Recent Advances in Discovery and Classification of RNA Viruses

Recent Advances in Discovery and Classification of RNA Viruses

The science of virus classification has been on a contentious ride since its beginnings. One of the issues that compounds the problem is whether viruses are living entities. There continues to be an ongoing debate regarding this issue, especially as rapid advances in the virome frontier are made (Forterre, 2016; Koonin & Dolja, 2018; Nasir & Caetano-Anolles, 2015). There remains ongoing debate regarding what was actually the first primordial indication of forthcoming life, primitive RNA based cells, RNA/RT viruses, or even separate RNA-like information-holding structures that were eventually assisted in reproducing by primitive enzymes (Greene & Reid, 2013; Wolf et al., 2018).

     Disagreement also exists regarding the viability of one of the most time-weathered virus classification systems (Koonin & Dolja, 2018; Forterre, 2016; Nasir & Caetano-Anolles, 2015; Wolf et al. 2018), the Baltimore Classification system conceived by David Baltimore over 50 years ago, that classifies viruses into seven classes or groups, depending on a researcher's concurrence: dsDNA viruses, ssDNA viruses, dsRNA viruses, positive-sense ssRNA viruses, negative-sense ssRNA viruses, positive-sense RNA reverse-transcribing (RT) RNA viruses replicated by DNA, and dsDNA viruses replicated by RT using an RNA intermediate (Greene & Reid, 2013; Koonin et al., 2021). In an alternate approach to viral classification, the International Committee on Taxonomy of Viruses (ICTV) classifies viruses into six realms based on highly conserved traits such as DNA/RNA genome, strandedness, capsid type, protein fold, and polymerase type (Greene & Reid, 2013; ICTV, 2021; Nasir & Caetano-Anolles, 2015; Zayed, 2022). The ICTV method of classification is also being subject to continual revision request by researchers as new viral genome families are discovered and massive data builds on large proportions of yet unclassified species (Wolf et al, 2018; Zayed, 2022). Due to huge volumes of data on virus fragments gathered during recent metaviromics studies yielding highly statistically-assured, yet-unclassified, non-in vitro/non-in vivo-experienced viruses -- the ICTV has decided to include viral species based only on the statistically-assured nature of the metaviromic data (Koonin & Dolja, 2018)

     A number of researchers are taking alternate approaches to classification by using phylogenetic analysis to derive phylogenetic trees that indicate RNA viruses and/or RT entities belong at the root and legacy is based on conserved characteristics (Wolf et al., 2018; Wolf et al., 2020; Zayed, 2022). The viral genomic space has grown to the degree that researchers segment some portion of this space for their research. Many researchers have concentrated their efforts on RNA viruses. This task is made easier by the fact that all RNA viruses conserve a form of enzyme and gene called RNA dependent RNA polymerase (RdRp) (National Center for Biotechnology Information, 2022b). Researcher have identified and use a specific portion of RdRp code that is conserved commonly between all virus species to isolate RNA viruses fragments from the pool of heterogeneous code obtained during metaviromic and holobiont studies (Koonin & Dolja, 2018; Simon et al., 2019; Wolf, 2018; Wolf et al., 2020; Zayed, 2022). In addition to the RdRp congruence of these classification trees, researchers are also cross-classifying the entire viral and RNA viral domains by identifying variables such as but not limited to conserved genes, gene families, and conserved structures such as protein and structures, statistical triplet condon occurrence, and statistical exon and exon/intron similarities (Nasir & Caetano-Anolles, 2015; Wolf et al., 2018; Wolf et al., 2020; Yoon, 2009; Zayed, 2022).

     The methods and technologies used in just the few studies mentioned in this post include iterative hidden Markov models (HMMs), Basic Local Alignment Search Tool (Blast), CLANS Clustering Analysis, network-based clustering, ) primary sequence–based clustering, meta-transcriptome sequencing, iterative computational procedures, bipartite networks, and genome sequence comparison ( Frickey & Lupas, 2004; Koonin & Dolja, 2018; Lin et al., 2022; National Center for Biotechnology Information, 2022a; Wolf et al., 2018; Wolf et al., 2020; Yoon, 2009; Zayed, 2022). We are at the beginning edge of discovery of the incredible number of viral species that exist on earth. Advances in technology have gradually, and at an ever accelerating pace, brought the details of the global virome into tighter focus and at the same time increased the macro-lens of viral diversity. Distention still exists on numerous issues regarding viruses. As the future progresses many issues will undoubtedly be settled, but many may still remain. From the pool of articles I did read, it does appear that viral linage may have first appeared in the form of primitive RNA type entities. Likely this issue will remain unresolved unless we find some way to ascertain the beginnings of life.

