The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: An open-label pilot study

Mina Fukuchi; Masaaki Sugita; Makoto Banjo; Keisuke Yonekura; Yasuhiro Sasuga

doi:10.1371/journal.pone.0262906

. 2022 Jan 27;17(1):e0262906. doi: 10.1371/journal.pone.0262906

The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: An open-label pilot study

Mina Fukuchi ¹, Masaaki Sugita ^2,^¤a, Makoto Banjo ^2,^¤b, Keisuke Yonekura ³, Yasuhiro Sasuga ^1,^*

Editor: Krzysztof Durkalec-Michalski⁴

PMCID: PMC8794134 PMID: 35085328

Abstract

Diet and exercise can alter the gut microbiota, but recent studies have assessed the impact of athletic competition on gut microbiota and host metabolites. We designed an open-label pilot study to investigate the effects of both official competition and a multi-strain lactic acid bacteria-fermented soymilk extract (LEX) on the gut microbiota in Japanese college endurance athletes. The analysis of fecal 16S rRNA metagenome and urinary metabolites was used to identify changes in gut microbiota composition and host metabolism. When the fecal microbiota were investigated before and after a race without using of a supplement (pre-observation period), there was an increase in the phylum Firmicutes and decrease in Bacteroidetes. However, no changes in these phyla were seen before and after a race in those who consumed LEX. Before and after LEX ingestion, changes in urinary metabolites included a significant reduction in yeast and fungal markers, neurotransmitters, and mitochondrial metabolites including the TCA cycle. There were several correlations between urinary metabolites and the composition of fecal microbiota. For example, the level of tricarballylic acid was positively correlated with the composition ratio of phylum Firmicutes (Pearson’s r = 0.66; p < 0.01). The bacterial species Parabacteroides distasonis was also found to correlate moderately with several urinary metabolites. These findings suggest two possibilities. First, endurance athletes experience significant fluctuations in gut microbiota after a single competition. Second, LEX ingestion may improve yeast and fungal overgrowth in the gastrointestinal tract and enhancing mitochondrial metabolic function.

Introduction

The gut microbiota is essential for health, playing a role in nutrient uptake, vitamin synthesis, energy harvest, inflammatory modulation, and host immune response [1, 2]. The use of next-generation sequencing techniques has greatly expanded our knowledge of microbiota composition and its relationship to diseases [3]. Numerous intrinsic and extrinsic factors can affect the gut microbiota, resulting in a highly dynamic and complex gut environment. The diet is the main modifying factor affecting the composition of human microbiota, and dietary components act as substrates for microbial metabolism, influencing both microbiome composition and function [4]. Additionally, recent studies suggest the ability of physical exercise to induce changes in the gut microbiota, which may also affect exercise performance. For instance, when compared with sedentary control groups, athletes had relative increases in pathways (e.g., amino acid, antibiotic biosynthesis, carbohydrate metabolism) and fecal metabolites (e.g., short-chain fatty acids) associated with enhanced muscle turnover [5].

Several studies showed that elite athletes and those who train frequently have higher bacterial species richness (α-diversity) than those with a sedentary lifestyle or low fitness level [6–8]. An increase in the symbiotic species Akkermansia muciniphila and Faecalibacterium prausnitzii was observed in athletes and highly active individuals [6, 9].

Excessive exercise stresses the gastrointestinal (GI) tract and increases the likelihood of multiple symptoms associated with the disruption of the gut microbiota and decreased performance [10]. Supplements aimed at improving the intestinal environment, including probiotics, have traditionally focused on the health status of athletes in terms of reducing exercise-induced stress, improving the host immunity, reducing the symptoms of GI and upper respiratory tract infections [11]. In addition to probiotic products, various other supplements such as fermented bacterial products are expected to improve GI tract symptoms. The extract of multi-strain lactic acid bacteria (LAB)-fermented soy milk (LEX) is one of them, and the effect of improving the metabolites derived from gut microbiota has been reported [12]. Another study demonstrated that LEX ingestion can prevent colon cancer and activate intestinal immunity [13, 14].

Understanding whether gut microbiota and its environment play a vital role in athletic performance and daily conditioning is particularly interesting to athletes. Additionally, such knowledge can bring benefits to human health. Further studies are needed to understand the daily changes in gut microbiota and host metabolites or beneficial food effects on athletes’ day-to-day health.

Endurance exercises are activities, which are performed during longer time intervals and used aerobic metabolism. They can be defined as long-term cardiovascular exercise and include activities such as running, cross-country skiing, cycling, aerobic exercise, or swimming. Physiological adaptations to endurance exercise include correcting electrolyte imbalances, increases in systemic inflammatory responses, and decreases in glycogen storage, oxidative stress, intestinal permeability, and muscle damage [15]. Endurance athletes are more likely to have the upper respiratory tract infections and GI disorders, including increased permeability of the gastrointestinal epithelial wall, known as “leaky gut,” disruption of mucus thickness, and increased bacterial migration [16].

We conducted a pilot study modeled on long-distance runners to investigate changes in gut microbiota during excessive exercise and competition. Furthermore, we investigated the effects of LEX ingestion on the gut microbiota and urinary metabolites as an example of a supplement for affecting GI environment. Here, we use fecal microbe metagenome sequencing and urinary biological metabolome analysis to characterize changes before and after race and LEX ingestion on long-distance runners.

Materials and methods

Study design and participants

This was an open-label study to evaluate the impact of both official sports competition and the efficacy of LEX on endurance athletes. This study enrolled Japanese college long-distance runners aged 19 to 21 years old and a continuous period was selected so that there was one official race for each observation period. A total of 13 participants, nine males and four females, were recruited. Those who were allergic to soybeans, a raw material of the test article, were excluded. The study consisted of two 4-week periods, where each four weeks, the participants competed in one race. The first four-week pre-observation period (before LEX ingestion) measured changes in the gut microbiota due to athletic performance alone. The second four-week period examined the effect of daily LEX ingestion on gut microbiota.

Furthermore, to investigate the effect of LEX ingestion on host metabolites, we observed changes in urinary metabolites before and after the LEX ingestion. The study was conducted according to the Declaration of Helsinki. Informed consent was obtained from each participant and ethical approval was obtained from the Ethics Committee of Faculty of Education, Mie University (registration number: 2016–4, Mie, Japan).

Test supplement

The test supplement was a commercially available LAB dietary supplement (brand name LACTIS) that was derived from a multi-strain LAB-fermented soymilk (LEX) extract consisting of sixteen LAB strains (Lacticaseibacillus paracasei R0101, R0301, R0401, R0601, R0701, R0901, R1001, R1402, R1502, R1602, Lactiplantibacillus plantarum R0502, R0801, R1101, Y1201, Levilactobacillus brevis R0201, R1305). LAB was cultivated in soymilk on the order of 10¹² bacteria/g, and then extracted with ethanol, and clear extract was added to the product, as equivalent ca. 10¹⁰ bacteria/mL with lactic acid and citric acid. The test article was a 10 mL volume liquid, and participants ingest it twice in the morning and evening before meals during the LEX ingestion period. The test article was obtained from B&S Corporation Co., Ltd. (Tokyo, Japan).

Procedure

Participants were recruited from university track and field clubs, and 13 long-distance runners were enrolled from October 20 to December 16, 2016. The participants were provided with the test article, four feces collection kits, and two urine collection kits and were instructed to ingest the test supplement twice (10 mL/once) daily for four weeks after a four-week pre-observation period.

An official race was held twice during the entire observation period, and the schedule was set so that a race would occur in the middle of both the pre-observation period and the LEX-ingestion period. The official races were the second Long-distance challenge race (2016. 11. 12, Aichi, Japan, Organizer: Aichi Association of Athletics) and the 78th The Inter-University Athletic Unions of Tokai Ekiden Road Relay Championship (2016. 12. 4, Aichi, Japan, Organizer: The Inter-University Athletic Union of Tokai), which were held during the pre-observation period and the LEX ingestion period, respectively. The primary outcome was to measure the composition of fecal microbiota and urinary metabolites. Fig 1A shows the timing of races and sample collection during the test period. The composition of fecal microbiota was measured four times: before a race and without supplementation in a pre-observation period (PRE_b), at the end of the pre-observation period (post-race) with no supplementation (PRE_a), before a race in the LEX-ingestion period (POST_b), and at the end of the LEX-ingestion (post-race) period (POST_a). Additionally, urinary metabolites were measured twice at PRE_a and POST_a to evaluate the efficacy of LEX ingestion. During the observation period, participants wrote a diary about their physical condition during practice, total training time, mileage, condition of breakdown, and exercise intensity to evaluate exercise specificity. The exercise intensity was entered in 11 levels from 0 to 10 (0: practice break, 1: low, 10: high), and the same evaluation was given to the exercise load of the competition when participating in the races.

Fig 1 — (A) Race schedule and sampling timing of feces and urine. (B) Participant-tracking flowchart.

