Pathogen Genomics in Public Health

Abstract

Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" through more effective investigations of outbreaks of foodborne illnesses, better-targeted tuberculosis control, and more timely and granular influenza surveillance to inform the selection of vaccine strains. In this article, we describe how public health agencies have been adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given the ongoing development in sequencing and sequencing-related technologies.

Figure 1:. Example of sequencing for outbreak detection and investigation.

An important purpose of infectious disease surveillance is identifying outbreaks for investigation and intervention. Discovering patterns in the epidemiologic data—i.e., finding common exposures among cases that cluster in time and location—can help distinguish outbreaks from the often much larger background of sporadic cases. Molecular subtyping has played an increasingly central role in this process by detecting cases with isolates that share a common molecular “fingerprint.” In this figure, we schematically represent surveillance data for a foodborne pathogen, Salmonella enterica serovar Enteritidis, reported from one region of the United States in 2018; in that year, some states in the region were already sequencing Salmonella isolates in real time and others had not yet started. In the three panels, each dot represents a case of Salmonella Enteritidis gastroenteritis. Gray dots represent cases that were later determined to be “sporadic” (i.e., not linked to outbreaks) and colored dots represent cases that were eventually linked to outbreaks. The largest of these outbreaks (red dots) began as two distinct clusters of disease associated with restaurants in two different states. Whole-genome sequencing (WGS) linked these two clusters together and to several other cases outside the region. The first panel (“Unsorted”), displays cases randomly, without regard to molecular subtyping. The second panel represents a grouping of cases based on results of pulsed-field gel electrophoresis (PFGE), a molecular subtyping technology that US public health agencies have used since the 1990s. In this example, PFGE was mostly successful at grouping cases from the largest (red) outbreak; however, the group includes many cases unrelated to the outbreak, complicating the investigation and reducing the likelihood of finding the food source. In the third panel, we show that the finer resolution afforded by whole-genome sequencing (WGS) was more effective in segregating the red outbreak cases from others. This gave investigators more confidence in the cluster definition and allowed them to focus on cases that were more likely part of the same outbreak. In this case, epidemiologic investigation identified shell eggs as the likely source, which was quickly confirmed by isolating Salmonella Enteritidis from the implicated eggs and confirming that its WGS matched that from the outbreak cases. In addition to the outbreak in red, this panel shows four additional outbreaks. Cases in blue were part of a restaurant-associated outbreak linked to chicken in a single state. Two cases (green) were linked to live poultry exposure as part of a much larger, multistate, multi-strain outbreak that occurred mostly outside the region shown here. The five cases in light pink were investigated as an outbreak but no food source was identified. The fifteen cases in light orange occurred in a state where real-time WGS had not yet been implemented; their isolates were not sequenced until a later date, after the apparent outbreak had ended. This figure summarizes relationships identified by WGS using a simplified graph; in practice, however, the data would be represented as a phylogenetic tree, which contains additional detail that more precisely represents the relationships among sequences.

Figure 2.

Typical Pathogen Genomics Workflow. From pathogens (a) collected in the course of disease surveillance, genomic DNA (b) or RNA are extracted, shorn into shorter segments, labeled, and subjected to next-generation (high-throughput) sequencing (c). The raw data from the sequencer are sorted, reassembled and aligned to other genomes for comparison (d). The assembled genomes are used for several purposes including determining relatedness (e) and predicting phenotypic traits such as virulence, antimicrobial resistance, and serotype (f). Increasingly, the data made publicly available in real-time for use by researchers and for the development of diagnostics, therapeutics and vaccines (g).