Genomic Surveillance and Deployable Molecular Diagnostics for Emerging Infectious Diseases

Abstract

Emergence of novel pathogens and outbreaks of existing biothreats have significant social and economic impacts. Detecting emerging infectious disease (EID) threats early and accurately is critical for timely public health intervention and development of vaccines and therapeutics. In this work, we applied unbiased metagenomic sequencing to detect and characterize both viral and bacterial pathogens in plasma samples from a cohort of febrile patients and healthy controls in Thiès, Senegal. We identified relapsing fever Borrelia, an underrecognized tick-borne bacterial pathogen, as the most common cause of non-malarial febrile illness. Second, we took a genomics-informed approach to designing a deployable reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for Lassa virus (LASV), a seasonal hemorrhagic fever virus endemic to West Africa. We developed a high-throughput system for testing RT-LAMP primer set activity across diverse in vitro transcribed RNA targets. This massive-scale primer set screening generated important insights on the factors affecting RT-LAMP amplification speed and guided our design of candidate RT-LAMP assays for the two most prevalent lineages of LASV, Lineage II (LII) and Lineage IV (LIV). We evaluated the performance of candidate assays on clinical samples and showed they could detect LASV RNA (sensitivity compared to gold-standard qRT-PCR: Broad LII v1.1 45%, Broad LII v2 50%, Broad LIV-Liberia 33%), especially in samples with a high viral RNA load (sensitivity in samples with qRT-PCR Ct 35: Broad LII v1.1 87%, Broad LII v2 77%). Finally, we used our empirical dataset of over 3,800 unique Lassa virus RT-LAMP primer set (LPS)-target pairs to predict amplification in silico with high precision and recall (SS: Precision = 0.887, Recall = 0.873; WS: Precision = 0.952, Recal = 0.714) and explored the potential for expanding our empirical dataset and applying a biological sequence optimized machine learning architecture to create a tool for rapid RT-LAMP assay design for emerging viral threats.