Genomic Surveillance and Pandemic Preparedness

By Rajesh Pandey

Genomic Surveillance and Pandemic Preparedness offers practical, in-depth instruction in where, how, and why genomic surveillance may be applied. Drawing heavily from the learnings during the COVID-19 pandemic, this book covers different aspects of microbes with a focus on viral genome sequencing and analysis, implementation and inference of genomic surveillance, and global data sharing best practices for future pandemic preparedness. Here, more than a dozen international authors consider host and pathogen genome interaction, population-level prevalence, individual disease susceptibility and gene protection, pathogen mutation evolutionary dynamics, RNA modulation of infection, pathogen diversity, and the role of coinfections and comorbidities in disease severity. A wide range of examples, from bacterial pathogens to fungal and viral infections, with an emphasis on recent COVID-19 analysis, are discussed. Finally, the authors provide step-by-step instruction in experimental and computational approaches for genomic surveillance, including nucleic acid isolation, sequencing library preparation, functional sequencing analysis, and bioinformatics pipelines. Throughout this book, importance is placed on inference—moving from genomic sequencing to meaningful genomic surveillance that bolsters pandemic preparedness.

• Highlights different aspects of genomic surveillance, from theory to implementation to inferences
• Covers a broad range of infectious diseases, with emphasis on recent COVID-19 studies
• Shares methods and approaches for sequencing-based pathogen detection, diagnostics, and surveillance
• Facilitates international collaboration in drug and vaccine development

• Language: English
• Publisher: Academic Press
• Release date: Jun 29, 2023
• ISBN: 9780443187681

Book preview

Preface

Gene–Genome–Genomics–Genomic Surveillance; Pathogen–Pathogenome–Pathogenicity—these are the concepts which would be our friend for time to come as we prepare for informed preparedness for timely response to future infectious disease/s. In the post-COVID-19 era, the theme of One Health , having an integrated understanding (preemptive), inclusive of the genome information encoded within a pathogen/potential pathogen, host response/differential host response, and transcriptionally active microbes (TAMs) within the infected host, needs unified understanding toward: What determines differential disease severities (mild/moderate/severe) and clinical outcomes (recovered/mortality) albeit infected by the same/similar pathogen? Complementary to the multi–omics-based approaches, there is need for an integrative genomics approach toward understanding different facets of disease from a single clinicogenomics dataset, for example, using RNA-seq data to ask questions pertaining to RNA virus genome encoded information, host protein coding functional response, modulatory role of noncoding RNA, alternate transcript isoforms, and TAMs constituting the inherent human microbiota/microbiome.

One of the pillars of the host response is the resilience of the immune system vis-a-vis underlying cellular heterogeneity, in modulating differential immune responses in time and space. Several studies during the COVID-19 pandemic have highlighted the differential immune escape potential of SARS-CoV-2 variants of concern (VOCs), like Alpha, Delta, and Omicron. It is important to delineate the individual role of the pathogen genome–elicited immune response from the host as well as host genetic makeup underlying the response to the infection. This is in addition to the different pathogens, for example, bacteria, virus, fungi, helminthes, etc., which by virtue of differential genome composition in size, backbone (DNA/RNA), single, or double stranded and AT/GC contents are also an important component of this layered host–pathogen interaction.

Subsequently, in addition to bulk RNA-seq, single cell genomics coupled with cytokine profiling in COVID-19 patients have emphasized cell type dynamics and hence immune response in modulating disease severity. Furthermore, using standard model systems, immune perturbation experiments are essential and can provide large immune response datasets and an integrated framework to connect immune features to clinical trajectories of disease. The human genome always surprises by the hierarchical layers of regulation which includes the cross-talk between the genetic parasites, in other words transposable elements, and their nonrandom integration within the human genome with functional consequences. Previous viral infections have been causally associated with the Alzheimer disease (AD)/Parkinson's disease (PD). This highlights the long-term effect on the human health of previous infections.

