Journal of Microbiology (2017) Vol. 55, No. 8, pp. 648–654 DOI 10.1007/s12275-017-6636-8

eISSN 1976-3794 pISSN 1225-8873

Composition and abundance of microbiota in the pharynx in patients with laryngeal carcinoma and vocal cord polyps§ Hongli Gong1†, Boyan Wang2†, Yi Shi3*, Yong Shi1, Xiyan Xiao1, Pengyu Cao1, Lei Tao1, Yuezhu Wang4, and Liang Zhou1*

Keywords: microbiota, bacterial communities, pharynx, laryngeal cancer, vocal cord polyps Introduction

1

Shanghai Key Clinical Disciplines of Otorhinolaryngology, Department of Otorhinolaryngology, Eye, Ear, Nose, and Throat Hospital, Fudan University, Shanghai, 200031, P. R. China 2 Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, 200032, P. R. China 3 Department of Clinical Laboratory, Branch of Shanghai First People's Hospital, Shanghai, 200081, and Department of Clinical Laboratory, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, P. R. China 4 Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Sequencing Center, Shanghai, 201203, P. R. China (Received Dec 13, 2016 / Revised Jul 13, 2017 / Accepted Jul 13, 2017)

The pharynx is an important site of microbiota colonization, but the bacterial populations at this site have been relatively unexplored by culture-independent approaches. The aim of this study was to characterize the microbiota structure of the pharynx. Pyrosequencing of 16S rRNA gene libraries was used to characterize the pharyngeal microbiota using swab samples from 68 subjects with laryngeal cancer and 28 subjects with vocal cord polyps. Overall, the major phylum was Firmicutes, with Streptococcus as the predominant genus in the pharyngeal communities. Nine core operational taxonomic units detected from Streptococcus, Fusobacterium, Prevotella, Granulicatella, and Veillonella accounted for 21.3% of the total sequences detected. However, there was no difference in bacterial communities in the pharynx from patients with laryngeal cancer and vocal cord polyps. The relative abundance of Firmicutes was inversely correlated with Fusobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes. The correlation was evident at the genus level, and the relative abundance of Streptococcus was inversely associated with Fusobacterium, Leptotrichia, Neisseria, Actinomyces, and Prevotella. This study presented a profile for the overall structure of the microbiota in pharyngeal swab samples. Inverse correlations were found between Streptococcus and other bacterial communities, suggesting that potential antagonism may exist among pharyngeal microbiota. †

These authors contributed equally to this work *For correspondence. (Y. Shi) E-mail: [email protected]; Tel.: +86-1362182-0215; Fax: +86-21-5235-5019 / (L. Zhou) E-mail: [email protected]; Tel.: +86-21-6437-7134-290; Fax: +86-21-64377151 § Supplemental material for this article may be found at http://www.springerlink.com/content/120956. Copyright G2017, The Microbiological Society of Korea

The human body houses large numbers of symbiotic species, and each person is an assemblage of human cells and microbial communities (Peterson et al., 2009; Costello et al., 2012). We rely on those species to acquire nutrition, resist pathogens, and shape the immune system (Costello et al., 2009; Hooper et al., 2012; Tremaroli and Backhed, 2012). The human body could be viewed as a biologic island that is colonized by mounting symbiotic species built via processes of dispersal, local diversification, environmental selection, and ecological drift (Costello et al., 2012). Human health might be a result of ecosystem interactions, and many clinical diseases are related to perturbation of microbiota structure (Costello et al., 2012; Relman, 2012). Therefore, it is essential to understand the synergistic activities between humans and the microbiotas living in and on them (Heintz and Mair, 2014). Recently, human microbiotas at different biological sites were described using the 16S rRNA gene sequencing technique. An accumulating number of studies have reported the bacterial community profiles in the oral cavity, and numerous communities play critical roles in the maintenance of a normal oral physiological niche or in the development of oral diseases (Koren et al., 2011; Kraneveld et al., 2012; Benitez-Paez et al., 2014). The pharynx is an anatomical site that connects the oral, nasal, upper respiratory, and upper digestive tracts. This organ is constantly exposed to both nasally inhaled and orally ingested microbial communities (Abreu et al., 2012). The microbiota of the upper respiratory and upper digestive tract, which is cleared by mucociliary mechanisms, must also transit through the pharynx (Lemon et al., 2010; Odutola et al., 2013). This open body habitat houses microbial communities that are different from those housed in its connected anatomic location. The ecological niche of the pharynx is a bioecological environment for many common bacteria, such as Corynebacterium propinquum and Fusobacterium necrophorum, as well as some potentially invasive pathogens, such as Streptococcus pneumonia, Neisseria meningitides, and Haemophilus influenza (Bogaert et al., 2011; Odutola et al., 2013). Bacterial communities build a microenvironment on the surface of epithelial cells with strong synergism; however, the understanding of the structure and prevalence of microbiota in the pharynx is insufficient to date. In this study, we sought to characterize the profiles of the bacterial communities in the pharynx. To achieve this ob-

