Biochemical pathways analysis of microarray results: regulation of myogenesis in pigs
© te Pas et al; licensee BioMed Central Ltd. 2007
Received: 08 January 2007
Accepted: 13 June 2007
Published: 13 June 2007
Combining microarray results and biological pathway information will add insight into biological processes. Pathway information is widely available in databases through the internet.
Mammalian muscle formation has been previously studied using microarray technology in pigs because these animals are an interesting animal model for muscle formation due to selection for increased muscle mass. Results indicated regulation of the expression of genes involved in proliferation and differentiation of myoblasts, and energy metabolism. The aim of the present study was to analyse microarrays studying myogenesis in pigs. It was necessary to develop methods to search biochemical pathways databases.
PERL scripts were developed that used the names of the genes on the microarray to search databases. Synonyms of gene names were added to the list by searching the Gene Ontology database. The KEGG database was searched for pathway information using this updated gene list. The KEGG database returned 88 pathways. Most genes were found in a single pathway, but others were found in up to seven pathways. Combining the pathways and the microarray information 21 pathways showed sufficient information content for further analysis. These pathways were related to regulation of several steps in myogenesis and energy metabolism. Pathways regulating myoblast proliferation and muscle fibre formation were described. Furthermore, two networks of pathways describing the formation of the myoblast cytoskeleton and regulation of the energy metabolism during myogenesis were presented.
Combining microarray results and pathways information available through the internet provide biological insight in how the process of porcine myogenesis is regulated.
Microarray technology can simultaneously measure the differential expression of large numbers of genes in a tissue and thereby identify the genes involved in the regulation of different stages of a process. Typically, microarray experiments produce long lists of genes that are differentially expressed between two different situations. In order to understand the biology of these data it may be relevant to include physiological information of the genes in the study. Many databases such as the Kyoto Encyclopaedia of Genes and Genomes (KEGG) contain information on biological pathways . Combination of microarray data and pathway data may highlight the processes taking place in the cell providing information on the tissue- and process-specific functioning of the genome.
Mammalian myogenesis, the formation of multinucleated muscle fibres from mononucleated precursor cells called myoblasts, is an exclusive prenatal process [2, 3]. Muscle fibre formation takes place in two waves, the primary and secondary muscle fibre formation . Each wave consists of proliferation of myoblasts and fusion to form new muscle fibres. Primary muscle fibres are formed de novo. Secondary myofibres are formed using the primary fibres as a template. Muscle fibre numbers may be related to body and muscle hypertrophy, muscle strength and muscle function . Complex genetic regulatory mechanisms underlie spatial and temporal myogenesis. Central to this regulatory process is the MRF (muscle regulatory factors) gene family, four basic helix-loop-helix transcription factors regulating differentiation stage-specific gene expression [6, 7]. A network of genes is involved in the regulation of the expression of the MRF genes thereby regulating the progress through the myogenesis process [8–14].
Pig breeding has mainly focused during the past decades on improving growth rate and muscularity . Furthermore, pig breeds differ in muscle traits such as muscularity, muscle fibre type, colour, etc [16, 17]. It can be expected that differences in myogenesis are a major underlying mechanism for the observed phenotypes. This makes the pig an attractive species to study mammalian myogenesis.
Using microarray technology we previously reported on the expression of genes known to affect myogenesis in laboratory animals and in vitro model systems [18–21]. Using two extreme pig breeds (Duroc and Pietrain) known to differ for muscle fibre characteristics [16, 17], we showed differential expression of myogenesis related genes. The results suggest that the heavier muscled Pietrain breed is delayed in myogenesis and expression of the genes during secondary myogenesis is higher than in the less muscled Duroc breed [18, 20]. Furthermore, expression of genes of energy metabolism was higher in the heavy muscle breed compared to the less muscled pig breed. We also reported on the expression profiles during muscle development in Duroc foetuses [19–21]. The profiles showed that both myoblast proliferation and differentiation during primary and to a lesser extent during secondary muscle fibre formation are associated with differential expression of many genes known to regulate these processes. Furthermore, different energy metabolism mechanisms (i.e. pathways) seems to be involved in proliferation (high expression of genes related to oxidative phosphorylation energy mechanism) and differentiation (expression of genes related to glycolysis mechanism).
