Changes in the Oropharyngeal Microbiome in Moderate-to-Severe Tobacco Dependence Before and After 30 Days of Smoking Cessation

doi:10.21203/rs.3.rs-3865545/v1

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Changes in the Oropharyngeal Microbiome in Moderate-to-Severe Tobacco Dependence Before and After 30 Days of Smoking Cessation

https://doi.org/10.21203/rs.3.rs-3865545/v1

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Smoking considerably changes the oral microbiota vital for maintaining oral health; this possibly results in the development of diseases. Therefore, to restore the healthy oral microbiota, smoking cessation is a beneficial strategy. However, at present, the relationship between smoking cessation duration and oral microbial recovery remains unclear, and previous studies have not undertaken self-comparisons before and after smoking cessation. In the present study, we evaluated 30 healthy adult men with moderate-to-severe tobacco dependence who willingly quit smoking. Oropharyngeal swab samples were collected before and on day 30 of smoking cessation (experimental group). Simultaneously, samples were collected once from 30 never-smokers (control group). Metagenomic next-generation sequencing revealed differences in the β-diversity and relative abundance of the oral microbial species in both groups. Furthermore, linear discriminant effect size analysis identified the top 10 dominant species, which primarily belonged to the phyla Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Verrucomicrobia. From before to day 30 after cessation, oropharyngeal microbiota composition gradually increased in the experimental group; thereafter, it gradually became similar to the composition in the control group. There is significant heterogeneity in the oral microbiota between smokers and never-smokers. A 30-day smoking cessation intervention can initiate a restorative trend in the oral microbiota of smokers.

smoking cessation

metagenomic next-generation sequencing

oropharyngeal microbiome

dynamic changes

nicotine dependence

The oral cavity is not only the primary communication channel between the human body and the external environment but also the initial digestive organ that degrades carbohydrates and dietary lipids. The mouth of healthy individuals comprises more than 600 bacteria^[1]; therefore, the oral cavity contains the second most complex microbiota after the gut flora^[2], which comprises bacteria, phages, viruses, fungi, archaea, and protozoa. The predominant component is the bacterial microbiome, comprising obligate aerobic bacteria such as Neisseria and Rothia; facultative anaerobes such as Streptococcus and Actinomyces; and obligate anaerobes such as Firmicutes, Bacteroidetes, and Spirochaetes^[1]. Furthermore, saprophytic protozoa such as Entamoeba gingivalis and Trichomonas tenax and fungi such as Candida albicans and Saccharomyces cerevisiae are native residents of the oral microbiota^[3].

Imbalance in the oral microflora can lead to various oral diseases, including dental caries, periodontitis, pulpitis, alveolar osteitis, and tonsillitis^[3]; this phenomenon is also associated with other systemic diseases, including infectious pulmonary diseases, infective endocarditis, stroke, preterm birth, diabetes, head and neck tumors, and gastrointestinal tumors^[4–9]. Smoking, dietary habits, alcohol drinking, and oral hygiene encompass some of the factors that affect the structure of the oral microbiota^[10]. Among them, smoking is considered an important factor affecting oral microbiota composition. Burning cigarette smoke comprises approximately 7,000 chemicals, many of which are toxic^[11]. Smoking can change the structure of the oral microecology via different mechanisms, including affecting the immune status of the host, increasing salivary acidity and oxygen depletion in the mouth, strengthening bacterial adhesion to the mucosal surface, promoting bacterial biofilm formation, and altering bacterial metabolism^[12–14]. Furthermore, it can significantly alter the oral microbiota and may eventually result in disease development. Notably, smoking cessation may restore smoking-induced oral microbial changes; therefore, it is a good approach to restore oral microecology. However, only few studies on smoking cessation and oral microecological changes are available, and the relationship between smoking cessation duration and oral microecological recovery remains unclear. Furthermore, in previous studies, longitudinal comparisons before and after smoking cessation were missing.

Traditional culture techniques fail to detect approximately 60% of human-associated microorganisms^[15]. However, continuous advances in microbial detection technology have gradually led to the development of second-generation sequencing technologies; this has promoted the identification of microorganisms in different biological environments as well as the microbiota in different parts of the human body^[16]. There are two effective methods for microbial sequencing: 16S rRNA and microbial metagenome sequencing. However, the accuracy and specificity of 16S rRNA sequencing are inadequate for identification at various material levels, with blind spots in virus and fungal identification. Furthermore, there is genetic information exchange in some microorganisms between genera, resulting in the recombination of the 16S rRNA gene; this makes it difficult to explain the relationship between species based on the phylogenetic relationships derived from this gene. These findings collectively suggest that full-length 16S rRNA sequencing cannot accurately and entirely identify oral bacteria, including Actinobacteria, Streptococcus, Neisseria, Veillonella, and Porphyromonas^[3]. Moreover, 16S rRNA sequencing is time-consuming, with the sequencing of only 100 cloned genes per sample; this is a relatively shallow analysis for a highly complex population. The limitations of the overly complex 16s rRNA sequencing can be partially overcome by performing metagenomic next-generation sequencing (mNGS). In this sequencing technology, nucleic acids in samples can be directly extracted for high-throughput sequencing; furthermore, it can be combined with bioinformatics analysis, achieving the simultaneous detection of bacteria, fungi, viruses, parasites, and other microorganisms.

In the present study, to improve the understanding of the effect of smoking and smoking cessation on oral microbiome, we performed mNGS to explore the changes in the pharyngeal microbiota before and after smoking cessation in individuals with moderate-to-severe tobacco dependence.

In total, 67 participants (35 in the experimental group and 32 in the control group) were included in this study. We excluded five participants in the experimental group who relapsed. Furthermore, we excluded two participants in the control group: intracranial space-occupying lesions were detected in cranial magnetic resonance imaging of one participant, and blood biochemistry examination revealed increased blood glucose levels in one participant. Finally, 30 participants were included for both the experimental and control groups. Both patient groups were not statistically significantly different in terms of baseline data, including age, body mass index, average smoking age, initial smoking age, or nicotine dependence level (Table 1). On day 1, the concentration of urinary cotinine, a nicotine metabolite, significantly decreased in smokers and was almost consistent with the concentration in the participants in the control group (never-smokers). Furthermore, this decrease in concentration remained constant on days 7 and 30 (Figure 1).

Table 1 Baseline characteristics of the study participants

Group	Experimental group	Control group
Gender (Male/Female)	30/0	30/0
Age (Mean±SD, years)	34.83±8.12	29±4
BMI（Mean±SD）	24.48±3.17	22.8±2.64
Daily Cigarette Consumption (Mean±SD, cigarettes)	20.23±6.28	-
Initial Smoking Age (Mean±SD, years)	17.87±3.05	-
Smoking Duration (Mean±SD, years)	18.02±9.25
Smoking Index (Mean±SD, cigarettes/year)	366±188
FTND Score (Mean±SD, points)	6.6±1.35	-
≤4	0	-
5-6	53.33%	-
≥7	46.67%	-

Fig. 1 Changes in urinary cotinine concentration in smokers who quit smoking

2.1 Analysis of microbial community diversity

In the analysis of α-diversity of the microbiota, we consistently observed no statistically significant differences in the species richness of the oropharyngeal microbiota between the experimental and control groups at days 0 and 30 (Figures 2A and 2B). Next, we evaluated the β-diversity of three microbial communities including control group, experimental group at days 0 and 30. When comparing control group with experimental group, the ecological distance between the microorganisms was much farther, community composition was more heterogeneous, and throat microbial structure was significantly different (Figures 2C and 2D). In contrast, in the experimental group, the microbial ecological distance between days 0 and 30 was closer, indicating that the microbial composition before and after smoking cessation was more similar. However, during the smoking cessation period, the throat microbial structure of the experimental group gradually became more similar to that of the control group.

Fig. 2 Diversity analysis of the oral microecology

Note: Graphs A and B represent the Shannon and Simpson diversity indexes, respectively; graphs C and D represent the Bray–Curtis distance and unweighted Unifrac distance in community diversity, respectively. The results were visualized using PCoA.

2.2 Microbial community composition and differential analysis

Analysis of the relative abundance of the species revealed that the top 10 species in all three groups were bacteria, including Prevotella melaninogenica, Prevococcus jejuni, Rothia mucilagionosa, Streptococcus salivarius, Haemophilus parainfluenzae, Neisseria subflava, Fusobacterium pseudoperiodonticum, Streptococcus mitis, Prevococcus intermediate, and Veillonella atypica; there were slight differences in their relative abundance in the three groups (Figure 3A). Furthermore, the top 10 fungi in the three groups were as follows: Malassezia restricta, Malassezia globosa, Pyricularia oryzae, S. cerevisiae, Aspergillus sydowii, Cladosporium sphaerospermum, Puccinia striiformis, Malassezia japonica, Albugo laibachii, and C. albicans (Figure 3B). On days 0 and 30, the relative abundance of Malassezia and C. albicans was higher in the experimental group than in the control group. In contrast,

the relative abundance of Pyricularia and rice blast fungus decreased in the experimental group compared with the control group. In terms of relative abundance, the top 10 viruses in the three groups were Human herpesvirus-7, BeAn-58058 virus, Human gammaherpesvirus-4, Paramecium bursaria Chlorella virus FR483, Invertebrate iridescent virus-31, Human alphaherpesvirus-1, Torque teno virus, Human betaherpesvirus-6B, Ungulate tetraparvovirus-1, and Cyprinid herpesvirus-1 (Figure 3C).

