Making genomic surveillance deliver: A lineage classification and nomenclature system to inform rabies elimination

Abstract

The availability of pathogen sequence data and use of genomic surveillance is rapidly increasing. Genomic tools and classification systems need updating to reflect this. Here, rabies virus is used as an example to showcase the potential value of updated genomic tools to enhance surveillance to better understand epidemiological dynamics and improve disease control. Previous studies have described the evolutionary history of rabies virus, however the resulting taxonomy lacks the definition necessary to identify incursions, lineage turnover and transmission routes at high resolution. Here we propose a lineage classification system based on the dynamic nomenclature used for SARS-CoV-2, defining a lineage by phylogenetic methods for tracking virus spread and comparing sequences across geographic areas. We demonstrate this system through application to the globally distributed Cosmopolitan clade of rabies virus, defining 96 total lineages within the clade, beyond the 22 previously reported. We further show how integration of this tool with a new rabies virus sequence data resource (RABV-GLUE) enables rapid application, for example, highlighting lineage dynamics relevant to control and elimination programmes, such as identifying importations and their sources, as well as areas of persistence and routes of virus movement, including transboundary incursions. This system and the tools developed should be useful for coordinating and targeting control programmes and monitoring progress as countries work towards eliminating dog-mediated rabies, as well as having potential for broader application to the surveillance of other viruses.

Molecular Analysis of Rabies Virus Using RNA Extracted from Used Lateral Flow Devices

“…We successfully constructed a phylogenetic tree with all positive samples using sequencing primers ( n = 34) for groups 1 and 2. In this study, phylogenetic analysis of the N gene revealed that all sequences were grouped into the Asian clade and the SEA4 subclade ( 27 ). We performed N gene sequencing for phylogenetic analysis in this study since N gene analysis is essential for classifying the lineage ( 27 ).…”

“…In this study, phylogenetic analysis of the N gene revealed that all sequences were grouped into the Asian clade and the SEA4 subclade ( 27 ). We performed N gene sequencing for phylogenetic analysis in this study since N gene analysis is essential for classifying the lineage ( 27 ). The virus subclades are identical because this is a single-institute analysis using samples from one region.…”

Examining the molecular clock hypothesis for the contemporary evolution of the rabies virus

“…((48), Fig S1), despite different methods and RABV datasets being used in their calculations. The mean per-generation substitution rate produced through the methods described could also potentially be used in the future to estimate the size of rabies outbreaks from genetic data and calculate predicted rates of RABV lineage emergence based on the classification guidelines of MADDOG (41).…”

“…The beta regression curve and prediction interval in Figure 2C was generated using the 'betareg' R package (40). RABV lineages were assigned using MADDOG (41).…”

Uncovering the endemic circulation of rabies in Cambodia

“…To analyse the distribution of Cambodian lineages within a global RABV phylogeny, maximum likelihood phylogenetic inference was performed using the program IQ‐TREE v2.0.6 (Minh et al, 2020), with 1000 bootstrap replicates and the substitution model (GTR + I + Γ) selected by the IQ‐TREE's ModelFinder tool. This first phylogenetic analysis was based on all the Cambodian N genes ( n = 354) and a balanced subset of 300 N genes isolated across all continents, and was used to visualize the RABV clades and subclades of the non‐Cambodian and Cambodian sequences (Campbell et al, 2022). The discrete phylogeographic analysis used to investigate the signature of endemicity was performed with the discrete diffusion model (Lemey et al, 2009) implemented in the software package BEAST 1.10 (Suchard et al, 2018).…”