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ANIMAL GENETICS |
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* DIPROVAL University of Bologna, Sezione Allevamenti Zootecnici, Italy; and
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Wageningen University and Research Centre, Animal Sciences Group, ID-Lelystad, Division of Animal Resource Development, Animal Genomics Group, Lelystad, The Netherlands
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: embryo • expression profile • microarray • muscle • pig breed • transcriptome
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). As a result, pig breeds differ in muscle traits such as muscularity, muscle fiber type, color, etc. Duroc pigs are slower growing with relatively high i.m. fat content (Sellier, 1998) and redder muscle fiber types. Pietrain pigs are faster-growing pigs with relatively low i.m. fat content (Jones, 1998; Sellier, 1998) and whiter muscle fiber types. In addition, overall fatness of Duroc pigs is greater than that of Pietrain pigs. These 2 breeds are considered to represent extremes of modern Western pig breeds.
Many genes regulating myogenesis are known. Often these genes affect the activity and/or expression of the muscle regulatory factors (MRF) and myocyte-specific enhancer binding factor2 (Mef2) gene families, which take central positions in the regulation of myogenesis. The MRF gene family consists of 4 genes regulating proliferation (myf-5 and MyoD), terminal differentiation (myogenin), and maintenance of muscle tissue (Olson, 1990; Weintraub et al., 1991). The Mef2 genes (Mef2-A,-B,-C,-D) are members of the MADS gene family, and Mef2-D is present in undifferentiated myoblasts and may participate in the earliest commitment events leading to myogenesis (Wagenknecht et al., 2003). The Mef2 proteins are expressed in many tissues, but Mef2 only activates transcription in developing muscle.
Using microarray technology, we studied expression of the many known genes simultaneously affecting myogenesis. Microarray technology can simultaneously measure the differential expression of a large number of genes in a given tissue and may identify the genes related to the different muscle phenotypes. The aim of this study was to compare purebred Duroc and Pietrain prenatal muscle tissue transcriptome expression levels at different stages of prenatal development to get insight into the differences in muscle tissue development in these pig breeds.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Eighty-nine purebred Duroc and 87 Pietrain sows or gilts were bred with Duroc or Pietrain purebred boars, respectively. The sows were slaughtered around d 14, 21, 35, 49, 63, 77, and 91 after insemination, and the embryos/fetuses were collected. The litters of at least 14 sows per gestational age were collected. All handling of sows, embryos, and fetuses was done according to Dutch laws. The gestational ages of the litters varied for 4 d around the sampling ages. Fourteen-day-old embryos were collected by flushing the uterus. Thus, these samples represented all the embryos in a uterine horn. Twenty-one-day-old embryos were individually collected as whole embryos. Embryonic length was determined for each embryo. Fetuses of 35 to 91 d were weighed. For embryos with gestational age of 49 to 91 d, the gender was determined. Although the LM itself was not clearly visible at 35 d of development, the area where the muscle developed appeared and was extracted. Longissimus muscle tissue was dissected from the 49- to 91-d-old embryos. All collected materials were snap frozen in liquid N2 and stored at –80°C until RNA isolation. The gestational ages covered the whole period of myogenesis.
Microarray Construction
The microarray contained PCR fragments of 509 genes. A literature survey resulted in 200 genes with known effects on myogenesis, energy metabolism, and muscle structural genes. Some of the genes were members of larger gene families (See Supplemental Table; available online at http://jas.fass.org). A few other members of such gene families also were included in the microarray. The genes were partially cloned using a pool of RNA consisting of 2 samples per gestational age. Gene-specific primers were constructed based on sequence information from GenBank and used to amplify a PCR fragment on this cDNA pool. The fragments were cloned and partially sequenced to verify identity when doubt on identity existed. Furthermore, 309 clones of an adult pig muscle cDNA library (Davoli et al., 1999, 2002) were added. GenBank accession numbers, references, primers, and pathway information of all genes on the microarray are supplied in the Supplemental Table.
The clones were amplified in four 50-µL PCR reactions to obtain 200 µL total volume of PCR product for each clone, collected in a single well of a 96-well microtiter plate, and purified through Sephadex G100 (Sigma Aldrich, Zwijndrecht, The Netherlands). The purified products were precipitated and resuspended in 20 µL spotting buffer (1M phosphate buffer, pH 5.8; 50% dimethyl sulfoxide). After that, the length and quality of each fragment were checked on a 1% agarose gel. The fragments were spotted on glass slides in duplicate.
Microarray Hybridization
The Trizol-Phenol (Life Technologies, Breda, The Netherlands) method was used to isolate RNA from 2 embryos or fetuses per breed, except for the 14-d gestational age stage, for which Qiagen RNeasy kit (Qiagen, Cologne, Germany) was used. The RNA samples were pooled, and 2 µg of RNA for each stage and breed was labeled with Cy3 or Cy5 using the TSA labeling and amplification kit protocol (Perkin Elmer Life Sciences, Inc., Langen, Germany). Each microarray was hybridized with samples from a single gestational age with a sample from Duroc labeled with one dye and a sample from Pietrain labeled with the other dye. For each gestational age, hybridizations were performed in duplicate and in duplicate dye swap. A total of 7 gestational ages were investigated; thus, the number of microarray hybridizations was 28. Hybridization was done for 16 h at 65°C. After hybridization, the slides were rinsed according to the stringent washing protocol recommended by the manufacturer.
Microarray Analysis
Microarrays were scanned using the GeneTac2000 scanner (Genomics Solutions, Ann Arbor, MI). Each microarray was analyzed independently using the following steps. The first step was normalization of raw scanning data, which included background correction. The background signal was determined using blank spots. The background was normalized by including 33 blank spots per patch, which were evenly distributed. Blank spots are spots for which only spot buffer was used for printing and no DNA material. The blanks spots are therefore a better reference to account for nonspecific expression than local background surrounding expressed spots. Beyond that, we found that the number of lost spots attributable to negative values after background correction was less using blank spots for background correction. Normalization also was done 1) using all spots, and 2) per patch, all intensity-dependent using a locally weighted scatterplot smoothing (LOWESS) fit (Cleveland, 1974; Park et al., 2003), following the procedure described by Yang et al. (2002) and Pool et al. (2003). Spots are represented by their M- and A-values: M = log2 (Cy5/Cy3); thus, the M-value indicates the differential expression between the Cy5 and Cy3 labeled RNA samples; and A = (log2 [Cy5 * Cy3])/2. Therefore, the A-value indicates a weighted mean expression level of the Cy5- and Cy3-labeled RNA samples (Yang et al., 2002). Additionally, the significance of the difference is indicated by the 2-sided P-value tested on a log-logistic distribution.
The second step in microarray analysis was that normalized spots with the difference Cy3 – Cy5 having P > 0.05 were discarded. The third step was that the remaining spots were analyzed for up- and/or downregulation of expression by comparing the differential expression in the breeds using the M-values, and for mean expression levels (i.e., A-values). The M-values were regarded significant if –1.58 < M > 1.58 (i.e., if the differential expression is larger by at least 3 times, as recommended by the manufacturer of the labeling kit; Perkin Elmer). Using the Spotfire Pro 7 software (BioASP, Amsterdam, The Netherlands), clustering was performed with the K-means clustering option after a principal component analysis using the M- and A-values. Clustering is based on A-values using Euclid distances. The software was arbitrarily set to divide the spots into 8 clusters. Finally, the last step was that the results were analyzed with biological interpretation of data using available data on the physiology of the genes (i.e., involved in energy metabolism, myogenesis, or muscle structural genes).
The P-values were calculated without accounting for multiple comparisons; thus, there is the need for protection against Type I errors. The Bonferroni test was not used because it is too conservative, especially for microarray data. We avoided the risk of Type I errors by considering multiple copies of the clones on the array. The false error rate of a single spot of a gene is 0.05 and is 0.05 x 0.05 = 0.0025 for 2 spots. If no effect (false positive), positive measurements are expected to occur only in 1 spot of a gene (with the chance to occur in 2 spots also being extremely small). To be included in the list of up- or downregulated genes, a gene should show 1 to 4 spots significant per microarray on at least 2 microarrays, of which at least 1 microarray should be a dye swap microarray, making a Type I error extremely unexpected. Therefore, finding multiple significant up-or downregulated copies of a gene on the array increases the overall chance of a gene to be differentially expressed on the array (i.e., the more copies are found significant, the smaller the risk of detecting falsely significant designated genes due to Type I errors). Furthermore, the chance of having a false positive upregulated copy would be as large as for a false positive downregulation. If the experiment had no power at all, one would expect as many false positive down-regulations as up-regulations. We have accounted for this by suggesting that a group of genes only has an effect when the number of either up- or down-regulated genes is more than one-half of the genes within that group. Therefore, we believe that we avoided the risk of Type I errors in this experiment.
