Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
Natl Vet Inst SVA, Dept Virol Parasitol & Immunobiol VIP, Uppsala, Sweden.;OIE Collaborating Ctr Biotechnol Based Diag Infec, Ulls Vag 2B & 26, SE-75689 Uppsala, Sweden..
OIE Collaborating Ctr Biotechnol Based Diag Infec, Ulls Vag 2B & 26, SE-75689 Uppsala, Sweden.;Swedish Univ Agr Sci SLU, Dept Biomed Sci & Vet Publ Hlth BVF, Uppsala, Sweden..
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab. Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland..ORCID iD: 0000-0002-0766-8789
2016 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 17, 529Article in journal (Refereed) Published
Abstract [en]

Background: 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.

Results: 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).

Conclusions: 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.

Place, publisher, year, edition, pages
2016. Vol. 17, 529
Keyword [en]
Influenza A virus, Host adaptation, Combinatorial signatures, Host-specific signatures, MCFS, Rosetta, Rough sets
National Category
Basic Medicine
Identifiers
URN: urn:nbn:se:uu:diva-302696DOI: 10.1186/s12864-016-2919-4ISI: 000380665200001PubMedID: 27473048OAI: oai:DiVA.org:uu-302696DiVA: diva2:970642
Funder
eSSENCE - An eScience CollaborationSwedish Research Council Formas, 2011-1692
Available from: 2016-09-14 Created: 2016-09-08 Last updated: 2017-09-22Bibliographically 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

Open Access in DiVA

fulltext(2216 kB)104 downloads
File information
File name FULLTEXT01.pdfFile size 2216 kBChecksum SHA-512
e7770cbe746923e507d521030c403a5b6f7d912dc0f4b6e731ae1e1fa150aa3b815d47c0f44f177045945dbdc23f0b281d154d5eb5e890db0a812ad6cce984b0
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Khaliq, ZeeshanKomorowski, Jan
By organisation
Computational Biology and BioinformaticsScience for Life Laboratory, SciLifeLab
In the same journal
BMC Genomics
Basic Medicine

Search outside of DiVA

GoogleGoogle Scholar
Total: 104 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 317 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf