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 Table of Contents  
Year : 2021  |  Volume : 10  |  Issue : 3  |  Page : 260-267

Microbiome in sputum as a potential biomarker of chronicity in pulmonary resistant to rifampicin-tuberculosis and multidrug-resistant-tuberculosis patients

1 Department of Medical Microbiology, Faculty of Medicine; Doctoral Study Program of Medical Science, Faculty of Medicine, Airlangga University, Yogyakarta, Indonesia
2 Department of Medical Microbiology, Faculty of Medicine; Department of Tuberculosis, Laboratory of Tuberculosis, Institute of Tropical Disease, Airlangga University, Yogyakarta, Indonesia
3 Department of Biochemistry, Faculty of Veterinary Medicine, Gadjah Mada University, Yogyakarta, Indonesia
4 Department of Medical Microbiology, Faculty of Medicine, Airlangga University, Yogyakarta, Indonesia; Department of Bacteriology, School of Medicine, Niigata University, Niigata, Japan

Date of Submission18-Jun-2021
Date of Acceptance27-Jul-2021
Date of Web Publication03-Sep-2021

Correspondence Address:
Ni Made Mertaniasih
Jl. Mayjen Prof. Dr. Moestopo No. 47 Surabaya 60131
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijmy.ijmy_132_21

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Background: Cases of tuberculosis (TB) and multidrug-resistant TB (MDR-TB) in South-east Asia including Indonesia are still high. The presence of mixed infections in TB cases has been reported. Several studies revealed the role of the human microbiome in TB. This study purposes to characterize microbiome which can be a potential biomarker of chronicity in TB or MDR-TB. Methods: Sputum samples of pulmonary TB patients confirmed MDR-TB and resistant to rifampicin TB (RR-TB) were conducted Metagenomic next-generation sequencing. Principal coordinate analysis of UniFrac's showing the community structure of microbiome in MDR-TB comorbid diabetes mellitus (DM) is different from RR-TB noncomorbid DM (P = 0.003). Results: Proteobacteria microbiome in MDR-TB comorbid DM was more abundant than in RR-TB noncomorbid DM. Actinobacteria found in the small quantity in RR-TB and MDR-TB. Diversity of microbiome genera was greater in RR-TB. The linear discriminant analysis effect size analysis represents a genus biomarker whose abundance shows significant differences between groups, genus Rothia as a potential biomarker for RR-TB noncomorbid DM. Conclusions: Interesting findings is the community structure of microbiome in MDR-TB and RR-TB. In chronic TB such as recurrent, associated MDR-TB should attention to the findings of a small number of Actinobacteria could be a biomarker of TB which is also a determinant in patient taking combined anti-TB drugs of choice.

Keywords: Multidrug-resistant tuberculosis patients, microbiome, potential biomarker, resistant to rifampicin TB

How to cite this article:
Wiqoyah N, Mertaniasih NM, Artama WT, Matsumoto S. Microbiome in sputum as a potential biomarker of chronicity in pulmonary resistant to rifampicin-tuberculosis and multidrug-resistant-tuberculosis patients. Int J Mycobacteriol 2021;10:260-7

How to cite this URL:
Wiqoyah N, Mertaniasih NM, Artama WT, Matsumoto S. Microbiome in sputum as a potential biomarker of chronicity in pulmonary resistant to rifampicin-tuberculosis and multidrug-resistant-tuberculosis patients. Int J Mycobacteriol [serial online] 2021 [cited 2022 Jan 25];10:260-7. Available from: https://www.ijmyco.org/text.asp?2021/10/3/260/325495

  Introduction Top

Tuberculosis (TB) is the leading cause of death from a single infectious agent in the world. TB can be cured but is still a health problem because of the high number of incidents, especially in TB high burden country, which infects an average of 9 million people each year. Although TB is cured, more than 1 million people die each year.[1]

The discovery of TB cases with a quick and precise diagnosis will accelerate the treatment cure rate and decrease the spread of TB so that the discovery of TB cases will be an important factor in efforts to stop TB transmission and can reduce the incidence of TB, and finally, TB management can be successful. Until now, it has developed various molecular methods for TB and multidrug-resistant TB (MDR-TB).[2] The molecular method is a method by detecting the specific genes of Mycobacterium TB (MTB) that have specific genetic variations from a specific geographic region to detect the most mutations in different areas.[3]

