Summary: Evolution of RNA viruses occurs through disequilibria of collections of closely related mutant spectra or mutant clouds termed viral quasispecies. Here we review the origin of the quasispecies concept and some biological implications of quasispecies dynamics. Two main aspects are addressed: (i) mutant clouds as reservoirs of phenotypic variants for virus adaptability and (ii) the internal interactions that are established within mutant spectra that render a virus ensemble the unit of selection. The understanding of viruses as quasispecies has led to new antiviral designs, such as lethal mutagenesis, whose aim is to drive viruses toward low fitness values with limited chances of fitness recovery. The impact of quasispecies for three salient human pathogens, human immunodeficiency virus and the hepatitis B and C viruses, is reviewed, with emphasis on antiviral treatment strategies. Finally, extensions of quasispecies to nonviral systems are briefly mentioned to emphasize the broad applicability of quasispecies theory.

Viral Quasispecies Evolution

Most humans are in contact with animals in a way or another. A zoonotic disease is a disease or infection that can be transmitted naturally from vertebrate animals to humans or from humans to vertebrate animals. More than 60% of human pathogens are zoonotic in origin. This includes a wide variety of bacteria, viruses, fungi, protozoa, parasites, and other pathogens. Factors such as climate change, urbanization, animal migration and trade, travel and tourism, vector biology, anthropogenic factors, and natural factors have greatly influenced the emergence, re-emergence, distribution, and patterns of zoonoses. As time goes on, there are more emerging and re-emerging zoonotic diseases. In this review, we reviewed the etiology of major zoonotic diseases, their impact on human health, and control measures for better management. We also highlighted COVID-19, a newly emerging zoonotic disease of likely bat origin that has affected millions of humans along with devastating global consequences. The implementation of One Health measures is highly recommended for the effective prevention and control of possible zoonosis.

/pdf/zoonotic-diseases-etiology-impact-and-control-3f9bm52mdq.pdf

Zoonotic diseases: etiology, impact, and control

The 2019 novel coronavirus (SARS-CoV-2) pandemic has caused a global health emergency. The outbreak of this virus has raised a number of questions: What is SARS-CoV-2? How transmissible is SARS-CoV-2? How severely affected are patients infected with SARS-CoV-2? What are the risk factors for viral infection? What are the differences between this novel coronavirus and other coronaviruses? To answer these questions, we performed a comparative study of four pathogenic viruses that primarily attack the respiratory system and may cause death, namely, SARS-CoV-2, severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and influenza A viruses (H1N1 and H3N2 strains). This comparative study provides a critical evaluation of the origin, genomic features, transmission, and pathogenicity of these viruses. Because the coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 is ongoing, this evaluation may inform public health administrators and medical experts to aid in curbing the pandemic's progression.

/pdf/comparative-review-of-sars-cov-2-sars-cov-mers-cov-and-5f6ivw3bqa.pdf

Comparative Review of SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza A Respiratory Viruses.

The influenza A virus is an important infectious cause of morbidity and mortality in humans and was responsible for 3 pandemics in the 20th century. As the replication of the influenza virus is based on its host's machinery, codon usage of its viral genes might be subject to host selection pressures, especially after interspecies transmission. A better understanding of viral evolution and host adaptive responses might help control this disease. Relative Synonymous Codon Usage (RSCU) values of the genes from segment 1 to segment 6 of avian and human influenza viruses, including pandemic H1N1, were studied via Correspondence Analysis (CA). The codon usage patterns of seasonal human influenza viruses were distinct among their subtypes and different from those of avian viruses. Newly isolated viruses could be added to the CA results, creating a tool to investigate the host origin and evolution of viral genes. It was found that the 1918 pandemic H1N1 virus contained genes with mammalian-like viral codon usage patterns, indicating that the introduction of this virus to humans was not through in toto transfer of an avian influenza virus. Many human viral genes had directional changes in codon usage over time of viral isolation, indicating the effect of host selection pressures. These changes reduced the overall GC content and the usage of G at the third codon position in the viral genome. Limited evidence of translational selection pressure was found in a few viral genes. Codon usage patterns from CA allowed identification of host origin and evolutionary trends in influenza viruses, providing an alternative method and a tool to understand the evolution of influenza viruses. Human influenza viruses are subject to selection pressure on codon usage which might assist in understanding the characteristics of newly emerging viruses.

/pdf/codon-usage-bias-and-the-evolution-of-influenza-a-viruses-k5jk7he76m.pdf

Codon usage bias and the evolution of influenza A viruses. Codon Usage Biases of Influenza Virus.

Hemolysis and Related Phenomena

Viral zoonosis, the transmission of a virus from its primary vertebrate reservoir species to humans, requires ubiquitous cellular proteins known as receptor proteins. Zoonosis can occur not only through direct transmission from vertebrates to humans, but also through intermediate reservoirs or other environmental factors. Viruses can be categorized according to genotype (ssDNA, dsDNA, ssRNA and dsRNA viruses). Among them, the RNA viruses exhibit particularly high mutation rates and are especially problematic for this reason. Most zoonotic viruses are RNA viruses that change their envelope proteins to facilitate binding to various receptors of host species. In this study, we sought to predict zoonotic propensity through the analysis of receptor characteristics. We hypothesized that the major barrier to interspecies virus transmission is that receptor sequences vary among species--in other words, that the specific amino acid sequence of the receptor determines the ability of the viral envelope protein to attach to the cell. We analysed host-cell receptor sequences for their hydrophobicity/hydrophilicity characteristics. We then analysed these properties for similarities among receptors of different species and used a statistical discriminant analysis to predict the likelihood of transmission among species. This study is an attempt to predict zoonosis through simple computational analysis of receptor sequence differences. Our method may be useful in predicting the zoonotic potential of newly discovered viral strains.