The present study aimed to relate these myogenesis microarray results with known cellular physiological processes accessible through online pathways databases. Knowing these relationships may provide a better understanding of the regulation of mammalian myogenesis. Pathway database information of species with low genomic information is less than species with sequenced genomes. Software packages usually use species-specific gene IDs to find pathway information in databases. However, the concept of comparative genomics enables to use information from related species. Therefore, we have developed and present here software tools that streamline the process of searching for pathways in online databases followed by combining pathway and microarray data. This enabled us to identify relevant pathways from the KEGG database. Combination of the microarray results with physiologic/biochemical pathway information highlighted regulatory mechanisms of porcine myogenesis.
Number of pathways returned by the KEGG database search using the PERL script
Total Number of pathways
Pathways involved in regulation of porcine myogenesis
(Sub)Pathways further analyzed in detail for common expression profile
No. of genes on the pathway
No genes with microarray data
Bile acid biosynthesis
Biosynthesis of steroids
Calcium signalling pathway
Fatty acid metabolism
Hedgehog signalling pathway
Jak-STAT signalling pathway
MAPK signalling pathway
Notch signalling pathway
TGF-beta signalling pathway
WNT signalling pathway
Notch signalling pathway
WNT signalling pathway
The WNT family of ligands are signalling proteins that regulate developmental processes such as myogenesis and adipogenesis . The WNTs activates the Frizzled receptors during somitogenesis. In the early somite the activation of the pathway leads to down regulation of myf-5 expression. This affects commitment of cells to become myoblasts. Subsequently myoblast proliferation is induced . Figure 5 shows that the KEGG pathway consists of three subpathways (Figure 5A). All genes on the microarray are located on the top subpathway directed to influence the cell cycle, i.e. proliferation of myoblasts in myogenesis. The expression profiles (Figure 5B) give some indication for attenuation of secondary myofibre formation thought the peak expression in the period of 50 – 70 days of gestation.
Calcium signalling pathway
Myogenic differentiation involves the fusion of myoblasts to become multinucleated myofibres. Calcium seems to be involved in the IGF-calcineurin-NFATc3 signalling pathway to enhance myogenic differentiation . Figure 6A shows the KEGG derived calcium signalling pathway with the genes on the microarray encircled. The pathway is complex and can be divided in at least five different subpathways all around the calcium ion. The expression profile of all genes of the pathway on the microarray (Figure 6B) is complex, but might indicate that subsets of the genes or subpathways could have common regulation. Therefore subpathways were analysed independently. Figure 6C shows that the genes in the centre subpathway have similar expression pathways suggesting a common regulation, while genes outside this subpathway (Figure 6D) have different expression profiles. Furthermore, these genes do not belong to a single biochemical subpathway and their expression profiles are not related suggesting also different regulatory mechanisms.
Energy metabolism has been shown to be involved in myogenesis [18–21, 27–29]. ATP itself may act via the SWI/SNF complex to modulate chromatin structure to modulate expression of myogenesis differentiation stage-specific genes to block terminal differentiation . Figure 7 shows the ATP synthesis pathway derived from the KEGG database with the genes on the microarray encircled. This pathway differs from the pathways before as this indicates how the ATP synthesis complex is composed. The complex consists of three groups of proteins linked together. The genes on the microarray are divided over the three groups but are mainly focussed on the top group. Figure 7B shows the expression pattern of the genes on our microarray, which is complex and suggest regulation on several different levels. The top line genes show a more similar expression profile (Figure 7C) suggesting common regulation distinct from the genes on the other two lines.
Genes active in more than one pathway (Figure 3) may strengthen the network. Eight of such genes were found linking pathway components of the network regulating the cytoskeleton formation leading to proliferation and differentiation of myoblasts and contraction of myofibres. Furthermore, 14 genes connected diverse pathways components of the energy metabolism network. Six of them provided a direct connection between the oxidative phosphorylation pathway and ATP synthesis, eight of them connected the Glycolysis/Glucogenesis pathway with several other pathway components of the energy metabolism network.
Merge microarray data with biological database information
To produce biological meaningful results from the expression patterns of many genes (up to whole genome scans) resulting from microarray experiments we use databases with physiological pathway information. Biochemical pathways such as stored in the KEGG database describe physiological processes taking place in the cell. The physiology of a process may differ between species. Therefore both general (called reference) pathways and species-specific pathways can be searched. Porcine-specific pathways are often not (yet) provided. However, it is noticed that pig physiology closely resembles human physiology . Therefore we used the human pathways information and always compared them with the reference pathways, which were in these experiments always similar. Therefore we decided that we could extrapolate these pathways to be used for the pig.
