Genética y evolución en la pandemia de COVID-19

La pandemia ocasionada por el coronavirus SARS-CoV-2 ha alterado la vida, la economía, las relaciones sociales y personales en nuestro planeta y, desgraciadamente, ha producido más de 5 millones de muertes (datos a noviembre de 2021). Por otra parte, ha puesto de manifiesto el papel esencial que tiene la investigación biomédica y lo dependiente que es nuestra sociedad de muchos trabajos científicos que solían pasar inadvertidos. Además de mostrar el poder que se deriva de la acción coordinada y cooperativa de grupos de investigación, la pandemia también ha puesto un foco en disciplinas que, en gran medida, han sido ignoradas hasta el momento. La aparición de variantes de preocupación e interés y su relación con los incrementos en la incidencia de las infecciones en diferentes olas o la posibilidad de que representen escapes vacunales ha llevado a la OMS a establecer la vigilancia genómica como una de las herramientas fundamentales para conocer y controlar la expansión del virus. En este artículo, vamos a analizar algunos aspectos fundamentales de la evolución y genética de las poblaciones del virus y cómo nos pueden ayudar a interpretar adecuadamente los datos derivados de los análisis de secuencias completas del genoma viral, método ideal para realizar dicha vigilancia

La teoría de la evolución en los juzgados

La teoría de la evolución y sus implicaciones en otros campos, concretamente en este artículo se incide en el aspecto judicial. Resulta que los mismos postulados evolutivos que son discutidos y reprobados en algunos tribunales son aceptados en otros como evidencias científicas en casos criminales. La teoría evolutiva ha hecho una irrupción insospechada hace apenas unos años en los tribunales de justici

Comparative analysis of variation and selection in the HCV genome

Genotype 1 of the hepatitis C virus (HCV) is themost prevalent of the variants of this virus. Its two main subtypes, HCV-1a and HCV-1b, are associated to differences in epidemic features and risk groups, despite sharing similar features in most biological properties. We have analyzed the impact of positive selection on the evolution of these variants using complete genome coding regions, and compared the levels of genetic variability and the distribution of positively selected sites. We have also compared the distributions of positively selected and conserved sites considering different factors such as RNA secondary structure, the presence of different epitopes (antibody, CD4 and CD8), and secondary protein structure. Less than 10% of the genome was found to be under positive selection, and purifying selection was the main evolutionary process acting in both subtypes. We found differences in the number of positively selected sites between subtypes in several genes (Core, HVR2 in E2, P7, helicase in NS3 and NS4a). Heterozygosity values in positively selected sites and the rate of non-synonymous substitutions were significantly higher in subtype HCV-1b. Logistic regression analyses revealed that similar selective forces act at the genome level in both subtypes: RNA secondary structure and CD4 T-cell epitopes are associated with conserved sites, while CD8 T-cell epitopes are associatedwith positive selection in both subtypes. These results indicate that similar selective constraints are acting along HCV-1a and HCV-1 b genomes, despite some differences in the distribution of positively selected sites at independent genes

Phylogenetic signal and functional categories in Proteobacteria genomes

BACKGROUND: A comprehensive evolutionary analysis of bacterial genomes implies to identify the hallmark of vertical and non-vertical signals and to discriminate them from the presence of mere phylogenetic noise. In this report we have addressed the impact of factors like the universal distribution of the genes, their essentiality or their functional role in the cell on the inference of vertical signal through phylogenomic methods. RESULTS: We have established that supermatrices derived from data sets composed mainly by genes suspected to be essential for bacterial cellular life perform better on the recovery of vertical signal than those composed by widely distributed genes. In addition, we show that the "Transcription" category of genes seems to harbor a better vertical signal than other functional categories. Moreover, the "Poorly characterized" category performs better than other categories related with metabolism or cellular processes. CONCLUSION: From these results we conclude that different data sets allow addressing different questions in phylogenomic analyses. The vertical signal seems to be more present in essential genes although these also include a significant degree of incongruence. From a functional perspective, as expected, informational genes perform better than operational ones but we have also shown the surprising behavior of poorly annotated genes, which points to their importance in the genome evolution of bacteria

The substitution rate of HIV-1 subtypes: a genomic approach

HIV-1M causes most infections in the AIDS pandemic. Its genetic diversity is defined by nine pure subtypes and more than sixty recombinant forms. We have performed a comparative analysis of the evolutionary rate of five pure subtypes (A1, B, C, D, and G) and two circulating recombinant forms (CRF01_AE and CRF02 AG) using data obtained from nearly complete genome coding sequences. Times to the most recent common ancestor (tMRCA) and substitution rates of these HIV genomes, and their genomic partitions, were estimated by Bayesian coalescent analyses. Genomic substitution rate estimates were compared between the HIV-1 datasets analyzed by means of randomization tests. Significant differences in the rate of evolution were found between subtypes, with subtypes C and A1 and CRF01_AE displaying the highest rates. On the other hand, CRF02_AG and subtype D were the slowest evolving types. Using a different molecular clock model for each genomic partition led to more precise tMRCA estimates than when linking the same clock along the HIV genome. Overall, the earliest tMRCA corresponded to subtype A1 (median = 1941, 95% HPD = 1943-55), whereas the most recent tMRCA corresponded to subtype G and CRF01_AE subset 3 (median = 1971, 95% HPD = 1967-75 and median = 1972, 95% HPD = 1970-75, respectively). These results suggest that both biological and epidemiological differences among HIV-1M subtypes are reflected in their evolutionary dynamics. The estimates obtained for tMRCAs and substitution rates provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes