Exercise and Diet: Impact on Gut Microbial Diversity
Exercise and Diet: Impact on Gut Microbial Diversity
Male elite professional rugby players (n=40) were recruited for this study; the mean age of the athletes was 29 (±4) years and they had a mean BMI of 29.1 (±2.9). Healthy male controls were recruited from the Cork city and county region of Ireland; the mean age of controls was 29 (±6) years. Two groups of control were specifically recruited based on their physical size (BMI) relative to the athletes, with group 1 (n=23) having a BMI ≤25 and group 2 (n=23) having a BMI >28 (Table 1). All subjects except one (Indian ethnicity) were of Irish ethnicity and all subjects gave written informed consent prior to the beginning of the study. This study was approved by the Cork Clinical Research Ethics Committee. Exclusion criteria included having a BMI between 25 and 28, antibiotic treatment within the previous 2 months or suffering from any acute or chronic cardiovascular, GI or immunological condition.
Faecal and blood samples were collected from all participants. DNA was extracted from fresh stool samples which were stored on ice prior to use. Each participant was interviewed by a nutritionist and completed a detailed food frequency questionnaire (FFQ). Body composition analysis data from dual-energy X-ray absorptiometry scans were received from the Irish Rugby Football Union for all athletes, dual-energy X-ray absorptiometry scans for controls were performed in University Hospital Cork and waist:hip measurements were taken for athletes and controls.
Dietary data were collected by means of a FFQ which was administered by a research nutritionist. The FFQ was an adapted version of that used in the UK arm of the European Prospective Investigation into Cancer (EPIC) study which in turn, is based on the original Willett FFQ. To more comprehensively reflect the Irish diet, the 130-food item EPIC FFQ was extended to include an additional 57 food items. Participants were asked to recall dietary intakes over the previous 4 weeks. A photographic food atlas was used to pictorially quantify foods and beverages. Manufacturer's weights on packaging and household measures were also used to quantify foods. Intakes of nutritional supplements were recorded. Completed FFQs were coded and quantified by researchers and entered in the Weighed Intake Software Package (WISP, Tinuviel Software, Anglesey, UK), which uses McCance and Widdowson's The Composition of Foods, sixth edition plus all supplemental volumes to generate nutrient intake data. Data were subsequently imported into SPSS V.18 (SPSS, Chicago, USA) for analysis. Dietary data was visualised with correspondence analysis (R statistical package V.2.13.1). Fasting blood samples were collected and analysed at the Cork Mercy University Hospital clinical laboratories. Commercial multispot microplates (Meso Scale Diagnostics) were used to measure cytokines.
As the athletes were involved in a rigorous training camp we needed to assess the physical activity levels of both control groups. To determine this we used an adapted version of the EPIC-Norfolk questionnaire. T-Tests were carried out to compare high BMI and low BMI controls.
Stool samples were stored on ice until processed. DNA was purified from fresh stool samples using the QIAmp DNA Stool Mini Kit (Qiagen, Crawley, West Sussex, UK) according to manufacturer's instructions with addition of a bead-beating step (30 s×3) and stored at −20°C. The microbiota composition of the samples was established by amplicon sequencing of the 16S rRNA gene V4; universal 16S rRNA primers estimated to bind to 94.6% of all 16S rRNA genes (ie, the forward primer F1 (5'AYTGGGYDTAAAGNG) and a combination of four reverse primers R1 (5'TACCRGGGTHTCTAAAGNG), R2 (TACCAGAGTATCTAATTC), R3 (5'CTACDSRGGTMTCTAATC) and R4 (5'TACNVGGGTATCTAATC) (Ribosomal Database Projects Pyrosequencing Pipeline: http://pyro.cme.msu.edu/pyro/help.jsp) were employed for PCR amplification. Molecular identifier tags were attached between the 454 adaptor sequence and the target-specific primer sequence, allowing for identification of individual sequences from the pooled amplicons. Ampure purification system (Beckman Coulter, Takeley, UK) was used to clean the amplicons before being sequenced on a 454 Genome Sequencer FLX platform (Roche Diagnostics, Burgess Hill, West Sussex, UK) in line with 454 protocols at the Teagasc high throughput sequencing centre. DNA sequence reads from this study are available from the Sequence Read Archive (accession number PRJEB4609).
