COVID-19 as a Model for PCR Viral Respiratory Disease Diagnostics

COVER Michal Pohludka Lenka Piherová COVID-19 as a Model for PCR Viral Respiratory Disease Diagnostics KAROLINUM The COVID-19 pandemic has affected the whole world and influenced almost all areas of human life. Although each country has approached the situation differently, they had one activity in common – the effort to slow down the spread of the virus to prevent an overload of national health systems. The preferred testing method for medical diagnostics as well as for elimination of the spread within the population is the Real-Time Quantitative Reverse Transcription PCR (RT-qPCR). In this book, we focus in detail on the methodology, good practice, and troubleshooting. The aim of the publication is to provide a practical advanced manual that can be utilized in any diagnostic laboratory using the PCR method. Good practice in laboratory diagnostics is essential for medical as well as for epidemiological reasons. During an epidemic outbreak, laboratory diagnostics helps physicians to treat patients effectively and the laboratory big data provides information about the spread of the virus in different sociological groups. This is illustrated in the presented experiment studying spread of the virus in a child population at schools. Covid 19 as a Model_hrb 5mm.indd 1Covid 19 as a Model_hrb 5mm.indd 1 16.09.2024 9:3416.09.2024 9:34 COVID-19 as aModel for PCR Viral Respiratory Disease Diagnostics Michal Pohludka, Lenka Piherová Reviewers: RNDr. Gabriel Minárik, PhD. RNDr. Martin Radina Published by Charles University Karolinum Press Prague 2024 Edited by Pavlína Píhová Typeset by DTP Karolinum Press First edition This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. © Charles University, 2024 © Michal Pohludka, Lenka Piherová, 2024 ISBN 978-80-246-5949-7 ISBN 978-80-246-5985-5 (pdf) https://doi.org/10.14712/9788024659855 Charles University Karolinum Press www.karolinum.cz [email protected] CONTENTS PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Development of PCR Diagnostics for Detection of SARS-CoV-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Laboratory PCR Diagnostics as a Key Parameter for Planning a Treatment . . . . . . . . . . . . . . . . . . . . . . . . 10 Barriers to Increasing Laboratory Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Evolution of the Virus and Related Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 New Epidemic Waves and a Prediction of New Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 INTRODUCTION TO VIROLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Non-cellular Organisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Structure and Composition of Viral Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Human Coronaviruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Coronavirus SARS-CoV-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 The SARS-CoV-2 Genome Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 The Genomic Changes of SARS-CoV-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 The SARS-CoV-2 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 The Life Cycle of SARS-CoV-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 BIOLOGICAL MATERIALS SUITABLE FOR SARS-COV-2 DETECTION . . . . . . . . . . . . . . . . . . . . 24 EXTRACTION OF VIRAL RNA/DNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Detailed Experimental Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 POLYMERASE CHAIN REACTION IN DIAGNOSTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 DETECTION OF VIRAL PRESENCE BY RT-QPCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Evaluation of PCR Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Evaluation of Results from RT-qPCR Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Control Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Threshold Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Evaluation of Results and Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Problems Faced During Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 EVALUATION PROCESS OF SALIVA TESTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Evaluation of SARS-CoV-2 Detection in Saliva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Validation of Functionality of Salivette for SARS-CoV-2 Detection in Saliva . . . . . . . . . . . . . . . . . . . . . . 42 Sample Pooling for a Consequent Detection of SARS-CoV-2 by RT-qPCR . . . . . . . . . . . . . . . . . . . . . . . . 43 Pooling as a Tool for High-Throughput SARS-CoV-2 PCR Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 CHARACTERIZATION OF SARS-COV-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Screening of Different SARS-CoV-2 Variants in the Population by RT-qPCR . . . . . . . . . . . . . . . . . . . . . . 46 Sequencing of SARS-CoV-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Next Generation Sequencing (NGS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Sequencing of Total RNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 PCR Amplicon Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 MONITORING OF THE SARS-COV-2 INFECTION AT SCHOOLS DURING A PANDEMIC PEAK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 CT VALUES CALCULATED ON A WEEKLY BASIS MIGHT BE USED FOR EPIDEMIOLOGICAL PREDICTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 LIST OF ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 AUTHORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7 PREFACE The COVID-19 pandemic has affected the whole world and influenced almost all areas of human life . Although each country has approached the situation differently, they had one activity in common – the effort to slow down the spread of the virus to prevent an overload of national health systems . The preferred testing method for medical diagnostics as well as for elimination of the spread within the population is the Real-Time Quantitative Reverse Transcription PCR (RT-qPCR) . In this book, we focus in detail on the methodology, good practice, and troubleshooting . The aim of the publication is to provide an advanced practical manual that can be utilized in any diagnostic laboratory using the PCR method . Good practice in laboratory diagnostics is essential for medical as well as for epidemiological reasons . During an epidemic outbreak, laboratory diagnostics helps physicians to treat patients effectively and the laboratory big data provides information about the spread of the virus in different sociological groups . This is illustrated in the presented experiment studying the spread of the virus in child population at schools . 8 INTRODUCTION The COVID-19 pandemic caused a global crisis affecting almost all aspects of our lives . Many countries implemented restrictions to prevent their health systems from overloading and to stop, or at least control, the spread of the infection . Governments applied widespread anti-epidemic measures that were kept for a long time . However, the infection and transmission of the SARS-CoV-2 virus has a fundamentally different course, consequences, and intensity than any other infection their expert teams have experienced . For less at-risk populations, these widespread measures had questionable, sometimes even negative effects . The most affected groups were children and students . One of the key tools in the fight against the COVID-19 pandemic is laboratory diagnostics based on PCR methodology . Prior to the pandemic, there were just a few parameters measured by this technique . Currently, the PCR testing for SARS-CoV-2 is the most measured laboratory parameter and PCR has become one of the best-known methods even among the general public . During the pandemic, we were experiencing a significant technological progress in SARSCoV-2 PCR diagnostics, as there was a huge demand for the delivery of fast, reliable, and sensitive patient results . In a therapeutical context, it is essential to quickly deliver information of SARS-CoV-2 positivity to the physician to provide the patient with an effective treatment that works early in the course of the infection . From an epidemiological perspective, it is beneficial to look at the evolution of the virus and its spread in the population with a specific focus on different demographic and sociological groups . The PCR method has become the diagnostic gold standard for determination of SARSCoV-2 presence . It was used for monitoring of the spread within the population and consequently for the management of restrictive interventions to slow the spread of the virus and to keep the healthcare system running. It also makes it possible to provide