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 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 1343. Prophylactic Dosing of Baloxavir Acid Eliminates Mortality in Mice Lethal Influenza A Virus Infection Model

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S410-S411
Author(s):  
Shinya Shano ◽  
Keita Fukao ◽  
Takeshi Noshi ◽  
Kenji Sato ◽  
Masashi Sakuramoto ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Weight ◽  
Virus Infection ◽  
Influenza A Virus ◽  
Influenza A ◽  
Lethal Dose ◽  
Body Weight Loss ◽  
Prophylactic Efficacy ◽  
Body Weights ◽  
Single Dosing

Abstract Background Baloxavir acid (BXA), an active form of orally available prodrug baloxavir marboxil (BXM, formerly S-033188), is a novel small molecule inhibitor of cap-dependent endonuclease (CEN) of influenza A and B virus, and was recently launched for the treatment of acute and uncomplicated influenza with single dosing of BXM (the trade name XOFLUZA™) in Japan in March 2018. Here, we evaluated the prophylactic efficacy of BXA in mice lethally infected with influenza A virus. Methods T1/2 of BXA in human is more than 10 times longer than that in mice. Therefore, suspension of BXA was subcutaneously administered at 0.8 or 1.6 mg/kg in mice to maintain the plasma concentration of BXA as seen in humans, and then mice were intranasally inoculated with a lethal dose of A/PR/8/34 strain at 48, 72, or 96 hours after the administration of BXA. Survival time and body weight change were then monitored through a 28-day period after virus infection. Mice were euthanized and regarded as dead if their body weights were lower than 70% of the initial body weights according to humane endpoints. Results Single dosing of BXA (1.6 mg/kg) completely eliminated mortality in mice, when the mice were administrated the drug at 48, 72, or 96 hours before virus infection (Figure 1). BXA treatment also significantly prevented body weight loss, consistent with the prolonged survival. Conclusion Prophylactic dosing of BXA exhibited significant protective efficacy against mortality and body weight loss in mice following a lethal infection with influenza A virus. The significant prophylactic efficacy observed in our mouse model suggests the potential utility of BXM for the prophylaxis of influenza in human. Disclosures S. Shano, Shionogi & Co., Ltd.: Employee, Salary. K. Fukao, Shionogi & Co., Ltd.: Employee, Salary. T. Noshi, Shionogi & Co., Ltd.: Employee, Salary. K. Sato, Shionogi & Co., Ltd.: Employee, Salary. M. Sakuramoto, Shionogi & Co., Ltd.: Employee, Salary. K. Baba, Shionogi TechnoAdvance Research & Co., Ltd.: Employee, Salary. T. Shishido, Shionogi & Co., Ltd.: Employee, Salary. A. Naito, Shionogi & Co., Ltd.: Employee, Salary.

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 EXPERIMENTAL INFECTION WITH INFLUENZA A VIRUS IN MICE

1941 ◽  
Vol 73 (1) ◽  
pp. 43-55 ◽  
Author(s):  
R. M. Taylor
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Lethal Dose ◽  
Virus Titer ◽  
Immune Serum ◽  
Normal Serum ◽  
Sublethal Dose ◽  
Virus Content ◽  
Proportional Increase ◽  
Intranasal Instillation

Following intranasal inoculation of influenza A virus (strain PR8) there is a rapid increase of the virus in the lungs which with large doses reaches a maximum within 24 hours. With smaller doses, although the proportional increase is greater, the maximum concentration is not reached until 48 hours following inoculation. If a lethal dose is administered, the ultimate concentration of the virus in the lungs is the same, irrespective of the size of the dose. If a sublethal dose is given, the titer of the virus in the lungs does not achieve the titer reached in mice receiving a lethal dose. Within 48 hours following inoculation of a sublethal dose the lungs of a mouse may contain at least 76,000 M.L.D., yet the mouse survives. The intranasal instillation of sterile fluid (distilled water, varying concentrations of NaCl, broth, or 10 per cent normal serum) into a mouse sublethally infected produces a sharp rise in the virus content of the lung usually followed by death within 3 to 8 days. If, however, the instillate consists of 10 per cent immune serum, there is no rise in the virus titer, and no apparent harm results from the instillation. The implications of these phenomena are discussed and an hypothesis presented to explain their occurrence.

scholarly journals VirPreNet: A weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all 8 segments

2020 ◽  
Author(s):  
Rui Yin ◽  
Zihan Luo ◽  
Pei Zhuang ◽  
Zhuoyi Lin ◽  
Chee Keong Kwoh
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Influenza A Virus ◽  
Influenza A ◽  
Lethal Dose ◽  
Influenza Viruses ◽  
Numerical Representation ◽  
Influenza A Viruses ◽  
Linear Layer

AbstractMotivationInfluenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics.ResultsIn this paper, we propose a weighted ensemble convolutional neural network for the virulence prediction of influenza A viruses named VirPreNet that uses all 8 segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the 8-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble convolutional neural network is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet’s main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza [email protected] and ImplementationCodes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet

scholarly journals DNA-Epitope Vaccine Provided Efficient Protection to Mice Against Lethal Dose of Influenza A Virus H1N1

2014 ◽  
Vol 27 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Huiling Wei ◽  
Stephen D. Lenz ◽  
David H. Thompson ◽  
Roman M. Pogranichniy
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Lethal Dose ◽  
Virus H1n1 ◽  
Epitope Vaccine ◽  
Efficient Protection

scholarly journals Protective Role of Beta Interferon in Host Defense against Influenza A Virus

2006 ◽  
Vol 81 (4) ◽  
pp. 2025-2030 ◽  
Author(s):  
Iris Koerner ◽  
Georg Kochs ◽  
Ulrich Kalinke ◽  
Siegfried Weiss ◽  
Peter Staeheli
Keyword(s):  
Influenza A Virus ◽  
Host Defense ◽  
Virus Strain ◽  
Influenza A ◽  
Lethal Dose ◽  
Influenza Viruses ◽  
Type I ◽  
Type I Ifn ◽  
Embryo Fibroblasts ◽  
Deficient Mice

ABSTRACT Type I interferon (IFN), which includes the IFN-α and -β subtypes, plays an essential role in host defense against influenza A virus. However, the relative contribution of IFN-β remains unresolved. In mice, type I IFN is effective against influenza viruses only if the IFN-induced resistance factor Mx1 is present, though most inbred mouse strains, including the recently developed IFN-β-deficient mice, bear only defective Mx1 alleles. We therefore generated IFN-β-deficient mice carrying functional Mx1 alleles (designated Mx-BKO) and compared them to either wild-type mice bearing functional copies of both IFN-β and Mx1 (designated Mx-wt) or mice carrying functional Mx1 alleles but lacking functional type I IFN receptors (designated Mx-IFNAR). Influenza A virus strain SC35M (H7N7) grew to high titers and readily formed plaques in monolayers of Mx-BKO and Mx-IFNAR embryo fibroblasts which showed no spontaneous expression of Mx1. In contrast, Mx-wt embryo fibroblasts were found to constitutively express Mx1, most likely explaining why SC35M did not grow to high titers and formed no visible plaques in such cells. In vivo challenge experiments in which SC35M was applied via the intranasal route showed that the 50% lethal dose was about 20-fold lower in Mx-BKO mice than in Mx-wt mice and that virus titers in the lungs were increased in Mx-BKO mice. The resistance of Mx-BKO mice to influenza A virus strain PR/8/34 (H1N1) was also substantially reduced, demonstrating that IFN-β plays an important role in the defense against influenza A virus that cannot be compensated for by IFN-α.

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.

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