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

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

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.

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.

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.