Assessment of Remediation Through Measuring Nuclear Respiratory Factor 1 (nrf1) Environmental Stressor Transcriptomic Gene Signatures

摘要: Heavy metals are toxic substances that have caused great ecological damage, can sorb with sediments, become mobile in surface waters and infiltrate the food chain. As a result, long-term exposures associated with ingestion and recreational exposures lead to environmental and human health-associated risks. The present work aims to monitor environmental and human health risk factors, using Nuclear respiratory factor 1 (nrf1), transcription factor (TF) and its targets as molecular markers of heavy metal toxicity in adult zebrafish. Here, a novel Machine Learning (ML) of Molecular Stressor of Surveillance Framework (MsSF) focuses on molecular risk factors associated with heavy metal toxic stress and was designed to assist in the long-term monitoring and detection of heavy metals found in surface waters. Public datasets were mined to monitor the transcription factor activity of nrf1 and its target genes. Results show nrf1 was downregulated and its responses in the hepatic system produced significant differential expression among heavy metal treated samples. Three novel Dynamic Bayesian Networks (DBN) were trained to present the optimal scenario of gene markers to discretize toxicity associated with arsenic, cadmium, and mercury. Comparative analysis in among male zebrafish described in GSE3048, GSE41622 and GSE18861 shows a total of 117 genes discovered by the DBN model. Among the toxicity responsive genes, are the nrf1 molecular signatures, whereas 5 genes are common to arsenic, cadmium and mercury. Enriched stress pathways revealed apoptotic signaling pathway, transcription regulator activity and catalytic activity in …