Quantitative Real-Time PCR
To validate the results of the microarrays, 5 genes showing differential expression were selected and analyzed with real-time PCR using the Lightcycler (Roche Diagnostics, Almere, The Netherlands). Genes were chosen in different pathways: myogenesis-affecting (EPO-receptor, ß-catenin, and TGF ß2), energy metabolism (GAPDH), and muscle structural (collagen 3A1). Primers were designed on the cDNA sequence to amplify a 100 to 150 bp fragment, and probes containing fluorescein were designed according to the rules set by the manufacturer (Table 1). All reactions had an annealing temperature of 60°C, except TGF-ß2 (55°C), and a magnesium concentration of 3 mM, except ß-catenin (5 mM). For real-time PCR, each gestational age was represented by individual RNA samples isolated from 6 embryos or fetuses different from the animals and litters used for microarray analysis. Mean values are presented.
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et al., 2004). In the microarray, normalization as a function of all genes on the microarray was used, which cannot be done with real-time PCR. Therefore, we also tried 18S rRNA; however, we showed that 18S rRNA also is strongly regulated during the 14-and 21-d gestational ages, as has been shown previously (Voronina, 2002; Radoni et al., 2004). Using 18S rRNA normalization, the 35- to 91-d period resulted in unrealistic differences between 21 and 35 d because of extremely low 18S rRNA levels (data not shown). This period is a very important stage in primary muscle differentiation. Therefore, we decided to use nonnormalized real-time PCR values. The mean value of 6 animals is presented. The results are presented as the log(Duroc/Pietrain); the sign of the results indicates the ratio and the direction of the differential expression between Duroc and Pietrain. It is important to note that no new data are produced with real-time PCR. These experiments were only used to verify the microarray data. |
RESULTS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Genes were grouped into 3 major groups: myogenesis, energy metabolism, and muscle structural genes. The first 2 groups have been subdivided into pathway-specific subgroups (Table 1
). Results were analyzed for 1) up- or downregulation (i.e., the ratio between the expression level in Duroc and the expression level in Pietrain; M-value), and 2) for general expression level (A-value).
Duroc-Pietrain Expression Ratio
Data were analyzed for spots with P < 0.05 and for spots showing more than 3 times differential expression. Although the number of spots is substantially lower in the latter analysis, the results for each group are similar. The results are shown in Table 2. Column N shows the total number of genes on a microarray for each (sub)group, whereas column N-list indicates the total number of genes for each (sub)group of genes showing statistically significant differential expression on at least 1 gestational age. To be included, a gene should show 1 to 4 spots significant per microarray on at least 2 microarrays, of which at least 1 microarray should be a dye swap microarray. A (sub)group was considered significant when at least 50% of the genes was differentially expressed on at least 1 gestational age. Note that especially in the myogenesis group, genes may be included in 2 subgroups (e.g., differentiation-inhibiting and proliferation-stimulating effects).
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) indicate a major difference between the energy metabolism in Duroc and Pietrain embryos and fetuses at all gestational ages. At all gestational ages except 35 d of gestation, the energy metabolism in the Pietrain was at a greater level than in the Duroc. At d 35 the situation was reversed. The genes of the fatty acid metabolism had a different general expression profile. The results indicate that fatty acid metabolism was at a higher level in early Duroc embryos (d 14 to 49 of gestational age) compared with Pietrain embryos, whereas the reverse situation was found in older fetuses from d 63 of gestation and onward.
Finally, with the exception of the ATP metabolism subgroup, all subgroups had lower numbers of differentially expressed genes at 91 d of gestation. This finding may indicate that at birth the Duroc and Pietrain breeds differ less in energy metabolism.
Myogenesis.
Table 2 indicates that, in general, myogenesis started earlier in Duroc than in Pietrain because the expression levels of all myoblast proliferation and differentiation affecting groups of genes were greater in Duroc embryos of 14 to 35 d than the expression levels in Pietrain. From 49 d and onwards, with the exception of the differentiation-inhibiting group of genes, Pietrain showed increased myogenesis. It should be noted that not all functional groups showed significant differential expression on all gestational ages.
Muscle Structural Genes. The expression pattern of the muscle structural genes showed similarities with the myogenesis genes. In the early embryo until d 49, the expression of the genes was greater in Duroc than in Pietrain. Fetuses older than 49 d of gestation showed the opposite expression pattern.
