Crowder Dickey (flycup08)

Moreover, we have constructed a phylogenetic tree for the novel coronavirus (COVID-19) and related coronaviruses by ENJ, which shows that COVID-19 and SARS-CoV are closer than other coronaviruses. Because it differs from the existing phylogenetic trees for those coronaviruses, we constructed a phylogenetic network for them. The network shows those species have had a reticulate evolution.Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases. Identification of lncRNA biomarkers and lncRNA-disease associations is central to diagnoses and treatment. However, traditional experimental methods are expensive and time-consuming. Enormous amounts of data present in public biological databases are available for computational methods used to predict lncRNA-disease associations. In this study, we propose a novel computational method to predict lncRNA-disease associations. More specifically, a heterogeneous network is first constructed by integrating the associations among microRNA (miRNA), lncRNA, protein, drug, and disease, Second, high-order proximity preserved embedding (HOPE) was used to embed nodes into a network. Finally, the rotation forest classifier was adopted to train the prediction model. In the 5-fold cross-validation experiment, the area under the curve (AUC) of our method achieved 0.8328 ± 0.0236. We compare it with the other four classifiers, in which the proposed method remarkably outperformed other comparison methods. Otherwise, we constructed three case studies for three excess death rate cancers, respectively. The results show that 9 (lung cancer, gastric cancer, and hepatocellular carcinomas) out of the top 15 predicted disease-related lncRNAs were confirmed by our method. In conclusion, our method could predict the unknown lncRNA-disease associations effectively.Mitochondrial dysfunction is a metabolic hallmark of cancer cells. In search of molecular factors involved in this dysregulation in hepatocellular carcinoma (HCC), we found that the nuclear-encoded long noncoding RNA (lncRNA) MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) was aberrantly enriched in the mitochondria of hepatoma cells. Using RNA reverse transcription-associated trap sequencing (RAT-seq), we showed that MALAT1 interacted with multiple loci on mitochondrial DNA (mtDNA), including D-loop, COX2, ND3, and CYTB genes. MALAT1 knockdown induced alterations in the CpG methylation of mtDNA and in mitochondrial transcriptomes. This was associated with multiple abnormalities in mitochondrial function, including altered mitochondrial structure, low oxidative phosphorylation (OXPHOS), decreased ATP production, reduced mitophagy, decreased mtDNA copy number, and activation of mitochondrial apoptosis. These alterations in mitochondrial metabolism were associated with changes in tumor phenotype and in pathways involved in cell mitophagy, mitochondrial apoptosis, and epigenetic regulation. We further showed that the RNA-shuttling protein HuR and the mitochondria transmembrane protein MTCH2 mediated the transport of MALAT1 in this nuclear-mitochondrial crosstalk. This study provides the first evidence that the nuclear genome-encoded lncRNA MALAT1 functions as a critical epigenetic player in the regulation of mitochondrial metabolism of hepatoma cells, laying the foundation for further clarifying the roles of lncRNAs in tumor metabolic reprogramming.MicroRNAs (miRNAs) regulate the expression of genes associated with the development of diseases, including type 2 diabetes mellitus (T2DM). However, the use of miRNAs to predict T2DM remission has been poorly studied. Therefore, we aimed to investigate whether circulating miRNAs could be used to predict the probability of T2DM remission in patients with coronary heart disease. We included the newly diagnosed T2DM (n = 190) of the 1,002 patients from the CORDIOPREV study. Seventy-three patients reve