Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)

ilustraciones, graficas, mapas

Autores:: Rojas Cruz, Alexis Felipe

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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oai_identifier_str	oai:repositorio.unal.edu.co:unal/81628
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
dc.title.translated.eng.fl_str_mv	Molecular evolution of zoonotic Betacoronavirus associated with Acute Respiratory Distress Syndrome (ARDS)
title	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
spellingShingle	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA) 610 - Medicina y salud::616 - Enfermedades Coronavirus Betacoronavirus Evolución molecular Transmisión inter-especies Estructura secundaria de RNA Selección positiva RNA pequeño derivado del virus Molecular evolution Inter-species transmission RNA secondary structure Positive selection Virus-derived small RNA
title_short	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
title_full	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
title_fullStr	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
title_full_unstemmed	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
title_sort	Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)
dc.creator.fl_str_mv	Rojas Cruz, Alexis Felipe
dc.contributor.advisor.none.fl_str_mv	Bermúdez Santana, Clara Isabel Gallego Gómez, Juan Carlos
dc.contributor.author.none.fl_str_mv	Rojas Cruz, Alexis Felipe
dc.contributor.researchgroup.spa.fl_str_mv	RNómica Teórica y Computacional
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud::616 - Enfermedades
topic	610 - Medicina y salud::616 - Enfermedades Coronavirus Betacoronavirus Evolución molecular Transmisión inter-especies Estructura secundaria de RNA Selección positiva RNA pequeño derivado del virus Molecular evolution Inter-species transmission RNA secondary structure Positive selection Virus-derived small RNA
dc.subject.other.none.fl_str_mv	Coronavirus Betacoronavirus
dc.subject.proposal.spa.fl_str_mv	Evolución molecular Transmisión inter-especies Estructura secundaria de RNA Selección positiva RNA pequeño derivado del virus
dc.subject.proposal.eng.fl_str_mv	Molecular evolution Inter-species transmission RNA secondary structure Positive selection Virus-derived small RNA
description	ilustraciones, graficas, mapas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-23T16:18:25Z
dc.date.available.none.fl_str_mv	2022-06-23T16:18:25Z
dc.date.issued.none.fl_str_mv	2022-06-20
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81628
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81628 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abascal, F., Zardoya, R., & Telford, M. J. (2010). TranslatorX: Multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Research, 38(Web Server issue), W7-13. https://doi.org/10.1093/nar/gkq291 Abdullahi, I. N., Emeribe, A. U., Ajayi, O. A., Oderinde, B. S., Amadu, D. O., & Osuji, A. I. (2020). Implications of SARS-CoV-2 genetic diversity and mutations on pathogenicity of the COVID-19 and biomedical interventions. Journal of Taibah University Medical Sciences, 15(4), 258-264. https://doi.org/10.1016/j.jtumed.2020.06.005 Abdullahi, I. N., Emeribe, A. U., Mustapha, J. O., Fasogbon, S. A., Ofor, I. B., Opeyemi, I. S., Obi-George, C., Sunday, A. O., & Nwofe, J. (2020). Exploring the genetics, ecology of SARS-COV-2 and climatic factors as possible control strategies against COVID-19. Le Infezioni in Medicina, 28(2), 166-173. Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4, e05005. https://doi.org/10.7554/eLife.05005 Alouane, T., Laamarti, M., Essabbar, A., Hakmi, M., Bouricha, E. M., Chemao-Elfihri, M. W., Kartti, S., Boumajdi, N., Bendani, H., Laamarti, R., Ghrifi, F., Allam, L., Aanniz, T., Ouadghiri, M., El Hafidi, N., El Jaoudi, R., Benrahma, H., Attar, J. E., Mentag, R., … Ibrahimi, A. (2020). Genomic Diversity and Hotspot Mutations in 30,983 SARS-CoV-2 Genomes: Moving Toward a Universal Vaccine for the “Confined Virus”? Pathogens, 9(10), 829. https://doi.org/10.3390/pathogens9100829 Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C., & Garry, R. F. (2020). The proximal origin of SARS-CoV-2. Nature Medicine, 26(4), 450-452. https://doi.org/10.1038/s41591-020-0820-9 Andrews, R. J., O’Leary, C. A., Tompkins, V. S., Peterson, J. M., Haniff, H. S., Williams, C., Disney, M. D., & Moss, W. N. (2021). A map of the SARS-CoV-2 RNA structurome. NAR Genomics and Bioinformatics, 3(2), lqab043. https://doi.org/10.1093/nargab/lqab043 Ashour, H. M., Elkhatib, W. F., Rahman, M. M., & Elshabrawy, H. A. (2020). Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks. Pathogens, 9(3), 186. https://doi.org/10.3390/pathogens9030186 Åsjö, B., & Kruse, H. (2006). Zoonoses in the Emergence of Human Viral Diseases. Perspectives in Medical Virology, 16, 15-41. https://doi.org/10.1016/S0168-7069(06)16003-6 Aydemir, M. N., Aydemir, H. B., Korkmaz, E. M., Budak, M., Cekin, N., & Pinarbasi, E. (2021). Computationally predicted SARS-COV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways. Gene Reports, 22, 101012. https://doi.org/10.1016/j.genrep.2020.101012 Balmeh, N., Mahmoudi, S., Mohammadi, N., & Karabedianhajiabadi, A. (2020). Predicted therapeutic targets for COVID-19 disease by inhibiting SARS-CoV-2 and its related receptors. Informatics in Medicine Unlocked, 20, 100407. https://doi.org/10.1016/j.imu.2020.100407 Banaganapalli, B., Al-Rayes, N., Awan, Z. A., Alsulaimany, F. A., Alamri, A. S., Elango, R., Malik, M. Z., & Shaik, N. A. (2021). Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection. Computers in Biology and Medicine, 135, 104570. https://doi.org/10.1016/j.compbiomed.2021.104570 Barreda-Manso, M. A., Nieto-Díaz, M., Soto, A., Muñoz-Galdeano, T., Reigada, D., & Maza, R. M. (2021). In Silico and In Vitro Analyses Validate Human MicroRNAs Targeting the SARS-CoV-2 3′-UTR. International Journal of Molecular Sciences, 22(11), 6094. https://doi.org/10.3390/ijms22116094 Battaglia, R., Alonzo, R., Pennisi, C., Caponnetto, A., Ferrara, C., Stella, M., Barbagallo, C., Barbagallo, D., Ragusa, M., Purrello, M., & Di Pietro, C. (2021). MicroRNA-Mediated Regulation of the Virus Cycle and Pathogenesis in the SARS-CoV-2 Disease. International Journal of Molecular Sciences, 22(24), 13192. https://doi.org/10.3390/ijms222413192 Bernard, M. A., Zhao, H., Yue, S. C., Anandaiah, A., Koziel, H., & Tachado, S. D. (2014). Novel HIV-1 MiRNAs Stimulate TNFα Release in Human Macrophages via TLR8 Signaling Pathway. PLOS ONE, 9(9), e106006. https://doi.org/10.1371/journal.pone.0106006 Bernhardt, H. S. (2012). The RNA world hypothesis: The worst theory of the early evolution of life (except for all the others)(a). Biology Direct, 7, 23. https://doi.org/10.1186/1745-6150-7-23 Bernier, A., & Sagan, S. M. (2018). The Diverse Roles of microRNAs at the Host–Virus Interface. Viruses, 10(8), 440. https://doi.org/10.3390/v10080440 Betel, D., Koppal, A., Agius, P., Sander, C., & Leslie, C. (2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology, 11(8), R90. https://doi.org/10.1186/gb-2010-11-8-r90 Boni, M. F., Lemey, P., Jiang, X., Lam, T. T.-Y., Perry, B. W., Castoe, T. A., Rambaut, A., & Robertson, D. L. (2020). Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nature Microbiology, 5(11), 1408-1417. https://doi.org/10.1038/s41564-020-0771-4 Brister, J. R., Ako-adjei, D., Bao, Y., & Blinkova, O. (2015). NCBI Viral Genomes Resource. Nucleic Acids Research, 43(Database issue), D571-D577. https://doi.org/10.1093/nar/gku1207 Callahan, V., Hawks, S., Crawford, M. A., Lehman, C. W., Morrison, H. A., Ivester, H. M., Akhrymuk, I., Boghdeh, N., Flor, R., Finkielstein, C. V., Allen, I. C., Weger-Lucarelli, J., Duggal, N., Hughes, M. A., & Kehn-Hall, K. (2021). The Pro-Inflammatory Chemokines CXCL9, CXCL10 and CXCL11 Are Upregulated Following SARS-CoV-2 Infection in an AKT-Dependent Manner. Viruses, 13(6), 1062. https://doi.org/10.3390/v13061062 Canakoglu, A., Pinoli, P., Bernasconi, A., Alfonsi, T., Melidis, D. P., & Ceri, S. (2021). ViruSurf: An integrated database to investigate viral sequences. Nucleic Acids Research, 49(D1), D817-D824. https://doi.org/10.1093/nar/gkaa846 Cao, C., Cai, Z., Xiao, X., Rao, J., Chen, J., Hu, N., Yang, M., Xing, X., Wang, Y., Li, M., Zhou, B., Wang, X., Wang, J., & Xue, Y. (2021). The architecture of the SARS-CoV-2 RNA genome inside virion. Nature Communications, 12(1), 3917. https://doi.org/10.1038/s41467-021-22785-x Carrasco-Hernandez, R., Jácome, R., López Vidal, Y., & Ponce de León, S. (2017). Are RNA Viruses Candidate Agents for the Next Global Pandemic? A Review. ILAR Journal, 58(3), 343-358. https://doi.org/10.1093/ilar/ilx026 Ceraolo, C., & Giorgi, F. M. (2020). Genomic variance of the 2019-nCoV coronavirus. Journal of Medical Virology, 92(5), 522-528. https://doi.org/10.1002/jmv.25700 Chan, Agnes. P., Choi, Y., & Schork, N. J. (2020). CONSERVED GENOMIC TERMINALS OF SARS-COV-2 AS CO-EVOLVING FUNCTIONAL ELEMENTS AND POTENTIAL THERAPEUTIC TARGETS. bioRxiv. https://doi.org/10.1101/2020.07.06.190207 Chan, J. F.-W., Kok, K.-H., Zhu, Z., Chu, H., To, K. K.-W., Yuan, S., & Yuen, K.-Y. (2020). Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerging Microbes & Infections, 9(1), 221-236. https://doi.org/10.1080/22221751.2020.1719902 Chen, C.-Y., Ping, Y.-H., Lee, H.-C., Chen, K.-H., Lee, Y.-M., Chan, Y.-J., Lien, T.-C., Jap, T.-S., Lin, C.-H., Kao, L.-S., & Chen, Y.-M. A. (2007). Open Reading Frame 8a of the Human Severe Acute Respiratory Syndrome Coronavirus Not Only Promotes Viral Replication but Also Induces Apoptosis. The Journal of Infectious Diseases, 196(3), 405-415. https://doi.org/10.1086/519166 Chen, L., Song, W., Davis, I. C., Shrestha, K., Schwiebert, E., Sullender, W. M., & Matalon, S. (2009). Inhibition of Na+ transport in lung epithelial cells by respiratory syncytial virus infection. American Journal of Respiratory Cell and Molecular Biology, 40(5), 588-600. https://doi.org/10.1165/rcmb.2008-0034OC Chen, Y., & Wang, X. (2020). miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Research, 48(D1), D127-D131. https://doi.org/10.1093/nar/gkz757 Chen, Y., Ye, W., Zhang, Y., & Xu, Y. (2015). High speed BLASTN: An accelerated MegaBLAST search tool. Nucleic Acids Research, 43(16), 7762-7768. https://doi.org/10.1093/nar/gkv784 Chen, Z., Liang, H., Chen, X., Ke, Y., Zhou, Z., Yang, M., Zen, K., Yang, R., Liu, C., & Zhang, C.-Y. (2016). An Ebola virus-encoded microRNA-like fragment serves as a biomarker for early diagnosis of Ebola virus disease. Cell Research, 26(3), 380-383. https://doi.org/10.1038/cr.2016.21 Chow, J. T.-S., & Salmena, L. (2020). Prediction and Analysis of SARS-CoV-2-Targeting MicroRNA in Human Lung Epithelium. Genes, 11(9). https://doi.org/10.3390/genes11091002 Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., Land, S. J., Lu, X., & Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly, 6(2), 80-92. https://doi.org/10.4161/fly.19695 Conzade, R., Grant, R., Malik, M. R., Elkholy, A., Elhakim, M., Samhouri, D., Ben Embarek, P. K., & Van Kerkhove, M. D. (2018). Reported Direct and Indirect Contact with Dromedary Camels among Laboratory-Confirmed MERS-CoV Cases. Viruses, 10(8), 425. https://doi.org/10.3390/v10080425 Corman, V. M., Ithete, N. L., Richards, L. R., Schoeman, M. C., Preiser, W., Drosten, C., & Drexler, J. F. (2014). Rooting the Phylogenetic Tree of Middle East Respiratory Syndrome Coronavirus by Characterization of a Conspecific Virus from an African Bat. Journal of Virology, 88(19), 11297-11303. https://doi.org/10.1128/JVI.01498-14 Cui, J., Li, F., & Shi, Z.-L. (2019). Origin and evolution of pathogenic coronaviruses. Nature Reviews Microbiology, 17(3), 181-192. https://doi.org/10.1038/s41579-018-0118-9 Cullen, B. R. (2004). Transcription and Processing of Human microRNA Precursors. Molecular Cell, 16(6), 861-865. https://doi.org/10.1016/j.molcel.2004.12.002 Cullen, B. R. (2010). Five questions about viruses and microRNAs. PLoS Pathogens, 6(2), e1000787. https://doi.org/10.1371/journal.ppat.1000787 Cyranoski, D. (2020). Did pangolins spread the China coronavirus to people? https://www.nature.com/articles/d41586-020-00364-2 da Silva, P. G., Mesquita, J. R., de São José Nascimento, M., & Ferreira, V. A. M. (2021). Viral, host and environmental factors that favor anthropozoonotic spillover of coronaviruses: An opinionated review, focusing on SARS-CoV, MERS-CoV and SARS-CoV-2. The Science of the Total Environment, 750, 141483. https://doi.org/10.1016/j.scitotenv.2020.141483 Danecek, P., & McCarthy, S. A. (2017). BCFtools/csq: Haplotype-aware variant consequences. Bioinformatics (Oxford, England), 33(13), 2037-2039. https://doi.org/10.1093/bioinformatics/btx100 Daniloski, Z., Jordan, T. X., Ilmain, J. K., Guo, X., Bhabha, G., tenOever, B. R., & Sanjana, N. E. (2020). The Spike D614G mutation increases SARS-CoV-2 infection of multiple human cell types. BioRxiv, 2020.06.14.151357. https://doi.org/10.1101/2020.06.14.151357 Darriba, D., Taboada, G. L., Doallo, R., & Posada, D. (2012). jModelTest 2: More models, new heuristics and high-performance computing. Nature methods, 9(8), 772. https://doi.org/10.1038/nmeth.2109 De, M. N., Walker, C., Borges, R., Weilguny, L., Slodkowicz, G., & Goldman, N. (2020). Issues with SARS-CoV-2 sequencing dat. https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473 Denison, M. R., Graham, R. L., Donaldson, E. F., Eckerle, L. D., & Baric, R. S. (2011). Coronaviruses. RNA Biology, 8(2), 270-279. https://doi.org/10.4161/rna.8.2.15013 Dickey, L. L., Worne, C. L., Glover, J. L., Lane, T. E., & O’Connell, R. M. (2016). MicroRNA-155 enhances T cell trafficking and antiviral effector function in a model of coronavirus-induced neurologic disease. Journal of Neuroinflammation, 13(1), 240. https://doi.org/10.1186/s12974-016-0699-z Domingo, E., Martínez-Salas, E., Sobrino, F., de la Torre, J. C., Portela, A., Ortín, J., López-Galindez, C., Pérez-Breña, P., Villanueva, N., Nájera, R., VandePol, S., Steinhauer, D., DePolo, N., & Holland, J. (1985). The quasispecies (extremely heterogeneous) nature of viral RNA genome populations: Biological relevance — a review. Gene, 40(1), 1-8. https://doi.org/10.1016/0378-1119(85)90017-4 Duffy, S., Shackelton, L. A., & Holmes, E. C. (2008). Rates of evolutionary change in viruses: Patterns and determinants. Nature Reviews Genetics, 9(4), 267-276. https://doi.org/10.1038/nrg2323 Duy, J., Honko, A. N., Altamura, L. A., Bixler, S. L., Wollen-Roberts, S., Wauquier, N., O’Hearn, A., Mucker, E. M., Johnson, J. C., Shamblin, J. D., Zelko, J., Botto, M. A., Bangura, J., Coomber, M., Pitt, M. L., Gonzalez, J.