RESUMEN
SARS-CoV-2 variants with concerning characteristics have emerged since the end of 2020. Surveillance of SARS-CoV-2 variants was performed on a total of 4,851 samples from the capital city and 10 provinces of Argentina, during 51 epidemiological weeks (EWs) that covered the end of the first wave and the ongoing second wave of the COVID-19 pandemic in the country (EW 44/2020 to EW 41/2021). The surveillance strategy was mainly based on Sanger sequencing of a Spike coding region that allows the identification of signature mutations associated with variants. In addition, whole-genome sequences were obtained from 637 samples. The main variants found were Gamma and Lambda, and to a lesser extent, Alpha, Zeta, and Epsilon, and more recently, Delta. Whereas, Gamma dominated in different regions of the country, both Gamma and Lambda prevailed in the most populated area, the metropolitan region of Buenos Aires. The lineages that circulated on the first wave were replaced by emergent variants in a term of a few weeks. At the end of the ongoing second wave, Delta began to be detected, replacing Gamma and Lambda. This scenario is consistent with the Latin American variant landscape, so far characterized by a concurrent increase in Delta circulation and a stabilization in the number of cases. The cost-effective surveillance protocol presented here allowed for a rapid response in a resource-limited setting, added information on the expansion of Lambda in South America, and contributed to the implementation of public health measures to control the disease spread in Argentina.
RESUMEN
The study of long non-coding RNAs (lncRNAs), greater than 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based machine learning method called FGGA-lnc for the automatic gene ontology (GO) annotation of lncRNAs across the three GO subdomains. We build upon FGGA (factor graph GO annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. Raw GO annotations obtained in this way are unaware of the GO structure and therefore likely to be inconsistent with it. The message-passing algorithm embodied by factor graph models overcomes this problem. Evaluations of the FGGA-lnc method on lncRNA data, from model and non-model organisms, showed promising results suggesting it as a candidate to satisfy the huge demand for functional annotations arising from high-throughput sequencing technologies.
RESUMEN
The main objective of this study was to estimate the genetic diversity levels and haplotype traceability in pacu Piaractus mesopotamicus from the breeding program located in Brazil by analyses of the mitochondrial DNA control region (mtDNA). Moreover, broodstocks from eight commercial fish farms were used for comparative evaluation, four from Brazil (Br1-Br4) and four from Argentina (Ar1-Ar4). The descriptive results revealed 47 polymorphic sites and 51 mutations, which evidenced 34 haplotypes. Ten haplotypes were shared among fish farms and 24 were exclusive. The nucleotide diversity (π) ranged from 0.00031 to 0.01462 and haplotype diversity (Hd) from 0.125 to 0.868. The analysis of molecular variance (AMOVA) indicated high structure present in the analyzed stocks (FST = 0.13356 and ФST = 0.52707). The genetic diversity was high in most of the commercial broodstocks, especially those from Brazil. We observed seven haplotypes in the genetic breeding population, of which four were exclusive and three shared among the commercial fish farms. The genetic diversity was moderate (π = 0.00265 and Hd = 0.424) and considered appropriated for this breeding population of pacu. Our results provide support for the genetic diversity maintenance and mtDNA traceability of pacu commercial broodstocks.
RESUMEN
Molecular diagnostics based on reverse transcription (RT)-PCR are routinely complicated by the lack of stable internal controls, leading to falsely negative results. We describe a strategy to produce a stable competitive internal control (CIC) based on a Qbeta phage derivative (recombinant Qbeta [rQbeta]) bearing primers KY78 and KY80, which are widely used in the detection of hepatitis C virus (HCV). rQbeta was RNase resistant and stable at 4 degrees C for 452 days in SM medium (0.1 M NaCl, 8 mM MgSO(4).7H(2)O, 50 mM Tris HCl [pH 7.5], 2% gelatin) and for 125 days after lyophilization and reconstitution. rQbeta performance as a CIC was evaluated. rQbeta was added to HCV-positive samples, followed by RNA extraction and a CIC-HCV RT-PCR assay. This method combines RT-PCR, liquid hybridization with nonradioactive probes, and enzyme immunoanalysis. No influence of the CIC on qualitative HCV detection was observed independently of viral load, and results had high concordance with those of commercial kits. In conclusion, we describe a versatile, low-cost alternative strategy to armored RNA technology that can be adapted for detection or real-time applications of any RNA target. Moreover, the CIC reported here is an essential reagent for HCV screening in blood banks in resource-limited settings.