Bacterial DNA isolation and 16S rRNA sequencing

Fecal samples were self-collected in polyethylene sample collection containers, placed in a freezer immediately, transport to lab while frozen and stored at -80℃ for further analysis. Total genome DNA from fecal samples was extracted using a commercial DNA extraction kit (ISOFECAL for Beads Beating, NIPPON GENE CO., LTD., Tokyo, Japan) according to the manufacturer’s instructions and stored at −30℃. The V3–V4 region of the bacterial 16S rRNA gene was amplified by PCR using universal primers (forward: 5′- ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNCCTACGGGNGGCWGCAG-3′; reverse: 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNGACTACHVGGGTATCTAATCC-3′). PCR reactions were performed in 20 μL reactions with ExTaq HS polymerase (TaKaRa BIO INC., Shiga, Japan), 0.5 μM forward and reverse primers, and 1-ng template DNA. Thermal cycling consisted of the initial denaturation at 94℃ for 2 min, followed by 20 cycles of denaturation at 94℃ for 30 s, annealing at 55℃ for 30 s, elongation at 72℃ for 30 s, and ending with samples at 72℃ for 5 min. Samples were indexed in the second PCR using index sequence inserted primers and sequenced on an Illumina MiSeq sequencer, using MiSeq Reagent Kit v3 (Illumina, CA, United States) with paired-end 300-base-pair reads. High-throughput sequencing was performed at Bioengineering Lab. Co., Ltd. (Kanagawa, Japan). Sequences were screened for chimeras using the uchime algorithm (USEARCH V8.1.1861), and putative chimeras were removed from the data set. Sequence data were processed using the Quantitative Insight into Microbial Ecology pipeline (QIIME V1.9.1). Operational taxonomic units were defined on the basis of 97% similarity clustering using QIIME with default parameters. The bacterial taxonomy assignment was performed using the Greengenes V13_8 data base. β-Diversities were also calculated on the sequence reads based on weighted and unweighted UniFrac distance matrices; subsequently, principal coordinate analysis (PCoA) was performed on the samples.

Analysis of urinary metabolites

Midstream urine from the first-morning void was collected in a sterile screw-cap container. The urine samples were placed in a freezer immediately to avoid bacterial growth and transported after freezing for three hours or more. The urine samples were assayed by the Great Plains Laboratory, Inc. (Lenexa, KS, United States). The urinary metabolites were quantified as to their trimethylsilyl (TMS) ethers or esters, and GC-MS was performed as described in previous research [17]. Due to limitations in available data, only concentrations of 74 metabolites were reported from the spectrum analysis. All concentrations of metabolites were corrected by urinary creatinine (Cr) concentration to minimize variability of urine concentration.

Statistical analysis

There were some cases of data loss or non-participation in the race, so each analysis used the maximum number of obtained data (at least eight participants). The number of samples used in each analysis is shown in Fig 1B. The differences in bacterial taxa characterizing the groups were evaluated by linear discriminant analysis (LDA) Effect Size (LEfSe) method with default setting on website https://huttenhower.sph.harvard.edu/galaxy/root [18]. A diagnostic check (Shapiro-Wilk test of normality) was performed prior to analysis. When normality and equal variance between sample groups were achieved, repeated measures ANOVA followed by a Bonferroni’s correction or paired t-test was performed to find significant differences. For non-normally distribution data, Friedman’s test with a Scheffé’s multiple comparison method was performed. The difference in variance was determined using Fisher’s F-test for α-diversity of fecal microbiota. Pearson’s correlation coefficients were used to analyse associations between microbial composition and urinary metabolites. Multiple comparisons for urinary metabolites and the associations of microbial composition and urinary metabolites were adjusted using the false discovery rate (FDR) method [19]. For the difference in rate of perceived exertion as exercise load between both periods, Wilcoxon signed-rank test was conducted. Differences were considered significant at p < 0.05. The results of bacterial taxa were visualized as box-and-whisker plots showing: the median and the interquartile (midspread) range (boxes containing 50% of all values). Urinary metabolites were described as a mean and standard deviation. Statistical analysis was conducted using XLSTAT v19.4 software (Addinsoft, Paris, France) and BellCurve for Excel v.3.21 (Addinsoft, Social Survey Research Information Co., Ltd., Tokyo, Japan).

Results

Study participants

A total of 13 Japanese college long-distance runners (nine male, four female) were enrolled, with a 4-week pre-observation period followed by a 4-week LEX-ingestion period. The baseline characteristics of the participants was as follows [shown by mean (± SD)]; height: male 173.6 (±5.1), female 158.7 (±4.9), body fat percentage: male 9.9 (±2.0), female 22.9 (±3.7), BMI: male 19.2 (±0.9), female 21.3 (±1.2). All participants ate three meals a day and had a balanced diet of common meat, fish, and vegetables, and did not intervene in their diet during the study period. Additionally, eight of 13 participants (61.5%) were taking yogurt, but during the test period, the intake was the same as usual. Two official races were held once during each period. Fig 1B shows the participant-tracking flowchart. Nine (five males and four females) of the 13 participants participated in two races. Two urine samples (two female) were not received before the start of LEX ingestion due to physical conditions not suitable for urinalysis. Of the 52 samples of fecal DNA collected, three male samples were defective, and no sequence results were obtained. As a result, the analyses of fecal microbiota (samples from five males and three females) and urinary metabolites (samples from six males and two females) were performed for those who participated in the two races. To analyse associations between fecal microbiota composition and urinary metabolites, data for nine participants was incorporated regardless of participation in two races.

S1 Fig shows self-reported total training time and mileage for each four-week study interval, as well as the rate of perceived exertion (RPE) for each of the two races. Athletes reported that both the training intensity and total training time were higher (approximately 25% and 17%, respectively) during the pre-observation period than during the LEX-ingestion period.

The total distance of the first race during the pre-observation period was 5,000 m for men and 3,000 m for women, and the total distance for the second race during the LEX-ingestion period was 5.4 to 12.3 km for men and 3.7 to 8.1 km for women. Thus, the RPE reported by athletes was significantly (p = 0.048 for the analysis of fecal microbiota, p = 0.038 for the analysis of urinary metabolites) higher in the second race (during the LEX-ingestion period).

Changes in the gut microbiota composition during the test period

A total 1,115,654 (1.1 million) 16S rRNA reads were generated from fecal samples provided by eight runners, with mean of 39,095 (±7,899 SD), 30,933 (±11,006), 36,016 (±12,926), and 33,414 (±11,309) reads for PRE_b (pre-observation, before race), PRE_a (pre-observation, after race), POST_b (LEX ingestion, before race), and POST_a (LEX ingestion, after race), respectively. Reads corresponding to 12 phyla, 61 families, and 90 genera were detected in eight runners. PCoA based on weighted UniFrac distances of 16S rRNA sequences highlighted a clear differentiation of the microbial populations before and after a single race (Fig 2A). However, during the LEX-ingestion period, the changes observed in microbial populations due to race participation were small, and rather the POST_a state tended to return to the PRE_b state.

Fig 2 — (A) Principal coordinate analysis (PCoA) plots (weighted UniFrac analysis). (B) Linear discriminant analysis (LDA) effect size (LEfSe) analysis plot in fecal microbiota between four groups (PRE_b, PRE_a, POST_b, and POST_a). LDA scores (log10) > 2 and p < 0.05 are listed. (C) Histogram of the relative distribution of phyla *Bacteroidetes* and *Firmicutes*. PRE: pre-observation period. POST: LEX-ingestion period. b: before the race. a: after the race. For all box-and-whisker plots, center lines represent the median and the box edges represent the first and the third quartiles with mean (Red +). Statistical significance was determined using repeated measures ANOVA with Bonferroni’s correction for PCoA plot. * and *** indicate significant differences between p < 0.05 and p < 0.001, respectively.

To discover significant alteration of bacterial taxa abundance in response to the race and the LEX-ingestion, we conducted LEfSe analysis. The LDA score showed higher associations of the phylum Bacteroidetes (including class Bacteroidia) and the phylum Firmicutes (including class Clostridia) with before and after the race during pre-observation period (Fig 2B). The phylum Bacteroidetes composition decreased and the phylum Firmicutes composition increased significantly after the race (Fig 2C). The Firmicutes to Bacteroidetes (F / B) ratio was significantly (p = 0.038) higher after the race and these trends continued even after 14 days with the LEX-ingestion period (POST_b) (S2 Fig). At the end of LEX-ingestion (POST_a), despite the increased RPE (increased effort required) reported by athletes for the race in this period, no differentially abundant features was found in the LEfSe analysis between PRE_b and POST_a. In addition, the F / B ratio at POST_a also tended to mirror those obtained during the pre-observation period (PRE-b).

Further analysis of the α-diversity of the fecal samples based on the phylum level revealed no significant differences in Chao1 and Shannon’s index. However, the variance of Shannon’s index before and after the race during pre-observation period was significantly different (F-test: p = 0.001) (S3 Fig). Thus, the Chao1 index represents the estimated richness of a community structure, while the Shannon’s index represents both the richness and evenness of it is species diversity, indicating that the evenness of a microbial community structure is likely to fluctuate before and after the race.

Changes in urinary metabolites due to ingestion of LEX

Seventy-three urinary metabolites were analyzed for changes in both GI microbiota and metabolism due to LEX ingestion. These compounds included fungal and yeast metabolites, bacterial metabolites, TCA cycle metabolites, neurotransmitter metabolites, and other metabolites. Patterns of these urinary metabolites from individual samples were subjected to principal component analysis (PCA), as shown in Fig 3. Conspicuously, urinary metabolites displayed remarkable differences before and after LEX ingestion in participants who could collect urine samples and participated in two races. The differences were also clear in the cases of focusing on 18 compounds of bacterial markers and 46 compounds of metabolism markers. Additionally, it was observed that the PCA score of the bacterial markers was aggregated to a particular score after ingestion of LEX compared with the metabolic marker.

Fig 3 — (Left panel) Total: 73 compounds. (Center panel) Bacterial marker: 18 compounds. (Right panel) Metabolism marker: 46 compounds. PRE_a: before LEX-ingestion (blue closed circle), POST_a: after LEX ingestion (red closed circle).

