- Short Report
- Open Access
An Always Correlated gene expression landscape for ovine skeletal muscle, lessons learnt from comparison with an “equivalent” bovine landscape
© Sun et al.; licensee BioMed Central Ltd. 2012
- Received: 1 August 2012
- Accepted: 7 November 2012
- Published: 13 November 2012
We have recently described a method for the construction of an informative gene expression correlation landscape for a single tissue, longissimus muscle (LM) of cattle, using a small number (less than a hundred) of diverse samples. Does this approach facilitate interspecies comparison of networks?
Using gene expression datasets from LM samples from a single postnatal time point for high and low muscling sheep, and from a developmental time course (prenatal to postnatal) for normal sheep and sheep exhibiting the Callipyge muscling phenotype gene expression correlations were calculated across subsets of the data comparable to the bovine analysis. An “Always Correlated” gene expression landscape was constructed by integrating the correlations from the subsets of data and was compared to the equivalent landscape for bovine LM muscle. Whilst at the high level apparently equivalent modules were identified in the two species, at the detailed level overlap between genes in the equivalent modules was limited and generally not significant. Indeed, only 395 genes and 18 edges were in common between the two landscapes.
Since it is unlikely that the equivalent muscles of two closely related species are as different as this analysis suggests, within tissue gene expression correlations appear to be very sensitive to the samples chosen for their construction, compounded by the different platforms used. Thus users need to be very cautious in interpretation of the differences. In future experiments, attention will be required to ensure equivalent experimental designs and use cross-species gene expression platform to enable the identification of true differences between different species.
- Longissimus Muscle
- Gene Expression Correlation
- Muscle Module
- Mitochondrial Module
- Gene Expression Platform
The availability of gene expression datasets derived from the same tissue from animals with different genetic backgrounds, different developmental stages, and different environmental perturbations facilitates the construction of informative tissue specific gene expression correlation networks. The “Always Correlated” (AC) landscape approach provides a simple method for the construction of informative networks from relatively small datasets . In particular the approach facilitates the identification of coherent modules of functionally related genes. The availability of equivalent tissue specific networks from different species would enable comparison between species for the same tissue and potentially the identification of common and/or species specific features.
Constructing the ovine AC skeletal muscle transcriptional landscape and identification of modules
Sources of gene expression data contributing to the analysis groups
Number of arrays2
Number of genes in network
80d3, 100d, 120d, T04, P10d5, P20d, P30d, T12
80d, 100d, 120d, T0, P10d, P20d, P30d, T12
Prenatal (Callipyge and normal)
80d, 100d, 120d,
Postnatal (Callipyge and normal)
T0, P10d, P20d, P30d, T12
High-Low (high and low muscling phenotypes)
80d, 100d, 120d, T0, P10d, P20d, P30d, T12, T78
intersection of above networks
Identification of functional modules in the AC landscape
Description of the key GO term
GO enrichment P-value
mitochondrial electron transport, NADH to ubiquinone
muscle contraction, muscle system process
Regulation of ubiquitin-protein ligase activity
negative regulation of ubiquitin-protein ligase activity
Comparison of the ovine and bovine AC landscapes
The ovine skeletal muscle co-expression landscape contains just under half the nodes, but ~60% more edges per node, and an eight times larger percentage of negative correlations than the cattle landscape . The latter values were somewhat surprising, given that the two analyses started with a similar number of genes and achieving a similar ratio of edges to nodes in the two networks is predicted to lead to a very small ovine AC landscape. It is not clear whether these differences reflect the source of the samples used, the gene expression platform (in particular the use of a bovine microarray for analysis of the ovine samples), the quality of the gene expression data, or a combination of all the above.
Overlap of the gene composition of modules in the ovine and bovine AC landscapes
Ovine module name
Number of genes
Bovine module name
Mitochondria (nuclear encoded)
Full landscape – annotation overlap1
Full landscape – annotation overlap
In a more detailed comparison of the two landscapes we observed that only eighteen identical edges were present in both landscapes. Given that the module structure appeared to be conserved between the landscapes, but contained different genes, this may be due to the differences between the genes correlated within modules as a consequence of the phenotypes rather than sampling or microarray platform differences. However, if this was the result of platform related issues, this may have also led to different performances of the probes on the arrays for the same genes, equally impacting the correlations leading to the final network. Indeed, of the 14,041 genes confidently annotated on the Affymetrix microarray and 17,101 genes confidently annotated on the Agilent microarray only 11,712 genes could be confidently linked between the two datasets. It also appears that although the objective was to obtain a core network the design of the experiments still had an impact on the genes represented and the modules observed. For example, there was no “cell cycle” or “fat” module in the ovine network and no “regulation of ubiquitin-protein ligase activity” module in the bovine network, although genes from these modules were represented on the arrays and probes returned informative data.
