Swine influenza A viruses cause severe illness among pigs and financial losses on pig farms worldwide. These viruses can also infect humans and have caused deadly human pandemics in the past. Influenza A viruses are dangerous because viruses can be transferred between humans, birds and pigs. These co-infections can allow the viruses to swap genetic material. Viral genetic exchanges can result in new virus strains that are more dangerous or that can infect other types of animals more easily.

Farmers vaccinate their pigs to control the swine influenza A virus. The vaccines are regularly updated to match circulating virus strains. But the virus evolves rapidly to escape vaccine-induced immunity, and infections are common even in vaccinated pigs. Learning about how vaccination affects the evolution of influenza A viruses in pigs could help scientists prevent outbreaks on pig farms and avoid spillover pandemics in humans.

Li et al. show that influenza A viruses are less likely to swap genetic material in vaccinated and boosted pigs than in unvaccinated animals. In the experiments, Li et al. collected swine influenza A samples from the lungs of pigs that had received different vaccination protocols. Next, Li et al. used next-generation sequencing to identify new mutations in the virus or genetic swaps among different strains. In pigs infected with both the H1N1 and H3N2 strains of influenza, the two viruses began trading genes within a week. But less genetic mixing occurred in vaccinated and boosted pigs because they spent less time infected with both viruses than in unvaccinated pigs. The vaccination status of the pig did not have much effect on how many new mutations occurred in the viruses.

The experiments show that vaccinating and boosting pigs against influenza A viruses may protect against genetic swapping among influenza viruses. If future studies on pig farms confirm the results, the information gleaned from the study could help scientists improve farm vaccine protocols to further reduce influenza risks to animals and people.

Influenza A viruses (IAV) are important pathogens in humans, birds, and pigs globally. In the United States, IAV infection causes a significant disease burden on the healthcare system and society, resulting in 12,000–61,000 human deaths plus estimated losses of $11.2 billion annually (Centers for Disease control and Prevention, 2020; Putri et al., 2018). IAV infections in pigs are also considered one of the top disease concerns for the US swine industry when influenza-induced respiratory disease severely reduces pig health and subsequently the pork producers’ profitability and sustainability. Human and swine share the same IAV subtypes (Ryt-Hansen et al., 2021; Vincent et al., 2014), which have similar tangled evolutionary histories contributing to the bidirectional transmission of IAV between both species (Anderson et al., 2021). Pigs pose a risk for generating novel IAV strains with zoonotic and pandemic potential, which represents an unpredictable threat to both the swine industry and global public health.

IAV exhibits an extraordinary ability for cross-species transmission and immune evasion by continually expanding its genetic diversity through antigenic drift and shift. This genetic diversity permits the rapid evolution of IAV, maximizing the virus’s opportunity to remain viable following significant changes in the environment and making the use of vaccines extremely difficult for disease prevention (Martin and Brooke, 2019; Sandbulte et al., 2015; Vincent et al., 2017). Therefore, minimizing IAV diversity should be a key strategy for One Health purposes to efficiently control IAV transmission between humans and pigs (Centers for Disease Control and Prevention, 2017). As swine are susceptible to avian, human, and swine-origin influenza virus, IAV introduction from multiple hosts immensely enriches the genetic pool of swine IAV and is responsible for the emergence of distinct H1 and H3 IAV lineages during the last 20 years in pigs (Lewis et al., 2016; Ma et al., 2009). The distribution of IAV receptors in the swine respiratory tract also promotes IAV co-infections with strains from various hosts and facilitates virus reassortment that may result in new viruses (Van Reeth, 2007). A mathematical model estimated an overall probability of a co-infection event in a farrow-to-finish pig farm, possibly leading to reassortment, to be 16.8% (Cador et al., 2017). This estimate seems reasonable given that the recurrent and co-circulation of distinct IAV subtypes within swine herds is quite common and that over 74 different H1 genotypes have been detected in the US pig population alone from 2009 to 2016 (Gao et al., 2017; Nirmala et al., 2021; Rose et al., 2013). With such diverse viral populations, swine should be considered one of the potential sources for novel IAV variants of zoonotic and pandemic infections. The 2009 H1N1 pandemic virus (H1N1pdm09), a reassortant virus that originated in pigs, contained gene segments of avian, swine, and human influenza viruses that led to the first influenza pandemic of the 21st century (Garten et al., 2009). Within the first year of circulation, between 151,700 and 575,400 people died worldwide due to the 2009 H1N1 virus infection (Centers for Disease Control and Prevention, 2019). Due to the continued evolution, the H1N1pdm09 virus has generated a complex hemagglutinin (HA) clade system (https://nextstrain.org/flu/seasonal/h1n1pdm/ha/12y; Hadfield et al., 2018). Moreover, the H1N1pdm09 virus has further reassorted with endemic IAV strains in pigs and has generated reassortants with distinct genetic constellations in many countries (Charoenvisal et al., 2013; Howard et al., 2011; Lam et al., 2011; Rajão et al., 2017; Wong et al., 2012). Some reassortants of specific genotypes have already become the predominant circulating strains in swine populations and have caused fatal infections in people in contact with pigs (Resende et al., 2017; Sun et al., 2020).

Vaccination against IAV is the primary measure to prevent influenza in pigs. In the United States, influenza vaccination is implemented in over 80% of large breeding herds and more than 95% of breeding females in these herds receive at least two doses of vaccines before their first farrowing (USDA, 2016). Even though live-attenuated influenza vaccine (LAIV) and RNA-based influenza vaccines have recently been licensed in the United States, whole-cell inactivated vaccines with oil-based adjuvants are still the most common vaccine types in pigs (Aphis, 2020; Van Reeth and Ma, 2013). To maximize cross-protective immunity against multiple viruses circulating in pig herds, most commercial vaccines contain multiple antigenically distinct IAV strains. However, IAV breakthrough infections are common in pigs as the virus evolves rapidly to escape host immunity (Murcia et al., 2012), resulting in the circulation of the virus in vaccinated herds (Chamba Pardo et al., 2021). Besides, the mismatch between vaccine and field strains raises concerns about the vaccine-associated enhanced respiratory disease (VAERD), which has been reported in pigs vaccinated with a univalent whole inactivated vaccine (WIV) and experimentally challenged with a heterologous virus (Khurana et al., 2013; Rajão et al., 2016). VAERD is attributed to non-neutralizing antibodies induced by vaccines failing to neutralize the virus and instead exacerbate virus replication and disease enhancement (Crowe, 2013; Khurana et al., 2013). Therefore, understanding how immunity induced by swine IAV vaccination shapes within-host virus evolution in pigs is key to controlling the disease and the emergence of novel antigenic variants. Previous studies have characterized the IAV mutational spectra within naïve and vaccinated pigs and other mammals to simulate the impact of immune pressure on within-host diversity (Debbink et al., 2017; Hoelzer et al., 2010; McCrone et al., 2018; Murcia et al., 2013; Murcia et al., 2012). However, in vivo studies that explore how vaccination impacts reassortment between multiple subtypes of IAVs in pigs are lacking. Besides, most of the knowledge on IAV within-host diversity in pigs so far is based on samples taken from nasal cavities and studies that quantified the IAV within-host variation in pig lungs are lacking (Illingworth et al., 2014; Murcia et al., 2012). Considering that tissue tropism plays a vital role in IAV dissemination along the swine respiratory tract (Zhang et al., 2018), with the pig lungs harboring IAV populations with the most extensive genomic variations, the effect of immune pressure on IAV within-host diversity may differ by anatomical location (Takayama et al., 2021; Xue et al., 2018; Zhang et al., 2018). Therefore, in vivo studies are needed to provide an integrated picture of how vaccine-induced immunity affects IAV evolutionary trajectories occurring in the swine lower respiratory tract by evaluating the extent of IAV reassortment and mutational spectra taking place concurrently in naïve and vaccinated pigs.

