Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Abstract

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Transcriptomic Analysis of Tight Junction Proteins Demonstrates the Aberrant Expression and Function of Zona Occludens 2 (ZO-2) Protein in Stanford Type A Aortic Dissection

“…To the best of our knowledge, this is the first evidence suggesting that these miRNA families are implicated in the pathogenesis of aortic aneurysm and dissection. Previously, there has been evidence in the literature showing the implication of hsa-miR-155-5p, hsa-miR-1-3p, and hsa-miR-218-5p in endothelial dysfunction, pulmonary hypertension, and atrial fibrillation [28][29][30]. In addition, we demonstrated the primary biological functions related to the ZO-2 protein.…”

Comprehensive Review on the Virulence Factors and Therapeutic Strategies with the Aid of Artificial Intelligence against Mucormycosis

“…For instance, high cotH gene copy numbers are linked to increased virulence. Transcriptome analysis has identified Wex1, a novel exonuclease involved in virulence. , Virtual screening and AI applications in antifungal drug discovery : AI algorithms aid in predicting novel protein structures, drug-target interactions, virtual screening of drug libraries, and assessing molecular features like drug toxicity. , Researchers have applied these methods to identify potential inhibitors for mucormycosis, some of which are summarized in Table . , Machine learning (ML) in drug repurposing for fungal infections involves exploring extensive compound databases (HTVS for computer aided drug designing), property predictions (ADME/T), QSAR, and pharmacophore analysis . ML algorithms systematically screen existing drug databases for potential candidates against fungal infections, including mucormycosis.…”

“…Virtual screening and AI applications in antifungal drug discovery : AI algorithms aid in predicting novel protein structures, drug-target interactions, virtual screening of drug libraries, and assessing molecular features like drug toxicity. , Researchers have applied these methods to identify potential inhibitors for mucormycosis, some of which are summarized in Table . , …”