During an outbreak investigation, Cer et al., showed the utilization of metagenomics by comparing genomic variants to measure human–human spread and mutation rates of the COVID-19 virus. Comparing two different outbreaks of COVID-19—one in a group prior to vaccine development and one in a group with high vaccination rates—a larger number of variants were observed within the unvaccinated group. The study highlighted further questions and discussion on the impacts of sampling bias, the impact of purifying selection in creating clonal viral populations in vaccinated individuals, and how that can inform and support vaccination efforts.

Pandemics such as the Ebola in 2014–2016 and COVID-19 have shown that global security is compromised when response requires advanced training, special tools, and established infrastructure to accurately survey and assess the outbreak situation, where delays ultimately cost lives. It was during the Ebola outbreak that portable sequencing technology (MinION) was piloted in the field to speed up detection and public health response with a simpler, pared down protocol and analysis workflow. COVID-19 sequencing protocol using the ARTIC protocol from Illumina can be time consuming, limiting the potential use of molecular epidemiology for control measures. To reduce sequencing time during an outbreak, Arévalo et al., modified a COVID-19 whole genome sequencing assay that produced high quality metagenomics data relatively quickly in an isolated, low resource setting. Adaptation of current protocols to LMIC infrastructure and resources while providing a quick turnaround for results will be invaluable in controlling future outbreaks.

Clinically, metagenomics can be used to identify unknown or rare pathogens that might go undetected using traditional methods. An example of this was outlined by Tian et al., to detect viral infections in kidney transplant recipients (KTRs) during the COVID-19 pandemic. An NGS approach detected rare viruses in multiple sample types including bronchoalveolar lavage fluid (BALF), urine and blood. This method identified infections or co-infections in vulnerable immunosuppressed kidney transplant recipients, which was then used to inform treatment strategies.

Li et al., used NGS on clinical samples of patients who were suspected of having spinal tuberculosis (TB). Over half of the samples (45/82) tested had bacteria that was not Mycobacterium tuberculosis. This approach quickly and simultaneously confirmed patients with spinal infection if they had TB or non-TB infections. This approach also distinguished and identified other pathogens in these patients, which helped guide patient's clinical treatment. This study evaluated the efficacy and application of NGS as clinical diagnostic assay for suspected spinal TB infection in clinical laboratories.

Altantogtokh et al., tested pools of Dermacentor nuttalli ticks (n = 1,773) from 15 provinces of Mongolia using NGS. Rickettsia spp. was detected in 88.33% of screened tick pools, with Khentii aimag having the highest detection rate for Rickettsia spp. Rickettsia raoultii and Rickettsia sibirica were detected within this study, aligning with previous findings that applied NGS to livestock samples in Mongolia (Chaorattanakawee et al., 2022). Anaplasma spp. and Bartonella spp. were detected in 3.18 and 0.79% of pools, respectively, with Anaplasma spp. only detected in ticks collected from livestock, as seen elsewhere (von Fricken et al., 2020). This study represents the first detection of Bartonella melophagi in ticks tested from Mongolia.

Pollio et al., discovered a novel Rickettsia spp. in mosquitoes which shared a close identity with Rickettsia bellii based on data from NGS. While it is unknown if R. bellii is pathogenic in humans, detecting this microorganism in mosquitoes may hold value down the road should this disease emerge. The use of metagenomics to screen mosquito samples elsewhere in the world may hold value for the identification and characterization of potential pathogenic Rickettsia spp. within an atypical vector host.

Human-livestock transmission is one mode for infection of zoonotic pathogens. The likelihood of infection is increased as animals pass pathogens between species similar to the spread of Escherichia coli in Hickman et al., This interspecies exchange is conducive to pathogen mutations, and passage of mobile genetic elements or plasmids via clonal or horizontal gene transfer, increasing the risk of spreading to humans potentially increasing the severity of infection, and resistance to antibiotic interventions.

Ergunay et al., used NGS to screen 75 blood-fed ticks (Rhipicephalus spp. and Amblyomma spp.) collected from wild and domestic animals in Kenya for pathogens. Fifty-six human or veterinary pathogenic bacterial species were detected including Escherichia coli (62.8%), Proteus mirabilis (48.5%), Coxiella burnetii (45.7%), and Francisella tularensis (14.2%). Additional pathogens detected include fungal species, filarial pathogens, protozoa, and environmental and water/foodborne pathogens. Jingmen tick virus was detected within 13% of these samples, representing the first report of this virus in Kenya. Interestingly, in these wild and domestic animal blood-bags, human pathogens, including Plasmodium falciparum were detected, indicating back spill-over of pathogens of human pathogens to animals.

Finally, the adoption of a One Health focused surveillance strategy is described in detail by Achee et al., through the Remote Emerging Disease Intelligence—NETwork (REDI-NET), which has implemented MinION-based xenosurveillance strategies in the United States, Kenya and Belize. Comprehensive standard operating procedures (SOPs) were developed for the deployment of metagenomic surveillance at high-risk disease envelopes. The ultimate goal of REDI-NET is to apply agnostic pathogen detection to a variety of sample types including water, ticks, soil, and leeches across Gold- (reach-back laboratories with greater infrastructure) and Silver- (remote, field-forward laboratories) tiered laboratory systems. This study outlined the framework required for working with metagenomic data, efficient data pipeline strategies, and systematic processes for collection and testing of samples for deeper analysis.

This Research Topic highlights the diversity of metagenomic application across disciplines, geographical regions, and both research and clinical settings. The field of metagenomics is growing rapidly, with all papers in this Research Topic published within the past year. Utilizing metagenomics from a One Health perspective will expand surveillance and characterization of existing and new pathogens in humans and animals, which will likely continue to be adopted into existing systems as next generation sequencing becomes more accessible and cost-effective.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.