Xu Yan

Exercise training and DNA methylation in humans

The response to exercise training (trainability) has been shown to have a strong heritable component. There is growing evidence suggesting that traits such as trainability do not only depend on the genetic code, but also on epigenetic signals. Epigenetic signals play an important role in the modulation of gene expression, through mechanisms such as DNA methylation and histone modifications. There is an emerging evidence to show that physical activity influences DNA methylation in humans. The present review aims to summarize current knowledge on the link between DNA methylation and physical activity in humans. We have critically reviewed the literature and only papers focused on physical activity and its influence on DNA methylation status were included; a total of 25 papers were selected. We concluded that both acute and chronic exercises significantly impact DNA methylation, in a highly tissue‐ and gene‐specific manner. This review also provides insights into the molecular mechanisms of exercise‐induced DNA methylation changes, and recommendations for future research.

ACTN3 R577X Gene Variant is Associated with Muscle-related Phenotypes in elite Chinese Sprint/Power Athletes

Abstract Yang, R, Shen, X, Wang, Y, Voisin, S, Cai, G, Fu, Y, Xu, W, Eynon, N, Bishop, DJ, and Yan, X. ACTN3 R577X gene variant is associated with muscle-related phenotypes in elite Chinese sprint/power athletes. J Strength Cond Res 31(4): 1107–1115, 2017—The ACTN3 R577X polymorphism (rs1815739) has been shown to influence athletic performance. The aim of this study was to investigate the prevalence of this polymorphism in elite Chinese track and field athletes, and to explore its effects on athletes level of competition and lower-extremity power. We compared the ACTN3 R577X genotypes and allele frequencies in 59 elite sprint/power athletes, 44 elite endurance athletes, and 50 healthy controls from Chinese Han origin. We then subcategorized the athletes into international level and national level and investigated the effects of ACTN3 genotype on lower-extremity power. Genotype distribution of the sprint/power athletes was significantly different from endurance athletes (p = 0.001) and controls (p < 0.001). The frequency of the RR genotype was significantly higher in international-level than that in the national-level sprint/power athletes (p = 0.004), with no international-level sprint/power athletes with XX genotype. The best standing long jump and standing vertical jump results of sprint/power athletes were better in the RR than those in the RX + XX genotypes (p = 0.004 and p = 0.001, respectively). In conclusion, the ACTN3 R577X polymorphism influences the level of competition and lower-extremity power of elite Chinese sprint/power athletes. Including relevant phenotypes such as muscle performance in future studies is important to further understand the effects of gene variants on elite athletic performance.

ACVR1B rs2854464 Is Associated with Sprint/Power Athletic Status in a Large Cohort of Europeans but Not Brazilians

Skeletal muscle strength and mass, major contributors to sprint/power athletic performance, are influenced by genetics. However, to date, only a handful of genetic variants have been associated with sprint/power performance. The ACVR1B A allele (rs rs2854464) has previously been associated with increased muscle-strength in non-athletic cohort. However, no follow-up and/or replications studies have since been conducted. Therefore, the aim of the present study was to compare the genotype distribution of ACVR1B rs2854464 between endurance athletes (E), sprint/power (S/P) athletes, mixed athletes (M), and non-athletic control participants in 1672 athletes (endurance athletes, n = 482; sprint/power athletes, n = 578; mixed athletes, n = 498) and 1089 controls (C) of both European Caucasians (Italian, Polish and Russians) and Brazilians. We have also compared the genotype distribution according to the athlete’s level of competition (elite vs. sub-elite). DNA extraction and genotyping were performed using various methods. Fishers exact test (adjusted for multiple comparisons) was used to test whether the genotype distribution of rs2854464 (AA, AG and GG) differs between groups. The A allele was overrepresented in S/P athletes compared with C in the Caucasian sample (adjusted p = 0.048), whereas there were no differences in genotype distribution between E athletes and C, in neither the Brazilian nor the Caucasian samples (adjusted p > 0.05). When comparing all Caucasian athletes regardless of their sporting discipline to C, we found that the A allele was overrepresented in athletes compared to C (adjusted p = 0.024). This association was even more pronounced when only elite-level athletes were considered (adjusted p = 0.00017). In conclusion, in a relatively large cohort of athletes from Europe and South America we have shown that the ACVR1B rs2854464 A allele is associated with sprint/power performance in Caucasians but not in Brazilian athletes. This reinforces the notion that phenotype-genotype associations may be ethnicity-dependent.

