The lung microbiota in nontuberculous mycobacterial pulmonary disease

Abstract

Background

The role of bacterial microbiota in the pathogenesis of nontuberculous mycobacterial pulmonary disease (NTM-PD) is unclear. We aimed to compare the bacterial microbiome of disease-invaded lesions and non-invaded lung tissue from NTM-PD patients.

Methods

We analyzed lung tissues from 23 NTM-PD patients who underwent surgical lung resection. Lung tissues were collected in pairs from each patient, with one sample from a disease-involved site and the other from a non-involved site. Lung tissue microbiome libraries were constructed using 16S rRNA gene sequences (V3–V4 regions).

Results

Sixteen (70%) patients had Mycobacterium avium complex (MAC)-PD, and the remaining seven (30%) had Mycobacterium abscessus-PD. Compared to non-involved sites, involved sites showed greater species richness (ACE, Chao1, and Jackknife analyses, all p = 0.001); greater diversity on the Shannon index (p = 0.007); and genus-level differences (Jensen-Shannon, PERMANOVA p = 0.001). Analysis of taxonomic biomarkers using linear discriminant analysis (LDA) effect sizes (LEfSe) demonstrated that several genera, including Limnohabitans, Rahnella, Lachnospira, Flavobacterium, Megamonas, Gaiella, Subdoligranulum, Rheinheimera, Dorea, Collinsella, and Phascolarctobacterium, had significantly greater abundance in involved sites (LDA >3.00, p <0.05, and q <0.05). In contrast, Acinetobacter had significantly greater abundance at non-involved sites (LDA = 4.27, p<0.001, and q = 0.002). Several genera were differentially distributed between lung tissues from MAC-PD (n = 16) and M. abscessus-PD (n = 7), and between nodular bronchiectatic form (n = 12) and fibrocavitary form (n = 11) patients. However, there was no genus with a significant q-value.

Conclusions

We identified differential microbial distributions between disease-invaded and normal lung tissues from NTM-PD patients, and microbial diversity was significantly higher in disease-invaded tissues.

Trial registration

Clinical Trial registration number: NCT00970801.

Introduction

Nontuberculous mycobacteria (NTM) are ubiquitous organisms found in natural environments and human communities. NTM infection is caused by complex interactions among immune status, microbiological factors, and environmental exposure. Most NTM infections lead to chronic pulmonary disease (PD), and the global burden of NTM-PD is increasing [1, 2]. Among more than 190 species of NTM, the Mycobacterium avium complex (MAC), mainly composed of M. avium and M. intracellulare, is the most common pathogen followed by M. abscessus in many countries [3].

The challenging aspect of NTM-PD management is that NTM-PD has a remarkably heterogeneous clinical course, unlike other respiratory infectious diseases [4, 5]. NTM-PD patients can show spontaneous negative conversion without antibiotic therapy or remain stable for several years. However, some patients exhibit rapid worsening of NTM-PD that involves cavity formation. Other obstacles include low treatment success rates, despite long-term multidrug antibiotic therapy [6]. In addition, clinicians commonly encounter mixed infections with various species of NTM, as well as repeated reinfection by different genotypes [7]. To date, there are limited data explaining the heterogeneity of the disease course or pathogenesis.

The microbiome, an ecological niche of microbial communities (e.g., bacteria, viruses, fungi, and archaea), has recently been reported to affect the clinical course and pathogenesis of respiratory diseases [8, 9]. Culture-independent techniques that involve targeted sequencing (e.g., analysis of the 16S ribosomal RNA [rRNA] gene) have been beneficial for detecting and identifying microbes. Recent studies of respiratory samples from patients with cystic fibrosis, bronchiectasis, or pulmonary fibrosis revealed differences in microbiota composition (i.e., dysbiosis), depending on the clinical course of the disease [10–12].

To explain the heterogeneity of the disease course or pathogenesis of NTM-PD, microbiome studies have been performed using various respiratory samples (e.g., alveolar fluid, oral washings, and sputum), and they have suggested the involvement of several dominant bacterial taxa [13–16]. However, these studies had some limitations, such as between-study discrepancies, possible sample contamination, insufficient sample sizes, or difficulties in the selection of controls. Therefore, the role of the microbiota in NTM-PD pathogenesis remains unclear. In this study, we conducted microbiome analysis using lung tissue specimens obtained under sterile conditions from NTM-PD patients who had undergone surgical lung resection. We compared the bacterial microbiome of disease-invaded lesions to non-invaded lung tissue in each patient. We also compared differences in bacterial distribution among patients with different NTM species or radiological phenotypes of NTM-PD. Our findings are useful for the characterization of microbial environments in the lung tissue of patients with NTM-PD.