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jective, we conducted a cross-sectional study based on a culture-independent approach. We collected swab samples from the pharynx of 40 laryngeal cancer patients and 28 vocal cord polyp patients, and we completely analyzed the composition and abundance of the pharyngeal microbiotas from these subjects. Materials and Methods Subject recruitment and sample collection Forty patients with laryngeal squamous cell carcinoma (LSCC) (58.8%) and 28 patients with vocal cord polyps (41.2%) were enrolled in this study. Subjects were recruited from August 2010 to July 2012 at the Department of Otorhinolaryngology, Eye, Ear, Nose, and Throat Hospital of Fudan University of Shanghai. There were 62 men (91.2%) and 6 women (8.8%). The LSCC patients underwent laryngectomy and were confirmed to have tumors by histopathology. The vocal cord polyps patients underwent laryngoscopic surgery and were confirmed to be cancer free via histopathology. Prior to sample collection, none of the enrolled participants had undergone any medical therapy in the previous three months. Participants with a history of use of hormones or antibiotics use during the previous three months or an active bacterial or viral infection in another part of the body were excluded from the current study (Rezaii et al., 2008; Gong et al., 2012). The mean age of these subjects was 57.1 (±11.5) years, with a range of 24–81 years. The study protocols were approved by the Ethics Committee of the Eye, Ear, Nose, and Throat Hospital of Fudan University. All participants were informed and signed a written consent form according to the committee’s regulations. Swab samples were obtained from the pharynx behind the oral cavity. The samples were collected using the Human Microbiota Project (HMP) protocol (see http://hmpdacc.org/doc/HMP_Clinical_Protocol.pdf) (Li et al., 2012). All swab samples were placed in collection tubes and maintained at -80°C prior for later DNA extraction. Bacterial DNA extraction and purification Bacterial DNA was extracted from 68 swab samples using an enzymatic cocktail lysis and bead-beating protocol combined with a QIAamp DNA Mini Kit (QIAGEN) as previously described (Gong et al., 2013; Hou et al., 2013). Genomic DNA purification was performed with a DNeasy Kits (QIAGEN, USA) according to the manufacturer’s instructions. The negative controls were used during these steps. Prior to further analyses, isolated DNA was stored at -80°C until DNA had been isolated from all samples. 16S rRNA gene library preparation Universal primers 27F and 534R were applied for polymerase chain reaction (PCR) amplification of the 16S rRNA gene variable region 1 and region 3 (V1–V3) (Sangon, Shanghai). The PCR was performed using the following parameters: 2 min of denaturation at 95°C, followed by 30 cycles of 20 sec at 95°C (denaturing), 30 sec at 56°C (annealing), and 5 min at 72°C (elongation) (Gong et al., 2013). The 16S rRNA gene V1–V3 region was used for each sample independently to

