Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment

Integration of leave-one-out method and real-time live cell reporter array system to assess the toxicity of mixtures

The ever-increasing number of chemicals and complex mixtures demands a time-saving and cost-effective platform for environmental risk assessment. However, there is limit promising tool for evaluating the contribution of each component to the total toxicity effects of the mixture. Here, four widely distributed environmental pollutants with different mode-of-actions, i.e., cadmium chloride (Cd), nitrofurazone (NFZ), triclosan (TCS), and tris(2-chloroethyl) phosphate (TCEP), were selected as components of artificial mixture. Integration of leave-one-out method and high-dimensional live cell array system was used to explore relative contribution of each component from the mixture. A quaternary mixture (All_4_chems) and four ternary mixtures (Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP) were investigated by Escherichia coli (E. coli) live cell array system with 90 environmental stress genes modified by green fluorescent protein (GFP) expressing reporter vectors. E. coli cytotoxicity tests demonstrated that TCS has antagonism effect with other three chemicals (Cd, NFZ and TCEP), while it was additive effect in other three binary combinations. A total of 26, 23, 13, 31 and 23 genes were significantly altered with fold-change greater than 2 over the 4 h exposure by All_4_chems, Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP, respectively. Clustering analysis based on time-series gene expression patterns and transcriptional effect level index (TELI) showed that Leave_TCEP has similar profiles with All_4_chems, demonstrating TCEP has the least contribution among four components to the quaternary mixture. Leave_NFZ has the least number of significantly altered genes, implying NFZ has the largest toxicity effect contribution to the quaternary mixture. The relative contribution in different pathways indicated that Cd has the most contribution to the mixture in redox stress, while TCS has the least contribution in DNA stress pathway. Collectively, our results demonstrated the utility of high-dimensional toxicogenomics data and leave-one-out method in prioritizing the relative contribution of each component in mixture.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus salmoides)

In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass ( Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 μg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.