Study on the airway microecology of pulmonary tuberculosis based on 16S rRNA sequencing

doi:10.21203/rs.3.rs-1667910/v1

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Study on the airway microecology of pulmonary tuberculosis based on 16S rRNA sequencing

https://doi.org/10.21203/rs.3.rs-1667910/v1

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Background: Pulmonary tuberculosis is a chronic infectious disease of the respiratory system. It is still one of the causes of death from a single infectious disease, but it has been stuck in the study of a single pathogen. Recent studies have shown that many diseases are related to the microbiota. We clarified the occurrence of tuberculosis and the correlation between drug resistance and respiratory flora. High-throughput 16S rRNA gene sequencing was used to characterize the respiratory microbiota composition of 30 tuberculosis (TB) patients and compared with 30 health (H) controls. According to their Gene Xpert results, 30 pulmonary tuberculosis patients were divided into 12 persons in the sensitive group (DS0) and 18 persons in the drug-resistant group (DR0). The flora of the two were compared with the H group.

Results: The data generated by sequencing showed that Firmicutes, Proteus, Bacteroides, Actinomyces and Fusobacterium were the five main bacterial phyla detected, and they constituted more than 96% of the microbial community. The relative abundance of Fusobacterium, Haemophilus, Porphyromonas, Neisseria, TM7, Spirochetes, SR1, and tunica in the TB group was lower than that of the H group, and Granulicatella was higher than the H group. The PcoA diagrams of the two groups had obvious clustering differences. The Alpha diversity of the TB group is lower than that of the H group, and the Beta diversity is higher than that of the H group (P<0.05). The relative abundance of Streptococcus in the DS0 group was much higher than that in the DR0 group (P<0.005).

Conclusion: Pulmonary tuberculosis can cause disorders of the respiratory tract flora, in which the relative abundance of Streptococcus was significantly different between rifampicin-sensitive and drug-resistant patients.

Tuberculosis

Microecology

16S rRNA gene sequencing

Tuberculosis is an ancient respiratory infectious disease and continues to current days. According to the World Health Organization (WHO) "Global Tuberculosis Report 2021" released in October, 2021: There were nearly 2 billion people latently infected with tuberculosis worldwide. In 2020, there were approximately 9.87 million new cases of tuberculosis in the world. The Report also pointed out that about 1.50 billion death cases were found globally in 2020 with the death rate 15%, higher than 14% in 2019. The estimated number of new cases of tuberculosis in China is 833,000, of which about 65,000 were resistant to the main anti-tuberculosis drug -- rifampicin. Tuberculosis remains the leading cause of death from a single infectious disease ¹.

Mycobacterium tuberculosis causes tuberculosis and is mostly found in the lungs and respiratory tract. In healthy people, the lungs and airways have not been proved germfree. There were five major bacterial phyla in lung and airways of healthy people: Bacteroides, Firmicutes, Proteobacteria, Actinomycetes and Fusobacteria ². The microbial flora could act as a biological barrier to prevent pathogen invasion and participate in human immune regulation, which is mutually beneficial to the human body. Therefore, the microbial flora is closely related to the occurrence and development of diseases.

16S rRNA, one of the small subunits of ribosome, is a key tool for researches of microbial communities ^3,4. Ribosomal RNA (rRNA) is the most abundant RNA in cells, accounting for about 80% of the total RNA. There are three types of rRNA in prokaryotes. According to their settlement coefficients, rRNAs can be divided into 5S, 16S, and 23S ⁵. They are combined with different nucleosome proteins to form the large subunit and small subunit of the nucleosome ⁶. The four rRNAs of eukaryotes also use similar methods to form the large and small subunits of the ribosome ⁵. 16S rRNA gene sequences contain hypervariable regions which can identify bacteria for it provides species-specific signature sequences ⁷. 16S rRNA was originally used in bacteria identification, which was found lately in reclassifying new species ⁸, even genera ⁹. Currently, 16S rRNA has become prevalent in medical microbiology and infectious diseases ¹⁰.

To this end, we used 16S rRNA sequencing to detect the composition and diversity of sputum microflora in patients with tuberculosis infection, and compared them with healthy people to explore.

Participants

30 healthy medical staffs as controls and 30 tuberculosis patients in Guangzhou Chest Hospital were selected, 29 males and 31 females, aged 20–60. In the health (H) group, the medical staffs were non-smokers, who had no history of chronic diseases of respiratory system, no history of respiratory infection in the past 3 weeks, no heart, lung, brain and other diseases, allergic diseases; physical examination and chest X-ray showed no abnormality. The sputum smears of patients in the tuberculosis group (TB) were positive for acid-fast bacilli, which were identified as Mycobacterium tuberculosis by nucleic acid test, and no anti-tuberculosis therapy was used at the time of enrolment. Exclusion criteria: (1) patients with severe disease (malignant hypertension, uncontrolled diabetes, myocardial infarction, etc.), malignant tumor, and pulmonary infection on admission; (2) Oral/intravenous antibiotics were used within 3 weeks before enrolment; (3) Limited understanding ability or inability to cooperate with the examiner; (4) Patients requiring long-term oxygen inhalation (> 12h/ day). All the subjects had signed the informed consent.

According to the rifampicin resistance results of Gene Xpert MTB/RIF in the patients' initial sputum, we divided the tuberculosis patients into drug-sensitive (DS) group (12 patients) and drug-resistant (DR) group (18 patients). Sputum samples were collected in H group: 3% saline ultrasound spray was used to extract induced sputum from the respiratory tract. 60 sputum samples from the H group and the TB group were sequenced by 16S rRNA. After sample preparation, DNA extraction, amplification, database building, up-machine sequencing, data filtering, reads splicing into Tags, Tags clustering into OTU and comparing with the database, species annotation and other statistical analyses were conducted.

Molecular analysis

1. Genomics DNA extraction

The sputum was liquefied by 4% NaOH and buffered in pH 6.8 phosphate buffer then was centrifuged and the supernatant was discarded. The DNA was extracted after the precipitate was taken. The microbial community DNA was extracted using NEB next microbiome DNA enrichment kit (New England Biolabs, Ipswich, MA, US) following instructions of the manufacturer. DNA was quantified with a Qubit Fluorometer by using Qubit® dsDNA BR Assay kit (Invitrogen, USA) and the quality was checked by running aliquot on 1% agarose gel.

2. Library Construction

Variable regions V1–V3 of bacterial 16S rRNA gene was amplified with degenerate PCR primers, 8F (5’-AGAGTTTGATYMTGGCTCAG-3’) and 518R (5’-ATTACCGCGGCTGCTGG-3’). Both forward and reverse primers were tagged with Illumina adapter, pad and linker sequences. PCR enrichment was performed in a 50 µL reaction containing 30ng template, fusion PCR primer and PCR master mix. PCR cycling conditions were as follows: 94℃ for 3 minutes, 30 cycles of 94℃ for 30 seconds, 50℃ for 45 seconds, 72℃ for 45 seconds and final extension for 10 minutes at 72℃ for 10 minutes. The PCR products were purified with AmpureXP beads and eluted in Elution buffer. Libraries were qualified by the Agilent 2100 bioanalyzer (Agilent, USA). The validated libraries were used for sequencing on Illumina HiSeq platform (BGI, Shenzhen, China) following the standard pipelines of Illumina, and generating 2 ×300 bp paired-end reads.

3. Sequencing and bioinformatics analysis

Raw reads were filtered to remove adaptors and low-quality and ambiguous bases, and then paired-end reads were added to tags by the Fast Length Adjustment of Short reads program (FLASH, v1.2.11)¹¹ to get the tags. The tags were clustered into OTUs with a cutoff value of 97% using UPARSE software (v7 .0.1090)¹² and chimera sequences were compared with the Gold database using UCHIME (v4.2.40)¹³ to detect. Then, OTU representative sequences were taxonomically classified using Ribosomal Database Project (RDP) Classifier v.2.2 with a minimum confidence threshold of 0.6, and trained on the Greengenes database v201305 by QIIME v1.8.0. The USEARCH_global¹⁴ was used to compare all Tags back to OTU to get the OTU abundance statistics table of each sample.

Alpha and beta diversity were estimated by MOTHUR (v1.31.2)¹⁵ and QIIME (v1.8.0) at the OTU level, respectively. Sample cluster was conducted by QIIME (v1.8.0) based on UPGMA. Barplot of different classification levels was plotted with R package v3.4.1. GraPhlan map of species composition was created using GraPhlAn. Principal Coordinate Analysis (PCoA) was performed by QIIME (v1.8.0). LEfSe cluster or LDA analysis was conducted by LEfSe. Significant Species or function were determined by R(v3.4.1) based on Wilcox-test or Kruskal-Test.

1. The bacterial flora of the respiratory sputum specimens of the healthy medical staff and the tuberculosis patients were diverse, with five main phyla: Proteus, Firmicutes, Fusobacterium, Actinomycetes and Bacteroides.

