O. Eidelman et al., Control of the proinflammatory state in cystic fibrosis lung epithelial cells by genes from the TNF-alpha R/NF kappa B pathway, MOL MED, 7(8), 2001, pp. 523-534
Citations number
37
Categorie Soggetti
Research/Laboratory Medicine & Medical Tecnology","Medical Research General Topics
Background: Cystic fibrosis (CF) is the most common, lethal autosomal reces
sive disease affecting children in the United States and Europe. Extensive
work is being performed to develop both gene and drug therapies. The princi
pal mutation causing CF is in the CFTR gene ([Delta F508]CFTR). This mutati
on causes the mutant protein to traffic poorly to the plasma membrane, and
degrades CFTR chloride channel activity. CPX, a candidate drug for CF, bind
s to mutant CFTR and corrects the trafficking deficit. CPX also activates m
utant CFTR chloride channel activity. CF airways are phenotypically inundat
ed 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-alpha
R/NF kappaB pathway, and it is possible that the CF phenotype is due to dys
function of genes from this pathway. In addition, because drug therapy with
CPX and gene therapy with CFTR have the same common endpoint of raising th
e levels of CFTR, we have hypothesized that either approach should have a c
ommon genomic endpoint.
Materials and Methods: To test this hypothesis, we studied IL-8 secretion a
nd 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 w
ith 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 un
der the different conditions. A novel bioinformatic algorithm (GENESAVER) w
as 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. H
owever, 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 a
nalysis 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, ba
sed on similarity of global gene expression, also supports this conclusion.
The GENESAVER algorithm, using the IL-8 secretion level as a physiologic v
ariable, identifies a subset of genes from the TNF-alphaR/NF kappaB pathway
that is expressed in phase with IL-8 secretion from CF epithelial cells. C
ertain other genes, previously known to be positively associated with CF al
so fall into this category. Identified genes known to code for known inhibi
tors are expressed inversely, out of phase with IL-8 secretion.
Conclusions: Wild-type CFTR and CPX both suppress proinflammatory IL-8 secr
etion from CF epithelial cells. The mechanism, as defined by pharmacogenomi
c analysis, involves identified genes from the TNF-alphaR/NF kappaB pathway
. The close relationship between IL-8 secretion and genes from the TNF-alph
aR/NF kappaB pathway suggests that molecular or pharmaceutical targeting of
these novel genes may have strategic use in the development of new therapi
es for CF. From the perspective of global gene expression, both gene and dr
ug therapy have similar genomic consequences. This is the first example sho
wing equivalence of gene and drug therapy in CF, and suggests that a gene t
herapy-defined endpoint may prove to be a powerful paradigm for CF drug dis
covery. Finally, because the GENESAVER algorithm is capable of isolating di
sease-relevant genes in a hypothesis-driven manner without recourse to any
a priori knowledge about the system, this new algorithm may also prove usef
ul in applications to other genetic diseases.