TTN-AS1 Genotype (rs1001238) and its Influence on Inflammatory Responses in Muscle Tissues in Soccer Players

Abstract

Genetic factors demonstrate influence on the components of athletic performance, but also on the activation and resolution of inflammation, muscle tissue immunity and other phenotypes. Titin (TTN) is a giant sarcomere protein that plays an essential role in muscle contraction with actin and myosin filaments. Its polymorphism may contribute to the inter-individual variation in the structural and functional properties of the muscle and tendon and its response to mechanical load, which may imply susceptibility to muscle injury. The identification of genes that may influence the levels of molecules involved in the sarcomeric structure can help to elucidate the factors and mechanisms related to muscle damage and consequent inflammatory process. The aim of the present study was to investigate the association of a TTN polymorphism, TTN-AS1 (RS1001238), with inflammation-related phenotypes in soccer players after a training session with predominance of eccentric actions. The sample consisted of 47 under-20 men's soccer players belonging to clubs in the first division of Brazilian soccer. Blood samples were collected before, 24, and 48 hours after the training session to evaluate the inflammatory responses (hematological neutrophil analysis, high sensitivity quantitative protein (CRP), tumor necrosis factor (TNFα)), muscle damage (creatine kinase (CK)), and insulin-like growth factor 1 (IGF-1). It was observed that individuals with the CC genotype for the TTN-AS1 Polymorphism (RS1001238) showed greater inflammatory responses (p<0.01) in relation to the TT and TC genotypes, with greater damage verified by CK (p<0.01) concentrations, TTN-AS1 (RS1001238) phenotypes and its possible structural alterations in skeletal striated muscle sarcomeres.

Keywords: Genetics; Polymorphism; TTN-AS1; Football; Inflammatory

HIGHLIGHTS

Analysis of the TTN-AS1 (rs1001238) polymorphism in soccer players.

Athletes with the CC genotype have greater muscle inflammatory responses.

Athletes with the CC genotype have with greater muscle damage verified by CK concentrations.

INTRODUCTION

The specialized cytoskeleton of striated muscle cells consists of highly ordered structures, the sarcomeres made from myosin, actin, titin filaments, and many other structural and regulatory proteins [¹]. The cytoskeleton is no longer seen as a static skeleton that fixes each cellular component but is a dynamic and sensitive cellular organizer that responds to several extracellular signaling. Muscle cells are no exception; however, myocytes' responses to external signals are unique because of their specialized ordered protein composition and sarcomere arrangement [²-⁴]. Titin (TTN) is a giant protein in the sarcomere that plays an essential role in muscle contraction with actin and myosin filaments. However, their usefulness goes beyond mechanical functions, extending to versatile and complex parts in sarcomere organization and maintenance, passive force, mechanosensitization, and signaling [⁵-⁶].

TTN's multiple functions are partial attributed to its large size and modular structures that interact with many protein partners [⁵-⁶]. Among the TTN domains, the N2A element is one of the unique segments contributing to compliance, contraction, structural stability, and signaling functions through protein-protein interactions with actin filaments, chaperones, stress-sensitive proteins, and proteases [¹,³,⁷,⁸,⁹]. The giant TTN gene (2q31.2) encoding TTN consists of 365 exons and transcribes an mRNA over 100kb in length. Due to extensive mRNA splicing, the human TTN gene generates seven isoforms with different physiological properties: the longer isoforms are more elastic, while the shorter isoforms are more rigid [⁹]. The main isoform of skeletal striated muscle is N2A [⁵], the longest known, expressing 312 exons but without exon 49, which encodes the N2B domain in the I-band region. In contrast, the heart-specific isoforms are shorter, and two major isoforms, N2B (191 exons) and N2BA (313 exons), were identified [³]. Depending on the type of muscle and stage of human development, different lengths of these titin isoforms are produced, generating distinct muscle phenotypes [³,⁷].

TTN polymorphisms can affect the contractile characteristics of TTN filaments, depending on their genotype, and can be a favorable or unfavorable factor for muscle performance [⁵, ⁸,¹⁰]. The TTN gene has been described as one of the major human disease genes; recently, variants of the TTN gene have been associated with numerous hereditary diseases of skeletal and cardiac muscle [⁶,^{11 12}]; Nevertheless, its relationship to physical performance, athletic status, and injury risk is almost unknown. Recent publications have shown that genetic factors have a major influence not only on components of athletic performance but also on activation and resolution of inflammation, muscle tissue regeneration, and other phenotypes [¹³-¹⁴].