Fig. 3 Relative abundance of the top 10 species in the pharyngeal microbiota

Note: In figures A, B, and C, CS represents day 0, H represents the control group, and RS represents day 30. The different colored squares on the right represent the top 10 microbial species based on their relative abundances.

Next, LEFSe analysis was conducted to detect the species with differential abundance in the three groups. Forty-eight differential taxa were detected when comparing the experimental group at day 0 and control group (Supplementary Table 2). Among them, the species with the most significant differential abundance in the day 0 group included Pseudomonas aeruginosa, P. melaninogenica, S. salivarius, Abiotrophia defectiva, and Candidatus Nanosynbacter lyticus; these bacteria were primarily concentrated in Actinobacteria, Bacteroidetes, Glycobacteria, Firmicutes, and Proteobacteria (Figure 4A). These phyla were more significantly enriched before smoking cessation, and Proteobacteria was more abundant in the control group. On the other hand, when comparing the experimental group at day 30 and control group, 40 differential taxa were identified (Supplementary Table 3). The species with the most significant differential abundance included P. aeruginosa, P. melaninogenica, S. salivarius, A. defectiva, and Candidatus Nanosynbacter lyticus. By classifying these differential taxa at the phylum level, we observed that Firmicutes, Saccharibacteria, and Proteobacteria were still enriched in the day 30 group compared with the control group (Figure 4B). Finally, when comparing days 0 and 30, 15 differential taxa were identified. Among them, nine bacteria, including S.mitis and Delftia acidovorans, were enriched at day 30. They were primarily concentrated in the family Actinomycetaceae and phylum Actinobacteria. Notably, six bacteria, including P. aeruginosa, were more significantly enriched at day 0 (Supplementary Table 4, Figure 4C).

Fig 4 Phylogenetic tree of the differential species

Note: Figures 4A, 4B, and 4C illustrate the evolutionary relationships of the differential species. The relationships were arranged from inside to outside and by boundary, door, class, order, family, genus, and species. In Figures 4A and 4B, red represents the species enriched in the control group, green represents the enriched species before and 30 days after smoking cessation, and yellow represents the species with no differences between the groups. In Figure 4C, red and green represent the species enriched in the control and day 0 groups, respectively. Yellow represents the species with no differences between the groups.

2.3 Species correlations in the microbiota

SparCC correlation analysis between the species revealed a cooperative relationship among 17 bacterial groups, whereas an antagonistic interaction in one bacterial group (Figure 5). However, the correlations between them were relatively weak, with correlation coefficients (R) of <0.5.

Fig 5 Association among the species

Note: The red line represents a positive correlation, whereas the green line represents a negative correlation.

2.4 Association between species and smoking-related clinical indicators

Correlation analysis of relative microbial abundance with changes in cotinine concentration revealed that 13 bacteria were positively correlated with cotinine concentration, whereas seven bacteria were negatively correlated. However, the R-value was only >0.3 (Figure 6A). When analyzing the association between species and nicotine dependence, we observed that two bacteria, namely, Streptococcus and Actinobacteria, were positively correlated with nicotine dependence, whereas three bacteria, including Actinobacteria, were negatively correlated (R > 0.5) (Figure 6B). Furthermore, we observed that Curvibacter and Ruminococcus were positively correlated with daily smoking quantity, whereas Clostridium, Porphyromonas, and Corynebacterium were negatively correlated (R < 0.5). When analyzing the association between relative microbial abundance and smoking duration, Oribacterium exhibited a positive correlation, whereas Desulfovibrio exhibited a negative correlation (R > 0.5) (Figure 6C). Finally, when analyzing the correlation with initial smoking age, only Bacillus exhibited an association (R > 0.5) (Figure 6D).