government officials with the necessary data on the development of the epidemic with the possibility of predicting its future state . The COVID-19 pandemic has opened up unsuspected possibilities in PCR diagnostics . This is one of the few positive effects of the pandemic . Nowadays, discussions are conducted on how to use all the equipment and laboratories to determine parameters other than SARSCoV-2 . This might result in a much faster and precise delivery of results to physicians for other infectious disease parameters such as HCV, HBV, or STDs . The global pandemic of SARS-CoV-2 has brought and continues to bring dramatic changes in all social areas. There are significant ones in the healthcare sector, but perhaps the greater effects permeate into business, services, and the everyday activities of people . Even 9 though this is a global problem, different countries are approaching the complex issue of the pandemic and its impact differently . Some are using restrictive measures to slow the spread of the virus . Others are letting the epidemic run and instead focus on quality diagnosis and treatment, which is currently the most functional approach in the initial phases of any epidemic. In this context, the global strategy in the fight against the pandemic strongly influences laboratory diagnostics . In some countries, the priority is to test just people with symptoms and effectively use the testing capacities for patients, so that an efficient treatment can be applied already in the early stage of the infection . With such an approach, the results are delivered faster in comparison with countries that test the population more broadly for epidemiological reasons to prevent the transmission of the infection . DEVELOPMENT OF PCR DIAGNOSTICS FOR DETECTION OF SARS-COV-2 At the beginning of the pandemic of COVID-19, knowledge regarding the virus and its impact was minimal . However, soon after, the viral genome has been sequenced and characterized and the function of individual genes of the virus has been determined . This was crucial for the establishment of the PCR methodology. In the early days of fighting the pandemic, the World Health Organization (WHO, https://www .who .int/) and Centers for Disease Control and Prevention (CDC, https://www .cdc .gov/) provided assistance to research teams worldwide in developing protocols and setting recommendations for PCR methodologies . PCR diagnostics were set up very carefully in the early days . Two or even more genes were detected simultaneously in a multiplex assay, targeting conserved parts of the virus sequence, so the analysis would not be affected by possible mutations. This posed difficulties in terms of evaluation and there were situations where laboratories were not even able to evaluate the results easily . The lack of knowledge of the life cycle of the virus in the organism came into play . Samples were collected from people at different stages of infection, from the first contact with the virus all the way to the stage when the infection was no longer present in the organism, but residual mRNA remained on the mucous membranes of the nasopharynx . In all these cases, due to the sensitivity of the PCR methodology, positive results were reported . There were cases when only one gene came out positive, even after the analysis was repeated to rule out a faulty outcome . It was later found out that the mRNA stays on the mucosa longer and degrades gradually, with the E gene, which is one of the analyzed genes, being the last to degrade. Out of caution, these samples were considered positive, which caused a significantly negative social effect . People have been kept in quarantine for unreasonably long periods and in some cases had to undergo multiple exit tests . This problem was solved by changing the PCR diagnostic setup . PCR kits that had two genes as targets in one channel during the analysis became tolerated . With this setup, there were no longer controversial results that would keep people in quarantines for unreasonably long periods. At the same time, this approach significantly increased throughput of laboratories that could automate the entire process . With this change, PCR diagnostics suddenly became one of the key tools not only for diagnosing people with symptoms, but also for tracing people potentially infected from a contact with a positive individual . This was the point at which laboratories began to automate the whole process and rely on automated evaluation of results, which led to a rapid increase in testing capacity . 16 A – Helical: The virus structure has a capsid with a central cavity or hollow tube that is made up of proteins arranged in a circular fashion, creating a disc-like shape . The disc shapes are attached helically . Usually, plant viruses belong to this group . B – Icosahedral: An icosahedron is a geometric shape with 20 sides, each composed of an equilateral triangle . A typical example of viruses from this group are polioviruses or herpesviruses . C – Enveloped: Viral envelopes consist of a lipid bilayer that closely surrounds a shell of virus-encoded, membrane-associated proteins . The exterior of the bilayer is studded with virus-encoded, glycosylated (trans-)membrane proteins . Animal viruses are frequently enveloped . D – Head-and-tail: This is a variant of the icosahedral viral shape found in bacteriophages . Some viruses, regardless of their protein capsid shape, are enveloped with a lipid bilayer around their cap . There is also a classification of viruses according to the Baltimore classification groups, which is based on their mechanism of mRNA synthesis . Characteristics directly related to this include whether the genome is made out of DNA or RNA, which can be either singleor double-stranded and either positive or negative . There are seven groups (Tab . 1) . RNA Capsid protein subunit VPg Capsid RNA Genome Fig. 1 Main morphological virus types: A – helical, B – icosahedral, C – head-and-tail, D – enveloped A C B D 17 Tab. 1 Groups of viruses according to the Baltimore classification groups Group Name Type of genetic information Group I double-stranded DNA viruses dsDNA Group II single-stranded DNA viruses ssDNA Group III double-stranded RNA viruses dsRNA Group IV positive sense single-stranded RNA viruses (+)ssRNA Group V negative sense single-stranded RNA viruses (–)ssRNA Group VI single-stranded RNA viruses with a DNA intermediate in their life cycle ssRNA-RT Group VII double-stranded DNA viruses with an RNA intermediate in their life cycle dsRNA-RT HUMAN CORONAVIRUSES Human coronaviruses are among the viruses that cause respiratory diseases with varying severity . The severity scale ranges from common cold, through bronchiolitis, to death (Pene et al ., 2003) . In recent years, we have seen the human coronaviruses appear periodically in different places all around the world . The major issue with such outbreaks is high infectivity with fatal pneumonia in a significant number of cases (Wu et al., 2020). The first human coronavirus outbreak started in November 2002 in Foshan, China (Ge et al ., 2015) . This turned to a global issue with a significant lethal rate around 10% (Lee et al., 2003). Then, the globe faced another pandemic a decade later . The virus was called MERS-CoV and appeared in June 2012 in Saudi Arabia with a global fatality rate of over 30% (de Groot et al., 2013). The third pandemic started in December 2019 in Wuhan, China . The virus was called “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2) and it caused a disease known as COVID-19 . Even though all of the mentioned pandemics share the same development (Zhu et al ., 2020), the pandemic of SARS-CoV-2 had much stronger negative effects compared with the previous outbreaks, and it affected all the aspects of human life, sometimes with catastrophic consequences . Human coronaviruses are enveloped viruses that contain non-segmented, single-stranded, positive-sense RNA genome (Masters, 2006) . Their primary hosts are vertebrates . From the perspective of the classification, the coronaviruses are distributed into 39 species. What is important: they are genotypically and serologically separated into four major general AlphaCoV, BetaCoV, GammaCoV, and DeltaCoV, as established by the International Committee for Taxonomy of Viruses (Wu et al ., 2020) . The phylogenetic tree of human coronaviruses is shown in Fig . 2 . 