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

2019 ◽  
Author(s):  
Fransiskus Xaverius Ivan ◽  
Chee Keong Kwoh
Keyword(s):  
Random Forest ◽  
Influenza A Virus ◽  
Influenza A ◽  
Meta Analysis ◽  
Viral Proteins ◽  
Mouse Strains ◽  
Learning Approaches ◽  
Rule Based ◽  
Averaged Model ◽  
Better Than

AbstractBackgroundInfluenza 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. Virus adaptation through serial lung-to-lung passaging and reverse genetic engineering and mutagenesis approaches have been widely used in the studies. 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.MethodsVirulence information of IAV infections and the corresponding virus and mouse strains were documented from literature. Using the mouse lethal dose 50, time series of weight loss or percentage of survival, the virulence of the infections was classified as avirulent or virulent for two-class problems, and as low, intermediate or high for three-class problems. On the other hand, protein sequences were decoded from the corresponding IAV genomes or reconstructed manually from other proteins according to mutations mentioned in the related literature. IAV virulence models were then learned from various datasets containing IAV proteins whose amino acids at their aligned position and the corresponding two-class or three-class virulence labels. Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling, and top protein sites and synergy between protein sites were identified from the models.ResultsMore than 500 records of IAV infections in mice whose viral proteins could be retrieved were documented. The BALB/C and C57BL/6 mouse strains and the H1N1, H3N2 and H5N1 viruses dominated the infection records. PART models learned from full or subsets of datasets achieved the best performance, with moderate averaged model accuracies ranged from 65.0% to 84.4% and from 54.0% to 66.6% for two-class and three-class datasets that utilized all records of aligned IAV proteins, respectively. Their averaged accuracies were comparable or even better than the averaged accuracies of random forest models and should be preferred based on the Occam’s razor principle. Interestingly, models based on a dataset that included all IAV strains achieved a better averaged accuracy 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 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 the virulence of IAV infections is a challenging problem. Rule-based models generated using only viral proteins are useful for its advantage in interpretation, but only achieve moderate performance. Development of more advanced machine learning approaches that learn models from features extracted from both viral and host proteins must be considered for future works.

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

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Fransiskus Xaverius Ivan ◽  
Chee Keong Kwoh
Keyword(s):  
Random Forest ◽  
Influenza A Virus ◽  
Survival Data ◽  
Influenza A ◽  
Meta Analysis ◽  
Lethal Dose ◽  
Viral Proteins ◽  
Learning Approaches ◽  
Rule Based ◽  
Better Than

Abstract Background Influenza 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. Results IAV 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. Conclusion Modelling 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.

scholarly journals Host Shutoff in Influenza A Virus: Many Means to an End

Viruses ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 475 ◽  
Author(s):  
Rachel Levene ◽  
Marta Gaglia
Keyword(s):  
Immune Responses ◽  
Influenza A Virus ◽  
Influenza A ◽  
Rna Synthesis ◽  
Viral Proteins ◽  
Host Gene Expression ◽  
Structural Protein ◽  
Infected Cells ◽  
Host Shutoff ◽  
Virus Replication Cycle

Influenza A virus carries few of its own proteins, but uses them effectively to take control of the infected cells and avoid immune responses. Over the years, host shutoff, the widespread down-regulation of host gene expression, has emerged as a key process that contributes to cellular takeover in infected cells. Interestingly, multiple mechanisms of host shutoff have been described in influenza A virus, involving changes in translation, RNA synthesis and stability. Several viral proteins, notably the non-structural protein NS1, the RNA-dependent RNA polymerase and the endoribonuclease PA-X have been implicated in host shutoff. This multitude of host shutoff mechanisms indicates that host shutoff is an important component of the influenza A virus replication cycle. Here we review the various mechanisms of host shutoff in influenza A virus and the evidence that they contribute to immune evasion and/or viral replication. We also discuss what the purpose of having multiple mechanisms may be.

scholarly journals Long-Lasting Mucosal and Systemic Immunity against Influenza A Virus Is Significantly Prolonged and Protective by Nasal Whole Influenza Immunization with Mucosal Adjuvant N3 and DNA-Plasmid Expressing Flagellin in Aging In- and Outbred Mice

Vaccines ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 64 ◽  
Author(s):  
Jorma Hinkula ◽  
Sanna Nyström ◽  
Claudia Devito ◽  
Andreas Bråve ◽  
Steven E. Applequist
Keyword(s):  
Immune Responses ◽  
Influenza A Virus ◽  
Influenza A ◽  
Influenza Infection ◽  
Inbred Mouse ◽  
Herd Immunity ◽  
Serum Antibody ◽  
Mouse Strains ◽  
Virus Challenge ◽  
Influenza A H1n1

