Effets de l’entraînement en résistance, de la performance ...
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HAL Id: tel-01342188 https://tel.archives-ouvertes.fr/tel-01342188 Submitted on 5 Jul 2016 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Effets de l’entraînement en résistance, de la performance à l’unité contractile Antony Philippe To cite this version: Antony Philippe. Effets de l’entraînement en résistance, de la performance à l’unité contractile. Médecine humaine et pathologie. Université Montpellier, 2015. Français. NNT: 2015MONT4002. tel-01342188
Transcript of Effets de l’entraînement en résistance, de la performance ...
HAL Id: tel-01342188https://tel.archives-ouvertes.fr/tel-01342188
Submitted on 5 Jul 2016
HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.
Effets de l’entraînement en résistance, de la performanceà l’unité contractile
Antony Philippe
To cite this version:Antony Philippe. Effets de l’entraînement en résistance, de la performance à l’unité contractile.Médecine humaine et pathologie. Université Montpellier, 2015. Français. �NNT : 2015MONT4002�.�tel-01342188�
Délivré par Université de Montpellier
Préparée au sein de l’école doctorale
Sciences du Mouvement Humain
Et de l’unité de recherche
UMR 866 Dynamique Musculaire et
Métabolisme
Spécialité : Physiologie de l’exercice
Présentée par Antony PHILIPPE
Soutenue le 4 décembre 2015 devant le jury composé de
Mr Jean-René LACOUR, Professeur émérite, Université Lyon II Rapporteur
Mr Carlo REGGIANI, PU, Université de Padoue Rapporteur
Mr Fabio BORRANI, HDR, Université de Lausanne Examinateur
Mr Julien LAGARDE, MCU, Université de Montpellier
Mme Corinne LIONNE, DR, CNRS
Examinateur
!"#$%&'()'*&+*,+"-
Mr Robin CANDAU, PU, Université de Montpellier Directeur
Effets de l’entraînement en résistance, de
la performance à l’unité contractile
…à toi Guilhem, qui m’a mis des rêves plein la tête,
et toi Aude, avec qui je veux les réaliser…
!"
!!"
Remerciements
Je tiens avant tout à remercier Jean René LACOUR et Carlo REGGIANI pour avoir accepté d’être
les rapporteurs de ce jury. Je suis très honoré de vous présenter ces travaux. Je remercie également
Julien LAGARDE, pour avoir accepté d’être membre de ce jury et pour m’avoir confié des
enseignements de Neurosciences. Merci à Fabio BORRANI, avec qui j’ai beaucoup appris à travers
nos discussions téléphoniques. Une partie des travaux n’aurait pu être réalisée sans ta contribution
précieuse et ton expérience. Merci pour ta patience, et pardon de t’avoir harcelé de mails de
dernière minute ! Merci à Corinne LIONNE, examinatrice, marraine de thèse mais également
présidente du jury. Tu es pour moi LA référence en matière de myofibrilles et de cinétiques. J’ai
énormément appris de ton expérience et de tes conseils aussi précieux que clairs. Les journées
passées au Rapid Flow Quench vont me manquer !
Robin. A la fois directeur de thèse, mentor et ami, rien de tout ce qui est présenté ici n’aurait pu
être réalisé sans toi, pas même un sport et vie ! Tu m’as réellement transmis cette passion pour la
recherche, l’enseignement, et même pour les équations ! La réflexion autour de la recherche
pouvait se faire aussi bien au labo avec un café, qu’aux pieds des voies. Pour parler de fonctions de
transfert ou de moteurs moléculaires, y’a pas d’heures ou de lieux ! J’espère que tu as apprécié ces 3
dernières années autant que moi. Je t’appelle dimanche et on se fait une petite corrélation
charge/perf au Thorac !
Parce que la recherche ne peut pas se faire seul, je tiens également à remercier toute l’équipe de la
DMEM et particulièrement, Vincent, Guillaume, Anne et Arnaud pour nos discussions
constructives et vos conseils. Je pense aussi à toute l’équipe technique (notamment Florance pour
les entraînements à l’animalerie) et zootechnique. Florian, tu m’as beaucoup aidé depuis le début, je
me félicite de t’avoir rencontré, je suis arrivé au bon endroit. On continuera les bonnes soirées
comme avant ! Thomas, merci pour tes conseils sur la rédaction et pour m’avoir soutenu sur la fin
(j’ai dû te transmettre pas mal de mon stress !). Merci aussi de m’avoir fait découvrir la slack au
labo, ça à été une révélation ! Spéciale dédicace à Allan. Je te l’ai déjà dit, tu as énormément
contribué à ce travail. Quand je pense aux RT-qPCR, tu as été mon professeur pour ces manips, et
pour bien d’autres aussi ! Je te remercie aussi Gwen, pour tes conseils avisés quand je suis arrivé au
labo. Faire une thèse avec vous, on ne pouvait pas rêver mieux
!!!"
Parce que la recherche ne peut pas se faire sans le soutien des proches, je ne peux pas oublier mes
amis. Jess, on en a fait du chemin depuis les soirées plage à la Réunion ! A toi ma colloc’
internationale, ma presque-sœur, je suis fière de te présenter mon travail de ces 3 dernières années.
Anne, toi aussi super colloc’, merci beaucoup ! Heureusement que tu étais là dans les coups durs !
Jonathan Sensei, depuis médecine, on s’est bien débrouillé hein ? Tu m’a suivi et soutenu depuis le
début, tu as été ma bonne conscience, ta contribution à ce travail est significative, là je dis merci !
Saïd et Gabrielle, merci de m’avoir hébergé, et pour votre soutien. Depuis le temps, vous avez
toujours été là.
Je remercie bien sûr ma famille. Après m’avoir transmis vos passions pour la mer (papa) et pour
l’escalade (maman), c’est à mon tour de vous transmettre la mienne. Même si vous n’avez jamais
douté de moi, je ne pensais pas vous présenter un jour cet ouvrage.
Résumé
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Abstract
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'+'5%'%)23."+9"09.4*$".:9$*$%%):9$"$3"&1523.$"/"93"$3%&'83$0$3%"+$"&1.).%'34$".23%"+1%')**1$."
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+1($*2551$."+'3."*$."59W*)4'%)23."5&1.$3%1$.;"
!"#$%"&'&()#*+,-*&.................................................................................................&/0
(1,2*&3+434#&........................................................................................................&0
<).%$"+$."#$%&'()*'+,-"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^.....................^^^^^^^^^^^^^^"."
!"#$%&'%#&()*+,"($"-.#&////////////////////////////////////////////////////////////....................////////////."""
<).%$"+$."59W*)4'%)23."^^^^^^^^^^^^^^^^^^^^^^^^^.....................^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^/."!"
ETAT DE LA LITTERATURE 5
Chapitre 1. MODELISATION DES EFFETS DE L’ENTRAINEMENT 6
O&)34)5$"`"<'"%,12&)$"+$.".=.%-0$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"H!
F; m9'3%)6)4'%)23"+$."4,'&?$."$%"5$&62&0'34$"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"G
F;F"m9'3%)6)4'%)23"+$"*7$3%&')3$0$3%" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"G
F;F;F;"_1%,2+$."2W]$4%)($." ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FM!
F;F;B;"[3$"01%,2+$".9W]$4%)($"W'.1$".9&"*'"5$&4$5%)23"+$"*7$662&%"`"\$..)23^KOe" ^^^^^^^^"FF!
F;B;"m9'3%)6)4'%)23"+$"*'"5$&62&0'34$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FB!
B; Y$."02+-*$."%,12&):9$."'9X"02+-*$."5,=.)2*2?):9$." ^^^^^^^^^^^^^^^^^^^^^^^^^^"Fh
B;F;"_2+-*$"+$"E'3).%$&" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"Fh
B;B;"<$."02+-*$."/"5'&'0-%&$."('&)'W*$." ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FC
B;N;"<$."02+-*$."0)X%$." ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"Fn
B;N;"e(2*9%)23."($&."+$."02+-*$."5,=.)2*2?):9$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"BM
N; o'*)+)%1"+$."02+-*$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"BN
N;F;"T]9.%$0$3%"+$."5'&'0-%&$."+9"02+-*$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"BN
N;F;F;"<'"01%,2+$"+$."02)3+&$."4'&&1." ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"BN!
N;B;B;"<'"01%,2+$"+$."'*?2&)%,0$."?131%):9$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"Bh!
N;B;"<1?)%)0)%1"+$."02+-*$."/"4205*$X)%1"4&2)..'3%$"`"<$"?')3"+$"('&)'34$" ^^^^^^^^^^"NM!
h; T552&%" +$" *'" 02+1*).'%)23" +'3." *$" +20')3$" +$" *7$3%&'83$0$3%" $%" +$" *'
5,=.)2*2?)$"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"NN!
h;F;"p5%)0).'%)23"+$"*'"5$&62&0'34$"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"NN!
h;B;"#'&'4%1&).'%)23"+$."014'3).0$." .29.^]'4$3%." /" *'"5$&62&0'34$"`"<$." *)0)%$."+$"*7'%,*-%$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"Nn!
pW]$4%)6."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"hF!
!"""
Chapitre 2. DE LA CONTRACTION ! LA PLASTICIT" MUSCULAIRE 43
F; #23%&'4%)23"09.49*')&$"$%"TUO"`"[3"4=4*$"$3"029($0$3%"^^^^^^^^^^^^^^^^^^^^^"hh
F;F;"<'"&$*'%)23".%&94%9&$"^"6234%)23"+9"09.4*$".:9$*$%%):9$"^^^^^^^^^^^^^^^^^^^^^^^^^^^^"hI
F;F;F;"<'"6)W&$"$%".$."0=26)W&)**$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"hC!
F;F;B;"<$".'&420-&$"$%".$."5&2%1)3$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"hH!
F;F;N;"#=4*$"014'32^4,)0):9$"`"[3$",).%2)&$":9)"3$"%29&3$"5'."&23+" ^^^^^^^^^^^^^^^^^^^^^^"In!
F;B;"<7'4%)()%1"TUO'.):9$".9&"0=26)W&)**$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"CG!
F;B;F;"<'"0=26)W&)**$"4200$"02=$3"+71%9+$"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"CG!
F;B;B;"[3$"4)31%):9$"%&-."&'5)+$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"HF!
B; e3%&'83$0$3%"$3"&1.).%'34$A"5&$3$c"*'"62&4$"+9"W23"4S%1" ^^^^^^^^^^^^^^^^^^^^^^^"HI
B;F;"<$."02+-*$."+7$3%&'83$0$3%"$3"&1.).%'34$"4,$c"*$"&'%"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"HC
B;F;F;"<7$3%&'83$0$3%"5'&"0)01%).0$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"HC!
B;F;B"<7$3%&'83$0$3%"(2*23%')&$"'($4"29".'3."4,24."1*$4%&):9$." ^^^^^^^^^^^^^^^^^^^^^^^^^^^^"Hn!
B;F;N;"<$"02+-*$"+7$.4'*'+$"`"Y$"q'&'.,$.b="/"E-?9$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"HG!
B;B;"<'"5*'.%)4)%1"+$."6)W&$."09.49*')&$."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"nF!
B;B;F;"T+'5%'%)23.":9'3%)%'%)($.A"/"*714,$**$"09.49*')&$"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"nB!
B;B;B;"T+'5%'%)23.":9'*)%'%)($.A"/"*714,$**$"+$"*'"0=26)W&)**$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"Gh!
pW]$4%)6."^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FMC!
ETUDES REALISEES AU COURS DE LA THESE 107
Etude 1 : Quantification de la charge d’entrainement et de la performance au
judo 108
!3%&2+94%)23"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FMG!
T&%)4*$."`"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FFM"
O$&4$)($+"%&')3)3?")3%$3.)%="'3+"5$&62&0'34$"4,'3?$.":9'3%)6)4'%)23")3"]9+2;"
J Strength Cond Res;"BMFI"j93r"BGDCJ`FIHM^H;"
#234*9.)23" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FFn!
!"""
Etude 2 : Modélisation de la réponse à l’entrainement en résistance dans une
étude expérimentale chez l’animal 119
!3%&2+94%)23"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FBM!
T&%)4*$"`"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FBF"
_2+$*)3?"&$.523.$"%2"&$.).%'34$"%&')3)3?")3"'3"'3)0'*"$X5$&)0$3%".%9+=;"
Biomed Res Int;"BMFI"r"BMFI`GFhnCM"
#234*9.)23" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FBn!
Etude 3 : La réponse musculaire à l’entraînement en résistance s’exprime
essentiellement par une plasticité de l’activité ATPasique de la myosine 129
!3%&2+94%)23"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FNM!
T&%)4*$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FNB"
_9.4*$"5*'.%)4)%=")3"&$.523.$"%2"&$.).%'34$"%&')3)3?")."0')3*="423%&2**$+"W="'"0=2.)3"
TUO'.$"'+'5%'%)23;"Manuscrit en préparation"
#234*9.)23" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FIC!
Etude 4 : "volution du modèle classique des effets de l’entraînement vers une
structure physiologique 158
T&%)4*$" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FIG"
_'39.4&)%"$3"5&15'&'%)23
DISCUSSION GENERALE ET PERSPECTIVES 170
"."
TRAVAUX SUPPLEMENTAIRES 176
O="sA"K'023'%X2"#A"\)&($3%"OA"\'34,$c"T_jA"O,)*)55$"TsA"Y29)**'&+"TA"s'*W-."
pA"<)233$"#A"E233)$9"TA"#,25'&+"TA"et al. BMFI;"#,&23)4"4*$3W9%$&2*"%&$'%0$3%"
4205&20).$." 62&4$" 5&2+94%)23" a)%,29%" +)&$4%*=" '*%$&)3?" .b$*$%'*" 09.4*$"
423%&'4%)*$"0'4,)3$&=;"The Journal of Physiology IGN`"BMHF^BMnh;"^^^^^^^^^^^^"FHH!
_2&:9$%%$"OA"o$&+)$&"YA"d'+'*'"TA"t1%,)-&$"jA"O,)*)55$"TsA"K2W)%')**$"KA"d2*%'"
T;" BMFI;" T3" '.%&24=%$^+$5$3+$3%" 0$4,'3).0" 62&" 3$9&23'*" &,=%,02?$3$.).;"
Nature Neuroscience"Fn`"nhh^nIh;" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"FHn
REFERENCES BIBLIOGRAPHIQUES 204
."
.0"
Liste des publications
O9W*)4'%)23.")3+$X1$."!\!"
- Philippe, A. G.A"O=A"s;A"t'()$&A"t;"E;A"\'34,$cA"T;"_;"j;A"E233)$9A"T;A"E9..2A"U;A"u"#'3+'9A"K;"
DBMFIJ;"_2+$*)3?"%,$"&$.523.$." %2"&$.).%'34$"%&')3)3?")3"'3"'3)0'*"$X5$&)0$3%".%9+=;"BioMed
Research InternationalA"2015A"GFhnCM;",%%5`kk+2);2&?kFM;FFIIkBMFIkGFhnCM"
- O=A" s;A" K'023'%X2A" #;A" \)&($3%A" O;A" \'34,$cA" T;" _;" j;A" Philippe, A. G.A" Y29)**'&+A" T;A" v"
#'3+'9A" K;" E;" DBMFIJ;" #,&23)4" 4*$3W9%$&2*" %&$'%0$3%" 4205&20).$." 62&4$" 5&2+94%)23"a)%,29%"
+)&$4%*=" '*%$&)3?" .b$*$%'*" 09.4*$" 423%&'4%)*$" 0'4,)3$&=;" The Journal of PhysiologyA" 593DnJA"
BMHFwBMnh;",%%5`kk+2);2&?kFM;FFFNk]5,=.)2*;BMFh;BnHMCM"
- _2&:9$%%$A"O;A"o$&+)$&A"Y;A"d'+'*'A"T;A"t1%,)-&$A"j;A"Philippe, A. G.A"K2W)%')**$A"K;A"u"d2*%'A"T;"
DBMFIJ;" T3" '.%&24=%$^+$5$3+$3%" 0$4,'3).0" 62&" 3$9&23'*" &,=%,02?$3$.).;" Nature
NeuroscienceA"18DCJA"nhhwnIh;",%%5`kk+2);2&?kFM;FMNnk33;hMFN"
- T?2.%)3,2A"_;"t;A"Philippe, A. G.A"_'&42*)32A"s;"\;A"O$&$)&'A"e;"K;A"E9..2A"U;A"#'3+'9A"K;"E;A"
u"t&'34,)3)A"e;"DBMFhJ;"U&')3)3?"5$&4$)($+")3%$3.)%="'3+"5$&62&0'34$"4,'3?$.":9'3%)6)4'%)23")3"
]9+2;" Journal of Strength and Conditioning Research / National Strength & Conditioning
Association;",%%5`kk+2);2&?kFM;FIFGkj\#;MMMMMMMMMMMMMHHH"
T&%)4*$."$3"429&."+714&)%9&$"
- Philippe A.G.A" <)233$" #;A" O'?'32" T;t;" $%" #'3+'9" K;E;" _9.4*$" 5*'.%)4)%=" )3" &$.523.$" %2"
&$.).%'34$"%&')3)3?")."0')3*="423%&2**$+"W="'"0=2.)3"TUO'.$"'+'5%'%)23"
- Philippe A.G.A" E2&&'3)" t;A" #'3+'9" K;E;" e(2*9%)23" +$." 02+-*$." $05)&):9$." +$." $66$%." +$"
*7$3%&'83$0$3%"($&."93$".%&94%9&$"5,=.)2*2?):9$"
#"""
#,'5)%&$"+$"*)(&$"
- Philippe A.G.A" O=" s;A" \'34,$c" T;_;j;A" t2&0)4,)" Y;A" #,25'&+" T;A" #'9+'9" K;E;" DBMFNJ;"
O&1($3%)23" +$." %&29W*$." 09.49*2^.:9$*$%%):9$." 4,$c" *$" .52&%)6;" #,'5)%&$"`" m9'3%)6)4'%)23" +$."
4,'&?$." +$" &1$3%&'83$0$3%" +'3." *$" 4'+&$" +$" *'" &1,'W)*)%'%)23;" #22&+2331" 5'&" _;" j9*)'A" j<"
#&2).)$&A"T"Y95$=&23A"#"l1&)..23;"\'9&'05."_1+)4'*"e+)%$9&"
T&%)4*$."+$"+)669.)23"+$."4233')..'34$."
- #'3+'9"K;E;A"Philippe AGA"\'34,$c"T_jA"E2&&'3)"t;"#200$3%"0)$9X" 6)*$&" '9"($3%" DBMFIJ;"
\52&%"$%"o)$"gxFIF;"
- Philippe A.G;A"#'3+'9"K;E;A"\'34,$c"T;_;j;A"j29&."+$"6Z%$"DBMFhJ;"\52&%"$%"o)$"gxFhC;"
- Philippe A.G;A" \'34,$c"T;_;j;A"#'3+'9"K;E;"_'*)3" 4200$"93"09.4*$" DBMFhJ;" \52&%" $%"o)$"
gxFhN"
- Philippe A.G;A"#'3+'9"K;E;A"<'"W'*'+$"+$"*'"0$&"?$*1$"DBFMNJ;"\52&%"$%"o)$"gxFhF;"
#20093)4'%)23."2&'*$."