Fig 6 Associations between species and clinical indicators

Note: A chart: relationship between changes in cotinine concentration and microorganisms; B chart: association between nicotine dependence and microorganisms; C chart: association between daily smoking and microorganisms; D chart: association between smoking duration and microorganisms; and E chart: relationship between initial smoking age and microorganisms. The red line in the A, B, C, D, and E charts represents a positive correlation, whereas the green line represents a negative correlation.

Subjects and methods

We recruited 67 participants in this study: 35 healthy smokers in the experimental group and 32 non-smokers in the control group. The participants were recruited from January 1, 2020 to December 25, 2020, at the First Affiliated Hospital of Kunming Medical University. They were young men aged 20–45 years. All participants underwent baseline assessments, including blood routine, blood biochemistry, urine routine, chest computed tomography, lung function, body fat analysis, and cranial magnetic resonance imaging. All participants were healthy (without illnesses and were not undergoing any medical treatment). Moderate-to-severe tobacco dependence was defined as continuous smoking for at least 5 years, with a Fagerstrom Test of Nicotine Dependence score of at least 5 and a smoking index (daily smoking years) of > 200 cigarettes/year. On the day of enrollment into the experimental group (day 0), after smoking their last cigarette, the participants were asked to stop smoking entirely and to report their smoking cessation via phone and WeChat. Furthermore, they were asked to complete the Daily Completion Scale Patient Health Questionnaire (PHQ-9), which was developed by Pfizer American Pharmaceuticals in 1999, and the Generalized Anxiety Disorder Scale until the complete disappearance of the withdrawal symptoms. PHQ-9 is a universally accepted international depression detection scale as well as the depression screening scale stated in the Work Program of Exploring the Characteristic Services for Depression Prevention and Treatment released by the official website of the National Health Commission in 2020. No drug-assisted withdrawal treatment was provided to the participants in the experimental group throughout the smoking cessation period, except for psychological support, including group treatment and individual psychological counseling. The Ethics Committee of Kunming Medical University approved this study (Ethics number: 2020 review L No. 44). All enrolled participants signed the informed consent form.

Sample collection

Urine samples were collected from the participants in the experimental group on the day before smoking cessation (day 0), 1st day of cessation (day 1), 7th day of cessation (day 7), and 30th day of cessation (day 30). Simultaneously, for a one-time comparison, urine samples were collected from the participants in the control group. Moreover, on days 0 and 30, oral swabs were collected from the participants in the experimental group. The collected throat swabs were stored in a refrigerator at − 80℃.

Microbial mNGS

Oropharyngeal swab samples (3 ml) were collected and centrifuged at 8000 rpm for 5 min. A sample of 600 µl was drawn into a 1.5 ml centrifuge tube containing 500 µl of glass beads and vigorously shaken at 3000 rpm for 30 min. A 300-µl sample volume was absorbed and transferred to a new 1.5 ml centrifuge tube for nucleic acid extraction. The QIAamp Micro DNA Kit (Qiagen, Hilden, Germany) was used to extract DNA. The QIAamp Viral RNA Mini Kit (Qiagen, Hilden, Germany) was used to extract RNA. After nucleic acid extraction, QIAseq Ultralow Input Library kit for Illumina (Qiagen, Hilden, Germany) was used to built the libraries. Qubit (Thermo Fisher Scientific, MA, USA) was used to measure the nucleic acid concentration. Then, nucleic acid fragmentation, end repair, joint ligation, purification, library amplification, repurification, and library quantification were performed. The Fastp 0.20.0 was used to filter the raw data to get high-quality data. Reads containing adapter sequences, those with more than 10% nitrogen content, those with A bases, low-quality reads (bases of quality Q20 > 50% of the whole reads), and reads with a Q30 quality value of statistical sequencing data (Q30 > 90%) were removed. After library construction, the library was homogenized. The Illumina NextSeq 550 platform was used to perform library pooling, library denaturation, library concentration dilution, and sequencing. Followed by human host DNA reads after mapping to human reference database (hg38). Database alignment was used to remove human sequences. Select representative microbial combinations according to Kraken 2 standards from NCBI and genome databases (https://benlangmead.github.io/aws-indexes/k2), choosing pathogens and their genomes or fragments. The analysis for species annotation utilized the least common ancestor.

Metabolomic analysis of urinary cotinine

Smoking cessation was monitored by measuring the changes in urinary cotinine concentration.

