A prostate cell line model of persistent Zika virus infection
RHYS ELUMI IZUAGBE BSc Biomedical Science
Submitted in fulfilment of the requirements of the degree of HL84 Master of Applied Science (Research)
Institute of Health and Biomedical Innovations (IHBI) Faculty of Health School of Biomedical Sciences Queensland University of Technology 2019
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Keywords
Zika virus (ZIKV); prostate; sexual transmission; persistent infection; RNAseq; qRT-PCR; RWPE-1; reservoir; interferons
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Abstract
Zika virus (ZIKV) is an arthropod-borne virus (arbovirus) that has caused significant public health concern in recent years. Long neglected as a virus of limited importance, recent epidemics in French Polynesia and South/Latin America have demonstrated that ZIKV can cause teratogenic malformations in humans, most notably microcephaly. Although the primary mode of human infection is via mosquito bite, ZIKV can also be sexually transmitted. It is now well established that ZIKV can persist in the male reproductive tract, with reports of infectious virus being detectable in semen up to six months following infection. Although persistent and often asymptomatic infection can have devastating consequences for foetal development, the viral reservoirs that facilitate sexual transmission are less understood. The prostate gland may act as a reservoir for ZIKV, informed by studies reporting the presence of virus in the semen of vasectomised men. However, the molecular mechanisms underlying viral persistence in the prostate are unknown. Here, we established a human prostate cell line model to study the persistence of three ZIKV isolates, belonging to the two main virus lineages, over a 30-day period. We found that intracellular ZIKV RNA remains detectable up to 30 days post-infection (dpi) in RWPE-1 prostate cells. Extracellular viral RNA was also detectable for all ZIKV isolates up to 30 dpi post-infection. Remarkably, a French Polynesian isolate persisted at relatively high levels of both intracellular and extracellular virus until 30 dpi, reaching maxima of 2.7x106 genome copies/ ng RNA (6 dpi) and 3.2x108 genome copies/ ml
(24 dpi) respectively. By contrast, the prototype African strain MR766 peaked at an intracellular concentration of 1.9x106 genome copies/ ng RNA (12 dpi) and extracellular concentration of 2x106 genome copies/ ml (3 dpi). Persistent infection with any of the three examined ZIKV strains caused no loss in host cell viability. RNA sequencing and differential gene expression analyses were performed for ZIKV-infected and control cells, at 6 and 21 dpi.
Strong upregulation of antiviral response genes was observed at 6 dpi, in the form of type-I iv
interferon and multiple viral recognition pathways however, only minor recruitment of systemic immunity. At 21 dpi antiviral response was largely abated and replaced with a strong transcriptomic profile of terminal keratinocyte cornification.
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Table of Contents
Abstract ...... iv List of Figures ...... viii List of Tables ...... x List of Appendices ...... xi List of Abbreviations ...... xii Statement of Original Authorship ...... xiii Acknowledgments ...... xiv
Chapter 1: Introduction ...... 1 1.1 Background ...... 1 1.2 Significance, Scope and Definitions ...... 2 1.3 Hypothesis and Aims ...... 2
Chapter 2: Literature Review ...... 3 2.1 Chronology of Zika virus emergence ...... 3 2.2 ZIKV classification and virion structure ...... 5 2.3 Clinical manifestations and sequelae ...... 8 2.4 Mosquito vectors of ZIKV ...... 10 2.5 Sexual transmission of ZIKV ...... 12 2.6 The prostate gland as a viral reservoir ...... 13 2.7 Innate antiviral responses during persistent infection ...... 14 2.7.1 Interferon ...... 15 2.7.2 Toll-like receptors ...... 17 2.7.3 RIG-like receptors ...... 17 2.7.4 Nod-like receptors ...... 18 2.8 Hypothesis and Aims ...... 18
Chapter 3: Methods ...... 19 3.1 Aim 1 Methodology and Experimental Design ...... 19 3.1.1 Virus strains and cells ...... 19 3.1.2 Infection of non-malignant human prostate cells ...... 20 3.1.3 Plaque assays to titre infectious virus ...... 22 3.1.4 Prostate cell viability following infection ...... 22 3.1.5 Quantification of ZIKV genome copies ...... 24 3.1.6 Statistical analysis ...... 25 vi
3.2 Aim 2 Methodology and Experimental Design ...... 25 3.2.1 Selection of samples, RNA extraction and RNA-Seq ...... 25 3.2.2 Transcriptome Analysis ...... 26 3.2.3 Validation of differentially transcribed genes using qRT-PCR ...... 28 3.2.4 Statistical analysis ...... 29 3.3 Ethics ...... 29
Chapter 4: Results ...... 30 4.1 Aim 1 Results ...... 30 4.1.1 Extracellular ZIKV RNA production in RWPE-1 cells following infection at low MOI ...... 30 4.1.2 Intracellular ZIKV RNA production in RWPE-1 cells following infection at low MOI ...... 31 4.1.3 Infectious ZIKV titers in RWPE-1 cells following infection at low MOI ...... 32 4.1.4 Extracellular ZIKV RNA production in RWPE-1 cells following infection at high MOI ...... 33 4.1.5 Intracellular ZIKV RNA production in RWPE-1 cells following infection at high MOI ...... 34 4.1.6 ZIKV titers following infection at high MOI 10 ...... 35 4.1.7 RWPE-1 cell viability following ZIKV infection at low MOI ...... 37 4.1.8 RWPE-1 cell viability following ZIKV infection at high MOI ...... 38 4.1.9 Cellular toxicity of ZIKV ...... 39 4.2 Aim 2 Results ...... 41 4.2.1 Bioinformatic assembly ...... 41 4.2.3 Differentially expressed genes following ZIKV infection ...... 42 4.2.4 DEG validation by qRT-PCR ...... 46 4.2.4 GO term enrichment classifications ...... 48
Chapter 5: Discussion ...... 55 An in vitro model of persistent infection ...... 56 Antiviral responses ...... 58 Keratinocyte specific responses ...... 60 Viral carcinogenesis ...... 61 Further remarks ...... 63 Limitations ...... 64
Bibliography ...... 67 Appendices ...... 81
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List of Figures
Figure 2.1. ZIKAV genome structure...... 5
Figure 2.2. Schematic of flavivirus replication cycle and membrane fusion...... 7
Figure 2.3. Subversion of IFN signalling by flaviviruses ...... 16
Figure 3.1 Design of time-course of infection experiments...... 21
Figure 3.2. Schematic of LDH cytotoxicity assay...... 24
Figure 3. 3. Galaxy Differentially Expressed Gene (DEG) bioinformatics workflow...... 27
Figure 4.1. Extracellular virus produced by three strains of ZIKV over 30 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 1...... 31
Figure 4.2. Intracellular virus produced by three strains of ZIKAV over 30 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 1 ...... 32
Figure 4.3. Infectious titre produced by three strains of ZIKAV over 30 days, following infection of RWPE-1 cells at MOI 1...... 33
Figure 4.4. Extracellular virus production of two strains of ZIKAV over 36 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 10...... 34
Figure 4.5. Intracelluar virus production for two strains of Zika over 36 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 10...... 35
Figure 4.6. Infectious titre produced by two strains of ZIKAV over 36 days, following infection of RWPE-1 cells at MOI 10...... 36
Figure 4.7 Viability of RWPE-1 cells following infection at MOI 1 with three ZIKAV strains...... 37
Figure 4.8 Viability of RWPE-1 cells following infection at MOI 10 with two ZIKAV strains...... 38
Figure 4.9. Cytotoxicity measured at 3 dpi with ZIKAV H/PF/2013 infection...... 39
viii Figure 4.10 Cytotoxicity measured at 6 dpi with ZIKAV H/PF/2013 infection...... 40
Figure 4.11. Significant differentially expressed genes at 6 dpi in RWPE-1 cells infected with ZIKAV strain H/PF/2013...... 43
Figure 4.12. Significant differentially expressed genes at 21dpi of RWPE-1 cells infected with ZIKAV strain H/PF/2013 ...... 45
Figure 4.13. Relative expression of IFI44L and XAF-1 in RWPE-1 cells following ZIKAV strain H/PF/2013 infection at 6 dpi...... 46
Figure 4.14. Relative expression of IFI44L and XAF-1 genes in RWPE-1 cells following ZIKAV strain H/PF/2013 infection at 21 dpi...... 47
Figure 4.15 Enrichment of predominant biological processes at 6 dpi as determined from gene ontology assessment of Differentially Expressed Genes...... 49
Figure 4.16. Enrichment of predominant biological processes arising at 21 dpi as determined from gene ontology assessment of Differentially Expressed Genes...... 51
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List of Tables
Table 1. RNA-Seq DEG validation primer sets...... 29 Table 2. Mapping rates of sequence data obtained from each sample...... 41 Table 3. Top 5 differentially expressed genes at 6 days post-infection ...... 44 Table 4. Top five DEGS at 21 dpi of RWPE-1 cells infected with ZIKV...... 45 Table 5. Major Gene Ontology term results of DEG at 6 dpi...... 48 Table 6. Major Gene Ontology results of DEG at 21 dpi...... 50
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List of Appendices
Appendix 1. pUc19-RpS7-wsp-RpS17-ZIKV control plasmid ...... 82 Appendix 2. ZIKV qRT-PCR primer set and alignment to tested strains ...... 83 Appendix 3. Bioanalyser RNA integrity analysis ...... 84 Appendix 4.1. 6 dpi; RNA-Seq expression of significant DEG (1 of 3) ...... 85 Appendix 5.1. 21 dpi; RNA-Seq expression of significant DEG (1 of 2) ...... 88
Appendix 6.1. Prostate biomarker RNA-Seq expression and DEG, 6 dpi ...... 90 Appendix 6.2. Prostate biomarker RNA-Seq expression, 21 dpi ...... 91 Appendix 7.1. Day 6 Gene Ontology analysis; Type-III Interferon regulation ...... 92 Appendix 7.2. Day 6 Gene Ontology analysis; Virus sensing ...... 93 Appendix 7.3. Day 6 Gene Ontology analysis; Interferon-ß positive regulation ...... 94 Appendix 7.4. Day 6 Gene Ontology analysis; Cytoplasmic PAMP recognition ...... 95 Appendix 7.5. Day 6 Gene Ontology analysis; Interferon-α positive regulation ...... 96 Appendix 7.6. Day 6 Gene Ontology analysis; Type-I Interferon negative regulation ...... 97 Appendix 7.7. Day 21 Gene Ontology analysis; Cornification ...... 98 Appendix 8.1. Gene ontology results of day 6 DEG (1 of 2) ...... 99 Appendix 8.2. Gene ontology results of day 21 DEG ...... 101
Appendix 9.1. Day 6 DEG Prominent KEGG maps; Measles defence ...... 102 Appendix 9.2. Day 6 DEG Prominent KEGG maps; Influenza A defence ...... 103 Appendix 9.3. Day 6 DEG Prominent KEGG maps; RIG-1 signalling pathway ...... 104 Appendix 9.4. Day 6 DEG Prominent KEGG maps; Human papillomavirus defence ...... 105 Appendix 9.5. Day 6 DEG Prominent KEGG; Herpes simplex virus defence ...... 106 Appendix 9.6. Day 21 DEG prominent KEGG; RAS signalling pathway...... 107 Appendix 9.7. Day 21 DEG prominent KEGG; Compliment and coagulation cascades ...... 108 Appendix 9.8. Day 21 DEG prominent KEGG; IL-17 pathway ...... 109
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List of Abbreviations
ADE Antibody-Dependent Enhancement AGRF Australian Genomic Research Facility BPB Blood Prostate Barrier CARD Caspase Activation and Recruitment Domain CNS Central Nervous System CZS Congenital Zika Syndrome DAMP Damage-Associated Molecular Pattern DC Dendritic Cell DEG Differentially Expressed Genes DENV Dengue virus DP Defective Particle Dpi Days post infection FI Fluorescence Intensity GBS Guillain–Barré Syndrome GO Gene Ontology HSV Herpes Simplex Virus IFA Immuno-Fluorescence Assay IFN Interferon IFNAR Interferon-Alpha/Beta Receptor IFNGR Interferon-Gamma Receptor ISG Interferon Stimulated Gene IP Interfering particle Kbs Kilo-Base pairs KEGG Kyoto Encyclopedia of Genes and Genome LDH Lactate Dehydrogenase LNCaP Lymph Node Carcinoma of the Prostate MOI Multiplicity of Infection: ratio of infectious particles to inoculated cells MSCs Mesenchymal Stem Cells NLR Nod-Like Receptor NSP Non-Structural Protein PAMP Pathogen Associated Molecular Pattern PCa Prostate Cancer Pfu Plaque Forming Units PHEIC Public Health Emergency of International Concern PRR Pattern Recognition Receptor qRT-PCR Quantitative Reverse Transcription Polymerase Chain Reaction RLR Rod-Like Receptor SFM Serum-Free Media STI Sexually Transmitted Infection TLR Toll-Like Receptor WHO World Health Organisation WNV West Nile Virus ZF Zika Fever ZIKV Zika virus
xii Statement of Original Authorship
The work contained in this thesis has not been previously submitted and meets the requirements for an award at this or any other higher education institution. To the best of my knowledgeand belief, the thesis contains no material previously published or written by another person except where due reference is made.
Signature: QUT Verified Signature
Date:
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Acknowledgments
I wish to extend my greatest thanks and recognition to Dr Francesca D. Frentiu who has guided me throughout the course of my studies and continues to provide her kindness and support in any matter I bring to her. I would also like to acknowledge the other members of my supervisory team, Professor Kirsten Spann, Associate Professor Daniela Loessner and
Professor Flavia Huygens, for providing their multidisciplinary expertise to my research topic.
My thanks also go to Dr Liesel Stassen, Dr Narayan Gyawali, Dr Omezie Ekwudu, Randika
Badal Yapa, Deborah Phillips and Jessica La for their involvement in my research. It would be remiss of me not to recognise the wider support offered by my friends and colleagues in the
Institute for Health and Biomedical Innovation at QUT.
Finally, I wish to acknowledge sources of funding for this research: the IHBI Student
Allocation, the School of Biomedical Sciences Student Support Fund and an IHBI Mid-Career
Researcher Funding Scheme grant awarded to Francesca D. Frentiu.
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Chapter 1: Introduction
1.1 Background
Zika virus (ZIKV) has caused public health alarm in recent years because of its ability to cause neural defects in human neonates, most notably microcephaly. ZIKV is an enveloped, positive- sense ssRNA virus of ~11Kb in length, which belongs to the family Flaviviridae and genus
Flavivirus [1]. The genus includes other significant human pathogens, such as the Japanese encephalitis, West Nile (WNV), dengue (DENV) and yellow fever viruses. The principal vectors of ZIKV are mosquito species of the Aedes (Stegomyia) genus [2-4]. ZIKV infection in adults is largely asymptomatic, with estimates of silent infections ranging from 50-80% of cases [5-7]. Where symptoms occur, ZIKV is observed as a self-limiting febrile illness which may be accompanied by arthralgia or a maculopapular rash of the chest, trunk and limbs [8].
At the time of writing, at least 84 countries have reported local circulation of ZIKV [9].
Recent evidence of the full extent of ZIKV pathologies and teratogenic effects in humans has led to an urgency for research on this virus. ZIKV, unlike other arboviruses, can be sexually transmitted, primarily by males to females [10]. The majority of cases of sexual transmission occur in asymptomatic individuals [11,12] and duration between symptomatic onset and transmission is estimated to be in the range of 32-41 days [13]. Currently, 14 countries in non-endemic regions have reported direct person to person transmission of the virus [14]. Incursions of ZIKV, through pan-continental travel, into countries otherwise free of vectors known to transmit the virus have also been reported [12,15]. Facilitating sexual transmission is the ability of ZIKV to persist in reservoirs of the male reproductive tract, such as the prostate gland. Reports of ZIKV presence in semen from vasectomised men have indicated that the prostate is an important but poorly understood reservoir for the virus.
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1.2 Significance, Scope and Definitions
Given the capacity of ZIKV to persist in the male reproductive tract and the potentially devastating consequences of sexual transmission to pregnant women, it is critical to understand the mechanisms facilitating persistence. Using a cell line model, we explore the role of the prostate as a potentially significant reservoir of persistent ZIKV infection. The knowledge gained here may help design better vaccines and/or antivirals to treat ZIKV infection in men.