- Philippe A.G;A"O="sA"\'34,$c"T_jA"t2&0)4,)"YA"#,25'&+"TA"#'3+'9"K"DBMFNJ;"m9'3%)6)4'%)23"
+$."4,'&?$."+$"&1$3%&'83$0$3%"+'3."*$"4'+&$"+$"*'"&1,'W)*)%'%)23;"hF-0$"e3%&$%)$3."+$"_1+$4)3$"
O,=.):9$"$%"K1'+'5%'%)23"De_OKJ;"_23%5$**)$&A"C^n"0')"BMFN"
- Philippe A.G;A" <)233$" #;A" O'?'32" T;t;A" #'3+'9" K;E;A" DBMFIJ;" BNMR" +$" ?')3" +$" 62&4$" `"
5*'.%)4)%1" 0142339$" +$." %Z%$." +$" 0=2.)3$;" FF-0$" ]29&31$" +$" *7e42*$" Y24%2&'*$" \4)$34$." +9"
_29($0$3%"l90')3;"BG"0')"BMFIA"g)4$;"F$&"5&)X"420093)4'%)23"2&'*$;"
#""""
Liste des figures
Figure 1 :"K$5&1.$3%'%)23".4,10'%):9$"+9"02+-*$"4*'..):9$"+$."$66$%."+$"*7$3%&'83$0$3%""""H"
Figure 2 : K1523.$"/"93$"4,'&?$"+7$3%&')3$0$3%"+$."6234%)23."+7'5%)%9+$"$%"+$"6'%)?9$"+9"
02+-*$"+$"4*'..):9$" ------------------------------------------------------------------------------- FI"
Figure 3 : p&?'3)?&'00$"+$."'*?2&)%,0$."?131%):9$. ----------------------------------------"""BI"
Figure 4 : K$5&1.$3%'%)23".4,10'%):9$"+$"32%&$"'*?2&)%,0$"?131%):9$ ---------------------"""BH"
Figure 5"`"_1%,2+$"+$"+1%$&0)3'%)23"+$."5'&'0-%&$."+9"02+-*$""----------------------------"""BG"
Figure 6 :"eX$05*$"+7']9.%$0$3%"+$"B"02+-*$."/"4205*$X)%1"+)661&$3%$""""------------------"""NF"
Figure 7 :"K$*'%)23"$3%&$"*'"4,'&?$"+7$3%&'83$0$3%":92%)+)$33$"k"5$&62&0'34$""""----------"""Nh"
Figure 8 :"e(2*9%)23"+$"*'"5$&62&0'34$"$%"+$.")36*9$34$."$3"&1523.$"/"*7$3%&'83$0$3% - NC"
Figure 9 :"e(2*9%)23"+9"02+-*$"+7)3%$&'4%)23"'4%)3$^0=2.)3$" ------------------------------- ""hh"
Figure 10 : p&?'3).'%)23".%&94%9&$**$"+9"09.4*$".%&)1".:9$*$%%):9$" -------------------------- hI"
Figure 11 : K$5&1.$3%'%)23".4,10'%):9$"+793".'&420-&$"""------------------------------------ ""hn"
Figure 12 :"K$5&1.$3%'%)23"$3"&9W'3"+$"*7'4%)3$^s"--------------------------------------------- "hG"
Figure 13 : \%&94%9&$"+9"4205*$X$"'4%)3$"k"%&2520=2.)3$" ------------------------------------"""IB"
Figure 14 : \%&94%9&$"4&).%'**)3$"+9"6&'?0$3%"\F"+$"*'"0=2.)3$"!!""""""------------------------ Ih"
Figure 15 : e%'%.".%&94%9&$*."42339."+$"*'"0=2.)3$""--------------------------------------------"""IC"
Figure 16 : _2+-*$."6234%)233$*."5&252.1."529&"*$."02%$9&."'4%)3$^0=2.)3$"""------------ ""CM"
Figure 17 : _2+-*$"+7)3)%)'%)23"+9"52a$&.%&2b$"5'&"*$"O)"""------------------------------------"""CC"
Figure 18 : _2+-*$."5&252.1."529&"*7)3)%)'%)23"+9"52a$&.%&2b$"------------------------------ ""CH"
Figure 19 :"O&)34)5$"+9"K'5)+"t*2a"m9$34,"----------------------------------------------------"""HF"
Figure 20 : #29&W$"%=5):9$"+$"4)31%):9$"+$"5&2+94%)23"+$"O)"2W%$39$"5'&"Ktm" ---------"""HB"
Figure 21 :"o2)$."+7'4%)('%)23"+$"_UpK"*2&."+$"*7$3%&'83$0$3%"$3"&1.).%'34$" ----------"""nI"
Figure 22 : O&24$..9."+$"&1?9*'%)23"+$"*7$X5&$..)23"?13):9$."+$."Myh .9)%$"/"*7$3%&'83$0$3%"$3"
&1.).%'34$""--------------------------------------------------------------------------------------------- GF"
Figure 23 : K$*'%)23 4,'&?$"k"()%$..$"+$"423%&'4%)23"""------------------------------------------ GN"
Figure 24 : K$*'%)23"4234$3%&'%)23"$3"0=2.)3$"k"%$3.)23".514)6):9$""------------------------ Gn"
Figure 25 : e66$%"+$"*7$3%&'83$0$3%".9&"*'"#\TA"OM"$%"OMk#ST"""""----------------------------- FMM"
Figure 26 : #2y%"13$&?1%):9$"$%"'4%)()%1"TUO'.):9$"+$.").262&0$."+$"0=2.)3$""-----------"FMB"
Figure 27 : e66$%."+$"*'"%$051&'%9&$".9&"*7'4%)()%1"TUO'.):9$"$3"423+)%)23!"'4%)(1$"$%"&$*'X$"
-------------------------------------------------------------------------------------------------------- FMN"
Liste des !"#$%&!'&()*
#"!
!"#$%&'()*)!"#$%&)+$,-.&%#-$/&
!"#$%&'0)*)!"#$%&)1,234,-$/&
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I"
ETAT DE LA
LITTERATURE
La nature ne fait rien sans objet
T&).%2%$"
C"
- CHAPITRE 1 -
MODELISATION DES EFFETS DE
L’ENTRAINEMENT
H"
Principe : La théorie des systèmes
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1. Quantification des charges et performance
1.1 Quantification de l’entrainement
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#
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Contexte
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2.2.2.2. P0/CSA
Définition
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2.2.2.3. Adaptations de l’activité ATPasique de la myosine
Définitions
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PERCEIVED TRAINING INTENSITY AND PERFORMANCE
CHANGES QUANTIFICATION IN JUDO
MARCUS F. AGOSTINHO,1,2 ANTONY G. PHILIPPE,3 GILVAN S. MARCOLINO,2 EWERTON R. PEREIRA,2
THIERRY BUSSO,4 ROBIN B. CANDAU,3 AND EMERSON FRANCHINI1,3
1Martial Arts and Combat Sports Research Group, School of Physical Education and Sport, University of Sao Paulo, Sao Paulo,Brazil; 2Barueri Recreation Association and Cotia Sports Secretary, Brazil; 3Faculty of Sports Sciences, UMR 866, Universityof Montpellier, Montpellier, France; and 4Laboratory of Physiology and Exercise, University of Saint-Etienne, Saint-Etienne,France
ABSTRACT
Agostinho, MF, Philippe, AG, Marcolino, GS, Pereira, ER, Busso, T,
Candau, RB, and Franchini, E. Perceived training
intensity and performance changes quantification in judo.
J Strength Cond Res 29(6): 1570–1577, 2015—The objective of
this study was to determine the methods of quantification for
training and performance, which would be the most appropriate
for modeling the responses to long-term training in cadet and
junior judo athletes. For this, 10 young male judo athletes
(15.9 6 1.3 years, 64.9 6 10.3 kg, and 170.8 6 5.4 cm) com-
peting at a regional/state level volunteered to take part in this
study. Data were collected during a 2-year training period (i.e.,
702 days) from January 2011 to December 2012. Their mean
training volume was 6.52 6 0.43 hours per week during the
preparatory periods and 4.75 6 0.49 hours per week during
the competitive periods. They followed a training program pre-
scribed by the same coach. The training load (TL) was quantified
through the session rating of perceived exertion (RPE) and ex-
pressed in arbitrary unit (a.u.). Performance was quantified from 5
parameters and divided into 2 categories: performance in com-
petition and performance in training. The evaluation of perfor-
mance in competition was based on the number of points per
level. Performance in training was assessed through 4 different
tests. A physical test battery consisting of a standing long jump, 2
judo-specific tests that were the maximal number of dynamic chin-
up holding the judogi, and the Special Judo Fitness Test was
used. System modeling for describing training adaptations con-
sisted of mathematically relating the TL of the training sessions
(system input) to the change in performance (system output). The
quality of the fit between TL and performance was similar, whether
the TL was computed directly from RPE (R2 = 0.55 6 0.18) or
from the session RPE (R2 = 0.56 6 0.18) and was significant in
8 athletes over 10, excluding the standing jump from the com-
putation of the TL, leading to a simplest method. Thus, this study
represents a first attempt to model TL effects on judo-specific
performance and has shown that the best relationships between
amounts of training and changes in performance were obtained
when training amounts were quantified simply from RPE.
KEY WORDS combat sports, training load, specific tests,
season, rating of perceived exertion
INTRODUCTION
Competitive judo is a grappling combat sport in
which opponents try to throw, imobilize, or sub-
mit (by choke or elbow joint-lock technique) each
other to win the match, demanding a high tech-
nical and tactical development to reach success (18). Because
the match is composed by high-intensity intermittent ac-
tions, with periods of effort near 30 seconds and pauses of
approximately 10 seconds, until the end of the time limit
(3–5 minutes depending on the age category), physical fit-
ness also plays an important role in this sport (15). In gen-
eral, adult judo athletes take part in 7–12 competitions per
year (23), and this model has been introduced to cadet and
junior judo athletes (24), with high-level competitions as
Youth Olympic Games, World, Continental, and National
Championships being part of the competitive season of
young athletes. In high-level judo, hormonal (31) and bio-
chemical control (25) are used to monitor the training
responses. However, the resources—especially concerning
physical testing—for young judo athletes are smaller than for
adult judo athletes. Thus, affordable training monitoring pro-
cedures would help to increase the control of training pre-
scription for these athletes, improving performance and
avoiding problems related to overtraining, injury, and burnout.
Commonly, the use of rating of perceived exertion (RPE)
scales to monitor training responses has gained popularity
among coaches, trainers, and researchers (11). Among many
scales available (11), the approach proposed by Foster et al.
Address correspondence to Emerson Franchini, [email protected].
29(6)/1570–1577
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(12) is one of the most used one in combat sports (21,22,28–
30,32,38,40). Furthermore, specific physical fitness tests are
also recommended to monitor training responses, especially
in complex sports where the measurement during the activ-
ity is difficulty to conduct (2). The most simple and used
specific physical tests to evaluate judo athletes are the Spe-
cial Judo Fitness Test (SJFT) (14,15,35,36) and the dynamicchin-up holding the judogi (1,16). Both tests seem to dis-
criminate judo athletes from different competitive levels
(16,20) and to demand similar metabolic and fatigue profile
as that observed during the competitive match (16,17,19),
although long-term training monitoring using these tests is
lacking. Lower-body muscle power has also been reported
as important to judo technique execution (43), with vertical
jumping performance being a discriminant variable between
judo athletes from different competitive levels (44).
One important approach to improve the understanding
about training responses and prescription is to model the
training process (9), allowing coaches to make better deci-sion making about the stimulus needed to increase athletes’
performance. This approach has been used in more con-
trolled aerobic cyclical activities (42), sprint (37), and
strength training (8). Sports involving a high technical com-
ponent, such as artistic gymnastics, were only recently
Figure 1. Tested correlations between performances and training loads. Black lines are the correlations between performances and training load. Gray lines are
the correlations between performances and RPE value. GTP = global training performance; GTPs = simplified global training performance; SJFT index = Special
Judo Fitness Test index; duration is expressed in minutes.
TABLE 1. Individual coefficients of determination between actual and modeled performance.*†
Athlete
1
Athlete
2
Athlete
3
Athlete
4
Athlete
5
Athlete
6
Athlete
7
Athlete
8
Athlete
9
Athlete
10 Mean 6 SD
R2GTP/TL 0.79z 0.75z 0.70z 0.66z 0.57z 0.51z 0.48z 0.37 0.35 0.30 0.55 6 0.18
R2GTPs/TL 0.79z 0.76z 0.71z 0.65z 0.56z 0.48z 0.48z 0.36 0.35 0.30 0.54 6 0.18
R2GTP/
RPE
0.80z 0.79z 0.70z 0.67z 0.58z 0.48z 0.47z 0.42z 0.38 0.33 0.56 6 0.18
R2GTPs/
RPE
0.79z 0.79z 0.70z 0.67z 0.58z 0.44z 0.47z 0.41z 0.34 0.26 0.54 6 0.19
R2Points/
RPE
0.04 0.002 0.03 0.01 0.11 0.07 0.05 0.06 0.02 0.13 0.05 6 0.04
R2Points/
TL
0.02 0.003 0.23 0.001 0.01 0.05 0.06 0.04 0.05 0.11 0.06 6 0.07
R2SJFT
index/
RPE
0.31 0.01 0.06 0.49 0.33 0.01 0.04 0.25 0.07 0.05 0.10 6 0.12
*GTP = global training performance; TL = training load; RPE = rating of perceived exertion; SJFT index = special judo fitness testindex.
†Individual values and mean 6 SD of different coefficient of determination expressed without unit. R2GTP/TL, R
2GTPs/TL, R
2GTP/RPE,
R2GTPs/RPE, R
2Points/TL, R
2Points/RPE, and R2
SJFT/RPE have been obtained by computing model 1.zp # 0.05.
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investigated (33). However, no long-term training modeling
study about combat sports was found. Studies investigating
training modeling in sports with high technical-tactical com-ponents and dual confrontations are needed to verify the
feasibility of such approach in these sports. Additionally,
the comparison of different quantification procedures of
both training loads (TLs) and performance in combat sports
was not extensively tested. Thus, the objective of this study
was to determine the methods of quantification for training
and performance, which would be the most appropriate for
modeling the responses to long-term training in cadet andjunior judo athletes.
METHODS
Experimental Approach to the Problem
The TL was quantified through the session RPE and
expressed in arbitrary unit (a.u.). Rating of perceived exertion
was measured from the Foster session RPE scale (12), which
was presented to the athletes 30 minutes after each training
session. Training load was calculated from 2 methods, that is,the product of the duration of the training session in minute
and the RPE value and directly from the RPE value provided
by the Foster scale. This method was previously validated
for combat sports athletes (21,22,28–30,32,40), and the
Portuguese version was already used in combat sports ath-
letes (28,29,32,40).
At the beginning of the general preparatory period, the
10 judo athletes were submitted to technical, randori
(combat simulation), and strength and conditioning train-
ing sessions twice with 1-week interval. Because the train-
ing sessions were identical and had the same duration, we
used the RPE and TL of each type of session to calculate
the reliability of these variables. The following results
were found: (a) technical training session (80-minute
Figure 2. Application of the model on subject 2. A) Modelled (grey line) and actual (black dots) performance in response to training load. B) Evolution of training
load, quantified directly from the RPE value (black bar = session 1; grey bar = session 2) over the 702 days of experiment. RPE = rating of perceived exertion.
Judo Training Modeling
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duration)—RPE session 1 = 3.5 6 0.7 a.u.; TL session 1 =
276 6 58 a.u.; RPE session 2 = 4.1 6 1.1 a.u.; TL session2 = 324 6 85 a.u.; RPE intraclass correlation coefficient
(ICC) = 0.64; RPE limits of agreement = 22.47 to 1.27 (mean
difference =20.60); TL ICC = 0.64; TL limits of agreement =
2197.9 to 101.9 (mean difference =248.0); (b) randori training
session (80-minute duration)—RPE session 1 = 6.5 6 1.0 a.u.;
TL session 1 = 516 6 77 a.u.; RPE session 2 = 6.5 6 1.2 a.u.;
TL session 2 = 5206 100 a.u.; RPE ICC = 0.94; RPE limits of
agreement = 21.15 to 1.05 (mean difference = 0.05);TL ICC = 0.94; TL limits of agreement =292.0 to 84.0 (mean
difference = 24.0); and (c) strength and conditioning training
session (60-minute duration)—RPE session 1 = 5.2 6 0.8 a.u.;
TL session 1 = 312 6 47 a.u.; RPE session 2 = 4.9 6 0.9 a.u.;
TL session 2 = 294 6 54 a.u.; RPE ICC = 0.79; RPE limits of
agreement = 21.13 to 1.73 (mean difference = 0.30); TL
ICC = 0.79; TL limits of agreement = 267.8 to 103.8 (mean
difference = 18.0). Thus, good reliability values were obtainedusing this method in different types of training sessions in
young judo athletes.
Subjects
For the present analysis, 10 young male judo athletes
competing at a regional/state level took part in the study
with mean 6 SD age, body mass, and height of, respectively,
15.9 6 1.3 years, 64.9 6 10.3 kg, and 170.8 6 5.4 cm at thebeginning of the study. Their mean training volume was
6.52 6 0.43 hours per week during the preparatory periods
and 4.75 6 0.49 hours per week during the competitive peri-
ods. Their time of judo practice was 6.06 1.9 years, including
2.7 6 1.1 years registered at the Sao Paulo State Judo Feder-
ation. Data were collected during a 2-year training period
(i.e., 702 days) from January 2011 to December 2012. They
followed a training program prescribed by the same coach.The athletes and their parents were fully informed and pro-
vided their written consent before entering the investigation.
All procedures were approved by the local Ethics Committee.
Performance Quantification
Performance was quantified from 5 parameters and divided
into 2 categories: performance in competition and perfor-
mance in training. The evaluation of performance incompetition was based on the number of points per level.
This value was specifically given by the coach for each
competition considering the level (departmental, regional,
state) and the number of matches won in each competition.
Briefly, points per match won varied from 4 (regional and
friendly competitions) to 40 points (national competitions).
This was an adaptation of the Cadet and Junior International
Ranking List proposed by the International Judo Federation.Performance was assessed through 4 different tests.
A physical test battery consisting of a standing long jump,
2 judo-specific tests that were the maximal number of
dynamic chin-up holding the judogi (1,16), the number of
throws in the SJFT (15,35,36), and a specific index devel-
oped for judo (number of throws/[HRmax 2 HRrecovery])
(SJFT index) (15,17,19,35) was used. These tests were per-
formed on Sunday, after a standardized short warm-up.The standing jump test consisted in 3 trials with at least
1-minute rest in-between. The best value was retained as the
performance criterion. The number of dynamic chin-ups test
from a single maximal execution was considered. The total
number of throws during the 3 sets (A = 15 seconds, B and
C = 30 seconds each, with a 10-second rest between
TABLE 2. Indicators of goodness-of-fit of performance for various system models of training effects.*
Subject N
Model 1 (df = 2) Model 2 (df = 4) R2 gain between model 2 and 1
R2model 1 Adj R2 R2
model 2 Adj R2 f-ratio p
1 15 0.79 0.76† 0.82 0.75† 0.833 0.4632 15 0.79 0.76† 0.79 0.71† 0 13 15 0.70 0.65†4 15 0.67 0.62†5 15 0.58 0.51† 0.65 0.51† 1.000 0.4026 15 0.44 0.35† 0.46 0.24 0.185 0.8347 15 0.47 0.38† 0.77 0.68† 6.522 0.015z8 15 0.41 0.31† 0.45 0.23 0.364 0.7049 15 0.34 0.23 0.35 0.09 0.077 0.92710 15 0.26 0.14 0.44 0.22 1.607 0.248Mean 6 SD 0.55 6 0.19 0.51 6 0.2 0.59 6 0.19 0.46 6 0.26
*Model 1, model using 1 first-order component; model 2, model using 2 first-order components; N, number of measurements ofperformance; Adj R2, adjusted coefficient of determination.
†Significance of the fit: p # 0.05.zStatistical difference from model 1 components: p # 0.05.
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consecutive sets) of the SJFT was registered from a singleexecution. Athletes were familiarized with all procedures,
and these tests were described as highly reliable (10,15,34).
The ICC for the SJFT variables in our laboratory, using
a similar sample, was as follows: throws (0.73), heart rate
(HR) after the test (0.93), HR 1 minute after the test
(0.89), and index (0.89). The chin-up holding the judogi test
had an ICC of 0.98 and limits of agreement of 22.9 to 2.3
repetitions (mean difference of 20.3 repetition). The stand-ing jump test presents a high ICC (0.95) and low coefficient
of variation (2.4%) (26). We retained 2 combinations of these
tests in the calculation of the training performance, with the
objective to check whether TL would be related better with
3 or 2 tests combination, which would provide the coach the
possibility to use a smaller number of test to monitor the
athletes’ performance. The global training performance
(GTP) was determined as follows:
GTP ¼ ka standing jump
þ kb number of dynamic chin-ups
þ kc total number of throws;
where ka, kb, and kc are the respective weighting coefficients
for each test. The values of these coefficients are 0.05, 0.45,
and 0.50, respectively. They were determined based on time-
motion analysis (27) and physiological analysis (13) of judo
matches, which have a high demand on repeated all-out
efforts as presented in the SJFT, upper-body strength endur-
ance as evaluated by the chin-up dynamic test, and a lower
number of actions involving lower-body muscle power dur-ing technique application, which was evaluated by the long-
standing jump. For the second combination, simplified GTP
was computed as follows:
GTPs ¼ k number of dynamic chin-ups
þ k total number of throws;
where k is 0.5 dynamic chin-ups and the total number of
throws having an equivalent weighting factor in this
simplified performance criterion.
Mathematical Modeling
System modeling for describing training adaptations con-
sisted of mathematically relating the TL of the trainingsessions (system input) to the change in performance
(system output) (Figure 1).
For this study, 1 mathematical model based on only 1 first-
order filter effect of training on performance with an impulse
response k1:e2t=t1 was used (7).
Performance p(t) is obtained by adding the basic level of
performance p0 to the convolution product of training dose
w(t) with the impulse response. The w(t) is considered asa discrete function, that is, a series of impulse each day, wi at
day i. The convolution product becomes a summation in
which model performance pnon day n is estimated by math-
ematical recursion from the series of wi and is, thus, esti-
mated as follows:
pn¼ p0 þ k1
Xn21
i¼1
wi e2ðn2i Þ=t1 : (1)
The 2 parameters of the model were obtained from the
fitting of the measured performances and the modeled onesby the minimization of the residual sum of squares (RSS)
between them
RSS ¼XN
n¼1
½pn2pn&2; (2)
with n taking the N values corresponding to the days of
measurement of the measured performances.
Statistical Analyses
Selected data were expressed as mean 6 SD. The goodness
of the fit of performance was assessed with the coefficient of
determination (R2). The statistical significance of the model
fit was tested for each subject by using analysis of variance of
the residuals in accordance with the degree of freedom ofthe model (df = 2).
RESULTS
The quality of the fit between TL and performance was
similar, whether the TL was computed directly from RPE or
from the classical method of Foster (i.e., session RPE)according to which the TL is calculated from the product
of RPE by time of training session. Indeed, R2GTP/TL and
R2GTP/RPE were, respectively, 0.55 6 0.18 and 0.56 6 0.18
and significant in 7 (R2GTP/TL) and 8 (R2
GTP/RPE) subjects
over 10 (Table 1).
Concerning the quantification of performance variations,
no significant relation could be successfully established
between the number of points per level in competition andTL or RPE value (respectively, R2
Points/TL and R2Points/RPE;
p. 0.05; Table 1). Thus, we looked for training performance
criteria. Both GTP and GTPs provided good correlations
with RPE because R2GTP/RPE and R2
GTPs/RPE were 0.56 6
0.18 and 0.54 6 0.19, respectively (Table 1), excluding the
standing jump from the computation of the TL, leading to
a simplest method. Furthermore, no significant correlation
between SJFT and TL could be evidenced (p . 0.05; Table1). In this analysis, the optimal combination retained was the
correlation between RPE and GTPs. The typical curve of
correlation between actual and modeled performance in
response to TL is shown in Figure 2.
For the 10 athletes, the variation of the mean TL between
the first and the second year was not significant (p . 0.05),
whereas the GTP increased by 27.09 6 10.7% during the
2 years of the studied period (p , 0.001) (Table 2).
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DISCUSSION
This study is the first attempt to model the training response
of a combat sport. The most important finding of this study
was the significant relationship between TL and perfor-
mance quantification. Surprisingly, (a) the method of Foster
developed (i.e., session RPE) to quantify TLwas less adapted
than the simplest method based only on the scale (i.e., RPE
value) to appreciate the intensity of the training sessions and
(b) the classical SJFT index was less appropriated to describe
the performance variations than the combination of dynamic
chin-up holding the judogi and the number of throws during
the SJFT.
Wallace et al. (41) used, among other methods, the session
RPE to quantify TL and to verify its relationship with per-
formance in runners and observed a correlation of
0.60 6 0.10 between modeled and actual performance. As
a possible explanation for the moderate relationship found,
authors reported that they had observed a poor sensitivity to
small changes in intensity during moderate-to-hard exercise
when using the session RPE scale and recommended the
substitution of the CR-10 scale by a CR-100 or 6–20 RPE
scales as a possible solution to improve the sensibility of TL
quantification. The quantification of the TL by the Borg
scale takes into consideration the difficulty of the training
session by appreciating both its intensity and duration. The
Foster method consisting in multiplying both difficulty and
duration of the daily TL takes into account in reality twice
the duration leading to a minimization of the intensity
impact on the TL. Consequently, the Foster method was
less adapted than the simple quantification by the scale
(i.e., RPE value). Alternative methods based on HR zones
(4) or number of movement repetitions weighted by specific
coefficients (6,33) would be also tested in future studies with
judo athletes.
Otherwise, in this study, the best criteria to quantify
performance would have been to use directly the results in
competition. However, our analysis indicated that the num-
ber of points per level did not constitute a good indicator, as
no significant correlation could be established between this
criterion and the dynamic of TL (Table 1). This result is easily
comprehensible, so far as the level of the opponent was not
included in this performance criterion. Except for the inter-
national level judo athletes who are ranked by the Interna-
tional Judo Federation, following the same criteria, this aspect
is hard to evaluate for other athletes. Alternatively, to consider
the results in competitions, a technically and tactically homo-
geneous group of judo athletes taking part in the same com-
petitions should be analyzed, which is quite difficult to
investigate and control during such a long period. Thus, the
natural alternative solution would have been to use a perfor-
mance criterion included in the training schedule of judo
athletes. The specific test developed for judo, SJFT index,
was the first candidate test. Nevertheless, no significant rela-
tionship has been evidenced between this index and RPE
(p . 0.05; Table 1). Interestingly, better results were obtainedby simplifying this test to the combination of number of
throws to the number of dynamic chin-ups and standing long
jump (i.e., GTP). The best relationship between this global
training test and the dynamic of TL was obtained. Finally, the
simplification of the GTP by excluding the standing long
jump (i.e., GTPs) provided similar results (Table 1).
The practice of judo requires tactical, technical, and
physical development. Modeling training response couldlead to an optimization of the training prescription and can
improve our knowledge on the effects of training on
performance. Nevertheless, the limitations of the modeling
approach need to be taken in consideration. The mean R2
value obtained in this study (0.54 6 0.19; n = 10) could be
compared with the results obtained for swimmers (0.56 6
0.06) (39), reflecting the high degree of complexity of the
judo practice especially as this result was obtained froma model taking into account 1 transfer function, whereas in
the study on swimmers, the extended model proposed by
Busso (5) was successfully applied because of a greater num-
ber of performance tests (32 6 3.8).
The model proposed by Banister et al. (3) was the most
used one, which includes 2 components: 1 reflecting the
long-term adaptation to training (positive effect as for the
simpler model used in this study) and 1 reflecting the neg-ative effects of training on performance (i.e., fatigue). We
have unsuccessfully tested the model of Banister on our data.
For example, when using RPE value and GTPs for quanti-
fying the training and the performance, respectively, the fit-
ting was significant in only 4 subjects for the model of
Banister because of the addition of further parameters. It is
noteworthy that the df of this model is 4, whereas the df ofthe model used in this study was only 2. The R2 adjusted tothe number of model parameters was lower with the model
of Banister (0.46 6 0.26) than the model with only 1 com-
ponent used in this study (0.51 6 0.2). Furthermore, an
F-test on the decrease in residuals according to the increase
in model parameters (8) showed that the addition of a neg-
ative component in the model gave a significantly better fit
in only 1 subject. It was the reason why we have retained the
model with only 1 component in this study (Table 2).A possible explanation of the unsuccessful application of
the Banister model could be that the measures of perfor-
mance were to space out. It suggests that for sports involving
a highly complex coordination and both technical and
physical capacities, performance needs to be measured more
frequently if the negative component of training (i.e., fatigue)
wants to be evidenced as it has been shown in the study of
Sanchez et al. (33), where 33 performance measures havebeen collected in 3 months in elite gymnasts. Thus, by
including several times a week after the training session
the simple testing procedure including number of throws
and the number of dynamic chin-ups, it would be possible
to reduce the average lapse time between 2 measures of
performance (i.e., 48.5 6 1.7 days in our study).
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The TL stayed significantly constant between the 2 years ofthe experiment, suggesting that the perception of the training
session intensity expressed by the RPE was reliable to
estimate TL because all the athletes had the same coach
and a constant training program over the 2 years of the
experiment. However, it is important to emphasize that
the GTP increased by 27.09 6 10.7% during the 2 years of
the studied period. This can be explained by the fact that the
performance test battery is based on both physical and tech-nical development, especially the number of throws in the
SJFT. Thus, a technical improvement may have resulted in
increased performance independently from the TL. More-
over, the use of the training impulse (TRIMPS) method of
Banister on the whole session including the recovery period
could constitute a solution in so far as the anaerobic compo-
nent that slips from measure of the HR during the exercise
becomes perceptible if the recovery HR is also taken intoaccount. Nevertheless, the session RPE method for TL quan-
tification can be considered as a good indicator for most of the
athletes, as the TL stayed constant.
The aim of this study was to determine whether the
session RPE training quantification method was appropriate
to modeling the responses to long-term training in cadet and
junior judo athletes. In conclusion, this study has shown that
the best relationships between amounts of training andchanges in performance were obtained when training
amounts were quantified simply from RPE. Furthermore,
best results were obtained when the performance was
quantified using a combined criterion that includes maximal
specific movement repetitions of chin-ups gripping the
judogi and throws during the SJFT, excluding the standing
jump test so far, as including this test in the GTP did not
enhance the correlation coefficient. Based on this prelimi-nary study, it is now possible to move to a systematic
approach of training response especially with a frequent
monitoring of performance based on simple tests included in
the training program of judo athletes. The goal is to attain
a reliable description of the training process in complex
disciplines such as combat sports to have access to models to
simulate training strategies and to optimize the planning of
future training.
PRACTICAL APPLICATIONS
Proper training monitoring is a key component to under-
stand training adaptation and improve training prescription.
Although judo-specific tests have been developed, the
comprehension of the impact of TL on performance change
in these tests thoroughout the season was not investigated
previously. The results of this study demonstrated thatsimply monitoring the RPE can predict more than 50% of
the variance on performance changes in judo-specific tests
(i.e., gripping strength endurance performance and number
of throws during a high-intensity intermittent test). Thus,
coaches should include the monitoring of RPE in each
training session to better understand the variation on
judo-related performance. Additionally, this approach canbe improved over the years and specific decision making
concerning the proper stimulus for each athlete can be
developed, which can guarantee the application of the
individualization training principle.
ACKNOWLEDGMENTS
The authors thank the Brazilian Sports Ministry and the
CNPq for the financial support to conduct this study(process number 487302/2013-3).
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Hindawi Publishing CorporationBioMed Research InternationalArticle ID 914860
Research Article
Modeling the Responses to Resistance Training in an AnimalExperiment Study
Antony G. Philippe,1,2,3 Guillaume Py,1,2,3 François B. Favier,1,2,3 Anthony M. J. Sanchez,4
Anne Bonnieu,1,2,3 Thierry Busso,5 and Robin Candau1,2,3
1INRA, UMR866 Dynamique Musculaire et Metabolisme, 34000Montpellier, France2UFR STAPS, Universite Montpellier 1, 34000Montpellier, France3Universite Montpellier 2, 34000Montpellier, France4Laboratoire Europeen Performance Sante Altitude, Departement STAPS, Universite de Perpignan EA 4604, Via Domitia, 66120 Font-Romeu, France5Laboratoire de Physiologie de l’Exercice, Universite de Lyon, 42000 Saint-Etienne, France
Correspondence should be addressed to Antony G. Philippe; [email protected] and Robin Candau;[email protected]
Received 16 October 2014; Accepted 26 December 2014
Academic Editor: Heide Schatten
Copyright © Antony G. Philippe et al.(is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
(e aim of the present studywas to test whether systemsmodels of training e)ects on performance in athletes can be used to explorethe responses to resistance training in rats. 11 Wistar Han rats (277± 15 g) underwent 4 weeks of resistance training consisting inclimbing a ladder with progressive loads. Training amount and performance were computed from total work and mean powerduring each training session. (ree systems models relating performance to cumulated training bouts have been tested: (i) witha single component for adaptation to training, (ii) with two components to distinguish the adaptation and fatigue produced byexercise bouts, and (iii) with an additional component to account for training-related changes in exercise-induced fatigue. Modelparameters were ,tted using amixed-e)ectsmodeling approach.(emodel with two components was found to be themost suitableto analyze the training responses (!2 = 0.53;" < 0.001). In conclusion, the accuracy in quantifying training loads and performancein a rodent experiment makes it possible to model the responses to resistance training.(is modeling in rodents could be used infuture studies in combination with biological tools for enhancing our understanding of the adaptive processes that occur duringphysical training.
1. Introduction
Adaptations to training are related to the amount of workperformed during the exercise sessions. (e sum of theseinputs yields increases and decreases in performance capacitybecause both adaptation and fatigue are produced by exercisebouts. Systems models have been developed to quantifythese antagonistic e)ects of physical exercise on humanperformance. In 1975, Banister et al. [1] proposed the ,rst andmost frequently usedmodel, which includes two componentsin order to distinguish the adaptations and fatigue that occurwith training. A simplermodel with only one component wasalso proposed to analyze the biological responses inducedby endurance training [2]. (e most complex model is an
extension of that proposed by Banister et al., in which theresponse to a single exercise depends on past training [3].Using suchmodels with data from animal experiments wouldo)er the opportunity to go beyond the simple quanti,cationof the relationship between the amount of exercise trainingand performance and would thereby improve our knowledgeabout the nature of the adaptive processes that take placeduring training.
Modeling training e)ects in rodents presents severaladvantages over models in athletes. Animal models allow themeasurement of training e)ects for a broad range of trainingsituations, loads, and intensities, which would be unethicalin athletes. Moreover, the training load and performance
2 BioMed Research International
Table 1: Change in additional loads li'ed by rats during the trainingprogram.
Training sessions Load (% body mass) Mean load ± SD (g)
1 to 5 50 143.8 ± 10.26 80 248 ± 20.17 and 8 100 312.6 ± 24.69 to 13 120 397.1 ± 34.714 to 16 130 450.9 ± 37.817 and 18 140 497.5 ± 41.619 150 539.7 ± 47.6
can be controlled with high precision, especially in thecontext of resistance exercise (RE) on a climbing ladder.0isprecision enables researchers to capture small details of thetraining process and, ultimately, to optimize the structureof the model itself. Rodent models authorize greater inva-siveness, yield more biological information, and thereforeprovide greater insight into the adaptive processes that occurduring training, particularly regarding the link between theadaptive cell mechanisms and training e1ects. In addition,the animal model could reduce the sources of variability inresponse to training compared with a human model. 0einterindividual variability is naturally decreased in animalswith the same genetic background. Obviously, parametersexternal to training (nutrition, sleep quality, fatigue relatedto activities other than training, etc.) are controlled inanimals as opposed to humans. 0is homogeneity in theresponses to physical exercise in animals allows us to takeadvantage ofmixed-e1ectsmodeling to analyze the responsesof a group of animals, taking interindividual variability intoconsideration. When repeated measurements are made onseveral related statistical units,mixed-e1ectsmodeling allowsa more robust estimation of model parameters than usingonly available individual data [4–6]. 0e single-individualmodel has generally been used in human studies, with theexception of one work in which the mixed-e1ects model wasapplied to a group of elite swimmers [7].
Among the training programs, RE is particularly suitablefor animal studies because RE is associated with high gainsin performance, muscle strength, and muscle 2ber cross-sectional area. RE is characterized by exercise performedbetween 60% and 80% of the maximum load, and severalexperimentalmodels have been developed to evaluatemuscleand physical performance in response to RE. In rats, vol-untary exercise based on ladder climbing activity has beenshown to induce muscle hypertrophy, changes in muscletypology, and increased force and power output [8]. One ofthe 2rst studies using ladder climbing as amodel of resistancetraining [9] showed that, a'er 26weeks of resistance training,the trained rats were able to climb 40 cmwhile carrying up to140%of their bodymass, without changes in the ratio betweenbody andmuscle (EDL and soleus) mass, in comparison withcontrols. More recently we found that rats could climb 1
meter while carrying 150% of their body mass a'er 4 weeksof resistance training, in association with hypertrophy of48% of 2ber IIx in FDP muscle [10]. A'er 8 weeks, the rats
Time
Performance
0
0
Positivecomponent
Negativecomponent
k1
k2
tn tg
k1 − k2
pg
Figure 1: Schematic representation of the response to 1 unit oftraining according to Model-2Comp. Performance results from thedi1erence between two training components. In the casewhere !2 isgreater than !1, performance decreases 2rst a'er the training bout.A'erwards, the negative component decreases more quickly thanthe positive component, in the case where "1 is greater than "2,resulting in performance recovery and peaking when the di1erencebetween the negative and positive components is the greatest. 0eresponse to a training bout is characterized by "!, the time necessaryto recover initial performance a'er the training session, "", the timenecessary to reachmaximal performance, and #", the maximal gainin performance for 1 training unit.
could li' up to 210% of their body mass. Another study [11]demonstrated a 287% increase in the maximal amount ofbody weight that the animals could carry a'er 8 weeks oftraining (3 sessions a week).
REmodel o1ers the opportunity to quantify both trainingwork and performance in animal with a great accuracy.0us, the twofold aim of the present study was to (i) testwhether the systems models used to describe the trainingresponse in athletes could be applied in rats and (ii) verify theapplicability of the mixed-e1ects model in animals with thesame genetic background in order to improve the statisticalstrength of the training response model.
2. Methods
2.1. Animals and Experimental Design
BioMed Research International 3
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20 25 30
Perform
ance output (W
)
Time (days)
0
20
40
60
80
0 5 10 15 20 25 30
Training input (J)
Time (days)
Figure 2: Quanti(cation of training (systems input) and performance (systems output). Values are expressed in mean ± SEM. Note that, forthe training input, the variability is very low because the animals had the same age and the same training load calculated as a percentage ofbody mass.)us, SEM bars are hardly visible.
2.1.1. Ethics Statement. )is study was approved by theCommittee on the Ethics of Animals Experiment of Langue-doc Roussillon in accordance with the guidelines from theFrench National Research Council for the Care and Use ofLaboratory Animals (permit number: CEEA-LR-1069).
2.1.2. Animal Model. Eight-week-old Wistar Han rats (277 ±15 g; ! = 11) obtained from Charles River Laboratories(L’Arbresle, Rhone, France) were housed at a constant roomtemperature and humidity and maintained in a 12 : 12 h light-dark cycle. )ey had access to standard rat chow (A04,Scienti(c Animal Food & Engineering, Augy, France) andwater ad libitum.
2.1.3. Resistance Training Protocol. )e rats underwent 4
weeks of progressive resistance training. )e exercise con-sisted of climbing a 1-meter-high homemade ladder inclinedat 85∘ ten times. )e ladder was adapted from the apparatusof Lee et al. [12]. Training sessions were held in the a1ernoon,(ve times aweek.A cloth bag containingweightswas attachedto the base of the tail with tape. )ree days before training,the rats were familiarized with the apparatus by climbingit twice with 50% of body weight. In accordance with theprotocol proposed by Begue et al. [10], the initial weightattached to the tail was 50% of the rat body weight and wasincreased progressively until 150% a1er 4 weeks (Table 1).Each training session consisted in one set of 10 repetitionswith 2min rest between trials. All rats were able to performten climbs per training session. Rats from the same cage weretrained together. Precisely, rats were placed on a platform onthe top of the ladder and one of them was put on the 2oorat the base of the ladder. )e working rat quickly joined itscongeners spontaneously.
2.2. Training and Performance Quanti$cation. Training work(TW in J) was calculated as the potential work developedduring the training sessions:
TW = (#load +#rat) ⋅ & ⋅ Δℎ ⋅ ), (1)
where mass (#) is expressed in kg, & is the constant of thegravity on earth expressed in m⋅s−2, ℎ is the distance climbedin m, and) is the number of repetitions.
Performance was the power output developed during thefull climbing session, computed as the work done againstgravity (TW) divided by total climbing time (s) and expressedin W:
Performance = TW
time. (2)
Each climb generally lasted between 3 and 25 s dependingon the load carried by the rats.
2.3. Modeling of the Training E%ects
2.3.1. Basic Frameworks. Since the original work of Banisterand coworkers [1], systemsmodeling has been used to analyzethe adaptations to physical training in subjects enrolled incontrolled experiments or in athletes in real-life situations[13, 14].)is approach considers the body as a system whoseoutput is the performance varying with the amounts oftraining ascribed to input. Systems theory allows the analysisof a dynamical process using abstraction from mathematicalmodels. A system is characterized by at least one input andone output, and the system behavior is characterized by atransfer function *(+, ,) relating output at a given time toprevious inputs. Assuming the formulation of the transferfunction, the set of parameters characterizing a subject’sbehavior (noted ,) is estimated by (tting the model outputto the actual data. )e number of parameters which canbe introduced in the model is limited by the precision ofthe data that can be collected to quantify training inputand performance output. An analysis of the goodness-of-(t is needed to test the statistical signi(cance of the model,especially to compare models di3ering in complexity, that is,the number of equations and related parameters giving thedegrees of freedom of the competing models (df).
)e transfer function *(+, ,) gives the model perfor-mance at time + by using the product of convolution asfollows:
- (+) = - (0) + . (+) ∗ * (+, ,) , (3)
4 BioMed Research International
where !(0) is the initial performance and the product ofconvolution is de"ned by
" (#) ∗ % (#, &) = ∫!
0" (() ⋅ % (# − (, &) +(. (4)
#e discretization of (2) gives
! (,Δ#) = ! (0) +"−1∑$=1" (/Δ#) ⋅ % ((, − /)Δ#, &) , (5)
where # = ,Δ# and "(0) is assumed to be equal to 0.Fixing the value of Δ# to 1 day led us to consider "(#) as adiscrete function, that is, a series of impulses each day: "$on day /, and the product of convolution as a summation inwhich the model performance !" on day , is estimated by
mathematical recursion from the series of "$ before day ,.2.3.2. Systems Models. #e most o(en used model initiallyproposed by Banister et al. [1] is named Model-2Comp inthe present study (Figure 1). #e system operates in accor-dance with a transfer function resulting from the di)erencebetween two components: one acting positively on perfor-mance ascribed to training adaptations and the second actingnegatively on performance ascribed to the fatiguing e)ectsof exercise. Responses to training are thus characterized bythe set of model parameters including two gain-terms 11 and12 and two time constants 21 and 22.#e equation of Model-2Comp is
!" = ! (0) + 11 ⋅"−1∑$=1"$ ⋅ 3−("−$)/%1 − 12 ⋅
"−1∑$=1"$ ⋅ 3−("−$)/%2 . (6)
To assess the statistical signi"cance of Model-2Comp, itsgoodness-of-"t was compared with that of a systems modelcomprising only one training component (Model-1Comp),whose equation is
!" = ! (0) + 11 ⋅"−1∑$=1"$ ⋅ 3−("−$)/%1 . (7)
It was shown that the "tting of performance can besigni"cantly improved with a model with 12 varying overtime in accordance with system input [3]. We tested thismodel, noted here as Model-3Comp, whose equation is
!" = ! (0) + 11 ⋅"−1∑$=1"$ ⋅ 3−("−$)/%1
−"−1∑$=1(1 (0) − Δ1$2) ⋅ "$ ⋅ 3−("−$)/%2
(8)
in which the value of 12 at day / is estimated by mathematicalrecursion using a "rst-order "lter with a gain term 13 and atime constant 23
Δ1$2 = 13 ⋅$∑&=1"& ⋅ 3−($−&)/%3 . (9)
We added the value of 12 at time 0 in this study, noted12(0).
0.00
0.20
0.40
0.60
0.80
0 5 10 15 20 25 30
Time (days)
In!uence of training (W
)
ip
in
Figure 3: Mean ± SEM of the sum of positive and negativein4uences of training on performance.
2.3.3. Estimation of Model Parameters and Statistics. #eparameters for the models were determined by "tting themodel performances to actual performances for the entiregroup of rats using a mixed-e)ects model. #is modelincorporated a systematic component for the mean responseof the population and a random component for each animal’sresponse around the mean. #e general model included (i)common time constants: 21 for Model-1Comp, 21 and 22for Model-2Comp, and 21, 22, and 23 for Model-3Comp;(ii) a subject-speci"c intercept !(0); and (iii) subject-speci"cmultiplying factors for each component: 11 for Model-1Comp, 11 and 12 forModel-2Comp, and 11, 12(0), and 13 forModel-3Comp. #e set of model parameters was calculatedto produce the equation that most closely "t the data points.Using the generalized reduced gradient (GRG) algorithmin the Excel solver, the parameters were determined byminimizing the residual sum of squares (RSS) between themodeled and measured performances given by
RSS ='∑(=1
)∑"=1(!"( − !"( )2 , (10)
where 8 is an integer corresponding to each rat (total number9 being 11) and , to each day during which performance wasmeasured (total number being 19 for each rat).!"( is the actualperformance and !"( is themodel performance at day , for rat8.
Indicators of goodness-of-"t were estimated for eachmodel used in this study. #e Shapiro-Wilk test was used tocheck the normality of the distribution of both the trainingloads, that is, input of the model, and the performances, thatis, input of the model. #e statistical signi"cance of the "twas tested by analysis of variance of the RSS in accordancewith the degrees of freedom (df) of eachmodel: 12 forModel-1Comp, 24 for Model-2Comp, and 36 for Model-3Comp.#eadjusted coe5cient of determination (Adj.92) was computedto take into account the di)erence in df between the models.#e mean square error on performance estimation (SE) was
BioMed Research International 5
computed as RSS/(!−df−1)."e level of con#dence for eachlevel ofmodel complexity was tested by analysis of variance ofthe related decrease in residual variation."e decrease in RSSexplained by the introduction of further model parameterswas tested using the #-ratio test in accordance with theincrease in df as described previously [15]. Data in the textand Table 1 are expressed as means ± SD and the responses totraining are showed with SEM in Figures 2 and 3.
2.3.4. Modeled Responses to Training. With Model-2Comp,the time response of performance to a single training boutwas characterized by $!, the time to recover performance, and$", the time to peak performance a'er training completion[16], computed as
$! = %1%2%1 − %2 ln('2'1) , $" =
%1%2%1 − %2 ln(
%1'2%2'1) . (11)
)", the maximal gain in performance for 1 unit of training,was estimated as
)" = '1*−$!/%1 − '2*−$!/%2 . (12)
To distinguish the short-term negative e)ect of thetraining doses from the long-term bene#t, the positive andnegative in*uences of training on performance (ip andin, resp.) were estimated as described previously [17]. "eamount of training on day + had an e)ect on performance onday , quanti#ed by
-( +,) = '10&*−(!−&)/%1 − '20&*−(!−&)/%2 . (13)
"e values of in and ip on day , were estimated from thesum of in*uences of each past training amount, dependingon whether the result was negative or positive:
in! =!−1∑&=1
2222222- (+,)2222222 , when -( +,) < 0
ip! =!−1∑&=1
2222222- (+,)2222222 , when -( +,) > 0.
(14)
3. Results
Figure 2 shows the evolution in training work and per-formance. Table 2 shows the statistics for the #tting ofperformance with the three tested models. Although the#t was statistically signi#cant for all models, only Model-2Comp signi#cantly improved the #t when compared withModel-1Comp (3 < 0.05). "e third component in Model-3Comp failed to give a description of performance variationscomparedwithModel-1CompandModel-2Comp (3 > 0.05).It is noteworthy that the coe-cient of determination adjustedto the model df was lower for Model-3Comp than for Model-2Comp.
Because of its statistical signi#cance, the results fromModel-2Comp were retained for the analysis of the e)ects oftraining. With the estimates of parameters of Model-2Comp
Table 2: Statistics of model #tting.
Model 42 Adj.42 # ratio df 3 SE
Model-1Comp 0.48 0.45 14.97 12, 196 <0.001 0.209
Model-2Comp 0.53∗ 0.47 8.78 24, 184 <0.001 0.202
Model-3Comp 0.54 0.45 5.68 36, 172 <0.001 0.198
Model-1Comp, model using one #rst-order component; Model-2Comp,model using two #rst-order components; Model-3Comp, model with twocomponents where the gain term for the negative component varies by usingone further #rst-order #lter. Adj.(2, adjusted coe-cient of determination;df, degrees of freedom; SE, standard error. Statistical di)erence compared toModel-1Comp: ∗) < 0.05.
(%1 = 5.31 days, %2 = 4.3 days, '1 = 0.0186 ± 0.0134, and'2 = 0.0200± 0.0157 s−1), the response to a training bout wascharacterized by $! = 1.07 ± 1.46 days, $! = 5.29 ± 2.04 days,and )" = 0.0011 ± 0.0005W. "e variations in ip and in areshown on Figure 3. ip, which can be regarded as an index ofthe adaptations to physical training, increased progressivelyall along the experiment, whereas in, the index of fatigue,increased during the #rst days of training each week before itplateaued with the daily sessions."e 2 days without trainingbetween weeks allowed a complete recovery of past sessions.
4. Discussion
In the present study, Model-2Comp was retained as theoptimal model because statistically it provided the bestdescription of the e)ect of the response to resistance trainingin rats. Contrary to the results in a previous report [3],Model-3Comp did not statistically improve the #t of the model,possibly because of an insu-cient amount of training work."is model is based on the assumption that the relation-ship between daily training work and performance has aninverted-U shape, which implies that when the amount oftraining exceeds an optimal value of daily work, performancewill decline because of the transient oversolicitation. "eamounts of training in the present study may not have beengreat enough to allow the detection of such an e)ect. "evariations in fatigue elicited by the exercise over the entirestudy period were relatively small and Model-3Comp didnot increase the response to training compared to Model-2Comp."is is supported by the estimates for the small valuesobtained for $! and $", which suggested that the rats copedwell with the trainingwork. Nevertheless,Model-3Comp is ofa great interest for exercise prescription because it allows formore detailed analysis of the detrimental e)ects of trainingwith heavy/supraoptimal loads. For this reason, this prelim-inary study with an experimental animal model provides abasis for further research using Model-3Comp. Indeed, tooptimally capture the process of training, it will be necessaryto increase the amount of training work and to use contrastedtraining programs with periods of more intensi#ed trainingfollowed by reduced training work. Moreover, this systematicmathematical procedure of modeling o)ers the possibility ofsimulating training e)ects in order to test di)erent strategies,and it may thus be useful for advocating individualizedtraining programs, which constitute the optimal adaptive
+- ( ),
+- ( ),+- ( ),
+- ( ),+- ( ),
6 BioMed Research International
stimulus. "is type of approach was developed to optimizethe training process in athletes [18, 19], but, with the animal asan experimental model, it could be extended to those chronicdiseases for which exercise presents curative properties asalready employed in cardiac rehabilitation [20, 21]. It wouldthus be of interest to extend these strategies of rehabilitationprograms to rodent models su(ering from other chronicdiseases (e.g., ob/obmice, db/dbmice for type 2 diabetes, andthe streptozotocinmodel for type 1 diabetes), as direct testingin patients would not be ethical.
Another advantage of the animal model compared withhuman modeling of training e(ects is the high precision inthe quanti)cation of training work and performance. In thepresent study, the training work was directly computed bythe mechanical work of the center of mass [22]. Here, theunit was the joule, whereas the training load for athletes isindirectly evaluated by the variation in heart rate, as initiallyproposed by Banister, or the number of repetitions in eachexercise bout [17, 23, 24]. "e measure of performance isalso more accurate because it is computed from the powerdeveloped according to the reference method of the center ofmass [22]. "is measure in each training session also allowsthe collection of a high number of performance values neededto )t the model.
"is study is the )rst to blend the mixed-e(ects modelin that proposed by Banister, that is, Model-2Comp. "isadvance in the technical sophistication of the modelingled us to pool the data of the entire group of animalswhich o(ers two main advantages over the classical single-individual model."e )rst advantage is that it provides greatrobustness in the determination of the model parametersand insofar it increases the number of performance criteria,without increasing the degrees of freedom of the modelin the same proportion. "e second advantage is that ito(ers the possibility of sacri)cing several animals duringtraining to gain information about the dynamics of thebiological processes involved, without appreciably degradingthe precision of the training response quanti)cation."e onlyprecaution that needs to be taken is to adapt the numberof animals included in the study according to the numberof biological measures planned at di(erent times so that thetraining response at the end of the training period is stillrepresentative with regard to a su-cient sample size.
Last, comparedwith studies on training e(ects in athletes,the animal model o(ers optimal conditions to link both thepositive andnegative e(ects of training to the transitory adap-tivemechanisms induced by the cell signaling pathways.Untilnow, the process of training adaptation was considered to belike a black box, wherein performance output is the responseto training work. With an animal model that conforms to thestandards for the ethical treatment of experimental animals,it is possible to give the real physiological signi)cation tothe components of the transfer function used to describe thetraining e(ects on performance. New hypotheses can thusbe formulated to explain the positive and negative traininge(ects on performance. For example, is the positive in.uence(ip) linked to the main protein synthesis-signaling pathwayunder the control of the mechanistic (or mammalian) targetof rapamycin MTOR or is it related to the signaling sca(old
that is responsible for morphological adaptions (phenotype,ATPase activity, and hyperplasia)? On the other hand, canthe negative in.uence (in) be explained by exercise-inducedproteolysis, a phenomenon which seems to be attenuatedat least in part by resistance training through attenuatedinduction of atrogenes, such as the muscle ring )nger 1
(MuRF-1) [25]?
5. Conclusion
Modeling the e(ects of resistance training is fully applicablein rodent and allows for the detailed analysis of the trainingadaptation process. Model-2Comp was the most appropriatemodel to describe the training responses in the presentstudy. "e addition of contrasted periods to our trainingprogram may be promising for the application of Model-3Comp, which would yield information on the optimalvalue of daily training work, a major focus in researchon individualized training and rehabilitation programs. "emixed-e(ects model o(ers two main advantages comparedwith individual classicalmodeling, with (i) greater robustnessin the determination of the model parameters and (ii) thepossibility to determine the kinetic of the biological parame-ters by sacri)cing several animals at critical times during thetraining program."e accuracy in quantifying training loadsand performance in the experimental condition of resistancetraining with rodents, as well as the possibility of tightlycontrolling external factors, makes it possible to upgradethe structure of the training e(ects model and establish thebiological signi)cance of the model components.
Conflict of Interests
"e authors declare that there is no con.ict of interestsregarding the publication of this paper.
Authors’ Contribution
"ierry Busso and Robin Candau have equally contributed tothis work.
Acknowledgments
"e authors would like to thank Marie-Amelie Le Fur andFlorence Sabatier and Sara Laatar for her precious help intraining the animals and Catherine Carmeni for revising theEnglish paper.
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Conclusion
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Introduction
"
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Muscle plasticity in response to resistance training is mainly controlled by a
myosin ATPase adaptation
T3%23="s;"Ol!<!OOeFA�A"#2&)33$"<!pggeBA"T**'3"t;"OTsTgpF"'3+"K2W)3"#TgYT[FA�"
"1Université de Montpellier, INRA, UMR866 Dynamique Musculaire et Métabolisme, F-34060,
Montpellier, France"2Centre d’Etudes d’Agents Pathogènes et Biotechnologies pour la Santé, FRE3689, CNRS,
Montpellier, France
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Introduction
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Results
"
RT induces a moderate hypertrophy but marked strength and power output gains
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"
Time course of m-ATPase is triphasic
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"
RT increases the rate constant of the initial ADP burst phase of m-ATPase
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RT induces a marked increase of the fast steady-state rate constant of m-ATPase
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Effects of resistance training on the slow steady-state of m-ATPase
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RT induces a shift toward an intermediate phenotype
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MHC2A and MHC2B ATPase activity increases after RT
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The rate-limiting step of the cross-bridge cycle is accelerated after RT
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Discussion
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Material and Methods
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Ethic statement
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Resistance training protocol and performance quantification
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Myofibril preparation
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"
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"
Modeling of the chemical kinetics
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Modeling of the cross-bridge cycle
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Estimation of the training effect on m-ATPase activity
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Myosin isoform determination
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Statistical analysis
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Figure 1"
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Figure 1: Effects of RT on mass, strength and performance;"DaJ"e(2*9%)23"26"W2+="0'.."D)3"?J"
2($&"%)0$"62&"#s"Da,)%$"5*2%.A"3LI"'3)0'*.J"'3+"Us"DW*'4b"5*2%.A"3LFI"'3)0'*.J;"g2"+)66$&$34$"
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+$*%2)+" 62&" #s" D3LFM" '3)0'*." '3+" 3LBM"09.4*$.r" a,)%$" 42*903.J" '3+" Us" D3LFM" '3)0'*." '3+"
3LBM"09.4*$.r"W*'4b"42*903.J;"Y'%'".,2a"'".)?3)6)4'3%"$66$4%"26"%&')3)3?"23"&$*'%)($"09.4*$"0'.."
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Figure 2: Time courses of ADP production by myofibrils from control (white plots) and
trained (black plots) animals."DaJ"U=5)4'*"49&($"2W%')3"W="K'5)+"t*2a"m9$34,"'3+"lO<#")3"Us"
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62&"+$*%2)+;"DdJ"TYO"5&2+94%)23"2($&"%)0$"62&"423%&2*"D3LN×hNJ"'3+"%&')3$+"D3LI×hIJ"423+)%)23."
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Figure 3: Training effect on myofibrillar ATPase activity at 4°C." U,$" '3'*=.)." 26" %,$" %)0$"
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Figure 4: Effect of training on MHC composition."#2052.)%)23"26"_l#").262&0")3"DaJ"W)4$5.A"
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Figure 5: !! is increased in MHC2A and MHC2B fibers after training;"e66$4%"26"KU"23"kt")3"
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Figure 6: Effect of training on the kinetic constant of the cross-bridge cycle." DaJ" \)05*$"
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W="KU" DP" �" M;MIJ;"T**"P^('*9$."a$&$" 4'*49*'%$+" 9.)3?" '" %a2^%')*$+"\%9+$3%7." %^%$.%;"#s" Da,)%$"
42*903.J"'3+"Us"DW*'4b"42*903.J"423+)%)23."'&$"$X5&$..$+"'."0$'3"V".;$;0;"
"
"
"
"
"
Table 1. PCR Primer set sequences
""" "" ""
s$3$" t2&a'&+"DIf"^"NfJ" K$.$&.$""DIf"^"NfJ"
" " "_l#F" sTssTsTsss#ssT#TUU" T#U#UU#TUU#Tss###UUs"
_l#B'" UssUsU###UsUUsTTTsTT" UUsTU#Ts#UssU#s#TU#"
_l#BW" Uss#U#UsTUs#TTsTsTTT" U#Us#TU#Ts#UTTs##TU#"
_l#BX" sTTss##TTsTTss##TU#" ssU#T#UUU##Us#UUUssT"
"
!
Conclusion
#$%%$"1%9+$"$.%"*'"5&$0)-&$"/"023%&$&".'3."'0W)?9�%1."93$"'+'5%'%)23"+$"*7'4%)()%1"TUO'.):9$"
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W)$3" .951&)$9&" '9X" ?')3." +$" BN"R" $%" Bh"R" 2W%$39." 5&141+$00$3%" .9)%$" /" 93" $3%&'83$0$3%"
+7$3+9&'34$" D\4,*9%$&"u" t)%%.A" FGGhr" K2$*." et al.A" BMMnJ;" <7'9?0$3%'%)23" +$" kt" .71%$3+" +$"
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.951&)$9&"/"6'%)?9$&"*2&."+793$"'.4$3.)23"DY$=,*$"et al.A"BMFIJ;"#$%%$")05*)4'%)23"0']$9&$"+9"
tYO"+'3."*7$.4'*'+$".$&')%"+$"3'%9&$"/"$X5*):9$&"*7'9?0$3%'%)23"5'&%)49*)-&$0$3%"0'&:91$"+$"
kt"'9".$)3"+$"4$"5$%)%"09.4*$"4205'&1"'9X"+$9X"'9%&$."09.4*$."1%9+)1."+'3."32%&$"1%9+$;"
O29&" .7'..9&$&" :9$" *$." ?')3." +7'4%)()%1" TUO'.):9$" 371%')$3%" 5'." *$" &$6*$%" +$" 4,'3?$0$3%."
5,132%=5):9$.A" 329." *7'(23." $X5&)01$" $3" 6234%)23" +9" 529&4$3%'?$" +$" 6)W&$." _l#BT" $%"
_l#BE;" <$." &1.9*%'%." 5&1.$3%1." )4)" 023%&$3%" 529&" *'" 5&$0)-&$" 62)." 93$" '9?0$3%'%)23" +$"
*7'4%)()%1" TUO'.):9$" +$" *72&+&$" FMMR" 529&" 4$." ).262&0$." .29." *7$66$%" +$" *7$3%&'83$0$3%;"
<7'55*)4'%)23"+793"02+-*$".)05*)6)1"+9"4=4*$"014'32^4,)0):9$"329."'"5$&0)."+7)+$3%)6)$&"*'"
*24'*).'%)23" +$." '+'5%'%)23." 02*149*')&$.;" <$." &1.9*%'%." 023%&$3%" :9$" 4$." '+'5%'%)23."
)3%$&()$3+&')$3%"0']2&)%')&$0$3%" *2&."+$" *71%'5$"+$" *)W1&'%)23"+$."5&2+9)%."+$" *7,=+&2*=.$A" $%"
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'($4"*'"%,12&)$"4*'..):9$".$*23"*':9$**$"*'"*)W1&'%)23"+$."5&2+9)%."+$"*7,=+&2*=.$"$.%"*71%'5$"*'"
5*9." *$3%$" $%" +234" *)0)%'3%$" +9" 4=4*$"014'32^4,)0):9$" +'3." 32." 423+)%)23." $X51&)0$3%'*$."
