Tissue and host tropism of influenza viruses: Importance of quantitative analysis

2009 ◽  
Vol 52 (12) ◽  
pp. 1101-1110 ◽  
Author(s):  
Hong Zhang
Keyword(s):  
Quantitative Analysis ◽  
Influenza Viruses ◽  
Host Tropism

A Study on Host Tropism Determinants of Influenza Virus Using Machine Learning

2020 ◽  
Vol 15 (2) ◽  
pp. 121-134 ◽  
Author(s):  
Eunmi Kwon ◽  
Myeongji Cho ◽  
Hayeon Kim ◽  
Hyeon S. Son
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Influenza Virus ◽  
Random Forest ◽  
Physicochemical Properties ◽  
Protein Sequences ◽  
Influenza Viruses ◽  
Host Tropism ◽  
Post Hoc ◽  
Ha Protein

Background: The host tropism determinants of influenza virus, which cause changes in the host range and increase the likelihood of interaction with specific hosts, are critical for understanding the infection and propagation of the virus in diverse host species. Methods: Six types of protein sequences of influenza viral strains isolated from three classes of hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest neighbor algorithms were used for host classification. The Java language was used for sequence analysis programming and identifying host-specific position markers. Results: A machine learning technique was explored to derive the physicochemical properties of amino acids used in host classification and prediction. HA protein was found to play the most important role in determining host tropism of the influenza virus, and the random forest method yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific differences were also selected and verified, and they were found to be useful position markers for host classification. Finally, ANOVA analysis and post-hoc testing revealed that the physicochemical properties of amino acids, comprising protein sequences combined with position markers, differed significantly among hosts. Conclusion: The host tropism determinants and position markers described in this study can be used in related research to classify, identify, and predict the hosts of influenza viruses that are currently susceptible or likely to be infected in the future.

scholarly journals Gain of Function Research: the Clairvoyant Lens on Pandemics

2021 ◽  
Vol 28 (3) ◽  
pp. 269-274
Author(s):  
Sudhir BHANDARI ◽  
◽  
Amitabh DUBE ◽  
Bhoopendra PATEL ◽  
Amit TAK ◽  
...  
Keyword(s):  
Head Start ◽  
Culture Medium ◽  
Antimicrobial Agents ◽  
Selective Pressure ◽  
Influenza Viruses ◽  
Combination Therapies ◽  
Medical Sciences ◽  
Gain Of Function ◽  
Passive Immunotherapy ◽  
Host Tropism

Pandemic influenza viruses have emerged three times in this century. It is important to examine the potential risk of novel microorganisms/viruses through the add-on research mechanism of Gain of Function Research (GoFR). This mechanism consists of the practice of serial passaging of microorganisms to increase their transmissibility, virulence, immunogenicity, and host tropism through the inclusive feature of selective pressure of culture medium. Although, the GoFR can be a double-edged sword that has the potential to give an insight and better appreciation of current and future pandemics with antecedent apprehension of initiating a pandemic, itself. Moreover, with its inherent potential to give a head start on a virus, GoFR has the potential to develop vaccines or therapeutics, before the virus emerges in its true virulent form. Likewise, the GoFR studies can be vital in research on antivirals and antimicrobial agents and can help inform the development of combination therapies. Passive immunotherapy, which often includes a combination of products, is particularly dependent on GoFR experiments for evaluating efficacy. GoFR if made use of meticulously and with caution could help Medical Sciences and Humankind tremendously.

Computational identification of physicochemical signatures for host tropism of influenza A virus

2018 ◽  
Vol 16 (06) ◽  
pp. 1840023 ◽  
Author(s):  
Rui Yin ◽  
Xinrui Zhou ◽  
Jie Zheng ◽  
Chee Keong Kwoh
Keyword(s):  
Influenza A ◽  
High Performance ◽  
Computational Models ◽  
Prediction Models ◽  
Migratory Birds ◽  
Economic Loss ◽  
Influenza Viruses ◽  
Species Barrier ◽  
Van Der Waals Volume ◽  
Host Tropism

Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross species barrier and infect humans. In this study, we have constructed computational models for host tropism prediction on human-adapted subtypes of influenza HA proteins using random forest. The feature vectors of the prediction models were generated based on seven physicochemical properties of amino acids from influenza sequences of three major hosts. Feature aggregation and associative rules were further applied to select top 20 features and extract host-associated physicochemical signatures on the combined model of nonspecific subtypes. The prediction model achieved high performance ([Formula: see text], [Formula: see text], [Formula: see text]). Support and confidence rates were calculated for the host class-association rules. The results indicated that secondary structure and normalized Van der Waals volume were identified as more important physicochemical signatures in determining the host tropism.

scholarly journals Establishment of a reverse genetic system from a bovine derived Influenza D virus isolate

2021 ◽  
Author(s):  
Melle Holwerda ◽  
Laura Laloli ◽  
Manon Wider ◽  
Lutz Schönecker ◽  
Jens Becker ◽  
...  
Keyword(s):  
Genetic System ◽  
Influenza Viruses ◽  
Reverse Genetic System ◽  
Sequence Length ◽  
Reverse Genetic ◽  
Host Tropism ◽  
Culture Models ◽  
Conventional Cell ◽  
Viral Determinants

