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- Khaliq, Zeeshan, et al.
(author)
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A complete map of potential pathogenicity markers of avian influenza virus subtype H5 predicted from 11 expressed proteins
- 2015
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In: BMC Microbiology. - : Springer Science and Business Media LLC. - 1471-2180. ; 15
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Journal article (peer-reviewed)abstract
- 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.
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| 3. |
- Khaliq, Zeeshan, et al.
(author)
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Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes
- 2016
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In: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 17
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Journal article (peer-reviewed)abstract
- 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.
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| 5. |
- Khaliq, Zeeshan
(author)
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Understanding Complex Diseases and Disease Causative Agents : The Machine Learning way
- 2017
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Doctoral thesis (other academic/artistic)abstract
- 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).
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