scholarly journals Australia as a global sink for the genetic diversity of avian influenza A virus

2021 ◽  
Author(s):  
Michelle Wille ◽  
Victoria Grillo ◽  
Silvia Ban de Gouvea Pedroso ◽  
Graham W. Burgess ◽  
Allison Crawley ◽  
...  

Most of our understanding of the ecology and evolution of avian influenza A virus (AIV) in wild birds is derived from studies conducted in the northern hemisphere on waterfowl, with a substantial bias towards dabbling ducks. However, relevant environmental conditions and patterns of avian migration and reproduction are substantially different in the southern hemisphere. Through the sequencing and analysis of 333 unique AIV genomes collected from wild birds collected over 15 years we show that Australia is a global sink for AIV diversity and not integrally linked with the Eurasian gene pool. Rather, AIV are infrequently introduced to Australia, followed by decades of isolated circulation and eventual extinction. The number of co-circulating viral lineages varies per subtype. AIV haemagglutinin (HA) subtypes that are rarely identified at duck-centric study sites (H8-12) had more detected introductions and contemporary co-circulating lineages in Australia. Combined with a lack of duck migration beyond the Australian-Papuan region, these findings suggest introductions by long-distance migratory shorebirds. In addition, we found no evidence of directional or consistent patterns in virus movement across the Australian continent. This feature corresponds to patterns of bird movement, whereby waterfowl have nomadic and erratic rainfall-dependant distributions rather than consistent intra-continental migratory routes. Finally, we detected high levels of virus gene segment reassortment, with a high diversity of AIV genome constellations across years and locations. These data, in addition to those from other studies in Africa and South America, clearly show that patterns of AIV dynamics in the Southern Hemisphere are distinct from those in the temperate north.

PLoS ONE ◽  
2012 ◽  
Vol 7 (6) ◽  
pp. e38256 ◽  
Author(s):  
Josanne H. Verhagen ◽  
Vincent J. Munster ◽  
Frank Majoor ◽  
Pascal Lexmond ◽  
Oanh Vuong ◽  
...  

2010 ◽  
Vol 57 (7-8) ◽  
pp. e184-e194 ◽  
Author(s):  
V. Lang ◽  
M. Rinder ◽  
A. Hafner-Marx ◽  
S. Rabl ◽  
K. H. Bogner ◽  
...  

2012 ◽  
Vol 20 (2) ◽  
pp. 140-145 ◽  
Author(s):  
Kyu-Jun Lee ◽  
Jun-Gu Choi ◽  
Hyun-Mi Kang ◽  
Kwang-Il Kim ◽  
Choi-Kyu Park ◽  
...  

ABSTRACTOutbreaks of avian influenza A virus infection, particularly the H5N1 strains that have affected birds and some humans for the past 15 years, have highlighted the need for increased surveillance and disease control. Such measures require diagnostic tests to detect and characterize the different subtypes of influenza virus. In the current study, a simple method for producing reference avian influenza virus antisera to be used in diagnostic tests was developed. Antisera of nine avian influenza A virus neuraminidases (NA) used for NA subtyping were produced using a recombinant baculovirus. The recombinant NA (rNA) proteins were expressed in Sf9 insect cells and inoculated intramuscularly into specific-pathogen-free chickens with the ISA70 adjuvant. The NA inhibition antibody titers of the rNA antiserum were in the ranges of 5 to 8 and 6 to 9 log2units after the primary and boost immunizations, respectively. The antisera were subtype specific, showing low cross-reactivity against every other NA subtype using the conventional thiobarbituric acid NA inhibition assay. These results suggest that this simple method for producing reference NA antisera without purification may be useful for the diagnosis and surveillance of influenza virus.


2005 ◽  
Vol 79 (15) ◽  
pp. 9926-9932 ◽  
Author(s):  
Kyoko Shinya ◽  
Masato Hatta ◽  
Shinya Yamada ◽  
Ayato Takada ◽  
Shinji Watanabe ◽  
...  

ABSTRACT In 2003, H5N1 avian influenza virus infections were diagnosed in two Hong Kong residents who had visited the Fujian province in mainland China, affording us the opportunity to characterize one of the viral isolates, A/Hong Kong/213/03 (HK213; H5N1). In contrast to H5N1 viruses isolated from humans during the 1997 outbreak in Hong Kong, HK213 retained several features of aquatic bird viruses, including the lack of a deletion in the neuraminidase stalk and the absence of additional oligosaccharide chains at the globular head of the hemagglutinin molecule. It demonstrated weak pathogenicity in mice and ferrets but caused lethal infection in chickens. The original isolate failed to produce disease in ducks but became more pathogenic after five passages. Taken together, these findings portray the HK213 isolate as an aquatic avian influenza A virus without the molecular changes associated with the replication of H5N1 avian viruses in land-based poultry such as chickens. This case challenges the view that adaptation to land-based poultry is a prerequisite for the replication of aquatic avian influenza A viruses in humans.


2007 ◽  
Vol 13 (11) ◽  
pp. 1667-1674 ◽  
Author(s):  
Michael Lierz ◽  
Hafez M. Hafez ◽  
Robert Klopfleisch ◽  
Dörte Lüschow ◽  
Christine Prusas ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Fahad Humayun ◽  
Fatima Khan ◽  
Nasim Fawad ◽  
Shazia Shamas ◽  
Sahar Fazal ◽  
...  

Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1–H16 and NA sequences to subtypes N1–N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.


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