AbstractThe ruminant-associated Influenza D virus (IDV) has a broad host tropism and was shown to have zoonotic potential. To identify and characterize molecular viral determinants influencing the host spectrum of IDV, a reverse genetic system is required. For this, we first performed 5′ and 3′ rapid amplification of cDNA ends (RACE) of all seven genomic segments, followed by assessment of the 5′ and 3′ NCR activity prior to constructing the viral genomic segments of a contemporary Swiss bovine IDV isolate (D/CN286) into the bidirectional pHW-2000 vector. The bidirectional plasmids were transfected in HRT-18G cells followed by viral rescue on the same cell type. Analysis of the segment specific 5′ and 3′ non-coding regions (NCR) highlighted that the terminal 3′ end of all segments harbours an Uracil instead of a Cytosine nucleotide, similar to other Influenza viruses. Subsequent analysis on the functionality of the 5′ and 3′ NCR in a minireplicon assay revealed that these sequences were functional and that the variable sequence length of the 5′ and 3′ NCR influences reporter gene expression. Thereafter, we evaluated the replication efficiency of the reverse genetic clone on conventional cell lines of human, swine and bovine origin by use of an in vitro model recapitulating the natural replicate site of IDV in bovine and swine. This revealed that the reverse genetic clone D/CN286 replicates efficiently in all cell culture models. Combined, these results demonstrate the successful establishment of a reverse genetic system from a contemporary bovine IDV isolate that can be used for future identification and characterization of viral determinants influencing the broad host tropism of IDV.

scholarly journals Establishment of a Reverse Genetic System from a Bovine Derived Influenza D Virus Isolate

Viruses ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 502
Author(s):  
Melle Holwerda ◽  
Laura Laloli ◽  
Manon Wider ◽  
Lutz Schönecker ◽  
Jens Becker ◽  
...  
Keyword(s):  
Genetic System ◽  
Influenza Viruses ◽  
Reverse Genetic System ◽  
Sequence Length ◽  
Reverse Genetic ◽  
Host Tropism ◽  
Culture Models ◽  
Conventional Cell ◽  
Viral Determinants

The ruminant-associated influenza D virus (IDV) has a broad host tropism and was shown to have zoonotic potential. To identify and characterize molecular viral determinants influencing the host spectrum of IDV, a reverse genetic system is required. For this, we first performed 5′ and 3′ rapid amplification of cDNA ends (RACE) of all seven genomic segments, followed by assessment of the 5′ and 3′ NCR activity prior to constructing the viral genomic segments of a contemporary Swiss bovine IDV isolate (D/CN286) into the bidirectional pHW2000 vector. The bidirectional plasmids were transfected in HRT-18G cells followed by viral rescue on the same cell type. Analysis of the segment specific 5′ and 3′ non-coding regions (NCR) highlighted that the terminal 3′ end of all segments harbours an uracil instead of a cytosine nucleotide, similar to other influenza viruses. Subsequent analysis on the functionality of the 5′ and 3′ NCR in a minireplicon assay revealed that these sequences were functional and that the variable sequence length of the 5′ and 3′ NCR influences reporter gene expression. Thereafter, we evaluated the replication efficiency of the reverse genetic clone on conventional cell lines of human, swine and bovine origin, as well as by using an in vitro model recapitulating the natural replication site of IDV in bovine and swine. This revealed that the reverse genetic clone D/CN286 replicates efficiently in all cell culture models. Combined, these results demonstrate the successful establishment of a reverse genetic system from a contemporary bovine IDV isolate that can be used for future identification and characterization of viral determinants influencing the broad host tropism of IDV.

scholarly journals SARS-CoV-2 Evolutionary Adaptation toward Host Entry and Recognition of Receptor O-Acetyl Sialylation in Virus–Host Interaction

2020 ◽  
Vol 21 (12) ◽  
pp. 4549 ◽  
Author(s):  
Cheorl-Ho Kim
Keyword(s):  
Gene Transfer ◽  
Specific Interaction ◽  
Virus Evolution ◽  
Influenza Viruses ◽  
Host Cells ◽  
Type I ◽  
Evolutionary Adaptation ◽  
Type Ii ◽  
Health Crisis ◽  
Host Tropism