Among the 10,713,472 raw 16S rRNA readings obtained, 5,090,698 raw Reads were from the TB group samples, and 5,622,774 raw Reads were from H group samples. 3,783,786 clean Reads in TB group were obtained by filtering process, which removed 25.67% of original sequence Reads. For H group, 4,138,850 clean Reads were retained and 26.39% original sequence Reads were deleted. After removing the primer sequence, the average length is about 252 bp. The clean Reads were spliced into Tags through the overlap relationship between the reads, and each sample retained 67,519 tags on average. Finally, 600 OTUs were obtained. The TB group had 168 OTUs, and the H group had 70 OTUs. The two groups had 362 shared OTUs. All OTUs used Greengenes comparison to obtain the five main bacterial phyla: Proteus, Firmicutes, Fusobacterium, Actinomycetes and Bacteroides. Other bacteria were also detected, such as TM7, Spirochace, SR1, Cyanobacteria, and Tenericutes, which together accounted for < 4% of the total readings analyzed.

2. The TB group had significant difference from the H group in both Alpha and Beta diversity. However, there was no significant difference in Alpha and Beta diversity between DS0 group and DR0 group.

By phylogenetic-based weighting (Unifrac distance metric), the PcoA principal coordinate analysis between samples in different groups was generated, showing that tuberculosis samples (TB) and samples from health controls (H) have different clusters (Fig. 1). Shannon diversity Kruskal-Wallis Test was used to analyze the differences between the drug-sensitive (DS0) group, drug-resistant (DR0) group and H group. The median values were 2.63, 2.34 and 3.08, respectively, P < 0.001. There were statistical differences between DS0, DR0 and H, and the diversity in the samples of TB patients in the above two groups was lower than that of H (Fig. 2). Beta diversity cluster analysis was conducted for samples from the DS0, DR0 and H groups based on the distance between samples, and the median values were 0.56, 0.58 and 0.30, respectively, P < 0.001, indicating that there was a statistical difference between DS0, DR0 and H (Fig. 3). The inter-individual diversity of samples from TB patients in the above two groups was higher than that of H. There is no significant difference on both Alpha diversity and Beta diversity between DS0 group and DR0 group (P > 0.05).

3. There were differences in the relative abundance of species between the tuberculosis and healthy respiratory samples.

60 samples were sequenced and clustered into 600 OTUs by 16S rRNA amplicon. Greengenes annotation was used to identify Actinobacteria, Fusobacteria, Bacteroidetes, Proteobacteria and Firmicutes as five main phyla. The dominant phyla were Proteobacteria and Firmicutes. The corresponding dominant bacteria genera were Neisseria, Haemophilus, Streptococcus and Veillonella. In addition, the main genus of Fusobacteria is Fusobacteriales, while the main genus of Bacteroidetes is Prevotella (Fig. 4). On the level of genus, the top 15 species in abundance were Actinobacillus, Campylobacter, Capnocytophaga, Lautropia, Actinomyces, Granulicatella, Rothia, Fusobacterium, Leptotrichia, Porphyromonas, Haemophilus, Veillonella, Streptococcus, Neisseria and Prevotella (Fig. 5).

In phylum level, the relative abundance of Proteobacteria (25.89% vs 36.69%), Fusobacteria (4.76% vs 9.51%), TM7 (0.96% vs 1.75%), Spirochaetes (0.07% vs 0.56%), SR1 (0.18% vs 0.34%), Tenericutes (0.06% vs 0.16%) in TB group was lower than H group (P < 0.05) and the relative abundance of Firmicutes (40.30% vs 26.75%) and Actinobactria (6.30% vs 2.95%) in TB group was higher than H group (P < 0.05) (Fig. 6).

The relative abundance of the first 15 bacterial genera in sputum samples of the TB and H groups (Fig. 5), and the differences of key species are shown in Fig. 7. The Relative Abundance of Neisseria, Haemophilus, Porphyromonas, Fusobacterium and Granulicatella were different. The Relative Abundance of the first 4 genera of bacteria in the above two groups were 17.23% and 20.40%, 4.61% and 10.70%, 2.71% and 4.43%,1.65% and 6.74%, respectively. Wilcox rank sum test showed that the p-values were 0.04, 0, 0.001, 0, respectively, which indicated that the genera mentioned above in TB group was lower than that in H group. However, Granulicatella in TB group is higher than H group: 3.94% and 0.74%, p-value was 0.03. At the species level, the relative abundance of Veillonella dispar was 10.82% and 3.75% higher than that of H, and the p-value was 0.01. Neisseria subflava, Haemophilus parainfluenzae, Prevotella pallens and Prevotella nanceiensis were lower in TB than in H (P < 0.05).