While understanding the pathogen genome, the backbone of the genome (DNA/RNA) is a critical factor as it underlines the mutation rate which in turn determines the rate of evolution of the pathogen. It assumes significance as it directly impacts the infectivity rate, medical and healthcare infrastructure, disease interventions, drug regimen, and potential vaccine development. As we have globally experienced challenges posed by the RNA viruses, like SARS-CoV-2, H1N1, Ebola, influenza, HCV, it is important to understand the functional outcome of the RNA–RNA interactions, both between multiple copies of the virus within the host as well as pathogen–host RNA. The secondary symptoms manifested in the infected individuals are managed clinically wherein antibiotics is one of the major medications used. These antibiotics while they help control secondary bacterial infections they also contribute to antibiotic resistance. Sequencing of bacterial populations, across multiple diseases, has highlighted the effects of mutation, recombination, and selection pressure leading to enhanced understanding of antibody resistance, host evasion, and adaptation to new environments and pathogenicity, thus driving bacterial evolution and infection. Thus, in alignment with the future disease management, we should be expanding on the functional role of antimicrobial resistance (AMR) in the host which would impair future treatment options. One of the possible ways would be digital biobanking of the AMR profiling in the patients, subsequent to their successful treatment to aid/augment future decision-making towards drugs to be used. The understanding would be enhanced during specific disease situations, both for the RNA viruses or specific bacterial infections.

As we understand specific pathogens, locally and globally, and the differential host response, in this exceedingly mobile and interconnected world, we also travel to different geographical terrains, including high altitude with relatively lower oxygen levels. It has been known before that underlying medical condition, like respiratory tract infections, is a risk factor toward a persons' adjustment or adaptation to high altitude for a nonnatural habitant. Focused research in this direction will help and aide increasing travel priorities to new places. Adding further layer to differential disease severities is the functional role of resident microbes within the infected host. Exploring and elucidating the role of coinfections/copresence of the microbes, in addition or during the primary pathogen infection, will help understand layers of regulation modulating the disease. Limited yet important research highlights an impressive role of functional metagenome in modulating the disease severity and clinical outcome. The findings are expected to aid in exploring specific role of microbes as probiotics as well as its potential therapeutic usage. This would help cut the risk of AMR which is staring at the global community as we grapple with the post-COVID world order.

This book highlights the importance of preemptive understanding for quicker response for future challenges and replacing/revisiting the old order of reactive response to a pathogenic challenge. Thus, Genomic Surveillance needs to align with future pandemic preparedness with inclusion but not limited to (i) expanding the definition of genomic surveillance to include pathogen, host, as well as resident microbes, (ii) trained manpower for federated genomics-based approach for taking sequencing lab to the people for priority sequencing at the point of care, (iii) global data sharing for federated learning to augment learning together, (iv) seamless ingestion of the data for AI/ML-based prediction models, and (v) strengthening the One Health paradigm for sustainable development and pandemic response.

This book would have been incomplete without the timely contributions from the authors who albeit their busy schedule and direct/indirect challenges posed by the COVID-19 locally/globally contributed to specific chapters in this book. It was wonderful to work 24 × 7 with authors across the global time zones which includes initial reaching out to potential authors to clarifying the specific queries and then leading toward timely submission. Lot of learning gained during the COVID times vis-à-vis clinical and community surveillance has been the pivot which cajoled us to share a reference book emphasizing the need for genomic surveillance as well as the skill set required for the future pandemic preparedness. It was enriching experience to see that the research skill set was directly being utilized as a critical component of public health decision-making and disease management as well as lab researchers working at the interface of public health.

Heartfelt thank you is due to the support system during the COVID times that includes but not limited to family members, friends, clinicians, neighborhood, funding bodies, and self-help groups. I also take this opportunity to wish Happy Reading of this book to the future readers covering diverse aspects of host–pathogen interaction with a functional role in disease severities (mild, moderate, severe) and clinical outcomes (recovered and mortality).

Lastly, take this opportunity to pay tribute to Dr. Bani Baral, an Academic par excellence. She was a constant source of inspiration who kept reminding me that I should be converting the learnings made during COVID-19 into a book for the larger audience to read and academically strengthen future pandemic preparedness. She left for heavenly abode in early 2022 but her discussions are always going to act as guiding light.

Rajesh Pandey, PhD

Principal Scientist, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India

https://www.igib.res.in/?q=RajeshPandey

Section A

Pathogens and host: genomics perspective

Outline

1. Population-level differences in COVID-19 prevalence, severity, and clinical outcome

2. Host genetics in disease susceptibility and protection

3. RNA as modulators of infection outcome: potential usage for genomic surveillance

1: Population-level differences in COVID-19 prevalence, severity, and clinical outcome

Ishita Dasgupta ¹ , a , Sandeep Saini ² , a , Md Abuzar Khan ¹ , and Kumardeep Chaudhary ¹ , ³       ¹ CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India      ² Department of Biophysics, Panjab University, Chandigarh, India      ³ Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India

Abstract

Initial measures to control the outbreak of COVID-19 seemed to be insufficient due to the lack of preparedness for this unexpected pandemic of the 21st century. A key aspect of pandemic preparedness is understanding the population-level differences. In this chapter, we discussed the role of population-level differences in the prevalence, severity, and clinical outcome of COVID-19. During the COVID-19 pandemic, it was found that the disease has affected different populations disproportionately. For instance, usually, the prevalence was found to be more in Southern Asia and African regions as compared to the Western pacific. Furthermore, the infected proportion of the male population was higher than that of females. On the basis of race, the prevalence in the black population was higher than in the white. Population-based severity and clinical outcomes based on age, sex, genetic and socioeconomic status in COVID-19 also demand effective and focused measures to control the pandemic situations.

