Exon and intron sharing in opposite direction-an undocumented phenomenon in human genome-between Pou5f1 and Tcf19 genes

Background Overlapping genes share same genomic regions in parallel (sense) or anti-parallel (anti-sense) orientations. These gene pairs seem to occur in all domains of life and are best known from viruses. However, the advantage and biological significance of overlapping genes is still unclear. Expressed sequence tags (ESTs) analysis enabled us to uncover an overlapping gene pair in the human genome. Results By using in silico analysis of previous experimental documentations, we reveal a new form of overlapping genes in the human genome, in which two genes found on opposite strands (Pou5f1 and Tcf19), share two exons and one intron enclosed, at the same positions, between OCT4B3 and TCF19-D splice variants. Conclusions This new form of overlapping gene expands our previous perception of splicing events and may shed more light on the complexity of gene regulation in higher organisms. Additional such genes might be detected by ESTs analysis also of other organisms.

Investigation of the Mechanisms of Chuankezhi Injection in the Treatment of Asthma Based on the Network Pharmacology Approach

Background. Chuankezhi injection (CKZI) was an effective traditional Chinese medicine (TCM) injection in adjuvant bronchial asthma therapy. In this report, we used a network pharmacology method to reveal the mechanisms of CKZI for the treatment of asthma. Methods. The candidate compounds in CKZI were determined by searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and China National Knowledge Infrastructure website (CNKI). The targets of candidate compounds were searched in the TCMSP, DrugBank 5.0, and SwissTargetPrediction. The disease targets were screened from the Online Mendelian Inheritance in Man (OMIM) and GeneCards. The overlapping gene symbols between candidate compounds and disease were filtered via a Venn diagram and were considered as potential targets. A protein-protein interaction (PPI) network and disease-related candidate compound-target-pathway (DC-T-P) network were visualized by Cytoscape 3.6.1. Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by metascape to determine the pathways related to asthma. Results. A total of 70 overlapping gene symbols were recognized as potential targets. Cytokines (IL6, TNF, and IL1B) and chemokines (CXCL8 and CCL2) could be recognized as hub genes. Asthma-related candidate compounds were mainly flavonoids, such as quercetin, luteolin, and kaempferol. The cytokine-mediated signaling pathway, cytokine receptor binding, and membrane craft were the most significant biological process (BP), molecular function (MF), and cellular component (CC) of GO function results, respectively. The relevant pathways of CKZI against asthma mainly include IL-17, NF-kappa B, HIF-1, calcium, and PI3K-Akt signaling pathways. Conclusion. Our research provided a theoretical basis for further investigating the mechanisms of CKZI in the treatment of asthma.

GeneSetCluster: a tool for summarizing and integrating gene-set analysis results

Background Gene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are over-represented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNA-sequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biological insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights. Results We present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identifies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations. Conclusions GeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://github.com/TranslationalBioinformaticsUnit

Dynamically evolving novel overlapping gene as a factor in the SARS-CoV-2 pandemic

Understanding the emergence of novel viruses requires an accurate and comprehensive annotation of their genomes. Overlapping genes (OLGs) are common in viruses and have been associated with pandemics but are still widely overlooked. We identify and characterize ORF3d, a novel OLG in SARS-CoV-2 that is also present in Guangxi pangolin-CoVs but not other closely related pangolin-CoVs or bat-CoVs. We then document evidence of ORF3d translation, characterize its protein sequence, and conduct an evolutionary analysis at three levels: between taxa (21 members of Severe acute respiratory syndrome-related coronavirus), between human hosts (3978 SARS-CoV-2 consensus sequences), and within human hosts (401 deeply sequenced SARS-CoV-2 samples). ORF3d has been independently identified and shown to elicit a strong antibody response in COVID-19 patients. However, it has been misclassified as the unrelated gene ORF3b, leading to confusion. Our results liken ORF3d to other accessory genes in emerging viruses and highlight the importance of OLGs.

Dynamically evolving novel overlapping gene as a factor in the SARS-CoV-2 pandemic

Understanding the emergence of novel viruses requires an accurate and comprehensive annotation of their genomes. Overlapping genes (OLGs) are common in viruses and have been associated with pandemics, but are still widely overlooked. We identify and characterize ORF3d, a novel OLG in SARS-CoV-2 that is also present in Guangxi pangolin-CoVs but not other closely related pangolin-CoVs or bat-CoVs. We then document evidence of ORF3d translation, characterize its protein sequence, and conduct an evolutionary analysis at three levels: between taxa (21 members of Severe acute respiratory syndrome-related coronavirus), between human hosts (3978 SARS-CoV-2 consensus sequences), and within human hosts (401 deeply sequenced SARS-CoV-2 samples). ORF3d has been independently identified and shown to elicit a strong antibody response in COVID-19 patients. However, it has been misclassified as the unrelated gene ORF3b, leading to confusion. Our results liken ORF3d to other accessory genes in emerging viruses and highlight the importance of OLGs.