Different polymorphisms may contribute to interindividual variation in the structural and functional properties of muscle and tendon and their response to mechanical load, potentially implying susceptibility to muscle injury [¹⁵]. Thus, identifying genes that may influence the levels of molecules involved in the sarcomeric structure can help to elucidate the factors and mechanisms related to muscle damage and the consequent inflammatory process [¹³,¹⁶,¹⁷].

Some studies have found associations between single nucleotide polymorphisms (SNPs) of the inflammatory cascade protein genes with several diseases [¹⁵,¹⁸,¹⁹], however, very little is known about the genetic association with the inflammatory response resulting from sports practice [¹³,¹⁴,²⁰]. Recently, Baumert and coauthors (2022) [¹⁷] found associations between two polymorphisms (SNP) for Titin, the (rs3731749) and (rs1001238), associated with muscle pain after eccentric exercises (120 maximal eccentric knee-extension on an isokinetic dynamometer) in vivo and with changes in the migration of muscle stem cells after muscle damage in vitro respectively (performed through biopsies of the vastus lateralis), both SNPs were in high linkage disequilibrium, providing evidence of a novel genetic mechanism underlying the inflammatory response to muscle damage in humans [¹⁷]. The TT genotype of the TTN-AS1 SNP (rs1001238) could lead to more elastic and plastic properties of the muscle stem cell body, positively affecting the migration efficiency of muscle stem cells at the cellular level, which in turn has an impact overall on muscle regeneration after muscle microdamage from the physical exercise. In this context, identifying different inflammatory responses after high physical demand training and associating them with different genetic profiles can be useful in determining different recovery strategies and elaborating training methods with the individualization of the load imposed on athletes [²¹-²³] Therefore, the objective of the study was to evaluate the inflammatory responses resulting from training with a predominance of eccentric actions inherent to soccer and its relationship with the expression of the TTN gene for the SNP rs1001238.

MATERIAL AND METHODS

Ethical concern

This study complied with all the norms established by the National Health Council (Resolution 466/12) involving research with human beings and was approved by the Research Ethics Committee (69253417.1.0000.5149). All procedures, risks and benefits related to the research were duly passed on to the volunteers before signing the consent form for participation in the study.

Sample

The sample consisted of 46 male under-20 soccer players with at least 06 years of experience in systematized soccer training. The athletes belong to clubs in the first division of Brazilian football and compete in competitions organized and/or recognized by the Brazilian Football Confederation (CBF). The inclusion criteria adopted were: a) age group between 18 and 20 years old; b) be bonded to an elite Brazilian soccer club; c) participate in regular training; d) no history of kidney disease; e) not be under the effect of drug treatment; f) not using any diuretic or antiseptic; g) not having a fever up to 07 days before the beginning of the study; h) read and sign the free and informed consent form. The exclusion criteria adopted were: a) presenting a complaint of pain accompanied by a diagnosis of musculoskeletal microinjury in the lower limbs, pelvis, and lumbar spine, and b) presenting a feverish or infectious state during the study period.

Procedure

The study was carried out in the third week of the pre-season of the participating teams. This period was chosen so the athletes are in equal physical conditioning and that there is not a possible inflammatory response that is too discrepant from the real situation in the case of detraining of athletes due to the vacation period before the study.

On the first day of the sample characterization study, subjects underwent a physical assessment in which body mass, height, and skinfolds were measured to calculate the fat percentage [²⁴]. On the same day, the first blood collection was performed, which was used to extract genomic DNA. The maximum oxygen consumption values (VO²max) were obtained by an evaluation carried out two weeks before the beginning of the study using the field test YoYo Endurance Test level 2 [²⁵]. The athletes were genotyped for the TTN-AS1 gene and then grouped into their respective genotypes. Table 1 shows the results of the sample characterization for age, body mass, height, fat percentage, and VO²máx variables. Values referring to the physical demand of the applied training protocol are shown in Table 2.

After sample characterization, on the second day of the study, the second blood collection was performed to obtain the baseline of inflammatory biomarkers and, after one hour, the training session began. On the third day of the study, 24 hours after training, the third blood collection was performed for biomarkers analysis. The fourth collection of blood samples was performed on the following day (day 3), 48 hours after the end of the training session.

During the study, from the sample characterization procedures until 48 hours after the training session, athletes were accommodated in the club's facilities, where all meals followed the guidance of a sports nutritionist. Participants had not taken any medication or dietary supplement with an anti-inflammatory action for at least 02 weeks before the study. Athletes able to perform training but still recovering off recent injuries were excluded from the sample, as they are constantly submitted to treatments and recovery protocols that alter inflammatory responses.