We developed a PERL script to find all the pathways in the database of the genes on the microarray. The PERL script provides a link to the entire pathway including the genes on the microarray and those not on the microarray. Software tools that can be found on the internet (such as Bioconductor , Whole Pathway Scope , GOminer , and GenMapp ) identify genes with their species-specific IDs. As a result only species-specific pathways can be found in pathway databases. However, for many species, including many livestock species like pigs, such data are not available for many pathways resulting in missing information. Therefore, we use the names and synonyms of the genes which find also pathways from other species using the concept of comparative genomics. Therefore, it is possible to analyse data from species with relative low physiological and/or sequence information in the database, giving our software an advantage for those species over the other software tools. However, due to the unlimited finding of pathways from other species it is important to screen for false positive pathways such as photosynthesis metabolism in these analyses.
Searching the KEGG database did not return pathway information for each gene. The main reason for this is that no pathway in the KEGG was found related to these particular genes. For many genes the information on how they act in the cell is still lacking, or not included in the database. Alternatively, a gene may be in the KEGG database with a name not specified in the Gene Ontology database resulting in missing information. This influences the completeness of analyses. Future analyses using other databases may add new data. However, from this point onwards we analyzed the data from the genes with known pathway information and discarded the remaining genes.
The number of pathways per gene differed from one to seven, with almost 75% of the genes with known pathway information belonging to a single pathway. Physiological processes within a cell are not separated but usually linked together in a network. The genes that are active in more than one pathway could be the point of intersection that joins together the pathways into a network.
Due to the relative low number of genes on the microarray which was aimed at studying the myogenesis regulatory processes approximately 60% of the pathways harboured only a low number of genes (i.e. one to three genes). However, most pathways can be divided into subpathways making analysis with only a limited number of genes within the pathway possible. In this study we analysed subpathways harbouring at least two genes on the microarray. Furthermore, statistical analysis showed that expression profiles within subpathways were more similar than within pathways. Biologically, the effect of a pathway on a trait may often be regulated via one of the subpathways of a pathway. Therefore, we argue that where possible pathways analyses always should be analyzed within subpathways.
Pathways associated with (regulation of) myogenesis
Muscle development is an important characteristic of meat deposition in livestock animals [2, 3]. In pigs muscle development has been selected for growth rate and muscularity during the last decades . Therefore, it can be expected that the muscle regulatory mechanism has been under selection pressure that may have influenced the pathways found. Up regulation of positive acting genes and/or down regulation of negative regulatory genes may be more pronounced because of selection. This divergent selection makes the pig an excellent species to study the regulation of myogenesis in mammals.
Expression profiles of genes within a (sub) pathway were compared with the expression profiles of all the genes within the pathway, and with the genes outside the subpathway but in the pathway. Subpathways where the genes showed similar expression profile changes during the gestational period studied were considered to be under a common regulation associated with myogenesis. Therefore, such pathways may highlight the regulatory mechanism underlying muscle development itself. Of these pathways 21 of the 88 pathways had information of at least 2 genes in a subpathway, which we found sufficient for further detailed analyses.
Previously we reported myogenesis differentiation phase-specific regulation of the gene expression using microarrays. Known information regarding the physiology of the genes in the general cell division mechanism and myoblast-specific cell division and cell fusion was used to associate the expression levels of the genes the proliferation and differentiation phase so primary and secondary myofibre formation [19–21]. The results included changes in the expression of energy metabolism genes which were compared to the existing literature on some candidate genes [27–29]. Differences in the timing of these processes between pig breeds were also reported . The present study reports the pathways in which the genes participate aiming to elucidate the underlying regulatory mechanisms.
