Diet is the strongest modulator of intestinal microbiota (Moore and Stanley 2016 ). In a mouse model, shifts in gut microbial populations were found to be driven by dietary changes including, but not limited to dietary fibre intake (Zhang et al . 2016 ) and high‐fat obesogenic diet (Lin et al . 2012 ). It is now accepted that a Western diet can turn microbiota into obesity promoting: faecal transplant of this microbiota to lean subjects will cause obesity in recipients regardless of the diet (Ridaura et al . 2013 ; Bradlow 2014 ). Additionally, a high level of dietary fructose has been implicated as one of the main causes of western obesity via high consumption of high‐fructose corn syrup through beverages (Bray et al . 2004 ). Western diets are also extremely low in fibre. Microbiota uses fibre to produce health‐promoting short chain fatty acids (SCFA), such as butyrate (Simpson and Campbell 2015 ). Recent studies have shown that high‐fibre diets can prevent development of asthma (Thorburn et al . 2015 ) and colitis (Macia et al . 2015 ) via interactions of fibre and microbiota to produce SCFA, while diets low in fibre have the opposite effect. Although links between physical activity, diet and gut microbiota have been recognized, previous studies have not compared the effect of different activity types/intensities and diets on gut microbiota which may be important in better understanding how changes to activity and diet can influence health via gut microbiota. We selected a variation of Western high‐fat, high‐fructose, low‐fibre and protein diet (HF) to evaluate the interaction of nutrition and activity on the composition of the gut microbiota. Due to the combination of high‐fat, high‐fructose and low‐fibre levels, the Western diet appears designed for ill health and obesity. Despite this conundrum, the Western diet has regrettably become a symbol of western society and wealth and the scope of its influence on health is yet to be fully comprehended. Based on the current knowledge surrounding exercise and microbiota, we hypothesized that different bacterial cohorts will drive microbiota shifts relative to the intensity of activity undertaken in standard chow (SC) and HF diets. We used a rat model to investigate the effects of different activity intensities and diets on intestinal microbiota. Our data suggest that HF diet negatively influences the benefits of exercise that may come from the reshaping of intestinal microbiota.

To date, there is evidence indicating that physical activity is a potential external agent that can change gut microbiota diversity (Bermon et al . 2015 ). This was first observed by Matsumoto et al . ( 2008 ) who reported alteration in the microbiota content in nonobese voluntary wheel‐exercised rats compared to control (sedentary) rats. Voluntary wheel running was found to alter gut microbiota by increasing the number of Bifidobacterium and Lactobacillus sp. in male rats (Queipo‐Ortuño et al . 2013 ) and decreasing the number of Erysipelotrichaceae in mice that had significant body weight reduction (Choi et al . 2013 ). Furthermore, moderate intensity treadmill training has been shown to alter the composition and the diversity of the gut bacteria at genus level in nonobese, obese and hypertensive rats (Petriz et al . 2014 ).

Emerging research has provided compelling evidence demonstrating the importance of gut microbiota for the promotion of healthy body weight and overall health (Duncan et al . 2008 ). Bacterial cells in the gut outnumber host cells and contribute 100 times more genetic potential when compared to host genetic contribution (Guarner and Malagelada 2003 ; Qin et al . 2010 ). Gastrointestinal tract (GIT) microbiota plays a significant role in weight management (Guarner and Malagelada 2003 ). ‘Germ‐free’ animals need 30% more calories to compensate for the lack of presence of bacteria capable of digesting otherwise indigestible food (Guarner and Malagelada 2003 ; Sears 2005 ). Moreover, faecal transplant from obese to lean mice induced obesity in the recipient mice with no change in their diet (Ley et al . 2006 ). In a more recent study, Ridaura et al . ( 2013 ) transplanted microbiota from lean and from obese individuals into germ‐free mice, and were able to repeat these phenotypes. GIT microbiota has also been reported to regulate fat storage (Backhed et al . 2004 ), with normal control mice shown to have 47 and 42% more gonadal fat and total fat respectively, compared to intestinal microbiota‐free mice (Backhed et al . 2004 ).

At the other end of the activity spectrum, sedentary behaviours are activities of low energy expenditure (<20% VO 2 max) characterized by prolonged sitting or reclining. This behaviour contributes to the development of obesity which in turn increases cardiovascular disease risk (Barnes 2012 ). Replacing sedentary behaviour with light‐intensity physical activity has been suggested as a useful way to integrate more physical activity into the day and gain health benefits associated with increased light‐intensity physical activity.

