends on the interaction between temperature

and mosquito genotype. Proceedings of the Royal Society B: Biological Sciences, 284(1864).

doi:10.1098/rspb.2017.1506

Gloria-Soria, A., Chiodo, T. G., & Powell, J. R. (2018). Lack of evidence for natural

Wolbachia infections in Aedes aegypti (Diptera: Culicidae). Journal of Medical Entomology,

55(5), 1354-1356. doi:10.1093/jme/tjy084

Gould, E. A., & Higgs, S. (2009). Impact of climate change and other factors on

emerging arbovirus diseases. Transactions of The Royal Society of Tropical Medicine and

Hygiene, 103(2), 109-121. doi:10.1016/j.trstmh.2008.07.025

Gubler, D. J. (2011). Dengue, urbanization and globalization: The unholy trinity of the

21(st) century. Tropical Medicine and Health, 39(4 Suppl), 3-11. doi:10.2149/tmh.2011-S05

15

Gubler, D. J., & Clark, G. G. (1996). Community involvement in the control of Aedes

aegypti. Acta Tropica, 61(2), 169-179. doi:10.1016/0001-706x(95)00103-l

Guzman, M. G., & Harris, E. (2015). Dengue. The Lancet, 385(9966), 453-465.

doi:https://doi.org/10.1016/S0140-6736(14)60572-9

Hall, M. D., Bento, G., & Ebert, D. (2017). The Evolutionary consequences of stepwise

infection processes. Trends in Ecology & Evolution, 32(8), 612-623.

doi:10.1016/j.tree.2017.05.009

Hertig, M., & Wolbach, S. B. (1924). Studies on rickettsia-like micro-organisms in

insects. The Journal of medical research, 44(3), 329-374.327.

Hilgenboecker, K., Hammerstein, P., Schlattmann, P., Telschow, A., & Werren, J. H.

(2008). How many species are infected with Wolbachia?--A statistical analysis of current data.

FEMS microbiology letters, 281(2), 215-220. doi:10.1111/j.1574-6968.2008.01110.x

Hoffmann, A. A., Iturbe-Ormaetxe, I., Callahan, A. G., Phillips, B., Billington, K., &

Axford, J. K. (2014). Stability of the wMel Wolbachia infection following invasion into Aedes

aegypti populations. PLoS Neglected Tropical Diseases, 8. doi:10.1371/journal.pntd.0003115

Hurk, A. F., Hall-Mendelin, S., Pyke, A. T., Frentiu, F. D., McElroy, K., & Day, A.

(2012). Impact of Wolbachia on infection with chikungunya and yellow fever viruses in the

mosquito vector Aedes aegypti. PLoS Neglected Tropical Diseases, 6. doi:1 0.1371/ journal.

.0001892

Indriani, C., Tantowijoyo, W., Rancès, E., Andari, B., Prabowo, E., Yusdi, D., Ansari, M.

R., Wardana, D. S., Supriyati, E., Nurhayati, I., Ernesia, I., Setyawan, S., Fitriana, I., Arguni, E.,

Amelia, Y., Ahmad, R. A., Jewell, N. P., Dufault, S. M., Ryan, P. A., Green, B. R., McAdam, T.

F., O'Neill, S. L., Tanamas, S. K., Simmons, C. P., Anders, K. L., & Utarini, A. (2020). Reduced

dengue incidence following deployments of Wolbachia-infected Aedes aegypti in Yogyakarta,

Indonesia: a quasi-experimental trial using controlled interrupted time series analysis. Gates open

research, 4, 50-50. doi:10.12688/gatesopenres.13122.1

Jones, R. T., Ant, T. H., Cameron, M. M., & Logan, J. G. (2021). Novel control strategies

for mosquito-borne diseases. Philosophical Transactions of the Royal Society B: Biological

Sciences, 376(1818), 20190802. doi:10.1098/rstb.2019.0802

Kandul, N. P., Liu, J., Sanchez C, H. M., Wu, S. L., Marshall, J. M., & Akbari, O. S.

(2019). Transforming insect population control with precision guided sterile males with

demonstration in flies. Nature Communications, 10(1), 84. doi:10.1038/s41467-018-07964-7

Kauffman, E. B., & Kramer, L. D. (2017). Zika virus mosquito vectors: competence,

biology, and vector control. The Journal of Infectious Diseases, 216(suppl_10), S976-s990.

doi:10.1093/infdis/jix405

Keatinge, A. F. (1949). A hundred years of insecticides and repellents in the army; a

historical summary. Journal of the Royal Army Medical Corps, 92(6), 290-312.

16

Khoo, C. C. H., Doty, J. B., Held, N. L., Olson, K. E., & Franz, A. W. E. (2013).

Isolation of midgut escape mutants of two American genotype dengue 2 viruses from. Virology

Journal, 10(1), 257. doi:10.1186/1743-422X-10-257

Kounatidis, I., Chtarbanova, S., Cao, Y., Hayne, M., Jayanth, D., Ganetzky, B., &

Ligoxygakis, P. (2017). NF-κB Immunity in the brain determines fly lifespan in healthy aging

and age-related neurodegeneration. Cell Reports, 19(4), 836-848. Do i:10. 1016 /j.celre p.2017

.04.007

Lambrechts, L., Chevillon, C., Albright, R. G., Thaisomboonsuk, B., Richardson, J. H.,

Jarman, R. G., & Scott, T. W. (2009). Genetic specificity and potential for local adaptation

between dengue viruses and mosquito vectors. BMC Ecology and Evolution, 9, 160.

doi:10.1186/1471-2148-9-160

Lambrechts, L., Fansiri, T., Pongsiri, A., Thaisomboonsuk, B., Klungthong, C.,

Richardson, J. H., Ponlawat, A., Jarman, R. G., & Scott, T. W. (2012). Dengue-1 virus clade

replacement in Thailand associated with enhanced mosquito transmission. Journal of Virology,

86(3), 1853-1861. doi:10.1128/jvi.06458-11

Le Flohic, G., Porphyre, V., Barbazan, P., & Gonzalez, J. P. (2013). Review of climate,

landscape, and viral genetics as drivers of the Japanese encephalitis virus ecology. PLoS

Neglected Tropical Diseases, 7(9), e2208. doi:10.1371/journal.pntd.0002208

Lequime, S., Fontaine, A., Ar Gouilh, M., Moltini-Conclois, I., & Lambrechts, L. (2016).

Genetic drift, purifying selection and vector genotype shape dengue virus intra-host genetic

diversity in mosquitoes. PLoS Genetics, 12(6), e1006111. doi:10.1371/journal.pgen.1006111

Leta, S., Beyene, T. J., De Clercq, E. M., Amenu, K., Kraemer, M. U. G., & Revie, C. W.

(2018). Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes

albopictus. International Journal of Infectious Diseases, 67, 25-35. doi: htt ps: //do i.or g/1 0.101

6/j.ijid.2017.11.026

Lin, X., & Wang, G. (2015). Development of a RNAi-based release of insects carrying a

dominant lethal (RIDL) system in Drosophila melanogaster. Science Bulletin, 60(3), 356-362.

doi:https://doi.org/10.1007/s11434-014-0667-x

Lobo, N. F., Achee, N. L., Greico, J., & Collins, F. H. (2018). Modern vector control.

Cold Spring Harbor perspectives in medicine, 8(1), a025643. doi:10.1101/cshperspect.a025643

Lounibos, L. P. (1981). Habitat segregation among African treehole mosquitoes.

Ecological Entomology, 6(2), 129-154. doi:https://doi.org/10.1111/j.1365-2311.1981.tb00601.x

Macdonald, G. (1957). The epidemiology and control of malaria. Amen House, Warwick

Square, London E.C.4.: Oxford University Press.

Macias, V. M., Ohm, J. R., & Rasgon, J. L. (2017). Gene drive for mosquito control:

where did it come from and where are we headed? International Journal of Environmental

Research and Public Health, 14(9). doi:10.3390/ijerph14091006

17

Maciel-de-Freitas, R., Avendanho, F. C., Santos, R., Sylvestre, G., Araújo, S. C., Lima, J.

B. P., Martins, A. J., Coelho, G. E., & Valle, D. (2014). Undesirable consequences of insecticide

resistance following Aedes aegypti control activities due to a dengue outbreak. PLoS ONE, 9(3),

e92424. doi:10.1371/journal.pone.0092424

Marcombe, S., Darriet, F., Tolosa, M., Agnew, P., Duchon, S., Etienne, M., Yp Tcha, M.

M., Chandre, F., Corbel, V., & Yébakima, A. (2011). Pyrethroid resistance reduces the efficacy

of space sprays for dengue control on the island of Martinique (Caribbean). PLoS Neglected

Tropical Diseases, 5(6), e1202. doi:10.1371/journal.pntd.0001202

McFarlane, M., Arias-Goeta, C., Martin, E., O'Hara, Z., Lulla, A., Mousson, L., Rainey,

S. M., Misbah, S., Schnettler, E., Donald, C. L., Merits, A., Kohl, A., & Failloux, A.-B. (2014).

Characterization of Aedes aegypti innate-immune pathways that limit chikungunya virus

replication. PLoS Neglected Tropical Diseases, 8(7), e2994. doi:10.1371/journal.pntd.0002994

McGraw, E. A., & O'Neill, S. L. (1999). Evolution of Wolbachia pipientis transmission

dynamics in insects. Trends in Microbiology, 7(7), 297-302. doi:10.1016/s0966-842x(99)01531-0

McGregor, B. L., & Connelly, C. R. (2020). A review of the control of Aedes aegypti

(Diptera: Culicidae) in the continental United States. Journal of Medical Entomology, 58(1), 10-

25. doi:10.1093/jme/tjaa157

McNeill, J. R. (2010). Mosquito empires: Ecology and war in the greater Caribbean,

1620–1914. Cambridge: Cambridge University Press.

Miller, B. R., Mitchell, C. J., & Ballinger, M. E. (1989). Replication, tissue tropisms and

transmission of yellow fever virus in Aedes albopictus. Transactions of The Royal Society of

Tropical Medicine and Hygiene, 83(2), 252-255. doi:10.1016/0035-9203(89)90667-6

Moreira, L. A., Iturbe-Ormaetxe, I., Jeffery, J. A., Lu, G., Pyke, A. T., Hedges, L. M.,

Rocha, B. C., Hall-Mendelin, S., Day, A., Riegler, M., Hugo, L. E., Johnson, K. N., Kay, B. H.,

McGraw, E. A., van den Hurk, A. F., Ryan, P. A., & O'Neill, S. L. A Wolbachia symbiont in

Aedes aegypti limits infection with dengue, chikungunya, and plasmodium. Cell, 139(7), 1268-

1278. doi:10.1016/j.cell.2009.11.042

Morrison, T. E. (2014). Reemergence of chikungunya virus. Journal of Virology, 88(20),

11644-11647. doi:10.1128/jvi.01432-14

Moyes, C. L., Vontas, J., Martins, A. J., Ng, L. C., Koou, S. Y., Dusfour, I.,

Raghavendra, K., Pinto, J., Corbel, V., David, J.-P., & Weetman, D. (2017). Contemporary status

of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS

Neglected Tropical Diseases, 11(7), e0005625. doi:10.1371/journal.pntd.0005625

Murray, N. E., Quam, M. B., & Wilder-Smith, A. (2013). Epidemiology of dengue: past,

present and future prospects. Clinical Epidemiology, 5.

18

Ohm, J. R., Baldini, F., Barreaux, P., Lefevre, T., Lynch, P. A., Suh, E., Whitehead, S.

A., & Thomas, M. B. (2018). Rethinking the extrinsic incubation period of malaria parasites.

Parasites & Vectors, 11(1), 178. doi:10.1186/s13071-018-2761-4

Perera, R., & Kuhn, R. J. (2008). Structural proteomics of dengue virus. Current opinion

in microbiology, 11(4), 369-377. doi:10.1016/j.mib.2008.06.004

Petersen, L. R., & Powers, A. M. (2016). Chikungunya: epidemiology. F1000Research,

5, F1000 Faculty Rev-1082. doi:10.12688/f1000research.7171.1

Phuc, H. K., Andreasen, M. H., Burton, R. S., Vass, C., Epton, M. J., Pape, G., Fu, G.,

Condon, K. C., Scaife, S., Donnelly, C. A., Coleman, P. G., White-Cooper, H., & Alphey, L.

(2007). Late-acting dominant lethal genetic systems and mosquito control. BMC Biology, 5(1),

11. doi:10.1186/1741-7007-5-11

Pinto, S. B., Riback, T. I. S., Sylvestre, G., Costa, G., Peixoto, J., Dias, F. B. S.,

Tanamas, S. K., Simmons, C. P., Dufault, S. M., Ryan, P. A., Neill, S. L., Muzzi, F. C., Kutcher,

S., Montgomery, J., Green, B. R., Smithyman, R., Eppinghaus, A., Saraceni, V., Anders, K. L.,

Durovni, B., & Moreira, L. A. (2021). Effectiveness of Wolbachia infected mosquito

deployments in reducing the incidence of dengue and chikungunya in Niterói, Brazil: a quasi-

experimental study. medRxiv, 2021.2001.2031.21250726. doi:10.1101/2021.01.31.21250726

Plernsub, S., Saingamsook, J., Yanola, J., Lumjuan, N., Tippawangkosol, P., Walton, C.,

& Somboon, P. (2016). Temporal frequency of knockdown resistance mutations, F1534C and

V1016G, in Aedes aegypti in Chiang Mai city, Thailand and the impact of the mutations on the

efficiency of thermal fogging spray with pyrethroids. Acta Tropica, 162, 125-132.

doi:https://doi.org/10.1016/j.actatropica.2016.06.019

Powell, J. R. (2016). New contender for most lethal animal. Nature, 540(7634), 525-525.

doi:10.1038/540525c

Powell, J. R. (2018). Mosquito-Borne Human Viral Diseases: Why Aedes aegypti? The

American Society of Tropical Medicine and Hygiene, 98(6), 1563-1565. doi:10.4269/ajtmh.17-

0866

Powell, J. R., & Tabachnick, W. J. (2013). History of domestication and spread of Aedes

aegypti - A Review. Memórias do Instituto Oswaldo Cruz, 108(Suppl 1), 11-17.

doi:10.1590/0074-0276130395

Quintero-Gil, D. C., Ospina, M., Osorio-Benitez, J. E., & Martinez-Gutierrez, M. (2014).

Differential replication of dengue virus serotypes 2 and 3 in coinfections of C6/36 cells and Aedes

aegypti mosquitoes. The Journal of Infection in Developing Countries, 8(7), 876-884.

doi:10.3855/jidc.3978

Reed, W., & Carroll, J. (1901). The Prevention of yellow fever. Public health papers and

reports, 27, 113-129.

19

Ritchie, S. A. (2014). 24 Dengue Vector Bionomics: Why Aedes aegypti is such a good

vector: CABI International Oxford, UK.

Ritchie, S. A., Pyke, A. T., Hall-Mendelin, S., Day, A., Mores, C. N., Christofferson, R.

C., Gubler, D. J., Bennett, S. N., & van den Hurk, A. F. (2013). An explosive epidemic of DENV-

3 in Cairns, Australia. PLoS ONE, 8(7), e68137. doi:10.1371/journal.pone.0068137

Rivero, A., Vézilier, J., Weill, M., Read, A. F., & Gandon, S. (2010). Insecticide control

of vector-borne diseases: When is insecticide resistance a problem? PLoS Pathogens, 6(8),

e1001000. doi:10.1371/journal.ppat.1001000

Rogan, W. J., & Chen, A. (2005). Health risks and benefits of bis(4-chlorophenyl)-1,1,1-

trichloroethane (DDT). The Lancet, 366(9487), 763-773. doi:10.1016/S0140-6736(05)67182-6

Roiz, D., Wilson, A. L., Scott, T. W., Fonseca, D. M., Jourdain, F., Müller, P.,

Velayudhan, R., & Corbel, V. (2018). Integrated Aedes management for the control of Aedes-

borne diseases. PLoS Neglected Tropical Diseases, 12(12), e0006845-e0006845.


Rückert, C., & Ebel, G. D. (2018). How do virus-mosquito interactions lead to viral

emergence? Trends in Parasitology, 34(4), 310-321. doi:10.1016/j.pt.2017.12.004

Rückert, C., Weger-Lucarelli, J., Garcia-Luna, S. M., Young, M. C., Byas, A. D.,

Murrieta, R. A., Fauver, J. R., & Ebel, G. D. (2017). Impact of simultaneous exposure to

arboviruses on infection and transmission by Aedes aegypti mosquitoes. Nature Communications,

8, 15412. doi:10.1038/ncomms15412

Sanchez-Vargas, I., Olson, K. E., & Black, W. C. t. (2021). The Genetic basis for salivary

gland barriers to arboviral transmission. Insects, 12(1). doi:10.3390/insects12010073

Shrinet, J., Srivastava, P., & Sunil, S. (2017). Transcriptome analysis of Aedes aegypti in

response to mono-infections and co-infections of dengue virus-2 and chikungunya virus.

Biochemical and Biophysical Research Communications, 492(4), 617-623. doi:10.1 0 16/ j.b brc

.2017.01.162

Shrivastava, S., Tiraki, D., Diwan, A., Lalwani, S. K., Modak, M., Mishra, A. C., &

Arankalle, V. A. (2018). Co-circulation of all the four dengue virus serotypes and detection of a

novel clade of DENV-4 (genotype I) virus in Pune, India during 2016 season. PLoS ONE, 13(2),


Sigle, L. T., & McGraw, E. A. (2019). Expanding the canon: Non-classical mosquito

genes at the interface of arboviral infection. Insect Biochemistry and Molecular Biology, 109, 72-

80. doi:10.1016/j.ibmb.2019.04.004

Silva, L. A., & Dermody, T. S. (2017). Chikungunya virus: epidemiology, replication,

disease mechanisms, and prospective intervention strategies. Journal of Clinical Investigation,

127(3), 737-749. doi:10.1172/jci84417

20

Smith, D. L., Battle, K. E., Hay, S. I., Barker, C. M., Scott, T. W., & McKenzie, F. E.

(2012). Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted

pathogens. PLoS Pathogens, 8(4), e1002588. doi:10.1371/journal.ppat.1002588

Smith, D. L., Perkins, T. A., Reiner, R. C., Jr., Barker, C. M., Niu, T., Chaves, L. F.,

Ellis, A. M., George, D. B., Le Menach, A., Pulliam, J. R., Bisanzio, D., Buckee, C., Chiyaka, C.,

Cummings, D. A., Garcia, A. J., Gatton, M. L., Gething, P. W., Hartley, D. M., Johnston, G.,

Klein, E. Y., Michael, E., Lloyd, A. L., Pigott, D. M., Reisen, W. K., Ruktanonchai, N., Singh, B.

K., Stoller, J., Tatem, A. J., Kitron, U., Godfray, H. C., Cohen, J. M., Hay, S. I., & Scott, T. W.

(2014). Recasting the theory of mosquito-borne pathogen transmission dynamics and control.

Transactions of The Royal Society of Tropical Medicine and Hygiene, 108(4), 185-197.

doi:10.1093/trstmh/tru026

Souza-Neto, J. A., Powell, J. R., & Bonizzoni, M. (2019). Aedes aegypti vector

competence studies: A review. Infection, Genetics and Evolution 67, 191-209.

doi:https://doi.org/10.1016/j.meegid.2018.11.009

Staples, J. E., & Monath, T. P. (2008). Yellow fever: 100 Years of discovery. JAMA,

300(8), 960-962. doi:10.1001/jama.300.8.960

Tabachnick, W. J. (1991). Evolutionary genetics and arthropod-borne disease: The

yellow fever mosquito. American Entomologist, 37(1), 14-26. doi:10.1093/ae/37.1.14

Tanabe, I. S. B., Tanabe, E. L. L., Santos, E. C., Martins, W. V., Araújo, I. M. T. C.,

Cavalcante, M. C. A., Lima, A. R. V., Câmara, N. O. S., Anderson, L., Yunusov, D., & Bassi, Ê.

J. (2018). Cellular and molecular immune response to chikungunya virus infection. Frontiers in

cellular and infection microbiology, 8, 345-345. doi:10.3389/fcimb.2018.00345

Terradas, G., Allen, S. L., Chenoweth, S. F., & McGraw, E. A. (2017). Family level

variation in Wolbachia-mediated dengue virus blocking in Aedes aegypti. Parasites & Vectors,

10(1), 622-622. doi:10.1186/s13071-017-2589-3

Tjaden, N. B., Thomas, S. M., Fischer, D., & Beierkuhnlein, C. (2013). Extrinsic

incubation period of dengue: Knowledge, backlog, and applications of temperature dependence.

PLoS Neglected Tropical Diseases, 7(6), e2207. doi:10.1371/journal.pntd.0002207

Ty Hang, V. T., Holmes, E. C., Veasna, D., Quy, N. T., Tinh Hien, T., Quail, M.,

Churcher, C., Parkhill, J., Cardosa, J., Farrar, J., Wills, B., Lennon, N. J., Birren, B. W., Buchy,

P., Henn, M. R., & Simmons, C. P. (2010). Emergence of the Asian 1 genotype of dengue virus

serotype 2 in Viet Nam: In vivo fitness advantage and lineage replacement in South-East Asia.


Vazeille, M., Gaborit, P., Mousson, L., Girod, R., & Failloux, A. B. (2016). Competitive

advantage of a dengue 4 virus when co-infecting the mosquito Aedes aegypti with a dengue 1

virus. BMC Infectious Diseases, 16, 318. doi:10.1186/s12879-016-1666-0

21

Vogels, C. B. F., Rückert, C., Cavany, S. M., Perkins, T. A., Ebel, G. D., & Grubaugh, N.

D. (2019). Arbovirus coinfection and co-transmission: A neglected public health concern? PLoS

Biology, 17(1), e3000130. doi:10.1371/journal.pbio.3000130

Wahid, B., Ali, A., Rafique, S., & Idrees, M. (2017). Global expansion of chikungunya

virus: mapping the 64-year history. Int The Journal of Infectious Diseases, 58, 69-76.

doi:10.1016/j.ijid.2017.03.006

Walker, T., Johnson, P. H., Moreira, L. A., Iturbe-Ormaetxe, I., Frentiu, F. D.,

McMeniman, C. J., Leong, Y. S., Dong, Y., Axford, J., Kriesner, P., Lloyd, A. L., Ritchie, S. A.,

O/'Neill, S. L., & Hoffmann, A. A. (2011). The wMel Wolbachia strain blocks dengue and

invades caged Aedes aegypti populations. Nature, 476(7361), 450-453. doi:http: //www.natur e . c

om/nature/journal/v476/n7361/abs/nature10355

Watkinson-Powell, B., & Alphey, N. (2017). Resistance to genetic insect control:

Modelling the effects of space. Journal of Theoretical Biology, 413, 72-85. doi:https://doi.org/

10.1016/j.jtbi.2016.10.014

Weeks, A. R., & Breeuwer, J. A. (2001). Wolbachia-induced parthenogenesis in a genus

of phytophagous mites. Proceedings. Biological sciences, 268(1482), 2245-2251.

doi:10.1098/rspb.2001.1797

Werren, J. H. (1997). Biology of Wolbachia. The Annual Review of Entomology, 42, 587-

609. doi:10.1146/annurev.ento.42.1.587

Werren, J. H., Baldo, L., & Clark, M. E. (2008). Wolbachia: master manipulators of

invertebrate biology. Nature Reviews Microbiology, 6(10), 741-751. doi:10.1038/nrmicro1969

Wilke, A. B. B., Vasquez, C., Carvajal, A., Medina, J., Chase, C., Cardenas, G., Mutebi,

J.-P., Petrie, W. D., & Beier, J. C. (2020). Proliferation of Aedes aegypti in urban environments

mediated by the availability of key aquatic habitats. Scientific Reports, 10(1), 12925.

doi:10.1038/s41598-020-69759-5

Wilson, A. L., Courtenay, O., Kelly-Hope, L. A., Scott, T. W., Takken, W., Torr, S. J., &

Lindsay, S. W. (2020). The importance of vector control for the control and elimination of vector-

borne diseases. PLoS Neglected Tropical Diseases, 14(1), e0007831. doi:10.1371/journal.pntd.

0007831

Yactayo, S., Staples, J. E., Millot, V., Cibrelus, L., & Ramon-Pardo, P. (2016).

Epidemiology of chikungunya in the Americas. The Journal of Infectious Diseases, 214(suppl 5),

S441-s445. doi:10.1093/infdis/jiw390

Yen, J. H., & Barr, A. R. (1971). New hypothesis of the cause of cytoplasmic

incompatibility in Culex pipiens L. Nature, 232(5313), 657-658. doi:10.1038/232657a0

Yen, P.-S., & Failloux, A.-B. (2020). A Review: Wolbachia-based population

replacement for mosquito control shares common points with genetically modified control

approaches. Pathogens (Basel, Switzerland), 9(5), 404. doi:10.3390/pathogens9050404

22

Zabalou, S., Riegler, M., Theodorakopoulou, M., Stauffer, C., Savakis, C., & Bourtzis, K.

(2004). Wolbachia induced cytoplasmic incompatibility as a means for insect pest population

control. Proceedings of the National Academy of Sciences, 101(42), 15042-15045.

doi:10.1073/pnas.0403853101

Zhang, L., Xu, W., Gao, X., Li, W., Qi, S., Guo, D., Ajayi, O. E., Ding, S.-W., & Wu, Q.

(2020). lncRNA Sensing of a Viral Suppressor of RNAi Activates non-canonical innate immune

signaling in Drosophila. Cell Host & Microbe, 27(1), 115-128.e118.

doi:https://doi.org/10.1016/j.chom.2019.12.006

Zhang, R., Zhu, Y., Pang, X., Xiao, X., Zhang, R., & Cheng, G. (2017). Regulation of

antimicrobial peptides in Aedes aegypti Aag2 Cells. Frontiers in Cellular and Infection

Microbiology, 7, 22. doi:10.3389/fcimb.2017.00022

Zhao, L., Alto, B. W., Jiang, Y., Yu, F., & Zhang, Y. (2019). Transcriptomic analysis of

Aedes aegypti innate immune system in response to ingestion of chikungunya virus. International

Journal of Molecular Sciences, 20(13), 3133. doi:10.3390/ijms20133133

Zheng, X., Zhang, D., Li, Y., Yang, C., Wu, Y., Liang, X., Liang, Y., Pan, X., Hu, L.,

Sun, Q., Wang, X., Wei, Y., Zhu, J., Qian, W., Yan, Z., Parker, A. G., Gilles, J. R. L., Bourtzis,

K., Bouyer, J., Tang, M., Zheng, B., Yu, J., Liu, J., Zhuang, J., Hu, Z., Zhang, M., Gong, J.-T.,

Hong, X.-Y., Zhang, Z., Lin, L., Liu, Q., Hu, Z., Wu, Z., Baton, L. A., Hoffmann, A. A., & Xi, Z.

(2019). Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature,

572(7767), 56-61. doi:10.1038/s41586-019-1407-9

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Chapter 2

Intra-host growth kinetics of dengue virus in the mosquito Aedes aegypti

Citation: Novelo M, Hall MD, Pak D, Young PR, Holmes EC, McGraw EA. Intra-host

growth kinetics of dengue virus in the mosquito Aedes aegypti. PLoS Pathogens.

2019;15(12):e1008218

2.1 Abstract

Dengue virus (DENV) transmission by mosquitoes is a time-dependent process that

begins with the consumption of an infectious blood-meal. DENV infection then proceeds

stepwise through the mosquito from the midgut to the carcass, and ultimately to the salivary

glands, where it is secreted into saliva and then transmitted anew on a subsequent bite. We

examined viral kinetics in tissues of the Ae. aegypti mosquito over a finely graded time

course, and as per previous studies, found that initial viral dose and serotype strain diversity

control infectivity. We also found that a threshold level of virus is required to establish

body-wide infections and that replication kinetics in the early and intermediate tissues do

not predict those of the salivary glands. Our findings have implications for mosquito GMO

design, modeling the contribution of transmission to vector competence and the role of

mosquito kinetics in the overall DENV epidemiological landscape.

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2.2 Author summary

DENV infection in the mosquito is a complex and dynamic process. Following

ingestion of an infected blood meal, DENV enters the mosquito midgut epithelial cells,

where it replicates. Subsequently, the virus disseminates and infects other tissues, including

hemocytes, fat body and reproductive organs, ultimately reaching the salivary glands. The

kinetics of infection are influenced by genetic variation in the virus. Comparisons between

strains within single serotypes, have revealed variation in infection rates in mosquitoes. To

explore the role of infectious dose, serotype and tissue in viral infection kinetics we

sampled DENV loads in populations of infected mosquitoes over numerous, sequential

time-points. We reveal that the kinetics of DENV infection in the midgut, carcass and

salivary glands of the mosquito Ae. aegypti are strikingly different among the strains

selected for this study, and that these differences are also driven by the initial infectious

dose.

Keywords: mosquito; Aedes aegypti; DENV; infection dynamics; within-vector

infection; EIP, intra-host; viral kinetics; dengue virus.

25

2.3 Introduction

Dengue is the most prevalent arboviral disease globally (Murray, Quam, & Wilder-

Smith, 2013), with estimates of more than 390 million infections per year (Bhatt et al.,

2013) and over half of the world’s population at risk (Brady et al., 2012). Although

mortality is generally low, the morbidity caused by dengue disease is associated with a

substantial socioeconomic burden (Duane J. Gubler, 2011). Dengue virus (DENV),

transmitted to humans through the bite of the Ae. aegypti mosquito (Simmons , Farrar , van

Vinh Chau , & Wills 2012), is spreading in large part due to the expanding geographic

range of the vector (Powell & Tabachnick, 2013). Previously confined to Africa, Ae.

aegypti is now present in tropical and temperate regions worldwide, and its spread is

assisted by climate change, globalization and ineffective vector control programs (Colón-

González, Fezzi, Lake, & Hunter, 2013).

DENV infection in the mosquito is a complex and dynamic process (Lequime,

Fontaine, Ar Gouilh, Moltini-Conclois, & Lambrechts, 2016). The virus must circumvent

multiple tissue barriers, including the midgut and salivary glands, and infect a range of

intermediate tissues in a stepwise fashion (Franz, Kantor, Passarelli, & Clem, 2015).

Following ingestion, DENV enters the midgut epithelial cells, where it replicates.

Subsequently, the virus disseminates and infects secondary tissues, including hemocytes,

fat body and reproductive tissue, ultimately reaching the salivary glands (Khoo, Doty,

Held, Olson, & Franz, 2013). The midgut is thought to represent the primary barrier to the

process of infection (Franz et al., 2015), capable of preventing many mosquitoes from

reaching the stage of disseminated infections (Cox, Brown, & Rico-Hesse, 2011). The rate

26

of this progression dictates the extrinsic incubation period (EIP), or the delay before a

mosquito can infect another human on a subsequent bite (Black Iv et al., 2002). The EIP

plays an important role in shaping transmission rates (Macdonald, 1957), with longer time

windows reducing the number of opportunities for pathogen transmission over a

mosquito’s lifetime.

The kinetics of infection are equally influenced by genetic variation in the virus.

Dengue fever is caused by four different serotypes of virus (DENV 1–4) (Deen et al., 2006)

that are 65–70% similar at the DNA sequence level across their ~11-kb genomes (Perera

& Kuhn, 2008), while also exhibiting a high-average within-serotype diversity of ~3% at

the amino acid level (Azhar et al., 2015). Comparisons between strains within single

serotypes, have revealed variation in both infection rates and EIP in mosquitoes (Anderson

& Rico-Hesse, 2006; Armstrong & Rico-Hesse, 2003), most likely due to differences in

viral replication rates (Cox et al., 2011). In humans, genetic variation within and between

serotypes also determines relative viral fitness (replication rate), epidemic potential and

virulence (Lambrechts et al., 2012; Ty Hang et al., 2010), with particular strains linked to

more severe clinical manifestations. The intra-host/vector diversity of DENV can also play

a role in transmission, such as variants with a replicative advantage can spread more rapidly

overall, eventually displacing those with lower fitness (Guzman & Harris, 2015). For

example, an uncharacteristically large outbreak of dengue in Cairns, Australia in

2008/2009 was attributed to the very short EIP of the DENV-3 strain in the mosquito

(Ritchie et al., 2013).

27

Surprisingly, little is known about DENV kinetics in the mosquito and how the

virus interacts with individual tissues during infection. Mosquitoes have evolved both

systemic and tissue specific antiviral mechanisms to limit viral replication (Cheng, Liu,

Wang, & Xiao, 2016); thus, viral kinetics may differ between tissues. Given that the virus

moves in a stepwise fashion, selective pressures in an initial tissue might therefore have

cascading effects on the viral kinetics in downstream tissues (Hall, Bento, & Ebert, 2017).

Additionally, different tissue types may offer a diversity of cell types and cellular niches

that may vary in their capacity to support DENV replication. For example, the midgut

epithelium is a dynamic niche, with cells being shed frequently (Zieler, Garon, Fischer, &

Shahabuddin, 2000), whereas other cell types in intermediate tissues may be more stable

sites of virus production.

A better understanding of intra vector kinetics will assist with developing optimal

intervention points for genetic modification, improve our model of vectorial capacity

(Macdonald, 1957) by adding components of dose and serotype, etc. (Christofferson &

Mores, 2011) and, expand our understanding of DENV epidemiology, and vector-virus

interactions such as peaks in viral load and latency periods in the host. Herein we compared

midgut, carcass and salivary gland loads for 4 strains of DENV representing each of the 4

serotypes, fed at either high or low infectious doses, daily over a period of 3 weeks. In so

doing, we were able to assess how the factors of dose and serotype strain diversity define

susceptibility, EIP and transmissibility but also how tissue specific differences and the inter

relationships between tissue kinetics drive transmissibility.

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2.4 Methods

2.4.1 Ae. aegypti rearing and virus preparation

Approximately 8000 eggs of an Ae. aegypti inbred line collected in Townsville,

Australia 13 generations previously were vacuum hatched and divided into individual 30

× 40 × 8-cm plastic trays containing 1 liter of reverse osmosis (RO) autoclaved water.

Larvae (~150) were placed in individual trays containing 3 liters of RO autoclaved water,

supplemented with common fish food (Tetramin, Melle, Germany). Pupae were collected

and placed into breeding cages, containing approximately 450 mosquitoes each. Adults

were provided 10% sucrose ad libitum. All mosquitoes were reared in a controlled

environment at 26°C, 75% relative humidity and a 12-hr light/dark cycle.

The DENV serotypes/strains used for this experiment are listed in Table 2.1. The

virus was propagated in cell culture, as described previously (Frentiu, Robinson, Young,

McGraw, & O'Neill, 2010). Ae. albopictus C6/36 cells were grown at 26°C in RPMI 1640

medium (Invitrogen, Carlsbad, CA) supplemented with 10% fetal bovine serum (FBS), 1×

Glutamax (Invitrogen) and HEPES buffer. Cells were first allowed to form monolayers of

around 60–80% confluence in T175 flasks (Sigma Aldrich, St. Louis, MO), and then

inoculated with DENV and maintained in RPMI medium supplemented with 2% FBS.

After 7 days post-inoculation, live virus was harvested, titrated via absolute quantification

PCR and adjusted to two final viral loads of 105 and 108 DENV copies per ml. Single-use

aliquots were stored at −80°C for subsequent plaque assay titration.

29

2.4.2 Mosquito infections

Prior to infection, female mosquitoes were sorted and placed in 1-liter plastic cups

with a density of ~150 individuals. Sucrose was removed from the mosquitoes 24 hrs, prior

to oral infection. Double-chamber glass feeders were covered with pig intestine previously

immersed in a 10% sucrose solution. Water heated to 37°C was circulated in the outer

chamber of the feeders, and a 1:1 mix of defibrinated sheep blood and the previously

titrated DENV virus was placed inside the feeder. The mosquitoes were split into 2 feeding

groups, each allowed to feed for ~2 hrs. One group was fed with a DENV infectious blood

meal concentration of 108 and the other with 105 DENV copies/ml. After 24 hrs, blood fed

mosquitoes were identified by visual inspection and separated into cups of 10 individuals.

2.4.3 Mosquito dissection and RNA extraction

Blood fed Ae. aegypti mosquitoes were collected daily, 10 per feeding group, over

20 days. Mosquitoes were anesthetized by chilling and dissected on a chill plate in a drop

of sterile phosphate buffer saline (PBS). Midgut, salivary glands and carcass tissues were

Table 2.1. The DENV strains used in this study

Serotype Strain Passage Accession

number

Place of origin Collection date

DENV-1 FR-50 10 FJ432734.1 Vietnam 2007

DENV-2 ET-300 11 EF440433.1 East Timor 2000

DENV-3 Cairns/08/09 9 JN406515.1 Australia 2008

DENV-4 33-188 12 None Vietnam NA

NA: not available

30

placed in separated 1.5-ml microcentrifuge tubes (Sarstedt, Nümbrecht, Germany)

containing 200 µl of TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and 2-mm glass

beads. Samples were homogenized and frozen at −80°C. RNA extraction was performed

using the TRIzol RNA extraction protocol according to the manufacturer’s instructions,

and RNA was eluted in 25 µl of nucleic acid-free water. Samples were DNase treated (Life

Technologies, Carlsbad, CA, USA) according to the manufacturer instructions (Amuzu,

Simmons, & McGraw, 2015). Total RNA was determined with a NanoDropTM lite

spectrophotometer (Thermo Scientific, Waltham, MA).

2.4.4 DENV absolute quantification via RT-qPCR

Quantitative real-time PCR (RT-qPCR) reactions for DENV detections were

performed with 4× TaqMan® fast virus 1-step master mix (Roche Applied Science,

Switzerland), PCR-grade water, 10 µM of DENV primers and probe and 2.5 µl of RNA in

a final volume of 10 µl. Reactions were run in microseal 96 microplates (Life Technologies

Life, Technologies, Carlsbad, CA) covered with optically clear film. LightCycler 480

(Roche Applied Science, Switzerland) thermal cycling conditions were 50°C for 5 min for

reverse transcription, 95°C for 10 s for RT inactivation/denaturation followed by 50

amplification cycles of 95°C for 3 s, 60°C 30 s and 72°C for 1 s.

Standard curves were generated from triplicate samples on each plate spanning the

range of 10 to 108 copies/reaction of DENV fragment copies. DENV standards were

constructed as described elsewhere (Ye et al., 2015), and they contained the 3’UTR of the

DENV genome. The limit of detection was set at 10 copies in this experiment; water was

31

used as a negative control and standards were run in triplicates. The concentration of

DENV genome copies in each sample was extrapolated from the standard curve as DENV

copies per nanogram of total RNA.

The primer sequences used for the detection of DENV, as previously used (Ye et

al., 2014), were F: 5′-AAGGACTAGAGGTTAGAGGAGACCC-3′, R: 5′CGTTC TG TG

CCTGGAATGATG-3′ and P: 5′-HEX- AACAG CATATTGA CGCTGGGAGAG ACC

AGA-BHQ1-3′.

2.4.5 Survival assay

A second set of Townsville mosquitoes were reared in the same conditions as

above. Mosquitoes were blood fed with the two viral concentrations as above (108 and 105

DENV copies/ml). A total of 80 fed mosquitoes per concentration were separated into clear

200-ml cups, each containing 10 individuals. They were monitored daily to assess death

until all had expired. Neither DENV serotypes nor infectious doses had an effect on

survival compared to a blood-only fed control (Appendix A).

2.4.6 Data analysis

Data analysis was carried out in R v 3.6.0 (http://www.r-project.org/). For

statistical analysis of the infection frequency, quasibinomial GLM was used to correct

for overdispersion of data, and Tukey for contrasts was used for post hoc comparisons.

For the kinetics of DENV virus, first we fit a 3-parameter logistic DRC model to DENV

loads in each tissue, dose and serotype combination, which modeled the upper asymptote

http://www.r-project.org/

32

or the maximum DENV load, the slope or growth rate at the midpoint, and the midpoint or

infection age which is halfway between the lower and upper asymptotes (i.e. ED50). We

then estimated separate trends for each treatment combination. From the candidate models,

the best fitting model for each treatment combination was selected using Akaike’s

information criterion (AIC). For MG and CA tissues the best fitting model was the 3-

parameter logistic model, while for the SG tissue we identified a linear relationship

between DPI and DENV load. DRC parameters and pairwise comparisons were estimated

with the DRC package (Ritz, Baty, Streibig, & Gerhard, 2016). The best model was

selected based on the AIC (yielding the lowest) values. In general, for the salivary

glands, the best fit was a linear model; thus, they were subsequently analyzed in this

fashion. For the average DENV load at DPI 20, significant differences were based on

Tukey post hoc comparison following ANOVAs. All DENV loads were reported on a log

scale given the value range.

2.5 Results

We orally challenged inbred wild type Ae. aegypti mosquitoes with two infectious

doses (108 and 105 DENV copies/ml), representative of plasma viremia ranges in humans

(Duong et al., 2015), of the 4 DENV serotypes. In each vector competence experiment,

mosquitoes were blood fed with a strain representing each of the 4 DENV serotypes at the

2 infectious doses. Ten individuals were then collected daily for 20 days, starting from the

first day post-infection (DPI), to measure DENV infection status and infection kinetics in

3 key tissues - midgut (MG), carcass (CA) and salivary glands (SG) - per mosquito.

33

2.5.1 The proportion of DENV-infected mosquitoes is affected by both infectious dose

and DPI

Infection status was determined by analyzing the proportion of individuals positive

for DENV in each tissue at each DPI, based on the presence of a positive qRT-PCR.

Infection prevalence (Figure 2.1) in the MG, CA and SG in all the serotypes was

significantly influenced by the infectious dose and DPI (Table 2.2). In only one case was

there a significant interaction between these 2 factors (Table 2.2, DENV-3 SG, GLM,

F=5.23, p=0.02), indicating that, in general, dose and age of infection act independently to

shape infection status.

Mosquito populations fed with higher doses had higher infection rates across

tissues and strains (Figure 2.1). The DENV-1 and -2 strains exhibited a higher infection

rate in all tissues and at both infectious doses compared to the DENV-3 and -4 strains.

DENV-4 exhibited especially low infection rates in all tissues at the low infectious dose.

The significance of DPI demonstrates that infection status is heavily time-dependent. For

the DENV-1 and -2 strains, in the high infectious dose, infection rates were constantly high

throughout 20 DPI in contrast to the low infectious dose, where there is noticeable

difference between the infection rates between early and late DPI. For the DENV-3 strain,

both infectious doses peaked in infection rate at ~12 days in all tissues. In the DENV-4

strain, infection levels are weaker than the other strains, which may contribute to the greater

variation in the effect of time across tissues and doses.

34

2.5.2 EIP is affected by the serotype strain and initial infectious dose

If we take SG detection of viral load as a proxy for presence of virus in the saliva,

the susceptibility data (Figure 2.1) can also be used to shed light on EIP. EIP can be

measured in multiple ways, including the day of first arrival of virus in the tissue/saliva or

the point at which 50% of the population has infected tissue/saliva. For all strains and

infectious doses, viral load can be detected in the SG already by day 2. In many cases,

especially for low doses, viral load appears in the SG and then detection is lost for several

days before returning and becoming stable (Figure 2.1). If we calculate an EIP50 for

populations where infection continues for more than two days, we see that infectious dose

and serotype strain diversity are predictors; for the strains representing DENV 1-4 at low-

dose, they are 5, 6, 9 and 15 days, respectively, while for the high-dose they are 1, 3, 7 and

7 days.

35

Figure 2.1: Susceptibility of DENV strains by DPI, tissue and infectious dose. The percentage

of DENV infectious mosquitoes calculated daily out of 10 individuals. Low infectious dose in red

and high infectious dose in blue. Stars represent EIP50 at the salivary gland for both infectious

doses. Statistical analysis is presented in Table 2.3.

36

Table 2.2. Susceptibility by Strain, Tissue, Dose and DPI

Serotype Tissue Factor F df p-value Sig.

DENV-1

MIDGUT

DOSE 29.79 38 <0.001 ***

DPI 19.38 37 <0.001 ***

DOSE: DPI 0.4 36 0.50 ns

CARCASS

DOSE 49.33 38 <0.001 ***

DPI 30.68 37 <0.001 **

DOSE: DPI 0.31 36 0.57 ns

SALIVARY

GLANDS

DOSE 31.78 38 <0.001 ***

DPI 12.09 37 0.001 **

DOSE: DPI 0.40 36 0.52 ns

DENV-2

MIDGUT

DOSE 16.76 38 <0.001 ***

DPI 4.67 37 0.03 *

DOSE: DPI 0.30 36 0.58 ns

CARCASS

DOSE 15.86 38 <0.001 ***

DPI 7.49 37 0.001 **

DOSE: DPI 0.12 36 0.72 ns

SALIVARY

GLANDS

DOSE 5.21 38 0.02 *

DPI 30.71 37 <0.001 ***

DOSE: DPI 0.25 36 0.61 ns

DENV-3

MIDGUT

DOSE 16.28 38 <0.001 ***

DPI 18.52 37 <0.001 ***

DOSE: DPI 0.08 36 0.77 ns

CARCASS

DOSE 15.48 35 <0.001 ***

DPI 6.24 34 0.01 *

DOSE: DPI 1.04 33 0.31 ns

SALIVARY

GLANDS

DOSE 6.95 38 0.01 *

DPI 13.72 37 <0.001 ***

DOSE: DPI 5.23 36 0.02 *

DENV-4

MIDGUT

DOSE 30.69 37 <0.001 ***

DPI 17.35 36 <0.001 ***

DOSE: DPI 1.47 35 0.23 ns

CARCASS

DOSE 20.78 33 <0.001 ***

DPI 14.01 32 <0.001 ***

DOSE: DPI 0.66 34 0.42 ns

SALIVARY

GLANDS

DOSE 10.62 38 0.002 **

DPI 18.07 37 <0.001 ***

DOSE: DPI 0.12 36 0.73 ns

*P<0.05, **P<0.01, ***P<0.001. ns. not significant.

37

2.5.3 Intra-host DENV kinetics are influenced by initial infectious dose and strain

To quantify the kinetics of DENV infection, we assessed whether the cumulative

change in DENV load in each tissue followed a trend that was either a sigmoidal or linear

over time. Thus, we first fit a 3-parameter logistic model to DENV loads in each tissue,

dose and serotype combination, which modeled the upper asymptote or the maximum

DENV load, the slope or growth rate at the midpoint, and the midpoint or infection age

which is halfway between the lower and upper asymptotes (i.e., ED50). We then estimated

separate trends for each treatment combination. From the candidate models, the best fitting

model for each treatment combination was selected using Akaike’s information criterion

(Appendix A). For MG and CA tissues the best fitting model was the 3-parameter logistic

model, while for the SG tissue we identified a linear relationship between DPI and DENV

load. We used the slope and maximum DENV load parameters to compare the behavior of

the different serotypes at different infectious doses and across tissues and times post-

infection (Figure 2.2).

2.5.4 Some treatments do not show substantial DENV replication and others exhibit

subpopulation structure of DENV load in infected mosquitoes

For a number of treatments DENV loads did not rise to very high levels (Figure

2.2). For example, in the low-dose categories neither the DENV-3 nor -4 strains reached a

load higher than log 103 in any tissue as measured by population mean. In these cases,

although we were able to fit a dose response curve (DRC) to the data, the parameters of

max load and growth rate are not informative (Appendix A). Therefore, the subsequent

38

discussion of growth parameter estimates focuses only on those treatments with substantial

DENV loads and replication (Tables 2.3 and 2.4). For the remaining treatments we aimed

to fit a single DRC to the different treatment combinations (infectious dose, tissue and

strain). In several treatments, however, we identified potential subpopulation structure,

with a proportion of the mosquitoes exhibiting high loads and the rest exhibiting very low

loads. Those treatments were the low infectious dose in the MG and CA for strains of

serotype DENV-1 and -2 and the high infectious dose in the MG and CA for strains of

serotypes DENV-3 and -4 (Figure 2.2). After noticing subpopulation structure in our data,

we applied Hartigan’s dip test for all datasets. For any with a significant result, we then

used a bimodality coefficient test to partition the data. We found that using a coefficient of

bimodality of >0.75 effectively split our datasets into two clear subpopulations (Appendix

A) using the following criteria: (1) for the y-axis, we selected the lowest load in the

histogram located between the two highest peaks (Appendix A); and (2) for the x-axis, we

identified the DPI where the two populations began to diverge.

39

Figure 2.2: DENV kinetics by strain, tissue and infectious dose. For the midgut and carcass,

lines represent the fitted parametric growth curves for DENV loads. Growth curves were fitted on

logarithmic transformed data. For the salivary glands, lines represent a linear regression for DENV

load. Linear regressions were fitted on logarithmic transformed data. For all tissues and serotypes,

points represent individual mosquito values (n=10); in red, low infectious dose (105 DENV

copies/ml); in blue, high infectious dose (108 DENV copies/ml). Points for the subpopulations have

been “jittered” to avoid overlap. Dashed lines represent subpopulations.

40

Table 2.3 DRC parameters for successful infections by tissue, infectious dose and strain

Infectious

dose Serotype Tissue Parameter

Parameter

estimate

Standard

error p-value Sig.

HIGH

DENV-3

MIDGUT

Growth rate

0.66 0.07 <0.001 ***

DENV-1 0.64 0.21 0.003 **

DENV-4 0.59 0.11 <0.001 ***

DENV-2 0.31 0.04 <0.001 ***

DENV-4

CARCASS

1.36 0.48 0.005 **

DENV-3 0.73 0.1 <0.001 ***

DENV-2 0.4 0.03 <0.001 ***

DENV-1 0.21 0.05 <0.001 ***

DENV-2

MIDGUT

Max DENV

load

12.2 0.2 <0.001 ***

DENV-3 11.5 0.17 <0.001 ***

DENV-4 9.74 0.26 <0.001 ***

DENV-1 9.11 0.27 <0.001 ***

DENV-2

CARCASS

9.53 0.16 <0.001 ***

DENV-1 8.99 1.24 <0.001 ***

DENV-3 8.85 0.19 <0.001 ***

DENV-4 6.44 0.21 <0.001 ***

DENV-1

MIDGUT

ED50

3.6 0.38 <0.001 ***

DENV-2 3.6 0.43 0.28

DENV-3 2.94 0.19 <0.001 ***

DENV-4 0.46 0.31 <0.001 ***

DENV-1

CARCASS

9.61 1.72 <0.001 ***

DENV-4 8.06 0.32 <0.001 ***

DENV-3 7.56 0.18 <0.001 ***

DENV-2 6.69 0.19 <0.001 ***

LOW

DENV-1 MIDGUT

Growth rate

0.92 0.27 0.001 ***

DENV-2 0.46 0.05 <0.001 ***

DENV-1 CARCASS

0.45 0.07 <0.001 ***

DENV-2 0.36 0.08 <0.001 ***

DENV-2 MIDGUT

Max DENV

load

11.24 0.27 <0.001 ***

DENV-1 8.28 0.27 <0.001 ***

DENV-1 CARCASS

8.7 0.44 <0.001 ***

DENV-2 8.1 0.37 <0.001 ***

DENV-2 MIDGUT

ED50

4.44 0.26 <0.001 ***

DENV-1 3.88 0.33 <0.001 ***

DENV-1 CARCASS

10.1 0.53 <0.001 ***

DENV-2 5.77 0.69 <0.001 ***

*P<0.05, **P<0.01, ***P<0.001. p-value indicates if parameter estimate is different than zero.

Standard error of parameter estimates. Treatments ranked by parameter estimate.

41

Table 2.4 DRC parameter contrasts for successful infections between strains, infectious

dose and subpopulations

Infectious

dose Serotype contrast Tissue

Parameter

estimate

Standard

error p-value Sig.