-P., Schoepp, R. J., Goff, A. J., & Minogue, T. D. (2018). Virus-encoded miRNAs in Ebola virus disease. Scientific Reports, 8(1), 6480. https://doi.org/10.1038/s41598-018-23916-z Elizondo, V., Harkins, G. W., Mabvakure, B., Smidt, S., Zappile, P., Marier, C., Maurano, M., Perez, V., Mazza, N., Beloso, C., Ifran, S., Fernandez, M., Santini, A., Perez, V., Estevez, V., Nin, M., Manrique, G., Perez, L., Ross, F., … Duerr, R. (2020). SARS-CoV-2 genomic characterization and clinical manifestation of the COVID-19 outbreak in Uruguay. medRxiv, 2020.10.08.20208546. https://doi.org/10.1101/2020.10.08.20208546 El-Sayed, A., & Kamel, M. (2021). Coronaviruses in humans and animals: The role of bats in viral evolution. Environmental Science and Pollution Research, 28(16), 19589-19600. https://doi.org/10.1007/s11356-021-12553-1 Fan, Y., Zhao, K., Shi, Z.-L., & Zhou, P. (2019). Bat Coronaviruses in China. Viruses, 11(3), 210. https://doi.org/10.3390/v11030210 Farkas, C., Mella, A., & Haigh, J. J. (2020). Large-scale population analysis of SARS-CoV-2 whole genome sequences reveals host-mediated viral evolution with emergence of mutations in the viral Spike protein associated with elevated mortality rates (p. 2020.10.23.20218511). https://doi.org/10.1101/2020.10.23.20218511 Farrag, M. A., Amer, H. M., Bhat, R., & Almajhdi, F. N. (2021). Sequence and phylogentic analysis of MERS-CoV in Saudi Arabia, 2012–2019. Virology Journal, 18(1), 90. https://doi.org/10.1186/s12985-021-01563-7 Farrag, M. A., Amer, H. M., Bhat, R., Hamed, M. E., Aziz, I. M., Mubarak, A., Dawoud, T. M., Almalki, S. G., Alghofaili, F., Alnemare, A. K., Al-Baradi, R. S., Alosaimi, B., & Alturaiki, W. (2021). SARS-CoV-2: An Overview of Virus Genetics, Transmission, and Immunopathogenesis. International Journal of Environmental Research and Public Health, 18(12), 6312. https://doi.org/10.3390/ijerph18126312 Fehr, A. R., & Perlman, S. (2015). Coronaviruses: An Overview of Their Replication and Pathogenesis. En H. J. Maier, E. Bickerton, & P. Britton (Eds.), Coronaviruses: Methods and Protocols (pp. 1-23). Springer. https://doi.org/10.1007/978-1-4939-2438-7_1 Ferron, F., Subissi, L., Morais, A. T. S. D., Le, N. T. T., Sevajol, M., Gluais, L., Decroly, E., Vonrhein, C., Bricogne, G., Canard, B., & Imbert, I. (2018). Structural and molecular basis of mismatch correction and ribavirin excision from coronavirus RNA. Proceedings of the National Academy of Sciences, 115(2), E162-E171. https://doi.org/10.1073/pnas.1718806115 Forni, D., Cagliani, R., Clerici, M., & Sironi, M. (2017). Molecular Evolution of Human Coronavirus Genomes. Trends in Microbiology, 25(1), 35-48. https://doi.org/10.1016/j.tim.2016.09.001 Frutos, R., Serra-Cobo, J., Pinault, L., Lopez Roig, M., & Devaux, C. A. (2021). Emergence of Bat-Related Betacoronaviruses: Hazard and Risks. Frontiers in Microbiology, 12. https://doi.org/10.3389/fmicb.2021.591535 Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012). CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 28(23), 3150-3152. https://doi.org/10.1093/bioinformatics/bts565 Fulzele, S., Sahay, B., Yusufu, I., Lee, T. J., Sharma, A., Kolhe, R., & Isales, C. M. (2020). COVID-19 Virulence in Aged Patients Might Be Impacted by the Host Cellular MicroRNAs Abundance/Profile. Aging and Disease, 11(3), 509-522. https://doi.org/10.14336/AD.2020.0428 Gasparello, J., Finotti, A., & Gambari, R. (2021). Tackling the COVID-19 “cytokine storm” with microRNA mimics directly targeting the 3’UTR of pro-inflammatory mRNAs. Medical Hypotheses, 146, 110415. https://doi.org/10.1016/j.mehy.2020.110415 Geoghegan, J. L., Duchêne, S., & Holmes, E. C. (2017). Comparative analysis estimates the relative frequencies of co-divergence and cross-species transmission within viral families. PLoS Pathogens, 13(2), e1006215. https://doi.org/10.1371/journal.ppat.1006215 Gojobori, T., Moriyama, E. N., & Kimura, M. (1990). Molecular clock of viral evolution, and the neutral theory. Proceedings of the National Academy of Sciences of the United States of America, 87(24), 10015-10018. https://doi.org/10.1073/pnas.87.24.10015 Graepel, K. W., Lu, X., Case, J. B., Sexton, N. R., Smith, E. C., & Denison, M. R. (2017). Proofreading-deficient coronaviruses adapt over long-term passage for increased fidelity and fitness without reversion of exoribonuclease-inactivating mutations. BioRxiv, 175562. https://doi.org/10.1101/175562 Gralinski, L. E., & Menachery, V. D. (2020). Return of the Coronavirus: 2019-nCoV. Viruses, 12(2), 135. https://doi.org/10.3390/v12020135 Gruber, A. R., Findeiß, S., Washietl, S., Hofacker, I. L., & Stadler, P. F. (2010). RNAz 2.0: Improved noncoding RNA detection. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 69-79. Guan, Y., Zheng, B. J., He, Y. Q., Liu, X. L., Zhuang, Z. X., Cheung, C. L., Luo, S. W., Li, P. H., Zhang, L. J., Guan, Y. J., Butt, K. M., Wong, K. L., Chan, K. W., Lim, W., Shortridge, K. F., Yuen, K. Y., Peiris, J. S. M., & Poon, L. L. M. (2003). Isolation and Characterization of Viruses Related to the SARS Coronavirus from Animals in Southern China. Science, 302(5643), 276-278. https://doi.org/10.1126/science.1087139 Guzzi, P. H., Mercatelli, D., Ceraolo, C., & Giorgi, F. M. (2020). Master Regulator Analysis of the SARS-CoV-2/Human Interactome. Journal of Clinical Medicine, 9(4), 982. https://doi.org/10.3390/jcm9040982 Hadfield, J., Megill, C., Bell, S. M., Huddleston, J., Potter, B., Callender, C., Sagulenko, P., Bedford, T., & Neher, R. A. (2018). Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics, 34(23), 4121-4123. https://doi.org/10.1093/bioinformatics/bty407 Han, G.-Z. (2020). Pangolins Harbor SARS-CoV-2-Related Coronaviruses. Trends in Microbiology, 28(7), 515-517. https://doi.org/10.1016/j.tim.2020.04.001 Hasan, M. M., Akter, R., Ullah, M. S., Abedin, M. J., Ullah, G. M. A., & Hossain, M. Z. (2014). A Computational Approach for Predicting Role of Human MicroRNAs in MERS-CoV Genome. Advances in Bioinformatics, 2014, e967946. https://doi.org/10.1155/2014/967946 Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic Acids Research, 31(13), 3429-3431. Hon, C.-C., Lam, T.-Y., Shi, Z.-L., Drummond, A. J., Yip, C.-W., Zeng, F., Lam, P.-Y., & Leung, F. C.-C. (2008). Evidence of the Recombinant Origin of a Bat Severe Acute Respiratory Syndrome (SARS)-Like Coronavirus and Its Implications on the Direct Ancestor of SARS Coronavirus. Journal of Virology, 82(4), 1819-1826. https://doi.org/10.1128/JVI.01926-07 Hu, J., Peng, P., Cao, X., Wu, K., Chen, J., Wang, K., Tang, N., & Huang, A. (2022). Increased immune escape of the new SARS-CoV-2 variant of concern Omicron. Cellular & Molecular Immunology, 1-3. https://doi.org/10.1038/s41423-021-00836-z Huang, H.-Y., Lin, Y.-C.-D., Li, J., Huang, K.-Y., Shrestha, S., Hong, H.-C., Tang, Y., Chen, Y.-G., Jin, C.-N., Yu, Y., Xu, J.-T., Li, Y.-M., Cai, X.-X., Zhou, Z.-Y., Chen, X.-H., Pei, Y.-Y., Hu, L., Su, J.-J., Cui, S.-D., … Huang, H.-D. (2020). miRTarBase 2020: Updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Research, 48(D1), D148-D154. https://doi.org/10.1093/nar/gkz896 Hussain, M., & Asgari, S. (2014). MicroRNA-like viral small RNA from Dengue virus 2 autoregulates its replication in mosquito cells. Proceedings of the National Academy of Sciences, 111(7), 2746-2751. https://doi.org/10.1073/pnas.1320123111 Hussain, S., Pan, J., Chen, Y., Yang, Y., Xu, J., Peng, Y., Wu, Y., Li, Z., Zhu, Y., Tien, P., & Guo, D. (2005). Identification of Novel Subgenomic RNAs and Noncanonical Transcription Initiation Signals of Severe Acute Respiratory Syndrome Coronavirus. Journal of Virology, 79(9), 5288-5295. https://doi.org/10.1128/JVI.79.9.5288-5295.2005 Huston, N. C., Wan, H., Strine, M. S., de Cesaris Araujo Tavares, R., Wilen, C. B., & Pyle, A. M. (2021). Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms. Molecular Cell, 81(3), 584-598.e5. https://doi.org/10.1016/j.molcel.2020.12.041 Ito, K., & Murphy, D. (2013). Application of ggplot2 to Pharmacometric Graphics. CPT: Pharmacometrics & Systems Pharmacology, 2, e79. https://doi.org/10.1038/psp.2013.56 John, G., Sahajpal, N. S., Mondal, A. K., Ananth, S., Williams, C., Chaubey, A., Rojiani, A. M., & Kolhe, R. (2021). Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology. Current Issues in Molecular Biology, 43(2), 845-867. https://doi.org/10.3390/cimb43020061 Kalvari, I., Nawrocki, E. P., Ontiveros-Palacios, N., Argasinska, J., Lamkiewicz, K., Marz, M., Griffiths-Jones, S., Toffano-Nioche, C., Gautheret, D., Weinberg, Z., Rivas, E., Eddy, S. R., Finn, R. D., Bateman, A., & Petrov, A. I. (2021). Rfam 14: Expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research, 49(D1), D192-D200. https://doi.org/10.1093/nar/gkaa1047 Kelly, J. A., Woodside, M. T., & Dinman, J. D. (2021). Programmed −1 Ribosomal Frameshifting in coronaviruses: A therapeutic target. Virology, 554, 75-82. https://doi.org/10.1016/j.virol.2020.12.010 Keng, C.-T., Choi, Y.-W., Welkers, M. R. A., Chan, D. Z. L., Shen, S., Gee Lim, S., Hong, W., & Tan, Y.-J. (2006). The human severe acute respiratory syndrome coronavirus (SARS-CoV) 8b protein is distinct from its counterpart in animal SARS-CoV and down-regulates the expression of the envelope protein in infected cells. Virology, 354(1), 132-142. https://doi.org/10.1016/j.virol.2006.06.026 Kim, D., Lee, J.-Y., Yang, J.-S., Kim, J. W., Kim, V. N., & Chang, H. (2020). The Architecture of SARS-CoV-2 Transcriptome. Cell, 181(4), 914-921.e10. https://doi.org/10.1016/j.cell.2020.04.011 Kim, W. R., Park, E. G., Kang, K.-W., Lee, S.-M., Kim, B., & Kim, H.-S. (2020). Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2. Molecules and Cells, 43(11), 953-963. https://doi.org/10.14348/molcells.2020.0177 Kopecky-Bromberg, S. A., Martínez-Sobrido, L., Frieman, M., Baric, R. A., & Palese, P. (2007). Severe Acute Respiratory Syndrome Coronavirus Open Reading Frame (ORF) 3b, ORF 6, and Nucleocapsid Proteins Function as Interferon Antagonists. Journal of Virology, 81(2), 548-557. https://doi.org/10.1128/JVI.01782-06 Korber, B., Fischer, W. M., Gnanakaran, S., Yoon, H., Theiler, J., Abfalterer, W., Foley, B., Giorgi, E. E., Bhattacharya, T., Parker, M. D., Partridge, D. G., Evans, C. M., Silva, T. de, Group, on behalf of the S. C.-19 G., LaBranche, C. C., & Montefiori, D. C. (2020). Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2. BioRxiv, 2020.04.29.069054. https://doi.org/10.1101/2020.04.29.069054 Kosakovsky Pond, S. L., & Frost, S. D. W. (2005). Not so different after all: A comparison of methods for detecting amino acid sites under selection. Molecular Biology and Evolution, 22(5), 1208-1222. https://doi.org/10.1093/molbev/msi105 Kozomara, A., Birgaoanu, M., & Griffiths-Jones, S. (2019). miRBase: From microRNA sequences to function. Nucleic Acids Research, 47(D1), D155-D162. https://doi.org/10.1093/nar/gky1141 Krol, J., Sobczak, K., Wilczynska, U., Drath, M., Jasinska, A., Kaczynska, D., & Krzyzosiak, W. J. (2004). Structural Features of MicroRNA (miRNA) Precursors and Their Relevance to miRNA Biogenesis and Small Interfering RNA/Short Hairpin RNA Design *. Journal of Biological Chemistry, 279(40), 42230-42239. https://doi.org/10.1074/jbc.M404931200 Krüger, J., & Rehmsmeier, M. (2006). RNAhybrid: MicroRNA target prediction easy, fast and flexible. Nucleic Acids Research, 34(Web Server issue), W451-W454. https://doi.org/10.1093/nar/gkl243 Lam, T. T.-Y., Jia, N., Zhang, Y.-W., Shum, M. H.-H., Jiang, J.-F., Zhu, H.-C., Tong, Y.-G., Shi, Y.-X., Ni, X.-B., Liao, Y.-S., Li, W.-J., Jiang, B.-G., Wei, W., Yuan, T.-T., Zheng, K., Cui, X.-M., Li, J., Pei, G.-Q., Qiang, X., … Cao, W.-C. (2020). Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature, 583(7815), 282-285. https://doi.org/10.1038/s41586-020-2169-0 Larsson, A. (2014). AliView: A fast and lightweight alignment viewer and editor for large datasets. Bioinformatics, 30(22), 3276-3278. https://doi.org/10.1093/bioinformatics/btu531 Latinne, A., Hu, B., Olival, K. J., Zhu, G., Zhang, L., Li, H., Chmura, A. A., Field, H. E., Zambrana-Torrelio, C., Epstein, J. H., Li, B., Zhang, W., Wang, L.-F., Shi, Z.-L., & Daszak, P. (2020). Origin and cross-species transmission of bat coronaviruses in China. Nature Communications, 11(1), 4235. https://doi.org/10.1038/s41467-020-17687-3 Lau, S. K. P., Woo, P. C. Y., Li, K. S. M., Huang, Y., Tsoi, H.-W., Wong, B. H. L., Wong, S. S. Y., Leung, S.-Y., Chan, K.-H., & Yuen, K.-Y. (2005). Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proceedings of the National Academy of Sciences, 102(39), 14040-14045. https://doi.org/10.1073/pnas.0506735102 Li, F. (2013). Receptor recognition and cross-species infections of SARS coronavirus. Antiviral Research, 100(1), 246-254. https://doi.org/10.1016/j.antiviral.2013.08.014 Li, F. (2016). Structure, Function, and Evolution of Coronavirus Spike Proteins. Annual Review of Virology, 3(1), 237-261. https://doi.org/10.1146/annurev-virology-110615-042301 Li, H. (2018). Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics (Oxford, England), 34(18), 3094-3100. https://doi.org/10.1093/bioinformatics/bty191 Li, X., Fu, Z., Liang, H., Wang, Y., Qi, X., Ding, M., Sun, X., Zhou, Z., Huang, Y., Gu, H., Li, L., Chen, X., Li, D., Zhao, Q., Liu, F., Wang, H., Wang, J., Zen, K., & Zhang, C.-Y. (2018). H5N1 influenza virus-specific miRNA-like small RNA increases cytokine production and mouse mortality via targeting poly(rC)-binding protein 2. Cell Research, 28(2), 157-171. https://doi.org/10.1038/cr.2018.3 Li, X., Giorgi, E. E., Marichannegowda, M. H., Foley, B., Xiao, C., Kong, X.-P., Chen, Y., Gnanakaran, S., Korber, B., & Gao, F. (2020). Emergence of SARS-CoV-2 through recombination and strong purifying selection. Science Advances, eabb9153. https://doi.org/10.1126/sciadv.abb9153 Li, X., & Zou, X. (2019). An overview of RNA virus-encoded microRNAs. ExRNA, 1(1), 37. https://doi.org/10.1186/s41544-019-0037-6 Li, Z., Luo, Q., Xu, H., Zheng, M., Abdalla, B. A., Feng, M., Cai, B., Zhang, X., Nie, Q., & Zhang, X. (2017). MiR-34b-5p Suppresses Melanoma Differentiation-Associated Gene 5 (MDA5) Signaling Pathway to Promote Avian Leukosis Virus Subgroup J (ALV-J)-Infected Cells Proliferaction and ALV-J Replication. Frontiers in Cellular and Infection Microbiology, 7, 17. https://doi.org/10.3389/fcimb.2017.00017 Liu, Q., Du, J., Yu, X., Xu, J., Huang, F., Li, X., Zhang, C., Li, X., Chang, J., Shang, D., Zhao, Y., Tian, M., Lu, H., Xu, J., Li, C., Zhu, H., Jin, N., & Jiang, C. (2017). MiRNA-200c-3p is crucial in acute respiratory distress syndrome. Cell Discovery, 3(1), 1-17. https://doi.org/10.1038/celldisc.2017.21 Liu, Y., Sun, J., Zhang, H., Wang, M., Gao, G. F., & Li, X. (2016). Ebola virus encodes a miR-155 analog to regulate importin-α5 expression. Cellular and Molecular Life Sciences, 73(19), 3733-3744. https://doi.org/10.1007/s00018-016-2215-0 Liu, Y., Wimmer, E., & Paul, A. V. (2009). Cis-acting RNA elements in human and animal plus-strand RNA viruses. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, 1789(9), 495-517. https://doi.org/10.1016/j.bbagrm.2009.09.007 Lokugamage, K. G., Hage, A., de Vries, M., Valero-Jimenez, A. M., Schindewolf, C., Dittmann, M., Rajsbaum, R., & Menachery, V. D. (2020). Type I Interferon Susceptibility Distinguishes SARS-CoV-2 from SARS-CoV. Journal of Virology, 94(23), e01410-20. https://doi.org/10.1128/JVI.01410-20 Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 Lu, D., Chatterjee, S., Xiao, K., Riedel, I., Wang, Y., Foo, R., Bär, C., & Thum, T. (2020). MicroRNAs targeting the SARS-CoV-2 entry receptor ACE2 in cardiomyocytes. Journal of Molecular and Cellular Cardiology, 148, 46-49. https://doi.org/10.1016/j.yjmcc.2020.08.017 Lu, G., Wang, Q., & Gao, G. F. (2015). Bat-to-human: Spike features determining «host jump» of coronaviruses SARS-CoV, MERS-CoV, and beyond. Trends in Microbiology, 23(8), 468-478. https://doi.org/10.1016/j.tim.2015.06.003 Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., Bi, Y., Ma, X., Zhan, F., Wang, L., Hu, T., Zhou, H., Hu, Z., Zhou, W., Zhao, L., … Tan, W. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. The Lancet, 395(10224), 565-574. https://doi.org/10.1016/S0140-6736(20)30251-8 Ma, X., Zhao, X., Zhang, Z., Guo, J., Guan, L., Li, J., Mi, M., Huang, Y., & Tong, D. (2018). Differentially expressed non-coding RNAs induced by transmissible gastroenteritis virus potentially regulate inflammation and NF-κB pathway in porcine intestinal epithelial cell line. BMC Genomics, 19(1), 747. https://doi.org/10.1186/s12864-018-5128-5 Madhugiri, R., Fricke, M., Marz, M., & Ziebuhr, J. (2016). Chapter Four—Coronavirus cis-Acting RNA Elements. En J. Ziebuhr (Ed.), Advances in Virus Research (Vol. 96, pp. 127-163). Academic Press. https://doi.org/10.1016/bs.aivir.2016.08.007 Mallick, B., Ghosh, Z., & Chakrabarti, J. (2009). MicroRNome Analysis Unravels the Molecular Basis of SARS Infection in Bronchoalveolar Stem Cells. PLOS ONE, 4(11), e7837. https://doi.org/10.1371/journal.pone.0007837 Masters, P. S. (2006). The Molecular Biology of Coronaviruses. En Advances in Virus Research (Vol. 66, pp. 193-292). Academic Press. https://doi.org/10.1016/S0065-3527(06)66005-3 Matarese, A., Gambardella, J., Sardu, C., & Santulli, G. (2020). miR-98 Regulates TMPRSS2 Expression in Human Endothelial Cells: Key Implications for COVID-19. Biomedicines, 8(11), 462. https://doi.org/10.3390/biomedicines8110462 Meckiff, B. J., Ramírez-Suástegui, C., Fajardo, V., Chee, S. J., Kusnadi, A., Simon, H., Grifoni, A., Pelosi, E., Weiskopf, D., Sette, A., Ay, F., Seumois, G., Ottensmeier, C. H., & Vijayanand, P. (2020). Single-cell transcriptomic analysis of SARS-CoV-2 reactive CD4+ T cells. Social Science Research Network, 3641939. https://doi.org/10.2139/ssrn.3641939 Memish, Z. A., Cotten, M., Meyer, B., Watson, S. J., Alsahafi, A. J., Al Rabeeah, A. A., Corman, V. M., Sieberg, A., Makhdoom, H. Q., Assiri, A., Al Masri, M., Aldabbagh, S., Bosch, B.-J., Beer, M., Müller, M. A., Kellam, P., & Drosten, C. (2014). Human infection with MERS coronavirus after exposure to infected camels, Saudi Arabia, 2013. Emerging Infectious Diseases, 20(6), 1012-1015. https://doi.org/10.3201/eid2006.140402 Menachery, V. D., Graham, R. L., & Baric, R. S. (2017). Jumping species—A mechanism for coronavirus persistence and survival. Current Opinion in Virology, 23, 1-7. https://doi.org/10.1016/j.coviro.2017.01.002 Merino, G. A., Raad, J., Bugnon, L. A., Yones, C., Kamenetzky, L., Claus, J., Ariel, F., Milone, D. H., & Stegmayer, G. (2020). Novel SARS-CoV-2 encoded small RNAs in the passage to humans. Bioinformatics, 36(24), 5571-5581. https://doi.org/10.1093/bioinformatics/btaa1002 Mi, H., Muruganujan, A., Ebert, D., Huang, X., & Thomas, P. D. (2019). PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Research, 47(D1), D419-D426. https://doi.org/10.1093/nar/gky1038 Michel, C. J., Mayer, C., Poch, O., & Thompson, J. D. (2020). Characterization of accessory genes in coronavirus genomes. Virology Journal, 17(1), 131. https://doi.org/10.1186/s12985-020-01402-1 Mignone, F., Grillo, G., Licciulli, F., Iacono, M., Liuni, S., Kersey, P. J., Duarte, J., Saccone, C., & Pesole, G. (2005). UTRdb and UTRsite: A collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Research, 33(Database issue), D141-146. https://doi.org/10.1093/nar/gki021 Miller, M. A., Pfeiffer, W., & Schwartz, T. (2010). Creating the CIPRES Science Gateway for inference of large phylogenetic trees. 2010 Gateway Computing Environments Workshop (GCE), 1-8. https://doi.org/10.1109/GCE.2010.5676129 Mishra, R., Kumar, A., Ingle, H., & Kumar, H. (2020). The Interplay Between Viral-Derived miRNAs and Host Immunity During Infection. Frontiers in Immunology, 10, 3079. https://doi.org/10.3389/fimmu.2019.03079 Morales, L., Oliveros, J. C., Fernandez-Delgado, R., tenOever, B. R., Enjuanes, L., & Sola, I. (2017). SARS-CoV-Encoded Small RNAs Contribute to Infection-Associated Lung Pathology. Cell Host & Microbe, 21(3), 344-355. https://doi.org/10.1016/j.chom.2017.01.015 Morel, B., Barbera, P., Czech, L., Bettisworth, B., Hübner, L., Lutteropp, S., Serdari, D., Kostaki, E.-G., Mamais, I., Kozlov, A. M., Pavlidis, P., Paraskevis, D., & Stamatakis, A. (2021). Phylogenetic Analysis of SARS-CoV-2 Data Is Difficult. Molecular Biology and Evolution, 38(5), 1777-1791. https://doi.org/10.1093/molbev/msaa314 Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky Pond, S. L., & Scheffler, K. (2013). FUBAR: A fast, unconstrained bayesian approximation for inferring selection. Molecular Biology and Evolution, 30(5), 1196-1205. https://doi.org/10.1093/molbev/mst030 Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., & Kosakovsky Pond, S. L. (2012). Detecting individual sites subject to episodic diversifying selection. PLoS Genetics, 8(7), e1002764. https://doi.org/10.1371/journal.pgen.1002764 Nelson, J. W., & Breaker, R. R. (2017). The lost language of the RNA World. Science Signaling, 10(483). https://doi.org/10.1126/scisignal.aam8812 Nowick, K., Walter Costa, M. B., Höner zu Siederdissen, C., & Stadler, P. F. (2019). Selection Pressures on RNA Sequences and Structures. Evolutionary Bioinformatics Online, 15. https://doi.org/10.1177/1176934319871919 Omoto, S., Ito, M., Tsutsumi, Y., Ichikawa, Y., Okuyama, H., Brisibe, E. A., Saksena, N. K., & Fujii, Y. R. (2004). HIV-1 nef suppression by virally encoded microRNA. Retrovirology, 1(1), 44. https://doi.org/10.1186/1742-4690-1-44 Omrani, A. S., Al-Tawfiq, J. A., & Memish, Z. A. (2015). Middle East respiratory syndrome coronavirus (MERS-CoV): Animal to human interaction. Pathogens and Global Health, 109(8), 354-362. https://doi.org/10.1080/20477724.2015.1122852 Oostra, M., de Haan, C. A. M., & Rottier, P. J. M. (2007). The 29-Nucleotide Deletion Present in Human but Not in Animal Severe Acute Respiratory Syndrome Coronaviruses Disrupts the Functional Expression of Open Reading Frame 8. Journal of Virology, 81(24), 13876-13888. https://doi.org/10.1128/JVI.01631-07 Pang, K. C., Frith, M. C., & Mattick, J. S. (2006). Rapid evolution of noncoding RNAs: Lack of conservation does not mean lack of function. Trends in Genetics, 22(1), 1-5. https://doi.org/10.1016/j.tig.2005.10.003 Paradis, E., Claude, J., & Strimmer, K. (2004). APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics (Oxford, England), 20(2), 289-290. https://doi.org/10.1093/bioinformatics/btg412 Peng, X., Gralinski, L., Ferris, M. T., Frieman, M. B., Thomas, M. J., Proll, S., Korth, M. J., Tisoncik, J. R., Heise, M., Luo, S., Schroth, G. P., Tumpey, T. M., Li, C., Kawaoka, Y., Baric, R. S., & Katze, M. G. (2011). Integrative deep sequencing of the mouse lung transcriptome reveals differential expression of diverse classes of small RNAs in response to respiratory virus infection. MBio, 2(6). https://doi.org/10.1128/mBio.00198-11 Pham, T. N., Lukhele, S., Hajjar, F., Routy, J.-P., & Cohen, É. A. (2014). HIV Nef and Vpu protect HIV-infected CD4+ T cells from antibody-mediated cell lysis through down-modulation of CD4 and BST2. Retrovirology, 11(1), 15. https://doi.org/10.1186/1742-4690-11-15 Pickett, B. E., Greer, D. S., Zhang, Y., Stewart, L., Zhou, L., Sun, G., Gu, Z., Kumar, S., Zaremba, S., Larsen, C. N., Jen, W., Klem, E. B., & Scheuermann, R. H. (2012). Virus Pathogen Database and Analysis Resource (ViPR): A Comprehensive Bioinformatics Database and Analysis Resource for the Coronavirus Research Community. Viruses, 4(11), 3209-3226. https://doi.org/10.3390/v4113209 Pierce, J. B., Simion, V., Icli, B., Pérez-Cremades, D., Cheng, H. S., & Feinberg, M. W. (2020). Computational Analysis of Targeting SARS-CoV-2, Viral Entry Proteins ACE2 and TMPRSS2, and Interferon Genes by Host MicroRNAs. Genes, 11(11), 1354. https://doi.org/10.3390/genes11111354 Piskol, R., & Stephan, W. (2008). Analyzing the Evolution of RNA Secondary Structures in Vertebrate Introns Using Kimura’s Model of Compensatory Fitness Interactions. Molecular Biology and Evolution, 25(11), 2483-2492. https://doi.org/10.1093/molbev/msn195 Plante, J. A., Liu, Y., Liu, J., Xia, H., Johnson, B. A., Lokugamage, K. G., Zhang, X., Muruato, A. E., Zou, J., Fontes-Garfias, C. R., Mirchandani, D., Scharton, D., Bilello, J. P., Ku, Z., An, Z., Kalveram, B., Freiberg, A. N., Menachery, V. D., Xie, X., … Shi, P.-Y. (2021). Spike mutation D614G alters SARS-CoV-2 fitness. Nature, 592(7852), 116-121. https://doi.org/10.1038/s41586-020-2895-3 Pond, S. L. K., Murrell, B., & Poon, A. F. Y. (2012). Evolution of viral genomes: Interplay between selection, recombination, and other forces. Methods in Molecular Biology (Clifton, N.J.), 856, 239-272. https://doi.org/10.1007/978-1-61779-585-5_10 Prasad, A. N., Ronk, A. J., Widen, S. G., Wood, T. G., Basler, C. F., & Bukreyev, A. (2020). Ebola Virus Produces Discrete Small Noncoding RNAs Independently of the Host MicroRNA Pathway Which Lack RNA Interference Activity in Bat and Human Cells. Journal of Virology, 94(6). https://doi.org/10.1128/JVI.01441-19 Qu, X.-X., Hao, P., Song, X.-J., Jiang, S.-M., Liu, Y.-X., Wang, P.-G., Rao, X., Song, H.-D., Wang, S.-Y., Zuo, Y., Zheng, A.-H., Luo, M., Wang, H.-L., Deng, F., Wang, H.-Z., Hu, Z.-H., Ding, M.-X., Zhao, G.-P., & Deng, H.-K. (2005). Identification of two critical amino acid residues of the severe acute respiratory syndrome coronavirus spike protein for its variation in zoonotic tropism transition via a double substitution strategy. The Journal of Biological Chemistry, 280(33), 29588-29595. https://doi.org/10.1074/jbc.M500662200 Qu, Z., & Adelson, D. (2012). Evolutionary conservation and functional roles of ncRNA. Frontiers in Genetics, 3, 205. https://doi.org/10.3389/fgene.2012.00205 R Core Team. (2021). R: The R Project for Statistical Computing. https://www.r-project.org/ Rabaan, A. A., Al-Ahmed, S. H., Sah, R., Alqumber, M. A., Haque, S., Patel, S. K., Pathak, M., Tiwari, R., Yatoo, Mohd. I., Haq, A. U., Bilal, M., Dhama, K., & Rodriguez-Morales, A. J. (2021). MERS-CoV: Epidemiology, molecular dynamics, therapeutics, and future challenges. Annals of Clinical Microbiology and Antimicrobials, 20(1), 8. https://doi.org/10.1186/s12941-020-00414-7 Rabi, F. A., Al Zoubi, M. S., Kasasbeh, G. A., Salameh, D. M., & Al-Nasser, A. D. (2020). SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far. Pathogens, 9(3), 231. https://doi.org/10.3390/pathogens9030231 Rahaman, M., Komanapalli, J., Mukherjee, M., Byram, P. K., Sahoo, S., & Chakravorty, N. (2021). Decrypting the role of predicted SARS-CoV-2 miRNAs in COVID-19 pathogenesis: A bioinformatics approach. Computers in Biology and Medicine, 136, 104669. https://doi.org/10.1016/j.compbiomed.2021.104669 Raj, V. S., Mou, H., Smits, S. L., Dekkers, D. H. W., Müller, M. A., Dijkman, R., Muth, D., Demmers, J. A. A., Zaki, A., Fouchier, R. A. M., Thiel, V., Drosten, C., Rottier, P. J. M., Osterhaus, A. D. M. E., Bosch, B. J., & Haagmans, B. L. (2013). Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC. Nature, 495(7440), 251-254. https://doi.org/10.1038/nature12005 Rambaut, A., Drummond, A. J., Xie, D., Baele, G., & Suchard, M. A. (2018). Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Systematic Biology, 67(5), 901-904. https://doi.org/10.1093/sysbio/syy032 Rangan, R., Zheludev, I. N., & Das, R. (2020). RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses. BioRxiv, 2020.03.27.012906. https://doi.org/10.1101/2020.03.27.012906 Ricagno, S., Egloff, M.-P., Ulferts, R., Coutard, B., Nurizzo, D., Campanacci, V., Cambillau, C., Ziebuhr, J., & Canard, B. (2006). Crystal structure and mechanistic determinants of SARS coronavirus nonstructural protein 15 define an endoribonuclease family. Proceedings of the National Academy of Sciences of the United States of America, 103(32), 11892-11897. https://doi.org/10.1073/pnas.0601708103 Rivas, E. (2020). RNA structure prediction using positive and negative evolutionary information. PLOS Computational Biology, 16(10), e1008387. https://doi.org/10.1371/journal.pcbi.1008387 Saçar Demirci, M. D., & Adan, A. (2020). Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection. PeerJ, 8. https://doi.org/10.7717/peerj.9369 Saini, S., Saini, A., Thakur, C. J., Kumar, V., Gupta, R. D., & Sharma, J. K. (2020). Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity. Molecular Biology Research Communications, 9(2), 83-91. https://doi.org/10.22099/mbrc.2020.36507.1487 Salamatbakhsh, M., Mobaraki, K., Sadeghimohammadi, S., & Ahmadzadeh, J. (2019). The global burden of premature mortality due to the Middle East respiratory syndrome (MERS) using standard expected years of life lost, 2012 to 2019. BMC Public Health, 19(1), 1523. https://doi.org/10.1186/s12889-019-7899-2 Shang, J., Ye, G., Shi, K., Wan, Y., Luo, C., Aihara, H., Geng, Q., Auerbach, A., & Li, F. (2020). Structural basis of receptor recognition by SARS-CoV-2. Nature, 581(7807), 221-224. https://doi.org/10.1038/s41586-020-2179-y Shi, J., Wen, Z., Zhong, G., Yang, H., Wang, C., Huang, B., Liu, R., He, X., Shuai, L., Sun, Z., Zhao, Y., Liu, P., Liang, L., Cui, P., Wang, J., Zhang, X., Guan, Y., Tan, W., Wu, G., … Bu, Z. (2020). Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS-coronavirus 2. Science (New York, N.Y.), 368(6494), 1016-1020. https://doi.org/10.1126/science.abb7015 Shu, Y., & McCauley, J. (2017). GISAID: Global initiative on sharing all influenza data – from vision to reality. Eurosurveillance, 22(13). https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494 Simmonds, P. (2020). Pervasive RNA secondary structure in the genomes of SARS-CoV-2 and other coronaviruses – an endeavour to understand its biological purpose. BioRxiv, 2020.06.17.155200. https://doi.org/10.1101/2020.06.17.155200 Siniscalchi, C., Di Palo, A., Russo, A., & Potenza, N. (2021). Human MicroRNAs Interacting With SARS-CoV-2 RNA Sequences: Computational Analysis and Experimental Target Validation. Frontiers in Genetics, 12, 760. https://doi.org/10.3389/fgene.2021.678994 Smith, E. C., Blanc, H., Vignuzzi, M., & Denison, M. R. (2013). Coronaviruses Lacking Exoribonuclease Activity Are Susceptible to Lethal Mutagenesis: Evidence for Proofreading and Potential Therapeutics. PLOS Pathogens, 9(8), e1003565. https://doi.org/10.1371/journal.ppat.1003565 Sola, I., Mateos-Gomez, P. A., Almazan, F., Zuñiga, S., & Enjuanes, L. (2011). RNA-RNA and RNA-protein interactions in coronavirus replication and transcription. RNA Biology, 8(2), 237-248. https://doi.org/10.4161/rna.8.2.14991 Stamatakis, A. (2014). RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England), 30(9), 1312-1313. https://doi.org/10.1093/bioinformatics/btu033 Sticht, C., De La Torre, C., Parveen, A., & Gretz, N. (2018). miRWalk: An online resource for prediction of microRNA binding sites. PLoS ONE, 13(10). https://doi.org/10.1371/journal.pone.0206239 Sweileh, W. M. (2017). Global research trends of World Health Organization’s top eight emerging pathogens. Globalization and Health, 13. https://doi.org/10.1186/s12992-017-0233-9 Tang, H., Gao, Y., Li, Z., Miao, Y., Huang, Z., Liu, X., Xie, L., Li, H., Wen, W., Zheng, Y., & Su, W. (2020). The noncoding and coding transcriptional landscape of the peripheral immune response in patients with COVID-19. Clinical and Translational Medicine, 10(6), e200. https://doi.org/10.1002/ctm2.200 Tang, X., Wu, C., Li, X., Song, Y., Yao, X., Wu, X., Duan, Y., Zhang, H., Wang, Y., Qian, Z., Cui, J., & Lu, J. (2020). On the origin and continuing evolution of SARS-CoV-2. National Science Review, 7(6), 1012-1023. https://doi.org/10.1093/nsr/nwaa036 Teng, Y., Wang, Y., Zhang, X., Liu, W., Fan, H., Yao, H., Lin, B., Zhu, P., Yuan, W., Tong, Y., & Cao, W. (2015). Systematic Genome-wide Screening and Prediction of microRNAs in EBOV During the 2014 Ebolavirus Outbreak. Scientific Reports, 5(1), 9912. https://doi.org/10.1038/srep09912 Toyoshima, Y., Nemoto, K., Matsumoto, S., Nakamura, Y., & Kiyotani, K. (2020). SARS-CoV-2 genomic variations associated with mortality rate of COVID-19. Journal of Human Genetics, 65(12), 1075-1082. https://doi.org/10.1038/s10038-020-0808-9 Verma, S., Dwivedy, A., Kumar, N., & Biswal, B. K. (2020). Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets (p. 2020.11.02.365049). https://doi.org/10.1101/2020.11.02.365049 Vlachos, I. S., & Hatzigeorgiou, A. G. (2017). Functional Analysis of miRNAs Using the DIANA Tools Online Suite. Methods in Molecular Biology (Clifton, N.J.), 1517, 25-50. https://doi.org/10.1007/978-1-4939-6563-2_2 Walls, A. C., Park, Y.-J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler, D. (2020). Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell, 181(2), 281-292.e6. https://doi.org/10.1016/j.cell.2020.02.058 Walter Costa, M. B., Höner zu Siederdissen, C., Dunjić, M., Stadler, P. F., & Nowick, K. (2019a). SSS-test: A novel test for detecting positive selection on RNA secondary structure. BMC Bioinformatics, 20(1), 151. https://doi.org/10.1186/s12859-019-2711-y Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223), 470-473. https://doi.org/10.1016/S0140-6736(20)30185-9 Wang, N., Shang, J., Jiang, S., & Du, L. (2020). Subunit Vaccines Against Emerging Pathogenic Human Coronaviruses. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.00298 Wang, Q., Qi, J., Yuan, Y., Xuan, Y., Han, P., Wan, Y., Ji, W., Li, Y., Wu, Y., Wang, J., Iwamoto, A., Woo, P. C. Y., Yuen, K.-Y., Yan, J., Lu, G., & Gao, G. F. (2014). Bat origins of MERS-CoV supported by bat coronavirus HKU4 usage of human receptor CD26. Cell Host & Microbe, 16(3), 328-337. https://doi.org/10.1016/j.chom.2014.08.009 Weaver, S., Shank, S. D., Spielman, S. J., Li, M., Muse, S. V., & Kosakovsky Pond, S. L. (2018). Datamonkey 2.0: A Modern Web Application for Characterizing Selective and Other Evolutionary Processes. Molecular Biology and Evolution, 35(3), 773-777. https://doi.org/10.1093/molbev/msx335 Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 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). https://doi.org/10.1128/mBio.02329-18 Wong, M. C., Cregeen, S. J. J., Ajami, N. J., & Petrosino, J. F. (2020). Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019. BioRxiv, 2020.02.07.939207. https://doi.org/10.1101/2020.02.07.939207 World Health Organization (WHO). (2022, de Enero). WHO Coronavirus Disease (COVID-19). https://covid19.who.int Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., Yuan, M.-L., Zhang, Y.-L., Dai, F.-H., Liu, Y., Wang, Q.-M., Zheng, J.-J., Xu, L., Holmes, E. C., & Zhang, Y.-Z. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579(7798), 265-269. https://doi.org/10.1038/s41586-020-2008-3 Wyler, E., Mösbauer, K., Franke, V., Diag, A., Gottula, L. T., Arsiè, R., Klironomos, F., Koppstein, D., Hönzke, K., Ayoub, S., Buccitelli, C., Hoffmann, K., Richter, A., Legnini, I., Ivanov, A., Mari, T., Del Giudice, S., Papies, J., Praktiknjo, S., … Landthaler, M. (2021). Transcriptomic profiling of SARS-CoV-2 infected human cell lines identifies HSP90 as target for COVID-19 therapy. iScience, 24(3), 102151. https://doi.org/10.1016/j.isci.2021.102151 Xing, Y., Li, X., Gao, X., & Dong, Q. (2020). MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences. Frontiers in Microbiology, 0. https://doi.org/10.3389/fmicb.2020.01502 Xu, K., Zheng, B.-J., Zeng, R., Lu, W., Lin, Y.-P., Xue, L., Li, L., Yang, L.-L., Xu, C., Dai, J., Wang, F., Li, Q., Dong, Q.-X., Yang, R.-F., Wu, J.- R., & Sun, B. (2009). Severe acute respiratory syndrome coronavirus accessory protein 9b is a virion-associated protein. Virology, 388(2), 279-285. https://doi.org/10.1016/j.virol.2009.03.032 Yang, D., & Leibowitz, J. L. (2015). The structure and functions of coronavirus genomic 3′ and 5′ ends. Virus Research, 206, 120-133. https://doi.org/10.1016/j.virusres.2015.02.025 Yang, H.-C., Chen, C., Wang, J.-H., Liao, H.-C., Yang, C.-T., Chen, C.-W., Lin, Y.-C., Kao, C.-H., Lu, M.-Y. J., & Liao, J. C. (2020). Analysis of genomic distributions of SARS-CoV-2 reveals a dominant strain type with strong allelic associations. Proceedings of the National Academy of Sciences, 117(48), 30679-30686. https://doi.org/10.1073/pnas.2007840117 Yang, Y., Liu, C., Du, L., Jiang, S., Shi, Z., Baric, R. S., & Li, F. (2015). Two Mutations Were Critical for Bat-to-Human Transmission of Middle East Respiratory Syndrome Coronavirus. Journal of Virology, 89(17), 9119-9123. https://doi.org/10.1128/JVI.01279-15 Ye, Z.-W., Yuan, S., Yuen, K.-S., Fung, S.-Y., Chan, C.-P., & Jin, D.-Y. (2020). Zoonotic origins of human coronaviruses. International Journal of Biological Sciences, 16(10), 1686-1697. https://doi.org/10.7150/ijbs.45472 Yones, C., Raad, J., Bugnon, L. A., Milone, D. H., & Stegmayer, G. (2021). High precision in microRNA prediction: A novel genome-wide approach with convolutional deep residual networks. Computers in Biology and Medicine, 134, 104448. https://doi.org/10.1016/j.compbiomed.2021.104448 Zhan, S., Wang, Y., & Chen, X. (2020). RNA virus-encoded microRNAs: Biogenesis, functions and perspectives on application. ExRNA, 2(1), 15. https://doi.org/10.1186/s41544-020-00056-z Zhang, J., Nielsen, R., & Yang, Z. (2005). Evaluation of an Improved Branch-Site Likelihood Method for Detecting Positive Selection at the Molecular Level. Molecular Biology and Evolution, 22(12), 2472-2479. https://doi.org/10.1093/molbev/msi237 Zhang, L., Jackson, C. B., Mou, H., Ojha, A., Rangarajan, E. S., Izard, T., Farzan, M., & Choe, H. (2020). The D614G mutation in the SARS-CoV2 spike protein reduces S1 shedding and increases infectivity. BioRxiv, 2020.06.12.148726. https://doi.org/10.1101/2020.06.12.148726 Zhang, L., Jackson, C. B., Mou, H., Ojha, A., Rangarajan, E. S., Izard, T., Farzan, M., & Choe, H. (2020). The D614G mutation in the SARS-CoV2 spike protein reduces S1 shedding and increases infectivity. BioRxiv, 2020.06.12.148726. https://doi.org/10.1101/2020.06.12.148726 Zhang, X., Chu, H., Wen, L., Shuai, H., Yang, D., Wang, Y., Hou, Y., Zhu, Z., Yuan, S., Yin, F., Chan, J. F.-W., & Yuen, K.-Y. (2020). Competing endogenous RNA network profiling reveals novel host dependency factors required for MERS-CoV propagation. Emerging Microbes & Infections, 9(1), 733-746. https://doi.org/10.1080/22221751.2020.1738277 Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., Chen, H.-D., Chen, J., Luo, Y., Guo, H., Jiang, R.-D., Liu, M.-Q., Chen, Y., Shen, X.-R., Wang, X., … Shi, Z.-L. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579(7798), 270-273. https://doi.org/10.1038/s41586-020-2012-7
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bermúdez Santana, Clara Isabel4640436fa6ecd6a7d3ab0cad7b367eaeGallego Gómez, Juan Carlos68a924cabd006c7d97d15bfa5910da69Rojas Cruz, Alexis Felipeb45f2a0104be8ea07acefdb54d1946eeRNómica Teórica y Computacional2022-06-23T16:18:25Z2022-06-23T16:18:25Z2022-06-20https://repositorio.unal.edu.co/handle/unal/81628Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficas, mapasLos Betacoronavirus han causado a su paso epidemias mortales, como el brote de SARS-CoV de 2002 y la continua prevalencia del MERS-CoV que se detectó por primera vez en 2012. A finales de 2019, se inició la pandemia de COVID-19, lo que impulsó a los científicos de todo el mundo a aplicar sus respectivos conocimientos para abordar cómo el SARS-CoV-2 infecta a los humanos. La principal estrategia ha sido la implementación de sistemas convencionales de vigilancia sanitaria para identificar, intervenir y controlar las infecciones virales causadas por estos virus emergentes. Aunque el seguimiento de la evolución genética del virus ha sido de gran importancia, se desconoce hasta qué punto es posible la transmisión zoonótica entre especies animales susceptibles y no susceptibles, así como la eventual funcionalidad de la arquitectura estructural del genoma de RNA de los Betacoronavirus en la fisiopatología, principalmente para SARS-CoV, MERS-CoV y SARS-CoV-2. Para llenar este vacío de conocimiento y facilitar el desarrollo de tratamientos eficaces, se realizó un estudio amplio de los genomas de los Betacoronavirus mediante el análisis de 1,252,952 secuencias virales reportadas en bases de datos que han circulado desde el 2002 pasando de reservorios naturales a huésped intermedio y humanos. Este trabajo considera dos enfoques diferentes de representar la información genómica, como se presentan y discuten en el capítulo 2: Análisis de secuencia. Esta parte del trabajo presenta un análisis evolutivo de transmisión horizontal en las secuencias virales para caracterizar y describir completamente la variación intra-hospedera de los Betacoronavirus. Los resultados revelan que cambios de aminoácidos en la subunidad S1 de la proteína S de SARS-CoV (G > T; A577S), MERS-CoV (C > T; S746R y C > T; N762A) y SARS-CoV-2 (A > G; D614G) con señales de selección