1.3 Hypothesis and Aims
It is hypothesised that prostate cells can harbour persistent ZIKV infection and that such persistence is facilitated through alteration of gene expression in host cells. This hypothesis will be addressed through the following aims:
Aim 1: To establish a non-malignant, human prostate cell line model of persistent ZIKV infection;
Aim 2: To characterise the transcriptomic response of cells persistently infected with ZIKV, using RNA-Seq.
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Chapter 2: Literature Review
2.1 Chronology of Zika virus emergence
ZIKV was first discovered in 1947 from a feverish Rhesus Macaque identified during sentinel screening for yellow fever in the Zika forest of Uganda. Shortly after, in 1948, the virus was isolated from Aedes (Stegomyia) africanus mosquitoes, providing the first indication that ZIKV was an arbovirus [16]. Human infection with ZIKV was not reported until 1952, where it was determined that human sera screened for yellow fever antibodies also displayed ZIKV neutralising antibodies [17,18]. The first cases of Zika fever (ZF) were reported in 1954 when three symptomatic Nigerian patients were identified [19]. A clear understanding of clinical symptoms did not develop until 1956 when ZF was observed as a self-limiting febrile illness marked by rash, moderate fever and headache [20]. For more than six decades following discovery ZIKV incidence in humans was minor, with only 14 reported cases [6]. During this time, ZIKV reached Asia and was circulating there in 1966 when it was isolated from
Malaysian populations of Aedes aegypti. Serological surveys of Indonesia in the 1980’s indicated that ZIKV continued to circulate in Asia, with low prevalence without outbreaks of
ZF [3].
The potential of ZIKV as a human pathogen was largely neglected until the first mass outbreak occurred in 2007 on the Micronesian island of Yap [6,21]. During the Yap epidemic,
185 cases of rash, arthralgia and conjunctivitis were recorded. A further 900 infections were estimated to have remained unreported, due to subclinical or asymptomatic manifestations [6].
Overall, the Yap outbreak was estimated to have affected ~73% of the island’s population. A phylogenetic analysis determined the Yap outbreak was caused by ZIKV belonging to the
Asian lineage of the virus, estimated to have diverged from African strains in the 1960’s [22].
A strain from the Asian lineage was again the culprit of another epidemic in 2013 across the
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French Polynesian islands, when 8,510 suspected infections were reported [23] and leading to an estimated 32,000 total infections or ~11.5% of the population [24].
A notable number of neurological complications arising from infection were also reported during the French Polynesian outbreak, including the first instances of ZIKV- associated Guillain–Barré syndrome (GBS) [25,26], an autoimmune condition characterised by destruction of nerve myelination. Perinatal transmission was also observed in two pregnant women during this outbreak [27]. The direct mechanism of transmission was not identified, although intrauterine and infected breast milk were proposed as likely pathways [27]. An investigation from the outbreak in French Polynesia also demonstrated some cases of extended viruria following ZIKV infection [28,29]. Urine obtained from these cases did not contain recoverable infectious virus, however.
A third ZIKV epidemic occurred from 2015 to 2016 in Brazil, at the greatest scale seen to date [30], which then spread to the rest of America. It is estimated that 1.5 million infections have occurred in Brazil alone [31]. During the event, at least 1,708 cases of GBS were also reported, as were an unprecedented 6,776 cases of microcephaly, of which 208 were fatal [32].
These coinciding rates of GBS and microcephaly represented 19% and 20-fold increases, respectively, compared to previous annual reporting [31-33]. On March 1, 2016, the World
Health Organisation (WHO) announced a public health emergency of international concern
(PHEIC) declaration in response to the spread of ZIKV [34]. Although the PHEIC ended in
November 2016, the WHO has urged sustained long-term research and vigilant public health responses to combat the virus.
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2.2 ZIKV classification and virion structure
ZIKV belongs to the Flaviviridae, a family containing positive-sense, single stranded RNA viruses. The family includes more than 70 known human pathogens [35]. ZIKV clusters in the flavivirus genus, which is broadly divided into vector-borne and non-vectored viruses. The vectored group of viruses can be further divided into mosquito and tick-borne clusters [36].
ZIKV is phylogenetically most closely related to Spondweni, a mosquito-borne flavivirus found in Africa, and Dengue (DENV) viruses [37]. Due to shared geographic range and serological IgM cross-reactivity of ZIKV with DENV, mis-reporting of true ZIKV cases as
DENV cases has likely occurred since its first discovery [38,39].
The ZIKV genome is approximately 11Kb in length and codes for seven non-structural proteins [40] and three structural, the Capsid (C) pre-membrane (prM/M) and Envelope (E) proteins (Figure 2.1). Mature virions are ~ 50nm in diameter and have an icosahedral shell of
180 E-proteins anchored to a lipid membrane by M proteins surrounding the capsid [41].
Figure 2.1. ZIKV genome structure [42].
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The E protein is essential for the initial binding of virus to the host cell, endocytosis and genome uncoating (Figure 2.2). It contains four domains, three ecto-domains and one trans- membrane. The extracellular domains include the DII dimerisation interface and fusion loop as well as the DIII continuous peptide segment involved in receptor binding. E proteins in a pre-infection state are arranged as dimers covering the entire surface in a “herringbone” formation. Exposure to the endosome environment results in the dissociation of E protein dimerization and the subsequent exposure of their fusion loops (Figure 2.2). The C protein is essential for the packaging and assembly of viral RNA into the nucleocapsid [43]. The specific interactions occurring during assembly are unknown however the positively charged “bottom layer” of the C protein existing as a hexameric arrangement of three dimers, readily binds RNA and is essential for migration into the luminal endoplasmic reticulum for processing of immature virions (Figure 2.2) [43]. It is suspected that C protein hexameric structures are also able to bind and identify other species of nucleic acid including dsRNA and DNA. This has been observed in other flaviviruses and may facilitate subversion of host responses such as
RNA silencing [43]. In an immature state a precursor-membrane (prM) protein exists in place of an M-protein. It is hypothesised that while in a prM state, the protein is required for E- protein folding [44]. The furin-like protease cleaves the prM protein into the mature M protein which conveys the primary function of anchorage of E-proteins to the lipid membrane and pore formation during host membrane fusion [37].
Direct interactions between host and viral proteins are required for replication through the ZIKV lifecycle (Figure 2.2). Endocytosis is mediated through host cell receptor targets and following cytoplasmic compartmentalisation, an acidic environment triggers envelope protein dissociation and conformational alteration. The E-protein fusion loops initially folded parallel to the virion surface, are extended to form trimers bound which bind to the host membrane of the vesicle. Fusion of the viral and host membrane then allows for the intracellular release of
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the viral genome from the capsid. The genome is then translated as a single polyprotein and replicated by the endoplasmic reticulum before it relocates to the endoplasmic reticulum where viral and co-opted host proteases cleave the polyprotein into the ten distinct viral proteins.
Assembly of structural viral proteins also occurs in the endoplasmic reticulum with further matured within the Golgi body. Via this replication both infectious particles and non-infectious sub-viral particles lacking the capsid or genome are created and released through endocytosis.
Figure 2.2. Schematic of flavivirus replication cycle (A) and membrane fusion (B) [37].
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The non-structural proteins serve many purposes including replication, packaging and immune evasion. The NS1 protein is necessary for viral RNA replication and is involved in direct and antibody‐mediated attack onto target cells [45,46]. The NS2A protein binds the 3’
UTR during replication, processing and secretion of mature virions, but it also modulates host antiviral IFN responses [47]. NS2B, in conjunction with host proteases, processes the viral polyprotein during the replication cycle [48,49]. Similarly, the NS3 protein has protease activity, cleaving the polyprotein but further carries helicase activity to unwind RNA and links to NS2B to provide NTPase/RTPase genome capping [50]. NS4A/B proteins are known to inhibit the intracellular Akt-m TOR signalling pathway which is involved in regulation of the cell cycle, proliferation and quiescence [51]. Finally, the NS5 protein is a RNA-dependent
RNA polymerase providing 5’ capping of the viral genome and further supressing IFN signalling [45].
2.3 Clinical manifestations and sequelae
Asymptomatic infection with ZIKV is reported to occur in 50-80% of cases [5-7]. Where symptoms are present, they typically persist for less than one week and are largely similar to those following infection with arboviruses such as DENV and chikungunya, albeit more subdued by comparison [52]. Early epidemiologic surveillance and human challenge studies reported that ZIKV presents symptomatically as a “dengue-like illness” characterised by mild fever, muscle aches, arthritis, arthralgia, eye pain, conjunctivitis, prostration and maculopapular rash [53,54]. The similarity of symptoms to other major pathogens likely contributed to the low reported prevalence of ZIKV prior to its recent emergence. Traditional diagnostic assays have suffered from serological cross-reactivity of ZIKV with DENV and, in
Africa, the more closely related Spondweni virus [37]. This cross-reactivity has been observed in IgM antibody assays and has resulted in erroneous misidentification as DENV [21].
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Infection with contemporary strains of ZIKV can result in novel and serious neuro- degenerative sequelae: Congenital Zika Syndrome (CZS) in neonates and Guillain-Barre
Syndrome in adults [25]. Susceptibility of developing embryonic neurological tissues to ZIKV infection can lead to serious neurological damage, of which microcephaly has been the most alarming form. Microcephaly arises during prenatal development, is distinguished by an abnormal smallness in proportions of the skull, and is often observed alongside additional neurological abnormalities [55]. The first confirmed cases of ZIKV causing microcephaly were retrospectively characterised following reports made by the Brazilian Ministry of Health detailing over ten thousand such afflicted infants between November 2015 and December 2016
[32]. Later investigation of a 20-fold increase in the prevalence of microcephaly reported in
Brazil and Colombia during the 2014-2015 period was also linked to coinciding outbreaks of
ZIKV in these countries [31,33]. Further analysis established that ZIKV infection in the first trimester of pregnancy is a critical point of exposure for the onset of neurological malformations, following vertical transmission of the virus from mother to the developing fetus
[56]. Furthermore, maternal infection and subsequent vertical transmission can cause fetal demise [57,58]. Murine models have demonstrated a capacity for ZIKV to invade placental tissue and subsequently infect cortical progenitor cells, giving rise to CZS [59-61].
Documented cases of vertical ZIKV transmission in humans detail the presence of ZIKV RNA,
IgM and IgG antibodies in maternal sera during gestation followed by observations of fetal cranial malformations, cerebral tissue atrophy and the presence of virus in fetal brain tissue and umbilical cord [62,63].
In adults, neurological symptoms also occur, most seriously in the form of GBS [5], a virally initiated autoimmune response directed against myelinated nerves of the peripheral nervous system. The resultant degradation of the myelin sheath and underlying nerve axon can result in varying degrees of paralysis, muscle weakness and loss of sensation.
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While GBS is known to occur following infection with several different viruses, including
DENV and chikungunya [64-66], it was not associated with ZIKV until the outbreak in French
Polynesia [67]. Meningitis, meningoencephalitis and myelitis are also potential consequences of infection and were described during the French Polynesian Outbreak. In the seven decades since the first isolation of ZIKV, significant differences have been observed between early isolates from Africa and contemporary Asian lineage strains associated with recent epidemic events. The ability of the virus to affect neurological tissues has been known from those early days [56,62,65,68]. Testing of early isolates in murine models demonstrated the virus was capable of causing neurological damage [16,17]. While early passages of the virus were less pathogenic, later passages displayed high neurotropism, paralysis and fatality resulting from intracerebral inoculation [17]. Neurotropism was also observed in a few subsequent rabbit,
Guinea pig and monkey studies [17]. Juvenile mice were also observed to be more susceptible to cerebral infection than adult counterparts.
2.4 Mosquito vectors of ZIKV
The virus is predominantly transmitted to vertebrate hosts by mosquito vectors. Aedes mosquitoes are well established as the predominant vectors of ZIKV, with the virus detected from a number of different species during various ecological surveys. In Africa, the major vector of ZIKV is suspected to be A. africanus [69]. This mosquito readily bites humans and many non-human hosts but most notably primates. Populations of A. vittatus taken from rural areas of Senegal were reported to have replicative ZIKV, and given their propensity for human and animal biting, are now implicated in the circulation of ZIKV in those areas [69]. Outside of Africa, A. aegypti is the principal human vector and harboured the first known instances of
ZIKV in Asia [3,70]. A. albopictus has a wide and still expanding geographical distribution, making it a potent vector species and second only to A. aegypti in terms of the perceived threat
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of human ZIKV infection [71,72]. It is also opportunistic in its feeding, readily taking meals of numerous warm and cold-blooded hosts, though humans remain an important target [73].
The species is a known vector of over 20 arboviruses [74]. Following the 2015-2016 epidemic,
2.6 billion people were estimated to be at risk of contracting ZIKV [75], based on the geographic distribution of the major vectors A. aegypti and A. albopictus. Of note, A. hensilli is believed to have been central to the 2007 Yap epidemic, based on abundance estimated via entomological survey [6]. In vivo laboratory infections of A. hensilli also demonstrated high infection and dissemination of ZIKV [76].
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2.5 Sexual transmission of ZIKV
Unusually for an arbovirus, ZIKV is also sexually transmissible [12,15,77-79]. This additional mode of transmission could potentially increase epidemic duration and size [80,81]. No other known arbovirus to date is sexually transmitted, making predictive modelling of ZIKV outbreak dynamics a challenge [72]. In 2008, the first report of sexual transmission of ZIKV occurred after a viremic person travelled from Senegal to the United States [15]. Sexual transmission is thought to have played a significant role in the 2015-2016 Brazilian epidemic, as women of reproductive age were more likely to be infected than male counterparts [79].
Models of ZIKV accounting for both sexual and vectored transmission typically conclude that sexual transmission can maintain ZIKV reservoirs to facilitate the acquisition of virus by mosquitoes [82]. At least 14 countries have reported accounts of sexual transmission, whereby infection of men returning from endemic areas has led to the infection of partners in non- endemic areas [[78,83,84]. Asymptomatic, subclinical and indicative infections have all been observed from cases of sexual transmission [11,12]. ZIKV RNA in semen has persisted in many cases long after convalescence and virus clearance from blood serum [85]. One report has shown detectable concentrations of virus in semen for as long as 188 days post symptomatic onset and following viral clearance from sera. A recent cohort study of 150 human participants
[86] determined that ZIKV RNA was detectable on average until 14, 8 and 34 days post onset of symptoms for sera, urine and semen respectively. Four percent of male participants reported positive for ZIKV infection from semen samples at 125 days following symptomatic onset.
Not only does persistent infection of the male reproductive tract strongly increase the risk of sexual transmission of ZIKV, but it is also problematic for diagnostics based on detection of viremia that solely utilise blood serum. While ZIKV RNA can be detected at a higher concentration for longer in urine than in blood [87], semen analysis of men residing in
ZIKV-infected areas may be the most useful detection method for persistent infection. Lending
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evidence to this, ZIKV has been isolated from the semen of asymptomatic blood donors who were infected during a 2016 outbreak in Puerto Rico [88]. The ability to infect urogenital and reproductive tissues through sexual contact with infectious fluids is present to some extent in many arboviruses [89,90] but does not describe true sexual transmission. Adventitious infection may occur through micro-abrasions or viruria during unprotected sexual activities.
However, sexual transmission of ZIKV is distinct from this in that it can occur without viremia, virurea or any other apparent systemic infection, requiring only that virus is localised to the reproductive tissues of an infected person. ZIKV is the only arbovirus capable of true sexual transmission in humans, with only Hepatitis C (not an arbovirus) within the Flaviviridae family sharing this mode of infection. Spondweni virus is the closest relative of ZIKV, clustering in the same serogroup and with less than 20% variation in E protein sequence between them [37].
Surprisingly, it shows negligible shedding in semen in murine models [91] suggesting ZIKV has acquired a sexual mode of transmission independently. The evolutionary changes that have allowed for tropism in the male reproductive tract and sexual transmission remain to be elucidated.
2.6 The prostate gland as a viral reservoir
ZIKV RNA and infectious virus have been detected from, and transmitted through sexual contact with infected vasectomised men [84,92]. Therefore, the prostate is a likely candidate as a ZIKV virus reservoir due to its secretory capacity being maintained following vasectomy.