Muscle structural subunit genes in the ovine AC transcriptional landscape
Location of the genes encoding muscle structural protein subunits in the AC landscape
Slow twitch fibres1
Fast twitch fibres
Both slow and fast twitch fibres
Fibre type specificity is not known
TNNT1, FHL1, TMOD4, MYOZ1
TRIM54, TCAP, MYOT, DES, MYBPC1
TNNC2, MYLPF, MYL1
Near “regulation of ubiquitin–protein ligase activity”
Elsewhere in AClandscape
Not in the AClandscape
MYL3, MYH7, TNNC1, TNNI1, TPM3
MYH1, MYBPC2, TNNI2,
MYOM2, MYOM3, MYBPC3, TNNI3, TNNT2, TPM2, MYPN, KBTBD10, KBTBD5, CSRP3, LMOD2, UNC45B, SGCA, CMYA5, PDLIM3, LRRC39, XIRP2, TRIM63
Potential impact of sample choice on AC modules
The final gene in the muscle module encoded FAF1, FAS-associated factor one, which is highly expressed in skeletal muscle . Although probes for FAF1 are present on the bovine Agilent array platform and return informative signals (consistent with expression in muscle contractile cells) FAF1 was not present in the bovine AC landscape. FAF1 contains a ubiquitin-binding motif and has recently been reported to associate with the valosin-containing protein (VCP) purified from muscle, the resulting complex may interact transiently with the 26S proteosome . Mutations in VCP cause inclusion body myopathies, it has been proposed that VCP plays a role in protein homeostasis, extracting proteins from protein complexes for degradation by the 26S proteosome  and that disruption of this role leads to accumulation of undegraded proteins . The ubiquitin-dependent proteolytic system is the major proteolytic system in skeletal muscle . The Callipyge mutation has been proposed to increase muscle mass through a reduction in the rate of muscle protein degradation, although this has been proposed to be through increased levels of calpastatin, rather than decreased activity of the 26S proteosome [21, 22]. In addition, the proteosome was identified as a potential determinant of the muscling trait in the high-low muscling animals used in the analysis reported here . Interestingly, no “regulation of ubiquitin-protein ligase activity” module was present in the bovine network, although genes from these modules were represented on the arrays and probes returned informative data. The ubiquitin-ligases play a role in the targeting of proteins to the 26S proteosome for degradation. This difference between the two AC landscapes is suggestive of the source of the samples influencing the resulting networks and that the comparison has potentially identified a difference related to a role of the 26S Proteosome in the Callipyge animals and more generally in high and low muscling phenotypes in sheep compared to another high muscling genotype, Myostatin deficiency , in cattle.
Identification of putative key transcription factors
Assignment of Transcription Factors to robust modules
TFs in a module in the AC and identified by “Module-to-Regulator” analysis
TFs in a module in the AC landscape only
Top 10 TFs identified by the “Module-to-Regulator” analysis only1
KLF9, COPS5, HIF1AN, PREB, TCF7L2, SMARCA1, SMARCAD1, CHD1, CSDA, MEOX2 
SMARCAD1, CHD1, TCF7L2, HIF1AN2, SMARCA1, BPTF, PREB, MEOX2, YBX1 
BTF3, GTF2H5, CAMTA1, ZHX1, YY1 , BMI1, NR3C1, SUB1, ZBTB1, RBL2
Regulation of ubiquitin–protein ligase activity
YBX1, TAF10, PHB2, ASH1L, TULP4, TBX3, RBM39, MLL3, RBL2
For the “regulation of ubiquitin-protein ligase activity” module, SUZ12 has a GO annotation for “histone ubiquination”. TCEB1 has a GO annotation for “ubiquitin-ligase complex” and “ubiquitin-dependent protein catabolic process”. TAF9 has a GO annotation of “regulation of proteosomal ubiquitin-dependant protein catabolic process”. In addition, COPS5 regulates exosomal protein deubiquitination and sorting , SOX4 interacts with ubiquitin-conjugating enzyme 9 (UBC9), which represses the transcriptional activity of SOX4 , and TCF4 regulates the expression of ubiquitin c-terminal hydrolase L1 (UCHL1) . Thus, of the 11 proteins encoded by genes identified by the “Module-to-Regulator” analysis, six have a link with processes involving ubiquitin. In contrast, for example only 1 of the proteins identified in the analysis for the translation module is annotated with a GO term which includes the word ubiquitin.
However, even though the analyses in the two species both appear to correctly identify some TFs involved in the regulation of the function of the module there is only one gene, HIF1AN, in the overlap between the transcriptional regulators identified in the ovine and bovine “Module-to-Regulator” analyses (Table 5). Again it is likely that the experiment specific factors described above have contributed to this small overlap, which is not significant (hypergeometric test of an overlap of one gene in the mitochondrial module p-value = 0.23), and that a significant rate of false positives may be generated using these methods.
Despite apparent similarities between the datasets, a development time course overlaid with a muscle growth contrast, the differences in the composition of the experimental samples and design appears to have significantly impacted the final landscapes generated using the AC approach. The detection of true differences between cattle and sheep LM muscle awaits the availability of appropriately generated orthologous datasets using, for example, transcript sequencing techniques from as close as possible equivalent experiments. However, generating a truly orthologous dataset between two different species, even for equivalent tissues, with equivalent analysis parameters may not be a trivial process.
Availability of supporting data
With the exception of the data for the 80d, 100d and 120d LM muscle sheep callipyge genotype samples which is unpublished (Personal communication RL Tellam, K Byrne, T Vuocolo and N Cockett), the sheep gene expression data sets supporting the results of this article are available in the NCBI GEO repository, GSE5195 (10d, 20d, 30d, LM muscle sheep, callipyge and normal genotypes), GSE5955 (T0 and T12 LD muscle sheep, callipyge and normal genotypes), GSE20112 (80d, 100d, 120d LM muscle sheep normal genotypes), GSE20552 (T78, LM muscle sheep, High-Low).
The other data sets supporting the results of this article are included within the article and its additional files.
We would like to thank Noelle Cockett for permission to analyse unpublished data, Nathan S. Watson-Haigh for help with Bioconductor, and Wes Barris for the annotation of the probes on the microarray.
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