Viruses are one of the most abundant biological entities in the world and can infect different organisms, such as mammals, invertebrates, plants, and microorganisms. Viral identification can be challenging due to laborious requirements for isolation in the laboratory, and the absence of conserved regions found in all viral genomes that could be used as universal markers. Alternatively, the advent of massively parallel sequencing and proteomics strategies have revolutionized the detection, identification and characterization of new viral sequences in different biological settings. These methodologies have driven the exponential increase in the number of studies and description of viral sequences in the past years, which directly impacted the diversity of viruses and contributed to the understanding of virus evolution and pathogenesis.
It is well known that the relationship among viruses and their hosts is a coevolutionary process as the virus require the host`s cellular machinery to translate their proteins. The host can develop new strategies to fight viral infections, while viruses can develop counter-defense measures. Since viruses are subject to the same evolutionary pressures that shape the host genome composition and codon usage, they can share similar characteristics. Therefore, hosts and virus coding regions tend to share common features, such as dinucleotide composition and codon usage patterns, which can be used to determine possible virus hosts. Another strategy with the potential to determine a possible viral host is the assessment of virus-derived small RNAs. Small RNAs are generated by the activation of the RNA interference (RNAi) pathway. RNAi induces silencing of self and non-self RNAs based on sequence similarity and can be used as a hallmark of host antiviral response. This strategy is capable of differentiating between endogenous and exogenous viral sequences and also characterizing the virus genome. Thanks to the vast availability of public libraries and ongoing viral diversity studies, the number of descriptions of new viral species has increased significantly. Nevertheless, many issues are still found in the identification and characterization of viral genomes and the designation of their putative hosts. Molecular characteristics of the host and the virus, such as codon usage, di-nucleotide composition and virus-derived small RNAs (indicative of RNAi pathways activation) can help in the identification and characterization of viral sequences and also reveal aspects of the virus-host interactions.
This Research Topic aims to highlight the methods and approaches developed by researchers to investigate virus-host signatures. We welcome original research, reviews, short reports, and perspectives. Articles surrounding the areas listed below are welcomed:
Molecular viral signatures;
Comparative viral genomics;
Methodologies to virus discovery;
Use of viral molecular patterns to characterize host-pathogen interactions;
Researchers are encouraged to carry out interdisciplinary studies using laboratory methods, Artificial Intelligence, Bioinformatics and statistical modeling.
Viruses are one of the most abundant biological entities in the world and can infect different organisms, such as mammals, invertebrates, plants, and microorganisms. Viral identification can be challenging due to laborious requirements for isolation in the laboratory, and the absence of conserved regions found in all viral genomes that could be used as universal markers. Alternatively, the advent of massively parallel sequencing and proteomics strategies have revolutionized the detection, identification and characterization of new viral sequences in different biological settings. These methodologies have driven the exponential increase in the number of studies and description of viral sequences in the past years, which directly impacted the diversity of viruses and contributed to the understanding of virus evolution and pathogenesis.
It is well known that the relationship among viruses and their hosts is a coevolutionary process as the virus require the host`s cellular machinery to translate their proteins. The host can develop new strategies to fight viral infections, while viruses can develop counter-defense measures. Since viruses are subject to the same evolutionary pressures that shape the host genome composition and codon usage, they can share similar characteristics. Therefore, hosts and virus coding regions tend to share common features, such as dinucleotide composition and codon usage patterns, which can be used to determine possible virus hosts. Another strategy with the potential to determine a possible viral host is the assessment of virus-derived small RNAs. Small RNAs are generated by the activation of the RNA interference (RNAi) pathway. RNAi induces silencing of self and non-self RNAs based on sequence similarity and can be used as a hallmark of host antiviral response. This strategy is capable of differentiating between endogenous and exogenous viral sequences and also characterizing the virus genome. Thanks to the vast availability of public libraries and ongoing viral diversity studies, the number of descriptions of new viral species has increased significantly. Nevertheless, many issues are still found in the identification and characterization of viral genomes and the designation of their putative hosts. Molecular characteristics of the host and the virus, such as codon usage, di-nucleotide composition and virus-derived small RNAs (indicative of RNAi pathways activation) can help in the identification and characterization of viral sequences and also reveal aspects of the virus-host interactions.
This Research Topic aims to highlight the methods and approaches developed by researchers to investigate virus-host signatures. We welcome original research, reviews, short reports, and perspectives. Articles surrounding the areas listed below are welcomed:
Molecular viral signatures;
Comparative viral genomics;
Methodologies to virus discovery;
Use of viral molecular patterns to characterize host-pathogen interactions;
Researchers are encouraged to carry out interdisciplinary studies using laboratory methods, Artificial Intelligence, Bioinformatics and statistical modeling.