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The adenovirus E4orf1 protein initiates a feedback loop involving insulin and growth factor receptors, AKT, and NF-κB, leading to abnormal DNA content in infected cells

Moore, M.; Kong, J.; Akmel, A.; Thomas, M. A.

2025-05-13 cancer biology
10.1101/2025.05.12.653445 bioRxiv
Show abstract

Abnormal DNA levels, such as aneuploidy and polyploidy, can indicate cellular transformation and cancer; however, the mechanisms remain poorly understood. All tumor viruses inherently cause abnormal DNA content in cells due to their oncogenes. During infections, adenovirus (Ad) oncogenes--early region 1A (E1A), early region 4 open reading frame 3 (E4orf3), and E4 open reading frame 1 (E4orf1)--promote the abnormal buildup of cellular DNA. Previous studies have described how E1A and E4orf3 lead infected cells to accumulate abnormal DNA content; however, the role of E4orf1 remains speculative. In this study, we generated cells that express E4orf1 to investigate its role in abnormal DNA content. The E4orf1-expressing cells initially exhibited no increase in DNA content compared to the control group. However, after Ad infection, they displayed higher ploidy levels. To detail how E4orf1 influences ploidy levels in Ad-infected cells, we employed pharmacological agents that target E4orf1 signaling. Our results indicate that E4orf1 enhances signaling from insulin and growth factor receptors to AKT and NF-{kappa}B, creating a feedback loop that elevates levels of cellular DNA in Ad-infected cells. Author SummaryThe early region 4 open reading frame 1 of adenovirus (E4orf1) is recognized for its ability to initiate signals that convert normal cells into cancerous ones. In the initial stages of cancer, cells exhibit DNA content that exceeds the typical levels seen during the G2 and M phases of the cell cycle. This study demonstrates that E4orf1 can trigger a feedback loop involving EGFR, INSR, IGF1R, AKT, and NF-kB, which is both dependent on and independent of PI3 kinase and leads to the accumulation of abnormal DNA content.

Published in PLOS Pathogens (predicted rank #10) · training set

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