We previously published a vaccine-challenge study assessing IAV infections in pigs vaccinated with five distinct prime-boost vaccine combinations after simultaneous infection with both an H1N1 and an H3N2 IAV strain using a seeder pig model (Li et al., 2020). The bronchoalveolar lavage fluid (BALF) samples obtained from the aforementioned study enabled us to evaluate, in this study, how IAV reassorts and mutates in the swine lower respiratory tract under immune pressure. We hypothesized that the dual-subtype IAV co-infection model better represents the conditions of IAV co-infection encountered in the field and that the findings could contribute to the body of knowledge of within-host virus evolution in pigs (Nirmala et al., 2021). Here, we performed next-generation sequencing directly on BALF samples and IAV plaques purified from the BALF samples to identify the virus mutations and reassortment that happened in swine lungs.

Understanding the impact of vaccination on IAV within-host evolution is critical for improving overall animal health and productivity while also decreasing the emergence of novel antigenic variants with pandemic potential (Diaz et al., 2017; Nirmala et al., 2021; USDA, 2016). To better mimic field scenarios, we set up an in vivo co-infection model where we challenged naïve pigs with either an H1N1 (gamma clade 1A.3.3.3) or an H3N2 (human-like clade 3.2010.1) virus belonging to the dominant subtypes and lineages identified during US pig surveillance (Anderson et al., 2013). Pigs were co-housed in contact with nonvaccinated pigs or pigs vaccinated by licensed multivalent vaccines following multiple vaccination protocols (Li et al., 2020). The co-infection model allowed us to simultaneously assess the IAV genetic variation and reassortment under immune pressure in pigs and provided a rare view of the full impact of vaccination on IAV evolution. The dataset includes pigs with different levels of virus shedding and immune responses that help us define factors that affect IAV evolution (Li et al., 2020). We directly sequenced and performed the isolation of viral plaques on selected BALF samples without additional virus propagation in cells to ensure that no mutation or reassortants were developed during cell culture. Our results suggested that the pigs receiving two doses of vaccines (PRIME BOOST) had a lower proportion of IAV reassortants in fewer days when the pigs were co-infected with both challenge viruses compared to nonvaccinated pigs. A significant number of mutations with abundant functional relevant amino acid changes were present within pigs in H1N1 and H3N2 viral populations regardless of vaccination status. However, we did not detect a major effect of vaccination on IAV within-host diversity as there were few antigenic variants detected in the IAV populations, and we found no differences in the number and frequency of identified SNVs, the evolutionary rates, nor in the nucleotide diversity of IAV in pigs of the different treatment groups.

Within the 7 days of the study period, we identified a large number of IAV reassortants with multiple distinct genotypes in the pigs that had been co-infected with the H1 and H3 viruses. This finding is particularly notable when considering the complex ecosystem and management practices implemented in swine farms, and the pigs with various vaccination and infection statuses present in the farms (Garrido-Mantilla et al., 2021; Reynolds et al., 2014; White et al., 2017). The fluidity of ages and immune statuses of the pigs in swine farms enable viruses to continually infect susceptible pigs and create an ideal breeding ground for IAV to reassort and circulate (Diaz et al., 2017; Chamba Pardo et al., 2018). Given the background that the coexistence of viruses with distinct lineages and genotypes is common in pig populations, the frequency of IAV reassortment could be extensive, especially considering that most of the pigs need to be housed approximately for 24–26 weeks in farms (Nirmala et al., 2021). Our study provides initial evidence that vaccination can play a role in decreasing the generation and emergence of new reassortant viruses in pigs. These results, if confirmed in field conditions, show the significance of IAV vaccination in pig populations not only by restricting the virus circulation and clinical impact but also by reducing the IAV genotypic diversity that is important for the overall IAV control at the human–animal interface (Dwyer and Kirkland, 2011).

The factors driving reassortment are complicated, and for IAV reassortment to occur two parental viruses need to reach multiple criteria (Lowen, 2018). The co-infection of distinct IAV viruses at the cellular level is an essential prerequisite for IAV reassortment (Marshall et al., 2013). Numerous factors affecting IAV cellular co-infections have been elucidated in vitro or under experimental conditions, including the IAV challenge dose, the time interval between primary and secondary infection, and the location of initial virus replication (Richard et al., 2018; Tao et al., 2014). Our study found that pigs co-infected for a longer duration tended to generate more reassortants, which showed that vaccination could minimize IAV reassortment by reducing the possibility of pigs being co-infected by distinct viruses. It has been shown that most IAV reassortants emerge with lower fitness compared with parental viruses, and evidence suggests that the strength of negative selection on the reassortants is positively associated with the genetic distance between their parental strains (Villa and Lässig, 2017). Tissue tropism also appears to play a role in the generation of novel reassortants in pigs, with most reassortants generated in pigs being recovered in the middle and lower respiratory tissues. In contrast, fewer reassortants were identified in the nasal cavity (Zhang et al., 2018). It is likely that the high temperature and presence of various types of sialic acid receptors in the lungs benefit infection and replication of the reassortant viruses (de Graaf and Fouchier, 2014; Nelli et al., 2010; Zhang et al., 2018), which might help explain the significant number of IAV reassortants we identified in the BALF samples of this study. We observed that the IAV internal genes were exchanged fairly easily between the two challenge, that is, parental, viruses compared to the antigenic genes. Considering the nucleotide homology of internal genes between the challenge viruses was much higher (over 90%) than that of the antigenic genes (~53%), the mismatch of package signals and functional proteins could explain these results given that these restraining factors facilitate virus reassortment between similar strains rather than in distantly related viruses (Lowen, 2017; Lowen, 2018; Richard et al., 2018). Besides, there are other environmental conditions and other host factors that may affect the fitness of the reassortants in a populations (Lowen, 2018).

The high plasticity of the IAV genome and the fast-expanding nature of IAV favors the generation of progeny virions with slightly different mutation ‘signatures’ (Lauring and Andino, 2010; Martínez et al., 2012). Although most progeny variants have a deleterious effect on the overall fitness of the viral populations, some of the beneficial mutants may go through positive selection and can quickly dominate after sudden changes in the environment thereby shifting the fitness landscape, which may maximize the possibility of IAV to replicate in harsh conditions (Martínez et al., 2012). As a result, IAV may increase mutation rates at the cost of fitness to enrich the genetic pool and accumulate more beneficial variants with high frequencies favored by positive selection (Elena and Sanjuán, 2005; Sprouffske et al., 2018). In a stable environment, gaining fitness is a priority for IAV rather than the accumulation of genetic variation at the consensus level (Martínez et al., 2012). Therefore, purifying selection takes place as the major selective force that continually removes new variants and generates IAV populations with a high proportion of low-frequency variants (Moncla et al., 2020). Previous studies have identified that host immunity could drive positive selection on the IAV antigenic proteins in the long term (Bush et al., 1999; Li et al., 2011; Nelson and Holmes, 2007). However, in our study, we rarely observed the inference of the same selection pressures at any coding regions for the H1N1 and H3N2 viruses, which is consistent with the results of within-host diversity on H5N1 virus (Moncla et al., 2020). Instead, the within-host diversity of both subtypes was dominated by low-frequency variants and exhibited similar or higher synonymous polymorphisms compared to the nonsynonymous polymorphisms. The variation patterns of IAV suggested that the viral populations may be broadly shaped by purifying selection and genetic drift within pigs regardless of vaccination statuses. In addition to the close mutational spectra we observed on H1N1 and H3N2 viruses in pigs from various treatment groups, we detected limited shared diversity, which developed independently in at least two PRIME BOOST, SINGLE LAIV, or NO VAC pigs for both viruses. Our observation suggested that vaccination did not drive the progress of IAV genome convergence, at least in the swine lower respiratory tract, when we measured the virus’s shared diversity between pigs, which in turn reflected the limited selective forces raised by vaccination on viral within-host diversity.

