IC18 mainly Sotrastaurin cost identified alginate biosynthesis alg genes (PA3540-PA3551) and flagellum and type Selleckchem PF01367338 IV pilus biogenesis genes (Figure 4 and Additional file 1, Table S1). Besides common adaptations shared by a group of P. aeruginosa CF isolates, the ICA also showed that P. aeruginosa CF isolates from early infection stage employed multiple patient-specific strategies of adaptation in the CF airways. IC2 revealed that the early stage B12-4 and B12-7 isolates induced the expression of genes related to MexAB-OprM efflux system, iron uptake

as well as citronellol/leucine catabolism (Figure 4 and Additional file 1, Table S1). IC4 revealed that the early stage B6-0 and B6-4 isolates

up-regulated expression of LPS biosynthesis wbp genes (PA3146-PA3159) and down-regulated expression of genes involved in the flagellum biogenesis (Figure 4 and Additional file 1, Table S1). IC16 revealed that the early stage CF114-1973 isolate up-regulated the expression of genes involved in fimbrial biogenesis while down-regulated expression of the PA0632-PA0639 genes (Figure 4 and Additional file 1, Table S1). IC20 revealed that the late stage CF66-2008 isolate up-regulated the expression of ARS-1620 mouse the LPS biosynthesis wbp genes (PA5448-PA5454) (Figure 4 and Additional file 1, Table S1). ICA enhanced identification of co-regulated genes for adaptation of P. aeruginosa to the CF airways We further compared the power of ICA and Linear Models for Microarray Data (LIMMA) [16] to identify co-changed genes using the kdp genes (PA1632-PA1635) and arn genes PLEK2 (PA3552-PA3559) as examples (Figure 6). In ICA analysis, the kdp genes and arn genes were identified from IC6 and IC10 respectively and they are ranked at the top of the short gene lists generated from these ICs (Figure 6). In contrast, when the P. aeruginosa microarray dataset from the early stage isolates and late stage

isolates were grouped and compared using LIMMA analysis, the kdp genes and arn genes are not the most significant genes identified (Figure 6), thus can be easily missed during the analysis. By decomposing and extracting genes from the microarray dataset simultaneously, ICA is superior to established single-gene method LIMMA on identifying novel patterns of co-regulated genes. Figure 6 Enrichment of co-regulated genes with output from ICA and LIMMA analysis. The ranks of selected genes are plotted. Discussion Understanding the bacterial adaptation is a great challenge for scientists and medical doctors to battle infectious diseases. Bacterial cells have a high level of mutation rate and can adapt to the dynamic host environments by selecting mutants which are more fit to the condition.