Training protocol

Athletes were familiar with the proposed training [¹⁸], but did not perform these types of exercises at least one month before the study, even though they were training regularly for 02 weeks, participating in at least 07 training sessions per week, performing both cardiovascular and resistance exercises. After the warm-up exercises, the athletes were randomly divided and did, twice, a circuit consisting of five stations with intermittent exercise combining jumps, changes of direction, accelerations, and decelerations. The permanence at each station had a duration of 03 minutes with 30-second intervals for changing stations. The activities were carried out at maximum speed and accompanied by verbal encouragement from the coaches. The total duration of training was 45 minutes. The training session load analysis was performed by recording the heart rate (HR) and other variables like the total distance covered and actions in high intensity. These variables were recorded through heart-rate-integrated GPS Polar brand devices. (Polar Team Pro®, Finland). The environmental conditions of temperature and relative humidity were 26ºC e 68% RH during the training protocol (Digital Thermo-Hygrometer, Instrutherm® HT-260).

Blood sample collections

Blood collections were performed at sample characterization moments, pre-training, 24, and 48 hours after training. In each collection, 4mL of blood was collected from each athlete in an EDTA tube (catalog: 454036 - Greiner®), e 24mL (six tubs) of serum (catalog: 454071 - Greiner®). EDTA samples were refrigerated at 4ºC, and serological samples were centrifuged at 3000g for 10 minutes and refrigerated at 4ºC and subsequently analyzed.

Hematological and biochemical analysis

Genomic DNA extractions and hematological analyses (hemogram) were carried out in the samples collected in EDTA tubes. Biomarkers were analyzed in serum samples: For analysis of muscle damage, creatine phosphokinase (CK) was used; for analysis of inflammatory state, high sensitivity quantitative reactive protein (CRP), tumor necrosis factor (TNFα) was used, and insulin-like growth factor type 1 (IGF-1). Automated cell count was obtained through the hemogram cytometry performed by the Roche XN® Series equipment (Roche Laboratories, Brazil). CK was measured using the ultraviolet kinetic method and CRP by turbidimetry, using the Atellica® Solution analyzer (Siemens Healthineers, Brazil) for both methods. Cytokines (TNFα) and IGF-1 were quantified by the Immulite® analyzer (Siemens Healthineers, Brazil) using chemiluminescence and radioimmunoassay methods.

Genotyping of TTN-AS1 SNP (rs1001238)

Genomic DNA was extracted from 500μl of whole blood collected on the first day of the study using a salting out method [²⁶] and sample quality and integrity were tested by spectrophotometry (Nanodrop, Thermo Fisher Scientific GE, MA, United States). To determine the rs1001238 polymorphism of the TTN gene, the site of interest was amplified from the genomic DNA using the following primers TTAATTTTGGCCACAATGTTAACAT (forward), GGTTCCTTCTTCAACCTCCATGAAT (reverse). And molecular probes VIC and FAM are interpreted as: 5'-TCCAACTT [ C/T] AGGTTCTT-3'. Applied Biosystems, MA, Estados Unidos Allele discrimination was performed using a genomic sequence detection system (StepOnePlus Real-Time PCR System, Applied Biosystems, MA, United States). In each well of the qPCR plate, 10-15ng of DNA (1μL) were pipetted, in addition to 12.5μL of Master mix for genotyping (TaqMan Genotyping Master Mix® - 2✕), 1.3μL of specific primers and probes (TaqMan genotyping assay mix® - 20✕) and 11,2μL of DNase-free and RNase-free water, totaling a final volume of 25μL for each sample. The amplification process started with denaturation at 95°C for 10min followed by 40 cycles of 94°C for 15 seconds and 60 seconds at 60°C. The alleles were distinguished by allelic discrimination based on the relationship between the different fluorescence generated in the StepOnePlus™ Software v2.3.

