UNIVERSITY OF GHANA COLLEGE OF BASIC AND APPLIED SCIENCES THE EFFECTS OF SARS-CoV-2 INFECTION ON CANCER-LIKE PHENOTYPES AND CYTOKINE PRODUCTION IN CANCER CELL LINES BY ALBERTA SERWAA (10877284) A THESIS SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE AWARD OF THE DEGREE OF MASTER OF PHILOSOPHY IN MOLECULAR BIOLOGY DEPARTMENT OF BIOCHEMISTRY, CELL, AND MOLECULAR BIOLOGY JANUARY 2023 University of Ghana http://ugspace.ug.edu.gh ii DECLARATION I, Alberta Serwaa, herewith declare that except for cited references, this MPhil thesis titled “The Effects of SARS-CoV-2 Infection on Cancer-like Phenotypes and Cytokine Production in Cancer Cell Lines” was aptly completed by myself at the Department of Biochemistry, Cell, and Molecular Biology, University of Ghana. I also certify that all results obtained therein are the true reflection of the work completed under the guidance and supervision of Dr. Anastasia Rosebud Aikins and Dr. Peter Kojo Quashie. Signature:…………………………………………Date: ……………………………………… Alberta Serwaa (Student) Signature:…… …… Date: …23rd October, 2023………………. Anastasia R. Aikins (Supervisor) Signature: ………Date: …23 October 2023………………… Dr. Peter Kojo Quashie. (Supervisor) 23rd October, 2023 University of Ghana http://ugspace.ug.edu.gh iii ABSTRACT The occurrences of cancer and cancer-related mortality are a growing burden worldwide and co- infections with other pathogens such as viruses might contribute to disease pathogenesis. Cancer is one of the leading comorbidities for the recent global pandemic ‘Coronavirus Disease 2019’ (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The infection causes severe respiratory injury, organ failure, and hyper-production of inflammatory cytokines. The causative virus initiates infection by attaching to the human Angiotensin Converting Enzyme 2 (ACE2) receptor through the receptor-binding domain (RBD) within the viral spike protein. Also, successful infection is dependent on the activation of the spike by host proteases like the Transmembrane serine protease 2 (TMPRSS2). Studies have shown that ACE2 and TMPRSS2 are present in healthy cells and may play important roles in regulating cellular function. Other studies have also shown aberrant ACE2 expression in cancer cells. This research sought to study how ACE2 expressed on cancer cells interacts with SARS-CoV-2 spike protein and pseudoviruses (PV’s), and how this interaction affects cancer phenotypic properties and cytokine expression. Gene expression analysis for ACE2 and TMPRSS2 in breast, colorectal, and prostate tumors tissues and normal tissues was carried out using Gene Expression Profiling Interactive Analysis (GEPIA) software. The protein expression levels of ACE2 on seven cancer cell lines (MDA-MB-231, MDA-MB-468, DLD-1, COLO205, HCT-15, 22RV1, and BPH1) were screened using dot blot assay. In vitro, analysis of the interaction of Spike and ACE2 was examined by producing SARS-CoV-2 pseudovirus (PV) to infect the cell lines. The transduction efficiency of the PVs was measured by the quantification of luciferase activity. Cancer cell proliferation, viability, migration, and angiogenic markers were analyzed post-infection using3-(4,5- Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay, cell titer Glo viability University of Ghana http://ugspace.ug.edu.gh iv assay, wound healing assay, and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Cytokines expression post-infection was investigated using qRT-PCR. The effects were further confirmed with live virus (LV) infections using MTT and qRT-PCR. Using in silico analysis, ACE2 was shown to be expressed highly in colorectal tissues and low in breast and prostate tissues. In vitro, ACE2 expression data was consistent with the in-silico analysis. However, out of 7 cell lines, a significant PV infection was observed only in 22RV1 (prostate) and DLD-1 (colorectal). PV decreased 22RV1 proliferation but have an inconclusive effect on DLD- 1. Proliferation and migration were increased in LV- infected 22RV1 but were decreased in LV- infected DLD-1. Infections increased cytokine levels in 22RV1. Similarly, PV increased cytokine expression for DLD-1 cells. However, LV downregulated IL-1β and IL-8 in DLD-1. LV infection affected the expression of genes involved in proliferation, migration, angiogenesis, and the expression of cytokines. Although ACE2 expression might not ensure possible infection of SARS- CoV-2. The current findings suggest that infection of 22RV1 and DLD-1 with the virus may affect their cellular properties and gene expression. The infection can upregulate and downregulate the expression of cytokines in cancer contributing to the progression or regression of these cancers. Additionally, PV might not be an appropriate model for the study on the viral-host response partly because LV infections and PV infections have contrasting effects on cancer properties in different cell lines. University of Ghana http://ugspace.ug.edu.gh v DEDICATION I dedicate this to my late father Mr. Anthony Opoku Mensah and all other persons with cancer who suffered a great deal during this COVID-19 pandemic. University of Ghana http://ugspace.ug.edu.gh vi ACKNOWLEDGEMENTS My deepest thanks and sincere appreciation to my supervisors Dr. Anastasia R. Aikins and Dr. Peter K. Quashie for their support and guidance throughout this project. I am profoundly grateful to them for dedicating their time and resources to shape and improve the quality of the research and myself as a researcher. I would like to acknowledge the West African Genetic Medicine Center (WAGMC) and the Quashie lab for funding this work. I wish to also acknowledge the West African Center for Cell Biology of Infectious Pathogens (WACCBIP). I am grateful to Fatima Oyawoye for her enthusiastic guidance and consistent assistance, and to Charles Olwal, Irene Amoakoh Owusu, and Bernardine Tuah for their help during the project. I also want to express my gratitude to the Quashie Lab and Aikins Lab for their assistance with the project. I would also like to appreciate my friends; Jennifer Boah, Kwadwo Fosu, Kate Sagoe, Mabel Adu Boahen, Miriam Yarley Yartey, Edem Adika, Bernice Sarsah, and Judy Puplampu for their words of encouragement during the work. A big thanks to Daniel Dosoo, Nelson Edu, and the members of the protein expression lab (WACCBIP) for their support during the work. I would like to thank my siblings, most especially Dr. Eric Mensah Sarpong for their prayers, mental support, and financial support during my study. Finally, I would like to acknowledge my colleagues and the entire staff of the Department of Biochemistry, Cell and Molecular Biology, University of Ghana, especially Dr. Abiola Isawumi. I am grateful to God. University of Ghana http://ugspace.ug.edu.gh vii TABLE OF CONTENTS DECLARATION .......................................................................................................................................... ii ABSTRACT ................................................................................................................................................. iii DEDICATION .............................................................................................................................................. v ACKNOWLEDGEMENTS ......................................................................................................................... vi TABLE OF CONTENTS ............................................................................................................................ vii LIST OF FIGURES .................................................................................................................................... xii LIST OF TABLES ...................................................................................................................................... xiv LIST OF ABBREVIATIONS ..................................................................................................................... xv CHAPTER ONE ........................................................................................................................................... 1 1.0 INTRODUCTION .............................................................................................................................. 1 1.1 BACKGROUND ................................................................................................................................ 1 1.2 PROBLEM STATEMENT ................................................................................................................. 4 1.3 RATIONALE ...................................................................................................................................... 5 1.4 HYPOTHESES ................................................................................................................................... 5 1.5 AIM ..................................................................................................................................................... 6 1.6 OBJECTIVES ..................................................................................................................................... 6 CHAPTER TWO .......................................................................................................................................... 7 2.0 LITERATURE REVIEW ................................................................................................................... 7 2.1 Epidemiology of cancer ...................................................................................................................... 7 2.2 Characteristics of cancers (cancer hallmarks) ................................................................................... 10 University of Ghana http://ugspace.ug.edu.gh viii 2.3 Infectious agents and cancer ............................................................................................................. 13 2.4 Coronaviruses (CoVs) ....................................................................................................................... 14 2.5 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) ............................................... 15 2.6 Angiotensin-converting enzyme 2 (ACE2) ....................................................................................... 19 Transmembrane serine protease 2 (TMPRSS2) ...................................................................................... 21 2.7 Coronavirus disease 2019 (COVID-19) ............................................................................................ 21 2.8 Impact of COVID-19 and cancer comorbidity.................................................................................. 27 2.8.1 Impact of COVID-19-related immune response on cancer ............................................................ 28 2.8.2 Impact of COVID-19-related inflammation on cancer .................................................................. 31 CHAPTER THREE .................................................................................................................................... 33 3.0 MATERIALS AND METHODS ...................................................................................................... 33 3.1 Study design ...................................................................................................................................... 33 3.2 In silico gene expression analysis ..................................................................................................... 33 3.3 In vitro analysis for ACE2 expression in breast, colorectal, and prostate cancer cell lines .............. 33 3.3.1 Cell Culture ................................................................................................................................ 33 3.3.2 Dot blot ...................................................................................................................................... 34 3.4 Pseudovirus production and infection ............................................................................................... 