The murine lung as a factory to produce secreted intrapulmonary and circulatory proteins.

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.

Translational utility of a hierarchical classification strategy in biomolecular data analytics.

Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.