Tuberculosis (TB) remains a global threat in the 21st century. Traditional studies of the disease are focused on the single pathogen Mycobacterium tuberculosis. Recent studies have revealed associations of some diseases with an imbalance in the microbial community. Characterization of the TB microbiota could allow a better understanding of the disease.
Here, the sputum microbiota in TB infection was examined by using 16S rRNA pyrosequencing. A total of 829,873 high-quality sequencing reads were generated from 22 TB and 14 control sputum samples. Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria were the five major bacterial phyla recovered, which together composed over 98% of the microbial community. Proteobacteria and Bacteroidetes were more represented in the TB samples and Firmicutes was more predominant in the controls. Sixteen major bacterial genera were recovered. Streptococcus, Neisseria and Prevotella were the most predominant genera, which were dominated by several operational taxonomic units grouped at a 97% similarity level. Actinomyces, Fusobacterium, Leptotrichia, Prevotella, Streptococcus, and Veillonella were found in all TB samples, possibly representing the core genera in TB sputum microbiota. The less represented genera Mogibacterium, Moryella and Oribacterium were enriched statistically in the TB samples, while a genus belonging to the unclassified Lactobacillales was enriched in the controls. The diversity of microbiota was similar in the TB and control samples.
The composition and diversity of sputum microbiota in TB infection was characterized for the first time by using high-throughput pyrosequencing. It lays the framework for examination of potential roles played by the diverse microbiota in TB pathogenesis and progression, and could ultimately facilitate advances in TB treatment.
Citation: Cheung MK, Lam WY, Fung WYW, Law PTW, Au CH, et al. (2013) Sputum Microbiota in Tuberculosis as Revealed by 16S rRNA Pyrosequencing. PLoS ONE 8(1): e54574. doi:10.1371/journal.pone.0054574
Editor: Stefan Bereswill, Charité-University Medicine Berlin, Germany
Received: October 24, 2012; Accepted: December 12, 2012; Published: January 24, 2013
Copyright: © 2013 Cheung et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Research Fund for the Control of Infectious Diseases (RFCID) from the Health, Welfare and Food Bureau of the Hong Kong SAR Government (Ref. No. 09080442). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Tuberculosis (TB) is an airborne infectious disease caused by the bacterium Mycobacterium tuberculosis. In 2010, there were an estimated 8.5 million cases of TB worldwide, causing 1.2 million deaths . Global control of TB has relied on the 90-year-old and largely ineffective Bacille Calmette-Guérin (BCG) vaccine and a few decades-old drugs . The growing epidemics of HIV/AIDS and multidrug-resistant TB strains have further complicated the effectiveness of disease control . As with most classical disease investigations, TB studies have been focused on a single causative agent. However, recent understandings suggest that disease phenotypes are more likely to be a result of complex microbial interactions . Characterizing the whole microbial communities in TB infections might provide further insights into the disease.
The advent of the cloning-independent and massively parallel 454 pyrosequencing technology  has facilitated investigations of the soil microbial populations , marine microbial communities  and the human gut microbiota . Just recently, the microbiota of respiratory diseases such as cystic fibrosis (CF) , , chronic obstructive pulmonary disease (COPD)  and nosocomial pneumonia  were also characterized by using this approach. By revealing the composition and diversity of microbiota associated with these diseases, these studies allow the development of hypotheses relating the microbial communities and the disease states. Information on the respiratory microbiota in TB infection is currently unavailable. Here, based on 16S rRNA pyrosequencing, the composition and diversity of sputum microbiota in TB infection was examined for the first time. The microbiota of non-TB case-matched sputum samples was also characterized for comparison.
A total of 964,556 raw 16S rRNA reads were obtained in this study. About 578,000 reads were from the 22 TB samples and the remaining ~387,000 reads were from the 14 controls (Table 1). The filtering process removed about 14% of the raw sequencing reads. There were about 499,000 and 331,000 high-quality reads for the TB and control samples, respectively. The average read length was about 370 bp, after removal of the primer sequences. The average number of qualified reads for the TB group was 22,660, and that for the control group was 23,667. Rarifying the number of reads to 13,210, there was an average of 486 and 487 OTUs in the TB and control samples, respectively. The average Chao1 value calculated was 832 in the TB samples and 870 in the controls. The average Shannon index was 4.85 in the TB samples and 4.80 in the controls.
Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria were the major bacterial phyla recovered, which together composed over 98% of the microbial community (Figure 1). Firmicutes, Proteobacteria and Bacteroidetes were among the most abundant phyla in both the TB and control samples, composing 37.6%, 31.2% and 19.2% in the TB samples and 43.6%, 27.1% and 17.0% in the controls, respectively. Whereas the relative abundance of Actinobacteria and Fusobacteria between the two groups was similar, Proteobacteria and Bacteroidetes were more represented in the TB samples and Firmicutes was more dominant in the controls. Breakdown of the data revealed a large variation in the community structure among the individuals (Figure 2). This was supported by PCoA analysis, in which no obvious differential clustering was observed between members of the TB and control groups (Figure 3).
Only phyla >1% in either group were included.
Only phyla >1% in either group were included. The 22 samples on the left were from the TB patients, and the 14 samples on the right were from the control group.
The plots were based on a) weighted and b) unweighted UniFrac distances. The two principal coordinates combined explained 66.44% and 24.31% of the variations in the weighted and unweighted cases, respectively. Red dots represent TB samples; blue dots represent control samples.
Sixteen major bacterial genera were recovered in the sputum samples here (Figure 4). The most abundant genera in the TB samples were Neisseria (28.0%), Streptococcus (27.8%) and Prevotella (16.8%), whereas those for the controls were Streptococcus (31.8%), Neisseria (22.0%) and Prevotella (14.4%). Neisseria and Prevotella were more represented in the TB samples and Streptococcus was more predominant in the controls. Lactococcus, Pseudomonas and unclassified Enterobacteriaceae were less dominant and prevalent in the TB samples (Figures 4 and 5). Within the 16 major genera, only Actinomyces, Fusobacterium, Leptotrichia, Prevotella, Streptococcus, and Veillonella were recovered in all the TB samples, and all these genera besides Leptotrichia were found in all the 36 samples (Figure 5). Breakdown of the data again revealed inter-member variations in the community structure at the genus level (Figure 6).
Only genera >1% in either group were included.
Only genera >1% in either group were included.
Only genera >1% in either group were included. The 22 samples on the left were from the TB patients, and the 14 samples on the right were from the control group.
Two OTUs together composed more than 90% of the dominant genus Neisseria in both sample groups (Table 2). OTU 6783 was most similar to the uncultured clone NSV3Q1b18 sampled from a showerhead swab , and contributed 57.2% and 88.7% of the genus in the TB and control samples, respectively. On the other hand, OTU 7988 was most similar to the uncultured clone 7H59 from the subgingival plaque of a periodontitis-free patient , and it was more represented in Neisseria in the TB samples (37.4%) than in the controls (4.1%). About one-third of another major genus Streptococcus was represented by three OTUs in both sample groups (Table 2). OTU 1734 was most similar to a S. mitis clone sampled from the oral cavity , and was more represented in Streptococcus in the TB samples (12.0%) than in the controls (1.6%). On the other hand, OTU 6370 was most similar to a S. parasanguinis clone sampled from the oral cavity, and was more represented in the control samples (16.3%) than in the TB samples (5.6%). OTU 7204 was most similar to the S. salivarius strain ATCC 7073, and its contribution in the genus was relatively comparable in both groups (12.4% in TB and 14.6% in control). Two OTUs together composed more than one-third of the genus Prevotella in both the TB and control samples (Table 2). They were most similar to uncultured bacterial clones sampled from the human oral cavity.
In terms of the less represented taxa, genera Moryella, Mogibacterium and Oribacterium were found statistically enriched in the TB samples (p<0.05) whereas a genus belonging to the unclassified Lactobacillales was enriched in the control samples (p<0.05) (Table 3). At the species level, eight OTUs, including those showing 99% identity to Prevotella melaninogenica, Lactobacillus crispatus, Streptococcus anginosus, and Parvimonas micra, were found enriched in the TB samples (p<0.05). Two other OTUs, including one that is 99% identical to Aggregatibacter aphrophilus, were enriched in the control samples (p<0.05) (Table 3).
