Genomic and Metagenomic Approaches for Predictive Surveillance of Emerging Pathogens and Antibiotic Resistance

Abstract

Antibiotic-resistant organisms (AROs) are a major concern to public health worldwide. While antibiotics have been naturally produced by environmental bacteria for millions of years, modern widespread use of antibiotics has enriched resistance mechanisms in human-impacted bacterial environments. Antibiotic resistance genes (ARGs) continue to emerge and spread rapidly. To combat the global threat of antibiotic resistance, researchers must develop methods to rapidly characterize AROs and ARGs, monitor their spread across space and time, and identify novel ARGs and resistance pathways. We review how high-throughput sequencing-based methods can be combined with classic culture-based assays to characterize, monitor, and track AROs and ARGs. Then, we evaluate genomic and metagenomic methods for identifying ARGs and biosynthetic pathways for novel antibiotics from genomic data sets. Together, these genomic analyses can improve surveillance and prediction of emerging resistance threats and accelerate the development of new antibiotic therapies to combat resistance.

Figure 1

Biological and environmental samples can be interrogated for ARO and ARG enrichment using whole genome sequencing (WGS) and metagenomics analyses. A) Samples can be collected from diverse origin, including biological samples such as tissue, blood, or fecal samples from animal or patient samples. Environmental samples can be collected directly from built environment surfaces or natural environments such as soil and water sources. B) In culture-based methods, isolates are extracted from samples and processed individually to determine species identification and antibiotic susceptibility. After DNA extraction, each isolate can be sequenced using WGS. WGS analyses can determine taxonomic identification, antibiotic resistance genotype, and epidemiological genes. These characteristics can be compared to culture-based results, creating genotype-phenotypic characterization of isolates and improved characterization of species and resistome. WGS analyses can also be used to improve sequence based searches for metagenomics analyses. C) In metagenomics analyses, genomic DNA is extracted directly from a sample, then 16S or whole metagenomics sequencing is done. After 16S amplification and sequencing, taxonomic composition is determined. After WMS, taxonomic composition, antibiotic resistance gene abundance, and functional gene abundance can be analyzed. All of these data can be combined to improve diagnosis, treatment, and ARO and ARG surveillance.

Figure 2

Antibiotic remnants from antibiotic use in humans and animals increases antibiotic concentration and ARO and ARG abundance in wastewaters and in sink reservoirs in hospitals due to human shedding. After treatment, wastewater progresses to reservoirs such as rivers, resulting in ARG enrichment in downstream agricultural and human water sources. From rivers, ARG enrichment may be passed on to household water supplies, resulting in an ARG enrichment in sinks due to increased ARO and ARG abundance in source water. ARG enrichment in rivers also results in increased ARG abundance in sediment and other environmental water sources. This cycle can result in ARG enrichment in human pathogens and persistent environmental reservoirs of AROs and ARGs.

Figure 3

The application of NGS to bacterial isolates and metagenomes has accelerated drug development by enabling the discovery of novel antibiotics and informing the rational design of drugs that reverse resistance mechanisms. Genomic data can enable intelligent surveillance methods for tracking the origin and spread of ARGs, as well as insights into the evolutionary context behind emergence of a particular resistance mechanism. This can be done through sequence-based computational tools (top) or function-based methods, such as functional metagenomics methods (middle). Note the vertical arrows depicting how sequence- and function-based methods can be combined to make inferences about a particular resistance mechanism, such as through deep mutational scanning (bottom).