Myogenesis differentiation stage regulating pathways
Myogenesis in mammals proceed in two distinct waves. The Notch signalling pathway described above shows two peak expressions coinciding with the timing of the two waves of myofibre formation [2, 3]. The role of the Notch signalling pathway in myogenesis is to keep the cells in an undifferentiated proliferative state by up regulation of proliferation stimulatory genes (Myf-5) and down regulation of differentiation switch genes (MyoD) [22, 23]. The two peaks of the expression profile coincide with the two waves of myofibre formation suggesting that myoblasts at least partly regulate proliferation and postponement of terminal differentiation through the Notch pathway. The subpathway of the calcium signalling pathway leading to proliferation of cells showed a similar expression profile, while other subpathways of the same calcium signalling pathway showed different unrelated expression profiles. Contrary to the Notch pathway the calcium signalling pathway enhances the differentiation of myoblasts probably through involvement in the initiation of the fusion of the myoblast cells. Both pathways show peak expression at the switch moment of proliferation to differentiation in primary and secondary myogenesis in pigs [2, 3, 19–21]. Although speculative, these results may suggest that the balance between the two pathways decide when the cells move from the proliferative state into the differentiation state.
The WNT signalling pathway is suggested to be important for early development in commitment of cells to become myoblasts and start proliferating in somites . The WNT pathway of the KEGG database suggests that most of our genes are on the subpathway leading to cell cycle, so initiate proliferation. Surprisingly not all genes on this subpathway show similar expression profiles. Again there are two peak expressions, but different genes seem to be involved in primary and secondary waves of muscle fibre formation. Some genes may indicate that they may be at a peak expression during somite phase, but in this experiment data on this early time point in gestation are missing. So, although the results are at least suggestive it is uncertain to conclude that the WNT signalling pathway is involved in porcine myogenesis. Furthermore, the Focal adhesion pathway which is suggested to be involved in fusion of myoblasts shows a similar expression profile to WNT signalling (data not shown, see additional data). A similar conclusion as for the WNT signalling pathway can be drawn. Alternatively, it can be suggested that genes within these pathways may be important for different waves of myofibre formation as there are important differences between the two waves of myogenesis [2, 3]. During primary muscle fibre formation myofibres are formed de novo while during the secondary muscle fibre formation the newly formed myofibres use the primary myofibres as a template to form. This may require different gene expression especially from the WNT and Focal adhesion pathways.
In conclusion these results show that different physiological pathways were found to be involved in the regulation of the proliferation and differentiation steps in myogenesis. These results therefore indicate the way the genome functions to regulate a developmental process.
Myogenesis differentiation phase energy metabolism pathways
The ATP synthesis pathway also shows an expression profile similar to the Notch signalling pathway and the calcium signalling pathway. ATP synthesis has been shown to modulate myogenesis by stimulating chromatin structure modulating mechanisms to block differentiation . Previously we reported that we observed ATP metabolism genes (synthesis and associated ATPases) expressions associated with proliferations while glycolysis metabolism related more to differentiation . We argued that the higher energy requiring proliferation may be supported by ATP metabolism while differentiation probably needed reduction of energy support. This is in agreement with the present finding that the ATP synthesis pathway is associated with the proliferative state before terminal differentiation of myoblasts takes place.
Other energy metabolism pathways show more complex expression profiles. The oxidative phosphorylation pathway shows the formation of five complexes. Of these complexes III and V show similar expression profiles suggesting that there may be involvement of the oxidative phosphorylation pathway too, but the other three complexes show different expression profiles making conclusion difficult (data not shown, see additional information). The glycolysis/gluconeogenesis pathway does not show a similar expression profile. Moreover, there seems to be several different profiles linked to one biochemical subpathway. This may either suggest that the regulation of the expression of the genes in this pathway is not associated with myogenesis or that unknown other phases are involved (data not shown, see additional information).
Networks of pathways
The output of a pathway may be the input of the next pathway. Alternatively, genes may be active in more than one pathway, thereby linking the pathways biochemically. Thus, either directly or indirectly pathways act in a network to fulfil their task in the cell. However, it should be remembered that inside the cell pathways may be separated by compartmentalization or because of activity is separated in time. This may hamper the reported interaction. Networks of pathways should be checked with physiological research.
We found evidence of two networks of pathways, one regulating the formation of the muscle structure, and one showing interactions between the several mechanisms supplying energy to the cells. We reported previously that both mechanisms take a central position during myogenesis [18–21].