The recently emerged SARS-CoV-2 is the cause of the global health crisis of the coronavirus disease 2019 (COVID-19) pandemic. No evidence is yet available for CoV infection into hosts upon zoonotic disease outbreak, although the CoV epidemy resembles influenza viruses, which use sialic acid (SA). Currently, information on SARS-CoV-2 and its receptors is limited. O-acetylated SAs interact with the lectin-like spike glycoprotein of SARS CoV-2 for the initial attachment of viruses to enter into the host cells. SARS-CoV-2 hemagglutinin-esterase (HE) acts as the classical glycan-binding lectin and receptor-degrading enzyme. Most β-CoVs recognize 9-O-acetyl-SAs but switched to recognizing the 4-O-acetyl-SA form during evolution of CoVs. Type I HE is specific for the 9-O-Ac-SAs and type II HE is specific for 4-O-Ac-SAs. The SA-binding shift proceeds through quasi-synchronous adaptations of the SA-recognition sites of the lectin and esterase domains. The molecular switching of HE acquisition of 4-O-acetyl binding from 9-O-acetyl SA binding is caused by protein–carbohydrate interaction (PCI) or lectin–carbohydrate interaction (LCI). The HE gene was transmitted to a β-CoV lineage A progenitor by horizontal gene transfer from a 9-O-Ac-SA–specific HEF, as in influenza virus C/D. HE acquisition, and expansion takes place by cross-species transmission over HE evolution. This reflects viral evolutionary adaptation to host SA-containing glycans. Therefore, CoV HE receptor switching precedes virus evolution driven by the SA-glycan diversity of the hosts. The PCI or LCI stereochemistry potentiates the SA–ligand switch by a simple conformational shift of the lectin and esterase domains. Therefore, examination of new emerging viruses can lead to better understanding of virus evolution toward transitional host tropism. A clear example of HE gene transfer is found in the BCoV HE, which prefers 7,9-di-O-Ac-SAs, which is also known to be a target of the bovine torovirus HE. A more exciting case of such a switching event occurs in the murine CoVs, with the example of the β-CoV lineage A type binding with two different subtypes of the typical 9-O-Ac-SA (type I) and the exclusive 4-O-Ac-SA (type II) attachment factors. The protein structure data for type II HE also imply the virus switching to binding 4-O acetyl SA from 9-O acetyl SA. Principles of the protein–glycan interaction and PCI stereochemistry potentiate the SA–ligand switch via simple conformational shifts of the lectin and esterase domains. Thus, our understanding of natural adaptation can be specified to how carbohydrate/glycan-recognizing proteins/molecules contribute to virus evolution toward host tropism. Under the current circumstances where reliable antiviral therapeutics or vaccination tools are lacking, several trials are underway to examine viral agents. As expected, structural and non-structural proteins of SARS-CoV-2 are currently being targeted for viral therapeutic designation and development. However, the modern global society needs SARS-CoV-2 preventive and therapeutic drugs for infected patients. In this review, the structure and sialobiology of SARS-CoV-2 are discussed in order to encourage and activate public research on glycan-specific interaction-based drug creation in the near future.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

Quantification of Silica Uptake by Alveolar Macrophages - An Emperical Scanning Electron Microprobe Method

1982 ◽  
Vol 40 ◽  
pp. 332-333
Author(s):  
V. V. Damiano ◽  
R. P. Daniele ◽  
H. T. Tucker ◽  
J. H. Dauber
Keyword(s):  
Quantitative Analysis ◽  
Alveolar Macrophages ◽  
Bulk Sample ◽  
Silica Particles ◽  
Empirical Method ◽  
Phagocytic Cells ◽  
Calibration Standards ◽  
X Ray ◽  
Accurate Quantitative Analysis ◽  
Scanning Electron

An important example of intracellular particles is encountered in silicosis where alveolar macrophages ingest inspired silica particles. The quantitation of the silica uptake by these cells may be a potentially useful method for monitoring silica exposure. Accurate quantitative analysis of ingested silica by phagocytic cells is difficult because the particles are frequently small, irregularly shaped and cannot be visualized within the cells. Semiquantitative methods which make use of particles of known size, shape and composition as calibration standards may be the most direct and simplest approach to undertake. The present paper describes an empirical method in which glass microspheres were used as a model to show how the ratio of the silicon Kα peak X-ray intensity from the microspheres to that of a bulk sample of the same composition correlated to the mass of the microsphere contained within the cell. Irregular shaped silica particles were also analyzed and a calibration curve was generated from these data.

Lateral resolution of the electron microbeam analysis

1978 ◽  
Vol 36 (1) ◽  
pp. 542-543
Author(s):  
H.J. Dudek
Keyword(s):  
Quantitative Analysis ◽  
Depth Resolution ◽  
Lateral Resolution ◽  
Cylindrical Particle ◽  
Correction Procedure ◽  
X Ray ◽  
Element Concentrations ◽  
Microbeam Analysis ◽  
The Matrix

The chemical inhomogenities in modern materials such as fibers, phases and inclusions, often have diameters in the region of one micrometer. Using electron microbeam analysis for the determination of the element concentrations one has to know the smallest possible diameter of such regions for a given accuracy of the quantitative analysis.In th is paper the correction procedure for the quantitative electron microbeam analysis is extended to a spacial problem to determine the smallest possible measurements of a cylindrical particle P of high D (depth resolution) and diameter L (lateral resolution) embeded in a matrix M and which has to be analysed quantitative with the accuracy q. The mathematical accounts lead to the following form of the characteristic x-ray intens ity of the element i of a particle P embeded in the matrix M in relation to the intensity of a standard S

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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