The HA protein is the major target of the host adaptive immune response and crucial to IAV evolution. Within our dataset, there were few amino acid changes that fell in the H1 and H3 antigenic regions. Furthermore, the nucleotide divergence of the HA segments did not correlate with the intensity of humoral and cellular immune responses induced by vaccination. These observations are concordant with the results from Debbink et al., 2017, which showed the limited effect of vaccination on antigenic diversity of H3N2 viruses in humans. However, other factors like host type, IAV strain, infection dose, challenge/infection model, virus passage among animals, and observation period may cause discrepancies in the results of selection of the antigenic variants between the studies that explore the influence of immune pressure on IAV within-host diversity (Debbink et al., 2017; Hoelzer et al., 2010; Murcia et al., 2013; Murcia et al., 2012). Taken together, we did not find evidence that vaccination influenced IAV genetic variation in the lower respiratory tract of pigs. The observation of our study aligns with the data from Murcia et al., 2012, which suggested that vaccine-induced immunity had a minimal impact on the genetic variation of IAV populations in both upper and lower respiratory tracts of pigs.

Evaluating IAV evolution in vaccinated hosts is often difficult to perform. One challenge is to obtain enough qualifying samples in pigs that are well-protected by vaccination as there may be low levels of, or even no, replicating virus. The lack of virus detection in samples from vaccinated pigs may introduce sample selection bias. That is, samples from vaccinated pigs with replicating virus may represent pigs with poor immunity that may not adequately reflect the effect of vaccination on IAV within-host evolution. However, in our study this may not be the case since samples submitted for direct sequencing or plaque assay from PRIME BOOST pigs had significantly higher H1 and H3-specific antibody titers compared to SINGLE LAIV or NO VAC pigs. Besides, our findings on IAV within-host diversity in vaccinated and unvaccinated pigs agree with those of similar studies in pigs and other hosts (Debbink et al., 2017; Murcia et al., 2013; Murcia et al., 2012). Our results may have also been influenced by the degree of shedding of the seeder pigs in each of the rooms as some pigs may have shed more than others, which could influence the likelihood of co-infections to occur in the in-contact pigs, biasing the likelihood of detecting reassortants. The viruses from the seeders may also contain subgenomic variants that transmit to in-contact pigs. We found that the three super-reassortant pigs (pig #4479, #4490, and #5174) came from three different rooms, which shows that IAV reassortment did not have a ‘room effect.’ In addition, it was not the study’s primary aim to detect the selection of individual IAV SNVs or reassortants specific to any vaccination protocol but rather to compare the impact of vaccination on the overall evolutionary trends of virus populations in pigs. The PRIME BOOST samples analyzed in this study included samples from pigs receiving multiple prime-boost vaccination protocols. Different vaccine combinations may introduce variability in the profiles of antibody populations raised among PRIME BOOST pigs. In our study, this did not seem to cause significant differences between groups because pigs in different groups had similar HA-specific adaptive immune responses and exhibited a comparable level of protective efficacy on pigs against the challenge viruses (Li et al., 2020). However, whether and how the different homology between vaccine and challenge viruses, especially in prime-boost vaccination schemes, will change the IAV evolutionary process within pigs still needs further exploration. We also had a limited number of BALF samples available for plaque purification and the number of plaques isolated from each sample was not equal. Although we acknowledge this as a limitation, the overall conclusions were supported by the statistical methods employed to assess the relationship between the percent of reassortants and the treatment group at the individual pig level, which accounted for the impact of unequal sample quantity from different treatment groups and the variability of total plaques isolated from each sample. As our statistical analysis relies on the asymptotic theory, which assesses how the estimators or tests behave as the sample size becomes larger (Höpfner, 2014), further research with a larger number of samples collected from both experimental and field settings should help validate the concept of using IAV vaccines to mitigate IAV reassortment in pigs.

Finally, understanding the association between IAV vaccination and evolution is hard but necessary. IAV within-host evolution plays a vital role in disease outcomes since mutations and reassortment events can significantly influence the biological characteristics of IAV. In this study, we conclude that pig vaccination should be explored as a way to reduce the generation and emergence of reassortant viruses, and this may be particularly important in farm animals where large populations are housed together. In contrast, we did not observe the same significant effect of vaccination on IAV genetic variation. Our research results provide insights into the complexity of IAV evolution in pigs and will help develop more effective influenza control programs to mitigate the burden of IAV infections in pigs while decreasing the risk of zoonotic infections and preserving public health. We believe our study assembles a comprehensive recognition of IAV evolutionary strategies under immune pressure but does not fully reflect the situation in the field. Therefore, more studies are needed to verify the extent of IAV mutations and reassortment in vaccinated and unvaccinated pigs and understand what factors contribute to the transmission of reassortant viruses under field conditions, which is essential for developing IAV vaccines and surveillance strategies.

The raw sequence reads generated in this study have been deposited in SRA (NCBI) under Bioproject accession number PRJNA813974. All the raw datasets and custom python scripts generated in this study are available in the GitHub repository: https://github.com/TorremorellLabUMN/Swine-IAV-within-host-evolution (copy archived at swh:1:rev:522346731fd666f239122fe9e3dada0e0d3ea141).

1. 1. Li C

   (2022) NCBI Sequence Read Archive

   ID PRJNA813974. Influenza A virus reassortment and genetic variation in lower respiratory tracts of vaccinated and unvaccinated pigs.

   https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA813974
2. 1. Li C

   (2022) GitHub repository

   ID IAV-within-host-evolution. Swine-IAV-within-host-evolution.

   https://github.com/TorremorellLabUMN/Swine-IAV-within-host-evolution

1. 1. Li C

   (2020) NCBI GenBank

   ID MT377725. influenza A virus / Heterologous prime-boost study.

   https://www.ncbi.nlm.nih.gov/nuccore/?term=MT377710:MT377725[accn]

1. 1. Anderson TK
   2. Nelson MI
   3. Kitikoon P
   4. Swenson SL
   5. Korslund JA
   6. Vincent AL

   (2013) Population dynamics of cocirculating swine influenza A viruses in the united states from 2009 to 2012

   Influenza and Other Respiratory Viruses 7 Suppl 4:42–51.

   https://doi.org/10.1111/irv.12193

   • PubMed
   • Google Scholar
2. 1. Anderson TK
   2. Chang J
   3. Arendsee ZW
   4. Venkatesh D
   5. Souza CK
   6. Kimble JB
   7. Lewis NS
   8. Davis CT
   9. Vincent AL

   (2021) Swine influenza A viruses and the tangled relationship with humans

   Cold Spring Harbor Perspectives in Medicine 11:a038737.

   https://doi.org/10.1101/cshperspect.a038737

   • PubMed
   • Google Scholar
3. Software

   1. Andrew S

   (2010) Fast QC: A quality control tool for high throughput sequence data

   Babraham Bioinformatics.

   https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
4. Book

   1. Aphis U

   (2020)

   Veterinary Biological Products, Licensees and Permittees Prepared April 1

   United States: Department of Agriculture.

