Non-coding segments of the human genome are enriched in cis-regulatory modules that constitute functional elements, such as transcriptional enhancers and Super-enhancers. A hallmark of cancer pathogenesis is the dramatic dysregulation of the “archetype” gene expression profiles of normal human cells. Genomic variations can promote such deficiencies when occurring across enhancers and Super-enhancers, since they affect their mechanistic principles, their functional capacity and specificity, and the epigenomic features of the chromatin microenvironment across which these regulatory elements reside. Here, we comprehensively describe: fundamental mechanisms of gene expression dysregulation in cancers that involve genomic abnormalities within enhancers’ and Super-enhancers’ (SEs) sequences, which alter the expression of oncogenic transcription factors (TFs); cutting-edge technologies applied for the analysis of variation-enriched hotspots of the cancer genome; and pharmacological approaches for the treatment of Super-enhancers’ aberrant function. Finally, we provide an intratumor meta-analysis, which highlights that genomic variations in transcription-factor-driven tumors are accompanied overexpression of genes, a portion of which encodes for additional cancer-related transcription factors.

Viral RNA sensing triggers innate antiviral responses in humans by stimulating signaling pathways that include crucial antiviral genes such as interferon. RNA viruses have evolved strategies to inhibit or escape these mechanisms. Coronaviruses use multiple enzymes to synthesize, modify, and process their genomic RNA and sub-genomic RNAs. These include Nsp15 and Nsp16, whose respective roles in RNA capping and dsRNA degradation play a crucial role in coronavirus escape from immune surveillance. Evolutionary studies on coronaviruses demonstrate that genome expansion in Nidoviruses was promoted by the emergence of Nsp14-ExoN activity and led to the acquisition of Nsp15- and Nsp16-RNA-processing activities. In this review, we discuss the main RNA-sensing mechanisms in humans as well as recent structural, functional, and evolutionary insights into coronavirus Nsp15 and Nsp16 with a view to potential antiviral strategies.

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.

Cancer is a disease caused by (epi)genomic and gene expression abnormalities and characterized by metabolic phenotypes that are substantially different from the normal phenotypes of the tissues of origin. Metabolic reprogramming is one of the key features of tumors, including those established in the human nervous system. In this work, we emphasize a well-known cancerous genomic alteration: the amplification of MYCN and its downstream effects in neuroblastoma phenotype evolution. Herein, we extend our previous computational biology investigations by conducting an integrative workflow applied to published genomics datasets and comprehensively assess the impact of MYCN amplification in the upregulation of metabolism-related transcription factor (TF)-encoding genes in neuroblastoma cells. The results obtained first emphasized overexpressed TFs, and subsequently those committed in metabolic cellular processes, as validated by gene ontology analyses (GOs) and literature curation. Several genes encoding for those TFs were investigated at the mechanistic and regulatory levels by conducting further omics-based computational biology assessments applied on published ChIP-seq datasets retrieved from MYCN-amplified- and MYCN-enforced-overexpression within in vivo systems of study. Hence, we approached the mechanistic interrelationship between amplified MYCN and overexpression of metabolism-related TFs in neuroblastoma and showed that many are direct targets of MYCN in an amplification-inducible fashion. These results illuminate how MYCN executes its regulatory underpinnings on metabolic processes in neuroblastoma.