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A complete map of potential pathogenicity markers of avian influenza virus subtype H5 predicted from 11 expressed proteins
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
2015 (English)In: BMC Microbiology, ISSN 1471-2180, E-ISSN 1471-2180, Vol. 15, 128Article in journal (Refereed) Published
Abstract [en]

Background: Polybasic cleavage sites of the hemagglutinin (HA) proteins are considered to be the most important determinants indicating virulence of the avian influenza viruses (AIV). However, evidence is accumulating that these sites alone are not sufficient to establish high pathogenicity. There need to exist other sites located on the HA protein outside the cleavage site or on the other proteins expressed by AIV that contribute to the pathogenicity. Results: We employed rule-based computational modeling to construct a map, with high statistical significance, of amino acid (AA) residues associated to pathogenicity in 11 proteins of the H5 type viruses. We found potential markers of pathogenicity in all of the 11 proteins expressed by the H5 type of AIV. AA mutations S-43(HA1)-D, D-83(HA1)-A in HA; S-269-D, E-41-H in NA; S-48-N, K-212-N in NS1; V-166-A in M1; G-14-E in M2; K-77-R, S-377-N in NP; and Q-48-P in PB1-F2 were identified as having a potential to shift the pathogenicity from low to high. Our results suggest that the low pathogenicity is common to most of the subtypes of the H5 AIV while the high pathogenicity is specific to each subtype. The models were developed using public data and validated on new, unseen sequences. Conclusions: Our models explicitly define a viral genetic background required for the virus to be highly pathogenic and thus confirm the hypothesis of the presence of pathogenicity markers beyond the cleavage site.

Place, publisher, year, edition, pages
2015. Vol. 15, 128
Keyword [en]
Avian Influenza virus, Pathogenicity, Virulence, MCFS, Rosetta, Rough sets
National Category
Microbiology
Identifiers
URN: urn:nbn:se:uu:diva-258765DOI: 10.1186/s12866-015-0465-xISI: 000356912800001PubMedID: 26112351OAI: oai:DiVA.org:uu-258765DiVA: diva2:842506
Funder
eSSENCE - An eScience CollaborationEU, FP7, Seventh Framework Programme, 219235Swedish Research Council Formas, 2011-1692
Available from: 2015-07-20 Created: 2015-07-20 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Understanding Complex Diseases and Disease Causative Agents: The Machine Learning way
Open this publication in new window or tab >>Understanding Complex Diseases and Disease Causative Agents: The Machine Learning way
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Diseases can be caused by foreign agents – pathogens – such as viruses, bacteria and other parasites, entering the body or by an internal malfunction of the body itself. The partial understanding of diseases like cancer and the ones caused by viruses, like the influenza A viruses (IAVs) and the human immunodeficiency virus, means we still do not have an efficient cure or defence against them. In this thesis we developed and applied combinations of machine learning strategies to study some of the deadliest human diseases and the agents causing them. The results obtained in this study further our understanding about them, paving the way for the development of more efficient and more reliable counter strategies against them.

In Paper I we studied the genetic make up of the highly pathogenic (HP) avian influenza viruses and identified a viral genetic background that could potentially transform a low pathogenic (LP) strain into HP. In Paper II we identified combinatorial signatures in the IAVs genome that potentially could affect their adaptation to humans.

Candidate HIV vaccine studies are usually carried out in nonhuman primate models. In Paper III we analysed the host responses of immunized Rhesus Macaques against the simian immunodeficiency virus infection. We found that protection in Rhesus Macaques is mediated by a gradually built up immune response, in contrast to a strong initial immune response, which we found to be detrimental to protection.

In Paper IV we analysed 9 different cancer types and identified 38 novel long noncoding RNAs (lncRNAs) that have a disrupted expression in multiple cancer types – pan-cancer differentially expressed (DE) lncRNAs. In addition, we also found 308 novel lncRNAs whose dysregulation was specific to a certain cancer type (cancer-specific DE lncRNAs).

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 53 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1567
Keyword
Pathogens, Influenza A viruses, Human immunodeficiency virus, Simian immunodeficiency virus, Pathogenicity, Cancer, long noncoding RNAs, Machine learning, Host specificity, Host-specific signatures
National Category
Bioinformatics and Systems Biology Genetics
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-329948 (URN)978-91-513-0081-8 (ISBN)
Public defence
2017-11-10, B/B7:113a, Uppsala Biomedicinska Centrum BMC, Husarg. 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2017-10-18 Created: 2017-09-22 Last updated: 2017-10-18

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