Clustering Analysis
Clustering analysis was performed to investigate the absolute expression levels of the genes in both breeds in relation to function. A typical clustering analysis with principal component analysis (PCA) is shown in Figure 1, in which the PCA1 is the A-value and the PCA2 is the M-value. The principal component analysis and clustering analysis decreased the amount of individual data by grouping the genes according to their differential expression patterns.
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indicate that genes with similar biological function often cluster together, indicating pathway-regulated expression levels. Genes involved in myogenesis seemed to be an exception; however, myogenesis is a multiprocess pathway as indicated in the subgroups of Table 2. Although less clearly interpretable, there seems to be a coclustering of proliferation-stimulating and differentiation-inhibiting genes. Genes of other functional groups cluster more widespread in different clusters (data not shown).
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To validate the microarray results, we chose 5 genes, 3 myogenesis related (EPO-receptor, ß-catenin and TGF-ß2) and all belonging to both proliferation-stimulating and differentiation-inhibiting myogenesis sub-groups, an energy metabolism household gene (GAPDH) more specifically belonging to the glycolysis subgroup, and a muscle structural gene (collagen 3A1), a gene expressed early during myogenesis (i.e., from d 14 and onward) to represent the groups of genes.
Real-time PCR validated the results of the microar-rays (Figure 2). The results of the real-time PCR for each gene are shown in Figure 2 as the log(Duroc/Pie-train). Presented in this way, the results directly indicate the direction of the differential expression: if the expression level in Duroc fetuses was greater than in Pietrain fetuses, the log of the ratio is positive (indicated with a bar to the right); the opposite differential expression is indicated by a bar to the left.
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The quantitative real-time PCR differential expression of Col3A1 indicated greater Duroc expression levels in 14- to 35-d-old embryos compared with Pietrain embryos, with the opposite being the case for older fetuses. The Col3A1 gene expression matched with the microarray muscle structural genes expression patterns, except for d 35 and 49. Because Col3A1 is expressed very early in prenatal development, the ratio-switch in expression from Duroc to Pietrain also may be early, so instead of after d 49, the ratio-switch in the expression of this gene occurs before d 49. It also should be noted that at d 14 of gestation, the gene is expressed in Duroc, but even real-time PCR was unable to detect expression in Pietrain.
Quantitative real-time PCR of GAPDH showed that the expression level was greatest in Pietrain fetuses, except for d 21. These results are similar to the microarray results, except that the changes in the expression in the microarray were at d 35, whereas the real-time PCR indicated the changes were at d 21.
In general, the results of the real-time PCR indicated that the microarray data were reliable for these 5 genes. Therefore, we extrapolate these findings to the reliability of all microarray data.
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The formation of new skeletal muscle fibers or myogenesis is an exclusively prenatal process, determining muscle characteristics such as fiber numbers, which may be related to muscle strength and function (Rehfeldt et al., 2000). Myogenesis is under complex genetic regulation. The MRF and Mef2 gene families of transcription factors take central positions in the regulation of myogenesis. The MRF gene family consists of 4 genes regulating proliferation (myf-5 and MyoD), terminal differentiation (myogenin), and maintenance of muscle tissue (Olson, 1990; Weintraub et al., 1991). The Mef2 genes (Mef2A,-B,-C,-D) are members of the MADS gene family. Their proteins bind to a conserved A/T-rich sequence in the control regions of numerous muscle-specific genes. Although Mef2A and Mef2C proteins are specific to differentiated skeletal and cardiac muscle, Mef2D is present even in undifferentiated myoblasts, and it may participate in the earliest commitment events leading to myogenesis. (Wagenknecht et al., 2003). The Mef2 proteins are expressed in many tissues, but it is only in the developing cardiac, skeletal, and smooth muscle that Mef2 activates transcription. After birth, the expression of Mef2 is downregulated, but it is reinduced during skeletal muscle regeneration (Parker et al., 2003). In pigs, genetic variation in myogenin is related to muscle and general body growth (Soumillion et al., 1997; te Pas et al., 1999b), whereas no such relationship was reported for myf-5 (te Pas et al., 1999a). Furthermore, postnatal expression levels of myf-5, MyoD, and myogenin also were related to growth rate (te Pas et al., 2000). The activity and expression levels of the MRF genes are regulated by a network of genes together modulating skeletal muscle fiber development and differentiation and thereby regulating muscle and body growth potential. The MRF proteins act in concert with the Mef2 family in orchestrating differentiation.