It is now well recognised that ZIKV can maintain persistent infection in a number of reproductive tissues, including testes [93]. Given that the prostate contributes the majority of the volume of total ejaculate, the prostate may play a key role in persistent ZIKV infection. A few studies of ZIKV’s propensity to infect prostate cells have used murine and macaque models
[94-98]. Some murine models showed the presence of prostate infection, but not all [14]. The
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Rhys Izuagbe n8535761 relevance of murine models for studying prostate infections is unclear because the majority of models of infection to date are immunosuppressed and ZIKV infection is quickly cleared in wildtype mice. In macaques, the virus has been detectable 28 days after infection in the prostate
[99], but molecular details of infection in this model have not been resolved. In a study investigating the potential for ZIKV to infect the human prostate [100], prostate mesenchymal stem cells (MSCs), lymph node carcinoma of the prostate (LNCaP) cells and organoids comprised of both, were infected with three clinical isolates of ZIKV. The virus remained detectable up to 14 days post-infection (dpi), and most notably, MSCs were found to maintain highly stable concentrations of ZIKV RNA. A survey of cell types susceptible to ZIKV also identified LNCaPs as able to be infected without any cytopathic effects [101]. Although these studies indicate prostate cells are susceptible to ZIKV, neither explicitly considered persistent infection within their experimental design or offered insights into the mechanistic basis of persistence.
2.7 Innate antiviral responses during persistent infection
RNA virus persistence is defined as the persistence of infectious virus and/or virus products after the acute phase of infection has passed [102]. To establish persistent infection, viruses must develop mechanisms to avoid being eliminated by the host cell’s immune system. This may be achieved through either suppression of apoptosis of infected cells and/or dampening down of the innate antiviral response, for example by circumventing the interferon (IFN) response [103]. This thesis will focus on the innate immune response as it is the first barrier within the host cells to combat virus replication and transmission.
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2.7.1 Interferon
Antiviral defence cytokines are important in the innate immune response, in particular IFNs.
IFNs comprise a family of small signalling molecules that can trigger transcriptional changes which impede viral replication through mRNA degradation, apoptotic signalling and immune cell recruitment. In some circumstances, IFNs can also be enacted following intracellular bacterial infections or in cancer cells, but they are considered primarily as the first line of defence against viruses. IFNs are classified as Type I (α & ß), Type II (γ) or Type III (λ) based upon functional characteristics of the proteins they stimulate and the type of cells that produce them. Type I IFN can be produced by the majority of cells in response to viral infection and convey resistance in surrounding cells while signalling the occurrence to immune cells through upregulation of a repertoire of interferon stimulated genes (ISGs). Type I signalling will often have the net effect of sensitising the infected host cell to apoptosis, [104,105]. Type II IFN is only produced by natural killer (NK) cell and T lymphocytes, in addition to select lymphoid cells. Its primary action is to recruit immune action against infected cells through the expression of Class II MHC molecules. Type II IFN also alters transcription of some genes to establish immune-regulatory cellular responses. Type III IFN plays a role in the innate anti-viral response much like type I, however it is enacted through a distinct pathway utilising a separate receptor complex. Expression of associated type III IFN receptors is more specific than those binding type I, enabling further compartmentalisation of specific IFN activity. Type III elicits a synonymous antiviral response with that of type I, with common ISGs and transcriptional mediations. Monocytes and dendritic cells (DC) are recruited to mediate both innate and adaptive immune activity through the secretion of signal factors including IFN. Given that
ZIKV is known to infect monocytes and differentiated DCs readily, usurpation of IFN response is implicated in facilitating infection and has been subject to several investigations [106-110].
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The receptors IFNAR1/2 (Type I IFN) and IFNGR1/2 (Type II IFN) recognise autocrine and paracrine IFNs and initiate JAK-STAT signalling with the STAT1/STAT2 dimer complex serving as a stimulator of antiviral genes when bound to promoter regions of host genes. JAK-STAT signalling has been identified as a common point of exploitation amongst several viruses, including many Flaviviruses. The existence of antagonising non-structural viral proteins (NSP) which degrade signalling has been demonstrated in ZIKV and other flaviviruses, such as DENV, YFV and WNV [111-113]. These NSPs effectively arrest the antiviral effects of Type I IFN, circumventing innate immune responses [109,114,115] (Figure
2.3). The ZIKV NS5 non-structural protein antagonises human STAT2, dampening IFN Type
I signal cascade to impede antiviral responses [109]. DENV NS5 also reduces STAT2 signalling, however, requires the UBR4 ligase to do so; an obligate ligase of ZIKV antagonism of STAT2 is unknown [109] (Figure 2.3).
Figure 2.3. Subversion of IFN signalling by flaviviruses [109]. ZIKV and DENV are able to impede downstream IFN induced genes through inhibition of the STAT1-STAT2 heterodimer complex. The mechanism by which ZIKV achieves this is uncertain however the viral NS5 protein is known to bind and stimulate degradation of STAT2 transcriptional activator.
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2.7.2 Toll-like receptors
Toll-like receptors (TLRs) serve as an integral part of innate immunity, acting against a wide range of pathogenic organisms including bacteria, fungi and viruses, and induce antimicrobial and pro-inflammatory conditions including downstream IFN pathways. A total of 10 human
TLRs have been identified, at least four of which have the ability to recognise viral nucleic acid motifs and initiate combative responses (TLRs 3, 7, 8, 9). Further, TLRs 2 and 4 are able to detect some viral structural and non-structural proteins [116,117]. Many somatic cells express TLR3 including a large number of epithelial cells that are targeted by viruses, although dendritic cells and macrophages are the only immune cells capable of expressing TLR3. TLR3 is expressed natively in various specialised tissues including the lung, liver, heart, brain and within central nervous tissues, neurons, astrocytes and microglia cells. TLR s 3 and 7 are strong antiviral inducers against WNV and DENV [118-121].
2.7.3 RIG-like receptors
RIG-Like Receptors (RLRs) are cytosolic proteins implicated in antiviral defence through their recognition of viral RNA species. RLRs are expressed by the majority of human somatic cells and serve as repressors of the Caspase Recruitment Domain (CARD) which directs apoptosis via mitochondrial caspase release. Subsets of CARD proteins also display the ability to recognise viral motifs and utilise them as triggers of intrinsic apoptosis. RIG-1 recognises viral
5’-ppp-dsRNA, short dsRNA or 5’-ppp-ssRNA. In addition to repressing CARD, RIG-1 directs downstream partner of the mitochondrial antiviral signalling protein promoting production of
IRF3/7 IFN-I/III and NF-kappa B, an important regulator of DNA transcription during viral infection. RIG-1 is an important combatant against a number of human-infecting viruses including measles, Influenza A and HSV.
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2.7.4 Nod-like receptors
Nod-like receptors (NLRs) are pathogen associated molecular pattern receptors that serve a similar purpose to the TLRs albeit as cytoplasmic rather than transmembrane proteins. NLRs function in autophagy, signal de-transduction, transcription activation and inflammasome formation [122] which are necessary for resolution of WNV and DENV infections [123].
Increased expression of TLRs and NLRs was triggered by an Asian strain of ZIKV at six hours post-infection, while these responses were delayed by 24 hours following infection with an
African strain [124]. Subversion of innate immune responses by ZIKV may facilitate persistent replication in reproductive niches.
2.8 Hypothesis and Aims
The central hypothesis of this study is that ZIKV is able to persistently replicate in the prostate by dampening antiviral innate immune responses. This hypothesis will be tested through the following two aims:
Aim 1. To establish a non-malignant, human prostate cell line model of persistent ZIKV infection.
Aim 2. To characterise the transcriptomic response of cells persistently infected with ZIKV, using RNA-Seq.
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Chapter 3: Methods
3.1 Aim 1 Methodology and Experimental Design
3.1.1 Virus strains and cells
There are three distinct genetic lineages of ZIKV (West African, North African and Asian), all of which are capable of causing pathology in humans. In this study, strains of two of these lineages were utilised, representing older as well as more recent, clinically relevant, examples of ZIKV. Strain MR766 (Genbank ID AY632535.1), considered to be the prototypic ZIKV strain, is derived from the original 1947 isolation and belongs to the West African lineage.
Strains BeH815744 (Genbank ID KU365780.1) and H/PF/2013 (Genbank ID KJ776791) are isolates obtained from the 2015 Brazilian and 2013 French Polynesian outbreaks, respectively.
The two latter strains belong to the Asian lineage. Virus stocks were grown in the Aedes albopictus C6/36 cell line using RPMI1640 with a HEPES-modification (Sigma-Aldrich,
R5886), supplemented with 10% foetal bovine serum (FBS; Gibco) and 1% Glutamax
(ThermoFisher). Mosquito cells were incubated at 27°C. Vero (African green monkey kidney) cells were used to titer infectious virus. They were maintained in Dulbecco’s Modified Eagles
Media (DMEM (Sigma-Aldrich) with 10% FBS, 1% Glutamax, and incubated at 37°C under
5% CO2. The human epithelial prostate cell line RWPE-1 (ATCC CRL-11600) [125] was maintained in Keratinocyte serum-free medium (KSFM) including bovine pituitary extract and epithelial growth factor 1-53 (ThermoFisher) and supplemented with 1% penicillin- streptomycin and incubated at 37°C under 5% CO2.
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3.1.2 Infection of non-malignant human prostate cells
RWPE-1 cells were utilised for time course assays of ZIKV infection (Figure 3.1). Two experiments were conducted: firstly, RWPE-1 cells were infected at a multiplicity of infection
(MOI) of 1 with strains MR766, BeH815744 and H/PF/2013, and secondly, RWPE-1 cells were infected at a MOI of 10 with MR766 and BeH815744 (This experiment chronologically preceded and H/PF/2013 was omitted as the strain was not yet available). The second experiment at higher MOI was designed to test whether viral kinetics during persistent infection are consistent despite varying initial inocula. Under both experiments, virus inoculum was allowed to adsorb over 4 hours before aspiration, washing of monolayers in sterile 1xPBS and replacement with maintenance media.
Over the course of 30 days and at 3-day intervals, supernatants and cells were harvested for further characterisation of viral replication kinetics. For the MOI-10 experiment, a 36-day time-course with 3-day intervals conducted until 24 days and 6-day intervals thereafter. All supernatants and cell harvests were aliquoted and frozen at -80°C prior to further assessment
(Figure 3.1). Medium was refreshed every 3 days for remaining cells (Figure 3.1, Time point
2). To assess production of infectious viral particles, plaque assays were performed from collected cell supernatants, detailed as per the methods below (see Chapter 3.1.4). Plaque assays were performed in duplicates per sample. Running simultaneously to the viral kinetics assay and using the same time points, an assessment of cell viability in response to infection was conducted (Figure 3.1).
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Time‐point 1
Time‐point 2
Media replacement and return to incubation
1. Supernatant collection Plaque titration
2. Cell viability assay
3. Cell collection RNA extraction Quantitative RT‐PCR
Figure 3.1 Design of time-course of infection experiments. Supernatants and cells were collected every 3 days from each of three independent wells (A), over a 30-day time course (MOI-1 experiment) or every 3 days till 24 dpi and every 6 days thereafter until 36 dpi (MOI-10 experiment). For wells where cells were not collected (B), only media replacement was performed. For example, wells that were sampled at 6 dpi only had media replaced at 3 dpi.
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3.1.3 Plaque assays to titre infectious virus
Vero cells seeded in 24-well culture plates and at 80-90% confluency (~1.6-1.8x105 cells/well) were infected with ZIKV in serum-free medium. The inoculum was allowed to adsorb for 2 hours post-infection before immobilization with a carboxymethyl cellulose (CMC) overlay
(Sigma-Aldrich, C5013). High-viscosity CMC (2% w/v) was dissolved in ddH2O and mixed
1:1 with double strength M199 medium (Sigma-Aldrich). Cells were then incubated for 4 days at 37°C and 5% CO2. At the end of the incubation period, cells were fixed and stained with
0.05% crystal violet formaldehyde solution and inspected for plaque formation. Total concentrations of infectious virus were then reported as plaque forming units /ml (pfu/ml).
3.1.4 Prostate cell viability following infection
To test if ZIKV infection leads to host cell mortality over the time course, viability assays were used to examine the metabolic health of cell populations. AlamarBlue® (Thermo Fisher) also known as resazurin, is a metabolic indicator which can be used to quantify the rate of reduction occurring in a given cellular environment. AlamarBlue® allows us to examine the impact of persistent infection better than endpoint assays, as it can be used on the same cell population over a time course without cell sacrifice or damage. A 4% AlamarBlue® solution made in
KSFM, supplemented with 1% penicillin/streptomycin, was prepared fresh and in parallel to time point sampling of supernatants. Uninfected control cells and ZIKV-infected cells were washed of existing medium and replaced with 4% AlamarBlue® solution, followed by a 4 h incubation at 37°C in a 5% CO2 atmosphere. As resazurin is UV sensitive, incubations were performed in darkness and shielded from direct UV sources. After incubation, 100µl of now colourised (reduced) supernatants were loaded into a 96-well clear bottom, black plate and fluorescence quantified using a plate reader (BMG Labtech- CLARIOStar, FLUOstar) with emission and excitation wavelengths at 544 and 590 nm respectively. Four replicate
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measurements were taken for each well and orbital averaging was used to average multiple measures of each well. Alongside cellular supernatants, non-reduced and completely reduced
4% AlamarBlue® were measured as controls, as per manufacturer’s instructions. Total reduction of AlamarBlue® solution was achieved by autoclaving at 121°C for 15 min as per manufacturer’s instructions.
In order to validate the utility of the AlamarBlue® cell viability assay, a lactate dehydrogenase (LDH) cytotoxicity assay was also performed at MOIs of 1 and 10 and at two time points post-infection. RWPE-1 and Vero cells were seeded into 96-well clear bottom, black plates at 1.25x104 cells per well, with six replicate wells per cell line and MOI combination (Figure 3.2). Growth medium for RWPE-1 cells was as above. Growth medium for Vero cells constituted of DMEM / 5% FBS / 1% GlutaMax. Cells were incubated at 37°C and 5% CO2 until they reached 90-100% confluence. Cells were then infected with the ZIKV strain H/PF2013, as appropriate, at MOIs of 1 and 10. Maintenance medium for Vero cells during infection was as above but supplemented with only 2% FBS. The inoculum was made up in relevant serum-free growth medium for each cell line, and plates were rocked every 30 min during infection. Following incubation, wells were washed with sterile 1xPBS and 150µl of relevant growth medium was added. Due to the potentially inhibitory effect of FBS on viral replication and downstream influence on LDH quantification, Vero cells were grown in low
FBS (2%). Plates were returned to incubation for 3 days, at which point the first plate was collected for CytoTox-ONE™ LDH assessment. The second plate was similarly collected at 6 dpi. The CytoTox-ONE™ Homogeneous Membrane Integrity Assay (Promega) was used to assay LDH release, following the manufacturer’s instructions. Measurement was performed using a ClarioStar fluorescence plate reader (BMG labtech) utilising orbital averaging.
Background fluorescence of each growth medium was normalized using the cell free control preparation which was subtracted from all other wells as appropriate. Results were expressed
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Transfer supernatant to new plate for LDH assay
Figure 3.2. Schematic of LDH cytotoxicity assay. Cells grown within a 96well plate were infected as appropriate with H/PF/2013 and incubated for 3 or 6 days. Six biological replicates of each treatment were sampled and prepared for LDH release assays.
3.1.5 Quantification of ZIKV genome copies
Extracellular viral RNA was extracted from time course supernatants (section 3.1.3 above) using the QIAmp viral RNA kit (Qiagen), following the manufacturer's protocol. Intracellular viral RNA was extracted using the RNeasy RNA extraction kit (Qiagen) and Trizol (Life
Technologies), following the manufacturer's protocol. The number of virus genome copies was analysed via one-step qRT-PCR utilising the Taqman FastVirus 1-Step Master Mix
(Thermo Scientific), on a Rotorgene 6000q platform (Qiagen). Absolute quantification of virus genome copies was achieved by comparing to control plasmid standards (Appendix 1) (pUc19-
RpS7-wsp-RpS17-ZIKV; synthesised by Genscript, New Jersey, USA) and containing a known copy number of a 225 bp sequence derived from the MR766 ZIKV strain (Genbank ID
AY632535.2). Detection of this sequence and experimental samples was achieved using the
ZIKV 835-Forward, 911C-Reverse primer set and 860 FAM-labelled probe targeted to the pre- membrane gene [38] (Appendix 2). In order to account for intracellular RNA amount, the number of virus copies was also normalised against a β-actin housekeeping gene (PrimeTime
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qPCR assay, ITD DNA). The ΔΔCT method of standardisation was used to determine ZIKV copies as normalised against housekeeping gene expression, provided that amplification efficiency fell within 10% variance.
3.1.6 Statistical analysis
Significant differences between experimental groups were determined using non-parametric and parametric t-tests, one-way and two-way ANOVA as appropriate. For all tests, a value of p<0.05 was considered to represent a statistically significant result.
3.2 Aim 2 Methodology and Experimental Design
3.2.1 Selection of samples, RNA extraction and RNA-Seq
In order to characterise the transcriptional changes arising from infection, cells infected with
H/PF/2013 (MOI-1) as described previously (section 3.1.3 above) and uninfected RWPE-1 mRNAs were sampled at 6 and 21 dpi. These time points and ZIKV strain were selected as being representative of a persistent infection based upon demonstrated viral kinetics and infectivity over the 30-day time course (see Chapter 4). Three biological replicates were used per time point and either ZIKV infection or control (mock infection) combination. Intracellular
RNA samples were generated from cell suspensions with extractions performed using the
RNeasy RNA isolation kit (Qiagen) and relevant manufacturer protocols. Eluted RNA was then
DNAse treated on column using the washing digestion and elution protocols of the Reliaprep
RNA Cell system (Promega). Final elutions were made in 30 µl nuclease-free water and RNA stored at -80°C. RNA quality and integrity were assessed using an Agilent 1200 Bioanalyzer and samples passing QC (RIN scores ≥ 7.40) were used for sequencing (see Appendix 3 for
RIN report). Samples were sequenced at the Australian Genome Research Facility (AGRF) on an Illumina HiSeq, with resultant reads compiled using the bcl2fastq 2.20.0.422 pipeline.
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3.2.2 Transcriptome Analysis
Sequence data was returned in the form of Fastq.gz files which were grouped as replicates according to treatment. Bioinformatic analysis was conducted using the Galaxy software package (Version 0.36.4) [126] with Differentially Expressed Gene (DEG) analysis performed
(Figure 3.3). Reads were screened for quality using the FastQC tool before grooming to remove poor quality reads using the FastQ Groomer tool. Illumina sequence adapters were then removed and poor quality read ends were trimmed using the Trimmomatic tool [127].
Following QC and trimming, reads were assembled against an annotated human reference genome (hg38, http://genome.ucsc.edu). Alignment, assembly and expression analysis were performed using the Tuxedo Suite (Bowtie2, TopHat, Cufflinks, Cuffdiff). TopHat (including
BowTie2) [128] was used to map and annotate human sequences while simultaneously excluding reads that contained sequences which were unalignable to a human reference.
Outputs of replicate assemblies were merged as single BAM files (containing averaged gene/sequence counts according to assemblies) for DEG analysis. Merged BAM files of infected vs uninfected conditions within time-points were submitted to the Cuffdiff tool [129] alongside the human hg38 reference genome used to annotate sequence data and a false discovery rate of 0.05 was applied as a corrective cut-off for test statistics (Benjamini-
Hochberg method). Output of the Cuffdiff DEG analysis provided tabular data of differential gene counts between infected and uninfected cells according to FPMK (fragments per kilobase of exon per million fragments mapped) determination. A gene with FPKM >0 was considered as expressed and significant differential expression concluded if the p value of the test statistic was greater than that of the false discovery rate following correction.
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Figure 3. 3. Galaxy Differentially Expressed Gene (DEG) bioinformatics workflow.
27 A Prostate cell line model of Zika virus infection Gene Ontology Enrichment analysis was also performed using statistically significant DEGs applied to determine biological pathways being enriched as a result of infection. The analysis was conducted using the Panther classification system (pantherdb.org) and only enriched pathways with false discovery rates <0.05 were assessed. Differential expression of each gene was then modelled and described as a percentage change against control sample expression and visualised through KEGG plotting (www.genome.jp), providing maps of altered biological processes.
3.2.3 Validation of differentially transcribed genes using qRT-PCR
In order to validate results obtained from RNA-Seq, two genes highly dysregulated at 6 dpi
(see Chapter 4.2.4) were selected for qRT-PCR analysis (Table 1). RNA from infected and uninfected cells at 6 dpi and 21 dpi, three biological replicates per combination and two technical replicates per sample, were utilized for qRT-PCR. These samples were derived from the same source as those in the RNA-Seq analysis above but prior to DNAase treatment (see
Chapter 3.2.1). Relative expression RT-qPCR was performed, using samples standardised to
40ng total RNA/ reaction and normalised to expression of a housekeeping genome analysed in parallel. Each validating primer set (Table 1) was prepared with the 2x GoTaq Green Master
Mix (Promega) and the 50x GoScript Reverse Transcriptase (Promega). Reactions were run and analysed on a Rotorgene 6000q platform and associated proprietary software.
Quantification of RNA was expressed as a relative change in gene expression arising from
ZIKV infection. Results obtained from RNA-Seq results were considered validated if RT- qPCR results were observed with a direction of difference in expression consistent with DEG data.
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Table 1. RNA-Seq DEG validation primer sets.
Gene and Reference Primers 5' – 3'
IFI44L [130] FW: TGC AGA GAG GAT GAG AAT ATC
RV: ACT AAA GTG GAT GAT TGC AG
XAF1 [131] FW: AGC AGG TTG GGT GTA CGA TG
RV: CCT GGC ACT CAT TGG CCT TA
3.2.4 Statistical analysis
Significant differences between ZIKV infected and control cells were determined through utilisation of nonparametric and parametric t-test, one-way and two-way ANOVA as appropriate. For all tests, a value of p<0.05 was considered to represent a statistically significant result. Bioinformatic QC and DEG data was governed by default settings of statistical cut-off within the Tuxedo Suite toolset. DEG specifically was strictly assessed with false discovery rates of below 0.05 (Cuffdiff default cutoff) [129] and bias correction using a reference genome.
3.3 Ethics
Experiments with human cell lines were performed under QUT approval number 1700000243.
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Chapter 4: Results 4.1 Aim 1 Results
4.1.1 Extracellular ZIKV RNA production in RWPE-1 cells following infection at low
MOI
We observed persistent production of ZIKV RNA from RWPE-1 cells over the course of 30 days following initial infection at MOI 1, for all strains of virus tested (Figure 4.1). The highest levels of RNA were observed for the French Polynesian strain H/PF/2013, with a peak value of 3.23x108 genome copies/ml at 6 dpi. Viral RNA was maintained for this strain at levels above 3.74x105 genome copies/ml throughout the 30-day time course. Following detection of peak virus, H/PF/2013 was maintained above 1.9x107 genome copies/ml. For the African strain MR766, a peak of 2.02x106 genome copies/ml was detected at 3 dpi. From 6 to 15 dpi, virus concentrations were maintained at their lowest levels, between 2.42x103 and 2.64x103 genome copies/ml, respectively (Figure 4.1). Extracellular virus amounts significantly increased by 18 dpi at 2.47x105 genome copies/ml and continued to increase until 27 dpi, with
1.12x106 genome copies/ml. A slight reduction was then observed at 30 dpi, to 3.45x105 genome copies/ml. The Brazilian epidemic strain BeH815744 produced consistently low amounts of virus at MOI-1 but was still detectable throughout the time course (Figure 4.1).
Extracellular BeH815744 was detected at 1.91x105 genome copies/ml at 3 dpi. The highest amount of virus was observed at 9 dpi at 4.51x105 genome copies/ml before a steady decline to plateau starting from 18 dpi at 3.35x103 genome copies/ml with a minimum concentration
27 dpi of 2.27x103 genome copies/ml.
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Figure 4.1. Comparison of extracellular viral RNA produced by three strains of ZIKV over 30 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 1. ZIKV RNA was detected using qRT-PCR. Copy numbers were calculated against a control plasmid. Means ±SD from three biological replicates are shown. Statistical significance between time points was analysed by One-way ANOVA.
4.1.2 Intracellular ZIKV RNA production in RWPE-1 cells following infection at low
MOI
Constant levels of intracellular RNA were also observed from RWPE-1 cells infected with three different ZIKV strains at low MOI (Figure 4.2). The H/PF/2013 strain showed consistently high levels throughout the experiment, consistently maintained above its 3 dpi concentration of 3.16x105 genome copies/ng of RNA. H/PF/2013 gradually increased and plateaued over time. Peak virus was observed on day 24 post-infection at 2.7x106 genome copies/ng RNA. The BeH815744 strain also displayed consistent levels of intracellular RNA albeit less than that of the H/PF/2013 strain (Figure 4.2). BeH815744 was maintained above
7.04x103 genome copies/ng RNA from an initial concentration at day 3 of 3.61x104 genome copies/ng RNA. Peak virus was observed at 15 dpi at 2.20x105 copies/ng RNA. MR766 of the
African ZIKV lineage was maintained above 1.67x104 copies/ng RNA, with an initial concentration of 2.16x105 copies/ng of RNA and peak concentration of 1.94x106 copies/ng.
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Figure 4.2. Comparison of intracellular viral RNA produced by three strains of ZIKV over 30 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 1. ZIKV RNA was detected using qRT-PCR. Copy numbers were calculated against a control plasmid. Means ±SD from three biological replicates are shown. Statistical significance between time points was analysed by One- way ANOVA.
4.1.3 Infectious ZIKV titers in RWPE-1 cells following infection at low MOI
We investigated the dynamics of infectious virus production by RWPE-1 cells over the time course of infection. Following collection from each time point, supernatants taken from
MR766, BeH815744 and H/PF/2013 infected RWPE-1 cells were tested for infectious virus using plaque forming assays on Vero cells. Dynamic kinetics were observed for all three examined strains, whereby multiphasic titres were described. The infectious titers observed were low, particularly for the epidemic strains of BeH815744 and H/PF/2013 (Figure 4.3).
MR766 displayed the highest titre of all three strains tested despite comparatively unremarkable levels of extracellular ZIKV RNA (Figure 4.1). MR766 peak infectious titre was seen at 27 dpi with 4x104pfu/ml. Of all the strains tested, BeH815744 displayed the lowest infectivity (Figure 4.3), as from 6 dpi time onwards infectious titers were below the threshold of detection for most time points sampled. This was not dissimilar to the low extracellular
RNA observations (Figure 4.1). Infectious virus production did briefly recover following day
12 and 18 dpi. H/PF/2013 showed low infectious titers despite the high extracellular viral RNA
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detected. The strain displayed fluctuations in the production of infectious particles ranging from 1.83x101-1.25x103 pfu/ml with peak infectivity at 18 dpi.
Limit of detection
Figure 4.3. Comparison of infectious titre produced by three strains of ZIKV over 30 days, following infection of RWPE-1 cells at MOI 1. Infectious titre is expressed as plaque forming units/ml. Inoculum of titration assays was derived from cell supernatants collected at 3-day intervals from 0 to 30 days post-infection. Means ±SD of three biological replicates are shown.
4.1.4 Extracellular ZIKV RNA production in RWPE-1 cells following infection at high
MOI
In infections using a higher inoculum (MOI 10), extracellular virus production was relatively low for MR766 and BeH815744 but remained detectable over the full 30-day time course
(Figure 4.4). Levels of extracellular ZIKV RNA were substantially lower than those observed in infections at the lower MOI (Figure 4.1). Peaks of extracellular virus were observed at 6 dpi for both strains, with MR766 expressed at 1.0x104 and BeH815744 at 2.96x104 genome copies/ml. Both strains displayed downward trends in RNA production until minimum values of 37.45 and 35.38 genome copies/ml respectively were reached at 24 dpi. Despite a dramatic reduction to near undetectable levels at day 24 post-infection, viral RNA recovered and continued to increase (Figure 4.4).
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Figure 4.4. Comparison of extracellular viral RNA production of two strains of ZIKV over 36 days, following infection of RWPE-1 non-malignant prostate epithelial cells at MOI 10. ZIKV RNA was detected using qRT-PCR. Copy numbers were calculated against a control plasmid. Means ±SD of four biological replicates are shown. Statistical significance between time points was analysed by One- way ANOVA.
4.1.5 Intracellular ZIKV RNA production in RWPE-1 cells following infection at high
MOI
For MR766 infected RWPE-1 cells, the highest concentrations of intracellular virus were observed at 3 dpi at 3.3x105 copies/ ng RNA (Figure 4.5). Dramatic reductions then followed at 9 dpi but were by contrast, stably expressed at 15 dpi. Minimal concentrations of virus were observed at cessation of the time course assay at 36 dpi. The initial concentration of MR766 far exceeded that of BeH815744 however from 9 dpi the higher concentrations of viral RNA were observed in BeH815744-infected cells which peaked at 9 dpi at 1.46x105 genome copies/ml (Figure 4.5).
BeH815744 intracellular concentration was underscored as a more consistent and persistent infection with an accumulation of virus peaking at 9 dpi. Far greater retention of intracellular infection was observed over remaining time points and final concentrations at 36 dpi were more than 15-fold greater than that of MR766.
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Figure 4.5. Comparison of intracellular viral RNA production for two strains of Zika over 36 days. MR766 and BeH815744 intracellular copy number/ng RNA after MOI-10 infection, informed by qRT-PCR detection. Absolute quantification of ZIKV was achieved through a standard curve of the Puc19-ZIKV control plasmid. Normalisation of disparate concentrations of RNA was achieved by using the -actin housekeeping gene which was stably expressed. Means of four biological replicates are presented, alongside ±SD. Statistical significance between time points was analysed by One-way ANOVA.
4.1.6 ZIKV titers following infection at high MOI 10
The highest titres following infection at MOI 10 were observed at 3 dpi across both examined strains MR766 and BeH815744, at 4.25x106 and 1.75x104 pfu/ml respectively (Figure 4.6).
This represents a far greater initial titre in comparison to infection at MOI-1 (Figure 4.3).
Significant reductions by 6 dpi were also observed and led to steadily plateauing concentrations of infectious virus. An 85.3% reduction in infectious virus was observed of MR766 between 3 and 6 dpi which was a smaller reduction than that of BeH815744 observed at 90%. Greater maintenance of infectious virus was seen of MR766. No infectious virus was detected after 27 days of MR766 infection or 21 days of BeH815744. Extension of sampling time points from
3 to 6 commencing from 24 dpi was observed to be insufficient to recover infectious extracellular virus.
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Limit of detection
Figure 4.6. Comparison of infectious titre produced by two strains of ZIKV over 36 days, following infection of RWPE-1 cells at MOI 10. Infectious titre is expressed as plaque forming units/ml. Inoculum of titration assays was derived from cell supernatants collected at 3 day intervals from 0 to 30 days post-infection. Means ±SD of four biological replicates are shown.
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4.1.7 RWPE-1 cell viability following ZIKV infection at low MOI
Following infection with MR766, BeH815744 and H/PF/2013 at MOI 1, no difference in viability between ZIKV infected and uninfected cells was observed at any time post-infection
(Figure 4.7), as detected with an AlamarBlue® assay. Furthermore, no difference arose between strain infections at any time-point.
Figure 4.7 Viability of RWPE-1 cells following infection at MOI 1 with three ZIKV strains. Viability was assessed using AlamarBlue® reagent, over the course of 30 days. Fluorescence Intensity (FI) was measured using a BMG Labtech CLARIOStar microplate reader at Emission λ:544 and Excitation λ:590. Three biological replicates were used per each time point and infection combination. Four FI readings were taken per each biological replicate.
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4.1.8 RWPE-1 cell viability following ZIKV infection at high MOI
Following infection with MR766 and BEH815744 at MOI 10, no difference in viability between ZIKV infected and uninfected cells was observed at any time post-infection (Figure
4.8). Infection with any one examined strain did not produce any difference in viability compared to any other strain.
Figure 4.8 Viability of RWPE-1 cells following infection at MOI 10 with two ZIKV strains. Viability was assessed using AlamarBlue® reagent, over the course of 30 days. Fluorescence Intensity (FI) was measured using a BMG Labtech CLARIOStar microplate reader at Emission λ:544 and Excitation λ:590. Four biological replicates were used per each time point and infection combination. Four FI readings were taken per each biological replicate.
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4.1.9 Cellular toxicity of ZIKV
Following 3 days of infection with H/PF/2013, the LDH assay indicated the absence of any significant cytotoxicity in RWPE-1 cells infected at either MOI-1 or MOI-10 (Figure 4.9).
Significant cytotoxicity was, however, observed in Vero cells with MOI-1 and MOI-10 infections producing a 9.65-fold and 13.11-fold increase in LDH release respectively. No significant difference between uninfected RWPE-1 and Vero cells was identified at 3 dpi.
