RNA sequencing data from neutrophils of patients with cystic fibrosis reveals potential for developing biomarkers for pulmonary exacerbations

Abstract

Background: There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses.

Methods: RNA sequencing of peripheral blood neutrophils from CF patients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level > 1.0 and a false discovery rate (FDR) < 0.05 were used in a cosine K-nearest neighbor (KNN) model. Real time PCR was used to corroborate RNA sequencing expression differences in both neutrophils and whole blood samples from an independent cohort of CF patients. Furthermore, sandwich ELISA was conducted to assess plasma levels of MRP8/14 complexes in CF patients before and after therapy.

Results: We found differential expression of 136 transcripts and 83 isoforms when we compared neutrophils from CF patients before and after therapy (>1.5 fold change, FDR-adjusted P < 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CF patients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CF patients were essentially unchanged by therapy.

Conclusions: Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.

Keywords: Biomarkers; Cystic fibrosis; Neutrophils; RNA; Sequencing; Transcriptome.

Figure 1

Principal component analysis (PCA) of the expression of model classifier genes in neutrophils from cystic fibrosis patients before (flare) and after (convalescent) therapy. The PCA scatter plot shows that the two groups of samples form distinct clusters with minimal overlap based on expression of these 29 classifier genes.

Figure 2

Receiver operating curve (ROC) analysis of the cosine KNN model built using 29 classifier genes in training cohort (A) and testing cohort (B). Model is able to distinguish between flare and convalescent samples in both cohorts with an AUC of 0.975 in the training cohort and 0.66667 in the testing cohort.

Figure 3

Hierarchical cluster analysis of differentially expressed gene in neutrophils from CF patients before and after therapy. The result shows that flare patients generally clustered together and convalescent patients clustered together

Figure 4

Validation of RNA-Seq data for selected genes by quantitative real-time PCR. Differentially expressed genes in neutrophils from flare CF patients were compared to convalescent patients. Fifteen of nineteen differentially expressed genes in CF patients between before and after therapy in the RNA-Seq analysis were also differentially expressed in neutrophils (A) and ten of nineteen in whole blood (B) in the qRT-PCR in an independent cohort. Six genes of the all validated genes were differentially expressed in both neutrophils and whole blood samples in qRT-PCR in the independent cohort.

Figure 5

MRP 8/14 concentration in plasma from CF patients. Plasma MRP 8/14 levels were measured by ELISA. The data shows there is no significant difference in plasma MRP 8/14 concentration from CF patients between before and after therapy.