It has been reported that mixed infections can cause increased infection rates in areas with high TB incidence. It is known that colonized microorganisms in the human body play an important role in the physiological process of infection. Some studies show that the causes of TB infection and pathogenesis of MTB are multifactorial including human genetic factors, microbiome, antibiotic resistance, and comorbid diabetes mellitus (DM).[4]

The balance of the microbiome is impaired in pulmonary infections and causes changes in the diversity of the pulmonary microbiome. The microbiome that wins the competition is the dominant microbiome in the lungs. The abundant growth of a bacterial species causes a decrease in the pulmonary microbiome species and is related to lung disease conditions.[4],[5],[6] Disruption of the balance of the microbiome community in the body can be caused by several factors such as antibiotics or infections.[6]

This allows for changes in microbiome diversity in TB sufferers related to major anti-TB drug resistance in both resistant to rifampicin TB (RR-TB) and MDR-TB. Krishna et al. shows the association between disease and the imbalance of interaction between bacteria in the microbiome.[4] Until now, there are still very few and limited investigations about the role of the microbiome in the development of MDR-TB. Sputum is a source of bacterial discovery that can potentially be a TB biomarker. The next-generation sequencing method based on 16SrRNA gene can analyze the composition, diversity, and abundance of the microbiome with high precision. It is, therefore, very important to explore the microbiome in sputum to look for potential microbiotic biomarkers that play a role in TB or MDR-TB infections, thus opening new pathways for the diagnosis of TB patients with anti-TB drug resistance.

It is very important to analyze the microbiome in sputum which has the potential as a biomarker in RR or MDR-TB patients who are not or accompanied by comorbid DM.

  Methods Top

Subject and sample collection

A total of 75 sputum samples of pulmonary TB (PTB) patients with confirmed resistance to rifampicin and or isoniazid. Samples of sputum were obtained from TB patients in Dr. Soetomo Academic Hospital Surabaya, Indonesia. The criteria of sputum samples from TB patients that can be passed on for further examination were MTB positively RR TB confirmed by Xpert MTB/RIF (Cepheid, CA, USA), and the resistance to Rifampicin and Isoniazid as MDR-TB confirmed by method MGIT 960 BACTEC System. This research has been approved by the Ethics Committee at Dr. Soetomo Academic Hospital Surabaya, Indonesia. Informed Consent was obtained from all patient admission for TB patient care. During the patient's sputum collection, patient data information was obtained from the medical record including age, gender, previous clinical history, and RR test.

Genomic DNA extraction

Sputum sample processing using Alkali Petroff method, DNA extracted using Qiagen DNA extraction kit.

Genome DNA concentration measurement

The size of the quality and DNA levels assessed using the nanodrop spectrophotometer tool. DNA concentration of ≥50 ng/μl, OD260/280 = 1.8 ~ 2.0 was obtained.

Amplification of V3-V4 region of 16S rRNA gene by polymerase chain reaction method

16S rRNA genes of distinct regions (16SV3-V4) were amplified using specific primer with the barcode, primer 341F (5'CCTAYGGGRBGCASCAG3') and 806R (5' GGACTACNNGGGTATCTAAT 3'). All polymerase chain reactions (PCR) are performed with Phusion® High-Fidelity PCR Master Mix (New England Biolabs).

Polymerase chain reaction products quantification and qualification

PCR products were mixed with loading buffer volume 1X (contain SYB green). PCR products were detected by 2% electrophoresis gel. Samples with bright main strips between 400bp and 450bp were selected for further examinations.

Polymerase chain reaction products mixing and purification

PCR products were mixed at equal density ratios. The mixed PCR products were purified with Qiagen Gel Extraction Kit (Qiagen, Germany).

Library preparation

Libraries generated with NEBNext® UltraTM DNA Library Prep Kit for Illumina and measured through Qubit and quantitative PCR will be analyzed by the Illumina platform.