Keywords

COVID-19; Population; Prevalence; Risk; SARS-CoV-2; Serosurvey

Abbreviations

AMR    Americas region

ARDS    Acute respiratory distress syndrome

CFR    Case fatality ratio

COPD    Chronic obstructive pulmonary disease

COVID-19    Coronavirus disease-2019

CREST    Cas13-based, rugged, equitable, scalable testing

EMR    Eastern Mediterranean region

EUR    Europe region

FELUDA    FnCas9 (Francisella novicida Cas9) Editor Linked Uniform Detection Assay

HIC    High income country

IFR    Infection fatality rate

LMIC    Lower-middle income country

MERS-CoV    Middle East respiratory syndrome coronavirus

POC    Point-of-care

PR    Prevalence ratio

R&D    Research and development

RR    Relative risk

SARS-CoV-2    Severe acute respiratory syndrome coronavirus-2

WHO    World Health Organization

WPR    Western Pacific region

Introduction

The first list of blueprint priority diseases was published by the World Health Organization (WHO) in December 2015 after the outbreak of the Ebola virus disease in 2014 in West Africa. This list contains the list of diseases and pathogens that are prioritized for research and development (R&D) in the context of public health emergencies. Later, it was updated in January 2017 and the latest list (Fig. 1.1) was compiled in February 2018 and updated with coronavirus disease 2019 (COVID-19) later on (World Health Organization, 2022). All the priority diseases in the WHO list are caused by viruses which reflects the importance of these pathogens in controlling future outbreaks and pandemics. Moreover, the inclusion of viral diseases in the priority list is obvious as the world has seen many fatal viral outbreaks in the recent past caused by vector-borne diseases (Lassa fever, Yellow fever, Ebola and Marburg hemorrhagic fever, Dengue fever, West Nile fever, Chikungunya, and Zika fever) and zoonotic diseases (Swine flu, Middle East respiratory syndrome (MERS), and Severe acute respiratory syndrome (SARS)) where the current COVID-19 emerges as the most severe pandemic of the 21st century (Trovato et al., 2020; Gates, 2020).

Initiated in Wuhan, Hubei province, China, COVID-19 is caused by a new strain of the coronavirus family and thus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Huang et al., 2020; Zhu et al., 2020). Being an infectious disease, COVID-19 rapidly infected the world population and thus was declared a global pandemic by WHO on March 11, 2020 (World Health Organization, 2020). At the time of compiling this chapter, COVID-19 had infected more than 650 million people and the death toll exceeded 6 million worldwide (WHO Coronavirus, 2023).

Figure 1.1  The diseases and pathogens prioritized for research and development (R&D) by WHO. (∗Pathogen unknown).

In the past, it has been seen that infectious diseases impact the populations of different geographical regions, sex, age, occupations, and socioeconomic status around the world in different ways (Lessler et al., 2016; Justman et al., 2018; Busico et al., 1999; Gonzalez et al., 2000). The same pattern has also been observed during COVID-19 in which the prevalence, severity, and clinical outcome were found to be different among the world populations (He et al., 2021; Jordan and Adab, 2020; Franceschi et al., 2021). The estimation of these patterns at global, regional, and local levels helps health policymakers in making better epidemiological strategies to curb the outbreaks depending on the magnitude of population-level differences in disease prevalence, severity, and clinical outcomes (Pearce et al., 2020; Stringhini et al., 2021).

Hence, this chapter intends to provide insight into the population-level differences in COVID-19 prevalence, severity, and clinical outcome.

Measuring the burden of disease: prevalence and incidence

In the context of epidemiology, the disease burden or frequency is usually measured in the terms of prevalence and incidence. These indicators of disease burden serve as the basis for framing and evaluating healthcare policies and provide directionality to scientific research (Spronk et al., 2019). Both terms have specific meanings as defined below.

Prevalence

Prevalence is the proportion of a population that has a specific condition or disease at a particular period (Bhopal, 2016). The prevalence of a disease can be calculated by the following formula:

It can be stated as a percentage (10%, or 10 out of 100 persons), or as the number of instances per 10,000 or 100,000 people (National Institute of Mental Health (NIMH), 2023).