Methods

Study participants

We screened consecutive NTM-PD patients who underwent adjuvant surgical lung resection, in addition to antibiotic therapy, between July 2012 and February 2019 at Samsung Medical Center, Seoul, South Korea (S1 Fig). We identified 40 patients with NTM-PD who consented to the collection of lung tissue. Lung tissue was collected in pairs from each patient, with one sample taken from a disease-invaded lesion and the other taken from normal tissue. We initially attempted to evaluate both bacterial and fungal microbiomes. A bacterial microbiome library was constructed using the V3-V4 region of 16S rRNA gene [17]. However, library construction failed with samples from 17 patients because of quality control (QC) failures or low valid read count (<200). Although a fungal microbiome library was constructed using the ITS 2 region [18], only one sample had a valid read count that allowed microbiome analysis. Therefore, in this study, fungal microbiome analysis was excluded.

Finally, the paired lung tissues of 23 NTM-PD patients were included in the analysis (S1 Table) and only the bacterial microbiomes were examined. The median duration of antibiotic therapy before surgery was 22.7 (interquartile range, 12.6–48.4) months. Details of antibiotics administered are shown in S2 and S3 Tables. We compared the microbiome data of patients according to disease involvement, NTM species, and radiological phenotype. Data were obtained from an ongoing prospective observational cohort (ClinicalTrials.gov identifier: NCT00970801). Written informed consent was obtained from the study participants and the Institutional Review Board of the Samsung Medical Center approved this study (IRB no. 2012–05–001).

Lung tissue specimens

Lung tissue specimens were obtained under sterile conditions in the operating room from surgical material of patients who underwent lobectomy or pneumonectomy for NTM-PD. Specimens were acquired from two distinct sites in each patient: i) a cavity wall lesion in fibrocavitary (FC) form or a bronchiectasis lesion in nodular bronchiectatic (NB) form (hereafter, involved site), and ii) a site that appeared grossly normal without radiological involvement of NTM-PD from the resected lung (hereafter, non-involved site). These lung tissues were immediately dissected into multiple segments with a diameter of 5 mm. Dissected samples were snap-frozen in liquid nitrogen and stored at -80°C.

DNA extraction from lung tissue and MiSeq sequencing

DNA extraction from lung tissue was performed using a Maxwell® RSC PureFood GMO and Authentication Kit (Promega, Madison, WI, USA) in accordance with the manufacturer’s protocol. The concentration and quality of the extracted DNA were measured using an Epoch™ Spectrometer (BioTek, Winooski, VT, USA).

To analyze the bacterial microbiome of the lung tissue DNA, region V3 to V4 from the bacterial 16S rRNA gene was amplified using primers 341F (TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG) and 805R (GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC). Amplification was performed in a C1000 Touch thermal cycler polymerase chain reaction (PCR) system (Bio-Rad, Hercules, CA, USA). The PCR cycling conditions were as follows: initial denaturation at 95°C for 3 min; 25 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and elongation at 72°C for 30 s; and final extension at 72°C for 5 min.

Then, in accordance with the MiSeq system protocol for preparation of a 16S metagenomics sequencing library, a second PCR (Index PCR) was performed using a Nextera XT index kit (Illumina, San Diego, CA, USA) to attach an index code and Illumina sequencing adapter to the products of the first PCR. The second PCR cycling conditions were as follows: initial denaturation at 95°C for 3 min; 8 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and elongation at 72°C for 30 s; and final extension at 72°C for 5 min.

The final PCR products were purified using a QIAquick PCR purification kit (Qiagen, Valencia, CA, USA) and quantified using a Quant-iT PicoGreen dsDNA Assay Kit (Promega). The quality of the final library was measured using a Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA). Samples that did not pass QC during the DNA extraction and library production steps were excluded from further analysis. Libraries that passed QC were sequenced by CJ Bioscience, Inc. (Seoul, Korea) using a MiSeq Reagent Kit v2 (500-cycles) based on the Illumina MiSeq sequencing platform (Illumina) in accordance with the manufacturer’s instructions. The raw data were registered in the Sequence Read Archive (accession number PRJNA931790).

Sequence analysis

Microbiome profiling was conducted using the 16S-based Microbial Taxonomic Profiling (MTP) platform of the EzBioCloud application with the 16S database version PKSSU.4.0 [19]. All raw sequencing data were re-analyzed into each individual MTP using the EzBioCloud pipeline. MTP sets were constructed by grouping these individual MTPs. Comparative analysis between MTP sets was performed after normalization of gene copy numbers.

