Omics A Journal of Integrative Biology最新文献

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic disease of the lung with poor prognosis. Fibrosis results from remodeling of the interstitial tissue. A wide range of gene expression changes are observed, but the role of micro RNAs (miRNAs) and circular RNAs (circRNA) is still unclear. Therefore, this study aimed to establish an messenger RNA (mRNA)-miRNA-circRNA competing endogenous RNA (ceRNA) regulatory network to uncover novel molecular signatures using systems biology tools. Six datasets were used to determine differentially expressed genes (DEGs) and miRNAs (DEmiRNA). Accordingly, protein-protein, mRNA-miRNA, and miRNA-circRNA interactions were constructed. Modules were determined and further analyzed in the Drug Gene Budger platform to identify potential therapeutic compounds. We uncovered common 724 DEGs and 278 DEmiRNAs. In the protein-protein interaction network, TMPRSS4, ESR2, TP73, CLEC4E, and TP63 were identified as hub protein coding genes. The mRNA-miRNA interaction network revealed two modules composed of ADRA1A, ADRA1B, hsa-miR-484 and CDH2, TMPRSS4, and hsa-miR-543. The DEmiRNAs in the modules further analyzed to propose potential circRNA regulators in the ceRNA network. These results help deepen the understanding of the mechanisms of IPF. In addition, the molecular leads reported herein might inform future innovations in diagnostics and therapeutics research and development for IPF.

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.

Cigarette smoking is the major cause of chronic inflammatory diseases such as chronic obstructive pulmonary disease (COPD). It is paramount to develop pharmacological interventions and delivery strategies against the cigarette smoke (CS) associated oxidative stress in COPD. This study in Wistar rats examined cysteamine in nanoemulsions to counteract the CS distressed microenvironment. In vivo, 28 days of CS and 15 days of cysteamine nanoemulsions treatment starting on 29th day consisting of oral and inhalation routes were established in Wistar rats. In addition, we conducted inflammatory and epithelial-to-mesenchymal transition (EMT) studies in vitro in human bronchial epithelial cell lines (BEAS2B) using 5% CS extract. Inflammatory and anti-inflammatory markers, such as tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, IL-1β, IL-8, IL-10, and IL-13, have been quantified in bronchoalveolar lavage fluid (BALF) to evaluate the effects of the cysteamine nanoemulsions in normalizing the diseased condition. Histopathological analysis of the alveoli and the trachea showed the distorted, lung parenchyma and ciliated epithelial barrier, respectively. To obtain mechanistic insights into the CS COPD rat model, "shotgun" proteomics of the lung tissues have been carried out using high-resolution mass spectrometry wherein genes such as ABI1, PPP3CA, PSMA2, FBLN5, ACTG1, CSNK2A1, and ECM1 exhibited significant differences across all the groups. Pathway analysis showed autophagy, signaling by receptor tyrosine kinase, cytokine signaling in immune system, extracellular matrix organization, and hemostasis, as the major contributing pathways across all the studied groups. This work offers new preclinical findings on how cysteamine taken orally or inhaled can combat CS-induced oxidative stress.

Kidney renal cell carcinoma (KIRC) is the most common type of renal cancer. Kidney malignancies have been ranked in the top 10 most frequently occurring cancers. KIRC is a prevalent malignancy with a poor prognosis. The disease has risen for the last 40 years, and robust biomarkers for KIRC are needed for precision/personalized medicine. In this bioinformatics study, we utilized genomic data of KIRC patients from The Cancer Genome Atlas for biomarker discovery. A total of 314 samples were used in this study. We identified many differentially expressed genes (DEGs) categorized as upregulated or downregulated. A protein-protein interaction network for the DEGs was then generated and analyzed using the Search Tool for the Retrieval of Interacting Genes plugin of Cytoscape. A set of 10 hub genes was selected based on the Maximum Clique Centrality score defined by the CytoHubba plugin. The elucidated set of genes, that is, CALCA, CRH, TH, CHAT, SLC18A3, FSHB, MYH6, CAV3, KCNA4, and GBX2, were then categorized as potential candidates to be explored as KIRC biomarkers. The survival analysis plots for each gene suggested that alterations in CHAT, CAV3, CRH, MYH6, SLC18A3, and FSHB resulted in decreased survival of KIRC patients. In all, the results suggest that genomic alterations in selected genes can be explored to inform biomarker discovery and for therapeutic predictions in KIRC.

Idiopathic pulmonary arterial hypertension (IPAH) is a progressive disease that affects the pulmonary arteries, resulting in increased pulmonary vascular resistance and right ventricular dysfunction, which can ultimately lead to heart failure and death. The molecular substrates of IPAH are poorly understood while diagnostics and therapeutics innovation remain as unmet needs for this debilitating disease. In this study, a network-based methodology was used to uncover the salient molecular mechanisms of IPAH to inform drug and diagnostic discovery, and personalized medicine. Expression profiling datasets associated with IPAH were obtained from the Gene Expression Omnibus database: GSE15197, GSE113439, GSE53408, and GSE67597. The comparative analysis of mRNA and miRNA expression data and the modular analysis of a transcriptome-based weighted gene coexpression network unraveled disease-specific gene and miRNA signatures. DEAD-box helicase 52 (DDx52), ESF1 nucleolar pre-RNA processing protein (ESF1), heterogeneous nuclear ribonuclearprotein A3 (MNRNPA3), Myosin VA (MYO5A), replication factor C subunit 1 (RFC1), and arginine and serine rich coiled coil 1 (RSRC1) were detected as the salient genes for IPAH. In addition, the salient gene-based drug repositioning analysis identified alvespimycin, tanespimycin, geldanamycin, LY294002, cephaeline, digoxigenin, lanatoside C, helveticoside, trichostatin A, phenoxybenzamine, genistein, pioglitazone, and rosiglitazone as potential drug candidates for IPAH. In conclusion, this study provides new molecular signatures in relation to IPAH and attendant potential drug candidates for further experimental and translational clinical research for patients with IPAH.

Erythrocytosis is characterized by an increase in red cells in peripheral blood. Polycythemia vera, the commonest primary erythrocytosis, results from pathogenic variants in JAK2 in ∼98% of cases. Although some variants have been reported in JAK2-negative polycythemia, the causal genetic variants remain unidentified in ∼80% of cases. To discover genetic variants in unexplained erythrocytosis, we performed whole exome sequencing in 27 patients with JAK2-negative polycythemia after excluding the presence of any mutations in genes previously associated with erythrocytosis (EPOR, VHL, PHD2, EPAS1, HBA, and HBB). We found that the majority of patients (25/27) had variants in genes involved in epigenetic processes, including TET2 and ASXL1 or in genes related to hematopoietic signaling such as MPL and GFIB. Based on computational analysis, we believe that variants identified in 11 patients in this study could be pathogenic although functional studies will be required for confirmation. To our knowledge, this is the largest study reporting novel variants in individuals with unexplained erythrocytosis. Our results suggest that genes involved in epigenetic processes and hematopoietic signaling pathways are likely associated with unexplained erythrocytosis in individuals lacking JAK2 mutations. With very few previous studies targeting JAK2-negative polycythemia patients to identify underlying variants, this study opens a new avenue in evaluating and managing JAK2-negative polycythemia.

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.