HIIT has been suggested to offer a range of cardio‐protective benefits and is therefore used and prescribed clinically (Shiraev and Barclay 2012 ) in treating obesity (Shiraev and Barclay 2012 ), metabolic syndrome (Shiraev and Barclay 2012 ; Weston et al . 2014 ) and diabetes (Higgins et al . 2014 ; Francois and Little 2015 ). The growing interest in HIIT as a training modality remains closely correlated with its ability to utilize lipid substrates, especially from stored abdominal fat deposits, which improves overall fat distribution and safeguards against obesity incidence (Burgomaster et al . 2008 ; Boutcher 2011 ; Keating et al . 2014 ). HIIT has also been shown to improve insulin sensitivity and glucose control (Tjonna et al . 2008 ) in patients with metabolic syndrome and attenuate oxidative stress responses in healthy populations (Bogdanis et al . 2013 ).

It is widely accepted that physical activity and exercise serve to improve health. The efficacy of physical activity and exercise are primarily influenced by the frequency, intensity and duration of the exercise (Pate et al . 1995 ). The intensity of an activity can be classified as sedentary, light, moderate, vigorous or high and public health guidelines for exercise activity have typically emphasized the importance of accumulating moderate to vigorous intensity activity across the week (Blair et al . 2004 ). With large proportions of the global population engaging in insufficient levels of exercise activity (Hallal et al . 2012 ) and in sedentary behaviours (Bauman et al . 2011 ), there is growing interest in the health benefits of different activity intensities to those that are typically promoted in public health guidelines. Two such approaches are high‐intensity activity performed across shorter bouts and low‐intensity activity completed across longer durations. Both approaches are intended to increase daily activity levels and are at different ends of the intensity spectrum. An emerging area of interest is the modification of traditional interval training to develop alternative forms of exercise referred to as high‐intensity interval training (HIIT) and light‐intensity training (LIT). HIIT is defined as an enhanced form of interval training comprised of brief high‐intensity exercise (>85% VO 2 max) separated by periods of low‐intensity movement (20–40% VO 2 max) (Trapp et al . 2008 ). In contrast, LIT is typically performed continuously at intensities equivalent to 20–40% VO 2 max. Both forms have been suggested to offer a range of metabolic benefits and improve cardiovascular health.

Statistical comparisons between the groups were done using QIIME‐based anova, alpha diversity significance was calculated using two‐sample nonparametric t ‐test and up to 10 5 Monte Carlo permutations. Beta diversity statistics was based on Adonis and up to 10 6 permutations. Other tools used in the data analysis included the EzTaxon database (Chun et al . 2007 ) and an R phylogenetic package, ade4 (Analysis of Data functions for Ecological and Environmental data in the framework of Euclidean Exploratory) (Chessel et al . 2004 ). The complete annotated sequence dataset is publically available on the MG‐RAST database under ID 4663683.3. Some data were visualized using Calypso (Zakrzewski et al . 2016 ).

Faecal samples were collected for microbiota analysis. DNA was extracted as previously described (Wu et al . 2011 ) with Bioline Isolate Faecal DNA kit, cat.no. BIO‐52082. Primers were selected to amplify the V3–V4 region of 16S rRNA genes: forward ACTCCTACGGGAGGCAGCAG and reverse GGACTACHVGGGTWTCTAAT, and also contained barcodes, spacer and Illumina sequencing linker sequences as detailed by Fadrosh et al . ( 2014 ). Sequencing was performed on the Illumina MiSeq platform using 2 × 300 bp paired end sequencing. A total of 54 samples were successfully sequenced (1 045 266 quality trimmed sequences with an average length of 481 nt). The number of samples analysed for each treatment was as follows: CTL.SC ( n = 6), CTL.HF ( n = 8), SED.SC ( n = 8), SED.HF ( n = 9), LIT.SC ( n = 3), LIT.HF ( n = 7), HIIT.SC ( n = 6) and HIIT.HF ( n = 7). Due to unexpected mortality and health issues of some rats, the LIT.SC group was reduced to three rats. However, in one‐way anova, as well as in multivariate analyses, inequality of sample sizes is not a particular problem as long as there is homogeneity of variance. To maintain the comparisons to the underrepresented LIT group, individual group to group comparisons were included. Analysis of microbial communities was completed using QIIME ver. 1.8.0 (Caporaso et al . 2010 ) and QIIME default parameters unless stated otherwise. Operational taxonomic units (OTUs) were clustered at 97% similarity using Uclust (Edgar 2010 ), Pintail (Ashelford et al . 2005 ) was used to inspect for chimeric sequences and taxonomic assignments were performed against the GreenGenes database (DeSantis et al . 2006 ). OTUs with relative abundance of <0·01% were removed.