Statistics

Descriptive data are presented as mean ± standard deviation (X ± SD). The normality of data distribution was verified using the Kolmogorov-Smirnov test. For variables with a Gaussian distribution, comparisons over time were performed using a two-way analysis of variance (ANOVA two-way), with repeated measures on the second factor (Group x Moment), followed by Tukey's post-hoc when appropriate. To interpret the effect size for the ANOVA statistical differences, the eta-square was used, classified as small (0.01 < 𝜂2 ≤ 0.06), medium (0.06 < 𝜂2 ≤ 0.14) and large (𝜂2 > 0.14) [²⁷,²⁸]. All analyses were done using the JAMOVI software (The jamovi project, 2019). The significance level was set at p < 0.05. For statistical analysis, plasma concentration data were relativized according to equation 1 [²⁹]. The equation is carried out to understand the kinetics of the concentration of muscle damage biomarkers such as CK and systemic inflammatory processes such as CRP, with peaks observed in 24 hours [³⁰-³¹], serum concentrations of CRP can remain significantly higher concerning concentrations baseline for up to 48 hours [³²].

$\mathrm{Re}lativizedvalue=\frac{valueofthemoment}{highestindividualvalue}x100.$ (1)

RESULTS

Table 1
Sample characterization

Variables	Mean±SD CC Group N:15	Mean±SD TC Group N:22	Mean±SD TT Group N:9
Age (years)	19±1	18±1	18±1
Body Mass (kg)	74.3±7.3	74.3±8.3	52.3±8.3
Height (cm)	178.1±6.6	181.1±5.6	165.1±5.6
% Fat	10.3±2.2	9.2±3.2	8.2±3.2
VO2máx (mL.kg-1.min-1)	56.3±3.1	57.3±2.1	58.3±2.1

• Data are presented as mean ± standard deviation

Table 2
Physical demands of the training protocol.

Variables	Mean±SD	Minimum	Maximum
Distance (m)	3.102±166.2	2.702	3.377
High intensity running (m)	160.6±37.3	86.5	270
Sprints	20±5	13	31
High intensity accelerations	61±7	50	72
High intensity decelerations	52±9	30	72
Total high intensity actions	132±18	90	175
%HR	79.8±3.4	75.5	90.2

• The data referring to the physical demand of the session are in Table 2.

TTN AS1 genotype distributions were within the Hardy-Weinberg equilibrium (p= 0.247). The athletes were grouped by Genotypes 15 CC, 22 TC e 9 TT. There was a significant interaction between genotypes and CK concentration (Figure 1) over time (F = 3.95, p = 0.001, η2 = 0.342). In the CC group, CK concentrations were higher at 48 hours compared to the TC and TT groups (p<0.01). At baseline and 24 hours, no significant differences were observed between genotypes (p<0.526). A significant effect of time on blood CK concentrations was observed (F = 4.67, p < 0.01, η2 = 0.350).

Figure 1
CK Basal CK concentrations, 24 and 48 hours after the Protocol

There was a significant interaction between genotypes and CRP concentrations (Figure 2) over time (F = 2.34, p < 0.048, η2 = 0.059). In the CC group, CRP concentrations were higher at 48 hours compared to the TT and TC groups (p<0.01). At pre and 24 hours, no significant differences were observed between genotypes (p<0.28). A significant effect of time on CRP concentrations was observed. (F = 4.41, p < 0.01, η2 = 0.168). (F = 4.41, p < 0.01, η2 = 0.168).

Figure 2
Basal CRP concentrations, 24 and 48 hours after the Protocol

Figure 3
Baseline IGF-1 concentrations, 24 and 48 hours after the Protocol

Regarding IGF-1 concentrations (Figure 3), there was a significant interaction between genotypes and their kinetics (F = 6.47, p = 0.045, η2 = 0.14.) The genotype factor had a high effect on IGF-1 values -1 (F = 5.93, p < 0.02, η2 = 0.35) In the TT and TC groups, the IGF-1 concentrations at 24 hours were significantly higher than in the CC group (p < 0.01).

In the TT and TC groups, IGF-1 concentrations at 48 hours were significantly lower than in the CC group (There was a significant interaction between genotypes and TNFα kinetics (Figure 4) (F = 12.36, p < 0.01, η2 = 0.43). There was a large and significant effect of time (F = 9.14, p < 0.01, η2 = 0.38) and genotypes (F = 11.21, p < 0.01, η2 = 0.34) on serum TNFα concentrations. In the TT group, the values for the 48-hour time point were significantly lower compared to the CC group (p < 0.01) and different for TC and CC at the 24-hour time point (p<0.02).

Figure 4
Baseline TNF α concentrations, 24 and 48 hours after the Protocol

The neutrophils obtained through the hemogram used in the present study are presented in (Figure 5). There was a significant interaction between genotypes and neutrophil activity over time (f = 5.87, p = 0.01, η2 = 0.48). The neutrophil activity was significantly higher in the CC group compared to the TT and TC groups at the 48-hour time point (p < 0.01).