Mammalian myogenesis takes place in 2 distinct waves: the primary and secondary waves of muscle fiber formation in the pig occur from d 30 to 60 and from d 54 to 90 of gestation, respectively (Wigmore and Stickland, 1983; Wigmore and Evans, 2002). Our samples are composed of both of these periods and the period preceding the primary muscle fiber formation when the myoblasts, precursor cells for myogenesis, are formed. It should be noted that especially the very young embryonic samples differed from the fetal samples that were older. The 14-d embryo samples were pools of all embryos in a uterine horn, and the 21-d embryos were composed of whole embryo samples. All other samples were LM specific. Therefore, comparison of the data of different gestational ages within the range of the young embryos should be treated with caution; however, we only present the comparison of the expression patterns between breeds for each specific gestational age. Because these data are derived from similar isolates, these comparisons are not affected by these sample differences. Nonetheless, it should be kept in mind that the nonmuscle tissue in these samples might affect the observed expression level, indicating expression that probably was not related to myogenesis. Because we selected genes involved in myogenesis, this is unlikely but cannot be excluded.
It is well known that pig-breed-specific differences in muscle fiber numbers exist (Dwyer and Stickland, 1991); however, the molecular background of this difference has never been investigated. Therefore, we compared the myogenesis of 2 pig breeds differing for muscle fiber characteristics. The high muscularity of the Pietrain pigs is mainly related to hypertrophy of muscle fibers leading to mostly white muscle fiber typing (Jones, 1998; Sellier, 1998). Contrary to this, Duroc pigs have smaller, redder fiber types (Sellier, 1998). The prenatal muscle tissue transcriptomes of both breeds were compared using microarray technology.
We performed a literature search to find as many genes as possible that have been related to myogenesis using data of all animal species and from in vitro studies. The pig homologues of these genes were cloned and placed on a microarray to investigate their expression profiles simultaneously. Because energy availability in the developing muscle tissue during myogenesis seemed to affect myogenesis, we included genes from several energy metabolism pathways on the microarray. Energy availability might be important to form mature muscle fibers that also contain enough energy for the period shortly after birth. If this fails, the perinatal muscles might be insufficient, leading to muscle failure such as splay leg and other leg weaknesses (Jorgensen and Vestergaard, 1990). Furthermore, a direct connection between energy metabolism and myogenesis has recently been suggested because energy content of satellite cell cultures has been related to hypertrophy of muscle fibers in vitro (Louis et al., 2004). Adding creatine to satellite cell cultures increases IGF-I level and expression of the 4 MRF genes, although only myogenin was increased more than 3-fold. Moreover, differential regulation of the expression of 6-phosphofructo-2-kinase — key regulatory enzymes in glycolysis and gluconeogenesis (Riera et al., 2003) and BMP9 — regulating the expression of phosphoenolpyruvate carboxykinase (PEPCK) and Akt kinase activity, a key enzyme in glycolysis and a key regulatory protein in differentiated myotubes, respectively (Chen et al., 2003), indicates a direct connection between energy metabolism and myogenesis.
Comparison of Myogenesis in Prenatal Duroc and Pietrain
Expression profile differences of myogenesis-related genes suggest that muscle fiber formation in Duroc was initiated earlier than in Pietrain because myogenesis-affecting genes were expressed to a greater level in young (14 to 35 d) prenatal Duroc pigs than in Pietrain pigs. The expression profile differences of muscle structural genes was similar, also suggesting that muscle fiber formation is taking place earlier in Duroc embryos compared with Pietrain embryos. Delayed muscle fiber formation has been related to increased muscle fiber numbers in selected quail lines (Coutinho et al., 1993). The delay may be associated with a prolonged proliferation period, not necessarily proliferation rate, which may be unaffected, giving rise to an increased numbers of myoblasts; this may result in increased primary muscle fiber numbers. Increased numbers of primary muscle fibers may give rise to increased secondary muscle fiber formation. Muscle fiber numbers have been related to muscle mass (Stickland and Goldspink, 1973; Dwyer et al., 1993); however, the increased muscle mass of Pietrain pigs has been related more to hypertrophy rather than to hyperplasia (Jones, 1998; Sellier, 1998). Alternatively, the delayed muscle fiber formation may result in the formation of a larger pool of satellite cells. The increased proliferation process during later developmental ages in Pietrain as compared with Duroc may enhance the number of satellite cells further, thereby increasing the hypertrophic capacity of Pietrain muscles further. Because hypertrophy is mainly a postnatal trait of muscle fibers, this seems to be related to the low expression level of energy metabolism genes relative to Duroc prenatal pigs. A high creatine level, suggestive of a high energy level, was reported to be associated with hypertrophic growth (Louis et al., 2004). Although speculative, this also may relate to the postnatal Pietrain leanness. Postnatal Pietrain pigs, which have highly hypertrophied muscles, may use a high energy metabolism to increase muscle hypertrophy, leaving less energy for fat deposition.
