Different Impacts of Heat-Killed and Viable Lactiplantibacillus plantarum TWK10 on Exercise Performance, Fatigue, Body Composition, and Gut Microbiota in Humans

Abstract

Lactiplantibacillus plantarum TWK10, a probiotic strain, has been demonstrated to improve exercise performance, regulate body composition, and ameliorate age-related declines. Here, we performed a comparative analysis of viable and heat-killed TWK10 in the regulation of exercise performance, body composition, and gut microbiota in humans. Healthy adults (n = 53) were randomly divided into three groups: Control, TWK10 (viable TWK10, 3 × 10¹¹ colony forming units/day), and TWK10-hk (heat-killed TWK10, 3 × 10¹¹ cells/day) groups. After six-week administration, both the TWK10 and TWK10-hk groups had significantly improved exercise performance and fatigue-associated features and reduced exercise-induced inflammation, compared with controls. Viable TWK10 significantly promoted improved body composition, by increasing muscle mass proportion and reducing fat mass. Gut microbiota analysis demonstrated significantly increasing trends in the relative abundances of Akkermansiaceae and Prevotellaceae in subjects receiving viable TWK10. Predictive metagenomic profiling revealed that heat-killed TWK10 administration significantly enhanced the signaling pathways involved in amino acid metabolisms, while glutathione metabolism, and ubiquinone and other terpenoid-quinone biosynthesis pathways were enriched by viable TWK10. In conclusion, viable and heat-killed TWK10 had similar effects in improving exercise performance and attenuating exercise-induced inflammatory responses as probiotics and postbiotics, respectively. Viable TWK10 was also highly effective in regulating body composition. The differences in efficacy between viable and heat-killed TWK10 may be due to differential impacts in shaping gut microbiota.

Keywords: Lactiplantibacillus plantarum TWK10; anti-fatigue; exercise performance; heat-killed; microbiota; postbiotics; probiotics; viable.

Figure 1

Effects of TWK10 on exercise endurance performance and fatigue-associated blood indicators. (A) Endurance performance was evaluated under 85% VO_2max exercise intensity before and after TWK10 administration. Statistical differences among groups were analyzed by two-way repeated-measures ANOVA with Tukey post-hoc test. The significance of differences between parameters before and after administration was analyzed by two-way repeated-measures ANOVA with Bonferroni post-hoc test. ** P < 0.01, *** P < 0.001. During fixed intensity and period exercise tests, blood samples were collected for (B) lactate, (C) ammonia, (D) glucose, and (E) CK measurements at the indicated time points after TWK10 administration. Data are presented as mean ± SD. Statistical differences among groups were analyzed by one-way ANOVA with Tukey post-hoc test. Different letters (a, b, c) indicate significant differences among groups at P < 0.05.

Figure 1

Effects of TWK10 on exercise endurance performance and fatigue-associated blood indicators. (A) Endurance performance was evaluated under 85% VO_2max exercise intensity before and after TWK10 administration. Statistical differences among groups were analyzed by two-way repeated-measures ANOVA with Tukey post-hoc test. The significance of differences between parameters before and after administration was analyzed by two-way repeated-measures ANOVA with Bonferroni post-hoc test. ** P < 0.01, *** P < 0.001. During fixed intensity and period exercise tests, blood samples were collected for (B) lactate, (C) ammonia, (D) glucose, and (E) CK measurements at the indicated time points after TWK10 administration. Data are presented as mean ± SD. Statistical differences among groups were analyzed by one-way ANOVA with Tukey post-hoc test. Different letters (a, b, c) indicate significant differences among groups at P < 0.05.

Figure 2

Effects of TWK10 on pro-inflammatory indicators after exercise challenge. Blood samples were collected and analyzed at 120 min after fixed intensity and period exercise challenges. (A) Neutrophil to lymphocyte ratio (NLR), and (B) platelet to lymphocyte ratio (PLR) were examined. Data are presented as mean ± SD. The significance of differences among groups were analyzed by two-way repeated-measures ANOVA with Tukey post-hoc test. Differences before and after administration were analyzed by two-way repeated-measures ANOVA with Bonferroni post-hoc test. * P < 0.05, ** P < 0.01.

Figure 3

Comparisons of bacterial diversity among groups and its changes in response to intervention: (A) Box plots showing differences among the three groups (Control, TWK10, and TWK10-hk) in α-diversity indices (Shannon index and observed ASVs) before and after administration. Each box plot illustrates the median, interquartile range, minimum, and maximum values. ASV: Amplicon Sequence Variants; (B) NMDS plots of bacterial β-diversity based on weighted UniFrac distance (left panel) and unweighted UniFrac distance (right panel); (C) Distances of bacterial β-diversity based on the weighted UniFrac distance (left panel) and unweighted UniFrac distance (right panel). Comparisons of bacterial composition based on the top 10 phyla (D) and top 20 families (E) of bacteria in all samples. Others, remaining phyla or families with lower relative abundance.

Figure 4

Co-occurrence network analysis of the gut microbiota. Bacterial networks were generated using SparCC correlation coefficients, based on relative abundances at the family level: (A) In the TWK10 group before administration (TWK10_before), there were 37 nodes, and correlation coefficients ranged from |0.60| to |0.92|; (B) In the TWK10 group after administration (TWK10_after), there were 28 nodes, and correlation coefficients ranged from |0.60| to |0.74|; (C) In the TWK10-hk group before administration (TWK10-hk_before), there were 30 nodes, and the correlation coefficients ranged from |0.61| to |1.00|; (D) In the TWK10-hk group after administration (TWK10-hk_after), there were 25 nodes, and correlation coefficients range from |0.60| to |0.85|. Nodes represent bacteria families; grey and orange color edges represent negative and positive correlation coefficients, respectively. The size and the degree of the green color of nodes in the network represent the relative abundance of each taxon in each group.

Figure 5

LEfSe analysis of metabolic function profiles using PICRUSt in subjects receiving viable or heat-killed TWK10. Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed significant differences in functional profiles between before (negative score) and after (positive score) administration in the (A) TWK10 and (B) TWK10-hk groups. LDA scores (log₁₀) > 2.0 and P < 0.1 are shown.

Figure 6

Heatmap of Spearman’s correlation analysis between gut microbiota and functional parameters in subjects who received viable or heat-killed TWK10 administration. Spearman’s correlation values were calculated between the abundances of the top 20 most abundant bacterial families and phenotypic changes following administration of viable or heat-killed TWK10. Red squares, positive correlations; blue squares, negative correlations. * P < 0.05, ** P < 0.01, *** P < 0.001.