Next-generation sequencing: approaches to leverage its power for genomic surveillance, pathogen detection, and outbreak investigation

Published on April 25th, 2024

Public health surveillance and detection is defined as the ongoing, systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health actions to reduce morbidity and mortality and to improve health (1). In recent years, whole genome sequencing (WGS) by next-generation sequencing (NGS) technologies has enabled rapid, precise, accurate, and cost-effective characterization of pathogens to support clinical interventions and public health responses (2). NGS technologies now allow sequence data to be generated faster and more affordably than ever before. These technologies have many applications in public health, such as the sequencing of entire genomes to provide insights into genetic changes, variations, mutations, phylogeny, and virulence; metagenomics—the process of sequencing all genetic material in a biological or environmental sample for total characterization; and targeted sequencing that focuses on specific genomic regions of interest. During the COVID-19 pandemic large-scale WGS of SARS-CoV-2 contributed to identifying the different variants and guided public health preparedness and responses, while highlighting the role WGS can play in a public health emergency. In their Frontiers in Science lead article, Struelens et al. advocate for a broader global implementation of WGS to enhance our control of infectious diseases and antimicrobial resistance (AMR), learning from the experience of COVID-19 (3). Here we outline a few of the policy measures necessary to help achieve this goal. 

Building capability and capacity  

In the public health arena, the deployment of NGS capability and capacity has focused mainly on operational issues. Many countries, particularly low- and middle-income countries (LMICs), have lacked access to NGS platforms (4) as well as the bioinformatics capabilities required to analyze the data. In Africa, up to 71% of next-generation sequencers (N=206) were concentrated in just five countries (South Africa, Nigeria, Kenya, Morocco, and Egypt), with most of the capacity (144/206) located outside of national public health institutes (5). The lack of access to NGS was not limited to any particular WHO region (4). A skilled workforce and stable funding for operations and sustainment of such technological capabilities are additional issues that LMICs must address before this technology can be adopted globally. These and other hurdles, such as limited experience in bacterial genomics (e.g., knowledge regarding expected genome size, mobile genetic elements, and expected variability within a given bacterial species) must be overcome if WGS results are to be correctly interpreted (6) and the recent scientific and technological advances in WGS are to benefit public health. 

Policymakers, particularly those in LMICs, could also benefit from learning about the benefits of WGS for disease and AMR surveillance. Unfortunately, most of the studies describing the benefits of WGS were done in high-income countries (HICs). Limited resources might favor a One Health approach (aiming to balance and optimize the health of people, animals, and the environment) owing to cost saving, ease of coordination, and efficient resource mobilization (7). This would help address concerns over AMR, which disproportionately affect LMICs—where antimicrobial use is mostly unregulated and agriculture and livestock farming play a major role in their economies (8).  

Data policies 

It is also essential to address non-laboratory issues associated with the use of NGS technologies in public health. NGS generates massive amounts of data and results that vary in terms of clinical relevance (9). NGS poses particular challenges in several ethical areas: privacy, informed consent, responsible use of genomic data, benefits of sharing with affected community, and return of results. Furthermore, NGS data often moves back and forth between research and public health contexts (i.e., data collected for public health surveillance can be used for research and vice versa). There are differences in the ethical obligations and standards applicable in research and public health. This blurred boundary also has implications for informed consent and the return of results.  

Guidelines support policies to promote the sharing of genomic data among public health agencies, research institutions, and international partners to enhance surveillance efforts, detect emerging threats, including AMR genes, design rapid molecular detection assays such as real-time polymerase chain reaction (PCR) and outbreak investigation by tracking the spread and identifying the cause of infectious diseases. For example, the genomic epidemiologic database for global identification of microorganisms or Global Microbial Identifier (GMI) is a platform for storing WGS data of microorganisms (bacteria, viruses, fungi, and parasites), for the identification of relevant genes and for the comparison of genomes to detect, track, and trace infectious disease outbreaks and emerging pathogens. The database holds two types of information: i) genomic information of microorganisms, linked to ii) metadata for those organisms, such as epidemiological details (9). This international initiative began in 2011 and currently has >270 contributor scientists in 55 countries. To use genomes for infectious disease investigations and epidemiology, metadata are essential. The more details captured in the metadata, the more efficient the tracking of microorganisms and understanding the cause and linkages to support the public health mission. However, with a higher level of detail also comes potential political, privacy, and ethical complications. Thus, policies need to be established concerning what constitutes essential metadata, who has access to it, and its security. In addition, ownership and intellectual property rights can affect timely sharing of genetic data from pathogens and thus affect the public health response (10). There is a continued lack of understanding and guidance on equitable data sharing (11). To address this, robust governance and policy structures need to be created at international, national, and institutional levels (11). 

Ethical guidelines should be established for the collection, use, and sharing of genomic data (12). The sequence data produced by NGS can be used for phylogenetic analyses to understand infectious disease transmission dynamics (i.e., who most likely infected whom) and thus inform effective public health interventions. For example, HIV phylogenetics, which has been restricted to large, highly regulated laboratories, might become more widely distributed across geographic areas. This raises issues including stigmatization and risk of social harm to individuals or groups, and concerns about the privacy, confidentiality, and security of data (13). 

Analyses of WGS data are only as good as the quality of the data. Quality control measures ensure accuracy, reproducibility, and reliability of WGS data (14). Quality assurance establishes standards for laboratory practices and procedures to ensure the accuracy, reliability, and reproducibility of WGS data (15). Three aspects of NGS, irrespective of chemistry, instrumentation or software are: i) sample preparation, ii) sequencing, and iii) data analysis and interpretation. Budowle et al. developed a framework of criteria that should be considered to validate these steps (16). 

Conclusion 

WGS has the potential to revolutionize disease surveillance, detection and diagnosis, and molecular epidemiology, as well as to monitor and track AMR in the environment by providing unprecedented resolution. By developing appropriate policies and guidelines, public health can leverage the power of genomics to prevent and control infectious diseases, ultimately improving public health outcomes worldwide. 

Conflict of interest 

SAM is currently employed by IHRC, Inc. SPP declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 

This article reflects the views of the author (SPP) and should not be construed to represent FDA's views or policies. 

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