The Stoney supercomputer at the Irish Centre for High End Computing was used for the following analysis. Raw sequences were quality trimmed using the Qiime Suite of programmes, any reads not meeting the quality criteria of a minimum quality score of 25 and sequence length shorter than 150 bps for 16S amplicon reads. The SILVA 16S rRNA (V.106) database was employed to BLAST the trimmed fasta sequence files using default parameters. Parsing of the resulting BLAST output files was achieved through MEtaGenome ANalyzer which uses a lowest common ancestor algorithm to assign reads to the National Center for Biotechnology Information taxonomies. Filtering was carried out within MEtaGenome ANalyzer using bit scores prior to tree construction and summarisation, similar to previous studies a bit-score cut-off of 86 was selected. Clustering of sequence reads into operational taxonomical units at 97% identity level was achieved using Qiime. The ChimeraSlayer program was used to remove chimaeras from aligned operational taxonomical units and the FastTreeMP tool generated a phylogenetic tree. α Diversity indices and rarefaction curves were generated using Qiime. β Diversities were also calculated on the sequence reads based on weighted and unweighted Unifrac and Bray-Curtis distance matrices; subsequently principal coordinate analysis (PCoA) and unweighted pair group method with arithmetic mean clustering was performed on the samples. KiNG viewer and Dendroscope software were used to visualise PCoA plots and unweighted pair group method with arithmetic mean clustering, respectively. Enterotype clustering was carried out according to the approach previously described.
Statistical analysis was carried out using GraphPad Prism V.5.04 (La Jolla, California, USA) R statistical package (V.2.13.1) and SPSS software package V.18 (SPSS, Chicago, USA). Kruskal-Wallis and Mann-Whitney tests were used to find significant differences in microbial taxa, α diversity, and clinical and biochemical measures. Adjustment for multiple testing was estimated using the false discovery rate functions (phylum and family level) in the R statistical package (V.2.13.1) using the Benjamini and Hochberg method.
Materials and Methods
Subjects Characteristics
Male elite professional rugby players (n=40) were recruited for this study; the mean age of the athletes was 29 (±4) years and they had a mean BMI of 29.1 (±2.9). Healthy male controls were recruited from the Cork city and county region of Ireland; the mean age of controls was 29 (±6) years. Two groups of control were specifically recruited based on their physical size (BMI) relative to the athletes, with group 1 (n=23) having a BMI ≤25 and group 2 (n=23) having a BMI >28 (Table 1). All subjects except one (Indian ethnicity) were of Irish ethnicity and all subjects gave written informed consent prior to the beginning of the study. This study was approved by the Cork Clinical Research Ethics Committee. Exclusion criteria included having a BMI between 25 and 28, antibiotic treatment within the previous 2 months or suffering from any acute or chronic cardiovascular, GI or immunological condition.
Experimental Design
Faecal and blood samples were collected from all participants. DNA was extracted from fresh stool samples which were stored on ice prior to use. Each participant was interviewed by a nutritionist and completed a detailed food frequency questionnaire (FFQ). Body composition analysis data from dual-energy X-ray absorptiometry scans were received from the Irish Rugby Football Union for all athletes, dual-energy X-ray absorptiometry scans for controls were performed in University Hospital Cork and waist:hip measurements were taken for athletes and controls.
Nutritional and Clinical Data Collection
Dietary data were collected by means of a FFQ which was administered by a research nutritionist. The FFQ was an adapted version of that used in the UK arm of the European Prospective Investigation into Cancer (EPIC) study which in turn, is based on the original Willett FFQ. To more comprehensively reflect the Irish diet, the 130-food item EPIC FFQ was extended to include an additional 57 food items. Participants were asked to recall dietary intakes over the previous 4 weeks. A photographic food atlas was used to pictorially quantify foods and beverages. Manufacturer's weights on packaging and household measures were also used to quantify foods. Intakes of nutritional supplements were recorded. Completed FFQs were coded and quantified by researchers and entered in the Weighed Intake Software Package (WISP, Tinuviel Software, Anglesey, UK), which uses McCance and Widdowson's The Composition of Foods, sixth edition plus all supplemental volumes to generate nutrient intake data. Data were subsequently imported into SPSS V.18 (SPSS, Chicago, USA) for analysis. Dietary data was visualised with correspondence analysis (R statistical package V.2.13.1). Fasting blood samples were collected and analysed at the Cork Mercy University Hospital clinical laboratories. Commercial multispot microplates (Meso Scale Diagnostics) were used to measure cytokines.