The relative abundance of sequences was compared between MTP sets using the Wilcoxon rank-sum test. Alpha (α) diversity was analyzed using the species richness (number of operational taxonomic units [OTUs], ACE, Chao1, and Jackknife) and the Shannon diversity index. Beta (β) diversity was evaluated using Jensen-Shannon divergence and generalized UniFrac distances, and the results were visualized by principal component analysis. Permutational multivariate analysis of variance (PERMANOVA) was used to analyze statistical differences in beta diversity. To identify differentially distributed taxa between MTP sets, which could potentially be used as microbial biomarkers, we performed taxonomic biomarker analysis based on linear discriminant analysis (LDA) effect sizes (LEfSe) and phylogenetic investigation of communities through the reconstruction of unobserved state algorithms [20, 21]. Taxa that demonstrated significant results in both relative abundance (p <0.05) and LEfSe analysis (LDA score >3.00) were considered significant biomarkers. Statistical significance was set at p <0.05. Hotelling’s t-test was used to compare bacterial profiles among categories. To correct for the false discovery rate, q-values were computed after correction for multiple tests to identify significant bacterial taxa in each category [22].

Results

Study participants

The characteristics of the 23 patients at the time of lung resection are shown in Table 1. The median age was 58 years, and 16 (70%) patients were women. Sixteen patients had MAC-PD (nine M. avium and seven M. intracellulare), and the remaining seven patients had M. abscessus subsp. abscessus (hereafter, M. abscessus)-PD. Approximately half (48%) of the patients had positive results in acid-fast bacilli smears and exhibited FC disease. Two (8%) patients achieved culture conversion status at the time of surgery.

[Figure omitted. See PDF.]

Comparison of microbiota proportions between involved and non-involved sites

We compared the relative proportions of microbial taxa between involved and non-involved sites at the phylum and genus taxonomic levels (Fig 1). The same four phyla were dominant at involved and non-involved sites, albeit at different proportions: involved sites, Proteobacteria (39.9%), Firmicutes (22.2%), Actinobacteria (13.3%), and Bacteroidetes (12.4%); non-involved sites, Proteobacteria (41.2%), Actinobacteria (17.7%), Firmicutes (16.1%), and Bacteroidetes (13.3%). In relative abundance analysis, the proportion of Verrucomicrobia was significantly higher at involved sites than at non-involved sites (p = 0.010) (S2 Fig).

[Figure omitted. See PDF.]

At the genus level, Mycobacterium (5.6%), Bacteroides (5.1%), Sphingomonas (3.6%), and Limnohabitans (3.4%) were the dominant genera at involved sites. At non-involved sites, Prevotella (4.6%), Acinetobacter (4.5%), Mycobacterium (4.5%), and Sphingobium (4.0%) were dominant. In relative abundance analysis, the proportions of Rahnella (p = 0.002), Aquabacterium (p = 0.005), Oscillibacter (p = 0.005), Limnohabitans (p = 0.003), Ruminococcus (p = 0.006), Blautia (p = 0.026), and Faecalibacterium (p = 0.011) were significantly higher at involved sites than at non-involved sites, while the proportions of Acinetobacter (p<0.001) and Enhydrobacter (p = 0.022) were significantly higher at non-involved sites (S3 Fig).

Major respiratory pathogens at the genus level

We compared the proportions of major respiratory pathogens at the genus level between involved and non-involved sites. Clinical microbiological data and proportions of Mycobacterium in each patient are shown in Table 2. The read counts tended to be higher at involved sites than at non-involved sites, but the differences were not statistically significant (p = 0.339). There were no correlations between clinical microbiological data (e.g., acid-fast bacilli smear and culture status results in sputum) and read counts of Mycobacterium in lung tissue. Mycobacterium was also detected at involved sites in two patients who achieved culture conversion in sputum samples at the time of lung resection (Mint-8 and Mint-10).

[Figure omitted. See PDF.]

The read counts of other major respiratory pathogens (e.g., Stenotrophomonas, Haemophilus, Burkholderia, and Pseudomonas) are shown in S4 Table. Burkholderia was found in only one sample from a non-involved site, but there were no significant differences in the relative abundances of Stenotrophomonas (p = 0.583), Haemophilus (p = 0.733), or Pseudomonas (p = 0.423).

Microbial diversities at involved and non-involved sites

The α-diversity of the microbiomes showed statistically significant differences between involved and non-involved sites. Involved sites had significantly higher numbers of OTUs when compared to non-involved sites (p = 2.9e-4), and showed greater richness in ACE, Chao 1, and Jackknife analyses (all p = 0.001). The Shannon index also indicated greater levels of diversity at involved sites (p = 0.007) (Fig 2).

[Figure omitted. See PDF.]

Species richness (ACE p = 0.001, Chao1 p = 0.001, and Jackknife p = 0.001) and diversity index (Shannon p = 0.007), by Wilcoxon rank-sum test.