TB remains a global threat in the 21st century. Lead by the World Health Organization, the Stop TB Partnership has planned to halve the global TB prevalence and mortality by 2015 and to eliminate the disease as a public health problem by 2050 . A better understanding of the disease from an angle different from the traditional single-pathogen-based approach could help to meet these targets. Indeed, instead of the presence of a single causative pathogen, it was shown that some diseases are associated with and might result from an imbalance in the microbial community, a condition known as dysbiosis. Inflammatory bowel disease  and obesity  are two of the well-known examples. The advent of next-generation sequencing technologies, such as 454 pyrosequencing, has greatly advanced studies of the human microbiota . However, unlike the widely studied gut microbiota, the investigation of the lung-associated microbiota is relatively new . In this study, the sputum microbiota in TB infection was characterized by using 16S rRNA pyrosequencing for the first time, aiming to advance understanding of the disease in the microbial community level.
About one million raw sequencing reads were generated for a total of 36 sputum samples in this study. However, the throughput is still insufficient to capture every member in the microbial community. An example particularly worth mentioning is M. tuberculosis. Although being the causative agent of the disease, M. tuberculosis represents only a very small portion of the whole microbiota in the TB sputum samples (Figure 4). To obtain positive results in the culture and the smear tests, there should be at least 100 M. tuberculosis cells/ml and ~104 acid-fast bacilli (AFB) cells/ml, respectively . Because the average total bacterial load in sputum samples could reach 109 cells/ml , 105 reads may be needed to recover a single read of M. tuberculosis in a particular TB sample. Since the average sequencing throughput was <27,000 reads per sample here, it is not unexpected that no reads of M. tuberculosis were recovered in some of the TB sputum samples. Nevertheless, the sequencing throughput is sufficient to reveal the major phyla and genera of the sputum microbiota, and it allows meaningful comparisons between the TB and control groups.
Five major bacterial phyla were recovered in the sputum samples collected in this study; they were the Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria. They are also the predominant phyla in other human body sites, including the oral cavity, skin and colon . In addition, they dominate the microbiota in the CF sputum  and the bronchial tract of COPD patients . The three most dominant phyla retrieved in this study (Firmicutes, Proteobacteria and Bacteroidetes) are also the most commonly identified phyla in the normal lung microbiota . These collectively suggest the prevalence of these major phyla in normal and diseased lung microbiota, as well as the human microbiota in general. Within the 16 major genera recovered in this study, only Actinomyces, Fusobacterium, Leptotrichia, Prevotella, Streptococcus, and Veillonella were found in all the TB samples, and they may represent the core genera in the TB sputum microbiota. Similarly, except Leptotrichia, the remaining five genera were found in all 36 samples, and they may represent the core genera in the sputum microbiota in general. However, additional studies are needed to confirm this.
Streptococcus, Neisseria and Prevotella were the three most predominant genera recovered in the sputum samples collected in the current study. They are also the major genera retrieved in the sputum of patients with COPD , nosocomial pneumonia  and CF , . This is in contrast to the normal lung microbiota, in which the genus Pseudomonas dominates . The genus Streptococcus encompasses both commensal and pathogenic Gram-positive bacteria inhabiting various human body sites, including the oral cavity and the upper respiratory tract. Streptococcus pneumoniae, for instance, is a well-known pathogen associated with pneumonia . Neisseria is a group of Gram-negative bacteria that colonizes human mucosal epithelia. Commensal species include N. lactamica and N. mucosa, while pathogenic species such as N. meningitidis could cause pneumonia . Prevotella contains obligate anaerobes commonly isolated from the human oral and intestinal tracts, and it can cause lower respiratory tract infections. Members within this genus are believed to cause pulmonary exacerbations through polymicrobial interactions that enhance the virulence of the established pathogens .
