Assessing inflammatory protein biomarkers in COPD subjects with and without alpha-1 antitrypsin deficiency

Abstract

Rationale: Individuals homozygous for the Alpha-1 Antitrypsin (AAT) Z allele (Pi*ZZ) exhibit heterogeneity in COPD risk. COPD occurrence in non-smokers with AAT deficiency (AATD) suggests that inflammatory processes may contribute to COPD risk independently of smoking. We hypothesized that inflammatory protein biomarkers in non-AATD COPD are associated with moderate-to-severe COPD in AATD individuals, after accounting for clinical factors.

Methods: Participants from the COPDGene (Pi*MM) and AAT Genetic Modifiers Study (Pi*ZZ) were included. Proteins associated with FEV₁/FVC were identified, adjusting for confounders and familial relatedness. Lung-specific protein-protein interaction (PPI) networks were constructed. Proteins associated with AAT augmentation therapy were identified, and drug repurposing analyses performed. A protein risk score (protRS) was developed in COPDGene and validated in AAT GMS using AUROC analysis. Machine learning ranked proteomic predictors, adjusting for age, sex, and smoking history.

Results: Among 4,446 Pi*MM and 352 Pi*ZZ individuals, sixteen blood proteins were associated with airflow obstruction, fourteen of which were highly expressed in lung. PPI networks implicated regulation of immune system function, cytokine and interleukin signaling, and matrix metalloproteinases. Eleven proteins, including IL4R, were linked to augmentation therapy. Drug repurposing identified antibiotics, thyroid medications, hormone therapies, and antihistamines as potential adjunctive AATD treatments. Adding protRS improved COPD prediction in AAT GMS (AUROC 0.86 vs. 0.80, p = 0.0001). AGER was the top-ranked protein predictor of COPD.

Conclusions: Sixteen proteins are associated with COPD and inflammatory processes that predict airflow obstruction in AATD after accounting for age and smoking. Immune activation and inflammation are modulators of COPD risk in AATD.

Fig. 1

Schematic of study design. COPDGene = Genetic Epidemiology of COPD study. FEV₁ = forced expiratory volume in 1 s. FVC = forced vital capacity. LASSO = least absolute shrinkage and selection operator. Pi = alpha-1 antitrypsin protease inhibitor. AUC = area-under-the-receiver-operating-characteristic curve. STRING = search tool for the retrieval of interacting proteins

Fig. 2

The top 16 proteins associated with FEV₁/FVC in both Alpha-1 Genetic Modifiers Study and COPDGene were mapped to the human lung single cell atlas (https://cellxgene.cziscience.com/gene-expression). *ADCYAP1 is another name for PACAP

Fig. 3

STRING network built from top 14 proteins associated with FEV1/FVC in both Alpha-1 Genetic Modifiers Study and COPDGene with high expression in lung cells (top quartile of expression). Medium confidence interactions were included (> 0.4). Edge thickness indicates level of confidence. Edges with 10 interactors in the first shell and 5 in the second shell were permitted. MCL clustering was performed with and inflation factor of 3 to define clusters, which are shown in different colors

Fig. 4

A Receiver-operating-characteristic curves and area-under-the-curve measures for models trained in COPDGene and tested in the Alpha-1 Genetic Modifiers Study. The clinical model included age, sex, and pack-years of smoking. ProtRS = protein risk score. ‘Combined’ indicates the combination of clinical variables and the protRS. B Random forest model-based variable importance measures for proteins in the protein risk score after adjusting for age, sex, and pack-years of smoking