In this study, we report the first case of intra-host SARS-CoV-2 recombination during a coinfection by the variants of concern (VOC) AY.33 (Delta) and P.1 (Gamma) supported by sequencing reads harboring a mosaic of lineage-defining mutations. By using next-generation sequencing reads intersecting regions that simultaneously overlap lineage-defining mutations from Gamma and Delta, we were able to identify a total of six recombinant regions across the SARS-CoV-2 genome within a sample. Four of them mapped in the spike gene and two in the nucleocapsid gene. We detected mosaic reads harboring a combination of lineage-defining mutations from each VOC. To our knowledge, this is the first report of intra-host RNA-RNA recombination between two lineages of SARS-CoV-2, which can represent a threat to public health management during the COVID-19 pandemic due to the possibility of the emergence of viruses with recombinant phenotypes.

In the present study, we provide a retrospective genomic epidemiology analysis of the SARS-CoV-2 pandemic in the state of Rio de Janeiro, Brazil. We gathered publicly available data from GISAID and sequenced 1927 new genomes sampled periodically from March 2021 to June 2021 from 91 out of the 92 cities of the state. Our results showed that the pandemic was characterized by three different phases driven by a successive replacement of lineages. Interestingly, we noticed that viral supercarriers accounted for the overwhelming majority of the circulating virus (>90%) among symptomatic individuals in the state. Moreover, SARS-CoV-2 genomic surveillance also revealed the emergence and spread of two new variants (P.5 and P.1.2), firstly reported in this study. Our findings provided important lessons learned from the different epidemiological aspects of the SARS-CoV-2 dynamic in Rio de Janeiro. Altogether, this might have a strong potential to shape future decisions aiming to improve public health management and understanding mechanisms underlying virus dispersion.

Despite being developed from one zygote, heterokaryotypic monozygotic (MZ) co-twins exhibit discordant karyotypes. Epigenomic studies in biological samples from heterokaryotypic MZ co-twins are of the most significant value for assessing the effects on gene- and allele-specific expression of an extranumerary chromosomal copy or structural chromosomal disparities in otherwise nearly identical germline genetic contributions. Here, we use RNA-Seq data from existing repositories to establish within-pair correlations for the breadth and magnitude of allele-specific expression (ASE) in heterokaryotypic MZ co-twins discordant for trisomy 21 and maternal 21q inheritance, as well as homokaryotypic co-twins. We show that there is a genome-wide disparity at ASE sites between the heterokaryotypic MZ co-twins. Although most of the disparity corresponds to changes in the magnitude of biallelic imbalance, ASE sites switching from either strictly monoallelic to biallelic imbalance or the reverse occur in few genes that are known or predicted to be imprinted, subject to X-chromosome inactivation or A-to-I(G) RNA edited. We also uncovered comparable ASE differences between homokaryotypic MZ twins. The extent of ASE discordance in MZ twins (2.7%) was about 10-fold lower than the expected between pairs of unrelated, non-twin males or females. The results indicate that the observed within-pair dissimilarities in breadth and magnitude of ASE sites in the heterokaryotypic MZ co-twins could not solely be attributable to the aneuploidy and the missing allelic heritability at 21q.

Predicting the physical or functional associations through protein-protein interactions (PPIs) represents an integral approach for inferring novel protein functions and discovering new drug targets during repositioning analysis. Recent advances in high-throughput data generation and multi-omics techniques have enabled large-scale PPI predictions, thus promoting several computational methods based on different levels of biological evidence. However, integrating multiple results and strategies to optimize, extract interaction features automatically and scale up the entire PPI prediction process is still challenging. Most procedures do not offer an in-silico validation process to evaluate the predicted PPIs. In this context, this paper presents the PredPrIn scientific workflow that enables PPI prediction based on multiple lines of evidence, including the structure, sequence, and functional annotation categories, by combining boosting and stacking machine learning techniques. We also present a pipeline (PPIVPro) for the validation process based on cellular co-localization filtering and a focused search of PPI evidence on scientific publications. Thus, our combined approach provides means to extensive scale training or prediction of new PPIs and a strategy to evaluate the prediction quality. PredPrIn and PPIVPro are publicly available at https://github.com/YasCoMa/predprin and https://github.com/YasCoMa/ppi_validation_process.

IntroductionCell entry of SARS-CoV-2 causes genome-wide disruption of the transcriptional profiles of genes and biological pathways involved in the pathogenesis of COVID-19. Expression allelic imbalance is characterized by a deviation from the Mendelian expected 1:1 expression ratio and is an important source of allele-specific heterogeneity. Expression allelic imbalance can be measured by allele-specific expression analysis (ASE) across heterozygous informative expressed single nucleotide variants (eSNVs). ASE reflects many regulatory biological phenomena that can be assessed by combining genome and transcriptome information. ASE contributes to the interindividual variability associated with the disease. We aim to estimate the transcriptome-wide impact of SARS-CoV-2 infection by analyzing eSNVs.MethodsWe compared ASE profiles in the human lung cell lines Calu-3, A459, and H522 before and after infection with SARS-CoV-2 using RNA-Seq experiments.ResultsWe identified 34 differential ASE (DASE) sites in 13 genes (HLA-A, HLA-B, HLA-C, BRD2, EHD2, GFM2, GSPT1, HAVCR1, MAT2A, NQO2, SUPT6H, TNFRSF11A, UMPS), all of which are enriched in protein binding functions and play a role in COVID-19. Most DASE sites were assigned to the MHC class I locus and were predominantly upregulated upon infection. DASE sites in the MHC class I locus also occur in iPSC-derived airway epithelium basal cells infected with SARS-CoV-2. Using an RNA-Seq haplotype reconstruction approach, we found DASE sites and adjacent eSNVs in phase (i.e., predicted on the same DNA strand), demonstrating differential haplotype expression upon infection. We found a bias towards the expression of the HLA alleles with a higher binding affinity to SARS-CoV-2 epitopes.DiscussionIndependent of gene expression compensation, SARS-CoV-2 infection of human lung cell lines induces transcriptional allelic switching at the MHC loci. This suggests a response mechanism to SARS-CoV-2 infection that swaps HLA alleles with poor epitope binding affinity, an expectation supported by publicly available proteome data.