HIGH

DENV-1 vs DENV 2

MIDGUT

Growth rate 0.18 0.08 ns

Max DENV

load 0.36 <0.001 ***

CARCASS

Growth rate 0.05 0.001 **

Max DENV

load 1.02 0.58 ns

LOW

MIDGUT


Max DENV

load 0.39 <0.001 ***

CARCASS


Max DENV

load 0.59 0.31 ns

HIGH DENV-1 vs DENV-3

MIDGUT


Max DENV

load 0.34 <0.001 ***

CARCASS

Growth rate 0.13 <0.001 ***

Max DENV

load 1.00 0.88 ns


MIDGUT


Max DENV

load 0.33 0.08 ns

CARCASS


Max DENV

load 0.99 0.01 **


MIDGUT


Max DENV

load 0.33 0.05 ns

CARCASS


Max DENV

load 0.37 0.04 **


MIDGUT


Max DENV

load 0.30 <0.001 ***

CARCASS

Growth rate 0.46 0.04 ***

Max DENV

load 0.30 <0.001 ***


MIDGUT


Max DENV

load 0.32 <0.001 ***

CARCASS


Max DENV

load 0.29 <0.001 ***

*P<0.05, **P<0.01, ***P<0.001; ns, not significant.

42

2.5.5 DENV load is always higher in the midgut than other tissues for high infectious

dose

In all serotypes, maximum DENV loads were consistently higher in the MG than

the CA. Because we fit linear models to the SG data, we examined the average DENV load

at the last DPI (20 days) in lieu of a maximum load (Appendix A). For all serotypes and

high infectious doses, the SG load at this point was lower than the CA loads. This strong

pattern suggests a “trickledown” effect for DENV load across the sequentially infected

tissues (Figure 2.3). In contrast, low infectious doses resulted in low DENV loads in all

tissues and for all strains, and DENV-3 and -4 could not be evaluated due to their lack of

successful infection and failure of the selected model to run. Additionally, we ran

generalized linearized models on DENV load in the MG and CA (for high dose only) as a

predictor for the SG DENV load. We found no significant predictive ability regardless of

the serotype and tissue (Appendix A).

43

2.5.6 Tissue-specific DENV kinetics vary by strain in the high infectious dose

In the MG, the maximum loads of DENV-2 (12.2 log c/ng) and -3 (11.5 log c/ng)

are similarly high and greater than the strains representing serotypes 1 (9.11 log c/ng) and

4 (9.74 log c/ng) (Table 2.3). In the CA, the DENV-1 (8.99 log c/ng) and -2 (9.53 log c/ng)

strains have the highest loads and are both significantly different from DENV-3 (8.85 log

c/ng) and -4 (6.44 log c/ng) strains (Table 2.4). In the SG, the average final load (DPI 20)

for the DENV-2 strain is not different from the DENV-1 and -3 strains but is greater than

the DENV-4 (Appendix A). Although we have examined only one representative strain

from each serotype, these patterns suggest that DENV-2 may be better at replicating in

Figure 2.3: “Trickledown” or declining viral load with tissue progression. Average 20 DPI

DENV load for the 4 dengue serotypes across infectious dose and tissues. Box plots show median

values (horizontal line in the box), 25–75% interquartile range (upper–lower limits of the box),

95% range of data (error lines).

44

mosquitoes and that the other serotypes do not show consistent patterns of dominance over

one another across tissues. For growth rate, no one strain appeared to dominate over the

others across multiple tissues (Tables 2.4 and 2.5). For example, the DENV-3 strain has

the highest growth rate (change in viral copies/day) in the MG (0.66 log DENV copies per

DPI), whereas DENV-2 strain has the lowest (0.31 log DENV copies per DPI). In the CA,

DENV-4 has the highest growth rate (1.36 log DENV copies per DPI), whereas DENV-1

has the lowest (0.21 log DENV copies per DPI). Interestingly, in the SG all strains have

distinctly different growth rates, ranked in the following order: DENV-1, 2, 3, and 4 (Table

2.5). This suggests that the growth rates in early process/upstream tissues are not predictive

of rates in the final tissue controlling transmissibility.

2.5.7 The DENV-2 strain has a higher maximum DENV load than the DENV-1 strain

in the low infectious dose

For the low infectious dose, we only considered mosquitoes infected with either

DENV-1 or -2 strains, as the remaining strains did not exhibit “successful” infections

meaning that even though infection rate was high, the actual DENV load was substantially

low. Here, we saw that the DENV-2 strain had a significantly higher max DENV load in

the MG and a higher average final load in the SG than the DENV-1 strain. For growth rate,

there were no differences between the DENV-1 and DENV-2 strains in the MG or CA, but

in SG, the DENV-1 strain was significantly higher (Table 2.6).

45

Table 2.5 Linear regression model for SG

Infectious

dose Serotype Term Slope

Standard

error F p-value Sig

LOW

DENV-1 DPI 0.23 0.03 6.12 <0.01 **

DENV-2 DPI 0.13 0.03 3.51 <0.01 **

DENV-3 DPI −0.06 0.03 -1.81 0.073 ns

DENV-4 DPI 0.04 0.02 2.31 0.023 *

HIGH

DENV-1 DPI 0.38 0.03 10.98 <0.01 **

DENV-2 DPI 0.34 0.03 11.38 <0.01 **

DENV-3 DPI 0.23 0.04 5.24 <0.01 **

DENV-4 DPI 0.12 0.04 2.87 <0.01 **

*P<0.05, **P<0.01, ***P<0.001. Tukey for contrasts. ns, not significant.

Table 2.6 Serotype contrasts for growth rate in SG

Infectiou

s dose Serotype contrast

Standard

error df p-value Sig

LOW

DENV-1 – DENV-2 0.16 1026 0.24 ns

DENV-1 – DENV-3 1.81 1026 <.0001 ***

DENV-1 – DENV-4 2.14 1026 <.0001 ***

DENV-2 – DENV-3 2.95 1026 <.0001 ***

DENV-2 – DENV-4 3.49 1026 <.0001 ***

DENV-3 – DENV-4 0.40 1026 0.97 ns

HIGH

DENV-1 – DENV-2 0.08 1026 0.0001 ***

DENV-1 – DENV-3 0.58 1026 0.004 **

DENV-1 – DENV-4 1.15 1026 <.0001 ***

DENV-2 – DENV-3 1.64 1026 <.0001 ***

DENV-2 – DENV-4 3.27 1026 <.0001 ***

DENV-3 – DENV-4 0.51 1026 0.054 ns

*P<0.05, **P<0.01, ***P<0.001. Tukey for contrasts. ns, not significant.

46

2.6 Discussion

To explore the role of infectious dose, serotype and tissue in viral infection kinetics

we sampled DENV loads in populations of infected mosquitoes over numerous, sequential

time-points of the mosquito’s lifespan relevant to transmission in the field. We reveal that

the kinetics of DENV infection in the midgut, carcass and salivary glands of the mosquito

Ae. aegypti are strikingly different among the strains selected for this study, and that these

differences are also driven by the initial infectious dose. Specifically, we showed that (1)

initial infectious dose dictates infection frequency, with the DENV-1 and -2 strains

analyzed here infecting a greater proportion of mosquitoes than the DENV-3 and -4 strains;

(2) for DENV-3 and -4 strains only, the high infectious dose led to strong infection, with

lower doses producing lower viral replication; (3) viral growth rates in particular tissues

did not predict the total DENV load in that same tissue, with some strains having higher

growth rate but lower total DENV load; (4) in the salivary glands all strains have

independent growth rates not predicted by growth rates in previous tissues; and (5) for

every tissue and infectious dose, the DENV-2 strain reached a higher viral load according

to both the average final load at 20 DPI and max DENV load.

The initial infectious dose has been shown to influence the likelihood of a mosquito

becoming infected following an infectious blood meal and whether there is ultimately

dissemination to the salivary glands after a blood meal (D. J. Gubler, Nalim, Tan, Saipan,

& Sulianti Saroso, 1979; Rosen, Roseboom, Gubler, Lien, & Chaniotis, 1985). The amount

of virus in human blood required to infect ~50% of a mosquito population varies between

serotypes, with DENV-1 and -2 requiring ~10-fold less than DENV-3 and -4. This same

47

rank order is observed here. However, strain variation within each serotype is substantial

(Fontaine et al., 2018), so further comparisons across multiple strains within each serotype

are clearly needed to draw any conclusions about whether the strains we have selected are

predictive of the entire serotype.

We were also able to identify a potential threshold of virus needed in the blood

meal to initiate successful infections and assure likely transmissibility via the salivary

glands. A similar threshold effect has been observed in Drosophila melanogaster

populations challenged with systemic bacterial infections (Duneau et al. 2017). The study

identified two possible outcomes of infection; (1) where hosts died with high bacterial

burden or (2) where hosts survived with chronic infection and low pathogen load. Bacterial

load and the time it took the host to establish an immune response were found to determine

the trajectory of infection. Similarly, we saw either high infection (leading to

transmissibility) or low/loss of infection. Interestingly, for our study, the threshold effect

was driven by the initial infectious dose and by serotype. DENV-1 and -2 strains required

less virus to establish an infection, whereas, DENV-3 and -4 strains required substantially

more. The failure to initiate a successful infection could be due to poor replication in the

midgut or the midgut preventing dissemination (Cox et al., 2011; Khoo et al., 2013;

Taracena et al., 2018). This pattern is not unique to the midgut, however, with low and

inefficient replication also in disseminated tissues, such as the carcass. The data may

simply be the result of low population sizes of virus in combination with the large number

of vacant cellular niches. During infection DENV selectively annexes and manipulates host

metabolism and machinery to increase viral translation and replication (Chotiwan et al.,

48

2018; Choy, Sessions, Gubler, & Ooi, 2015; Reid et al., 2018), while also enhancing

replication of mosquito cells to increase niche availability in the mosquito (Helt & Harris,

2005). The more virions that infect a given cell, the more efficient this process becomes

and, thus, more viruses are produced. Initial viral load combined with inherent replicative

differences between strains is likely to produce the pattern we see in our data, that the

DENV-1 and 2 strains require a lower dose to produce successful infections.

Interestingly, higher viral growth rates in tissues did not necessarily lead to higher

DENV loads. For example, for the high infectious dose of DENV-1, the midgut had the

highest max DENV load compared to the carcass, however its growth rate was smaller.

This relationship was seen for the other treatments as well. It is possible that the migration

of viruses between tissues via the tracheal system or hemolymph (Forrester, Guerbois,

Seymour, Spratt, & Weaver, 2012) or directly from nearby tissues is impacting the total

viral counts. In support of tracheal or hemolymph travel is the initial spike in infection rate

in salivary glands followed by a reduction to zero and then a second front of infection

several days later. It is possible that the latter wave represents the larger migration of virus

from bodily tissues that then establishes permanent infection. Given how the circulatory

system of the mosquito works, viral particles could be carried from tissues that are not in

immediate proximity to the salivary glands. This process could directly affect growth rate

in upstream tissues. The salivary glands also represent a special case of the entry and exit

balance, with virus being excreted into the saliva and hence lost from tissue measures.

Under a more natural setting additional factors would likely affect kinetics including,

exposure to non-infectious blood meals prior to consumption of an infectious (Zhu et al.,

49

2017), consumption of sequential dengue infectious meals (Muturi, Buckner, & Bara,

2017) or interactions between DENV and other viruses present in blood meals or the

mosquito body (Le Coupanec et al., 2017).

The disconnect between kinetics in intermediate tissues and those of the salivary

glands suggests that there are either stochastic processes driving kinetics in early tissues

that do not predict how many viruses make it to the salivary glands, or that factors affecting

replication rate in the salivary glands are somewhat independent (Cheng et al., 2016). For

example, previous research has demonstrated that the strength and efficacy of immunity

varies across tissues (Cheng et al., 2016) and in response to different serotypes (Smartt,

Shin, & Alto, 2017). It is also possible that DENV is modulating the vector immune

response in a strain- or tissue-dependent manner (Sim & Dimopoulos, 2010). Differences

between the salivary glands and other tissues may also be due to the number of cellular

niches, and/or unique replication rates in those niches. For example, in contrast to the

midgut epithelium, DENV tropism in the salivary glands appears to be heterogenous within

the tissue; this might be due to the distribution of cellular receptors (Cao-Lormeau, 2009),

with a possible higher concentration of viral entry receptors in the lateral distal lobes of the

tissue (Salazar, Richardson, Sánchez-Vargas, Olson, & Beaty, 2007). In future studies, it

would be useful to quantify the production rate of virus using negative strand strain-specific

PCR methods (Raquin & Lambrechts, 2017) in the midgut and in the salivary glands

relative to the availability of cellular niches.

The differences in viral kinetics between the DENV strains analyzed here,

particularly in the salivary glands, might explain disparities in transmission potential in the

50

field. A range of studies have linked different genotypes to overall dominance in terms of

circulation in human populations both at local and global scales (Dumre et al., 2017;

Liebman et al., 2012; Sasmono et al., 2018; Shrivastava et al., 2018). Additionally, DENV

variants with a replicative advantage in both the host and vector can spread more rapidly,

eventually displacing those with lower fitness (Guzman & Harris, 2015). Our findings

indicate that 2 of the 4 DENV strains studied here show greater capacity to replicate in the

mosquito. In some cases, these differences may have consequences for public health, as

some specific variants of DENV have been linked to more severe clinical manifestations

than others (Balmaseda et al., 2006; Halsey et al., 2012; Nisalak et al., 2000; OhAinle et

al., 2011). These findings also have implications for the mosquito’s contribution to the

DENV epidemiological landscape, with the understanding that aspects of human

immunity, including cross-reactivity and non-specific serotype responses also shape the

nature of circulating variants (Reich et al., 2013). Lastly, our work provides better insight

into ideal design of vector competence experiments and the selection of gene candidates

for the engineering of virus refractory mosquitoes. Specifically, our findings support a

greater focus on salivary gland or saliva-based measures of infection and genes that either

completely reduce viral loads in the midgut or that control viral replication in the endpoint

tissue, salivary glands.

2.7 Acknowledgments

The authors would like to thank Emily Kerton, for her help with insect rearing and

sample preparation.

51

2.8 References

Amuzu, H. E., Simmons, C. P., & McGraw, E. A. (2015). Effect of repeat human blood

feeding on Wolbachia density and dengue virus infection in Aedes aegypti. Parasites & Vectors,

8, 246. doi:10.1186/s13071-015-0853-y


by the infecting genotype of dengue virus. The American Society of Tropical Medicine and

Hygiene, 75.

Armstrong, P. M., & Rico-Hesse, R. (2003). Efficiency of dengue serotype 2 virus strains

to infect and disseminate in Aedes aegypti. The American Society of Tropical Medicine and

Hygiene, 68.

Azhar, E. I., Hashem, A. M., El-Kafrawy, S. A., Abol-Ela, S., Abd-Alla, A. M., &

Sohrab, S. S. (2015). Complete genome sequencing and phylogenetic analysis of dengue type 1

virus isolated from Jeddah, Saudi Arabia. Virology Journal, 12. doi:10.1186/s12985-014-0235-7

Balmaseda, A., Hammond, S. N., Perez, L., Tellez, Y., Saborio, S. I., & Mercado, J. C.

(2006). Serotype-specific differences in clinical manifestations of dengue. The American Society

of Tropical Medicine and Hygiene, 74.

Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L.,

Drake, J. M., Brownstein, J. S., Hoen, A. G., Sankoh, O., Myers, M. F., George, D. B., Jaenisch,

T., Wint, G. R. W., Simmons, C. P., Scott, T. W., Farrar, J. J., & Hay, S. I. (2013). The global

distribution and burden of dengue. Nature, 496(7446), 504-507.Doi:10.1038/nature12060

Black Iv, W. C., Bennett, K. E., Gorrochótegui-Escalante, N., Barillas-Mury, C. V.,

Fernández-Salas, I., de Lourdes Muñoz, M. a., Farfán-Alé, J. A., Olson, K. E., & Beaty, B. J.

(2002). Flavivirus susceptibility in Aedes aegypti. Archives of Medical Research, 33(4), 379-388.

doi:http://dx.doi.org/10.1016/S0188-4409(02)00373-9

Brady, O. J., Gething, P. W., Bhatt, S., Messina, J. P., Brownstein, J. S., Hoen, A. G.,

Moyes, C. L., Farlow, A. W., Scott, T. W., & Hay, S. I. (2012). Refining the global spatial limits

of dengue virus transmission by evidence-based consensus. PLoS Neglected Tropical Diseases,


Cao-Lormeau, V.-M. (2009). Dengue viruses binding proteins from Aedes aegypti and

Aedes polynesiensis salivary glands. Virology Journal, 6(1), 35. doi:10.1186/1743-422x-6-35



Chotiwan, N., Andre, B. G., Sanchez-Vargas, I., Islam, M. N., Grabowski, J. M., Hopf-

Jannasch, A., Gough, E., Nakayasu, E., Blair, C. D., Belisle, J. T., Hill, C. A., Kuhn, R. J., &

Perera, R. (2018). Dynamic remodeling of lipids coincides with dengue virus replication in the

52

midgut of Aedes aegypti mosquitoes. PLoS Pathogens, 14(2), e1006853.


Choy, M. M., Sessions, O. M., Gubler, D. J., & Ooi, E. E. (2015). Production of

infectious dengue virus in Aedes aegypti is dependent on the ubiquitin proteasome pathway. PLoS

Neglected Tropical Diseases, 9(11), e0004227-e0004227. doi:10.1371/journal.pntd.0004227

Christofferson, R. C., & Mores, C. N. (2011). Estimating the magnitude and direction of

altered arbovirus transmission due to viral phenotype. PLoS ONE, 6(1), e16298.

doi:10.1371/journal.pone.0016298

Colón-González, F. J., Fezzi, C., Lake, I. R., & Hunter, P. R. (2013). The effects of

weather and climate change on dengue. PLoS Neglected Tropical Diseases, 7(11), e2503.


Cox, J., Brown, H. E., & Rico-Hesse, R. (2011). Variation in vector competence for

dengue viruses does not depend on mosquito midgut binding affinity. PLoS Neglected Tropical


Deen, J. L., Harris, E., Wills, B., Balmaseda, A., Hammond, S. N., Rocha, C., Dung, N.

M., Hung, N. T., Hien, T. T., & Farrar, J. J. (2006). The WHO dengue classification and case

definitions: time for a reassessment. The Lancet, 368(9530), 170-173. doi:10.1016/S0140-

6736(06)69006-5

Dumre, S. P., Bhandari, R., Shakya, G., Shrestha, S. K., Cherif, M. S., Ghimire, P.,

Klungthong, C., Yoon, I.-K., Hirayama, K., Na-Bangchang, K., & Fernandez, S. (2017). Dengue

virus serotypes 1 and 2 responsible for major dengue outbreaks in Nepal: Clinical, laboratory, and

epidemiological features. The American Society of Tropical Medicine and Hygiene, 97(4), 1062-

1069. doi:10.4269/ajtmh.17-0221

Duneau, D., Ferdy, J. B., Revah, J., Kondolf, H., Ortiz, G. A., Lazzaro, B. P., & Buchon,

N. (2017). Stochastic variation in the initial phase of bacterial infection predicts the probability of

survival in D. melanogaster. eLife, 6. doi:10.7554/eLife.28298

Duong, V., Lambrechts, L., Paul, R. E., Ly, S., Lay, R. S., Long, K. C., Huy, R.,

Tarantola, A., Scott, T. W., Sakuntabhai, A., & Buchy, P. (2015). Asymptomatic humans transmit

dengue virus to mosquitoes. Proceedings of the National Academy, 112(47), 14688-14693.

doi:10.1073/pnas.1508114112





Forrester, N. L., Guerbois, M., Seymour, R. L., Spratt, H., & Weaver, S. C. (2012).

vector-borne transmission imposes a severe bottleneck on an RNA virus population. PLoS

Pathogens, 8(9), e1002897. doi:10.1371/journal.ppat.1002897

53

Franz, A. W. E., Kantor, A. M., Passarelli, A. L., & Clem, R. J. (2015). Tissue Barriers to

Arbovirus infection in mosquitoes. Viruses, 7(7), 3741-3767. doi:10.3390/v7072795

Frentiu, F. D., Robinson, J., Young, P. R., McGraw, E. A., & O'Neill, S. L. (2010).

Wolbachia-mediated resistance to dengue virus infection and death at the cellular level. PLoS

ONE, 5(10), e13398. doi:10.1371/journal.pone.0013398

Gubler, D. J. (2011). Dengue, urbanization and globalization: The unholy trinity of the

21(st) century. Tropical Medicine and Health, 39(4 Suppl), 3-11. doi:10.2149/tmh.2011-S05

Gubler, D. J., Nalim, S., Tan, R., Saipan, H., & Sulianti Saroso, J. (1979). Variation in

susceptibility to oral infection with dengue viruses among geographic strains of Aedes aegypti.

The American Society of Tropical Medicine and Hygiene, 28(6), 1045-1052.



Hall, M. D., Bento, G., & Ebert, D. (2017). The evolutionary consequences of stepwise

infection processes. Trends in Ecology & Evolution, 32(8), 612-623.

doi:10.1016/j.tree.2017.05.009

Halsey, E. S., Marks, M. A., Gotuzzo, E., Fiestas, V., Suarez, L., Vargas, J., Aguayo, N.,

Madrid, C., Vimos, C., Kochel, T. J., & Laguna-Torres, V. A. (2012). Correlation of serotype-

specific dengue virus infection with clinical manifestations. PLoS Neglected Tropical Diseases,

6(5), e1638-e1638. doi:10.1371/journal.pntd.0001638

Helt, A.-M., & Harris, E. (2005). S-phase-dependent enhancement of dengue virus 2

replication in mosquito cells, but not in human cells. Journal of Virology, 79(21), 13218-13230.

doi:10.1128/jvi.79.21.13218-13230.2005

Khoo, C. C., Doty, J. B., Held, N. L., Olson, K. E., & Franz, A. W. (2013). Isolation of

midgut escape mutants of two American genotype dengue 2 viruses from Aedes aegypti. Virology

Journal, 10(1), 1-11. doi:10.1186/1743-422x-10-257




86(3), 1853-1861. doi:10.1128/jvi.06458-11

Le Coupanec, A., Tchankouo-Nguetcheu, S., Roux, P., Khun, H., Huerre, M., Morales-

Vargas, R., Enguehard, M., Lavillette, D., Misse, D., & Choumet, V. (2017). Co-infection of

mosquitoes with chikungunya and dengue viruses reveals modulation of the replication of both

viruses in midguts and salivary glands of Aedes aegypti mosquitoes. International Journal of

Molecular Sciences, 18(8). doi:10.3390/ijms18081708

Lequime, S., Fontaine, A., Ar Gouilh, M., Moltini-Conclois, I., & Lambrechts, L. (2016).

Genetic drift, purifying selection and vector genotype shape dengue virus intra-host genetic

diversity in mosquitoes. PLoS Genetics, 12(6), e1006111. doi:10.1371/journal.pgen.1006111

54

Liebman, K. A., Stoddard, S. T., Morrison, A. C., Rocha, C., Minnick, S., Sihuincha, M.,

Russell, K. L., Olson, J. G., Blair, P. J., Watts, D. M., Kochel, T., & Scott, T. W. (2012). Spatial

dimensions of dengue virus transmission across interepidemic and epidemic periods in Iquitos,

Peru (1999-2003). PLoS Neglected Tropical Diseases, 6(2), e1472.doi:10.1371/journal.pntd.

0001472

Macdonald, G. (1957). The Epidemiology and Control of Malaria. Amen House,

Warwick Square, London E.C.4.: Oxford University Press.



Muturi, E. J., Buckner, E., & Bara, J. (2017). Superinfection interference between

dengue-2 and dengue-4 viruses in Aedes aegypti mosquitoes. Tropical Medicine & International

Health, 22(4), 399-406. doi:10.1111/tmi.12846

Nisalak, A., Raengsakulrach, B., Endy, T. P., Vaughn, D. W., Kalayanarooj, S.,

Nimmannitya, S., Suntayakorn, S., Rothman, A. L., Ennis, F. A., Green, S., & Innis, B. L. (2000).

Dengue Viremia Titer, Antibody Response Pattern, and Virus Serotype Correlate with Disease

Severity. The Journal of Infectious Diseases, 181(1), 2-9. doi:10.1086/315215

OhAinle, M., Balmaseda, A., Macalalad, A. R., Tellez, Y., Zody, M. C., Saborio, S.,

Nunez, A., Lennon, N. J., Birren, B. W., Gordon, A., Henn, M. R., & Harris, E. (2011). Dynamics

of dengue disease severity determined by the interplay between viral genetics and serotype-

specific immunity. Science Translational Medicine, 3(114), 114ra128.

doi:10.1126/scitranslmed.3003084

Perera, R., & Kuhn, R. J. (2008). Structural proteomics of dengue virus. Current opinion

in microbiology, 11(4), 369-377. doi:10.1016/j.mib.2008.06.004

Powell, J. R., & Tabachnick, W. J. (2013). History of domestication and spread of Aedes

aegypti - A review. Memórias do Instituto Oswaldo Cruz, 108(Suppl 1), 11-17.

doi:10.1590/0074-0276130395

Raquin, V., & Lambrechts, L. (2017). Dengue virus replicates and accumulates in Aedes

aegypti salivary glands. Virology, 507, 75-81. doi:https://doi.org/10.1016/j.virol.2017.04.009

Reich, N. G., Shrestha, S., King, A. A., Rohani, P., Lessler, J., Kalayanarooj, S., Yoon,

I.-K., Gibbons, R. V., Burke, D. S., & Cummings, D. A. T. (2013). Interactions between

serotypes of dengue highlight epidemiological impact of cross-immunity. Journal of The Royal

Society Interface, 10(86). doi:10.1098/rsif.2013.0414

Reid, D. W., Campos, R. K., Child, J. R., Zheng, T., Chan, K. W. K., Bradrick, S. S.,

Vasudevan, S. G., Garcia-Blanco, M. A., & Nicchitta, C. V. (2018). Dengue virus selectively

annexes endoplasmic reticulum-associated translation machinery as a strategy for co-opting host

cell protein synthesis. Journal of Virology, 92(7), e01766-01717. doi:10.1128/jvi.01766-17

55

Ritchie, S. A., Pyke, A. T., Hall-Mendelin, S., Day, A., Mores, C. N., Christofferson, R.

C., Gubler, D. J., Bennett, S. N., & van den Hurk, A. F. (2013). An explosive epidemic of DENV-

3 in Cairns, Australia. PLoS ONE, 8(7), e68137. doi:10.1371/journal.pone.0068137

Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2016). Dose-response analysis using R.

PLoS ONE, 10(12), e0146021. doi:10.1371/journal.pone.0146021

Rosen, L., Roseboom, L. E., Gubler, D. J., Lien, J. C., & Chaniotis, B. N. (1985).

Comparative susceptibility of mosquito species and strains to oral and parenteral infection with

dengue and Japanese encephalitis viruses. The American Society of Tropical Medicine and

Hygiene, 34(3), 603-615.

Salazar, M. I., Richardson, J. H., Sánchez-Vargas, I., Olson, K. E., & Beaty, B. J. (2007).

Dengue virus type 2: replication and tropisms in orally infected Aedes aegypti mosquitoes. BMC

Microbiology, 7, 9-9. doi:10.1186/1471-2180-7-9

Sasmono, R. T., Taurel, A. F., Prayitno, A., Sitompul, H., Yohan, B., Hayati, R. F.,

Bouckenooghe, A., Hadinegoro, S. R., & Nealon, J. (2018). Dengue virus serotype distribution

based on serological evidence in pediatric urban population in Indonesia. PLoS Neglected

Tropical Diseases, 12(6), e0006616. doi:10.1371/journal.pntd.0006616





Sim, S., & Dimopoulos, G. (2010). Dengue virus inhibits immune responses in Aedes

aegypti cells. PLoS ONE, 5(5), e10678. doi:10.1371/journal.pone.0010678

Simmons , C. P., Farrar , J. J., van Vinh Chau , N., & Wills , B. (2012). Dengue. New

England Journal of Medicine, 366(15), 1423-1432. doi:doi:10.1056/NEJMra1110265

Smartt, C. T., Shin, D., & Alto, B. W. (2017). Dengue serotype-specific immune

response in Aedes aegypti and Aedes albopictus. Memórias do Instituto Oswaldo Cruz, 112(12),

829-837. doi:10.1590/0074-02760170182

Taracena, M. L., Bottino-Rojas, V., Talyuli, O. A. C., Walter-Nuno, A. B., Oliveira, J. H.

M., Angleró-Rodriguez, Y. I., Wells, M. B., Dimopoulos, G., Oliveira, P. L., & Paiva-Silva, G.

O. (2018). Regulation of midgut cell proliferation impacts Aedes aegypti susceptibility to dengue

virus. PLoS Neglected Tropical Diseases, 12(5), e0006498. doi:10.1371/journal.pntd.0006498

Ty Hang, V. T., Holmes, E. C., Veasna, D., Quy, N. T., Tinh Hien, T., Quail, M.,

Churcher, C., Parkhill, J., Cardosa, J., Farrar, J., Wills, B., Lennon, N. J., Birren, B. W., Buchy,

P., Henn, M. R., & Simmons, C. P. (2010). Emergence of the Asian 1 genotype of dengue virus

serotype 2 in Viet Nam: In vivo fitness advantage and lineage replacement in South-East Asia.


56

Ye, Y. H., Carrasco, A. M., Frentiu, F. D., Chenoweth, S. F., Beebe, N. W., van den

Hurk, A. F., Simmons, C. P., O'Neill, S. L., & McGraw, E. A. (2015). Wolbachia reduces the

transmission potential of dengue-infected Aedes aegypti. PLoS Neglected Tropical Diseases, 9(6),

e0003894. doi:10.1371/journal.pntd.0003894

Ye, Y. H., Ng, T. S., Frentiu, F. D., Walker, T., van den Hurk, A. F., O'Neill, S. L.,

Beebe, N. W., & McGraw, E. A. (2014). Comparative susceptibility of mosquito populations in

North Queensland, Australia to oral infection with dengue virus. The American Society of

Tropical Medicine and Hygiene, 90(3), 422-430. doi:10.4269/ajtmh.13-0186

Zhu, Y., Zhang, R., Zhang, B., Zhao, T., Wang, P., Liang, G., & Cheng, G. (2017). Blood

meal acquisition enhances arbovirus replication in mosquitoes through activation of the

GABAergic system. Nature Communications, 8(1), 1262. doi:10.1038/s41467-017-01244-6

Zieler, H., Garon, C. F., Fischer, E. R., & Shahabuddin, M. (2000). A tubular network

associated with the brush-border surface of the Aedes aegypti midgut: implications for pathogen

transmission by mosquitoes. Journal of Experimental Biology, 203(Pt 10), 1599-1611.

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Chapter 3

The effects of DENV serotype competition and co-infection on viral kinetics

in Wolbachia-infected and uninfected Aedes aegypti mosquitoes

Citation: Novelo M, Audsley M, McGraw EA. The impact of DENV serotype competition

and co-infection viral kinetics in Wolbachia-infected and uninfected Aedes aegypti

mosquitoes. Parasites & Vectors. 2021. In press.

3.1 Abstract

Background: The Ae. aegypti mosquito is responsible for the transmission of

several medically important arthropod-borne viruses, including multiple serotypes of

dengue virus (DENV-1, -2, -3, and -4). Competition within the mosquito between DENV

serotypes can affect viral infection dynamics, modulating the transmission potential of the

pathogen. Vector control remains the main method for limiting dengue fever. The insect

endosymbiont Wolbachia pipientis is currently being trialed in field releases globally as a

means of biological control because it reduces virus replication inside the mosquito. It is

not clear how co-infection between DENV serotypes in the same mosquito might alter the

pathogen-blocking phenotype elicited by Wolbachia in Ae. aegypti.

Methods: Five- to 7-day-old female Ae. aegypti from two lines, namely, with

(wMel) and without Wolbachia infection (WT), were fed virus-laden blood through an

artificial membrane with either a mix of DENV-2 and -3 or the same DENV serotypes

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singly. Mosquitoes were subsequently incubated inside environmental chambers and

collected on the following days post-infection: 3, 4, 5, 7, 8, 9, 11, 12, and 13. Midgut,

carcass, and salivary glands were collected from each mosquito at each timepoint and

individually analyzed to determine the percentage of DENV infection and viral RNA load

via RT-qPCR.

Results: We saw that for WT mosquitoes DENV-3 grew to higher viral infection

rates across multiple tissues when co-infected with DENV-2 than when it was in a mono-

infection. Additionally, we saw a strong pathogen-blocking phenotype in wMel mosquitoes

independent of co-infection status.

Conclusion: In this study, we demonstrated that the wMel mosquito line is capable

of blocking DENV serotype co-infection in a systemic way across the mosquito body.

Moreover, we showed that for WT mosquitoes, serotype co-infection can affect infection

frequency in a tissue- and time-specific manner and that both viruses have the potential of

being transmitted simultaneously. Our findings suggest that the long-term efficacy of

Wolbachia pathogen blocking is not compromised by arthropod-borne virus co-infection.

Key words: dengue; co-infection, Aedes aegypti; serotype; Wolbachia; infection

dynamics

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3.2 Introduction

Dengue viruses (DENVs) are medically important arthropod-borne viruses

(arboviruses) responsible for up to 300 million cases of dengue fever a year, and it can be

caused by any of the four related but antigenically distinct DENV serotypes (DENV-1 to -

4) (Murray, Quam, & Wilder-Smith, 2013). In regions with endemic transmission of all

four serotypes of DENV, varying predominance of certain serotypes has been observed

between seasons (Vasilakis & Weaver, 2008). Differences in the DENV infection kinetics

and transmission potential are influenced by the genetic diversity of the four different

DENV serotypes. The overall genome sequence-level differences between serotypes are

estimated at 20-30% (Holmes & Twiddy, 2003; Rico-Hesse, 2003).

Substantial evidence indicates that variation in the DENV genome of serotypes and

strains can have epidemiological significance by altering the extrinsic incubation period

(EIP) (Fontaine et al., 2018; Ko, Salem, Chang, & Chao, 2020; L. Lambrechts et al., 2009),

defined as the time it takes for the pathogen to be transmitted by the vector (Macdonald,

1957), and therefore has a powerful effect on the scale and speed of epidemics. DENV-2

strains from the American and Southeast Asian genotypes differ in their EIP lengths, with

the Southeast Asian genotypes having shorter EIPs. This shorter EIP was thought, in part,

to explain the displacement of the American DENV strains in South America by the Asian

lineage (Anderson & Rico-Hesse, 2006). Additionally, different DENV serotypes exhibit

various degrees of infectivity across the same mosquito populations (C. V. F. Carrington,

Foster, Pybus, Bennett, & Holmes, 2005; Ekwudu et al., 2020; Gubler, Nalim, Tan, Saipan,

& Sulianti Saroso, 1979; Novelo et al., 2019; Weaver & Vasilakis, 2009). Moreover, oral

60

susceptibility to DENV-1 was shown to be up to four times higher than that of DENV-3 in

Ae. aegypti from Senegal, with DENV-1 having higher infection and dissemination rates

(Gaye et al., 2014). Finally, systematic analyses of DENV replication kinetics of all four

DENV serotypes found significant differences in the infection rate and EIP between

serotypes (Ekwudu et al., 2020; Novelo et al., 2019).

Competition between DENV strains and serotypes can also affect viral population

dynamics within the vector, thus modulating the transmission potential (Louis Lambrechts

et al., 2012; Vogels et al., 2019). This happens in nature when mosquitoes take multiple

blood meals from several different hosts that are each infected with a different or multiple

DENV serotypes (Shrivastava et al., 2018). In controlled laboratory experiments, using

field-derived mosquito populations, there were no differences in dissemination and

transmission rates between DENV-1 and DENV-4 mono-infections in Ae. aegypti, but

during co-infection, DENV-4 had a much higher dissemination rate, leading to the

exclusive presence of DENV-4 in the saliva for this particular experimental mosquito

population (M. Vazeille, Gaborit, Mousson, Girod, & Failloux, 2016). Furthermore,

differential replication between DENV-2 and DENV-3 has been shown, with DENV-2

exhibiting a much higher replication efficiency both in vitro and in vivo during co-infection

(Quintero-Gil, Ospina, Osorio-Benitez, & Martinez-Gutierrez, 2014). Additionally, the

effect of co-infection with different families of arboviruses on vector competence has just

been recently studied. For example, mosquitoes exposed to double or triple infections with

chikungunya virus (CHIKV; Togaviridae), Zika virus (ZIKV; Flaviviridae), and DENV

(Flaviviridae) were capable of transmitting all pathogens concurrently, without noticeable

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changes to mosquito infection and dissemination rates (Rückert et al., 2017). Co-infection

studies may shed light on the outcome of competitive processes in field mosquitoes, even

if rare, but also importantly allow for direct comparisons of the transmissibility between

viruses.

Given the ease of rearing Ae. aegypti in the laboratory, vector competence

experiments are an important tool with which to study the effect and interaction between

DENV and mosquito genotypes in the transmission potential of the virus. However, one of

the main issues with artificial vector competence experiments is that there is too much

heterogeneity between experiments that results from environmental variation and its

interaction with genetic variation in both the mosquitoes and viruses (Souza-Neto, Powell,

& Bonizzoni, 2019). Individual vector competence experiments using single DENV

serotypes or strains often give varying results in both infection and transmission rates,

making pairwise comparisons difficult to interpret. Moreover, limited data are available on

co-infections with different serotypes, with some experiments suggesting competitive

disadvantage or superinfection interference between DENV serotypes (Muturi, Buckner,

& Bara, 2017). Additionally, it is not clear how these viral dynamics and interactions may

be altered in the presence of Wolbachia infection, which is currently being trialed in global

releases as a means of reducing virus transmission to humans (Dorigatti, McCormack,

Nedjati-Gilani, & Ferguson, 2018) and that is known to reduce viral replication in serotype-

specific ways (Bell, Katzelnick, & Bedford, 2019; L. B. Carrington et al., 2018; Ferguson

et al., 2015). To the best of our knowledge, only one study has looked at the effects of

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arboviral co-infection in Wolbachia-infected mosquitoes using ZIKV and DENV (Dutra et

al., 2016).

To assess the effect of DENV serotype co-infection on transmissibility and any

corresponding interactions with Wolbachia infection, we used two interdependent

approaches. First, we challenged two Ae. aegypti mosquito lines that were either

Wolbachia infected (wMel) or Wolbachia uninfected (WT) in both mono- and co-infection

vector competence experiments with DENV-2 and DENV-3. We collected midgut,

carcass, and salivary glands at nine timepoints post-infection. We then used the infection

rate and viral RNA load data to assess the effects of competing serotypes and evaluate their

performance in individual mosquitoes and between wMel and WT lines. Second, we

determined whether serotype co-infection altered viral infection dynamics relative to the

mono-infected state by comparing viral RNA load and infection rate between the two

vector competence experiments.

3.3 Methods

3.3.1 Mosquito lines and rearing

The mosquito lines used for this work have been described previously (Terradas,

Allen, Chenoweth, & McGraw, 2017; Walker et al., 2011). The wMel line was collected

from the Wolbachia release zone in Cairns, Australia, as part of the Eliminate Dengue

Program, whereas the WT line, naturally free from Wolbachia, was collected outside the

Wolbachia release zone. Both lines were identified morphologically and with genetic

markers as well as screened for the presence/absence of pathogens before being used in

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our study (31). WT and wMel larvae were fed Tetramin® fish food (Melle, Germany), and

adults were maintained on 10% sucrose. All mosquitoes were reared in a controlled

environment at 26°C, 75% relative humidity, and a 12-hr light/dark cycle.

3.3.2 Virus culture and titration

The DENV serotypes/strains used for this experiment are listed in Table 3.1. The

virus was propagated in cell culture, as described previously (Frentiu, Robinson, Young,

McGraw, & O'Neill, 2010). Briefly, Ae. albopictus C6/36 cells were grown at 26°C in

RPMI 1640 medium (Invitrogen, Carlsbad, CA) supplemented with 10% fetal bovine

serum (FBS), 1× Glutamax (Invitrogen), and HEPES buffer. Cells were first allowed to

form monolayers of around 60–80% confluence in T175 flasks (Sigma Aldrich, St. Louis,

MO), and then, they were inoculated with DENV and maintained in RPMI medium

supplemented with 2% FBS. After 7 days post-inoculation, live virus was harvested,

titrated via absolute quantification PCR (qPCR) and plaque forming unit assay (as per

below), and adjusted to a final concentration of ~4 × 105 plaque-forming units (PFU)/ml

for both serotypes (Table 3.2) prior to mixing with blood. Live virus was used for all vector

competence experiments.

Prior to the above steps, we isolated the viruses at different timepoints from C6/36

cells and assessed their viral RNA loads by qPCR and plaque assay to select the most

appropriate day to harvest virus for the vector competence experiments. These pilot

experiments also revealed the relationship between viral RNA load estimates by qPCR and

live virus estimates by plaque assay. In general, we saw that higher viral RNA loads

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correlated with higher plaque assay titers (Appendix B) with viral RNA loads ranging from

~10- to 2000-fold higher than live virus titers, as expected (Choy, Ellis, Ellis, & Gubler,

2013; Richardson, Molina-Cruz, Salazar, & Black, 2006). The average ratio of viral RNA

copies relative to PFU/ml was larger for DENV-3 (1300-fold) than for DENV-2 (270-fold),

indicating that RT-qPCR was more sensitive for DENV-3. This effect was consistent

across the two tested collection timepoints.

3.3.3 Mosquito infections

The methods for mosquito oral infections have been described previously (Novelo et al.,

2019; Terradas et al., 2017). Briefly, prior to oral DENV infections, 6- to 7-day-old adult

female mosquitoes were sorted and starved for 24 hrs. For mono-infections, a 1:1 mix of

Table 3.1 Dengue serotypes and strains use

Serotype Strain Passage Accession number Place of origin Collection

date

DENV-2 ET-300 11 EF440433.1 East Timor 2000

DENV-3 Cairns 2008 9 JN406515.1 Australia 2008

Table 3.2. Viral titers

Serotype

Undiluted RT-

qPCR titer of

infected

supernatant

(copies/ml)

Final plaque

assay titer for

the blood meal

(PFU/ml)

Fold change

difference

between RNA

copies/mL and

PFU/mL

Average ratio of

viral RNA

copies/ml relative

to PFU/ml

DENV-2 4 × 107 ~3 × 105 146 277.1

DENV-3 8 × 108 ~4 × 105 2046 1382.1

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virus culture and defibrinated sheep blood was prepared. For co-infections, 1 ml of each

DENV serotype was combined, and from that blend, 1 ml was combined with 1 ml of

defibrinated sheep blood. Glass feeders with double chambers were covered with pig

intestine, and water heated to 37°C was circulated in the outer chamber of the feeders. The

mosquitoes were allowed to feed for ~2 hrs. Immediately after blood feeding, mosquitoes

were knocked down and sorted on ice. Unfed females were discarded. The remaining

mosquitoes were returned to 70-ml plastic cups and maintained on 10% sucrose. At days

post-infection (DPI) 3, 4, 5, 7, 8, 9, 11, 12, and 13, mosquitoes were anesthetized and

dissected in sterile phosphate-buffered saline (PBS). We collected midgut, carcass, and

salivary glands from individual mosquitoes. For our vector competence experiments, the

mosquito carcass was the collection of tissues that remained after dissecting the midgut

and salivary glands. We used the carcass as a proxy for viral dissemination from the

mosquito midgut. All tissue collections were conducted on live mosquitoes. Individual

tissues were collected in 1.5-ml microcentrifuge tubes (Sarstedt, Nümbrecht, Germany)

containing 200 µl of Trizol reagent (Invitrogen) and 2-mm glass beads. Samples were

homogenized and frozen at −80°C until RNA extraction.

3.3.4 DENV absolute quantification via RT-qPCR

The RT-qPCR mixture contained 2.5 μl of LC480 master mix (Roche); 4.25 μl of

PCR-grade water; 0.25 μl of 10 μM forward (F) primer, reverse (R) primer, and probe (P)

specific to each DENV serotype; and 2 μl of RNA. Reactions were run in the LightCycler

480 instrument (Roche), and the thermal cycling conditions were 95°C for 3 minutes, 30

cycles of 95°C for 30 seconds, 50°C for 1 min, and 68°C for 1 min, finalizing with 68°C

66

for 5 min. Standard curves were generated by triplicate on each plate by analyzing 100 to

107 copies/reaction of DENV fragment copies with a limit of detection of set at 100 copies.

DENV genome copies were extrapolated from the standard curve as DENV copies per

tissue. The standards for both serotypes were generated using a ~100-bp conserved region

of the NS5 protein of the DENV genome. The detection threshold for both DENV-2 and

DENV-3 was set at ~35 Ct. The primers and probes used for the detection of specific

DENV serotypes were as follows:

DENV-2-ET300: F primer (primer 9873681; TCCATACACGCCACACATGAG),

R primer (primer 98736818; GGGATTTCCTCCCATGATTC) and Probe-FAM (probe

98084286; 56-fam/AGGGTGTGGATTCGAGAAAACCCATGG/3BHQ_1).

DENV-3-Cairns08/09: F primer (primer 98644632; TTTCTG CTCCCAC CAC

TTTC), R primer (primer 98451787; CCATCCYGCTCCCTGAGA) and Probe-LC640

(probe9845178; 5LtC640N /AAGAA AGTTGGT AGTCCCC TGCAGACC TCA/3IAb R

QSp).

3.3.5 Statistical analysis

All statistical analysis used R v3.6.0 (http://www.r-project.org/). For the analysis

of the infection frequency, a one-way ANOVA was fitted, and Tukey for contrasts was

used for post hoc comparisons. Viral RNA load analysis was carried out using Kruskal-

Wallis by rank test for the non-parametric data. All DENV RNA loads were reported on a

log10 scale given the value range.

http://www.r-project.org/

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3.4 Results

In this study, we challenged both WT and wMel Ae. aegypti mosquitoes with two

serotypes in the co- and mono-infected states. Specifically, we investigated (1) the relative

infection dynamics of two DENV serotypes inside the mosquito by competing them

directly and (2) how individual serotypes behave when they are in a co-infection relative

to mono-infection. Mosquitoes were blood fed with DENV-2 and DENV-3, both separately

and together. In the case of mono-infections, a 1:1 mix of each DENV serotype and blood

was used. For co-infections, the two serotypes were first combined, and from that blend, a

1:1 mix of blood and the two serotypes was used. Overall, the viral titers for both

experiments were equivalent. We used RT-qPCR with serotype-specific TaqMan probes

to quantify DENV RNA load in midgut, carcass, and salivary gland tissues to assess

dissemination and infectiousness at nine timepoints post-infection.

3.4.1 DENV serotype competition in co-infection

By competing the serotypes in same mosquitoes, we were able to powerfully

compare their performance, controlling some of the substantial variation that can occur

across vector competence experiments. Infection rates in the WT mosquito line indicate

that DENV-3 was a better competitor than DENV-2 but that the magnitude of this

difference changed depending on the tissue and DPI. In the co-infection experiments, we

classified the infection rates as uninfected (no viral RNA load detectable for either

serotype), only DENV-2 infected (DENV-2/alone), only DENV-3 infected (DENV-

3/alone), or co-infected (both serotypes detected). We saw significant variation in the

68

percentage of infected WT mosquitoes between serotypes (df=1, F=22.2, P < 0.0001). In

the midgut, most mosquitoes were co-infected at all DPIs (infection rate: 25% to 100%).

Mosquitoes were DENV-3/alone at 6 timepoints with an infection rate between 10 and

50% and DENV-2/alone at DPI 4 and 9 with an infection rate of ~15% (Figure 3.1, midgut).

In the carcass, most WT mosquitoes were either co-infected or DENV-3/alone, but

we saw no DENV-2/alone mosquitoes at any timepoint (Figure 3.1, carcass). For the

salivary glands, most WT mosquitoes were similarly either co-infected or DENV-3/alone,

and there was only one timepoint at which we observed DENV-2/alone mosquitoes, with

an infection frequency of ~10% at DPI 3 (Figure 3.1, salivary glands). For wMel

mosquitoes, no pairwise comparison between serotypes was possible, as there were too few

infected mosquitoes to perform statistical analysis, due to the action of Wolbachia-

mediated pathogen blocking. Overall, our results indicate that DENV-3 is better at

replicating and disseminating in mosquitoes, regardless of tissue or timepoint, than DENV-

2.

69

We also compared viral RNA loads between serotypes across tissues and DPI.

Overall, we saw no significant differences between DENV-2 and DENV-3 (Figure 3.2, Chi

square = 1.66, P = 0.19, df =1). The viral RNA load for both serotypes in the WT

mosquitoes ranged from ~103 to ~108 in the midgut, ~102 to ~107 in the carcass, and ~102

to ~106 log10 DENV copies per tissue in the salivary glands. In contrast, the viral RNA

load in the wMel mosquitoes was diminished and only observed in 10 timepoints across all

Figure 3.1. Mosquito susceptibility to DENV in co-infections by DPI, tissue, and line. Ae.

aegypti were orally challenged with DENV-2 (orange) and DENV-3 (navy) simultaneously,

whereby mosquitoes were fed a blood meal containing both viruses at ~3 × 105 DENV genome

copies/ml. Mosquito tissues (midgut, carcass, and salivary glands) were collected at nine

timepoints post-infection. DENV RNA load was determined via RT-qPCR using serotype-specific

probes. Each bar represents the proportion of mosquitoes positive for either (orange or navy) or

both (blue) serotypes for each day post-infection. Total number of mosquitoes screened per day

was n = 7 for the wild type line and n = 30 for wMel.

70

tissues, ranging from ~102 to ~105 in the midgut, ~102 to ~108 in the carcass, and ~102

log10 DENV copies per tissue in the salivary glands. Additionally, in the wMel

mosquitoes, viral RNA loads from both serotypes were observed at only one timepoint

(Figure 3.1, wMel, carcass, DPI 5).

Figure 3.2. DENV load in co-infection by DPI, tissue, and mosquito line. Ae. aegypti were

orally challenged with DENV-2 (orange circles) and DENV-3 (navy diamonds) simultaneously,

and both viruses were fed to mosquitoes at ~3 × 105 DENV genome copies/ml. Mosquito tissues

(midgut, carcass, and salivary glands) were collected at nine days post-infection, and DENV

RNA load was determined via RT-qPCR using serotype-specific probes. Mosquitoes with

undetectable viral RNA load are not represented in this graph. Black bars represent treatment

medians. Each symbol represents a single mosquito sample. Total number of mosquitoes

screened per day was n=7 for the wild type line and n=30 for wMel.

Viral RNA loads from both serotypes were expressed as DENV-2/DENV-3 ratio

(Figure 3.3). Although for all tissues there was a general trend of co-infected samples

71

having higher levels of DENV-3 than DENV-2, they were not significantly different

from one another. For the wMel mosquitoes, only DPI 5 had sufficient data with which

to calculate a ratio due to the action of Wolbachia pathogen blocking; in this case,

DENV-3 appeared higher than DENV-2, but with so few data points, statistical

comparisons were not possible.

Figure 3.3. Co-infection serotype ratio by DPI and tissue. Graphs depict the log10 DENV-

2/DENV-3 ratio for all wild type (grey) and wMel (red) samples found to be positive for infection

with both viruses via RT-qPCR for each tissue. Ratios greater than zero indicate that DENV-2

levels were higher than DENV-3 levels for that sample. Color lines represent mean DENV-

2/DENV-3 ratio and the standard error estimate of the mean ratio.