Rats were fed ad libitum following their allocated diet (SC or HF). The composition and energy value of both animal diets are provided in Table S1 and Fig. S1. Food, water intake, systolic blood pressure and heart rate measurements were taken on all rats at intervention weeks 0, 4, 8 and 12 to estimate overall performance and cardiovascular health. At the end of the 12‐week intervention period, all animals were euthanized and faecal samples from the colorectal tissue were immediately collected and stored at −80°C for molecular analyses.

CTL rats housed in standard plastic cages (400 cm 2 /400 g rat), were not exercised but were allowed normal cage activity. SED rats were not exercised and were housed in small standard plastic cages with 1080 cm 2 floor area (240 cm 2 /400 g rat) to limit physical activity. Sharp et al . ( 2003 ) demonstrated that housing male rats with a similar floor area (230 cm 2 /400 g rat) does not produce crowding stress but initiates significant reductions in physical activity ( P < 0·05). LIT rats were housed in standard plastic cages (400 cm 2 /400 g rat) and were trained daily using a motor‐driven treadmill, 5 days a week. Activity was performed at 8 m min −1 speed, 0% incline for 125 min divided into four bouts (30–30–30–35 min) separated by 2 h of rest between bouts (~40–50% VO 2 max) (Lee et al . 2001 ). HIIT‐trained rats were housed in standard plastic cages (400 cm 2 /400 g rat) and trained by running 5 days a week using a motor‐driven treadmill, beginning with 10 min day −1 at a speed and grade of 10 m min −1 and 10% respectively. Across the study, activity intensity was progressively increased until the animals were performing four 2·5 min work bouts, at 50 m min −1 speed, 10% grade each separated by a 3 min rest period as previously designed (Matsunaga et al . 2007 ), a running speed that typically approaches maximal aerobic capacity in rats (>90% of VO 2 max) (Brooks and White 1978 ).

Rodents were randomly assigned to the following conditions: control (CTL), sedentary (SED), light‐intensity trained and high‐intensity interval trained. The rats were bred at the Central Queensland University (CQU) small animal facility. Male Wistar rats were grown on a SC diet until the beginning of the study when they weighed average 433 g. At 12 weeks of age, 57 rats were assigned to activity intervention which consisted of CTL, SED, LIT and HIIT groups. For each group, rats were allocated into SC (Riverina Stockfeeds; South Brisbane, QLD, Australia) and HF (Poudyal et al . 2010 ) diets. The main difference in diet composition was in fat (5% in SC and 24% in HF), protein (25% in SC and 6% in HF), fibre (6% in SC and 1% in HF) and fructose (25% in HF) composition (Fig. S1 and Table S1). Turnbaugh et al . ( 2009 ) showed that introducing a comparable Western diet (high in fat and sugar, low in fibre) and chow‐based plant diet rich in polysaccharides both altered microbiota within a day, independent of weight gain or loss, with changes stabilized across a week. Considering that microbiota responds to a wide range of external stimuli almost immediately within days (David et al . 2014 ), we proceeded with the activity intervention for the duration of 12 weeks allowing enough time for the effects of both diet and exercise to be clearly differentiated.

Compared to the SC diet, the HF diet prompted moderate changes in microbiota relative to activity intensity. There was no statistical difference in alpha diversity using chao1, observed species, Shannon, Simpson, dominance, richness, equitability or evenness indices between activity groups in the HF diet consistent with SC diet samples. There were significant differences introduced in microbiota by activity intervention based on Unweighted ( P = 0·0039) but not Weighted UniFrac ( P = 0·3018). This trend is opposite to the SC diet where greater differences were found in changed abundance rather than in presence/absence of phylotypes, indicating that different activity intervention in HF communities had different participation in terms of bacteria. Phylotypes responding to exercise type in the HF diet are represented in Fig. 4 . There were a number of species of Clostridium induced by exercise (HIIT and LIT) including members of family Clostridiaceae: C. celatum , C. disporicum and C. polysaccharolyticum . There were also Clostridium species with ambiguous taxonomy such as members of the lineage Firmicutes/Clostridia/Clostridiales/Lachnospiraceae: Clostridium aldenense , C. lavalense, C. aerotolerans and C. saccharolyticum . Exercise responsive OTUs shared high sequence similarity (Fig. 4 ) to multiple species listed above, demonstrating their close relationships in the V3–V4 region of 16S rRNA genes.