Figure 5
Basal Neutrophil concentrations, 24 and 48 hours after the Protocol

DISCUSSION

This is the first study to identify the relationship between the TTN-AS1 genotype (rs1001238) and its influence on inflammatory responses in muscle tissues in soccer players.

Due to normal biological variation, we propose that each athlete's tolerable load is unique, thus resulting in a wide interindividual variation in musculoskeletal soft tissue response to load or other stimuli. A proportion of individuals will, intrinsically have an increased risk of injury, while others may have a reduced risk of such injuries [¹⁵]. Researchers should collectively apply molecular genetics tools integrated with other scientific disciplines to elucidate the biological mechanisms of injury by helping clinicians and trainers customize the training load for the individual, allowing them to achieve optimal performance and reduce the risk of injury [²⁰,²²].

Skeletal striated muscles are characterized by the expression of the N2A titin isoform, encoded by up to 312 exons - always without exon 49 (coding for the N2B element) [³⁵]. A common feature of all titin N2A isoforms in skeletal muscle is the more extended PEVK segment than cardiac N2BA and N2B isoforms [³⁶]. Due to the alternative splicing processes that occur during skeletal muscle development, many titin N2A isoforms of different lengths are generated in various types of skeletal muscles [⁹], as well as depending on the stage of human development [³⁷,³⁸]. Leońska-duniec and coauthors (2022) [³⁹] identified a TTN mutation that alters isoform splicing in mice, demonstrating an association between isoform size and sarcomere length, with significantly greater resting sarcomere lengths corresponding to the greatest mutant titin isoforms, assuming a linear relationship between the length of the fascicle and the number of sarcomeres in series, in which fascicles expressing larger titin isoforms may be longer than those expressing more minor titin isoforms [³⁷,⁴⁰, ⁴¹, ⁴²]. PERRIN, NOSAKA and STEELE (2017) [¹²] state that differences titin length may explain the risk of muscle injury due to the mechanism of early contractile stimulation linking titin to actin, placing more pressure on titin during stretching; this observation also explains why peak torque during a session of exercise is higher in contractions initiated at long muscle lengths compared to short muscle lengths, in the same direction. BUTTERFIELD and HERZOG (2006) [⁴⁰] concluded that activation time and muscle length before stretching could influence muscle injury by significantly increasing the magnitude of fiber tension, and fiber dynamics is a more relevant variable than muscle strength dynamics in influencing muscle strength than the magnitude of muscle injury.

When comparing the genotypes for the same external load demand, our results have shown higher activity of neutrophils in individuals with the CC genotype. With this, it is possible to suggest the likelihood of athletes with this genotype present higher demands for immune system cells, which may be related to a longer acute phase of the inflammatory process, corroborating the findings of Baumert and coauthors (2022) [¹⁷] in migration responses of a mixed population of myoblasts and fibroblasts, associated with the rate of muscle recovery after an artificial assay of in vitro wound healing and muscle injury in vivo. Baumert and coauthors (2022) [¹⁷], studying the TTN-AS1 (rs1001238) polymorphism have found higher migration of myoblasts in TT homozygotes (n = 8), more cells (24h: 66.1 ± 9.56 cells; 48h: 96.9 ± 10.0 cells) for the region with muscle injury in in vitro experiments, compared to individuals with at least one C allele (n = 4; 24h: 57.0 ± 6.00 cells; 48h: 78.8 ± 10.1 cells). The kinetics of CK and CRP concentrations for the CC genotype point to a more catabolic and inflammatory outline when compared to the TT and TC genotypes, showing an elongated response to the acute inflammatory phase. The CC group showed higher significant values at 48 hours moment, while the TT and TC groups showed a curve of reduction of concentrations. [¹⁷]

Apparently, individuals with the CC genotype showed higher and more prolonged inflammatory responses and a relative delay in tissue repair in CC athletes compared to TT and TC individuals. Individuals with the T allele may probably have more elastic characteristics, producing less muscle damage and, consequently, less CK and CRP leakage. In addition, TT and TC individuals may have greater efficiency in muscle stem cell migration, which, in turn, impacts muscle regeneration, making faster tissue repair [¹⁷]. The results found in this study also demonstrate that TT and TC individuals have higher concentrations of IGF-1 24 hours after exercise compared to the CC group, evidencing a faster regenerative response by the action of IGF-1. IGF-1 is a potent mitogen produced in the liver or locally, and it induces myoblast proliferation and cell survival. It may even be considered one of the main factors regulating muscle growth and regeneration. IGF-1 stimulates myoblasts' proliferation, differentiation, and fusion in injured muscle fibers and is critical for growth and regeneration [³⁸]. Furthermore, in vitro studies have shown that IGF-1 and IGF-2 are responsible for modulating regulatory factors and promoting satellite cell proliferation and myoblast differentiation [⁴³-⁴⁵].