Four-hundred microliter of precooled methanol/acetonitrile (Merck, 1499230-935)/aqueous solution (4:4:2, v/v) was added to the sample, followed by vortex mixing for 20 min and centrifugation at 14000 g and 4°C for 20 min. The supernatant was collected and subjected to vacuum drying. Then, 100 µl of acetonitrile: water solution (acetonitrile: water = 1:1, v/v) was added for redissolution, followed by vortexing and centrifugation at 14000 g and 4°C for 15 min. Sample analysis was performed using 2 µl of the supernatant. Quality control samples were prepared by mixing equal amounts of the samples.

The ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 µm, Waters, USA) was used for chromatographic separation. The column temperature was maintained at 40°C, and the mobile phase flow rate was 0.3 ml/min. Mobile phase A was water and 0.1% formic acid and mobile phase B was acetonitrile. The following gradient was employed for metabolite elution: 0–0.5 min, 5% B; 0.5–1.0 min, 5% B; 1.0–9.0 min, 5–100% B; 9.0–12.0 min, 100% B; and 12.0–15.0 min, 5% B. The upper sample volume for each sample was 5 µl. The samples were placed in an autosampler at 4°C throughout the analysis. To avoid the influence of signal fluctuations during instrument detection, the samples were continuously analyzed in a random order. In the sample cohort, quality control samples were inserted after each sample and used to monitor and evaluate system stability and experimental data reliability.

Electrospray ionization was performed in the positive and negative ion modes. Samples were subjected to mass spectrometric analysis using the Q-Exactive four-level rod-electrostatic field orbitrap high-resolution mass spectrometer (Thermo Fisher Scientific).

The ESI source conditions after HILIC chromatographic separation were as follows: ion source gas 1: 60, ion source gas 2: 60, curtain gas: 30, source temperature: 320°C, ion spray voltage floating: ±3500 V (both positive and negative modes), MS m/z scan range: 80–1200 Da, product ion scan resolution: 17500, MS scan accumulation time: 0.20 s/spectra, and product ion scan accumulation time: 0.05 s/spectra. Secondary mass spectrometry was performed using information-dependent acquisition (IDA). In the high-sensitivity mode, the declustering potential was ± 60 V (positive and negative modes) and collision energy was 35 ± 15 eV. The IDA settings were as follows: isotopes within 4 Da were excluded, and six candidate ions were monitored per cycle.

The raw data obtained after mass spectrometric analysis were preprocessed for peak extraction, peak alignment, peak correction, and standardization using Compound Discoverer 3.0 software (Thermo Fisher Scientific). The output was a 3D data matrix comprising sample name, spectral peak information (including retention time and molecular weight), and peak area. The structures of the metabolites were identified using accurate mass number matching (< 25 ppm) and secondary spectrum matching and by searching the self-built databases of BioCyc, HMDB, Metlin, HFMDB, and Lipid Maps.

Statistical analysis

Measurement data with normal distribution were analyzed by t-test and expressed as mean ± standard deviation. The Shannon and Simpson indexes were calculated using the vegan package in R language to assess the alpha (α) diversity of the microbiota of each participant. The UniFrac distance was calculated and the master coordinates (principal coordinate analysis [PCoA]) were visually analyzed using the phyloseq package in R language to determine microbiota diversity. The Adonis function was used to calculate P-values. Kraken2 and bracken software and the MiniKraken2 database (minikraken2_v2_8GB_201904_UPDATE) were used to list the original composition of the species. Taxa with differences in abundance between different tax were identified using linear discriminant effect size analysis (LEFSe); the difference was judged as an LDA value of > 2. The associations between microorganisms and microorganisms and the environment were determined using SparCC software. Cytoscape was used to visualize the correlation network. All statistical tests were two-tailed. A P-value of < 0.05 was considered statistically significant. SPSS 26.0 statistical software and R language were used to perform statistical analyses.

In this study, we observed that the richness and composition of the oropharyngeal microbiota were significantly different between smokers and non-smokers before and after smoking cessation. Before smoking cessation, specific bacteria, including P. aeruginosa, P. melaninogenica, S. salivarius, A. defectiva, and Candidatus Nanosynbacter lyticus, were more significantly enriched in the experimental group compared with the control group. These bacteria were primarily concentrated in the phyla Actinobacteria, Bacteroidetes, Firmicutes, and Ascomycota before smoking cessation, whereas the phylum Proteobacteria was more abundant in the control group. After 30 days of smoking cessation, the most differential species were similar to those before smoking cessation. Comparisons before and after smoking cessation revealed significant changes in the family Actinomycetaceae and phylum Bacteroidetes; the most noticeable differences were observed in Prevotella intermedia in the phylum Bacteroidetes. This suggests that smoking cessation can reverse smoking-related changes in the oral microbiota. Furthermore, correlation analysis between microorganisms and microorganisms and clinical indicators revealed that the microbial population was unique among smokers. In addition to their association with smoking, synergistic and antagonistic relationships were observed among the microorganisms. Moreover, these microbial interactions were associated with changes in cotinine concentration, smoking duration, daily smoking quantity, and initial smoking age. Our study findings innovatively suggest the relationships among oral microorganisms and the association between microorganisms and smoking status.