18 Coronavirus SARS-CoV-2 The coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) belongs to the genus Beta-CoV and is responsible for the disease called coronavirus disease (COVID-19) . Primarily, the coronaviruses cause infections in birds and other mammals . However, they have the ability to cross species barriers even from birds to humans (Menachery et al ., 2017) . Coronaviruses have the largest genome out of all the RNA viruses and SARS-CoV-2 is not any exception and its viral transcript consists of a 5’-cap structure and a 3’poly-A tail (Lai & Stohlman, 1981) . The SARS-CoV-2 Genome Composition The genome architecture of SARS-CoV-2 is depicted in Fig . 3 . The length of the genome is around 30,000 bp . In general, the genome of coronaviruses includes a variable number of open reading frames (ORF). The first 50 ORFs (ORF1a/b) correspond to about two thirds of the whole genome and is translated into pp1a (polyprotein 1a) and pp1ab proteins that are cleaved by proteases . This results in sixteen non-structural proteins called nsp1-16 (non-structural protein) . The last third containing 30 ORFs consists of genes coding structural and accessory proteins (Khailany et al ., 2020) . There are four major genes encoding structural proteins: S protein, E protein, M protein, and N protein . The spike (S) protein is able to recognize the receptor of the host cell . The penetration of the virus into the host cell is mediated through angiotensin converting enzyme 2 (ACE2) (Wu et al ., 2020) . Next, the SARS-CoV-2 virus contains additional six proteins, encoded by the ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes . The functions of these proteins are still unclear . Most of the proteins encoded in the region of ORF1a and ORF1ab are crucial for virus replication and for the adaption to a new host (Yoshimoto, 2020) . Some of the nsp proteins are important for creation of the replication α-COVs 0.05 β-COVs δ-COVs γ-COVs HCoV-229E HCoV-NL63 TGEV Bat-SL ZC45 Bat-SL ZXC21 SARS-CoV-2 SARS-CoV MERS-CoV HCoV-OC43 HKU-1 MHV-A59 IBV SW1 HKU11 HKU17 Fig. 2 Phylogenetic tree of human coronaviruses (Biswas et al ., 2020) 19 and transcription complex, e .g ., nsp 12 is the RNA-dependent RNA polymerase . However, other proteins, such as nsp 7 and nsp 8, most probably even other nsp proteins, are also essential for its functionality . The Genomic Changes of SARS-CoV-2 Coronaviruses are relatively stable due to their proofreading mechanism . Although SARSCoV-2 is no exception and its mechanism is active in the process of replication, due to the enormous replication frequency, various mutations in the SARS-CoV-2 virus sequence occur . These come in all forms, from point deletions and insertions to more complex changes in the virus genome . However, only a very limited number of mutations have the possibility to gain an evolutionary advantage and start replicating to form a new variant . Therefore, despite the massive spread of the virus in the population, only a few virus variants are clinically relevant and have significantly different infectivity and evolutionary advantage over the previous forms . Nevertheless, the current character of the global world is an ideal environment for the most evolutionarily favored variant to prevail . All the known sequences and variants of SARS-CoV-2 are reported into the GISAID database (https://www .gisaid .org/) . The SARS-CoV-2 Structure The SARS-CoV-2 consists of phosphorylated nucleocapsid (N) protein with genomic RNA as a core enveloped by a bilayer of phospholipids . The particle has a spherical shape with a diameter between 80 and 120 nm . A characteristic property is the outer surface projecting the spike protein . The structure of the SARS-CoV-2 is shown in Fig . 4 . Generally, human coronaviruses, SARS-CoV-2 included, are composed of the following proteins: spike (S), membrane (M), envelope (E), nucleocapsid (N), and hemagglutinin (HA) . The S, M, and E proteins are embedded in the viral envelop, while N protein protects viral RNA genome located in the core of the virus (Zhou et al ., 2020) . The S protein is heavily glycosylated and contains the receptor-binding domain that is critical for its binding with the ACE2 receptor . There are additional important parts in the protein sequence of S protein, such as polybasic cleavage sites (RRAR/S) . They allow digestion by host’s furin-like protease 0 5000 10000 ORF1b 15000 20000 25000 30000 bp ORF1a ORF1a ORF1b S ORF3a1 E 4405 aa 2595 aa 1282 aa 1282 aa 75 aa M ORF6 ORF7a ORF8 N ORF10 222 aa 61 aa 121 aa 121 aa 419 aa 38 aa S N M ORF3a E ORF6 ORF7a ORF10 ORF8 Fig. 3 Genome architecture of SARS-CoV-2 20 during viral replication, which is most likely important for the infectivity (Nao et al ., 2017) . However, the whole functionality of the polybasic sites in still unknown . The Life Cycle of SARS-CoV-2 All the SARS-coronaviruses, SARS-CoV, MERS-CoV, and SARS-CoV-2, exhibit a common strategy for replication and translation following infection in the host cells . The initial step is the binding of SARS-CoV-2 virus to a cell receptor, and it determines the severity of infection and pathogenesis . The binding takes place through host cell surface by a densely glycosylated S protein . The protein is a trimeric fusion protein and consist of two major subunits: a receptor binding domain (S1; also known as RBD) and a second domain (S2) mediating viral fusion with host cell membrane . The fusion process of the SARS-CoV-2 membrane with the host cell membrane starts when the S1 domain binds to a host cell ACE2 receptor (Li et al ., 2003; Wu et al ., 2020) . There are other receptors, such as CD209L (C-type lectin, also called L-SIGN) and dipeptidyl peptidase-4 (DPP4, also known as CD26), which can be used for the fusion of the virus with the host cells, but those have much lower affinity than ACE2 (Jeffers et al ., 2004; Raj et al ., 2013) . The ACE2 receptors are widely distributed on epithelial cells . The number of receptors is individual and in general, children have a much lower concentration of them compared with adults . In term of the epithelial cells, ACE2 receptors are present in the cells of trachea, bronchi, bronchial serous glands, and alveoli (Liu et al ., 2011), as well as alveolar monocytes and macrophages (Kuba et al ., 2005) . ACE2 is also diffusely expressed on the endothelial cells of arteries and veins, cerebral neurons, immune cells, tubular epithelial cells of kidneys, mucosal cells of intestines, and epithelial cells of renal tubules (Gu & Korteweg, 2007; Guo et al ., 2008) . The SARS-CoV-2 attacks these cells with ACE2 receptors and virions are released to infect other new targets . Once the virus is attached to host cells via ACE2 receptors, the virus entry follows . There are two known mechanisms based on the availability of the host cell protease to activate receptor-attached spike protein (Simmons et al., 2013). The first one is SARS-CoV-2 entering host cells as an endosome, which is mediated by clathrin-dependent and clathrinindependent endocytosis (Kuba et al ., 2010) . This leads to structural and conformational E protein S protein M protein Fig. 4 Protein structure of SARS-CoV-2 21 Human Coronavirus SARS-CoV-1 & SARS-CoV-2 MERS-CoV Membrane fusion Membrane fusion Endocytosis Endocytosis S protein S protein ACE2 DPP4 TMPRSS2 TMPRSS2 Viral RNA release Viral RNA release Airway cell Airway cell Attachment Attachment Infection Infection Activation of S protein by proteolytic cleavage Activation of S protein by proteolytic cleavage Fig. 5 The attachment of SARS-CoV-2/MERS-CoV and entry into airway cells 22 changes in the viral particle, specifically in the S protein, fusing the viral envelope with the endosomal wall (Simmons et al ., 2013) . The second possible way is a direct invasion of the virus into the host cell . This is possible thanks to the proteolytic cleavage of receptorattached spike protein by the host’s transmembrane serine protease 2 (TMPRSS2) or transmembrane serine protease 11D (TMPRSS11D) on the cell surface (Heurich et al ., 2014) . Both paths are depicted in Fig . 