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generate PCR amplicons. These PCR products were purified before sequencing. Using the standard 454/Roche GS-FLX protocols, amplicon pyrosequencing was conducted. DNA samples were transformed into single-stranded template DNA (sstDNA) libraries using the GS DNA Library Preparation kit (Roche Applied Science). The sstDNA libraries were clonally amplified in the bead-immobilized form with the GS emPCR kit (Roche Applied Science) and sequenced on the 454 Genome Sequencer FLX Titanium platform (Gong et al., 2013). The pyrosequencing was conducted at the Chinese National Human Genome Sequencing Center (Shanghai). The negative controls were used during 16S rRNA gene library preparation. Sequencing data processing and analysis We obtained the complete hypervariable region of V1–V2 after trimming primers and the incomplete V3 region. Sequences that did not reach the quality standards were abandoned, as described in previous protocols (Gong et al., 2013). A strategy to reduce noise was conducted by Mothur using Chris Quince’s PyroNoise algorithm (Schloss et al., 2009). Taxonomic classification of bacteria in the samples was performed with the Ribosomal Database Project (RDP) (Wang et al., 2007). Operational taxonomic units (OTUs) were determined at level of 97% similarity via UCLUST software. The Shannon weaver index, Simpson diversity, ACE, Chao1, and Good’s coverage were engaged to evaluate the quality of reads via Mothur analysis (Schloss et al., 2009). Metastats was used to calculate the relative abundances of phyla and genera of different groups, and the results were adjusted by the false discovery rate (FDR). Sequences were assigned to the hierarchical taxa at a cut-off of 80% (Cole et al., 2009). A principal coordinate analysis (PCoA) was conducted, and the results were analyzed using UniFrac software and visualized with R software. The P values related to these two methods were tested with the R package CrossMatch based on the UniFrac sample distance. Linear regression model and multiple linear regression model were performed to analyze the correlations among bacterial communities. The sequences reported in this article have been deposited in the GenBank Sequence Read Archive (accession nos. SRP032176 and SRP047093). Results The pharyngeal bacterial diversities of the subjects were characterized by pyrosequencing. The V1–V3 regions of the 16S rRNA gene that had been amplified from the total genomic DNA were isolated from each sample. In total, 117,141 reads were acquired, with an average of 1,723 reads from each sample. After trimming the uncompleted V3 region, the dataset consisted of high-quality sequences of V1–V2 with an average length of 321 ± 8 bp. OTUs were selected based on 97% sequence similarity, and taxonomic data were assigned to each representative sequence via the classification algorithm of the RDP. We identified 2,279 OTUs from the 68 samples. To provide characterization of the sequencing reads, the coverage percentage (Good), richness estimators (ACE and Chao1), and diversity indices (Shannon and Simpson) based

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Fig. 1. Distributions of the major genera in the pharynx of the enrolled subjects. Stacked columns for each of the individuals presented the proportional distributions of the sequences corresponding to a given genus. Twenty-five genera were included. The top 15 genera were the bacteria with proportions that were at least 0.5% of the communities. The upper color bars on the right indicated the genera that were detected in this study. The lower color bars on the right indicated the laryngeal cancer patients and vocal cord polyps patients. Each color was an individual genus, and each stacked column was a swab sample.

on the estimated OTUs were used. The number of OTUs was close to the total number of OTUs estimated by the Chao1 and ACE parameters, and Good’s coverage was 99% (Supplementary data Table S1). We found that the pharyngeal niche harbors a wide range of microbial communities. Overall, sequences were assigned to 12 different phyla. The averaged phylum-level distribution pattern demonstrated that Firmicutes (55.0%) accounted for the majority at this site, which was followed by Fusobacteria (12.7%), Proteobacteria (11.2%), Actinobacteria (10.8%), and Bacteroidetes (9.6%). The main bacterial genera that constituted more than 0.5% of the communities were Streptococcus (39.0%), Neisseria (9.1%), Prevotella (7.8%), Leptotrichia (7.0%), Veillonella (6.9%), Actinomyces (6.6%), Rothia (4.6%), Fusobacterium (4.5%), Gemella (3.8%), Granulicatella (2.9%),

Megasphaera (0.9%), Porphyromonas (0.7%), Capnocytophaga (0.6%), and Solobacterium (0.6%). Interpersonal variability was identified in the community makeup, and the findings at the phylum and genus levels were displayed (Supplementary data Figs. S1 and S2). Moreover, the detailed percentages of the main phyla and the 25 main genera for each sample were presented (Supplementary data Fig. S3 and Fig. 1). The relative abundances of the core OTUs displayed variation across all individuals. With respect to the core OTU analyses, we defined “core OTUs” as those that were distributed in at least 90% of all samples from the pharynx site (Huse et al., 2012). We detected nine core OTUs that constituted the common populations, and the cumulative proportions of these core communities accounted for 21.3% of all the sequences (Table 1 and Supplementary data Fig. S4).

Table 1. Nine core OTUs in the pharyngeal site of 68 subjects at the level of 90% OTUs OTU521 OTU1312 OTU727 OTU899 OTU1867 OTU1260 OTU2119 OTU1187 OTU1140 a

Phyla Firmicutes Firmicutes Firmicutes Fusobacteria Bacteroidetes Firmicutes Firmicutes Firmicutes Firmicutes

Genera Streptococcus Streptococcus Streptococcus Fusobacterium Prevotella Granulicatella Veillonella Veillonella Veillonella

indicated the average relative abundance of each OTU in swab sample of 68 subjects. indicated the lowest abundance of each OUT in swab sample of one subject. indicated the higheast abundance of each OUT in swab sample of one subject. Operational taxonomic units (OTUs). b c