   • Google Scholar
5. Report

   1. Aphis U

   (2022) Influenza A virus in swine Surveillance - Fiscal Year 2021 Quarterly Report

   United States Department of Agriculture.

   https://www.aphis.usda.gov/animal_health/animal_dis_spec/swine/downloads/fy2021quarter4swinereport.pdf

   • Google Scholar
6. 1. Bloom JD

   (2014) An experimentally determined evolutionary model dramatically improves phylogenetic fit

   Molecular Biology and Evolution 31:1956–1978.

   https://doi.org/10.1093/molbev/msu173

   • PubMed
   • Google Scholar
7. 1. Bolger AM
   2. Lohse M
   3. Usadel B

   (2014) Trimmomatic: a flexible trimmer for illumina sequence data

   Bioinformatics 30:2114–2120.

   https://doi.org/10.1093/bioinformatics/btu170

   • PubMed
   • Google Scholar
8. 1. Brownlee GG
   2. Fodor E

   (2001) The predicted antigenicity of the haemagglutinin of the 1918 spanish influenza pandemic suggests an avian origin

   Philosophical Transactions of the Royal Society of London. Series B 356:1871–1876.

   https://doi.org/10.1098/rstb.2001.1001

   • Google Scholar
9. 1. Bush RM
   2. Fitch WM
   3. Bender CA
   4. Cox NJ

   (1999) Positive selection on the H3 hemagglutinin gene of human influenza virus A

   Molecular Biology and Evolution 16:1457–1465.

   https://doi.org/10.1093/oxfordjournals.molbev.a026057

   • PubMed
   • Google Scholar
10. 1. Cador C
    2. Andraud M
    3. Willem L
    4. Rose N

    (2017) Control of endemic swine flu persistence in farrow-to-finish pig farms: a stochastic metapopulation modeling assessment

    Veterinary Research 48:58.

    https://doi.org/10.1186/s13567-017-0462-1

    • PubMed
    • Google Scholar
11. 1. Caton AJ
    2. Brownlee GG
    3. Yewdell JW
    4. Gerhard W

    (1982) The antigenic structure of the influenza virus A/PR/8/34 hemagglutinin (H1 subtype)

    Cell 31:417–427.

    https://doi.org/10.1016/0092-8674(82)90135-0

    • PubMed
    • Google Scholar
12. Website

    1. Centers for Disease Control and Prevention

    (2017) Prioritizing zoonotic diseases for multisectoral, One Health collaboration in the United States: workshop summary. Washington, DC

    Accessed April 25, 2019.

    https://www.cdc.gov/onehealth/what-we-do/zoonotic-disease-prioritization/us-workshops.html
13. Website

    1. Centers for Disease Control and Prevention

    (2019) 2009 H1N1 Pandemic (H1N1pdm09 virus)

    Accessed June 11, 2019.

    https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html
14. Website

    1. Centers for Disease control and Prevention

    (2020) Disease Burden of Influenza

    Accessed October 5, 2020.

    https://www.cdc.gov/flu/about/burden/index.html
15. 1. Chamba Pardo FO
    2. Schelkopf A
    3. Allerson M
    4. Morrison R
    5. Culhane M
    6. Perez A
    7. Torremorell M

    (2018) Breed-to-wean farm factors associated with influenza A virus infection in piglets at weaning

    Preventive Veterinary Medicine 161:33–40.

    https://doi.org/10.1016/j.prevetmed.2018.10.008

    • PubMed
    • Google Scholar
16. 1. Chamba Pardo FO
    2. W Allerson M
    3. R Culhane M
    4. B Morrison R
    5. R Davies P
    6. Perez A
    7. Torremorell M

    (2021) Effect of influenza A virus sow vaccination on infection in pigs at weaning: A prospective longitudinal study

    Transboundary and Emerging Diseases 68:183–193.

    https://doi.org/10.1111/tbed.13688

    • PubMed
    • Google Scholar
17. Book

    1. Chambers J

    (2008) Software for Data Analysis: Programming with R

    New York, NY: Springer Science & Business Media.

    https://doi.org/10.1007/978-0-387-75936-4

    • Google Scholar
18. 1. Chang J
    2. Anderson TK
    3. Zeller MA
    4. Gauger PC
    5. Vincent AL

    (2019) OctoFLU: automated classification for the evolutionary origin of influenza A virus gene sequences detected in u.s. swine

    Microbiology Resource Announcements 8:e00673-19.

    https://doi.org/10.1128/MRA.00673-19

    • PubMed
    • Google Scholar
19. 1. Charoenvisal N
    2. Keawcharoen J
    3. Sreta D
    4. Chaiyawong S
    5. Nonthabenjawan N
    6. Tantawet S
    7. Jittimanee S
    8. Arunorat J
    9. Amonsin A
    10. Thanawongnuwech R

    (2013) Genetic characterization of thai swine influenza viruses after the introduction of pandemic H1N1 2009

    Virus Genes 47:75–85.

    https://doi.org/10.1007/s11262-013-0927-x

    • PubMed
    • Google Scholar
20. 1. Crowe JE

    (2013) Universal flu vaccines: primum non nocere

    Science Translational Medicine 5:200fs34.

    https://doi.org/10.1126/scitranslmed.3007118

    • PubMed
    • Google Scholar
21. Software

    1. Davies R

    (2022) Samtools, version swh:1:rev:58b93039661b12e4430eef7f35d38b75d9b5484d

    GitHub.

    https://github.com/samtools/samtools
22. 1. de Graaf M
    2. Fouchier RAM

    (2014) Role of receptor binding specificity in influenza A virus transmission and pathogenesis

    The EMBO Journal 33:823–841.

    https://doi.org/10.1002/embj.201387442

    • PubMed
    • Google Scholar
23. 1. Debbink K
    2. McCrone JT
    3. Petrie JG
    4. Truscon R
    5. Johnson E
    6. Mantlo EK
    7. Monto AS
    8. Lauring AS

    (2017) Vaccination has minimal impact on the intrahost diversity of H3N2 influenza viruses

    PLOS Pathogens 13:e1006194.

    https://doi.org/10.1371/journal.ppat.1006194

    • PubMed
    • Google Scholar
24. 1. Diaz A
    2. Marthaler D
    3. Culhane M
    4. Sreevatsan S
    5. Alkhamis M
    6. Torremorell M

    (2017) Complete genome sequencing of influenza A viruses within swine farrow-to-wean farms reveals the emergence, persistence, and subsidence of diverse viral genotypes

    Journal of Virology 91:e00745-17.

    https://doi.org/10.1128/JVI.00745-17

    • PubMed
    • Google Scholar
25. 1. Dwyer DE
    2. Kirkland PD

    (2011) Influenza: one health in action

    New South Wales Public Health Bulletin 22:123–126.

    https://doi.org/10.1071/NB11005

    • PubMed
    • Google Scholar
26. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • PubMed
    • Google Scholar
27. 1. Elena SF
    2. Sanjuán R

    (2005) Adaptive value of high mutation rates of RNA viruses: separating causes from consequences

    Journal of Virology 79:11555–11558.