Thus, the main physiological effect of TNF-α is to promote the immune response by recruiting neutrophils and monocytes to the site of infection and activating them [²⁹]. It has been described that elevated TNFα attenuates myoblast fusion and differentiation, which may impair muscle regeneration [⁴⁶]. TNFα cytosine is associated with catabolic pathways and suppression of protein synthesis in skeletal muscle [⁴⁷]. Moreover, TNF infusion in mice led to a significant drop in IGF-1 concentrations, suggesting a negative influence of TNFα on the IGF-1 system [⁴⁸]. With the data shown in the present investigation, it is suggested that individuals with the TT genotype have lower concentrations of TNFα, which may positively influence the tissue repair process and explain the higher concentrations of IGF-1 observed for the T allele in the present study. Based on heredity, considering the principle of biological individuality, and adjustments in the training load distribution, different recovery strategies can be elaborated to optimize the adaptations to the training. For sessions with great physical demand, CC individuals would need a longer recovery time between training sessions and the distribution of training content in a differentiated way to avoid overtraining and the onset of injuries.

CONCLUSION

The findings suggest that, for soccer players, having the CC alleles increases muscle damage and acute inflammatory responses to eccentric training compared to those with TT and TC alleles. More studies are needed on the phenotypes of TTN-AS1 (rs1001238) and its possible structural alterations in sarcomeres of skeletal striated muscles.

