H. Woelders, T. Pas, M. F. Bannink, A. Veerkamp, R. F. Smits et al., Systems biology in animal sciences, Animal, vol.5, pp.1036-1047, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02646452

M. Bonnet, I. Cassar-malek, Y. Chilliard, and B. Picard, Ontogenesis of muscle and adipose tissues and their interactions in ruminants and other species, Animal, vol.4, pp.1093-1109, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02667753

T. Chaze, B. Meunier, C. Chambon, C. Jurie, and B. Picard, In vivo proteome dynamics during early bovine myogenesis, Proteomics, vol.8, pp.4236-4248, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02663688

H. Taga, Y. Chilliard, B. Meunier, C. Chambon, B. Picard et al., Cellular and molecular large-scale features of fetal adipose tissue: is bovine perirenal adipose tissue brown?, J Cell Physiol, vol.227, pp.1688-1700, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02648870

T. Chaze, B. Meunier, C. Chambon, C. Jurie, and B. Picard, Proteome dynamics during contractile and metabolic differentiation of bovine foetal muscle, Animal, vol.3, pp.980-1000, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02668303

B. Picard, I. Cassar-malek, N. Guillemin, and M. Bonnet, Quest for Novel Muscle Pathway Biomarkers by Proteomics in Beef Production, Comprehensive Biotechnology, pp.395-405, 2011.

N. J. Hudson, R. E. Lyons, A. Reverter, P. L. Greenwood, and B. P. Dalrymple, Inferring the in vivo cellular program of developing bovine skeletal muscle from expression data, Gene Expr Patterns, vol.13, pp.109-125, 2013.

M. Bionaz and J. J. Loor, Ruminant metabolic systems biology: reconstruction and integration of transcriptome dynamics underlying functional responses of tissues to nutrition and physiological state, Gene Regul Syst Bio, vol.6, pp.109-125, 2012.

K. Shahzad and J. J. Loor, Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism, Curr Genomics, vol.13, pp.379-394, 2012.

J. M. Romao, J. W. He, M. Mcallister, T. Guan-le, and L. , Elucidation of molecular mechanisms of physiological variations between bovine subcutaneous and visceral fat depots under different nutritional regimes, PLoS One, vol.8, p.83211, 2013.

S. Carbon, A. Ireland, C. J. Mungall, S. Shu, B. Marshall et al., AmiGO: online access to ontology and annotation data, Bioinformatics, vol.25, pp.288-289, 2009.

E. Eden, R. Navon, I. Steinfeld, D. Lipson, and Z. Yakhini, GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists, BMC Bioinformatics, vol.10, p.48, 2009.

D. Binns, E. Dimmer, R. Huntley, D. Barrell, C. O'donovan et al., QuickGO: a web-based tool for Gene Ontology searching, Bioinformatics, vol.25, pp.3045-3046, 2009.

M. Safran, I. Dalah, J. Alexander, N. Rosen, I. Stein et al., GeneCards Version 3: the human gene integrator, Database (Oxford), vol.2010, p.20, 2010.

V. G. Tarcea, T. Weymouth, A. Ade, A. Bookvich, J. Gao et al., Michigan molecular interactions r2: from interacting proteins to pathways, Nucleic Acids Res, vol.37, pp.642-646, 2009.

S. Kerrien, B. Aranda, L. Breuza, A. Bridge, F. Broackes-carter et al., The IntAct molecular interaction database in 2012, Nucleic Acids Res, vol.40, pp.841-846, 2012.

A. Chatr-aryamontri, B. J. Breitkreutz, S. Heinicke, L. Boucher, A. Winter et al., The Bio-GRID interaction database: 2013 update, Nucleic Acids Res, vol.41, pp.816-823, 2013.

B. Aranda, H. Blankenburg, S. Kerrien, F. S. Brinkman, A. Ceol et al., PSICQUIC and PSISCORE: accessing and scoring molecular interactions, Nat Methods, vol.8, pp.528-529, 2011.

A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Res, vol.41, pp.808-815, 2013.

J. Hernandez-toro, C. Prieto, and J. De-las-rivas, APID2NET: unified interactome graphic analyzer, Bioinformatics, vol.23, pp.2495-2497, 2007.