Table 1 summarizes the urinary metabolites that were significantly different before and after LEX ingestion for four weeks. There are significant changes before and after LEX ingestion in 27 of the 73 compounds. For example, arabinose is one of the yeast markers, and the urinary arabinose level before LEX ingestion was 48.25 ± 17.17 (SD) mmol/mol Cr, which was higher than the reference range [male (≥age 13): ≤20 mmol/mol Cr, female (≥age 13): ≤29 mmol/mol Cr]. However, the mean arabinose in urine after LEX ingestion decreased significantly (p = 0.016) to 17.00 ± 5.21 mmol/mol Cr, within the reference range. Other yeast and fungal markers, such as 3-oxoglutaric acid, tartaric acid, and tricarballylic acid, also had significantly reduced levels after LEX ingestion. Additionally, 3-methylglutaric acid and 3-hydroxyglutaric acid, two markers for amino acid metabolism in mitochondria, were also elevated before LEX ingestion compared to reported reference levels [3-methylglutaric acid: male (≥age 13): 0.02–0.38 mmol/mol Cr, female (≥age 13): ≤0.76 mmol/mol Cr, 3-hydroxyglutaric acid: male (≥age 13): ≤4.6 mmol/mol Cr, female (≥age 13): ≤6.2 mmol/mol Cr], but significant decreases were observed after LEX ingestion (3-methylglutaric acid: p = 0.023, 3-hydroxyglutaric acid: p = 0.029). Furthermore, many significant reductions were observed after LEX ingestion in the TCA cycle metabolites (2-oxoglutaric acid: p = 0.005, aconitic acid: p = 0.005) and neurotransmitter metabolites (homovanillic acid: p = 0.006, vanillylmandelic acid: p = 0.006, quinolinic acid: p = 0.0003, kynurenic acid: p = 0.0004).

Table 1. Changes in urinary metabolites before and after LEX ingestion; showing only those with significant differences.

Category	Component	Mean (Standard deviation)		p-value after FDR adjustment
		mmol/mol Creatinine
		PRE	POST
Yeast and Fungi	3-Oxoglutaric acid	0.06 (0.05)	0.00 (0.01)	0.029
	Tartaric acid	0.32 (0.18)	0.07 (0.10)	0.049
	Arabinose	48.25 (17.17)	17.00 (5.21)	0.016
	Tricarballylic acid	0.11 (0.08)	0.02 (0.05)	0.024
Bacteria	2-Hydroxyphenylacetic acid	0.33 (0.12)	0.23 (0.18)	0.037
Bacteria	4-Hydroxyhippuric acid	6.79 (3.31)	1.94 (1.15)	0.044
Clostridia	4-Cresol	8.75 (7.37)	2.76 (3.22)	0.027
TCA Cycle	2-Oxoglutaric acid	9.30 (5.03)	5.68 (4.81)	0.005
TCA Cycle	Aconitic acid	9.78 (1.35)	3.15 (2.10)	0.005
Mitochondrial Amino Acid	3-Methylglutaric acid	0.59 (0.31)	0.21 (0.15)	0.023
	3-Hydroxyglutaric acid	7.59 (4.65)	2.09 (1.62)	0.029
	3-Methylglutaconic acid	1.15 (0.38)	0.73 (0.42)	0.011
Neurotransmitter	Homovanillic acid	1.95 (0.43)	0.81 (0.28)	0.006
	Vanillylmandelic acid	1.41 (0.17)	0.75 (0.36)	0.006
	Quinolinic acid	1.58 (0.33)	0.58 (0.28)	<0.001
	Kynurenic acid	1.18 (0.25)	0.39 (0.25)	<0.001
Pyrimidine	Thymine	0.18 (0.05)	0.09 (0.07)	0.049
Ketone and Fatty Acid Oxidation	Methylsuccinic acid	1.13 (0.28)	0.50(0.23)	0.004
Vitamin	Pantothenic acid	1.51 (0.53)	0.49 (0.32)	0.012
	3-Hydroxy-3-methylglutaric acid	11.13 (2.54)	4.91 (2.30)	0.011
	Methylcitric acid	0.57 (0.18)	0.21 (0.10)	0.011
Detoxification	Pyroglutamic acid	21.25 (3.01)	13.14 (4.07)	0.011
	Orotic acid	0.31 (0.16)	0.14 (0.12)	0.024
	2-Hydroxyhippuric acid	0.78 (0.68)	0.21 (0.31)	0.044
Amino Acid	Phenylpyruvic acid	0.85 (0.25)	0.50 (0.43)	0.037
Amino Acid	4-Hydroxyphenyllactic acid	0.22 (0.08)	0.06 (0.03)	0.011
Mineral	Phosphoric acid	3.58 (0.84) *1	2.67 (1.01) *1	0.039

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*1: mol/mol Creatinine

Association between gut microbiota and urinary metabolites

Subsequently, we explored the relationship between the proportion of fecal microbiota and urinary metabolite concentration. The results showed that there were several moderate correlations between microbial proportions and concentration of metabolites. Fig 4 shows a summary of these correlations. Data on tricarballylic acid and carboxylic acid, one of the fungal and yeast markers, are shown as scatter plots (Fig 5), which indicates that phylum Firmicutes was positively correlated with tricarballylic acid [r = 0.662 (p = 0.003)] and carboxylic acid [r = 0.689 (p = 0.002)].

Fig 4 — The color strip (down legend) represents the correlation between each item. The darker color indicates that the correlation coefficient increases gradually. For example, red shows a positive correlation, while blue shows a negative correlation. The values indicate Pearson’s correlation coefficients (r), and bold letters indicate p < 0.05.

Fig 5 — Phylum *Firmicutes* versus tricarballylic acid (A) and carboxylic acid (B). Open and closed circles represent values in the pre-observation period (PRE) and LEX-ingestion period (POST), respectively. Pearson’s correlation coefficients (r) are shown for each plot with p-values. The analysis was conducted using data from 18 points from two samples given by nine participants.

At the family level, moderate correlations were observed with several metabolites in Porphyromonadaceae (Figs 4 and S4). They were correlated with 3-metylglutaconic acid (r = -0.609, mitochondrial amino acid metabolite) kynurenic acid (r = -0.529, tryptophan metabolite), and pyrimidine metabolites (uracil: r = -0.680, thymine: r = -0.625). A species of Parabacteroides distasonis (PD, phylum Bacteroidetes, family Porphyromonadaceae) was also found to correlate with several metabolites (Figs 4 and S4). These moderate correlations included 3-metylglutaconic acid (r = -0.630), neurotransmitter metabolites (vanillylmandelic acid: r = -0.602, quinolinic acid: r = -0.570, and kynurenic acid: r = -0.630), or pyrimidine metabolites (thymine: r = -0.554).

To discover significant alteration of bacterial taxa abundance in response to LEX ingestion, we conducted LEfSe analysis for 11 participants regardless of whether they participated in the two races. As a result, the LDA scores showed significant bacterial differences before and after LEX ingestion (S5 Fig). The composition of Odoribacter (phylum Bacteroidetes, family Odoribacteraceae), Turicibacter (phylum Firmicutes, family Turicibacteraceae), and PD increased, and Oribacterium (phylum Firmicutes, family Lachnospiraceae) composition decreased after LEX ingestion. PD was the only biomarker detected at species level and has the highest abundance ratio among these biomarkers.

Discussion

This study provides two insights into the competition and supplement ingestion on endurance athletes as a pilot study. To the best of our knowledge, this is the first study combining the analysis of changes in the gut microbiota and urinary metabolites of endurance athletes, with and without LEX supplementation. This study demonstrated that athletes who competed had changes in the composition of the gut microbiota. It was also suggested that LEX ingestion suppresses yeast and fungal overgrowth and improves mitochondrial metabolism, including the TCA cycle.

There are several limitations to this study. First, we acknowledge limitation to this study because it lacked a control group, and that the placebo effect may have explained the observed effects of LEX ingestion. Also, it was not possible to distinguish whether changes in microbiota on LEX or training with the used study design. Additionally, we adjusted the schedule so that participants could compete in two races during the research period. Still, it was challenging to align the competition intensity with the observation period. Second, there is a lack of physiological data on exercise and clinical data on the GI tract. Third, an in-depth analysis of the participants’ diets was not provided in this study due to various factors. The diet and exercise are the two factors that significantly influence the gut microbiota [4, 5]. Therefore, a randomized placebo-controlled study needs to be conducted with data on their physiological data on exercise and diet to analyze the role of diet, including supplements and exercise in influencing the gut microbiota taxonomic composition in athletes.

In this study, fluctuations in phylum-level composition of the gut microbiota were observed during the pre-observation (non-supplementation) period, with a decrease in Bacteroidetes and an increase in Firmicutes before and after the race. It is important to note that samples were collected on the day before and six days after the race, but the trend continued for at least 14 days. Several studies in animal models and humans have found correlations between specific alterations in the gut microbial community structure and exercise, although these were studies of the effects of long-term exercise. Most of the published studies on mouse models have examined the combined effects of exercise, dietary interventions, and diseases. Choi et al. (2013) reported that mice using a running wheel exercise method had an increase in phylum Firmicutes but decreases in phyla Tenericutes and Bacteroidetes, which attenuated the changes in gut microbiota induced by oral exposure to polychlorinated biphenyls (PCB) [20]. Similarly, in both type-two diabetic and control mice, exercise resulted in a greater abundance of Firmicutes species and lower Bacteroides/Prevotella genera than sedentary mice [21]. In contrast, Evans et al. (2014) described increases in the phylum Bacteroidetes and decreases in the phylum Firmicutes in a manner that was proportional to the distance ran by mice fed a high-fat diet [22]. In a short-term human study, Zhao et al. (2018) reported that a half-marathon race induced increasing richness in the families Coriobacteriaceae and Succinivibrionaceae, which belong to the phyla Actinobacteria and Proteobacteria, respectively [23]. Therefore, it is thought that exercise affects gut microbiota composition and can fluctuate due to a single competition. The difference in the dispersion of the Shannon’s index before and after the race suggests that the bacterial composition is relatively unstable after the race. Participants in this study undergo daily training, and changes in the gut microbiota at the phylum level may be due to the rigorous physical demands of an official race and the psychological effects of the competition. It is interesting that this change continues for a relatively long period, and further research is needed to delve into how the microbiome is related to the conditioning of athletes.