Composition of the pharyngeal microecological communities in smokers

Cigarette smoke contains more than 7000 chemicals^[11]. Smoking decreases commensal bacteria and increases the colonization of pathogenic bacteria in the oral cavity^[17]. An early in vitro study based on cultivation methods has revealed that cigarette smoke strongly inhibits Neisseria growth; however, Streptococcus is less affected by cigarette smoke^[18]. Comprehensive analysis of the oral bacteria using high-throughput 16S rRNA sequencing revealed that the abundance of Neisseria, Porphyromonas, and Gemella significantly decreased in smokers^[19]. Furthermore, another study has demonstrated the enrichment of Capnocytophaga, Streptococcus, and Veillonella and reduction of Capnocytophaga, Fusobacterium, and Neisseria in the oral cavity of smokers^[20]. In addition, a large-scale cohort study including 1204 individuals in the United States revealed an increase in the abundance of the genera Veillonella and Streptococcus but a decrease in Neisseria and Capnocytophaga in current smokers^[14]. In addition, at the operational taxonomic unit (OTU) level, 249 OTUs were different between current and never-smokers. These differing taxa were primarily concentrated in the genera Streptococcus and Actinomyces; on the other hand, 57 OTUs in the genus Fusobacterium and the phylum Bacteroidetes were decreased in smokers^[14]. The differences in the findings of these studies may be owing to 11 different oral partitions^[21], each with distinct microbiota^[22]. In a previous study, the bacterial composition of seven different surface samples in the oral cavity was evaluated; similar microbial communities were observed in the buccal mucosa, gingiva, and hard palate; however, different microbial communities were observed in the saliva, tongue, tonsils, throat, and above and below the gingiva^[22]. Mohammad et al.^[10] conducted 16S rRNA sequencing using oropharyngeal swab samples and reported that the oral microbiota composition is primarily represented by the phyla Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria in smokers. Furthermore, they identified the genera Prevotella and Veillonella^[10]. These findings are highly consistent with those of the present study; however, because the primary focus of the study was on the population in the Middle East, which exhibits obvious differences in ethnicity and dietary habits compared with our population, and different microbial gene sequencing methods were utilized, we observed variations in the classification at the microbial species level. We observed that Candidatus Nanosynbacter lyticus was enriched in smokers. This bacterium, originally known as TM7, has been recently discovered and is classified under the Candidate Phyla Radiation, along with the phyla Saccharibacteria and Absconditabacteria. TM7 is associated with different mucosal diseases, including vaginal infections, inflammatory bowel disease, halitosis, and periodontal diseases^[23–25]. Furthermore, TM7 is prevalent in the oral cavity and exhibits a positive correlation with oral disease development^[26]; in addition, it can modulate immune responses in humans^[27].

In this study, we also observed the presence of fungi and viruses in the oral microbiota of smokers. The top 10 fungi in all three groups were M. restricta, M. globosa, P. oryzae, S. cerevisiae, A. sydowii, C. sphaerospermum, P. striiformis, M. Japan, A. laibachii, and C. albicans. Among them, the relative abundance of M.globosa and C. albicans was higher in smokers than in non-smokers; however, the difference was not statistically significant. In a survey of the fungal communities in mouthwash samples from 20 healthy individuals, 74 cultivable and 11 uncultivable fungal genera were identified. The dominant genera included Candida, Aspergillus, Penicillium, Saccharomyces, Fusarium, Fusobacterium, and Cryptococcus^[28]. This was the first study to analyze the “baseline mycobiome” in healthy individuals. Our study results were similar to some extent to the results of this study; however, whether the identified genera represent the colonization of fungal nutritional forms remains unclear at present. In addition, the fungal reproduction cycle is relatively long, and significant changes may not occur in the short term. Analysis of the relative abundance of viruses revealed that herpesviruses are the predominant virus type in the oral cavity; however, the observed viruses did not exhibit significant differences in the three groups. In many recent studies, next-generation sequencing techniques have been applied to reveal the oral virome in humans under different conditions, including in healthy individuals, those with prolonged antibiotic use, and individuals with oral viromes associated with periodontal diseases^[29–31]. The oral virome in healthy individuals includes eukaryotic viruses and bacteriophages, with similarities to the oral bacterial community; the viral community remains stable over time^{[32, 33]}. The most common eukaryotic virus families in the oral cavity are Herpesviridae, Papillomaviridae, and Anelloviridae; they can exist asymptomatically in healthy individuals^[31]. A study has demonstrated a relationship between herpesviruses and periodontal disease^[34]; however, the effect of viruses on oral diseases remains unknown. In the present study, oral herpesviruses were not significantly different in the three groups; this may be owing to limitations in sample type and quantity. Therefore, in future studies, the sample size should be increased or other oral samples should be used for analysis.