REFERENCES

• ¹ Clark KA, Mcelhinny AS, Beckerke MC, Gregorio CC. Striated muscle cytoarchitecture: an intricate web of form and function. Annu Rev Cell Dev Biol. 2002; 8: 637-706.
• ² Krüger M, Linke WA. The giant protein titin: A regulatory node that integrates myocyte signaling pathways. J Biol Chem. 2011 Mar 25;286(12):9905-12.
• ³ Labeit S, Lahmers S, Burkart C, Fong C, McNabb M, Witt S, et al. Expression of distinct classes of titin isoforms in striated and smooth muscles by alternative splicing, and their conserved interaction with filamins. J Mol Biol. 2006 Sep 29;362(4):664-81.
• ⁴ Yamasaki R, Berri M, Wu Y, Trombitás K, McNabb M, Kellermayer MS, et al. Titin-actin interaction in mouse myocardium: passive tension modulation and its regulation by calcium/S100A1. Biophys J. 2001 Oct; 81(4):2297-313.
• ⁵ Adewale AO, Ahn YH. Titin N2A Domain and Its Interactions at the Sarcomere. Int J Mol Sci. 2021 Jul 15;22(14):7563.
• ⁶ Tharp CA, Haywood ME, Sbaizero O, Taylor MRG, Mestroni L. The Giant Protein Titin's Role in Cardiomyopathy: Genetic, Transcriptional, and Post-translational Modifications of TTN and Their Contribution to Cardiac Disease. Front Physiol. 2019 Nov 28;10:1436.
• ⁷ Chauveau C, Rowell J, Ferreiro A. A rising titan: TTN review and mutation update. Hum Mutat. 2014 Sep;35(9):1046-59.
• ⁸ Leońska-Duniec A, Maciejewska-Skrendo A. TTN gene’s variants as potential markers associated with muscle tissue’s disfunctions and physical performance. Acta kinesiol. 2021;15 Suppl 1:119-26.
• ⁹ Prado LG, Makarenko I, Andresen C, Kruger M, Opitz CA, Linke WA. Isoform diversity of giant proteins in relation to passive and active contractile properties of rabbit skeletal muscles. J Gen Physiol. 2005 Nov; 126(5): 461-80.
• ¹⁰ Stebbings GK, Williams AG, Herbert AJ, Lockey SJ, Heffernan SM, Erskine RM, et al. TTN genotype is associated with fascicle length and marathon running performance. Scand J Med Sci Sports. 2018 Feb;28(2):400-06.
• ¹¹ Kellermayer D, Smith JE, Granzier H. Titin mutations and muscle disease. Pflugers Arch. 2019 May; 471(5):673-82.
• ¹² Perrin C, Nosaka K, Steele J. Could titin have a role in strain-induced injuries? J Sport Health Sci. 2017 Jun; 6(2):143-4.
• ¹³ Baumert P, Hall EC, Erskine RM. The genetic association with exercise-induced muscle damage and muscle injury risk. Sport Exerc Nutr Genom. 2019. 375-407.
• ¹⁴ Larruskain J, Celorrio D, Barrio I, Odriozola A, Gil SM, Fernandez-Lopez JR, et al. Genetic Variants and Hamstring Injury in Soccer: An Association and Validation Study. Med Sci Sports Exerc. 2018 Feb;50(2):361-68.
• ¹⁵ Collins M, September AV, Posthumus M. Biological variation in musculoskeletal injuries: current knowledge, future research and practical implications. Br J Sports Med. 2015 Dec;49(23):1497-503.
• ¹⁶ Baumert P, Lake MJ, Stewart CE, Drust B, Erskine RM. Genetic variation and exercise-induced muscle damage: implications for athletic performance, injury and ageing. Eur J Appl Physiol. 2016 Sep;116(9):1595-625.
• ¹⁷ Mohr M, Draganidis D, Chatzinikolaou A, Barbero-Álvarez JC, Castagna C, Douroudos I, et al. Polygenic mechanisms underpinning the response to exercise-induced muscle damage in humans: In vivo and in vitro evidence. J Cell Physiol. 2022 Jul;237(7):2862-76.
• ¹⁸ Pimenta EM, Coelho DB, Cruz IR, Figueiredo Morandi R., Veneroso CE, Santos Carvalho MR, et al. The ACTN3 genotype in soccer players in response to acute eccentric training. Eur J Appl Physiol. 2012 Apr;112(4):1495-503.
• ¹⁹ Todendi PF, Possuelo LG, Klinger EI, Reuter CP, Burgos MS, Moura DJ, et al. Low-grade inflammation markers in children and adolescents: Influence of anthropometric characteristics and CRP and IL6 polymorphisms. Cytokine. 2016 Dec;88: 177-83.
• ²⁰ Pickering C, Suraci B, Semenova EA, Boulygina EA, Kostryukova ES, Kulemin NA, et al. A Genome-Wide Association Study of Sprint Performance in Elite Youth Football Players. J Strength Cond Res. 2019 Sep;33(9):2344-51.
• ²¹ Coso JD, Hiam D, Houwelling P, Perez LM, Eynon N, Lucia A. More than a ‘speed gene’: ACTN3 R577X genotype, trainability, muscle damage, and the risk for injuries. Eur J Appl Physiol. 2019;119: 49-60.
• ²² Massidda M, Voisin S, Culigioni C, Piras F, Cugia P, Yan X, et al. ACTN3 R577X Polymorphism Is Associated with the Incidence and Severity of Injuries in Professional Football Players. Clin J Sport Med. 2019 Jan; 29(1):57-61.
• ²³ Ruiz JR, Arteta D, Buxens A, Artieda M, Gómez-Gallego F, Santiago C. Can we identify a power-oriented polygenic profile? J Appl Physiol (1985). 2010 Mar;108(3):561-6.
• ²⁴ Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr. 1978 Nov;40(3):497-504.
• ²⁵ Bangsbo J. Fitness Training in Football - a scientific approach. Copenhagen: August Krogh Institute; 1994. 336p.