In terms of β-diversity, there were clear genus-level differences between involved and non-involved sites (Jensen-Shannon PERMANOVA, p = 0.001) (Fig 3). Generalized UniFrac distances also indicated significant differences in genus-level differences between involved and non-involved sites (p = 0.001).

[Figure omitted. See PDF.]

Taxonomic biomarker analysis according to disease involvement

We performed LEfSe analysis to identify specific taxa that were differentially distributed between involved and non-involved sites. Taxa that showed significant results in both relative abundance and LEfSe analyses are shown in Table 3. The abundances of the phyla Verrucomicrobia, Planctomycetes, and Nitrospirae were higher at involved sites than at non-involved sites (p < 0.05). However, considering the cut-off value of the q-value, only the abundance of Nitrospirae was significantly higher at involved sites than at non-involved sites.

[Figure omitted. See PDF.]

At the genus level, several genera, including Limnohabitans, Faecalibacterium, Rahnella, Blautia, Ruminococcus, Oscillibacter, Lachnospira, and Methylotenera were more abundant in involved sites than at non-involved sites. As many genera showed higher abundance at involved sites, only taxa ranking within the top 10 are described in Table 3 (the complete dataset is shown in S5 Table). Among all taxa found, the genera showing significant q-values were Limnohabitans, Rahnella, Lachnospira, Flavobacterium, Megamonas, Gaiella, Subdoligranulum, Rheinheimera, Dorea, Collinsella, and Phascolarctobacterium (S5 Table).

However, only Acinetobacter and Enhydrobacter were abundant at non-involved sites in the complete dataset. Among them, Acinetobacter was predominant at non-involved sites with significant q-value. The detailed read counts of these genera in each patient are shown in S6 Table. Of them, only Acinetobacter showed a significant q-value.

Taxonomic biomarker analysis according to NTM-PD etiology

Next, we identified specific genera that were differentially distributed between lung tissues from MAC-PD patients and M. abscessus-PD patients (Table 4). At involved sites, Bacteroides, Rahnella, Streptococcus, Neisseria, Bacillus, and Agathobaculum were significantly more abundant in tissues from MAC-PD patients. However, there was no genus with a significant q-value. There were no differences between MAC-PD and M. abscessus-PD patients in α-diversity at involved sites. However, there was a significant difference in β-diversity at these sites (Jensen-Shannon PERMANOVA, p = 0.013 and generalized UniFrac p = 0.023).

[Figure omitted. See PDF.]

At non-involved sites, Staphylococcus and Bacteroides were more abundant in M. abscessus-PD patients than in MAC-PD patients. However, there were no differences in either α- or β-diversity between MAC-PD patients and M. abscessus-PD patients at non-involved sites. No genus had a significant q-value.

Taxonomic biomarker analysis according to radiological phenotype

Finally, we evaluated differentially distributed genera according to the NTM-PD radiological phenotype (Table 5). At involved sites, several genera, including Mycobacterium, Bacteroides, Faecalibacterium, and Blautia were significantly more abundant in the FC form than in the NB form. As there were many genera showing a higher distribution in involved sites, only those ranked within the top five are described in Table 5 (the complete dataset is shown in S7 Table). There was no genus with a significant q-value. There were no differences in α-diversity between these forms at involved sites. However, there was a significant difference in β-diversity between the two forms (Jensen-Shannon PERMANOVA p = 0.005 and generalized UniFrac p = 0.009).

[Figure omitted. See PDF.]

At non-involved sites, Escherichia and Streptococcus were significantly more abundant in the FC form than in the NB form. There was no genus with a significant q-value. There were also no differences in either α- or β-diversity between the two forms.

Discussion

We performed microbiome analysis using pairs of disease-invaded and non-invaded lung tissue from NTM-PD patients and identified several predominant taxa according to disease involvement. One of the most notable findings was that Acinetobacter and Enhydrobacter were more abundant at non-involved sites of NTM-PD lung tissues than the involved sites. In particular, Acinetobacter was predominant at non-involved sites even after correcting for false-positive rates. These findings imply that perturbations in microbial communities occur in the NTM-PD and also suggest that it is important to evaluate the relationships between the reduced presence of specific taxa, such as Acinetobacter, and deterioration of NTM-PD.

Acinetobacter is a genus of aerobic Gram-negative coccobacilli, which can be part of the normal flora of the skin and human respiratory secretions but which can also cause various human infections, including pneumonia or wound infections [23]. Previous studies in patients with obstructive respiratory disease, pneumonia, COVID-19, and tuberculosis have reported increased abundances of Acinetobacter [24–26], whereas interstitial pneumonia patients exhibited a reduced abundance of Acinetobacter in their alveolar fluid compared to healthy volunteers [26]. However, recent microbiome studies have not identified Acinetobacter species in bronchial washing fluid or sputum of NTM-PD patients [14, 15, 27]. In contrast to these studies, in our study, Acinetobacter was more abundant at non-involved sites of lung tissues, suggesting that the abundance of Acinetobacter decreased with invasion of the lung by various other bacteria, including NTM, possibly due to a competitive relationship with other bacteria. Only one South Korean study has reported that Enhydrobacter abundance is decreased in bronchial washing fluid from NTM-PD patients, but they did not find significant clinical implications [16]. Further studies are required to evaluate whether Acinetobacter plays a role in the pathogenesis of NTM-PD, whether it exhibits antagonism against NTM, and how it affects the host’s innate immune system.