Each of the three most predominant genera was dominated by two to three major OTUs that together composed one-third to 90% of the groups. OTU 1734 of the genus Streptococcus is 100% identical to S. mitis and was more represented in Streptococcus in the TB samples than in the controls. Although usually commensal, S. mitis could become a significant pathogen in immunocompromised patients as well as individuals implicated in a wide range of diseases, including pneumonia . Indeed, genome study of the S. mitis B6 strain has revealed homologues of many previously identified S. pneumoniae virulence factors such as autolysins . Also, incubation of S. mitis in the gingival epithelial cells was reported to induce the expression of human β-defensin 2 that can kill other oral pathogens . It is thus reasonable to speculate that the increased relative abundance of the opportunistic pathogen in TB infections could alter the microbial community in TB lung and affect TB pathogenesis and progression. Unlike OTU 1734, OTU 6370, which is 100% identical to S. parasanguinis, was more represented in the control samples. S. parasanguinis is a member of the viridans streptococci that constitute the major population of the human oral microbiota. It is also one of the early colonizers of the tooth surface . OTU 7204 is most similar to S. salivarius and was of relatively comparable abundance in Streptococcus in both groups. Although being another member of the viridans streptococci, S. salivarius commonly causes bacteremia . The two dominant Neisseria OTUs and the two dominant Prevotella OTUs are most similar to uncultured clones with limited information. Additional research works on these organisms are urged to better understand the potential effects of these abundant members in TB or sputum microbiota.
The less abundant taxa may also affect the dynamics of the microbial community and clinical outcomes . Mogibacterium, Moryella and Oribacterium were the genera enriched statistically in the TB samples collected in this study. Mogibacterium is a group of Gram-positive anaerobic bacteria, in which members such as M. timidum have been identified in cases of acute lung infections . On the other hand, Moryella and Oribacterium are genera of anaerobic bacteria that were identified in the recent decade , . Little information is available since their discovery. At the species level, eight less represented OTUs were found significantly enriched in the TB samples, each with an over 10-fold difference in relative abundance between the sample groups. Four of them are with high similarity (99%) to known bacterial species, including Prevotella melaninogenica, Lactobacillus crispatus, Streptococcus anginosus, and Parvimonas micro. P. melaninogenica is a Gram-negative anaerobic bacterium that represents a ubiquitous member of the oral commensal microbiota. However, putative virulence factors such as hemagglutination were reported in some strains . Lactobacilli are Gram-positive bacteria that are usually regarded as beneficial to human health. However, L. crispatus strain M206119 was found to exacerbate dextran sulfate sodium (DSS)-induced colitis in mice by interfering with their murine inflammatory responses . It was reported that S. anginosus infections might simulate tuberculosis . Indeed, three cases of co-infection by S. anginosus and M. tuberculosis were reported previously . P. micra is a Gram-positive anaerobe usually isolated from the human oral cavity. However, association of the bacterium with respiratory and gastrointestinal tract infections was reported previously . The enrichment of these opportunistic pathogens in the TB sputum revealed here suggests their association with TB pathogenesis or progression.
Bacterial community diversity in CF sputum was found to be positively correlated with the patient stability , . It was also reported that a decrease in microbial diversity in the gastrointestinal tract is associated with an increased incidence of inflammatory bowel disease . However, bacterial diversity was found to be significantly higher among asthmatic patients than in controls . In our study, the microbial diversity was found to be similar in the TB and the control sputum samples. Thus, there does not seem to be a general relationship between microbial diversity and the disease state. Indeed, this is not unexpected because different diseases may involve different sets of microbiota, within which interactions could be complex, including both synergistic and antagonistic effects among members .
The current study represents the first characterization of the sputum microbiota in TB infection based on 16S rRNA pyrosequencing. The work allows future examination of potential roles played by the diverse microbiota in TB pathogenesis and progression, and it could ultimately facilitate advances in TB treatment. However, additional studies on the uncultured taxa are urged. Further investigations based on metatranscriptomics, metaproteomics and metabolomics on the RNA, protein and metabolite levels, respectively, are also expected to provide further insights into understanding the disease.
Materials and Methods
The study was approved by the Joint CUHK-NTEC Clinical Research Ethical Committee at the Prince of Wales Hospital in Hong Kong. Written informed consent was obtained from all the studied subjects for sample collection and subsequent analysis.
Sample Collection and DNA Extraction
Sputum samples were collected from the Tuberculosis Reference Laboratory of the Hong Kong government in a routine TB screening exercise. All samples in this study were from patients who are ethnically Hong Kong Chinese. All the patients were free of HIV. No anti-TB or other antibiotic medication was given to the patients within four weeks of sputum collection. Smear-positive and culture-positive TB sputum samples were collected from 22 patients (13 males and eight females, aged between 20 and 66; data missing for one patient) (Table 1, Dataset S1). Control sputum samples were also collected from 14 individuals (six males and eight females, aged between 22 and 82) with TB-resembling coughing symptoms but being culture-negative for comparison. The sputum samples were treated with a 3% NaOH solution, neutralized with a phosphate buffer and then centrifuged. About 200 µl supernatant was subjected to genomic DNA extraction using the QIAamp DNA Mini Kit (Qiagen, Valencia, CA, USA).
