miRNAs as markers for the development of individualized training regimens: A pilot study

Abstract

Small, non-coding RNAs (microRNAs) have been shown to regulate gene expression in response to exercise in various tissues and organs, thus possibly coordinating their adaptive response. Thus, it is likely that differential microRNA expression might be one of the factors that are responsible for different training responses of different individuals. Consequently, determining microRNA patterns might be a promising approach toward the development of individualized training strategies. However, little is known on (1) microRNA patterns and their regulation by different exercise regimens and (2) possible correlations between these patterns and individual training adaptation. Here, we present microarray data on skeletal muscle microRNA patterns in six young, female subjects before and after six weeks of either moderate-intensity continuous or high-intensity interval training on a bicycle ergometer. Our data show that n = 36 different microRNA species were regulated more than twofold in this cohort (n = 28 upregulated and n = 8 downregulated). In addition, we correlated baseline microRNA patterns with individual changes in VO₂ max and identified some specific microRNAs that might be promising candidates for further testing and evaluation in the future, which might eventually lead to the establishment of microRNA marker panels that will allow individual recommendations for specific exercise regimens.

Keywords: individual training adaptation; microRNAs; physical exercise; skeletal muscle.

FIGURE 1

Regulation of miRs ‐1(‐3p), ‐21(‐5p), ‐133a(‐3p), and ‐133b in skeletal muscle samples of all six subjects in response to exercise. Normalized array data (log2‐transformed) are shown for all participants (left panels), right panels show corresponding qPCR data. Circles mark subjects that performed MICT, triangles subjects that performed HIIT training. Bottom panels show qPCR data for miR‐21‐5p in a larger cohort of subjects (MICT: n = 13, HIIT: n = 12). Left and right panels represent subjects performing HIIT and MICT, respectively. Grey lines represent subjects only included in the qPCR, but not in the microarray analysis. miR‐21‐5p was regulated 1.46‐fold (p = 0.028*), and was stronger in subjects performing MICT (1.73‐fold; p = 0.038*; n = 13) when compared to HIIT (1.19‐fold; p = 0.471; n = 12)

FIGURE 2

Expression patterns of miRs ‐379‐5p, ‐487b‐3p, ‐497‐5p and ‐503‐5p as assessed by miR microarray and qPCR analysis. Circles mark subjects that performed MICT, triangles subjects that performed HIIT training. Bottom panels show qPCR data for miR‐503‐5p in a larger cohort of subjects (MICT: n = 13, HIIT: n = 12). Left and right panels represent subjects performing HIIT and MICT, respectively. Grey lines represent subjects only included in the qPCR, but not in the microarray analysis. Overall induction was 2.15‐fold (p = 0.002**), and was stronger in subjects performing MICT (2.82‐fold; p = 0.004**; n = 13) when compared to HIIT (1.58‐fold; p = 0.188*; n = 12)

FIGURE 3

Correlation of baseline miR concentrations in skeletal muscle samples of all six subjects and ΔVO₂max. miRs were screened for a potential correlation of baseline expression levels and gains in VO₂max (ml kg⁻¹ min⁻¹) with training. Data for miRs with correlation coefficients of <−0.7 or >0.7 are shown. Circles mark subjects that performed MICT, triangles subjects that performed HIIT training