35 3.4.1 Bacterial transformation ............................................................................................................. 35 3.4.2 DNA extraction .......................................................................................................................... 36 3.4.3 Pseudovirus production .............................................................................................................. 37 University of Ghana http://ugspace.ug.edu.gh ix 3.4.4 Pseudovirus luciferase assay ...................................................................................................... 38 3.5 Cell proliferation assay ..................................................................................................................... 38 3.6 Cell viability assay ............................................................................................................................ 39 3.7 Wound healing assay ........................................................................................................................ 39 3.8 Gene expression analysis .................................................................................................................. 40 3.9 Live virus infection ........................................................................................................................... 42 3.10 Statistical Analysis .......................................................................................................................... 43 CHAPTER FOUR ....................................................................................................................................... 44 4.0 RESULTS ......................................................................................................................................... 44 4.1. ACE2 and TMPSS2 genes are expressed differentially in normal and tumor tissues ..................... 44 4.2 In vitro analysis of ACE2 expression in some cancer cell lines ....................................................... 46 4.3 SARS-CoV-2 PV infection of cancer cell lines ................................................................................ 48 4.4 Phenotypic and molecular analysis of PV-infected 22RV1 cell line ................................................ 50 4.4.1 Effects on the proliferation and viability of 22RV1 ................................................................... 50 4.4.2 Effects of PV on the migration of 22RV1 cell lines .................................................................. 52 4.4.3 Effects on the expression of angiogenic growth factor VEGF in 22RV1 .................................. 54 4.5 Phenotypic and molecular effects of PV-infected DLD-1 cell lines ................................................. 55 4.5.1 Effects on the proliferation and viability of DLD-1 ................................................................... 55 4.4.2 Effects of infection on the migration of DLD-1 cell lines ......................................................... 57 4.4.3 Effects of infection on the expression of angiogenic growth factor VEGF in DLD-1 .............. 59 University of Ghana http://ugspace.ug.edu.gh x 4.5 Cytokine profiling of PV-infected cancer cell lines .......................................................................... 60 4.5.1 Effects of infection on the expression of TNF-α, IL-1β, IL-6, and IL-8 in 22RV1 ................... 60 4.5.2 Effects of infection on the expression of IL-1β, IL-6 and IL-8 in DLD-1 ................................. 62 4.6 Live virus (LV) infection effect on cancer cell phenotype and gene expression .............................. 64 4.6.1 LV Effects on the proliferation of 22RV1 ................................................................................. 64 4.6.2 LV effects on the migration of 22RV1 cell lines ....................................................................... 66 4.6.3 LV Effects on the expression of angiogenic growth factor VEGF in 22RV1 ............................ 67 4.7 Effects of LV infection in DLD-1 cell lines...................................................................................... 68 4.7.1 Effects on the proliferation and viability of DLD-1 ................................................................... 68 4.7.2 LV Effects on the migration of DLD-1 cell lines ...................................................................... 70 4.7.3 Effects of infection on the expression of angiogenic growth factor VEGF in DLD-1 .............. 71 4.8 Cytokine profiling of LV-infected cancer cell lines ......................................................................... 72 4.8.1 Effects of infection on the expression of TNF-α, IL-1β, IL-6, and IL-8 in 22RV1 ................... 72 4.8.2 Effects of infection on the expression of IL-1β, IL-6 and IL-8 in DLD-1 ................................. 74 CHAPTER FIVE ........................................................................................................................................ 76 5.0 DISCUSSION, CONCLUSION, AND RECOMMENDATIONS ................................................... 76 5.1 DISCUSSION ................................................................................................................................... 76 5.1.1 SARS-CoV-2 PV infectivity of cancer cells .............................................................................. 77 5.1.2 Effects of PV infections on cancer cell lines ............................................................................. 78 5.1.3 Effects of LV infections on cancer properties ............................................................................ 80 University of Ghana http://ugspace.ug.edu.gh xi 5.2 CONCLUSION ................................................................................................................................. 81 5.3 RECOMMENDATIONS .................................................................................................................. 82 REFERENCES ........................................................................................................................................... 83 APPENDIXES .......................................................................................................................................... 110 University of Ghana http://ugspace.ug.edu.gh xii LIST OF FIGURES Figure 2.1 Regional distribution of the estimated number of new cases for all cancers (Globocan 2020) ............................................................................................................................................... 8 Figure 2.2 (A) Number of new cancer cases worldwide (B)Number of new cancer cases in Ghana. The data represents both sexes and all ages in 2020 (Globocan 2020) .......................................... 9 Figure 2.3: SARS-CoV-2 virion structure (A) and genome organization(B) (Alsobaie, 2021) ... 17 Figure 2.4: SARS-CoV-2 infection cycle (Malone et al., 2022) .................................................. 19 Figure 2.5: SARS-CoV-2 infections immune response and cytokine storm (Yang et al, 2021) .. 26 Figure 2.6: Possible impacts of COVID-19 on cancer progression (Saha & Anirvan, 2020) Error! Bookmark not defined. Figure 4.1: Gene expression levels of ACE2 and TMPRSS2 in tumor and matched normal tissues (A&B). .......................................................................................................................................... 45 Figure 4.2: Protein expression quantification of ACE2 in cancer cell lines. ................................ 47 Figure 4.3: Infection analysis of cell lines with SARS-CoV-2 Spike PV. A significant infection was detected in DLD-1 and 22RV1 .............................................................................................. 49 Figure 4.4: PV reduces proliferation and viability of 22RV1. ...................................................... 51 Figure 4.5: Effects of infection on the migration of 22RV1 cells. ............................................... 53 Figure 4.6: Infection causes the upregulation of VEGF in 22RV1............................................... 54 Figure 4.7: Proliferative effect of PV in DLD-1 cell line. ............................................................ 56 Figure 4.8: Effects of infection on the migration of DLD-1 cells. ............................................... 58 Figure 4.9: Effects of infection on angiogenic growth factor VEGF. .......................................... 59 Figure 4.10: Infection increases the mRNA expression levels of some cytokines. ...................... 61 Figure 4.11: Effects of infection on expression of TNF-α, IL-1β, IL-6, and IL-8........................ 63 University of Ghana http://ugspace.ug.edu.gh xiii Figure 4.12: LV increases proliferation of 22RV1. ...................................................................... 65 Figure 4.13: Effects of infection on the migration of 22RV1 cells. ............................................. 66 Figure 4.14: Infection causes the upregulation of VEGF in 22RV1............................................. 67 Figure 4.15: Proliferative effect of LV in DLD-1 cell line. .......................................................... 69 Figure 4.16: LV reduces migration markers of DLD-1 cells. ....................................................... 70 Figure 4.17: Effects of LV infection on angiogenic growth factor VEGF. .................................. 71 Figure 4.18: Infection increases the mRNA expression levels of some cytokines. ...................... 73 Figure 4.19: Effects of infection on expression of TNF-α, IL-1β, IL-6, and IL-8........................ 75 University of Ghana http://ugspace.ug.edu.gh xiv LIST OF TABLES Table 1: List of cell lines .............................................................................................................. 34 Table 2: List of primers for gene expression analysis .................................................................. 41 Table 3: RT-qPCR cycling condition for gene expression analysis ............................................. 42 University of Ghana http://ugspace.ug.edu.gh xv LIST OF ABBREVIATIONS 22RV1 Human prostatic carcinoma epithelial cell line ACE2 Angiotensin-converting enzyme 2 BCL2 B-cell lymphoma 2 BRCA Breast Invasive carcinoma COAD Colon adenocarcinoma CoV Coronavirus COVID-19 Coronavirus disease 2019 DLD-1 Colorectal adenocarcinoma cell line DNA Deoxyribonucleic Acid E Envelope IL Interleukin KI-67 Marker of proliferation LB Luria Broth LV Live Virus M Membrane MERS-CoV Middle East respiratory syndrome coronavirus MMP9 Matrix metallopeptidase 9 MTT 3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyltetrazolium Bromide N Nucleocapsid Nsp Non-structural protein ORF Open reading frame University of Ghana http://ugspace.ug.edu.gh xvi Pp Poly protein PRAD Prostate adenocarcinoma PV Pseudovirus RNA Ribonucleic acid q-RT PCR Quantitative-Reverse transcription polymerase chain reaction S Spike SARS-CoV Severe acute respiratory coronavirus SARS-COV-2 Severe acute respiratory coronavirus 2 TCID50 Median Tissue Culture Infectious Dose TMPRSS2 Transmembrane serine protease 2 TNF Tumor necrosis factor VEGF Vascular endothelial growth factor VIM Vimentin WHO World Health Organization BSA Bovine serum albumin PBS Phosphate buffer saline PBS-T Phosphate buffer saline-tween 20 BSA-PBS-T Bovine serum albumin-Phosphate buffer saline-tween 20 University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1.0 INTRODUCTION 1.1 BACKGROUND As one of the leading causes of death worldwide, cancer continues to be a global burden (Hu & Fu, 2012). The GLOBOCAN 2020 estimates of cancer incidence and mortality show that there were 10.3 million deaths and 19.3 million new cancer incidents worldwide (Sung et al., 2021). In Ghana and some parts of Africa, cancer is a rising and generally unrecognized public health concern (Adam & Koranteng, 