Sequencing data processing

Paired-end readings were assigned to samples based on their unique barcodes and cut by cutting barcodes and primary sequences. The average base quality is required to be Q20≥ (that is, the average accuracy of the base order is ≥99%). Paired-end readings were combined using FLASH (V1.2.7) Quality filtering of raw tags is formed under specific filtering conditions to obtain high-quality clean tags by the Qiime quality control process (V1.7.0) Tags compared to reference databases (Gold databases) use the UCHIME algorithm to detect chimera sequences. After Chimera sequences were removed, Effective Tag is finally obtained.[7],[8],[9],[10]

Operational taxonomic units cluster and taxonomic annotation

Sequence analysis was performed by Uparse software (Uparse v7.0.1001, http://drive5.com/uparse/) using all effective tags. The order with the ≥97% similarity is assigned to the same operational taxonomic units (OTUs). The order of representatives for each OTU was filtered for further annotations. For each representative sequence, Mothur software was performed against the SSUrRNA database of SILVA. Database for the annotation of species in each taxonomic rating (Threshold: 0.8 ~ 1) (kingdom, phylum, class, order, family, genus, species). To obtain a phylogenetic relationship of all OTUs representative sequences, MUSCLE (Version 3.8.31) can compare multiple sequences quickly OTUs abundance information is normalized using standard sequence numbers that correspond to the sample in the least order. Further analysis of alpha diversity and beta diversity was all done based on this normalized data output.[9],[11],[12],[13]

Sequencing data analysis

Library sequence preparation

There was a certain proportion of “Dirty Data” in Raw Data obtained on sequencing. Raw data would be merged and then filtered to get Clean Data. After that, the grouping of OTUs is carried out based on effective data. According to the OTUs grouping results, taxonomic annotations are created for the order of representatives of each OTU to obtain appropriate taxa information and taxa-based abundance distribution. At the same time, OTUs are analyzed for Alpha diversity analysis, beta diversity analysis to obtain wealth and abundance information in samples, general and unique OTUs information among samples or different sample groups. Differences between each sample or among the sample group about the structure of microbial communities were explained through dimensional reduction (principal coordinate analysis [PcoA]), Non-Metric Multi-Dimensional Scaling (NMDS).[14]

Alpha diversity

Alpha diversity was applied in analyzing biodiversity complexity for samples through Shannon index. All of these were calculated with QIIME (Version 1.7.0) and displayed with R software (Version 2.15.3)[15]

Beta diversity

Beta diversity analysis was used to evaluate the sample differences in species community complexity, Beta diversity in unifrac was not weighted calculated by QIIME software (Version 1.7.0). Cluster analysis using key coordinate analysis (PCoA) was performed to obtain the principal coordinates and visualize the complex and multidimensional data. The unifrac distance matrix was not weighted between previously obtained samples converted into a new set of orthogonal axes, where the maximum variation factor is indicated by the first principal coordinate, and the second maximum by the second principal coordinate, and so on PCoA analysis was displayed by WGCNA packages, stat packages, and ggplot2 packages in R software (version 2.15.3)[16],[17],[18]

Linear discriminant analysis effect size analysis

Linear discriminant analysis effect size (LEfSe) analysis was done by LEfSe software.

Statistical analysis

T-test is performed represents significant variation between the groups (P < 0.05), MetaStat test represents highly significant difference (q < 0.01). Several other nonparametric tests were used to analyze the differences in the microbiome community between the two sample groups. ANOSIM analysis represent statistical significance (P < 0.05), multi-response permutation procedure (MRPP) analysis represent statistical significance (P < 0.05), Adonis analysis represent statistical significance (P < 0.05), Analysis of molecular variance (AMOVA) represents statistical significance (P < 0.05).[19]

  Results Top

Characteristics of tuberculosis patients

Seventy-five samples of PTB patients have been collected, which have been tested with Xpert MTB/RIF (Cepheid, CA, USA), confirmed 25 samples showed rifampicin resistance (RR/TB), and by MGIT 960 System were 50 samples confirmed RR and isoniazid resistance (MDR-TB). Among the 50 samples, 17 samples were RR, isoniazid, and kanamycin. The profile of PTB patients was male 53 people (70,67%) are more numerous than female 22 (29,33%), with an age range from 15 years old to 64 years.