    https://doi.org/10.1128/JVI.79.18.11555-11558.2005

    • PubMed
    • Google Scholar
28. 1. Gao S
    2. Anderson TK
    3. Walia RR
    4. Dorman KS
    5. Janas-Martindale A
    6. Vincent AL

    (2017) The genomic evolution of H1 influenza A viruses from swine detected in the united states between 2009 and 2016

    The Journal of General Virology 98:2001–2010.

    https://doi.org/10.1099/jgv.0.000885

    • PubMed
    • Google Scholar
29. 1. Garrido-Mantilla J
    2. Sanhueza J
    3. Alvarez J
    4. Culhane MR
    5. Davies P
    6. Allerson MW
    7. Torremorell M

    (2021) Impact of nurse sows on influenza A virus transmission in pigs under field conditions

    Preventive Veterinary Medicine 188:105257.

    https://doi.org/10.1016/j.prevetmed.2021.105257

    • PubMed
    • Google Scholar
30. 1. Garten RJ
    2. Davis CT
    3. Russell CA
    4. Shu B
    5. Lindstrom S
    6. Balish A
    7. Sessions WM
    8. Xu X
    9. Skepner E
    10. Deyde V
    11. Okomo-Adhiambo M
    12. Gubareva L
    13. Barnes J
    14. Smith CB
    15. Emery SL
    16. Hillman MJ
    17. Rivailler P
    18. Smagala J
    19. de Graaf M
    20. Burke DF
    21. Fouchier RAM
    22. Pappas C
    23. Alpuche-Aranda CM
    24. López-Gatell H
    25. Olivera H
    26. López I
    27. Myers CA
    28. Faix D
    29. Blair PJ
    30. Yu C
    31. Keene KM
    32. Dotson PD Jr
    33. Boxrud D
    34. Sambol AR
    35. Abid SH
    36. St George K
    37. Bannerman T
    38. Moore AL
    39. Stringer DJ
    40. Blevins P
    41. Demmler-Harrison GJ
    42. Ginsberg M
    43. Kriner P
    44. Waterman S
    45. Smole S
    46. Guevara HF
    47. Belongia EA
    48. Clark PA
    49. Beatrice ST
    50. Donis R
    51. Katz J
    52. Finelli L
    53. Bridges CB
    54. Shaw M
    55. Jernigan DB
    56. Uyeki TM
    57. Smith DJ
    58. Klimov AI
    59. Cox NJ

    (2009) Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans

    Science 325:197–201.

    https://doi.org/10.1126/science.1176225

    • PubMed
    • Google Scholar
31. 1. Hadfield J
    2. Megill C
    3. Bell SM
    4. Huddleston J
    5. Potter B
    6. Callender C
    7. Sagulenko P
    8. Bedford T
    9. Neher RA

    (2018) Nextstrain: real-time tracking of pathogen evolution

    Bioinformatics 34:4121–4123.

    https://doi.org/10.1093/bioinformatics/bty407

    • PubMed
    • Google Scholar
32. 1. Hoelzer K
    2. Murcia PR
    3. Baillie GJ
    4. Wood JLN
    5. Metzger SM
    6. Osterrieder N
    7. Dubovi EJ
    8. Holmes EC
    9. Parrish CR

    (2010) Intrahost evolutionary dynamics of canine influenza virus in naive and partially immune dogs

    Journal of Virology 84:5329–5335.

    https://doi.org/10.1128/JVI.02469-09

    • PubMed
    • Google Scholar
33. Book

    1. Höpfner R

    (2014) In Asymptotic Statistics

    de Gruyter.

    https://doi.org/10.1515/9783110250282

    • Google Scholar
34. 1. Howard WA
    2. Essen SC
    3. Strugnell BW
    4. Russell C
    5. Barass L
    6. Reid SM
    7. Brown IH

    (2011) Reassortant pandemic (H1N1) 2009 virus in pigs, united kingdom

    Emerging Infectious Diseases 17:1049–1052.

    https://doi.org/10.3201/eid/1706.101886

    • PubMed
    • Google Scholar
35. 1. Illingworth CJR
    2. Fischer A
    3. Mustonen V

    (2014) Identifying selection in the within-host evolution of influenza using viral sequence data

    PLOS Computational Biology 10:e1003755.

    https://doi.org/10.1371/journal.pcbi.1003755

    • PubMed
    • Google Scholar
36. 1. Kearse M
    2. Moir R
    3. Wilson A
    4. Stones-Havas S
    5. Cheung M
    6. Sturrock S
    7. Buxton S
    8. Cooper A
    9. Markowitz S
    10. Duran C
    11. Thierer T
    12. Ashton B
    13. Meintjes P
    14. Drummond A

    (2012) Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data

    Bioinformatics 28:1647–1649.

    https://doi.org/10.1093/bioinformatics/bts199

    • PubMed
    • Google Scholar
37. 1. Keller MW
    2. Rambo-Martin BL
    3. Wilson MM
    4. Ridenour CA
    5. Shepard SS
    6. Stark TJ
    7. Neuhaus EB
    8. Dugan VG
    9. Wentworth DE
    10. Barnes JR

    (2018) Direct RNA sequencing of the coding complete influenza A virus genome

    Scientific Reports 8:1–8.

    https://doi.org/10.1038/s41598-018-32615-8

    • PubMed
    • Google Scholar
38. 1. Khurana S
    2. Loving CL
    3. Manischewitz J
    4. King LR
    5. Gauger PC
    6. Henningson J
    7. Vincent AL
    8. Golding H

    (2013) Vaccine-induced anti-HA2 antibodies promote virus fusion and enhance influenza virus respiratory disease

    Science Translational Medicine 5:200ra114.

    https://doi.org/10.1126/scitranslmed.3006366

    • PubMed
    • Google Scholar
39. 1. Koboldt DC
    2. Chen K
    3. Wylie T
    4. Larson DE
    5. McLellan MD
    6. Mardis ER
    7. Weinstock GM
    8. Wilson RK
    9. Ding L

    (2009) VarScan: variant detection in massively parallel sequencing of individual and pooled samples

    Bioinformatics 25:2283–2285.

    https://doi.org/10.1093/bioinformatics/btp373

    • PubMed
    • Google Scholar
40. Software

    1. Koboldt DC

    (2019) Varscan, version swh:1:rev:aec03a55b256986581674cf756fd34ccc81acb0b

    GitHub.

    https://github.com/dkoboldt/varscan
41. 1. Lam TT-Y
    2. Zhu H
    3. Wang J
    4. Smith DK
    5. Holmes EC
    6. Webster RG
    7. Webby R
    8. Peiris JM
    9. Guan Y

    (2011) Reassortment events among swine influenza A viruses in china: implications for the origin of the 2009 influenza pandemic

    Journal of Virology 85:10279–10285.

    https://doi.org/10.1128/JVI.05262-11

    • PubMed
    • Google Scholar
42. 1. Lauring AS
    2. Andino R

    (2010) Quasispecies theory and the behavior of RNA viruses

    PLOS Pathogens 6:e1001005.

    https://doi.org/10.1371/journal.ppat.1001005

    • PubMed
    • Google Scholar
43. 1. Lee MS
    2. Chen JSE

    (2004) Predicting antigenic variants of influenza A/H3N2 viruses

    Emerging Infectious Diseases 10:1385–1390.

