Speaker
Description
Spillover of emerging pathogens increasingly challenges human health and societal stability. One of the deadliest endemic diseases in West Africa, Lassa mammarenavirus (LASV), shows increasing potential for international outbreaks. Despite this, LASV remains undersampled and lacks a standardized approach to classifying the current sampled diversity. To address this gap, we assembled and analyzed the most complete whole-genome phylogeny of LASV. We collected 2,811 publicly available sequences under the organism name “Lassa mammarenavirus” from the National Center for Biotechnology Information (NCBI), GenBank, and curated full genome and segment-specific alignments. Corresponding phylogenetic trees were inferred using a maximum likelihood model framework. In agreement with previous studies, we find that significantly greater diversity exists among LASV lineages compared to within them. These observations highlight the need for a lineage-specific reference panel. To select representative reference sequences, we use closeness centrality, a network-based centrality metric that quantifies each node's closeness to all others based on the shortest-path length to it. This statistic is ideal for identifying phylogenetically central sequences, as it reflects relative proximity to all other sampled genomes in a given lineage and thus the sequence most representative of the lineage in tree space. We show that our lineage-specific reference panel accurately classifies LASV sequences in BLAST and short read mapping applications relative to the previously used Pinneo reference strain. Our open-source reference panel accurately characterizes the known diversity of LASV, providing a framework for molecular epidemiological analyses of future outbreaks. We further analyze our alignments to show, consistent with previous work, that LASV diversity is strongly partitioned by geography rather than by host type. Then, in a Bayesian framework, we analyze the origins and spread of LASV in West Africa. Taken together, our analyses both provide novel epidemiological tools and advance our understanding of LASV, an emerging pathogen of concern.
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