Average abundancea % 5.1 4.5 3.3 2.2 1.8 1.7 1.2 1.1 0.4

Ranging scopes low % 0.04 0.08 0.07 0.05 0.07 0.06 0.07 0.07 0.04

b

high % 37.43 26.06 20.21 15.68 10.39 7.97 5.71 6.49 2.41

c

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Seven core OTUs were from the Firmicutes phylum, with a cumulative population of 17.7% of the entire dataset. OTU # 899 was from the Fusobacteria phylum, and OTU # 1867 was from the Bacteroidetes phylum. Three dominant core OTUs were from the Streptococcus genus with an aggregate population of 12.8%, and three minor abundant core OTUs were from the Veillonella genus, with a cumulative population of 2.7%. OTU # 521 was the most abundant core OTUs, with an average abundance of 5.1% in all subjects, and it showed variability ranging from 0.04% to 37.4%. However, OTU # 521 had a low prevalence and was not detected in five samples. Moreover, when we defined core OTUs as those present in at least 95% of the pharyngeal samples (Huse et al., 2012), we observed four core OTUs in the samples: OTU # 1187, OTU # 1260, OTU # 1312, and OTU # 2119. These OTUs all comprised the Firmicutes phylum, and their aggregate abundance accounted for 8.6% of the total reads detected. OTU # 1187 and OTU # 2119 were highly prevalent; they were absent in only one sample and had low abundances in all individuals (Table 1). The microbiota profiles of the different clinical groups were analyzed. Comparing the spatial diversity between the laryngeal cancer patients and vocal cord polyps patients via PCoA analysis, we observed no difference between these two groups (P = 0.11). Moreover, we analyzed the relative abundance of bacterial communities in these two groups using metastats and FDR methods, and no difference was detected (P > 0.05). Two groups were stratified according to age: those younger than 60 years and those 60 years or older. No dif-

(A)

(B)

(C)

(D)

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ference in microbial structure was found by the PCoA analysis (P = 0.42), and no diversity of communities of the relative abundance between these two groups was observed by metastats and FDR (P > 0.05). With respect to differences in the microbiota based on sex, we recognized no discordance between the groups of male and female patients through the PCoA analysis (P = 0.28) or metastats and FDR (P > 0.05). Inverse correlations in the relative abundances of bacterial communities were found in the pharynx. The relative abundance of Firmicutes was inversely correlated with Fusobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes using linear regression model analyses (Fig. 2). After multiple linear regression model analyses, we confirmed that the relative prevalence of Firmicutes was negatively related to these four phyla in the pharyngeal communities. The correlations were as follows: ŷFirmicutes = 1.002 – 1.014xFusobacteria – 1.015xProteobacteria – 1.005xActinobacteria – 1.054xBacteroidetes [R2 = 0.998, p < 0.001, DW (Durbin – Watson) = 2.125, VIF (Variance Inflation Factor): Fusobacteria (1.131); Proteobacteria (1.082); Actinobacteria (1.066); Bacteroidetes (1.140)]. Furthermore, these correlations were evident at the genus level in the pharyngeal communities. We observed that the relative populations of Streptococcus were negatively associated with Fusobacterium, Leptotrichia, Neisseria, Actinomyces, Prevotella, Campylobacter, Megasphaera, Eubacterium, Solobacterium, Mogibacterium, TM7_genera_incertae_sedis, and SR1_genera_incertae_sedis (P < 0.05) (Supplementary data Fig. S5). These correlations were adjusted using multiple linear regression model analyses, and the related correla-

Fig. 2. Inverse relationships of the relative abundance between Firmicutes and another phylum using linear regression model analyses. (A) Linear regression in the relative abundances of Firmicutes and Fusobacteria in the pharyngeal communities (ŷFirmicutes = 0.695 – 1.209xFusobacteria; 2 R = 0.440; Pearson correlation: P < 0.001; Linear regression model: P < 0.001). (B) Linear regression in the relative abundances of Firmicutes and Proteobacteria in the pharyngeal communities (ŷFirmicutes = 2 0.646 – 0.695xProteobacteria; R = 0.212; Linear regression model: P < 0.001; Pearson correlation: P < 0.001). (C) Linear regression of the relative abundances of Firicutes and Actinobacteria in the pharyngeal communities (ŷFirmicutes = 0.675 – 0.898xActinobacteria; 2 R = 0.161; Linear regression model: P = 0.001; Pearson correlation: P = 0.001). (D) Linear regression in the relative abundances of Firmicutes and Bacteroidetes in the pharyngeal communities (ŷFirmicutes = 0.684 – 1.227xBacteroidetes; R2 = 0.163; Linear regression model: P = 0.001; Pearson correlation: P = 0.001).