There were no differences in alpha diversity between activity groups in the SC diet; however, beta diversity difference between SC groups was very high in both weighted (Adonis, P < 10 −6 , 10 6 permutations) and unweighted ( P = 1·5 × 10 −4 ) UniFrac. There were more differences in OTU abundance rather than presence or absence of different OTUs between activity groups. The significant differences in abundance were evident at a phylum level (Fig. 3 ) with phyla Tenericutes, Actinobacteria, and Firmicutes ( P < 0·05) different between activity groups. However, the phyla were not equally affected by all activity groups. Tenericutes and Actinobacteria were induced in LIT only, with no significant modification in HIIT. Known families responsible for such a strong increase of the above phyla in LIT are the Tenericutes family Erysipelotrichaceae ( P = 2·07 × 10 −15 ) and the Actinobacteria families Bifidobacteriaceae ( P = 3·91 × 10 −8 ) and Coriobacteriaceae ( P = 8·84 × 10 −6 ). These families were almost exclusively present in the LIT group to abundances of up to 12% and absent or rare in other groups. Significantly affected OTUs are provided in Fig. S6. The closest isolates (with EzTaxon database % similarity to type strain) of significant OTUs that increased in LIT‐ and HIIT‐exercised rats include Parasutterella excrementihominis (100%) and Lactobacillus johnsonii (100%) increased in LIT, Clostridium saccharolyticum (96·29%) increased in HIIT, Clostridium geopurificans (94·31%) increased in HIIT and LIT.

PCA plot generated in the Ade4 R phylogenetic package. Duality diagram functions (dudi) was used to perform Between‐Class Analysis (bca) between the activity groups (A) and separately in all groups used in the study (B). Monte Carlo test based on 10permutations showed that all groups are significantly (Monte Carlo< 10) different owing mostly to diet induced sample separation. There is an overlap between exercised rats (A) microbiota. All communities from rats fed HF diet are overlapping (B) regardless of activity intensity, while SC diet allows differentiation of microbiota according to activity intervention. The graph shows sample and group relationship with respect to microbiota structure. [Colour figure can be viewed at wileyonlinelibrary.com

Observing the overall relationship between different activity groups using ade4 (Chessel et al . 2004 ) and between all (diet plus activity) groups analysis (Fig. 1 a,b), we identified overlap between HIIT and LIT rat microbial communities, as well as between SED and CTL, regardless of diet (Fig. 1 a). Microbial communities from rats fed a SC diet were highly differentiated based on activity intervention, with the HIIT.SC group rats having the highest similarity in microbiota between rats within the same group, but distinct to other activity groups. Other activity groups on a SC diet induced greater differences between rats within the group. The high separation of microbiota based on activity intervention observed in the SC diet was not manifested in rats fed a HF diet, where very similar communities were apparent regardless of activity intervention (Fig. 1 b). The foremost differences were driven by diet. Some bacterial families were more abundant in the SC diet rats and others in HF diet rats (Fig. 2 ). Diet‐induced differences were at a magnitude high enough to mask exercise‐induced changes. In order to better understand the changes from the activity intervention, we analysed the four activity groups separately in the SC and HF diets.

Both diet and activity intensity correlated with significant changes in microbiota between the eight groups. The differences in community richness were not significant at an OTU or a genus level. However, significant differences in richness index between diet treatments were observed at a family level (Fig. S2). Overall, significant differences between diet treatments (comparing all samples on SC vs HF) were evident at a phylum level (Canberra distance, P < 10 −6 at 10 6 permutations, Fig. S3), whereas there were no significant differences at the phylum level between activity groups ( P = 0·3852), thus samples were grouped by diet and not by activity intensity (Fig. S3). Several phyla were significantly responsive to diet (Fig. S4) and this was reflected in the downstream lineage to lower taxonomic levels (Fig. S5).

All rats appeared healthy throughout the experimental period. By the end of the third week, all the rats became fully accustomed to the intervention and were running steadily at the target speed. No adverse reaction to HIIT was documented in this trial. One rat (from LIT.SC group) died during the study and two rats (from LIT.SC group) were taken out of the study due to severe tooth defects. HF‐fed animals failed to significantly gain weight (Fig. S1a) compared to their matched SC groups. There was an initial weight loss (from week 2 to week 4 of intervention) on HF diet. CTL.HF and SED.HF groups started to gain weight before week 12 of intervention. No between‐group differences in weight were observed at week 0 or 12 of the intervention.

