The oral microbiome profile of young water polo players

doi:10.21203/rs.3.rs-3306302/v1

Objectives.

Adverse changes in the constitution of oral cavity microbiota may have serious health consequences. We hypothesized that the oral cavity microbiota community in water polo players may differ from others. Our aims were to determine the composition of the oral microbiome of elite water polo players and to compare their oral microbiome to that of non-athletes.

Materials and Methods

Elite water polo players (n: 29) and non-athletes (n: 16) aged between 16–20 were studied. The oral microbiome was determined from a saliva sample, DNA was isolated by the QIAmp DNA Blood Mini Kit. The 16S rRNA gene amplicon sequencing method was used to analyse the microbiome community. PCR primers were trimmed from the sequence reads with cutadapt. R library DADA2 was used to process reads in the abundance analysis.

Results

In general, Streptococcus, Veilonella and Prevotella genera constituted more than 50% of the oral microbiome community of the studied youth. The oral microbial profile had significant sexual dimorphism and differed between water polo players and the non-athletes. Males had a significantly higher relative abundance of the Atopobium and Pravotella_7 genera and a significantly lower relative abundance of the Fusobacterium, Gemella and Streptococcus genera as compared to their female counterparts. Compared to non-athletes, water polo players had significantly higher relative abundance of the genus Veillonella, and lower relative abundance of the genus Gemella.

Conclusion

The results confirm that regular water training can alter the composition of the oral microbial community.

young water polo players

oral microbiome

DADA2 library

sexual dimorphism

Formal studies of the human oral microbiome have identified viruses, fungi, protozoa, archaea and bacteria in the community of microorganisms that colonise the surfaces of teeth and the soft tissues of the oral mucosa. The oral microbiome is an ecological community of symbiotic, commensal and pathogenic microorganisms. Among them 500–700 bacterial species, while 3000–7000 discernible operational taxonomic unit level (OTU, used to differentiate bacterial organisms below the genus level) phylotypes were evidenced in bacterial community of the oral cavity. The most frequent salivary bacterial phyla are Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria [1, 2]. The oral microbiome has important roles in maintaining oral homeostasis, protecting the oral cavity, preventing disease development by digesting food, generating energy, promoting the maturation of the host’s mucosa and immune system, controlling fat storage and metabolic regulation, detoxicating environmental chemicals and preventing the invasion of pathogens [3]. Any changes in the microbiota community of the oral cavity (which may be caused by, for example, the altered metabolism of the microbial community or the host, or changes in the factors affecting the biofilm in the oral cavity) have important health consequences. In dysbiosis, when the equilibrium of the oral ecosystem is disrupted, the disease-promoting bacteria manifest and result in adverse consequences for human health.

Beside the host genetic determination of salivary microbiome composition, a proportionally bigger variation in the salivary microbiome due to lifestyle factors was also evidenced [4]. Age, sex, body structural parameters, ethnicity, geographic location, sociodemographic factors, smoking and alcohol consumption were evidenced to form the oral microbiome composition [5–12].

Regular exercise has several positive influences on the biological status of the human body, e.g. decreases the risk of obesity, insulin resistance, improves skeleton-muscular robusticity, stress tolerance and enhances the capability of the immune system. However, it is evidenced that mild-to-moderate intensity exercises can be protective against chronic diseases, while acute strenuous exercise can provoke the typical injuries and illnesses in athletes, for example, overuse injuries on the upper extremities in swimming, or gastrointestinal tract symptoms and illnesses (nausea, vomiting, abdominal pain, diarrhoea) due to gut ischemia in running [13–14].

Water polo players are at risk for a variety of traumatic injuries due to intense physical contact, as well as overuse injury. In addition to these injuries, water polo players are prone to experience otitis, skin allergic issues, eye irritation, asthma and exercise-induced bronchospasm due to the aquatic environment and prolonged exposure to chlorine [15]. The influence of aquatic environment on the communities of microorganisms in water polo players’ gastrointestinal tract (oral and faeces microbiomes) has not been studied yet, although the second most common, following upper respiratory tract infections, illnesses are the gastrointestinal tract infections (1.5–3.0 times higher than in non-athletes) [14, 16–17]. Their higher exposure to gastrointestinal tract pathogens during swimming is evidenced by several studies [18, 19]. Cryptosporidium, Giardia intestinalis and Adenovirus strains 4 and 7 are the most common cause of swimming-pool related gastrointestinal illness, as they are partially chlorine resistant [20].