Long-distance runners need sustained energy for a long time of continuous exercise and their enduring workouts. The metabolic demands for skeletal muscle, liver, kidneys, and adipose tissue are increased within a day or a few days after running. Studies of the correlation between microbiota composition and food intake have reported many correlations. Fat and energy intake were the largest connected components in the network between bacterial taxa [23]. Changes in the bacterial composition may be induced to regulate energy and hormonal balance, or to response to changes in the diet ingested. However, athletes are doing high load training on a daily basis. Considering this, the psychological impact of competition may also be related to the changes in the bacterial composition in a single competition. Also, these changes may have major impact on maintaining the athlete’s post-competition condition. Therefore, it is necessary to conduct the study while accurately grasping the dietary intake and mental status before and after the competition.

The change in microbial composition before and after the race during the LEX-ingestion period were smaller than that seen during the pre-observation period, as the bacterial composition on the final day of LEX ingestion (POST_a) was similar to the initial state (PRE_b). However, directly comparing the two study periods is difficult because sampling after the race was longer (6 days) during the LEX-ingestion period. Moreover, it is possible that not enough time elapsed between the pre-observation (pre-ingestion) period and the LEX-ingestion period to allow the microbiome to return to baseline, which would potentially confound the results seen. In addition, it is necessary to consider that the exercise intensity may differ slightly depending on the observation period, potentially impacting the microbiota. However, because the change in the POST_b microbiota is not maintained (i.e., reverts to the original pre-race microbial composition), we expect that LEX ingestion will have the ability to suppress changes in the gut microbial resulting from the participation in an official competition.

We also investigated changes in urinary metabolites before and after the ingestion of LEX in this study. Unfortunately, it is necessary to refrain from consuming sports drinks and fruits that affect urine metabolites from the day before the urine collection and considering the maintenance of the condition before the race. The analysis of urinary metabolites on the day before the competition could not be carried out at the same time as the fecal collection. Before LEX ingestion, the arabinose concentration (48.25 mmol/mol Cr on mean) is considerably higher than the reference range. Arabinose is a marker for Candida species (yeast), and increasing arabinose levels in urine correlate with yeast overgrowth [24]. After ingestion of LEX, urinary arabinose levels significantly decreased. Additionally, as yeast and fungal markers, significant reductions in 3-oxoglutaric acid, tartaric acid, and tricarballylic acid were also observed [25–27]. Generally, these results suggest that continuous ingestion of LEX suppresses yeast and fungal overgrowth.

In humans, fungi colonize the gut shortly after birth [28]. Human fecal gut mycobiome is low in diversity compared to its bacterial composition, and dominated by yeast including Saccharomyces, Malassezia, and Candida [29]. The relationship between diet and fungi has also been studied, and Candida is abundant in relation to recent carbohydrate consumption [30]. The presence of fungi is associated with the exacerbation of several human diseases, including inflammatory bowel disease and colorectal cancer [31–33]. It has also been reported that the growth of fungi in the intestine is involved in the inflammatory response and enhances the allergic response [34]. Endurance athletes routinely perform long-distance running training (for the participants of this study, the monthly mean of running distance was about 260km), and this excessive exercise load may reduce the momentum of gut microbiota, resulting in the growth of yeast and fungi. The overgrowth of fungi in the gut is involved in facilitating systemic inflammation and increasing fatigue, which affecting the maintenance of an athlete’s physical condition. Ingestion of LEX may suppress the excessive growth of fungi and may be effective for daily conditioning.

Of the nine markers related to bacterial growth, three showed a significant decrease after LEX ingestion. These metabolites are tyrosine and phenylalanine metabolites, and these results suggest that the overgrowth of specific bacteria, including Clostridia and microbial dysbiosis is suppressed. Furthermore, changes in urinary metabolites reflecting neurotransmitter levels were observed in relation to the metabolism of phenylalanine, tyrosine, or tryptophan. We recently reported that LEX ingestion decreases urinary indoxyl sulfate, a tryptophan metabolite derived from the metabolism of gut microbiota [12]. Changes in these metabolites are also presumed to be mediated by the gut microbiota.

Another notable change in urinary metabolites before and after LEX ingestion are markers for mitochondrial metabolites, including the TCA cycle. Of the nine compounds in these markers, the values before LEX ingestion were within the standard range, but five compounds showed a significant decrease after LEX ingestion. We hypothesize that continuous ingestion of LEX improves mitochondrial metabolism, leading to the maintenance of energy acquisition and substrate availability for assimilation processes such as lipogenesis. 3-Methylglutaric acid and 3-methylglutaconic acid are leucine metabolites, and leucine is known as a potent stimulator of protein synthesis by stimulating the mammalian target of rapamycin (mTOR) [35]. Considering these points, the maintenance of GI environment may enhance recovery from fatigue and muscle repair by modulating inflammation in daily training.

Vitamin B contributes to the process of the TCA cycle as cofactors/enzymes such as FAD (B2) and NAD (B3), as components of CoA (B5), or as coenzyme Q10 (B5) [36]. In relation to vitamins, significant changes were observed in pantothenic acid (B5), 3-hydroxy-3-methylglutaric acid (precursor of coenzyme Q10), and methylcitric acid (an indicator of biotin). Although dietary B vitamins are absorbed through the small intestine, they can also be supplied by the gut microbiota biosynthesis. Generally, it is hypothesized that the activation of mitochondrial metabolism and TCA cycle by LEX ingestion may be mediated through gut microbiota.

We then analyzed the relationship between the composition of gut microbiota and urinary metabolites. Correlations were detected between phylum Firmicutes, and yeast and fungal markers, including tricarballylic acid and carboxycitric acid, positively correlated. It is hypothesized that the imbalance of the gut microbiome may induce excessive growth of yeast and fungi. This has been demonstrated previously. For example, antibiotics treatment in mice leads to major fungi expansions [34, 37], suggesting that the balance of bacterial composition controls the prevalence of the fungus in the gut. In the composition of human gut microbiota phyla Firmicutes and Bacteroidetes are the two major dominant [38]. The Firmicutes to Bacteroidetes ratio has been extensively examined for human and mouse gut microbiota. Multiple studies reveal that the F/B ratio is correlated with obesity and other diseases [39–42]. It is also presumed that the balance between phyla Firmicutes and Bacteroidetes is involved in controlling fungal occupancy in athletes.

PD is the only bacterial species found to correlate with urinary metabolites at the species level. The genus Parabacteroides, including PD are a gram-negative anaerobe and defined as one of the 18 core members of the gut microbiota of humans [43]; thus, it is thought to be involved in important physiological functions in the host. The abundance of PD is relatively lower in patients with obesity, nonalcoholic fatty liver disease (NAFLD), inflammatory bowel disease (IBD), and multiple sclerosis [44–47]. Moreover, treatment with live PD in mice was shown to have anti-inflammatory effects, reduce weight gain, improve glucose homeostasis, correct obesity-related abnormalities, and induce regulatory T lymphocytes from naïve CD4⁺ T cells [48, 49]. It has also been suggested that these effects are regulated by PD via succinic acid and secondary bile acid production or suppression of TLR4 and AKT signals [49]. These findings support the gut PD as a promising symbiont that can modulate host metabolism to potentially alleviate metabolic dysfunction. In this study, many urinary metabolites were correlated with PD composition, suggesting associations to leucine, phenylalanine, tyrosine, and tryptophan metabolism. As a constituent of gut microbiota, PD may also be an important marker for athlete conditioning as it is also associated with anti-inflammatory responses. For reference, changes in PD composition due to LEX ingestion significantly increase in comparison before and after LEX ingestion regardless of whether they participated in two races. Although PD administration has been suggested as a probiotic, it is important to identify food ingredients that regulate endogenous PD rather than supplemental PD.

Conclusively, this study provides insight into the impact of competition and health effects of oral ingestion of LEX of endurance athletes, focusing on their microbiome composition. Our data reveal that a single, official endurance race can rapidly induce striking compositional changes in gut microbiota. These alterations can also potentially last for a relatively long period. Analysis of urinary metabolites suggested that the participants may also be experiencing yeast and fungal overgrowth in the gastrointestinal system. Therefore, LEX ingestion may also have an overall beneficial effect on this aspect as well. These data suggested that LEX ingestion may also improve mitochondrial metabolism and amino acid metabolism and may effectively maintain the daily metabolic homeostatic condition of athletes. A relationship between fungal growth and occupancy of the phylum Firmicutes was also observed, and PD, which is linked with many urinary metabolites, may also be an important indicator for athlete condition. Further studies, however, must elucidate the impact of competitive events on elite athletes’ gut microbiome and explore options for mitigating adverse effects on the GI environment, such as in the case of LEX administration.

Supporting information

S1 Fig. Exercise loads in the pre-observation period and the LEX-ingestion period.