4. There were differences in the distribution of bacteria in the drug-sensitive group (DS0) and drug-resistant group (DR0)

There were differences in the relative abundance of DS0 and DR0 (Fig. 8). The Streptococcus relative abundance of DS0 group is higher than that of DR0 group :25.35% and 12.41, mainly reflected in the species level of Streptococcus infantis, P < 0.05, showing statistical difference.

After 16S rRNA sequencing data analysis, the respiratory secretions of 30 tuberculosis patients and 30 healthy medical staffs were clustered into 600 OTU, including five major bacterial phyla: Proteus, Firmicutes, Fusobacterium, Actinomycetes and Bacteroidetes, and 15 major bacterial genera (Fig. 5), which was similar to most reports ^16–18. There were 168 unique OTU in the TB group and 70 unique OTU in the H group, indicating that there were more exotic species in the TB group, which was similar to that reported by Zelin Cui ¹⁸. PcoA principal coordinate analysis showed that there was clustering difference between TB and H groups, which was shown in both Alpha diversity and Beta diversity. The data showed that the intra - individual diversity of PTB respiratory flora samples decreased, but the inter - individual diversity increased.

The airway microecology is unbalanced in many chronic respiratory diseases. In bronchiectasis, the airways became widened and mucociliary clearance fails, resulting in increased adhesion of potential pathogenic microorganisms (PPMs) such as Pseudomonas and Haemophilus ¹⁹. In chronic obstructive pulmonary disease (COPD), Haemophilus influenzae and Moraxella were associated with disease progression and exacerbation ²⁰. Cystic fibrosis (CF) is associated with infections of Staphylococcus aureus and Ceratocystis cepacia ²¹. Our study on the airway microorganism of PTB is slightly different and has no correlation with the PPMs of Proteobacteria mentioned above. The reason is that the diseases mentioned above are chronic infections caused by the increased abundance of colonizing bacteria in the respiratory tract. However, the pathogen of PTB is Mycobacterium tuberculosis (MTB), which belongs to foreign bacteria and is caused by MTB breaking through the respiratory barrier and invading the lungs due to the low resistance of the body or the destruction of lung structure or the reduced ability of cilia clearance. Our data showed that no co-infection of other PPMs occurred. In addition, our study showed that the relative abundance of Firmicutes and Actinomycetes in the airway of PTB increased, while the relative abundance of Actinomycetes in the airway of PTB decreased, which was similar to the report of Jing Wu ²² and P. Krishna ¹⁷. Some other reports on the respiratory tract of PTB are different from our data, such as Zelin Cui ¹⁸ reported that Stenotrophomonas, Cupriavidus, Pseudomonas, Thermus, Sphingomonas and other foreign bacteria are unique to the airway of PTB; Yuhua Zhou reported ¹⁶: the dominant genus of bacteria in the tuberculosis patient's lower respiratory tract is Cuprophyll rather than Streptococcus. In terms of bacterial genera in this study, Neisseria, Haemophilus, Porphyromonas and Fusobacterium were lower than those in H group. Granulicatella and Veillonella_dispar were higher than those of H group. There are 15 Species of unique biomarkers in TB group, which are derived from Firmicutes, Actinobacteria and Proteobacteria. The results are different from the above reports, which may be due to the different sequencing depth and bacteria detected in each report. On the other hand, the environment, region, diet, race and underlying diseases of patients in each report are also different.

The disorder of the microbial flora in the airway of PTB is a second-degree disorder, which is a manifestation of chronic disease, also known as a localized flora disorder, which can slowly recover after rapid removal of MTB pathogens and reduced inflammation. We speculate that after the MTB into the lungs, the body to play the inherent immune function of lung, most bacteria was neutrophil infiltration, system effectively removal of phagocyte, or adaptive immunity will be activated in a few days, the specific immune amplification, such as T cells, the secretion of inflammatory cytokines help to remove pathogen. During this process, the strong host immune response may kill or clear the normal bacterial flora in the respiratory tract (such as Proteobacteria, Fusobacteria, etc.), and at the same time cause inflammation and damage to the lung tissue, resulting in more mucus production and changes in the airway microenvironment such as local hypoxia, temperature rise, pH value and nutrition. It is conducive to the reproduction of Gram-positive bacteria such as Firmicutes and Actinomycetes. The cell wall of the above bacteria is composed of a thick and dense layer of peptidoglycan and phosphogeicylic acid, and it has a relatively strong resistance to the outside world. The Relative Abundance of Mycobacteriaceae in this study is not high ²², but as a pathogen, it is Gram-positive bacteria. Is there a synergistic effect with elevated Firmicutes and Actinomycetes that predicts progression of PTB disease? In addition, the relative abundance of Granulicatella increased in TB group. Recently, Granulicatella has caused endocarditis, bacteremia, peritonitis, arthritis, etc. ²³, and whether Granulicatella has caused double infection of respiratory tract is also a concern.