D<)233$"et al.A"FGGIJ;"
"
"
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<7$3%&'83$0$3%" '" )3+9)%" +$." ?')3." +$" 59)..'34$" 014'3):9$" +$" FNCAHR;" #$%%$" 59)..'34$A"
+1%$&0)31$"5'&"*$"%&'(')*"52%$3%)$*"+1($*2551"423%&$"*'"?&'()%1"+)().1"5'&"*$"%$05."+$"023%1$"
+9"&23?$9&A"$.%"/"0$%%&$"$3"&$*'%)23"'($4"*$."?')3."+$"59)..'34$"4,)0):9$."+$"FBNR"4)%1."4)^
+$..9.;"e3"4234*9.)23A"*$."'+'5%'%)23."+$"*7'4%)()%1"TUO'.):9$"D?')3"+$"59)..'34$"4,)0):9$J"
.$&')$3%"+$"3'%9&$"/"$X5*):9$&"*$."?')3."+$"5$&62&0'34$"0'..)6."2W.$&(1."D?')3."+$"59)..'34$"
014'3):9$J;"
"
" "
FIn"
Etude 4 : "volution du modèle classique des effets de
l’entraînement vers une structure physiologique
"
"
"
"
"
_'39.4&)%"$3"5&15'&'%)23"
Philippe AGA"E2&&'3)"tA"E-?9$"sA"E233)$9"T"$%"#'3+'9"KE"
"
" "
Evolution des modèles empiriques des effets de l’entraînement vers une
structure physiologique
"
""
Ol!<!OOe"T;s;F�A"EpKKTg!""t;BA"Ees[e"s;FA"Epgg!e["TF;"$%"#TgYT["K;EF�;""
1Université de Montpellier, INRA, UMR866 Dynamique Musculaire et Métabolisme, F-
34060, Montpellier, France 2!Institute of Sport Sciences and Department of Physiology, University of Lausanne,
Lausanne, Switzerland
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""""Introduction
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5&141+$00$3%"{h|;"
"
Méthode
"
Animal, protocole d’entraînement et quantification des charges et performances.
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Résultats et discussion préliminaires
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Conclusion
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Figure et tables ""
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.1&)$;""
""""""""""""""""""""""
Table 1
"
Modèle R2 Adj.R2 F ratio ddl P
A2 * 0,71 0,65 12,24 3 < 0,001
Banister 0,79 0,73 13,16 4 < 0,001
A2F1 0,76 0,67 8,23 5 < 0,01
"�" 02+-*$" &$%$39" 4'&" *$." ?')3." +$" ('&)'34$" 529&" *$." 02+-*$." 5*9." 4205*$X$." 371%')$3%" 5'."
.)?3)6)4'%)6."
"
Table 2
Modèle kA tau A1 tau A2 kF tau F
A2 0,0025 ± 0,0016 0,61 3,68 none none
Banister 1,556 ± 3,099 none 48,34 1,79 ± 3,27 49,12
A2F1 0,0037 ± 0,0025 0,33 5,72 0,0025 ± 0,0015 49,93
"
"
!
! !
References
F; E'3).%$&" e�A" l'0)*%23" #<;" o'&)'%)23." )3" )&23" .%'%9." a)%," 6'%)?9$" 02+$**$+" 6&20%&')3)3?")3"6$0'*$"+).%'34$"&933$&.;"e9&"j"T55*"O,=.)2*;"FGnIrIhDFJ`FCwBN;"B; E9..2"UA"l�bb)3$3"dA"O'b'&)3$3"TA"#'&'..2"#A"<'429&"jKA"d20)"OoA"$%"'*;"T".=.%$0.02+$*"26"%&')3)3?"&$.523.$."'3+")%."&$*'%)23.,)5"%2",2&023'*"&$.523.$.")3"$*)%$"a$)?,%^*)6%$&.;"e9&"j"T55*"O,=.)2*;"FGGMrCFDF^BJ`hnwIh;""N; E'3).%$&" e�A" #'*($&%" U�A" \'('?$" _oA" E'4," U;" T" .=.%$0." 02+$*" 26" %&')3)3?" 62%'%,*$%)4"5$&62&0'34$;"T9.%"j"\52&%."_$+;"FGHIr(2*;"H`IHwCF;"h; O,)*)55$"TsA"O="sA"t'()$&"tEA"\'34,$c"T_jA"E233)$9"TA"E9..2"UA"$%"'*;"_2+$*)3?%,$" &$.523.$." %2" &$.).%'34$" %&')3)3?" )3" '3" '3)0'*" $X5$&)0$3%" .%9+=;" E)2_$+" K$." !3%;"BMFIrBMFI`GFhnCM;""I; #'3+'9" KA" E9..2" UA" <'429&" jK;" e66$4%." 26" %&')3)3?" 23" )&23" .%'%9." )3" 4&2..^4293%&=.b)$&.;"e9&"j"T55*"O,=.)2*;"FGGBrChDCJ`hGHwIMB;"C; E9..2" U;" o'&)'W*$" +2.$^&$.523.$" &$*'%)23.,)5" W$%a$$3" $X$&4).$" %&')3)3?" '3+5$&62&0'34$;"_$+"\4)"\52&%."eX$&4;"BMMN"j9*rNIDHJ`FFnnwGI;"H; \'34,$c" T_jA" s'*W-." pA" t'W&$^s9$&=" tA" U,20'." <A" Y29)**'&+" TA" O=" sA" $%" '*;_2+$**)3?" %&')3)3?" &$.523.$" )3" $*)%$" 6$0'*$" ?=03'.%." '3+" 25%)0'*" .%&'%$?)$." 26" 2($&*2'+"%&')3)3?"'3+"%'5$&;"j"\52&%."\4);"BMFNrNFDFhJ`FIFMwG;""n; U,20'." <A" E9..2" U;" T" %,$2&$%)4'*" .%9+=" 26" %'5$&" 4,'&'4%$&).%)4." %2" 25%)0)c$5$&62&0'34$;"_$+"\4)"\52&%."eX$&4;"BMMI"\$5rNHDGJ`FCFIwBF;"G; U,20'."<A"_9])b'"!A"E9..2"U;"T"02+$*".%9+="26"25%)0'*"%&')3)3?"&$+94%)23"+9&)3?"5&$^$($3%"%'5$&")3"$*)%$".a)00$&.;"j"\52&%."\4);"BMMn"T5&rBCDCJ`ChNwIB;"FM; E$?9$" sA" Y29)**'&+" TA" s'*W$." pA" K2..'32" EA" o$&39." EA" #'3+'9" KA" $%" '*;" e'&*='4%)('%)23"26"&'%".b$*$%'*"09.4*$"!<^Ck\UTUFk\UTUN"+$5$3+$3%"?$3$"$X5&$..)23")3"&$.).%'34$"$X$&4).$"*)3b$+"%2",=5$&%&25,=;"O*2\"p3$;"BMFNrnDBJ`$IHFhF;""FF; T+'0."sKA" l'%,$&" E_A" E'*+a)3" d_A" Y9+*$=" sT;" \b$*$%'*" 09.4*$"0=2.)3" ,$'(=4,')3" 42052.)%)23" '3+" &$.).%'34$" %&')3)3?;" j" T55*" O,=.)2*" E$%,$.+'" _+" FGnI;" FGGN"t$WrHhDBJ`GFFwI;""FB; U&'55$" \A" #2.%)**" YA" U,20'." K;" e66$4%" 26" .a)0" %'5$&" 23" a,2*$" 09.4*$" '3+" .)3?*$09.4*$"6)W$&"423%&'4%)*$"5&25$&%)$.;"_$+"\4)"\52&%."eX$&4;"BMMM"Y$4rNBDFBJ`hnwIC;"FN; E9..2"UA"t*�4b"_;"T"0)X$+^$66$4%."02+$*"26" %,$"+=3'0)4" &$.523.$"26"09.4*$"?$3$%&'3.4&)5%"$X5&$..)23"%2"$3+9&'34$"$X$&4).$;"e9&"j"T55*"O,=.)2*;"BMFN"_'=rFFNDIJ`FBHGwGM;"Fh; <$$"\A"E'&%23"eKA" \a$$3$="l<A" t'&&'&"KO;"o)&'*" $X5&$..)23" 26" )3.9*)3^*)b$" ?&2a%,6'4%2&^!"$3,'34$."09.4*$",=5$&%&25,=")3"&$.).%'34$^%&')3$+"&'%.;"j"T55*"O,=.)2*"E$%,$.+'"_+"FGnI;"BMMh"_'&rGCDNJ`FMGHwFMh;""FI; !c:9)$&+2"_A"!W'�$c"jA"s2&2.%)'?'"eA"s'&&9$."_A"���)?'"TA"T3%�3"TA"$%"'*;"_'X)0'*.%&$3?%,"'3+"52a$&"4,'&'4%$&).%)4." )3" ).20$%&)4"'3+"+=3'0)4"'4%)23."26" %,$"955$&"'3+" *2a$&"$X%&$0)%)$.")3"0)++*$^'?$+"'3+"2*+$&"0$3;"T4%'"O,=.)2*"\4'3+;"FGGG"\$5rFCHDFJ`IHwCn;""FC; O'?'32"TtA" O="sA" E$&3'&+)" lA" #'3+'9" KEA" \'34,$c"T_j;" T9%25,'?=" '3+" 5&2%$)3%9&32($&" .)?3'*)3?" )3" .*2a^%a)%4," 09.4*$" +9&)3?" $X$&4).$;" _$+" \4)" \52&%." eX$&4;" BMFh"j9*rhCDHJ`FNFhwBI;""FH; E'*+a)3"d_A"l'++'+"tA"O'3+2&6"#eA"K2="KKA"e+?$&%23"oK;"T*%$&'%)23." )3"09.4*$0'.." '3+" 423%&'4%)*$" 5,$32%=5$" )3" &$.523.$" %2" 93*2'+)3?" 02+$*.`" &2*$" 26"%&'3.4&)5%)23'*k5&$%&'3.*'%)23'*"0$4,'3).0.;"t&23%"O,=.)2*;"BMFNrh`Bnh;""
DISCUSSION GENERALE
ET PERSPECTIVES "
" "
FHF"
Plasticité de la myosine ATPase en réponse à l’entraînement.
L’entraînement en résistance est responsable, au sein du muscle strié squelettique, d’un
remodelage profond qui s’opère à différents niveaux d’organisation du muscle. D’un point de
vue quantitatif, les adaptations sont relativement bien connues. En réponse à un entraînement
en résistance, elles sont dirigées essentiellement par une hypertrophie et par un changement
phénotypique en faveur des fibres intermédiaires représentées par les fibres de type 2A.
Compte tenu des variations relativement discrètes de la masse musculaire (+ 23% et + 31%
respectivement pour le deltoïde et le FDP) et des changements phénotypiques vers un profil
plus lent, les fibres de type 2X et 2B étant partiellement converties en 2A, induits par notre
protocole d’entraînement en résistance, nous avons suspecté une adaptation plus fine, d’ordre
qualitatif, qui pourrait expliquer les gains de puissance observés. Les résultats indiquent une
augmentation de la puissance chimique de la myosine ATPase de plus de 110%. Une
représentation simplifiée du cycle mécano-chimique, réduit aux 3 étapes fondamentales (i.e.
collision de l’ATP sur le complexe actine-myosine, l’hydrolyse de l’ATP et la libération des
produits de l’hydrolyse) suggère que ces gains de puissance chimique sont liés principalement
à une augmentation de la vitesse de libération des produits de l’hydrolyse de l’ATP (+ 164%,
+ 40% et + 37% respectivement pour le FDP, le biceps et le deltoïde) et dans une moindre
mesure, à une augmentation de la vitesse de l’étape d’hydrolyse (+ 95%, + 30% et + 47%
pour le FDP, le biceps et le deltoïde, respectivement). Cependant, la technique de Rapid Flow
Quench utilisée pour établir la cinétique de production de phosphate par les myofibrilles en
conditions activées (i.e. en présence de calcium) ne permet pas de mesurer avec certitude la
vitesse de l’étape de collision de l’ATP. Bien que nous ayons mesuré la cinétique du burst et
que la vitesse de fixation de l’ATP à concentration saturante soit suffisamment rapide pour ne
pas influencer l’activité ATPasique à l’état stable, nous ne pouvons pas exclure totalement un
effet de l’entraînement sur la vitesse de fixation de l’ATP. La mesure de la vitesse de
formation du complexe actine-myosine-ATP est permise par les techniques de chasse à l’ATP
froid (cold ATP chase experiments en anglais) (Barman & Travers, 1985). Cette étape
irréversible est dirigée par kcoll, la constante de vitesse de formation du complexe collisionnel
avec kcoll = k2[ATP]/(K1 + [ATP]) où K1 est la constante de dissociation de l’ATP du
complexe actine-myosine et k2 et la constante de vitesse d’isomérisation de la myosine
(Lionne et al., 2002).
FHB"
Une augmentation du nombre de ponts acto-myosine sous l’effet de l’entraînement ?
L’activité ATPasique de la myosine est définie comme la production de phosphate (ou
d’ADP) par tête de myosine, et par seconde. Dans nos méthodes, nous avons mesuré par
HPLC la quantité totale d’ADP libre et lié au complexe actine-myosine) produit au cours du
temps en conditions d’activation maximale. Cette méthode ne fournit pas d’information
directe quant au nombre de têtes de myosine pleinement compétentes d’un point de vue
enzymatique et encore moins sur le nombre de têtes actives à l’instant t. L’augmentation de la
production de phosphate au cours du temps observée dans nos conditions entraînées pourrait
ainsi être liée à une augmentation de la fraction de têtes de myosine pleinement compétentes
d’un point de vue enzymatique. Une titration des têtes de myosine permettrait de mesurer ce
nombre de têtes compétentes (Lionne et al., 2002). Cependant, dans une étude antérieure sur
les effets de l’entraînement en endurance, une augmentation de plus de 20% de l’activité
ATPasique a été observée, et ce, sans augmentation du nombre de têtes de myosine
compétentes (Roels et al., 2008). Dans ces conditions, nous pouvons difficilement associer
l’augmentation de l’activité ATPasique mesurée à une augmentation du nombre de têtes
compétentes. En revanche, nous ne pouvons pas exclure une augmentation du nombre de têtes
de myosine effectivement actives. Cette proportion de tête en interaction forte avec l’actine à
l’instant t est inférieure à 10% lors du raccourcissement à vitesse maximale, mais peut être
grandement majorée dans certaines situations. En effet, lors des contractions isométriques et
surtout excentriques, la proportion de tête active peut augmenter, essentiellement sous l’effet
de la diminution de l’encombrement stérique et d’une position des têtes de myosine plus
proche et plus favorable à une interaction avec les sites actifs de l’actine (Brunello et al.,
2007). Dans les conditions expérimentales de raccourcissement à vitesse maximale de notre
étude, une augmentation du nombre de têtes actives ne peut être exclue. Les méthodes pour
explorer ces deux hypothèses restent à être imaginées.
La force par pont actine-myosine est-t-elle augmentée à l’entraînement ?
L’entraînement en résistance induit une augmentation de la force isométrique (P0). Rapportée
à la surface de section des fibres, une augmentation de P0/CSA signifie une adaptation
qualitative du muscle squelettique. Une telle plasticité ne fait pas l’objet d’un consensus.
Certaines études ont montré une augmentation de P0/CSA en réponse à l’entraînement en
résistance (Harber et al., 2004; D’Antona et al., 2006), et d’autres non (Widrick et al., 2002;
Shoepe et al., 2003). Chez nos animaux entraînés, une augmentation extrêmement marquée de
FHN"
la puissance mécanique et de la force développées en condition d’escalade avec charges
lourdes en l’absence d’une hypertrophie proportionnelle suggère fortement une augmentation
de la tension spécifique. Une augmentation de la force par pont est probablement
envisageable. Cependant, comment cette force pourrait-elle augmenter sous l’effet de
l’entraînement ? Un accroissement des liaisons hydrophobiques lié à une optimisation de la
surface de contact actine-myosine représente une hypothèse que l’on ne peut écarter. Une
analyse structurelle du complexe acto-myosine en état de post-powerstroke par
cristallographie pourrait apporter des éléments de réponse. Les changements de conformation
qui interviennent sous l’effet du calcium au sein des filaments épais se font plus rapidement
que la fixation de la tête de myosine sur l’actine, suggérant que la génération de force pourrait
être contrôlée par ces cinétiques de changements structurels au sein de la myosine (Reconditi
et al., 2011; Fusi et al., 2014). Les chaines légères régulatrices (RLC) seraient en mesure de
réguler l’orientation des têtes de myosines si bien qu’à température physiologique et en
conditions relaxe, deux populations de têtes de myosines coexistent : certaines têtes orientées
perpendiculairement et d’autres parallèlement aux filaments de myosines (Fusi, Huang, &
Irving, 2015). Une modulation de l’équilibre conformationnel des régions RLC des têtes de
myosines serait alors de nature à expliquer les variations potentielles de la force par pont et/ou
leur nombre sous l’effet de l’entraînement. Dans l’étude antérieure conduite sur les effets de
l’endurance, ni les changements d’isoformes des RLC, ni leur états de phosphorylation n’ont
permis d’expliquer les gains d’activité ATPasique mesurés suggérant que d’autres
mécanismes impliquant d’autres protéines sarcomériques pourraient être impliquées dans
cette plasticité des moteurs moléculaires (Roels et al., 2008).
Un modèle physiologique des effets de l’entraînement en résistance.
Les gains de puissance chimique développée lors de l’entraînement semblent déterminer
l’essentiel des gains de puissances mécaniques mesurés. Cependant, à quelle vitesse survient
cette adaptation de l’activité ATPasique ? Dans nos conditions, 4 semaines d’entraînement
étaient suffisantes pour induire une augmentation de l’activité ATPasique par un facteur 2.
Les animaux étaient capables de soulever 280 ± 20% de leur masse corporelle lors du test de
répétition maximale. Les résultats de travaux précédents réalisés dans mon laboratoire
d’accueil indiquent que la performance continue d’augmenter si l’entraînement perdure.
Après 10 semaines d’entraînement selon le même protocole d’escalade, les animaux
pouvaient soulever 350 ± 23% de leur masse corporelle lors du test de répétition maximale
(Bègue, 2013). Ces résultats suggèrent que les gains d’activité ATPasique obtenus dans nos
FHh"
conditions (i.e. 4 semaines d’entraînement) n’ont pas encore atteint de plateau et que la
capacité d’adaptation de l’activité ATPasique de la myosine peut dépasser les 110%
d’augmentation.
Des études antérieures ont montré une augmentation de l’activité ATPasique de l’ordre de
20% suite à un entraînement en endurance réalisé sur des périodes de 8 à 12 semaines
(Schluter & Fitts, 1994; Roels et al., 2008). L’intensité, mais également la durée de l’exercice
physique seraient alors déterminants dans l’amplitude des gains d’activité ATPasique. A
partir du modèle physiologique proposé dans l’étude 4, il deviendra possible d’établir une
relation entre les gains de performance et les gains d’activité ATPasique. Des mesures
d’activité ATPasique effectuées à différents temps au cours de la période d’entraînement
permettront de déterminer sa cinétique de mise en place. Combinée à des mesures réalisées
lors d’entraînement à différentes intensités (i.e. différentes charges de travail), la relation
dose-réponse pourra être établie. Le modèle physiologique appliqué indique une performance
maximale 2,4 jours après une charge de travail isolée. Cette donnée est en adéquation avec les
cinétiques d’activation de la voie canonique de synthèse des protéines. En effet, MTOR, chef
d’orchestre de cette voie, ainsi que 4E-BP1 (un effecteur activé par MTOR), peuvent rester
activés jusqu’à 24h post exercice (Pagano et al., 2014). De la même manière, la vitesse de
réponse des transcrits géniques fait apparaître une réponse maximale 24 h après entraînement
(Busso & Flück, 2013). Les paramètres du modèle physiologique semblent correspondre ainsi
à la réalité des processus adaptatifs biologiques responsables des gains de performances
observés à l’entraînement. De plus, la nature exponentielle du modèle proposé, par opposition
aux modèles classiques où la réponse est impulsionnelle, reflète mieux la nature également
exponentielle des processus adaptatifs biologiques cités ci-dessus. Des mesures biologiques
complémentaires [activité ATPasique et de flux de synthèse protéique grâce notamment à la
puromycine (Schmidt et al., 2009)] permettraient d’établir un modèle physiologique détaillé
où la performance serait expliquée par l’action combinée des mécanismes adaptatifs
musculaires :
!! ! !! ! !!"##$%&'! ! !!"#!!!!!!"#$%$&"!!" ! !!!"#$%&
Où !!"##$%&'! ,!!!"#! , !!!!"#$%&"!!" et !!"#$%& sont les fonctions de transfert propres à
décrire les adaptations respectives de la commande motrice, du phénotype, des flux de
synthèse protéique et de l’activité ATPasique par tête de myosine, !!"#$%&.
FHI"
A notre connaissance, ce travail est le premier à montrer une étonnante possibilité adaptative
des unités contractiles et que cette adaptation de l’activité ATPasique, indépendante du
phénotype, est à l’origine des gains de puissance mécanique observés suite à un entraînement
de résistance. Un modèle simple du cycle mécano-chimique de la myosine ATPase indique
une augmentation des vitesses des étapes d’hydrolyse de l’ATP et surtout de libération des
produits de l’ATP. Bien que des mécanismes adaptatifs connus soient suspectés, aucun
d’entre eux ne permet d’expliquer une augmentation aussi marquée de l’activité ATPasique.