positiva son factores fundamentales que subyacen al posible salto de barrera de los murciélagos al huésped intermedio. El capítulo 3: Análisis estructural, es una sección que explora los Betacoronavirus a nivel estructural como propuesta para descubrir si el plegamiento de estructuras secundarias de RNA conservadas podrían actuar como loci putativos para procesar RNAs pequeños virales, con una posible función asociada a la patogénesis en proceso de selección. Más del 87.58% de estas estructuras de RNA indican que 12 regiones portan RNAs pequeños en los Betacoronavirus, sugiriendo la posibilidad de modular la reprogramación transcripcional del nuevo huésped después de la infección. Los hallazgos de este estudio proporcionan una serie de significativos patrones moleculares que contribuyen a expandir las fronteras de la terapéutica humanos en el contexto de la actual crisis sanitaria mundial.Betacoronavirus have caused earlier deadly epidemics, including the 2002 SARS-CoV outbreak and the ongoing prevalence of MERS-CoV, which was first detected in 2012. In late 2019, the emergence of the COVID-19 pandemic encouraged scientists around the globe to apply their respective insights to address how SARS-CoV-2 infects humans. The main strategy has been the implementation of standard health surveillance systems to identify, manage and control viral infections caused by these emerging viruses. Even though monitoring the genetic evolution of the virus has been of high significance, to what extent zoonotic transmission across susceptible and non-susceptible animal species is possible, as well as eventual functionality the structural architecture of the RNA genome of Betacoronavirus in the pathophysiology, mainly for SARS-CoV, MERS-CoV and SARS-CoV-2 is unclear. To fill this knowledge gap and facilitate the development of effective treatments, a comprehensive study of Betacoronavirus genomes was performed by means of the analysis of 1,252,952 viral sequences reported in databases which have circulated since 2002 from natural reservoirs to intermediate hosts and humans. This study includes two different approaches to represent genomic information, as introduced and discussed in Chapter 2: Sequence analyses. This part of the work represents an evolutionary analysis of horizontal transmission in viral sequences to thoroughly characterize and describe the intra-host variation and transmission routes of Betacoronavirus. The results reveal that amino acid changes within S protein S1 subunit of SARS-CoV (G > T; A577S), MERS-CoV (C > T; S746R and C > T; N762A) and SARS-CoV-2 (A > G; D614G) with signals of positive selection are pivotal factors underlying the possible jumping from bats barrier to intermediate host. Chapter 3: Structural analyses, is a section that explores Betacoronavirus at the structural level as a proposal to discover whether the folding of conserved RNA secondary structures may act as putative loci for processing virus-derived small RNAs, with a potential function associated with pathogenesis in the process of selection. Over 87.58% of these RNA structures indicate that 12 regions carry small RNAs in Betacoronavirus, suggesting the possibility of modulation of transcriptional re-programming of the new host upon infection. The findings of this study provide a collection of significant molecular signatures that contribute to pushing the frontiers of human therapeutics in the context of the current global health crisis.Servicio Alemán de Intercambio Académico (DAAD)MaestríaMagíster en Ciencias - BiologíaEvolución viral y bioinformática de RNAxviii, 90 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - BiologíaDepartamento de BiologíaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - EnfermedadesCoronavirusBetacoronavirusEvolución molecularTransmisión inter-especiesEstructura secundaria de RNASelección positivaRNA pequeño derivado del virusMolecular evolutionInter-species transmissionRNA secondary structurePositive selectionVirus-derived small RNAEvolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)Molecular evolution of zoonotic Betacoronavirus associated with Acute Respiratory Distress Syndrome (ARDS)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbascal, F., Zardoya, R., & Telford, M. J. (2010). TranslatorX: Multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Research, 38(Web Server issue), W7-13. https://doi.org/10.1093/nar/gkq291Abdullahi, I. N., Emeribe, A. U., Ajayi, O. A., Oderinde, B. S., Amadu, D. O., & Osuji, A. I. (2020). Implications of SARS-CoV-2 genetic diversity and mutations on pathogenicity of the COVID-19 and biomedical interventions. Journal of Taibah University Medical Sciences, 15(4), 258-264. https://doi.org/10.1016/j.jtumed.2020.06.005Abdullahi, I. N., Emeribe, A. U., Mustapha, J. O., Fasogbon, S. A., Ofor, I. B., Opeyemi, I. S., Obi-George, C., Sunday, A. O., & Nwofe, J. (2020). Exploring the genetics, ecology of SARS-COV-2 and climatic factors as possible control strategies against COVID-19. Le Infezioni in Medicina, 28(2), 166-173.Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4, e05005. https://doi.org/10.7554/eLife.05005Alouane, T., Laamarti, M., Essabbar, A., Hakmi, M., Bouricha, E. M., Chemao-Elfihri, M. W., Kartti, S., Boumajdi, N., Bendani, H., Laamarti, R., Ghrifi, F., Allam, L., Aanniz, T., Ouadghiri, M., El Hafidi, N., El Jaoudi, R., Benrahma, H., Attar, J. E., Mentag, R., … Ibrahimi, A. (2020). Genomic Diversity and Hotspot Mutations in 30,983 SARS-CoV-2 Genomes: Moving Toward a Universal Vaccine for the “Confined Virus”? Pathogens, 9(10), 829. https://doi.org/10.3390/pathogens9100829Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C., & Garry, R. F. (2020). The proximal origin of SARS-CoV-2. Nature Medicine, 26(4), 450-452. https://doi.org/10.1038/s41591-020-0820-9Andrews, R. J., O’Leary, C. A., Tompkins, V. S., Peterson, J. M., Haniff, H. S., Williams, C., Disney, M. D., & Moss, W. N. (2021). A map of the SARS-CoV-2 RNA structurome. NAR Genomics and Bioinformatics, 3(2), lqab043. https://doi.org/10.1093/nargab/lqab043Ashour, H. M., Elkhatib, W. F., Rahman, M. M., & Elshabrawy, H. A. (2020). Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks. Pathogens, 9(3), 186. https://doi.org/10.3390/pathogens9030186Åsjö, B., & Kruse, H. (2006). Zoonoses in the Emergence of Human Viral Diseases. Perspectives in Medical Virology, 16, 15-41. https://doi.org/10.1016/S0168-7069(06)16003-6Aydemir, M. N., Aydemir, H. B., Korkmaz, E. M., Budak, M., Cekin, N., & Pinarbasi, E. (2021). Computationally predicted SARS-COV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways. Gene Reports, 22, 101012. https://doi.org/10.1016/j.genrep.2020.101012Balmeh, N., Mahmoudi, S., Mohammadi, N., & Karabedianhajiabadi, A. (2020). Predicted therapeutic targets for COVID-19 disease by inhibiting SARS-CoV-2 and its related receptors. Informatics in Medicine Unlocked, 20, 100407. https://doi.org/10.1016/j.imu.2020.100407Banaganapalli, B., Al-Rayes, N., Awan, Z. A., Alsulaimany, F. A., Alamri, A. S., Elango, R., Malik, M. Z., & Shaik, N. A. (2021). Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection. Computers in Biology and Medicine, 135, 104570. https://doi.org/10.1016/j.compbiomed.2021.104570Barreda-Manso, M. A., Nieto-Díaz, M., Soto, A., Muñoz-Galdeano, T., Reigada, D., & Maza, R. M. (2021). In Silico and In Vitro Analyses Validate Human MicroRNAs Targeting the SARS-CoV-2 3′-UTR. International Journal of Molecular Sciences, 22(11), 6094. https://doi.org/10.3390/ijms22116094Battaglia, R., Alonzo, R., Pennisi, C., Caponnetto, A., Ferrara, C., Stella, M., Barbagallo, C., Barbagallo, D., Ragusa, M., Purrello, M., & Di Pietro, C. (2021). MicroRNA-Mediated Regulation of the Virus Cycle and Pathogenesis in the SARS-CoV-2 Disease. International Journal of Molecular Sciences, 22(24), 13192. https://doi.org/10.3390/ijms222413192Bernard, M. A., Zhao, H., Yue, S. C., Anandaiah, A., Koziel, H., & Tachado, S. D. (2014). Novel HIV-1 MiRNAs Stimulate TNFα Release in Human Macrophages via TLR8 Signaling Pathway. PLOS ONE, 9(9), e106006. https://doi.org/10.1371/journal.pone.0106006Bernhardt, H. S. (2012). The RNA world hypothesis: The worst theory of the early evolution of life (except for all the others)(a). Biology Direct, 7, 23. https://doi.org/10.1186/1745-6150-7-23Bernier, A., & Sagan, S. M. (2018). The Diverse Roles of microRNAs at the Host–Virus Interface. Viruses, 10(8), 440. https://doi.org/10.3390/v10080440Betel, D., Koppal, A., Agius, P., Sander, C., & Leslie, C. (2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology, 11(8), R90. https://doi.org/10.1186/gb-2010-11-8-r90Boni, M. F., Lemey, P., Jiang, X., Lam, T. T.-Y., Perry, B. W., Castoe, T. A., Rambaut, A., & Robertson, D. L. (2020). Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nature Microbiology, 5(11), 1408-1417. https://doi.org/10.1038/s41564-020-0771-4Brister, J. R., Ako-adjei, D., Bao, Y., & Blinkova, O. (2015). NCBI Viral Genomes Resource. Nucleic Acids Research, 43(Database issue), D571-D577. https://doi.org/10.1093/nar/gku1207Callahan, V., Hawks, S., Crawford, M. A., Lehman, C. W., Morrison, H. A., Ivester, H. M., Akhrymuk, I., Boghdeh, N., Flor, R., Finkielstein, C. V., Allen, I. C., Weger-Lucarelli, J., Duggal, N., Hughes, M. A., & Kehn-Hall, K. (2021). The Pro-Inflammatory Chemokines CXCL9, CXCL10 and CXCL11 Are Upregulated Following SARS-CoV-2 Infection in an AKT-Dependent Manner. Viruses, 13(6), 1062. https://doi.org/10.3390/v13061062Canakoglu, A., Pinoli, P., Bernasconi, A., Alfonsi, T., Melidis, D. P., & Ceri, S. (2021). ViruSurf: An integrated database to investigate viral sequences. Nucleic Acids Research, 49(D1), D817-D824. https://doi.org/10.1093/nar/gkaa846Cao, C., Cai, Z., Xiao, X., Rao, J., Chen, J., Hu, N., Yang, M., Xing, X., Wang, Y., Li, M., Zhou, B., Wang, X., Wang, J., & Xue, Y. (2021). The architecture of the SARS-CoV-2 RNA genome inside virion. Nature Communications, 12(1), 3917. https://doi.org/10.1038/s41467-021-22785-xCarrasco-Hernandez, R., Jácome, R., López Vidal, Y., & Ponce de León, S. (2017). Are RNA Viruses Candidate Agents for the Next Global Pandemic? A Review. ILAR Journal, 58(3), 343-358. https://doi.org/10.1093/ilar/ilx026Ceraolo, C., & Giorgi, F. M. (2020). Genomic variance of the 2019-nCoV coronavirus. Journal of Medical Virology, 92(5), 522-528. https://doi.org/10.1002/jmv.25700Chan, Agnes. P., Choi, Y., & Schork, N. J. (2020). CONSERVED GENOMIC TERMINALS OF SARS-COV-2 AS CO-EVOLVING FUNCTIONAL ELEMENTS AND POTENTIAL THERAPEUTIC TARGETS. bioRxiv. https://doi.org/10.1101/2020.07.06.190207Chan, J. F.-W., Kok, K.-H., Zhu, Z., Chu, H., To, K. K.-W., Yuan, S., & Yuen, K.-Y. (2020). Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerging Microbes & Infections, 9(1), 221-236. https://doi.org/10.1080/22221751.2020.1719902Chen, C.-Y., Ping, Y.-H., Lee, H.-C., Chen, K.-H., Lee, Y.-M., Chan, Y.-J., Lien, T.-C., Jap, T.-S., Lin, C.-H., Kao, L.-S., & Chen, Y.-M. A. (2007). Open Reading Frame 8a of the Human Severe Acute Respiratory Syndrome Coronavirus Not Only Promotes Viral Replication but Also Induces Apoptosis. The Journal of Infectious Diseases, 196(3), 405-415. https://doi.org/10.1086/519166Chen, L., Song, W., Davis, I. C., Shrestha, K., Schwiebert, E., Sullender, W. M., & Matalon, S. (2009). Inhibition of Na+ transport in lung epithelial cells by respiratory syncytial virus infection. American Journal of Respiratory Cell and Molecular Biology, 40(5), 588-600. https://doi.org/10.1165/rcmb.2008-0034OCChen, Y., & Wang, X. (2020). miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Research, 48(D1), D127-D131. https://doi.org/10.1093/nar/gkz757Chen, Y., Ye, W., Zhang, Y., & Xu, Y. (2015). High speed BLASTN: An accelerated MegaBLAST search tool. Nucleic Acids Research, 43(16), 7762-7768. https://doi.org/10.1093/nar/gkv784Chen, Z., Liang, H., Chen, X., Ke, Y., Zhou, Z., Yang, M., Zen, K., Yang, R., Liu, C., & Zhang, C.-Y. (2016). An Ebola virus-encoded microRNA-like fragment serves as a biomarker for early diagnosis of Ebola virus disease. Cell Research, 26(3), 380-383. https://doi.org/10.1038/cr.2016.21Chow, J. T.-S., & Salmena, L. (2020). Prediction and Analysis of SARS-CoV-2-Targeting MicroRNA in Human Lung Epithelium. Genes, 11(9). https://doi.org/10.3390/genes11091002Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., Land, S. J., Lu, X., & Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly, 6(2), 80-92. https://doi.org/10.4161/fly.19695Conzade, R., Grant, R., Malik, M. R., Elkholy, A., Elhakim, M., Samhouri, D., Ben Embarek, P. K., & Van Kerkhove, M. D. (2018). Reported Direct and Indirect Contact with Dromedary Camels among Laboratory-Confirmed MERS-CoV Cases. Viruses, 10(8), 425. https://doi.org/10.3390/v10080425Corman, V. M., Ithete, N. L., Richards, L. R., Schoeman, M. C., Preiser, W., Drosten, C., & Drexler, J. F. (2014). Rooting the Phylogenetic Tree of Middle East Respiratory Syndrome Coronavirus by Characterization of a Conspecific Virus from an African Bat. Journal of Virology, 88(19), 11297-11303. https://doi.org/10.1128/JVI.01498-14Cui, J., Li, F., & Shi, Z.