According to the condition or status of TB suffered by the patient, TB patients are categorized into new TB, TB fails treatment, TB relapses, and Do treatment. Among 53 male patients fall into the category of TB failed treatment as many as 17 patients (32.07%), TB relapsed 30 (56.60%) and 11.33% were new TB. Among 22 female patients, no one the new TB category, TB failed treatment cases 8 (36.36%), and 14 (63,64%) with TB relapsed. Among male TB patients detected 18 (33.96%), MDR-TB patients have comorbid DM.

Patient characteristics associated with Microbiome profile

Of the 75 samples examined, 6 sputum DNA samples can be further examined, they were 3 samples of MDR-TB patients with comorbid DM with sample number 13, 14, 15, and 3 samples of RR-TB noncomorbid DM (no. 16, 27, 69). The average age of MDRTB and RR-TB patients is 48 years [Table 1].
Table 1: Patient characteristics related to biomarker microbiome profile and potential analysis

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Operational taxonomic units identification

Analysis of sputum microbiome between MDR-TB comorbid DM patients and RR-TB noncomorbid DM based on OTU levels. There is an average of 629.67 OTUs in the MDR-TB comorbid DM patients and 1012 OTUs in the noncomorbid DM RR-TB.

Phylum Proteobacteria MDR-TB comorbid DM patients are more abundant than RR-TB noncomorbid DM. Differences in microbiome profile of phylum levels in MDR-TB comorbid DM patients and noncomorbid RR-TB DM patients can be viewed in [Table 2] and [Figure 1]. The abundance of phylum microbiome of each sample is shown in [Figure 2].
Figure 1: Profile of microbiome phylum in multidrug-resistant – tuberculosis comorbid diabetes mellitus (a) and resistant to rifampicin - tuberculosis noncomorbid diabetes mellitus (b) patients

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Figure 2: Microbiome abundance at phylum rank from sample number 13, 14, 15 of multidrug-resistant-tuberculosis, and no. 16, 27, and no. 69 of resistant to Rifampicin -tuberculosis

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Table 2: The abundance of 5 dominant microbiome phylum in comorbid diabetes mellitus multidrug-resistant tuberculosis and noncomorbid diabetes mellitus rifampicin-resistant tuberculosis patients

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There is 10 phylum in the sputum microbiome with 5 dominant phyla obtained in this study, as shown in [Table 2]. Phylum proteobacteria was more abundant in MDR-TB comorbid DM patients, while phylum Firmicutes was more abundant in noncomorbid DM RR-TB patients.

Analysis of microbiome diversity in the sample

Analysis of microbiome sputum diversity in the sample of MDR-TB comorbid DM samples and noncomorbid DM RR-TB by Alpha diversity test with Shanon index. Shannon index values in the MDR-TB and RR-TB groups are 4.3 and 4.7, respectively.

The Alpha diversity test using Shannon index showed differences in diversity in both the number and abundance of microbiome between MDR-TB patients and RR-TB whose meaning was tested with t-test (P = 0.003). The results of the microbiome diversity analysis are shown in [Figure 3].
Figure 3: Alpha diversity analysis with Shannon index showing microbiome diversity differs between multidrug-resistant - tuberculosis comorbid diabetes mellitus patients and non-comorbid diabetes mellitus resistant to rifampicin - tuberculosis (P = 0.003)

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Actinobacteria phylum of Microbiome showed higher abundance in RR-TB patients than in MDR-TB. It also means that DM affects the abundance of the microbiome, the abundance of the microbiome in TB patients who are not exposed to DM is greater than the group affected by DM. The MetaStatistics test also showed a distinctly distinct microbiome abundance between the two sample groups.

Composition of microbiome genera abundance in multidrug-resistant - tuberculosis and resistant to rifampicin- tuberculosis samples

Proteobacteria phylum of the microbiome in MDR-TB comorbid DM patients was more abundant than in the RR-TB noncomorbid DM patients. Differences between microbiome phylum of MDR-TB Comorbid DM patients and noncomorbid RR-TB DM patients are shown in [Table 3] and [Figure 1]. Of the 35 microbiome genera detected, the microbiome genera diversity was greater in RR-TB noncomorbid DM samples than in comorbid D M MDR-TB samples. The large abundance of microbiome genus in RR-TB noncomorbid DM samples were Streptococcus (25.6%),  Neisseria More Details (14.3%), Rothia (8.63%), Prevotella (6.3%), while its small abundance is Veillonella (1.3%), Porphyromonas (1.3%), and Actinomyces (1.3%). The large abundant microbiome genus in MDR-TB comorbid DM is Neisseria (30.5%), Streptococcus (21%) and Prevotella (6.3%), Alloprevotella (5,6). The smallest microbiome genus in MDR-TB and RR-TB samples was Actinobacillus (1.3%)
Table 3: The abundance of 5 dominant microbiome phylum based on tuberculosis status