Background: Vaccination is commonly used to prevent and control influenza infection in humans. However, improvements in the ease of delivery and strength of immunogenicity could markedly improve herd immunity. The aim of this pre-clinical study is to test the potential improvements to existing intranasal delivery of formalin-inactivated whole Influenza A vaccines (WIV) by formulation with a cationic lipid-based adjuvant (N3). Additionally, we combined WIV and N3 with a DNA-encoded TLR5 agonist secreted flagellin (pFliC(-gly)) as an adjuvant, as this adjuvant has previously been shown to improve the effectiveness of plasmid-encoded DNA antigens. Methods: Outbred and inbred mouse strains were intranasally immunized with unadjuvanted WIV A/H1N1/SI 2006 or WIV that was formulated with N3 alone. Additional groups were immunized with WIV and N3 adjuvant combined with pFliC(-gly). Homo and heterotypic humoral anti-WIV immune responses were assayed from serum and lung by ELISA and hemagglutination inhibition assay. Homo and heterotypic cellular immune responses to WIV and Influenza A NP were also determined. Results: WIV combined with N3 lipid adjuvant the pFliC(-gly) significantly increased homotypic influenza specific serum antibody responses (>200-fold), increased the IgG2 responses, indicating a mixed Th1/Th2-type immunity, and increased the HAI-titer (>100-fold). Enhanced cell-mediated IFNγ secreting influenza directed CD4+ and CD8+ T cell responses (>40-fold) to homotypic and heterosubtypic influenza A virus and peptides. Long-term and protective immunity was obtained. Conclusions: These results indicate that inactivated influenza virus that was formulated with N3 cationic adjuvant significantly enhanced broad systemic and mucosal influenza specific immune responses. These responses were broadened and further increased by incorporating DNA plasmids encoding FliC from S. typhimurum as an adjuvant providing long lasting protection against heterologous Influenza A/H1N1/CA09pdm virus challenge.

scholarly journals A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records

2016 ◽  
Vol 23 (5) ◽  
pp. 934-941 ◽  
Author(s):  
Tasnia Tahsin ◽  
Davy Weissenbacher ◽  
Robert Rivera ◽  
Rachel Beard ◽  
Mari Firago ◽  
...  
Keyword(s):  
Influenza A Virus ◽  
Full Text ◽  
Influenza A ◽  
Geographic Information ◽  
Geospatial Data ◽  
Rule Based ◽  
Pubmed Central ◽  
Geospatial Metadata ◽  
High Level ◽  
F Measure

Abstract Objective The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases. Materials and Methods We developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus. Results We found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively. Discussion Our system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction. Conclusion Our system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles.

scholarly journals Semisynthetic Cardenolides Acting as Antiviral Inhibitors of Influenza A Virus Replication by Preventing Polymerase Complex Formation

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4853
Author(s):  
Laurita Boff ◽  
André Schreiber ◽  
Aline da Rocha Matos ◽  
Juliana Del Sarto ◽  
Linda Brunotte ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Influenza A Virus ◽  
Virus Replication ◽  
Influenza A ◽  
Ex Vivo ◽  
Influenza Virus Infection ◽  
Human Tumor ◽  
Viral Proteins ◽  
Public Health Issue ◽  
Ex Vivo Model

Influenza virus infections represent a major public health issue by causing annual epidemics and occasional pandemics that affect thousands of people worldwide. Vaccination is the main prophylaxis to prevent these epidemics/pandemics, although the effectiveness of licensed vaccines is rather limited due to the constant mutations of influenza virus antigenic characteristics. The available anti-influenza drugs are still restricted and there is an increasing viral resistance to these compounds, thus highlighting the need for research and development of new antiviral drugs. In this work, two semisynthetic derivatives of digitoxigenin, namely C10 (3β-((N-(2-hydroxyethyl)aminoacetyl)amino-3-deoxydigitoxigenin) and C11 (3β-(hydroxyacetyl)amino-3-deoxydigitoxigenin), showed anti-influenza A virus activity by affecting the expression of viral proteins at the early and late stages of replication cycle, and altering the transcription and synthesis of new viral proteins, thereby inhibiting the formation of new virions. Such antiviral action occurred due to the interference in the assembly of viral polymerase, resulting in an impaired polymerase activity and, therefore, reducing viral replication. Confirming the in vitro results, a clinically relevant ex vivo model of influenza virus infection of human tumor-free lung tissues corroborated the potential of these compounds, especially C10, to completely abrogate influenza A virus replication at the highest concentration tested (2.0 µM). Taken together, these promising results demonstrated that C10 and C11 can be considered as potential new anti-influenza drug candidates.

scholarly journals Roles of the Non-Structural Proteins of Influenza A Virus

Pathogens ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 812
Author(s):  
Wenzhuo Hao ◽  
Lingyan Wang ◽  
Shitao Li
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Rna Virus ◽  
Critical Role ◽  
Viral Proteins ◽  
Host Cells ◽  
Structural Proteins ◽  
Host Interactions ◽  
New Findings ◽  
Seasonal Epidemics

Influenza A virus (IAV) is a segmented, negative single-stranded RNA virus that causes seasonal epidemics and has a potential for pandemics. Several viral proteins are not packed in the IAV viral particle and only expressed in the infected host cells. These proteins are named non-structural proteins (NSPs), including NS1, PB1-F2 and PA-X. They play a versatile role in the viral life cycle by modulating viral replication and transcription. More importantly, they also play a critical role in the evasion of the surveillance of host defense and viral pathogenicity by inducing apoptosis, perturbing innate immunity, and exacerbating inflammation. Here, we review the recent advances of these NSPs and how the new findings deepen our understanding of IAV–host interactions and viral pathogenesis.