    https://doi.org/10.3201/eid1008.040107

    • PubMed
    • Google Scholar
44. 1. Lewis NS
    2. Russell CA
    3. Langat P
    4. Anderson TK
    5. Berger K
    6. Bielejec F
    7. Burke DF
    8. Dudas G
    9. Fonville JM
    10. Fouchier RA
    11. Kellam P
    12. Koel BF
    13. Lemey P
    14. Nguyen T
    15. Nuansrichy B
    16. Peiris JM
    17. Saito T
    18. Simon G
    19. Skepner E
    20. Takemae N
    21. ESNIP3 consortium
    22. Webby RJ
    23. Van Reeth K
    24. Brookes SM
    25. Larsen L
    26. Watson SJ
    27. Brown IH
    28. Vincent AL

    (2016) The global antigenic diversity of swine influenza A viruses

    eLife 5:e12217.

    https://doi.org/10.7554/eLife.12217

    • PubMed
    • Google Scholar
45. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/map format and samtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
46. 1. Li W
    2. Shi W
    3. Qiao H
    4. Ho SYW
    5. Luo A
    6. Zhang Y
    7. Zhu C

    (2011) Positive selection on hemagglutinin and neuraminidase genes of H1N1 influenza viruses

    Virology Journal 8:1–9.

    https://doi.org/10.1186/1743-422X-8-183

    • PubMed
    • Google Scholar
47. 1. Li C
    2. Culhane MR
    3. Cheeran M
    4. Galina Pantoja L
    5. Jansen ML
    6. Amodie D
    7. Mellencamp MA
    8. Torremorell M

    (2020) Exploring heterologous prime-boost vaccination approaches to enhance influenza control in pigs

    Veterinary Research 51:1–15.

    https://doi.org/10.1186/s13567-020-00810-z

    • PubMed
    • Google Scholar
48. Software

    1. Li C

    (2022) Swine-IAV-within-host-evolution, version swh:1:rev:522346731fd666f239122fe9e3dada0e0d3ea141

    GitHub.

    https://github.com/TorremorellLabUMN/Swine-IAV-within-host-evolution
49. 1. Lowen AC

    (2017) Constraints, drivers, and implications of influenza A virus reassortment

    Annual Review of Virology 4:105–121.

    https://doi.org/10.1146/annurev-virology-101416-041726

    • PubMed
    • Google Scholar
50. 1. Lowen AC

    (2018) It’s in the mix: reassortment of segmented viral genomes

    PLOS Pathogens 14:e1007200.

    https://doi.org/10.1371/journal.ppat.1007200

    • PubMed
    • Google Scholar
51. 1. Ma W
    2. Kahn RE
    3. Richt JA

    (2009) The pig as a mixing vessel for influenza viruses: human and veterinary implications

    Journal of Molecular and Genetic Medicine 03:158.

    https://doi.org/10.4172/1747-0862.1000028

    • Google Scholar
52. 1. Marshall N
    2. Priyamvada L
    3. Ende Z
    4. Steel J
    5. Lowen AC

    (2013) Influenza virus reassortment occurs with high frequency in the absence of segment mismatch

    PLOS Pathogens 9:e1003421.

    https://doi.org/10.1371/journal.ppat.1003421

    • PubMed
    • Google Scholar
53. 1. Martin BE
    2. Brooke CB

    (2019) Flu shows the power of diversity

    Cell 176:9–10.

    https://doi.org/10.1016/j.cell.2018.12.017

    • PubMed
    • Google Scholar
54. Book

    1. Martínez MA
    2. Martrus G
    3. Capel E
    4. Parera M
    5. Franco S
    6. Nevot M

    (2012) Quasispecies dynamics of RNA viruses

    In: Witzany G, editors. Viruses: Essential Agents of Life. Springer. pp. 21–42.

    https://doi.org/10.1007/978-94-007-4899-6

    • Google Scholar
55. 1. McCrone JT
    2. Lauring AS

    (2016) Measurements of intrahost viral diversity are extremely sensitive to systematic errors in variant calling

    Journal of Virology 90:6884–6895.

    https://doi.org/10.1128/JVI.00667-16

    • PubMed
    • Google Scholar
56. 1. McCrone JT
    2. Woods RJ
    3. Martin ET
    4. Malosh RE
    5. Monto AS
    6. Lauring AS

    (2018) Stochastic processes constrain the within and between host evolution of influenza virus

    eLife 7:e35962.

    https://doi.org/10.7554/eLife.35962

    • PubMed
    • Google Scholar
57. Software

    1. Moncla LH

    (2019) Figure 5b: H5N1 shared snps permutation test

    GitHub.

    https://github.com/blab/h5n1-cambodia/blob/master/figures/figure-5b-shared-sites-permutation-test.ipynb
58. 1. Moncla LH
    2. Bedford T
    3. Dussart P
    4. Horm SV
    5. Rith S
    6. Buchy P
    7. Karlsson EA
    8. Li L
    9. Liu Y
    10. Zhu H
    11. Guan Y
    12. Friedrich TC
    13. Horwood PF

    (2020) Quantifying within-host diversity of H5N1 influenza viruses in humans and poultry in cambodia

    PLOS Pathogens 16:e1008191.

    https://doi.org/10.1371/journal.ppat.1008191

    • PubMed
    • Google Scholar
59. 1. Murcia PR
    2. Hughes J
    3. Battista P
    4. Lloyd L
    5. Baillie GJ
    6. Ramirez-Gonzalez RH
    7. Ormond D
    8. Oliver K
    9. Elton D
    10. Mumford JA
    11. Caccamo M
    12. Kellam P
    13. Grenfell BT
    14. Holmes EC
    15. Wood JLN

    (2012) Evolution of an eurasian avian-like influenza virus in naïve and vaccinated pigs

    PLOS Pathogens 8:e1002730.

    https://doi.org/10.1371/journal.ppat.1002730

    • PubMed
    • Google Scholar
60. 1. Murcia PR
    2. Baillie GJ
    3. Stack JC
    4. Jervis C
    5. Elton D
    6. Mumford JA
    7. Daly J
    8. Kellam P
    9. Grenfell BT
    10. Holmes EC
    11. Wood JLN

    (2013) Evolution of equine influenza virus in vaccinated horses

    Journal of Virology 87:4768–4771.

    https://doi.org/10.1128/JVI.03379-12

    • PubMed
    • Google Scholar
61. 1. Nei M
    2. Gojobori T

    (1986) Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions

    Molecular Biology and Evolution 3:418–426.

    https://doi.org/10.1093/oxfordjournals.molbev.a040410

    • PubMed
    • Google Scholar
62. 1. Nelli RK
    2. Kuchipudi SV
    3. White GA
    4. Perez BB
    5. Dunham SP
    6. Chang KC

    (2010) Comparative distribution of human and avian type sialic acid influenza receptors in the pig

    BMC Veterinary Research 6:4.

    https://doi.org/10.1186/1746-6148-6-4

    • PubMed
    • Google Scholar
63. 1. Nelson MI
    2. Holmes EC

    (2007) The evolution of epidemic influenza

    Nature Reviews. Genetics 8:196–205.

    https://doi.org/10.1038/nrg2053

    • PubMed
    • Google Scholar
64. 1. Nelson CW
    2. Hughes AL

    (2015) Within-host nucleotide diversity of virus populations: insights from next-generation sequencing

    Infection, Genetics and Evolution 30:1–7.

    https://doi.org/10.1016/j.meegid.2014.11.026

    • PubMed
    • Google Scholar
65. Software

    1. Nelson CW

    (2021) SNPGenie, version swh:1:rev:3a6916f2527eee0d9ab1a024d1d2b96581706bd5

    GitHub.

