Discovering disease-causing pathogens in resource-scarce Southeast Asia using a global metagenomic pathogen monitoring system

Abstract

Understanding the regional pathogen landscape and surveillance of emerging pathogens is key to mitigating epidemics. Challenges lie in resource-scarce settings, where outbreaks are likely to emerge, but where laboratory diagnostics and bioinformatics capacity are limited. Using unbiased metagenomic next generation sequencing (mNGS), we identified a variety of vector-borne, zoonotic and emerging pathogens responsible for undifferentiated fevers in a peri-urban population in Cambodia. From March 2019 to October 2020, we enrolled 473 febrile patients aged 6 months to 65 years of age presenting to a large peri-urban hospital in Cambodia. We collected sera and prepared sequencing libraries from extracted pathogen RNA for unbiased metagenomic sequencing and subsequent bioinformatic analysis on the global cloud-based platform, IDseq. We employed multivariate Bayesian models to evaluate specific pathogen risk causing undifferentiated febrile illness. mNGS identified vector-borne pathogens as the largest clinical category with dengue virus (124/489) as the most abundant pathogen. Underappreciated zoonotic pathogens such as Plasmodium knowlesi, leptospirosis, and co-infecting HIV were also detected. Early detection of chikungunya virus presaged a larger national outbreak of more than 6,000 cases. Pathogen-agnostic mNGS investigation of febrile persons in resource-scarce Southeast Asia is feasible and revealing of a diverse pathogen landscape. Coordinated and ongoing unbiased mNGS pathogen surveillance can better identify the breadth of endemic, zoonotic or emerging pathogens and deployment of rapid public health response.

Untangling an insect virome from its endogenous viral elements

“…Potential bacterial reads were distinguished from contaminating environmental sequences by establishing z-score metrics ≥10, alignment length over 50 matching nucleotides (NT L ≥50), and a minimum of five reads per million aligning to the reference protein database (NR rPM ≥ 5). Potential viruses were established by z-score metrics of ≥1, NT L ≥50, and NR rPM ≥ 5 [46,54,55]. Bacterial and viral hits were confirmed with BLASTX and BLASTN manual searches.…”

“…Lastly, we removed a contig nearly identical to the pLeu plasmid found in Buchnera aphidicola and a small portion of two large contigs matching the Buchnera genome. The final annotation was assessed using BUSCO v.5.3.2 [69] with the MetaEuk gene predictor [70] Characterizing endogenous viral elements in the M. euphorbiae genome: DNA Illumina raw reads were used as input to the CZ ID platform pipeline V7.1 (https://czid.org) and a z-score metrics of ≥1 and NT L ≥50 as described above [46,54]. Additionally, to screen for actively transcribed Ambidensovirus-like EVES in the M. euphorbiae genome, we used BLASTN searches using the seven viral hits provided in individual Trinity contigs flagged by TRAVIS (supplementary file contigs_TRAVIS.fasta) against the genome scaffolds.…”

Metagenomic next-generation sequencing to characterize potential etiologies of non-malarial fever in a cohort living in a high malaria burden area of Uganda

“…falciparum is unlikely to be the cause of febrile illness. Taken together, the limited availability of diagnostics for pathogens beyond malaria results in many undiagnosed illnesses [ 4 ], missed opportunities for targeted treatments [ 5 ], unnecessary empiric use of antibiotics [ 6 ], and public health surveillance systems that provide an incomplete picture of the pathogen landscape [ 7 ].…”

Metagenomic next-generation sequencing to characterize etiologies of non-malarial fever in a cohort living in a high malaria burden area of Uganda

“…In individuals with parasitemia who have acquired high levels of clinical immunity, P. falciparum is unlikely to be the cause of febrile illness. Taken together, the limited availability of diagnostics for pathogens beyond malaria results in many undiagnosed illnesses [4], missed opportunities for targeted treatments [5], unnecessary empiric use of antibiotics [6], and public health surveillance systems that provide an incomplete picture of the pathogen landscape [7].…”