2020). Most developing countries, including Ghana, lack regionally pertinent information or cancer data repositories. Despite efforts to overcome this concern, the lack of population-based research data with a suitable representative population remains a challenge (Laryea et al., 2014). Some reports predict that low or medium-development index countries would have the biggest relative rises in cancer incidence by 2040 (Arnold et al., 2022; Wilson et al., 2018). However, there are still large disparities between high and low-income nations Over the years, research has been undertaken to identify the true etiology of cancer. According to reports, a person is likely to acquire cancer from both hereditary and environmental elements (Garcia-Closas et al., 2014; William Audeh, 2012). The development of some malignancies has been linked to natural elements like hormones. Food additives and medications containing synthetic materials may also promote the growth of cancer. Infectious agents like viruses, bacteria, and parasites may also influence cancer development (Blackadar, 2016). Consequently, it is possible to categorize the emergence and spread of cancer as multifaceted. Normally, the human immune system mounts an immunological response upon the detection of tumor antigens. These responses include the activation of immune cells and the induction of University of Ghana http://ugspace.ug.edu.gh 2 inflammatory responses, characterized by the production of various cytokines (Candeias & Gaipl, 2016). Malignant cells can, however, develop defense mechanisms to evade the body's adaptive immune response. Examples include producing immunosuppressive substances like cytokines and becoming immune cell resistant (Loose & Wiele, 2009). Additionally, those who have cancer often have altered immune systems, which lowers the body's capacity to defend itself against infections. The mounted resistance to immune response, drugs, and other forms of treatment, all contributes to factors that weaken the immune system. Consequently, this increases the risk of bacterial and viral infections in cancer patients (Steele, 2012). Previous studies have shown that community respiratory viruses are a common cause of respiratory disease in immunocompromised persons with cancer (Whimbey, 1997; Devine & Wingard, 1994). That is, the immunocompromised nature of cancer patients makes them more susceptible to viral infections compared to others. It could thus be inferred that cancer patients are at a higher risk of infection, symptom severity, and even death from SARS-CoV-2 infection. The etiological cause of the Coronavirus disease 2019 (COVID-19), SARS-CoV-2 is a highly contagious and pathogenic virus that surfaced during the last quarter of 2019 (Hu et al., 2021). The disease was declared a global pandemic by the World Health Organization (WHO) in the first quarter of 2020 after being detected in numerous countries. Research has revealed that the virus initiates infection via attachment to the ACE2 receptor on host cell surfaces using its receptor binding domain within the spike(S) protein (Shang et al., 2020). The process is aided by the proteolytic processing of the spike protein, to allow entry into the host cell. This proteolytic cleavage is brought about by proteases produced by the host. Examples include the endoprotease furin, the lysosomal protease cathepsin L, and the transmembrane protease serine 2 (TMPRSS2) (Hoffmann et al., 2020). University of Ghana http://ugspace.ug.edu.gh 3 These proteins involved in the successful entry of the virus are expressed in a range of organs and are involved in several biological processes (Cao et al., 2021; Hikmet et al., 2020). In a normal cell, ACE2 expression is crucial in regulating cellular function by generating small proteins that regulate processes such as blood pressure, wound healing, and inflammation (Sriram et al., 2020). Also, the expression of these proteins on cancer cells and their role in cancer progression have been the subject of several studies (Cheng et al., 2021; Dai et al., 2020). The expression of ACE2 on cancer cell surfaces has been shown to have diverse biological functions. Studies reported on the impact of ACE2 in cancer are thus far inconclusive; ACE2 may have both favorable and unfavorable outcomes in cancer development and this differs with different cancer types. One study has shown that the receptor has a beneficial effect in reducing certain cancer properties in different cancer types such as lung cancer, breast cancer, colon cancer, and pancreatic cancer (Xu et al., 2017). The in vitro overexpression of the protein in non-small cell lung cancer was shown to reduce the production of vascular endothelial growth factor (VEGF) and inhibit cell growth (Aydiner et al., 2015; Catarata et al., 2020). Similarly, Zhang Q et al., 2019, reported a decrease in breast cancer angiogenesis by ACE2. Nonetheless, it has been shown to enhance the migratory and invasive capabilities of renal adenocarcinomas thereby having a negative effect (Almutlaq et al., 2021; Zheng et al., 2015). Either way, the way cells, particularly cancer cells communicate is through the release of different stimulatory molecules, one of which is broadly referred to as cytokines. Reports indicate that a hyper-inflammatory condition known as "cytokine storm" is the primary cause of the majority of the severe symptoms that occur after SARS-CoV-2 infection (Song et al., 2020). The term refers to a host-driven severe hyper-inflammatory immune response to cancers and infections, such as those caused by SARS-CoV-2 (Bhaskar et al., 2020). One of COVID-19's University of Ghana http://ugspace.ug.edu.gh 4 more prevalent comorbidities is cancer, but the immunological import of this is not yet understood. For a SARS-CoV-2-cancer comorbid infection, hyperproduction of cytokines may result in novel cytokine combinations within the tumor microenvironment. This may in turn affect the cancer-like properties of the cancerous cells. Cancerous cells produce and release certain pro-inflammatory cytokines into the tumor microenvironment which can drive cancer pathogenesis (Dehne et al., 2017). However, numerous inflammatory factors may be implicated in mechanisms that promote tumor development (Singh et al., 2019). Cytokines which are produced as a result of infection and infection-related inflammation may serve to reduce cancer development (Maeda & Omata, 2008). Alternatively, these cytokines can be utilized by cancers to promote growth, attenuate apoptosis, and facilitate invasion and metastasis (Lu et al., 2006). 1.2 PROBLEM STATEMENT One key factor in COVID-19 disease susceptibility, severity, and mortality was associated with comorbidities/persons with underlying medical conditions (Fang et al., 2020; Sanyaolu et al., 2020). COVID-19 comorbidity describes the coexistence of any long-term disease with the COVID-19 disease. The pandemic was shown to have a devastating effect on persons with cancer leading to significantly high mortality risk. The disease's influence on cancer diagnosis, compared with the data before the pandemic observed that the number of deaths caused by breast, colorectal, lung, and esophageal cancer will increase by 7.9–9.6%, 15.3–16.6%, 4.8–5.3%, and 5.8–6.0% in five years, respectively (Maringe et al., 2020). In 2020, most countries suspended routine screening for cancer in response to the COVID-19 pandemic. Also, COVID-19 has been shown to cause dysregulation in immune reactions resulting in a ‘‘cytokine storm’’ (Prompetchara et al., 2020). This poses a critical challenge to cancer patients by increasing poor prognosis (Disis et al., 2020). In cancer patients with COVID-19, the impact of increased health loss varies, making it more University of Ghana http://ugspace.ug.edu.gh 5 difficult to forecast disease progression and manage illness symptoms. Data on how SARS CoV- 2 may influence the growth of cancer cells or its spread however is limited. Also, there is limited data on the effect of SARs-CoV-2 on cytokine production in cancer. 1.3 RATIONALE Cancer cells expressing the ACE2 receptor for cellular function may be susceptible to SARS-CoV- 2 infection. The ACE2 receptor's functionality and signaling may be impacted by this potential infection of cancer cells, which may also have an impact on the phenotypic characteristics of cancer cells. Therefore, it is important to know the relationship between ACE2 levels, viral infectivity, and infection intensity in cancer cells. Again, cytokines are important in the pathophysiology of cancer and have been studied as potential diagnostic and prognostic indicators of cancer. Cytokines can either promote or repress tumor growth when they are generated in response to infections, inflammation, and immune responses. As a result, COVID-mediated cytokine production in cancer cells may stimulate or suppress tumor growth. It is therefore crucial to comprehend the impact of cytokine storms on cancers amid this pandemic. While cancer has proved over time to be a global health concern, co-infection with the new SARS-CoV-2 may contribute to disease pathogenesis and its implications on the worldwide cancer burden and global cancer care are unknown. This in-depth investigation into the plausible interaction and/or infection of the SARS-CoV-2 virus on cancer cells will aid in understanding its impact on cancer characteristics and cytokine generation by cancer cells. 1.4 HYPOTHESES 1. Differential expression of the ACE2 receptor in cancer tissues correlates with the ability to be infected by SARS-CoV-2 virions and pseudoparticles. University of Ghana http://ugspace.ug.edu.gh 6 2. SARS-CoV-2 interaction with ACE2 receptor on cancer cells alters cancer properties and cytokine production 1.5 AIM To investigate the effect of SARS-CoV-2 infection on cancer-like properties and cytokine production in different cancer cell lines. 1.6 OBJECTIVES Specifically, the study sought to: 1. Determine SARS-CoV-2 infectivity of some cancer cell lines and the effects of infection on some selected cancer phenotypes (Proliferation, Migration, and Angiogenesis) 2. Determine the effects of SARS-CoV-2 infection on the expression of pro-inflammatory cytokines TNF-α, IL-1β, IL-6, and IL-8 in some cancer cell lines University of Ghana http://ugspace.ug.edu.gh 7 CHAPTER TWO 2.0 LITERATURE REVIEW 2.1 Epidemiology of cancer Cancer is a non-communicable disease that develops as a result of the abnormal growth of cells. This unnatural cell proliferation could lead to cancer cells invading other cell types in the body and spreading their properties (Boutayeb & Boutayeb, 2005; Chaker et al., 2015; Preethi et al., 2021). According to the WHO, the term “cancer” refers to a broad range of illnesses that affect various parts of the body. There are a variety of factors that contribute to its genesis. Mostly, cancer is referred to as a genetic disease brought about by diverse genetic factors (Peto & Houlston, 2001). However, many epidemiological studies have revealed environmental factors, which could be physical, chemical, or biological may be crucial in the disease’s genesis and progression (Montesano & Hall, 2001). A few such environmental factors include infections, radiation, lifestyle choices such as diet, smoking, alcohol, and other occupational exposures (Clapp et al., 2008; Czene et al., 2002). Data compiled by the WHO implies that cancer is a leading cause of mortality globally, accounting for around 10 million fatalities. Regionally, most cancer cases and related mortality were recorded in Asia, which was 49.3% and 58.3% respectively. Africa recorded about 5.7% cases and 7.2% deaths in 2020. The regional distribution of the number of new cases is shown in Figure 2.1 (Sung et al., 2021). University of Ghana http://ugspace.ug.edu.gh 8 Figure 2.1 Regional distribution of the estimated number of new cases for all cancers (Globocan 2020) However, as shown in Figure 2.2a below, the breast (11.7%), lung (11.4%), colorectum (10%), prostate (7.3%), stomach (5.6%), and liver (4.7%) were