5 . When the virus and the host cell membrane fuse, the virus releases the whole nucleocapsid with packed genomic RNA into the cytoplasm . The viral genome behaves as any other mRNA and the cell’s ribosome translates two-thirds of this RNA . For the rest of the genetic information, there is a special mechanism . From the sequence responsible for ORF, two large overlapping polyproteins (pp) are formed: pp1a and pp1ab. More specifically, it comes to a translation of different proteins due to a frame shift triggered by slippery sequence (UUUAAAC) and downstream RNA pseudo knot at end of ORF1a (Masters, 2006) . Protease Human Coronavirus 1 2 3 4 6 7 8 9 Binding and viral entry via membrane fusion or endocytosis Membrane Fusion Exocytosis Cytoplasm Formation of mature virion Release of viral genome Virus inside Golgi vesicle TMPRSS2 Receptor Ribosome RNA genome (+ sense) Viral Polymerase Translation of viral polymerase protein S, E, and M protein combine with nucleocapsid RNA replication RNA genome (- sense) Genomic and Subgenomic RNA (+ sense) Airway cell Genomic replication 5Subgenomic transcription Nucleocapsid (N) Spike (S) Membrane (M) Envelope (E) Translation of viral structural proteins S, M, and E at ER Membrane N in cytoplasm Viral genome ERGIC Nucleocapsid N Endoplasmic reticulum (ER) Fig. 6 An overview of the life cycle of HCoV in the host-cell (Song et al ., 2019; Zumla et al ., 2015) 23 activities follow and a whole spectrum of different proteins is created, such as nsp1-nsp16, RdRP, RNA helicase, and exoribonuclease (Fehr & Perlman, 2015) . The majority of the newly translated nsp proteins, together with structural proteins like N protein form a complex called RTC (multi-protein replicase-transcriptase complex), are responsible for the viral genome replication and transcription (Fehr & Perlman, 2015) . The main protein in the complex is RdRP, which synthesizes negative-sense sub-genomic RNA strands of viral RNA from corresponding positive-sense mRNAs . Afterwards, it is used as a template for the production of positive-sense strands (mRNAs) . These newly synthesized RNA strands represent genome for the generation of new viral progeny . The final step of the SARS-CoV-2 life cycle is an assembly and release. The viral RNA translation process is driven inside the endoplasmic reticulum . The formation of structural proteins (S, E, M) is the leading step, and they are moving along the secretory pathway into the Golgi intermediate compartment . The mechanism is following: N protein packs the newly produced RNA genome and a nucleocapsid is constructed . Next, M protein is responsible for virion assembly through multiple protein–protein interactions, which assist in the incorporation of the nucleocapsid, envelope, and spike proteins into a virus particle (Fehr & Perlman, 2015) . Finally, there is a process of secretion . The virion fuses with the plasma membrane and it is finally secreted from the host cell by exocytosis (Fehr & Perlman, 2015; Lim et al., 2016; see Fig . 6) . 24 BIOLOGICAL MATERIALS SUITABLE FOR SARS-COV-2 DETECTION Material commonly used worldwide for SARS-CoV-2 detection is a nasopharyngeal swab . The RT-qPCR detection of SARS-CoV-2 mRNA from the swab is the gold standard for diagnosing the COVID-19 infection both in symptomatic patients and asymptomatic individuals (Piras et al ., 2020) . This sample collection is based on a trivial procedure which, however, should be done by a trained operator to avoid false-negative results, as sufficient contact of the swab with the epithelial cells is essential for the proper sampling . The swab is then placed into a transport medium – we use the viRNAtrap collection tube (GeneSpector, Czech Republic) – and the sample is ready for transport to a laboratory for analysis . The viRNAtrap solution is a strong chaotropic and denaturing agent . It allows to destroy the viral capsid and due to it making the material non-infectious . We have developed a method for SARS-CoV-2 detection from saliva that is currently used in several European states . The sample collection is based on Salivette (Sarstedt, Germany), a product originally used for cortisol testing (Costa et al ., 2021) . Major advantages over other solutions or a swab collection are its simplicity, low invasiveness, and a possibility of selfcollection with no need for trained personnel and no leftover infectious waste . This method represents a very safe method of collecting biological material with virtually no risk of contamination of the environment with droplets or body fluids of the patient and lower aerosol formation compared with gargle methods . This type of collection is easy to perform . It is also painless, so it is suitable for more sensitive individuals or children over 3 years of age . In practice, due to the above-mentioned properties and the overall simple procedure, this sample collection method is commonly used for children’s testing (Fig . 7) . 25 Fig. 7 User manual for the saliva sample collection using Salivette 123 ≥ 30 min. 1–2 min. 5 7 8 6 4 9 123 ≥ 30 min. 1–2 min. 5 7 8 6 4 9 123 ≥ 30 min. 1–2 min. 5 7 8 6 4 9 32 Tab. 4 The program used for RT-qPCR Reverse transcription 42 °C 10 min Initial denaturation 95 °C 3 min Denaturation 95 °C 10 sec 45 cycles Annealing and elongation + fluorescence acquisition 60 °C 30 sec EVALUATION OF PCR DATA The obtained data were evaluated using the CFX Manager 3 .1 software (BioRad, USA) . The detection kit “gb SARS-CoV-2 Combi” contains two fluorophores with excitation in different wavelengths (excitation/emission FAM 495/520 nm; HEX 537/553 nm) . Probes labeled with FAM fluorophore are used for detection of presence of two genes located on the SARS-CoV-2 genome – RdRP and E gene . The presence of SARS-CoV-2 is determined by the value of the cycle threshold CT in FAM channel and the internal control, present throughout the complete process of isolation, in HEX channel . CT values inversely correlate with the viral load in the sample (i .e ., the lower the CT value, the higher the viral titer) and their interpretation is specific to each amplicon. The described method is semiquantitative as there is no housekeeping gene used for a normalization process . A positive semiquantitative result, indicating the presence of SARSCoV-2 RNA, is determined when the cycle threshold (CT) value is less than 38 (Tab . 5) . If the internal control has the cycle threshold (CT) value of less than 35 and the FAM channel detecting the presence of SARS-CoV-2 has a negative signal, the result is negative with either no presence of the virus in the sample or in a concentration below the level of detection . If both channels have no signal, then the reaction and the result are invalid and must be repeated from the beginning . Tab. 5 Interpretation of results from RT-qPCR assay Valid results FAM HEX Invalid results FAM HEX Negative - + Failed extraction; inhibition of RT-qPCR – – Positive CT < 35 +/– Weak positive CT < 38 + The level of detection is 3,000 viral particles when using the combination of the “viRNAtrap Extraction Kit” (GeneSpector, Czech Republic) and the “gb SARS-CoV-2 Combi” (Generi Biotech, Czech Republic) PCR detection kit . The theoretical LOD (limit of detection) of the PCR kit is 3 .5 copies per reaction . The detection limits are shown in Fig . 11 . The data are automatically connected to a LIS (laboratory information system) and consequently reported to physicians, national authorities, or the tested individuals . In general, there are two approaches to controlling the whole RT-qPCR process . The one described in this book is using the internal control system, which is very widely used, but does 33 not detect an incorrect sampling collection . Therefore, some laboratories use a housekeeping gene as control. This is a parallel detection of a specific human gene that is always present in the mucous membranes of the nasopharynx . Its positive signal in the appropriate detection channel also indicates a good quality sample and a valid result for the detection of the presence of SARS-CoV-2 . However, at the time of the peak of testing, laboratories began to move away from this approach, because the frequency of poor-quality collections was high and it was also very difficult to arrange a new patient collection, especially from people who were tested for epidemic purposes and were not symptomatic . Their willingness to be tested again was very low, which caused complications and slowed down the whole process in laboratories . EVALUATION OF RESULTS FROM RT-QPCR DETECTION Measuring one sample requires a different approach than analyzing thousands of samples in one day . The main difference is the level of automation that goes into the sample evaluation and subsequent reporting . However, both options share the same basic features and steps leading to the analytical outcomes . Control Mechanisms After the PCR run, the data is analyzed and all reaction controls are checked (Fig . 12) . Fig. 11 Calibration curve and determination of the limit of detection using the combination of the viRNAtrap Extraction Kit and the gb SARS-CoV-2 Combi PCR detection kit ϯϬ ϯϮ ϯϰ ϯϲ ϯϴ ϰϬ ϰϮ ϯ,ϬϬϬ ϯϬ,ϬϬϬ ϯϬϬ,ϬϬϬ T ǀĂůƵĞ ŵZEŝŶƉƵƚ;ĐŽƉŝĞƐͿ ŶƵŵďĞƌŽĨĐŽƉŝĞƐŽĨǀŝƌƵƐ^Z^ͲŽsͲϮ ĨĨŝĐŝĞŶĐLJŽĨZEŝƐŽůĂƚŝŽŶ 34 In the first step, positive and negative controls are run simultaneously alongside each set of the analyzed samples . The expected runs are shown in Fig . 