Figure 4.9. Cytotoxicity measured at 3 dpi with ZIKV H/PF/2013 infection. Cytotoxicity was measured by Fluorescence Intensity (FI) derived from LDH release following infection of RWPE-1 and Vero cells with ZIKV H/PF/2013 at MOI-1 and 10. Six biological replicates are plotted with ±SD.
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At 6 dpi following infection with H/PF/2013, no significant cytotoxicity was observed in
RWPE-1 cells infected at either MOI-1 or MOI-10 (Figure 4.10). A significant decrease in extracellular LDH was however observed in Vero cells at both MOI-1 and MOI-10. Fold decreases of 3.31 and 5.26, respectively, were observed in MOI-1 and MOI-10 infections. No significant difference between uninfected RWPE-1 and Vero cells was determined at 6 dpi.
This pattern is likely due to significant death of Vero cells following ZIKV infection, such that very few cells are observed to be left alive in culture at 6 dpi. This explanation is also consistent with the significantly higher cytotoxicity (cell death) observed in MOI-10 infection at 3 dpi.
Figure 4.10 Cytotoxicity measured at 6 dpi with ZIKV H/PF/2013 infection. Cytotoxicity was measured by Fluorescence Intensity (FI) derived from LDH release following infection of RWPE-1 and Vero cells with ZIKV H/PF/2013 at MOI-1 and 10. Six biological replicates are plotted with ±SD.
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4.2 Aim 2 Results 4.2.1 Bioinformatic assembly
RNA-Seq data was submitted to Galaxy as .gz files and quality checking was performed using the FastQC toolset. Metrics of basic statistics such as base quality scores, sequence quality scores, GC content per sequence, per base N content, sequence overrepresentation and adapter content all met the required thresholds. All samples efficiently assembled to the Homo sapiens hg38 reference genome, with no less than 92.6% of reads per any one replicate being successfully mapped (Table 2).
Table 2. Mapping rates of sequence data obtained from each sample.
RNA sample % of input reads Total mapped sequences (millions)
Uninfected Day 6 Replicate 1 93.7% 23.98 Replicate 2 93.7% 24.37 Replicate 3 94.1% 26.09
ZIKV MOI‐1 Day 6 Replicate 1 93.7% 23.02 Replicate 2 93.8% 22.99 Replicate 3 93.9% 24.27
Uninfected Day 21 Replicate 1 93.2% 24.82 Replicate 2 93.5% 23.65 Replicate 3 92.9% 24.89
ZIKV MOI‐1 Day 21 Replicate 1 92.6% 24.09 Replicate 2 92.9% 24.03 Replicate 3 92.6% 24.40
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4.2.3 Differentially expressed genes following ZIKV infection
Statistical analyses were performed on means of the three biological replicates sequenced per each ZIKV infection (or mock-infection) and time point combination. A total of 60,539 independent Ensembl entries were assessed, with 38,044 expressed at 6 dpi and 38,400 expressed at 21 dpi. At 6 dpi, 51 genes were significantly differentially expressed between
ZIKV-infected versus control RWPE-1 cells. Three genes (TSPAN32, PANK4, RP11-166B2.1; see Apendix 2 for ENTREZ numbers) had their expression reduced to zero in ZIKV-infected cells versus controls. Ten genes were were expressed de novo (or at least above detectable levels using RNA-Seq) in ZIKV-infected cells sampled at 6 dpi versus controls. These included CTD-2521M24.5, AC005307.3, CORO7, RP4-641G12.3, NRIR, RUFY4, C10orf11,
PLVAP, ZBP1 and FAP (see Appendix 4 for ENTREZ numbers). Thirty-eight genes were significantly up- or downregulated by greater than 2-logfold, with nine of these differentially expressed by greater than 5-logfold (Figure 4.11). A number of other genes involved in antiviral response were also observed to be upregulated in RWPE-1 cells infected with ZIKV.
A large number of the genes upregulated at 6 dpi play key roles in attacking the virus replication cycle in the host cell. For example, HERC5, HERC6 and ISG15 code for host proteins involved in viral protein degradation [132, 133] and reduced viral uncoating upon entry into the host cell; IFITM1 prevents fusion between virus and host endosome membrane, stopping release of the virion into the cytosol; OASL, DDX58 and DDX60 are involved in viral RNA recognition;
OAS1 and OAS3 degrade viral RNA; and PARP14 which inhibits transcription and translation of viral RNA [132]. The significantly upregulated gene XAF1 (Figure 4.11) plays a role in regulating apoptosis in virus infection [134]. Other genes with important roles in cytokine and chemokine production following virus infection were also upregulated, including MX1, IFI44 and CCL2 [132].
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Figure 4.11. Significant differentially expressed genes at 6 dpi in RWPE-1 cells infected with ZIKV. Level of differential expression is shown as a log-fold change from uninfected cells. A total of 10 genes were newly expressed; FAP, ZBP1, PLVAP, C10orf11, RUFY4, NRIR, RP4-641G12.3, CORO7, AC005307.3, CTD-2521M24.5 and a further 3 genes were no longer expressed following infection; TSPAN32, PANK4, RP11-166B2.1.
Among the top five upregulated DEGs observed at day 6 post infection were the interferon- stimulated genes IFI44L, IFI6 and IFI44 (Table 3). Overall, the DEG results indicated a strong antiviral response at day 6 post infection of RWPE-1 cells following ZIKV infection.
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Table 3. Top 5 differentially expressed genes at 6 days post-infection
Gene Gene Name Ensembl ID Function
IFI44l Interferon inducible protein ENSG00000137959 Targeted effector of IFN antiviral 44L response
IFI6 Interferon inducible protein ENSG00000126709 Targeted effector of IFN antiviral 6 response
KLHDC7B Kelch domain‐containing ENSG00000130487 Role in ubiquitination, induced by protein 7B IFN‐Ύ, TNFα and IL‐4. Believed to play some role in viral replication.
IFI44 Interferon inducible protein ENSG00000137965 Targeted effector of IFN antiviral 44 response
XAF‐1 XIAP associated‐factor 1 ENSG00000132530 Negative regulation of anti‐caspase activity.
At 21 dpi, 35 genes were found to be upregulated in ZIKV-infected cells versus controls
(Appendix 5). One gene (RP11-141J13.3) was observed to be expressed solely in ZIKV- infected cells. Only nine genes were upregulated by greater than 2-logfold (Figure 4.12).
Among these, the analysis performed using the Galaxy pipeline did not detect any upregulation of antiviral response genes observed in the 6 dpi response. The genes observed to differ at 21 dpi are primarily involved in cell cycle functions. Three of the five most upregulated genes have also been associated with tumorigenesis and different types of cancers (Table 4). At 21 dpi, KLK12 and KLK14 were significantly upregulated 2.5 and 1.2-logfold respectively. These genes have been implicated previously in prostate pathologies such as cancer [135-138].
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Figure 4.12. Significant differentially expressed genes at 21dpi of RWPE-1 cells with ZIKV. Level of differential expression is shown as a log-fold change from uninfected. A single gene was silenced following infection; RP11-141J13.3.
Table 4. Top five DEGS at 21 dpi of RWPE-1 cells infected with ZIKV.
Gene Gene Name Ensembl ID Function
S100A8 S100 calcium binding ENSG00000143546 Cell cycle progression, immune protein A8 inflammatory response and neutrophil chemotaxis. Implicated in tumorigenesis and metastasis.
VWF Von Willebrand factor ENSG00000110799 Modified platelet adhesion factor and protein transporter for homeostasis within the blood.
SPRR3 Small proline‐rich ENSG00000163209 Keratinocyte development. Associated with protein 3 oesophageal cancer.
FADS6 Fatty acid desaturase 6 ENSG00000172782 Biosynthesis of polyunsaturated fatty acids. Stress‐induced, notably heat.
S100A7 S100 calcium binding ENSG00000143556 Cell cycle progression, differentiation and protein A7 immune modulation. Implicated in tumorigenesis and metastasis
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4.2.4 DEG validation by qRT-PCR
Differential expression of two genes was tested using RT-qPCR to validate the RNA-Seq results above. At 6 dpi, presence of IFI44L mRNA was observed to be 5-log2fold higher in cells infected with the ZIKV strain H/PF/2013 relative to controls (Figure 4.13). This was consistent with RNA-Seq DEG results which reported an 8-log2fold increase in IFI44L, following infection (Figure 4.11). Expression of XAF-1 mRNA was observed to be significantly higher by 3.8-log2fold in ZIKV H/PF/2013 infected cells at 6 dpi (Figure 4.13).
This was consistent with RNA-Seq results which reported an increase in XAF-1 transcripts resulting from infection (Figure 4.11). High standard error in replicate values was observed for
XAF-1.
Figure 4.13. Relative expression of IFI44L and XAF-1 in RWPE-1 cells following ZIKV infection at 6 dpi. Assayed using RT-qPCR of 3 biological and 2 technical replicates, normalised to the BACT housekeeping gene and plotted as change from uninfected cell expression with ±SD.
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Validation genes were also tested at 21 dpi and it was found that IFI44L was increased 2.3- log2fold following infection (figure 4.14). This represented a lesser enrichment of the gene compared to 6 dpi (figure 4.13) but DEG analysis via RNA-Seq at this later time-point, determined no significant dysregulation (Figure 4.12). A 1.9-log2fold increase in XAF-1 expression was observed using RT-qPCR on 21 dpi samples (figure 4.14), which was opposed to RNA-Seq results which reported no significant differential expression (Figure 4.12).
Figure 4.14. Relative expression of IFI44L and XAF-1 genes in RWPE-1 cells following ZIKV infection at 21 dpi. Assayed using RT-qPCR of 3 biological and 2 technical replicates, normalised to the BACT housekeeping gene and plotted as change from uninfected cell expression with ±SD.
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4.2.4 GO term enrichment classifications
GO analysis of statistically significant DEGs at 6 dpi identified a dominant classification of antiviral response (figure 4.13). Major terms highly enriched following infection were type-1
IFN signalling, negative regulation of viral genome replication, negative regulation of Type I
IFN production, defense response to virus, positive regulation of response to cytokine stimulus, cellular response to IFN-gamma and regulation of innate immune response (Table 5) (See
Appendix 7 for GO maps).
Table 5. Major Gene Ontology term results of DEG at 6 dpi.
GO biological process Genes in Genes in Fold P‐value* Process DEG Enrichment
Type I interferon signalling pathway 66 12 91.09 2.38E‐16
Negative regulation of viral genome replication 53 8 75.62 2.90E‐09
Negative regulation of type I interferon production 44 5 56.93 3.81E‐04
Defense response to virus 191 15 39.35 2.87E‐16
Positive regulation of response to cytokine stimulus 52 4 38.54 4.03E‐02
Cellular response to interferon‐gamma 161 6 18.67 8.13E‐03
Regulation of innate immune response 368 8 10.89 5.74E‐03
*Only statistically significant GO results are shown, as determined by Bonferroni correction for P <0.05.
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Figure 4.15 Enrichment of predominant biological processes at 6 dpi as determined from gene ontology assessment of DEGs.
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At 21 dpi, GO term analysis identified that strongly enriched pathways related to biological processes of cornification and neutrophil degranulation (figure 4.14). The cornification pathway was observed to be enriched 38-fold (Table 6). Keratinocyte differentiation towards terminal epidermal development was represented with several intermediary processes (Table
6). Intermediate processes of neutrophil degranulation related to adaptive immune recruitment and leukocyte exocytosis (Table 6) (see Appendix 7 for GO maps).
Table 6. Major Gene Ontology results of DEG at 21 dpi.
GO biological process Genes in Process Genes in DEG Fold Enrichment P‐ value*
Cornification 112 7 38.68 6.31E‐6
Neutrophil degranulation 483 8 10.25 7.60E‐3
*Only statistically significant GO results are shown as determined by Bonferroni correction for P <0.05.
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Figure 4.16. Enrichment of predominant biological processes arising at 21 dpi as determined from gene ontology assessment of DEG.
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4.2.5 KEGG mapping of DEG at 6 dpi
The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides databases for the purpose of comparison and mapping of bioinformatic profiles based upon described biological processes. Utilising RNA-Seq DEG results of ZIKV infection at 6 dpi as inputs for KEGG analysis, several maps relating to viral infection were returned. Influenza A, herpes simplex, measles, hepatitis C, HIV-1 and Epstein-Barr viruses were all represented (Table 7) indicating a profile of both RNA and DNA viral response in ZIKV infected RWPE-1 cells. Notably, the antiviral RIG-1 and NLR signalling pathways were also represented. Genes identified to be dysregulated and mapped as being viral responses included OAS, IFI, ISG, DDX, MX1 and
CCL2 (see Appendix 9 for KEGG maps).
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Table 7. Top dysregulated KEGG ma pathways at 6 dpi.
Influenza A ‐ Homo sapiens (hsa05164) CCL2; C‐C motif chemokine ligand 2 DDX58; DExD/H‐box helicase 58 IFIH1; interferon induced with helicase C domain 1 MX1; MX dynamin like GTPase 1 OAS1; 2’‐5’‐oligoadenylate synthase 1 OAS3; 2’‐5’‐oligoadenylate synthase 3 RSAD2; radical S‐adenosyl methionine domain containing 2
Herpes simplex infection ‐ Homo sapiens (hsa05168) CCL2; C‐C motif chemokine ligand 2 DDX58; DExD/H‐box helicase 58 HLA‐F; major histocompatibility complex, class I, F IFIH1; interferon induced with helicase C domain 1 OAS1; 2’‐5’‐oligoadenylate synthase 1 OAS3; 2’‐5’‐oligoadenylate synthase 3
Measles ‐ Homo sapiens (hsa05162) DDX58; DExD/H‐box helicase 58 IFIH1; interferon induced with helicase C domain 1 MX1; MX dynamin like GTPase 1 OAS1; 2’‐5’‐oligoadenylate synthase 1 OAS3; 2’‐5’‐oligoadenylate synthase 3
Human papillomavirus infection ‐ Homo sapiens (hsa05165) HLA‐F; major histocompatibility complex, class I, F ISG15; ISG15 ubiqutin‐like modifier MX1; MX dynamin like GTPase 1 OASL; 2’‐5’‐oligoadenylate synthase like
Hepatitis C ‐ Homo sapiens (hsa05160) DDX58; DExD/H‐box helicase 58 OAS1; 2’‐5’‐oligoadenylate synthase 1 OAS3; 2’‐5’‐oligoadenylate synthase 3
RIG‐I‐like receptor signalling pathway ‐ Homo sapiens (hsa04622) DDX58; DExD/H‐box helicase 58 IFIH1; interferon induced with helicase C domain 1 ISG15; ISG15 ubiqutin‐like modifier
Epstein‐Barr virus infection ‐ Homo sapiens (hsa05169) DDX58; DExD/H‐box helicase 58 HLA‐F; major histocompatibility complex, class I, F VIM; vimentin
Human immunodeficiency virus 1 infection ‐ Homo sapiens (hsa05170) BST2; bone marrow stromal cell antigen 2 HLA‐F; major histocompatibility complex, class I, F SAMHD1; SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1
NOD‐like receptor signalling pathway ‐ Homo sapiens (hsa04621) CCL2; C‐C motif chemokine ligand 2 OAS1; 2’‐5’‐oligoadenylate synthase 1 OAS3; 2’‐5’‐oligoadenylate synthase 3
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4.2.6 KEGG mapping of DEG at 21 dpi
Utilising DEG results from RNA-Seq at 21 dpi as inputs for KEGG analysis, maps relating to cellular differentiation and survival were returned in addition to pro-inflammatory cytokine production. IL-17 signalling (Table 8), notably the antimicrobial signalling arm (see Appendix
9) was well represented, suggesting there is still a degree of immune response to ZIKV infection. A pro-survival arm of RAS signalling was observed in upregulation of PLA2GF, in addition to increased positive feedback of RAS stimulation through RASAL1. Complement and coagulation cascades were also returned from KEGG analysis with central stimulation via VWF and downstream stimulation of phagocytosis through CR4 (ITGAX) (see Appendix 9 for KEGG maps).
Table 8. Top dysregulated KEGG map pathways at 21 dpi.
