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Large-scale sequence analysis reveals novel human-adaptive markers in PB2 segment of seasonal influenza A viruses.

ABSTRACT: To elucidate the adaptive strategies of influenza A viruses (IAVs) to human, we proposed a computational approach to identify human-adaptive mutations in seasonal IAVs, which have not been analyzed comprehensively. We compared representative PB2 sequences of 1425 avian IAVs and 2176 human IAVs and identified a total of 42 human-adaptive markers, including 28 and 31 markers in PB2 proteins of seasonal viruses H1N1 and H3N2, respectively. Notably, this comprehensive list encompasses almost all the markers identified in prior computational studies and 21 novel markers including an experimentally verified mutation K526R, suggesting the predictive power of our method. The strength of our analysis derives from the enormous amount of recently available sequences as well as the recognition that human-adaptive mutations are not necessarily conserved across subtypes. We also utilized mutual information to profile the inter-residue coevolution in PB2 protein. A total of 35 and 46 coevolving site pairs are identified in H1N1 and H3N2, respectively. Interestingly, 13 out of the 28 (46.4%) identified markers in H1N1 and 16 out of the 31 (51.6%) in H3N2 are embraced in the coevolving pairs. Many of them are paired with well-characterized human-adaptive mutations, indicating potential epistatic effect of these coevolving residues in human adaptation. Additionally, we reconstructed the PB2 evolutionary history of seasonal IAVs and demonstrated the distinct adaptive pathway of PB2 segment after reassortment from H1 to H3 lineage. Our study may provide clues for further experimental validation of human-adaptive mutations and shed light on the human adaptation process of seasonal IAVs.

SUBMITTER: Wen L

PROVIDER: S-EPMC5874250 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Large-scale sequence analysis reveals novel human-adaptive markers in PB2 segment of seasonal influenza A viruses.

Wen Lei L Chu Hin H Wong Bosco Ho-Yin BH Wang Dong D Li Cun C Zhao Xiaoyu X Chiu Man-Chun MC Yuan Shuofeng S Fan Yanhui Y Chen Honglin H Zhou Jie J Yuen Kwok-Yung KY

Emerging microbes & infections 20180329 1

To elucidate the adaptive strategies of influenza A viruses (IAVs) to human, we proposed a computational approach to identify human-adaptive mutations in seasonal IAVs, which have not been analyzed comprehensively. We compared representative PB2 sequences of 1425 avian IAVs and 2176 human IAVs and identified a total of 42 human-adaptive markers, including 28 and 31 markers in PB2 proteins of seasonal viruses H1N1 and H3N2, respectively. Notably, this comprehensive list encompasses almost all the ...[more]

PMID: 29593225

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Project description:BackgroundThe explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation.ResultsOver 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from >/=88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections.ConclusionTo overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field.

Project description:BackgroundThe identification of mutations that confer unique properties to a pathogen, such as host range, is of fundamental importance in the fight against disease. This paper describes a novel method for identifying amino acid sites that distinguish specific sets of protein sequences, by comparative analysis of matched alignments. The use of mutual information to identify distinctive residues responsible for functional variants makes this approach highly suitable for analyzing large sets of sequences. To support mutual information analysis, we developed the AVANA software, which utilizes sequence annotations to select sets for comparison, according to user-specified criteria. The method presented was applied to an analysis of influenza A PB2 protein sequences, with the objective of identifying the components of adaptation to human-to-human transmission, and reconstructing the mutation history of these components.ResultsWe compared over 3,000 PB2 protein sequences of human-transmissible and avian isolates, to produce a catalogue of sites involved in adaptation to human-to-human transmission. This analysis identified 17 characteristic sites, five of which have been present in human-transmissible strains since the 1918 Spanish flu pandemic. Sixteen of these sites are located in functional domains, suggesting they may play functional roles in host-range specificity. The catalogue of characteristic sites was used to derive sequence signatures from historical isolates. These signatures, arranged in chronological order, reveal an evolutionary timeline for the adaptation of the PB2 protein to human hosts.ConclusionBy providing the most complete elucidation to date of the functional components participating in PB2 protein adaptation to humans, this study demonstrates that mutual information is a powerful tool for comparative characterization of sequence sets. In addition to confirming previously reported findings, several novel characteristic sites within PB2 are reported. Sequence signatures generated using the characteristic sites catalogue characterize concisely the adaptation characteristics of individual isolates. Evolutionary timelines derived from signatures of early human influenza isolates suggest that characteristic variants emerged rapidly, and remained remarkably stable through subsequent pandemics. In addition, the signatures of human-infecting H5N1 isolates suggest that this avian subtype has low pandemic potential at present, although it presents more human adaptation components than most avian subtypes.

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Large-scale sequence analysis reveals novel human-adaptive markers in PB2 segment of seasonal influenza A viruses.

Publications

Large-scale sequence analysis reveals novel human-adaptive markers in PB2 segment of seasonal influenza A viruses.

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