Regional patterns of genetic diversity in swine influenza A viruses in the United States from 2010 to 2016

Abstract

These data suggest that vaccine composition and control efforts should consider IAV diversity within swine production regions in addition to aggregated national patterns. This article is protected by copyright. All rights reserved.

Mitigating Pandemic Risk with Influenza A Virus Field Surveillance at a Swine-Human Interface

“…As our real-time Mia database contained human-cases of swine IAV, we performed additional post-field phylogenetic analyses using the outbreak data, the Mia database, and additional swine IAV sequences that represent the genetic diversity and evolutionary relationships of contemporary swine IAV 14,15,20 . When compared to 126 swine H1 sequences, our Mia identified swine-H1N2 viruses were in a monophyletic clade with contemporary 1B.2.1 (H1-δ2) HA genes from the human seasonal lineage (Figure 3).…”

“…We processed and assembled reads with IRMA's FLU-sensitive module 13 Each gene was aligned with MAFFT v7.294b 30 and the best-known maximum likelihood phylogeny for each alignment was inferred using IQ-TREE v1.6.2 31 , with the model of molecular evolution automatically selected during the tree search. Each gene was classified to evolutionary lineage and/or phylogenetic clade 15,17,20 . Subsequently, we selected 3-10 viruses from each named clade that captured the evolutionary relationships among clades, and that represented the major HA, NA, and whole genome constellations circulating in US swine 15,20 .…”

“…Each gene was classified to evolutionary lineage and/or phylogenetic clade 15,17,20 . Subsequently, we selected 3-10 viruses from each named clade that captured the evolutionary relationships among clades, and that represented the major HA, NA, and whole genome constellations circulating in US swine 15,20 . We then aligned the MiSeq and annotated sequences with MUSCLE v.3.8.31 25 and inferred maximum likelihood trees using IQ-TREE 24 v.1.6.8…”

Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks

“…In the past few decades, genetic analysis of rapidly evolving pathogens has become an integral part of animal disease surveillance systems worldwide (1)(2)(3)(4). Most current and past molecular surveillance studies of animal disease pathogens of both public health and economical importance such as influenza (5)(6)(7), foot-and-mouth disease (FMD) (8)(9)(10), and porcine reproductive and respiratory syndrome (PRRS) (11)(12)(13) viruses are dependent on classical epidemiological and phylogenetic methods. These studies or surveillance systems used classical phylogenetic methods, including parsimony, neighbor-joining, or maximum likelihood (ML) approaches to either genotype novel emerging strains, classify viral lineages, or assess tree topologies to distinguish between novel and emerging strains (6,7,13).…”

“…Most current and past molecular surveillance studies of animal disease pathogens of both public health and economical importance such as influenza (5)(6)(7), foot-and-mouth disease (FMD) (8)(9)(10), and porcine reproductive and respiratory syndrome (PRRS) (11)(12)(13) viruses are dependent on classical epidemiological and phylogenetic methods. These studies or surveillance systems used classical phylogenetic methods, including parsimony, neighbor-joining, or maximum likelihood (ML) approaches to either genotype novel emerging strains, classify viral lineages, or assess tree topologies to distinguish between novel and emerging strains (6,7,13). In addition, classical phylogenetic approaches were used to assess correlations between the similarities of nucleotide sequences and related epidemiological characteristics, while ignoring uncertainties associated with estimates of phylogenetic relationships, host, temporal, and spatial factors (7,10,11,14).…”