P. Rice, I. Longden, and A. Bleasby, EMBOSS: the European Molecular Biology Open Software Suite, Trends Genet, vol.16, pp.276-277, 2000.

K. Frank and M. J. Sippl, High-performance signal peptide prediction based on sequence alignment techniques, Bioinformatics, vol.24, pp.2172-2176, 2008.

T. N. Petersen, S. Brunak, G. Von-heijne, and H. Nielsen, SignalP 4.0: discriminating signal peptides from transmembrane regions, Nat Methods, vol.8, pp.785-786, 2011.

D. W. Huang, B. T. Sherman, and R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat Protoc, vol.4, pp.44-57, 2009.

F. Ramirez, G. Lawyer, and M. Albrecht, Novel search method for the discovery of functional relationships, Bioinformatics, vol.28, pp.269-276, 2012.

J. Chen, E. E. Bardes, B. J. Aronow, and A. G. Jegga, ToppGene Suite for gene list enrichment analysis and candidate gene prioritization, Nucleic Acids Res, vol.37, pp.305-311, 2009.

I. Ulitsky, A. Maron-katz, S. Shavit, D. Sagir, C. Linhart et al., Expander: from expression microarrays to networks and functions, Nat Protoc, vol.5, pp.303-322, 2010.

A. Nikitin, S. Egorov, N. Daraselia, and I. Mazo, Pathway studio-the analysis and navigation of molecular networks, Bioinformatics, vol.19, pp.2155-2157, 2003.

F. M. Mccarthy, C. R. Gresham, T. J. Buza, P. Chouvarine, L. R. Pillai et al., AgBase: supporting functional modeling in agricultural organisms, Nucleic Acids Res, vol.39, pp.497-506, 2011.

D. Caccia, M. Dugo, M. Callari, and I. Bongarzone, Bioinformatics tools for secretome analysis, Biochim Biophys Acta, vol.1834, pp.2442-2453, 2013.

T. Romacho, M. Elsen, D. Rohrborn, and J. Eckel, Adipose tissue and its role in organ crosstalk, Acta Physiol (Oxf), vol.210, pp.733-753, 2014.

M. Magrane and U. Consortium, UniProt Knowledgebase: a hub of integrated protein data, Database (Oxford), p.9, 2011.

N. R. Coordinators, Database resources of the National Center for Biotechnology Information, Nucleic Acids Res, vol.41, 2013.

F. Al-shahrour, R. Diaz-uriarte, and J. Dopazo, FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes, Bioinformatics, vol.20, pp.578-580, 2004.

Y. Benjamini and Y. Hochberg, Controlling the False Discovery Rate-a Practical and Powerful Approach to Multiple Testing, Journal of the Royal Statistical Society Series B-Methodological, vol.57, pp.289-300, 1995.

M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat Genet, vol.25, pp.25-29, 2000.

C. T. Lopes, M. Franz, F. Kazi, S. L. Donaldson, Q. Morris et al., Cytoscape Web: an interactive web-based network browser, Bioinformatics, vol.26, pp.2347-2348, 2010.

W. Nickel, The mystery of nonclassical protein secretion. A current view on cargo proteins and potential export routes, Eur J Biochem, vol.270, pp.2109-2119, 2003.

O. Emanuelsson, S. Brunak, G. Von-heijne, and H. Nielsen, Locating proteins in the cell using TargetP, SignalP and related tools, Nat Protoc, vol.2, pp.953-971, 2007.

O. Emanuelsson, H. Nielsen, S. Brunak, and G. Von-heijne, Predicting subcellular localization of proteins based on their N-terminal amino acid sequence, J Mol Biol, vol.300, pp.1005-1016, 2000.

S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, Basic local alignment search tool, J Mol Biol, vol.215, pp.403-410, 1990.

S. Hwang, S. W. Son, S. C. Kim, Y. J. Kim, H. Jeong et al., A protein interaction network associated with asthma, J Theor Biol, vol.252, pp.722-731, 2008.