Control Physical Activity Levels
As the athletes were involved in a rigorous training camp we needed to assess the physical activity levels of both control groups. To determine this we used an adapted version of the EPIC-Norfolk questionnaire. T-Tests were carried out to compare high BMI and low BMI controls.
DNA Extraction and High-throughput Amplicon Sequencing
Stool samples were stored on ice until processed. DNA was purified from fresh stool samples using the QIAmp DNA Stool Mini Kit (Qiagen, Crawley, West Sussex, UK) according to manufacturer's instructions with addition of a bead-beating step (30 s×3) and stored at −20°C. The microbiota composition of the samples was established by amplicon sequencing of the 16S rRNA gene V4; universal 16S rRNA primers estimated to bind to 94.6% of all 16S rRNA genes (ie, the forward primer F1 (5'AYTGGGYDTAAAGNG) and a combination of four reverse primers R1 (5'TACCRGGGTHTCTAAAGNG), R2 (TACCAGAGTATCTAATTC), R3 (5'CTACDSRGGTMTCTAATC) and R4 (5'TACNVGGGTATCTAATC) (Ribosomal Database Projects Pyrosequencing Pipeline: http://pyro.cme.msu.edu/pyro/help.jsp) were employed for PCR amplification. Molecular identifier tags were attached between the 454 adaptor sequence and the target-specific primer sequence, allowing for identification of individual sequences from the pooled amplicons. Ampure purification system (Beckman Coulter, Takeley, UK) was used to clean the amplicons before being sequenced on a 454 Genome Sequencer FLX platform (Roche Diagnostics, Burgess Hill, West Sussex, UK) in line with 454 protocols at the Teagasc high throughput sequencing centre. DNA sequence reads from this study are available from the Sequence Read Archive (accession number PRJEB4609).
Bioinformatic Analysis
The Stoney supercomputer at the Irish Centre for High End Computing was used for the following analysis. Raw sequences were quality trimmed using the Qiime Suite of programmes, any reads not meeting the quality criteria of a minimum quality score of 25 and sequence length shorter than 150 bps for 16S amplicon reads. The SILVA 16S rRNA (V.106) database was employed to BLAST the trimmed fasta sequence files using default parameters. Parsing of the resulting BLAST output files was achieved through MEtaGenome ANalyzer which uses a lowest common ancestor algorithm to assign reads to the National Center for Biotechnology Information taxonomies. Filtering was carried out within MEtaGenome ANalyzer using bit scores prior to tree construction and summarisation, similar to previous studies a bit-score cut-off of 86 was selected. Clustering of sequence reads into operational taxonomical units at 97% identity level was achieved using Qiime. The ChimeraSlayer program was used to remove chimaeras from aligned operational taxonomical units and the FastTreeMP tool generated a phylogenetic tree. α Diversity indices and rarefaction curves were generated using Qiime. β Diversities were also calculated on the sequence reads based on weighted and unweighted Unifrac and Bray-Curtis distance matrices; subsequently principal coordinate analysis (PCoA) and unweighted pair group method with arithmetic mean clustering was performed on the samples. KiNG viewer and Dendroscope software were used to visualise PCoA plots and unweighted pair group method with arithmetic mean clustering, respectively. Enterotype clustering was carried out according to the approach previously described.
Statistical Methods
Statistical analysis was carried out using GraphPad Prism V.5.04 (La Jolla, California, USA) R statistical package (V.2.13.1) and SPSS software package V.18 (SPSS, Chicago, USA). Kruskal-Wallis and Mann-Whitney tests were used to find significant differences in microbial taxa, α diversity, and clinical and biochemical measures. Adjustment for multiple testing was estimated using the false discovery rate functions (phylum and family level) in the R statistical package (V.2.13.1) using the Benjamini and Hochberg method.
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