Discussion

The rats in this study fed HF diet did not develop obesity nor develop an overweight condition for the duration of the experiment. The initial weight loss with the high‐fat diet on body weight has been reported in several rat obesity studies (Picchi et al. 2011; van Waveren et al. 2014). The initial weight loss on HF diet is in line with previous rodent studies suggesting diets high in fat and carbohydrates may naturally predispose rats to reduce food intake in order to maintain a stable body weight or fat composition (Picchi et al. 2011; Tillman et al. 2014; Crescenzo et al. 2015). In contrast, others (Charlton et al. 2011; Panchal et al. 2011) have shown increased food intake following HF diet. These inconsistencies across studies might be due to many factors, including variations in experimental design, response to carbohydrates relative to rat strain, the palatability of the diet and the ratio of fructose to fat (Vasselli et al. 2013). It has been hypothesized that rodents on high‐fat and high‐carbohydrate diets attempt to regulate the rate of excess fat gain only during the initial stage of diet change (up to 8–16 weeks) by significantly reducing food intake. The unexercised rats on HF diet (CTL and SED) started to regain weight before the experiment ended; however, there was no statistically significant difference in weights between the rats at the beginning or end of the 12 week experimental procedure. Thus, it has to be noted that the present study, although investigating the effects of diets and exercise on intestinal microbiota, should not be considered as in any way related to obesity. Intestinal microbiota responds to environmental changes including diet and activity levels within days (David et al. 2014). This response is especially robust in regards to dietary influence, which induces changes almost immediately independent of weight gain or loss (Turnbaugh et al. 2009). Therefore, producing obesity was not a requirement to examine the effects of either diet or exercise.

Physical activity is critically important for the maintenance of body composition and overall health. In this regard, different body metabolome profiles have been reported in athletes and people who regularly exercise and these profiles correlate with changes in intestinal microbiota (Petriz et al. 2014). Further, studies comparing the effects of HIIT and LIT have found these activity intensities to be very different in physiological adaptation (Tucker et al. 2015). In the present study, we investigated, for the first time, the combined effect of different activity and diet treatments on intestinal microbiota using a rat model. In recent attempts to humanize rodent microbiota by transferring faecal material from human donors into mice and rats, rats were effectively colonized with and closely reflected the communities of the donors, while humanized gnotobiotic mice were not able to effectively transfer low abundance microbiota (reviewed in Nguyen et al. 2015). Therefore, it can be suggested that using rats for microbiota studies appropriately models microbiota interactions in humans.

In a recent study, Petriz et al. (2014) investigated exercise‐induced microbiota changes in obese and nonobese, hypertensive rats. These researchers reported significant differences in microbiota induced by activity, where trained rats had a notable increase in the genera Allobaculum (in hypertensive), Pseudomonas and Lactobacillus (in obese). Results from our study did not reproduce Allobaculum induction; however, Allobaculum is a bacterial genus under the Erysipelotrichaceae family that was significantly (Fig. 3) induced in LIT rats consuming the SC diet. The LIT level of activity is more consistent with the treadmill routine used by Petriz et al. (2014) than the HIIT protocol. Similar to Petriz et al. (2014), we noted a significant increase in LIT.SC rats of a phylotype that was phylogenetically closest to the type strain of L. johnsonii (Fig. S6). The microbiota response to activity intensity will vary from experiment to experiment depending on the basal starting microbiota present in the particular cohort of animals used. The expectation is that general trends may be discernible and reproducible between studies even if the exact details vary.

Evans et al. (2014) compared microbial communities of mice running voluntarily on a wheel against sedentary mice following intake of SC and HF diets. The voluntary running is comparable with the LIT protocol in the present study. Despite obvious differences in the experimental design, the study conducted by Evans et al. (2014) and our investigation identified the same families involved in response to a HF diet and activity. However, although the influence of activity is in agreement for exercise‐induced families (Lachnospiraceae in SC and HIIT, and Clostridiaceae in HF), as well as those reduced from activity (Turicibacteraceae in SC and bifidobacteria in HF), we reported an opposite trend in Erysipelotrichaceae family. In our data, Erysipelotrichaceae were significantly higher in HF and increased in the LIT.SC group while Evans et al. (2014) recorded the opposite. This may indicate that different species of Erysipelotrichaceae were involved between the two studies. Although diets were named similarly as high fat and low fat (SC), they were not comparable based on composition and this would strongly influence microbiota responses for each treatment, as would differences in starting microbiota composition.

