Alternative SARS-CoV-2 detection protocol from self-collected saliva for mass diagnosis and epidemiological studies in low-incoming regions.

Until mass vaccination befalls, control of the new betacoronavirus-associated severe acute respiratory syndrome pandemic (SARS-CoV-2) is based on decreasing virus circulation by social distancing and blocking transmission foci after diagnosis. Globally adopted SARS-CoV-2 diagnostic criteria embrace viral RNA detection by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) on nasopharynx secretions, which requires healthcare facilities and specialized personnel for sample collection. To develop an alternative protocol, hydrophilic cotton as the material and saliva as the source for biological sample collection in qRT-PCR/RT-endpoint-PCR SARS-CoV-2 diagnostic methods prepared with local consumables were evaluated using 99 archived nasopharynx samples previously diagnosed as positive for SARS-CoV-2 and 111 prospective saliva samples pared with nasopharynx samples from patients attending the local reference ABC Medical School diagnostic laboratory. The kappa agreement coefficient between the SARS-CoV-2 qRT-PCR and RT-endpoint-PCR was k = 0.97 (95 % CI 0.92-1.00) and k = 0.90 (95 % CI 0.81-0.99), respectively, on SARS-CoV-2-positive archived samples, with the initial qRT-PCR CT under 25. The agreement coefficient of the SARS-CoV-2 alternative saliva diagnostic protocol, when used to test the paired nasopharynx samples, was k = 0.79 (95 % CI 0.56-1,00). These data support that the SARS-CoV-2 diagnostic assay based on self-collected saliva on cotton represents an alternative protocol for mass diagnosis and epidemiological studies in low-income regions.

Metabolism-driven post-translational modifications of H3K9 in early bovine embryos.

Metabolic and molecular profiles were reported as different for bovine embryos with distinct kinetics during the first cleavages. In this study, we used this same developmental model (fast vs slow) to determine if the relationship between metabolism and developmental kinetics affects the levels of acetylation or tri-methylation at histone H3 lysine 9 (H3K9ac and H3K9me3, respectively). Fast and slow developing embryos presented different levels of H3K9ac and H3K9me3 from the earliest stages of development (40 and 96 hpi) and up to the blastocyst stage. For H3K9me3, both groups of embryos presented a wave of demethylation and de novo methylation, although it was more pronounced in fast than slow embryos, resulting in blastocysts with higher levels of this mark. The H3K9ac reprogramming profile was distinct between kinetics groups. While slow embryos presented a wave of deacetylation, followed by an increase in this mark at the blastocyst stage, fast embryos reduced this mark throughout all the developmental stages studied. H3K9me3 differences corresponded to writer and eraser transcript levels, while H3K9ac patterns were explained by metabolism-related gene expression. To verify if metabolic differences could alter levels of H3K9ac, embryos were cultured with sodium-iodoacetate (IA) or dichloroacetate (DCA) to disrupt the glycolytic pathway or increase acetyl-CoA production, respectively. IA reduced H3K9ac while DCA increased H3K9ac in blastocysts. Concluding, H3K9me3 and H3K9ac patterns differ between embryos with different kinetics, the second one explained by metabolic pathways involved in acetyl-CoA production. So far, this is the first study demonstrating a relationship between metabolic differences and histone post-translational modifications in bovine embryos.

Tricarboxylic Acid Cycle Metabolites as Mediators of DNA Methylation Reprogramming in Bovine Preimplantation Embryos.

In many cell types, epigenetic changes are partially regulated by the availability of metabolites involved in the activity of chromatin-modifying enzymes. Even so, the association between metabolism and the typical epigenetic reprogramming that occurs during preimplantation embryo development remains poorly understood. In this work, we explore the link between energy metabolism, more specifically the tricarboxylic acid cycle (TCA), and epigenetic regulation in bovine preimplantation embryos. Using a morphokinetics model of embryonic development (fast- and slow-developing embryos), we show that DNA methylation (5mC) and hydroxymethylation (5hmC) are dynamically regulated and altered by the speed of the first cleavages. More specifically, slow-developing embryos fail to perform the typical reprogramming that is necessary to ensure the generation of blastocysts with higher ability to establish specific cell lineages. Transcriptome analysis revealed that such differences were mainly associated with enzymes involved in the TCA cycle rather than specific writers/erasers of DNA methylation marks. This relationship was later confirmed by disturbing the embryonic metabolism through changes in α-ketoglutarate or succinate availability in culture media. This was sufficient to interfere with the DNA methylation dynamics despite the fact that blastocyst rates and total cell number were not quite affected. These results provide the first evidence of a relationship between epigenetic reprogramming and energy metabolism in bovine embryos. Likewise, levels of metabolites in culture media may be crucial for precise epigenetic reprogramming, with possible further consequences in the molecular control and differentiation of cells.

SNPs selection using support vector regression and genetic algorithms in GWAS.

INTRODUCTION: This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. RESULTS: The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. CONCLUSIONS: The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.