• ²⁶ Miller AS, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988 Feb 11;16(3):1215.
• ²⁷ Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge; 1988. 567p.
• ²⁸ Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013 Nov 26;4: 863.
• ²⁹ Duarte W, Rodrigues Junior JL, Paula LV, Chagas MH, Andrade AGP, Veneroso CE, et al. C-Reactive Protein and Skin Temperature of the lower limbs of Brazilian elite soccer players like load markers following three consecutive games. J Therm Bio. 2022 Apr;105:103188.
• ³⁰ Ispirlidis I, Fatouros IG, Jamurtas AZ, Nikolaidis MG, Michailidis I, Douroudos I, et al. Time-course of changes in inflammatory and performance responses following a soccer game. Clin J Sport Med. 2008 Sep;18(5):423-31.
• ³¹ Souglis A, Bogdanis GC, Chryssanthopoulos C, Apostolidis N, Geladas ND. Time course of oxidative stress, inflammation, and muscle damage markers for 5 days after a soccer match: Effects of sex and playing position. J Strenght Cond Res. 2018 Jul;32(7):2045-54.
• ³² Mohr M, Draganidis D, Chatzinikolaou A, Barbero-Álvarez JC, Castagna C, Douroudos I, et al. Muscle damage, inflammatory, immune and performance responses to three football games in 1 week in competitive male players. Eur J Apply Physiol. 2016 Jan;116(1):179-93.
• ³³ Stolen T, Chamari K, Castagna C. Physiology of soccer: An update. Sport Med. 2005;35(6):501-36.
• ³⁴ Bernal-Orozco MF, Posada-Falomir M, Quiñónez-Gastélum CM, et al. Anthropometric and body composition profile of Young professional soccer players. J Strenght Cond Res. 2020 Jul;34(7):1911-23.
• ³⁵ Bang ML, Centner T, Fornoff F, Geach AJ, Gotthardt M, McNabb M, et al. The complete gene sequence of titin, expression of an unusual approximately 700-kDa titin isoform, and its interaction with obscurin identify a novel Z-line to I-band linking system. Circ Res. 2001 Nov 23;89(11):1065-72.
• ³⁶ Freiburg A, Trombitas K, Hell W, Cazorla O, Fougerousse F, Centner T, et al. Series of exon-skipping events in the elastic spring region of titin as the structural basis for myofibrillar elastic diversity. Circ Res. 2000 Jun 9;86(11):1114-21.
• ³⁷ Ottenheijm CAC, Knottnerus AM, Buck D, Luo X, Greer K, Hoying A, et al. Tuning passive mechanics through differential splicing of titin during skeletal muscle development. Biophys J. 2009 Oct 21; 97(8): 2277-86.
• ³⁸ Fry AC, Staron RS, James CB, Hikida RS, Hagerman FC. Differential titin isoform expression in human skeletal muscle. Acta Physiol Scand. 1997 Dec;161(4):473-9.
• ³⁹ Leońska-Duniec A, Borczyk M, Piechota M, Korostyński M, Brodkiewicz A, Cięszczyk P. TTN Variants Are Associated with Physical Performance and Provide Potential Markers for Sport-Related Phenotypes. Int J Environ Res Public Health. 2022 Aug 17;19(16):10173.
• ⁴⁰ Butterfield TA, Herzog W. Effect of altering starting length and activation timing of muscle on fiber strain and muscle damage. J Appl Physiol. 2006 May;100(5):1489-98.
• ⁴¹ Hessel AL, Lindstedt SL, Nishikawa KC. Physiological mechanisms of eccentric contraction and its applications: A role for the giant titin protein. Front Physiol. 2017;8:70.
• ⁴² Maciejewska-Skrendo A, Leznicka K, Leonska-Duniec A, Wilk M, Filip A, Cieszczyk P, et al. Genetics of Muscle Stiffness, Muscle Elasticity and Explosive Strength. J Hum Kinet. 2020 Aug; 74: 143-59.
• ⁴³ Ten Broek RW, Grefte S, Von Den Hoff JW. Regulatory factors and cell populations involved in skeletal muscle regeneration. J Cell Physiol. 2010 Jul;224(1):7-16.
• ⁴⁴ Chargé SB, Rudnicki MA. Cellular and molecular regulation of muscle regeneration. Physiol Rev. 2004 Jan;84(1):209-38.
• ⁴⁵ Bryan BA, Walshe TE, Mitchell DC, Havumaki JS, Saint-Gueniez M, Maharaj AS, et al. Coordinated vascular endothelial growth factor expression and signaling during skeletal myogenic differentiation. Mol Biol Cell. 2008 Mar;19(3):994-1006.
• ⁴⁶ Stewart CE, Newcomb PY, Holly JM. Multifaceted roles of TNF-alpha in myoblast destruction: a multitude of signal transduction pathways. J Cell Physiol. 2004 Feb;198(2):237-47.
• ⁴⁷ Ling PR, Schwartz JH, Bistrian BR. Mechanisms of host wasting induced by administration of cytokines in rats. Am J Physiol. 1997 Mar;272(3 Pt 1): E333-9.
• ⁴⁸ Llovera M, García-Martínez C, López-Soriano J, Carbó N, Agell N, López-Soriano FJ, et al. Role of TNF receptor 1 in protein turnover during cancer cachexia using gene knockout mice. Mol Cell Endocrinol. 1998 Jul 25; 142(1-2):183-9.

• Funding:
  No funding is associated with this study.

Edited by

• Editor-in-Chief:
  Paulo Vitor Farago

• Associate Editor:
  Marcelo Ricardo Vicari

Publication Dates

• Publication in this collection
  28 Oct 2024
• Date of issue
  2024

History

• Received
  23 Oct 2023
• Accepted
  21 May 2024