En effet, ni les phosphorylations des chaînes légères de myosine, ni même une augmentation
du nombre de têtes de myosine pleinement compétentes sur le plan enzymatique ne serait en
mesure d’expliquer le gain de plus de 100% de la puissance chimique observé suite à
l’entraînement de résistance. Combinées à d’autres mesures biologiques, ces données
permettraient d’établir un modèle physiologique, où la performance serait expliquée par les
différentes formes de plasticité qui s’expriment au sein du muscle strié squelettique.
"
"
FHC"
TRAVAUX
SUPPLEMENTAIRES
"
J Physiol 593.8 (2015) pp 2071–2084 2071
Th
eJo
urn
al
of
Ph
ysi
olo
gy
Chronic clenbuterol treatment compromises forceproduction without directly altering skeletal musclecontractile machinery
G. Py1,2, C. Ramonatxo1,2, P. Sirvent3, A. M. J. Sanchez1,2, A. G. Philippe1,2, A. Douillard1,2, O. Galbes1,2,
C. Lionne4, A. Bonnieu2, A. Chopard1,2, O. Cazorla5, A. Lacampagne5 and R. B. Candau1,2
1Faculte des Sciences du Sport, Universite Montpellier, 700 avenue du Pic Saint-Loup, F-34060 Montpellier, France2INRA, UMR866, Universite Montpellier, 2 Place Viala, F-34060 Montpellier, France3Clermont Universite, Universite Blaise Pascal, EA3533, Laboratoire des Adaptations Metaboliques a l’Exercice en Conditions Physiologiques et
Pathologiques (AME2P), BP 80026, F-63171 Aubiere Cedex, France4Centre d’Etudes d’Agents Pathogenes et Biotechnologies pour la Sante, UMR-5236 CNRS-UM1-UM1, France5U1046 INSERM, UMR 9214 CNRS, Physiologie et Medecine Experimentale du Cæur et des Muscles, University of Montpellier, CHRU Montpellier,
Montpellier, France
Key points
r Clenbuterol is an adrenergic receptor agonist known to induce skeletal muscle hypertrophyand a shift towards faster muscle fibres, when administered chronically at high doses.
r However, when normalized to the muscle surface area, the increase in muscle force is no longer
increased and even depressed.r We show that muscle contraction and relaxation force kinetics were significantly reduced
particularly in fast contracting muscles.r We show that action potential-elicited Ca2+ transients were depressed in the fast contracting
muscle.r Our data show that chronic clenbuterol treatment reduces contractile efficiency, with altered
contraction and relaxation kinetics, but without directly altering the contractile machinery.
Lower Ca2+ release during contraction could partially explain these deleterious effects.
Abstract Clenbuterol is a β2-adrenergic receptor agonist known to induce skeletal muscle hyper-
trophy and a slow-to-fast phenotypic shift. The aim of the present study was to test the effects of
chronic clenbuterol treatment on contractile efficiency and explore the underlying mechanisms,i.e. the muscle contractile machinery and calcium-handling ability. Forty-three 6-week-old male
Wistar rats were randomly allocated to one of six groups that were treated with either sub-
cutaneous equimolar doses of clenbuterol (4 mg kg−1 day−1) or saline solution for 9, 14 or21 days. In addition to the muscle hypertrophy, although an 89% increase in absolute maximal
tetanic force (Po) was noted, specific maximal tetanic force (sPo) was unchanged or even depressed
in the slow twitch muscle of the clenbuterol-treated rats (P < 0.05). The fit of muscle contraction
and relaxation force kinetics indicated that clenbuterol treatment significantly reduced the rateconstant of force development and the slow and fast rate constants of relaxation in extensor
digitorum longus muscle (P < 0.05), and only the fast rate constant of relaxation in soleus muscle
(P < 0.05). Myofibrillar ATPase activity increased in both relaxed and activated conditions insoleus (P < 0.001), suggesting that the depressed specific tension was not due to the myosin head
alteration itself. Moreover, action potential-elicited Ca2+ transients in flexor digitorum brevis
fibres (fast twitch fibres) from clenbuterol-treated animals demonstrated decreased amplitude
G. Py and C. Ramonatxo contributed equally to the study.
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society DOI: 10.1113/jphysiol.2014.287060
2072 G. Py and others J Physiol 593.8
after 14 days (−19%, P < 0.01) and 21 days (−25%, P < 0.01). In conclusion, we showedthat chronic clenbuterol treatment reduces contractile efficiency, with altered contraction andrelaxation kinetics, but without directly altering the contractile machinery. Lower Ca2+ releaseduring contraction could partially explain these deleterious effects.
(Received 10 November 2014; accepted after revision 27 January 2015; first published online 27 February 2015)
Corresponding author G. Py: Faculte des Sciences du Sport, Universite Montpellier, INRA, UMR866 Dynamique
Musculaire et Metabolisme, 2 place Viala, F-34060 Montpellier, France. E-mail: [email protected]
Abbreviations AP, action potential; CSA, cross-sectional area; EDL, extensor digitorum longus; FDB, flexor digitorum
brevis; SOL, soleus.
Introduction
β2-Adrenergic agonists, the most effective bronchodilatorsused to prevent asthma in humans, act by inhibitingsmooth muscle contraction and increasing epithelialmucus clearance. In addition to their anti-asthma effect,chronic administration of these agonists causes a dramaticincrease in skeletal muscle growth in various mammalianspecies (Kim et al. 1992; Mersmann, 1998; Kissel et al.2001) and enhances human sports performance (Collompet al. 2005; Sanchez et al. 2012; Hostrup et al. 2014a,b).
Synthetic β2-agonists such as cimaterol, clenbuterol,fenoterol, formoterol, salbutamol and salmeterol arebased on the chemical structure of adrenaline andpromote muscle growth through the stimulation ofβ2-adrenoceptors and subsequent activation of down-stream signalling pathways. Clenbuterol, one of theselective β2-adrenergic agonists, is known to inducea specific protein anabolic effect in skeletal muscle(Mersmann, 1998; Kissel et al. 2001; Bricout et al. 2004;Lynch & Ryall, 2008). Because of this effect, clenbuterolhas been extensively studied, especially for its therapeuticpotential in various diseases affecting muscle mass orweakness. Effects of β2-adrenergic agonist treatment onmuscle mass have been demonstrated in th contexts ofmuscular dystrophy, disuse (Ricart-Firinga et al. 2000),ageing, denervation and amyotrophic lateral sclerosis inboth animals and humans (Lynch & Ryall, 2008). Inaddition to the effect on muscle mass, chronic clenbuteroltreatment induces a fibre-type transition from slow to fasttwitch fibres in animal models (Zeman et al. 1988; Pollaet al. 2001; Mounier et al. 2007; Douillard et al. 2011). Thephenotypic and metabolic changes induced by clenbuterolhave raised questions about the impact of this treatmenton muscle contractile function and ATPase activity. Pre-vious studies have shown greater force-producing capacityin clenbuterol-treated muscle in both fast and slowtwitch muscles (Dodd et al. 1996). Nevertheless, whenmaximal tetanic contraction was corrected for musclecross-sectional area, muscle force was unchanged andeven altered in soleus muscle (McCormick et al. 2010).These results suggest a potentially deleterious effect ofchronic clenbuterol treatment on the skeletal muscle
contractile machinery. A preliminary study suggestedthat chronic clenbuterol treatment induces significantchanges in the calcium (Ca2+) signals associated withexcitation–contraction coupling in fast twitch skeletalmuscles (Sirvent et al. 2014). However, the mechanismsresponsible for the changes in clenbuterol-induced contra-ctility are not well understood.
Ca2+ handling is the primary regulator of forcegeneration by cross-bridges in striated muscle. A reductionin Ca2+ release from the sarcoplasmic reticulum ateach contraction would explain, at least in part, thereduced contractile function. However, muscle contractilefunction depends on contraction and relaxation processes.Relaxation involves at least three processes: termination ofCa2+ release, the kinetics of calcium dissociation fromtroponin and cross-bridge detachment, and Ca2+ uptakeby the sarcoplasmic reticulum and myoplasmic Ca2+
buffering (Poggesi et al. 2005). Strong evidence indicatesthat cross-bridge kinetics is the major determinant of thetime course of striated muscle relaxation and is responsiblefor ‘load-dependent’ changes in relaxation kinetics. Last,the kinetics of myosin head detachment from the thinfilament is related to ATP binding. Thus, beyond musclehypertrophy, an effect of clenbuterol on contraction andrelaxation kinetics is expected.
The aim of the present study was to test the effect ofchronic clenbuterol treatment on muscle force generationand relaxation kinetics in slow and fast twitch musclesand on myofibrillar ATPase activity in both relaxed andactivated conditions. In addition, Ca2+ signalling wasevaluated as it is the main regulator of contraction andrelaxation kinetics.
Methods
All experiments complied with the Guide for the Careand Use of Laboratory Animals, published by the NationalInstitutes of Health (NIH Publications No. 85-23, Revised1996). All experiments were approved by the ResearchEthics Committee of Languedoc-Roussillon region[‘Ethique sur l’Experimentation Animale’ approved theexperimental protocol (# 36)].
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J Physiol 593.8 Effect of clenbuterol on skeletal muscle contractility 2073
Animals
Forty-three 6-week-old male Wistar rats (body mass:236.3 ± 3.8 g; Charles River, Wilmington, MA, USA)were randomly allocated to one of six groups andhoused in standard cages with a 12:12 h light–dark cycleand food and water available ad libitum. Treated ratsreceived subcutaneous equimolar doses of clenbuterol(4 mg kg−1 day−1) for 9 (CB-G9, n = 7), 14 (CB-G14,n = 7) or 21 days (CB-G21, n = 8). Body weights weremeasured daily and injection doses adjusted accordingly.To control for maturation effects due to the treatmentduration, control rats were recruited and received adaily subcutaneous injection of an identical volume ofsaline solution for 9 (CTL-G9, n = 7), 14 (CTL-G14,n = 7) or 21 days (CTL-G21, n = 7). All measurementswere made 24 h after the last treatment. While animalswere anaesthetized with pentobarbital sodium, extensordigitorum longus (EDL), soleus (SOL) and flexordigitorum brevis (FDB) muscles of both left and righthindlimbs were rapidly dissected out. At the end of thesurgical procedure, the animals were killed with a lethaldose of pentobarbital.
Muscle phenotype
SOL and EDL muscle phenotypes were determined bythe Kaiser and Brooke myosin-ATPase coloration method(Brooke & Kaiser, 1970). All twitch fibres from each musclewere selected and identified as being type I fibres, type IIafibres or type IIx-IIb fibres.
Kinetics of muscle force activation and relaxation
kinetics
EDL and SOL muscles from the right hindlimb weresurgically exposed. The excised muscles were immediatelyplaced ib a custom-built Plexiglas chamber filled withKrebs-Ringer solution [composition (in mM): NaCl 11.9,KCl 0.5, CaCl2 0.125, MgSO4 0.1, KH2PO4 0.1, glucose1, NaHCO3 2.5, mannitol 0.11] equilibrated with 95%O2/5% CO2 gas and maintained at 25°C, pH 7.4.
Contractile properties of the right EDL and SOL wereassessed in vitro according to the methods describedin detail previously (Lynch et al. 2001; Ryall et al.2002). In vitro contractile measurements began with thedetermination of the muscle optimal length (Lo) for iso-metric tension development. After a 15 min equilibrationin the bath, the muscle was connected to an isotonic forcetransducer (model 305B; Cambridge Instruments, AuroraScientific, Inc., Ontario, Canada) and stimulated alongits entire length with platinum wire electrodes. After thedetermination of Lo from the maximum isometric twitchforce, all subsequent contractile properties were measured
at Lo. Optimum twitch fibre length (Lf) was determinedby multiplying Lo by previously determined ratios of fibrelength to muscle length: 0.44 for EDL and 0.71 for SOL(Brooks & Faulkner, 1988; Ryall et al. 2002). Isometrictetanic tension was determined at different frequencies ofstimulation (701B Stimulator; Aurora Scientific), rangingfrom 1 to 100 Hz for SOL and 150 Hz for EDL (pulseduration 0.5 ms, train duration 500 ms). A rest periodof 1 min was applied between each tetanic stimulation.Maximal isometric tetanic tension (Po) was determinedfrom the plateau of the frequency–force curve. After allmeasurements, the muscle was removed from the bath,trimmed of connective tissue, blotted dry and weighedon an analytical balance. Specific force (sPo; N cm−2) wascalculated for each muscle according to the well-acceptedprocedure that accounts for muscle cross-sectional area,determined after dividing muscle mass by the product ofLf and 1.06 mg mm−3, according to Lynch et al. (2001).
Fitting of muscle mechanical kinetics
A non-linear regression was used to fit the data obtainedin the CTL-G21 and CB-G21 groups only to a modelthat defines the kinetics of muscle force production andrelaxation in EDL and SOL muscles during a single500 ms twitch (pulse duration of 0.5 ms at 100 Hzfor SOL and 150 Hz for EDL). The model used to fitthe force development and relaxation kinetics for EDLand SOL and determine the parameter values is pre-sented in Fig. 1. Data were fitted using GraphPad Prism 4(GraphPad Prism Software, San Diego, CA, USA). Duringthe force production phase, the rate constant of theforce development (kACT, s−1) was determined. The timecourse of full force relaxation was biphasic, starting witha slow, seemingly linear, phase (rate constant, Slow kREL,s−1) followed, after a ‘shoulder’, by a fast, approximatelymonoexponential, relaxation phase (rate constant, FastkREL, s−1).
Myofibril preparation
SOL (slow twitch) muscle from CTL-G21, CB-G14 andCB-G21 groups as well as EDL (fast twitch) musclefrom CTL-G21, CB-G9, CB-G14 and CB-G21 groupswas used for these experiments. Briefly, muscles wereimmersed in ice-cooled Ringer buffer [composition (inmM): Tris-HCl 50, pH 7.0, NaCl 100, KCl 2, MgCl2 2,EGTA 1, dithiothreitol 1, phenylmethanesulfonyl fluoride0.2, leupeptin 0.01, pepstatin 0.005 and NaN3 0.5] at restlength. From these latter SOL and EDL muscles, myo-fibrils were prepared as previously described (Candauet al. 2003). The total myosin head concentration in themyofibrillar suspension was measured by absorbance at280 nm of a 1:10 dilution of the suspension in 2% SDS
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society
2074 G. Py and others J Physiol 593.8
(Houadjeto et al. 1992), based on the assumption that themolar extinction coefficient and percentage of myosin inSOL and EDL myofibrils are the same as in psoas myo-fibrils (Herrmann et al. 1994; Iorga et al. 2004).
Myofibrillar ATPase activity
ATPase measurements were carried out in a home-built,thermostatically controlled, Rapid Flow Quenchapparatus (Barman & Travers, 1985). Experiments wereperformed at 4°C to slow down the unloaded shorteningvelocity (V0), and thus to increase the duration of theunloaded shortening phase (tB) and improve the timeresolution. The chosen temperature of 4°C, although itslows the shortening velocity, still allows the myofibrilsto behave and contract normally (Candau et al. 2003).
In the Rapid Flow Quench apparatus, myofibrils weremixed with [γ-32P]ATP (2.5 µM myosin heads + 25 µM
[γ-32P]ATP in the reaction mixture at 4°C). Reactionmixtures were quenched in acid (22% trichloroacetic acidand 1 mM KH2PO4) at different times and the [32P]Piwas assayed. Reaction mixtures with incubation timesfrom 200 ms to 10 s were obtained with this apparatus.For longer incubation times, myofibrils and [γ-32P]ATPwere mixed in a beaker at 4°C, where one sample of thereaction mixture was taken and quenched every 10–15 sfor 3–10 min. The amounts of total Pi (i.e. free Pi plusmyosin head-bound Pi) were determined in the quenchedreaction mixtures by the filter paper method of Reimannand Umfleet (1978). Relaxing and activating bufferswere used to obtain relaxed and activated conditions,respectively, i.e. 50 mM Tris-acetate, pH 7.4, 100 mM
Figure 1. Fitting of force activation and
relaxation in EDL (A) and SOL (B) muscles
Recording shows the fitting curve (red line)
obtained from the data curve (black line)
recorded at 150 and 100 Hz for EDL and
SOL, respectively. The kinetics of force
development was described by one
exponential increase: a1(1–e−kACT∗t), where
a1 is the value of force at the maximal curve
asymptote (mN); e is the exponential
function; kACT (s−1) is the rate constant of
the force development; and x is the time
(ms). The kinetics of force relaxation was
described using two exponential slow and
fast decays: a2(1–e−slow kREL∗(t-td2)) + c2 and
a3(1–e− fast kREL∗(t-td3)) +c3 where c2 and c3
are the force levels at the beginning of each
decay; slow kREL and fast kREL are the rate
constants of the slow and fast phases of
tension decline, respectively; a2 and a3 are
the basal relaxation force level c2 and c3,
respectively; and td2 and td3 are the time
delays at the beginning of each decay.
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J Physiol 593.8 Effect of clenbuterol on skeletal muscle contractility 2075
potassium acetate, 5 mM KCl and either 0.1 mM CaCl2 and2 mM magnesium acetate for the activating buffer (withCa2+), or 2 mM EGTA and 5 mM magnesium acetate forthe relaxing buffer (without Ca2+).
Modelling ATPase activity kinetics
Although the first phase of steady-state ATP hydro-lysis was used as a measure of the ATPase activityduring unloaded shortening of the calcium-activated myo-fibrils, the entire kinetics of ATP hydrolysis was modelledwith a three-component model to determine the kineticparameters:
At t < tB, [Total Pi] /[
myosin heads]
=A+ [kF.t] (1)
At t > tB, [Total Pi] /[
myosin heads]
=A+ [kF.t]
+ [A+kF.tB+kS. (t−tB)] (2)
where the third component eqn (2) starts when t>tB,[Total Pi]/[myosin heads] is the amount of total phosphatedetermined per myosin head (mol of ATP hydrolysed permol of myosin heads), A is the amplitude (mol/mol)of the Pi burst, kF is the rate constant (s−1) of thefast steady state during the unloaded shortening phasein activated conditions, kS is the rate constant (s−1) ofthe steady state during ‘over-contraction’ when the thinfilaments of the two halves of the sarcomeres start to over-lap each other (a non-physiological state), and tB is thetime break (s) corresponding to the unloaded shorteningphase duration (Lionne et al. 1996). The model parameterswere determined using an iterative process that minimizesthe sum of the mean squares between the total phosphatemeasured and that predicted by the model. The ATP costof contraction during the unloaded shortening phase wasdetermined based on the number of ATPs consumed permyosin head (ATPtB) until tB.
The titration of the myofibril preparation ([active site]),i.e. the number of fully active ATPase sites per totalsites, was estimated from the amplitude (A) of the Piburst as proposed by Iorga et al. (2004) for Pi burstexperiments on SOL myofibrils. Titration experimentswere performed in relaxed conditions because (i) A issimilar in relaxed and Ca2+-activated conditions, (ii) thecleavage step that controls the Pi burst amplitude occursin the detached state whatever the condition and (iii) A isdetermined more accurately in relaxed than in activatedconditions, due a slower reaction compared with activatedconditions.
The limiting step of the cross-bridge cycle, the iso-merization step that occurs just before Pi release (k4),was evaluated from the catalytic activity (kcat) and theequilibrium constant of the ATP cleavage step (K3) (Iorgaet al. 2004):
k4 = kcat (1+K 3) /K 3 (3)
where kcat = ks/[active site], and K3>10.
Stopped-flow experiments
The low temperature retained for the enzyme kineticexperiments allowed measurement of the kinetics ofthe ATP-induced myosin head detachment from thethin filament and the ATP cleavage (K3) (Stehle &Brenner, 2000). The experiment was carried out in aHi-Tech Scientific stopped-flow apparatus (model SF-61DX2; Hi-Tech Ltd, Salisbury, UK). The excitation wave-length was 295 nm and emission was >320 nm using acut-off filter (WG320; Hi-Tech) to measure tryptophanfluorescence. For each experimental condition, a series of12 shots was carried out and averaged. The fluorescenceof the myofibrillar solution, without ATP, was set as 100%just before the start of the experiment. Fluorescence timecourses were fitted with a double exponential model of rateconstants kTrp fast and kTrp slow. The fast phase is related tothe ATP-induced detachment of the myosin head from theactin filaments. Its kinetics is hyperbolically dependent onATP concentration, and the initial slope of the dependencegives information on the rate of detachment. The slowphase kinetics is governed by the ATP cleavage step andis independent of ATP concentration. For further details,see Stehle and Brenner (2000).
