Overall, systemic indicators of inflammation have low sensitivity and show only modest increases during acute exacerbations. Thus, to date, no systemic marker of treatment response, and in particular of neutrophilic inflammation, has been validated in CF for assessing the therapeutic outcome. The main aims of this study are to understand if CF patients in acute exacerbation status display a transcriptome profile in their blood neutrophils different from that of control subjects’ neutrophils, and to determine whether antibiotic treatment for an acute exacerbation can be described by a change in gene expression in blood neutrophils. A further aim of this study was to find out whether gene expression profiles differ in sputum neutrophils compared with blood neutrophils before and after antibiotic therapy. Previous studies have shown that airway-derived neutrophils are different from blood neutrophils, in terms of cytokine production, functional and signaling pathways, although not in gene expression profiles. Thus, comparison of differential expression of neutrophil genes between blood and sputum samples may serve to generate additional MG132 hypotheses concerning disease pathogenesis, inflammatory response regulation, and new targets for CF therapy. Then clustering was performed on genomic samples in order to identify subtypes among the patients by means of a “correlation network”, which was built from the reduced datasets connecting those patients displaying correlated expression. In this network, the numerical weights on the edges are the absolute correlation coefficients, while the nodes represent the analyzed samples. We named the obtained correlation networks “communities”, with many edges joining vertices of the same community and comparatively few edges joining vertices of different communities. The control vs pre-therapy and prevs post-therapy datasets were further investigated through Principal Components Analysis as it is an excellent method for expression data and allowed us to summarize the ways in which gene expression profiles over samples vary under different conditions. We also examined the communities obtained by observing correlations between samples, and show how they are manifested in principal component space reducing multi-dimensional data and determining the key variables in a multidimensional data set that explain the differences in the observations. In CF, pulmonary exacerbations are defined based on increased symptoms, decrements in lung functions.