the cancer types with the highest incidence rates worldwide. Breast cancer was highest in women accounting for 24.5% incidence and 15.5% deaths followed by colorectal cancer which was 9.4% and 9.5% cases and death respectively. In men, lung cancer was seen to be the highest with 14.3% cases and 21.5% deaths and this was also followed by prostate cancer with 14.1% and 6.8% cases and death respectively (Sung et al., 2021). University of Ghana http://ugspace.ug.edu.gh 9 Figure 2.2 Number of (A) new cancer cases worldwide and (B) in Ghana. The data represents both sexes and all ages in 2020 (Globocan 2020) In Ghana, the case and mortality trends show that there was a total of 24009 new cases and 15802 cancer deaths according to GLOBOCAN 2020 reports. Out of this, cases of breast cancer accounted for 18.7%, liver cancer for 14.4%, uterine cancer for 11.6%, prostate cancer for 8.9%, non-Hodgkin lymphoma for 5%, and other cancers for 41.5%. as shown in figure 2.2b above. Again, breast cancer was the highest among females whilst liver and prostate were the highest cancer cases among men (Siegel et al., 2021). University of Ghana http://ugspace.ug.edu.gh 10 2.2 Characteristics of cancers (cancer hallmarks) Normal cells within the human body multiply by a firmly controlled cell division machinery which involves balancing the production and release of growth-promoting signals (Bahassi, 2007). Unlike normal cells, cancerous cells acquire numerous genetic and epigenetic alterations of genes that enable their continuous growth (Liu et al., 2014). This phenomenon is mostly brought about by diverse factors such as exposure of normal cells to infectious particles, chemicals, radiation, and other environmental factors (Czene et al., 2002). These alterations cause the gain of abnormal phenotypic properties which constitute the six biological hallmarks of cancer proposed by Hanahan and Weinberg: (i) increase in cellular proliferation, (ii) resistance to cell death, (iii) evasion of growth suppressors, (iv) enhancement of angiogenesis, (v) activating invasive and metastatic potentials and (vi) enabling replicative mortality (Hanahan & Weinberg, 2011). Studies have uncovered other hallmarks also implicated in cancer progression. These include avoidance of immune destruction, tumor-promoting inflammation, genome instability, and mutations, and lastly, deregulation of cellular energetics (Arakelyan et al., 2023; Pavlova et al., 2022; Pavlova & Thompson, 2016). 2.3 Phenotypic and molecular determinants of cancer progression 2.3.1 Tumor proliferation Tumor proliferation involves the abnormally rapid growth of cells brought about by the sustenance of signals that promote cell growth. Normal cells regulate the synthesis and the release of growth- promoting signals. However, this control is compromised in cancer cells, enabling the continual proliferation of cancer cells (Nussinov et al., 2017). Many cellular replication genes are overexpressed in cancers making them possible markers to measure cancer progression and diagnosis (Kristiansen, 2018). Some of such include markers of proliferation KI-67, University of Ghana http://ugspace.ug.edu.gh 11 minichromosomal maintenance protein complex (MCM), and proliferating cell nuclear antigen (PCNA) (Cuzick et al., 2011; Juríková et al., 2016). PCNA is mostly used as a cell cycle marker that functions importantly in the metabolism of nucleic acids, DNA replication, cell cycle control, and chromatin assembly. The MCM serves as a proliferation marker due to its important role in DNA replication. The most common proliferation marker is the KI-67 (Juríková et al., 2016). It is an important prognostic marker of cell growth and may reflect the aggressiveness of cancers such as breast cancer (Denkert et al., 2015). The KI-67 gene encodes proteins necessary for proliferation and it is detected in all the phases of the cell cycle process except at the resting stage. High KI-67 expression levels may indicate rapid cell division and vice versa (Sobecki et al., 2017). 2.3.2 Resistance to Apoptosis Apoptosis also referred to as programmed cell death is a controlled process of cellular self- destruction that ensures proper regulations of cellular processes such as development, tissue homeostasis, and removal of damaged cells (Chinnasamy et al., 2020). However, cancerous cells mutate and acquire sustained proliferative ability that reduces cell death hence altering the normal balance between cell proliferation and cell death (Evan & Vousden, 2001). Cellular apoptosis can be measured or detected with several assays. This includes dye exclusion text, lactate dehydrogenase leakage assay (LDH), annexin V-FITC assay, 3-(4,5-Dimethylthiazol-2-yl)-2,5- Diphenyltetrazolium Bromide (MTT) assay, DNA fragmentation assay, crystal violet assay, etc. (Chinnasamy et al., 2020). The apoptotic death cascade involves markers such as Bcl-2, Bcl-2 associated death (BAD) promoter, caspases, tumor protein 53, etc. (Smyth et al., 2002), and the measurement of these markers provides useful cancer information. The Bcl-2 gene can be used as both an apoptotic and proliferation marker. This is because cells that are resisting cell death and actively proliferating highly express the Bcl-2 (Chinnasamy et al., 2020; Smyth et al., 2002). University of Ghana http://ugspace.ug.edu.gh 12 2.3.3 Angiogenesis The process involves the generation of additional blood vessels which enhances the supply of oxygen and other nutrients necessary for tumor growth (Zare et al., 2013). The process may occur as a result of the generation of pro-angiogenic substances by tumor cells through an angiogenic switch (Viallard & Larrivée, 2017; Zhang et al., 2019). An example of a pro-angiogenic factor is VEGF which is the key regulator that causes the formation and proliferation of endothelial cells ( Zhang et al., 2019). 2.3.4 Cancer cell migration and metastasis Metastatic malignancies are defined by the invasion and migration of cancer cells. This is caused by the propagation of cancer cells from the primary infection tissue to a secondary organ in the body. The process may occur through migratory processes such as collective cell migration, mesenchymal cell migration, and amoeboid cell migration (Chaffer & Weinberg, 2011; Wu et al., 2021). During this process, the cells gain the ability to change their morphology and their capacity to attach to other cells as well as invade the extracellular matrix (ECM) (Hanahan & Weinberg, 2011). Several other factors contribute to the invasion and metastasis of tumors such as angiogenic factors, proteases (cathepsins, collagenase, and metalloproteases (MMPs)), and adhesion proteins (Chin et al., 2005). The type IV collagenase group MMP-2 and MMP-9 are the most reliable prognostic markers out of all the metalloproteases (Chin et al., 2005). MMP-9 proteolytically digests the ECM molecules to enable cell migration. Also, the epithelial-mesenchymal transition (EMT) is another factor implicated in this process where epithelial cancerous cells acquire mesenchymal properties (Tsai & Yang, 2013). Due to this process, mesenchymal markers like vimentin(VIM) express themselves more strongly than epithelial markers like E-cadherin (Qian et al., 2013). VIM is known to encode cytoskeletal proteins usually found in mesenchymal tissues University of Ghana http://ugspace.ug.edu.gh 13 and its higher expression indicates the acquisition of mesenchymal phenotypes through EMT which is indicative of metastasis. 2.3 Infectious agents and cancer Studies have shown that at least 20% of cancers are associated with infectious pathogens either viral, bacterial, or parasitic infections. It behooves that these infections may play a role in cancers and their progression (De Martel et al., 2012; Yasunaga & Matsuoka, 2018). These infectious organisms may contribute to the onset of cancer or the progression of an existing tumor. This is due to their ability to reside within the tumor microenvironment and operate as carcinogens or create oncoproteins that accelerate the course of cancer (Ahmed & Seddon, 2022; Banerjee et al., 2015). The infectious pathogens within the body may also activate the immune response and cause inflammations that could contribute to promoting cancer (Macleod & Mansbridge, 2015). Nonetheless, some of these pathogens tend to enable antitumor immunity and immune surveillance which is advantageous to cancer regression (Pope et al., 2017; Xuan et al., 2014). 2.3.1 Viruses and Cancer Certain cancers have been shown to have viral etiology. These may include human immunodeficiency virus (HIV), human papillomavirus (HPV), Epstein-Barr virus (EBV), Kaposi’s sarcoma-associated herpesvirus (KSHV), human T-lymphotropic virus 1 (HTLV-1), hepatitis B & C virus, among others and their effect can either be direct or indirect (Morales- sánchez & Fuentes-pananá, 2014). These viruses may have the potential to initiate cancer by expressing viral oncogenes, viral latency, or through prolonged infection and inflammation leading to cancer-causing mutations in healthy cells (Mitchell et al., 2015; Moore & Chang, 2010; Polansky & Schwab, n.d.). On the other hand, some viruses may tend to influence the existing tumor microenvironments primarily via modulating cytokine production (Mesri et al., 2014). University of Ghana http://ugspace.ug.edu.gh 14 Studies reveal that comorbid viral infections in cancer may increase the progression of cancer (Panigrahi & Ambs, n.d.; Yasunaga & Matsuoka, 2018). Viral infections may tend to be persistent to cause the activation of chronic inflammation that results in tissue and DNA damage. This implies that novel persistent viruses capable of causing severe inflammatory responses such as SARS-CoV-2 could contribute to cancer progression (Porta et al., 2011; Yasunaga & Matsuoka, 2018). Again, together with the inflammatory pathways, certain mechanisms may be adapted in viral infections to aid cancer progression. Some of these viruses are involved in interleukin (IL) mediated cellular proliferation and survival, ROS oxidative stress, and VEGF-mediated angiogenesis (Yasunaga & Matsuoka, 2018). However, not all viral infections cause carcinogenesis and cancer progression. Owing to their replication cycle, some could be beneficial in tumor regression which has led to recent research into therapeutic viruses (Miest & Cattaneo, 2013). 2.4 Coronaviruses (CoVs) Coronaviruses belong to a family of viruses that cause infections within the respiratory tract and intestines. They have an enormous host range that is inclusive of mammals and birds (Kim et al., 2020). They are named for the way they appear on electron micrographs; enveloped viral particles with club-like structures on their surfaces, giving the appearance of a crown or corona (Sofi et al., 2020; Xian et al., 2020). They form the biggest group of RNA viruses that are a member of the order Nodovirales. Coronaviridae constitutes one of the four families under this order. The family is classified into four main genera based on their genome structures and phylogeny. That is, Alphacoronaviruses, Betacoronaviruses, Gammacoronaviruses, and Deltacoronaviruses. They constitute about thirty-eight known species (Deep et al., 2020; Wu et al., 2020; Sofi et al., 2020). In general, coronaviruses have been linked to both minor and serious illnesses (Burrell et al., 2017). University of Ghana http://ugspace.ug.edu.gh 15 Alpha and beta CoVs usually cause infection in mammals while the Gamma and Delta CoVs have birds as their hosts. Currently, there are only seven coronaviruses known to cause infections in humans (Deep et al., 2020; Zhu et al., 2020). Of these, those that cause mild infections include the human Coronavirus 229E (HCoV-229E), human coronavirus OC43 (HCoV OC43), human Coronavirus NL63 (HCoV-NL63), and human Coronavirus HKU1 (HCoV-HKU1). They are mostly implicated in respiratory diseases such as the common cold and are used in coronavirus research (Fung & Liu, 2019). The genomes of coronaviruses range in size from 27 to 32 kilobase pairs (kb). They contain the codes for the four structural proteins envelope, membrane, spike, and nucleocapsids together with additional accessory proteins that determine virulence (Burrell et al., 2017). Extremely dangerous coronavirus strains include Middle East respiratory syndrome coronavirus (MERS-CoV) and Severe acute respiratory syndrome Coronavirus (SARS-CoV) (Babarinsa et al., 2021). They are known to cause MERS and SARS respectively. The first coronavirus known to cause serious illness in people was SARS-CoV. This highly pathogenic virus emerged in 2002, infecting around 8000 individuals and accounting for the deaths of more than 700 individuals (Pal et al., 2020). The MERS-CoV caused an outbreak in 2009 and has so far infected 2519 individuals. It caused the deaths of 866 individuals and is still responsible for sporadic cases yearly (Alyami et al., 2020). The most recent pathogenic strain that can cause life-threatening illnesses is SARS- CoV-2, which is what is causing the present global pandemic (Deep et al., 2020). 