IL‐17 signaling pathway ‐ Homo sapiens (hsa04657) LCN2; lipocalin 2 S100A7; S100 calcium binding protein A7 S100A8; S100 calcium binding protein A8 S100A9; S100 calcium binding protein A9
Ras signaling pathway ‐ Homo sapiens (hsa04014) PLA2G2F; phospholipase A2 group IIF RASAL1; RAS protein activator like 1
Complement and coagulation cascades ‐ Homo sapiens (hsa04610) ITGAX; integrin subunit alpha X VWF; von Willebrand factor
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Chapter 5: Discussion
The prostate gland has been suggested to act as a reservoir for ZIKV in the male reproductive tract, long after the virus has been cleared from blood sera. An understanding of the mechanisms underlying persistence in the prostate is urgently needed to develop vaccines, antiviral therapies and diagnostic tools to combat this virus reservoir. Epithelial cells are readily infected by ZIKV and are physically exposed during initial proximal infection such as during cases of reproductive tract infections. It is now well established that ZIKV is able to infect numerous human testicular cells including those of epithelial, somatic and germinal origins [139-142]. The prostate has been neglected by comparison but has the potential to exist as a further reservoir of ZIKV. Therefore, an understanding of ZIKV in healthy prostate epithelial cells is of great importance. The RWPE-1 cell line used in this study is derived from the normal epithelium of the prostate periphery and serves as a good model to fill this knowledge gap. Here, we found that ZIKV can establish persistent infection in this human epithelial prostate cell line, as determined by detection of infectious virus from infected supernatants and viral RNA both intracellularly and extracellularly following 30 days ZIKV infection. This persistent infection was distinct from that of a latent or slow infection do to the concurrent replication of infective virus and dynamic kinetics of viral RNA. Our findings are consistent with reports of ZIKV shedding in semen from vasectomised men [84,92] and macaque studies [96-98] that suggest the prostate gland can act as a reservoir for ZIKV. Our conclusion of the prostate’s permissibility and facilitation of a replicative, persistent infection is strengthened by our experimental design which involved the removal of the majority of virus present in the cell culture medium every 3 days. Our results demonstrate that, despite the continuous removal of new virions, there was persistent viral RNA production from RWPE-1 cells until the end of the experiments. Through RNA sequencing, we characterised both early
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Rhys Izuagbe n8535761 and late points of infection and observed an initial innate immune response directed through type-I IFN signalling, upregulation of viral sensing (RIG-1 and NLR) and NFκB transcription.
Notably absent was type-II IFN signalling as no IFN-γ transcription was upregulated. Gene ontology reported a response to IFN- γ however, associated genes were also ubiquitous with type-I IFN signalling and viral sensing. We observed that later in infection, the antiviral response is drastically reduced with only limited recruitment of systemic immunity observed.
In place of a strong antiviral response, several pro-survival pathways were upregulated including cornification, the terminal stage of keratinocyte differentiation underpinned by a tightly control programmed cell death distinct from apoptosis. Many genes associated with the significant pro-survival signals detected at late stage infection have also been implicated in cancer.
An in vitro model of persistent infection
It has now been recognised that ZIKV is able to maintain persistent infection in a number of reproductive tissues. Different viral pathogens are able to establish persistent or latent infections in the prostate, with varying degrees of pathology [143]. Polyomavirus, herpesviruses, human papillomavirus (HPV) and human cytomegalovirus are all known to infect the human prostate and cause pathology [143-145]. ZIKV infection however, is largely asymptomatic and includes cases of sexual transmission. A silent ZIKV colonisation of immune privileged sites such as the prostate poses a substantial challenge for the development of vaccines and antiviral therapies, as well as for control and surveillance during outbreaks.
Conventional vaccines may be sufficient to eliminate virus from sera, but serum immunoglobulins may be unable to penetrate reproductive tract barriers to fight local infection
[146,147]. Confirmation and characterisation of this silent and persistent infection is an essential first step in producing effective detection and treatment options for male reproductive
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ZIKV infection.
We observed limited impact of ZIKV infection on host cell health throughout the entire course of infection. Lack of cytopathic effect or reduced viability is consistent with previous observations of ZIKV infection in other prostate-derived cells. For example, prostate MSCs were found to maintain high rates of infection at stable and consistent concentrations over the course of infection as detected by qRT-PCR of ZIKV RNA [100]. Under infection with one particular strain, ZIKV was calculated at a peak concentration approaching 1x1010 genome copies/ml in MSC cells. We also observed differences among ZIKV strains in replication capacity in prostate cells, noting that the strain H/PF/2013 infection produced the highest numbers of genome copies during the experiment. A previous survey of cell types susceptible to ZIKV identified LNCaP human prostate cancer cells as capable of supporting infection with the virus, without any cytopathic effects [101]. LNCaP cells were derived from a metastatic lesion and maintained many properties of malignancy through immortalisation [148]. Recently, another prostate cancer cell line, DU-145, was found to sustain high ZIKV titers during short term infection, although persistent infection was not assessed [149]. However, the relevance of tumour cell lines to ZIKV biology is unclear, as most infections are likely to occur in previously healthy men. Furthermore, LNCaP alongside other androgen-dependent cancer cells have a notably differing proteome in contrast to non-malignant prostate epithelial cells [150].
The relevance of MSCs for understanding the persistence of ZIKV in the prostate is also unclear as they become quiescent during adulthood or differentiation [151-153]. Strong cornification transcript upregulation was seen following 21 days of ZIKV infection.
We observed a dramatic drop in infectious virus particles during both MOI-1 and MOI-
10 time-courses (except for MR766, MOI-1), with only two ZIKV infections able to be titred after 30 days. This was, however, observed in parallel to consistent output of viral RNA
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ZIKV viral RNA in the absence of infectious virus suggests that new viral genomes were being produced for the duration of the experiment but the majority fail to mature or lack characteristics of infective particles. Interestingly, our results are consistent with a recent systematic review that identified the persistence of viral RNA for a median of 34-35 days and infectious virus for a significantly lower median of 12 dpi [154]. Further studies are required to understand how this occurs and what it means for sexual transmission of ZIKV.
Antiviral responses
Investigations of the host cell’s transcriptomic response to ZIKV MOI-1 infection identified a clear and strong innate antiviral response at 6 dpi, a time point early in infection. These responses were most prominently represented by RNA virus sensing, type-I IFN regulation and
NFκB promotion pathways. Key genes upregulated within these pathways included MDA5,
OAS1, OAS3, Mx1, CCL2 and ISG15. These genes promote several antiviral proteins; IFI44 is a stimulator of type-I IFN; ISG15 is involved in post-transcriptional modification of NFκB;
CCL2 is a pro-inflammatory monocyte chemoattractant; MX1 and IFITM are inhibitors of viral entry and finally OAS is an inhibitor of viral translation and replication. We further determined that ZIKV also stimulated the upregulation of the ISGs HERC5 and USP18 which have been linked to the persistence of another sexually-transmitted flavivirus, Hepatitis C virus [155,156].
Many genes identified by DEG analysis have been previously identified in persistent ZIKV infection of other cell types [68,157,158]. A study of ZIKV infection of human skin fibroblasts determined a similar innate antiviral response whereby OAS2, ISG15 and MX1 were the most strongly enhanced genes [158], with DDX58 and IFIH1 also significantly dysregulated. The study also reported RIG-1 signalling upregulation as early as 6 hours following infection.
KEGG mapping of our data also identified a strong signature of antiviral response pathways.
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The RNA viruses, measles, influenza A and herpes were reported as was the DNA virus, HPV.
RIG-1 viral sensing and downstream IFN regulation were enhanced within all RNA virus
KEGG maps and represent an effective recognition of ZIKV pathogen associated molecular patterns (PAMPs) by the cellular innate immune system [158].
Strong type-I IFN signalling pathway activation has also been found in ZIKV-infected
Sertoli cells in the male reproductive tract [157], despite persistent replication and shedding of infectious virus. It is known that cytokines including type-I IFNs are important contributors towards persistent viral infections due to a modulating role which can be immunosuppressive via expression of negative regulatory molecules and down-regulation of T-cell responses [159-
161]. Under viral infection, type-I IFNs can suppress inflammation without suppression of wider responses, particularly in absence of significant DAMPs which sensitise cells to inflammation to overcome negative signalling [162]. From our model, this is congruous with the early observation of limited inflammatory signals despite maintained viral sensing and the lack of DAMPs concluded from cellular viability experiments. Chronic viral infections have been shown to induce an exhausted, dysfunctional immune state and have been concluded to be a host response limiting excessive immunopathology and aiding presistant viral infection
[161]. This dysregulation could also explain the loss of antiviral responses determined at the
21 dpi time-point, though some aspects of immune activation were still upregulated, namely
Il-17 activity of neutrophil recruitment against clearance of extracellular pathogens. Genes seen to be upregulated and involved in these processes were S100A7/8/9 and LCN2.
IFN- γ (a type-II IFN) is often considered as critical in order to overcome infection, due to its anti-proliferative profile and its recruitment of innate immune cells. Gene ontology results showed some upregulation of genes involved in enacting IFN-γ responses however, no significant upregulation of IFN-γ signalling was observed.
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The absence of IFN-γ, particularly during an otherwise active antiviral state at 6 dpi, implicates a failure to signal for recruitment of leukocytes and antigen presenting cells during infection. This is particularly interesting given that the RWPE-1 cell line can produce effective
IFN-γ responses [163]. Type-I IFNs are demonstrated as potential suppressors of IFN- γ
[164,165] and such a response is feasible but not confirmed from results herein, requiring further investigation.
Keratinocyte specific responses
At the later time point, we also observed that RWPE-1 cells exhibited an upregulation of keratinocyte differentiation and cornification in ZIKV-infected cells. Cornification is the process by which keratinocytes differentiate to become more like skin and terminally produce a mechanically resistant barrier of dead cells and extracellular protein deposits. Its hallmark is the initial replacement of organelles with cytoskeletal proteins such as keratin, development of a rigid envelope at the cell periphery and, finally, linkage of dead cells into a functional structure. Cornified barriers serve as an immediate protective barrier against invading microbes. Cornification involves highly controlled programmed cell death typically without upregulation of inflammatory response and the maintenance of pro-survival signalling prior to cell termination. Classical apoptosis, necrosis or pyroptosis during this process may lead to the development of DAMPs leading to inflammatory response and dysregulation of cornification, thus pro-survival factors helps ensure that cornification can be coordinated into a functional structure before termination of cells [166]. Despite development of the cornification pathway, we observed no significant loss of cell viability even at 30 dpi following MOI-1 and 36 dpi
MOI-10 infection. It is unlikely that upregulation therefore led to significant terminal differentiation of keratinocytes. ZIKV has been shown to infect other keratinocytes, including human skin [158] and ovarian epithelial cells [101], but upregulation of cornification has not
60
been previously observed, possibly due to shorter infection time in other studies.
The absence of IFN- γ may also play a specific role in relation to the observed persistence of ZIKV infection; IFN- γ has been demonstrated to inhibit viral reinfection of epithelial keratinocytes by suppressing keratinocyte differentiation, notably cornified envelope formation. In place of cornification, the cytokine directs the formation of tight junctions between cells thus preventing paracellular routes of infection and limiting tropism [167]. A better exploration of this may yet provide therapeutic insight towards resolving persistent prostatic ZIKV infection.
Viral carcinogenesis
At 21 dpi, we observed upregulation of a number of genes that have been implicated in cell cycle and the development of tumours, several of which are associated with prostate cancer
(PCa). Amongst these were KLKs 12 and 14, which are commonly dysregulated in prostate cancers. Members of the KLKs, namely KLK3 and KLK2, are widely utilised as biomarkers of
PCas. The KLK family plays a key role in the proliferation of cancer cells by mediating degradation of the extracellular matrix to facilitate proliferation and angiogenesis. KLK12 facilitates angiogenesis specifically by degrading fibronectin and tenascin. KLK14 may further promote tumorigenesis through stimulation of transforming growth factor ß1 [168], a potent immune-suppressor. KLK6 is the only family member that is known to mediate inflammatory response [169] but was not identified in our DEG analysis. Complement cascade also had representation through the upregulation of the gene CR4 (ITGAX), which promotes phagocytosis. ZIKV-infected cell survival pathways included RAS signalling and cornification/keratinocyte differentiation. Additionally, several cancer-associated genes were upregulated [170-172]. A number of these have been previously reported from prostate tumour transcriptomes (S100A9, S100P) [173,174]. S100P is a calcium-binding protein that has been
A Prostate cell line model of Zika virus infection 61
Rhys Izuagbe n8535761 identified as being upregulated in androgen-dependent and androgen-independent PCa [175].
It is overexpressed in recurrent PCa xenografts during androgen deprivation [176]. Androgen deprivation therapy is a common treatment of androgen-dependent PCas, however in some cases, the therapeutic outcome is diminished due to the restored induction of androgen- responsive genes [176]. Increased expression of S100P is observed in metastatic PCas [174], suggesting an important role in cell survival. The exact mechanism by which S100P promotes tumorigenesis has not yet been determined. However, the importance of S100P calcium signalling to PCa survival has been demonstrated [177] and further demonstrated in other cancers including breast cancer [178]. The upregulation of such genes suggests that persistent viral replication leads to a disturbance in normal gene regulation in the host cell, possibly leading to increased host cell survival. It is possible that prolonged dysregulation of the host cell environment might lead to the development of a tumorigenic environment.
We also observed through KEGG mapping the activation of RAS signalling.
Interestingly RAS signalling has been implicated in a number of viral infections including herpesvirus [179] and adenovirus [180]. Herpesvirus can commandeer RAS signalling in order to enhance its own replication, as RAS controls functions including cell proliferation, differentiation, migration and DNA repair [179]. Herpes viruses are causative agents of many cancers and RAS hijacking and subsequent dysregulation is a strong contributor. Notably, RAS genes are dysregulated in a number of cancers where cell growth and differentiation can be driven towards malignancy. Twenty-seven % of human tumours are estimated to contain RAS gene gain-of-function mutations [181] and are also highly associable with hyper-proliferative disorders [182]. Twenty percent of human cancers have an aetiology involving microbial infection and viral carcinogenesis is commonly observed amongst these [183]. Hepatitis viruses B and C [184], human papillomavirus [185] and Epstein-Barr are prominent examples of this aetiology. Polyomavirus and HPV have been described as persistently shedding DNA
62
from the prostate in prostate cancer patients [143]. Upregulation of RAS signalling could potentially lead to a tumorigenic environment in ZIKV-infected cells during persistent infection. Interestingly, the Fibroblast Activation Protein Alpha (FAP) was only expressed in
ZIKV-infected cells at 6 dpi, and is linked as having a role in tumour development [186]. Its presence is observed in >90% of epithelial cancers [187,188]. Under normal conditions, FAP is only expressed in cases of wound repair [189], therefore its expression under ZIKV infection without a loss in cell viability requires further scrutiny.
Further remarks
We also observed downregulation of the intermediate filament vimentin at both 6 and 21 dpi.
Vimentin is the major cytoskeletal filament in non-muscle cells and is highly expressed by fibroblast, endothelial cells, macrophages, melanocyte and diverse cells of the testis [190]. It is also a surface-expressed protein which has been implicated as an entry receptor in DENV infections [191]. It is unclear why vimentin is being downregulated following ZIKV infection, and additional experiments are needed to elucidate any potential role in virus entry. However, given its utilisation by the closely related DENV, down regulation of vimentin may be a host response to limit virion binding and infection. Additionally, the cytokine basic fibroblast growth factor (FGF2) which is reported to underlie persistence of virus in Sertoli cells [192], was also expressed in our infection model. In contrast to [192], which reported upregulation of
FGF2 arising from ZIKV infection, our transcriptomic analysis identified FGF2 as being stably expressed at both early and late stages of ZIKV infection and no different to control cells (see
Appendix 6). Viral persistence in RWPE-1 cells is likely to be driven by alternative means than in Sertoli cells, underscoring the importance of considering persistence in the prostate distincly from the testis.
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Limitations
Our cell line model provides a tractable and reproducible system to study some of the features of persistent ZIKV replication in the prostate. However, as a cell line model it cannot capture the role of functional innate or adaptive immunity in responding to such infection. Notably the monocyte chemoattractant protein CCL2 was significantly upregulated at 6 dpi and indicates attempts to recruit wider immune factors which may be sufficient to clear infection in vivo. A cell line model also fails to capture any level of prostate immune privilege. Future targeted approach to explore the lack of IFN-γ observed and its role in recruiting systemic immunity is of prominent importance as failed recruitment is commonly reported of persistent viral infections [193,194]. The lack of apoptosis following chronic infection in our model is a dramatic indication of the prostates potential as a persistent reservoir of ZIKV however validation of this result along with further characterisation of infection in vivo may yet greatly expand our understanding. Further investigation utilising more complex, 3-dimensional in vitro designs such as co-culture and spheroids, as well as animal models or ex vivo tissues, could help us better understand host responses to persistent infection. Challengingly, an in vivo model would be best conducted in non-human primates due to the differing prostate biology in more common animal models (murine).