Our study identified a number of bacteria that characterize microbiota shifts in response to training. There was a different cohort of phylotypes involved in LIT and HIIT. This was expected given LIT and HIIT induce different metabolic responses (Zouhal et al. 2008). The P. excrementihominis phylotype (100% identity across the amplicon) was strongly increased in LIT. This species was first described by Nagai et al. (2009) as strictly anaerobic, nonspore‐forming, nonmotile, Gram‐negative coccobacillus present as single cells or in pairs. It is an asaccharolytic species that does not respond to a range of sugars; however, it was found to have a very unique cellular fatty acid profile producing very high amounts of C18:1 omega 9c fatty acid (oleic acid) as well as multiple unknown fatty acids with equivalent chain lengths 15 and 17 (Nagai et al. 2009). Another phylotype that increased with LIT was identified as 100% identical to a known strain of L. johnsonii. Numerous Lactobacillus strains have been implicated as potential probiotic supplements for athletes due to a positive influence on performance (reviewed in Pyne et al. 2015); however, little is known on the specific involvement of L. johnsonii in athletic performance. HIIT rats had an increased abundance of an OTU related to C. saccharolyticum (96·29%), a phylotype that is likely to be able to degrade complex carbohydrates, including cellulose, and is also an acetic acid producer (Murray 1986). The SC diet used was rich in a range of carbohydrates that, when degraded, can increase gut acetate levels. The majority of microbiota‐produced acetate is readily transported to liver and metabolized, while remaining acetate supplies can be utilized by skeletal muscle as an energy source (Hijova and Chmelarova 2007). SCFA producers in the gut perform important roles in colonic health and can reduce the risk of multiple gastrointestinal disorders (Hijova and Chmelarova 2007). SCFA producers represent a major energy source in the intestine. The liver uses butyrate and propionate and butyrate is a major energy source for gut epithelium. Moreover, Matsumoto et al. (2008) reported increases in blood butyrate in rats engaging in physical activity. SCFA production would be especially depleted in HF diet due to low amounts of fibre, thus possible promotion of SCFA producing bacteria by HIIT could have beneficial effects via increased conversion of fibre to SCFA.

HF diet differential OTUs known to correlate with athletic performance were different to those selected in SC diets. However, detailed inspection of the species characteristics suggests the same mode of action as in SC, through increased abundance of polysaccharide degraders (C. polysaccharolyticum for multiple polysaccharides and C. aerotolerans (Van Gylswyk and Van Der Toorn 1987) and Roseburia intestinalis (Duncan et al. 2002) for cellulose and xylan) and SCFA producers (C. celatum (Hauschild and Holdeman 1974) for acetate, R. intestinalis for butyrate (Duncan et al. 2002; Mirande et al. 2010), C. aerotolerans for acetate and lactate (Van Gylswyk and Van Der Toorn 1987)). Roseburia intestinalis is also a known fibre degrader (Mirande et al. 2010). Strong induction of family Lachnospiraceae numbers is also not surprising as this butyrate‐producing family has multiple potentially beneficial effects and is diminished in gastrointestinal complications such as inflammatory bowel disease (Berry and Reinisch 2013), as well as chronic liver cirrhosis (Chen et al. 2011). Lachnospiraceae are recognized as beneficial plant degraders and strong SCFA producers (Biddle et al. 2013). Our results suggest that LIT and HIIT can induce microbial cohorts that break down carbohydrates more effectively for potentially improved performance through energy provision to working muscles as well as increased production of SCFAs for GI health. The HF diet however, induced similar but much weaker changes to SCFA‐producing microbiota which is most likely due to low fibre, demonstrating the superior influence of diet over activity on microbiota response.

The data we presented show numerous phylotypes differential between the SED and CTL groups. However, the level of difference is relatively low compared to differences observed between CTL or SED vs LIT or HIIT. With the exception of a single phylotype increased in SED, identified as 97·28% identical to C. saccharolyticum, they are in the range of P‐value significance between 0·05 and 0·01. This indicates that differences between nonexercised control and sedentary microbiota are by far superseded with influence of regular exercise.

In summary, the data presented demonstrate that microbiota can be remodelled by activity intensity, presumably to the benefit of the host. HF diet negatively influences the benefits of exercise that may come from the reshaping of intestinal microbiota. Exercise and diet impact on intestinal microbiota however, their relative contributions to health and disease are still unknown. The outcomes of this study extend knowledge of microbiota associated with exercise performance and provide insights regarding possible interventions, manipulations and commercial health‐based applications.