ISU FLUture: a veterinary diagnostic laboratory web-based platform to monitor the temporal genetic patterns of Influenza A virus in swine

Zeller, Michael A.; Anderson, Tavis K.; Walia, Rasna W; Vincent, Amy L.; Gauger, Phillip C.

doi:10.1186/s12859-018-2408-7

Cited by 55 publications

(55 citation statements)

References 43 publications

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“…a secondary spike March-April (Anderson et al 2013;Walia et al 2019), with a similar trend in Canada (Poljak et al 2014). However, contemporary influenza illness and diagnosis can be found at any time of the year (https://influenza .cvm.iastate.edu; Zeller et al 2018a), in nearly all age groups of pigs, even suckling pigs from sows with high titers of influenza-specific serum antibodies (Allerson et al 2013;Corzo et al 2013).…”

Section: Clinical Aspects Of Influenza In Pigsmentioning

confidence: 96%

“…Then, during the 2010-2011 human influenza season, a distinct human seasonal H3N2 virus transmitted to U.S. swine and sustained onward transmission (Rajão et al 2015). This more contemporary H3N2 lineage, 2010.1, was genetically and antigenically distinct from the 1998 H3N2 lineage C-IV viruses in the United States and quickly became dominant, but did not replace the C-IV H3 lineage in U.S. swine (Zeller et al 2018a). During the same decade, a second distinct human seasonal H3N2 virus transmitted to U.S. swine, 2010.2, and continues to be detected at low levels (Zeller et al 2018b).…”

Section: H3mentioning

confidence: 99%

“…These dynamics contributed to the generation of numerous genetically and antigenically distinct lineages cocirculating in swine around the world. Then, the repercussions of these evolutionary events were starkly shown with the swine-origin pandemic in 2009 (Mena et al 2016), the subsequent impact of the continued 2009 pandemic H1N1 (H1N1pdm09) introductions into pig populations (Nelson et al 2015c), and ongoing detections of contemporaneous human seasonal H3N2 spillovers into swine (Ngo et al 2012;Rajão et al 2015;Krog et al 2017;Wong et al 2018;Zeller et al 2018a). The genetic diversity of H1 and H3 viruses in swine is summarized in Figure 1A,B and the N1 and N2 neuraminidase genes in swine are summarized in Figure 2A,B.…”

mentioning

confidence: 99%

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Swine Influenza A Viruses and the Tangled Relationship with Humans

Anderson

Chang

Arendsee

et al. 2020

Cold Spring Harb Perspect Med

124

122

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Influenza A viruses (IAVs) are the causative agents of one of the most important viral respiratory diseases in pigs and humans. Human and swine IAV are prone to interspecies transmission, leading to regular incursions from human to pig and vice versa. This bidirectional transmission of IAV has heavily influenced the evolutionary history of IAV in both species. Transmission of distinct human seasonal lineages to pigs, followed by sustained within-host transmission and rapid adaptation and evolution, represent a considerable challenge for pig health and production. Consequently, although only subtypes of H1N1, H1N2, and H3N2 are endemic in swine around the world, extensive diversity can be found in the hemagglutinin (HA) and neuraminidase (NA) genes, as well as the remaining six genes. We review the complicated global epidemiology of IAV in swine and the inextricably entangled implications for public health and influenza pandemic planning.

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Section: Clinical Aspects Of Influenza In Pigsmentioning

confidence: 96%

Section: H3mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Swine Influenza A Viruses and the Tangled Relationship with Humans

Anderson

Chang

Arendsee

et al. 2020

Cold Spring Harb Perspect Med

124

122

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“…The second mode benefits applications where the tree-child property can be expected; swine IAV serve as a good example of such applications, as viruses involved in reassortment events typically represent successful virus lineages. These lineages are generally the major detectable genetic clades of endemically circulating viruses, and routine surveillance such as that conducted by the USDA Influenza A Virus in Swine Surveillance system can be expected to sample both the putative parental strains and the child strains [52,3]. Moreover, as shown by Bordewich et al [8] the second mode guarantees connectedness of layers of candidate networks under SNPR.…”

Section: Introductionmentioning

confidence: 99%

Robinson-Foulds Reticulation Networks

Markin

Anderson

Vadali

et al. 2019

Preprint

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Phylogenetic (hybridization) networks allow investigation of evolutionary species histories that involve complex phylogenetic events other than speciation, such as reassortment in virus evolution or introgressive hybridization in invertebrates and mammals. Reticulation networks can be inferred by solving the reticulation network problem, typically known as the hybridization network problem. Given a collection of phylogenetic input trees, this problem seeks a minimum reticulation network with the smallest number of reticulation vertices into which the input trees can be embedded exactly. Unfortunately, this problem is limited in practice, since minimum reticulation networks can be easily obfuscated by even small topological errors that typically occur in input trees inferred from biological data. We adapt the reticulation network problem to address erroneous input trees using the classic Robinson-Foulds distance. The RF embedding cost allows trees to be embedded into reticulation networks inexactly, but up to a measurable error. The adapted problem, called the Robinson-Foulds reticulation network (RF-Network) problem is, as we show and like many other problems applied in molecular biology, NP-hard. To address this, we employ local search strategies that have been successfully applied in other NP-hard phylogenetic problems. Our local search method benefits from recent theoretical advancements in this area. Further, we introduce inpractice effective algorithms for the computational challenges involved in our local search approach. Using simulations we experimentally validate the ability of our method, RF-Net, to reconstruct correct phylogenetic networks in the presence of error in input data. Finally, we demonstrate how RF-networks can help identify reassortment in influenza A viruses, and provide insight into the evolutionary history of these viruses. RF-Net was able to estimate a large and credible reassortment network with 164 taxa.

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“…Thus, the majority of vaccine antigens in use for IAV in swine are selected based solely on the genetic clade or percent amino acid identity. This effort is fraught with risk as there are at least 16 distinct HA genetic clades of IAV in swine derived from multiple human-to-swine interspecies transmission events and subsequent evolution in the swine host (8,11). Further, there is evidence for regional patterns in HA clade persistence (8,12), and the demonstration that as few as six amino acid mutations within the HA may affect the antigenic phenotype of a virus (13,14).…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction and experimental validation of antigenic drift in H3 influenza A viruses in swine

Zeller

Gauger

Arendsee

et al. 2020

Preprint

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The antigenic diversity of influenza A virus (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High throughput sequencing technologies are able to quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of non-linear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic dataset of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Error between predicted and measured distances of uncharacterized strains were 0.34, 0.70, 2.19, and 0.17 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen.

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ISU FLUture: a veterinary diagnostic laboratory web-based platform to monitor the temporal genetic patterns of Influenza A virus in swine

Cited by 55 publications

References 43 publications

Swine Influenza A Viruses and the Tangled Relationship with Humans

Swine Influenza A Viruses and the Tangled Relationship with Humans

Robinson-Foulds Reticulation Networks

Machine learning prediction and experimental validation of antigenic drift in H3 influenza A viruses in swine

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