Possible mechanisms by which smoking affects the pharyngeal microecology

Smoking primarily affects the structure of the oral microbiota by increasing salivary acidity, depleting oxygen in the oral cavity, exerting antibiotic effects, and affecting bacterial surface adhesion. Smokers exhibit low salivary acidity, significantly decreasing Neisseria abundance; on the other hand, acid-resistant Streptococcus and Rothia can flourish in smokers. Furthermore, aerobic metabolic pathways (including the tricarboxylic acid cycle and oxidative phosphorylation) were downregulated in the oral microbiota of smokers, whereas glycolysis and other anaerobic carbohydrate metabolism pathways significantly increase^[14]. This finding further confirms that smoking establishes favorable conditions for the growth of obligate or facultative anaerobic bacteria but detrimental conditions for the survival of obligate aerobic bacteria. In the present study, we observed that the abundance of Streptococcus, Prevotella, Veillonella, and Gemella increases in smokers. These genera comprise facultative or obligate anaerobic bacteria; the factors mentioned above partially explain their enrichment in the smoking environment. Furthermore, cigarette smoke promotes biofilm formation by Streptococcus and Staphylococcus aureus, making them more prone to mucosal adhesion^[35]. Therefore, this makes smokers more susceptible to respiratory infections and dental caries^{[9, 36]}.

The oral mucosa functions as a first-line defense against pathogen invasion; however, smoking compromises the oral mucosa, disrupts the integrity of the oral epithelium^{[37, 38]}, increases the autophagy and apoptosis of epithelial cells^{[39, 40]}, and decreases β-defensin production by oral epithelial cells^[41–44]. Furthermore, smoking inhibits the phagocytic capacity of neutrophils in the oral cavity as well as their ability to generate superoxide, while also decreasing the release of LL-37, an antimicrobial peptide vital for pathogen clearance^[45–47]. Simultaneously, smoking interferes with the ability of oral macrophages to recognize, engulf, and present pathogenic antigens^{[48, 49]}, thereby affecting DC cell count and inhibiting their maturation and differentiation as well as their ability to present antigens to reactive T cells ^[50–56]. Moreover, smoking interferes with natural killer cell cytotoxicity by decreasing interferon-γ and tumor necrosis factor-α secretion^[57–59]. Finally, studies have revealed that IgA and IgG levels are significantly decreased in the oral cavity of smokers^[60–62]. Therefore, smoking establishes a more favorable oral ecosystem for pathogenic bacteria and indirectly inhibits local defense functions, finally increasing the risk of oral diseases and other systemic diseases in smokers.

It is noteworthy that a complex and dynamic ecological relationship is formed between the host and the fungi and bacteria in the oral cavity. Smoking may lead to an increase in certain microbial communities, such as specific bacterial populations, thereby suppressing the long-term colonization of fungal communities through competition and interaction mechanisms. The interaction between fungi and commensal bacteria involves physical binding, communication through signaling molecules, and metabolic exchange during the co-adaptation process in the multiple oral micro-environmental niche^[63]. In addition, alterations in the host environment are essential in shaping the composition of the oral fungal microbiota and in the development of fungal diseases^[64]. However, studies of changes in oral fungal communities in immunosuppressed populations are still limited.

