Identification of genome-wide nucleotide sites associated with mammalian virulence in influenza A viruses

Peng, Yousong; Zhu, Wenfei; Feng, Zhaomin; Zhu, Zhaozhong; Zhang, Zheng; Chen, Yongkun; Liu, Suli; Wu, Aiping; Wang, Dayang; Shu, Yuelong; Jiang, Taijiao

doi:10.1101/416586

Cited by 3 publications

(3 citation statements)

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“…did not result in an amino acid change in the encoded protein, but it has been demonstrated previously that natural selection can also act on synonymous sites [ 75 , 76 ]. Nucleotide mutations may also influence pathogenicity of influenza viruses [ 77 ], as they can affect packaging, transcription and translation of the virus, interfere with the hosts’ immune response [ 78 – 82 ], and can be co-selected with other sites, supporting our approach of incorporating mutations at the nucleotide rather than amino acid level as potential predictors for severity. The PA T135C mutation would not have been detected as a predictor of severity when only looking at the amino acid level.…”

Section: Discussionmentioning

confidence: 95%

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

et al. 2021

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Background The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management. Methods 160 A(H3N2) influenza positive samples from the 2016–2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models. Results The model including viral mutations in addition to the host characteristics had an improved fit ($${X}^{2}$$ X 2 =12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732. Conclusions Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.

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Section: Discussionmentioning

confidence: 95%

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

et al. 2021

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“…Furthermore, virulence may not only be encoded at protein level, but also at nucleotide level. In a very recent study, synonymous codons were interestingly able to give rise different virulence levels [17].…”

Section: Introductionmentioning

confidence: 99%

“…Such approach, to our knowledge, has only been carried out using a Bayesian graphical model to investigate the viral protein sites important for virulence of influenza A/H5N1 in mammals [18]. Nevertheless, a meta-analysis approach using Naive Bayes approach at viral nucleotide level has recently been carried out to demonstrate the contribution of synonymous nucleotide mutations to IAV virulence [17]. In this paper we present a meta-analysis of viral protein sites that determine the virulence of infections with any subtype of IAV; however, instead of any mammal, we focus on the infections in mice.…”

Section: Introductionmentioning

confidence: 99%

Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice

Ivan

Kwoh

2019

Preprint

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7Background: Influenza A virus (IAV) poses threats to human health and life. Many individual 8 studies have been carried out in mice to uncover the viral factors responsible for the virulence 9 of IAV infections. Virus adaptation through serial lung-to-lung passaging and reverse genetic 10 engineering and mutagenesis approaches have been widely used in the studies. Nonetheless, a 11 single study may not provide enough confident about virulence factors, hence combining 12 several studies for a meta-analysis is desired to provide better views. 13 Methods: Virulence information of IAV infections and the corresponding virus and mouse 14 strains were documented from literature. Using the mouse lethal dose 50, time series of weight 15 loss or percentage of survival, the virulence of the infections was classified as avirulent or 16 virulent for two-class problems, and as low, intermediate or high for three-class problems. On 17 the other hand, protein sequences were decoded from the corresponding IAV genomes or 18 reconstructed manually from other proteins according to mutations mentioned in the related 19literature. IAV virulence models were then learned from various datasets containing IAV 20 proteins whose amino acids at their aligned position and the corresponding two-class or three-21 class virulence labels. Three proven rule-based learning approaches, i.e., OneR, JRip and 22 PART, and additionally random forest were used for modelling, and top protein sites and 23 synergy between protein sites were identified from the models. 24Results: More than 500 records of IAV infections in mice whose viral proteins could be 25 retrieved were documented. The BALB/C and C57BL/6 mouse strains and the H1N1, H3N2 26 and H5N1 viruses dominated the infection records. PART models learned from full or subsets 27 of datasets achieved the best performance, with moderate averaged model accuracies ranged 28 from 65.0% to 84.4% and from 54.0% to 66.6% for two-class and three-class datasets that 29 utilized all records of aligned IAV proteins, respectively. Their averaged accuracies were 30 comparable or even better than the averaged accuracies of random forest models and should be 31 preferred based on the Occam's razor principle. Interestingly, models based on a dataset that 32 included all IAV strains achieved a better averaged accuracy when host information was taken 33 into account. For model interpretation, we observed that although many sites in HA were highly 34 correlated with virulence, PART models based on sites in PB2 could compete against and were 35 often better than PART models based on sites in HA. Moreover, PART had a high preference 36 to include sites in PB2 when models were learned from datasets containing concatenated 37 alignments of all IAV proteins. Several sites with a known contribution to virulence were found 38 as the top protein sites, and site pairs that may synergistically influence virulence were also 39 uncovered. 40 Conclusion: Modelling the virulence of IAV infections is a challenging problem. Rule-bas...

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Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice

Ivan

Kwoh

2019

BMC Genomics

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BackgroundInfluenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence combining several studies for a meta-analysis is desired to provide better views. For this, we documented more than 500 records of IAV infections in mice, whose viral proteins could be retrieved and the mouse lethal dose 50 or alternatively, weight loss and/or survival data, was/were available for virulence classification.ResultsIAV virulence models were learned from various datasets containing aligned IAV proteins and the corresponding two virulence classes (avirulent and virulent) or three virulence classes (low, intermediate and high virulence). Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling. PART models achieved the best performance, with moderate average model accuracies ranged from 65.0 to 84.4% and from 54.0 to 66.6% for the two-class and three-class problems, respectively. PART models were comparable to or even better than random forest models and should be preferred based on the Occam’s razor principle. Interestingly, the average accuracy of the models was improved when host information was taken into account. For model interpretation, we observed that although many sites in HA were highly correlated with virulence, PART models based on sites in PB2 could compete against and were often better than PART models based on sites in HA. Moreover, PART had a high preference to include sites in PB2 when models were learned from datasets containing the concatenated alignments of all IAV proteins. Several sites with a known contribution to virulence were found as the top protein sites, and site pairs that may synergistically influence virulence were also uncovered.ConclusionModelling IAV virulence is a challenging problem. Rule-based models generated using viral proteins are useful for its advantage in interpretation, but only achieve moderate performance. Development of more advanced approaches that learn models from features extracted from both viral and host proteins shall be considered for future works.

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Identification of genome-wide nucleotide sites associated with mammalian virulence in influenza A viruses

Cited by 3 publications

References 41 publications

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice

Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice

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