72

3.4.2 Co-infection alters infection dynamics of DENV serotypes in wild type

mosquitoes

After examining the competition dynamics between DENV-2 and DENV-3, we

then sought to determine whether or not co-infection altered viral infection dynamics

relative to the mono-infected state. For the wMel mosquitoes, no pairwise comparisons

between mono- and co-infection for each serotype were possible due to the action of

Wolbachia-mediated viral blocking. In the WT line, we saw higher infection rates when

DENV-3 was in co-infection relative to mono-infection (Figure 3.4) but that the magnitude

of this difference varied by DPI and tissue (df=1, F=7.5, P < 0.005). In the salivary glands,

infection was only observed at four timepoints in the mono-infected state, whereas it was

present in eight out of the nine timepoints in the co-infected mosquitoes (Figure 3.4). For

viral RNA load, we saw no significant difference between DENV-3 mono- and DENV-3

co-infection (Figure 3.5, Chi square = 1.54, P = 0.21, df =1). For DENV-2, there were no

differences in either infection frequency (Figure 3.4, df= 1, F=0.97, P=0.33) or viral RNA

load (Figure 3.5, Chi square = 0.01, P = 0.91, df =1) between mono- and co-infection,

indicating that co-infection did not alter dynamics.

3.4.3 Tissue specific differences in viral dynamics

Overall, for DENV-2, the infection rates were highest in the midgut and declined

as the virus moved into the carcass and salivary glands (Figure 3.4). DENV-3, in contrast,

demonstrated the highest infection rates in the midgut, followed by the salivary glands

(Figure 3.4). Viral RNA loads for the two serotypes were highest in the midgut and

73

decreased in the carcass and then the salivary glands (Figure 3.5, DENV-2; Chi square =

57.48, P < 0.001, df =2; DENV-3; Chi square = 57.48, P < 0.001, df =2). The carcass was

the tissue most likely to have either serotype present despite the action of wMel-mediated

pathogen blocking. Dissemination rates of each of the two serotypes in the presence of

wMel were similar across all three tissues (Figure 3.1).

Figure 3.4. WT line susceptibility to DENV in co- and mono-infections by DPI, tissue, and

serotype. Ae. aegypti mosquitoes were orally challenged with DENV-2 and DENV-3 in both

mono- and co-infection experiments. Each bar represents the proportion of mosquitoes positive

for either serotype in the mono- (grey) and co-infection (blue) experiments for each day post-

infection. Mosquito tissues (midgut, carcass, and salivary glands) were collected at nine days

post-infection, and DENV RNA load was determined via RT-qPCR using serotype-specific

probes. Sample size n=7 per day.

74

3.5 Discussion

We sought to explore the role of DENV serotype co-infection and competition in

both Wolbachia-mediated blocking and within-vector infection dynamics. Particularly, we

examined DENV RNA loads and infection rates in DENV-2/DENV-3 co-infected Ae.

aegypti midguts, carcasses, and salivary glands of two mosquito lines with (wMel) and

without Wolbachia infection (WT). We identified that in WT mosquitoes, there was a

Figure 3.5. WT line DENV viral RNA load in co- and mono-infection by DPI, tissue, and

serotype. Ae. aegypti mosquitoes were orally challenged with DENV-2 (orange) and DENV-3

(navy) in both co-infection (square) and mono-infection (triangle) experiments, with viruses

offered at equivalent titers in both cases. Mosquito tissues (midgut, carcass, and salivary glands)

were collected at nine days post-infection, and DENV RNA load was determined via RT-qPCR

using serotype-specific probes. Mosquitoes with undetectable viral RNA load are not represented

in this graph. Black bars represent treatment medians. Each symbol represents a single mosquito

sample. Data were log transformed. Sample size n=7.

75

competitive advantage for DENV-3 when co-infected with DENV-2 across multiple tissues

compared to a mono-infection. Additionally, we saw a strong pathogen-blocking

phenotype in wMel mosquitoes independent of co-infection status, tissue, and DPI.

Our data showed that for the WT mosquitoes, DENV serotype co-infection altered

the infection frequency of each serotype in a tissue-specific manner, with DENV-3 having

a competitive advantage over DENV-2. This advantage was clearer at the dissemination

stage of infection once the virus reached the hemocoel from the midgut and ultimately at

the transmission level when the virus arrived at the salivary glands. Moreover, the DENV-

3 competitive advantage was confirmed when we compared each serotype in co-infected

mosquitoes vs mono-infected mosquitoes, and we identified that DENV-3 produced higher

infection rates in all tissues when mosquitoes were co-infected. Conversely, DENV-2

infection dynamics did not change significantly when mosquitoes were co-infected

compared to mono-infected. Only one other study has looked at the infection dynamics of

co-infection with DENV-2 and -3 both in vitro and in vivo (Quintero-Gil et al., 2014);

contrary to our findings, they showed an increase in replication efficiency of 1000-fold for

DENV-2. These contradictory results may be due to the use of different DENV and

mosquito genotypes.

When the replication capacities of the two DENV serotypes were assessed in WT

mosquitoes, no significant differences in viral RNA load between DENV-2 and DENV-3

in either mono- and co-infection were found. Differences in viral replication rates without

affecting the viral RNA load in each tissue have been previously reported (Novelo et al.,

2019), in which DENV serotypes that had high growth rates did not necessarily achieve

76

high viral RNA loads or high infection frequencies in experimental mosquito populations.

The disconnect between viral RNA load and infection frequency for each virus suggests

that stochastic processes are potentially taking place or/and genotype-by-genotype

interactions are affecting the virus infection dynamics. For example, previous research has

shown that the strength of the mosquito immune response can be tissue and serotype

dependent (Cheng, Liu, Wang, & Xiao, 2016; Smartt, Shin, & Alto, 2017; Taracena et al.,

2018) and could lead to scenarios in which mosquitoes are more susceptible to dengue

infection with a particular serotype (i.e., higher infection frequency) but can also mount a

relatively strong immune response in particular tissues (i.e., low viral RNA load).

Arboviral co-infection is not limited to DENV serotypes; although most reported

cases are with DENV, co-circulation of DENV with CHIKV and/or ZIKV is increasing

around the globe (Bisanzio et al., 2018; Rodriguez-Morales, Villamil-Gómez, & Franco-

Paredes, 2016). This co-circulation represents a major challenge for many national and

international public health organizations, particularly because there is little information

about the potential clinical and biological consequences of these interactions. Individual

case reports of arboviral co-infection in humans suggest enhanced disease severity. Co-

infection with ZIKV and CHIKV has been associated with severe meningoencephalitis in

a male patient, and co-infection with DENV and CHIKV was linked to severe metrorrhagia

in a female patient (Brito, Azevedo, Cordeiro, Marques, & Franca, 2017; Schilling,

Emmerich, Günther, & Schmidt-Chanasit, 2009). Increased disease severity may occur

when both viruses interfere with different parts of the same immune pathways. For

example, interferon signaling is a major part of the human antiviral response, and it is

77

mediated by the signal transducer and activator of transcription one and two (STAT-1 and

STAT-2) (Murira & Lamarre, 2016). DENV has been shown to block STAT-1, and

CHIKV can potentially interfere with STAT-2 (Hollidge, Weiss, & Soldan, 2011),

therefore blocking the activation cascade of interferon and potentially increasing disease

severity.

The wMel mosquitoes challenged in DENV serotype co-infection were far less

susceptible than the WT line, indicating that the pathogen-blocking phenotype caused by

Wolbachia infection is not affected by concomitant DENV serotypes. Additionally, the

effect of Wolbachia blocking was seen in all three tissues and was stable across nine

timepoints, from 3 to 13 DPI, encompassing days of the mosquito’s lifespan relevant to

viral transmission in the field. Pathogen blocking by Wolbachia-infected mosquitoes

challenged with co-infecting arboviruses has only been shown once before (Caragata et al.,

2019). Co-infection was performed using DENV/ZIKV challenges but not with multiple

DENV serotypes at the same time, and it was limited to three timepoints and only one

mosquito tissue. Although co-circulation of novel emerging arboviruses like ZIKV or

CHIKV coupled with DENV has been reported (Carrillo-Hernández, Ruiz-Saenz,

Villamizar, Gómez-Rangel, & Martínez-Gutierrez, 2018; dos Santos S. Marinho et al.,

2020; Villamil-Gómez et al., 2016), most countries where DENV is endemic have reported

co-circulation of all 4 DENV serotypes, resulting in hyperendemicity for the virus (Bastos

Mde et al., 2012; Villabona-Arenas et al., 2014). Specifically, for DENV-2 and DENV-3

infections, one study showed that from 303 human serum samples, up to 21% were infected

with both viruses (Senaratne, Murugananthan, Sirisena, Carr, & Noordeen, 2020).This

78

phenomenon has been linked to an increased frequency of severe dengue cases and an

overall increase of virulence (Guzman & Harris, 2015).

Another effect of co-transmission can be epidemiological, where co-infection

occurring from a single biting event can significantly increase disease burden.

Mathematical modeling using in silico data in Ae. aegypti has shown that co-transmission

events can potentially lead to an increased number of cases for both viruses (Vogels et al.,

2019). An additional unanswered question in co-infection in Ae. aegypti is how sequential

viral infections might affect pathogen transmission dynamics, a scenario that can also occur

in nature (Nuckols et al., 2015; Marie Vazeille, Mousson, Martin, & Failloux, 2010).

Whether or not viruses can potentially interact after sequential acquisition by the mosquito

has yet to be determined. Last, in our co-infection experiments, we used half as much of

each virus but the same overall viral RNA load as mono-infections; however, no

statistically significant differences were observed in viral RNA load between mono-

infection and co-infection for either serotype. This still begs the question of the potential

outcome of infecting the mosquitoes with twice the amount of each serotype and twice the

overall amount of virus in co-infection.

3.6 Conclusion

Here, we present the first examination of DENV serotype co-infection and its effect

on Wolbachia-mediated pathogen blocking. We demonstrated that the wMel mosquito

strain is capable of blocking DENV serotype co-infection in a systemic way across the

mosquito body. Moreover, we showed that for WT mosquitoes, serotype co-infection can

79

affect infection frequency in a tissue- and time-specific manner and that both viruses have

the potential of being transmitted simultaneously.

3.7 Acknowledgements

We would like to thank E. Kerton, H. Amuzu, G. Terradas, C. Koh, L. Jimenez, S,

Ford, and C. Hammer for technical support throughout the experiment. Additionally, we

would like to thank Ana Ramírez for sharing and letting us use Ae. aegypti illustrations.

80

3.8 References


by the infecting genotype of dengue virus. The American Society of Tropical Medicine and

Hygiene, 75.

Bastos Mde, S., Figueiredo, R. M., Ramasawmy, R., Itapirema, E., Gimaque, J. B.,

Santos, L. O., Figueiredo, L. T., & Mourão, M. P. (2012). Simultaneous circulation of all four

dengue serotypes in Manaus, State of Amazonas, Brazil in 2011. Revista da Sociedade Brasileira

de Medicina Tropical, 45(3), 393-394. doi:10.1590/s0037-86822012000300022

Bell, S. M., Katzelnick, L., & Bedford, T. (2019). Dengue genetic divergence generates

within-serotype antigenic variation, but serotypes dominate evolutionary dynamics. eLife, 8,

e42496. doi:10.7554/eLife.42496

Bisanzio, D., Dzul-Manzanilla, F., Gomez-Dantés, H., Pavia-Ruz, N., Hladish, T. J.,

Lenhart, A., Palacio-Vargas, J., González Roldan, J. F., Correa-Morales, F., Sánchez-Tejeda, G.,

Kuri Morales, P., Manrique-Saide, P., Longini, I. M., Halloran, M. E., & Vazquez-Prokopec, G.

M. (2018). Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida,

Mexico. PLoS Neglected Tropical Diseases, 12(3), e0006298. doi:10.1371/journal.pntd.0006298

Brito, C. A. A., Azevedo, F., Cordeiro, M. T., Marques, E. T. A., Jr., & Franca, R. F. O.

(2017). Central and peripheral nervous system involvement caused by Zika and chikungunya

coinfection. PLoS Neglected Tropical Diseases, 11(7), e0005583.


Caragata, E. P., Rocha, M. N., Pereira, T. N., Mansur, S. B., Dutra, H. L. C., & Moreira,

L. A. (2019). Pathogen blocking in Wolbachia-infected Aedes aegypti is not affected by Zika and

dengue virus co-infection. PLoS Neglected Tropical Diseases, 13(5), e0007443-e0007443.


Carrillo-Hernández, M. Y., Ruiz-Saenz, J., Villamizar, L. J., Gómez-Rangel, S. Y., &

Martínez-Gutierrez, M. (2018). Co-circulation and simultaneous co-infection of dengue,

chikungunya, and Zika viruses in patients with febrile syndrome at the Colombian-Venezuelan

border. BMC Infectious Diseases, 18(1), 61. doi:10.1186/s12879-018-2976-1

Carrington, C. V. F., Foster, J. E., Pybus, O. G., Bennett, S. N., & Holmes, E. C. (2005).

Invasion and maintenance of dengue virus type 2 and type 4 in the Americas. Journal of Virology,

79(23), 14680. doi:10.1128/JVI.79.23.14680-14687.2005

Carrington, L. B., Tran, B. C. N., Le, N. T. H., Luong, T. T. H., Nguyen, T. T., Nguyen,

P. T., Nguyen, C. V. V., Nguyen, H. T. C., Vu, T. T., Vo, L. T., Le, D. T., Vu, N. T., Nguyen, G.

T., Luu, H. Q., Dang, A. D., Hurst, T. P., O’Neill, S. L., Tran, V. T., Kien, D. T. H., Nguyen, N.

M., Wolbers, M., Wills, B., & Simmons, C. P. (2018). Field and clinically derived estimates of

Wolbachia mediated blocking of dengue virus transmission potential in Aedes aegypti

mosquitoes. Proceeds of the National Academy of Science, 115(2), 361.

doi:10.1073/pnas.1715788115

81



Choy, M. M., Ellis, B. R., Ellis, E. M., & Gubler, D. J. (2013). Comparison of the

mosquito inoculation technique and quantitative real time polymerase chain reaction to measure

dengue virus concentration. The American Society of Tropical Medicine and Hygiene, 89(5),

1001-1005. doi:10.4269/ajtmh.13-0100

Dorigatti, I., McCormack, C., Nedjati-Gilani, G., & Ferguson, N. M. (2018). Using

Wolbachia for dengue control: insights from modelling. Trends in Parasitology, 34(2), 102-113.

doi:10.1016/j.pt.2017.11.002

dos Santos S. Marinho, R., Sanz Duro, R. L., Santos, G. L., Hunter, J., da Aparecida

Rodrigues Teles, M., Brustulin, R., de Padua Milagres, F. A., Sabino, E. C., Diaz, R. S., &

Komninakis, S. V. (2020). Detection of coinfection with chikungunya virus and dengue virus

serotype 2 in serum samples of patients in state of Tocantins, Brazil. Journal of Infection and

Public Health, 13(5), 724-729. doi:https://doi.org/10.1016/j.jiph.2020.02.034

Dutra, Heverton Leandro C., Rocha, Marcele N., Dias, Fernando Braga S., Mansur,

Simone B., Caragata, Eric P., & Moreira, Luciano A. (2016). Wolbachia blocks currently

circulating Zika virus isolates in Brazilian Aedes aegypti mosquitoes. Cell Host & Microbe,

19(6), 771-774. doi:https://doi.org/10.1016/j.chom.2016.04.021

Ekwudu, O. m., Marquart, L., Webb, L., Lowry, K. S., Devine, G. J., Hugo, L. E., &

Frentiu, F. D. (2020). Effect of serotype and strain diversity on dengue virus replication in

Australian mosquito vectors. Pathogens (Basel, Switzerland), 9(8), 668.

doi:10.3390/pathogens9080668

Ferguson, N. M., Kien, D. T. H., Clapham, H., Aguas, R., Tuan Trung, V. T., & Chau, T.

N. B. (2015). Wolbachia-mediated interference of dengue virus infection in Aedes aegypti and its

projected epidemiological impact on transmission. Science Translational Medicine, 7.

doi:10.1126/scitranslmed.3010370






Wolbachia-mediated resistance to dengue virus Infection and death at the cellular level. PLoS


Gaye, A., Faye, O., Diagne, C. T., Faye, O., Diallo, D., Weaver, S. C., Sall, A. A., &

Diallo, M. (2014). Oral susceptibility of Aedes aegypti (Diptera: Culicidae) from Senegal for

dengue serotypes 1 and 3 viruses. Tropical Medicine & International Health, 19(11), 1355-1359.

doi:10.1111/tmi.12373

82






Hollidge, B. S., Weiss, S. R., & Soldan, S. S. (2011). The role of interferon antagonist,

non-structural proteins in the pathogenesis and emergence of arboviruses. Viruses, 3(6), 629-658.

doi:10.3390/v3060629

Holmes, E. C., & Twiddy, S. S. (2003). The origin, emergence and evolutionary genetics

of dengue virus. Infection, genetics and evolution : journal of molecular epidemiology and

evolutionary genetics in infectious diseases, 3(1), 19-28. doi:https://doi.org/10.1016/S1567-

1348(03)00004-2

Ko, H.-Y., Salem, G. M., Chang, G.-J. J., & Chao, D.-Y. (2020). Application of next-

generation sequencing to reveal how evolutionary dynamics of viral population shape dengue

epidemiology. Frontiers in Microbiol, 11(1371). doi:10.3389/fmicb.2020.01371




doi:10.1186/1471-2148-9-160




86(3), 1853-1861. doi:10.1128/jvi.06458-11

Macdonald, G. (1957). The epidemiology and control of malaria. Amen House, Warwick

Square, London E.C.4.: Oxford University Press.

Murira, A., & Lamarre, A. (2016). Type-I interferon responses: from friend to foe in the

battle against chronic viral infection. Frontiers in Immunology, 7(609).

doi:10.3389/fimmu.2016.00609



Muturi, E. J., Buckner, E., & Bara, J. (2017). Superinfection interference between

dengue-2 and dengue-4 viruses in Aedes aegypti mosquitoes. Tropical Medicine & International

Health, 22(4), 399-406. doi:10.1111/tmi.12846

Novelo, M., Hall, M. D., Pak, D., Young, P. R., Holmes, E. C., & McGraw, E. A. (2019).

Intra-host growth kinetics of dengue virus in the mosquito Aedes aegypti. PLoS Pathogens,


83

Nuckols, J. T., Huang, Y. J., Higgs, S., Miller, A. L., Pyles, R. B., Spratt, H. M., Horne,

K. M., & Vanlandingham, D. L. (2015). Evaluation of simultaneous transmission of chikungunya

virus and dengue virus type 2 in infected Aedes aegypti and Aedes albopictus (Diptera:

Culicidae). The Journal of Medical Entomology, 52(3), 447-451. doi:10.1093/jme/tjv017

Quintero-Gil, D. C., Ospina, M., Osorio-Benitez, J. E., & Martinez-Gutierrez, M. (2014).

Differential replication of dengue virus serotypes 2 and 3 in coinfections of C6/36 cells and Aedes

aegypti mosquitoes. The Journal of Infection in Developing Countries, 8(7), 876-884.

doi:10.3855/jidc.3978

Richardson, J., Molina-Cruz, A., Salazar, M. I., & Black, W. t. (2006). Quantitative

analysis of dengue-2 virus RNA during the extrinsic incubation period in individual Aedes

aegypti. The American Society of Tropical Medicine and Hygiene, 74(1), 132-141.

Rico-Hesse, R. (2003). Microevolution and virulence of dengue viruses. Advances in

Virus Research, 59, 315-341. doi:10.1016/s0065-3527(03)59009-1

Rodriguez-Morales, A. J., Villamil-Gómez, W. E., & Franco-Paredes, C. (2016). The

arboviral burden of disease caused by co-circulation and co-infection of dengue, chikungunya and

Zika in the Americas. Travel Medicine and Infectious Disease, 14(3), 177-179.

doi:https://doi.org/10.1016/j.tmaid.2016.05.004




8, 15412. doi:10.1038/ncomms15412

Schilling, S., Emmerich, P., Günther, S., & Schmidt-Chanasit, J. (2009). Dengue and

chikungunya virus co-infection in a German traveller. Journal of Clinical Virology, 45(2), 163-

164. doi:10.1016/j.jcv.2009.04.001

Senaratne, U. T. N., Murugananthan, K., Sirisena, P. D. N. N., Carr, J. M., & Noordeen,

F. (2020). Dengue virus co-infections with multiple serotypes do not result in a different clinical

outcome compared to mono-infections. Epidemiology & Infection, 148, e119-e119.

doi:10.1017/S0950268820000229





Smartt, C. T., Shin, D., & Alto, B. W. (2017). Dengue serotype-specific immune

response in Aedes aegypti and Aedes albopictus. Memórias do Instituto Oswaldo Cruz, 112(12),

829-837. doi:10.1590/0074-02760170182


competence studies: A review. Infection, Genetics and Evolution 67, 191-209. doi:http s : / /

doi.org /10.1016/ j. meegid.2018.11.009

84

Taracena, M. L., Bottino-Rojas, V., Talyuli, O. A. C., Walter-Nuno, A. B., Oliveira, J. H.

M., Angleró-Rodriguez, Y. I., Wells, M. B., Dimopoulos, G., Oliveira, P. L., & Paiva-Silva, G.

O. (2018). Regulation of midgut cell proliferation impacts Aedes aegypti susceptibility to dengue

virus. PLoS Neglected Tropical Diseases, 12(5), e0006498. doi:10.1371/journal.pntd.0006498



10(1), 622-622. doi:10.1186/s13071-017-2589-3

Vasilakis, N., & Weaver, S. C. (2008). The history and evolution of human dengue

emergence. Advances in Virus Research, 72, 1-76. doi:10.1016/s0065-3527(08)00401-6

Vazeille, M., Gaborit, P., Mousson, L., Girod, R., & Failloux, A. B. (2016). Competitive

advantage of a dengue 4 virus when co-infecting the mosquito Aedes aegypti with a dengue 1

virus. BMC Infectious Diseases, 16, 318. doi:10.1186/s12879-016-1666-0

Vazeille, M., Mousson, L., Martin, E., & Failloux, A.-B. (2010). Orally co-infected

Aedes albopictus from la Reunion island, Indian ocean, can deliver both dengue and chikungunya

infectious viral particles in their saliva. PLoS Neglected Tropical Diseases, 4(6), e706.


Villabona-Arenas, C. J., de Oliveira, J. L., Capra, C. d. S., Balarini, K., Loureiro, M.,

Fonseca, C. R. T. P., Passos, S. D., & Zanotto, P. M. d. A. (2014). Detection of four dengue

serotypes suggests rise in hyperendemicity in urban centers of Brazil. PLoS Neglected Tropical

Diseases, 8(2), e2620-e2620. doi:10.1371/journal.pntd.0002620

Villamil-Gómez, W. E., Rodríguez-Morales, A. J., Uribe-García, A. M., González-

Arismendy, E., Castellanos, J. E., Calvo, E. P., Álvarez-Mon, M., & Musso, D. (2016). Zika,

dengue, and chikungunya co-infection in a pregnant woman from Colombia. International

Journal of Infectious Diseases, 51, 135-138. doi:10.1016/j.ijid.2016.07.017

Vogels, C. B. F., Rückert, C., Cavany, S. M., Perkins, T. A., Ebel, G. D., & Grubaugh, N.

D. (2019). Arbovirus coinfection and co-transmission: A neglected public health concern? PLoS

Biol, 17(1), e3000130. doi:10.1371/journal.pbio.3000130

Walker, T., Johnson, P. H., Moreira, L. A., Iturbe-Ormaetxe, I., Frentiu, F. D., &

McMeniman, C. J. (2011). The wMel Wolbachia strain blocks dengue and invades caged Aedes

aegypti populations. Nature, 476. doi:10.1038/nature10355

Weaver, S. C., & Vasilakis, N. (2009). Molecular evolution of dengue viruses:

contributions of phylogenetics to understanding the history and epidemiology of the preeminent

arboviral disease. Infection, genetics and evolution : journal of molecular epidemiology and

evolutionary genetics in infectious diseases, 9(4), 523-540. doi:10.1016/j.meegid.2009.02.003

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Chapter 4

Family level variation in the mosquito Aedes aegypti susceptibility and

immune response to dengue and chikungunya viral infection.

4.1 Abstract

Aedes aegypti is the main vector of the arboviruses dengue (DENV) and

chikungunya (CHIKV). Viral susceptibility and transmissibility in mosquito populations

depends on the GxG interactions between the virus and the vector. Both genotypes can also

have a great impact in the evolutionary potential of the vector:virus association. Here, we

used a modified full-sibling design to estimate the contribution of mosquito genetic

variation to the susceptibility and immune response to DENV and CHIKV. We

demonstrated substantial genetic variation in the susceptibility to both viruses.

Heritabilities were significant, but much higher for DENV susceptibility than to CHIKV,

at 40 and 18%, respectively. The differences are largely driven by greater within family

variation for CHIKV. Interestingly, we showed lack of genetic correlation between DENV

and CHIKV susceptibility between siblings of the same families. These data suggest that

the mosquito is much better at controlling DENV infection, and that the mosquito responds

to the viruses with two very different mechanisms. These disparities may result from

different coevolutionary histories, with CHIKV only recently emerging and acting as a

selective agent in mosquito populations. Last, in we tested for correlations between viral

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susceptibility and the expression levels of several immune genes, identifying two genes

that may underpin the insect’s response to CHIKV (TEP3 and CECH).

4.2 Introduction

Arboviral diseases account for more than 17% of all infectious diseases globally

(Kading, Brault, & Beckham, 2020), among the most prevalent are dengue and

Chikungunya, with over half of the world’s population at risk and no effective vaccines or

treatments (Wilder-Smith et al., 2017). Although mortality is generally low, the morbidity

caused by these pathogens is associated with a substantial socioeconomic burden. In some

cases, infection with any of the four dengue virus serotypes (DENV-1, 2, 3, 4) can lead to

dengue shock syndrome in all ages (Rajapakse, 2011), and chikungunya virus (CHIKV)

infection can be severe for infants, with nearly half of infected newborns experiencing

encephalopathy and multiple organ dysfunction (Mehta et al., 2018). Both DENV and

CHIKV are single stranded RNA viruses, belonging to the Flaviridae and Togaviridae

viral families respectively, and are transmitted primarily through the bite of the mosquito

Ae. aegypti (Huang, Higgs, Horne, & Vanlandingham, 2014; Lim, Lee, Madzokere, &

Herrero, 2018). Unlike DENV, CHIKV is less well studied, largely as it was a relatively

unknown pathogen causing small outbreaks around the Indian ocean until recently

(Rougeron et al., 2015). In 2013 the virus was first reported in the Americas, and by 2018

there have been more than 3 million cases in 45 countries (Yactayo, Staples, Millot,

Cibrelus, & Ramon-Pardo, 2016; Zeller, Van Bortel, & Sudre, 2016).

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Arboviral infection in the mosquito is a complex and dynamic process, as the

kinetics of infection can be influenced by the genetic variation of both the mosquito and

the virus (Novelo et al., 2019). Natural Ae. aegypti populations have been shown to vary

in their susceptibility to multiple arboviruses, including DENV and CHIKV (Gaye et al.,

2014; Gokhale, Paingankar, Sudeep, & Parashar, 2015; Gubler, Nalim, Tan, Saipan, &

Sulianti Saroso, 1979; Reiskind, Pesko, Westbrook, & Mores, 2008; Y. H. Ye et al., 2014),

however, studies have shown that diverse mosquito populations from the Americas, India

and the Pacific region have, are highly susceptible to CHIKV infection (Alto et al., 2017;

Chen et al., 2015; Gokhale et al., 2015; Tesh, Gubler, & Rosen, 1976; Vega-Rúa, Zouache,

Girod, Failloux, & Lourenço-de-Oliveira, 2014). Transmission efficiency in Ae. aegypti

populations from America have been shown to be up to 83% for multiple CHIKV

genotypes (Vega-Rúa et al., 2014). Moreover, CHIKV infectious particles have been

detected in Ae. aegypti salivary glands as early as 2 days post infection (DPI), indicating

that the dissemination of the virus within the mosquito is rapid and concomitant with

infection of the salivary glands (Dubrulle, Mousson, Moutailler, Vazeille, & Failloux,

2009). Comparatively, infectious DENV particles have been detected in the mosquito

salivary glands 4 DPI (Salazar, Richardson, Sánchez-Vargas, Olson, & Beaty, 2007).

Overall, the differences in susceptibility between CHIKV and other arboviruses, such as

DENV, have been linked to viral replication inside the mosquito, where genetic variation

and specific pathogen infection mechanisms can have an important role in determining

susceptibility and transmission.

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Upon ingestion of a viral infected blood meal, the molecular interactions between

the mosquito cells and the pathogen starts with the viral particle attachment to cellular

receptors, facilitating viral endocytosis or activating signaling pathways to gain

intracellular access (Alonso-Palomares, Moreno-García, Lanz-Mendoza, & Salazar, 2018).

For DENV, the specific identity of mosquito cellular receptors remains elusive, although

several putative receptors have been suggested (Cruz-Oliveira et al., 2015; Smith, 2012).

To date, only one DENV cell expressed receptor has been characterized, Prohibitin, a

ubiquitously expressed conserved protein shown to interact with DENV-2 (Kuadkitkan,

Wikan, Fongsaran, & Smith, 2010). CHIKV cellular internalization is thought to be

mediated through clathrin-mediated endocytosis (CME), a key process in vesicular

trafficking (Dong, Balaraman, et al., 2017; Hoornweg et al., 2016; Lee et al., 2013). Upon

entry, both viruses are uncoated through intracellular specific conditions, such as pH, and

replicate in the cell cytoplasm (Rodriguez, Muñoz, Segura, Rangel, & Bello, 2019).

Cellular tropism for both DENV and CHIKV appears widespread to multiple mosquito

organs, including midgut, fat body tissue, nervous, tracheal and reproductive systems and

salivary glands (Kantor, Grant, Balaraman, White, & Franz, 2018; Matusali et al., 2019;

Salazar et al., 2007; Vega-Rúa, Schmitt, Bonne, Krijnse Locker, & Failloux, 2015; Wong,

Chan, Sam, Sulaiman, & Vythilingam, 2016; Zhang et al., 2010; Ziegler et al., 2011).

Interestingly, variation in the replication rates between viruses have been reported inside

Ae. aegypti. Specifically, one study measuring viral RNA copies of each virus (Le

Coupanec et al., 2017), showed that in the mosquito midguts, DENV had a lower copy

number than CHIKV at 3 DPI, similarly, in the salivary glands, CHIKV was detected as

early as 4 DPI whereas DENV was detected no earlier than 10 DPI. Moreover, co-infection

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with DENV and CHIKV has shown to increased overall mosquito infection rates up to

100% for both viruses, while also enhancing CHIKV and DENV viral replication in the

midgut and salivary glands, compared to each virus alone (Le Coupanec et al., 2017).

Viral infection triggers cellular and humoral immune responses inside the

mosquito. The core pathways of the insect immune response are the Janus Kinase-Signal

Transducer Activator of transcription (JAK/STAT), Toll, Immune Deficiency (IMD) and

RNA interference (RNAi) (Alonso-Palomares et al., 2018). Viral components also interact

with pattern recognition receptors, triggering processes, such as melanization and the

production of anti-microbial peptides (AMP), including acting, defensin and cecropin

among others (Agaisse & Perrimon, 2004; Antonova, Alvarez, Kim, Kokoza, & Raikhel,

2009; Sim & Dimopoulos, 2010). Additionally, complement related proteins in the

mosquito hemolymph, such as thioester containing proteins (TEP), can recognize

arboviruses and trigger further AMP production (Mukherjee, Das, Begum, Mal, & Ray,

2019). Interestingly, some of the traditional innate immune pathways (Toll,

JAK/STAT,IMD) that been shown to contribute to the reduction of DENV replication both

in vitro and in vivo, play little role in limiting CHIKV infection. Specifically, exogenous

activation of the JAK-STAT pathway has been shown to modulate DENV infection, but

resulted in no enhanced resistance to CHIKV (Jupatanakul et al., 2017). Moreover, CHIKV

infection has also been shown to significantly repress the Toll pathway stimulation, and

neither the IMD pathway has been found to mediate viral activities (McFarlane et al.,

2014). Of the mayor insect immune pathways known to control infection of arboviruses

such as DENV, only the RNAi pathway has been shown to play a vital role in limiting

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CHIKV replication. Knockdown of Argonaute2 (AGO2), an RNAi effector molecule,

resulted in significant increase of viral RNA replication and titers (McFarlane et al., 2014).

Transcriptomic profiles of DENV and CHIKV infected mosquitoes have shown to

be distinct, with little overlap between responsive genes (Shrinet, Srivastava, & Sunil,

2017). Expression profiles of CHIKV-infected mosquitoes have revealed upregulation of

immune genes encoding AMPs, such as defensin and cecropin (CECH), and TEP (Dong,

Behura, & Franz, 2017). Both TEP and CECH have been shown to be highly upregulated

upon CHIKV infection up 10 DPI. In the same study, TEP3 expression was shown to be

highly induced with up to 16-fold greater expression than other genes (Zhao, Alto, Jiang,

Yu, & Zhang, 2019). Another study in malaria infected Anopheles gambie mosquitoes,

revealed that TEP molecules can function as positive immune regulators, reducing

pathogen intensity (Clayton, Dong, & Dimopoulos, 2014). Further reports have shown

increased expression of the CECH gene upon pathogen infection (Antonova et al., 2009;

Kokoza et al., 2010; Muturi, Blackshear, & Montgomery, 2012), including with Sindbis

virus (SINV) infection, a member of the Togaviridae family (Kim & Muturi, 2013).

Overall, the current data indicates that these genes might be important for CHIKV infection

response inside the vector.

There have been several recent discoveries of novel non-canonical antiviral genes

in the fly (Magwire et al., 2012; Piontkivska et al., 2016) and in Ae. aegypti (Ford et al.,

2019) revealed by examining population level genetic variation in the antiviral response.

Studying the natural genetic variation in Ae. aegypti could lead to insights in the basis of

the vector’s own natural immune response to CHIKV and DENV, and may offer a means

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to identify candidate antiviral genes to target with emerging insect genetic tools, such as

CRISPR-cas9, (Terradas, Allen, Chenoweth, & McGraw, 2017; Terradas & McGraw,

2019). Here, we used a modified full-sib breeding design to assess the family level

variation in CHIKV and DENV susceptibility, using a U.S. population of Ae. aegypti. By

examining the susceptibility trait for both viruses in the same population, we can show the

contribution of the genetic variation to the viral load variance. We then used the families

showing extreme phenotypes in viral load to screen candidate genes relate to infection

susceptibility. The set of candidate genes selected, were either common responders to viral

infection of both CHIKV and DENV, or were shown to have a specific effect only in

CHIKV-infection based on the literature.

4.3. Methods

4.3.1 Mosquito line and stock practices

An F3 Ae. aegypti mosquito line originating from Monterrey, Mexico, was reared in the

laboratory for 3 generations to expand and adapt the colony to laboratory feeding regimes.

Mosquitoes were reared under standard conditions; 26°C, 65% relative humidity and a 12

h light/dark cycle. Larvae were maintained on fish food (Tetramin, Tetra). Adults were fed

with 10% sucrose solution ad libitum. Mosquitoes were fed human blood (BioIVT,

Hicksville, NY, USA) for egg collection using a Hemotek system (Hemotek).

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4.3.2. Breeding design

A modified full – sib breeding design (William G. Hill & Mackay, 2004; Terradas

et al., 2017; Yixin H. Ye et al., 2016) was carried out on the mosquitoes in combination

with DENV and CHIKV infection. After expanding the mosquito line over 3 generations,

F6 eggs were hatched in synchrony and reared at low density (~150 larvae per 3 ltr of RO

water). After pupation, males and females were separated and transferred to 30 x 30 x 30

cm cages at a density of ~250 individuals per cage. Six to seven- day old virgin adult

females were blood-fed and then 250 virgin males were then added for mating. A total of

600 families were created by placing 600 blood-fed and mated females in small individual

housings containing moist filter paper. Egg papers were collected and dried 3-4 days later,

for short term storage. We selected female lineages that had produced a minimum of 60

eggs. For each family, eggs were hatched separately and after pupation, females were

separated and split into two cups with a minimum of 8 individuals per cup. Mosquitoes

were maintained on sucrose until vector competence experiments.

4.3.3. Virus culture

All experiments were carried out using the CHIKV strain 20235-St Martin 2013

(BEI Resources), and the DENV-2 strain 429557-Mex 2005 (BEI Resources), both from

Latin American outbreaks. Virus was cultured in C6/36 cells as previously reported

(Frentiu, Robinson, Young, McGraw, & O'Neill, 2010; Novelo et al., 2019; Terradas et al.,

2017; Yixin H. Ye et al., 2016). Briefly, C6/36 Ae albopictus cells were grown in RPMI

1640 media (Life Technologies, Carlsband, CA, USA) and supplemented with 10% heat-

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inactivated fetal bovine serum (FBS, Life Technologies), 1% Glutamax (Life

Technologies) and 25 mM HEPES (Sigma-Aldrich, St. Louis, MO, USA). Cells were

grown to a confluency of 80% and then independently infected with DENV-2 and CHIKV.

Infected culture flasks were incubated at 27 oC. For DENV-2, after 7 days post-infection,

supernatant was harvested at a titer of 1.0 x 105 focus forming units per ml (FFU/ml). For

CHKV, supernatant was harvested at 2 days post infection at a titer of 8.7 x 105 and

adjusted to a final concentration of 1.0 x 105 FFA/ml. Both viruses were store at -80oC in

1 ml single use aliquots for vector competence experiments.

4.3.4. Mosquito infections

Methods for oral mosquito infections have been fully described previously

(Amuzu, Simmons, & McGraw, 2015; Novelo et al., 2019; Terradas et al., 2017). Prior to

infections, mosquitoes were starved for 24 hrs. Half of the 6–7-day old females of each

family were challenged with DENV-2 (1st cup) and the other half (2nd cup) with CHIKV

at equal viral titers using a 1:1 mix of the frozen titrated aliquots and human blood.

Mosquitoes were allowed to feed for 30 minutes and shortly after they were anesthetized

with CO2 and unfed individuals were removed and discarded. All CHIKV work was carried

out at the Penn State Eva J. Pell ABSL-3 laboratory.

4.3.5. Viral quantification

After 7 days post infection, whole mosquitoes were collected, homogenized and

stored in trizol (Invitrogen, Carlsbad, CA, USA) reagent at -80 oC for downstream

molecular analysis. RNA was extracted according to the manufacturer’s protocol. RNA

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was eluted in 25 ul of DNA/RNA free water and DNAse treated (Life Technologies,

Carlsbad, CA, USA). DENV and CHIKV was quantified using 4× TaqMan fast virus 1-

step master mix (Applied biosystems, Foster city, CA, USA) in individual 10 ul reactions

containing virus specific primers and probes (Table 4.1). The RT-qPCR reactions were run

in a LightCycler 480 instrument (Roche Applied Science, Switzerland). The thermal

cycling conditions were 50°C for 5 min for reverse transcription, 95°C for 10s for RT

inactivation/denaturation followed by 50 amplification cycles of 95°C for 3s, 60°C for 30s

and 72°C for 1s. Standard curves for both DENV and CHIKV were generated as described

elsewhere (Rückert et al., 2017; Yixin H. Ye et al., 2016) and contained a ~100 bp fragment

of the 3’UTR of the DENV genome and a ~140 bp fragment of the CHIKV genome,

respectively. The standard curve spanned from 107 to 10 copies/reaction with a limit of

detection of 100 copies for both viruses. The viral load in each sample was extrapolated

from the standard curve as copies per mosquito.

4.3.6. Selection of immune genes for expression analysis

Both viral load and infection intensity have been shown to be modulated by the

mosquito immune response (Blair & Olson, 2014). To assess the potential role of specific

immune genes in viral infection, we used families with extreme viral loads to test the for

associations with gene expression. The null genetic correlation between DENV and

CHIKV infected mosquitoes suggests a genetic decoupling in the mechanisms that drive

the susceptibility to either virus in mosquitoes. Therefore, we aimed to test immune genes

that represent mosquito immune pathways that have 1) shown to be of great importance in

modulating infection for both viruses, and 2) that have might be important in the immune

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response against CHIKV. We examined whether virus type and viral load group altered the

expression level of several immune related genes. We compared the expression levels of

four genes associated with viral infection in Ae. aegypti. For the first group, we focused in

two genes, AGO2 and DCR2, that are part of the mosquito RNA RNAi pathway which

have been shown to modulate both CHIKV and DENV. For the second group, we tested

the antimicrobial peptide (AMP), cecropin (CECH), a TOLL pathway-regulated immune

effector (Mukherjee et al., 2019; Ramirez & Dimopoulos, 2010; Xi et al., 2008) and the

complement - related immune protein TEP3 (Blandin & Levashina, 2004; Mukherjee et

al., 2019) which have been shown to be upregulate in CHIKV infection but not DENV.

4.3.7. Immune gene expression

Retrotranscription of RNA to cDNA and gene expression analysis was carried out

on a LightCycler 480 instrument (Roche) using the Script One-step SYBR Green qRT-

PCR (Quantabio, Beverly, MA, USA) according to the manufacturer’s protocol. All CT

values were normalized to the housekeeping Ae. aegypti gene RpS17. Gene expression

ratios were obtained using the ∆∆Ct method (Livak & Schmittgen, 2001). PCR

amplification cycled 45 times at 95° C for 3 sec and 60° C for 30 sec, and the final cycle

was followed by a melting curve analysis. All primers for candidate genes and viral

quantification are listed in table 4.1.

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4.3.8. Analysis of genetic variance

We tested for genetic variation and broad-sense heritability (H2) for DENV and

CHIKV loads across our family design. We estimated the parameters using a modified full-

sib breeding design and the random effect linear model previously described (Terradas et

al., 2017; Yixin H. Ye et al., 2016):

zij = fi + εij (1)

Zij is the trait value for the jth female from the ith family, f i is the random effect of

the ith family and ε ij is the unexplained error. To test if the genetic variation was

significant, the family term was fitted as a random effect and the model (1) was compared

to a reduce model without the family term. Loglikelihoods of both models were compared

and twice the difference was tested against a chi-squared distribution with a single degree

Table 4.1. Primers and probes for qPCR gene expression and viral quantification.

Gene ID Target Direction Sequence (5’-3’)

AY713296 DICER 2 Forward GAATTCCTCGGCGATGCGGTATTA

Reverse GATGCCGACTCTGCCAGGATGT

AAEL008607 TEP-3 Forward AGTGTCCGTTGAGTCTCCTG

Reverse TCTACCGATCCCTTGCCATC

AAEL004175 RPS17 Forward TCCGTGGTATCTCCATCAAGCT

Reverse CACTTCCGGCACGTAGTTGTC

NC_001474.2 DENV

Forward AAGGACTAGAGGTTAGAGGAGACCC

Reverse CGTTCTGTGCCTGGAATGATG

Probe

FAM-

AACAGCATATTGACGCTGGGAGAGACCAG

A-BHQ1

MT228632 CHIKV

Forward CACCCGAAGTAGCCCTGAATG

Reverse TCCGAACATCTTTCCTCCCG

Probe 5CY5-GAGAATAGCCCGCTGTCTAGATCCAC-

3BHQ2

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of freedom. Broad sense heritability was estimated as twice the family variance component

(σ family) divided by the total phenotypic variance (σ family + σ error), (Saxton, 2004). All

models were constructed in SAS studio version 3.8 (SAS Institute, Cary, NC, USA).

Genetic correlations between DENV and CHIKV infected siblings was estimated using a

bivariate version of model (1) and fitting unrestrictive covariance correlations at the family

level (TYPE=UNR option) suing SAS Proc MIXED command. Significance of the genetic

correlation was tested by loglikelihood ratio tests.

4.4 Results

4.4.1. Family level variation in the heritability of DENV and CHIKV viral loads

To test for differences in the heritability across DENV and CHIKV viral loads we

assayed a total of 800 DENV and CHIKV fed individual mosquitoes from 50 families in a

modified full-sib design. Overall, we obtained 37 families for DENV and 38 for CHIKV

that had enough infected mosquitoes for further analysis. Infected mosquitoes were tested

for both viruses at day 7 post infection. After RNA extraction, we quantified DENV and

CHIKV via RT-qPCR in 3-5 individuals per family. The broad sense heritability (H2) of

DENV load was estimated to be 0.40 (Figure 4.1A. LRT: X2=24.8, P=>0.001) and for

CHIKV load, H2 was 0.18 (Figure 4.1B. LRT: X2= 5.9, P=0.015) across all the families.

Both heritability values were significantly greater than zero, indicating a role for multi-

genetic interactions in determining the viral load trait across the mosquito families for

DENV and CHIKV. The range of viral load was extremely variable across the families,

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spanning from hundreds to millions of copies; 102-107 per body for DENV and 102-108 per

body for CHIKV.

Figure 4.1. Ranked families by viral load. A) DENV load ranked families and B) CHIKV load

ranked families. Each point represents a single infected mosquito. Box plots show mean viral load

± SEM of each distinct Ae. aegypti family. Whole mosquito viral load was log10 transformed. N=3-

5 individuals per family.

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4.4.2. Genetic correlation between DENV and CHIKV across mosquito families

We found no correlation between DENV and CHIKV loads between siblings of the

same family based on 37 families (Figure 4.2. Pearson’s r = 0.0018, p = 0.99). Furthermore,

the correlation was not significantly greater than zero (LRT: X2= >0.001, P= 1.00). The

decoupling between susceptibility traits for siblings indicates a lack of shared control

mechanisms for the two viruses.

Figure 4.2. Scatter plot showing the relationship between mean DENV and CHIKV loads

from individual families. Gray area represents the 95% confidence interval of the regression line.

N = 37 mosquito families.

4.4.3 Phenotypic extremes of viral loads for both DENV and CHIKV infection in the

mosquito families

We selected the five most extreme mosquito families with respect to mean viral

load for both viruses. We then tested for differences between the viral loads between these

groups and found a statistically significant difference between our family extremes for both

DENV (Figure 4.3A, w=256, df=1, p= <0.0001) and CHIKV (Figure 4.3B, w=393, df=1,

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p=<0.0001). We then used these families to study the comparative expression of key

immunity genes relevant to the mosquito response to each virus as determined by the

literature.

Figure 4.3. Whole mosquito viral loads per family representing the extremes of the

distribution. A) DENV extreme families and B) CHIKV extreme families. Each point represents

a single infected mosquito. Whole mosquito viral load was log10 transformed. Boxplots show

family mean and SEM. N=3-5 individuals per family. * 0.05 < p < 0.01, ***0.001 < 0.0001, ****

p < 0.0001.

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4.4.4. Immune genes relative expression

We evaluate the relative expression of two genes known to be involved in viral

control of both CHIKV and DENV, DCR2 (AAEL006794) and AGO2 (AAEL017251),

part of the RNAi pathway. For DENV infected mosquitoes, there was a significant

difference in relative AGO2 expression between family groups, with higher expression of

the gene in the High viral load group (Figure 4.4A, w=236, df=1, p=0.001). The average

gene expression fold change of was 1.49 between High/Low viral load groups. For DCR2

expression, there was no significant difference between extreme family groups (Fig 4.4C,

w= 116, df=1, = 0.48) with a average fold change in gene expression of 1.22 between

High/Low viral load groups. For CHIKV infected mosquitoes, we saw no significant

difference in relative AGO2 gene expression between extreme family groups (Fig 4.4B,

w= 288, df=1, p= 0.08). The average gene expression fold change of High/Low viral load

groups was 1.39. However, there was a significant difference in DCR2 expression between

family groups were the High viral load group had higher expression (Fig 4.4D, w=352,

df=1, p=<0.001) with an average gene expression fold change of 2.98 over the Low viral

load group.

The second set of genes we evaluate are two effector molecules that have been

shown to be important in the overall control of CHIKV but not DENV infection in Ae.

aegypti, CECH (AAEL017211) and TEP3 (AAEL008607). For DENV infected

mosquitoes we identify a significant difference between groups (Fig 4.5A, w=67, df=1,

p=0.01), with a higher expression of the TEP3 in the Low viral load group. The average

gene expression fold change between High/Low viral load groups was 0.17. However, we

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saw no significant difference in CECH gene expression between the High and Low viral

load groups (Fig 4.5C, w=115, df=1, p=0.31) with an average gene expression fold change

of 0.83. For CHIKV infected mosquitoes we saw a significant difference in both TEP3 (Fig

4.5B, w=342, df=1, p=<0.0001) and CECH (Fig 4.5D, w=394, df=1, p=<0.0001) and, with

higher gene expression in the High CHIKV group. The average gene expression fold

change between High/Low viral load groups was 3.84 for TEP3 and 2.98 for CECH.

Additionally, we looked at the relationship between gene expression and viral load

regardless of the family groups (i.e., High and Low) for both DENV and CHIKV infected

mosquitoes. This was done to take into account the variation in gene expression in families

that might not correspond to their viral load groups. For DENV infected mosquitoes the

only significant interaction was for AGO2 expression, that was positively correlated to viral

load (Appendix C, Spearman’s r = 0.63, p= .05). For CHIKV infected mosquitoes all genes

were positively correlated to viral load (Appendix C). In general, the correlation trends

corresponded to the viral load family groupings previously observed.

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Figure 4.4. AGO2 and DCR2 expression relative to the housekeeping gene RPS17 in family

groups classified as High and Low Viral loads. A) AGO2 expression in DENV infected families;

B) AGO2 expression in CHIKV infected families and C) DCR2 expression in DENV infected

families; D) DCR2 expression in CHIKV infected families. Boxplots show family mean with SEM.

N=3-5 individuals per family. * 0.05 < p < 0.01, ***0.001 < 0.0001, **** p < 0.0001. The average

fold change of High viral load /Low viral load groups is A) 1.49, B) 1.36, C) 1.22, D) 2.98.

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Figure 4.5. TEP3 and CECH expression relative to the housekeeping gene RPS17 in family

groups classified as High and Low Viral loads. A) TEP3 expression in DENV infected families;

B) TEP3 expression in CHIKV infected families and C) CECH expression in DENV infected

families; D) CECH expression in CHIKV infected families. Boxplots show family mean with SEM.

N=3-5 individuals per family. * 0.05 < p < 0.01, ***0.001 < 0.0001, **** p < 0.0001. The average

fold change of High viral load /Low viral load groups is A) 0.17, B) 3.84, C) 0.83, D) 2.98.

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4.5. Discussion

We performed a modified full-sib breeding design to study the contribution of the

mosquito’s genetic variation, while limiting environmental variation, to susceptibility to

each virus. By exploring susceptibility to the two viruses in sisters of the same family, we

were also able to measure the genetic correlation between the individual susceptibilities.

We used the distribution of susceptibilities to select families representing the phenotypic

extremes for each virus. In these extreme families we tested for differences in expression

for key genes previously shown to represent the mosquito immune response to each virus.

Our experiments demonstrate the role of multi-genic interactions in determining the viral

load trait across mosquito families for both DENV and CHIKV. Additionally, the lack of

genetic correlation between DENV and CHIKV viral loads across siblings, demonstrated

independence of the genetic response to these two viruses. Finally, our gene expression

analysis on the extreme families showed that, although some viral control mechanisms,

such as the RNAi pathway, play a universal role in modulating viral replication, the

mosquitos’ response to infection appears markedly different depending on the infecting

virus.

The heritability values of the viral susceptibility for both DENV and CHIKV were

significant but fell within different ranges. For DENV, the H2 was 40%, this is in

accordance with previous studies examining susceptibility in Ae. aegypti using multiple

tissues as proxy. Both Bosio et al. (Bosio, Beaty, & Black, 1998) and Ye et al. (Yixin H.

Ye et al., 2016) reported heritabilities of ~40% in the midgut, head and saliva of DENV

infected mosquitoes. This suggests, that for DENV infection susceptibility, the mosquito

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genotype differences contribute to vector competence almost equally as the viral genotype

or environmental effects do. Heritability of CHIKV susceptibility was estimated at 18%,

almost half that estimated for siblings in the same family. The lower heritability for CHIKV

suggests that environmental or stochastic factors as well as the viral genotype have a

greater role than the mosquito’s own genetic factors in determining the susceptibility to

infection (Charmantier & Garant, 2005; William G. Hill, Goddard, & Visscher, 2008;

William G. Hill & Mackay, 2004). CHIKV infected mosquitoes also exhibited a wider

range in in viral loads than DENV and reached higher viral loads than DENV infected

mosquitos. Overall, the wider range of viral loads in CHIKV is a key contributor to the

overall low heritability for this trait. The genetic correlation between siblings for the DENV

and CHIKV susceptibility trait was not significantly different than zero. This shows

independence between the genes that control the susceptibility trait between viruses.