Notably, we identified large numbers of genera predominantly distributed at the involved sites, including Limnohabitans, Rahnella, Lachnospira, Flavobacterium, Megamonas, Gaiella, Subdoligranulum, Rheinheimera, Dorea, Collinsella, and Phascolarctobacterium. Philley et al. recently reported that Lachnospira was predominant in circulating extracellular vesicles from NTM patients [15]. Previous studies have also mentioned a relation between these genera and respiratory or other diseases. Megamonas, Collinsella, and Phascolarctobacterium were reported to be more abundant in the intestines of tuberculosis patients than in normal subjects [28–30], and Lachnospira and Flavobacterium were more distributed in the intestines of asthma and obese patients, respectively [31, 32]. A recent study showed that Subdoligranulum was more predominant in the lung tissue of patients with sub-solid-nodule lung cancer than with solid-nodule lung cancer [33]. However, there is still limited evidence regarding the associations of these genera with pathogenesis of NTM-PD. The abovementioned phenomena are assumed to be due to the influx of various bacteria from the outside due to the severe destruction of lung parenchyma during the course of NTM-PD. Therefore, it is possible that the genera predominantly found in the disease-invaded sites of NTM-PD may have little direct influence on NTM-PD pathogenesis, and some may be simply bystanders. Therefore, longitudinal assessment is necessary to evaluate significant perturbations in the microbial community of NTM-PD.

We identified different taxa distributions according to the causative NTM species and detected significant differences in β-diversity between MAC- and M. abscessus-invaded lung tissues. Bacteroides, Rahnella, Streptococcus, Neisseria, Bacillus, and Agathobaculum were abundant in MAC. There are no previous reports on comparative microbiome analysis according to NTM species. In previous studies using alveolar fluid or sputum, Bacteroides and Bacillus were predominant in the NTM group, and Streptococcus and Neisseria were observed in both NTM and control groups [14, 15, 27]. However, in our study, the differences in distributions of the above taxa were not significant after adjusting for the false-positive rate. Further studies are needed to identify more favorable bacterial environments for the survival of specific NTM species.

The clinical phenotypes of NTM-PD are divided into the FC form and NB form, the so-called “Lady Windermere syndrome.” The latter cases typically present with bronchiectasis in the middle lung zones and have unique body morphotypes, including lower body mass index and body fat percentage or taller stature [34]. Previous studies have focused on factors related to the typical NTM-PD phenotype in terms of immunological or metabolic factors [35, 36]. However, no studies have involved analysis of the microbiome according to NTM-PD phenotype. There were more dominant genera at involved sites in the FC form than the NB form (Table 5). In addition, proportions of Mycobacterium were higher in the FC form (11.24% vs. 0.42%, respectively). These findings suggest that the bacterial burden in the FC form is much higher than that in the NB form, and can be an evidence for the disease course of the FC form may be more aggressive than the NB form. In contrast, the predominant genera at non-involved sites in the NB form were Bacteroides and Clostridium. Although the roles that these bacteria play in the pathogenesis of NTM-PD have not been elucidated, our data suggest that the microbial environment may vary according to phenotype. However, there were no taxa with a significant q-value, and several factors, such as low biomass in the lung tissue [37, 38] or limitations of the 16S rRNA technique [39], should be taken into consideration.

In our study, α-diversity was higher at involved sites than at non-involved sites. In contrast, disease exacerbation and decreased diversity have been reported in previous studies of other respiratory diseases, such as chronic obstructive pulmonary disease [40]. But, a recent microbiome study analyzed sputum from 42 NTM-PD patients and reported that the Shannon index and observed richness were higher in those receiving treatment for NTM [41]. These observations suggest that diversity increases as the disease worsens in NTM-PD and that worsening of NTM-PD is accompanied by an increase in the invasion of other bacteria. Further research of this issue is required.