Incidence

Incidence is the measure of the number of new cases of a specific condition or disease which occur in a population at a particular period (Bhopal, 2016). The incidence of a disease can be calculated by the following formula:

Types of prevalence

In epidemiology, there are three types of prevalence reported:

▪ Point prevalence: The number of people affected by a disease in a population at a particular point in time is referred to as point prevalence.

▪ Period prevalence: The number of individuals affected by a disease in a population during a certain period is referred to as period prevalence.

▪ Lifetime prevalence: The number of individuals affected by a disease in a population for their total lifetime is referred to as lifetime prevalence (National Institute of Medical Health (NIH), 2023).

Factors affecting prevalence

The prevalence of a disease is affected by a number of factors, including the age of the population, the number of cases observed, the seriousness of the disease, the advancement of treatment, and the timespan for which the disease is observed. A rise in prevalence may occur due to factors such as prolonged illness duration, an upgraded diagnostic infrastructure, an extension of patients' lives in the absence of a cure, an increase in the number of new cases, and the immigration of diseased individuals into the population. A decrease in prevalence, on the other hand, could be due to shortened illness duration, better treatment options, a rapid recovery, a short life expectancy due to the disease, a decrease in the number of new cases, the emigration of diseased individuals, and the immigration of healthy individuals into the population (Beaglehole et al., 1993; Dicker et al., 2006).

Seroprevalence: measuring infection status in the population

Seroprevalence studies (or serosurveys) are population-based epidemiological surveys that measure the presence of antibodies against a particular pathogen. These studies can estimate the number of individuals who have been infected in the population. Serosurveys provide information on the history of exposure to a pathogen, whereas RT-PCR-based methods provide a snapshot of current exposure (Pollán et al., 2020; McConnell et al., 2021). Moreover, during the COVID-19 many CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based viral diagnostic assays such as FELUDA (FnCas9 (Francisella novicida Cas9) Editor Linked Uniform Detection Assay) (Azhar et al., 2021; Kumar et al., 2021), CREST (Cas13-based, Rugged, Equitable, Scalable Testing) (Aralis et al., 2022), and CRISPR-COVID (Hou et al., 2020) were also developed for faster and point-of-care (POC) detection of COVID-19 infection.

Serosurveys can be conducted in a country, state, or a particular community population (e.g., hospitals, hotels, schools, colleges, and offices, etc.) (McConnell et al., 2021). Moreover, in infectious diseases like the COVID-19 pandemic, serosurvey becomes of utmost importance for formulating and assessing the overall healthcare strategy in response to outbreaks. The data collected by these surveys helps in the identification of the magnitude of disease prevalence in the studied population such as current and emerging hot spots and the reduction of infection in areas where transmission has been considerably decreased (Larremore et al., 2021).

Furthermore, the results of these surveys provide the true infection rate in the population based on age, gender, geographic and socio-economic status (Bergeri et al., 2022) and thus guide the policymakers to decide on the prioritization of the vaccine and treatment, relaxing lockdown and in disease modeling, etc. (Larremore et al., 2021). Strategically to conduct a serosurvey, the population needs to be randomly segregated into a small sample population, which is generally the representation of the complete population. For the detection of antigen-specific antibodies, blood samples are collected and analyzed by the serological test. Fig. 1.2 shows schematically the process of serosurveillance.

Different types of serological tests can be used to measure the presence or absence of pathogen-specific antibodies in the population. The outcome of the serosurveys mainly depends on the sensitivity and specificity of these tests (Vogl et al., 2021). Table 1.1 lists the main assays used in the serosurveillance studies.

Figure 1.2  Strategical approach for conducting serosurvey. (I) Selection of a random sample population from the total population, (II) blood samples collected from individuals for pathogen-specific antibody detection, (III) tests performed to identify the presence of antibodies, (IV) seroprevalence calculated from the number of seropositive individuals identified. CLIA, chemiluminescence immunoassay; ELISA, enzyme-linked immunosorbent assay; IgA, immunoglobulin A; IgG, immunoglobulin G; IgM, immunoglobulin M; LFIA, lateral flow immunoassay.

Table 1.1

CLIA, chemiluminescence immunoassay; ELISA, enzyme-linked immunosorbent assay; FIA, fluorescence immunoassay; LFIA, lateral flow immunoassay; PRNT, plaque reduction neutralization tests.

