Maladaptation of critical cellular functions in asthma: bioinformatic analysis

Agrawal A, Sinha A, Ahmad T, Aich J, Singh P, Sharma A, Ghosh B. Maladaptation of critical cellular functions in asthma: bioinformatic analysis. Physiol Genomics 40: 1-7, 2009. First published October 20, 2009; doi: 10.1152/physiolgenomics.00141.2009.-Small maladaptations in cellular response to environmental stressors may underlie diseases like asthma. However, genomewide transcriptional profile comparisons between case and controls only highlight the quantitatively largest changes. Critical cellular homeostatic pathways may be upregulated modestly during normal adaptation to stress but insufficiently during disease. To discover such pathways in asthma, we utilized public information on differential response of primary bronchial epithelial cells from asthmatic or normal subjects to stressors like ozone and viral infections. Genes that were upregulated by stressor conditions in normal cells but were relatively downregulated in cells from asthmatic subjects were selected for further analysis. Either a stringent selection based on quantitative criterion or a non-stringent selection followed by network-based analysis was used. At the individual gene level, decay accelerating factor-1 (DAF-1, CD55) was identified and selected for validation. In a mouse model of allergic airway inflammation (AAI) resembling asthma, protein expression of CD55 was reduced compared with normal mice and returned to normal upon resolution of the allergic response. This was consistent with our finding of relative downregulation of CD55 in asthmatic compared with normal subjects. Interestingly, at a network level, the results pointed to possible abnormalities in the inositol signaling pathway, a critical cell signaling mechanism. In the mouse model of AAI, we found downregulation of inositol polyphosphate 4 phosphatase A (INPP4A), a critical member of the inositol signaling pathway. This and previous genetic evidence supports a role for inositol signaling abnormalities in asthma. In summary, logic-gated hypothesis-free exploration of published data sets may be valuable in discovery of novel disease-associated pathways.