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  • Title: Control of the proinflammatory state in cystic fibrosis lung epithelial cells by genes from the TNF-alphaR/NFkappaB pathway.
    Author: Eidelman O, Srivastava M, Zhang J, Leighton X, Murtie J, Jozwik C, Jacobson K, Weinstein DL, Metcalf EL, Pollard HB.
    Journal: Mol Med; 2001 Aug; 7(8):523-34. PubMed ID: 11591888.
    Abstract:
    BACKGROUND: Cystic fibrosis (CF) is the most common, lethal autosomal recessive disease affecting children in the United States and Europe. Extensive work is being performed to develop both gene and drug therapies. The principal mutation causing CF is in the CFTR gene ([Delta F508]CFTR). This mutation causes the mutant protein to traffic poorly to the plasma membrane, and degrades CFTR chloride channel activity. CPX, a candidate drug for CF, binds to mutant CFTR and corrects the trafficking deficit. CPX also activates mutant CFTR chloride channel activity. CF airways are phenotypically inundated by inflammatory signals, primarily contributed by sustained secretion of the proinflammatory cytokine interleukin 8 (IL-8) from mutant CFTR airway epithelial cells. IL-8 production is controlled by genes from the TNF-alphaR/NFkappaB pathway, and it is possible that the CF phenotype is due to dysfunction of genes from this pathway. In addition, because drug therapy with CPX and gene therapy with CFTR have the same common endpoint of raising the levels of CFTR, we have hypothesized that either approach should have a common genomic endpoint. MATERIALS AND METHODS: To test this hypothesis, we studied IL-8 secretion and global gene expression in IB-3 CF lung epithelial cells. The cells were treated by either gene therapy with wild-type CFTR, or by pharmacotherapy with the CFTR-surrogate drug CPX. CF cells, treated with either CFTR or CPX, were also exposed to Pseudomonas aeruginosa, a common chronic pathogen in CF patients. cDNA microarrays were used to assess global gene expression under the different conditions. A novel bioinformatic algorithm (GENESAVER) was developed to identify genes whose expression paralleled secretion of IL-8. RESULTS: We report here that IB3 CF cells secrete massive levels of IL-8. However, both gene therapy with CFTR and drug therapy with CPX substantially suppress IL-8 secretion. Nonetheless, both gene and drug therapy allow the CF cells to respond with physiologic secretion of IL-8 when the cells are exposed to P. aeruginosa. Thus, neither CFTR nor CPX acts as a nonspecific suppressor of IL-8 secretion from CF cells. Consistently, pharmacogenomic analysis indicates that CF cells treated with CPX greatly resemble CF cells treated with CFTR by gene therapy. Additionally, the same result obtains in the presence of P. aeruginosa. Classical hierarchical cluster analysis, based on similarity of global gene expression, also supports this conclusion. The GENESAVER algorithm, using the IL-8 secretion level as a physiologic variable, identifies a subset of genes from the TNF-alphaR/NFkappaB pathway that is expressed in phase with IL-8 secretion from CF epithelial cells. Certain other genes, previously known to be positively associated with CF, also fall into this category. Identified genes known to code for known inhibitors are expressed inversely, out of phase with IL-8 secretion. CONCLUSIONS: Wild-type CFTR and CPX both suppress proinflammatory IL-8 secretion from CF epithelial cells. The mechanism, as defined by pharmacogenomic analysis, involves identified genes from the TNF-alphaR/NFkappaB pathway. The close relationship between IL-8 secretion and genes from the TNF-alphaR/NFkappaB pathway suggests that molecular or pharmaceutical targeting of these novel genes may have strategic use in the development of new therapies for CF. From the perspective of global gene expression, both gene and drug therapy have similar genomic consequences. This is the first example showing equivalence of gene and drug therapy in CF, and suggests that a gene therapy-defined endpoint may prove to be a powerful paradigm for CF drug discovery. Finally, because the GENESAVER algorithm is capable of isolating disease-relevant genes in a hypothesis-driven manner without recourse to any a priori knowledge about the system, this new algorithm may also prove useful in applications to other genetic diseases.
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