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Prevention of influenza by targeting host receptors using engineered proteins.

ABSTRACT: There is a need for new approaches for the control of influenza given the burden caused by annual seasonal outbreaks, the emergence of viruses with pandemic potential, and the development of resistance to current antiviral drugs. We show that multivalent biologics, engineered using carbohydrate-binding modules specific for sialic acid, mask the cell-surface receptor recognized by the influenza virus and protect mice from a lethal challenge with 2009 pandemic H1N1 influenza virus. The most promising biologic protects mice when given as a single 1-?g intranasal dose 7 d in advance of viral challenge. There also is sufficient virus replication to establish an immune response, potentially protecting the animal from future exposure to the virus. Furthermore, the biologics appear to stimulate inflammatory mediators, and this stimulation may contribute to their protective ability. Our results suggest that this host-targeted approach could provide a front-line prophylactic that has the potential to protect against any current and future influenza virus and possibly against other respiratory pathogens that use sialic acid as a receptor.

SUBMITTER: Connaris H

PROVIDER: S-EPMC4035977 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Prevention of influenza by targeting host receptors using engineered proteins.

Connaris Helen H Govorkova Elena A EA Ligertwood Yvonne Y Dutia Bernadette M BM Yang Lei L Tauber Sandra S Taylor Margaret A MA Alias Nadiawati N Hagan Robert R Nash Anthony A AA Webster Robert G RG Taylor Garry L GL

Proceedings of the National Academy of Sciences of the United States of America 20140414 17

There is a need for new approaches for the control of influenza given the burden caused by annual seasonal outbreaks, the emergence of viruses with pandemic potential, and the development of resistance to current antiviral drugs. We show that multivalent biologics, engineered using carbohydrate-binding modules specific for sialic acid, mask the cell-surface receptor recognized by the influenza virus and protect mice from a lethal challenge with 2009 pandemic H1N1 influenza virus. The most promis ...[more]

PMID: 24733924

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Project description:BackgroundMajority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.ResultsIn this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability.ConclusionsFrom the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.

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Prevention of influenza by targeting host receptors using engineered proteins.

Publications

Prevention of influenza by targeting host receptors using engineered proteins.

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