The Experts below are selected from a list of 9729 Experts worldwide ranked by ideXlab platform
Tin Wee Tan - One of the best experts on this subject based on the ideXlab platform.
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predicting zoonotic risk of influenza a viruses from Host Tropism protein signature using random forest
International Journal of Molecular Sciences, 2017Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the Host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct Host Tropism protein signatures of avian, human and zoonotic influenza strains obtained from Host Tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.
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distinct Host Tropism protein signatures to identify possible zoonotic influenza a viruses
PLOS ONE, 2016Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary Hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape Host species barrier and successfully infect a new Host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct Host Tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their Host Tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the Host Tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar Host Tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct Host Tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.
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predicting Host Tropism of influenza a virus proteins using random forest
BMC Medical Genomics, 2014Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Majority 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. In 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. From 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 .
Joo Chuan Tong - One of the best experts on this subject based on the ideXlab platform.
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predicting zoonotic risk of influenza a viruses from Host Tropism protein signature using random forest
International Journal of Molecular Sciences, 2017Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the Host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct Host Tropism protein signatures of avian, human and zoonotic influenza strains obtained from Host Tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.
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distinct Host Tropism protein signatures to identify possible zoonotic influenza a viruses
PLOS ONE, 2016Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary Hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape Host species barrier and successfully infect a new Host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct Host Tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their Host Tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the Host Tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar Host Tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct Host Tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.
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predicting Host Tropism of influenza a virus proteins using random forest
BMC Medical Genomics, 2014Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Majority 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. In 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. From 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 .
Christine L P Eng - One of the best experts on this subject based on the ideXlab platform.
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predicting zoonotic risk of influenza a viruses from Host Tropism protein signature using random forest
International Journal of Molecular Sciences, 2017Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the Host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct Host Tropism protein signatures of avian, human and zoonotic influenza strains obtained from Host Tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.
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distinct Host Tropism protein signatures to identify possible zoonotic influenza a viruses
PLOS ONE, 2016Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary Hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape Host species barrier and successfully infect a new Host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct Host Tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their Host Tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the Host Tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar Host Tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct Host Tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.
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predicting Host Tropism of influenza a virus proteins using random forest
BMC Medical Genomics, 2014Co-Authors: Christine L P Eng, Joo Chuan Tong, Tin Wee TanAbstract:Majority 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. In 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. From 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 .
Rafael Blasco - One of the best experts on this subject based on the ideXlab platform.
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recombinant swinepox virus expressing β galactosidase investigation of viral Host range and gene expression levels in cell culture
Virology, 1998Co-Authors: Juan Barcena, Rafael BlascoAbstract:Abstract Swinepox virus (SPV) has been proposed as a potential vector for generating recombinant vaccines for swine. However, little is known about important aspects of SPV biology, such as the functionality of SPV promoters or the Host range of SPV. Using a transient expression assay, well-characterized vaccinia virus promoters were shown to be active in cells infected with SPV. A recombinant SPV expressing β-galactosidase (β-gal) was constructed and characterized. The E. coli LacZ gene was placed under the control of a strong vaccinia synthetic early/late promoter and was inserted by homologous recombination in a noncoding region of the SPV genome. The recombinant SPV expressing β-gal was used to characterize the Host range of the virus by measuring protein expression and virus production in different cell lines. In general, SPV expressed more protein and grew more efficiently than vaccinia virus in porcine cell lines. Surprisingly, the recombinant SPV was able to infect and replicate in several cell lines of nonswine origin. The virus directed regulated early and late gene expression of β-gal in those cells and formed blue plaques in cell monolayers in the presence of X-gal. Upon infection with the recombinant SPV, there was a significant level of viral replication, and the virus can be serially passaged in some nonswine cell lines. The data presented suggest that despite the strict Host Tropism of SPV, the virus exhibits a relatively broad Host range in cell culture.
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recombinant swinepox virus expressing β galactosidase investigation of viral Host range and gene expression levels in cell culture
Virology, 1998Co-Authors: Juan Barcena, Rafael BlascoAbstract:Abstract Swinepox virus (SPV) has been proposed as a potential vector for generating recombinant vaccines for swine. However, little is known about important aspects of SPV biology, such as the functionality of SPV promoters or the Host range of SPV. Using a transient expression assay, well-characterized vaccinia virus promoters were shown to be active in cells infected with SPV. A recombinant SPV expressing β-galactosidase (β-gal) was constructed and characterized. The E. coli LacZ gene was placed under the control of a strong vaccinia synthetic early/late promoter and was inserted by homologous recombination in a noncoding region of the SPV genome. The recombinant SPV expressing β-gal was used to characterize the Host range of the virus by measuring protein expression and virus production in different cell lines. In general, SPV expressed more protein and grew more efficiently than vaccinia virus in porcine cell lines. Surprisingly, the recombinant SPV was able to infect and replicate in several cell lines of nonswine origin. The virus directed regulated early and late gene expression of β-gal in those cells and formed blue plaques in cell monolayers in the presence of X-gal. Upon infection with the recombinant SPV, there was a significant level of viral replication, and the virus can be serially passaged in some nonswine cell lines. The data presented suggest that despite the strict Host Tropism of SPV, the virus exhibits a relatively broad Host range in cell culture.