PCR and Pyrosequencing
PCR was performed using composite primers flanking the hypervariable V1–V2 region of the 16S rRNA gene: A-8F (5′-AGAGTTTGATCCTGGCTCAG-3′) and B-357R (5′-CTGCTGCCTYCCGTA-3′) . Sequencing adaptors and sample-specific multiplex identifiers (MIDs) were also added to the 5′ ends of the primers according to the manufacturer’s protocol. PCR reactions were carried out using the Platinum® PCR SuperMix High Fidelity Kit (Invitrogen, Carlsbad, CA, USA). The PCR thermal regime consisted of an initial denaturation of 30 s at 94°C, followed by 28 cycles of 30 s at 94°C, 30 s at 55°C and 40 s at 68°C. PCR products were purified using the MinElute PCR Purification Kit (Qiagen, Valencia, CA, USA) and the quality of the purified products was assessed using Agilent Bioanalyzer 2100. Pyrosequencing of the purified PCR products was performed uni-directionally from the A-8F primer end and on a GS FLX-Titanium platform in a single full-plate run. The sequencing data were submitted to the NCBI short read archive (accession number SRA058505).
Raw sequencing reads were demultiplexed, quality-filtered and analyzed using QIIME 1.4.0 . Briefly, reads shorter than 200 bp or longer than 1000 bp, with an average quality score lower than 25 in a sliding window of 50 bp, with mismatching primer sequences, or with ambiguous bases (Ns) were removed from downstream analyses. Quality-filtered reads were clustered into operational taxonomic units (OTUs) at 97% similarity. Taxonomic assignment of representative OTUs was performed using the RDP Classifier  at a 0.8 confidence threshold against the Greengenes core set . The dataset was rarified before alpha diversity calculations. Principal coordinate analysis (PCoA) was performed using the weighted and unweighted UniFrac distances . Statistical analyses were performed using SAS.
Information of patients and samples examined.
Conceived and designed the experiments: WYWF HSK SKWT. Performed the experiments: WYWF PTWL. Analyzed the data: MKC WYL CHA WN. Contributed reagents/materials/analysis tools: KMK. Wrote the paper: MKC WYL.
- 1. WHO (2011) Global tuberculosis control: WHO report 2011. Available: http://www.who.int/tb/publications/global_report/en/index.html. Accessed 2012 Oct 24.
- 2. Young DB, Perkins MD, Duncan K, Barry CE 3rd (2008) Confronting the scientific obstacles to global control of tuberculosis. J Clin Invest 118: 1255–1265. doi: 10.1172/jci34614
- 3. Comas I, Gagneux S (2009) The past and future of tuberculosis research. PLoS Pathog 5: e1000600. doi: 10.1371/journal.ppat.1000600
- 4. Clemente JC, Ursell LK, Parfrey LW, Knight R (2012) The impact of the gut microbiota on human health: an integrative view. Cell 148: 1258–1270. doi: 10.1016/j.cell.2012.01.035
- 5. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, et al. (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437: 376–380.
