The intensive physical activity causes changes in the composition of gut and oral microbiota

Study design and subject characteristics

A prospective cohort study was conducted on 20 professional football players from one of the Polish football clubs and 12 amateurs. The mean age of professional football players was 26 ± 7 ages. In the amateurs, the mean age was 24 ± 6. All subjects of this group were of Polish ethnicity. Amateur players had limited intensity training during the league season, in contrast to professional football players. The amateurs trained twice a week for 1,5 h, and they participated once to twice a month in football matches. The professional football players trained every day for 4 h and they participated in one to two matches per week. In contrast to the amateurs, professional football players created a more homogeneous group due to a similar lifestyle, training intensity, and a similar diet created and controlled by a dietitian. However, the type of physical activity in both groups was similar, and all participants were matched with similar age, BMI, and health status. In two study groups, basic morphological parameters, including CRP were measured, and all were in the referential ranges.

For both groups, the same inclusion criteria were age between 18 and 40, active participation in group training, and informed consent to participate in the study. The exclusion criteria included: antibiotic treatment within two months of the sample collection procedure, serious injuries/breaks from training within two weeks of the sample collection procedure, and suffering from any immunological, gastrointestinal, or cardiovascular conditions.

Written informed consent was obtained from all study participants. The Ethical Committee of Jagiellonian University Medical College approved the protocol of the Jagiellonian University Medical College study. The study was conducted following the Declaration of Helsinki and adhered to Good Clinical Practice guidelines.

Samples collection

Stool samples and oral swabs were collected from both participant groups at a one-time point in November. This month was the highest physical activity for professional football players in the middle of the league season. The training load in this group was relatively constant over the year. Oral swabs and stool samples were collected from professional players again twice. The first collection was performed in the middle of the league season, the period of the highest physical activity. The second collection was performed at the end of 4 week-long intra-season breaks between the second half of December and the first half of February. This period between holidays and the next practical camp is characterized by low physical activity levels, with no preplanned training regimes. All professional football players were asked to collect fecal and oral samples before being well-instructed about the collection procedure. Feces were collected using a fecal collection kit (EasySampler, ALPCO, Salem) containing RNAlater (Sigma-Aldrich). The BactiSwabTM NPG Collection and Transport System (ThermoFisher Scientific, Waltham, MA, USA) were applied for oral swab collection. Both stool and oral samples were immediately transported on ice and stored at − 80 °C until further processing. Both fecal and swab samples were collected with the highest avoidance of contamination, which was critical for this microbiome study. The bacterial genomic DNA was extracted using commercially available assays, QIAamp BiOstic Bacteremia DNA Kit (QIAGEN, Hilden, Germany) for buccal swabs, and QIAamp PowerFecal Pro DNA Kit (QIAGEN, Hilden, Germany) for fecal samples, respectively. Then, the quantity and quality of bacterial genetic material were measured using spectrophotometer NanoDrop ND-1000 (Thermo Electron Corporation, West Palm Beach, FL, USA) and fluorometer Qubit 4 (Invitrogen, Waltham, MA, USA), respectively. The DNA samples were stored at − 20 °C until further analysis.

Genetic library construction

The gene-specific sequences targeting the V3 and V4 regions of the 16S rRNA gene were used to construct libraries. The specific primers were adapted from the Klindworth et al. publication⁸. When creating libraries, the Protocol for Preparing 16S Ribosomal RNA Gene Amplicon for Illumina MiSeq System (Part#15044223Rev.B.) from Illumina was adjusted. The KAPA HiFi HotStart Ready Mix (ROCHE, Basel, Switzerland) was used to perform the PCR-based amplification. All procedural steps were performed according to the manufacturer’s recommendations.

The amplification was performed under the following thermal profile 95 ◦C for 1 min, 55 ◦C for 1 min, then 72 ◦C for 1 min for 30 cycles, followed by a final extension at 72 ◦C for 5 min. Then, the amplicons were indexed with specific sequencing adapters following the Nextera XT Index Kit v2 from Illumina. The thermal conditions of indexing were as follows: 95 °C for 3 min, eight cycles of 95 °C for 30 s, 55 °C, 72 °C for 30 s, followed by a final extension at 72 °C for 5 min, and hold at 4 °C. Before sequencing, the amplicons were measured using Qubit 4.0 Fluorometer (Invitrogen) and Bioanalyzer (Agilent, Santa Clara, CA, USA) using Bioanalyzer DNA 1000 chips. The libraries with appropriate integrity and size, about 630 bp, were pooled in equimolar concentrations and then sequenced. The sequencing was performed on the MiSeq instrument (Illumina, San Diego, CA, USA) using a 300 × 2 V3 Kit and PhiX Control V3 from Illumina.

Statistical methods and analysis

The raw data, collected as the FASTQ files, were classified using the Illumina16S Metagenomics workflow. The classification was based on the algorithm with the high-performance implementation of the Ribosomal Database Project (RDP) classifier, described by Wang Q. et al. in 2007⁹. The open-reference operational taxonomic unit (OUT).

s were prepared based on these classifications. Then, a further detailed analysis was performed. The Greengenes database version 13.5 was used to conduct the taxonomic assignment of individual datasets¹⁰. Alpha and beta diversity were calculated using QIIME 2.0 software with Python scripts¹¹. Alpha diversity was calculated based on the sequence similarity at 97%. The richness was calculated as the amount of unique OTUs found in each sample and presented as observed OTUs. The count of unobserved species based on low-abundance OTUs was presented as ACE and Chao1 indices. Beta diversity (the distance and dissimilarities in-between microbial communities) was determined based on Jaccard, Bray–Curtis, and Jensen-Shannon Divergence indices calculated by QIIME. The distances were visualized by principal coordinate analysis (PCoA)¹². The clustering and statistical analysis were performed with LEfSe and the Microbiome Analyst platform¹³. The characteristic features of oral and intestinal microbiota profiles were determined using the linear discriminant analysis (LDA) effect size with LEfSe. The discovered microbial biomarkers with statistical significance and biological relevance were described based on the normalized relative abundance matrix, Kruskal–Wallisly’s rank-sum test, the significant alpha at 0.05, and the effect size threshold of 2. The hierarchical structure of taxonomic classifications was characterized using the median abundance and the non-parametric Wilcoxon Rank Sum test to show the taxonomic differences between the microbial communities and the abundance profiles of the experimental groups.