Bacteriophage-Mediated Cancer Gene Therapy.
Gleb O PetrovMayya Alexandrovna DymovaVladimir RichterPublished in: International journal of molecular sciences (2022)
Bacteriophages have long been considered only as infectious agents that affect bacterial hosts. However, recent studies provide compelling evidence that these viruses are able to successfully interact with eukaryotic cells at the levels of the binding, entry and expression of their own genes. Currently, bacteriophages are widely used in various areas of biotechnology and medicine, but the most intriguing of them is cancer therapy. There are increasing studies confirming the efficacy and safety of using phage-based vectors as a systemic delivery vehicle of therapeutic genes and drugs in cancer therapy. Engineered bacteriophages, as well as eukaryotic viruses, demonstrate a much greater efficiency of transgene delivery and expression in cancer cells compared to non-viral gene transfer methods. At the same time, phage-based vectors, in contrast to eukaryotic viruses-based vectors, have no natural tropism to mammalian cells and, as a result, provide more selective delivery of therapeutic cargos to target cells. Moreover, numerous data indicate the presence of more complex molecular mechanisms of interaction between bacteriophages and eukaryotic cells, the further study of which is necessary both for the development of gene therapy methods and for understanding the cancer nature. In this review, we summarize the key results of research into aspects of phage-eukaryotic cell interaction and, in particular, the use of phage-based vectors for highly selective and effective systemic cancer gene therapy.
Keyphrases
- gene therapy
- induced apoptosis
- cancer therapy
- papillary thyroid
- cell cycle arrest
- pseudomonas aeruginosa
- poor prognosis
- genome wide
- squamous cell
- magnetic resonance
- cell death
- magnetic resonance imaging
- stem cells
- single cell
- genome wide identification
- cystic fibrosis
- dna methylation
- sars cov
- signaling pathway
- gene expression
- artificial intelligence
- big data
- electronic health record
- genetic diversity
- mesenchymal stem cells
- deep learning