/pdf/classification-of-viral-zoonosis-through-receptor-pattern-50c700fve3.pdf

Classification of viral zoonosis through receptor pattern analysis.

Previous researches noticed that a 3D backbone structure can be mathematically represented with a 1D ? and ? dihedral angle array. However, performance of the backbone dihedral angle alignment was not supported with sufficiently large test sets to be quantified; i.e. only 2 pairs or 4 pairs of proteins were analyzed. Here we showed that it is more effective to accurately anticipate homology among 1891 pairs of proteins of 62 different proteases with the string of ? and ? dihedral angle array than famous 3D structural alignment tool TM-align. Gapless global alignment between protein structures was conducted to validate the effectiveness of performing structural alignment with strings of backbone torsion angles. Representation of 3D structure by 1D torsion angle strings allows local alignment, profile construction, hidden Markov models to be implemented with minor modifications and with almost no loss of speed compared with sequence alignment. By our further validation from the previous studies, the utility of backbone dihedral angle method could be more evident.

https://www.longdom.org/open-access/validity-of-protein-structure-alignment-method-based-on-backbone-torsion-angles-jpb.1000192.pdf

Validity of Protein Structure Alignment Method Based on Backbone Torsion Angles

Prion diseases, including ovine scrapie, bovine spongiform encephalopathy (BSE), human kuru and Creutzfeldt–Jakob disease (CJD), originate from a conformational change of the normal cellular prion protein (PrPC) into abnormal protease-resistant prion protein (PrPSc). There is concern regarding these prion diseases because of the possibility of their zoonotic infections across species. Mutations and polymorphisms of prion sequences may influence prion-disease susceptibility through the modified expression and conformation of proteins. Rapid determination of susceptibility based on prion-sequence polymorphism information without complex structural and molecular biological analyses may be possible. Information regarding the effects of mutations and polymorphisms on prion-disease susceptibility was collected based on previous studies to classify the susceptibilities of sequences, whereas the BLOSUM62 scoring matrix and the position-specific scoring matrix were utilised to determine the distance of target sequences. The k-nearest neighbour analysis was validated with cross-validation methods. The results indicated that the number of polymorphisms did not influence prion-disease susceptibility, and three and four k-objects showed the best accuracy in identifying the susceptible group. Although sequences with negative polymorphisms showed relatively high accuracy for determination, polymorphisms may still not be an appropriate factor for estimating variation in susceptibility. Discriminant analysis of prion sequences with scoring matrices was attempted as a possible means of determining susceptibility to prion diseases. Further research is required to improve the utility of this method.

/pdf/discriminant-analysis-of-prion-sequences-for-prediction-of-4yashh2kac.pdf

Discriminant analysis of prion sequences for prediction of susceptibility.

Previous studies have shown that genetic quantitative trait loci (QTL), strain barriers, inoculation dose and inoculation method modulate the incubation period of prion diseases. We examined the relationship between a diverse set of physical, genetic and immunological characteristics and the incubation period of prion disease using correlation analyses. We found that incubation period was highly correlated with brain weight. In addition, mean corpuscular volume and cell size were strongly correlated with incubation period, indicating that the physical magnitude of prion-infected organs or individual cells may be important in determining the incubation period. Given the same prion inoculation dose, animals with a lower brain weight, mean corpuscular volume or cell size may experience more virulent disease, as the effective concentration of abnormal prion, which might regulate the attachment rate of prions to aggregates, is increased with smaller capacity of brains and cells. This is partly consistent with previous theoretical modeling. The strong correlations between incubation period and physical properties of the brain and cells in this study suggest that the mechanism underlying prion disease pathology may be physical, indicating that the incubation process is governed by simple chemical stoichiometry.

Correlation analysis for the incubation period of prion disease.

FGMD (fungal genetic marker database, http://lcbb.snu.ac.kr/fgmd), which contains genetic markers for publicly reported fungal species. The 17 genetic markers used in this study can be used either singly or in combination for the phylogenetic classification of fungi. This database can provide diverse combinations of genetic markers and differs from other databases because it allows for broad systematic analyses based on all species for which sequences are publicly available. We also compared classifications made using single genetic markers to those made using a multigene approach to identify the best combination of markers based on the FGMD for the phylogenetic classification of fungal species. A phylogenetic analysis was performed using 71 species representing four fungal phyla using combinations of three marker genes. Multigene analysis was used for the classification of each species from the species to phylum levels, and error rates for each combination of markers were calculated. Multigene analyses had lower error rates than single-gene analyses. Group prediction by discriminant analysis using multigene markers, which used combinations of Rpb2, Atpase6, and β-Tubulin genes as variables, displayed high accuracy (92%). A multigene approach, such as the one described here, may be applicable as a unified genetic marker for the phylogenetic classification of diverse fungal species.

FGMD : Database of Fungal Genetic Markers and Multigene Analysis in Fungal Classification

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