We found distinct differences among the three ZIKV strains in their ability to infect
RWPE-1 cells and produce infectious virus. We were only able to characterize the transcriptomic response to one ZIKV strain at two time-points. It is possible that other strains evoke different host cell environment alternations to enable persistence. Deep sequencing of viruses at different stages of persistent infection would also potentially identify genomic changes in the pathogen facilitating persistence. Exploration of additional time-points in infection would also be instrumental in validation of our results herein. For example, a very early response to infection (<12 hours) may depict alternate antiviral pathways such as IFN-γ
64
indicating their suppression further into infection. Direct detection of target cytokines from collected supernatants would also strengthen our conclusions. The identification of genes involved in tumorigenesis. Our gene validation results suggested that the bioinformatic pipeline employed here may be very conservative in identifying DEGs. For example, IFI44L was found to also be upregulated relative to control cells at later time points in infection using RT-qPCR yet was absent in our output using the Galaxy platform. The RT-qPCR results indicated that our analysis may only have returned very strongly differentially expressed genes. RT-qPCR results were seen as an agreement of differential expression at 6 dpi, (considered significant by
RNA-Seq) and divergence from RNA-Seq results at 21 dpi, showing lesser but still significant increase in expression. Further analysis with additional bioinformatics pipelines or software may allow us to identify other differentially expressed genes during ZIKV infection of prostate cells by applying different methods of DEG calculation. High error was observed in the XAF-
1 validation and as such further optimisation or selection of additional validation primers is necessary.
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Conclusion The non-malignant prostate epithelial RWPE-1 cell line was permissive to infection with strains from African and Asian lineages of ZIKV. Each strain displayed differing replication kinetics during the 30-day infection time-course employed. Cells continued to produce viral RNA until completion of the time-course without any significant reduction in cell viability or increased cytotoxicity compared to controls. RNA-Seq and differential gene expression analysis identified strong upregulation of antiviral response pathways early in the infection but a discernible lack of type-II IFN signalling associated with inflammation and systemic, innate cell recruitment. Genes that were highly upregulated early in infection featured viral sensors and members of type I and IFN pathways, indicating recognition of and response to presence of viral RNA. This response was lost later in infection and replaced with expression of pro-survival genes, suggesting a dysregulated host cell environment. Several of the genes identified herein have been associated with the development of prostate cancer. At
21 dpi the cornification and keratinocyte differentiation pathways were significantly upregulated in ZIKV-infected cells but apoptotic programmed cell death was not observed.
Suppression of inflammation during cornification may be a potential contributor to ZIKV persistence in the cells used in this study. We have established a tractable and reproducible model with which to study persistent ZIKV replication in the prostate gland, a known reservoir of the virus.
66
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[190] Feitz, W. F. J., Debruyne, F. M. J., & Ramaekers, F. C. S. (2008). Intermediate filament proteins as tissue specific markers in normal and neoplastic testicular tissue. International Journal of Andrology, 10(1), 51-56.
[191] Yang, J., Zou, L., Yang, Y., Yuan, J., Hu, Z., Liu, H., Et al. (2016). Superficial vimentin mediates DENV-2 infection of vascular endothelial cells. Scientific Reports, 6, 38372.
[192] Kumar, A., Jovel, J., Lopez-Orozco, J., Limonta, D., Airo, A. M., Hou, S., Et al. (2018). Human Sertoli cells support high levels of Zika virus replication and persistence. Scientific Reports, 8, 5477.
[193] Kahan, S. M., Wherry, E. J., & Zajac, A. J. (2015). T cell exhaustion during persistent viral infections. Virology, 479-480, 180-193.
[194] Li, S., Gowans, E. J., Chougnet, C., Plebanski, M., & Dittmer, U. (2008). Natural Regulatory T Cells and Persistent Viral Infection. [10.1128/JVI.01768-07]. Journal of Virology, 82(1), 21.
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Appendices
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Appendix 1. pUc19-RpS7-wsp-RpS17-ZIKV control plasmid
82
Appendix 2. ZIKV qRT-PCR primer set and alignment to tested strains
Sequence 5’ – 3’ 835‐Forward [38] TTGGTCATGATACTGCTGATTGC 911C‐Reverse [38] CCTTCCACAAAGTCCCTATTGC 860 FAM probe [38] CGGCATACAGCATCAGGTGCATAGGAG
Alignment homology MR766 BeH815744 H/PF/2013 835‐Forward 100% 100% 100% 911C‐Reverse 86.4% 100% 100% 860 FAM probe 92.6% 100% 100%
MR766
BeH815744
H/P/F2013
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Appendix 3. Bioanalyser RNA integrity analysis
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Appendix 4.1. 6 dpi; RNA-Seq expression of significant DEG (1 of 3)
Analysis Expression Expression log2 Gene Gene ID (Ensembl ID) Locus position Test statistic p_value q_value status Uninfected 6dpi H/PF/2013 6dpi (fold_change) chr19:28435387‐ AC005307.3 ENSG00000267243.5 OK 0 0.218437 inf ‐nan 5.00E‐05 0.025545 28727777 chr22:36253009‐ APOL1 ENSG00000100342.20 OK 0.542739 3.95377 2.8649 3.72387 5.00E‐05 0.025545 36267530 chr19:17402938‐ BST2 ENSG00000130303.12 OK 1.45249 214.325 7.20513 9.94112 5.00E‐05 0.025545 17405648 chr10:75430570‐ C10orf11 ENSG00000148655.14 OK 0 0.5297 inf ‐nan 5.00E‐05 0.025545 76560994 chr17:34255217‐ CCL2 ENSG00000108691.9 OK 0.621206 6.86246 3.46558 5.13515 5.00E‐05 0.025545 34257203 chr16:4314760‐ CORO7 ENSG00000262246.5 OK 0 0.379341 inf ‐nan 5.00E‐05 0.025545 4425705 CTD‐ chr19:17391428‐ ENSG00000269720.1 OK 0 1.05874 inf ‐nan 5.00E‐05 0.025545 2521M24.5 17392284 chr9:32455704‐ DDX58 ENSG00000107201.9 OK 4.83365 28.1025 2.53952 4.81371 5.00E‐05 0.025545 32526324 chr4:168216292‐ DDX60 ENSG00000137628.16 OK 2.56871 34.6092 3.75204 3.42764 5.00E‐05 0.025545 168318807 chr14:23630114‐ DHRS2 ENSG00000100867.14 OK 0.846352 11.7821 3.7992 5.58273 5.00E‐05 0.025545 23645639 chr1:96446929‐ EEF1A1P11 ENSG00000228502.1 OK 5.07787 0.599651 ‐3.08203 ‐4.89078 5.00E‐05 0.025545 96448318 chr4:105484697‐ EEF1A1P9 ENSG00000249264.1 OK 6.27447 0.85646 ‐2.87304 ‐4.67734 5.00E‐05 0.025545 105486080 chr14:81727779‐ EIF3LP1 ENSG00000258501.1 OK 39.7374 6.69111 ‐2.57018 ‐4.79948 5.00E‐05 0.025545 82030349 chr10:42566676‐ EIF3LP2 ENSG00000233837.1 OK 37.905 5.5978 ‐2.75946 ‐4.91512 5.00E‐05 0.025545 42567483 chr13:42886387‐ EPSTI1 ENSG00000133106.14 OK 0.560805 16.5038 4.87916 5.34969 5.00E‐05 0.025545 42992271 chr2:162159761‐ FAP ENSG00000078098.13 OK 0 0.404933 inf ‐nan 5.00E‐05 0.025545 162245151 chr4:88457116‐ HERC5 ENSG00000138646.8 OK 0.972303 7.03272 2.8546 4.70039 5.00E‐05 0.025545 88506163
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Appendix 4.1. 6 dpi; RNA-Seq expression of significant DEG (2 of 3)
Analysis Expression Expression log2 Gene Gene ID (Ensembl ID) Locus position Test statistic p_value q_value status Uninfected 6dpi H/PF/2013 6dpi (fold_change) chr4:88378738‐ HERC6 ENSG00000138642.14 OK 2.11679 36.1935 4.09578 5.35785 5.00E‐05 0.025545 88443111 chr6:29722774‐ HLA‐F ENSG00000204642.13 OK 2.5263 18.8445 2.89904 4.29437 5.00E‐05 0.025545 29749049 chr1:78649795‐ IFI44 ENSG00000137965.10 OK 1.17677 173.58 7.20462 9.75824 5.00E‐05 0.025545 78664078 chr1:78619921‐ IFI44L ENSG00000137959.15 OK 0.525549 136.181 8.01749 7.5001 5.00E‐05 0.025545 78646145 chr1:27666060‐ IFI6 ENSG00000126709.14 OK 4.07571 969.734 7.89439 12.7488 5.00E‐05 0.025545 27703063 chr2:162267078‐ IFIH1 ENSG00000115267.5 OK 2.4578 12.9466 2.39714 4.13718 5.00E‐05 0.025545 162318703 chr11:307630‐ IFITM1 ENSG00000185885.15 OK 26.0989 492.273 4.2374 4.96985 5.00E‐05 0.025545 315272 chr1:1001137‐ ISG15 ENSG00000187608.8 OK 8.1416 325.917 5.32305 9.46115 5.00E‐05 0.025545 1014541 chr22:50548032‐ KLHDC7B ENSG00000130487.5 OK 0.324382 5.65863 4.12469 6.81494 5.00E‐05 0.025545 50551023 chrM:15955‐ MT‐TP ENSG00000210196.2 OK 1447.26 11187.2 2.95045 4.86914 5.00E‐05 0.025545 16023 chr21:41420303‐ MX1 ENSG00000157601.13 OK 5.07048 187.325 5.20728 5.33402 5.00E‐05 0.025545 41459214 chr2:6828513‐ NRIR ENSG00000225964.5 OK 0 0.635661 inf ‐nan 5.00E‐05 0.025545 6840464 chr12:112906776‐ OAS1 ENSG00000089127.12 OK 3.80617 150.354 5.30388 2.09489 5.00E‐05 0.025545 113017751 chr12:112906776‐ OAS3 ENSG00000111331.12 OK 5.86215 132.281 4.49603 3.58859 5.00E‐05 0.025545 113017751 chr12:121019110‐ OASL ENSG00000135114.12 OK 2.01122 25.7912 3.68073 6.14176 5.00E‐05 0.025545 121039242 chr1:2508532‐ PANK4 ENSG00000157881.13 OK 0.338198 0 ‐inf ‐nan 5.00E‐05 0.025545 2526628
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Appendix 4.1. 6 dpi; RNA-Seq expression of significant DEG (3 of 3)
Analysis Expression Expression log2 Gene Gene ID (Ensembl ID) Locus position Test statistic p_value q_value status Uninfected 6dpi H/PF/2013 6dpi (fold_change) chr3:122680617‐ PARP14 ENSG00000173193.13 OK 4.05002 21.2639 2.3924 3.07245 5.00E‐05 0.025545 122730840 chr19:17351447‐ PLVAP ENSG00000130300.8 OK 0 0.479002 inf ‐nan 5.00E‐05 0.025545 17377350 chr2:6912276‐ RNF144A ENSG00000151692.14 OK 0.124887 1.02666 3.03926 2.61895 5.00E‐05 0.025545 7077880 chr16:11927372‐ RP11‐166B2.1 ENSG00000234719.8 OK 0.211147 0 ‐inf ‐nan 5.00E‐05 0.025545 11976643 chr1:78666271‐ RP4‐641G12.3 ENSG00000238015.2 OK 0 1.00751 inf ‐nan 5.00E‐05 0.025545 78666695 chr1:171683127‐ RPL4P3 ENSG00000230364.1 OK 14.3057 2.75288 ‐2.37758 ‐4.17131 0.0001 0.048084 171684438 chr2:6840569‐ RSAD2 ENSG00000134321.11 OK 0.259079 23.4831 6.50209 5.03664 5.00E‐05 0.025545 6898239 chr2:218034959‐ RUFY4 ENSG00000188282.12 OK 0 0.228899 inf ‐nan 5.00E‐05 0.025545 218090581 chr11:18231348‐ SAA2 ENSG00000134339.8 OK 7.22241 36.672 2.34413 3.84254 0.0001 0.048084 18248643 chr20:36876120‐ SAMHD1 ENSG00000101347.8 OK 8.27086 52.5211 2.66679 4.11293 5.00E‐05 0.025545 36951843 chr19:17871972‐ SLC5A5 ENSG00000105641.3 OK 0.357549 3.30033 3.2064 4.62299 0.0001 0.048084 17895174 chr11:2295644‐ TSPAN32 ENSG00000064201.15 OK 0.195185 0 ‐inf ‐nan 5.00E‐05 0.025545 2318200 chr11:57551655‐ UBE2L6 ENSG00000156587.15 OK 7.87675 69.273 3.13662 4.98615 5.00E‐05 0.025545 57568284 chr22:18149898‐ USP18 ENSG00000184979.9 OK 0.432572 3.78287 3.12847 5.05718 5.00E‐05 0.025545 18177397 chr10:17214238‐ VIM ENSG00000026025.13 OK 193.078 25.6475 ‐2.91229 ‐5.2676 5.00E‐05 0.025545 17237593 chr17:6755446‐ XAF1 ENSG00000132530.16 OK 0.744595 94.9212 6.99413 6.32726 5.00E‐05 0.025545 6776116 chr20:57603845‐ ZBP1 ENSG00000124256.14 OK 0 1.19294 inf ‐nan 5.00E‐05 0.025545 57620576
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Appendix 5.1. 21 dpi; RNA-Seq expression of significant DEG (1 of 2)
Analysis Expression Expression log2 Gene Gene ID (Ensembl ID) Locus position Test statistic p_value q_value status Uninfected 6dpi H/PF/2013 6dpi (fold_change) chr13:24680410‐ ATP12A ENSG00000075673.11 OK 0.780769 1.74885 1.16344 4.10891 5.00E‐05 0.040066 24712493 chr19:41708584‐ CEACAM6 ENSG00000086548.8 OK 0.202798 0.756499 1.8993 4.11245 5.00E‐05 0.040066 41786893 chr12:5948873‐ VWF ENSG00000110799.13 OK 0.128653 0.921466 2.84045 3.57744 5.00E‐05 0.040066 6124770 chr12:113098818‐ RASAL1 ENSG00000111344.11 OK 0.122956 0.745612 2.60029 4.28476 5.00E‐05 0.040066 113136239 chr20:45174875‐ PI3 ENSG00000124102.4 OK 176.458 491.994 1.47932 6.28869 5.00E‐05 0.040066 45176544 chr20:2296000‐ TGM3 ENSG00000125780.11 OK 1.27596 3.17296 1.31425 4.62067 5.00E‐05 0.040066 2341078 chr8:142740943‐ SLURP1 ENSG00000126233.1 OK 115.659 230.208 0.993058 4.1829 5.00E‐05 0.040066 142742411 chr22:38084888‐ BAIAP2L2 ENSG00000128298.16 OK 3.93563 11.2252 1.51207 4.73559 5.00E‐05 0.040066 38110670 chr19:51077494‐ KLK14 ENSG00000129437.9 OK 5.70341 13.9087 1.28609 4.7172 5.00E‐05 0.040066 51084245 chr1:203178930‐ CHI3L1 ENSG00000133048.12 OK 81.4065 172.728 1.08529 4.17664 5.00E‐05 0.040066 203186749 chr13:77535673‐ SCEL ENSG00000136155.16 OK 19.5271 39.5041 1.01652 3.92812 5.00E‐05 0.040066 77645263 chr9:97029678‐ CTSV ENSG00000136943.10 OK 86.6355 179.446 1.05052 4.34659 5.00E‐05 0.040066 97039643 chr11:76657055‐ LRRC32 ENSG00000137507.11 OK 0.486621 2.73913 2.49285 4.71459 5.00E‐05 0.040066 76670747 chr16:31355133‐ ITGAX ENSG00000140678.16 OK 1.09386 5.96133 2.4462 4.79299 5.00E‐05 0.040066 31382997 chr1:153390031‐ S100A8 ENSG00000143546.9 OK 29.3643 239.639 3.02873 11.9863 5.00E‐05 0.040066 153391188 chr1:153457743‐ S100A7 ENSG00000143556.8 OK 3.544 23.6864 2.74061 8.75478 5.00E‐05 0.040066 153460701 chr5:148268179‐ SPINK7 ENSG00000145879.10 OK 157.162 464.12 1.56225 4.52165 5.00E‐05 0.040066 148383783
