Modeling, optimization, and comparable efficacy of T cell and hematopoietic stem cell gene editing for treating hyper-IgM syndrome.
Valentina VavassoriElisabetta MercuriGenni E MarcovecchioMaria C CastielloGiulia SchiroliLuisa AlbanoCarrie MarguliesFrank BuquicchioElena FontanaStefano BerettaIvan MerelliAndrea CappelleriPaola Mv RancoitaVassilios LougarisAlessandro PlebaniMaria KanariouArjan LankesterFrancesca FerruaEugenio ScanzianiCecilia Cotta-RamusinoAnna VillaLuigi NaldiniPietro GenovesePublished in: EMBO molecular medicine (2021)
Precise correction of the CD40LG gene in T cells and hematopoietic stem/progenitor cells (HSPC) holds promise for treating X-linked hyper-IgM Syndrome (HIGM1), but its actual therapeutic potential remains elusive. Here, we developed a one-size-fits-all editing strategy for effective T-cell correction, selection, and depletion and investigated the therapeutic potential of T-cell and HSPC therapies in the HIGM1 mouse model. Edited patients' derived CD4 T cells restored physiologically regulated CD40L expression and contact-dependent B-cell helper function. Adoptive transfer of wild-type T cells into conditioned HIGM1 mice rescued antigen-specific IgG responses and protected mice from a disease-relevant pathogen. We then obtained ~ 25% CD40LG editing in long-term repopulating human HSPC. Transplanting such proportion of wild-type HSPC in HIGM1 mice rescued immune functions similarly to T-cell therapy. Overall, our findings suggest that autologous edited T cells can provide immediate and substantial benefits to HIGM1 patients and position T-cell ahead of HSPC gene therapy because of easier translation, lower safety concerns and potentially comparable clinical benefits.
Keyphrases
- wild type
- cell therapy
- crispr cas
- end stage renal disease
- newly diagnosed
- mouse model
- chronic kidney disease
- gene therapy
- poor prognosis
- prognostic factors
- stem cells
- case report
- adipose tissue
- mesenchymal stem cells
- bone marrow
- immune response
- high resolution
- genome wide
- metabolic syndrome
- big data
- insulin resistance
- candida albicans
- transcription factor