13 . The positive control run shows a standard curve containing a linear ground phase, an early exponential phase, a log-linear phase, and a plateau phase . Crucial is the CT value of the positive control in the SARS-CoV-2 detection channel (FAM fluorophore), which should be between 20 and 30 for the combination of the RNA extraction and PCR kit we used (Fig . 13A) . If the value is more than 30, then inhibition of the reaction can be expected . This may be mainly due to poor setup of the PCR instrument or inappropriate preparation of the PCR mix for the reaction . This control does not provide insight into the control of the extracFig. 13 The standard PCR profile of a positive (A) and negative (B) control in FAM and HEX channels 0 5 10 15 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 1 2 3 0 10 20 30 40 Cycles RFU 10^3 Amplification A B 0 10 20 30 40 0 10 20 30 40 Cycles Cycles Amplification Amplification RFU 103 RFU 103 15 3 10 2 51 00 0 5 10 15 20 0 10 20 30 40 Cycles RFU 10^3 Amplification Fig. 12 Results of a PCR run: blue curves – positive samples detected in FAM channel; green curves – internal controls detected in HEX channel RFU 103 35 tion process and the PCR reaction, as it is a combination of the PCR mix and the manufacturer’s pre-prepared control that is not going through the extraction procedure . The negative control is essential to monitor for adverse effects that may be caused by contamination or improper setup of the PCR protocol (Fig . 13B) . The expected curve in the SARS-CoV-2 detection channel is a linear ground phase from the beginning to the end of the analysis . Any exceedance of the threshold indicates a problem in the reaction, or a poor machine setup and the problem must be addressed . An internal control is added to each sample prior to the actual mRNA extraction to determine if the entire process, starting with the extraction followed by the PCR reaction, has gone well . This is detected in a different spectral channel than the detection of the SARS-CoV-2 sequence itself (HEX channel in the case of the kit we used) . The curve of the internal control can vary and, in most cases, differs from positive and negative samples for SARS-CoV-2 (Fig . 14) . For negative samples of SARS-CoV-2, a standard curve of the internal control with all the phases already mentioned can be expected, with a CT value between 25 and 35 . Usually, with the combination of the extraction and PCR kit we used, the CT value is around 30 (shown in Fig. 15), and a significant deviation from this value to those borderline values marks the beginning of potential problems at some stage of the measurement process . If the internal control for negative samples for SARS-CoV-2 is within the expected range and both positive and negative controls ran as expected, then the negative result is valid and ready to be reported to the physician, patient, or other authority, such as a regional health center . 0 1 2 3 4 0 10 20 30 40 Cycles RFU 10^3 Amplification Fig. 14 Curve of the internal control RFU 103 36 In case of positive samples of SARS-CoV-2, the curve shows a different profile (Fig . 16A) . This is because of a competition between two reactions – the production of a target for SARS-CoV-2 and the internal control . Since the same building molecules (deoxynucleotides) are used for their synthesis, the character of the internal control curve is dependent on the input SARS-CoV-2 mRNA concentration . At the same time, the reaction is designed (including the length of each product) to preferentially amplify SARS-CoV-2 fragments . In general, the higher the input mRNA concentration in the PCR reaction, the more is the synthesis of the internal control negatively affected or even does not occur at all . Realistically, it appears that in strongly positive samples of SARS-CoV-2, no synthesis of the internal control in the HEX channel occurs . However, this is not a given rule and during our analyses, strong samples of SARS-CoV-2 also appeared with synthesized internal Fig. 16 Different positive samples of SARS-CoV-2 and their effect on the synthesis of the internal control (a strongly positive and moderately positive samples of SARS-CoV-2) 0 5 10 15 20 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 1 2 0 10 20 30 40 Cycles RFU 10^3 Amplification A B 0 10 40 Cycles Cycles Amplification Amplification RFU 103 RFU 103 20 15 2 10 5 00 20 30 1 0 10 20 30 40 Fig. 15 Standard curve of the internal control in negative samples of SARS-CoV-2 0 1 2 3 4 0 10 20 30 40 Cycles RFU 10^3 Amplification RFU 103 37 control, albeit at a lower concentration, which is reflected by a significant shift of the CT value towards higher values . The different positive samples of SARS-CoV-2 and their effect on the synthesis of the internal control are shown in Fig . 16 . Therefore, for positive samples of SARS-CoV-2, we have established a rule that in case of positivity, it is not necessary to monitor the value of the internal control due to the uncontrollable process of its synthesis . In the case that positive samples of SARS-CoV-2 also have the expected results of positive and negative controls in the same run, then the positive result for SARS-CoV-2 is valid and is ready to be reported to the physician, patient, or other authority, such as a regional health center . Threshold Setting Setting the threshold value is also key . Each lab has slightly different rules for setting the threshold value, but in general, the setting should not change the actual test results . What must be said, however, is that the threshold setting significantly affects the CT value . This does not play a major role in qualitative or semi-quantitative testing of SARS-CoV-2 . The value of CT is important when the dynamics of ongoing infection are monitored, and in these studies, quantification is related to the CT value of the housekeeping gene and is evaluated in relative terms, or an absolute quantification is performed using a calibration curve. However, neither approach is performed in routine diagnostic laboratories because of the large number of samples and the clinical irrelevance of such an approach . Adjustment of the threshold value is generally done to ensure results independent of subjective influence, such as the person performing the evaluation of the results. Some laboratories chose the approach of applying percentage of the threshold value of the total signal . Usually, this percentage value is somewhere around 5–10%. Other labs use the absolute value of the threshold . This is possible if the lab uses the same RT-qPCR process including all kits and reagents every time . Even so, there are minor shifts between batches and individual measurements. However, in most cases, this shift is insignificant. For more accurate evaluation, various threshold correction functions are also used directly within the evaluation software . These allow better visualization and more accurate adjustment, but the main benefit is the elimination of poorly evaluated samples in the automatic evaluation, where some samples do not have a clean linear (straight) path in the initial cycles and could incorrectly cross the threshold value right at the beginning of the curve (Fig . 17) . In these cases, a false positive sample would be reported . 38 Evaluation of Results and Reporting Using PCR, it is not possible to determine whether an individual is at the beginning, in the middle, or at the end of an infection . This could be the basis for further medical and epidemiological action . However, it is possible to estimate from the character of the curve whether the individual is in a late stage of the infection, or an early stage of the infection . Both are characterized by a late CT value, which makes recognition very difficult. As this recognition is an advanced diagnosis, which is not supported by any study known to date and is based on the experience of people working in the laboratory, it is not possible to provide this information to doctors or patients . The late-phase infection curve shows a late threshold with a CT of more than 35 with a maximum absorbance significantly lower than the positive samples or the positive control . Another characteristic of the late phase of the infection is the shape of the curve, which is more tilted and does not show the standard axis shape . By contrast, the early-phase curve has a standard axis shape with a maximum absorbance close to that of the positive control . The difference can be seen in the two spectra in Fig . 