Data in Fig. 8 showed that the Relative Abundance of Streptococcus in DS0 was much higher than that of DR0, mainly Streptococcus infantis, which belonged to physiological Streptococcus virifolia and was the most powerful colonization bacteria in the oropharynx. Some studies have reported that, this bacterium had an inhibitory effect on the growth of Streptococcus suppurans, Streptococcus pneumoniae and Gram-negative bacilli ²⁴. Therefore, when other bacterial genera were not adapted to the external environment and weakened, Streptococcus grows rapidly, perhaps because of the increase in the Abundance of this protective bacterium, it indicated a good outcome of the disease. DR0 is shown as the disorder of normal flora, with no increase in Streptococcus and no increase in other PPMs. Drug-resistant TB patients mostly recurrent patients, early have anti-tb drugs or other antibiotic treatment, the imbalance of bacteria in the respiratory tract or there is a certain time, has selected resistant MTB, and any imbalance of the normal flora in the destruction of the immune response and colonization of a variety of pathogens ^25,26, so we assume: In the follow-up treatment of drug-resistant tuberculosis, especially MDR-TB, which lasts for about one year, the continuous administration of antibiotic drugs may aggravate the selection of pressure and drug resistance of respiratory flora, and the difficulty in clearing Mycobacterium tuberculosis also aggravates the destruction of lung structure, resulting in a vicious cycle of treatment.

The respiratory microecology of tuberculosis patients and health workers had different clustering, diversity of bacteria genus was different, and secondary dysregulation of bacteria community occurred. The increased bacterial load of Streptococcus in DS0 group may be one of the indicators of good prognosis of PTB, and the occurrence of bacterial disturbance in DR0 group is closely related to the occurrence and progress of PTB. Biological regulation of normal flora may be tuberculosis pathogenesis and drug resistance mechanism is an important issue, pay more attention to ecology, rather than only emphasize single pathogen separation, adjusted by probiotics to respiratory tract microecological, keep it in a healthy state of balance, the host of the potential change is likely to prevent the host - harmful microorganism group interaction comprehensive solution. Therefore, increasing the research on the species and function of respiratory microecology as well as the correlation with diseases can become a new idea for clinical prevention and treatment of PTB.

TB, Tuberculosis; RNA, Ribonucleic Acid; WHO, World Health Organization; MTB, Mycobacterium tuberculosis; RIF, Rifampicin; DNA, Deoxyribonucleic Acid; PTB, pulmonary tuberculosis.

Ethics approval and consent to participate

The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Guangzhou Chest Hospital. Written informed consent was obtained from individual or guardian participants.

Consent to publish

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article. Data can be accessed from NCBI Sequence Read Archive (SRA) and the number was PRJNA837186.

Competing interests

Xingshan Cai, Yang Luo, Yuanliang Zhang, Yuan Lin, Bitong Wu, Zhizhong Cao, Zuqiong Hu, Xingyi Wu and Shouyong Tan declare that they have no competing interests.

Funding

The work was supported by the Guangdong Provincial Health Commission [grant numbers: A2019342]. The funders provided financial and technical support for the study.

Authors’ Contributions

XC and YL progressed experimental design, data collection, molecular experiments, and manuscript writing. YZ made project development, and data collection. YL and BW made patient selection and analysis. ZC, ZH and XW did microbiology experiments, data collection and analysis. ST made protocol and project development, served as scientific advisor of experimental design, and manuscript editing. All authors have read and approved the manuscript.

Acknowledgements

We sincerely thank the Guangdong Provincial Health Commission for its funding support and the Key Laboratory of Respiratory Diseases of Guangzhou Chest Hospital for its basic support.

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You are reading this latest preprint version

Study on the airway microecology of pulmonary tuberculosis based on 16S rRNA sequencing

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Abstract

Figures

Background

Methods

Participants

Molecular analysis

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Status:

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