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Analysis of cluster or structure of microbiome sputum between multidrug-resistant - tuberculosis comorbid diabetes mellitus and resistant to rifampicin - tuberculosis noncomorbid diabetes mellitus

A beta diversity index is based on the PcoA of UniFrac's undirected distance showing the community structure of microbiome MDR-TB Comorbid DM group is different from RR-TB non-comorbid DM group (P = 0,003), the RR-TB noncomorbid DM group has a greater abundance of Actinobacteria phylum than the MDR-TB comorbid DM group, forming its cluster of noncomorbid DM RR-TB groups. Both sample groups showed the structure of the microbiome separately, this can be a significant diversity difference between MDR-TB and RR-TB patients [Figure 4].
Figure 4: Principal coordinate analysis is based on Unweighted UniFrac distance. PC1 68.08%, PC2 18.53%. (red: group multidrug-resistant-tuberculosis comorbid diabetes mellitus group, green: resistant to rifampicin-tuberculosis noncomorbid diabetes mellitus (P = 0.003)

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Microbiome community structure based on NMDS analysis shows that the microbiome community structure of the MDR-TB comorbid DM group differs significantly from the RR-TB noncomorbid DM (stress factor <0.001). There are differences in community structure diversity between the two sample groups, with separate clusters formed between the MDR-TB comorbid DM group and the noncomorbid DM RR-TB group (B). The noncomorbid DM RR-TB group has a greater abundance of Actinobacteria phylum than the MDR-TB comorbid DM group.

ANOSIM showed that the microbiome community structure in MDR-TB comorbid DM samples and noncomorbid DM RR-TB samples were no different real or equally diverse (R = −0, P = 0.4). Based on the MRPP analysis, the microbiome community structure between MDR-TB comorbid DM samples and noncomorbid DM RR-TB did not differ significantly either, or the microbiome diversity in the sample of both MDR-TB Comorbid DM group and noncomorbid DM RR-TB was equally diverse (significance = 0.4). Similarly, analysis of microbiome community differences using AMOVA showed the microbiome in the MDR-TB comorbid DM sample and the noncomorbid DM RR-TB sample were equally diverse, not significantly different between the two groups (P = 0.376).

The abundance of the microbiome is different as a potential Biomarker microbiome. To find out the composition of the microbiome abundance that differs between the MDR-TB comorbid DM and RR-TB noncomorbid DM groups, an analysis of LEfSe analysis was formed. The LEfSe analysis analyzed different microbiome that could potentially be biomarkers from both sample groups (Segata, et al., 2011). LEfSe analysis with an overview of the histogram of linear discriminant analysis (LDA) and Cladogram scores is displayed as an analysis result for evaluation. The histogram of LDA scores represents a genus or species (biomarker) whose abundance shows significant differences between groups with an LDA score greater than the set threshold of 4. LEfSe analysis shows the genus Rothia is a potential biomarker that plays a role in noncomorbid RR-TB patients [Figure 5].
Figure 5: Analysis of linear discriminant analysis effect size community microbiome group multidrug-resistant-tuberculosis comorbid diabetes mellitus and resistant to Rifampicin -tuberculosis noncomorbid diabetes mellitus with; (a) histogram scores of linear discriminant analysis >4, and (b) Cladograms that indicate the microbial lineage distribution associated with the sputum microbiome. The spouse indicate the phylogenetic levels from Kingdom to genus

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  Discussion Top

Knowledge of the mechanism of development or pathogenesis of TB or MDR-TB is important for the treatment and diagnosis of TB or MDR-TB. The microbiome is widely associated with TB status, which causes recurrent TB cases, failed treatments, chronicity, or severity. Research on the microbiome of TB patients, especially MDR-TB, is still very little. This study provides a report on the potential of the microbiome which plays an important role in patients with RR-TB or MDR-TB. The discovery of potential microbiome provides access to diagnostic and development of drug-resistant TB or MDR-TB patient treatment. A good understanding of the pulmonary microbiome can provide better strategic TB management or TB patient care for the diagnosis, treatment, and prevention.