Effects of smoking cessation on the pharyngeal microecology

Smoking cessation is a vital approach to prevent premature morbidity, disability, and death; furthermore, it is the most direct and effective approach to restore microecology. A study involving African Americans revealed that the effects of smoking on the structure of the oral microbiota were recovered after smoking cessation^[65]. In addition, using nested polymerase chain reaction and flow cytometry to analyze the common subgingival microbial community structure 12 months after smoking cessation revealed that the abundance of periodontal disease-associated pathogens significantly decreased, whereas that of health-related microorganisms was re-enriched^[66]. Simultaneously, previous studies have suggested that the oral microecological structure of never- and previous smokers is basically similar; however, at the OTU level, only 17 different OTUs were identified; therefore, smoking may have altered the oral microecology, with irreversible changes in some taxa^[14]. However, studies on smoking cessation and oral microecological changes are limited, and the duration of smoking cessation and oral microecological changes was at least 12 months; as a result, the effects of short-term smoking cessation on the microecology have not been studied. For the first time, in our 30-day short-term smoking cessation study, we observed that after smoking cessation, there were rapid changes in the smoking-associated dominant bacteria Prevococcus melanotica and the phylum Actinobacteria. Previous studies have revealed a definite relationship between Prevotella nigrescens production and the occurrence of halitosis and periodontal diseases^[67]. Simultaneously, this species was observed to be involved in the oral nitrate reduction reaction, which is impossible in mammalian cells. Under anaerobic conditions, mammalian cells use nitrate as the terminal respiratory electron receptor to produce nitrite; the conversion of nitrite to carcinogenic nitrosamines and the proinflammatory factor nitric oxide exerts a toxic effect^{[68, 69]}. Furthermore, the enrichment of oral P. nigrescens may be associated with the occurrence of intestinal and lung cancers. Finally, Actinomycetes can affect oral health and lead to halitosis in smokers by producing hydrogen sulfide^[70].

Smoking-related clinical indicators may affect the microecological structure

In this study, we also observed a weak correlation between microorganisms and concentration changes of cotinine, a metabolic end product of nicotine; however, the implications remain unclear. Finally, when analyzing the association between microorganisms and smoking-related clinical indicators, we observed that oral desulfurococci and smoking years were negatively correlated. Oral desulfurococci can adapt to the oral environment in humans by losing some biosynthetic and metabolic capabilities, thereby significantly decreasing environmental sensing and signal transduction abilities. Furthermore, they can spontaneously lose the gene responsible for synthesizing methylmercury, a potent neurotoxin. However, transcriptomics and proteomics analyses revealed that this bacterium can trigger inflammatory responses in oral epithelial cells; this suggests that it plays a role in periodontal diseases^[71]. Our study findings suggest a negative correlation between Desulfobulbus and smoking duration, implying that this change confers some positive implications on oral health. However, the exact meaning remains unclear and requires additional experimental validation.

This study has some limitations. The experimental research lacked a sufficient observation period; for oral microecological research, smoking cessation should be analyzed for at least 12 months. In a study on the effect of smoking cessation on the intestinal flora, the relationship with changes in the intestinal flora change was explored; the researchers observed that the relative abundance of Bacteroidetes and Firmicutes changed after 12 weeks^[72]. Therefore, the findings deserve our reference that appropriately extending the observation period may be necessary in future studies. Finally, some selective bias was observed in this study.

The pharyngeal microecology of smokers is significantly heterogeneous compared with that of non-smokers. Smoking cessation for a short period of 30 days can restore the pharyngeal microecology of smokers. Furthermore, the pharyngeal microecological structure of smokers may be associated with changes in cotinine concentration, nicotine dependence, smoking expenditure, smoking duration, and initial smoking age.

Funding

The funding for this study was provided by the "Ten Thousand Talents Program" in Yunnan Province, China, in 2019.

Ethics approval and consent to participate

The Ethics Committee of Kunming Medical University approved this study (Ethics number: 2020 review L No. 44). All enrolled participants signed the informed consent form. This study was performed in accordance with the declaration of Helsinki.

Consent for publication

All authors of this study have agreed, read, and approved the manuscript, and have given their written consent for submission and subsequent publication of the manuscript.

Availability of data and materials

The data generated or analyzed during this study are included in this published article. The raw data can be obtained from the corresponding author.

Competing interests

The author(s) declare that they have no conflict of interest.

Acknowledgements

The authors would like to thank TopEdit for its linguistic assistance during the preparation of this manuscrip.

Author contributions

All authors contributed to the study conception and design. Samples and data collection were performed by LCT, LL, LT, and ZYD. Data curation were performed by LZH and XYL. Data analysis and writing—original draft were performed by GGJ and CQZ. ZJQ revised the manuscript.

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No competing interests reported.

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Changes in the Oropharyngeal Microbiome in Moderate-to-Severe Tobacco Dependence Before and After 30 Days of Smoking Cessation

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