Alexander Ploss - One of the best experts on this subject based on the ideXlab platform.
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decoding type i and iii interferon signalling during viral infection
Nature microbiology, 2019Co-Authors: Emily V Mesev, Robert A Ledesma, Alexander PlossAbstract:Interferon (IFN)-mediated antiviral responses are central to Host defence against viral infection. Despite the existence of at least 20 IFNs, there are only three known cell surface receptors. IFN signalling and viral evasion mechanisms form an immensely complex network that differs across species. In this Review, we begin by highlighting some of the advances that have been made towards understanding the complexity of differential IFN signalling inputs and outputs that contribute to antiviral defences. Next, we explore some of the ways viruses can interfere with, or circumvent, these defences. Lastly, we address the largely under-reviewed impact of IFN signalling on Host Tropism, and we offer perspectives on the future of research into IFN signalling complexity and viral evasion across species.
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recapitulation of treatment response patterns in a novel humanized mouse model for chronic hepatitis b virus infection
Virology, 2017Co-Authors: Benjamin Y Winer, Michael V Wiles, Tiffany Huang, Benjamin E Low, Cindy Avery, Mihaialexandru Pais, Gabriela Hrebikova, Evelyn Siu, Luis Chiriboga, Alexander PlossAbstract:There are ~350 million chronic carriers of hepatitis B (HBV). While a prophylactic vaccine and drug regimens to suppress viremia are available, chronic HBV infection is rarely cured. HBV's limited Host Tropism leads to a scarcity of susceptible small animal models and is a hurdle to developing curative therapies. Mice that support engraftment with human hepatoctyes have traditionally been generated through crosses of murine liver injury models to immunodeficient backgrounds. Here, we describe the disruption of fumarylacetoacetate hydrolase directly in the NOD Rag1-/- IL2RγNULL (NRG) background using zinc finger nucleases. The resultant human liver chimeric mice sustain persistent HBV viremia for >90 days. When treated with standard of care therapy, HBV DNA levels decrease below detection but rebound when drug suppression is released, mimicking treatment response observed in patients. Our study highlights the utility of directed gene targeting approaches in zygotes to create new humanized mouse models for human diseases.
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genetic dissection of the Host Tropism of human tropic pathogens
Annual Review of Genetics, 2015Co-Authors: Florian Douam, Benjamin Y Winer, Jenna M Gaska, Qiang Ding, Markus Von Schaewen, Alexander PlossAbstract:Infectious diseases are the second leading cause of death worldwide. Although the Host multiTropism of some pathogens has rendered their manipulation possible in animal models, the human-restricted Tropism of numerous viruses, bacteria, fungi, and parasites has seriously hampered our understanding of these pathogens. Hence, uncovering the genetic basis underlying the narrow Tropism of such pathogens is critical for understanding their mechanisms of infection and pathogenesis. Moreover, such genetic dissection is essential for the generation of permissive animal models that can serve as critical tools for the development of therapeutics or vaccines against challenging human pathogens. In this review, we describe different experimental approaches utilized to uncover the genetic foundation regulating pathogen Host Tropism as well as their relevance for studying the Tropism of several important human pathogens. Finally, we discuss the current and future uses of this knowledge for generating genetically modified animal models permissive for these pathogens.
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determinants of hepatitis b and delta virus Host Tropism
Current Opinion in Virology, 2015Co-Authors: Benjamin Y Winer, Alexander PlossAbstract:Hepatitis B virus (HBV) infections are a global health problem afflicting approximately 360 million patients. Of these individuals, 15–20 million are co-infected with hepatitis delta virus (HDV). Progress toward curative therapies has been impeded by the highly restricted Host Tropism of HBV, which is limited to productive infections in humans and chimpanzees. Here, we will discuss different approaches that have been taken to study HBV and HDV infections in vivo. The development of transgenic and humanized mice has lead to deeper insights into HBV pathogenesis. An improved understanding of the determinants governing HBV and HDV species Tropism will aid in the construction of a small animal model with inheritable susceptible to HBV/HDV.