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tions were as follows: ŷStreptococcus = 0.721 – 1.013xFusobacterium – 0.778xLeptotrichia – 1.005xNeisseria – 1.125xActinomyces – 0.912xPrevotella [R2 = 0.724, P < 0.001, DW (Durbin – Watson) = 1.946; VIF: Fusobacterium (1.280); Leptotrichia (1.412); Neisseria (1.629); Actinomyces (1.446); Prevotella (1.222)]. Discussion Using 16S rRNA gene-based culture-independent techniques, we investigated the microbiota composition and abundance of the pharynx in 68 subjects. The Firmicutes phylum constituted more than half of the microbial communities, and the genus Streptococcus accounted for the largest percentage of the microbiota in the sequence samples. Nine pharyngeal core OTUs were detected and displayed variation across all individuals. No differences in the microbial profiles according to different disease statuses, age groups, or sex groups were found. Inverse correlations in the relative abundances of bacterial communities were detected in the pharynx. The characteristics of bacterial communities in the pharynx were different from those at its adjacent sites. Kraneveld et al. (2012) determined that the predominant genera in the oral cavity were Streptococcus (34%), Rothia (12%), Veillonella (11%), and Prevotella (11%). Bogaert et al. (2011) identified the prevalent communities in the nasal cavity as Moraxella (40%), Haemophilus (20%), Streptococcus (12%), and Flavobacterium (10%), and they suggested that the biological equilibria in the nose and mouth microbiomes are different. Jette et al. (2016) reviewed published results and found that the prevalent bacterial genera in the oral cavity and pharynx were Prevolleta, Streptococcus, Veillonella, and Haemophilus, but the main communities in the nose were Corynebacterium, Propionibacterium, Staphylococcus, and Moraxella. After analyzing the microbiota structure in the pharynx from our study, we found that the composition of the microbial community in the pharynx is similar to that in the mouth; however, the relative abundances of these shared communities are dissimilar between these two sites (Kraneveld et al., 2012; Jette et al., 2016). The oral cavity is a dynamic ecosystem, and the gingival structure, papillae on the dorsal surface of the tongue, keratinization of squamous epithelia, salivary gland, gingival crevicular fluid, and pH gradient of the mouth might influence structure of microbial community assembly (Proctor and Relman, 2017). Anatomic features of the anterior naris, the nasal vestibule, the nasal conchae, the nasal meatuses, and the nasal sinuses may influence the nasal flow velocity and blanket of mucosa, which may impact the microbial communities inhabiting the nasal cavity (Proctor and Relman, 2017). Chemical and physiological features of the nasal cavity, including the pH, sweat glands, sebaceous glands, pseudostratified columnar ciliated epithelium, temperature gradient, and moisture gradient, appear to be important factors that likely influence the gene expression of microbial communities in the nasal cavity (Proctor and Relman, 2017). The mouth, nose, and pharynx are anatomically connected. The salivary film of the mouth and the air of the nose are drained into the pharynx, which then connects through the esophagus to the stomach and connects through the trachea to the lung. The microbiota of the esophagus includes com-

