The murine lung as a factory to produce secreted intrapulmonary and circulatory proteins.
Michael C Paul-SmithKamila M PytelJean-François GelinasJenny McIntoshIan PringleLee DaviesMario ChanCuixiang MengRobyn BellLidia CammackCaroline MoranLoren CameronMakoto InoueShu TsugumineTakashi HironakaDeborah R GillStephen C HydeAmit NathwaniEric W F W AltonUta GriesenbachPublished in: Gene therapy (2018)
We have shown that a lentiviral vector (rSIV.F/HN) pseudotyped with the F and HN proteins from Sendai virus generates high levels of intracellular proteins after lung transduction. Here, we evaluate the use of rSIV.F/HN for production of secreted proteins. We assessed whether rSIV.F/HN transduction of the lung generates therapeutically relevant levels of secreted proteins in the lung and systemic circulation using human α1-anti-trypsin (hAAT) and factor VIII (hFVIII) as exemplars. Sedated mice were transduced with rSIV.F/HN carrying either the secreted reporter gene Gaussia luciferase or the hAAT or hFVIII cDNAs by nasal sniffing. rSIV.F/HN-hAAT transduction lead to therapeutically relevant hAAT levels (70 μg/ml) in epithelial lining fluid, with stable expression persisting for at least 19 months from a single application. Secreted proteins produced in the lung were released into the circulation and stable expression was detectable in blood. The levels of hFVIII in murine blood approached therapeutically relevant targets. rSIV.F/HN was also able to produce secreted hAAT and hFVIII in transduced human primary airway cells. rSIV.F/HN transduction of the murine lungs leads to long-lasting and therapeutically relevant levels of secreted proteins in the lung and systemic circulation. These data broaden the use of this vector platform for a large range of disease indications.
Keyphrases
- endothelial cells
- poor prognosis
- type diabetes
- gene expression
- metabolic syndrome
- oxidative stress
- adipose tissue
- machine learning
- crispr cas
- artificial intelligence
- electronic health record
- copy number
- endoplasmic reticulum stress
- transcription factor
- genome wide
- deep learning
- big data
- long non coding rna
- cell proliferation
- chronic rhinosinusitis