Modelling ATPase activity as a function of twitch
fibre type
To identify the changes in ATPase activity due to changes inthe twitch fibre type in response to clenbuterol treatment, amultilinear regression analysis including specific ATPasesfor each twitch fibre type was used:
ki = kfF-I.F-I+kfF-IIa
.F-IIa+kfF-IIx/b.F-IIx/b
+kssF-I.F-I+kss-IIa
.F-IIa+kssF-IIx/b.F-IIx/b (4)
where F-I, F-IIa and F-IIx/b were the percentages of twitchfibre types I, IIa and IIx/b, respectively, and kss was therelaxed ATPase steady state, assumed to be equal to 0.125kf.
Ca2+-transient measurement
Due to its relatively small size and the technicalconstraints associated with Ca2+-transient measurement,the FDB muscle (fast twitch) was selected, dissected andenzymatically dissociated at 37°C for 1 h with 3 mg ml−1
collagenase (type 1; Sigma, St Louis, MO, USA) dissolvedin an external medium (in mM: NaCl 145, KCl 4, CaCl21.8, MgSO4 1, Hepes 10 and glucose 10, pH 7.4). Bundlesof fibres were then transferred to an external mediumwithout collagenase, and the fibres were mechanicallyseparated by gentle trituration. Intact single fibres were
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2076 G. Py and others J Physiol 593.8
Table 1. Time course of clenbuterol-induced phenotype shift in EDL and SOL muscles
EDL SOL
CTL-G21 CB-G9 CB-G14 CB-G21 CTL-G21 CB-G9 CB-G14 CB-G21
Fibre types I 7.5 ± 2.2 7.3 ± 2.6 7.6 ± 2.8 5.4 ± 1.4 80.0 ± 7.4 72.0 ± 8.3 69.4 ± 9.5∗ 60.7 ± 8.5∗∗∗
(%) IIa 25.6 ± 1.9 14.7 ± 4.3∗ 9.9 ± 2.4∗∗ 11.0 ± 3.7∗∗ 14.8 ± 8.5 16.9 ± 8.4 20.4 ± 9.7 29.1 ± 10∗∗
IIx-IIb 66.9 ± 2.7 78.0 ± 4.3∗ 82.4 ± 3.1∗∗ 83.6 ± 4.7∗∗ 5.3 ± 3.6 11.1 ± 1.8∗ 10.2 ± 2.8∗ 10.2 ± 6.8∗
Values are mean percentages (± SD, n = 8) of the different fibre types in EDL and SOL. ∗Different from CTL-G21; ∗∗different from
CTL-G21 and CB-G9; ∗∗∗different from CTL-G21, CB-G9 and CB-G14. P < 0.05. Because non-statistically differences exist between
CTL-G9, CTL-G14 and CTL-G21 groups for both fibre type distribution and CSA, results of the CTL-G21 group only are given.
plated on extracellular matrix (Sigma)-coated coverslipsattached across a 12 mm hole in the bottom of 35 mmPetri dishes. FDB fibres bathed in an external mediumwere loaded for 30 min at room temperature withFluo-3 AM (5 µM; TefLabs, Austin, TX, USA). Actionpotential (AP)-induced Ca2+ transients were triggered byfield stimulation with a single 2 ms pulse just above thestimulation threshold. Fluorescence images were acquiredin line-scan mode (spatial [x] vs. temporal [t], 1.5 ms perline) with a confocal system (Zeiss LSM 510 Meta,63× objective, NA = 1.2, H2O immersion; Oberkochen,Germany). Fluo-3 was excited with an argon/krypton laserat 488 nm, and the emitted fluorescence was recordedat 525 nm. Single-fibre Ca2+ transients were identifiedon raw fluorescence images. Image strips of the manuallyidentified area of a single fibre were extracted. After sub-tracting the photomultiplier offset current, strip imageswere converted to 1F/F, where F is the mean restingfluorescence of the fibre calculated from the image areapreceding the AP stimulation and subsequent Ca2+ trans-ient. The temporal fluorescence time courses of each fibrewere generated by spatial compression of the 1F/F images,resulting in a mean spatial F value for each temporalcoordinate.
Statistics
Variables were compared between groups usingANOVA and the Newman–Keuls post hoc multiplecomparison procedure when significance was detected.The significance of the model parameters retained in thekinetic analysis was tested by an F-test. Significance wasset at P < 0.05. All values are expressed as mean ± SEM.
Results
Morphometric properties, shift in muscle phenotype
Clenbuterol treatment for 21 days resulted in significantincreases in muscle mass, more pronounced in EDL(+22%; 220.9 ± 5.4 mg CB-G21 vs. 176.3 ± 8.3 CTL-G21;P < 0.001) than SOL (+9%; 202.0 ± 5.8 mg CB-G21
vs. 176.7 ± 6.1 mg CTL-G21; P < 0.01). The musclemass/body mass ratio was not different compared withcontrol groups. In the clenbuterol-treated rats, the ratiowas unchanged in SOL (0.53±0.01 CB-G21 vs. 0.54±0.01CTL-G21) but significantly increased in EDL (0.59 ± 0.01CB-G21 vs. 0.52 ± 0.02 CTL-G21; P < 0.001).
Non-statistically significant differences were foundbetween CTL-G9, CTL-G14 and CTL-G21 groups forboth fibre type distribution or cross-sectional area (CSA)(data not shown for CTL-G9 and CTL-G14 groups) andthus we decided to present results of the CTL-G21 grouponly. EDL muscle underwent a phenotypic shift towardfaster muscle twitch fibres, as determined by the myo-sin ATP (Table 1). This shift was noticeable from day 9onward. The increased percentage of type IIx-IIb twitchfibres at day 9 was accompanied by a decreased percentageof type IIa twitch fibres. At day 21, a 17% (P < 0.05)increase and a 15% (P < 0.05) decrease in IIx-IIb and IIatwitch fibre types, respectively, were observed comparedwith CTL-G21. Similar results in the SOL muscle werefound (Table 1). At day 14, the proportion of type Imuscle fibres was 11% (P < 0.05) lower in the treatedgroup compared with CTL-G14 group. The percentageof type IIx-IIb twitch fibres was significantly higher inthe treated group compared with CTL-G9 at day 9 (+6%,P < 0.01). Later (i.e. between day 9 and day 21), no furtherincrease in the percentage of type IIx-IIb twitch fibres wasobserved. After 21 days of treatment, the percentage oftype IIa fibres was significantly increased from 14 to 29%(Table 1).
Kinetics of muscle force activation and relaxation
Muscle force development. In EDL muscles, maximalisometric tension (Po) exhibited an 89% increase inCB-G21 compared with CTL-G21 (2232 ± 192 vs.1177 ± 206 mN, respectively; P < 0.05, Fig. 2A). However,when Po was corrected to account for muscle CSA, sPo
was not different in EDL among groups (12.3 ± 1.0 vs.12.0 ± 1.8 N cm−2 in CB-G21 and CTL-G21, respectively,Fig. 2A).
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J Physiol 593.8 Effect of clenbuterol on skeletal muscle contractility 2077
In SOL muscle, no significant difference in Po wasobserved between CB-G21 and CTL-G21 (1648 ± 133vs. 1365 ± 50 mN, respectively) (Fig. 2B). However,clenbuterol treatment significantly reduced SOL sPo
in CB-G21 compared with CTL-G21 (11.9 ± 0.7 vs.
14.0 ± 0.6 N cm−2; P < 0.05, Fig. 2B).
Kinetics of force development and relaxation. Fittingof thekinetics of force activation and relaxation for EDL
and SOL gave high determination coefficients, with amean r
2 value greater than 0.997 and 95% confidenceintervals inferior to 1% for EDL and SOL. Figure 2C
and D illustrates representative recordings from fittingcurves obtained from G21-CB and CTL-G21 for bothEDL and SOL. The results obtained from fitting themuscle mechanical kinetics showing the clenbuterol effectafter 21 days of treatment are summarized in Table 2. InEDL, the main clenbuterol effect was observed for kACT
with a significant decrease compared with the untreated
Figure 2. Effect of clenbuterol treatment on maximal isometric tetanic power (Po), specific isometric
tetanic power (sPo), and force development and force relaxation kinetics in EDL (A, C) and SOL (B, D)
muscle
Mean (± SEM) EDL (A) and SOL (B) maximal isometric tetanic power (Po, left vertical axis) and specific maximal
isometric tetanic power (sPo, right vertical axis) recorded from CTL-G21 (white bar) and CB-G21 animals (dark bar).
C and D, recording shows representative fitting curves in a CB-treated rat (red line) and a CTL-treated rat (black
line) in EDL muscle (C) and SOL muscle (D). The kinetics of force development was described by one exponential
increase: a1(1–e−kACT∗t), where a1 is the value of force at the maximal curve asymptote (mN); e is the exponential
function; kACT (s−1) is the rate constant of the force development; and x is the time (ms). The kinetics of force
relaxation was described using two exponential slow and fast decays: a2(1–e−slow kREL∗(t-td2)) + c2 and a3(1–e− fast
kREL∗(t-td3)) +c3 where c2 and c3 are the force levels at the beginning of each decay; slow kREL and fast kREL are
the rate constants of slow and fast phases of tension decline, respectively; a2 and a3 are the basal relaxation
forces level c2 and c3, respectively; and td2 and td3 are the time delasy at the beginning of each decay. ∗P < 0.05
CTL-G21 vs. CB-G21.
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2078 G. Py and others J Physiol 593.8
Table 2. Clenbuterol alters contraction and relaxation kinetics in fast muscle
EDL SOL
CTL-G21 (n = 7) CB-G21 (n = 8) CTL-G21 (n = 7) CB-G21 (n = 8)
kACT (s−1) 23.1 ± 0.8 19.3 ± 1.2∗∗ 8.5 ± 0.8 7.6 ± 0.0
Slow kREL (s−1) 3.4 ± 0.1 1.8 ± 0.0∗∗ 1.8 ± 0.2 1.4 ± 0.1
Fast kREL (s−1) 38.7 ± 2.2 28.7 ± 1.6∗∗∗ 10.4 ± 1.7 6.3 ± 0.5∗
Kinetic parameters for force development and relaxation during maximal tetanic eletrostimuation following clenbuterol treatment
in EDL and SOL muscles. Values are expressed as means ± SEM. kACT, rate constant of force development; Slow kREL, rate constant
of the slow relaxation phase; Fast kREL, rate constant of the fast relaxation phase; CTL-G21, untreated control group; CB-G21,
clenbuterol-treated group. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001.
condition (Table 2; P < 0.01). In addition, clenbuteroltreatment significantly decreased Slow kREL and FastkREL (Table 2; P < 0.01 and P < 0.001, respectively).In SOL muscle, a significant decrease in Fast kREL wasobserved in CB-21 compared with CTL-G21 (Table 2;P < 0.05).
Chemical kinetics
Slow-type muscle. Relaxed condition. A significantincrease in myosin ATPase activity was observed duringclenbuterol treatment compared with CTL (Figs 3A and4A, P < 0.001) in myofibrils prepared from SOL muscle.
Figure 3. Time courses of SOL myosin ATPase in relaxed (A) or Ca2+-activated (B) myofibrils in control
group (filled circles), and after 21 days of clenbuterol treatment (open circles)
Reaction mixtures were 3 µM myofibrils (as myosin heads) plus 30 µM [γ -32P]ATP at 4°C. The reaction mixtures were
quenched at the times indicated and the total [32P]Pi concentrations were determined. In the relaxed condition,
the amplitude of Pi burst and ATPase activity were 0.34 ± 0.05 vs. 0.45 ± 0.07 mol mol−1, and 0.0032 ± 0.0005
vs. 0.0083 ± 0.0011 s−1 for the control and 21-day clenbuterol-treated groups, respectively. In the Ca2+-activated
condition, the slope of the fast linear phase (ATPase activity in unloaded shortening), slope of the slow linear phase,
duration of the unloaded shortening phase (dashed lines) and the ATP cost of shortening were 0.025 ± 0.004 vs.
0.065 ± 0.01 s−1, 0.016 ± 0.003 vs. 0.031 ± 0.006 s−1, 87 ± 9 vs. 50 ± 5 s and 2.4 ± 0.2 vs. 4.2 ± 0.4 mol mol−1
for the control (CTL-G21) and 21-day clenbuterol-treated groups (CB-G21), respectively.
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society
J Physiol 593.8 Effect of clenbuterol on skeletal muscle contractility 2079
This increase was explained by the twitch fibre shift fromslow to fast type (P < 0.001). The lack of significantchange (P > 0.05) in the titration index suggests thatthe number of myosin heads fully competent in the myo-fibril suspension, from an enzymatic point of view, was
not affected by the clenbuterol treatment. The fast rateconstant of the tryptophan fluorescence signal increasedand the steeper initial slope of the dependence with ATPconcentration (P < 0.01; Fig. 4B) indicated an increasein the rate constant of ATP binding-induced myosin
Figure 4. Effect of clenburerol on SOL myofibrilar ATPase activity in relaxed (A–E) and Ca2+-activated
(F–H) conditions at 4°C
Reaction mixture concentrations were 3 and 30 µM for myosin heads and [γ -32P]ATP concentrations in Rapid
Flow Quench experiments, respectively. In Stopped-Flow experiments (B and C), myosin head concentration was
1 µM reaction mixture, and ATP concentrations ranged from 30 µM to 1 mM. CTL-G21, control group; CB-G14,
14-day clenburerol-treated group; CB-G21, 21-day clenburerol-treated group; kSS, rate constant of the steady-state
phase in relaxed condition; k2/K1, ATP binding-induced myosin detachment obtained from the initial slope of the
regression of ktrypophan fast as a function ATP concentration; ktryprophan slow, rate constant observed for the slow
phase of tryptophan increase when mixing myofibrils with ATP; k4, rate constant of Pi release (the isomerization
step preceding diffusion of Pi); kF, rate constant of the fast steady-state phase in activated condition; tB, duration
of the unloaded shortening phase, an index of unloaded shortening velocity; ATPtB, ATP consumed during the
unloaded shortening phase. Because no significant difference was detected between CTL-G9, CTL-G14 and
CTL-G21 groups, results of the CTL-G21 group are presented. NS, not significant. ∗∗P < 0.01; ∗∗∗P < 0.001.
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society
2080 G. Py and others J Physiol 593.8
detachment with clenbuterol treatment compared withthe control group. In contrast, the rate constant of thetryptophan fluorescence slow phase was not altered byclenbuterol treatment (P > 0.05; Fig. 4C). As this slowphase was attributed to the ATP cleavage step (Stehle& Brenner, 2000), the present result suggests a lack ofclenbuterol effect on this ATP cleavage step. The increase inthe rate constant of Pi release under clenbuterol treatment(P < 0.001; Fig. 4D) suggests that the enhancement inrelaxed ATPase activity was due to an increase in the Pirelease step, a rate-limiting step in the steady state ofATPase activity.
Ca2+
-activated condition. In this condition and overthe present time scale, the time curve was biphasic(Fig. 3B). The fast steady state corresponds to the unloadedshortening phase (Lionne et al. 1996). During this phase,myofibrils were shortened at maximal velocity in theunloaded condition. The slow steady state correspondsto a pseudo-isometric phase (non-physiological phase).The time corresponding to the transition between thesetwo phases is the duration of the unloaded shortening(tB). A significant increase in the slope of the faststeady state (i.e. myosin ATPase activity) was observedunder clenbuterol treatment in activated SOL myofibrilscompared with controls (P < 0.001; Fig. 4E). In associationwith this increase, a shorter duration of the unloadedphase was noted (P < 0.001; Fig. 4F), suggesting anincrease in unloaded shortening velocity. Interestingly,
the amount of ATP hydrolysed during the unloadedshortening phase increased under clenbuterol treatment(Fig. 4G; P < 0.001). Similarly to the relaxed condition, anincrease (P < 0.001) in Pi release rate was also noted in theCa2+-activated condition, suggesting that the increasedmyofibrillar ATPase was due to an increase in the rateconstant of Pi release. The multilinear regression analysisrevealed that the increases in the Pi release rate constantand myofibrillar ATPase activity in SOL were mainlyexplained (P < 0.001) by the phenotype shift inducedby clenbuterol.
Fast-type muscle. No significant change was seen in myo-fibrillar ATPase activity (Fig. 5) in myofibrils preparedfrom EDL muscle. The rate constant of the fast andslow steady-state phases (Fig. 5A and B, respectively),the duration of the unloaded shortening phase (Fig. 5C)and the amount of ATP consumed during this shortening(Fig. 5D) were not modified by clenbuterol treatment.The multilinear regression analysis revealed that theincrease in the Pi release rate constant and myofibrillarATPase activity in EDL were not significantly explained bythe phenotype shift induced by clenbuterol.
Ca2+ transients
The action potential-elicited Ca2+ transients infield-stimulated intact FDB twitch fibres from theclenbuterol-treated animals showed a progressively
Figure 5. Effect of clenburerol on EDL myofibrilar ATPase activity in Ca2+-activated conditions at 4°C
Reaction mixture concentrations were 3 and 30 µM for myosin heads and [γ -32P]ATP concentrations in Rapid Flow
Quench experiments, respectively. CTL-G21, control group; CB-G9, 9-day clenbuterol-treated group; CB-G14,
14-day clenburerol-treated group; CB-G21, 21-day clenburerol-treated group; k4, rate constant of Pi release (the
isomerization step preceding diffusion of Pi); kF, rate constant of the fast steady-state phase in activated condition;
tB, duration of the unloaded shortening phase, an index of unloaded shortening velocity; ATPtB, ATP consumed
during the unloaded shortening phase. Because no significant difference was detected between CTL-G9, CTL-G14
and CTL-G21 groups, results of the CTL-G21 group are presented. NS, not significant.
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J Physiol 593.8 Effect of clenbuterol on skeletal muscle contractility 2081
decreased amplitude after 14 days (−19%, P < 0.01) and21 days (−25%, P < 0.01), but not after 9 days, whencompared with the control groups (Fig. 6).
Discussion
In this study, we showed that chronic clenbuteroladministration induced a strong side effect, in contrastto the positive functional effect associated with musclehypertrophy and absolute maximal tetanic force (Po).In agreement with the study of Head & Ha (2011)showing that clenbuterol treatment induces a loss in forceproduction, we demonstrated that this loss is linked to (i)a diminution of the contraction and relaxation kineticsduring muscle force generation, especially in fast-typemuscle, and (ii) calcium handling.
Chronic clenbuterol treatment leads to hypertrophy
but reduced specific force in slow twitch muscle
We confirmed previous studies showing that chronicclenbuterol administration increases both muscle massand absolute isometric force, especially in fast-type muscle(Mounier et al. 2007). In the present study, the 89%increase in EDL absolute Po in clenbuterol-treated rats(4 mg kg−1 day−1) is greater than the 31% increasefound by Zeman et al. (1988) for the same duration oftreatment with rats receiving about 1.6 mg kg−1 day−1
or the 20% increase in maximal isometric force inthe study of Ryall et al. (2002) with the commonlyused dose of 2 mg kg−1 day−1. Thus, it seems that adose–response effect exists between clenbuterol and therelated increase in maximal tetanic force for treatmentlasting at least 2–4 weeks (Zeman et al. 1988; Ryall et al.2002). Moreover at the dose of 1.6 mg kg−1 day−1 it seemsthat no further increase in type II fibre CSA or Po occurs inEDL muscle between 2 and 12 weeks of treatment (Zeman
Figure 6. Effect of clenbuterol treatment on FDB Ca2+
transient amplitude
Bar graphs comparing amplitude for single-fibre AP-elicited Ca2+
transients in FDB fibres of control (CTL; n = 7) or clenbuterol-treated
(CB; n = 8) rats after 9, 14 and 21 days of treatment. Amplitude was
calculated from individual exponential fits and averaged. ∗∗P < 0.01.
et al. 1988). Nevertheless, when normalized to CSA, themaximal tetanic force was unchanged and even depressedin slow twitch muscle. Moreover, it is well known that highdose clenbuterol administration, as in our study, causesinflammation and apoptonecrosis in muscle early on in thetreatment and that these effects seem to be greater in slowtwitch fibres (Burniston et al. 2005; Douillard et al. 2011).This could be due to higher β2-adrenoreceptor and proteinGαs subunit density in slow-type as opposed to fast-typemuscles (Martin et al. 1989). The main consequencesof the apoptonecrotic period could be delayed musclegrowth and an increased number of infiltrated cells anddamaged fibres. Thus, these transient deleterious effectsmight explain the subsequent decrease in the maximaltetanic contraction obtained at 9, 14 and 21 days in SOLand, to a lesser extent, the decrease in the maximal tetaniccontraction obtained at 14 days in EDL.