18 . Such atypical samples with late CT values are quite difficult to evaluate, especially if a larger number of samples is measured . We have optimized the automatic evaluation of results with subsequent transfer to the medical information system and government systems (IHIS) . Only information whether an individual is positive or negative for SARS-CoV-2 is reported together with the CT value, but without further comments and opinion from the laboratory personnel . The frequency of these atypical results with late CT values is influenced by the epidemiological situation in the population . As the strength of the epidemic increases and more people Fig. 17 Threshold settings: green arrow – the threshold line; red arrow – crossing point of a curve with false positive result 0 200 400 600 10 20 30 Cycles RFU Amplification 39 are tested, the number of such samples also increases . There are several reasons for this . The main one is the higher probability of detecting people positive for SARS-CoV-2, as well as the dysfunctional tracing of people to prevent the spread of the epidemic . With epidemic peaks of high testing demand, people are contacted late, and even asymptomatic people are going for PCR testing and are often caught at a late stage of infection . This brings the negative social effect that these particular people are then meaninglessly left in quarantine even though they are already non-infectious . However, most people tested as positive for SARS-CoV-2 show a standard curve, which allows for the aforementioned, and at least partial, automation in the evaluation of the results and fully automated transfers to the above-mentioned databases . Problems Faced During Detection Some of the spectra show atypical patterns, which may affect the results and subsequent treatment . In a very limited number of cases, this is due to a non-standard character of the sample itself . In most cases, the problem starts somewhere in the sample preparation and analysis process, i .e ., in the isolation or PCR reaction . Such problems can be either one-time or systemic . One-Time Problems In the context of testing nasopharyngeal specimens for SARS-CoV-2, one-time problems include single contaminations, poorly sealed PCR plates, failure to add some components of the reaction, sample mix-ups, etc. These problems can be identified by simply repeating the entire sample preparation followed by PCR analysis . This step helps to eliminate the problems and report the results to the patient or physician . The spectra of one-time problems can be seen in Fig . 19 . A B 0 5 10 15 20 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 5 10 15 0 10 20 30 40 Cycles RFU 10^3 Amplification Fig. 18 Spectra of late-phase (A) and early-phase (B) of infection . The red arrows show typical curves for these conditions 0 10 40 0 10 30 40 Cycles Cycles Amplification Amplification RFU 103 RFU 103 20 55 00 15 10 20 30 15 10 20 40 Systemic Problems The more difficult situation is the repeated reporting of non-standard spectra. The most common is systemic contamination, which is seen as positivity across the whole plate with a high CT value . At this point, it is critical to detect the problem, because false positive results are being reported and the problem is gradually becoming more serious, as the CT values across the PCR plate start to drop and the problem escalates . Another example of a systemic problem is when the internal control channel is oversaturated with the signal being extremely strong for physical or chemical reasons and the SARS-CoV-2 detection channel is silenced providing false-negative results . Spectra of systemic problems can be seen in Fig . 20 . In all cases, the expertise and experience of the people evaluating the results is essential to detect all forms of problems affecting the correct issue of patient results . −10 0 10 20 30 40 0 10 20 30 40 Cycles RFU Amplification 0 10000 20000 30000 0 10 20 30 40 Cycles RFU 10^3 Amplification Fig. 19 One-time problems spectra: A – negative sample for both internal control and SARS-CoV-2 (either no internal control or other component of the reaction is omitted); B – poorly sealed PCR plate A B 0 010 1040 40 Cycles Cycles Amplification Amplification RFU RFU 103 40 30 20 10 0 −10 20 2030 30 30,000 0 20,000 10,000 A B Fig. 20 Systemic problems: A – contamination, red arrow shows positive control, green arrow shows negative control; B – problems with internal control 0.0 2.5 5.0 7.5 10.0 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 2 4 010203 04 0 Cycles RFU 10^3 Amplification Cycles Cycles Amplification Amplification RFU 103 RFU 103 0 10 20 30 40 0 10 30 40 4 2 0 10.0 2.5 0.0 7.5 5.0 20 41 EVALUATION PROCESS OF SALIVA TESTING This chapter should be an inspiration for any other testing and evaluations in each laboratory . The test tube Salivette (with cotton swab, cap: white) is used to detect the presence of the virus in saliva . The method utilizes a tampon made from a cotton-based absorbent material and a tube for its storage and transport . Saliva collection is performed by inserting the cotton tampon into the mouth of the subject for one to two minutes and then placing it in the transport tube (see Fig . 7) . During evaluation, individuals who underwent a reference nasopharyngeal swab also underwent saliva collection using a saliva collection tube (Salivette, Sarstedt, Germany) . The fluid was separated from the cotton roll by centrifugation and RNA of SARS-CoV-2 was extracted using the viRNATrap extraction kit (total 200 μL) and detected by RT-qPCR analysis as described above . In parallel, a validation in order to demonstrate the functionality of the sampling system Salivette at the end of its shelf life to detect SARS-CoV-2 virus particles in collected saliva was conducted . The sample integrity was proven over the whole product life cycle . For this validation, 18 pieces of Salivette tubes were used . EVALUATION OF SARS-COV-2 DETECTION IN SALIVA Samples were collected from patients using both ways (reference – nasopharyngeal swab, test – Salivette) and were isolated and analyzed by RT-qPCR immediately within the same day . The study group consisted of 249 women and 345 men . Age range of this group was from 4 years to 95 years . The identified positivity or negativity of the sample obtained by both sample collection methods was compared . A match between both collection methods was found in 587 of 594 samples tested . A discrepancy was found in 7 of 594 samples tested, with 1 sample taken by the test method being false positive and 6 samples taken by the test method being false negative . Conformity between the reference and test method was found in 98.8% of cases. The 6 samples that were positive in the nasopharyngeal swab and negative in the saliva samples had high CT values, and therefore low viral loads . The one sample that came out positive in saliva and negative in a nasopharyngeal swab may be explained by the dynamics of viral RNA degradation, where in saliva, nucleic acid is more stable for a longer time . 48 This data is provided both to physicians and patients . For doctors, the information about the variant is essential, as they can determine the subsequent treatment accordingly . SEQUENCING OF SARS-COV-2 The extracted RNA was transcribed to cDNA using SuperScript IV (ThermoFisher) . For sequencing of the S-protein, we used 7 overlapped amplicons (Tab. 12). The used amplification program is described in Tab . 13 . These amplicons were sequenced using the version 3 .1 Dye Terminator cycle sequencing kit with electrophoresis on an ABI 3500XL Avant Genetic Analyzer (both ThermoFisher Scientific; Waltham, MA, USA). Fig. 23 Spectra of positive samples from two different dates: A – typical spectra of SARS-CoV-2-positive samples (ROX channel); B – green curves of Delta-positive samples from the beginning of year 2022; C – curves from the end of the January of 2022 when the Omicron was more common; D – samples suspected of Omicron variant 0.0 2.5 5.0 7.5 10.0 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 5 10 15 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 1 2 3 4 5 0 10 20 30 40 Cycles RFU 10^3 Amplification 0 1 2 3 4 0 10 20 30 40 Cycles RFU 10^3 Amplification A C B D 0 0 0 010 10 10 1030 30 30 40 40 40 40 Cycles Cycles Cycles Cycles Amplification Amplification Amplification Amplification RFU 103 RFU 103 10.0 15 7.5 5.0 2.5 0.0 0 10 20 20 20 3020 5 1 0 4 RFU 103 RFU 103 1 0 4 5 2 3 2 3 49 Tab. 12 Set of primers used for amplification of the S-protein of SARS-CoV-2 Name of primer set Upper primer Lower primer Length primer_Cov_s20720_D 10F 16R 718 primer_Cov_s20720_D 16F 22R 689 primer_Cov_s20720_D 22F 29R 795 primer_Cov_s20720_D 30F 36R 677 primer_Cov_s20720_D 36F 41R 583 primer_Cov_s20720_D 41F 46R 590 primer_Cov_s20720_D 46F 52R 708 Tab. 13 Program used for amplification of amplicons of the S-protein for sequencing Amplification protocol Initial denaturation 95 °C 3 min Denaturation 95 °C 10 sec 35 cycles Annealing and elongation 57 °C 1 min Next Generation Sequencing (NGS) The NGS kit is based on a designed panel of 682 oligonucleotides allowing the preparation of any type and combination of PCR amplicons of the SARS-CoV-2 genomic sequence, including the possibility of PCR amplification of the entire genomic sequence. This method is designed to allow targeted genotyping of individual mutations, sequencing of selected regions of the genome using Sanger sequencing, or automated preparation of DNA libraries in 96-well plate format for multiplex sequencing on all types of Illumina, Pacific BioSciences, or Oxford Nanopore sequencers . Sequencing of Total RNA Total RNA isolated from positive samples were sequenced using the NGS method . The obtained results contained not only a sequence of whole genome of SARS-CoV-2 (Fig . 