- 6. Palmer K, Biasi C, Horn MA (2012) Contrasting denitrifier communities relate to contrasting N2O emission patterns from acidic peat soils in arctic tundra. ISME J 6: 1058–1077. doi: 10.1038/ismej.2011.172
- 7. Cheung MK, Au CH, Chu KH, Kwan HS, Wong CK (2010) Composition and genetic diversity of picoeukaryotes in subtropical coastal waters as revealed by 454 pyrosequencing. ISME J 4: 1053–1059. doi: 10.1038/ismej.2010.26
- 8. Saulnier DM, Riehle K, Mistretta TA, Diaz MA, Mandal D, et al. (2011) Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. Gastroenterology 141: 1782–1791. doi: 10.1053/j.gastro.2011.06.072
- 9. Filkins LM, Hampton TH, Gifford AH, Gross MJ, Hogan DA, et al. (2012) Prevalence of streptococci and increased polymicrobial diversity associated with cystic fibrosis patient stability. J Bacteriol 194: 4709–4717. doi: 10.1128/jb.00566-12
- 10. Guss AM, Roeselers G, Newton IL, Young CR, Klepac-Ceraj V, et al. (2011) Phylogenetic and metabolic diversity of bacteria associated with cystic fibrosis. ISME J 5: 20–29. doi: 10.1038/ismej.2010.88
- 11. Cabrera-Rubio R, Garcia-Núñez M, Setó L, Antó JM, Moya A, et al. (2012) Microbiome diversity in the bronchial tract of patients with chronic obstructive pulmonary disease. J Clin Microbiol 50: 3562–3568. doi: 10.1128/jcm.00767-12
- 12. Zhou Y, Lin P, Li Q, Han L, Zheng H, et al. (2010) Analysis of the microbiota of sputum samples from patients with lower respiratory tract infections. Acta Biochim Biophys Sin (Shanghai) 42: 754–76.
- 13. Feazel LM, Baumgartner LK, Peterson KL, Frank DN, Harris JK, et al. (2009) Opportunistic pathogens enriched in showerhead biofilms. Proc Natl Acad Sci U S A 106: 16393–16399. doi: 10.1073/pnas.0908446106
- 14. You M, Mo S, Watt RM, Leung WK Prevalence and diversity of Synergistetes taxa in periodontal health and disease. J Periodontal Res. In press.
- 15. Dewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, et al. (2010) The human oral microbiome. J Bacteriol 192: 5002–5017. doi: 10.1128/jb.00542-10
- 16. Stop TB Partnership (2011) The global plan to stop TB 2011–2015. Available: http://www.stoptb.org/global/plan/. Accessed 2012 Oct 24.
- 17. Spor A, Koren O, Ley R (2011) Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol 9: 279–290. doi: 10.1038/nrmicro2540
- 18. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, et al. (2009) The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1: 6ra14. doi: 10.1126/scitranslmed.3000322
- 19. Cho I, Blaser MJ (2012) The human microbiome: at the interface of health and disease. Nat Rev Genet 13: 260–270. doi: 10.1038/nrg3182
- 20. Beck JM, Young VB, Huffnagle GB (2012) The microbiome of the lung. Transl Res 160: 258–266. doi: 10.1016/j.trsl.2012.02.005
- 21. Vignesh R, Balakrishnan P, Shankar EM, Murugavel KG, Hanas S, et al. (2007) Value of single acid-fast bacilli sputum smears in the diagnosis of tuberculosis in HIV-positive subjects. J Med Microbiol 56: 1709–1710. doi: 10.1099/jmm.0.47497-0
- 22. Zhao J, Schloss PD, Kalikin LM, Carmody LA, Foster BK, et al. (2012) Decade-long bacterial community dynamics in cystic fibrosis airways. Proc Natl Acad Sci U S A 109: 5809–5814. doi: 10.1073/pnas.1120577109
- 23. Nobbs AH, Lamont RJ, Jenkinson HF (2009) Streptococcus adherence and colonization. Microbiol Mol Biol Rev 73: 407–450. doi: 10.1128/mmbr.00014-09
- 24. Brigham KS, Sandora TJ (2009) Neisseria meningitidis: epidemiology, treatment and prevention in adolescents. Curr Opin Pediatr 21: 437–443. doi: 10.1097/mop.0b013e32832c9668
- 25. Field TR, Sibley CD, Parkins MD, Rabin HR, Surette MG (2010) The genus Prevotella in cystic fibrosis airways. Anaerobe 16: 337–344.