Our study had several limitations. First, a large number of patients were excluded due to QC failures or low valid read counts, a possible consequence of the relatively low biomass in lung tissue compared to other organs. Similar phenomena have also been observed in previous studies that have used lung tissues from patients with other chronic respiratory diseases [37, 38]. Second, there are limitations to using the 16S rRNA gene sequencing to identify mycobacteria. Previous studies have reported a lack of mycobacterial identification by 16S rRNA gene sequencing in samples with positive cultures for these organisms [27, 39, 42]. Sequencing of this gene cannot distinguish between M. tuberculosis and NTM, and detailed species or subspecies identification of NTM cannot be performed. In addition, there may be taxa that have not been identified by 16S rRNA gene sequencing. Third, as we analyzed patients who had been treated with antibiotics for a long time, it is possible that the distribution of the microbiome had already been affected by the use of antibiotics. Fourth, this study was conducted only on Asian patients treated at a single institution. Therefore, further longitudinal analyses in larger numbers of patients are required to confirm our results.

In conclusion, we identified distinct microbial distributions between disease-invaded and non-invaded lung tissues from NTM-PD patients. Diversity was significantly higher in disease-invaded tissues.

Supporting information

S1 Fig. Study samples.

https://doi.org/10.1371/journal.pone.0285143.s001

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S2 Fig. Taxonomic relative abundance of phyla compared between involved and non-involved sites.

Taxa <1% are not indicated.

https://doi.org/10.1371/journal.pone.0285143.s002

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S3 Fig. Taxonomic relative abundance of genera compared between involved and non-involved sites.

Taxa <1% are not indicated.

https://doi.org/10.1371/journal.pone.0285143.s003

(DOCX)

S1 Table. Library data on lung tissue samples included in the study.

https://doi.org/10.1371/journal.pone.0285143.s004

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S2 Table. Antibiotics administered before lung resection of all patients (n = 40).

https://doi.org/10.1371/journal.pone.0285143.s005

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S3 Table. Detailed information of antibiotics administered before lung resection of study patients (n = 23).

https://doi.org/10.1371/journal.pone.0285143.s006

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S4 Table. Proportions of other major respiratory pathogens compared between involved and non-involved sites in study patients (n = 23).

https://doi.org/10.1371/journal.pone.0285143.s007

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S5 Table. Taxonomic biomarker analysis for genera compared between involved and non-involved sites in study patients (n = 23).

https://doi.org/10.1371/journal.pone.0285143.s008

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S6 Table. Predominant genera in non-involved site compared to involved site identified by LEfSe analysis (n = 23).

https://doi.org/10.1371/journal.pone.0285143.s009

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S7 Table. Taxonomic biomarker analysis for genera compared between NB (n = 12) and FC (n = 11) forms in involved site.

https://doi.org/10.1371/journal.pone.0285143.s010

(DOCX)

Acknowledgments

We would like to express our heartfelt gratitude and respect to Dr. Won-Jung Koh, for giving us invaluable guidance and unfailing support for NTM-PD research. Dr. Won-Jung Koh passed away in August 2019. We dedicate this work to his memory.

Citation: Kim B-G, Kang N, Kim S-Y, Kim DH, Kim H, Kwon OJ, et al. (2023) The lung microbiota in nontuberculous mycobacterial pulmonary disease. PLoS ONE 18(5): e0285143. https://doi.org/10.1371/journal.pone.0285143

About the Authors:

Bo-Guen Kim

Contributed equally to this work with: Bo-Guen Kim, Noeul Kang

Roles: Conceptualization, Data curation, Formal analysis, Writing – original draft

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Noeul Kang

Contributed equally to this work with: Bo-Guen Kim, Noeul Kang

Roles: Conceptualization, Data curation, Writing – original draft

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

ORICD: https://orcid.org/0000-0003-0925-3891

Su-Young Kim

Roles: Conceptualization, Data curation, Writing – original draft

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Dae Hun Kim

Roles: Conceptualization, Data curation, Writing – original draft, Writing – review & editing

E-mail: [email protected] (BWJ); [email protected] (DHK)

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Hojoong Kim

Roles: Resources, Writing – review & editing

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

O. Jung Kwon

Roles: Conceptualization, Resources, Writing – review & editing

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Hee Jae Huh

Roles: Conceptualization, Resources, Writing – review & editing

Affiliation: Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Nam Yong Lee

Roles: Resources, Writing – review & editing

Affiliation: Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Byung Woo Jhun

Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

E-mail: [email protected] (BWJ); [email protected] (DHK)

Affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

ORICD: https://orcid.org/0000-0002-6348-8731

References

1. Lee H, Myung W, Koh WJ, Moon SM, Jhun BW. Epidemiology of nontuberculous mycobacterial infection, South Korea, 2007–2016. Emerg Infect Dis. 2019; 25:569–72. pmid:30789139

2. Daley CL, Iaccarino JM, Lange C, Cambau E, Wallace RJ Jr, Andrejak C, et al. Treatment of nontuberculous mycobacterial pulmonary disease: an official ATS/ERS/ESCMID/IDSA clinical practice guideline. Eur Respir J. 2020; 56:2000535. pmid:32636299