24 and 25), but also expressed human RNA from the nasal swab . By analyzing the data obtained from the total RNA sequencing, we were able to prepare a map of RNA/proteins that are expressed in nasopharyngeal epithelial cells. We identified that one important protein involved in the immune response is expressed in these cells . Based on further experiments, we found that expression levels of this protein (SAA1) correspond to the subsequent course of the infectious disease. This finding led to filing of a patent application describing a method of prediction of severity of infectious diseases based on this marker . 50 Fig. 24 Whole genome of one SARS-CoV-2 positive sample 51 Fig. 25 Analysis of SARS-CoV-2 genome with variants 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 52 PCR Amplicon Sequencing As the method of sequencing of total RNA is not useful for diagnostic laboratories, other simpler methods were developed . Whole Genome Sequencing We designed and optimized 6 PCR amplicon pairs (SC1–SC6) which enabled sequencing of selected regions or the entire SARS-CoV-2 genome using the Illumina platform . S-protein Sequencing We also designed and optimized 7 pairs of PCR amplicons (10F–52R) (Fig . 26) to sequence selected fragments or the entire S-protein gene region using targeted Sanger sequencing . Fig. 26 PCR amplifications of S-protein. Agarose gel with 7 PCR amplicons of S-protein 53 MONITORING OF THE SARS-COV-2 INFECTION AT SCHOOLS DURING A PANDEMIC PEAK To monitor the SARS-CoV-2 infection at schools, two experimental rounds of testing were conducted during spring 2021 with the aim to get a realistic picture of the prevalence of SARS-CoV-2 infection in children of different age groups in the Czech Republic at that time . The prevalence of infection and its evolution was correlated with the applied anti-epidemic measures . The data obtained were compared with the results of testing children in mainstream and predetermined schools of one region of the Czech Republic, which have different levels of risk in terms of infection and transmission within families . In the first round, 313 asymptomatic children from elementary schools in three different regions of the Czech Republic were tested for the presence of the virus . In the second round, 762 asymptomatic school-aged children were tested for the presence of the virus . The children have been visiting 66 different schools within one region of the Czech Republic . The locations of the schools and regions are shown in Fig . 27 . In both rounds, the samples were collected and delivered into an accredited laboratory, where they were analyzed by RT-PCR with previous RNA extraction according to previously described protocols . The data were then categorized by age and divided into nine sociological groups (Tab . 14) . We present data from 1 September 2020, which was also the start of the 2020/2021 school year . In another approach, CT values characterizing the input concentration of viral RNA were collected, divided by age, and analyzed . The methodology used has remained the same over time, therefore, despite many factors influencing the quantification, the obtained data can be compared in a semi-quantitative manner . With this in mind and in such a robust dataset, it is possible to get an idea of the infectivity rate in different age groups (Fig . 28 and Tab . 15) . As the CT value increases, the concentration of SARS-CoV-2 RNA in the tested material decreases . Thus, this parameter is directly related to the level of infectivity of a given age group (Monod et al ., 2021) . 54 Fig. 27 Locations of schools and regions used for the study: orange dots represent the 66 schools participating in the second round of the experiment and the regions where the first round took place are marked in light grey Tab. 14 Categorization by age to nine sociological groups Category Age 10–2 years Newborns and toddlers 23–5 years Preschool children 36–8 years Elementary school children I 49–13 years Elementary school children II 514–15 years Elementary school children III 616–18 years Youth I / high schools 719–26 years Youth II / universities 827–65 years Adults 9More than 66 years Seniors 55 Tab. 15 Distribution of number of viral particles of SARS-CoV-2 in different age groups Age group 0–2 years 3–5 years 6–8 years 9–13 years 14–15 years 16–18 years 19–26 years 27–65 years More than 66 years Number of tests 956 5 045 5 598 5 999 3 171 2 187 12 825 79 852 12 923 Number of positive tests 332 846 992 1 515 889 550 2 712 22 008 4 108 Number of detected viral particles 482 000 436 000 426 000 477 000 486 000 492 000 534 000 552 000 530 000 In the second round, testing was accompanied by a questionnaire regarding COVID-19 history in the family, such as the number of cases or their severity . The questions are listed in Tab . 16 and were shared with parents of the tested children via the Google Questionnaire application . All the parents were contacted in seven days distance by teachers or school management . However, the analysis of the questionnaire data has not shown any solid pattern or relevant conclusions . Fig. 28 Distribution of number of viral particles of SARS-CoV-2 in different age groups ϯϬϬϬϬϬ ϯϱϬϬϬϬ ϰϬϬϬϬϬ ϰϱϬϬϬϬ ϱϬϬϬϬϬ ϱϱϬϬϬϬ ϲϬϬϬϬϬ ϲϱϬϬϬϬ Ϭ ϭ Ϯ ϯ ϰ ϱ ϲ ϳ ϴ ϵ ϭϬ EƵŵďĞƌŽĨǀŝƌĂůƉĂƌƚŝĐůĞƐ ^Z^ͲŽsͲϮ ŐĞŐƌŽƵƉƐ ǀĞƌĂŐĞŶƵŵďĞƌŽĨǀŝƌĂůƉĂƌƚŝĐůĞƐŝŶĚŝĨĨĞƌĞŶƚ ĂŐĞŐƌŽƵƉƐ 56 Tab. 16 Questionnaire for the second round with questions about family history in relation to the presence and course of COVID-19 in the families 1 Was your child (tested at school) been previously diagnosed as positive for COVID-19 by PCR? Yes – 2020 Yes – 2021 No 2 If your child was previously positive for COVID-19, what kind of symptoms did he/she have? No COVID-19 before Temperature Cough Headache Loss of smell and/or taste Diarrhea and/or vomiting Cold Rash Other 3 What is your child’s health status 3 days after the testing at school? 4 How many adults live in the household? 5 How many children live in the household? 6 Have any of the adults in the shared household been previously diagnosed as positive for COVID-19 by PCR? 7 If any of the adults in the shared household were diagnosed as PCR positive, did they stay in the same household as the other members during the period of isolation/quarantine/illness? 8 Have any of the children (other than the child tested at school) from the shared household been previously diagnosed as positive for COVID-19 by PCR? 9 If any of the children (other than the child tested at school) from the shared household were previously diagnosed as positive for COVID-19 by PCR, did they stay in a shared household with other members during the period of isolation/quarantine/illness? 10 Has any member of the household been vaccinated against COVID-19? Analysis of the age composition shows statistically significantly lower rates in individuals below 26 years of age in comparison with the adult population (27–65 years) (Tab . 17) . The prevalence of infection in this group was not affected by the course of the epidemic in other age groups in the general public . An exception was the period after Christmas, probably caused by an infection transmission during family gatherings . 57 Tab. 17 Analysis of the age composition compared to the adult population (27–65 years) Age group OR 95% CI P value 3–5 years 0 .48 0 .46–0 .51 <0 .0001 6–8 years 0 .54 0 .52–0 .57 <0 .0001 9–13 years 0 .85 0 .82-0 .88 <0 .0001 14–15 years 0 .86 0 .82-0 .9 <0 .0001 16–26 years 0 .75 0 .73-0 .77 <0 .0001 Even though the vast majority of children was tested on the basis of an indication, meaning either the child’s current health problems or symptoms (cold, temperature, cough, headache, vomiting, diarrhea, loss of smell or taste) or an epidemiological indication, there was still a persistently low prevalence in the group of children aged 0–8 years . The prevalence of infection increases in the group of children aged 9 to 15 years and partially in the group aged 15 to 18 years after school closure (Fig . 