- 26. Mitchell J (2011) Streptococcus mitis: walking the line between commensalism and pathogenesis. Mol Oral Microbiol 26: 89–98. doi: 10.1111/j.2041-1014.2010.00601.x
- 27. Denapaite D, Brückner R, Nuhn M, Reichmann P, Henrich B, et al. (2010) The genome of Streptococcus mitis B6 - what is a commensal? PLoS One 5: e9426. doi: 10.1371/journal.pone.0009426
- 28. Eberhard J, Pietschmann R, Falk W, Jepsen S, Dommisch H (2009) The immune response of oral epithelial cells induced by single-species and complex naturally formed biofilms. Oral Microbiol Immunol 24: 325–330. doi: 10.1111/j.1399-302x.2009.00518.x
- 29. Geng J, Chiu CH, Tang P, Chen Y, Shieh HR, et al. (2012) Complete genome and transcriptomes of Streptococcus parasanguinis FW213: phylogenic relations and potential virulence mechanisms. PLoS One 7: e34769. doi: 10.1371/journal.pone.0034769
- 30. Han XY, Kamana M, Rolston KV (2006) Viridans streptococci isolated by culture from blood of cancer patients: clinical and microbiologic analysis of 50 cases. J Clin Microbiol 44: 160–165. doi: 10.1128/jcm.44.1.160-165.2006
- 31. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464: 59–65. doi: 10.1038/nature08821
- 32. Hill GB, Ayers OM, Kohan AP (1987) Characteristics and sites of infection of Eubacterium nodatum, Eubacterium timidum, Eubacterium brachy, and other asaccharolytic eubacteria. J Clin Microbiol 25: 1540–1545.
- 33. Carlier JP, K’ouas G, Bonne I, Lozniewski A, Mory F (2004) Oribacterium sinus gen. nov., sp. nov., within the family ‘Lachnospiraceae’ (phylum Firmicutes). Int J Syst Evol Microbiol 54: 1611–1615. doi: 10.1099/ijs.0.63060-0
- 34. Carlier JP, K’ouas G, Han XY (2007) Moryella indoligenes gen. nov., sp. nov., an anaerobic bacterium isolated from clinical specimens. Int J Syst Evol Microbiol 57: 725–729. doi: 10.1099/ijs.0.64705-0
- 35. Haraldsson G, Meurman JH, Könönen E, Holbrook WP (2005) Properties of hemagglutination by Prevotella melaninogenica. Anaerobe 11: 285–289.
- 36. Zhou FX, Chen L, Liu XW, Ouyang CH, Wu XP, et al. (2012) Lactobacillus crispatus M206119 exacerbates murine DSS-colitis by interfering with inflammatory responses. World J Gastroenterol 18: 2344–2356. doi: 10.3748/wjg.v18.i19.2344
- 37. Jacobs JA, Pietersen HG, Walenkamp GH, Stobberingh EE, Soeters PB (1994) Intervertebral infection caused by Streptococcus milleri. A case report. Clin Orthop Relat Res 302: 183–188. doi: 10.1097/00003086-199405000-00028
- 38. Rabuñal R, Corredoira J, Monte R, Coira A (2009) Co-infection by Streptococcus anginosus and Mycobacterium tuberculosis: three case reports. J Med Case Rep 3: 37. doi: 10.1186/1752-1947-3-37
- 39. Murdoch DA, Shah HN (1999) Reclassification of Peptostreptococcus magnus (Prevot 1933) Holdeman and Moore 1972 as Finegoldia magna comb. nov. and Peptostreptococcus micros (Prevot 1933) Smith 1957 as Micromonas micros comb. nov. Anaerobe 5: 555–559. doi: 10.1099/00207713-33-2-207
- 40. Ott SJ, Musfeldt M, Wenderoth DF, Hampe J, Brant O, et al. (2004) Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease. Gut 53: 685–93. doi: 10.1136/gut.2003.025403
- 41. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, et al. (2011) Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J Allergy Clin Immunol 127: 372–381. doi: 10.1016/j.jaci.2010.10.048
- 42. Pédron T, Sansonetti P (2008) Commensals, bacterial pathogens and intestinal inflammation: an intriguing ménage à trois. Cell Host Microbe 3: 344–347. doi: 10.1016/j.chom.2008.05.010
- 43. McKenna P, Hoffmann C, Minkah N, Aye PP, Lackner A, et al. (2008) The macaque gut microbiome in health, lentiviral infection, and chronic enterocolitis. PLoS Pathog 4: e20. doi: 10.1371/journal.ppat.0040020
- 44. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7: 335–336. doi: 10.1038/nmeth.f.303
- 45. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 5261–5267. doi: 10.1128/aem.00062-07
- 46. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, et al. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72: 5069–5072. doi: 10.1128/aem.03006-05
- 47. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71: 8228–8235. doi: 10.1128/aem.71.12.8228-8235.2005