Clenbuterol treatment did not increase specific
myofibrillar ATPase activity
Our results show that the myofibrillar ATPase activitiesand the content of type II twitch fibres increased inthe same proportion in slow twitch muscle duringclenbuterol treatment, indicating that the ATPase activityassociated with each twitch fibre type was not altered.This suggests that the depressed specific tension was notdue to the contractile machinery itself. Two myofilamentproteins are possibly phosphorylated in response toβ-adrenoceptor activation, i.e. troponin-I, which reducesthe affinity for Ca2+ binding to troponin-C, and myo-sin binding protein-C (C-protein), which increases themaximal myosin ATPase rate, although neither proteinseems phosphorylated in mammalian skeletal muscleunder β-adrenergic agonist treatment (Manning & Stull,1982; Gruen et al. 1999). The amino acid residues that arephosphorylated by protein kinase A in cardiac troponin-Ior C-protein are absent from the skeletal muscle iso-forms of these proteins (Shaffer & Gillis, 2010). Anotherprotein, myosin light-chain kinase, is phosphorylatedunder β-agonist treatment in smooth muscle, but againthis does not occur in skeletal muscle (Manning & Stull,1982). β-Agonists such as clenbuterol seem to have nonotable ergogenic effect on myosin head function inskeletal muscle, except for the well-known change in twitchfibre type.
Calcium handling is altered under chronic clenbuterol
treatment
Another explanation is that the loss of force productioninduced by clenbuterol reflects different aspects ofproblems in calcium handling.
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society
2082 G. Py and others J Physiol 593.8
We found an alteration in the calcium handling abilityin fast-type muscle with a reduction in Ca2+-transientamplitude that may explain, at least in part, thereduced kinetics of muscle contraction. Sarcolemma Ca2+
current and sarcoplasmic reticulum Ca2+ handling arealtered differently during acute β2-agonist administrationcompared with chronic administration. The earlierdemonstration that acute β2-agonists influence intra-cellular Ca2+ handling in intact skeletal muscle wasachieved by administering terbutaline to isolated fasttwitch FDB fibres from mice (Cairns & Dulhunty,1993). These authors found a potentiation of myoplasmic[Ca2+] response ([Ca2+]i) during tetanic stimulation(20–100 Hz), especially at high stimulation frequencies.A series of subsequent studies further highlighted theacute β2-agonist-induced augmentation in peak twitch ortetanic [Ca2+]i on skeletal muscles for doses ranging from1 to 50 µM (Bruton et al. 1996; Ha et al. 1999; Prakashet al. 1999). Overall, β2-agonists do not change the resting[Ca2+]i in rodent muscles (Prakash et al. 1999). A recentstudy by Rudolf et al. (2006) showed that isoprenaline(10 µM) facilitated emptying of Ca2+ from the lumenof the sarcoplasmic reticulum of mouse tibialis anteriormuscle in vivo and that this effect was greater with tetanicstimulation. This strongly suggests that the increasedamplitude of Ca2+ transients involves greater Ca2+ effluxfrom the sarcoplasmic reticulum. However, data on theeffect of chronic β2-agonist treatment on skeletal musclecalcium homeostasis are sparse (Sirvent et al. 2014). Inthis study, high-dose chronic clenbuterol treatment hada deleterious effect given that Ca2+-transient amplitudeswere decreased. Although we do not understand theprecise mechanisms that would explain why chronicclenbuterol treatment has the opposite effect on Ca2+
handling compared with that usually reported for acutetreatment, the observations by Burniston et al. (2005)and Douillard et al. (2011) suggest some directions forinvestigation. First, chronic high doses (4 mg kg−1 day−1)of clenbuterol induce transient apoptosis and necrosis infast skeletal muscle. In addition, Lavoie et al. (2002) usedthe same dose and observed a decrease of 45 and 40%in β2-adrenoreceptor density in SOL and gastrocnemiusmuscle, respectively.
Chronic clenbuterol treatment alters contraction and
relaxation kinetics
Beyond the specific maximal force, the original results ofour study were related to the changes in muscle contractionand relaxation kinetics induced by clenbuterol. Indeed,our data demonstrated altered contraction and relaxationkinetics during muscle force generation, especially in EDL,with significant decreases in kACT, Slow kREL and FastkREL. Regarding contraction development, kACT depends
on both the cross-bridge isomerization rate constant(dependent on Pi liberation) and the Ca2+ release velocity(i.e. Ca2+ transient). In EDL, our results do not provideevidence for the first mechanism because myosin k4
and ATPase activity did not change under clenbuteroltreatment. However, we showed a reduction in themuscle Ca2+ transient of FDB muscle that approximatesthe EDL muscle fast phenotype (Fig. 6). This couldcontribute to the reduced kinetics of muscle contractionand strongly suggests that clenbuterol acts on some ofthe processes involved in excitation–contraction coupling,thereby modulating force and contractility characteristics,probably via an alteration in calcium handling. It has beenwell documented that muscle contraction efficiency andresistance to fatigue are altered by clenbutetol treatment(Chen & Alway, 2001; Polla et al. 2001). Interestingly, basedon the fitting kinetics, we found altered muscle relaxationafter treatment. Slow kREL was dependent on the myo-sin head detachment rate constant and the velocity ofcalcium uptake, confirming the role of calcium handlingability in the alteration of the relaxation properties ofskeletal muscle. In our study, the mechanism responsiblefor the decreased relaxation rate in EDL induced by anyalteration of the myosin head detachment kinetics canbe ruled out because of the lack of significant change inthe ATP binding rate constant. The slowed time course ofskeletal muscle contraction and relaxation that clenbuterolinduced during force generation counterbalanced thepositive effects associated with skeletal muscle hyper-trophy and may have functional relevance for physicalactivities. As the rate constants of both force developmentand relaxation control the maximal shortening velocity,such treatment could be considered as having a negativeimpact on the activities of daily living. On the onehand, slowing muscle relaxation might be associatedwith altered agonist–antagonist coordination but, on theother, a reduced myosin head detachment rate wouldbe associated with reduced maximal shortening velocity.Another potentially negative effect for slow twitch musclelies in the increased ATP cost of shortening, suggestingan altered efficiency in mechanochemical transduction,which would explain, at least in part, the lower resistanceto fatigue observed with high doses (He et al. 2000). Theselast negative effects are of particular importance in sportswhere performance depends on endurance and muscleoxidative capacity. However, even with altered contractionand relaxation kinetics, there remains the increase inabsolute tetanic force that could confer to athletes theexpected ergogenic aid.
In conclusion, our study shows that chronic clenbuteroltreatment decreases skeletal muscle contraction andrelaxation kinetics, especially in fast-type muscle, andan increase in the ATP cost for contraction was noted,as well. Because we found a decreased Ca2+-transientamplitude, it seems that the reduced specific force
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society
J Physiol 593.8 Effect of clenbuterol on skeletal muscle contractility 2083
production or the increased fatigue was dependent ondepressed calcium homeostasis induced by clenbuterol.The beneficial anabolic effects of high-dose β2-agonists fortreatment of sarcopenia, muscle weakness and musculardystrophy seem counterbalanced, at least in part, bydeleterious effects on muscle contractility, relaxationkinetics and Ca2+ handling.
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Additional information
Competing interests
The authors declare that they have no conflict of interest.
Author contributions
Conception and design of the experiments: G.P. and R.C.;
collection, analysis and interpretation of data: G.P., C.R., R.C.,
P.S., A.M.J.S., A.G.P., A.D., O.G., C.L., A.B., A.C., O.C. and
A.L.; drafting the article or critically revising it for substantial
intellectual content: C.R., R.C., G.P., A.M.J.S., A.G.P. and A.C.
Funding
This work was supported by the World Anti-Doping Agency,
the Centre National d’Etudes Spaciales, the French Ministry of
Youth and Sports, and the Fondation Francaise des Jeux, and
it was funded in part by the University of Montpellier and
the Institut National de la Recherche Agronomique (INRA).
A.M.J.S., A.D. and A.G.P. received PhD scholarships from the
Ministe re de l’Enseignement la Recherche et de la Technologie
(MENRT).
C© 2015 The Authors. The Journal of Physiology C© 2015 The Physiological Society
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"
Méthode
"
Animal, protocole d’entraînement et quantification des charges et performances.
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5&2?&$..)($0$3%" 529&" '%%$)3+&$" FIMR" +$" *$9&" 52)+." +$" 42&5." /" *7)..9$" +$." h" .$0')3$."
+7$3%&'83$0$3%" {hAFM|;" <'" .1'34$" +7$3%&'83$0$3%" 423.).%$" $3" 93$" .1&)$" +$" FM" &151%)%)23."
$.5'41$."4,'493$"5'&"B"0)39%$."+$"&$52."/"&').23"+$"I".1'34$."5'&".$0')3$A"5$3+'3%"*7'5&-."
0)+);"<7$3%&'83$0$3%" 1%')%" '&&Z%1" .)" *7'3)0'*" &$69.')%" +$"?&)05$&"0'*?&1" *$." .2**)4)%'%)23."+$"
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"
Quantification des charges et performances
<'"5$&62&0'34$"1%')%"'55&14)1$"'9"%&'($&."+9"%&'(')*"52%$3%)$*"629&3)"423%&$"*'"62&4$"+9$"/"*'"
?&'()%1"D�"$3"jJ`""
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�,"$.%"*'"+).%'34$"?&)051$"$3"0A"$%"g"$.%"*$"320W&$"+$"&151%)%)23."Di.e."N"L"FMJ;""
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'++)%)233$**$."D�7"$3"�J"$3"5&$3'3%"$3"4205%$"*$"%&'(')*"629&3)"$%"*$"%$05."+7$.4'*'+$"`"
�7)"L"�)";"%"^F"
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"p}"*$"%$&0$"�R"&$5&1.$3%$"4,'&?$"$X5&)01$"$3"R"+$"*'"4,'&?$"0'X)0'*$;"
T9"6)3'*A"*'"5$&62&0'34$"&1$**$"$X5&)01$"$3"1:9)('*$3%"59)..'34$"/"4,'&?$"25%)0'*$"+15$3+"+$"
*'"59)..'34$"0$.9&1$"'9"]29&")"D�7)J"523+1&1$"5'&"93"6'4%$9&"+71:9)('*$34$"32%1"["`"
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"
Modélisation des effets de l’entraînement
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.'".2&%)$;"<$."5'&'0-%&$."+9"02+-*$"4'&'4%1&).'3%"*'"&1523.$"/"*7$3%&'83$0$3%"+9".9]$%"D32%1"!J"
.23%" $.%)01." 5'&" *7']9.%$0$3%" +$" *'" .2&%)$" +9" 02+-*$" D);$;" 5$&62&0'34$" 02+-*$J" /" *'"
5$&62&0'34$"&1$**$;"<'"6234%)23"+$"%&'3.6$&%"! !!! "+233$"*'"5$&62&0'34$"02+-*$"'9"%$05."%"
5'&"*$"5&2+9)%"+$"423(2*9%)23"`"
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<$"5&2+9)%"+$"423(2*9%)23"$.%"+16)3)%"5'&"*7)3%1?&'*$"`""
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4,'&?$."+7$3%&'83$0$3%"`"
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)059*.)233$**$."+7'5%)%9+$"$%"+$"6'%)?9$":9)"1(2*9$3%"$3"5'&'**-*$"`"
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+$"%$05."+$"+14&2)..'34$"529&"*7'5%)%9+$"$%"*'"6'%)?9$A"&$.5$4%)($0$3%;""
"
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+1*')" 314$..')&$" 529&" :9$" *'" 5,'.$" +$" +14&2)..'34$" 3$" 4200$34$" :97'5&-." *'" 5,'.$" +$"
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&$.5$4%)($0$3%"*$."423.%'3%$."+7'05*)%9+$"$%"+$"%$05."+$"*'"6'%)?9$;"
"
Estimation des paramètres des modèles
<$."5'&'0-%&$."+$."02+-*$." .23%"+1%$&0)31."5'&" ']9.%$0$3%"+$" *'"5$&62&0'34$"02+-*$" /" *'"
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.23%"420093."/"*7$3.$0W*$"+9"?&295$;"!4)A"*$."423.%'3%$."+$"%$05."23%"1%1"+1%$&0)31$."529&"
*7$3.$0W*$" +9" ?&295$" 5'&" *'" 01%,2+$" +$." '*?2&)%,0$." ?131%):9$." $%" *$." 423.%'3%$."
+7'05*)%9+$." )3+)()+9$**$." 23%" 1%1" +1%$&0)31$." 5'&" &1?&$..)23" *)31')&$A" $3" 0)3)0).'3%" *'"
.200$"+$."4'&&1."&1.)+9$*."$3%&$"*$."5$&62&0'34$."&1$**$"$%"02+-*$"`"
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p}"K\\"$%" *'" .200$"+$."4'&&1." &1.)+9$*.A"3"5&$3+"g"('*$9&."42&&$.523+'3%"'9X" ]29&."2}" *'"
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:9'*)%1"+$"*7']9.%$0$3%"$.%"'55&14)1$"5'&"*$"42$66)4)$3%"+$"+1%$&0)3'%)23"`"
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+$"*'"*)%%1&'%9&$"+9"5&1.$3%"+2490$3%;"
Résultats et discussion préliminaires
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Figure et tables ""
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Table 1
"
Modèle R2 Adj.R2 F ratio ddl P
A2 * 0,71 0,65 12,24 3 < 0,001
Banister 0,79 0,73 13,16 4 < 0,001
A2F1 0,76 0,67 8,23 5 < 0,01
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"
Table 2
Modèle kA tau A1 tau A2 kF tau F
A2 0,0025 ± 0,0016 0,61 3,68 none none
Banister 1,556 ± 3,099 none 48,34 1,79 ± 3,27 49,12
A2F1 0,0037 ± 0,0025 0,33 5,72 0,0025 ± 0,0015 49,93
"
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References
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F;"" E'3).%$&" e�A" l'0)*%23" #<;" o'&)'%)23." )3" )&23" .%'%9." a)%," 6'%)?9$" 02+$**$+" 6&20"%&')3)3?")3"6$0'*$"+).%'34$"&933$&.;"e9&"j"T55*"O,=.)2*;"FGnIrIhDFJ`FCwBN;""B;"" E9..2"UA"l�bb)3$3"dA"O'b'&)3$3"TA"#'&'..2"#A"<'429&"jKA"d20)"OoA"$%"'*;"T".=.%$0."02+$*"26"%&')3)3?"&$.523.$."'3+")%."&$*'%)23.,)5"%2",2&023'*"&$.523.$.")3"$*)%$"a$)?,%^*)6%$&.;"e9&"j"T55*"O,=.)2*;"FGGMrCFDF^BJ`hnwIh;""N;"" E'3).%$&" e�A" #'*($&%" U�A" \'('?$" _oA" E'4," U;" T" .=.%$0." 02+$*" 26" %&')3)3?" 62%"'%,*$%)4"5$&62&0'34$;"T9.%"j"\52&%."_$+;"FGHIr(2*;"H`IHwCF;""h;"" O,)*)55$"TsA"O="sA"t'()$&"tEA"\'34,$c"T_jA"E233)$9"TA"E9..2"UA"$%"'*;"_2+$*)3?"%,$" &$.523.$." %2" &$.).%'34$" %&')3)3?" )3" '3" '3)0'*" $X5$&)0$3%" .%9+=;" E)2_$+" K$." !3%;"BMFIrBMFI`GFhnCM;""I;"" #'3+'9" KA" E9..2" UA" <'429&" jK;" e66$4%." 26" %&')3)3?" 23" )&23" .%'%9." )3" 4&2..^4293%&=".b)$&.;"e9&"j"T55*"O,=.)2*;"FGGBrChDCJ`hGHwIMB;""C;"" E9..2" U;" o'&)'W*$" +2.$^&$.523.$" &$*'%)23.,)5" W$%a$$3" $X$&4).$" %&')3)3?" '3+"5$&62&0'34$;"_$+"\4)"\52&%."eX$&4;"BMMN"j9*rNIDHJ`FFnnwGI;""H;"" \'34,$c" T_jA" s'*W-." pA" t'W&$^s9$&=" tA" U,20'." <A" Y29)**'&+" TA" O=" sA" $%" '*;"_2+$**)3?" %&')3)3?" &$.523.$" )3" $*)%$" 6$0'*$" ?=03'.%." '3+" 25%)0'*" .%&'%$?)$." 26" 2($&*2'+"%&')3)3?"'3+"%'5$&;"j"\52&%."\4);"BMFNrNFDFhJ`FIFMwG;""n;"" U,20'." <A" E9..2" U;" T" %,$2&$%)4'*" .%9+=" 26" %'5$&" 4,'&'4%$&).%)4." %2" 25%)0)c$"5$&62&0'34$;"_$+"\4)"\52&%."eX$&4;"BMMI"\$5rNHDGJ`FCFIwBF;""G;"" U,20'."<A"_9])b'"!A"E9..2"U;"T"02+$*".%9+="26"25%)0'*"%&')3)3?"&$+94%)23"+9&)3?"5&$^$($3%"%'5$&")3"$*)%$".a)00$&.;"j"\52&%."\4);"BMMn"T5&rBCDCJ`ChNwIB;""FM;"" E$?9$" sA" Y29)**'&+" TA" s'*W$." pA" K2..'32" EA" o$&39." EA" #'3+'9" KA" $%" '*;" e'&*="'4%)('%)23"26"&'%".b$*$%'*"09.4*$"!<^Ck\UTUFk\UTUN"+$5$3+$3%"?$3$"$X5&$..)23")3"&$.).%'34$"$X$&4).$"*)3b$+"%2",=5$&%&25,=;"O*2\"p3$;"BMFNrnDBJ`$IHFhF;""FF;"" T+'0."sKA" l'%,$&" E_A" E'*+a)3" d_A" Y9+*$=" sT;" \b$*$%'*" 09.4*$"0=2.)3" ,$'(="4,')3" 42052.)%)23" '3+" &$.).%'34$" %&')3)3?;" j" T55*" O,=.)2*" E$%,$.+'" _+" FGnI;" FGGN"t$WrHhDBJ`GFFwI;""FB;"" U&'55$" \A" #2.%)**" YA" U,20'." K;" e66$4%" 26" .a)0" %'5$&" 23" a,2*$" 09.4*$" '3+" .)3?*$"09.4*$"6)W$&"423%&'4%)*$"5&25$&%)$.;"_$+"\4)"\52&%."eX$&4;"BMMM"Y$4rNBDFBJ`hnwIC;""FN;"" E9..2"UA"t*�4b"_;"T"0)X$+^$66$4%."02+$*"26" %,$"+=3'0)4" &$.523.$"26"09.4*$"?$3$"%&'3.4&)5%"$X5&$..)23"%2"$3+9&'34$"$X$&4).$;"e9&"j"T55*"O,=.)2*;"BMFN"_'=rFFNDIJ`FBHGwGM;""Fh;"" <$$"\A"E'&%23"eKA" \a$$3$="l<A" t'&&'&"KO;"o)&'*" $X5&$..)23" 26" )3.9*)3^*)b$" ?&2a%,"6'4%2&^!"$3,'34$."09.4*$",=5$&%&25,=")3"&$.).%'34$^%&')3$+"&'%.;"j"T55*"O,=.)2*"E$%,$.+'"_+"FGnI;"BMMh"_'&rGCDNJ`FMGHwFMh;""FI;"" !c:9)$&+2"_A"!W'�$c"jA"s2&2.%)'?'"eA"s'&&9$."_A"���)?'"TA"T3%�3"TA"$%"'*;"_'X)0'*".%&$3?%,"'3+"52a$&"4,'&'4%$&).%)4." )3" ).20$%&)4"'3+"+=3'0)4"'4%)23."26" %,$"955$&"'3+" *2a$&"$X%&$0)%)$.")3"0)++*$^'?$+"'3+"2*+$&"0$3;"T4%'"O,=.)2*"\4'3+;"FGGG"\$5rFCHDFJ`IHwCn;""FC;"" O'?'32"TtA" O="sA" E$&3'&+)" lA" #'3+'9" KEA" \'34,$c"T_j;" T9%25,'?=" '3+" 5&2%$)3"%9&32($&" .)?3'*)3?" )3" .*2a^%a)%4," 09.4*$" +9&)3?" $X$&4).$;" _$+" \4)" \52&%." eX$&4;" BMFh"j9*rhCDHJ`FNFhwBI;""FH;"" E'*+a)3"d_A"l'++'+"tA"O'3+2&6"#eA"K2="KKA"e+?$&%23"oK;"T*%$&'%)23." )3"09.4*$"0'.." '3+" 423%&'4%)*$" 5,$32%=5$" )3" &$.523.$" %2" 93*2'+)3?" 02+$*.`" &2*$" 26"%&'3.4&)5%)23'*k5&$%&'3.*'%)23'*"0$4,'3).0.;"t&23%"O,=.)2*;"BMFNrh`Bnh;""!
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BIBLIOGRAPHIQUES "
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BMI"
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