The influence of α-actinin-3 deficiency on bone remodelling markers in young men

There is a large individual variation in the bone remodelling markers (BRMs) osteocalcin (OC), procollagen 1 N-terminal propeptide (P1NP) and β-isomerized C-terminal telopeptide (β-CTx), as well as undercarboxylated osteocalcin (ucOC), at rest and in response to exercise. α-actinin-3 (ACTN3), a sarcomeric protein, is expressed in skeletal muscle and osteoblasts and may influence BRM levels and the cross-talk between muscle and bone. We tested the levels of serum BRMs in α-actinin-3 deficient humans (ACTN3 XX) at baseline, and following a single bout of exercise. Forty-three healthy Caucasian individuals were divided into three groups (ACTN3 XX, n=13; ACTN3 RX, n=16; ACTN3 RR, n=14). Participants completed a single session of High Intensity Interval Exercise (HIIE) on a cycle ergometer (8×2-min intervals at 85% of maximal power). Blood samples were taken before, immediately after, and three hours post exercise to identify the peak changes in serum BRMs. There was a stepwise increase in resting serum BRMs across the ACTN3 genotypes (XX>RX>RR) with significantly higher levels of tOC ~26%, P1NP ~34%, and β-CTX (~33%) in those with ACTN3 XX compared to ACTN3 RR. Following exercise BRMs and ucOC were higher in all three ACTN3 genotypes, with no significant differences between groups. Serum levels of tOC, P1NP and β-CTX are higher in men with ACTN3 XX genotype (α-actinin-3 deficiency) compared to RR and RX. It appears that the response of BRMs and ucOC to exercise is not explained by the ACTN3 genotype.

Elite athletes’ genetic predisposition for altered risk of complex metabolic traits

BackgroundGenetic variants may predispose humans to elevated risk of common metabolic morbidities such as obesity and Type 2 Diabetes (T2D). Some of these variants have also been shown to influence elite athletic performance and the response to exercise training. We compared the genotype distribution of five genetic Single Nucleotide Polymorphisms (SNPs) known to be associated with obesity and obesity co-morbidities (IGF2BP2 rs4402960, LPL rs320, LPL rs328, KCJN rs5219, and MTHFR rs1801133) between athletes (all male, nu2009=u2009461; endurance athletes nu2009=u2009254, sprint/power athletes nu2009=u2009207), and controls (all male, nu2009=u2009544) in Polish and Russian samples. We also examined the association between these SNPs and the athletes’ competition level (‘elite’ and ‘national’ level). Genotypes were analysed by Single-Base Extension and Real-Time PCR. Multinomial logistic regression analyses were conducted to assess the association between genotypes and athletic status/competition level.ResultsIGF2BP2 rs4402960 and LPL rs320 were significantly associated with athletic status; sprint/power athletes were twice more likely to have the IGF2BP2 rs4402960 risk (T) allele compared to endurance athletes (ORu2009=u20092.11, 95% CI = 1.03-4.30, P <0.041), and non-athletic controls were significantly less likely to have the T allele compared to sprint/power athletes (OR = 0.62, 95% CI =0.43-0.89, P <0.0009). The control group was significantly more likely to have the LPL rs320 risk (G) allele compared to endurance athletes (ORu2009=u20091.26, 95% CI = 1.05-1.52, P <0.013). Hence, endurance athletes were the “protected” group being significantly (pu2009<u20090.05) less likely to have the risk allele compared to sprint/power athletes (IGF2BP2 rs4402960) and significantly (pu2009<u20090.05) less likely to have the risk allele compared to controls (LPL rs320). The other 3 SNPs did not show significant differences between the study groups.ConclusionsMale endurance athletes are less likely to have the metabolic risk alleles of IGF2BP2 rs4402960 and LPL rs320, compared to sprint/power athletes and controls, respectively. These results suggest that some SNPs across the human genome have a dual effect and may predispose endurance athletes to reduced risk of developing metabolic morbidities, whereas sprint/power athletes might be predisposed to elevated risk.