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Appendix 5.2. 21 dpi; RNA-Seq expression of significant DEG (2 of 2)
Analysis Expression Expression log2 Gene Gene ID (Ensembl ID) Locus position Test statistic p_value q_value status Uninfected 6dpi H/PF/2013 6dpi (fold_change)
chr9:128149070‐ LCN2 ENSG00000148346.11 OK 73.5386 319.844 2.12079 8.2547 5.00E‐05 0.040066 128153455
chr21:30165563‐ CLDN17 ENSG00000156282.4 OK 5.65036 13.2969 1.23468 4.62033 5.00E‐05 0.040066 30166756
chr1:20139325‐ PLA2G2F ENSG00000158786.4 OK 0.493368 1.21951 1.30556 4.16479 5.00E‐05 0.040066 20150386
chr1:47183592‐ PDZK1IP1 ENSG00000162366.7 OK 5.80859 12.3549 1.08882 3.87987 5.00E‐05 0.040066 47191044
chr1:153001746‐ SPRR3 ENSG00000163209.14 K 55.3304 405.061 2.872 11.9195 5.00E‐05 0.040066 153003856
chr1:153357853‐ S100A9 ENSG00000163220.10 OK 85.2127 318.974 1.9043 8.01565 5.00E‐05 0.040066 153361027
chr4:6693068‐ S100P ENSG00000163993.6 OK 70.0758 198.239 1.50025 4.89817 5.00E‐05 0.040066 6697170
chr15:45430528‐ C15orf48 ENSG00000166920.10 OK 6.73102 26.8938 1.99837 4.61662 5.00E‐05 0.040066 45586304
chr17:74877298‐ FADS6 ENSG00000172782.11 OK 0.0865702 0.582503 2.75032 4.3007 5.00E‐05 0.040066 74893781
chr5:148202793‐ SPINK6 ENSG00000178172.6 OK 8.49378 19.2498 1.18036 4.2259 5.00E‐05 0.040066 148215137
chr7:150990994‐ ATG9B ENSG00000181652.18 OK 8.23792 53.323 2.69441 5.57307 5.00E‐05 0.040066 151024499
chr12:52768154‐ KRT76 ENSG00000185069.2 OK 34.0321 63.7388 0.905276 3.85639 5.00E‐05 0.040066 52777345
chr19:51029091‐ KLK12 ENSG00000186474.15 OK 3.06597 17.5521 2.51723 8.73724 5.00E‐05 0.040066 51035230
chr8:142750149‐ LYPD2 ENSG00000197353.3 OK 2.63086 10.0276 1.93037 6.21725 5.00E‐05 0.040066 142752534
chr1:145866559‐ ANKRD35 ENSG00000198483.12 OK 1.8435 4.3583 1.24132 4.65371 5.00E‐05 0.040066 145885866
chr3:58564116‐ FAM3D ENSG00000198643.6 OK 0.336444 1.44498 2.10261 4.71069 5.00E‐05 0.040066 58666848
chr1:153056112‐ SPRR2A ENSG00000241794.1 OK 64.5191 235.64 1.86879 7.87098 5.00E‐05 0.040066 153057537
Chr17:4565751‐ RP11‐141J13.3 ENSG0000026291.1 OK 0 0.3828 Inf ‐nan 5.00E‐05 0.040066 4571760 A Prostate cell line model of Zika virus infection 89
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Appendix 6.1. Prostate biomarker RNA-Seq expression and DEG, 6 dpi
Expression Expression Analysis log2 Test Gene Gene ID (Ensembl ID) Locus position Uninfected H/PF/2013 p_value q_value Significant status (fold_change) statistic 6dpi 6dpi chr4:122826707‐ FGF2 ENSG00000138685.12 OK 2.8293 4.39209 0.634464 0.999549 0.3078 0.999119 no 122922968 chr19:50819147‐ KLK1 ENSG00000167748.10 OK 0.25542 0.486676 0.930088 1.15673 0.2359 0.99912 no 50823787 chr19:50861567‐ KLK2 ENSG00000167751.12 NOTEST 0 0 0 0 1 1 no 50880567 chr19:50854914‐ KLK3 ENSG00000142515.14 NOTEST 0 0 0 0 1 1 no 50860764 chr19:50906351‐ KLK4 ENSG00000167749.11 NOTEST 0.0630183 0.0578171 ‐0.124275 0 1 1 no 50910738 chr19:50943302‐ KLK5 ENSG00000167754.12 OK 135.14 214.018 0.663273 1.00449 0.30555 0.99912 no 51012129 chr19:50943302‐ KLK6 ENSG00000167755.13 OK 6.58311 11.715 0.831509 0.283689 0.6874 0.99912 no 51012129 chr19:50943302‐ KLK7 ENSG00000169035.11 OK 24.2482 38.5556 0.669059 0.413705 0.6283 0.99912 no 51012129 chr19:50943302‐ KLK8 ENSG00000129455.15 OK 48.6444 75.1121 0.626773 0.185699 0.6105 0.99912 no 51012129 chr19:50943302‐ KLK9 ENSG00000213022.5 OK 1.04184 2.56001 1.29702 0.12447 0.5349 0.99912 no 51012129 chr19:51012738‐ KLK10 ENSG00000129451.11 OK 27.7776 59.5995 1.10138 1.8972 0.0427 0.99912 no 51020175 chr19:51022215‐ KLK11 ENSG00000167757.13 OK 12.5285 28.5111 1.18631 1.99191 0.04605 0.99912 no 51028039 chr19:51029091‐ KLK12 ENSG00000186474.15 OK 0.461006 0.612033 0.408825 0.545403 0.54195 0.99912 no 51035230 chr19:51056205‐ KLK13 ENSG00000167759.12 OK 0.537511 0.99389 0.886791 0.82477 0.37885 0.99912 no 51065114 chr19:51077494‐ KLK14 ENSG00000129437.9 OK 0.342643 0.436433 0.349054 0.460339 0.6295 0.99912 no 51084245 chr19:50825288‐ KLK15 ENSG00000174562.13 NOTEST 0.102214 0 0 0 1 1 no 50851089
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Appendix 6.2. Prostate biomarker RNA-Seq expression, 21 dpi
Expression Expression Analysis log2 Test Gene Gene ID (Ensembl ID) Locus position Uninfected H/PF/2013 p_value q_value Significant status (fold_change) statistic 21dpi 21dpi chr4:122826707‐ FGF2 ENSG00000138685.12 OK 4.02842 4.04082 0.00443472 0.016474 0.986 0.99965 no 122922968 chr19:50819147‐ KLK1 ENSG00000167748.10 OK 0.667331 1.03138 0.628106 1.77934 0.07045 0.99965 no 50823787 chr19:50861567‐ KLK2 ENSG00000167751.12 NOTEST 0 0.00273958 0 0 1 1 no 50880567 chr19:50854914‐ KLK3 ENSG00000142515.14 NOTEST 0 0 0 0 1 1 no 50860764 chr19:50906351‐ KLK4 ENSG00000167749.11 OK 0.107461 0.19306 0.845238 0.749189 0.30055 0.99965 no 50910738 chr19:50943302‐ KLK5 ENSG00000167754.12 OK 466.412 563.4 0.272553 0.838315 0.39225 0.99965 no 51012129 chr19:50943302‐ KLK6 ENSG00000167755.13 OK 32.9441 55.429 0.750621 0.640782 0.48475 0.99965 no 51012129 chr19:50943302‐ KLK7 ENSG00000169035.11 OK 164.574 233.751 0.506233 0.856885 0.3667 0.99965 no 51012129 chr19:50943302‐ KLK8 ENSG00000129455.15 OK 471.103 730.008 0.631872 0.586467 0.5764 0.99965 no 51012129 chr19:50943302‐ KLK9 ENSG00000213022.5 OK 0.667331 1.03138 0.628106 1.77934 0.07045 0.99965 no 51012129 chr19:51012738‐ KLK10 ENSG00000129451.11 NOTEST 0 0.00273958 0 0 1 1 no 51020175 chr19:51022215‐ KLK11 ENSG00000167757.13 NOTEST 0 0 0 0 1 1 no 51028039 chr19:51029091‐ KLK12 ENSG00000186474.15 OK 0.107461 0.19306 0.845238 0.749189 0.30055 0.99965 no 51035230 chr19:51056205‐ KLK13 ENSG00000167759.12 OK 466.412 563.4 0.272553 0.838315 0.39225 0.99965 no 51065114 chr19:51077494‐ KLK14 ENSG00000129437.9 OK 32.9441 55.429 0.750621 0.640782 0.48475 0.99965 no 51084245 chr19:50825288‐ KLK15 ENSG00000174562.13 OK 164.574 233.751 0.506233 0.856885 0.3667 0.99965 no 50851089
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Appendix 7.1. Day 6 Gene Ontology analysis; Type-III Interferon regulation
92 A Prostate cell line model of Zika virus infection
Appendix 7.2. Day 6 Gene Ontology analysis; Virus sensing
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Appendix 7.3. Day 6 Gene Ontology analysis; Interferon-ß positive regulation
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Appendix 7.4. Day 6 Gene Ontology analysis; Cytoplasmic PAMP recognition
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Appendix 7.5. Day 6 Gene Ontology analysis; Interferon-α positive regulation
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Appendix 7.6. Day 6 Gene Ontology analysis; Type-I Interferon negative regulation
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Appendix 7.7. Day 21 Gene Ontology analysis; Cornification
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Appendix 8.1. Gene ontology results of day 6 DEG (1 of 2)
GO biological process genes in genes in Fold P value process DEG enrichment
Type I interferon signalling pathway 66 12 91.09 2.38E‐16 Cytokine‐mediated signalling pathway 613 15 12.26 4.45E‐09 Cellular response to cytokine stimulus 928 16 8.64 1.11E‐07 Response to cytokine 1014 16 7.91 4.09E‐07 Response to organic substance 2840 19 3.35 5.21E‐03 Response to stimulus 8269 32 1.94 1.40E‐02 Cellular response to organic substance 2229 19 4.27 1.08E‐04 Cellular response to chemical stimulus 2737 21 3.84 7.70E‐05 Cellular response to type I interferon 66 12 91.09 2.38E‐16 Response to type I interferon 71 12 84.68 5.28E‐16 Innate immune response 739 21 14.24 8.65E‐16 Immune response 1813 23 6.36 2.77E‐10 Immune system process 2676 25 4.68 1.08E‐08 Defense response 1298 24 9.26 1.15E‐14 Response to stress 3413 27 3.96 3.40E‐08
Negative regulation of viral genome replication 53 8 75.62 2.90E‐09 Negative regulation of viral life cycle 77 8 52.05 4.57E‐08 Negative regulation of viral process 93 8 43.1 1.88E‐07 Negative regulation of multi‐organism process 173 8 23.17 2.02E‐05 Regulation of multi‐organism process 378 12 15.9 7.91E‐08 Regulation of viral process 179 8 22.39 2.61E‐05 Regulation of symbiosis, encompassing mutualism 208 8 19.27 8.11E‐05 through parasitism Regulation of viral life cycle 140 8 28.63 4.09E‐06 Regulation of viral genome replication 89 8 45.03 1.35E‐07
Negative regulation of type I interferon production 44 5 56.93 3.81E‐04 Regulation of type I interferon production 115 6 26.14 1.22E‐03 Regulation of cytokine production 652 11 8.45 4.19E‐04
Only statistically significant GO results are shown as determined by Bonferroni correction for P <0.05. denotes that the given process falls within the above process.
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Appendix 8.1. Gene ontology results of day 6 DEG (2 of 2)
GO biological process genes in genes in Fold P value process DEG enrichment
Defense response to virus 191 15 39.35 2.87E‐16 Defense response to other organism 515 16 15.57 1.66E‐11 Response to other organism 949 20 10.56 2.49E‐12 Response to external biotic stimulus 951 20 10.54 2.60E‐12 Response to external stimulus 2045 21 5.14 3.63E‐07 Response to biotic stimulus 977 20 10.26 4.31E‐12 Multi‐organism process 2406 21 4.37 7.41E‐06 Response to virus 281 16 28.53 1.67E‐15 Immune effector process 1068 15 7.04 9.64E‐06
Positive regulation of response to cytokine stimulus 52 4 38.54 4.03E‐02 Regulation of response to cytokine stimulus 165 6 18.22 9.34E‐03
Cellular response to interferon‐gamma 161 6 18.67 8.13E‐03 Response to interferon‐gamma 183 8 21.9 3.09E‐05
Regulation of innate immune response 368 8 10.89 5.74E‐03 Regulation of immune response 1050 11 5.25 4.39E‐02 Regulation of immune system process 1548 17 5.5 2.14E‐05 Regulation of defense response 763 10 6.57 1.83E‐02
Only statistically significant GO results are shown as determined by Bonferroni correction for P <0.05. denotes that the given process falls within the above process.
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Appendix 8.2. Gene ontology results of day 21 DEG
genes in genes in fold GO biological process process sample enrichment p value
Cornification 112 7 38.68 6.31E‐6 Keratinization 225 8 22 2.44E‐5 Keratinocyte differentiation 267 10 23.18 1.13E‐7 Epidermal cell differentiation 311 10 19.9 4.82E‐7 Epithelial cell differentiation 666 11 10.22 4.40E‐5 Epithelial development 1085 11 6.27 5.89E‐3 Epidermis development 411 10 15.06 6.76E‐6 Skin development 374 10 16.55 2.77E‐6 Programmed cell death 1029 13 7.82 3.00E‐5 Cell death 1068 13 7.53 4.66E‐5
Neutrophil degranulation 483 8 10.25 7.60E‐3 Neutrophil mediated immunity 494 8 10.02 8.97E‐3 Myeloid leukocyte mediated immunity 515 8 9.61 1.22E‐2 Neutrophil activation involved in immune response 487 8 10.17 8.07E‐3 Myeloid cell activation involved in immune response 517 8 9.58 1.25E‐2 Leukocyte activation involved in immune response 613 8 8.08 4.36E‐2 Cell activation 1043 10 5.93 3.53E‐2 Cell activation involved in immune response 617 8 8.02 4.57E‐2 Myeloid leukocyte activation 571 8 8.67 2.60E‐2 Neutrophil activation 495 8 10 9.11E‐3 Granulocyte degranulation 500 8 9.9 9.81E‐3 Leukocyte degranulation 505 8 9.8 1.06E‐2 Regulated exocytosis 693 9 8.04 1.04E‐2 Exocytosis 782 9 7.12 2.77E‐2
Only statistically significant GO results are shown as determined by Bonferroni correction for P <0.05. denotes that the given process falls within the above process.
A Prostate cell line model of Zika virus infection 101
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Appendix 9.1. Day 6 DEG Prominent KEGG maps; Measles defence
*Significant DEG from ZIKV infection denoted by red box
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Appendix 9.2. Day 6 DEG Prominent KEGG maps; Influenza A defence
*Significant DEG from ZIKV infection denoted by red box
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Appendix 9.3. Day 6 DEG Prominent KEGG maps; RIG-1 signalling pathway
*Significant DEG from ZIKV infection denoted by red box
104 A Prostate cell line model of Zika virus infection
Appendix 9.4. Day 6 DEG Prominent KEGG maps; Human papillomavirus defence
*Significant DEG from ZIKV infection denoted by red box
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Appendix 9.5. Day 6 DEG Prominent KEGG; Herpes simplex virus defence
*Significant DEG from ZIKV infection denoted by red box
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Appendix 9.6. Day 21 DEG prominent KEGG; RAS signalling pathway
*Significant DEG from ZIKV infection denoted by red box
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Appendix 9.7. Day 21 DEG prominent KEGG; Compliment and coagulation cascades
*Significant DEG from ZIKV infection denoted by red box
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Appendix 9.8. Day 21 DEG prominent KEGG; IL-17 pathway
*Significant DEG from ZIKV infection denoted by red box
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