Total mileage, training time, and rate of perceived exertion (RPE) are shown. (A) Exercise load of 8 participants in the analysis of fecal microbiota. n = 8 (5 male /3 female). (B) Exercise load of 8 participants in the analysis of urinary metabolites. n = 8 (6 male/2 female). PRE: pre-observation period. POST: LEX-ingestion period. Values represent scatter plot with median (black line) and mean (Red +). Statistical significance was determined using a paired t-test for millage and training time. Statistical significance was determined using the Wilcoxon signed-rank test for RPE. Significant differences between PRE and POST: *p < 0.05.

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S2 Fig. Changes in Firmicutes to Bacteroidetes ratio of fecal microbiota.

POST: LEX-ingestion period. b: Before the race. a: After the race. Values represent box-and-whisker plots with mean (Red +). Statistical significance was determined using Friedman’s test with a Scheffé’s multiple comparison method. * indicate a significant difference of p < 0.05.

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S3 Fig. Changes in α-diversity of fecal microbiota at the phylum level.

PRE: Pre-observation period. POST: LEX-ingestion period. b: Before the race. a: after the race. Values represent box-and-whisker plots with mean (Red +). Statistical significance was determined using repeated measures ANOVA with Bonferroni’s correction. The difference in variance was determined using Fisher’s F-test. †† indicate a different variance between the two samples: p < 0.01.

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S4 Fig. Correlation plots of fecal bacterial composition and urinary metabolites.

The results of Porphyromonadaceae and Parabacteroides distasonis are shown. Open and closed circles represent values on the pre-observation period (PRE) and LEX-ingestion period (POST), respectively. Pearson’s correlation coefficients (r) are shown for each plot with p-values. The analysis was performed using data from 18 points of nine participants.

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S5 Fig. Differential abundance of microbial taxa before and after LEX ingestion.

(A) Linear discriminant analysis (LDA) effect size (LEfSe) analysis plot of taxonomic biomarkers in fecal microbiota between PRE_a and POST_a. LDA scores (log10) > 2 and p < 0.05 are listed. (B) Histogram of the relative distribution of Parabacteroides distasonis. Values represent box-and-whisker plots with mean (Red +). The analysis included 11 participants who had data for fecal microbiota before and after LEX ingestion.

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Acknowledgments

We would like to thank all the participants who took part in the study. Additionally, we appreciate California Nutrients, Inc. and Bioengineering Lab. Co., Ltd. for their technical assistance in conducting urine analysis and high-throughput sequencing, respectively. The English in this document has been vetted by a professional service (Enago) and an editor who is a native English speaker.

Data Availability

Fastq files are deposited in the DDBJ database under the accession number DRA011638 with h BioProject ID PRJDB11304 and BioSample IDs SAMD00283406-SAMD00283454.

Funding Statement

Funding: This work was supported by B&S Corporation Co. Ltd. The funder provided support in the form of salaries for authors [MF, YK and YS], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

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PLoS One. doi: 10.1371/journal.pone.0262906.r001

Decision Letter 0

Krzysztof Durkalec-Michalski

24 Jun 2021

PONE-D-21-11683

The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: an open-label pilot study

PLOS ONE

Dear Dr. Sasuga,

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We look forward to receiving your revised manuscript.

Kind regards,

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

5. Review Comments to the Author

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Reviewer #1: The authors presented a very interesting pilot study in which they evaluated the effects of lactic acid bacteria-fermented soymilk extract on the microbiome and metabolome of athletes before and after exercise during competition. The researchers made several interesting observations; however, the conclusions they drew are not appropriate. The most important conclusions are that the primary and secondary aims and research hypotheses should be precisely defined; a randomized placebo-controlled study should be conducted (comparator without bacteria); the sample size should be calculated before the study (in the pilot study, authors obtained some data, which will be helpful to do it). Moreover, physiological data on exercise and clinical data on the gastrointestinal tract must be included in the analysis, and it is necessary to record the diet of athletes. The discussion should be rewritten in this direction. In addition, several comments on the manuscript should be included:

line 60: Please cite the following position: Kulecka M et al. The composition and richness of the gut microbiota differentiate the top Polish endurance athletes from sedentary controls. Gut Microbes. 2020 Sep 2;11(5):1374-1384.

Line 69: Not only the GI tract is the target of probiotics; please inform readers about other benefits for athletes receiving probiotics.

Line 71: Probiotics activity is strain-specific. In the study referenced 11, the list of strains is not shown; tehrefore, there are fermenting bacteria, not probiotics.

Line 95: What do authors mean: "improving GI environment", we do not know the healthy microbiota and only speculate about it.

Line 109: With the used study design, it is impossible to distinguish whether the microbiota changes depended on LEX or training; this issue must be clearly stated in the discussion.

Line 120: The composition of the product, the names of the strains and their quantities were not given.

Line 144: Why was urine not tested at the same time points as a stool?

Results: How the authors related the compositionality problem?

Reviewer #2: In the work The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: an open-label pilot study Fukuchi et al. describe the effects of supplement LEX on fecal bacteriome and urine metabolome of endurance athletes in competitive environment. The study itself seems well-designed but its description, especially the Methods section could be improved upon.

Major remarks:

1. Abstract

“When the fecal microbiota was investigated before and after a race without the use of a supplement (pre-observation period), there was an increase in the abundance of phylum Bacteroidetes and decrease in the abundance of Firmicutes.” The opposite is mentioned in the results section as well as in Figure 2B

2. Methods

a. General – statistical methods are poorly described – some of them, like exclusion of samples by Smirnov-Grubbs test (line 591) are only mentioned in results and/or figure description.

b. Line 178 – software versions for usearch and QIIME should be given

c. Line 181 – which OTU picking strategy from QIIME was used? What database was used to assign taxonomic classification?

d. Line 201 – no post-hoc test is identified for Friedman’s test

e. Line 202 – no mention of multiple comparison adjustment of p-value is given here yet it is present in the results

f. Line 204 –Friedman’s test (a non-parametric method) was used previously for differential analysis of microbial taxa which indicates the authors believe the taxa abundance distribution deviates from normal distribution. But here, Pearson’s correlation coefficient is computed which is best suited for variables with normal distribution. Could authors justify their choice? Wouldn’t Spearman correlation coefficient be better?

g. Line 205 and 201 – two different p-value adjustment strategies are used: FDR and Bonferroni. Could authors justify their choice here?

3. Results

a. It would be good to see some baseline characteristics of athletes other than age, such as their BMI. Information on diet is also missing – even general remarks (was anyone on vegan/vegetarian diet? Was any other supplementation received?) would be helpful if such information is available.

Minor remarks:

Some wording choice is unusual, such as Bonferroni’s correction coefficient (line 260). It would be better to say simply Bonferroni’s correction of Bonferroni’s p-value adjustement

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Jan 27;17(1):e0262906. doi: 10.1371/journal.pone.0262906.r002

Author response to Decision Letter 0

8 Jul 2021

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

Yasuhiro Sasuga, Ph.D

Hachioji Center for Research & Development

B&S Corporation Co., Ltd.

8th July 2021

Dear Durkalec-Michalski

Response to reviewers’ comments [PONE-D-21-11683]

Thank you for reviewing our manuscript and for the helpful comments provided.

Please find enclosed our response to the comments raised. The line numbers provided here are based on our revised manuscript with tracked changes.

1. Reviewer 1

■ The authors presented a very interesting pilot study in which they evaluated the effects of lactic acid bacteria-fermented soymilk extract on the microbiome and metabolome of athletes before and after exercise during competition. The researchers made several interesting observations; however, the conclusions they drew are not appropriate. The most important conclusions are that the primary and secondary aims and research hypotheses should be precisely defined; a randomized placebo-controlled study should be conducted (comparator without bacteria); the sample size should be calculated before the study (in the pilot study, authors obtained some data, which will be helpful to do it). Moreover, physiological data on exercise and clinical data on the gastrointestinal tract must be included in the analysis, and it is necessary to record the diet of athletes. The discussion should be rewritten in this direction.

We appreciate the reviewer’s comment. We have divided the conclusions into both primary and secondary. We reflected that in abstracts (line 46–51) and discussion (line 418–425). In the discussion section, we added the limitation of this study and stated that it is necessary to monitor physiological data on exercise and clinical data on the GI tract and/or diet (line426-440). Also, we have noted that a randomized placebo-controlled study should be conducted in the future (line 440–443).

■ In addition, several comments on the manuscript should be included:

We accepted this reviewer’s suggestion. Reference 8 (line 73 and line 738–743).

■ Line 69: Not only the GI tract is the target of probiotics; please inform readers about other benefits for athletes receiving probiotics.

We accepted this reviewer’s suggestion. We added some key words (stress, host immunity, upper respiratory tract infections). Line 78–80.

■ Line 71: Probiotics activity is strain-specific. In the study referenced 11, the list of strains is not shown; tehrefore, there are fermenting bacteria, not probiotics.

We accepted this reviewer’s suggestion. We have inserted the word “fermented bacterial products” in the previous sentence (line 81).

■ Line 95: What do authors mean: "improving GI environment", we do not know the healthy microbiota and only speculate about it.

We accepted this reviewer’s suggestion. Certainly no one knows a healthy microbiota. Therefore, it was rewritten to affect the GI environment (line 108).

■ Line 109: With the used study design, it is impossible to distinguish whether the microbiota changes depended on LEX or training; this issue must be clearly stated in the discussion.

We accepted this reviewer’s suggestion. We have described the discussion section (line 432–434). It also clearly stated that a randomized, placebo-controlled trial should be conducted (line 440–443).

