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Human Endogenous Retroviruses as Gene Expression Regulators: Insights from Animal Models into Human Diseases

  • Durnaoglu, Serpen (Department of Life Sciences, College of Natural Sciences, Hanyang University) ;
  • Lee, Sun-Kyung (Department of Life Sciences, College of Natural Sciences, Hanyang University) ;
  • Ahnn, Joohong (Department of Life Sciences, College of Natural Sciences, Hanyang University)
  • Received : 2021.12.04
  • Accepted : 2021.12.07
  • Published : 2021.12.31

Abstract

The human genome contains many retroviral elements called human endogenous retroviruses (HERVs), resulting from the integration of retroviruses throughout evolution. HERVs once were considered inactive junk because they are not replication-competent, primarily localized in the heterochromatin, and silenced by methylation. But HERVs are now clearly shown to actively regulate gene expression in various physiological and pathological conditions such as developmental processes, immune regulation, cancers, autoimmune diseases, and neurological disorders. Recent studies report that HERVs are activated in patients suffering from coronavirus disease 2019 (COVID-19), the current pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. In this review, we describe internal and external factors that influence HERV activities. We also present evidence showing the gene regulatory activity of HERV LTRs (long terminal repeats) in model organisms such as mice, rats, zebrafish, and invertebrate models of worms and flies. Finally, we discuss several molecular and cellular pathways involving various transcription factors and receptors, through which HERVs affect downstream cellular and physiological events such as epigenetic modifications, calcium influx, protein phosphorylation, and cytokine release. Understanding how HERVs participate in various physiological and pathological processes will help develop a strategy to generate effective therapeutic approaches targeting HERVs.

Keywords

Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2021R1F1A1049211) and by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program [Hanyang University]). We sincerely apologize to our colleagues in case we unwittingly omit their valuable works in this review.

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