Chlorine is the most common disinfectant that is used in swimming pools and can irritate or disturb the normal body microbial defence system. Subsequent changes in the oral microbiome result in changes in the composition of the microbiota community of the large intestine, growth of pathogenic microbes, and can eventually affect the performance of athletes. The microbiota community in the oral cavity of water polo players is assumed to be special due to regular water sports. We assume that, compared to the community composition corresponding to their age group, we may experience differences in the dominance of the microbial strains. The composition of the oral microbiota community changes continuously and dynamically from birth to old age, but its changes are most intensively demonstrated during the period of physical development (reference). The aforementioned was our argument to choose a subgroup of young adults (16–20 years old) in whom the heterogeneity of the sub-sample to be examined is not increased by the large individual variation in the composition of the microbiota community that appears during adolescence.

The body and bone structural analysis of the young water polo players (U10-U20) selected in the present study revealed that they had higher stature, bigger body mass, higher amount of relative muscle mass component and smaller amount of relative bone mass component, higher bone mineral density and smaller relative fat mass as compared to non-athlete age-peers, furthermore, their bone mineral density was lower as comparison to athletes in other types of sport. Thus, their overall body structure was well characterized. Our purpose was to study whether their specific training environment (chlorinated water) has a role in shaping their microbial community composition or not, therefore we analysed the salivary oral microbiome of young water polo players and non-athletes using 16S rRNA gene sequencing to determine whether specific bacterial signatures in the oral microbiome of water polo players could be identified.

2.1 Subjects

Altogether 124 water polo players (62 males and 62 females, aged between 9–20 years, Table 1) and 16 non-athlete youths (control group, 8 males and 8 females, aged between 16–20 years) who participated in the body structural examinations voluntarily agreed to participate in the study. The water polo players trained regularly, several occasions per day in many instances, and also regularly participated in competitions and tournaments on weekends. In a randomly selected subsample of water polo players (n: 29, aged between 16–20 years) saliva samples were also collected. Saliva samples were collected from all non-athlete children (n: 16, aged between 16–20 years).

Table 1

Case numbers of the water polo players by age-groups in the body structure analysis study.
Age-group (years)	Case numbers
9	6
10	8
11	8
12	21
13	16
14	15
15	8
16	16
17	6
18	10
19	2
20	8
Together	124

2.2 Methods

2.2.1 Study design

The project was approved by the Research Ethics Committee of the University of Physical Education in Budapest, Hungary (ID of approval: TE-KEB/No42/2019). The research was carried out following the rules of the Declaration of Helsinki of 1975 (https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/, accessed on 21 January 2023) as revised in 2013. Parents of athletes < 18 years old and the athletes were informed of the project details; both provided written informed consent. Details of the project were also provided for older athletes who also provided informed written consent.

The cross-sectional research was conducted in November 2022. Subjects were asked to avoid eating and drinking for at least 30 min prior to examination and to follow their habitual training regime during the week of the examination. The examinations took place between 9:00 and 12:00 in the morning.

Athletes were excluded from the body structural examinations if they:

1. had severe degenerative lesions or fractures/deformations in the measurement area;

2. were unable to reach the correct position and/or remain immobile during measurement;

3. were exposed to an enhanced X-ray/CT scan several days prior to the study;

2.2.2 Microbial analyses

Saliva samples were collected with commercially available equipment. Subjects were asked to avoid eating, drinking, chewing gum or brushing their teeth for 30 minutes before sampling. Saliva samples of 500 µL were collected during the anthropometric examinations in water polo players and in schools in the case of control group. The saliva samples were stored at -20°C (no longer than 4 weeks before the microbiome analysis). Saliva samples of 500 µL were used for DNA extraction. The DNA was isolated by the QIAmp DNA Blood Mini Kit (Qiagen, Beverly, MA, USA). 16S rRNA gene amplicon sequencing (16S analysis) method was used (by V3–V4 primer set) to analyse the microbiome community in the studied groups of athletes and non-athletes [21]. PCR primers were trimmed from the sequence reads with cutadapt (Version 3.5).

R library DADA2 (Version 1.24.0) was used to process reads in the abundance analysis process. DADA2 trimming process were used to trim read segments after 0 nucleotide quality score detected. Reads shorter than 196 nucleotides were filtered out. Maximum expected erroneous nucleotides in limit were 2 in the forward reads and 2 in the reverse reads. Reads with higher number of expected erroneous nucleotides were filtered out. Forward and reverse reads were merged if the overlapped region were at least 12 nucleotides long. In the overlapping region max 0 mismatch were enabled. The minimum length of merged amplicon sequence variant was set to 390.

In the taxonomy assignment both orientations of the merged reads were used to find best hit to the Silva 16S v138.1 reference sequence database on minimum Bootstrap value level of 75. During the DADA2 workflow 11202 unique amplicon sequence variants were found in 45 samples. Out of the 11202 amplicon sequence variants the process identified 8361 chimera sequences and 116 amplicon sequence variants that were shorter than the minimum amplicon length was set at 390 nucleotides.