The gene SMART study: Method, study design, and preliminary findings

The gene SMART (genes and the Skeletal Muscle Adaptive Response to Training) Study aims to identify genetic variants that predict the response to both a single session of High-Intensity Interval Exercise (HIIE) and to four weeks of High-Intensity Interval Training (HIIT). While the training and testing centre is located at Victoria University, Melbourne, three other centres have been launched at Bond University, Queensland University of Technology, Australia, and the University of Brighton, UK. Currently 39 participants have already completed the study and the overall aim is to recruit 200 moderately-trained, healthy Caucasians participants (all males 18–45 y, BMIxa0<xa030). Participants will undergo exercise testing and exercise training by an identical exercise program. Dietary habits will be assessed by questionnaire and dietitian consultation. Activity history is assessed by questionnaire and current activity level is assessed by an activity monitor. Skeletal muscle biopsies and blood samples will be collected before, immediately after and 3xa0h post HIIE, with the fourth resting biopsy and blood sample taken after four weeks of supervised HIIT (3 training sessions per week). Each session consists of eight to fourteen 2-min intervals performed at the pre-training lactate threshold (LT) power plus 40 to 70% of the difference between pre-training lactate threshold (LT) and peak aerobic power (Wpeak). A number of muscle and blood analyses will be performed, including (but not limited to) genotyping, mitochondrial respiration, transcriptomics, protein expression analyses, and enzyme activity. The participants serve as their own controls.xa0Even though the gene SMART study is tightly controlled, our preliminary findings still indicate considerable individual variability in both performance (in-vivo) and muscle (in-situ) adaptations to similar training. More participants are required to allow us to better investigate potential underlying genetic and molecular mechanisms responsible for this individual variability.

Nature versus Nurture in Determining Athletic Ability.

This overview provides a general discussion of the roles of nature and nurture in determining human athletic ability. On the nature (genetics) side, a review is provided with emphasis on the historical research and on several areas which are likely to be important for future research, including next-generation sequencing technologies. In addition, a number of well-designed training studies that could possibly reveal the biological mechanism (cause) behind the association between gene variants and athletic ability are discussed. On the nurture (environment) side, we discuss common environmental variables including deliberate practice, family support, and the birthplace effect, which may be important in becoming an elite athlete. Developmental effects are difficult to disassociate with genetic effects, because the early life environment may have long-lasting effects in adulthood. With this in mind, the fetal programming hypothesis is also briefly reviewed, as fetal programming provides an excellent example of how the environment interacts with genetics. We conclude that the traditional argument of nature versus nurture is no longer relevant, as it has been clearly established that both are important factors in the road to becoming an elite athlete. With the availability of the next-generation genetics (sequencing) techniques, it is hoped that future studies will reveal the relevant genes influencing performance, as well as the interaction between those genes and environmental (nurture) factors.

An overview of technical considerations when using quantitative real-time PCR analysis of gene expression in human exercise research

Gene expression analysis by quantitative PCR in skeletal muscle is routine in exercise studies. The reproducibility and reliability of the data fundamentally depend on how the experiments are performed and interpreted. Despite the popularity of the assay, there is a considerable variation in experimental protocols and data analyses from different laboratories, and there is a lack of consistency of proper quality control steps throughout the assay. In this study, we present a number of experiments on various steps of quantitative PCR workflow, and demonstrate how to perform a quantitative PCR experiment with human skeletal muscle samples in an exercise study. We also tested some common mistakes in performing qPCR. Interestingly, we found that mishandling of muscle for a short time span (10 mins) before RNA extraction did not affect RNA quality, and isolated total RNA was preserved for up to one week at room temperature. Demonstrated by our data, use of unstable reference genes lead to substantial differences in the final results. Alternatively, cDNA content can be used for data normalisation; however, complete removal of RNA from cDNA samples is essential for obtaining accurate cDNA content.