Additionally, a value less than one indicates the presence of genotype x environment

interactions (William G. Hill & Mackay, 2004). In general, the primary source of genetic

correlation is the pleiotropy of genes, though linkage disequilibrium can be a contributing

factor (Sgrò & Hoffmann, 2004; Slatkin, 2008). Our results could suggest that genes

controlling this trait are present in different loci among the mosquito population or that

they lack pleiotropic effect, meaning that different genes control susceptibility to infection

depending on the virus (W. G. Hill, 2013).

Phylogenetic studies on Ae. aegypti and DENV have revealed a coevolutionary

process between the virus and the co-indigenous mosquito populations (Alonso-Palomares

et al., 2018; Barón, Ursic-Bedoya, Lowenberger, & Ocampo, 2010; Sim & Dimopoulos,

2010) which has in turn shape the susceptibility to the virus. Since CHIKV is a re-emerging

107

disease, the lack of the coevolutionary process with naïve, such as American, Ae. aegypti

populations, could be primary source of the differences between the mechanisms that

control infection inside the vector (Alonso-Palomares et al., 2018). The global spread of

CHIKV and DENV was preceded by the global expansion of Ae. aegypti and Ae.

albopictus. While the expansion of Ae. aegypti started over 400 years ago, the geographic

expansion of Ae. albopictus, the secondary vector of DENV and CHIKV started since the

1960’s, previously being limited to Southeast Asia (Louis Lambrechts, Scott, & Gubler,

2010). Among the evolutionary forces that drive the evolution of arboviruses, vector

interactions may play an important role in selecting viral genotypes with low fitness costs

and high transmissibility rates (L. Lambrechts et al., 2009). CHIKV emergence in the

Indian Ocean was linked to a single mutation that enhanced transmission by Ae. albopictus

over Ae. aegypti (Tsetsarkin, Vanlandingham, McGee, & Higgs, 2007). It has been

hypothesized that the emergence of arboviruses is partially caused by adaptations to native

vector populations. Invasive viral genotypes, that outcompete local genotypes tend to have

enhanced viral replication in the vector (Chevillon & Failloux, 2003).

Using the natural genetic variation in a mosquito population, we were able to select

family extremes with respect to DENV and CHIKV viral loads. Using these families, we

then quantify the expression levels of four genes associated with the mosquito immunity

after viral infection with DENV and CHIKV. We tested two genes involve in the immune

response for both viruses and two immune genes that have been recently shown to be of

importance upon CHIKV infection. We decided for this this approach since very little is

known about Ae. aegypti immune response to CHIKV compared to DENV. Additionally,

108

the two genes of importance for CHIKV that we chose, have been shown to be upregulated

in transcriptomic studies of CHIKV infected mosquitoes (Zhao et al., 2019).

The first set of genes, AGO2 and DCR2, are part of the RNAi-induced silencing

complex (RISC) (Pratt & MacRae, 2009) and been shown to play a vital role in limiting

CHIKV and DENV replication (Franz et al., 2006; Liu, Swevers, Kolliopoulou, &

Smagghe, 2019; McFarlane et al., 2014; Mukherjee et al., 2019). Interestingly, the

expression pattern of both genes was different depending on the infecting virus. AGO2

expression was positively correlated to higher viral loads in DENV infected families but

not so for CHIKV. In contrast, DCR2 expression was positively correlated with load in

CHIKV infected families, but no correlation was seen for DENV loads. Induction of RISC

starts with DCR2 recognition and cleavage of exogenous viral double stranded RNA

(dsRNA) into small interfering RNAs (siRNAs). The siRNAs are loaded into RISC and

cleaved by AGO2 leading to further viral degradation (Blair, 2011). Arbovirus infection

studies in Ae. aegypti have indicate that, given the ability of viruses to replicate despite

RNAi response, that arboviruses must possess a mechanism of immune evasion or

suppression (Blair & Olson, 2014). Studies with Flock House Virus (FHV) infected

Drosophila have shown that some viral genome siRNAs are inefficiently loaded into RISC,

potentially reducing the efficacy of RNAi downstream of DCR2 and allowing the virus to

maintain replication (Flynt, Liu, Martin, & Lai, 2009). For DENV-2, it has been shown

that its genome contains an overrepresentation of DCR2 target sites suggesting a decoy

mechanism for RNAi evasion (Sánchez-Vargas et al., 2009). These hotspots have also been

shown to be a poor target for AGO2 cleavage potentially due to secondary intra-strand

structures (Siu et al., 2011). Additionally, RNAi targeting has been shown to produce viral

109

genotype diversification, leading to the proliferation of escape mutants within the same

viral population that are not easily recognized by the effectors in this pathway (Brackney,

Beane, & Ebel, 2009). Whether either virus genotype actively promotes these evasion

strategies, that result in differential RISC viral modulation, remains unknown.

The second set of genes, CECH and TEP3, are a TOLL pathway-regulated

(Mukherjee et al., 2019; Ramirez & Dimopoulos, 2010; Xi, Ramirez, & Dimopoulos, 2008)

antimicrobial peptide (AMP), and a complement - related immune protein (Blandin &

Levashina, 2004; Mukherjee et al., 2019), respectively. The expression pattern for both

genes was also different depending on the virus. TEP3 expression was negatively

correlated with DENV viral load while it was positively correlated to CHIKV viral load.

No expression pattern was seen for CECH in DENV infected families but it was positively

correlated to CHIKV viral load. The expression pattern of both genes in is in line with other

studies suggesting their role in CHIKV immune modulation (Zhao et al., 2019).

Interestingly, the inverse correlation between TEP3 expression and CHIKV and DENV

viral loads suggest a differential interaction with the viruses. For CHIKV, the TEP3

expression pattern suggest that the immune response mediated through the immune protein

is reactive to the pathogen while for DENV, it suggests viral suppression. The mechanisms

by which each virus might affect the expression of these genes remains unknown.

Our experimental design has some caveats that may limit the scope of its overall

interpretation. Immune effector expression can vary depending on the time post infection

and its possible that the expression levels of specific genes might peak immediately after

blood feeding and were missed from our sampling regime (Erler, Popp, & Lattorff, 2011;

Sim & Dimopoulos, 2010). Given the scale of the family-based design we could only

110

sample one single time point. The experimental conditions differ from past transcriptomic

and gene expression studies used to identify our gene candidates. The expression results

shown here do not exclude genes that might be relevant in different tissues and timepoints.

Finally, the immune response of Ae. aegypti is complex and dynamic and several questions

regarding the mosquitos’ immunity remain outstanding, since most of what is known about

the general immunity of arthropods has been elucidated in the Drosophila model system

(Wang et al., 2015). Specifically, the precise molecular details of Ae. aegypti antiviral

immune systems remains undescribed (Cheng, Liu, Wang, & Xiao, 2016).

From an evolutionary perspective, the differences in quantitative genetics shown

here indicate that the selective pressures that lead to DENV infected mosquitoes having a

higher heritability and more homogeneous viral loads are stronger than those for CHIKV,

suggesting that the selection for higher or lower viral loads within a mosquito population

would occur faster for DENV than CHIKV infection (William G. Hill & Mackay, 2004).

This would have implication in our ability to predict the strength and impact of new

outbreaks and the overall infection dynamics between Ae. aegypti and CHIKV. These

changes could also explain CHIKV re-emergence and explosion in the American continent

in naïve Aedes populations. Additionally, the results shown here suggest that the infection

dynamics and some of the mechanisms that control viral susceptibility in mosquito

populations are markedly different between DENV and CHIKV. From an epidemiological

perspective, if the genes that control viral susceptibility to multiple viruses are different

and are located at different loci in the mosquito genome, this could introduce a challenge

to vector control methods aiming to target and modify universal loci that could be used

against multiple arbovirus. In this case, more individualized gene editing approaches would

111

be necessary depending on the predominant virus present (Scott & Zhang, 2017). Our gene

expression results also provide with some candidate genes that are not traditionally

associated with antiviral activity, that should be further examined as potential targets to

make refractory mosquito populations through gene editing techniques.

112

4.6. References

Agaisse, H., & Perrimon, N. (2004). The roles of JAK/STAT signaling in Drosophila

immune responses. Immunological Reviews, 198(1), 72-82. doi:https://doi.org/10.1111/j.0105-

2896.2004.0133.x

Alonso-Palomares, L. A., Moreno-García, M., Lanz-Mendoza, H., & Salazar, M. I.

(2018). Molecular basis for arbovirus transmission by Aedes aegypti mosquitoes. Intervirology,

61(6), 255-264. doi:10.1159/000499128

Alto, B. W., Wiggins, K., Eastmond, B., Velez, D., Lounibos, L. P., & Lord, C. C.

(2017). Transmission risk of two chikungunya lineages by invasive mosquito vectors from

Florida and the Dominican Republic. PLoS Neglected Tropical Diseases, 11(7), e0005724.


Amuzu, H. E., Simmons, C. P., & McGraw, E. A. (2015). Effect of repeat human blood

feeding on Wolbachia density and dengue virus infection in Aedes aegypti. Parasites & Vectors,

8, 246. doi:10.1186/s13071-015-0853-y

Antonova, Y., Alvarez, K. S., Kim, Y. J., Kokoza, V., & Raikhel, A. S. (2009). The role

of NF-kappaB factor REL2 in the Aedes aegypti immune response. Insect biochemistry and

molecular biology, 39(4), 303-314. doi:10.1016/j.ibmb.2009.01.007

Barón, O. L., Ursic-Bedoya, R. J., Lowenberger, C. A., & Ocampo, C. B. (2010).

Differential gene expression from midguts of refractory and susceptible lines of the mosquito,

Aedes aegypti, infected with Dengue-2 virus. Journal of Insect Science, 10(1).

doi:10.1673/031.010.4101

Blair, C. D. (2011). Mosquito RNAi is the major innate immune pathway controlling

arbovirus infection and transmission. Future Microbiology, 6(3), 265-277. doi:10.2217/fmb.11.11

Blair, C. D., & Olson, K. E. (2014). Mosquito immune responses to arbovirus infections.

Current opinion in insect science, 3, 22-29. doi:10.1016/j.cois.2014.07.005

Blandin, S., & Levashina, E. A. (2004). Thioester-containing proteins and insect

immunity. Molecular Immunology, 40(12), 903-908. doi:https://doi.org/10.101 6/j.molim

m.2003.10.010

Bosio, C. F., Beaty, B. J., & Black, W. C. t. (1998). Quantitative genetics of vector

competence for dengue-2 virus in Aedes aegypti. The American Society of Tropical Medicine and

Hygiene, 59(6), 965-970.

Brackney, D. E., Beane, J. E., & Ebel, G. D. (2009). RNAi targeting of West Nile virus in

mosquito midguts promotes virus diversification. PLoS Pathogens, 5(7), e1000502.


Charmantier, A., & Garant, D. (2005). Environmental quality and evolutionary potential:

lessons from wild populations. Proceedings. Biological sciences, 272(1571), 1415-1425.

doi:10.1098/rspb.2005.3117

113

Chen, T.-H., Jian, S.-W., Wang, C.-Y., Lin, C., Wang, P.-F., Su, C.-L., Teng, H.-J., Shu,

P.-Y., & Wu, H.-S. (2015). Susceptibility of Aedes albopictus and Aedes aegypti to three

imported chikungunya virus strains, including the E1/226V variant in Taiwan. Journal of the

Formosan Medical Association, 114(6), 546-552. doi:https://doi.org/10.1016/j.jfma.2014.12.002



Chevillon, C., & Failloux, A. B. (2003). Questions on viral population biology to

complete dengue puzzle. Trends in Microbiology, 11(9), 415-421. doi:10.1016/s0966-

842x(03)00206-3

Clayton, A. M., Dong, Y., & Dimopoulos, G. (2014). The Anopheles innate immune

system in the defense against malaria infection. The Journal of Innate Immunity, 6(2), 169-181.

doi:10.1159/000353602

Cruz-Oliveira, C., Freire, J. M., Conceição, T. M., Higa, L. M., Castanho, M. A. R. B., &

Da Poian, A. T. (2015). Receptors and routes of dengue virus entry into the host cells. FEMS

Microbiology Reviews, 39(2), 155-170. doi:10.1093/femsre/fuu004

Dong, S., Balaraman, V., Kantor, A. M., Lin, J., Grant, D. G., Held, N. L., & Franz, A.

W. E. (2017). Chikungunya virus dissemination from the midgut of Aedes aegypti is associated

with temporal basal lamina degradation during bloodmeal digestion. PLoS Neglected Tropical

Diseases, 11(9), e0005976-e0005976. doi:10.1371/journal.pntd.0005976

Dong, S., Behura, S. K., & Franz, A. W. E. (2017). The midgut transcriptome of Aedes

aegypti fed with saline or protein meals containing chikungunya virus reveals genes potentially

involved in viral midgut escape. BMC Genomics, 18(1), 382-382. doi:10.1186/s12864-017-3775-

6

Dubrulle, M., Mousson, L., Moutailler, S., Vazeille, M., & Failloux, A.-B. (2009).

Chikungunya virus and Aedes mosquitoes: Saliva is infectious as soon as two days after oral

infection. PLoS ONE, 4(6), e5895. doi:10.1371/journal.pone.0005895

Erler, S., Popp, M., & Lattorff, H. M. G. (2011). Dynamics of immune system gene

expression upon bacterial challenge and wounding in a social insect (Bombus terrestris). PLoS


Flynt, A., Liu, N., Martin, R., & Lai, E. C. (2009). Dicing of viral replication

intermediates during silencing of latent Drosophila viruses. Proceedings of the National Academy

of Sciences, 106(13), 5270-5275. doi:10.1073/pnas.0813412106

Ford, S. A., Allen, S. L., Ohm, J. R., Sigle, L. T., Sebastian, A., Albert, I., Chenoweth, S.

F., & McGraw, E. A. (2019). Selection on Aedes aegypti alters Wolbachia-mediated dengue virus

blocking and fitness. Nature Microbiology, 4(11), 1832-1839. doi:10.1038/s41564-019-0533-3

Franz, A. W. E., Sanchez-Vargas, I., Adelman, Z. N., Blair, C. D., Beaty, B. J., James, A.

A., & Olson, K. E. (2006). Engineering RNA interference-based resistance to dengue virus type 2

in genetically modified Aedes aegypti. Proceeds of the National Academy of Science USA, 103.

doi:10.1073/pnas.0600479103

114


Wolbachia-mediated resistance to dengue virus Infection and death at the cellular level. PLoS


Gaye, A., Faye, O., Diagne, C. T., Faye, O., Diallo, D., Weaver, S. C., Sall, A. A., &

Diallo, M. (2014). Oral susceptibility of Aedes aegypti (Diptera: Culicidae) from Senegal for

dengue serotypes 1 and 3 viruses. Tropical Medicine & International Health, 19(11), 1355-1359.

doi:10.1111/tmi.12373

Gokhale, M. D., Paingankar, M. S., Sudeep, A. B., & Parashar, D. (2015). Chikungunya

virus susceptibility & variation in populations of Aedes aegypti (Diptera: Culicidae) mosquito

from India. The Indian journal of medical research, 142 Suppl(Suppl 1), S33-S43.

doi:10.4103/0971-5916.176614




Hill, W. G. (2013). Genetic Correlation. In S. Maloy & K. Hughes (Eds.), Brenner's

Encyclopedia of Genetics (Second Edition) (pp. 237-239). San Diego: Academic Press.

Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and theory point to mainly

additive genetic variance for complex traits. PLoS Genetics, 4(2), e1000008.

doi:10.1371/journal.pgen.1000008

Hill, W. G., & Mackay, T. F. C. (2004). D. S. Falconer and introduction to quantitative

genetics. Genetics, 167(4), 1529-1536.

Hoornweg, T. E., van Duijl-Richter, M. K. S., Ayala Nuñez, N. V., Albulescu, I. C., van

Hemert, M. J., & Smit, J. M. (2016). Dynamics of chikungunya virus cell entry unraveled by

single-virus tracking in living cells. Journal of Virology, 90(9), 4745-4756.

doi:10.1128/jvi.03184-15

Huang, Y.-J. S., Higgs, S., Horne, K. M., & Vanlandingham, D. L. (2014). Flavivirus-

mosquito interactions. Viruses, 6(11), 4703-4730. doi:10.3390/v6114703

Jupatanakul, N., Sim, S., Angleró-Rodríguez, Y. I., Souza-Neto, J., Das, S., Poti, K. E.,

Rossi, S. L., Bergren, N., Vasilakis, N., & Dimopoulos, G. (2017). Engineered Aedes aegypti

JAK/STAT pathway-mediated immunity to dengue virus. PLoS Neglected Tropical Diseases,


Kading, R. C., Brault, A. C., & Beckham, J. D. (2020). Global perspectives on arbovirus

outbreaks: A 2020 snapshot. Tropical medicine and infectious disease, 5(3), 142.

doi:10.3390/tropicalmed5030142

Kantor, A. M., Grant, D. G., Balaraman, V., White, T. A., & Franz, A. W. E. (2018).

Ultrastructural analysis of chikungunya virus dissemination from the midgut of the yellow fever

mosquito, Aedes aegypti. Viruses, 10(10), 571. doi:10.3390/v10100571

115

Kim, C.-H., & Muturi, E. J. (2013). Effect of larval density and Sindbis virus infection on

immune responses in Aedes aegypti. Journal of Insect Physiology, 59(6), 604-610.

doi:https://doi.org/10.1016/j.jinsphys.2013.03.010

Kokoza, V., Ahmed, A., Woon Shin, S., Okafor, N., Zou, Z., & Raikhel, A. S. (2010).

Blocking of plasmodium transmission by cooperative action of cecropin A and defensin A in

transgenic Aedes aegypti mosquitoes. Proceeds of the National Academy of Science U S A,

107(18), 8111-8116. doi:10.1073/pnas.1003056107

Kuadkitkan, A., Wikan, N., Fongsaran, C., & Smith, D. R. (2010). Identification and

characterization of prohibitin as a receptor protein mediating DENV-2 entry into insect cells.

Virology, 406(1), 149-161. doi:https://doi.org/10.1016/j.virol.2010.07.015




doi:10.1186/1471-2148-9-160

Lambrechts, L., Scott, T. W., & Gubler, D. J. (2010). Consequences of the expanding

global distribution of Aedes albopictus for dengue virus transmission. PLoS Neglected Tropical


Le Coupanec, A., Tchankouo-Nguetcheu, S., Roux, P., Khun, H., Huerre, M., Morales-

Vargas, R., Enguehard, M., Lavillette, D., Misse, D., & Choumet, V. (2017). Co-Infection of

mosquitoes with chikungunya and dengue viruses reveals modulation of the replication of both

viruses in midguts and salivary glands of Aedes aegypti mosquitoes. International Journal of

Molecular Sciences, 18(8). doi:10.3390/ijms18081708

Lee, R. C. H., Hapuarachchi, H. C., Chen, K. C., Hussain, K. M., Chen, H., Low, S. L.,

Ng, L. C., Lin, R., Ng, M. M.-L., & Chu, J. J. H. (2013). Mosquito cellular factors and functions

in mediating the infectious entry of chikungunya virus. PLoS Neglected Tropical Diseases, 7(2),

e2050-e2050. doi:10.1371/journal.pntd.0002050

Lim, E. X. Y., Lee, W. S., Madzokere, E. T., & Herrero, L. J. (2018). Mosquitoes as

suitable vectors for Alphaviruses. Viruses, 10(2). doi:10.3390/v10020084

Liu, J., Swevers, L., Kolliopoulou, A., & Smagghe, G. (2019). Arboviruses and the

challenge to establish systemic and persistent infections in competent mosquito vectors: The

interaction with the RNAi mechanism. Frontiers in Physiology, 10, 890-890.

doi:10.3389/fphys.2019.00890

Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using

real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods, 25(4), 402-408.

doi:10.1006/meth.2001.1262

Magwire, M. M., Fabian, D. K., Schweyen, H., Cao, C., Longdon, B., Bayer, F., &

Jiggins, F. M. (2012). Genome-wide association studies reveal a simple genetic basis of

resistance to naturally coevolving viruses in Drosophila melanogaster. PLoS Genetics, 8(11),

e1003057. doi:10.1371/journal.pgen.1003057

116

Matusali, G., Colavita, F., Bordi, L., Lalle, E., Ippolito, G., Capobianchi, M. R., &

Castilletti, C. (2019). Tropism of the chikungunya virus. Viruses, 11(2). doi:10.3390/v11020175

McFarlane, M., Arias-Goeta, C., Martin, E., O'Hara, Z., Lulla, A., Mousson, L., Rainey,

S. M., Misbah, S., Schnettler, E., Donald, C. L., Merits, A., Kohl, A., & Failloux, A.-B. (2014).

Characterization of Aedes aegypti innate-immune pathways that limit chikungunya virus

replication. PLoS Neglected Tropical Diseases, 8(7), e2994. doi:10.1371/journal.pntd.0002994

Mehta, R., Gerardin, P., de Brito, C. A. A., Soares, C. N., Ferreira, M. L. B., & Solomon,

T. (2018). The neurological complications of chikungunya virus: A systematic review. Reviews in

medical virology, 28(3), e1978-e1978. doi:10.1002/rmv.1978

Mukherjee, D., Das, S., Begum, F., Mal, S., & Ray, U. (2019). The Mosquito immune

system and the life of dengue virus: What we know and do not know. Pathogens (Basel,

Switzerland), 8(2). doi:10.3390/pathogens8020077

Muturi, E. J., Blackshear, M., Jr., & Montgomery, A. (2012). Temperature and density-

dependent effects of larval environment on Aedes aegypti competence for an alphavirus. Journal

of vector ecology : journal of the Society for Vector Ecology, 37(1), 154-161. doi:10.1111/j.1948-

7134.2012.00212.x

Novelo, M., Hall, M. D., Pak, D., Young, P. R., Holmes, E. C., & McGraw, E. A. (2019).

Intra-host growth kinetics of dengue virus in the mosquito Aedes aegypti. PLoS Pathogens,


Piontkivska, H., Matos, L. F., Paul, S., Scharfenberg, B., Farmerie, W. G., Miyamoto, M.

M., & Wayne, M. L. (2016). Role of host-driven mutagenesis in determining genome evolution of

sigma virus (DMelSV; Rhabdoviridae) in Drosophila melanogaster. Genome Biology and

Evolution, 8(9), 2952-2963. doi:10.1093/gbe/evw212

Pratt, A. J., & MacRae, I. J. (2009). The RNA-induced silencing complex: a versatile

gene-silencing machine. The Journal of biological chemistry, 284(27), 17897-17901.

doi:10.1074/jbc.R900012200

Rajapakse, S. (2011). Dengue shock. Journal of Emergencies, Trauma and Shock, 4(1),

120-127. doi:10.4103/0974-2700.76835

Ramirez, J. L., & Dimopoulos, G. (2010). The Toll immune signaling pathway control

conserved anti-dengue defenses across diverse Ae. aegypti strains and against multiple dengue

virus serotypes. Developmental & Comparative Immunology, 34. doi:10.1016/j.dci.2010.01.006

Reiskind, M. H., Pesko, K., Westbrook, C. J., & Mores, C. N. (2008). Susceptibility of

Florida mosquitoes to infection with chikungunya virus. The American Society of Tropical

Medicine and Hygiene, 78(3), 422-425.

Rodriguez, A. K., Muñoz, A. L., Segura, N. A., Rangel, H. R., & Bello, F. (2019).

Molecular characteristics and replication mechanism of dengue, zika and chikungunya

arboviruses, and their treatments with natural extracts from plants: An updated review. EXCLI

Journal, 18, 988-1006. doi:10.17179/excli2019-1825

117

Rougeron, V., Sam, I. C., Caron, M., Nkoghe, D., Leroy, E., & Roques, P. (2015).

Chikungunya, a paradigm of neglected tropical disease that emerged to be a new health global

risk. Journal of Clinical Virology, 64, 144-152. doi:10.1016/j.jcv.2014.08.032




8, 15412. doi:10.1038/ncomms15412

Salazar, M. I., Richardson, J. H., Sánchez-Vargas, I., Olson, K. E., & Beaty, B. J. (2007).

Dengue virus type 2: replication and tropisms in orally infected Aedes aegypti mosquitoes. BMC

Microbiology, 7, 9-9. doi:10.1186/1471-2180-7-9

Sánchez-Vargas, I., Scott, J. C., Poole-Smith, B. K., Franz, A. W. E., Barbosa-Solomieu,

V., Wilusz, J., Olson, K. E., & Blair, C. D. (2009). Dengue virus type 2 infections of Aedes

aegypti are modulated by the mosquito's RNA interference pathway. PLoS Pathogens, 5(2),

e1000299. doi:10.1371/journal.ppat.1000299

Saxton, A. M. (2004). Genetic analysis of complex traits using SAS. Cary, N.C: SAS

Institute.

Scott, D. A., & Zhang, F. (2017). Implications of human genetic variation in CRISPR-

based therapeutic genome editing. Nature medicine, 23(9), 1095-1101. doi:10.1038/nm.4377

Sgrò, C. M., & Hoffmann, A. A. (2004). Genetic correlations, tradeoffs and

environmental variation. Heredity, 93(3), 241-248. doi:10.1038/sj.hdy.6800532

Shrinet, J., Srivastava, P., & Sunil, S. (2017). Transcriptome analysis of Aedes aegypti in

response to mono-infections and co-infections of dengue virus-2 and chikungunya virus.