Measuring diversity is essential to understanding the structure and dynamic change of the microbe community in pulmonary tissue which chronic TB disease process. This study revealed the differences in the composition of lower respiratory tract microbes in patients with MDR-TB and RR-TB.

Five main phyla from the microbiome were obtained from noncomorbid DM RR-TB samples that accounted for more than 90% of the entire phylum microbiome, with Firmicutes showing the most abundance (30%–38.33%). Actinobacteria abundance was greater in noncomorbid DM RR-TB samples compared to comorbid DM MDR-TB samples. Phylum Actinobacteria included Familia Mycobacteriaceae, Micrococcaceae, and others. Phylum proteobacteria more abundance in MDR-TB than RR-TB patients, it is included genus Pseudomonas, Acinetobacter, and others, that could be related to pulmonary tissue immunity status in the chronic disease process with the transmission of microbe from the upper respiratory tract, especially in MDR-TB or RR-TB patients. The previous research reported that Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria are the most important bacteria, accounting for more than 97% of the sputum microbiome, and other bacteria were detected with a relatively small abundance of 0.06%.[20]

The largest abundance of phylum Proteobacteria in MDR-TB Comorbid DM samples was different from that of RR-TB patients but similar to previous researchers, Proteobacteria accounted for more than 50% of the microbiome.[20] It has also previously been reported that the five microbiome phylum are associated with the diversity of sputum microbiome TB patients.[21],[22],[23] It was reported that these four bacteria are associated with the diversity of sputum microbiome in TB patients, but are also the main microbiome present in the mouth, skin, and colon of humans.[21],[24]

The abundance of the sputum microbiome genus in this study was different between 2 groups of RR TB patients and patients with MDR-TB anti-TB treatment. There are 5 largest genera there are MDR-TB patients, with Neisseria showing the greatest abundance (30.5%). Previous research has shown different compositions of the microbiome genus.[2] The microbiome found in RR-TB patient samples showed the largest abundance of the microbiome in Streptococcus. The sputum microbiome found in this study was slightly different from previous studies.[23] A microbiome figure between TB patients and healthy people shows noticeable different results.[4] Another different study was the Prevotella genus identified in the bacterial microbiome of the oral cavity, very low in relapsed TB patients compared to healthy control and other TB patients, reduced abundance of Bulleidia, Atopobium and Treponema in recurrent TB patients compared to new TB patients.[25],[26] The imbalance of the microbiome in the respiratory tract can be one of the risk factors for poor prognosis of TB treatment.

The study reported, Prevotella, Campylobacter, Atopobium, Treponema, and Blastobacter are genera that have less TB relapse than TB fails treatment. Campylobacter used to be a pathogen in respiratory tract infections in the subtropical tropics. Mixed infections with certain bacteria such as Treponema cause severe respiratory disease.[26] Sputum microbiome destruction may pose a risk factor for a poor TB treatment prognosis. Other researchers also showed the TB sputum microbiome in Cui's study had higher diversity compared to healthy controls[27] while other researchers found that healthy microbiome is more diverse than TB although without static significance.[23]

Based on the status of TB patients, Bacteroidetes, Proteobacteria, and Fusobacteria phylum in TB treatment failed of MDR-TB showed a greater abundance compared to relapsed RR-TB patients. The Neisseria genus is most abundant in MDR-TB patients, while Streptococcus is the most common genus found in RR-TB patients. Failed treatment MDR-TB Patients are exposed to more antibiotics than relapsed RR-TB patients so that the lungs of failed treatment MDR-TB patients are likely to become more susceptible to the colonization of some foreign microorganisms, resulting in local inflammatory damage during the elimination of Mycobacterium, which can increase colonization of foreign pathogenic bacteria in the lungs, especially in immunocompromised patients.[28] It has been shown that Streptococcus is the dominant genera in TB patients in the Indian and Chinese populations.[4],[25]

Several other genera such as Veillonella, Stenotrophomonas, and Pseudomonas were dominant in sputum samples from some TB patients in previous studies, and another study revealed that the microbial community of the respiratory tract, the lower respiratory tract is closely related to the health immunity of patients with lung disease.[29] Microbiome community structure differences between RR-TB and MDR-TB samples showed that the microbiome residents can be affected by combined anti-TB drug resistance.