plex bacterial populations of Streptococcus (21%), Klebsiella (10%), Gemella (6%), and Eubacterium (5%) (Liu et al., 2013). Goddard et al. (2012) reported that the vast majority of the bacterial populations in the lung were Pseudomonas aeruginosa (76.5%), Achromobacter xylosoxidans (9.2%), and Burkholderia cepacia complex (7.6%), and they found that bacterial communities in the lung were different from those in the pharynx. The microbial compositions and abundances in the pharynx are considerably different from those in the lung and esophagus (Charlson et al., 2011; Goddard et al., 2012; Liu et al., 2013). These observations may be explained by noting that different body sites represent distinct environments for adaption, and host-associated factors in the upper aerodigestive tract, including anatomic structures, regional chemistry, tissue type, immune activity, and physiological conditions, may influence microbial community composition and abundance (Huse et al., 2012; Proctor and Relman, 2017). Taken together, these findings indicate that the oral cavity, nose, pharynx, lung, and esophagus reserve their own profiles of bacterial structures. The characteristics of the microbiota at these anatomic sites likely call for a better understanding of the multiple bacterial communities that exhibit distinct preferences across different body sites. Nine core OTUs with wide variation in the relative abundance were determined for the swab samples from the pharyngeal site. These core OTUs that originated from the Firmicutes, Fusobacteria, and Bacteroidetes phyla, accounting for approximately one-fifth of the total OTUs detected. The dominant core species in the human body are thought to be autochthonous members of the human microbiota (Tap et al., 2009; Martinez et al., 2013). The core OTUs in a stable status could shape the microbial community and support the homeostatic ecosystem (Martinez et al., 2013). These OTUs appear to function as an ecological unit and to resist to perturbations that are caused by diet and lifestyle changes (Rajilic-Stojanovic et al., 2012; Martinez et al., 2013). These communities were broad in scopes, being dominant in some samples and rare in others, even for the most prevalent OTUs. This distribution pattern, in which core OTUs show highly variation across different subjects, has been reported previously (Huse et al., 2012; Martinez et al., 2013; Dishaw et al., 2014; Dougal et al., 2014). The low abundance of microbial populations at an anatomic site in individuals might be a temporal product of changes in health, and some populations might become prevalent in response to certain variations in human health (Sogin et al., 2006). These populations might also profoundly impact the development and growth of human bacterial biofilms (Sogin et al., 2006). Some species seem to perform discrete and essential functions, and this functional redundancy within communities might serve as a marker of community stability and, potentially, as a feature of human health status (Lemon et al., 2010). These findings indicate that the core communities probably maintain the persistent microbial populations. No overall patterns in microbiota composition were identified as a basis for discriminating between the laryngeal cancer patients and vocal cord polyp subjects. The physiological functions and anatomic structures of these two sites are different, which may explain this observation (Proctor and Relman, 2017). When the larynx performs its breathing func-

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tion, the air flow is drained from the nose into the pharynx and then connects through the larynx to the lungs. When the larynx performs its swallowing function, the epiglottis closes to prevent aspiration. The epiglottis functions as a valve that diverts air flow to the lung and diverts liquid and food to the esophagus. The false vocal cord also helps prevent food and drink from entering the trachea during the swallowing process. Hanshew et al. (2014) compared the characteristics of the microbial community among four types of benign vocal fold lesions and observed no diversity. They also found that community diversity in the vocal cords was lower than that of pharyngeal communities from 15 healthy samples. The structure of the bacterial community in the false vocal cord differs from that of the vocal cords, although these two sites are closely anatomically connected, and the structures of common communities in the larynx are distinct from those of the pharynx (Jette et al., 2016). The bacterial communities inhabiting the pharynx might be separated from the larynx by the epiglottis and false vocal cord. These observations suggest that the pharyngeal microbial communities may be not impacted by microbiological changes in the tumor niche in the larynx. Inverse correlations in the relative abundances of bacterial communities were observed in the pharynx niche. Fusobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes were the main populations in the pharyngeal communities, and as a unit, they were probably negatively affected by the phylum of Firmicutes. Moreover, this relationship was evident at the genus level in the pharyngeal communities. The genus of Streptococcus, a member of the Firmicutes phylum, was mainly negatively correlated with five genera that were also the major bacterial populations at the pharynx site. Fusobacterium and Leptotrichia are members of the Fusobacteria phylum. Neisseria is a member of the Proteobacteria phylum. Actinomyces is a member of the Actinobacteria phylum. Prevotella is a member of the Bacteroidetes phylum. The compositions and associations of the microbial communities at pharyngeal mucosal sites may define their relative abundances (Abreu et al., 2012). It has been reported that Streptococcus promote the development of multispecies biofilms through several processes of interspecies communication. Streptococcus can recognize receptors of statherin, proline-rich proteins, and salivary α-amylase and can interact with other bacteria by communication signals, such as autoinducer 2 (AI2) and arginine deiminase (Kolenbrander et al., 2010). Bacterial communities in the pharynx likely work together as a unit, and competitive and cooperative relationships may exist in this multispecies community (Siqueira and Rocas, 2009). The findings of the present study suggest that potential antagonism probably exists between Streptococcus and other bacterial communities, and Streptococcus might impact the relative abundances of other microbes. Together, the results of this study exploring the characteristics of human-associated bacterial communities in the pharynx showed inverse correlations in the relative abundance between Streptococcus and other bacterial communities.

