Proteome and Dihydrorhodamine Profiling of Bronchoalveolar Lavage in Patients with Chronic Pulmonary Aspergillosis.
Kristian AssingChristian B LaursenAmanda Jessica CampbellHans Christian BeckJesper Rømhild DavidsenPublished in: Journal of fungi (Basel, Switzerland) (2024)
Neutrophil and (alveolar) macrophage immunity is considered crucial for eliminating Aspergillus fumigatus . Data derived from bronchoalveloar lavage (BAL) characterizing the human immuno-pulmonary response to Aspergillus fumigatus are non-existent. To obtain a comprehensive picture of the immune pathways involved in chronic pulmonary aspergillosis (CPA), we performed proteome analysis on AL of 9 CPA patients and 17 patients with interstitial lung disease (ILD). The dihydrorhodamine (DHR) test was also performed on BAL and blood neutrophils from CPA patients and compared to blood neutrophils from healthy controls (HCs). BAL from CPA patients primarily contained neutrophils, while ILD BAL was also characterized by a large fraction of lymphocytes; these differences likely reflecting the different immunological etiologies underlying the two disorders. BAL and blood neutrophils from CPA patients displayed the same oxidative burst capacity as HC blood neutrophils. Hence, immune evasion by Aspergillus involves other mechanisms than impaired neutrophil oxidative burst capacity per se. CPA BAL was enriched by proteins associated with innate immunity, as well as, more specifically, with neutrophil degranulation, Toll-like receptor 4 signaling, and neutrophil-mediated iron chelation. Our data provide the first comprehensive target organ-derived immune data on the human pulmonary immune response to Aspergillus fumigatus .
Keyphrases
- end stage renal disease
- ejection fraction
- interstitial lung disease
- toll like receptor
- pulmonary hypertension
- chronic kidney disease
- endothelial cells
- prognostic factors
- peritoneal dialysis
- immune response
- adipose tissue
- big data
- rheumatoid arthritis
- machine learning
- idiopathic pulmonary fibrosis
- deep learning
- induced pluripotent stem cells
- data analysis