Emerging Bioinformatics Methods and Resources in Drug Toxicology

Bioinformatics analysis of differentially expressed genes related to ischemia and hypoxia in spinal cord injury and construction of miRNA-mRNA or mRNA-transcription factor interaction network

BACKGROUND

Previous studies show that spinal cord ischemia and hypoxia is an important cause of spinal cord necrosis and neurological loss. Therefore, the study aimed to identify genes related to ischemia and hypoxia after spinal cord injury (SCI) and analyze their functions, regulatory mechanism, and potential in regulating immune infiltration.

METHODS

The expression profiles of GSE5296, GSE47681, and GSE217797 were downloaded from the Gene Expression Omnibus database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to determine the function and pathway enrichment of ischemia- and hypoxia-related differentially expressed genes (IAHRDEGs) in SCI. LASSO model was constructed, and support vector machine analysis was used to identify key genes. The diagnostic values of key genes were evaluated using decision curve analysis and receiver operating characteristic curve analysis. The interaction networks of miRNAs-IAHRDEGs and IAHRDEGs-transcription factors were predicted and constructed with the ENCORI database and Cytoscape software. CIBERSORT algorithm was utilized to analyze the correlation between key gene expression and immune cell infiltration.

RESULTS

There were 27 IAHRDEGs identified to be significantly expressed in SCI at first. These genes were mostly significantly enriched in wound healing function and the pathway associated with lipid and atherosclerosis. Next, five key IAHRDEGs (Abca1, Casp1, Lpl, Procr, Tnfrsf1a) were identified and predicted to have diagnostic value. Moreover, the five key genes are closely related to immune cell infiltration.

CONCLUSION

Abca1, Casp1, Lpl, Procr, and Tnfrsf1a may promote the pathogenesis of ischemic or hypoxic SCI by regulating vascular damage, inflammation, and immune infiltration.

State of the Art of Genomic Technology in Toxicology: A Review

The rapid growth of genomics techniques has revolutionized and impacted, greatly and positively, the knowledge of toxicology, ushering it into a "new era": the era of genomic technology (GT). This great advance permits us to analyze the whole genome, to know the gene response to toxicants and environmental stressors, and to determine the specific profiles of gene expression, among many other approaches. The aim of this work was to compile and narrate the recent research on GT during the last 2 years (2020-2022). A literature search was managed using the PubMed and Medscape interfaces on the Medline database. Relevant articles published in peer-reviewed journals were retrieved and their main results and conclusions are mentioned briefly. It is quite important to form a multidisciplinary taskforce on GT with the aim of designing and implementing a comprehensive, collaborative, and a strategic work plan, prioritizing and assessing the most relevant diseases, so as to decrease human morbimortality due to exposure to environmental chemicals and stressors.

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.