Despite recent success in reducing the regional incidence of Plasmodium falciparum malaria, cases of zoonotic malaria are on the rise in Southeast Asia. The Cambodian National Malaria Surveillance Program has previously relied on rapid diagnostic tests and blood smear microscopy with confirmatory polymerase chain reaction (PCR) testing in a subset of cases to further distinguish P. falciparum, P. malariae, P. ovale, and P. vivax species. Here, metagenomic next-generation sequencing identified P. knowlesi mono-infection in six Cambodian patients initially diagnosed with P. malariae by blood smear microscopy in February-May 2020. These findings of recent human infections with P. knowlesi in Cambodia led to the incorporation of P. knowlesi-specific PCR diagnostics to national malaria surveillance efforts.

Aedes mosquitoes are some of the most important and globally expansive vectors of disease. Public health efforts are largely focused on prevention of human-vector contact. A range of entomological indices are used to measure risk of disease, though with conflicting results (i.e. larval or adult abundance does not always predict risk of disease). There is a growing interest in the development and use of biomarkers for exposure to mosquito saliva, including for Aedes spp, as a proxy for disease risk. In this study, we conduct a comprehensive geostatistical analysis of exposure to Aedes mosquito bites among a pediatric cohort in a peri‑urban setting endemic to dengue, Zika, and chikungunya viruses. We use demographic, household, and environmental variables (the flooding index (NFI), land type, and proximity to a river) in a Bayesian geostatistical model to predict areas of exposure to Aedes aegypti bites. We found that hotspots of exposure to Ae. aegypti salivary gland extract (SGE) were relatively small (< 500 m and sometimes < 250 m) and stable across the two-year study period. Age was negatively associated with antibody responses to Ae. aegypti SGE. Those living in agricultural settings had lower antibody responses than those living in urban settings, whereas those living near recent surface water accumulation were more likely to have higher antibody responses. Finally, we incorporated measures of larval and adult density in our geostatistical models and found that they did not show associations with antibody responses to Ae. aegypti SGE after controlling for other covariates in the model. Our results indicate that targeted house- or neighborhood-focused interventions may be appropriate for vector control in this setting. Further, demographic and environmental factors more capably predicted exposure to Ae. aegypti mosquitoes than commonly used entomological indices.

SignificanceMetagenomic pathogen sequencing offers an unbiased approach to characterizing febrile illness. In resource-scarce settings with high biodiversity, it is critical to identify disease-causing pathogens in order to understand burden and to prioritize efforts for control. Here, metagenomic next-generation sequencing (mNGS) characterization of the pathogen landscape in Cambodia revealed diverse vector-borne and zoonotic pathogens irrespective of age and gender as risk factors. Identification of key pathogens led to changes in national program surveillance. This study is a "real world" example of the use of mNGS surveillance of febrile individuals, executed in-country, to identify outbreaks of vector-borne, zoonotic, and other emerging pathogens in a resource-scarce setting.

Background

We established the first prospective cohort to understand how infection with dengue virus is influenced by vector-specific determinants such as humoral immunity to Aedes aegypti salivary proteins.

Methods

Children aged 2-9 years were enrolled in the PAGODAS (Pediatric Assessment Group of Dengue and Aedes Saliva) cohort with informed consent by their guardians. Children were followed semi-annually for antibodies to dengue and to proteins in Ae. aegypti salivary gland homogenate using enzyme-linked immunosorbent assays and dengue-specific neutralization titers. Children presented with fever at any time for dengue testing.

Results

From 13 July to 30 August 2018, we enrolled 771 children. At baseline, 22% (173/770) had evidence of neutralizing antibodies to 1 or more dengue serotypes. By April 2020, 51 children had symptomatic dengue while 148 dengue-naive children had inapparent dengue defined by neutralization assays. In a multivariate model, individuals with higher antibodies to Ae. aegypti salivary proteins were 1.5 times more likely to have dengue infection (hazard ratio [HR], 1.47 [95% confidence interval {CI}, 1.05-2.06]; P = .02), particularly individuals with inapparent dengue (HR, 1.64 [95% CI, 1.12-2.41]; P = .01).

Conclusions

High levels of seropositivity to Ae. aegypti salivary proteins are associated with future development of dengue infection, primarily inapparent, in dengue-naive Cambodian children.

Clinical trials registration

NCT03534245.

Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.