Previously we reported on the regulation of the expression of muscle structural genes [19–21]. The expression of muscle structural genes is almost undetectable before myogenesis starts and increases rapidly during the very early stages of myogenesis, i.e. the proliferation phase of the first wave of myogenesis. The cytoskeleton of myoblasts will be different from the contractile apparatus forming the cytoskeleton of the muscle fibres. The network of pathways we observed is focused on regulating the actin cytoskeleton not specifically in muscle fibres. Regulation of proliferation processes but not of differentiation processes was observed. Therefore, we suggest that this network of pathways is especially dedicated to the regulation of the cytoskeleton in myoblasts while related but possibly different pathways regulate the formation of the cytoskeleton during muscle fibre formation. However, the network of pathways also regulates contraction of cells. This could be related to the general cytoskeleton in myoblasts, but may also indicate regulation of the mechanism of muscle cell functioning. The latter may be an indication for regulation of muscle-specific functioning.
We previously reported complex regulation of energy metabolism during porcine myogenesis. Mammalian myogenesis takes place in two waves of proliferation of myoblasts followed by differentiation of preformed myoblasts into multinucleated myofibres or muscle cells [2, 3]. During both proliferation phases the genes for ATP synthesis and oxidative phosphorylation reach peak levels while the genes for glycolysis are at a nadir of the expression profile. Contrary to this during differentiation phases the genes for glycolysis show peak levels in the expression profiles and the genes for ATP synthesis and oxidative phosphorylation are at the nadir [19–21]. The network of pathways regulating energy metabolism indeed show the pathways for glycolysis and the citrate cycle in the centre of the network with many connections to other pathways also suggesting a highly regulated network. The network shows that fuel for the energy metabolism may be delivered from several different biochemical routes, i.e. via the anaerobe glycolysis route, and via the oxidative phosphorylation route. Thus, different availability of the biochemical routes during phases in myogenesis may underlie the observed changes in the expression of the energy metabolism mechanisms.
In conclusion: Both networks of pathways provide the first step towards a holistic view of the biochemical reactions that together make up the cell. So, the approach we took has taken us from microarrays to PERL script and bioinformatics to the first step in systems biology. The next steps will be to study interactions of pathway between cells, and between different types of cells towards tissue functioning, organ functioning, etc.
Finally, these results were obtained using the data of a single database. However, there are many more databases accessible through the internet, such as Biocarta , Reactome , and many more. Adding the data from these databases could highlight more biochemical relationships related to the trait.
We have analysed data from microarray experiments using bioinformatics tools and biochemical pathways from the KEGG database. The results of combining these sources of data indicate several pathways and subpathways involved in the regulation of myogenesis, especially the crucial moment of the switch between the proliferation and differentiation state that is important for determining the number of muscle fibres [2, 3, 38, 39]. Furthermore, networks of pathways indicating complex regulation of cytoskeleton formation and energy metabolism during myogenesis were found to be active. The expression profiles of genes related to each other in physiological pathways show how the genome functions to regulate prenatal muscle tissue formation. The network of pathways provides a holistic view of the physiology inside the cells during this process. The method is ready to investigate more cellular types in the same way. Future combination of results of several cell or tissue types will highlight the functioning of the genome during tissue formation, a first step towards Systems Biology understanding of life.
Animals and tissue collection
Longissimus muscle tissue samples were collected from the foetuses of pregnant Duroc sows at 14, 21, 35, 49, 63, 77, 91 days of gestation characterising both waves of myogenesis in pigs. RNA was extracted from six foetuses taken from different litters for each gestational age. RNA samples were pooled per gestational age. For more details see te Pas et al. .
Microarrays and analysis
Microarrays were composed of 509 genes known to affect myogenesis and related processes such as fat and energy metabolism. Microarrays were hybridized with Cy3 and Cy5 labelled RNA pools. Each pool consists of RNA isolated from muscle tissue of 6 unrelated foetuses. All experiments comprised pools of two successive gestational ages. Each experiment was done in duplicate and dye swap duplicate. For more details on the microarrays including gene lists and the microarray hybridisations see te Pas et al. [19, 20] and Cagnazzo et al. . Differential gene expression was calculated using the Limma package of Bioconductor to correct for microarray-specific hybridization differences. Limma is a statistical method which can be used to identify differentially expressed genes in complex microarray experiments. This method analyzes each gene using a linear regression model. Empirical Bayesian methods are used to provide stable results even when the number of arrays is small. In the results file effects are given for all levels for each factor, compared to the reference. The reference level itself is therefore omitted. We used the measured value of day 14 as the reference value. Differential expression was denoted as the M-value [18, 19].