2.5 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Severe acute respiratory virus 2 (SARS-CoV-2), is an enveloped, positive sense single-stranded RNA (+ssRNA) virus of the genus Betacoronavirus (Jackson et al., 2022). The outbreak was originally discovered in Wuhan, China (Khan et al., 2020). Infection with the virus causes COVID- University of Ghana http://ugspace.ug.edu.gh 16 19, a serious respiratory infection in humans that is extremely contagious (Hsu et al., 2020; Rudrapal et al., 2020). This disease has spread around the world infecting several hundreds of millions of people, killing millions, and evolving many viral variants along the way. Variants of this virus have been detected around the globe, some with higher transmissibility (Zhang et al., 2020). 2.5.1 SARS-CoV-2 virion morphology and genome organization Electron microscopy of SARS-CoV-2 revealed that the virus particle is spherical and has a size of about 100 to 150 nm (Jackson et al., 2022; Kumar et al., 2019). As shown in Figure 2.3a, viral proteins include four structural proteins: spike (S), nucleocapsid (N), membrane(M), and envelop (E) (Santos et al., 2020). The membrane, envelope, and spike glycoprotein are rooted in the host- derived lipid bilayer. The bilayer encapsulates the nucleocapsid, which has the viral RNA (Kumar et al., 2019). The spike glycoprotein (S protein) has a molecular weight of 180-200 kDa after N- glycosylation (Casalino et al., 2020; Walls et al., 2020). It is a homotrimeric polypeptide that is embedded within the membrane of the virus to give it the appearance of a crown (Deep et al., 2020). The protein has 3 domains; an extracellular N-terminal which interacts with host proteins, a transmembrane domain which anchors it to the lipid bilayer, and an intracellular C-terminal segment which is usually short. The protein undergoes structural rearrangement upon interaction with the host receptor to facilitate viral entry (Walls et al., 2020; Zhou et al., 2020). The genome of SARS-CoV-2 is approximately 30 kb and contains 15 open reading frames (ORFs) encoding 27 proteins (Baruah et al., 2020). The 5’ end which is about 20kb consists of the ORF1a and ORF1b and encodes polyproteins that encode 11 and 5 non-structural proteins (nsp), respectively. These 16 non-structural proteins form part of the replicase and transcriptase complex and are University of Ghana http://ugspace.ug.edu.gh 17 conserved in coronaviruses of the same family (Alsobaie, 2021; Michel et al., 2020). A diagram showing the genome organization is shown in Figure 2.3B Figure 2.3: SARS-CoV-2 virion structure (A) and genome organization(B) (Alsobaie, 2021) 2.5.2 SARS-CoV-2 entry mechanism and replication cycle The SARS-CoV-2 replication cycle in the host begins when there is a successful infection of the host cells via specific interactions between the virus S glycoprotein and human ACE2 receptor and A B University of Ghana http://ugspace.ug.edu.gh 18 some proteases (Baggen et al., 2021; Peng et al., 2021). The S-protein is made of two domains, 1 and 2 which are separated by a protease cleavage site (Gobeil et al., 2021). The first domain interacts with the host receptor ACE2 and the second mediates viral fusion with the host membrane (Belouzard et al., 2012; Huang et al., 2020). Interaction of the S1 domain and ACE2 causes a conformational change in the S protein that leads to the exposure of the protease site for cleavage. This cleavage is done by host proteases like transmembrane serine protease 2 (TMPRSS2), cysteine protease cathepsin B (CatB), and cathepsin L (CatL) (Hoffmann et al., 2020). These interactions facilitate the entry of the virus into the host cell, replicates, and subsequently triggers the immune system (Zhang et al., 2021). Viral replication occurs in the cytoplasm, which begins with viral gene expression. The virus recruits host machinery such as ribosomes for the translation of its ORF1a and ORF1b to produce polyproteins pp1a and pp1ab (Malone et al., 2022; Q. Zhang et al., 2021) The polyproteins are processed and cleaved by viral proteases to produce 16 non- structural proteins that assemble into the replication-transcription complexes (RTCs). The RTCs produce a new set of genomic RNAs with ORFs which can either serve as a template for further RNA synthesis or encode the structural proteins spike, membrane, envelope, and nucleocapsid as well as additional accessory proteins to be packaged into new virions (Malone et al., 2022). At the end of viral replication, newly produced virions are coated with nucleocapsid proteins and obtain a lipid bilayer that contains all the necessary viral proteins by budding off through the ER-Golgi compartment (Malone et al., 2022). A graphical depiction of the replication cycle is shown in Figure 2.4 University of Ghana http://ugspace.ug.edu.gh 19 Figure 2.4: SARS-CoV-2 infection cycle (Malone et al., 2022) 2.6 Angiotensin-converting enzyme 2 (ACE2) ACE2 is an enzyme that belongs to the renin-angiotensin system (RAS) that plays a significant part in cellular functions (Zhang et al., 2019). The enzyme has been established as the canonical receptor for SARS-CoV-2 infection. That is, only cells expressing ACE2 enable efficient infection and replication of the virus. The receptor was already identified as the receptor that facilitated infection following the 2003 SARS- CoV epidemic (Prabakaran et al., 2004; Bian & Li, 2021). A study showed that the inhibition of ACE2 receptor expression prevented viral entry (Chen et al., University of Ghana http://ugspace.ug.edu.gh 20 2021). There is a necessary interaction that forms a salt bridge between the Lys417 of the S protein and Asp30 on ACE2 to create a spike-ACE2 complex. However, there can be variations within the receptor which can have a possible impact on the functionality of the receptor. This variation can also affect susceptibility and severity of infection (Hoffmann et al., 2020; Hou et al., 2020; Veeramachaneni et al., 2021) 2.6.1 Expression and function of ACE2 in tissues The enzyme has a crucial role in the renin-angiotensin system. This system’s primary role is to maintain electrolyte homeostasis and regulate blood pressure (Tikellis et al., 2012). The enzyme is a membrane-bound carboxypeptidase that catalyzes the hydrolysis of a vasoconstrictor, angiotensin II to vasodilator, angiotensin 1-7 to counterbalance the activity of ACE (Kuba et al., 2013; Menon et al., 2007). ACE2 is expressed in various tissues throughout the human body (Tang et al., 2021). It is expressed in increasing levels in the kidney, testis, small intestine, and heart tissue. But oddly, the level of gene or protein expression seen in the lungs is rather low in comparison to other organs (Ryu & Shin, 2021; Tang et al., 2021). 2.6.2 Expression and function of ACE2 in cancers Depending on the tumor type, ACE2 may either function to promote tumor progression by enhancing one of the many properties of cancer or inhibit such properties which in turn becomes beneficial in inhibiting tumor progression. ACE2 has been reported to inhibit certain cancer properties such as growth and survival, metastasis, and angiogenesis among others in numerous cancers including cancer of the breast, colon, prostate, lungs, kidney, etc. (Zhang et al., 2019). ACE2 is an indicator of a better prognosis in cancers like breast cancer and hepatocellular carcinoma (Zhang et al., 2019). University of Ghana http://ugspace.ug.edu.gh 21 Transmembrane serine protease 2 (TMPRSS2) Transmembrane serine protease 2 (TMPRSS2) is a membrane-bound serine protease that has been implicated in the entry of the SARS-CoV-2 virus. As was previously mentioned, the enzyme cleaves the S protein after binding to ACE2 which is necessary to ensure entry (Stopsack et al., 2020). However, the ability of the virus to utilize alternative endosomal proteases such as cathepsin L makes the function of TMPRSS2 in SARS-CoV-2 infection debatable (Thunders & Delahunt, 2020). It plays a role in pathological processes like tumor cell invasion and apoptosis as well as physiological functions like digestion, blood clotting, tissue modeling, and inflammatory response (Guo et al., 2020). TMPRSS2 is recognized to be involved in cell invasion and metastasis in cancers like prostate cancer. According to research, TMPRSS2 suppression reduced prostate cancer cell invasion and metastasis (Ko et al., 2020). Their expression on cancer cells may contribute to the susceptibility of the cell to SARS-CoV-2 infection 2.7 Coronavirus disease 2019 (COVID-19) Coronavirus disease refers generally to any disease emanating from coronavirus infection. Coronaviruses are an enormous group of viruses implicated in diseases like mild colds or severe pneumonia (Al-quteimat & Amer, 2020). The 2019 infectious coronavirus disease, however, is caused by SARS-CoV-2 and is transferrable from person to person (Acter et al., 2020). According to WHO, all infected persons, either symptomatic, pre-symptomatic, or asymptomatic could cause transmission of the virus. From November 2019- February 2020, there were increasing cases and deaths occurring in China and the disease was fast spreading, reaching up to about 25 countries. In March 2020, the disease was deemed a public health emergency of global concern by the WHO (Landman et al., 2020), and later that month as a pandemic disease. As of January 13, 2023, data from the WHO dashboard indicated that there have been 661,545,258 confirmed cases globally University of Ghana http://ugspace.ug.edu.gh 22 and about 6,700,519 deaths on the global front. In Ghana, the total number of confirmed cases was 171,070 between 3 January 2020 to January 13 2023 with 1461 deaths (WHO). 