Background: Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis. The data collection process can lead to different distribution, frequency, and structure of missing data points. They can be classified into four categories: Structurally Missing Data (SMD), Missing Completely At Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR). For the three later, and in the context of genomic data (especially non-coding data), we will discuss six imputation approaches using 31,245 variants collected from ClinVar and annotated with 13 genome-wide features. Results: Random Forest and kNN algorithms showed the best performance in the evaluated dataset. Additionally, some features show robust imputation regardless of the algorithm (e.g. conservation scores phyloP7 and phyloP20), while other features show poor imputation across algorithms (e.g. PhasCons). We also developed an R package that helps to test which imputation method is the best for a particular data set. Conclusions: We found that Random Forest and kNN are the best imputation method for genomics data, including non-coding variants. Since Random Forest is computationally more challenging, kNN remains a more realistic approach. Future work on variant prioritization thru genomic screening tests could largely profit from this methodology.

Cardiac cell fate specification occurs through progressive steps, and its gene expression regulation features are still being defined. There has been an increasing interest in understanding the coordination between transcription and post-transcriptional regulation during the differentiation processes. Here, we took advantage of the polysome profiling technique to isolate and high-throughput sequence ribosome-free and polysome-bound RNAs during cardiomyogenesis. We showed that polysome-bound RNAs exhibit the cardiomyogenic commitment gene expression and that mesoderm-to-cardiac progenitor stages are strongly regulated. Additionally, we compared ribosome-free and polysome-bound RNAs and found that the post-transcriptional regulation vastly contributes to cardiac phenotype determination, including RNA recruitment to and dissociation from ribosomes. Moreover, we found that protein synthesis is decreased in cardiomyocytes compared to human embryonic stem-cells (hESCs), possibly due to the down-regulation of translation-related genes. Our data provided a powerful tool to investigate genes potentially controlled by post-transcriptional mechanisms during the cardiac differentiation of hESC. This work could prospect fundamental tools to develop new therapy and research approaches.

Uruguay is one of the few countries in the Americas that successfully contained the coronavirus disease 19 (COVID-19) epidemic during the first half of 2020. Nevertheless, the intensive human mobility across the dry border with Brazil is a major challenge for public health authorities. We aimed to investigate the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains detected in Uruguayan localities bordering Brazil as well as to measure the viral flux across this ∼1,100 km uninterrupted dry frontier. Using complete SARS-CoV-2 genomes from the Uruguayan–Brazilian bordering region and phylogeographic analyses, we inferred the virus dissemination frequency between Brazil and Uruguay and characterized local outbreak dynamics during the first months (May–July) of the pandemic. Phylogenetic analyses revealed multiple introductions of SARS-CoV-2 Brazilian lineages B.1.1.28 and B.1.1.33 into Uruguayan localities at the bordering region. The most probable sources of viral strains introduced to Uruguay were the Southeast Brazilian region and the state of Rio Grande do Sul. Some of the viral strains introduced in Uruguayan border localities between early May and mid-July were able to locally spread and originated the first outbreaks detected outside the metropolitan region. The viral lineages responsible for Uruguayan urban outbreaks were defined by a set of between four and 11 mutations (synonymous and non-synonymous) with respect to the ancestral B.1.1.28 and B.1.1.33 viruses that arose in Brazil, supporting the notion of a rapid genetic differentiation between SARS-CoV-2 subpopulations spreading in South America. Although Uruguayan borders have remained essentially closed to non-Uruguayan citizens, the inevitable flow of people across the dry border with Brazil allowed the repeated entry of the virus into Uruguay and the subsequent emergence of local outbreaks in Uruguayan border localities. Implementation of coordinated bi-national surveillance systems is crucial to achieve an efficient control of the SARS-CoV-2 spread across this kind of highly permeable borderland regions around the world.

Understanding the intricate molecular mechanisms governing the fate of human adipose-derived stem cells (hASCs) is essential for elucidating the delicate balance between adipogenic and osteogenic differentiation in both healthy and pathological conditions. Long non-coding RNAs (lncRNAs) have emerged as key regulators involved in lineage commitment and differentiation of stem cells, operating at various levels of gene regulation, including transcriptional, post-transcriptional, and post-translational processes. To gain deeper insights into the role of lncRNAs’ in hASCs’ differentiation, we conducted a comprehensive analysis of the lncRNA transcriptome (RNA-seq) and translatome (polysomal-RNA-seq) during a 24 h period of adipogenesis and osteogenesis. Our findings revealed distinct expression patterns between the transcriptome and translatome during both differentiation processes, highlighting 90 lncRNAs that are exclusively regulated in the polysomal fraction. These findings underscore the significance of investigating lncRNAs associated with ribosomes, considering their unique expression patterns and potential mechanisms of action, such as translational regulation and potential coding capacity for microproteins. Additionally, we identified specific lncRNA gene expression programs associated with adipogenesis and osteogenesis during the early stages of cell differentiation. By shedding light on the expression and potential functions of these polysome-associated lncRNAs, we aim to deepen our understanding of their involvement in the regulation of adipogenic and osteogenic differentiation, ultimately paving the way for novel therapeutic strategies and insights into regenerative medicine.

Background: The etiology of many genetic diseases is challenging. This is especially true for developmental disorders of the central nervous system, since several genes can be involved. Many of such pathologies are considered rare diseases, since they affect less than 1 in 2000 people. Due to their low frequency, they present several difficulties for patients, from the delay in the diagnosis to the lack of treatments. Next‐generation sequencing techniques have improved the search for diagnosis in several pathologies. Many studies have shown that the use of whole‐exome/genome sequencing in rare Mendelian diseases has a diagnostic yield between 30% and 50% depending on the disease.Methods: Here, we present the case of an undiagnosed 6‐year‐old boy with severe encephalopathy of unclear cause, whose etiological diagnosis was achieved by whole‐genome sequencing.Results: We found a novel variant that has not been previously reported in patients nor it has been described in GnomAD. Segregation analysis supports a de novo mutation, since it is not present in healthy parents. The change is predicted to be harmful to protein function, since it falls in the first quarter of the protein producing an altered reading frame and generating a premature stop codon. Additionally, the variant is classified as pathogenic according to ACMG criteria (PVS1, PM2, and PP3). Furthermore, there are several reported frameshift mutations in nearby codons as well as nonsense mutations that are predicted as pathogenic in other studies.Conclusion: We found a novel de novo frameshift mutation in the PURA gene (MIM number 600473), c.151_161del, with sufficient evidence of its pathogenicity.