J. E. Stajich, D. Block, K. Boulez, S. E. Brenner, S. A. Chervitz et al., The Bioperl toolkit: Perl modules for the life sciences, Genome Res, vol.12, pp.1611-1618, 2002.

K. Hiller, A. Grote, M. Scheer, R. Munch, and D. Jahn, PrediSi: prediction of signal peptides and their cleavage positions, Nucleic Acids Res, vol.32, pp.375-379, 2004.

L. Kall, A. Krogh, and E. L. Sonnhammer, Advantages of combined transmembrane topology and signal peptide prediction-the Phobius web server, Nucleic Acids Res, vol.35, pp.429-432, 2007.

V. S. Martha, Z. Liu, L. Guo, Z. Su, Y. Ye et al., Constructing a robust protein-protein interaction network by integrating multiple public databases, BMC Bioinformatics, vol.12, p.7, 2011.

A. K. Henning, M. H. Groschup, T. C. Mettenleiter, and A. Karger, Analysis of the bovine plasma proteome by matrix-assisted laser desorption/ionisation time-of-flight tandem mass spectrometry, Vet J, vol.199, pp.175-180, 2014.

J. Naval, M. Calvo, J. Laborda, P. Dubouch, M. Frain et al., Expression of mRNAs for alpha-fetoprotein (AFP) and albumin and incorporation of AFP and docosahexaenoic acid in baboon fetuses, J Biochem, vol.111, pp.649-654, 1992.

C. Chiellini, O. Cochet, L. Negroni, M. Samson, M. Poggi et al., Characterization of human mesenchymal stem cell secretome at early steps of adipocyte and osteoblast differentiation, BMC Mol Biol, vol.9, p.26, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00288589

H. Roelofsen, M. Dijkstra, D. Weening, M. P. De-vries, A. Hoek et al., Comparison of isotope-labeled amino acid incorporation rates (CILAIR) provides a quantitative method to study tissue secretomes, Mol Cell Proteomics, vol.8, pp.316-324, 2009.

J. Zhong, S. A. Krawczyk, R. Chaerkady, H. Huang, R. Goel et al., Temporal profiling of the secretome during adipogenesis in humans, J Proteome Res, vol.9, pp.5228-5238, 2010.

H. Heid, S. Rickelt, R. Zimbelmann, S. Winter, H. Schumacher et al., On the formation of lipid droplets in human adipocytes: the organization of the perilipin-vimentin cortex, PLoS One, vol.9, p.90386, 2014.

S. Bag, S. Ramaiah, and A. Anbarasu, fabp4 is central to eight obesity associated genes: A functional gene network-based polymorphic study, J Theor Biol, 2014.

N. L. Anderson, M. Polanski, R. Pieper, T. Gatlin, R. S. Tirumalai et al., The human plasma proteome: a nonredundant list developed by combination of four separate sources, Mol Cell Proteomics, vol.3, pp.311-326, 2004.

D. J. Kwiatkowski, T. P. Stossel, S. H. Orkin, J. E. Mole, H. R. Colten et al., Plasma and cytoplasmic gelsolins are encoded by a single gene and contain a duplicated actin-binding domain, Nature, vol.323, pp.455-458, 1986.

S. Hartwig, S. Raschke, B. Knebel, M. Scheler, M. Irmler et al., Secretome profiling of primary human skeletal muscle cells, Biochim Biophys Acta, vol.1844, pp.1011-1017, 2014.

L. Bihan, M. C. Bigot, A. Jensen, S. S. Dennis, J. L. Rogowska-wrzesinska et al., In-depth analysis of the secretome identifies three major independent secretory pathways in differentiating human myoblasts, J Proteomics, vol.77, pp.344-356, 2012.

M. P. Krause, Y. Liu, V. Vu, L. Chan, A. Xu et al., Adiponectin is expressed by skeletal muscle fibers and influences muscle phenotype and function, Am J Physiol Cell Physiol, vol.295, pp.203-212, 2008.

B. Yang, L. Chen, Y. Qian, J. A. Triantafillou, J. A. Mcnulty et al., Changes of skeletal muscle adiponectin content in diet-induced insulin resistant rats, Biochem Biophys Res Commun, vol.341, pp.209-217, 2006.

