Antibodies of Influenza A(H1N1)pdm09 virus in pigs' sera cross-react with other influenza A virus subtypes. A retrospective epidemiological interpretation of Norway's serosurveillance data from 2009-2017

Abstract

In the 90s, Norway started an active surveillance program for several reportable pig diseases that included influenza A virus (IAVs). These diseases are exotic (except for IAVs since 2009) to Norway but endemic in the rest of the world. Since the incur-sion of the first IAVs, influenza A(H1N1)pdm09 virus in 2009, serosurveillance every year of the Norwegian pig population revealed the herd prevalence for influenza A(H1N1)pdm09 (HIN1pdm09) has stabilized between 40 to 50%. Between 30 Sep-tember 2009 and 14 September 2017, the Norwegian Veterinary Institute and Norwegian Food Safety Authority screened 35,551 pigs for antibodies to IAVs from 8,636 herds and found 26% or 8,819 pigs' sera ELISA positive (titre ≥40). Subtyping these IAV antibodies from 8,214 pigs in 3,629 herds, by routine haemaglutination inhibition test (HAIT) against four standard antigens produced 13,771 positive results (HAIT titre ≥40) of binding antibodies. The four antigen subtypes eliciting positive HAIT titre in descending frequencies were immunogen H1N1pdm09 (n=8200 or 99.8%), swine influenza A virus (SIVs) subtypes swH1N1 (n=5164 or 62%), swH1N2 (n=395 or 5%) and swH3N2 (n=12 or 0.1%). Of these 8,214 pig blood samples, 3,039 were homologous HAIT assays, mostly with immunogen H1N1pdm09 (n=3,026 or 99.6%). Using HAIT titre of pig and herd geometric mean titre (GMT) as two continuous outcome variables, and with the data structured hierarchically, we used mixed effects linear regression analysis to investigate the impact of predictors of interests had on the outcomes. For the full data, the predictors in the regression model include categorical predictors antigen subtype (H1N1pdm09, swH1N1, swH1N2 & swH3N2), and production type (sow herd or fattening herd), ordinal pre-dictors year (longitudinally from 2009 to 2017), and number of antigens in heterologous reactions (1, 2, 3, 4) in the same blood sample. The last predictor, the proportion of HAIT positive (antigen specific) in tested pigs within the herd, was a continuous predictor, which served as a proxy for days post infection (dpi) or humoral response time in the pig or herd. Regression analysis on individual pig HAIT titre showed that antigen as predictor, the coefficient for immunogen H1N1pdm09 was at least four fold higher (p<0.001) than the three SIVs antigen subtypes, whose much lower coefficients were statistically no different between the three SIVs antigen subtypes. Correspondingly, for herd GMT, immunogen H1N1pdm09 was 28 - 40 fold higher in coefficient than the three SIVs antigen subtypes. Excluding the HAIT data of the three SIVs antigen subtypes with the regression analysis focusing only on immunogen H1N1pdm09 produced the effect of increasing the coefficients of the predictors in the models, indicating that including HAIT data of the three SIVs subtypes in the regression analysis had biased the coefficients of our predictors towards the null. Homologous reactions (99.6% H1N1pdm09) have lower coefficient while the likelihood of number of antigens involved in HAIT heterologous reactions in a single blood sample increased with higher HAIT titre of immunogen H1N1pdm09. For predictor "production", sows and sow herds had higher HAIT titre and GMT compared to fattening pigs and fattening herds respectively. Herds with "higher proportion of pigs tested positive" also had higher HAIT titre in the pig and herd GMT.