scholarly journals Genome-wide CRISPR screen identifies host dependency factors for influenza A virus infection

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Bo Li ◽  
Sara M. Clohisey ◽  
Bing Shao Chia ◽  
Bo Wang ◽  
Ang Cui ◽  
...  
Keyword(s):  
Virus Infection ◽  
Influenza A Virus ◽  
Influenza A ◽  
Meta Analysis ◽  
Host Factors ◽  
Validation Data ◽  
Genome Wide ◽  
A Genome ◽  
Crispr Screen ◽  
Influenza A Virus Infection

AbstractHost dependency factors that are required for influenza A virus infection may serve as therapeutic targets as the virus is less likely to bypass them under drug-mediated selection pressure. Previous attempts to identify host factors have produced largely divergent results, with few overlapping hits across different studies. Here, we perform a genome-wide CRISPR/Cas9 screen and devise a new approach, meta-analysis by information content (MAIC) to systematically combine our results with prior evidence for influenza host factors. MAIC out-performs other meta-analysis methods when using our CRISPR screen as validation data. We validate the host factors, WDR7, CCDC115 and TMEM199, demonstrating that these genes are essential for viral entry and regulation of V-type ATPase assembly. We also find that CMTR1, a human mRNA cap methyltransferase, is required for efficient viral cap snatching and regulation of a cell autonomous immune response, and provides synergistic protection with the influenza endonuclease inhibitor Xofluza.

scholarly journals Effect of Phosphorylation of CM2 Protein on Influenza C Virus Replication

2017 ◽  
Vol 91 (22) ◽  
Author(s):  
Takanari Goto ◽  
Yoshitaka Shimotai ◽  
Yoko Matsuzaki ◽  
Yasushi Muraki ◽  
Ri Sho ◽  
...  
Keyword(s):  
Ion Channel ◽  
Influenza A Virus ◽  
Reporter Gene ◽  
Virus Replication ◽  
Influenza A ◽  
Phosphorylation Site ◽  
Viral Proteins ◽  
Wild Type ◽  
Channel Protein ◽  
Influenza C Virus

ABSTRACT CM2 is the second membrane protein of the influenza C virus and has been demonstrated to play a role in the uncoating and genome packaging processes in influenza C virus replication. Although the effects of N-linked glycosylation, disulfide-linked oligomerization, and palmitoylation of CM2 on virus replication have been analyzed, the effect of the phosphorylation of CM2 on virus replication remains to be determined. In this study, a phosphorylation site(s) at residue 78 and/or 103 of CM2 was replaced with an alanine residue(s), and the effects of the loss of phosphorylation on influenza C virus replication were analyzed. No significant differences were observed in the packaging of the reporter gene between influenza C virus-like particles (VLPs) produced from 293T cells expressing wild-type CM2 and those from the cells expressing the CM2 mutants lacking the phosphorylation site(s). Reporter gene expression in HMV-II cells infected with VLPs containing the CM2 mutants was inhibited in comparison with that in cells infected with wild-type VLPs. The virus production of the recombinant influenza C virus possessing CM2 mutants containing a serine-to-alanine change at residue 78 was significantly lower than that of wild-type recombinant influenza C virus. Furthermore, the virus growth of the recombinant viruses possessing CM2 with a serine-to-aspartic acid change at position 78, to mimic constitutive phosphorylation, was virtually identical to that of the wild-type virus. These results suggest that phosphorylation of CM2 plays a role in efficient virus replication, probably through the addition of a negative charge to the Ser78 phosphorylation site. IMPORTANCE It is well-known that many host and viral proteins are posttranslationally modified by phosphorylation, which plays a role in the functions of these proteins. In influenza A and B viruses, phosphorylation of viral proteins NP, M1, NS1, and the nuclear export protein (NEP), which are not integrated into the membranes, affects the functions of these proteins, thereby affecting virus replication. However, it was reported that phosphorylation of the influenza A virus M2 ion channel protein, which is integrated into the membrane, has no effect on virus replication in vitro or in vivo. We previously demonstrated that the influenza C virus CM2 ion channel protein is modified by N-glycosylation, oligomerization, palmitoylation, and phosphorylation and have analyzed the effects of these modifications, except phosphorylation, on virus replication. This is the first report demonstrating that phosphorylation of the influenza C virus CM2 ion channel protein, unlike that of the influenza A virus M2 protein, plays a role in virus replication.

scholarly journals Machine Learning Approaches for Auto Insurance Big Data

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 42 ◽  
Author(s):  
Mohamed Hanafy ◽  
Ruixing Ming
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Big Data ◽  
Random Forest ◽  
Decision Trees ◽  
Customer Service ◽  
Learning Approaches ◽  
Auto Insurance ◽  
New Methods ◽  
Better Than

The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.

Sign in / Sign up

Export Citation Format

Share Document