    https://github.com/chasewnelson/SNPGenie
66. 1. Nguyen L-T
    2. Schmidt HA
    3. von Haeseler A
    4. Minh BQ

    (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies

    Molecular Biology and Evolution 32:268–274.

    https://doi.org/10.1093/molbev/msu300

    • PubMed
    • Google Scholar
67. 1. Nirmala J
    2. Perez A
    3. Culhane MR
    4. Allerson MW
    5. Sreevatsan S
    6. Torremorell M

    (2021) Genetic variability of influenza A virus in pigs at weaning in midwestern united states swine farms

    Transboundary and Emerging Diseases 68:62–75.

    https://doi.org/10.1111/tbed.13529

    • PubMed
    • Google Scholar
68. 1. Noronha JM
    2. Liu M
    3. Squires RB
    4. Pickett BE
    5. Hale BG
    6. Air GM
    7. Galloway SE
    8. Takimoto T
    9. Schmolke M
    10. Hunt V
    11. Klem E
    12. García-Sastre A
    13. McGee M
    14. Scheuermann RH

    (2012) Influenza virus sequence feature variant type analysis: evidence of a role for NS1 in influenza virus host range restriction

    Journal of Virology 86:5857–5866.

    https://doi.org/10.1128/JVI.06901-11

    • PubMed
    • Google Scholar
69. 1. Pauly MD
    2. Procario MC
    3. Lauring AS

    (2017) A novel twelve class fluctuation test reveals higher than expected mutation rates for influenza A viruses

    eLife 6:e26437.

    https://doi.org/10.7554/eLife.26437

    • PubMed
    • Google Scholar
70. 1. Putri WCWS
    2. Muscatello DJ
    3. Stockwell MS
    4. Newall AT

    (2018) Economic burden of seasonal influenza in the united states

    Vaccine 36:3960–3966.

    https://doi.org/10.1016/j.vaccine.2018.05.057

    • PubMed
    • Google Scholar
71. 1. Rajão DS
    2. Chen H
    3. Perez DR
    4. Sandbulte MR
    5. Gauger PC
    6. Loving CL
    7. Shanks GD
    8. Vincent A

    (2016) Vaccine-associated enhanced respiratory disease is influenced by haemagglutinin and neuraminidase in whole inactivated influenza virus vaccines

    The Journal of General Virology 97:1489–1499.

    https://doi.org/10.1099/jgv.0.000468

    • PubMed
    • Google Scholar
72. 1. Rajão DS
    2. Walia RR
    3. Campbell B
    4. Gauger PC
    5. Janas-Martindale A
    6. Killian ML
    7. Vincent AL

    (2017) Reassortment between swine H3N2 and 2009 pandemic H1N1 in the united states resulted in influenza A viruses with diverse genetic constellations with variable virulence in pigs

    Journal of Virology 91:e01763-16.

    https://doi.org/10.1128/JVI.01763-16

    • PubMed
    • Google Scholar
73. Software

    1. Rambaut A

    (2009) FigTree, version v1. 3.1

    Tree.Bio.

    http://tree.bio.ed.ac.uk/software/figtree/
74. 1. Resende PC
    2. Born PS
    3. Matos AR
    4. Motta FC
    5. Caetano BC
    6. Carmo Debur M
    7. Siqueira MM

    (2017) Whole-genome characterization of A novel human influenza A (H1N2) virus variant brazil

    Emerging Infectious Diseases 23:152–154.

    https://doi.org/10.3201/eid2301.161122

    • Google Scholar
75. 1. Reynolds JJH
    2. Torremorell M
    3. Craft ME

    (2014) Mathematical modeling of influenza A virus dynamics within swine farms and the effects of vaccination

    PLOS ONE 9:e106177.

    https://doi.org/10.1371/journal.pone.0106177

    • PubMed
    • Google Scholar
76. 1. Richard M
    2. Herfst S
    3. Tao H
    4. Jacobs NT
    5. Lowen AC

    (2018) Influenza A virus reassortment is limited by anatomical compartmentalization following coinfection via distinct routes

    Journal of Virology 92:e02063-17.

    https://doi.org/10.1128/JVI.02063-17

    • PubMed
    • Google Scholar
77. 1. Rose N
    2. Hervé S
    3. Eveno E
    4. Barbier N
    5. Eono F
    6. Dorenlor V
    7. Andraud M
    8. Camsusou C
    9. Madec F
    10. Simon G

    (2013) Dynamics of influenza A virus infections in permanently infected pig farms: evidence of recurrent infections, circulation of several swine influenza viruses and reassortment events

    Veterinary Research 44:72.

    https://doi.org/10.1186/1297-9716-44-72

    • PubMed
    • Google Scholar
78. 1. Ryt-Hansen P
    2. Krog JS
    3. Breum SØ
    4. Hjulsager CK
    5. Pedersen AG
    6. Trebbien R
    7. Larsen LE

    (2021) Co-circulation of multiple influenza A reassortants in swine harboring genes from seasonal human and swine influenza viruses

    eLife 10:e60940.

    https://doi.org/10.7554/eLife.60940

    • PubMed
    • Google Scholar
79. 1. Sandbulte MR
    2. Spickler AR
    3. Zaabel PK
    4. Roth JA

    (2015) Optimal use of vaccines for control of influenza A virus in swine

    Vaccines 3:22–73.

    https://doi.org/10.3390/vaccines3010022

    • PubMed
    • Google Scholar
80. 1. Sanjuán R
    2. Nebot MR
    3. Chirico N
    4. Mansky LM
    5. Belshaw R

    (2010) Viral mutation rates

    Journal of Virology 84:9733–9748.

    https://doi.org/10.1128/JVI.00694-10

    • PubMed
    • Google Scholar
81. Software

    1. Seemann T

    (2017) Shovill: faster spades assembly of illumina reads, version 3.0

    Tilgjengelig Fra.

    https://github.com/tseemann/shovill
82. 1. Sprouffske K
    2. Aguilar-Rodríguez J
    3. Sniegowski P
    4. Wagner A

    (2018) High mutation rates limit evolutionary adaptation in Escherichia coli

    PLOS Genetics 14:e1007324.

    https://doi.org/10.1371/journal.pgen.1007324

    • PubMed
    • Google Scholar
83. 1. Sun H
    2. Xiao Y
    3. Liu J
    4. Wang D
    5. Li F
    6. Wang C
    7. Li C
    8. Zhu J
    9. Song J
    10. Sun H
    11. Jiang Z
    12. Liu L
    13. Zhang X
    14. Wei K
    15. Hou D
    16. Pu J
    17. Sun Y
    18. Tong Q
    19. Bi Y
    20. Chang KC
    21. Liu S
    22. Gao GF
    23. Liu J

    (2020) Prevalent eurasian avian-like H1N1 swine influenza virus with 2009 pandemic viral genes facilitating human infection

    PNAS 117:17204–17210.

    https://doi.org/10.1073/pnas.1921186117

    • PubMed
    • Google Scholar
84. 1. Takayama I
    2. Nguyen BG
    3. Dao CX
    4. Pham TT
    5. Dang TQ
    6. Truong PT
    7. Do TV
    8. Pham TTP
    9. Fujisaki S
    10. Odagiri T
    11. Hasegawa H
    12. Nakajima N

    (2021) Next-generation sequencing analysis of the within-host genetic diversity of influenza A(H1N1)pdm09 viruses in the upper and lower respiratory tracts of patients with severe influenza

    MSphere 6:e01043-20.

    https://doi.org/10.1128/mSphere.01043-20

    • PubMed
    • Google Scholar
85. 1. Tao H
    2. Steel J
    3. Lowen AC

    (2014) Intrahost dynamics of influenza virus reassortment

    Journal of Virology 88:7485–7492.

    https://doi.org/10.1128/JVI.00715-14

    • PubMed
    • Google Scholar
86. 1. Tobita K
    2. Sugiura A
    3. Enomote C
    4. Furuyama M

    (1975) Plaque assay and primary isolation of influenza A viruses in an established line of canine kidney cells (MDCK) in the presence of trypsin

    Medical Microbiology and Immunology 162:9–14.

    https://doi.org/10.1007/BF02123572

    • PubMed
    • Google Scholar
87. Book

    1. USDA

    (2016)

    Swine 2012 Part II: Reference of Swine Health and Health Management in the United States

    USDA-APHIS-VS-CEAH-NAHMS Fort Collins, CO.