References

Forterre, P. (2016). To Be or Not to Be Alive: How Recent Discoveries Challenge the Traditional Definitions of Viruses and Life. Studies in History and Philosophy of Biol & Biomed Sci, 59, 100–108. doi: 10.1016/j.shpsc.2016.02.013

Frickey, T. & Lupas, A. (2004). CLANS: A Java Application for Visualizing Protein Families Based on Pairwise Similarity. Bioinformatics (Oxford, England), 20(18), 3702–3704. doi.org/10.1093/bioinformatics/bth444

Greene, S. E. & Reid A. (2013). Viruses Throughout Life & Time: Friends, Foes, Change Agents: A Report on an American Academy of Microbiology Colloquium San Francisco. Washington (DC): American Society for Microbiology. doi: 10.1128/AAMCol.Dec.2012

ICTV. (2021, March). The International Code of Virus Classification and Nomenclature (ICVCN). Retrieved on September 17 from https://ictv.global/about/code

Koonin, E. V., Krupovic, M., & Agol, V. I. (2021). The Baltimore Classification of Viruses 50 Years Later: How Does It Stand in the Light of Virus Evolution?. Microbiology and Molecular Biology Reviews, 85(3), e0005321. doi.org/10.1128/MMBR.00053-21

Koonin, E. V., & Dolja, V. V. (2018). Metaviromics: A Tectonic Shift in Understanding Virus Evolution. Virus Research, 246, A1–A3. doi.org/10.1016/j.virusres.2018.02.001

Lin, Z., Laska, E., & Siegel, C. (2022). A General Iterative Clustering Algorithm. Statistical Analysis and Data Mining: The ASA Data Scence Journal, 15, 433– 446. doi.org/10.1002/sam.11573

Nasir, A. & Caetano-Anolles, G. (2015). A Phylogenomic Data-Driven Exploration of Viral Origins and Evolution. Science Advances, 1(8). doi: 10.1126/sciadv.1500527

National Center for Biotechnology Information. (2022a, March 17). BLAST Basic Local Alignment Search Tool. National Library of Medicine. Retrieved on September 17, 2022 from https://blast.ncbi.nlm.nih.gov/Blast.cgi

National Center for Biotechnology Information. (2022b, January 29). RdRP RNA-Directed RNA Polymerase. National Library of Medicine. Retrieved on September 17, 2022 from https://www.ncbi.nlm.nih.gov/nuccore/?term=RdRp

Simon, J. C., Marchesi, J. R., & Mougel, C. et al. (2019). Host-Microbiota Interactions: From Holobiont Theory to Analysis. Microbiome 7, 5. doi.org/10.1186/s40168-019-0619-4

Wolf, Y. I., Kazlauskas, D., Iranzo, J., Lucía-Sanz, A., Kuhn, J. H., Krupovic, M., Dolja, V. V., & Koonin, E. V. (2018). Origins and Evolution of the Global RNA Virome. mBio, 9(6), e02329-18. doi.org/10.1128/mBio.02329-18

Wolf, Y. I., Silas, S., Wang, Y., Wu, S., Bocek, M., Kazlauskas, D., Krupovic, M., Fire, A., Dolja, V. V., & Koonin, E. V. (2020). Doubling of the Known Set of RNA Viruses by Metagenomic Analysis of an Aquatic Virome. Nature Microbiology, 5(10), 1262–1270. doi.org/10.1038/s41564-020-0755-4

Yoon B. J. (2009). Hidden Markov Models and their Applications in Biological Sequence Analysis. Current Genomics, 10(6), 402–415. doi.org/10.2174/138920209789177575

Zayed, A. A., Wainaina, J. M., Dominguez-Huerta, G., Pelletier, E., Guo, J., Mohssen, M., Tian, F., Pratama, A. A., Bolduc, B., Zablocki, O., Cronin, D., Solden, L., Delage, E., Alberti, A., Aury, J.-M., Carradec, Q., da Silva, C., Labadie, K., Poulain, J., & Ruscheweyh, H.-J. (2022). Cryptic and Abundant Marine Viruses at the Evolutionary Origins of Earth’s RNA Virome. Science, 376(6589), 156–162. doi: 10.1126/science.abm5847