-L. (2019). Origin and evolution of pathogenic coronaviruses. Nature Reviews Microbiology, 17(3), 181-192. https://doi.org/10.1038/s41579-018-0118-9Cullen, B. R. (2004). Transcription and Processing of Human microRNA Precursors. Molecular Cell, 16(6), 861-865. https://doi.org/10.1016/j.molcel.2004.12.002Cullen, B. R. (2010). Five questions about viruses and microRNAs. PLoS Pathogens, 6(2), e1000787. https://doi.org/10.1371/journal.ppat.1000787Cyranoski, D. (2020). Did pangolins spread the China coronavirus to people? https://www.nature.com/articles/d41586-020-00364-2da Silva, P. G., Mesquita, J. R., de São José Nascimento, M., & Ferreira, V. A. M. (2021). Viral, host and environmental factors that favor anthropozoonotic spillover of coronaviruses: An opinionated review, focusing on SARS-CoV, MERS-CoV and SARS-CoV-2. The Science of the Total Environment, 750, 141483. https://doi.org/10.1016/j.scitotenv.2020.141483Danecek, P., & McCarthy, S. A. (2017). BCFtools/csq: Haplotype-aware variant consequences. Bioinformatics (Oxford, England), 33(13), 2037-2039. https://doi.org/10.1093/bioinformatics/btx100Daniloski, Z., Jordan, T. X., Ilmain, J. K., Guo, X., Bhabha, G., tenOever, B. R., & Sanjana, N. E. (2020). The Spike D614G mutation increases SARS-CoV-2 infection of multiple human cell types. BioRxiv, 2020.06.14.151357. https://doi.org/10.1101/2020.06.14.151357Darriba, D., Taboada, G. L., Doallo, R., & Posada, D. (2012). jModelTest 2: More models, new heuristics and high-performance computing. Nature methods, 9(8), 772. https://doi.org/10.1038/nmeth.2109De, M. N., Walker, C., Borges, R., Weilguny, L., Slodkowicz, G., & Goldman, N. (2020). Issues with SARS-CoV-2 sequencing dat. https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473Denison, M. R., Graham, R. L., Donaldson, E. F., Eckerle, L. D., & Baric, R. S. (2011). Coronaviruses. RNA Biology, 8(2), 270-279. https://doi.org/10.4161/rna.8.2.15013Dickey, L. L., Worne, C. L., Glover, J. L., Lane, T. E., & O’Connell, R. M. (2016). MicroRNA-155 enhances T cell trafficking and antiviral effector function in a model of coronavirus-induced neurologic disease. Journal of Neuroinflammation, 13(1), 240. https://doi.org/10.1186/s12974-016-0699-zDomingo, E., Martínez-Salas, E., Sobrino, F., de la Torre, J. C., Portela, A., Ortín, J., López-Galindez, C., Pérez-Breña, P., Villanueva, N., Nájera, R., VandePol, S., Steinhauer, D., DePolo, N., & Holland, J. (1985). The quasispecies (extremely heterogeneous) nature of viral RNA genome populations: Biological relevance — a review. Gene, 40(1), 1-8. https://doi.org/10.1016/0378-1119(85)90017-4Duffy, S., Shackelton, L. A., & Holmes, E. C. (2008). Rates of evolutionary change in viruses: Patterns and determinants. Nature Reviews Genetics, 9(4), 267-276. https://doi.org/10.1038/nrg2323Duy, J., Honko, A. N., Altamura, L. A., Bixler, S. L., Wollen-Roberts, S., Wauquier, N., O’Hearn, A., Mucker, E. M., Johnson, J. C., Shamblin, J. D., Zelko, J., Botto, M. A., Bangura, J., Coomber, M., Pitt, M. L., Gonzalez, J.-P., Schoepp, R. J., Goff, A. J., & Minogue, T. D. (2018). Virus-encoded miRNAs in Ebola virus disease. Scientific Reports, 8(1), 6480. https://doi.org/10.1038/s41598-018-23916-zElizondo, V., Harkins, G. W., Mabvakure, B., Smidt, S., Zappile, P., Marier, C., Maurano, M., Perez, V., Mazza, N., Beloso, C., Ifran, S., Fernandez, M., Santini, A., Perez, V., Estevez, V., Nin, M., Manrique, G., Perez, L., Ross, F., … Duerr, R. (2020). SARS-CoV-2 genomic characterization and clinical manifestation of the COVID-19 outbreak in Uruguay. medRxiv, 2020.10.08.20208546. https://doi.org/10.1101/2020.10.08.20208546El-Sayed, A., & Kamel, M. (2021). Coronaviruses in humans and animals: The role of bats in viral evolution. Environmental Science and Pollution Research, 28(16), 19589-19600. https://doi.org/10.1007/s11356-021-12553-1Fan, Y., Zhao, K., Shi, Z.-L., & Zhou, P. (2019). Bat Coronaviruses in China. Viruses, 11(3), 210. https://doi.org/10.3390/v11030210Farkas, C., Mella, A., & Haigh, J. J. (2020). Large-scale population analysis of SARS-CoV-2 whole genome sequences reveals host-mediated viral evolution with emergence of mutations in the viral Spike protein associated with elevated mortality rates (p. 2020.10.23.20218511). https://doi.org/10.1101/2020.10.23.20218511Farrag, M. A., Amer, H. M., Bhat, R., & Almajhdi, F. N. (2021). Sequence and phylogentic analysis of MERS-CoV in Saudi Arabia, 2012–2019. Virology Journal, 18(1), 90. https://doi.org/10.1186/s12985-021-01563-7Farrag, M. A., Amer, H. M., Bhat, R., Hamed, M. E., Aziz, I. M., Mubarak, A., Dawoud, T. M., Almalki, S. G., Alghofaili, F., Alnemare, A. K., Al-Baradi, R. S., Alosaimi, B., & Alturaiki, W. (2021). SARS-CoV-2: An Overview of Virus Genetics, Transmission, and Immunopathogenesis. International Journal of Environmental Research and Public Health, 18(12), 6312. https://doi.org/10.3390/ijerph18126312Fehr, A. R., & Perlman, S. (2015). Coronaviruses: An Overview of Their Replication and Pathogenesis. En H. J. Maier, E. Bickerton, & P. Britton (Eds.), Coronaviruses: Methods and Protocols (pp. 1-23). Springer. https://doi.org/10.1007/978-1-4939-2438-7_1Ferron, F., Subissi, L., Morais, A. T. S. D., Le, N. T. T., Sevajol, M., Gluais, L., Decroly, E., Vonrhein, C., Bricogne, G., Canard, B., & Imbert, I. (2018). Structural and molecular basis of mismatch correction and ribavirin excision from coronavirus RNA. Proceedings of the National Academy of Sciences, 115(2), E162-E171. https://doi.org/10.1073/pnas.1718806115Forni, D., Cagliani, R., Clerici, M., & Sironi, M. (2017). Molecular Evolution of Human Coronavirus Genomes. Trends in Microbiology, 25(1), 35-48. https://doi.org/10.1016/j.tim.2016.09.001Frutos, R., Serra-Cobo, J., Pinault, L., Lopez Roig, M., & Devaux, C. A. (2021). Emergence of Bat-Related Betacoronaviruses: Hazard and Risks. Frontiers in Microbiology, 12. https://doi.org/10.3389/fmicb.2021.591535Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012). CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 28(23), 3150-3152. https://doi.org/10.1093/bioinformatics/bts565Fulzele, S., Sahay, B., Yusufu, I., Lee, T. J., Sharma, A., Kolhe, R., & Isales, C. M. (2020). COVID-19 Virulence in Aged Patients Might Be Impacted by the Host Cellular MicroRNAs Abundance/Profile. Aging and Disease, 11(3), 509-522. https://doi.org/10.14336/AD.2020.0428Gasparello, J., Finotti, A., & Gambari, R. (2021). Tackling the COVID-19 “cytokine storm” with microRNA mimics directly targeting the 3’UTR of pro-inflammatory mRNAs. Medical Hypotheses, 146, 110415. https://doi.org/10.1016/j.mehy.2020.110415Geoghegan, J. L., Duchêne, S., & Holmes, E. C. (2017). Comparative analysis estimates the relative frequencies of co-divergence and cross-species transmission within viral families. PLoS Pathogens, 13(2), e1006215. https://doi.org/10.1371/journal.ppat.1006215Gojobori, T., Moriyama, E. N., & Kimura, M. (1990). Molecular clock of viral evolution, and the neutral theory. Proceedings of the National Academy of Sciences of the United States of America, 87(24), 10015-10018. https://doi.org/10.1073/pnas.87.24.10015Graepel, K. W., Lu, X., Case, J. B., Sexton, N. R., Smith, E. C., & Denison, M. R. (2017). Proofreading-deficient coronaviruses adapt over long-term passage for increased fidelity and fitness without reversion of exoribonuclease-inactivating mutations. BioRxiv, 175562. https://doi.org/10.1101/175562Gralinski, L. E., & Menachery, V. D. (2020). Return of the Coronavirus: 2019-nCoV. Viruses, 12(2), 135. https://doi.org/10.3390/v12020135Gruber, A. R., Findeiß, S., Washietl, S., Hofacker, I. L., & Stadler, P. F. (2010). RNAz 2.0: Improved noncoding RNA detection. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 69-79.Guan, Y., Zheng, B. J., He, Y. Q., Liu, X. L., Zhuang, Z. X., Cheung, C. L., Luo, S. W., Li, P. H., Zhang, L. J., Guan, Y. J., Butt, K. M., Wong, K. L., Chan, K. W., Lim, W., Shortridge, K. F., Yuen, K. Y., Peiris, J. S. M., & Poon, L. L. M. (2003). Isolation and Characterization of Viruses Related to the SARS Coronavirus from Animals in Southern China. Science, 302(5643), 276-278. https://doi.org/10.1126/science.1087139Guzzi, P. H., Mercatelli, D., Ceraolo, C., & Giorgi, F. M. (2020). Master Regulator Analysis of the SARS-CoV-2/Human Interactome. Journal of Clinical Medicine, 9(4), 982. https://doi.org/10.3390/jcm9040982Hadfield, J., Megill, C., Bell, S. M., Huddleston, J., Potter, B., Callender, C., Sagulenko, P., Bedford, T., & Neher, R. A. (2018). Nextstrain: Real-time tracking of pathogen evolution. Bioinformatics, 34(23), 4121-4123. https://doi.org/10.1093/bioinformatics/bty407Han, G.-Z. (2020). Pangolins Harbor SARS-CoV-2-Related Coronaviruses. Trends in Microbiology, 28(7), 515-517. https://doi.org/10.1016/j.tim.2020.04.001Hasan, M. M., Akter, R., Ullah, M. S., Abedin, M. J., Ullah, G. M. A., & Hossain, M. Z. (2014). A Computational Approach for Predicting Role of Human MicroRNAs in MERS-CoV Genome. Advances in Bioinformatics, 2014, e967946. https://doi.org/10.1155/2014/967946Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic Acids Research, 31(13), 3429-3431.Hon, C.-C., Lam, T.-Y., Shi, Z.-L., Drummond, A. J., Yip, C.-W., Zeng, F., Lam, P.-Y., & Leung, F. C.-C. (2008). Evidence of the Recombinant Origin of a Bat Severe Acute Respiratory Syndrome (SARS)-Like Coronavirus and Its Implications on the Direct Ancestor of SARS Coronavirus. Journal of Virology, 82(4), 1819-1826. https://doi.org/10.1128/JVI.01926-07Hu, J., Peng, P., Cao, X., Wu, K., Chen, J., Wang, K., Tang, N., & Huang, A. (2022). Increased immune escape of the new SARS-CoV-2 variant of concern Omicron. Cellular & Molecular Immunology, 1-3. https://doi.org/10.1038/s41423-021-00836-zHuang, H.-Y., Lin, Y.-C.-D., Li, J., Huang, K.-Y., Shrestha, S., Hong, H.-C., Tang, Y., Chen, Y.-G., Jin, C.-N., Yu, Y., Xu, J.-T., Li, Y.-M., Cai, X.-X., Zhou, Z.-Y., Chen, X.-H., Pei, Y.-Y., Hu, L., Su, J.-J., Cui, S.-D., … Huang, H.-D. (2020). miRTarBase 2020: Updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Research, 48(D1), D148-D154. https://doi.org/10.1093/nar/gkz896Hussain, M., & Asgari, S. (2014). MicroRNA-like viral small RNA from Dengue virus 2 autoregulates its replication in mosquito cells. Proceedings of the National Academy of Sciences, 111(7), 2746-2751. https://doi.org/10.1073/pnas.1320123111Hussain, S., Pan, J., Chen, Y., Yang, Y., Xu, J., Peng, Y., Wu, Y., Li, Z., Zhu, Y., Tien, P., & Guo, D. (2005). Identification of Novel Subgenomic RNAs and Noncanonical Transcription Initiation Signals of Severe Acute Respiratory Syndrome Coronavirus. Journal of Virology, 79(9), 5288-5295. https://doi.org/10.1128/JVI.79.9.5288-5295.2005Huston, N. C., Wan, H., Strine, M. S., de Cesaris Araujo Tavares, R., Wilen, C. B., & Pyle, A. M. (2021). Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms. Molecular Cell, 81(3), 584-598.e5. https://doi.org/10.1016/j.molcel.2020.12.041Ito, K., & Murphy, D. (2013). Application of ggplot2 to Pharmacometric Graphics. CPT: Pharmacometrics & Systems Pharmacology, 2, e79. https://doi.org/10.1038/psp.2013.56John, G., Sahajpal, N. S., Mondal, A. K., Ananth, S., Williams, C., Chaubey, A., Rojiani, A. M., & Kolhe, R. (2021). Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology. Current Issues in Molecular Biology, 43(2), 845-867. https://doi.org/10.3390/cimb43020061Kalvari, I., Nawrocki, E. P., Ontiveros-Palacios, N., Argasinska, J., Lamkiewicz, K., Marz, M., Griffiths-Jones, S., Toffano-Nioche, C., Gautheret, D., Weinberg, Z., Rivas, E., Eddy, S. R., Finn, R. D., Bateman, A., & Petrov, A. I. (2021). Rfam 14: Expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research, 49(D1), D192-D200. https://doi.org/10.1093/nar/gkaa1047Kelly, J. A., Woodside, M. T., & Dinman, J. D. (2021). Programmed −1 Ribosomal Frameshifting in coronaviruses: A therapeutic target. Virology, 554, 75-82. https://doi.org/10.1016/j.virol.2020.12.010Keng, C.-T., Choi, Y.-W., Welkers, M. R. A., Chan, D. Z. L., Shen, S., Gee Lim, S., Hong, W., & Tan, Y.-J. (2006). The human severe acute respiratory syndrome coronavirus (SARS-CoV) 8b protein is distinct from its counterpart in animal SARS-CoV and down-regulates the expression of the envelope protein in infected cells. Virology, 354(1), 132-142. https://doi.org/10.1016/j.virol.2006.06.026Kim, D., Lee, J.-Y., Yang, J.-S., Kim, J. W., Kim, V. N., & Chang, H. (2020). The Architecture of SARS-CoV-2 Transcriptome. Cell, 181(4), 914-921.e10. https://doi.org/10.1016/j.cell.2020.04.011Kim, W. R., Park, E. G., Kang, K.-W., Lee, S.-M., Kim, B., & Kim, H.-S. (2020). Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2. Molecules and Cells, 43(11), 953-963. https://doi.org/10.14348/molcells.2020.0177Kopecky-Bromberg, S. A., Martínez-Sobrido, L., Frieman, M., Baric, R. A., & Palese, P. (2007). Severe Acute Respiratory Syndrome Coronavirus Open Reading Frame (ORF) 3b, ORF 6, and Nucleocapsid Proteins Function as Interferon Antagonists. Journal of Virology, 81(2), 548-557. https://doi.org/10.1128/JVI.01782-06Korber, B., Fischer, W. M., Gnanakaran, S., Yoon, H., Theiler, J., Abfalterer, W., Foley, B., Giorgi, E. E., Bhattacharya, T., Parker, M. D., Partridge, D. G., Evans, C. M., Silva, T. de, Group, on behalf of the S. C.-19 G., LaBranche, C. C., & Montefiori, D. C. (2020). Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2. BioRxiv, 2020.04.29.069054. https://doi.org/10.1101/2020.04.29.069054Kosakovsky Pond, S. L., & Frost, S. D. W. (2005). Not so different after all: A comparison of methods for detecting amino acid sites under selection. Molecular Biology and Evolution, 22(5), 1208-1222. https://doi.org/10.1093/molbev/msi105Kozomara, A., Birgaoanu, M., & Griffiths-Jones, S. (2019). miRBase: From microRNA sequences to function. Nucleic Acids Research, 47(D1), D155-D162. https://doi.org/10.1093/nar/gky1141Krol, J., Sobczak, K., Wilczynska, U., Drath, M., Jasinska, A., Kaczynska, D., & Krzyzosiak, W. J. (2004). Structural Features of MicroRNA (miRNA) Precursors and Their Relevance to miRNA Biogenesis and Small Interfering RNA/Short Hairpin RNA Design *. Journal of Biological Chemistry, 279(40), 42230-42239. https://doi.org/10.1074/jbc.M404931200Krüger, J., & Rehmsmeier, M. (2006). RNAhybrid: MicroRNA target prediction easy, fast and flexible. Nucleic Acids Research, 34(Web Server issue), W451-W454. https://doi.org/10.1093/nar/gkl243Lam, T. T.-Y., Jia, N., Zhang, Y.-W., Shum, M. H.-H., Jiang, J.-F., Zhu, H.-C., Tong, Y.-G., Shi, Y.-X., Ni, X.-B., Liao, Y.-S., Li, W.-J., Jiang, B.-G., Wei, W., Yuan, T.