■ Line 120: The composition of the product, the names of the strains and their quantities were not given.

Since the test product is a commercial product and does not contain live bacteria, we did not mention the name of the strains, but we added the number of bacteria at the time of fermentation, the extraction process, and the expected bacterial equivalent of the extract, according to the opinion of reviewer’s suggestion (line 137–140).

■ Line 144: Why was urine not tested at the same time points as a stool?

It is necessary to refrain from consuming sports drinks and fruits that affect urine metabolites from the day before the urine collection. Therefore, considering the maintenance of the condition before the race, the urine collection on the day before the race could not be carried out at the same time as the fecal collection. We have described the discussion section (line 510–514).

■ Results: How the authors related the compositionality problem?

Thank you for pointing out the viewpoint. We think that the gut bacterial composition (including its metabolites) and dietary intake are closely related. This study did not investigate accurate dietary intake (especially before and after the race). Fat and energy have been reported to be the largest connected components in the network between bacterial taxa. Changes in the bacterial composition may be induced to regulate energy and hormonal balance, or to respond to changes in the diet ingested. It is also thought that the mental stress of competition is also involved. In any case, the changes in the bacterial composition due to a single competition may have major impact on maintaining the athlete's post-competition condition. These perspectives have been added to the discussion section (line 480–493).

2. Reviewer 2

■ 1. Abstract

We appreciate the reviewer’s comment. We collected the text (line 36 and 37).

■　2. Methods

a. General – statistical methods are poorly described – some of them, like exclusion of samples by Smirnov-Grubbs test (line 591) are only mentioned in results and/or figure description.

We appreciate the reviewer’s comment. We have added the description of statistical analysis (line 219–221). Also, since there was a lack of description of statistical methods for the item of exercise load, we added it (line 232–236).

b. Line 178 – software versions for usearch and QIIME should be given

We appreciate the reviewer’s comment. We have described the software versions for usearch and QIIME (line 194 and 196).

c. Line 181 – which OTU picking strategy from QIIME was used? What database was used to assign taxonomic classification?

We appreciate the reviewer’s comment. We used Greengenes 13_8 database, so, we have added it to the text (line 199).

d. Line 201 – no post-hoc test is identified for Friedman’s test

e. Line 202 – no mention of multiple comparison adjustment of p-value is given here yet it is present in the results

We appreciate the reviewer’s comment. We have described post-hoc test for Friedman’s test. We used a Scheffé's method　for Friedman’s test, as post-hoc test (line 224).

We appreciate the reviewer’s comment for statistical processing. Appropriate methods were not applied for some items for differential analysis of microbial taxa. The taxa abundance distribution of the phylum Bacteroidetes and Firmicutes were normal distribution. Therefore, we have changed to one-way ANOVA with Bonferroni’s correction (line 221–223, line 296–298). As for the F / B ratio, the normality was not recognized, so the Friedman ’s test was used (line 223–224, line298–299). Similarly, PCoA analysis confirmed normality, so we have changed to one-way ANOVA. Because the bacterial distribution was normal, the analysis of the relationship between urinary metabolites and bacterial composition remained Pearson’s correlation coefficient. Bacterial taxa and diversity analysis of variance in S2 Fig and S3 Fig were also normal, so we changed to ANOVA (line 651 and 659). We have changed some statistical analysis methods, but the results are not affected. Due to these changes, Fig 2(A and B), S2 Fig and S3 Fig have been replaced.

g. Line 205 and 201 – two different p-value adjustment strategies are used: FDR and Bonferroni. Could authors justify their choice here?

We appreciate the reviewer’s comment. If the number of multiple comparisons in the test was five or less, Bonferroni’s correction was used, and more than that, FDR was used. So, FDR was used to analyze the urinary metabolites and the relationship of bacterial composition and urinary metabolites, which have many items to be compared. Since the description was halfway, we have added it to the text (line 229–231).

■ M&M: following the reagents, the information of company, city, country should be included.

It describes the reagents we used. Also, regarding the contract examination, the company is described.

■　3. Results

We appreciate the reviewer’s comment. We added a paragraph describing the background of the participants (line 247–253).

■ Minor remarks:

Some wording choice is unusual, such as Bonferroni’s correction coefficient (line 260). It would be better to say simply Bonferroni’s correction of Bonferroni’s p-value adjustement.

We accepted this reviewer’s suggestion (line 297).

3. Additional changes

(i) Changed the notation of Co-author affiliation. The affiliation at the time of the study and the current affiliation were separated.

(ii) We have an English proofreader correct the detailed English wording and change it. See revised manuscript with tracked changes.

We hope that we have addressed all the issues raised, and would be happy to clarify further on any other issues. Thank you for consideration of our manuscript for publication in your journal.

Yours sincerely,

Yasuhiro Sasuga

Attachment

Submitted filename: Response to Reviewers.doc

Click here for additional data file.^{(60.5KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0262906.r003

Decision Letter 1

Krzysztof Durkalec-Michalski

1 Nov 2021

PONE-D-21-11683R1The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: an open-label pilot studyPLOS ONE

Dear Dr. Sasuga,

Thank you for resubmitting your manuscript to PLOS ONE and making your manuscript significantly improved. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. The work requires additional compliance with the recommendations of the reviewer and the editor's comments. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by November 30, 2021. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (Please be aware that comments are referenced to text with the track changes option):

Line 27 – correct “resent” to “recent”.

Line 66 – try to reduce even one "and" in this sentence - is repeated too often.

Line 133 - The heading "TEST ARTICLE" is unclear. Consider Improving - Maybe "Supplementation". Likewise, in the later sections (e.g. lines 147-148) - the "test article" is misleading.

Lines 151-153 - Please add the necessary clarifications what exactly was and how "An official race" was conducted.

Line 164 – unclear part – authors wrote “and exercise intensity in order to evaluate exercise intensity” – do the authors mean “and exercise specificity in order to evaluate exercise intensity”? Please revise it.

Lines 166-167 – unclear part – authors wrote “ exercise load of the race when participating in the race”. Maybe some of the terms "race" can be replaced with "exercise tests" or "competition".

The statements in the methodical part of biological material storage "as soon as possible" (lines 159 and 175) and are quite unstable and unclear - what does that mean? What was the criterion or the range of time?

Lines 216-241 - In the description of the statistical parts it should also be clearly indicated whether the minimum sample size has been achieved, bearing in mind some losses.

Line 354 - put "concentration" behind "arabinose".

Line 421 - the authors write "an official competition" - but what was the detailed specificity of these efforts. Were they real competitions in which competitors competed with other competitors (not participating in the project) or simulated competitions in which efforts imitating competitions were carried out?

Line 477 - try to use a more formal language and replace the word "fascinating".

Lines 480-481 - the authors wrote "During long-distance running, the total energy of the body is very consumed" - this sentence is confusing. Do the authors mean that the body's energy expenditure is significant / elevated?

Line 484 - You should probably put "intake" behind “Fat and energy”

Line 534 – change “is about 260km” to past tense.

Lines 570-571 - the sentence, however, may be too optimistic despite the assurance that this is a hypothetical opinion of the authors. One should approach this even more carefully and point to "possible" or "potential" activation and that LEX ingestion "may be" mediated.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Most of the comments have been taken into account by the authors. However, I maintain my opinion that the article should include a list of the strains included in the product tested. As shown for example in the work of van Baarlen P et al. (van Baarlen P et al. Differential NF-kappaB pathways induction by Lactobacillus plantarum in the duodenum of healthy humans correlating with immune tolerance. Proc Natl Acad Sci U S A. 2009;106(7):2371-6.) killed strains have their specific physiological activities and it can be suspected that it is strain dependent.

Authors should clarify the answer to the compositionality problem. Microbiome data are compositional (relative) and give no information about absolute abundances, regardless of normalization procedures (Gloor et al., 2017). Therefore relevant statistical methods, e.g., based on log-ratios must be used, to avoid false-positive results (Knight et al., 2018; Mandal et al.,2015; Weiss et al., 2017). Information on the increase in abundance of a particular bacterial genus must be linked to the identification of the reference point (i.e. another genus) against which this occurred (Christensen et al., 2009;Morton et al., 2019, 2017). (Christensen et al., 2009;Morton et al., 2019, 2017).

References:

Christensen, K., Doblhammer, G., Rau, R., Vaupel, J.W., 2009. Ageing populations: the challenges ahead. Lancet 374, 1196–1208.

Gloor, G.B., Macklaim, J.M., Pawlowsky-Glahn, V., Egozcue, J.J., 2017. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224.

Knight, R., Vrbanac, A., Taylor, B.C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., Melnik, A.V., Morton, J.T., Navas, J., Quinn, R.A., Sanders, J.G., Swafford, A.D., Thompson, L.R., Tripathi, A., Xu, Z.Z., Zaneveld, J.R., Zhu, Q., Caporaso, J.G., Dorrestein, P.C., 2018. Best practices for analyzing microbiomes. Nat. Rev. Microbiol. 16, 410–422.

Mandal, S., Van Treuren,W.,White, R.A., Eggesbø,M., Knight, R., Peddada, S.D., 2015. Analysis of composition ofmicrobiomes: a novel method for studyingmicrobial composition. Microb. Ecol. Health Dis. 26, 27663.

Morton, J.T., Sanders, J., Quinn, R.A., McDonald, D., Gonzalez, A., Vázquez-Baeza, Y., Navas- Molina, J.A., Song, S.J., Metcalf, J.L., Hyde, E.R., Lladser, M., Dorrestein, P.C., Knight, R., 2017. Balance trees reveal microbial niche differentiation. mSystems 2. https://doi. org/10.1128/mSystems.00162-16.

