Focal adhesion kinase signaling regulates the expression of caveolin 3 and beta1 integrin, genes essential for normal myoblast fusion
Authors: Thomas A Rando
Louis F Reichardt
Stefano Biressi
Navaline L Quach
Charles Keller
Journal: Molecular Biology of the Cell (2009)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: skeletal myogenesis; myoblast fusion; integrins; focal adhesion kinase (FAK); muscle regeneration
Affiliations: Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA., *Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305;, Department of Physiology, University of California, San Francisco, San Francisco, CA 94158;, Department of Cellular and Structural Biology, Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX 78229; and, Neurology Service, VA Palo Alto Health Care System, Palo Alto, CA 94304

Abstract:  An essential phase of skeletal myogenesis is the fusion of mononucleated myoblasts to form multinucleated myotubes. Many cell adhesion proteins, including integrins, have been shown to be important for myoblast fusion in vertebrates, but the mechanisms by which these proteins regulate cell fusion remain mostly unknown. Here, we focused on the role of focal adhesion kinase (FAK), an important nonreceptor protein tyrosine kinase involved in integrin signaling, as a potential mediator by which integrins may regulate myoblast fusion. To test this hypothesis in vivo, we generated mice in which the Fak gene was disrupted specifically in muscle stem cells ("satellite cells") and we found that this resulted in impaired myotube formation during muscle regeneration after injury. To examine the role of FAK in the fusion of myogenic cells, we examined the expression of FAK and the effects of FAK deletion on the differentiation of myoblasts in vitro. Differentiation of mouse primary myoblasts was accompanied by a rapid and transient increase of phosphorylated FAK. To investigate the requirement of FAK in myoblast fusion, we used two loss-of-function approaches (a dominant-negative inhibitor of FAK and FAK small interfering RNA [siRNA]). Inhibition of FAK resulted in markedly impaired fusion but did not inhibit other biochemical measures of myogenic differentiation, suggesting a specific role of FAK in the morphological changes of cell fusion as part of the differentiation program. To examine the mechanisms by which FAK may be regulating fusion, we used microarray analysis to identify the genes that failed to be normally regulated in cells that were fusion defective due to FAK inhibition. Several genes that have been implicated in myoblast fusion were aberrantly regulated during differentiation when FAK was inhibited. Intriguingly, the normal increases in the transcript of caveolin 3 as well as an integrin subunit, the beta1D isoform, were suppressed by FAK inhibition. We confirmed this also at the protein level and show that direct inhibition of beta1D subunit expression by siRNA inhibited myotube formation with a prominent effect on secondary fusion. These data suggest that FAK regulation of profusion genes, including caveolin 3 and the beta1D integrin subunit, is essential for morphological muscle differentiation. 

Retracted: No

PubPeer comments:
 The author considered in this paper the additive consistency of interval valued fuzzy preference relations. A fuzzy preference relation $A=(a_{ij})_{ntimes n}$ is additive consistent if and only if $$a_{ij} = 0.5(w_i - w_j) + 0.5 quad forall i, j$$ Where $w_i$ are nonnegative real values. However, such a characterization has nothing to do with the normalization condition $$w_1+ w_2 +cdots + w_n =1$$ This normalization condition was imposed and lead to a false characterization of additive consistency of fuzzy preference relations. This means that a given additive consistent preference relation would be treated as an inconsistent one and represented by some wrong weight values. Consequently, the proposed goal programming models, e.g., (M-2) and (M-3), cannot deal with the issues of additive consistency. The numerical results presented are all not reliable. ![file] ![file] , 
 #1 Dear Rhipidura Albiventris, Firstly, we must point out that the result u201cA fuzzy preference relation (FPR) $A=(a_{ij})_{ntimes n}$ is additive consistent (Consistency definition) if and only if $a_{ij} = 0.5(w_i - w_j) + 0.5 quad forall i,j$ (Consistency formula).u201d that you quote is wrong under the normalization condition $w_1+ w_2 +cdots + w_n =1$. Thus, your comments are based on a wrong result. In our papers, we only quote that consistency formula satisfies the consistency definition (a sufficient condition), but the converse does not hold in general (not necessary condition) and we cannot find your result in any published work at present. Hence, from the existing result about consistency, it is obvious that the exact weights for an additive consistent FPR are not always derived by the consistency formula. For this case, we have provided approximate weights by optimal model. We have pointed in the last reply that some other researchers were also devoted to investigating the relationship between consistency definition and consistency formula under some other constraint conditions. Our team have also made some further work on this, but not published at present. Secondly, as the example you provided, I think that you can easily compute the approximate weights by our formula in our work, but not an exact one. Thirdly, We think that the normalization condition is necessary to be applied to the weights in order to reach uniqueness, because we needs normalized priority weights in some cases. Thank you for your interest. , 
 #2 Dear Shen Zhang The characterization that you claimed to be wrong is from the following paper T. Tanino, Fuzzy preference orderings in group decision making, Fuzzy Sets Syst. 12 (1984) 117u2013131. , 
 #3 Dear Rhipidura Albiventris, For your new question, please refer to the reply in the link below. https: pubpeer.com publications 7867CDD08E699F6D01C0E11F1693E0#6 ,