    • Google Scholar
88. 1. Van Reeth K

    (2007) Avian and swine influenza viruses: our current understanding of the zoonotic risk

    Veterinary Research 38:243–260.

    https://doi.org/10.1051/vetres:2006062

    • PubMed
    • Google Scholar
89. 1. Van Reeth K
    2. Ma W

    (2013) Swine influenza virus vaccines: to change or not to change-that’s the question

    Current Topics in Microbiology and Immunology 370:173–200.

    https://doi.org/10.1007/82_2012_266

    • PubMed
    • Google Scholar
90. 1. Villa M
    2. Lässig M

    (2017) Fitness cost of reassortment in human influenza

    PLOS Pathogens 13:e1006685.

    https://doi.org/10.1371/journal.ppat.1006685

    • PubMed
    • Google Scholar
91. 1. Vincent A
    2. Awada L
    3. Brown I
    4. Chen H
    5. Claes F
    6. Dauphin G
    7. Donis R
    8. Culhane M
    9. Hamilton K
    10. Lewis N
    11. Mumford E
    12. Nguyen T
    13. Parchariyanon S
    14. Pasick J
    15. Pavade G
    16. Pereda A
    17. Peiris M
    18. Saito T
    19. Swenson S
    20. Van Reeth K
    21. Webby R
    22. Wong F
    23. Ciacci-Zanella J

    (2014) Review of influenza A virus in swine worldwide: A call for increased surveillance and research

    Zoonoses and Public Health 61:4–17.

    https://doi.org/10.1111/zph.12049

    • PubMed
    • Google Scholar
92. 1. Vincent AL
    2. Perez DR
    3. Rajao D
    4. Anderson TK
    5. Abente EJ
    6. Walia RR
    7. Lewis NS

    (2017) Influenza A virus vaccines for swine

    Veterinary Microbiology 206:35–44.

    https://doi.org/10.1016/j.vetmic.2016.11.026

    • PubMed
    • Google Scholar
93. 1. Visher E
    2. Whitefield SE
    3. McCrone JT
    4. Fitzsimmons W
    5. Lauring AS

    (2016) The mutational robustness of influenza A virus

    PLOS Pathogens 12:e1005856.

    https://doi.org/10.1371/journal.ppat.1005856

    • PubMed
    • Google Scholar
94. 1. White LA
    2. Torremorell M
    3. Craft ME

    (2017) Influenza A virus in swine breeding herds: combination of vaccination and biosecurity practices can reduce likelihood of endemic piglet reservoir

    Preventive Veterinary Medicine 138:55–69.

    https://doi.org/10.1016/j.prevetmed.2016.12.013

    • PubMed
    • Google Scholar
95. 1. Wiley DC
    2. Wilson IA
    3. Skehel JJ

    (1981) Structural identification of the antibody-binding sites of hong kong influenza haemagglutinin and their involvement in antigenic variation

    Nature 289:373–378.

    https://doi.org/10.1038/289373a0

    • PubMed
    • Google Scholar
96. 1. Wong KK
    2. Greenbaum A
    3. Moll ME
    4. Lando J
    5. Moore EL
    6. Ganatra R
    7. Biggerstaff M
    8. Lam E
    9. Smith EE
    10. Storms AD
    11. Miller JR
    12. Dato V
    13. Nalluswami K
    14. Nambiar A
    15. Silvestri SA
    16. Lute JR
    17. Ostroff S
    18. Hancock K
    19. Branch A
    20. Trock SC
    21. Klimov A
    22. Shu B
    23. Brammer L
    24. Epperson S
    25. Finelli L
    26. Jhung MA

    (2012) Outbreak of influenza A (H3N2) variant virus infection among attendees of an agricultural fair, pennsylvania, usa, 2011

    Emerging Infectious Diseases 18:1937–1944.

    https://doi.org/10.3201/eid1812.121097

    • PubMed
    • Google Scholar
97. 1. Xue KS
    2. Moncla LH
    3. Bedford T
    4. Bloom JD

    (2018) Within-host evolution of human influenza virus

    Trends in Microbiology 26:781–793.

    https://doi.org/10.1016/j.tim.2018.02.007

    • PubMed
    • Google Scholar
98. 1. Xue KS
    2. Bloom JD

    (2020) Linking influenza virus evolution within and between human hosts

    Virus Evolution 6:veaa010.

    https://doi.org/10.1093/ve/veaa010

    • PubMed
    • Google Scholar
99. 1. Zhang X
    2. Sun H
    3. Cunningham FL
    4. Li L
    5. Hanson-Dorr K
    6. Hopken MW
    7. Cooley J
    8. Long L-P
    9. Baroch JA
    10. Li T
    11. Schmit BS
    12. Lin X
    13. Olivier AK
    14. Jarman RG
    15. DeLiberto TJ
    16. Wan X-F

    (2018) Tissue tropisms opt for transmissible reassortants during avian and swine influenza A virus co-infection in swine

    PLOS Pathogens 14:e1007417.

    https://doi.org/10.1371/journal.ppat.1007417

    • PubMed
    • Google Scholar
100. 1. Zhou B
     2. Donnelly ME
     3. Scholes DT
     4. St George K
     5. Hatta M
     6. Kawaoka Y
     7. Wentworth DE

     (2009) Single-reaction genomic amplification accelerates sequencing and vaccine production for classical and swine origin human influenza a viruses

     Journal of Virology 83:10309–10313.

     https://doi.org/10.1128/JVI.01109-09

     • PubMed
     • Google Scholar

Author details

1. Chong Li

   College of Veterinary Medicine, University of Minnesota, Saint Paul, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   lixx5577@umn.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6375-0181

2. Marie R Culhane

   College of Veterinary Medicine, University of Minnesota, Saint Paul, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Declan C Schroeder

   College of Veterinary Medicine, University of Minnesota, Saint Paul, United States

   Contribution

   Resources, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Maxim C-J Cheeran

   College of Veterinary Medicine, University of Minnesota, Saint Paul, United States

   Contribution

   Resources, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

5. Lucina Galina Pantoja

   Zoetis, Parsippany, United States

   Contribution

   Resources, Funding acquisition, Writing – review and editing

   Competing interests

   Lucina Galina Pantoja is employed by Zoetis during the time of the study

6. Micah L Jansen

   Zoetis, Parsippany, United States

   Contribution

   Resources, Funding acquisition, Writing – review and editing

   Competing interests

   Micah L Jansen is employed by Zoetis during the time of the study

7. Montserrat Torremorell

   College of Veterinary Medicine, University of Minnesota, Saint Paul, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Writing – review and editing

   For correspondence

   torr0033@umn.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9626-6537

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