2.2.3 Statistical analyses

Principal coordinate analysis of the bacterial composition data was used in the analysis for the visualization of the microbiome profile of the studied subgroups. The ordination plot is a graphical representation of the relationship between sample compositions (e.g. taxa diversities) and helps to recognise patterns in the sample relationship or help to recognise environmental variables that may have influence on sample diversity. Samples with similar feature may be located in a distinct area of the plot if the feature has some significant effect on the taxonomy diversity.

An ordination statistical test was used to compare distances of samples within the same group to distances of samples from different groups. If the distance between samples from the different groups is much larger than samples from the same group, we conclude that the groups are not equal. Therefore, the compositional similarities between the different groups were studied with permutational multivariate analysis of variance (PERMANOVA) analysis and for the differential abundance testing the univariate analysis of relative abundance of genera was performed with ANOVA test with White’s correction for heteroscedastic data (comparing the relative abundances of males-females, water polo players-control group). Hypotheses were tested at the 5% level of random error.

In general, Streptococcus, Veilonella and Prevotella genera constituted more than 50% of the oral microbiome community of the studied youths aged between 16–18 years (Table 2).

Figures 1–2 show the bacterium contents at the genus level by sexes and in water polo players/control group, respectively. Visually a difference could be detected between male and female subjects’ bacterial flora and between water polo players’ and in control group subjects’ microbiome profiles. The common predominance of Streptococcus and Veillonella genera was visually bigger in the females than in males and in water polo players than in the control group. To check this assumed difference between the subgroups, principal coordinate analysis of the abundances of the most frequent taxas on genus level was carried out.

Table 2

Mean relative abundance of the most frequent bacteria in the oral microbiome in the studied sample of youth aged between 16–18 years
Bacterial genus	%
Atopobium	1.3
Fusobacterium	2.5
Gemella	4.2
Granulicatella	2.4
Neisseria	5.5
Oribacterium	1.6
Prevotella_7	16.6
Streptococcus	22.1
Veillonella	17.0

Figure 1 Bacterial community profiling of saliva samples by normalized abundances of the most frequent taxa on genus level (taxonomy rank abundances, only taxa with a relative abundance above 0.01 are shown) by sexes.

Figure 2 Bacterial community profiling of saliva samples by normalized abundances of the most frequent taxa on genus level (taxonomy rank abundances) in water polo players and control group.

The principal coordinate analysis of oral microbiome composition of subjects confirmed that the microbial profile had significant sexual dimorphism and differed between water polo players and children belonging to the control group (Figs. 3–4, Table 3). The data points showed an apparent pattern of group-specific clustering in both by considering the sex of the subjects (Fig. 3) and the water polo players - control group comparison as well (Fig. 4).

Figure 3 Beta diversity plot (method: principal coordinate analysis, distance calculation was done by Bray-Curtis method) by sexes (●: males, ●: females).

Figure 4 Beta diversity plot (method: principal coordinate analysis, distance calculation was done by Bray-Curtis method) in water polo players (●) and subjects belonging to the control group (●).

Table 3

Summary of permutational multivariate analysis of variance (PERMANOVA) examining differences in microbiome profile based on the 16S rRNA gene for total bacterial and amplicon sequence variants bacterial degraders.
	Df	Sums sqs	Mean sqs	F Model	R²	p
Sex	1	0.308	0.308	1.883	0.040	0.016
Water polo players vs. control group	1	0.466	0.466	2.850	0.061	< 0.001
Residuals	42	6.861	0.163	NA	0.899	NA
Total	44	7.634	NA	NA	1.000	NA

Df: degrees of freedom; Sum sqs: sum of squares, Mean sqs: mean squares, F: variance ratio, p: level of significance, values in bold indicate significant effects.

Males had significantly higher relative abundance of the following genera: Atopobium (1.6% vs. 1.0%), Pravotella_7 (20.4% vs. 12.2%, Table 4) and significantly lower relative abundance of the genera: Fusobacterium (1.9% vs. 3.1%), Gemella (3.4% vs. 5.0%) and Streptococcus (19.3% vs. 25.3%) than in females (Wilcoxon signed rank test, p < 0.05). Compared to control group, water polo players had significantly higher relative abundance of the genus Veillonella (18.3% vs. 14.6%), and significantly lower relative abundance of the genus Gemella (3.8% vs. 4.9%).

Table 4

Significance level of analysis of the variance (ANOVA) by sex (M-F) and water polo players - control group comparison (W-C), resp. on the relative abundance of the 16S rRNA gene OTUs identified at the genus level of bacteria.
Bacterium genus	M-F	W-C
Atopobium	0.041	0.248
Fusobacterium	0.029	0.919
Gemella	0.025	0.040
Granulicatella	0.262	0.836
Neisseria	0.722	0.760
Oribacterium	0.709	0.494
Prevotella_7	0.001	0.510
Streptococcus	0.002	0.426
Veillonella	0.257	0.046

Former microbiological studies reported that the composition of the oral microbiome is characterized by a great diversity and accompanied by the presence of high proportions of anaerobic bacteria [22–23]. Overall, species of Fusobacterium, Gemella, Granulicatella, Neisseria, Prevotella, Streptococcus and Veillonella were commonly detected in oral microbiome of humans [23–24].