Morton, J.T., Marotz, C.,Washburne, A., Silverman, J., Zaramela, L.S., Edlund, A., Zengler, K., Knight, R., 2019. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719.

Weiss, S., Xu, Z.Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., Lozupone, C., Zaneveld, J.R., Vázquez-Baeza, Y., Birmingham, A., Hyde, E.R., Knight, R., 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27.

Reviewer #2: Please correct some minor spelling mistakes while proofreading the final version of the manuscript - like "resent" instead of recent in the abstract.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2022 Jan 27;17(1):e0262906. doi: 10.1371/journal.pone.0262906.r004

Author response to Decision Letter 1

17 Nov 2021

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

Yasuhiro Sasuga, Ph.D

Hachioji Center for Research & Development

B&S Corporation Co., Ltd.

17th November 2021

Dear Dr. Durkalec-Michalski

Response to reviewers’ comments [PONE-D-21-11683R1]

Thank you for reviewing our manuscript and for the helpful comments provided.

Please find enclosed our response to the comments raised. The line numbers provided here are based on our revised manuscript with tracked changes.

1. Additional Editor Comments

1. Line 27 – correct “resent” to “recent”.

We appreciate the editor’s comment. We collected the text (line 21).

2. Line 66 – try to reduce even one "and" in this sentence - is repeated too often.

We appreciate the editor’s comment. We collected the text (line 56-57).

3. Line 133 - The heading "TEST ARTICLE" is unclear. Consider Improving - Maybe "Supplementation". Likewise, in the later sections (e.g. lines 147-148) - the "test article" is misleading.

We appreciate the editor’s comment. We collected the text from article to supplement (line 119, 120, and 138).

4. Lines 151-153 - Please add the necessary clarifications what exactly was and how "An official race" was conducted.

We appreciate the editor’s comment. We inserted the text (line 142-147).

5. Line 164 – unclear part – authors wrote “and exercise intensity in order to evaluate exercise intensity” – do the authors mean “and exercise specificity in order to evaluate exercise intensity”? Please revise it.

We thank the editor’s comment. We collected the text from “intensity” to “specificity” (line 158).

6. Lines 166-167 – unclear part – authors wrote “ exercise load of the race when participating in the race”. Maybe some of the terms "race" can be replaced with "exercise tests" or "competition".

We appreciate the editor’s comment. We collected the text from “race” to “competition” (line 160).

7. The statements in the methodical part of biological material storage "as soon as possible" (lines 159 and 175) and are quite unstable and unclear - what does that mean? What was the criterion or the range of time?

We appreciate the editor’s comment. We collected to “immediately” (line 168, line201). We also added the storage condition (line 168-169, Line 201-202).

8. Lines 216-241 - In the description of the statistical parts it should also be clearly indicated whether the minimum sample size has been achieved, bearing in mind some losses.

This study was a pilot trial with a small number of participants. Since some data could not be obtained at some points, we described how to handle them (Line 211-213).

9. Line 354 - put "concentration" behind "arabinose".

We have already described 17.00 ± 5.21 (SD) mmol / mol Cr as the arabinose concentration after ingestion of LEX (line 367).

10. Line 421 - the authors write "an official competition" - but what was the detailed specificity of these efforts. Were they real competitions in which competitors competed with other competitors (not participating in the project) or simulated competitions in which efforts imitating competitions were carried out?

It was the intention of real competitions. However, there is already research on athletic competition and gut microbiota, and this study was characterized by tracking changes in athletes' microbiota and urinary metabolites, so we changed the description to that (Line 441-443).

11. Line 477 - try to use a more formal language and replace the word "fascinating".

We appreciate the editor’s comment. We collected to “interesting” (line 490).

12. Lines 480-481 - the authors wrote "During long-distance running, the total energy of the body is very consumed" - this sentence is confusing. Do the authors mean that the body's energy expenditure is significant / elevated?

We appreciate the editor’s comment. We collected to describe that long-distance runner need sustained energy (Line 493-495).

13. Line 484 - You should probably put "intake" behind “Fat and energy”

We appreciate the editor’s comment. We put “intake” (Line 498).

14. Line 534 – change “is about 260km” to past tense.

We appreciate the editor’s comment. We collected (line 546).

15. Lines 570-571 - the sentence, however, may be too optimistic despite the assurance that this is a hypothetical opinion of the authors. One should approach this even more carefully and point to "possible" or "potential" activation and that LEX ingestion "may be" mediated.

We appreciate the editor’s comment. We collected to “may be” (Line 580).

2. Reviewer 1

■ Most of the comments have been taken into account by the authors. However, I maintain my opinion that the article should include a list of the strains included in the product tested. As shown for example in the work of van Baarlen P et al. (van Baarlen P et al. Differential NF-kappaB pathways induction by Lactobacillus plantarum in the duodenum of healthy humans correlating with immune tolerance. Proc Natl Acad Sci U S A. 2009;106(7):2371-6.) killed strains have their specific physiological activities and it can be suspected that it is strain dependent.

We appreciate the reviewer’s comment. We have described all strains (Line 123-125). Also, the description has been changed to novel genera to reflect the reclassification of the genus Lactobacillus (Zheng J et al. 2020).

（Reference）

・Zheng J, et al..: A taxonomic note on the genus Lactobacillus: Description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. Int J Syst Evol Microbiol. 2020 Apr; 70(4): 2782–2858.

■ Authors should clarify the answer to the compositionality problem. Microbiome data are compositional (relative) and give no information about absolute abundances, regardless of normalization procedures (Gloor et al., 2017). Therefore relevant statistical methods, e.g., based on log-ratios must be used, to avoid false-positive results (Knight et al., 2018; Mandal et al.,2015; Weiss et al., 2017). Information on the increase in abundance of a particular bacterial genus must be linked to the identification of the reference point (i.e. another genus) against which this occurred (Christensen et al., 2009;Morton et al., 2019, 2017). (Christensen et al., 2009;Morton et al., 2019, 2017).

We accepted this reviewer’s suggestion. We conducted linear discriminant analysis (LDA) Effect Size (LEfSe) method (Segata N et al. 2011) to detect the differences in bacterial taxa characterizing the groups. There was no change in the basic data interpretation due to the change in the analysis method.

Due to the change of analysis method, the description of the method (Statistical analysis section)　has been changed (Line 214-216).

Due to the change of analysis method, figures have also changed (Fig 2B, add Fig 2C, S5 Fig). Figure legends have also been changed (Line 292-296, Line 671-682).

The result of S2 Fig was partially inappropriate as a result of LEfSe analysis, so it was replaced with the F / B ratio (S2 Fig., and Line 648-655).

In the result section, the description has been changed based on the result of LEfSe analysis (Line 308-331, Line 422-436).

The analysis of Parabacteroides distasonis, since LEfSe analysis was widely performed, exclusion by Smirnov-Grubbs test was not performed, and analysis was performed at n = 11.　 Therefore, the description of Smirnov-Grubbs test was deleted (Line 220, Line 432-433 Line 681-682).

Due to the change of analysis method, one reference has been added (reference No. 18) (Line 781-784). Therefore, subsequent reference numbers have changed.

（Reference）

Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.

3. Additional changes

Organized the description in the Statistical analysis section.

◆Moved the software description to the end　（Line　240-242）.

◆Since ANOVA was a repeated measure, the description was changed from “one way ANOVA” to “repeated measures ANOVA” (Line 222, 302, 660).

◆The description of the F-test was missing, so we added it (Line 226-227).

Line 26: 16s → 16S

Line 195, 284: Unifrac → UniFrac

Line 405: tricarballyic → tricarballylic

Line 414: kynureic → kynurenic

Line 416: Added phylum and family of Parabacteroides distasonis.

Line 483: Actinovateria → Actinobacteria

Line 532: Additionaly → Additionally

Fig 1: Since the description of the number of analyzes in S5 FIg was missing, we added it. As a result, there are two “Fecal microbiota” in Data analysis, so we distinguished them (all and LEX).

We hope that we have addressed all the issues raised, and would be happy to clarify further on any other issues. Thank you for consideration of our manuscript for publication in your journal.

Yours sincerely,

Yasuhiro Sasuga

Attachment

Submitted filename: Response to Reviewers_R1.doc

Click here for additional data file.^{(61.5KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0262906.r005

Decision Letter 2

Krzysztof Durkalec-Michalski

6 Dec 2021

PONE-D-21-11683R2The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: an open-label pilot studyPLOS ONE

Dear Dr. Sasuga,

Thank you for resubmitting your manuscript to PLOS ONE and making your manuscript significantly improved. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. The manuscript has been assessed by a statistical reviewer and his valuable comments significantly enrich the work. They should be carefully considered. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by January 16.2022. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: No

**********

6. Review Comments to the Author

Reviewer #3: Thoroughly proofread the manuscript. The results are not presented in an intelligent fashion. Pay attention to awkward sentences so the reader can comprehend the information being provided.

Minor revisions:

1- Line 218: Specify that the distribution was non-normal rather than “If failed.”

2- Line 219: Clarify that Scheffé refers to Scheffé’s multiple comparison method.

3- Line 221: Pearson’s correlation coefficients are used to illustrate the linear relationship between two variables. Additionally, graphs are provided. Pearson’s correlation coefficients are not obvious by inspecting graphs. Rephrase this sentence to improve clarity.

4- Lines 224-226: Clarify the use of the Wilcoxon signed-rank test. The term “non-parametric” can be dropped as a descriptor for Wilcoxon signed-rank and Freidman’s tests since no parametric versions of these tests exist.