The KEGG data base  contains general information on biological pathways including gene names and information on species-specific pathways While searching the KEGG database with known pathways we found that genes may be represented with several synonyms that were not all linked to the pathways in the KEGG data base. Therefore, we first linked the microarray data with a local MySQL installation of the Gene Ontology database  which contains data of the monthly release of 2006-02-01 to collect all the common names (some of them obsolete) and added these to the file before searching the KEGG database. To automate the searching and retrieving of pathway data from the KEGG database  a PERL script was written using the KEGG API . Direct links to each pathway for each gene were added to the file. All database searches were performed with homemade PERL scripts . For additional information see te Pas et al. additional file 5 – software information.
Analysis combining microarray and database information
Expression profile information about genes (from both regulated and not-regulated genes – see additional data files te Pas et al. ) was added to each pathway. Some of the pathways could be divided into several subpathways. When a pathway as defined by the KEGG database was composed of more than one parallel biochemical reaction paths, each path was denoted as a subpathway. Both pathways and subpathway gene expression profiles were analysed. Using the combined information of the KEGG pathways and microarray expression profiles, networks of pathways influencing each other and interacting with each other were identified.
We tested the significance of a common expression pattern within subpathway compared to within pathway with an ANOVA model. Since we were interested in the patterns of the slopes, we calculated the absolute value of the slopes of the slopes. We used these values for fitting and comparing a model with a day*pathway interaction term to a model which included a day*subpathway interaction term. We used the Akaike Information Criterion  to contrast the goodness of fit of both models (AIC = -2·log Likelihood + 2·n parameters, and lower AIC values correspond to better models).
List of abbreviations
Kyoto Encyclopaedia of Genes and Genomes
muscle regulatory factors
Practical Extraction and Report Language
This research was supported by internal WUR funds (grant numbers 213.213.4000 and 4434.6057.00). The authors thank Prof. Dr. M.A.M. Groenen (Wageningen University, The Netherlands) for critically reading the manuscript before submission.
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucl Acids Res. 2006, 34: D354-D357. 10.1093/nar/gkj102.PubMed CentralView ArticlePubMedGoogle Scholar
- Rehfeldt C, Fiedler I, Stickland NC: Number and size of muscle fibres in relation to meat production. Muscle Development of Livestock Animals – Physiology, Genetics, and Meat Quality. Edited by: te Pas MFW, Everts ME, Haagsman HP. 2004, CABI publishers, Wallingford, Oxfordshire, UK, 1-38.View ArticleGoogle Scholar
- Stickland NC, Bayol S, Ashton C, Rehfeldt C: Manipulation of muscle fibre number during prenatal development. Muscle Development of Livestock Animals – Physiology, Genetics, and Meat Quality. Edited by: te Pas MFW, Everts ME, Haagsman HP. 2004, CABI publishers, Wallingford, Oxfordshire, UK, 69-82.View ArticleGoogle Scholar
- Wigmore PMC, Evans DJ: Molecular and cellular mechanisms involved in the generation of fiber diversity during myogenesis. Int Rev Cytol. 2002, 216: 175-232.View ArticlePubMedGoogle Scholar
- Rehfeldt C, Fiedler I, Dietl G, Ender K: Myogenesis and postnatal skeletal muscle cell growth as influenced by selection. Livest Prod Sci. 2000, 66: 177-188. 10.1016/S0301-6226(00)00225-6.View ArticleGoogle Scholar