ACE I/D gene variant predicts ACE enzyme content in blood but not the ACE, UCP2, and UCP3 protein content in human skeletal muscle in the Gene SMART study

Angiotensin-converting enzyme (ACE) is expressed in human skeletal muscle. The ACE I/D polymorphism has been associated with athletic performance in some studies. Studies have suggested that the ACE I/D gene variant is associated with ACE enzyme content in serum, and there is an interaction between ACE and uncoupling proteins 2 and 3 (UCP2 and UCP3). However, no studies have explored the effect of ACE I/D on ACE, UCP2, and UCP3 protein content in human skeletal muscle. Utilizing the Gene SMART cohort ( n = 81), we investigated whether the ACE I/D gene variant is associated with ACE enzyme content in blood and ACE, UCP2, and UCP3 protein content in skeletal muscle at baseline and following a session of high-intensity interval exercise (HIIE). Using a stringent and robust statistical analyses, we found that the ACE I/D gene variant was associated with ACE enzyme content in blood ( P < 0.005) at baseline but not the ACE, UCP2, and UCP3 protein content in muscle at baseline. A single session of HIIE tended (0.005 < P < 0.05) to increase blood ACE content immediately postexercise, whereas muscle ACE protein content was lower 3 h after a single session of HIIE ( P < 0.005). Muscle UCP3 protein content decreased immediately after a single session of HIIE ( P < 0.005) and remained low 3 h postexercise. However, those changes in the muscle were not genotype dependent. In conclusion, The ACE I/D gene variant predicts ACE enzyme content in blood but not the ACE, UCP2, and UCP3 protein content of human skeletal muscle. NEW & NOTEWORTHY This paper describes the association between ACE I/D gene variant and ACE protein content in blood and ACE, UCP2, and UCP3 protein content in skeletal muscle at baseline and after exercise in a large cohort of healthy males. Our data suggest that ACE I/D is a strong predictor of blood ACE content but not muscle ACE content.

P-86 The use of whole-genome expression to predict exercise training response in the gene smart study: preliminary results

Differences in gene expression patterns may explain, at least partly, the inter-individual variability in response to similar exercise training [1, 2]. Studies such as the Gene Skeletal Muscle Adaptive Response to Training (SMART) study (www.athlomeconsortium.org) are necessary to elucidate the molecular mechanisms underlying those individual responses. Here we examine the individual differences in gene expression following exercise and training in the Gene SMART study. Twenty-two moderately trained, healthy Caucasians participants (all males 20-45 y, BMI ≤ 30) completed a single session of High Intensity Interval Exercise (HIIE) on a cycle ergometer (8 × 2-min intervals at 85% of maximal power with 1 min of recovery between intervals), and a subset of those participants (n = 13) completed four weeks of High Intensity Interval Training (HIIT). Blood samples were collected before, immediately after HIIE, 3 h post HIIE and four weeks post HIIT. Total RNA extracted from whole blood was used for whole transcriptome analysis (GeneChip HTA 2.0 from Affymetrix UK Ltd, > 285,000 full-length transcripts). One-way repeated measures ANOVA analysis was used to identify differential expressed genes at those four time points. Changes considered significant at a 5% FDR and a fold change (FC) of 2. Compared to baseline, 123 genes were differentially expressed immediately post HIIE whereas 204 genes were differentially expressed 3 hours post HIIE (n = 22). Specifically, 34 genes were upregulated and 89 genes were downregulated immediately post HIIE, whereas 23 genes were upregulated and 181 were downregulated 3 hours post HIIE. Four transcripts overlapped between those two time points; RUNX3 and CTSW expression was significantly increased immediately post HIIE (FC = 2.6, FDR adj.p=0.007 and FC = 2.4, FDR adj.p=0.005, respectively), and significantly decreased 3 hours post HIIE (FC = −2.2, FDR adj.p=0.002 and FC = −2.1, FDR adj.p=0.003, respectively), compared to baseline. Additionally, the gene expression levels of TC21000168.hg.1 and TC21000719.hg.1 were significantly decreased (FC = −2.1, FDR adj.p=0.005 and FC = −2.1, FDR adj.p=0.007) immediately post HIIE and significantly increased 3 hours post HIIE (FC = 2.7, FDR adj.p=0.0003 and FC = 2.7, FDR adj.p=0.0003). No significant changes in gene expression were found after 4 weeks of HIIT compared to baseline (n = 13). Although a relatively small sample size, this preliminary whole-genome expression analysis from whole blood is encouraging and supports the idea of using molecular markers such as gene expression to predict individual response to exercise. More participants are currently being recruited to increase the sample size. References Bouchard C, et al. Genomic predictors of the maximal O(2) uptake response to standardised exercise training programs. J Appl Physiol 1985, 2011.110(5):1160–70. Timmons JA, et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol 1985, 2010;108(6):1487–96.