-T., Zheng, K., Cui, X.-M., Li, J., Pei, G.-Q., Qiang, X., … Cao, W.-C. (2020). Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature, 583(7815), 282-285. https://doi.org/10.1038/s41586-020-2169-0Larsson, A. (2014). AliView: A fast and lightweight alignment viewer and editor for large datasets. Bioinformatics, 30(22), 3276-3278. https://doi.org/10.1093/bioinformatics/btu531Latinne, A., Hu, B., Olival, K. J., Zhu, G., Zhang, L., Li, H., Chmura, A. A., Field, H. E., Zambrana-Torrelio, C., Epstein, J. H., Li, B., Zhang, W., Wang, L.-F., Shi, Z.-L., & Daszak, P. (2020). Origin and cross-species transmission of bat coronaviruses in China. Nature Communications, 11(1), 4235. https://doi.org/10.1038/s41467-020-17687-3Lau, S. K. P., Woo, P. C. Y., Li, K. S. M., Huang, Y., Tsoi, H.-W., Wong, B. H. L., Wong, S. S. Y., Leung, S.-Y., Chan, K.-H., & Yuen, K.-Y. (2005). Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proceedings of the National Academy of Sciences, 102(39), 14040-14045. https://doi.org/10.1073/pnas.0506735102Li, F. (2013). Receptor recognition and cross-species infections of SARS coronavirus. Antiviral Research, 100(1), 246-254. https://doi.org/10.1016/j.antiviral.2013.08.014Li, F. (2016). Structure, Function, and Evolution of Coronavirus Spike Proteins. Annual Review of Virology, 3(1), 237-261. https://doi.org/10.1146/annurev-virology-110615-042301Li, H. (2018). Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics (Oxford, England), 34(18), 3094-3100. https://doi.org/10.1093/bioinformatics/bty191Li, X., Fu, Z., Liang, H., Wang, Y., Qi, X., Ding, M., Sun, X., Zhou, Z., Huang, Y., Gu, H., Li, L., Chen, X., Li, D., Zhao, Q., Liu, F., Wang, H., Wang, J., Zen, K., & Zhang, C.-Y. (2018). H5N1 influenza virus-specific miRNA-like small RNA increases cytokine production and mouse mortality via targeting poly(rC)-binding protein 2. Cell Research, 28(2), 157-171. https://doi.org/10.1038/cr.2018.3Li, X., Giorgi, E. E., Marichannegowda, M. H., Foley, B., Xiao, C., Kong, X.-P., Chen, Y., Gnanakaran, S., Korber, B., & Gao, F. (2020). Emergence of SARS-CoV-2 through recombination and strong purifying selection. Science Advances, eabb9153. https://doi.org/10.1126/sciadv.abb9153Li, X., & Zou, X. (2019). An overview of RNA virus-encoded microRNAs. ExRNA, 1(1), 37. https://doi.org/10.1186/s41544-019-0037-6Li, Z., Luo, Q., Xu, H., Zheng, M., Abdalla, B. A., Feng, M., Cai, B., Zhang, X., Nie, Q., & Zhang, X. (2017). MiR-34b-5p Suppresses Melanoma Differentiation-Associated Gene 5 (MDA5) Signaling Pathway to Promote Avian Leukosis Virus Subgroup J (ALV-J)-Infected Cells Proliferaction and ALV-J Replication. Frontiers in Cellular and Infection Microbiology, 7, 17. https://doi.org/10.3389/fcimb.2017.00017Liu, Q., Du, J., Yu, X., Xu, J., Huang, F., Li, X., Zhang, C., Li, X., Chang, J., Shang, D., Zhao, Y., Tian, M., Lu, H., Xu, J., Li, C., Zhu, H., Jin, N., & Jiang, C. (2017). MiRNA-200c-3p is crucial in acute respiratory distress syndrome. Cell Discovery, 3(1), 1-17. https://doi.org/10.1038/celldisc.2017.21Liu, Y., Sun, J., Zhang, H., Wang, M., Gao, G. F., & Li, X. (2016). Ebola virus encodes a miR-155 analog to regulate importin-α5 expression. Cellular and Molecular Life Sciences, 73(19), 3733-3744. https://doi.org/10.1007/s00018-016-2215-0Liu, Y., Wimmer, E., & Paul, A. V. (2009). Cis-acting RNA elements in human and animal plus-strand RNA viruses. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, 1789(9), 495-517. https://doi.org/10.1016/j.bbagrm.2009.09.007Lokugamage, K. G., Hage, A., de Vries, M., Valero-Jimenez, A. M., Schindewolf, C., Dittmann, M., Rajsbaum, R., & Menachery, V. D. (2020). Type I Interferon Susceptibility Distinguishes SARS-CoV-2 from SARS-CoV. Journal of Virology, 94(23), e01410-20. https://doi.org/10.1128/JVI.01410-20Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8Lu, D., Chatterjee, S., Xiao, K., Riedel, I., Wang, Y., Foo, R., Bär, C., & Thum, T. (2020). MicroRNAs targeting the SARS-CoV-2 entry receptor ACE2 in cardiomyocytes. Journal of Molecular and Cellular Cardiology, 148, 46-49. https://doi.org/10.1016/j.yjmcc.2020.08.017Lu, G., Wang, Q., & Gao, G. F. (2015). Bat-to-human: Spike features determining «host jump» of coronaviruses SARS-CoV, MERS-CoV, and beyond. Trends in Microbiology, 23(8), 468-478. https://doi.org/10.1016/j.tim.2015.06.003Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., Bi, Y., Ma, X., Zhan, F., Wang, L., Hu, T., Zhou, H., Hu, Z., Zhou, W., Zhao, L., … Tan, W. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. The Lancet, 395(10224), 565-574. https://doi.org/10.1016/S0140-6736(20)30251-8Ma, X., Zhao, X., Zhang, Z., Guo, J., Guan, L., Li, J., Mi, M., Huang, Y., & Tong, D. (2018). Differentially expressed non-coding RNAs induced by transmissible gastroenteritis virus potentially regulate inflammation and NF-κB pathway in porcine intestinal epithelial cell line. BMC Genomics, 19(1), 747. https://doi.org/10.1186/s12864-018-5128-5Madhugiri, R., Fricke, M., Marz, M., & Ziebuhr, J. (2016). Chapter Four—Coronavirus cis-Acting RNA Elements. En J. Ziebuhr (Ed.), Advances in Virus Research (Vol. 96, pp. 127-163). Academic Press. https://doi.org/10.1016/bs.aivir.2016.08.007Mallick, B., Ghosh, Z., & Chakrabarti, J. (2009). MicroRNome Analysis Unravels the Molecular Basis of SARS Infection in Bronchoalveolar Stem Cells. PLOS ONE, 4(11), e7837. https://doi.org/10.1371/journal.pone.0007837Masters, P. S. (2006). The Molecular Biology of Coronaviruses. En Advances in Virus Research (Vol. 66, pp. 193-292). Academic Press. https://doi.org/10.1016/S0065-3527(06)66005-3Matarese, A., Gambardella, J., Sardu, C., & Santulli, G. (2020). miR-98 Regulates TMPRSS2 Expression in Human Endothelial Cells: Key Implications for COVID-19. Biomedicines, 8(11), 462. https://doi.org/10.3390/biomedicines8110462Meckiff, B. J., Ramírez-Suástegui, C., Fajardo, V., Chee, S. J., Kusnadi, A., Simon, H., Grifoni, A., Pelosi, E., Weiskopf, D., Sette, A., Ay, F., Seumois, G., Ottensmeier, C. H., & Vijayanand, P. (2020). Single-cell transcriptomic analysis of SARS-CoV-2 reactive CD4+ T cells. Social Science Research Network, 3641939. https://doi.org/10.2139/ssrn.3641939Memish, Z. A., Cotten, M., Meyer, B., Watson, S. J., Alsahafi, A. J., Al Rabeeah, A. A., Corman, V. M., Sieberg, A., Makhdoom, H. Q., Assiri, A., Al Masri, M., Aldabbagh, S., Bosch, B.-J., Beer, M., Müller, M. A., Kellam, P., & Drosten, C. (2014). Human infection with MERS coronavirus after exposure to infected camels, Saudi Arabia, 2013. Emerging Infectious Diseases, 20(6), 1012-1015. https://doi.org/10.3201/eid2006.140402Menachery, V. D., Graham, R. L., & Baric, R. S. (2017). Jumping species—A mechanism for coronavirus persistence and survival. Current Opinion in Virology, 23, 1-7. https://doi.org/10.1016/j.coviro.2017.01.002Merino, G. A., Raad, J., Bugnon, L. A., Yones, C., Kamenetzky, L., Claus, J., Ariel, F., Milone, D. H., & Stegmayer, G. (2020). Novel SARS-CoV-2 encoded small RNAs in the passage to humans. Bioinformatics, 36(24), 5571-5581. https://doi.org/10.1093/bioinformatics/btaa1002Mi, H., Muruganujan, A., Ebert, D., Huang, X., & Thomas, P. D. (2019). PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Research, 47(D1), D419-D426. https://doi.org/10.1093/nar/gky1038Michel, C. J., Mayer, C., Poch, O., & Thompson, J. D. (2020). Characterization of accessory genes in coronavirus genomes. Virology Journal, 17(1), 131. https://doi.org/10.1186/s12985-020-01402-1Mignone, F., Grillo, G., Licciulli, F., Iacono, M., Liuni, S., Kersey, P. J., Duarte, J., Saccone, C., & Pesole, G. (2005). UTRdb and UTRsite: A collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Research, 33(Database issue), D141-146. https://doi.org/10.1093/nar/gki021Miller, M. A., Pfeiffer, W., & Schwartz, T. (2010). Creating the CIPRES Science Gateway for inference of large phylogenetic trees. 2010 Gateway Computing Environments Workshop (GCE), 1-8. https://doi.org/10.1109/GCE.2010.5676129Mishra, R., Kumar, A., Ingle, H., & Kumar, H. (2020). The Interplay Between Viral-Derived miRNAs and Host Immunity During Infection. Frontiers in Immunology, 10, 3079. https://doi.org/10.3389/fimmu.2019.03079Morales, L., Oliveros, J. C., Fernandez-Delgado, R., tenOever, B. R., Enjuanes, L., & Sola, I. (2017). SARS-CoV-Encoded Small RNAs Contribute to Infection-Associated Lung Pathology. Cell Host & Microbe, 21(3), 344-355. https://doi.org/10.1016/j.chom.2017.01.015Morel, B., Barbera, P., Czech, L., Bettisworth, B., Hübner, L., Lutteropp, S., Serdari, D., Kostaki, E.-G., Mamais, I., Kozlov, A. M., Pavlidis, P., Paraskevis, D., & Stamatakis, A. (2021). Phylogenetic Analysis of SARS-CoV-2 Data Is Difficult. Molecular Biology and Evolution, 38(5), 1777-1791. https://doi.org/10.1093/molbev/msaa314Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky Pond, S. L., & Scheffler, K. (2013). FUBAR: A fast, unconstrained bayesian approximation for inferring selection. Molecular Biology and Evolution, 30(5), 1196-1205. https://doi.org/10.1093/molbev/mst030Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., & Kosakovsky Pond, S. L. (2012). Detecting individual sites subject to episodic diversifying selection. PLoS Genetics, 8(7), e1002764. https://doi.org/10.1371/journal.pgen.1002764Nelson, J. W., & Breaker, R. R. (2017). The lost language of the RNA World. Science Signaling, 10(483). https://doi.org/10.1126/scisignal.aam8812Nowick, K., Walter Costa, M. B., Höner zu Siederdissen, C., & Stadler, P. F. (2019). Selection Pressures on RNA Sequences and Structures. Evolutionary Bioinformatics Online, 15. https://doi.org/10.1177/1176934319871919Omoto, S., Ito, M., Tsutsumi, Y., Ichikawa, Y., Okuyama, H., Brisibe, E. A., Saksena, N. K., & Fujii, Y. R. (2004). HIV-1 nef suppression by virally encoded microRNA. Retrovirology, 1(1), 44. https://doi.org/10.1186/1742-4690-1-44Omrani, A. S., Al-Tawfiq, J. A., & Memish, Z. A. (2015). Middle East respiratory syndrome coronavirus (MERS-CoV): Animal to human interaction. Pathogens and Global Health, 109(8), 354-362. https://doi.org/10.1080/20477724.2015.1122852Oostra, M., de Haan, C. A. M., & Rottier, P. J. M. (2007). The 29-Nucleotide Deletion Present in Human but Not in Animal Severe Acute Respiratory Syndrome Coronaviruses Disrupts the Functional Expression of Open Reading Frame 8. Journal of Virology, 81(24), 13876-13888. https://doi.org/10.1128/JVI.01631-07Pang, K. C., Frith, M. C., & Mattick, J. S. (2006). Rapid evolution of noncoding RNAs: Lack of conservation does not mean lack of function. Trends in Genetics, 22(1), 1-5. https://doi.org/10.1016/j.tig.2005.10.003Paradis, E., Claude, J., & Strimmer, K. (2004). APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics (Oxford, England), 20(2), 289-290. https://doi.org/10.1093/bioinformatics/btg412Peng, X., Gralinski, L., Ferris, M. T., Frieman, M. B., Thomas, M. J., Proll, S., Korth, M. J., Tisoncik, J. R., Heise, M., Luo, S., Schroth, G. P., Tumpey, T. M., Li, C., Kawaoka, Y., Baric, R. S., & Katze, M. G. (2011). Integrative deep sequencing of the mouse lung transcriptome reveals differential expression of diverse classes of small RNAs in response to respiratory virus infection. MBio, 2(6). https://doi.org/10.1128/mBio.00198-11Pham, T. N., Lukhele, S., Hajjar, F., Routy, J.-P., & Cohen, É. A. (2014). HIV Nef and Vpu protect HIV-infected CD4+ T cells from antibody-mediated cell lysis through down-modulation of CD4 and BST2. Retrovirology, 11(1), 15. https://doi.org/10.1186/1742-4690-11-15Pickett, B. E., Greer, D. S., Zhang, Y., Stewart, L., Zhou, L., Sun, G., Gu, Z., Kumar, S., Zaremba, S., Larsen, C. N., Jen, W., Klem, E. B., & Scheuermann, R. H. (2012). Virus Pathogen Database and Analysis Resource (ViPR): A Comprehensive Bioinformatics Database and Analysis Resource for the Coronavirus Research Community. Viruses, 4(11), 3209-3226. https://doi.org/10.3390/v4113209Pierce, J. B., Simion, V., Icli, B., Pérez-Cremades, D., Cheng, H. S., & Feinberg, M. W. (2020). Computational Analysis of Targeting SARS-CoV-2, Viral Entry Proteins ACE2 and TMPRSS2, and Interferon Genes by Host MicroRNAs. Genes, 11(11), 1354. https://doi.org/10.3390/genes11111354Piskol, R., & Stephan, W. (2008). Analyzing the Evolution of RNA Secondary Structures in Vertebrate Introns Using Kimura’s Model of Compensatory Fitness Interactions. Molecular Biology and Evolution, 25(11), 2483-2492. https://doi.org/10.1093/molbev/msn195Plante, J. A., Liu, Y., Liu, J., Xia, H., Johnson, B. A., Lokugamage, K. G., Zhang, X., Muruato, A. E., Zou, J., Fontes-Garfias, C. R., Mirchandani, D., Scharton, D., Bilello, J. P., Ku, Z., An, Z., Kalveram, B., Freiberg, A. N., Menachery, V. D., Xie, X., … Shi, P.-Y. (2021). Spike mutation D614G alters SARS-CoV-2 fitness. Nature, 592(7852), 116-121. https://doi.org/10.1038/s41586-020-2895-3Pond, S. L. K., Murrell, B., & Poon, A. F. Y. (2012). Evolution of viral genomes: Interplay between selection, recombination, and other forces. Methods in Molecular Biology (Clifton, N.J.), 856, 239-272. https://doi.org/10.1007/978-1-61779-585-5_10Prasad, A. N., Ronk, A. J., Widen, S. G., Wood, T. G., Basler, C. F., & Bukreyev, A. (2020). Ebola Virus Produces Discrete Small Noncoding RNAs Independently of the Host MicroRNA Pathway Which Lack RNA Interference Activity in Bat and Human Cells. Journal of Virology, 94(6). https://doi.org/10.1128/JVI.01441-19Qu, X.-X., Hao, P., Song, X.-J., Jiang, S.-M., Liu, Y.-X., Wang, P.-G., Rao, X., Song, H.-D., Wang, S.-Y., Zuo, Y., Zheng, A.-H., Luo, M., Wang, H.-L., Deng, F., Wang, H.-Z., Hu, Z.-H., Ding, M.-X., Zhao, G.-P., & Deng, H.