3. Hoefsloot W, van Ingen J, Andrejak C, Angeby K, Bauriaud R, Bemer P, et al. The geographic diversity of nontuberculous mycobacteria isolated from pulmonary samples: an NTM-NET collaborative study. Eur Respir J. 2013; 42:1604–13. pmid:23598956

4. Hwang JA, Kim S, Jo KW, Shim TS. Natural history of Mycobacterium avium complex lung disease in untreated patients with stable course. Eur Respir J. 2017; 49:1600537. pmid:28275170

5. Kwon BS, Lee JH, Koh Y, Kim WS, Song JW, Oh YM, et al. The natural history of non-cavitary nodular bronchiectatic Mycobacterium avium complex lung disease. Respir Med. 2019; 150:45–50. pmid:30961950

6. Kwak N, Park J, Kim E, Lee CH, Han SK, Yim JJ. Treatment outcomes of Mycobacterium avium complex lung disease: a systematic review and meta-analysis. Clin Infect Dis. 2017; 65:1077–84. pmid:28582488

7. Jhun BW, Kim SY, Moon SM, Jeon K, Kwon OJ, Huh HJ, et al. Development of macrolide resistance and reinfection in refractory Mycobacterium avium complex lung disease. Am J Respir Crit Care Med. 2018; 198:1322–30. pmid:29877739

8. O’Dwyer DN, Dickson RP, Moore BB. The lung microbiome, immunity, and the pathogenesis of chronic lung disease. J Immunol. 2016; 196:4839–47. pmid:27260767

9. Charlson ES, Bittinger K, Haas AR, Fitzgerald AS, Frank I, Yadav A, et al. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am J Respir Crit Care Med. 2011; 184:957–63. pmid:21680950

10. Cuthbertson L, Walker AW, Oliver AE, Rogers GB, Rivett DW, Hampton TH, et al. Lung function and microbiota diversity in cystic fibrosis. Microbiome. 2020; 8:45. pmid:32238195

11. Rogers GB, Bruce KD, Martin ML, Burr LD, Serisier DJ. The effect of long-term macrolide treatment on respiratory microbiota composition in non-cystic fibrosis bronchiectasis: an analysis from the randomised, double-blind, placebo-controlled BLESS trial. Lancet Respir Med. 2014; 2:988–96. pmid:25458200

12. Han MK, Zhou Y, Murray S, Tayob N, Noth I, Lama VN, et al. Lung microbiome and disease progression in idiopathic pulmonary fibrosis: an analysis of the COMET study. Lancet Respir Med. 2014; 2:548–56. pmid:24767767

13. Andrianasolo RM, Kesse-Guyot E, Adjibade M, Hercberg S, Galan P, Varraso R. Associations between dietary scores with asthma symptoms and asthma control in adults. Eur Respir J. 2018; 52:1702572. pmid:29997182

14. Yamasaki K, Mukae H, Kawanami T, Fukuda K, Noguchi S, Akata K, et al. Possible role of anaerobes in the pathogenesis of nontuberculous mycobacterial infection. Respirology. 2015; 20:758–65. pmid:25824634

15. Philley JV, Kannan A, Olusola P, McGaha P, Singh KP, Samten B, et al. Microbiome diversity in sputum of nontuberculous mycobacteria infected women with a history of breast cancer. Cell Physiol Biochem. 2019; 52:263–79. pmid:30816674

16. Kang SY, Kim H, Jung S, Lee SM, Lee SP. The lung microbiota in Korean patients with non-tuberculous mycobacterial pulmonary disease. BMC Microbiol. 2021; 21:84. pmid:33736609

17. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013; 41:e1. pmid:22933715

18. Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol. 2010; 10:189. pmid:20618939

19. Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017; 67:1613–7. pmid:28005526

20. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011; 12:R60. pmid:21702898

21. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013; 31:814–21. pmid:23975157

22. Yi H, Yong D, Lee K, Cho YJ, Chun J. Profiling bacterial community in upper respiratory tracts. BMC Infect Dis. 2014; 14:583. pmid:25391813

23. Munoz-Price LS, Weinstein RA. Acinetobacter infection. N Engl J Med. 2008; 358:1271–81. pmid:18354105

24. Huang WC, Wu MF, Huang CC, Liu SY, Chen HC, Chen YY, et al. Dynamics of the lung microbiome in intensive care patients with chronic obstructive pulmonary disease and community-acquired pneumonia. Sci Rep. 2020; 10:11046. pmid:32632240

25. Gaibani P, Viciani E, Bartoletti M, Lewis RE, Tonetti T, Lombardo D, et al. The lower respiratory tract microbiome of critically ill patients with COVID-19. Sci Rep. 2021; 11:10103. pmid:33980943