From the results of differential microbiome analysis, Rothia genus was found to be very abundant compared to the MDR-TB group, so it can be a potential biomarker or potential diagnostic marker for RR-TB patients. This discovery is different from other researchers, who showed Delftia as a biomarker for RR-TB patients, Kingella and Chlamydophila as biomarkers for TB patients with some anti-TB resistance, and Bordetella as a biomarker for the identification of MDR-TB patients.[20] Rothia mucilaginosa, an opportunistic pathogen, most commonly seen in immunocompromised patients, is widespread in TB samples. However, R. mucilaginosa has also been reported to be the sole causative agent for pneumonia. R. mucilaginosa was found in all TB patients as a joint pathogen.[30] With this biomarker can be a way to clinically therapy in TB patients. Previously there have been other types of biomarkers that play an important role in the diagnosis of TB that has limitations.[31],[32],[33] This difference is likely due to socioeconomic factors, nutrition, immunity, anti-TB drug resistance is a factor that affects the diversity of the Microbiome. Management of different anti-TB treatments, different foods can produce different Microbiome community compositions. This is supported by Microbiome research in TB patients in several countries.[4],[22],[27]

  Conclusions Top

From this study, characterized of microbiome revealed genus Rothia as a potential biomarker for RR-TB patients. Another interesting finding is the characteristic of the community structure of the microbiome in MDR-TB and RR-TB. In addition, in chronic TB cases such as recurrent and failed treatment of TB, and associated with MDR-TB or RR-TB should attention that although the findings of a small number of phylum Actinobacteria could be a biomarker of TB which is also a determinant of clinical outcomes in patient care taking anti-TB drugs with a choice of the drug combination.

Ethical clearance


Financial support and sponsorship

This study was financially supported by Faculty of Medicine, Universitas Airlangga.

Conflicts of interest

There are no conflicts of interest.