5- Lines 227-230: Drop the portion of the sentence following the colon.

6- Line 240: Replace “background” with “baseline characteristics.”

7- Line 241-243: Upfront, state that the mean and standard deviation are being summarized. No need to repeat SD each time.

8- Line 245: Provide the percentage that corresponds to 8 of 13.

9- Lines 254-257: Clarify this sentence.

10- Line 266: Are the results statistically significant? If so, support the statement with a p-value.

11- The standard statistical term for average is mean.

12- Lines 274 -280: Clarify PRE_a, PRE_b, POST_a, POST_b.

13- Provide p-values to support statements indicating statistical significance.

14- Indicate the date range subjects were enrolled in the study.

15- The p-value associated with a correlation is a test of the null hypothesis: correlation equal to zero; however, the absolute magnitude of the coefficient indicates the strength of the linear relationship between two variables. In general, the strength or correlation coefficient is the more important statistic to focus on.

Below is a table for interpreting correlation coefficients:

Coefficient (absolute value) Interpretation

0.90 - 1.0 Very Strong

0.70 - 0.89 Strong

0.40 - 0.69 Moderate

0.10 - 0.39 Weak

less than 0.10 Negligible correlation

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

PLoS One. 2022 Jan 27;17(1):e0262906. doi: 10.1371/journal.pone.0262906.r006

Author response to Decision Letter 2

10 Dec 2021

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

Yasuhiro Sasuga, Ph.D

Hachioji Center for Research & Development

B&S Corporation Co., Ltd.

10th December 2021

Dear Dr. Durkalec-Michalski

Response to reviewers’ comments [PONE-D-21-11683R2]

Thank you for reviewing our manuscript and for the helpful comments provided.

Please find enclosed our response to the comments raised. The line numbers provided here are based on our revised manuscript with tracked changes.

1. Reviewer 3

■ Minor revisions:

■ 1- Line 218: Specify that the distribution was non-normal rather than “If failed.”

We appreciate the reviewer’s comment. We corrected to “non-normally distribution” (Line 218-219).

■ 2- Line 219: Clarify that Scheffé refers to Scheffé’s multiple comparison method.

We appreciate the reviewer’s comment. We corrected to “Scheffé’s multiple comparison method” (Lines 219, 630-631)

■ 3- Line 221: Pearson’s correlation coefficients are used to illustrate the linear relationship between two variables. Additionally, graphs are provided. Pearson’s correlation coefficients are not obvious by inspecting graphs. Rephrase this sentence to improve clarity.

We appreciate the reviewer’s comment. We rephrased this sentence (Lines 221-223).

■ 4- Lines 224-226: Clarify the use of the Wilcoxon signed-rank test. The term “non-parametric” can be dropped as a descriptor for Wilcoxon signed-rank and Freidman’s tests since no parametric versions of these tests exist.

We appreciate the reviewer’s comment. We dropped “non-parametric” (Lines 219, 228).

■ 5- Lines 227-230: Drop the portion of the sentence following the colon.

We appreciate the reviewer’s comment. We dropped the portion of the sentence following the colon (Lines 231-233).

■ 6- Line 240: Replace “background” with “baseline characteristics.”

We appreciate the reviewer’s comment. We replaced “background” with “baseline characteristics.” (Line 242).

■ 7- Line 241-243: Upfront, state that the mean and standard deviation are being summarized. No need to repeat SD each time.

We appreciate the reviewer’s comment. We corrected (Lines 243-245).

■ 8- Line 245: Provide the percentage that corresponds to 8 of 13.

We appreciate the reviewer’s comment. We described the percentage　（Line 248）.

■ 9- Lines 254-257: Clarify this sentence.

We appreciate the reviewer’s comment.　We rephrased this sentence to improve clarity (Lines 257-259).

■ 10- Line 266: Are the results statistically significant? If so, support the statement with a p-value.

We appreciate the reviewer’s comment.　We described p-value (Lines 271-273).

■ 11- The standard statistical term for average is mean.

We appreciate the reviewer’s comment. We replaced “average” with “mean.” (Lines 278, 298, 508, 525, 622, 629, 635, 652).

■ 12- Lines 274 -280: Clarify PRE_a, PRE_b, POST_a, POST_b.

We appreciate the reviewer’s comment. It has described in materials & methods. However, we added explanations to improve clarity (Lines 280-281).

■ 13- Provide p-values to support statements indicating statistical significance.

We appreciate the reviewer’s comment.　We described p-value (Lines 310, 320, 352, 360-361, 363-366).

■ 14- Indicate the date range subjects were enrolled in the study.

We appreciate the reviewer’s comment. We described the date range subjects were enrolled (Line 135).

■ 15- The p-value associated with a correlation is a test of the null hypothesis: correlation equal to zero; however, the absolute magnitude of the coefficient indicates the strength of the linear relationship between two variables. In general, the strength or correlation coefficient is the more important statistic to focus on.

■ Below is a table for interpreting correlation coefficients:

■ Coefficient (absolute value) Interpretation

■ 0.90 - 1.0 Very Strong

■ 0.70 - 0.89 Strong

■ 0.40 - 0.69 Moderate

■ 0.10 - 0.39 Weak

■ less than 0.10 Negligible correlation

We appreciate the reviewer’s comment. Focusing on the correlation coefficient, we described the coefficient interpretation (Lines 38, 376, 399, 405).

We dropped “significant” (Lines 378, 383, 388).

We added coefficient (absolute value) (Lines 401-403, 406-409).

2. Additional changes

Organized the description in the Statistical analysis section.

Line 225: relationship → associations

Line 279, 284: delete “SD”

Line 348: delete “average”

Line 352: delete “SD”

Lines 407-408: delete “phenylalanine and tyrosine metabolites”.

Lines 408-409: delete “tryptophan metabolites”.

Line 409: delete “uracil”

Line 645: 9 → nine

Multi-panel Figures: place all panels from a multi-part figure into a single page and single file (Fig 1, Fig 2).

We hope that we have addressed all the issues raised, and would be happy to clarify further on any other issues. Thank you for consideration of our manuscript for publication in your journal.

Yours sincerely,

Yasuhiro Sasuga

Attachment

Submitted filename: Response to Reviewers_R2.doc

Click here for additional data file.^{(51.5KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0262906.r007

Decision Letter 3

Krzysztof Durkalec-Michalski

10 Jan 2022

The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: an open-label pilot study

PONE-D-21-11683R3

Dear Dr. Sasuga,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Krzysztof Durkalec-Michalski, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

PLoS One. doi: 10.1371/journal.pone.0262906.r008

Acceptance letter

Krzysztof Durkalec-Michalski

12 Jan 2022

PONE-D-21-11683R3

The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: an open-label pilot study

Dear Dr. Sasuga:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Krzysztof Durkalec-Michalski

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Exercise loads in the pre-observation period and the LEX-ingestion period.

(JPG)

Click here for additional data file.^{(395.2KB, jpg)}

S2 Fig. Changes in Firmicutes to Bacteroidetes ratio of fecal microbiota.

(JPG)

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S3 Fig. Changes in α-diversity of fecal microbiota at the phylum level.

(JPG)

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S4 Fig. Correlation plots of fecal bacterial composition and urinary metabolites.

(JPG)

Click here for additional data file.^{(667.2KB, jpg)}

S5 Fig. Differential abundance of microbial taxa before and after LEX ingestion.

(JPG)

Click here for additional data file.^{(116.8KB, jpg)}

Attachment

Submitted filename: Response to Reviewers.doc

Click here for additional data file.^{(60.5KB, doc)}

Attachment

Submitted filename: Response to Reviewers_R1.doc

Click here for additional data file.^{(61.5KB, doc)}

Attachment

Submitted filename: Response to Reviewers_R2.doc

Click here for additional data file.^{(51.5KB, doc)}

Data Availability Statement

Fastq files are deposited in the DDBJ database under the accession number DRA011638 with h BioProject ID PRJDB11304 and BioSample IDs SAMD00283406-SAMD00283454.

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PERMALINK

The impact of a competitive event and the efficacy of a lactic acid bacteria-fermented soymilk extract on the gut microbiota and urinary metabolites of endurance athletes: An open-label pilot study

Mina Fukuchi

Masaaki Sugita

Makoto Banjo

Keisuke Yonekura

Yasuhiro Sasuga

Roles

Abstract

Introduction

Materials and methods

Study design and participants

Test supplement

Procedure

Fig 1. Study overview.

Bacterial DNA isolation and 16S rRNA sequencing

Analysis of urinary metabolites

Statistical analysis

Results

Study participants

Changes in the gut microbiota composition during the test period

Fig 2. Difference of fecal microbiota composition before and after the race during the pre-observation and LEX-ingestion periods.

Changes in urinary metabolites due to ingestion of LEX

Fig 3. Principal component analysis (PCA) score plots.

Table 1. Changes in urinary metabolites before and after LEX ingestion; showing only those with significant differences.

Association between gut microbiota and urinary metabolites

Fig 4. Pearson’s correlation heat map correlations between fecal microbiota and urinary metabolites.

Fig 5. Correlations between urinary metabolites and bacterial composition at the phylum level.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Krzysztof Durkalec-Michalski

Roles

Author response to Decision Letter 0

Decision Letter 1

Krzysztof Durkalec-Michalski

Roles

Author response to Decision Letter 1

Decision Letter 2

Krzysztof Durkalec-Michalski

Roles

Author response to Decision Letter 2

Decision Letter 3

Krzysztof Durkalec-Michalski

Roles

Acceptance letter

Krzysztof Durkalec-Michalski

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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