26. Vázquez-Pérez JA, Carrillo CO, Iñiguez-García MA, Romero-Espinoza I, Márquez-García JE, Falcón LI, et al. Alveolar microbiota profile in patients with human pulmonary tuberculosis and interstitial pneumonia. Microb Pathog. 2020; 139:103851. pmid:31715320

27. Sulaiman I, Wu BG, Li Y, Scott AS, Malecha P, Scaglione B, et al. Evaluation of the airway microbiome in nontuberculous mycobacteria disease. Eur Respir J. 2018; 52:1800810. pmid:30093571

28. Hu Y, Feng Y, Wu J, Liu F, Zhang Z, Hao Y, et al. The gut microbiome signatures discriminate healthy from pulmonary tuberculosis patients. Front Cell Infect Microbiol. 2019; 9:90. pmid:31001490

29. Khaliq A, Ravindran R, Afzal S, Jena PK, Akhtar MW, Ambreen A, et al. Gut microbiome dysbiosis and correlation with blood biomarkers in active-tuberculosis in endemic setting. PLoS One. 2021; 16:e0245534. pmid:33481833

30. Maji A, Misra R, Dhakan DB, Gupta V, Mahato NK, Saxena R, et al. Gut microbiome contributes to impairment of immunity in pulmonary tuberculosis patients by alteration of butyrate and propionate producers. Environ Microbiol. 2018; 20:402–19. pmid:29322681

31. Stiemsma LT, Arrieta MC, Dimitriu PA, Cheng J, Thorson L, Lefebvre DL, et al. Shifts in Lachnospira and Clostridium sp. in the 3-month stool microbiome are associated with preschool age asthma. Clin Sci (Lond). 2016; 130:2199–207. pmid:27634868

32. Palmas V, Pisanu S, Madau V, Casula E, Deledda A, Cusano R, et al. Gut microbiota markers associated with obesity and overweight in Italian adults. Sci Rep. 2021; 11:5532. pmid:33750881

33. Ma Y, Qiu M, Wang S, Meng S, Yang F, Jiang G. Distinct tumor bacterial microbiome in lung adenocarcinomas manifested as radiological subsolid nodules. Transl Oncol. 2021; 14:101050. pmid:33765542

34. Koh WJ, Moon SM, Kim SY, Woo MA, Kim S, Jhun BW, et al. Outcomes of Mycobacterium avium complex lung disease based on clinical phenotype. Eur Respir J. 2017; 50:1602503. pmid:28954780

35. Kartalija M, Ovrutsky AR, Bryan CL, Pott GB, Fantuzzi G, Thomas J, et al. Patients with nontuberculous mycobacterial lung disease exhibit unique body and immune phenotypes. Am J Respir Crit Care Med. 2013; 187:197–205. pmid:23144328

36. Kim RD, Greenberg DE, Ehrmantraut ME, Guide SV, Ding L, Shea Y, et al. Pulmonary nontuberculous mycobacterial disease: prospective study of a distinct preexisting syndrome. Am J Respir Crit Care Med. 2008; 178:1066–74. pmid:18703788

37. Maughan H, Cunningham KS, Wang PW, Zhang Y, Cypel M, Chaparro C, et al. Pulmonary bacterial communities in surgically resected noncystic fibrosis bronchiectasis lungs are similar to those in cystic fibrosis. Pulm Med. 2012; 2012:746358. pmid:22448327

38. Kitsios GD, Rojas M, Kass DJ, Fitch A, Sembrat JC, Qin S, et al. Microbiome in lung explants of idiopathic pulmonary fibrosis: a case-control study in patients with end-stage fibrosis. Thorax. 2018; 73:481–4. pmid:28802277

39. Caverly LJ, Carmody LA, Haig SJ, Kotlarz N, Kalikin LM, Raskin L, et al. Culture-independent identification of nontuberculous mycobacteria in cystic fibrosis respiratory samples. PLoS One. 2016; 11:e0153876. pmid:27093603

40. Wang Z, Bafadhel M, Haldar K, Spivak A, Mayhew D, Miller BE, et al. Lung microbiome dynamics in COPD exacerbations. Eur Respir J. 2016; 47:1082–92. pmid:26917613

41. Cowman SA, James P, Wilson R, Cookson WOC, Moffatt MF, Loebinger MR. Profiling mycobacterial communities in pulmonary nontuberculous mycobacterial disease. PLoS One. 2018; 13:e0208018. pmid:30533049

42. Macovei L, McCafferty J, Chen T, Teles F, Hasturk H, Paster BJ, et al. The hidden ’mycobacteriome’ of the human healthy oral cavity and upper respiratory tract. J Oral Microbiol. 2015; 7:26094. pmid:25683180