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Acknowledgements This work received financial support from National Natural Science Foundation of China (No. 81001203 and No. 81502343) and the Academic Leaders Training Program of Pudong Health Bureau of Shanghai (No. PWRd2012-11). We thank Prof. Larry J. Forney and Xia Zhou at Department of Biological Sciences, University of Idaho for fruitful discussions with this study. Conflicts of Interests The authors disclose no potential conflicts of interest. References Abreu, N.A., Nagalingam, N.A., Song, Y., Roediger, F.C., Pletcher, S.D., Goldberg, A.N., and Lynch, S.V. 2012. Sinus microbiome diversity depletion and corynebacterium tuberculostearicum enrichment mediates rhinosinusitis. Sci. Transl. Med. 4, 151ra124. Benitez-Paez, A., Belda-Ferre, P., Simon-Soro, A., and Mira, A. 2014. Microbiota diversity and gene expression dynamics in human oral biofilms. BMC Genomics 15, 311. Bogaert, D., Keijser, B., Huse, S., Rossen, J., Veenhoven, R., van Gils, E., Bruin, J., Montijn, R., Bonten, M., and Sanders, E. 2011. Variability and diversity of nasopharyngeal microbiota in children: A metagenomic analysis. PLoS One 6, e17035. Charlson, E.S., Bittinger, K., Haas, A.R., Fitzgerald, A.S., Frank, I., Yadav, A., Bushman, F.D., and Collman, R.G. 2011. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am. J. Respir. Crit. Care Med. 184, 957–963. Cole, J.R., Wang, Q., Cardenas, E., Fish, J., Chai, B., Farris, R.J., KulamSyed-Mohideen, A.S., McGarrell, D.M., Marsh, T., Garrity, G.M., et al. 2009. The ribosomal database project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141– 145. Costello, E.K., Lauber, C.L., Hamady, M., Fierer, N., Gordon, J.I., and Knight, R. 2009. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697. Costello, E.K., Stagaman, K., Dethlefsen, L., Bohannan, B.J., and Relman, D.A. 2012. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255– 1262. Dishaw, L.J., Flores-Torres, J., Lax, S., Gemayel, K., Leigh, B., Melillo, D., Mueller, M.G., Natale, L., Zucchetti, I., De Santis, R., et al. 2014. The gut of geographically disparate ciona intestinalis harbors a core microbiota. PLoS One 9, e93386. Dougal, K., de la Fuente, G., Harris, P.A., Girdwood, S.E., Pinloche, E., Geor, R.J., Nielsen, B.D., Schott, H.C., 2nd, Elzinga, S., and Newbold, C.J. 2014. Characterisation of the faecal bacterial community in adult and elderly horses fed a high fibre, high oil or high starch diet using 454 pyrosequencing. PLoS One 9, e87424. Goddard, A.F., Staudinger, B.J., Dowd, S.E., Joshi-Datar, A., Wolcott, R.D., Aitken, M.L., Fligner, C.L., and Singh, P.K. 2012. Direct sampling of cystic fibrosis lungs indicates that DNA-based analyses of upper-airway specimens can misrepresent lung microbiota. Proc. Natl. Acad. Sci. USA 109, 13769–13774. Gong, H., Shi, Y., Zhou, L., Tao, L., Shi, Y., Cao, W., and Cheng, L. 2012. Helicobacter pylori infection of the larynx may be an emerging risk factor for laryngeal squamous cell carcinoma. Clin. Transl. Oncol. 14, 905–910. Gong, H., Shi, Y., Zhou, L., Wu, C., Cao, P, Tao, L., Xu, C., Hou, D., and Wang, Y. 2013. The composition of microbiome in larynx

654

Gong et al.

