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Molecular Therapy. Nucleic Acids Sep 2023The year 2023 marks the 25th anniversary of the discovery of RNAi. RNAi-based therapeutics enable sequence-specific gene knockdown by eliminating target RNA molecules... (Review)
Review
The year 2023 marks the 25th anniversary of the discovery of RNAi. RNAi-based therapeutics enable sequence-specific gene knockdown by eliminating target RNA molecules through complementary base-pairing. A systematic review of published and ongoing clinical trials was performed. Web of Science, PubMed, and Embase were searched from January 1, 1998, to December 30, 2022 for clinical trials using RNAi. Following inclusion, data from the articles were extracted according to a predefined protocol. A total of 90 trials published in 81 articles were included. In addition, ongoing clinical trials were retrieved from ClinicalTrials.gov, resulting in the inclusion of 48 trials. We investigated how maturation of RNAi-based therapeutics and developments in delivery platforms, administration routes, and potential targets shape the current landscape of clinically applied RNAi. Notably, most contemporary clinical trials used either -acetylgalactosamine delivery and subcutaneous administration or lipid nanoparticle delivery and intravenous administration. In conclusion, RNAi therapeutics have gained great momentum during the past decade, resulting in five approved therapeutics targeting the liver for treatment of severe diseases, and the trajectory depicted by the ongoing trials emphasizes that even more RNAi-based medicines also targeting extra-hepatic tissues are likely to be available in the years to come.
PubMed: 37583575
DOI: 10.1016/j.omtn.2023.07.018 -
Journal of Virology May 2024The host-virus interactome is increasingly recognized as an important research field to discover new therapeutic targets to treat influenza. Multiple pooled genome-wide... (Meta-Analysis)
Meta-Analysis
UNLABELLED
The host-virus interactome is increasingly recognized as an important research field to discover new therapeutic targets to treat influenza. Multiple pooled genome-wide CRISPR-Cas screens have been reported to identify new pro- and antiviral host factors of the influenza A virus. However, at present, a comprehensive summary of the results is lacking. We performed a systematic review of all reported CRISPR studies in this field in combination with a meta-analysis using the algorithm of meta-analysis by information content (MAIC). Two ranked gene lists were generated based on evidence in 15 proviral and 4 antiviral screens. Enriched pathways in the proviral MAIC results were compared to those of a prior array-based RNA interference (RNAi) meta-analysis. The top 50 proviral MAIC list contained genes whose role requires further elucidation, such as the endosomal ion channel and the kinase . Moreover, MAIC indicated that , a component of the transcription export complex, has antiviral properties, whereas former knockdown experiments attributed a proviral role to this host factor. CRISPR-Cas-pooled screens displayed a bias toward early-replication events, whereas the prior RNAi meta-analysis covered early and late-stage events. RNAi screens led to the identification of a larger fraction of essential genes than CRISPR screens. In summary, the MAIC algorithm points toward the importance of several less well-known pathways in host-influenza virus interactions that merit further investigation. The results from this meta-analysis of CRISPR screens in influenza A virus infection may help guide future research efforts to develop host-directed anti-influenza drugs.
IMPORTANCE
Viruses rely on host factors for their replication, whereas the host cell has evolved virus restriction factors. These factors represent potential targets for host-oriented antiviral therapies. Multiple pooled genome-wide CRISPR-Cas screens have been reported to identify pro- and antiviral host factors in the context of influenza virus infection. We performed a comprehensive analysis of the outcome of these screens based on the publicly available gene lists, using the recently developed algorithm meta-analysis by information content (MAIC). MAIC allows the systematic integration of ranked and unranked gene lists into a final ranked gene list. This approach highlighted poorly characterized host factors and pathways with evidence from multiple screens, such as the vesicle docking and lipid metabolism pathways, which merit further exploration.
Topics: Humans; Influenza A virus; CRISPR-Cas Systems; Influenza, Human; Host-Pathogen Interactions; Virus Replication; Clustered Regularly Interspaced Short Palindromic Repeats; RNA Interference
PubMed: 38567969
DOI: 10.1128/jvi.01857-23