- Olson EN: Myo D family: A paradigm for development?. Genes Dev. 1990, 4: 1454-1461. 10.1101/gad.4.9.1454.View ArticlePubMedGoogle Scholar
- Weintraub H, Davis R, Tapscott S, Thayer M, Krause M, Benezra R, Blackwell TK, Turner D, Rupp R, Hollenberg S, Zhuang Y, Lassar A: The myoD gene family: Nodal point during specification of the muscle cell lineage. Science. 1991, 251: 761-766. 10.1126/science.1846704.View ArticlePubMedGoogle Scholar
- Olson EN: Signal transduction pathways that regulate skeletal muscle gene expression. Mol Endocrinol. 1993, 7: 1369-1378. 10.1210/me.7.11.1369.PubMedGoogle Scholar
- Rawls A, Olson EN: MyoD meets its maker. Cell. 1997, 89: 5-8. 10.1016/S0092-8674(00)80175-0.View ArticlePubMedGoogle Scholar
- Capdevilla J, Johnson RL: Hedgehog signaling in vertebrate and invertebrate limb patterning. Cell Mol Life Sci. 2000, 57: 1682-1694. 10.1007/PL00000651.View ArticleGoogle Scholar
- Dobosy JR, Selker EU: Emerging connections between DNA methylation and histone acetylation. Cell Mol Life Sci. 2001, 58: 721-727. 10.1007/PL00000895.View ArticlePubMedGoogle Scholar
- Kitzmann M, Fernandez A: Crosstalk between cell cycle regulators and the myogenic factor MyoD in skeletal myoblasts. Cell Mol Life Sci. 2001, 58: 571-579. 10.1007/PL00000882.View ArticlePubMedGoogle Scholar
- Lee JW, Lee YC, Na S-Y, Jung D-J, Lee S-K: Transcriptional coregulators of the nuclear receptor superfamily: coactovators and corepressors. Cell Mol Life Sci. 2001, 58: 289-297. 10.1007/PL00000856.View ArticlePubMedGoogle Scholar
- Zhu W, Foehr M, Jaynes JB, Hanes SD: Drosophila SAP18, a member of the Sin3/Rpd3 Histone deacetylase complex, interacts with Bicoid and inhibits its activity. Dev Genes Evol. 2001, 211: 109-117. 10.1007/s004270100135.View ArticlePubMedGoogle Scholar
- Merks JWM: One century of genetic changes in pigs and the future needs. The Challenge of Genetic Change in Animal Production. Occasional Publication. Brit Soc Anim Sci, Edinburgh. Edited by: Hill WG, Bishop SC, McGuirk B, McKay JC, Simm G, Webb AJ. 2000, , 27: 8-19.Google Scholar
- Jones GF: Genetic aspects of domestication, Common breeds and their origin. The Genetics of the Pig. Edited by: Rothschild MF, Ruvinsky A. 1998, CAB International, Oxon, UK, 17-50.Google Scholar
- Sellier P: Genetics of meat and carcass traits. The Genetics of the Pig. Edited by: Rothschild MF, Ruvinsky A. 1998, CAB International, Oxon, UK, 463-510.Google Scholar
- Cagnazzo M, te Pas MFW, Priem J, de Wit A, Pool MH, Davoli R, Russo V: Comparison of prenatal muscle tissue expression profiles of two pig breeds differing in muscle characteristics. J Anim Sci. 2006, 84: 1-10.PubMedGoogle Scholar
- Te Pas MFW, de Wit AAC, Priem J, Cagnazzo M, Davoli R, Russo V, Pool MH: Transcriptome expression profiles in prenatal pigs in relation to myogenesis. J Muscle Res Cell Motil. 2005, 26: 157-165. 10.1007/s10974-005-7004-6.View ArticlePubMedGoogle Scholar
- Te Pas MFW, Cagnazzo M, de Wit AAC, Priem J, Pool MH, Davoli R: Muscle transcriptomes of Duroc and Pietrain pig breeds during prenatal formation of skeletal muscle tissue using microarray technology. Arch Anim Breed (special issue). 2005, 48: 141-147.Google Scholar
- Te Pas MFW, Pool MH, Hulsegge I, Janss LLG: Analysis of the differential transcriptome expression profiles during prenatal muscle tissue development. Arch Anim Breed (special issue). 2006, 49: 110-115.Google Scholar
- Delfini MC, Hirsinger E, Pourquie O, Duprez D: Delta 1-activated Notch inhibits muscle differentiation without affecting Myf5 and Pax3 expression in chick limb myogenesis. Development. 2000, 127: 5213-5224.PubMedGoogle Scholar
- Hirsinger E, Malaper P, Dubrulle J, Delfini MC, Duprez D, Henrique D, Horowicz D, Pourquie O: Notch signalling acts in postmitotic avian myogenic cells to control MyoD activation. Development. 2001, 128: 107-116.PubMedGoogle Scholar
