RESUMENPotato yellow vein virus (PYVV), es uno de los fitopatógenos más limitantes para la producción de papa en la región de Los Andes. A pesar que se le ha detectado infectando tomate en Colombia, el conocimiento de las características biológicas de las cepas presentes en este hospedante es muy limitado. En este estudio, utilizando secuenciación masiva de nueva generación (NGS), se obtuvo la secuencia completa de los tres segmentos genómicos del PYVV en plantas de tomate en Marinilla (Antioquia) y se evalúo la utilidad de tres juegos de cebadores para su detección mediante pruebas de RT-PCR convencional y en tiempo real (RT-qPCR). El genoma de la secuencia consenso presentó tamaños de 8043 nt (ARN1), 5346 nt (ARN2) y 3896 nt (ARN3) y se identificaron los diez ORF previamente reportados en este virus, aunque, en general, éstos presentaron menores niveles de identidad que los registrados entre cepas de PYVV de papa. Análisis de variación y de selección identificaron dos regiones en los ORF MET/HEL y CPm que presentan selección positiva, lo que podría estar asociado a la adaptación por hospedante. Los tres juegos de cebadores amplificaron las regiones esperadas de la cápside de PYVV, siendo posible identificar, por diferencias en valores de temperatura de fusión (Tm) y por secuenciación Sanger, la ocurrencia de al menos dos variantes principales de este virus en el Oriente Antioqueño, lo que concuerda con los niveles moderados de polimorfismos encontrados en las secuencias obtenidas por NGS. Palabras clave:Crinivirus, RT-PCR, RT-qPCR, secuenciación de nucleótidos de alto rendimiento, Solanaceae. ABSTRACTPotato yellow vein virus (PYVV) is one of the most important pathogens of potato in the Andean region. In spite of having been detected in tomato crops in Colombia, knowledge on the biological characteristics of PYVV is limited on this host. In this study, next-generation sequencing (NGS) of a PYVV strain infecting tomato in Marinilla District (Antioquia) was performed; additionally, three primer set useful in RT-PCR and RT-qPCR detection were also tested. The consensus genome consisted of three RNA segments of 8043 nt (RNA1), 5346 nt (RNA2) and 3896 nt (RNA3) encoding ten ORF with slight lower sequence identity in relation to PYVV isolates from potato. Sequence analysis suggests the presence of regions potentially undergoing positive selection in the ORFs coding for MET/HEL and CPm possibly as a result of host adaptation. Experimental validation of primers resulted in amplicon with the expected size while melting temperature analysis and sequencing suggest the presence of at least two PYVV infecting S. lycopersicum in east Antioquia in agreement with the NGS data.

Phylogenetics has played a pivotal role in the genomic epidemiology of severe acute respiratory syndrome coronavirus 2, such as tracking the emergence and global spread of variants and scientific communication. However, the rapid accumulation of genomic data from around the world—with over two million genomes currently available in the Global Initiative on Sharing All Influenza Data database—is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2 and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into ‘variants’, generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neighbor-joining trees in RapidNJ that are converted into a majority-rule consensus tree for each lineage. Branches with support values below 50 per cent or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly sampled ancestral variants. Currently, we process about 2 million genomes in approximately 9 h on 52 cores. The resulting trees are visualized using the JavaScript framework D3.js as ‘beadplots’, in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

Phylogenetics has played a pivotal role in the genomic epidemiology of SARS-CoV-2, such as tracking the emergence and global spread of variants, and scientific communication. However, the rapid accumulation of genomic data from around the world - with over two million genomes currently available in the GISAID database - is testing the limits of standard phylogenetic methods. Here, we describe a new approach to rapidly analyze and visualize large numbers of SARS-CoV-2 genomes. Using Python, genomes are filtered for problematic sites, incomplete coverage, and excessive divergence from a strict molecular clock. All differences from the reference genome, including indels, are extracted using minimap2, and compactly stored as a set of features for each genome. For each Pango lineage (https://cov-lineages.org), we collapse genomes with identical features into 'variants', generate 100 bootstrap samples of the feature set union to generate weights, and compute the symmetric differences between the weighted feature sets for every pair of variants. The resulting distance matrices are used to generate neigihbor-joining trees in RapidNJ and converted into a majority-rule consensus tree for the lineage. Branches with support values below 50% or mean lengths below 0.5 differences are collapsed, and tip labels on affected branches are mapped to internal nodes as directly-sampled ancestral variants. Currently, we process about 1.6 million genomes in approximately nine hours on 34 cores. The resulting trees are visualized using the JavaScript framework D3.js as 'beadplots', in which variants are represented by horizontal line segments, annotated with beads representing samples by collection date. Variants are linked by vertical edges to represent branches in the consensus tree. These visualizations are published at https://filogeneti.ca/CoVizu. All source code was released under an MIT license at https://github.com/PoonLab/covizu.

Nef is an accessory protein unique to the primate HIV-1, HIV-2 and SIV lentiviruses. During infection, Nef functions by interacting with multiple host proteins within infected cells to evade the immune response and enhance virion infectivity. Notably, Nef can counter immune regulators such as CD4 and MHC-I, as well as the SERINC5 restriction factor in infected cells. In this study, we generated a posterior sample of time-scaled phylogenies relating SIV and HIV Nef sequences, followed by reconstruction of ancestral sequences at the root and internal nodes of the sampled trees up to the HIV-1 Group M ancestor. Upon expression of the ancestral primate lentivirus Nef protein within CD4+ HeLa cells, flow cytometry analysis revealed that the primate lentivirus Nef ancestor robustly downregulated cell surface SERINC5, yet only partially downregulated CD4 from the cell surface. Further analysis revealed that the Nef-mediated CD4 downregulation ability evolved gradually, while Nef-mediated SERINC5 downregulation was recovered abruptly in the HIV-1/M ancestor. Overall, this study provides a framework to reconstruct ancestral viral proteins and enable the functional characterization of these proteins to delineate how functions could have changed throughout evolutionary history.