Decoding COVID-19 prevalence: a population-based approach

The COVID-19 pandemic has affected populations around the world in different ways. While some countries and regions have been hit hard with high rates of infection and mortality, others have seen relatively low levels of infection and fewer deaths (Rostami et al., 2021a). In this section, we will explore the various factors that may contribute to these population-level differences in COVID-19 prevalence. Furthermore, we will highlight prevalence differences among the different population groups using data from published systematic or metanalysis studies.

One key factor that may contribute to differences in COVID-19 prevalence is the population's level of immunity to the virus. This can be influenced by a variety of factors, including the population's previous exposure to other coronaviruses, the presence of cross-reactive antibodies in the population, and the presence of preexisting conditions that may affect the immune system's ability to fight off the virus (Tso et al., 2021; Ravi, 2020). Another factor that may influence COVID-19 prevalence is the population's level of social and physical distancing measures. Populations that have implemented strong social distancing measures, such as lockdowns and mask mandates, may have lower rates of infection compared to those that have not. Similarly, populations that live in more densely populated areas may be at higher risk of infection due to increased potential for pathogen transmission (Rubin et al., 2020).

Several country-wide, regional, and worldwide studies have been conducted during the last 2 years for deciphering the population-level prevalence differences in COVID-19. The systematic and meta-analysis studies that have collected the literature over time provide adequate evidence to decipher these differences systematically. Usually, prevalence reported in these studies is in the form of pooled prevalence which involves reanalyzing the primary data from multiple studies to summarize results. This method allows for standardized modeling and control of variables, increased statistical power, and examination of variability in the population subgroups (Smith-Warner et al., 2006).

For instance, in a systematic review study that collected the worldwide literature data till August 14, 2020 from 47 studies (covering 23 countries), the global pooled estimate was 3.38%. On a regional basis, the pooled seroprevalence was highest among central and southern Asia (22.16%) and lowest in South-Eastern Asia (0.37%). A lower pooled seroprevalence of 4.21% was observed in Europe and North American regions. In the gender-pooled seroprevalence, the male population (5.33%) was found to be more infected as compared to the female (5.05%). This study also provides age-wise pooled seroprevalence in which the age group between 20 and 49 was more (3.22%) seropositive as compared to others (age ≤19 (2.28%), age 50–64 (2.98%), age ≥65 (2.57%)). The study also revealed that the general children were more seropositive (8.76%) as compared to the general adult (5.31%). On the ethnic basis, black, non-Hispanic were found to be more seropositive (9.96%) as compared to brown/Hispanic (8.76%), white/non-Hispanic (3.76%), and Asian and other races (5.78%) (Rostami et al., 2021a).

In another study that analyzed the literature from January 2020 to December 2020 (968 studies across 74 countries), the median seroprevalence of COVID-19 in the general population was estimated to be 4.5% whereas on the regional basis, the highest rates of infection were found in the sub-Sharan Africa (19.5%) and the lowest in Southeast Asia, East Asia, and Oceania (0.6%). The risk of infection was significantly higher among Black (prevalence ratio [PR]: 3.37) compared to Asian (PR: 2.47) individuals. On the age group basis, persons in-between 18 and 64 years of age were found to be more seropositive as compared to older adults (≥65 years) (Bobrovitz et al., 2021).

In another systematic analysis that was performed for the literature collected between December 2019 to December 2020 (404 studies from 6 regions), the global seroprevalence of COVID-19 was found to be 8.0% in the general population, with higher rates among close contacts of infected individuals and high-risk healthcare workers (18.0% and 17.1%, respectively). The prevalence of infection varied significantly between WHO regions, with the lowest rates in the Western Pacific region (1.7%) and the highest in the South-East Asia (19.6%). The age and gender pattern revealed that young and older individuals were less likely to be infected than those aged 20–64, and men had more infection rates than women (7.0% for men and 6.6% for women) in pooled seroprevalence. The ethnic prevalence was found to be highest among the Black and Asian individuals (Relative Risk (RR) of 2.70) and 1.91, respectively) than White individuals. An approximately similar pooled infection-to-case ratio was observed in the region of the Americas (6.9) and the European region (8.4) but was found to be higher in India (56.5) (Chen et al., 2021).

In a study based on healthcare workers reported by Hossain et al., that analyzed the literature data on seroprevalence, published from January 2020 to January 2021 (53 studies from 14 countries), the overall seroprevalence of COVID-19 was found to be 8.6%. The pooled seroprevalence was higher in the United States (12.4%) than in Europe (7.7%) and East Asia (4.8%) and male healthcare workers (9.4%) were found to be infected more as compared to female (7.8%). It was also discovered to be more prevalent among older healthcare workers in Europe and East Asia, and among younger healthcare workers in the United States (Hossain et al.,