N. Guillemin, M. Bonnet, C. Jurie, and B. Picard, Functional analysis of beef tenderness, J Proteomics, vol.75, pp.352-365, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02646580

Q. Zhang, H. G. Lee, S. K. Kang, M. Baik, and Y. J. Choi, Heat-shock protein beta 1 regulates androgen-mediated bovine myogenesis, Biotechnol Lett, vol.36, pp.1225-1231, 2014.

M. Bonnet, L. Bernard, S. Bes, and C. Leroux, Selection of reference genes for quantitative real-time PCR normalisation in adipose tissue, muscle, liver and mammary gland from ruminants, Animal, vol.7, pp.1344-1353, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02645011

S. Raschke, K. Eckardt, K. Bjorklund-holven, J. Jensen, and J. Eckel, Identification and validation of novel contraction-regulated myokines released from primary human skeletal muscle cells, PLoS One, vol.8, p.62008, 2013.

M. Scheideler, C. Elabd, L. E. Zaragosi, C. Chiellini, H. Hackl et al., Comparative transcriptomics of human multipotent stem cells during adipogenesis and osteoblastogenesis, BMC Genomics, vol.9, p.340, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00305737

R. Cancello, C. Henegar, N. Viguerie, S. Taleb, C. Poitou et al., Reduction of macrophage infiltration and chemoattractant gene expression changes in white adipose tissue of morbidly obese subjects after surgery-induced weight loss, Diabetes, vol.54, pp.2277-2286, 2005.

H. He and X. Liu, Characterization of transcriptional complexity during longissimus muscle development in bovines using high-throughput sequencing, PLoS One, vol.8, p.64356, 2013.

S. Welle, R. Tawil, and C. A. Thornton, Sex-related differences in gene expression in human skeletal muscle, PLoS One, vol.3, p.1385, 2008.

T. Sadkowski, A. Ciecierska, A. Majewska, J. Oprzadek, K. Dasiewicz et al., Transcriptional background of beef marbling-novel genes implicated in intramuscular fat deposition, Meat Sci, vol.97, pp.32-41, 2014.

S. Welle, A. Cardillo, M. Zanche, and R. Tawil, Skeletal muscle gene expression after myostatin knockout in mature mice, Physiol Genomics, vol.38, pp.342-350, 2009.

J. H. Yoon, P. Song, J. H. Jang, D. K. Kim, S. Choi et al., Proteomic analysis of tumor necrosis factor-alpha (TNF-alpha)-induced L6 myotube secretome reveals novel TNF-alpha-dependent myokines in diabetic skeletal muscle, J Proteome Res, vol.10, pp.5315-5325, 2011.

R. Calloni, E. A. Cordero, J. A. Henriques, and D. Bonatto, Reviewing and updating the major molecular markers for stem cells, Stem Cells Dev, vol.22, pp.1455-1476, 2013.

S. Lecourt, J. P. Marolleau, O. Fromigue, K. Vauchez, R. Andriamanalijaona et al., Characterization of distinct mesenchymal-like cell populations from human skeletal muscle in situ and in vitro, Exp Cell Res, vol.316, pp.2513-2526, 2010.

E. M. Mcmillan and J. Quadrilatero, Autophagy is required and protects against apoptosis during myoblast differentiation, Biochem J, vol.462, pp.267-277, 2014.

V. Skop, M. Cahova, H. Dankova, Z. Papackova, E. Palenickova et al., Autophagy inhibition in early but not in later stages prevents 3T3-L1 differentiation: Effect on mitochondrial remodeling. Differentiation, 2014.

R. Baerga, Y. Zhang, P. H. Chen, S. Goldman, and J. S. , Targeted deletion of autophagy-related 5 (atg5) impairs adipogenesis in a cellular model and in mice, Autophagy, vol.5, pp.1118-1130, 2009.

N. Martinez-lopez, D. Athonvarangkul, S. Sahu, L. Coletto, H. Zong et al., Autophagy in Myf5+ progenitors regulates energy and glucose homeostasis through control of brown fat and skeletal muscle development, EMBO Rep, vol.14, pp.795-803, 2013.