  References Top

WHO. Global Tuberculosis Report 2020. Geneva: World Health Organization; 2020. Available from: https://www.who.int/publications/i/item/9789240013131. [Last accessed on 2021 Jun15].  Back to cited text no. 1
Lin HC, Perng CL, Lai YW, Lin FG, Chiang CJ, Lin HA, et al. Molecular screening of multidrug-resistance tuberculosis by a designated public health laboratory in Taiwan. Eur J Clin Microbiol Infect Dis 2017;36:2431-9.  Back to cited text no. 2
Takawira FT, Mandishora RSD, Dhlamini Z, Munemo E, Stray-Pedersen B. Mutations in rpoB and katG genes of multidrug resistant Mycobacterium tuberculosis undetectable using genotyping diagnostic methods. Pan Afr Med J 2017;27:145.  Back to cited text no. 3
Krishna P, Jain A, Bisen PS. Microbiome diversity in the sputum of patients with pulmonary tuberculosis. Eur J Clin Microbiol Infect Dis 2016;35:1205-10.  Back to cited text no. 4
Touvier M, Deschasaux M, Montourcy M, Sutton A, Charnaux N, Kesse-Guyot E, et al. Determinants of vitamin D status in Caucasian adults: Influence of sun exposure, dietary intake, sociodemographic, lifestyle, anthropometric, and genetic factors. J Invest Dermatol 2015;135:378-88.  Back to cited text no. 5
Naidoo CC, Nyawo GR, Wu BG, Walzl G, Warren RM, Segal LN, et al. The microbiome and tuberculosis: State of the art, potential applications, and defining the clinical research agenda. Lancet Respir Med 2019;7:892-906.  Back to cited text no. 6
Magoč T, Salzberg SL. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011;27:2957-63.  Back to cited text no. 7
Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 2013;10:57-9.  Back to cited text no. 8
Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat Methods 2013;10:996-8.  Back to cited text no. 9
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011;27:2194-200.  Back to cited text no. 10
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007;73:5261-7.  Back to cited text no. 11
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res 2013;41:D590-6.  Back to cited text no. 12
Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 2004;32:1792-7.  Back to cited text no. 13
Noval Rivas M, Burton OT, Wise P, Zhang YQ, Hobson SA, Garcia Lloret M, et al. A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis. J Allergy Clin Immunol 2013;131:201-12.  Back to cited text no. 14
Li B, Zhang X, Guo F, Wu W, Zhang T. Characterization of tetracycline resistant bacterial community in saline activated sludge using batch stress incubation with high-throughput sequencing analysis. Water Res 2013;47:4207-16.  Back to cited text no. 15
Lozupone C, Knight R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005;71:8228-35.  Back to cited text no. 16
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: An effective distance metric for microbial community comparison. ISME J 2011;5:169-72.  Back to cited text no. 17
White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 2009;5:e1000352.  Back to cited text no. 18
Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 1992;131:479-91.  Back to cited text no. 19
Lin D, Wang X, Li Y, Wang W, Li Y, Yu X, et al. Sputum microbiota as a potential diagnostic marker for multidrug-resistant tuberculosis. Int J Med Sci 2021;18:1935-45.  Back to cited text no. 20
Cheung MK, Lam WY, Fung WY, Law PT, Au CH, Nong W, et al. Sputum microbiota in tuberculosis as revealed by 16S rRNA pyrosequencing. PLoS One 2013;8:e54574.  Back to cited text no. 21
Botero LE, Delgado-Serrano L, Cepeda ML, Bustos JR, Anzola JM, Del Portillo P, et al. Respiratory tract clinical sample selection for microbiota analysis in patients with pulmonary tuberculosis. Microbiome 2014;2:29.  Back to cited text no. 22
Hu Y, Cheng M, Liu B, Dong J, Sun L, Yang J, et al. Metagenomic analysis of the lung microbiome in pulmonary tuberculosis – A pilot study. Emerg Microbes Infect 2020;9:1444-52.  Back to cited text no. 23
Cho I, Blaser MJ. The human microbiome: At the interface of health and disease. Nat Rev Genet 2012;13:260-70.  Back to cited text no. 24
Wu J, Liu W, He L, Huang F, Chen J, Cui P, et al. Sputum microbiota associated with new, recurrent and treatment failure tuberculosis. PLoS One 2013;8:e83445.  Back to cited text no. 25
Adami AJ, Cervantes JL. The microbiome at the pulmonary alveolar niche and its role in Mycobacterium tuberculosis infection. Tuberculosis (Edinb) 2015;95:651-8.  Back to cited text no. 26
Cui Z, Zhou Y, Li H, Zhang Y, Zhang S, Tang S, et al. Complex sputum microbial composition in patients with pulmonary tuberculosis. BMC Microbiol 2012;12:276.  Back to cited text no. 27
Sansonetti PJ. Microbiota and the immune system, an amazing mutualism forged by co-evolution. Semin Immunol 2013;25:321-2.  Back to cited text no. 28
Namasivayam S, Sher A, Glickman MS, Wipperman MF. The microbiome and tuberculosis: Early evidence for cross talk. mBio 2018;9:1-11.  Back to cited text no. 29
Botero Palacio LE, Delgado Serrano L, Cepeda Hernández ML, Del Portillo Obando P, Zambrano Eder MM. The human microbiota: The role of microbial communities in health and disease. Acta Biol Colomb 2015;21:1-7. [doi: 10.15446/abc.v21n1.49761].  Back to cited text no. 30
Pedersen JL, Bokil NJ, Saunders BM. Developing new TB biomarkers, are miRNA the answer? Tuberculosis (Edinb) 2019;118:101860.  Back to cited text no. 31
Salgado-Bustamante M, Rocha-Viggiano AK, Rivas-Santiago C, Magaña-Aquino M, López JA, López-Hernández Y. Metabolomics applied to the discovery of tuberculosis and diabetes mellitus biomarkers. Biomark Med 2018;12:1001-13.  Back to cited text no. 32
Petruccioli E, Chiacchio T, Vanini V, Cuzzi G, Codecasa LR, Ferrarese M, et al. Effect of therapy on Quantiferon-Plus response in patients with active and latent tuberculosis infection. Sci Rep 2018;8:15626.  Back to cited text no. 33


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]

  [Table 1], [Table 2], [Table 3]


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