Clustering analysis indicated that the expressions of the genes involved in several myogenic processes are more or less similarly regulated. In particular, the inhibition of differentiation and stimulation of proliferation were related. This provides further molecular evidence that these 2 processes are often coregulated. Differentiating myoblasts leave cell division before terminal differentiation (Olson et al., 1991). Finally, expression levels of myogenesis affecting genes are often low compared with genes affecting energy metabolism. Considering that many of these genes are transcription factors involved in the regulation of the MRF genes, which also are transcription factors, and many energy metabolism genes are so-called household genes, this may not be surprising. Nonetheless, because clustering is based on A-values (i.e., mean expression level), this observed difference in expression level may interfere with clustering analysis. Because of this difference, genes of the energy pathways and the myogenesis pathways often do not cluster together, which may provide an alternative explanation for the observed clustering patterns. On the other hand, muscle structural gene expression level depends on the progression of myogenesis, so expression is low in early embryos and increases in time. Nevertheless, this expression pattern also does not interfere with pathway clustering.
Comparison of Energy Metabolism in Prenatal Duroc and Pietrain in Relation to Myogenesis
In general, the expression levels of the genes involved in energy metabolism were greater in prenatal Pietrain than in Duroc muscle tissue, except for d 35, where the opposite situation was found for all energy metabolism pathways. The period around d 35 of gestation is central to primary muscle fiber formation (Wigmore and Stickland, 1983; Wigmore and Evans, 2002). Using only Duroc prenatal developing muscle tissue for microarray analysis of daily comparison, we also observed that energy metabolism decreased during myogenic differentiation (te Pas et al., 2005). It seems clear that myogenic differentiation and energy metabolism are connected processes; however, a clear explanation for this observation is lacking. From the reported relationship between energy level and muscle fiber hypertrophy (Louis et al., 2004), one may suggest that muscle fiber hyperplasia is affected, but data are lacking.
The fatty acid metabolism genes show an opposite expression profile to energy metabolism genes. In early Duroc embryonic tissues, the expression level was greater than in Pietrain. Duroc pigs are known to have more adipose tissue than Pietrain pigs, which are considered the most lean pig breed. The inter- and intramuscular fat depots are especially larger in Duroc pigs (Sellier, 1998). During the isolation of the prenatal tissues, we observed that Duroc embryos from d 35 of gestation and onward showed a well-developed subcutaneous fat layer, whereas Pietrain did not. Thus, our results indicate that the fat depot differences are already initiated during early embryogenesis.
Finally, the differences between Duroc and Pietrain prenatal energy metabolism expression profiles seemed to be less in the 91-d-old fetuses than in younger fetuses, with the exception of ATP metabolism. This finding may indicate that at birth, general energy metabolism is more alike for these 2 pig breeds. On the other hand, ATP metabolism, especially, is a major determinant of energy metabolism, which by itself may have a large effect on energy availability in piglets after birth, with effects on survival rate and growth and body composition, including muscle fiber typing characteristics.
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Footnotes |
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2 These authors contributed equally to this work.
3 Corresponding author: roberta.davoli{at}unibo.it
Received for publication January 18, 2005. Accepted for publication August 25, 2005.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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, A., S. Thulke, I. M. Mackay, O. Landt, W. Siegert, and A. Nitsche. 2004. Guideline to reference gene selection for quantitative real-time PCR. Biochim. Biophys. Res. Comm. 313:856–862.[Medline]This article has been cited by other articles:
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  J. Tong, M. J. Zhu, K. R. Underwood, B. W. Hess, S. P. Ford, and M. Du AMP-activated protein kinase and adipogenesis in sheep fetal skeletal muscle and 3T3-L1 cells J Anim Sci, June 1, 2008; 86(6): 1296 - 1305. [Abstract] [Full Text] [PDF]
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 S. Ponsuksili, E. Murani, C. Walz, M. Schwerin, and K. Wimmers Pre- and postnatal hepatic gene expression profiles of two pig breeds differing in body composition: insight into pathways of metabolic regulation Physiol Genomics, May 11, 2007; 29(3): 267 - 279. [Abstract] [Full Text] [PDF]
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