Aim: This study aims to identify endoplasmic reticulum stress response elements (ERSE) in the human genome to explore potentially regulated genes, including kinases and transcription factors, involved in the endoplasmic reticulum (ER) stress and its related diseases.
Materials and methods: Python-based whole genome screening of ERSE was performed using the Amazon Web Services elastic computing system. The Kinome database was used to filter out the kinases from the extracted list of ERSE-related genes. Additionally, network analysis and genome enrichment were achieved using NDEx, the Network and Data Exchange software, and web-based computational tools. To validate the gene expression, quantitative RT-PCR was performed for selected kinases from the list by exposing the HeLa cells to tunicamycin and brefeldin, ER stress inducers, for various time points.
Key findings: The overall number of ERSE-associated genes follows a similar pattern in humans, mice, and rats, demonstrating the ERSE's conservation in mammals. A total of 2705 ERSE sequences were discovered in the human genome (GRCh38.p14), from which we identified 36 kinases encoding genes. Gene expression analysis has shown a significant change in the expression of selected genes under ER stress conditions in HeLa cells, supporting our finding.
Significance: In this study, we have introduced a rapid method using Amazon cloud-based services for genome-wide screening of ERSE sequences from both positive and negative strands, which covers the entire genome reference sequences. Approximately 10 % of human protein-protein interactomes were found to be associated with ERSE-related genes. Our study also provides a rich resource of human ER stress-response-based protein networks and transcription factor interactions and a reference point for future research aiming at targeted therapeutics.
Keywords: Cancer; Cloud computing; ER stress; Kinases; Metabolic diseases; Neurodegenerative diseases.
Copyright © 2023. Published by Elsevier Inc.