29 and Tab . 18) . 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Web sites WHO – https://www .who .int/ CDC – https://www .cdc .gov GISAID – https://www .gisaid .org/ 67 LIST OF FIGURES Fig. 1 Main morphological virus types: A – helical, B – icosahedral, C – head-and-tail, D – enveloped Fig. 2 Phylogenetic tree of human coronaviruses (Biswas et al ., 2020) Fig. 3 Genome architecture of SARS-CoV-2 Fig. 4 Protein structure of SARS-CoV-2 Fig. 5 The attachment of SARS-CoV-2/MERS-CoV and entry into airway cells Fig. 6 An overview of the life cycle of HCoV in the host-cell (Song et al ., 2019; Zumla et al ., 2015) Fig. 7 User manual for the saliva sample collection using Salivette Fig. 8 RNA/DNA isolation based on magnetic-beads extraction technology (GeneSpector, Czech Republic) Fig. 9 Standard profile of a real-time PCR curve: red arrow – CT value, number of cycles where the fluorescence signal of the reaction crosses the set threshold; green arrow – set threshold; x-axis – number of PCR cycles; y-axis – fluorescence intensity Fig. 10 Preparation of PCR reaction Fig. 11 Calibration curve and determination of the limit of detection using the combination of the viRNAtrap Extraction Kit and the gb SARS-CoV-2 Combi PCR detection kit Fig. 12 Results of a PCR run: blue curves – positive samples detected in FAM channel; green curves – internal controls detected in HEX channel Fig. 13 The standard PCR profile of a positive (A) and negative (B) control in FAM and HEX channels Fig. 14 Curve of the internal control Fig. 15 Standard curve of the internal control in negative samples of SARS-CoV-2 Fig. 16 Different positive samples of SARS-CoV-2 and their effect on the synthesis of the internal control (a strongly positive and moderately positive samples of SARS-CoV-2) Fig. 17 Threshold settings: green arrow – the threshold line; red arrow – crossing point of a curve with false positive result Fig. 18 Spectra of late-phase (A) and early-phase (B) of infection . The red arrows show typical curves for these conditions Fig. 19 One-time problems spectra: A – negative sample for both internal control and SARS-CoV-2 (either no internal control or other component of the reaction is omitted); B – poorly sealed PCR plate Fig. 20 Systemic problems: A – contamination, red arrow shows positive control, green arrow shows negative control; B – problems with internal control Fig. 21 Schema of different sequencing strategies for characterization of SARS-CoV-2 genome Fig. 22 Results of discriminated PCR runs: orange curves – positive samples detected in ROX channel; green curves are positive samples with L45R variant in HEX channel; blue curves 68 are positive samples with E484K variant in FAM channel; purple curves are internal controls detected in Cy5 channel Fig. 23 Spectra of positive samples from two different dates: A – typical spectra of SARS-CoV-2positive samples (ROX channel); B – green curves of Delta-positive samples from the beginning of year 2022; C – curves from the end of the January of 2022 when the Omicron was more common; D – samples suspected of Omicron variant Fig. 24 Whole genome of one SARS-CoV-2-positive sample Fig. 25 Analysis of SARS-CoV-2 genome with variants Fig. 26 PCR amplifications of S-protein. Agarose gel with 7 PCR amplicons of S-protein Fig. 27 Locations of schools and regions used for the study . Orange dots represent the 66 schools participating in the second round of the experiment and the regions where the first round took place are marked in light grey Fig. 28 Distribution of number of viral particles of SARS-CoV-2 in different age groups Fig. 29 Prevalence of positivity for SARS-CoV-2 in age groups by week in the Czech Republic between 1 September 2020 and 7 March 2021 . The heatmap shows the status by weeks, the darker the color the higher the percentage of positive ones 69 LIST OF TABLES Tab. 1 Groups of viruses according to the Baltimore classification groups Tab. 2 Example of RT-qPCR protocol Tab. 3 List of components necessary for PCR reaction Tab. 4 The program used for RT-qPCR Tab. 5 Interpretation of results from RT-qPCR assay Tab. 6 Results of validation of sample collection using Salivette Tab. 7 The principle of dilution of samples for pooling Tab. 8 Results of pooled samples Tab. 9 The program used for RT-qPCR Tab. 10 Validation of results from discriminated RT-qPCR assay Tab. 11 Interpretation of results from discriminated RT-qPCR assay Tab. 12 Set of primers used for amplification of the S-protein of SARS-CoV-2 Tab. 13 Program used for amplification of amplicons of the S-protein for sequencing Tab. 14 Categorization by age to nine sociological groups Tab. 15 Distribution of number of viral particles of SARS-CoV-2 in different age groups Tab. 16 Questionnaire for the second round with questions about family history in relation to the presence and course of COVID-19 in the families Tab. 17 Analysis of the age composition compared to the adult population (27–65 years) Tab. 18 Timeline of anti-pandemic precautions in Czech Republic between 1 September 2020 and 7 March 2021 Tab. 19 Categorization of the population into demographic groups according to age and educational level with the number of performed tests 70 AUTHORS Michal POHLUDKA Michal Pohludka studied biochemistry and biotechnology at the Institute of Chemical Technology in Prague and subsequently completed his postgraduate studies in the field of molecular pathology at the 1st Faculty of Medicine at Charles University . Then, he spent more than a decade working for a global American company in various management positions in Central and Eastern Europe . His main focus was leading business and application activities in the field of clinical diagnostics and life sciences. Later, he founded his own consulting company . With the arrival of the first wave of the COVID-19 epidemic, he put his professional activities on hold and went to help set up a newly established laboratory for PCR testing for the presence of SARS-CoV-2 in patient samples . There, he combined his previous professional and managerial experience . Within a few months, the laboratory became the largest in Central and Eastern Europe . Together with representatives of Charles University, SPADIA LAB, a manufacturing company, and a development company, he founded the spin-off company GeneSpector . The aim was to create a complete solution for PCR testing and offer it, together with the know-how of the whole process, to Czech hospitals and laboratories to increase testing capacity and deliver patient results as quickly as possible . During the COVID-19 epidemic, he set up or built one fifth of all Czech laboratories for SARS-CoV-2 PCR testing . This includes the complete sample pathway from arrival into the laboratory, through automated RNA isolation, PCR and its evaluation, and automatic reporting of results to information systems . Due to the size and complexity of the whole agenda, he decided to share his practical experience with both the professional and general public in the form of this book . The aim of this publication is also to show problems of the process and follow-up solutions, so he invited Lenka Piherová, application specialist and expert from the 1st Faculty of Medicine of Charles University, as a co-author . Lenka PIHEROVÁ Lenka Piherová studied biochemistry and biomedical engineering at the Institute of Chemical Technology in Prague and subsequently completed her postgraduate studies at the 1st Faculty of Medicine of Charles University in the field of molecular and cellular biology, genetics, and virology. She has been studying genetic heart diseases for more than ten years and the core of her work is the analysis of genetic data with the aim to discover the cause of cardiomyopathies . She is an author and co-author of several scientific publications in this field. When the COVID-19 epidemic started, she helped the General University Hospital in Prague with PCR testing for SARS-CoV-2 . Her focus was on isolation of nucleic acids form swabs, speeding up testing, and discovery and optimization of a new isolation technique based on available chemicals and laboratory equipment . This solution became one of the pillars of the spin-off company GeneSpector . Later, she became an application specialist, helping several laboratories to set up testing and solve problems that arose during testing . These experiences are included in this book .