Respiratory microbiota and radiomics features in the stable COPD patients

Abstract

Backgrounds: The respiratory microbiota and radiomics correlate with the disease severity and prognosis of chronic obstructive pulmonary disease (COPD). We aim to characterize the respiratory microbiota and radiomics features of COPD patients and explore the relationship between them.

Methods: Sputa from stable COPD patients were collected for bacterial 16 S rRNA gene sequencing and fungal Internal Transcribed Spacer (ITS) sequencing. Chest computed tomography (CT) and 3D-CT analysis were conducted for radiomics information, including the percentages of low attenuation area below - 950 Hounsfield Units (LAA%), wall thickness (WT), and intraluminal area (Ai). WT and Ai were adjusted by body surface area (BSA) to WT/[Formula: see text] and Ai/BSA, respectively. Some key pulmonary function indicators were collected, which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), diffusion lung carbon monoxide (DLco). Differences and correlations of microbiomics with radiomics and clinical indicators between different patient subgroups were assessed.

Results: Two bacterial clusters dominated by Streptococcus and Rothia were identified. Chao and Shannon indices were higher in the Streptococcus cluster than that in the Rothia cluster. Principal Co-ordinates Analysis (PCoA) indicated significant differences between their community structures. Higher relative abundance of Actinobacteria was detected in the Rothia cluster. Some genera were more common in the Streptococcus cluster, mainly including Leptotrichia, Oribacterium, Peptostreptococcus. Peptostreptococcus was positively correlated with DLco per unit of alveolar volume as a percentage of predicted value (DLco/VA%pred). The patients with past-year exacerbations were more in the Streptococcus cluster. Fungal analysis revealed two clusters dominated by Aspergillus and Candida. Chao and Shannon indices of the Aspergillus cluster were higher than that in the Candida cluster. PCoA showed distinct community compositions between the two clusters. Greater abundance of Cladosporium and Penicillium was found in the Aspergillus cluster. The patients of the Candida cluster had upper FEV1 and FEV1/FVC levels. In radiomics, the patients of the Rothia cluster had higher LAA% and WT/[Formula: see text] than those of the Streptococcus cluster. Haemophilus, Neisseria and Cutaneotrichosporon positively correlated with Ai/BSA, but Cladosporium negatively correlated with Ai/BSA.

Conclusions: Among respiratory microbiota in stable COPD patients, Streptococcus dominance was associated with an increased risk of exacerbation, and Rothia dominance was relevant to worse emphysema and airway lesions. Peptostreptococcus, Haemophilus, Neisseria and Cutaneotrichosporon probably affected COPD progression and potentially could be disease prediction biomarkers.

Keywords: COPD; Chest CT; Radiomics; Respiratory microbiota.

Fig. 1

3D analysis of chest CT using SYNAPSE VINCENT. (A) Lung lobe extraction and segmentation. (B) Low attenuation area (LAA%) distribution analysis, the closer to red means more severe emphysema, the closer to blue means less severe emphysema. (C) 3D CT reconstruction image of the airway tree. (D) Cross-sectional images of selected airways, with the red line indicating the outer wall and outer diameter of the airway and the green line indicating the inner wall and inner diameter of the airway. (E) Longitudinal section image of the selected airway, yellow dots are automatically marked branch points

Fig. 2

Composition of the respiratory bacterial community in 52 patients. (A) Community composition at the phylum level. (B) Community composition at the genus level

Fig. 3

Comparison of alpha diversity and community composition between the Streptococcus cluster and the Rothia cluster. (A) JSD cluster stratification for sputum bacteria in 52 COPD patients, 1 was defined as Streptococcus cluster (n = 33), cluster 2 was defined as Rothia cluster (n = 19). (B) Comparison of alpha diversity (Chao and Shannon indices) between the two bacterial clusters. (C) PCoA analysis based on the Weighted UniFrac distance. (D) Significantly differing bacteria between the Streptococcus cluster and the Rothia cluster at the phylum level. (E) Significantly differing bacteria between the Streptococcus cluster and the Rothia cluster at the genus level. (F) DLco/VA%pred and smoking duration were associated with Chao and Shannon indices of bacteria by linear regression. (G) Some bacteria at the genus level were associated with clinical characteristics and pulmonary function based on Spearman correlation. Red indicates positive correlation; blue indicates negative correlation(* 0.01 ≤ FDR P<0.05, ** 0.001 ≤ FDR P<0.01, *** FDR P < 0.001).

Fig. 4

Composition of the respiratory fungi community in 30 patients. (A) Community composition at the phylum level. (B) Community composition at the genus level

Fig. 5

Comparison of alpha diversity and community composition between Aspergillus cluster and Candida cluster. (A) JSD cluster stratification for sputum fungi in 30 COPD patients, cluster 1 was defined as Aspergillus cluster(n = 17), cluster 2 was defined as Candida cluster(n = 13). (B) Comparison of alpha diversity (Chao and Shannon indices) between the two fungal clusters. (C) PCoA analysis based on the Weighted UniFrac distance. (D) Significantly differing fungi between Aspergillus cluster and Candida cluster at the phylum level. (E) Significantly differing fungi between Aspergillus cluster and Candida cluster at the genus level. (F) DLco/VA%pred, smoking duration, and smoking pack year were associated with Chao and Shannon indices of bacteria by linear regression. (G) Some fungi at the genus level were associated with clinical characteristics and pulmonary function based on Spearman correlation. Red indicates positive correlation; blue indicates negative correlation(* 0.01 ≤ FDR P<0.05, ** 0.001 ≤ FDR P<0.01, *** FDR P < 0.001)

Fig. 6

Spearman correlation analysis for respiratory microbiota and radiomics features. (A) The correlations between radiomics features and respiratory bacterial microbiota. (B) The correlations between radiomics features and respiratory fungal microbiota. (* FDR P<0.05)