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Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes

Overview of attention for article published in BMC Genomics, July 2016
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Title
Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes
Published in
BMC Genomics, July 2016
DOI 10.1186/s12864-016-2919-4
Pubmed ID
Authors

Zeeshan Khaliq, Mikael Leijon, Sándor Belák, Jan Komorowski

Abstract

The underlying strategies used by influenza A viruses (IAVs) to adapt to new hosts while crossing the species barrier are complex and yet to be understood completely. Several studies have been published identifying singular genomic signatures that indicate such a host switch. The complexity of the problem suggested that in addition to the singular signatures, there might be a combinatorial use of such genomic features, in nature, defining adaptation to hosts. We used computational rule-based modeling to identify combinatorial sets of interacting amino acid (aa) residues in 12 proteins of IAVs of H1N1 and H3N2 subtypes. We built highly accurate rule-based models for each protein that could differentiate between viral aa sequences coming from avian and human hosts. We found 68 host-specific combinations of aa residues, potentially associated to host adaptation on HA, M1, M2, NP, NS1, NEP, PA, PA-X, PB1 and PB2 proteins of the H1N1 subtype and 24 on M1, M2, NEP, PB1 and PB2 proteins of the H3N2 subtypes. In addition to these combinations, we found 132 novel singular aa signatures distributed among all proteins, including the newly discovered PA-X protein, of both subtypes. We showed that HA, NA, NP, NS1, NEP, PA-X and PA proteins of the H1N1 subtype carry H1N1-specific and HA, NA, PA-X, PA, PB1-F2 and PB1 of the H3N2 subtype carry H3N2-specific signatures. M1, M2, PB1-F2, PB1 and PB2 of H1N1 subtype, in addition to H1N1 signatures, also carry H3N2 signatures. Similarly M1, M2, NP, NS1, NEP and PB2 of H3N2 subtype were shown to carry both H3N2 and H1N1 host-specific signatures (HSSs). To sum it up, we computationally constructed simple IF-THEN rule-based models that could distinguish between aa sequences of avian and human IAVs. From the rules we identified HSSs having a potential to affect the adaptation to specific hosts. The identification of combinatorial HSSs suggests that the process of adaptation of IAVs to a new host is more complex than previously suggested. The present study provides a basis for further detailed studies with the aim to elucidate the molecular mechanisms providing the foundation for the adaptation process.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
Germany 1 3%
Unknown 34 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 28%
Student > Master 7 19%
Researcher 4 11%
Professor 3 8%
Student > Bachelor 2 6%
Other 2 6%
Unknown 8 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 25%
Biochemistry, Genetics and Molecular Biology 6 17%
Computer Science 4 11%
Immunology and Microbiology 2 6%
Veterinary Science and Veterinary Medicine 2 6%
Other 4 11%
Unknown 9 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2016.
All research outputs
#14,857,703
of 22,881,964 outputs
Outputs from BMC Genomics
#6,147
of 10,666 outputs
Outputs of similar age
#226,141
of 365,421 outputs
Outputs of similar age from BMC Genomics
#159
of 271 outputs
Altmetric has tracked 22,881,964 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,666 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 365,421 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 271 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.