2.7.1 Symptoms, diagnosis, and treatment The symptoms of COVID-19 may range in severity from those that are asymptomatic or moderate to those that are serious enough to cause death. After exposure to the virus, the onset of symptoms may begin between 2-14 days but mostly after 5 days due to their incubation period (Acter et al., 2020). Without a known diagnosis, a symptomatic person according to the CDC may present with at least two or three of the following signs. That is cough, fever, fatigue, muscle pain, loss of smell, taste disorder, headache, sore throat, nausea, vomiting, diarrhea, congestion, difficulty breathing, confusion, and chest pain among others. Reports on the intense form of the disease are acute respiratory distress syndrome (ARDS) and severe respiratory injury with pneumonia (Al-quteimat & Amer, 2020). Asymptomatic cases, however, are individuals who test positive for the virus and show no sign or symptoms of the disease. These individuals were mostly identified through contact tracing and voluntary testing. However, these individuals could be silent transmitters who spread the virus to other persons for extended periods (Oran & Topol, 2020). Again, due to the asymptomatic nature, the percentage of infected people might not be reflected in the total number of reported cases. It is therefore important to widen the testing programs to include asymptomatic individuals to reduce the silent spreading of the virus and also help with the accuracy of reported cases. The diagnosis of the disease relies mainly on the clinical manifestation of the disease. However, symptoms of COVID-19 patients are not peculiar to the condition. Hence, there is an inefficiency in the use of clinical presentation as a stand-alone diagnostic tool. The accepted ‘gold standard’ in the determination of the presence of the virus is real-time quantitative polymerase chain reaction University of Ghana http://ugspace.ug.edu.gh 23 (RT-qPCR). It is a nucleic acid detection test in which primers and probes are used in the amplification of the ORF1b and N gene of SARS-CoV-2 (Chan et al., 2020; Hu et al., 2020; Tian et al., 2020). High throughput sequencing can be used as a nucleic acid detection method, however, due to the cost and limited equipment, RT-qPCR is the most acceptable. Other forms of diagnosis employed may include chest CT scans characterized by ground-glass opacity and/or double-side infiltrates in the lungs, antibody detection by lateral flow immunoassay and enzyme-linked immunosorbent assay (ELISA) (Biemond et al., 2020). In the initial stages, there was no specific treatment and medication or approved vaccines, hence common clinical management practices were used to curb infections. Most of the treatments offered were based on common symptoms presented by patients (Al-quteimat & Amer, 2020). These included pain management, fever management, and administering antibiotics. Other broad- spectrum antiviral drugs such as remdesivir and drugs such as chloroquine, azithromycin, doxycycline, zinc tablets, and tocilizumab were used for managing the condition (Geleris et al., 2020; J. Liu et al., 2020; Skipper et al., 2020). Novel strategies used in the management of the condition include the use of convalescent plasma, complement inhibitors, anti-SARS-CoV-2 antibody cocktails, and soluble ACE2 among others (Stone et al., 2020; Xu et al., 2020). In recent times, there is the availability of vaccines produced to help fight the disease. The vaccines produced were either DNA-based, RNA-based, or protein-based (Soo et al., 2021). Within six to nine months into 2021, there were several vaccine candidates but about 18 of those were approved to be used as an emergency response to curb the spread of the disease (Ndwandwe & Wiysonge, 2021). Some of the commonly known ones such as jcovden (Johnson and Johnson), spikevax (Moderna), comirnaty (Pfizer), vaxzevria (AstraZeneca), sputnik V (Gamaleya), and Serum Institute of India’s Covisheild were among those that were approved to be used in Ghana. These University of Ghana http://ugspace.ug.edu.gh 24 were delivered either as a single-dose therapy or a double-dose therapy. As of January 6, 2023, data from the WHO dashboard indicated that there have been 13,107,022,929 vaccine doses administered worldwide. In Ghana, a report on vaccination dates 11th December 2022 with a total of 21,400,939 doses administered (WHO). That is, more than half of the Ghanaian population have received the vaccines with about 37% receiving at least 1 dose and 28% fully vaccinated. 2.7.2 Immune Response Upon SARS-CoV-2 infection, pathogen-associated molecular patterns (PAMPs), which initiate the innate immune response, as well as damage-associated molecular patterns (DAMPs) induced by host cell damage are released (Zhou et al., 2020). PAMPs and DAMPs interact with some pathogen-recognition receptors (PRRs) in cells like alveolar macrophages. This results in a wave of inflammation-mediated secretion of chemokines and cytokines into the bloodstream (Ferreira et al., 2021). A clinical data characteristic analysis of death cases in Wuhan, China revealed elevated levels of IL-1, IL-6, IL-7, IL-8, IL-9, IL-10, growth factors, blood growth factors, etc., when compared to healthy individuals (Ferreira et al., 2021). These molecules act as a chemoattractant to mobilize other immune cells like Natural killer cells, T cells, B cells, neutrophils, macrophages, plasmacytoid and myeloid dendritic cells, and plasma cells to the site of infection (Huang et al., 2020). The immune response generated against SARS-CoV-2 is robust and also diverse across different groups as shown in University of Ghana http://ugspace.ug.edu.gh 25 Figure 2.5 University of Ghana http://ugspace.ug.edu.gh 26 Figure 2.5: SARS-CoV-2 infections immune response and cytokine storm (Yang et al, 2021) 2.7.3 Factors that determine COVID-19 severity Viruses utilize cell surface receptors to access the cell interior, where their replication process takes place (Wu et al., 2021). Receptors are crucial for the pathogen to enter host cells, and they may also control how a disease develops (Dimitrov, 2000). Studies have identified ACE2, and TMPRSS2, as receptors that interact with spike protein to mediate its entry, and blockage to the receptors does interrupt host cell invasions (Itoyama et al., 2005). Studies have suggested that genetic variation and the level of receptor expression may affect the severity and susceptibility of diseases (Hoffmann et al., 2020a; Kam et al., 2009; Shang, Wan, et al., 2020; Shang, Ye, et al., 2020). There were reports that older people and obesity may contribute to COVID-19 severity University of Ghana http://ugspace.ug.edu.gh 27 (Wolff et al., 2021). Additionally, some reports indicated that persons with a fatally severe form of the illness also had pre-existing medical issues (Fang et al., 2020; Sanyaolu et al., 2020). Among these comorbidities, some of the most common ones included HIV infection, Hepatitis B infection, respiratory diseases, renal diseases, hypertension, diabetes, cardiovascular disorders, asthma (Oduro-Mensah et al., 2021), and cancers (Guan et al., 2020; Kamyshnyi et al., 2020; Klein, 2020; Sanyaolu et al., 2020). 2.8 Impact of COVID-19 and cancer comorbidity Comorbid COVID-19 instances are those who have the virus but are also dealing with another illness. This could be a viral, bacterial, fungal, and parasite infection, or non-communicable conditions including cancer, diabetes, heart or lung disease (Fang et al., 2020). Patients living with any form of malignancy have a lowered immune defense which increases their risk of opportunistic infections (Al-quteimat & Amer, 2020; Liang et al., 2020). Cancers added to the severity of the COVID-19 condition, according to research on the relationship between comorbidities and disease severity (Fang et al., 2020). COVID-19-cancer coinfections were linked to an increase in mortality, admittance into the intensive care unit, and dependence on ventilators, as opposed to individuals who only had SARS-CoV-2 infection (Landman et al., 2020). Additionally, normal healthcare checks for cancer patients were no longer being performed due to the strain on healthcare facilities caused by the virus's rapid spread. Treatment therapies were also being delayed which could negatively impact the prognosis for cancer (Al-quteimat & Amer, 2020). There were also records on the linkage between chemotherapy and infection severity. Patients undergoing cancer treatments tended to have COVID-19 illness that was more severe than usual (Landman et al., 2020; Zhang et al., 2020) University of Ghana http://ugspace.ug.edu.gh 28 Several studies have reported on the relationship between cancer and COVID-19 coinfection. There have also been reviews addressing the possible effects that infection may have on cancer patients and the progression of their condition. Sanyaolu et al., 2020 reported in their study that about 1.5% of a total of 1789 recruited in the study had a form of malignancy. Additionally, a study conducted in China in the early phases of the pandemic revealed about 1.3% of cases were cancer coinfections and among them, lung cancer was the highest, at about 28%. They described their findings to be alarming because it was higher than the overall cancer incidence in the Chinese community (Landman et al., 2020). Other types of cancers discovered to be linked to COVID-19 disease included esophageal cancers, and breast cancers (Zhang et al., 2020). Reports are showing that compared to other types of malignancy, patients with lung cancer coinfection had the least severe complications with a probability of reduced lung volume (Zhang et al., 2020). Nonetheless, the direct effect on the cancer cells and the tumor microenvironment remains unclear. 2.8.1 Impact of COVID-19-related immune response on cancer As a way of understanding disease physiology and how it affects cancer, studies have predicted the possible pathways that may come to play to either promote or reduce cancer progression as a response to SARS-CoV-2 infection. As ACE2 interacts with the spike to initiate infection, there could be a deleterious effect on the renin-angiotensin system due to changes in its pathways (Verdecchia et al., 2020). This can affect cancer progression in diverse ways. As illustrated in Figure 2.6 below, successful entry could downregulate ACE2 and consequently cause an imbalance as well as elevated bradykinin levels. Either way, downstream processes may lead to reduced anti-proliferation action, angiogenesis, P13K augmentation, and activation of the mitogen-activated protein kinase (MAPK) pathway: all of which could contribute to cancer progression. Reports show there was an increase in cytokine levels observed in the plasma of University of Ghana http://ugspace.ug.edu.gh 29 severe COVID-19 patients (Bülow et al., 2021; Kovacs-kasa et al., n.d.). This COVID-19-induced hyperproduction of cytokines could also contribute to cancer progression in a person with cancer. The produced cytokines may activate inflammation-mediated cancer progression through the phosphorylation of certain tumor proteins and the activation of pathways such as EMT (Saha & Anirvan, 2020; Yun et al., 2019). However, there is a need for more and further analysis into how these changes and inflammation may affect the progression of cancer. University of Ghana http://ugspace.ug.edu.gh 30 Figure 2.6: Possible impacts of COVID-19 on cancer progression (Saha & Anirvan, 2020) University of Ghana http://ugspace.ug.edu.gh 31 2.8.2 Impact of COVID-19-related inflammation on cancer As has been discussed above, COVID-19 disease is linked to an increase in the inflammatory response. When the first line of immune response is activated in response to viral particles and replication, there is an increased inflammatory response that results in a cytokine storm. This could cause the activation of certain proteins that are involved in pathways that promote cancer progression ( Hu, et al., 2021; Saha & Anirvan, 2020). The mechanism that brings about this continuous and increased cytokine production may involve the rapid activation of T helper 1 (Th1) cells. The T helper 1 (Th1) cells are known to be involved in the release of IL-6. This interleukin can further activate immune cells to enable other cytokines to be released in higher quantities (Hu, et al., 2021). Continuous and increased cytokine production could also be brought about by ACE2- mediated activation of NF-κB whose signaling could lead to the release of IL-6 and TNF-α. Again, IL-6 signaling through STAT3 can cause the further release of IL-6 and IL-8 and other chemokines like VEGF (Hu, et al., 2021; Tang et al., 2020) Studies have reported on the various types of cytokines implicated in the COVID-19-related cytokine storm. It has been shown that there were elevated levels of IL-1β, IL-6, IL-8, IL-7, IL- 10, IL-12, IP-10, and TNF-α among others with IL-6 being the most implicated (Fara et al., 2020; Hu, et al., 2021; Soy et al., 2020; Tang et al., 2020). Within the tumor microenvironment, cytokines may act as drivers of tumorigenesis or as inhibitors of tumors. IL-6 has been known to be involved in signaling that promotes cancer progression and survival (Turnquist et al., 2020). Also, the binding of IL-6 to its receptor could activate pathways and proteins implicated in EMT. IL-1β has been shown to interact with other cytokines to initiate a cascade of events that results in promoting University of Ghana http://ugspace.ug.edu.gh 32 EMT. TNF-α within the tumor microenvironment can cause tumor proliferation as well as metastasis through EMT activation (Heydarian et al., 2021) University of Ghana http://ugspace.ug.edu.gh 33 CHAPTER THREE 3.0 MATERIALS AND METHODS 3.1 Study design The study was an empirical study that involved the use of SARS-CoV-2 PV produced in the Virology laboratory at the West African Centre for Cell Biology of Infectious Pathogens (WACCBIP). There was also the use of SARS-CoV-2 viral particles isolated at the Noguchi Memorial Institute for Medical Research (NMIMR) from respiratory samples of a patient infected with the wild-type SARS-CoV-2 strain. Cancer cell lines were originally acquired from American Type Culture Collection (ATCC). 