and the throat biodiversity between laryngeal squamous cell carcinoma patients and control population. PLoS One 8, e66476. Hanshew, A.S., Jette, M.E., and Thibeault, S.L. 2014. Characterization and comparison of bacterial communities in benign vocal fold lesions. Microbiome 2, 43. Heintz, C. and Mair, W. 2014. You are what you host: Microbiome modulation of the aging process. Cell 156, 408–411. Hooper, L.V., Littman, D.R., and Macpherson, A.J. 2012. Interactions between the microbiota and the immune system. Science 336, 1268–1273. Hou, D., Zhou, X., Zhong, X., Settles, M.L., Herring, J., Wang, L., Abdo, Z., Forney, L.J., and Xu, C. 2013. Microbiota of the seminal fluid from healthy and infertile men. Fertil. Steril. 100, 1261– 1269. Huse, S.M., Ye, Y., Zhou, Y., and Fodor, A.A. 2012. A core human microbiome as viewed through 16S rRNA sequence clusters. PLoS One 7, e34242. Jette, M.E., Dill-McFarland, K.A., Hanshew, A.S., Suen, G., and Thibeault, S.L. 2016. The human laryngeal microbiome: Effects of cigarette smoke and reflux. Sci. Rep. 6, 35882. Kolenbrander, P.E., Palmer, R.J.Jr., Periasamy, S., and Jakubovics, N.S. 2010. Oral multispecies biofilm development and the key role of cell-cell distance. Nat. Rev. Microbiol. 8, 471–480. Koren, O., Spor, A., Felin, J., Fak, F., Stombaugh, J., Tremaroli, V., Behre, C.J., Knight, R., Fagerberg, B., Ley, R.E., et al. 2011. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl. Acad. Sci. USA 108 Suppl 1, 4592–4598. Kraneveld, E.A., Buijs, M.J., Bonder, M.J., Visser, M., Keijser, B.J., Crielaard, W., and Zaura, E. 2012. The relation between oral candida load and bacterial microbiome profiles in dutch older adults. PLoS One 7, e42770. Lemon, K.P., Klepac-Ceraj, V., Schiffer, H.K., Brodie, E.L., Lynch, S.V., and Kolter, R. 2010. Comparative analyses of the bacterial microbiota of the human nostril and oropharynx. mBio 1, pii: e00129-10. doi: 10.1128/mBio.00129-10. Li, K., Bihan, M., Yooseph, S., and Methe, B.A. 2012. Analyses of the microbial diversity across the human microbiome. PLoS One 7, e32118. Liu, N., Ando, T., Ishiguro, K., Maeda, O., Watanabe, O., Funasaka, K., Nakamura, M., Miyahara, R., Ohmiya, N., and Goto, H. 2013. Characterization of bacterial biota in the distal esophagus of japanese patients with reflux esophagitis and barrett's esophagus. BMC Infect. Dis. 13, 130. Martinez, I., Muller, C.E., and Walter, J. 2013. Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species. PLoS One 8, e69621. Odutola, A., Antonio, M., Owolabi, O., Bojang, A., Foster-Nyarko,

E., Donkor, S., Adetifa, I., Taylor, S., Bottomley, C., Greenwood, B., et al. 2013. Comparison of the prevalence of common bacterial pathogens in the oropharynx and nasopharynx of gambian infants. PLoS One 8, e75558. Peterson, J., Garges, S., Giovanni, M., McInnes, P., Wang, L., Schloss, J.A., Bonazzi, V., McEwen, J.E., Wetterstrand, K.A., Deal, C., et al. 2009. The NIH human microbiome project. Genome Res. 19, 2317–2323. Proctor, D.M. and Relman, D.A. 2017. The landscape ecology and microbiota of the human nose, mouth, and throat. Cell Host Microbe 21, 421–432. Rajilic-Stojanovic, M., Heilig, H.G., Tims, S., Zoetendal, E.G., and de Vos, W.M. 2012. Long-term monitoring of the human intestinal microbiota composition. Environ. Microbiol. doi: 10.1111/ 1462-2920.12023. Relman, D.A. 2012. The human microbiome: Ecosystem resilience and health. Nutr. Rev. 70 Suppl 1, S2-9. Rezaii, J., Tavakoli, H., Esfandiari, K., Ashegh, H., Hasibi, M., Ghanei, G., Khosh-Batn, M., and Rashidi, A. 2008. Association between Helicobacter pylori infection and laryngo-hypopharyngeal carcinoma: A case-control study and review of the literature. Head Neck. 30, 1624–1627. Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., Lesniewski, R.A., Oakley, B.B., Parks, D.H., Robinson, C.J., et al. 2009. Introducing mothur: Open-source, platformindependent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–4751. Siqueira, J.F.Jr. and Rocas, I.N. 2009. Community as the unit of pathogenicity: An emerging concept as to the microbial pathogenesis of apical periodontitis. Oral. Surg. Oral. Med. Ora.l Pathol. Oral. Radiol. Endod. 107, 870–878. Sogin, M.L., Morrison, H.G., Huber, J.A., Mark Welch, D., Huse, S.M., Neal, P.R., Arrieta, J.M., and Herndl, G.J. 2006. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA 103, 12115–12120. Tap, J., Mondot, S., Levenez, F., Pelletier, E., Caron, C., Furet, J.P., Ugarte, E., Munoz-Tamayo, R., Paslier, D.L., Nalin, R., et al. 2009. Towards the human intestinal microbiota phylogenetic core. Environ. Microbiol. 11, 2574–2584. Tremaroli, V. and Backhed, F. 2012. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249. Wang, Q., Garrity, G.M., Tiedje, J.M., and Cole, J.R. 2007. Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261– 5267.