-K. (2005). Identification of two critical amino acid residues of the severe acute respiratory syndrome coronavirus spike protein for its variation in zoonotic tropism transition via a double substitution strategy. The Journal of Biological Chemistry, 280(33), 29588-29595. https://doi.org/10.1074/jbc.M500662200Qu, Z., & Adelson, D. (2012). Evolutionary conservation and functional roles of ncRNA. Frontiers in Genetics, 3, 205. https://doi.org/10.3389/fgene.2012.00205R Core Team. (2021). R: The R Project for Statistical Computing. https://www.r-project.org/Rabaan, A. A., Al-Ahmed, S. H., Sah, R., Alqumber, M. A., Haque, S., Patel, S. K., Pathak, M., Tiwari, R., Yatoo, Mohd. I., Haq, A. U., Bilal, M., Dhama, K., & Rodriguez-Morales, A. J. (2021). MERS-CoV: Epidemiology, molecular dynamics, therapeutics, and future challenges. Annals of Clinical Microbiology and Antimicrobials, 20(1), 8. https://doi.org/10.1186/s12941-020-00414-7Rabi, F. A., Al Zoubi, M. S., Kasasbeh, G. A., Salameh, D. M., & Al-Nasser, A. D. (2020). SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far. Pathogens, 9(3), 231. https://doi.org/10.3390/pathogens9030231Rahaman, M., Komanapalli, J., Mukherjee, M., Byram, P. K., Sahoo, S., & Chakravorty, N. (2021). Decrypting the role of predicted SARS-CoV-2 miRNAs in COVID-19 pathogenesis: A bioinformatics approach. Computers in Biology and Medicine, 136, 104669. https://doi.org/10.1016/j.compbiomed.2021.104669Raj, V. S., Mou, H., Smits, S. L., Dekkers, D. H. W., Müller, M. A., Dijkman, R., Muth, D., Demmers, J. A. A., Zaki, A., Fouchier, R. A. M., Thiel, V., Drosten, C., Rottier, P. J. M., Osterhaus, A. D. M. E., Bosch, B. J., & Haagmans, B. L. (2013). Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC. Nature, 495(7440), 251-254. https://doi.org/10.1038/nature12005Rambaut, A., Drummond, A. J., Xie, D., Baele, G., & Suchard, M. A. (2018). Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Systematic Biology, 67(5), 901-904. https://doi.org/10.1093/sysbio/syy032Rangan, R., Zheludev, I. N., & Das, R. (2020). RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses. BioRxiv, 2020.03.27.012906. https://doi.org/10.1101/2020.03.27.012906Ricagno, S., Egloff, M.-P., Ulferts, R., Coutard, B., Nurizzo, D., Campanacci, V., Cambillau, C., Ziebuhr, J., & Canard, B. (2006). Crystal structure and mechanistic determinants of SARS coronavirus nonstructural protein 15 define an endoribonuclease family. Proceedings of the National Academy of Sciences of the United States of America, 103(32), 11892-11897. https://doi.org/10.1073/pnas.0601708103Rivas, E. (2020). RNA structure prediction using positive and negative evolutionary information. PLOS Computational Biology, 16(10), e1008387. https://doi.org/10.1371/journal.pcbi.1008387Saçar Demirci, M. D., & Adan, A. (2020). Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection. PeerJ, 8. https://doi.org/10.7717/peerj.9369Saini, S., Saini, A., Thakur, C. J., Kumar, V., Gupta, R. D., & Sharma, J. K. (2020). Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity. Molecular Biology Research Communications, 9(2), 83-91. https://doi.org/10.22099/mbrc.2020.36507.1487Salamatbakhsh, M., Mobaraki, K., Sadeghimohammadi, S., & Ahmadzadeh, J. (2019). The global burden of premature mortality due to the Middle East respiratory syndrome (MERS) using standard expected years of life lost, 2012 to 2019. BMC Public Health, 19(1), 1523. https://doi.org/10.1186/s12889-019-7899-2Shang, J., Ye, G., Shi, K., Wan, Y., Luo, C., Aihara, H., Geng, Q., Auerbach, A., & Li, F. (2020). Structural basis of receptor recognition by SARS-CoV-2. Nature, 581(7807), 221-224. https://doi.org/10.1038/s41586-020-2179-yShi, J., Wen, Z., Zhong, G., Yang, H., Wang, C., Huang, B., Liu, R., He, X., Shuai, L., Sun, Z., Zhao, Y., Liu, P., Liang, L., Cui, P., Wang, J., Zhang, X., Guan, Y., Tan, W., Wu, G., … Bu, Z. (2020). Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS-coronavirus 2. Science (New York, N.Y.), 368(6494), 1016-1020. https://doi.org/10.1126/science.abb7015Shu, Y., & McCauley, J. (2017). GISAID: Global initiative on sharing all influenza data – from vision to reality. Eurosurveillance, 22(13). https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494Simmonds, P. (2020). Pervasive RNA secondary structure in the genomes of SARS-CoV-2 and other coronaviruses – an endeavour to understand its biological purpose. BioRxiv, 2020.06.17.155200. https://doi.org/10.1101/2020.06.17.155200Siniscalchi, C., Di Palo, A., Russo, A., & Potenza, N. (2021). Human MicroRNAs Interacting With SARS-CoV-2 RNA Sequences: Computational Analysis and Experimental Target Validation. Frontiers in Genetics, 12, 760. https://doi.org/10.3389/fgene.2021.678994Smith, E. C., Blanc, H., Vignuzzi, M., & Denison, M. R. (2013). Coronaviruses Lacking Exoribonuclease Activity Are Susceptible to Lethal Mutagenesis: Evidence for Proofreading and Potential Therapeutics. PLOS Pathogens, 9(8), e1003565. https://doi.org/10.1371/journal.ppat.1003565Sola, I., Mateos-Gomez, P. A., Almazan, F., Zuñiga, S., & Enjuanes, L. (2011). RNA-RNA and RNA-protein interactions in coronavirus replication and transcription. RNA Biology, 8(2), 237-248. https://doi.org/10.4161/rna.8.2.14991Stamatakis, A. (2014). RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England), 30(9), 1312-1313. https://doi.org/10.1093/bioinformatics/btu033Sticht, C., De La Torre, C., Parveen, A., & Gretz, N. (2018). miRWalk: An online resource for prediction of microRNA binding sites. PLoS ONE, 13(10). https://doi.org/10.1371/journal.pone.0206239Sweileh, W. M. (2017). Global research trends of World Health Organization’s top eight emerging pathogens. Globalization and Health, 13. https://doi.org/10.1186/s12992-017-0233-9Tang, H., Gao, Y., Li, Z., Miao, Y., Huang, Z., Liu, X., Xie, L., Li, H., Wen, W., Zheng, Y., & Su, W. (2020). The noncoding and coding transcriptional landscape of the peripheral immune response in patients with COVID-19. Clinical and Translational Medicine, 10(6), e200. https://doi.org/10.1002/ctm2.200Tang, X., Wu, C., Li, X., Song, Y., Yao, X., Wu, X., Duan, Y., Zhang, H., Wang, Y., Qian, Z., Cui, J., & Lu, J. (2020). On the origin and continuing evolution of SARS-CoV-2. National Science Review, 7(6), 1012-1023. https://doi.org/10.1093/nsr/nwaa036Teng, Y., Wang, Y., Zhang, X., Liu, W., Fan, H., Yao, H., Lin, B., Zhu, P., Yuan, W., Tong, Y., & Cao, W. (2015). Systematic Genome-wide Screening and Prediction of microRNAs in EBOV During the 2014 Ebolavirus Outbreak. Scientific Reports, 5(1), 9912. https://doi.org/10.1038/srep09912Toyoshima, Y., Nemoto, K., Matsumoto, S., Nakamura, Y., & Kiyotani, K. (2020). SARS-CoV-2 genomic variations associated with mortality rate of COVID-19. Journal of Human Genetics, 65(12), 1075-1082. https://doi.org/10.1038/s10038-020-0808-9Verma, S., Dwivedy, A., Kumar, N., & Biswal, B. K. (2020). Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets (p. 2020.11.02.365049). https://doi.org/10.1101/2020.11.02.365049Vlachos, I. S., & Hatzigeorgiou, A. G. (2017). Functional Analysis of miRNAs Using the DIANA Tools Online Suite. Methods in Molecular Biology (Clifton, N.J.), 1517, 25-50. https://doi.org/10.1007/978-1-4939-6563-2_2Walls, A. C., Park, Y.-J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler, D. (2020). Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell, 181(2), 281-292.e6. https://doi.org/10.1016/j.cell.2020.02.058Walter Costa, M. B., Höner zu Siederdissen, C., Dunjić, M., Stadler, P. F., & Nowick, K. (2019a). SSS-test: A novel test for detecting positive selection on RNA secondary structure. BMC Bioinformatics, 20(1), 151. https://doi.org/10.1186/s12859-019-2711-yWang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223), 470-473. https://doi.org/10.1016/S0140-6736(20)30185-9Wang, N., Shang, J., Jiang, S., & Du, L. (2020). Subunit Vaccines Against Emerging Pathogenic Human Coronaviruses. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.00298Wang, Q., Qi, J., Yuan, Y., Xuan, Y., Han, P., Wan, Y., Ji, W., Li, Y., Wu, Y., Wang, J., Iwamoto, A., Woo, P. C. Y., Yuen, K.-Y., Yan, J., Lu, G., & Gao, G. F. (2014). Bat origins of MERS-CoV supported by bat coronavirus HKU4 usage of human receptor CD26. Cell Host & Microbe, 16(3), 328-337. https://doi.org/10.1016/j.chom.2014.08.009Weaver, S., Shank, S. D., Spielman, S. J., Li, M., Muse, S. V., & Kosakovsky Pond, S. L. (2018). Datamonkey 2.0: A Modern Web Application for Characterizing Selective and Other Evolutionary Processes. Molecular Biology and Evolution, 35(3), 773-777. https://doi.org/10.1093/molbev/msx335Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686Wolf, 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). https://doi.org/10.1128/mBio.02329-18Wong, M. C., Cregeen, S. J. J., Ajami, N. J., & Petrosino, J. F. (2020). Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019. BioRxiv, 2020.02.07.939207. https://doi.org/10.1101/2020.02.07.939207World Health Organization (WHO). (2022, de Enero). WHO Coronavirus Disease (COVID-19). https://covid19.who.intWu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., Yuan, M.-L., Zhang, Y.-L., Dai, F.-H., Liu, Y., Wang, Q.-M., Zheng, J.-J., Xu, L., Holmes, E. C., & Zhang, Y.-Z. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579(7798), 265-269. https://doi.org/10.1038/s41586-020-2008-3Wyler, E., Mösbauer, K., Franke, V., Diag, A., Gottula, L. T., Arsiè, R., Klironomos, F., Koppstein, D., Hönzke, K., Ayoub, S., Buccitelli, C., Hoffmann, K., Richter, A., Legnini, I., Ivanov, A., Mari, T., Del Giudice, S., Papies, J., Praktiknjo, S., … Landthaler, M. (2021). Transcriptomic profiling of SARS-CoV-2 infected human cell lines identifies HSP90 as target for COVID-19 therapy. iScience, 24(3), 102151. https://doi.org/10.1016/j.isci.2021.102151Xing, Y., Li, X., Gao, X., & Dong, Q. (2020). MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences. Frontiers in Microbiology, 0. https://doi.org/10.3389/fmicb.2020.01502Xu, K., Zheng, B.-J., Zeng, R., Lu, W., Lin, Y.-P., Xue, L., Li, L., Yang, L.-L., Xu, C., Dai, J., Wang, F., Li, Q., Dong, Q.-X., Yang, R.-F., Wu, J.- R., & Sun, B. (2009). Severe acute respiratory syndrome coronavirus accessory protein 9b is a virion-associated protein. Virology, 388(2), 279-285. https://doi.org/10.1016/j.virol.2009.03.032Yang, D., & Leibowitz, J. L. (2015). The structure and functions of coronavirus genomic 3′ and 5′ ends. Virus Research, 206, 120-133. https://doi.org/10.1016/j.virusres.2015.02.025Yang, H.-C., Chen, C., Wang, J.-H., Liao, H.-C., Yang, C.-T., Chen, C.-W., Lin, Y.-C., Kao, C.-H., Lu, M.-Y. J., & Liao, J. C. (2020). Analysis of genomic distributions of SARS-CoV-2 reveals a dominant strain type with strong allelic associations. Proceedings of the National Academy of Sciences, 117(48), 30679-30686. https://doi.org/10.1073/pnas.2007840117Yang, Y., Liu, C., Du, L., Jiang, S., Shi, Z., Baric, R. S., & Li, F. (2015). Two Mutations Were Critical for Bat-to-Human Transmission of Middle East Respiratory Syndrome Coronavirus. Journal of Virology, 89(17), 9119-9123. https://doi.org/10.1128/JVI.01279-15Ye, Z.-W., Yuan, S., Yuen, K.-S., Fung, S.-Y., Chan, C.-P., & Jin, D.-Y. (2020). Zoonotic origins of human coronaviruses. International Journal of Biological Sciences, 16(10), 1686-1697. https://doi.org/10.7150/ijbs.45472Yones, C., Raad, J., Bugnon, L. A., Milone, D. H., & Stegmayer, G. (2021). High precision in microRNA prediction: A novel genome-wide approach with convolutional deep residual networks. Computers in Biology and Medicine, 134, 104448. https://doi.org/10.1016/j.compbiomed.2021.104448Zhan, S., Wang, Y., & Chen, X. (2020). RNA virus-encoded microRNAs: Biogenesis, functions and perspectives on application. ExRNA, 2(1), 15. https://doi.org/10.1186/s41544-020-00056-zZhang, J., Nielsen, R., & Yang, Z. (2005). Evaluation of an Improved Branch-Site Likelihood Method for Detecting Positive Selection at the Molecular Level. Molecular Biology and Evolution, 22(12), 2472-2479. https://doi.org/10.1093/molbev/msi237Zhang, L., Jackson, C. B., Mou, H., Ojha, A., Rangarajan, E. S., Izard, T., Farzan, M., & Choe, H. (2020). The D614G mutation in the SARS-CoV2 spike protein reduces S1 shedding and increases infectivity. BioRxiv, 2020.06.12.148726. https://doi.org/10.1101/2020.06.12.148726Zhang, L., Jackson, C. B., Mou, H., Ojha, A., Rangarajan, E. S., Izard, T., Farzan, M., & Choe, H. (2020). The D614G mutation in the SARS-CoV2 spike protein reduces S1 shedding and increases infectivity. BioRxiv, 2020.06.12.148726. https://doi.org/10.1101/2020.06.12.148726Zhang, X., Chu, H., Wen, L., Shuai, H., Yang, D., Wang, Y., Hou, Y., Zhu, Z., Yuan, S., Yin, F., Chan, J. F.-W., & Yuen, K.-Y. (2020). Competing endogenous RNA network profiling reveals novel host dependency factors required for MERS-CoV propagation. Emerging Microbes & Infections, 9(1), 733-746. https://doi.org/10.1080/22221751.2020.1738277Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., Chen, H.-D., Chen, J., Luo, Y., Guo, H., Jiang, R.-D., Liu, M.-Q., Chen, Y., Shen, X.-R., Wang, X., … Shi, Z.-L. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579(7798), 270-273. https://doi.org/10.1038/s41586-020-2012-7Detección de variabilidad molecular en el genoma del virus SARS-CoV-2 y otros Betacoronavirus: Hacia un sistema de vigilancia molecular pandémico y post-pandémicoDirección de Investigación y Extensión Bogotá (DIEB) - Universidad Nacional de ColombiaEstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1083904793-2022.pdf1083904793-2022.pdfTesis de Maestría en Ciencias - Biologíaapplication/pdf3903749https://repositorio.unal.edu.co/bitstream/unal/81628/3/1083904793-2022.pdf33264594c2c9d216ac1dbfd907b298ffMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81628/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1083904793-2022.pdf.jpg1083904793-2022.pdf.jpgGenerated 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Evolución molecular de Betacoronavirus zoonóticos asociados con el Síndrome de Distrés Respiratorio Agudo (SDRA)

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