3.2 In silico gene expression analysis The differential gene expression of ACE2 and TMPRSS2 was analyzed using Gene Expression Profiling Interactive Analysis (GEPIA) software. The program performs gene expression analysis based on 9736 tumors from The Cancer Genome Atlas (TCGA) and 8587 normal samples from The Genotype-Tissue Expression (GTEx) databases, using standard RNA sequencing data. Briefly, ACE2 and TMPRSS2 gene expression analysis was carried out utilizing the defined sample selections BRCA (Breast invasive carcinoma), COAD (Colon adenocarcinoma), and PRAD (Prostate adenocarcinoma). The expression profiles were presented as box plots comparing the tumor tissues to the normal tissues of the selected samples. 3.3 In vitro analysis for ACE2 expression in breast, colorectal, and prostate cancer cell lines 3.3.1 Cell Culture Every cancer cell line utilized in the study was grown and maintained in a culture medium that was supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (Pen-strep). University of Ghana http://ugspace.ug.edu.gh 34 The cells were kept at 37 °C while being exposed to 5% carbon dioxide. Weekly subcultures of cells were performed, and some were kept at -80°C. The list of the various cancer cell lines, their cell types, and their culture medium are shown in Table 1 below. Table 1: List of cancer cell lines Cancer Type Cell Lines Culture Media Breast Invasive Carcinoma (BRCA) MDA-MB-231 MDA-MB-468 DMEM Prostate Adenocarcinoma (PRAD) 22RV1 BPH-1 RPMI Colon Adenocarcinoma (COAD) DLD-1 COLO205 HCT-15 RPMI Controls HELA- Negative HEK293T- Positive DMEM 3.3.2 Dot blot A dot blot analysis was used to determine the level of ACE2 on the cancer cell lines. Briefly, cells were trypsinized and centrifuged at 1000 rpm for 5 minutes. The resuspended pellets were transferred into Eppendorf tubes. Cells were lysed following a 30-minute incubation on ice with 200ul phosphate lysis buffer containing 0.1% imidazole, 0.5% triton X, and a 1X protease inhibitor and then vortexed. 3ul of cell lysates were spotted on nitrocellulose membrane and left to dry for 30 minutes at room temperature. After that, the membrane was blocked for 1 h on the bench with University of Ghana http://ugspace.ug.edu.gh 35 3% bovine serum albumin (BSA) in 1X PBS-T. The membrane was incubated with the primary antibody (1 in 1000 dilution with 1% BSA-PBS-T) for 1h and 30 minutes before being rinsed thrice with 1X PBS-T for 10 minutes each. The membrane was then incubated with the secondary antibody (1 in 10000 dilutions with 1% BSA-PBS-T) for 1 h and washed five times with 1X PBS- T for 10 minutes each. The detection substrates were added (1:1 dilution of Super Signal West Pico Plus Stable Peroxide and Super Signal West Pico Plus Luminol Enhancer) and viewed using the AmershamTM Imager 600 (GE Life Sciences, UK). 3.4 Pseudovirus production and infection 3.4.1 Bacterial transformation All bacterial cultures (XL-10 competent cells) were prepared using LB broth and agar by the manufacturer's directions. Briefly, 25g of LB powder was weighed and added to 1000 ml of distilled water. The mixture was stirred gently to allow for a uniform mixture and autoclaved at 121°C and 15psi for 15 minutes before use. For the LB agar, 15g of agar and 25g of LB powder were weighed into 1000 ml of distilled water and stirred gently to mix. The medium was then autoclaved at autoclaving conditions and allowed to cool before pouring under a flame. All autoclaved broth and agar were stored at 4℃. For the transformation process, the DNA to be used for the transformation was prepared to a working concentration of 10ng/μl. Briefly, 50μl XL-10 competent cells (Stratagene, USA) were aliquoted in a pre-chilled Eppendorf tube and stored on ice. After that, 5μl of the DNA was then pipetted into the tube containing the competent cell and incubated on ice for 30 minutes. Competent cells only were used as a negative control for contamination. After the incubation, the samples were heat-shocked at 42℃ for 30 seconds in a water bath and immediately transferred University of Ghana http://ugspace.ug.edu.gh 36 back to the ice. 100μl of pre-warmed LB broth containing 50mg/ml of ampicillin was added to the tubes. The tubes were shaken at 180 rpm while being incubated for 1h at 37 °C. 30μl of the transformed cells were spread uniformly on the agar plate infused with 50mg/ml of ampicillin. The plates were placed at 37°C for 16 to 18hs. 3.4.2 DNA extraction Plasmid extractions were carried out using the QIAprep® Spin Miniprep Kit (QIAGEN, Germany) after single colonies from transformed cells were cultured in antibiotic LB broth at 37°C for up to 16hs. Briefly, a 6-minute centrifugation at 5000 rpm was performed on overnight bacteria cultures. After the supernatant was discarded, the pelleted cells were resuspended in 250μl of buffer P1 and then put into a 1.5 ml microcentrifuge tube with labels. 250μl of buffer P2 was added to each tube, and well mixed by inversion till a blue solution was visible. Then, 350μl of buffer N3 was added and thoroughly mixed till the solution became colorless. After that, the mixture was centrifuged for 10 minutes at 13,000 rpm. The supernatant was transferred into QIAprep 2.0 spin column and centrifuged at 13,000 rpm for 1 minute and the filtrate in the collection tubes was discarded. The spin column was washed with 500μl of buffer PB by centrifuging it for 1 minute at 13,000 rpm. The filtrate in the collection tubes was then discarded. Another round of washing was done using 750μl of Buffer PB. To remove the remaining wash buffer, the spin column was centrifuged a second time for 1 minute. The spin column was then transferred into a fresh 1.5 ml microcentrifuge tube and filled with 50μl pre-warmed buffer EB. This was centrifuged for 1 minute to elute the DNA after being incubated at room temperature for 1 minute. The extracted DNA was quantified using Nanodrop (Thermo Fisher Scientific, USA) and kept at 4°C for storage. University of Ghana http://ugspace.ug.edu.gh 37 3.4.3 Pseudovirus production SARS-CoV-2 PV was produced using the calcium phosphate precipitation method as described by Chen, 2011 with few modifications. Using the 3-plasmid system, HEK 293T cells were co- transfected with pcDNA 3.1 SARS-CoV-2 S, firefly luciferase-expressing plasmid PCSFLW, and a lentiviral packaging plasmid p8.91. Briefly, the transfection reagent was prepared as followed; 2M CaCl2 was prepared using CaCl2 and distilled water, and the solution was filtered under aseptic conditions using a 0.2𝜇m syringe driven filter and stored at room temperature. HBS at 2X was prepared with 50mM HEPES, 280mM NaCl, and 1.5mM Na2HPO4. The solution was filtered, aliquoted in microcentrifuge tubes, and stored at -20℃. About 3 x 105 cells per well were seeded in 6 well plates, with 2ml complete media, and incubated for 24 hours at 37℃ with 5% CO2. At 3 hours pre-transfection, cells were replenished with fresh media. For each transfection, two separate tubes A and B were prepared. Solution A was made up of 100𝜇l of 2X HBS and solution B was made up of 12.2𝜇l of CaCl2, about 4𝜇g of S DNA, 4μg of PCSFLW, and 4μg of p8.91. The solution was topped up to 100𝜇l using nuclease-free water. Solution B was added to solution A gently and kept at ambient temperature for 30 minutes. The resulting mixture was gently applied to cells and swirled gently to ensure uniform distribution. Following that, cells were cultured for 24hs at 37°C with 5% CO2. The media was changed after 24hs. Media containing SARS-CoV-2 PV were harvested after 48 and 72hs. The PV was filtered using a 0.45𝜇m syringe-driven filter to remove cellular debris. Harvested viruses were aliquoted and stored at -80℃ until use. Mock PV stock was made by transfecting HEK 293T cells with the firefly luciferase-expressing plasmid PCSFLW and a lentiviral packaging plasmid p8.91 only. University of Ghana http://ugspace.ug.edu.gh 38 3.4.4 Pseudovirus luciferase assay Briefly, cells were seeded and incubated for 24h at 37℃ with 5% CO2. About 300𝜇l of PV was added to each well and cultured for 48h at 37°C with 5% CO2. The luciferase activity was determined using the One-Step™ Luciferase Assay System (BPS Bioscience, USA) as per the manufacturer’s instructions with few changes. The luciferase reagent buffer (A) was thawed to room temperature. The working solution was prepared by making a 1:100 dilution of the luciferase reagent substrate (B) and luciferase reagent buffer (A). Media was discarded and a volume of 200𝜇l of the working solution was added to each well and kept for 15 minutes while rocking gently. After the incubation, the solution was mixed gently by pipetting up and down and about 100𝜇l of the solution in each well was aliquoted into 96 well white plates in duplicates. Luminescence was then determined using the GloMax®-Multi detection system (Promega, USA). Cells only and cells with the mock virus were used to determine the background signal. 3.5 Cell proliferation assay Roche cell proliferation kit 1 (MTT) (Sigma-Aldarich, USA) was used to examine infection effects on cell growth following the manufacturer’s protocol with few modifications. Briefly, cells were harvested and about 1x104 per well for 22RV1 and 5x103 per well for DLD-1 cells were seeded in a tissue-culture grade 96-well plate and incubated overnight. Cells were then replicatedly infected with 100μl of PV and incubated for 48h and 72h. Mock viruses and medium alone were used as controls while incubating cells. After each incubation period, 10μl of the MTT labeling reagent was added to each well and incubated for 4h. Afterward, 100μl of the solubilization buffer was added to each well and incubated overnight. The plate was then read with the Varioskan LUX microplate reader (Thermo Fisher, USA) at an absorbance of 570nm. All incubations were done at 37°C with 5% CO2. University of Ghana http://ugspace.ug.edu.gh 39 3.6 Cell viability assay Cell viability was assessed using the Promega CellTiter-Glo® luminescent cell viability assay following the manufacturer’s instructions with a few modifications. Briefly, cells were seeded into tissue-culture grade 24-well plate at 1.5x105 cells per well and incubated at 37°C with 5% CO2 for not more than 24h. The cells were then infected with 100μl of PV and incubated for 72h. Cells were incubated with media only and mock virus as controls. All assays were performed in triplicates. A volume of CellTiter-Glo® reagent equal to the volume of the cell culture medium present in each well was added and mixed gently on an orbital shaker for 2 minutes to lyse the cells. The plate was then incubated in the dark for 10 minutes at room temperature. The luminescence was read using the GloMax®-Multi detection system (Promega, USA). 