Metagenomic sequencing detects viruses and bacteria in a cross-sectional clinical cohort of undifferentiated febrile illness in Nigeria

Abstract

ABSTRACT Molecular and microscopy-based diagnostic capacity is often insufficient or unavailable in places where infectious disease burdens are highest, such as in West Africa. Rapid diagnostic testing (RDT) can provide quick and affordable diagnoses of common infections but is an imperfect solution due to limitations around detecting and dealing with false-negative and false-positive results. An alternative to RDT is unbiased metagenomic sequencing for pathogen surveillance. Here, we present data from unbiased metagenomic sequencing used to identify causes of undiagnosed febrile illness in Jos, Plateau State, Nigeria. Proof of concept for this approach has been demonstrated by several groups who have identified epidemic and endemic viral diseases like Lassa fever, yellow fever, and chikungunya. We show that unbiased deep sequencing and metagenomic analysis can be used to identify RNA viruses in clinical samples. We sequenced RNA from sera of patients (n = 343), many of whom were acutely febrile (76%), in a survey of clinics in Jos. We detected five human-infecting viruses in 39 (11 %) specimens. Among these were hepatitis B virus, human pegivirus, and several anelloviruses. While most of the viruses identified are unlikely to cause clinical symptoms in the patients we sampled, their presence demonstrates the validity of our approach. Additionally, our sequencing data allowed us to identify genetic material from potentially pathogenic bacteria, another possible etiological agent of febrile illness. IMPORTANCE In low-resource areas, fevers due to infectious pathogens are a major source of illness, but tools for detecting and identifying such pathogens are often limited. Unbiased approaches for identifying genetic material from all potentially infectious organisms in a sample represent an opportunity for discovering sources of fever. Metagenomic sequencing can improve insight into pathogen landscapes in low-resource settings, potentially providing early detection of disease outbreaks. However, unbiased metagenomic sequencing (mNGS) is no panacea; it is susceptible to contamination and false positives. We used mNGS to evaluate serum from >300 Nigerian clinic-goers in Jos, Nigeria, most of whom (>70%) had fevers of unknown origin. Our goal was to understand arbovirus prevalence in Jos, Nigeria, and identify the sources of infection not routinely monitored for at clinics. We detected hepatitis B virus, as well as nonpathogenic anelloviruses. Our study provides insight into the utility and limitations of mNGS for pathogen surveillance. In low-resource areas, fevers due to infectious pathogens are a major source of illness, but tools for detecting and identifying such pathogens are often limited. Unbiased approaches for identifying genetic material from all potentially infectious organisms in a sample represent an opportunity for discovering sources of fever. Metagenomic sequencing can improve insight into pathogen landscapes in low-resource settings, potentially providing early detection of disease outbreaks. However, unbiased metagenomic sequencing (mNGS) is no panacea; it is susceptible to contamination and false positives. We used mNGS to evaluate serum from >300 Nigerian clinic-goers in Jos, Nigeria, most of whom (>70%) had fevers of unknown origin. Our goal was to understand arbovirus prevalence in Jos, Nigeria, and identify the sources of infection not routinely monitored for at clinics. We detected hepatitis B virus, as well as nonpathogenic anelloviruses. Our study provides insight into the utility and limitations of mNGS for pathogen surveillance.