Promising Intestinal Microbiota Associated with Clinical Characteristics of COPD Through Integrated Bioinformatics Analysis

Figures & data

Table 1 Characteristics of the Study Participants

Parameters	Healthy Controls (n=35)	Patients with COPD (n=37)	P-value
Female/Male	17/18	15/22	0.636
Age, years	64.6 ± 1.16	66.4 ± 1.25	0.289
Smoking, pack-years	5.0 ± 1.6	30.1 ± 3.9	<0.01
FEV1, %predicted	94.9 ± 2.38	41.1 ± 2.44	<0.01
FEV1/FVC, %	84.1 ± 1.92	46.4 ± 1.68	<0.01
WBC (×109)	6.8 ± 0.23	8.5 ± 0.50	<0.01
Neutrophils (%)	59.3 ± 1.39	68.9 ± 2.06	<0.01
CAT scores	NA	15.8 ± 1.0	NA
Treatment regimens, n (%)
ICS/LABA	NA	21 (56.7)	NA
LAMA/LABA	NA	16 (43.2)	NA
SABA	NA	20 (54.1)	NA
Theophylline	NA	31 (83.7)	NA

Notes: Data are presented as mean ± SEM unless stated otherwise.

Abbreviations: CAT, COPD assessment test; WBC, white blood cells; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; COPD, chronic obstructive pulmonary disease; ICS, inhaled corticosteroid; LABA, long-acting β agonist; SABA, short-acting β2 agonist; LAMA, long-acting muscarinic agonist; NA, not applicable.

Figure 1 Shannon sparse curve of each sample OTU operational taxon unit.

Table 2 Description of methods and Results of Microbial Diversity and Evenness Assessment

	Sobs	Shannon	Simpson	Chao1	ACE	Pielou
COPD	614.4864865	4.876082167	0.900774647	892.8842121	871.4961703	0.526382406
Healthy control	603.8571429	5.137543605	0.922601047	876.6779727	870.4104571	0.556407099
P-value (Wilcoxon)	0.946097	0.125986	0.156596	0.796765	0.875444	0.093333

Figure 2 Beta diversity analysis. (A) NMDS of the unweighted_unifrac distance metric. (B) ANOSIM of the unweighted_unifrac distance. Between represents the distribution of two-sample distances between health and COPD. Each scatter point represents the distance between two samples.

Figure 3 Differences in intestinal microbiota. (A) A stacked bar chart showed the distribution of dominant intestinal microbiota at the phylum level. (B) A stacked bar chart showed the distribution of dominant intestinal microbiota at the genus level. (C) Wilcoxon test results showed the relative OTU abundance of 15 genus intestinal microbiotas in COPD patients was significantly different from healthy controls. Each scatter represents the relative abundance of this bacterium in each sample.

Figure 4 Heat map of Spearman correlation analysis regarding differential intestinal microbiota and clinical characteristics (A) and treatment regimens (B). The correlation index in COPD or health samples is indicated by the color bar on the right side of the heat map.

Figure 5 The ability of intestinal microbiota to identify patients with COPD. (A) The illustration of the error changed with the number of trees. (B) Illustration of the tuning of the mtry hyperparameters. (C) ROC curve of the training set and testing set of the Random Forest Model . (D) ROC curve of the Random Forest Model of differential intestinal microbiota. (E) The mean decreases in the Gini coefficient and accuracy plot.

Abbreviations: AUC, the area under the curve; ROC, receiver operating curve.

Figure 6 Disease-related network of key intestinal microbiota. (A and B) Diseases types associated with Lachnospira based on Literature-based associations (A) and Raw data-based associations (B). (C and D) Interventions change the abundance of Lachnospira based on Literature-based associations (C) and Raw data-based associations (D).

Notes: A circle node represents an intervention-related comparison, a square a gut microbe, and a triangle a phenotype-related comparison. A red link indicates an increase in gut microbes, a green link indicates a decrease in gut microbes, a blue link indicates the presence of a gut microbe, and an orange link indicates the absence of a gut Supplementary microbe.