3.7 Wound healing assay Cellular migration analysis was done using a wound-healing assay with silicone culture inserts (Ibidi GmbH, Munich, Germany) as described by Pijuan et al., 2019 with modifications. Briefly, cells were harvested and about 1x104 per well of silicone culture insert for 22RV1 and 5x103 per silicone insert well for DLD-1 cells were seeded in a tissue-culture grade 12-well plate and incubated at 37°C with 5% CO2 overnight to allow the cells to attach to create about 90-95% confluent monolayer. The silicone culture inserts were removed gently and aseptically with sterile forceps. The plate was then washed gently thrice with 1ml sterile 1X PBS to remove non-attached cells. The cells were then infected with 100μl of PV with media only and mock virus as controls to a media volume of 2ml and incubated at 37°C with 5% CO2. Assays were performed in triplicates for each experimental condition. Images were taken with an Optikalview light microscope (OPTIKA® Ponteranica, Italy) several times at a specific time interval for not more University of Ghana http://ugspace.ug.edu.gh 40 than 72h. Image analysis was done with ImageJ software to determine the percent (%) of the wound area. 3.8 Gene expression analysis 3.8.1 RNA extraction Each well in a 6-well plate was seeded with 3x105 cells. The cells were infected with PV, with media only, or mock virus, in triplicate, and incubated at 37°C with 5% CO2 overnight. Total RNA from infected and non-infected cells was extracted using Zymo Quick-RNA™ Miniprep Plus Kit per the manufacturer’s protocol. Briefly, cells were lysed with 600μl of RNA lysis buffer and transferred into the yellow Spin-Away™ column placed in a collection tube, and centrifuged at 13000g for 30 seconds. An equal volume of absolute ethanol was added to flow-through, mixed thoroughly, and transferred into the green Zymo-Spin™ column in a collection tube. DNase 1 treatment was performed by first washing the column with 400μl of RNA wash buffer. For each treatment, 5μl of DNase 1 and 75μl of DNA digestion buffer were mixed in a fresh nuclease-free tube. The mixture was then added directly to the column matrix and incubated at room temperature for 15 minutes. After DNase 1 treatment, 400μl of RNA prep buffer was added, centrifuged at 13000 g for 30 seconds and the flow-through was discarded. The column was washed twice with RNA wash buffer first with 700μl for 30 seconds and 400μl for 2 minutes at 13000g each. 50μl of DNase/RNase-Free Water was added and centrifuged at 13000g for 30 seconds to elute RNA. RNA was quantified using nanodrop and stored at -80°C. 3.8.2. RT-qPCR assay RT-qPCR primers were designed using the IDT- PRIMERBLAST platform. Primers were diluted with 1X TAE buffer as directed by manufacturers to a stock concentration of 100mM and further diluted to a working concentration of 10Mm. University of Ghana http://ugspace.ug.edu.gh 41 Table 2: List of primers for gene expression analysis Gene Forward primer sequence 5’>3’ Reverse primer sequence5’>3’ VEGF CTCTACCTCCACCATGCCAAGT TCGATTGGATGGCAGTAGCTG BCL2 TGCACCTGACGCCCTTCAC AGACAGCCAGGAGAAATCAAACAG KI-67 AATTCAGACTCCATGTGCCTGAG CTTGACACACACATTGTCCTCAGC MMP9 ACGCACGACGTCTTCCAGTA CCACCTGGTTCAACTCACTCC Vimentin (VIM) TCTCTGAGGCTGCCAACCG CGAAGGTGACGAGCCATTTCC TNF-α AGTGACAAGCCTGTAGCCC GCAATGATCCCAAAGTAGACC IL-1β GCACGATGCACCTGTACGAT CACCAAGCTTTTTTGCTGTGAGT IL-6 CAATGAGGAGACTTGCCTGGTGA TGGCATTTGTGGTTGGGTCAG IL-8 CACCGGAAGGAACCATCTCACT TCAGCCCTCTTCAAAAACTTCTCC GAPDH TGCACCACCAACTGCTTA GGATGCAGGGATGATGTTC University of Ghana http://ugspace.ug.edu.gh 42 The RT-qPCR assay was performed using the Luna Universal OneStep RT-qPCR kit (New England Biolabs, UK). For each reaction, 20μl PCR mix was made with 1μg of RNA, 0.8μl each of forward and reverse primers, 1μl of high-fidelity enzyme mix, 10μl of the reaction mix, and nuclease-free water. Table 3: RT-qPCR cycling condition for gene expression analysis 3.9 Live virus infection To confirm the effect of the PV on the cell lines, live virus infections were performed for all assays. Briefly, laboratory stocks of live SARS-CoV-2 virus (LV) isolated from respiratory samples of COVID-19 patients were obtained from the global immunology and immune sequencing for epidemic response (GIISER) project. For all the live virus assays, cells were infected with a viral multiplicity of infection (MOI) of 0.02 based on a TCID50 titration of the virus stock done at the time of viral isolation. All infections were done under appropriate biosafety conditions. Condition Temp (°C) Time Number of cycles Reverse transcription 55 10 minutes 1 Initial denaturation 95 1 minute 1 Denaturation 95 30 seconds Annealing 56-62 30 seconds 40 Extension 60 30 seconds University of Ghana http://ugspace.ug.edu.gh 43 3.10 Statistical Analysis Raw data were obtained and stored in Excel and data analysis was performed using GraphPad Prism version 9.1.2 (GraphPad Software, San Diego, USA). The One-way ANOVA was used to compare the difference between all groups of the pseudotyped virus infectivity on cancer cells. The paired t-test was used to compare the difference between all groups for LV infection. Descriptive statistics were presented as mean with standard deviation and percentages. Statistical significance was determined at a p-value that is less than or equal to 0.05. University of Ghana http://ugspace.ug.edu.gh 44 CHAPTER FOUR 4.0 RESULTS 4.1. ACE2 and TMPSS2 genes are expressed differentially in normal and tumor tissues To determine the difference in expression levels of ACE2 and TMPRSS2, the gene expression profiles of some selected tumor samples and paired normal tissues were obtained from the GEPIA database. ACE2 is expressed widely across various normal and tumor tissues and the expression levels differ based on the tissue type. ACE2 is downregulated in normal breast and prostate tissues. Also, the expression in breast cancer tissue, as well as prostate cancer tissues, was downregulated indicating that the receptor is generally not localized. Nonetheless, comparing the normal tissues to cancerous tissues, the level of ACE2 in breast and prostate tumors was lower compared to the normal tissues with no significant difference (Figure 4.1a). The level of ACE2 expression in colorectal tumors was significantly higher than that in normal tissue (Figure 4.1a). Similarly, TMPRSS2 is expressed in varying levels across different tissue types. However, TMPRSS2 was observed to be expressed in a highly significant manner in prostate tumor tissues compared to its paired normal tissues. On the other hand, expression was significantly high in normal breast and colorectal tissues compared to their paired tumor samples. Generally, among the selected samples, TMPRSS2 seems to be more localized in prostate tissues (Figure 4.1b). University of Ghana http://ugspace.ug.edu.gh 45 Figure 4.1: Relative expression levels of (A) ACE2 and (B) TMPRSS2 in selected tumors and corresponding normal tissues. The expression cut-off was set as 1 from the log2 fold change (Log2FC) with a p-value cut of 0.01. Data are presented as a fold change of log expression of ACE2 transcription per million (LogTPM). (BRCA= breast invasive adenocarcinoma, COAD= colon adenocarcinoma, PRAD= prostate adenocarcinoma, T=tumor (red), N= normal (blue), num=number) University of Ghana http://ugspace.ug.edu.gh 46 4.2 In vitro analysis of ACE2 expression in some cancer cell lines In vitro analysis for levels of ACE2 in seven cancer cell lines MDA-MB-231, MDA-MB-468, DLD-1, COLO205, HCT-15, 22RV1, and BPH1 was performed using a dot blot analysis (appendix 1). The assay is a simple direct blotting technique that allows for the analysis of the absence or presence of a protein. As a positive control, HEK29T cell lines transfected to stably overexpress ACE2 were employed, whereas HELA cell lines served as a negative control. The finding suggests that all of the cell lines expressed ACE2 in some amount. However, the data was quantified (Davarinejad, 2018) to check the varying levels of the expression for each cell line using the image j version 1.53t (image j software, National Institute of Health, USA). There was low ACE2 expression in the breast cancer cell lines MDA-MB-231 and MDA-MB-468 (Figure 4.2). The expression was also seen to be low in the prostate cancer cell lines 22RV1 and BPH1. However, it was shown that the expression of ACE2 was elevated in the DLD-1, COLO205, and HCT-15 cells. This correlated with the upregulation of ACE2 expression in colorectal cancer observed from the gene expression analysis from GEPIA shown in Figure 4.1a above. University of Ghana http://ugspace.ug.edu.gh 47 1.0 1.5 2.0 2.5 HELA ACE2 293 MDA-MB-231 MDA-MB-468 DLD-1 COLO 205 HCT 15 22RVI BPH-1 Relative expression ratio Figure 4.2: Protein expression quantification of ACE2 in cancer cell lines. ACE2 is expressed highly in DLD-1 among the selected cancer cell lines. The data represents expression levels as a ratio of each protein spot relative to the loading control. University of Ghana http://ugspace.ug.edu.gh 48 4.3 SARS-CoV-2 PV infection of cancer cell lines To access the infectivity of the SARS-CoV-2 PV on cancer cell lines, cultured cancer cells MDA- MB-231, MDA-MB-468, DLD-1, COLO205, HCT-15, 22RV1 and BPH1 (HELA and ACE2- HEK293T as negative and positive controls respectively) were infected with PV and mock PV (transfection control). There was little to no infectivity in MDA-MB-231, MDA-MB-468, COLO205, and HCT-15 (appendix 3). Significant infection was seen in only DLD-1, BPH-1, and 22RV1, all of which showed a relative luminescence above that of the negative control (fig 4.3). The infectivity observed in BPH-1 was statistically not significant but a statistically significant infection was seen in DLD-1 (P= 0.0032) and 22RV1 (P <0.0001) (figure 4.3). Comparing the ACE2 expression percentages of the cell lines to the percentage of the infectivity of the PV shows that higher expression does not necessarily correlate to higher infectivity. DLD-1 had a higher ACE2 expression (figure 4.2) but the least significant PV infection. University of Ghana http://ugspace.ug.edu.gh 49 H E LA H E K 29 3T A C E 2 29 3 D LD -1 B P H -1 22 R V I 10 100 1000 R e la ti v e l u m in is c e n c e u n it s (R L U -l o g 1 0 ) Cells only Cells + M Cells + PV ✱ ✱✱✱✱ ✱✱✱ ns Figure 4.3: Infection analysis of cell lines with PV. A significant infection was observed in DLD- 1 and 22RV1 only. The data is a representation of the Mean ± SD of experimental groups and significance levels were obtained with one-way ANOVA multiple comparisons University of Ghana http://ugspace.ug.edu.gh 50 4.4 Phenotypic and molecular analysis of PV-infected 22RV1 cell line 4.4.1 Effects on the proliferation and viability of 22RV1 To explore the PV effect on 22RV1 proliferation, MTT analysis was performed. Cells were infected with PV for 48 and 72 hours. There was a significant decrease in the percentage growth of 22RV1 by the PV for both 48 hours incubation (p<0.0001) and 72 hours incubation (p<0.0001). This demonstrates that the SARS-CoV-2 PV's and ACE2 interaction on 22RV1 cells suppresses the cell line's capacity to proliferate in a time-dependent way (Figure 4.4a). Based on this result, an analysis of the effects of the PV produced on the viability of the cells using the CellTiter-Glo luminous cell viability assay was made. The analysis was used to assess the possible cytotoxic nature of the PV by analyzing the total number of viable cells 72 h post- infection. The findings indicate that SARS-CoV-2 PV reduces the total number of viable 22RV1 cells post-infection as shown in Figure 4.4b. Confirmation of the antiproliferative and cytotoxic effect of the PV on 22RV1 was performed by detecting the expression levels of proliferation marker KI-67 and anti-apoptotic marker BCL2 using qRT-PCR. The relative gene expression level of KI-67 post-PV-infection was reduced significantly (P=0.0395) compared to the control in 22RV1 cell lines (Figure 4.4c). This analysis correlates to the results observed for the proliferative phenotypic effect of PV using MTT. The relative gene expression levels for BCL2 between the control and PV-infected cells were not significantly different (Figure 4.4d). University of Ghana http://ugspace.ug.edu.gh 51 22RV1 M PV 0 50 100 150 % C e ll G ro w th 48HRS 72HRS ✱✱✱✱ 22RV1 M PV 0 5×106 1×107 1.5×107 2×107 2.5×107 N u m b e r o f v ia b le c e ll s ✱✱✱✱ KI-67 0.0 0.5 1.0 1.5 2.0 2.5 F o ld