Published April 25, 2023 | Version v1
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Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease

Description

Background

Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population.

Methods

We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiological information. Differential gene expression was determined by DESeq2.

Results

No differences in demographics or composition of white blood cell populations between groups were identified at baseline. Out of 213 genes associated with NTM-PD in the non-CF population, only two were significantly different in our cystic fibrosis NTM-PD cohort. Gene set enrichment analysis of the differential expression results showed that CF individuals who developed NTM-PD had higher expression levels of genes involved in the interferon (α and γ), tumor necrosis factor, and IL6-STAT3-JAK pathways.

Conclusion

In contrast to the non-cystic fibrosis population, the gene expression signature of patients with cystic fibrosis who develop NTM-PD is characterized by increased innate immune responses.

Notes

The data sets are provided as comma-separated values and can be opened with standard statistical software or explored with a spreadsheet program. In our analyses, we employed R and the GUI R studio (v 4.1.1) for analysis. Raw sequencing processing and transcript counting was done in a CentOS high performance cluster, dependencies and commands ran are described inside markdown scripts. 

Funding provided by: Cystic Fibrosis Canada

Funding provided by: Michael Smith Foundation for Health Research
Award Number: Research Trainee Award (RT-2020-0493)

Funding provided by: Michael Smith Foundation for Health Research
Award Number: Research Scholar Award

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