- Ross SE, Hemati N, Longo KA, Bennett CN, Lucas PC, Erickson RL, MacDougald OA: Inhibition of adipogenesis by Wnt signalling. Science. 2000, 289: 950-953. 10.1126/science.289.5481.950.View ArticlePubMedGoogle Scholar
- Borello U, Coletta M, Tajbakhsh S, Leyns L, De Robertis EM, Buckingham M, Cossu G: Transplacental delivery of the Wnt antagonist Frzb1 inhibits development of caudal paraxial mesoderm and skeletal myogenesis in mouse embryos. Development. 1999, 126: 4247-4254.PubMedGoogle Scholar
- Delling U, Tureckova J, Lim HW, De Windt LJ, Rotwein P, Molkentin JD: A calcineurin-NFATc3-dependent pathway regulates skeletal muscle differentiation and slow myosin heavy-chain expression. Mol Cell Biol. 2000, 20: 6600-6611. 10.1128/MCB.20.17.6600-6611.2000.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen CK, Grzegorzewski J, Barash S, Zhao Q, Schneider H, Wang Q, Singh M, Pukac L, Bell AC, Duan R, Coleman T, Duttaroy A, Cheng S, Hirsch J, Zhang L, Lazard Y, Fischer C, Barber MC, Ma Z-D, Zhang Y-Q, Reavey P, Hong L, Teng B, Sanyal I, Ruben SM, Blondel O, Birse CE: An integrated functional genomics screening program reveals a role for BMP-9 in glucose homeostasis. Nature Biotech. 2003, 21: 294-301. 10.1038/nbt795.View ArticleGoogle Scholar
- Louis M, Van Beneden R, Dehoux M, Thissen JP, Francaux M: Creatine increases IGF-I and myogenic regulatory factor mRNA in C2C12 cells. FEBS Lett. 2004, 557: 243-247. 10.1016/S0014-5793(03)01504-7.View ArticlePubMedGoogle Scholar
- Riera L, Obach M, Navasrro-Sabaté A, Duran J, Perales JC, Viñals F, Rosa JL, Ventura F, Bartrons R: Regulation of ubiquitous 6-phosphofructo-2-kinase by the ubiquitin-proteasome proteolytic pathway during myogenic C2C12 cell differentiation. FEBS Lett. 2003, 550: 23-29. 10.1016/S0014-5793(03)00808-1.View ArticlePubMedGoogle Scholar
- De la Serna IL, Roy K, Carlson KA, Imbalzano AN: MyoD can induce cell cycle arrest but not muscle differentiation in the presence of dominant negative SWI/SNF chromatin remodelling enzymes. J Biol Chem. 2001, 276: 41486-41491. 10.1074/jbc.M107281200.View ArticlePubMedGoogle Scholar
- Pond WG, Houpt KA: The biology of the pig. 1978, Ithaca, N.Y.: ComstockGoogle Scholar
- Bioconductor. [http://www.bioconductor.org/]
- Whole Pathway Scope. [http://www.abcc.ncifcrf.gov/wps/wps_login.php?typ=download]
- GOminer. [http://discover.nci.nih.gov/gominer/]
- GenMapp. [http://www.genmapp.org/]
- Biocarta. [http://www.biocarta.com/]
- Reactome. [http://www.reactome.org/]
- Coutinho LL, Morris J, Marks HL, Buhr RJ, Ivarie R: Delayed somite formation in a quail line exhibiting myofiber hyperplasia is accompanied by delayed expression of myogenic regulatory factors and myosin heavy chain. Development. 1993, 117: 563-569.PubMedGoogle Scholar
- Te Pas MFW, Soumillion A: The use of physiologic and functional genomic information of the regulation of the determination of skeletal muscle mass in livestock breeding strategies to enhance meat production. Current Genomics. 2001, 2: 285-304. 10.2174/1389202013350788.View ArticleGoogle Scholar
- KEGG data base. [http://www.genome.ad.jp/kegg/]
- Gene Ontology database. [http://www.godatabase.org/cgi-bin/amigo/go.cgi]
- Kawashima S, Katayama T, Sato Y, Kanehisa M: KEGG API: a web service using SOAP/WSDL to access the KEGG system. Genome informatics. 2003, 14: 673-674.Google Scholar
- PERL scripts. [http://www.perl.com/]
- Akaike H: Information theory as an extension of the maximum likelihood principle. 2nd International symposium on information theory. Edited by: Petrov BN, Csaki F. 1973, Budapest: Academiai Kiado, 267-281.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.