Biochemical and Biophysical Research Communications, 492(4), 617-623.

doi:10.1016/j.bbrc.2017.01.162

Sim, S., & Dimopoulos, G. (2010). Dengue Virus Inhibits Immune Responses in Aedes

aegypti Cells. PLoS ONE, 5(5), e10678. doi:10.1371/journal.pone.0010678

Siu, R. W. C., Fragkoudis, R., Simmonds, P., Donald, C. L., Chase-Topping, M. E.,

Barry, G., Attarzadeh-Yazdi, G., Rodriguez-Andres, J., Nash, A. A., Merits, A., Fazakerley, J. K.,

& Kohl, A. (2011). Antiviral RNA interference responses induced by Semliki forest virus

infection of mosquito cells: Characterization, origin, and frequency-dependent functions of virus-

derived small interfering RNAs. Journal of Virology, 85(6), 2907-2917. doi:10.1128/jvi.02052-10

Slatkin, M. (2008). Linkage disequilibrium — understanding the evolutionary past and

mapping the medical future. Nature Reviews Genetics, 9(6), 477-485. doi:10.1038/nrg2361

Smith, D. R. (2012). An update on mosquito cell expressed dengue virus receptor

proteins. Insect Molecular Biology, 21(1), 1-7. doi:https://doi.org/10.1111/j.1365-

2583.2011.01098.x

118



10(1), 622-622. doi:10.1186/s13071-017-2589-3

Terradas, G., & McGraw, E. A. (2019). Using genetic variation in Aedes aegypti to

identify candidate anti-dengue virus genes. BMC Infectious Diseases, 19(1), 580-580.

doi:10.1186/s12879-019-4212-z

Tesh, R. B., Gubler, D. J., & Rosen, L. (1976). Variation among goegraphic strains of

Aedes albopictus in susceptibility to infection with chikungunya virus. The American Society of

Tropical Medicine and Hygiene, 25(2), 326-335. doi:10.4269/ajtmh.1976.25.326

Tsetsarkin, K. A., Vanlandingham, D. L., McGee, C. E., & Higgs, S. (2007). A single

mutation in chikungunya virus affects vector specificity and epidemic potential. PLoS Pathogens,


Vega-Rúa, A., Schmitt, C., Bonne, I., Krijnse Locker, J., & Failloux, A.-B. (2015).

Chikungunya virus replication in salivary glands of the mosquito Aedes albopictus. Viruses,

7(11), 5902-5907. doi:10.3390/v7112917

Vega-Rúa, A., Zouache, K., Girod, R., Failloux, A.-B., & Lourenço-de-Oliveira, R.

(2014). High level of vector competence of Aedes aegypti and Aedes albopictus from ten

American countries as a crucial factor in the spread of chikungunya virus. Journal of Virology,

88(11), 6294-6306. doi:10.1128/jvi.00370-14

Wang, Z., Wu, D., Liu, Y., Xia, X., Gong, W., Qiu, Y., Yang, J., Zheng, Y., Li, J., Wang,

Y.-F., Xiang, Y., Hu, Y., & Zhou, X. (2015). Drosophila Dicer-2 has an RNA interference–

independent function that modulates Toll immune signaling. Science Advances, 1(9), e1500228.

doi:10.1126/sciadv.1500228

Wilder-Smith, A., Gubler, D. J., Weaver, S. C., Monath, T. P., Heymann, D. L., & Scott,

T. W. (2017). Epidemic arboviral diseases: priorities for research and public health. The Lancet

Infectious Diseases, 17(3), e101-e106. doi:10.1016/S1473-3099(16)30518-7

Wong, H. V., Chan, Y. F., Sam, I. C., Sulaiman, W. Y., & Vythilingam, I. (2016).

Chikungunya virus infection of Aedes Mosquitoes. Methods in Molecular Biology, 1426, 119-

128. doi:10.1007/978-1-4939-3618-2_11

Xi, Z., Ramirez, J. L., & Dimopoulos, G. (2008). The Aedes aegypti Toll pathway

controls dengue virus infection. PLoS Pathogens, 4(7), e1000098.


Yactayo, S., Staples, J. E., Millot, V., Cibrelus, L., & Ramon-Pardo, P. (2016).

Epidemiology of chikungunya in the Americas. The Journal of Infectious Diseases, 214(suppl 5),

S441-s445. doi:10.1093/infdis/jiw390

Ye, Y. H., Chenoweth, S. F., Carrasco, A. M., Allen, S. L., Frentiu, F. D., van den Hurk,

A. F., Beebe, N. W., & McGraw, E. A. (2016). Evolutionary potential of the extrinsic incubation

period of dengue virus in Aedes aegypti. Evolution. doi:10.1111/evo.13039

119

Ye, Y. H., Ng, T. S., Frentiu, F. D., Walker, T., van den Hurk, A. F., O'Neill, S. L.,

Beebe, N. W., & McGraw, E. A. (2014). Comparative susceptibility of mosquito populations in

North Queensland, Australia to oral infection with dengue virus. The American Society of

Tropical Medicine and Hygiene, 90(3), 422-430. doi:10.4269/ajtmh.13-0186

Zeller, H., Van Bortel, W., & Sudre, B. (2016). Chikungunya: Its history in Africa and

Asia and its spread to new regions in 2013–2014. The Journal of Infectious Diseases,

214(suppl_5), S436-S440. doi:10.1093/infdis/jiw391

Zhang, M., Zheng, X., Wu, Y., Gan, M., He, A., Li, Z., Liu, J., & Zhan, X. (2010).

Quantitative analysis of replication and tropisms of Dengue virus type 2 in Aedes albopictus. The

American Society of Tropical Medicine and Hygiene, 83(3), 700-707. doi:10.4269/ajtmh.2010.10-

0193

Zhao, L., Alto, B. W., Jiang, Y., Yu, F., & Zhang, Y. (2019). Transcriptomic analysis of

Aedes aegypti innate immune system in response to ingestion of chikungunya virus. International

Journal of Molecular Sciences, 20(13), 3133. doi:10.3390/ijms20133133

Ziegler, S. A., Nuckols, J., McGee, C. E., Huang, Y.-J. S., Vanlandingham, D. L., Tesh,

R. B., & Higgs, S. (2011). In vivo imaging of chikungunya virus in mice and Aedes mosquitoes

using a Renilla luciferase clone. Vector borne and zoonotic diseases (Larchmont, N.Y.), 11(11),

1471-1477. doi:10.1089/vbz.2011.0648

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Chapter 5

Synthesis and General Discussion

Aedes aegypti remains as one of the deadliest animals in the world (McGregor &

Connelly, 2020). Because of this, the development of vector control methods remains as

the primary tool to fight the spread of vector borne diseases. A key part of development of

optimal and effective vector control strategies depends on a deep understanding of the

vector-virus interactions that take place when a mosquito takes pathogen-laden blood meal.

The viral infection process inside the mosquito is a complex and dynamic process that is

poorly understood. (Guzman & Harris, 2015). Additionally, the strength and efficacy of

the mosquito immunity varies across tissues and in response to different serotypes and

viruses. These vector - virus interactions can ultimately shape the transmission potential of

multiple arboviruses (Bennett et al., 2002; Gubler, Nalim, Tan, Saipan, & Sulianti Saroso,

1979).

Chapter 2 focused on comparing tissue - specific mosquito responses to DENV

infection using four DENV serotypes. Chapter 3 further expanded this work by examining

the effect of DENV serotype co-infection in tissue-specific responses to infection, for both

a Wolbachia infected and uninfected mosquito lines. Finally, in Chapter 4, we explored

the mosquito infection dynamics from a population perspective and between different

121

arboviral families. We tested the role of the Ae. aegypti own genetic variation in

determining susceptibility to DENV and CHIKV.

Differences between individual infections of viruses of the same family, in our case,

of DENV serotypes, had markedly different dynamics in our vector competence assays in

Chapter 2, DENV-1 and 2 outperform DENV-3 and 4, reaching higher viral and infection

rates. This can potentially explain the disparities in the transmission potential in the field

between the different DENV serotypes, since DENV variants with replicative advantages

can move more rapidly in susceptible vector or host population. If individual infections of

DENV serotypes have different dynamics inside the mosquito, how do they behave when

they are co-infecting the vector? moreover, how can this scenario affect the development

and deployment of vector control methods? In Chapter 3 we try to answered these

questions by analyzing the co-infection process of DENV-2 and DENV-3 inside a

Wolbachia - infected Ae. aegypti mosquito line. We saw that DENV co-infection has little

effect in Wolbachia’s ability to block pathogen replication and that Wolbachia’s blocking

phenotype was systemic across the mosquito body. From a vector control point of view,

this seems promising, since it suggests that the long-term efficacy of Wolbachia won’t be

compromised by DENV-coinfections. In Wolbachia-free mosquitoes, DENV co-infection

had a significant effect in the dynamics of each DENV serotype. While in Chapter 2,

DENV-2 ha greater capacity to replicate in the mosquito than DENV-3, when in co-

infection, DENV-3 had a competitive advantage over DENV-2, suggesting either

enhancement of DENV-3 or competitive inhibition of DENV-2, although the mechanisms

by which this occur remain unknown. These chapters support the notions that the infection

122

process inside the vector, that ultimately affects transmissibility, is variable and dynamic

and can be depended on the infecting virus.

If the infection dynamics of DENV differ across (chapter 2) and between serotypes

(chapter 3) at an individual level, how do the differences in the infectious process translate

to a populations level? And how much the mosquito population’s own genetic variation

can impact this process, when infection is caused by an arbovirus from a different viral

family? In chapter 4, we demonstrate not only that the heritability to the susceptibility to

DENV and CHIKV is different within a population but that some of the immune

mechanisms that control it are different. We showed that indeed, the mosquitoes immunity

responds differently to each virus and that although some viral control mechanisms, such

as the RNAi pathway, play a universal role in modulating viral replication, the mosquitos’

response to infection appears markedly different depending on the infecting virus.

While these experiments shed light on the G x G interaction on the susceptibility

and viral transmission potential, I was only able to test individual viral and mosquito

strains, thus extrapolating the results for other genotypes is not without limitations. Further

expedients could use our framework with a diverse arrange of mosquito and viral genotypes

incorporating different variables such as more initial infectious doses, different modes of

co-infection and different immune genes (Souza-Neto, Powell, & Bonizzoni, 2019).

Presently, my findings from chapter 2 have been further expanded not only by testing

more DENV strains per serotype but also doing with Ae. albopictus, in the work done by

Ekwudu et al. (2020). Additionally, the tissue specific infection dynamics and the presence

of a viral subpopulation structure caused by a dose-dependent effect was reported in

123

CHIKV infected mosquitoes by Merwaiss et al. (2021). Similarly, chapter’s 3 results are

in agreement with the work done by Caragata et al. (2019) showing that co-infection does

not adversely affect pathogen blocking caused by Wolbachia, currently being trialed

worldwide.

The selection for viral genotypes that have the potential for rapid and explosive

spread is actively driven by withing vector selection (Chevillon & Failloux, 2003). Single

adaptive mutations within the viral genotype have been linked to the re-emergence and

spread of viruses like CHIKV. Viruses with such selective advantages can displace local

and more mild viral genotypes of the same or different viral families (Tsetsarkin,

Vanlandingham, McGee, & Higgs, 2007). Outcompeted genotypes tend to be milder

clinical manifestations and slow disease transmission within mosquito populations. How

much does the viral populations reflect the genetic structure of their vectors? Worldwide,

Ae. aegypti populations consists of connected islands of genetically different populations

(Apostol, Black, Reiter, & Miller, 1996). The vector competence within a population is, up

to some degree, determine by the G x G interactions, therefore viral genotype structure is

a result from adaptations to the vector (Lambrechts et al., 2009). The transmission potential

of a virus is therefore dependent on the vector-virus compatibility, i.e., the combination of

initial viral load, host and pathogen genotypes.

Through my work, I demonstrate the importance of the viral genetic diversity,

initial viral dose and tissues - specific response in dictating infectivity. We showed that a

threshold level of virus is required to stablish body-wide infection and that this threshold

might be different between DENV serotypes. We also showed that the pathogen blocking

124

effect in Wolbachia-infected mosquitoes is not affected by DENV serotype co-infection.

Finally, we showcase the contribution of mosquito’s genetic variation in the differential

susceptibility to infection between DENV and CHIKV. Taken together, my dissertation

addresses different areas of the complex vector-virus interactions and their effect on

pathogen transmissibility. While many unanswered questions remained, my research

provides valuable insight into the infection dynamics, immunity and feasibility of vector

control strategies.

125

5.1 References

Apostol, B. L., Black, W. C., Reiter, P., & Miller, B. R. (1996). Population

genetics with RAPD-PCR markers: the breeding structure of Aedes aegypti in Puerto

Rico. Heredity, 76(4), 325-334. doi:10.1038/hdy.1996.50

Bennett, K. E., Olson, K. E., Munoz Mde, L., Fernandez-Salas, I., Farfan-Ale, J.

A., Higgs, S., Black, W. C., & Beaty, B. J. (2002). Variation in vector competence for

dengue 2 virus among 24 collections of Aedes aegypti from Mexico and the United

States. American Journal of Tropical Medicine and Hygiene, 67.

Caragata, E. P., Rocha, M. N., Pereira, T. N., Mansur, S. B., Dutra, H. L. C., &

Moreira, L. A. (2019). Pathogen blocking in Wolbachia-infected Aedes aegypti is not

affected by Zika and dengue virus co-infection. PLOS Neglected Tropical Diseases,

13(5), e0007443-e0007443. doi:10.1371/journal.pntd.0007443

Chevillon, C., & Failloux, A. B. (2003). Questions on viral population biology to

complete dengue puzzle. Trends in Microbiology, 11(9), 415-421. doi:10.1016/s0966-

842x(03)00206-3

Ekwudu, O. m., Marquart, L., Webb, L., Lowry, K. S., Devine, G. J., Hugo, L. E.,

& Frentiu, F. D. (2020). Effect of serotype and strain diversity on dengue virus

replication in Australian mosquito vectors. Pathogens (Basel, Switzerland), 9(8), 668.

doi:10.3390/pathogens9080668

Gubler, D. J., Nalim, S., Tan, R., Saipan, H., & Sulianti Saroso, J. (1979).

Variation in susceptibility to oral infection with dengue viruses among geographic strains

of Aedes aegypti. American Journal of Tropical Medicine and Hygiene, 28(6), 1045-

1052.



Lambrechts, L., Chevillon, C., Albright, R. G., Thaisomboonsuk, B., Richardson,

J. H., Jarman, R. G., & Scott, T. W. (2009). Genetic specificity and potential for local

adaptation between dengue viruses and mosquito vectors. BMC Ecology and Evolution,

9, 160. doi:10.1186/1471-2148-9-160

McGregor, B. L., & Connelly, C. R. (2020). A Review of the Control of Aedes

aegypti (Diptera: Culicidae) in the Continental United States. Journal of Medical

Entomology, 58(1), 10-25. doi:10.1093/jme/tjaa157

Merwaiss, F., Filomatori, C. V., Susuki, Y., Bardossy, E. S., Alvarez, D. E., &

Saleh, M. C. (2021). Chikungunya virus replication rate determines the capacity of

126

crossing tissue barriers in mosquitoes. Journal of Virology, 95(3). doi:10.1128/jvi.01956-

20


competence studies: A review. Infection, Genetics and Evolution, 67, 191-209.

doi:https://doi.org/10.1016/j.meegid.2018.11.009

Tsetsarkin, K. A., Vanlandingham, D. L., McGee, C. E., & Higgs, S. (2007). A

single mutation in chikungunya virus affects vector specificity and epidemic potential.

PLoS Pathogens, 3(12), e201. doi:10.1371/journal.ppat.0030201

127

Appendices

Appendix A: Supplementary materials from Chapter 2

Appendix A: DRC estimates for unsuccessful infections by tissue, infectious dose and DENV

strain

Infectious

dose Serotype Tissue

Subpopulation

threshold Parameter Estimate SE

p-value Sig

LOW

DENV-1

MIDGUT

Unsuccessful

Growth

rate 1.25 0.81

0.12

Max

DENV

load

1.96 0.14

<0.001 ***

ED50 1.14 0.69 0.1

CARCASS

Growth

rate 0.13 0.15

0.38

Max

DENV

load

1.64 0.58

0.001

ED50 1.91 5.16 0.71

DENV-2

MIDGUT

Growth

rate 0 0.03

0.79

Max

DENV

load

6.56 8.63

0.81

ED50 1.14 0.68 1.67

CARCASS

Growth

rate 0.04 NA

NA

Max

DENV

load

4.04 NA

NA

ED50 0 NA NA

HIGH DENV-3

MIDGUT

Growth

rate -0.03 0.03

0.93

Max

DENV

load

6.22 9.66

0.64

ED50 0 78.7 1

LOW DENV-3 NTA

Growth

rate -0.3 0.17

0.08

Max

DENV

load

1.85 0.26

<0.001 ***

ED50 17.1 1.84 <0.001 ***

128

HIGH DENV-3

CARCASS

Unsuccessful

Growth

rate 0.31 0.13

0.02

Max

DENV

load

2.96 0.28

<0.001 ***

ED50 4.56 1.09 <0.001 ***

LOW DENV-3 NTA

Growth

rate -0.02 0.03

0.49

Max

DENV

load

2.44 4.99

0.62

ED50 0 90.7 1

HIGH DENV-4

MIDGUT

Unsuccessful

Growth

rate -0.04 .03

0.1552

Max

DENV

load

4.93 0.19

<0.001 ***

ED50 0 0.3 1

LOW DENV-4 NTA

Growth

rate 0.15 0.3

0.6

Max

DENV

load

1.38 0.52

0.01 *

ED50 0 .55 1

HIGH DENV-4

CARCASS

Unsuccessful

Growth

rate 0.02 0.06

0.74

Max

DENV

load

3.63 20.4

0.85

ED50 17.12 745 0.9

LOW DENV-4 NTA

Growth

rate 0.64 0.43

0.14

Max

DENV

load

1.31 0.16

<0.001 ***

ED50 7.25 2.17 0.001 **

*NA, not available due to poor fitting to the log logistic model. *NTA no threshold applied. *P<0.05,

**P<0.01, ***P<0.001. SE of the estimate, p-value indicates if estimate is different than zero.

129

Appendix A: Multimodal distribution analysis

Treatments Hartigan’s dip test Bimodality coefficient

DENV-1 CA LOW <2.2e-16 0.81

DENV-1 MG LOW 2.60E-05 0.86

DENV-2 CA LOW 0.001386 0.72

DENV-2 MG LOW 4.24E-05 0.79

DENV-3 CA HIGH 7.32E-06 0.73

DENV-3 MG HIGH <2.2e-16 0.75

DENV-4 CA HIGH 5.18E-06 0.89

DENV-4 MG HIGH <2.2e-16 0.93

DENV-1 CA HIGH 7.83E-06 0.67

DENV-1 MG HIGH 0.5921 0.63

DENV-2 CA HIGH 0.0001161 0.75

DENV-2 MG HIGH 0.9904 0.63

If the p value is >0.05, it suggests bimodality.

Appendix A: Survival curve statistical analysis

Treatment N Observed Expected (O-E)^2/E (O-E)^2/V

Control 80 80 88.5 0.82 1.06

DENV-1 High 80 80 98.7 3.52 4.93

DENV-1 Low 80 80 75.2 0.31 0.39

DENV-2 High 80 80 64.7 3.62 4.44

DENV-2 Low 80 80 86.4 0.47 0.60

DENV-3 High 80 80 81.1 0.02 0.03

DENV-3 Low 80 80 82.1 0.05 0.07

DENV-4 High 80 80 68.7 1.85 2.29

DENV-4 Low 80 80 74.3 0.43 0.54

Chisq = 12.9 on 8 degrees of freedom, p= 0.01.

130

Appendix A: AIC values for the DRC model

Serotype Dose Tissue Subpopulation threshold AIC

DENV-1 High MG NA 967

DENV-1 Low MG Successful 318

DENV-1 Low MG Unsuccessful 341

DENV-2 High MG NA 775

DENV-2 Low MG Successful 386

DENV-2 Low MG Unsuccessful 360

DENV-3 High MG Successful 392

DENV-3 High MG Unsuccessful 306

DENV-3 Low MG NA 263

DENV-4 High MG Successful 605



DENV-1 High CA NA 786

DENV-1 Low CA Successful 181

DENV-1 Low CA Unsuccessful 300

DENV-2 High CA NA 621

DENV-2 Low CA Successful 199

DENV-2 Low CA Unsuccessful 340

DENV-3 High CA Successful 340

DENV-3 High CA Unsuccessful 301

DENV-3 Low CA NA 160

DENV-4 High CA Successful 362



Appendix A: Results of the GLM fitted to salivary glands DENV load

Estimate SE t-value P=value

Intercept 1.06 2.36 0.44 0.66

DENGUE-2 41.94 75.43 0.55 0.58

DENGUE-3 0.14 2.41 0.05 0.95

DENGUE-4 -29.65 20.55 -1.44 0.16

Carcass load 0.13 0.29 0.44 0.66

Midgut load 0.05 0.24 0.21 0.83

DENGUE-2:Carcass load -4.69 8.34 -0.56 0.58

DENGUE-3:Carcass load -0.03 0.30 -0.13 0.89

DENGUE-4:Carcass load 6.15 =4.24 1.44 0.16

DENGUE-2:Midgut load -3.50 6.34 -0.55 0.58

DENGUE-3:Midgut load -0.01 0.24 -0.07 0.94

DENGUE-4:Midgut load 3.02 2.09 1.44 0.16

Carcass load:Midgut load -0.01 0.03 -0.33 0.73

DENGUE-2:Carcass load:Midgut load 0.39 0.70 0.56 0.58

DENGUE-3:Carcass load:Midgut load 0.008 0.03 0.27 0.78

DENGUE-4:Carcass load:Midgut load -0.62 0.42 -1.45 0.16

131

Appendix A: Density plots. Dotted line indicates data split point

DENV-1 MG LOW DENV-2 MG LOW

DENV-1 CA LOW DENV-2 CA LOW

DENV-3 MG HIGH DENV-4 MG HIGH

132

DENV-3 CA HIGH DENV-4 CA HIGH

DENV-1 CA HIGH DENV-2 CA HIGH

DENV-1 MG HIGH DENV-2 MG HIGH

133

Appendix A: Average salivary gland DENV load at the last DPI (20 days).

Average DENV load at DPI 20 for all 4 DENV serotypes at 2 infectious doses (High: 1 × 108, low:

1 × 105 DENV copies/ml). All box plots show median and interquartile ranges (n = 10 per

treatment). Significant differences are based on Tukey post hoc comparison following ANOVAs

on log-transformed data. Only significant differences are shown. *p<0.05.

134

Appendix A: Survival curves for all treatments

Survival curve analysis of control and the 4 DENV serotypes at 2 infectious doses (High: 1 × 108,

low: 1 × 105 DENV copies/ml). There was no significant difference between treatments (χ2 = 12.9,

df = 8, p = 0.1). Dotted outside lines represent 95% confidence intervals. Each line at the top

represents individual treatments.

Treatment

135

Appendix B: Supplementary materials from Chapter 3

Appendix B: Supplementary figure 1. Relationship between PFU and viral RNA copies

for both DENV-2 and DENV-3

Relationship between PFU and viral RNA copies for both DENV-2 and DENV-3. Viruses were

harvested from C6/36 cells at DPI 5 and 7 and on the day of the experiment (DPI7-Exp). A) PFU/ml

for both DENV-2 and DENV-3 for DPI5 (red), DPI7 (blue), and on the day of the experiment DPI7

(black). B) Log10 viral RNA copies/ml for both DENV-2 and DENV-3 for DPI5 (red), DPI7 (blue),

and on the day of the experiment DPI7 (black). All assays were done using live virus.

136

Appendix C: Supplementary materials from Chapter 4

Appendix C: Supplementary figure 1. Relationship between DENV viral load and gene

expression levels.

Relationship between DENV viral load and gene expression levels in extreme DENV load

mosquito families. DENV viral load was correlated with expression levels of A) AGO2 (Spearmans

r = 0.63, p = 0.05), B) DCR2 (Spearmans r = 0.0022, p = 0.95), C) TEP3 (Spearmans r = -0.46, p

= 0.18) and D) CECH (Spearmans r = -0.35, p = 0.32). Each point represents a mosquito family.

N=10 families.

137

Appendix C: Supplementary figure 3. Relationship between DENV viral load and gene

expression levels.

Relationship between CHIKV viral load and gene expression levels in extreme CHIKV load

mosquito families. CHIKV viral load was correlated with expression levels of A) AGO2

(Spearmans r = 0.7, p = 0.016), B) DCR2 (Spearmans r = 0.64, p = 0.035), C) TEP3 (Spearmans r

= 0.76, p = 0.0061) and D) CECH (Spearmans r = 0.62, p = 0.043). Each point represents a mosquito

family. N=10 families.

VITA

Mario de Jesús Novelo Canto

Education

Publications

• Novelo M, Hall MD, Pak D, Young PR, Holmes EC, McGraw EA. Intra-host

kinetics of dengue virus in the mosquito Aedes aegypti. PLOS Pathogens.

2019;15(12): e1008218. Novelo M, Audsley M, McGraw EA.

• Novelo M, Audsley M, McGraw EA. The impact of DENV serotype competition

and co-infection viral kinetics in the mosquito Aedes aegypti. Parasites & Vectors.

2021. In press.

Awards

• Jose de la Tore Endowment Opportunity for Graduate students from Latin America

and the College of Agricultural Sciences Graduate Student Travel Award - Penn

State (USA).

• Dean's Postgraduate Research Scholarship and Deans International Postgraduate

Research Scholarship - Monash University (AUS).

• National Council of Science and Technology Scholarship - CONACyT (MEX)

Teaching experience

• Teaching assistant: ENT202 The Insect Connection (Spring 2018)

• Teaching assistant: ENT216 Plagues Through the Ages (Spring 2020)

2018 – 2021 PhD in Entomology

Penn State University, University Park, PA, USA

2014 – 2016 Master of Science in Molecular Medicine (Distinction)

University of Sheffield, United Kingdom

2009 – 2014 Bachelor of Science in Biology

Universidad Autónoma de Yucatán, México

arbovirus infection dynamics in the mosquito …

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