The development and changes of oral microbiome throughout the human lifecycle has not been fully explored. It is well known that the structure of the oral microbiome community is quite simple during infancy and becomes gradually more complex by the appearance of new species (stages: predentate imprinting, eruption of primary teeth) in childhood. Puberty (and pregnancies) are also accompanied by significant changes in the oral microbial community due to changes of sex hormone levels. The composition and proportions of the oral microbes are quite stable in adulthood. It was confirmed that the relative abundance of genera of Enterobacterium, Pseudomonas, Staphylococcus and Candida increase by ageing [25].

Wang et al. [26] reported that Streptococcus, Veillonella, Neisseria and Haemophilus, were the most abundant genera (20%, 15%, 16%, 10%) in adults aged between 20 and 50 years (in saliva samples). Mashima et al. [27] found that Streptococcus, Veillonella, Prevotella, Gemella and Porphyromonas genera (32%, 20%, 8%, 6%, 7%) gave more than 70% of the microbial community in children aged between 7 and 15 years (with having moderate oral hygiene). Takeshita et al. [28] found that the proportion of Streptococcus, Veillonella and Prevotella genera was higher than 70% in elderly people aged 65 and over (Fig. 5). The microbial analysis of studied 16-20-year-old youths revealed that more than 55% of the microbial community was constituted by Streptococcus, Veillonella and Prevotella. Their oral microbiome was more similar to the microbial community of adults than children’s microbiome.

Figure 5 Mean genus abundances in the lifecycle stages in humans (sources: Takeshita et al. 2011,²⁸ Mashima et al. 2017,²⁷ Wang et al. 2022²⁶ and the present analysis)

The special activity environment of water polo players (chlorinated water) suggested to check for microbes that can be found only in water polo players in the studied sample of youths. Corynebacterium argentoratense (n: 3 subjects, infections in the throat, respiratory tract) [29], Fusobacterium naviforme (n: 3 subjects, gingivitis) [30], Howardella ureilytica (n: 7 subjects, observed in humans in connection with decreased food consumption) [31], Peptoniphilus lacrimalis (n: 3 subjects, vaginal infections) [32], Prevotella marshii (n: 3 subjects, supragingival plaque, subgingival plaque) [33] were found only in the water polo players’ saliva samples. Most of them can cause infections in the oral cavity, respiratory system, vagina or inflammations in the gingiva and plaques. Only Howardella ureilytica species was out of this pathogen line, since the presence of this microbe was found to be related with food restriction.

As a summary of the presented microbial analysis of 16-20-year-old youths we can conclude that

the microbial profile of the studied age-group fitted in the developmental and aging progression of the oral microbiome characterised by former studies,

significant sexual dimorphism was found in the microbial community composition in the studied subsample,

water polo players’ oral microbiome differed from the non-athlete age-peers’ profile in the proportion of its members (the dominant genera were present in both groups’ microbiome but in different proportions) and also had some genera that were missing from the non-athlete age-peers’ microbiome (that genera cause inflammations in the oral cavity, respiratory system, vagina).

Ethics approval and consent to participate

The project was approved by the Research Ethics Committee of the University of Physical Education in Budapest, Hungary (ID of approval: TE-KEB/No42/2019). The research was carried out following the rules of the Declaration of Helsinki of 1975 (https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/, accessed on 21 January 2023) as revised in 2013. Parents of athletes <18 years old and the athletes were informed of the details of the project; both provided written informed consent. Details of the project were also provided for older athletes who also provided informed written consent.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

The study was supported by the TEKA grant (EFKTA002T) of the Hungarian University of Sports Science, Budapest, Hungary. AZs’s work in the study was funded by the Scientific Foundations of Education Research Program of the Hungarian Academy of Sciences.

Authors’ contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Irina Kalabiska, Dorina Annar and Annamaria Zsakai. The analysis was performed and the manuscript was written by all authors. All authors read and approved the final manuscript.

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No competing interests reported.

The oral microbiome profile of young water polo players

Status:

Version 1

Abstract

Objectives.

Materials and Methods

Results

Conclusion

Figures

1 Introduction

2 Subjects and methods

2.1 Subjects

2.2 Methods

2.2.1 Study design

2.2.2 Microbial analyses

2.2.3 Statistical analyses

3 Results

4 Conclusions

Declarations

References

Additional Declarations

Status:

Version 1