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Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

Gu Q, Nagaraj SH, Hudson NJ, Dalrymple BP, Reverter A - BMC Genomics (2011)

Bottom Line: To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine.Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational and Systems Biology, CSIRO Food Futures Flagship and CSIRO Livestock Industries, 306 Carmody Rd, St. Lucia, Brisbane, Queensland 4067, Australia.

ABSTRACT

Background: Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.

Results: We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively.

Conclusion: The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

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TF network for bovine skeletal muscle. (A) Overall view of the 1,395 connections among 333 TFs; (B) Subnetwork spanned by the first neighbors of the 10 most connected TFs (GATA2, MAZ, NFE2L1, NFKB1, NKX25, NKX61, PAX4, PRRX2, TFCP2, and ZBTB7B), details of which are listed in Table 3; (C) Subnetwork of module-specific and muscle-expressed TFs; (D) Network among TFs with absolute correlation (r) >0.9 and number of common TGs (N) >100. Black and red edges correspond to positive and negative correlations, respectively. The network reveals the central role of TGIF1 which also contains a binding motif in the promoter region of myostatin (MSTN). Colors represent functional modules: cell cycle (green), fat (yellow), immune (purple), mitochondria (cyan), and muscle/glycolysis (red). Big and small nodes represent TFs with and without detectable expression in muscle, respectively.
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Figure 4: TF network for bovine skeletal muscle. (A) Overall view of the 1,395 connections among 333 TFs; (B) Subnetwork spanned by the first neighbors of the 10 most connected TFs (GATA2, MAZ, NFE2L1, NFKB1, NKX25, NKX61, PAX4, PRRX2, TFCP2, and ZBTB7B), details of which are listed in Table 3; (C) Subnetwork of module-specific and muscle-expressed TFs; (D) Network among TFs with absolute correlation (r) >0.9 and number of common TGs (N) >100. Black and red edges correspond to positive and negative correlations, respectively. The network reveals the central role of TGIF1 which also contains a binding motif in the promoter region of myostatin (MSTN). Colors represent functional modules: cell cycle (green), fat (yellow), immune (purple), mitochondria (cyan), and muscle/glycolysis (red). Big and small nodes represent TFs with and without detectable expression in muscle, respectively.

Mentions: We used the log-odds ratio (LOD) to investigate the relationship between the co-expression correlation observed for genes pairs and the number of shared TFs. We observed that the LOD-value is dependent on the type of transcriptional regulatory motifs (TRMn) defining the motif of n common TFs jointly regulating the same set of TGs, and with n = 0, 1, 2,... up to n ≥ 10 (Additional File 2). These results corroborated and, to a degree, formally validated our previous observation that gene pairs sharing TFBS showed an increased co-expression correlation (Figure 2A). A similar result was observed for gene pairs sharing from more than 1 to more than 10 TFBSs (Additional File 2). Again, the trend is more pronounced when only positive correlations are considered, in line with a higher reliability for positive correlations [32]. As expected, the distribution of the co-expression correlations for pairs of genes with TRMs of 1, 5 or 10 genes shows an increased bias to highly positive correlations with increased size of the TRM (Figure 4B). Likewise, extreme positive correlations (i.e. within the interval {0.8,1.0}) are more frequent among high-order TRMs than extreme negative correlations (i.e. within the interval {-1.0,-0.8}) (Additional File 2).


Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

Gu Q, Nagaraj SH, Hudson NJ, Dalrymple BP, Reverter A - BMC Genomics (2011)

TF network for bovine skeletal muscle. (A) Overall view of the 1,395 connections among 333 TFs; (B) Subnetwork spanned by the first neighbors of the 10 most connected TFs (GATA2, MAZ, NFE2L1, NFKB1, NKX25, NKX61, PAX4, PRRX2, TFCP2, and ZBTB7B), details of which are listed in Table 3; (C) Subnetwork of module-specific and muscle-expressed TFs; (D) Network among TFs with absolute correlation (r) >0.9 and number of common TGs (N) >100. Black and red edges correspond to positive and negative correlations, respectively. The network reveals the central role of TGIF1 which also contains a binding motif in the promoter region of myostatin (MSTN). Colors represent functional modules: cell cycle (green), fat (yellow), immune (purple), mitochondria (cyan), and muscle/glycolysis (red). Big and small nodes represent TFs with and without detectable expression in muscle, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3025955&req=5

Figure 4: TF network for bovine skeletal muscle. (A) Overall view of the 1,395 connections among 333 TFs; (B) Subnetwork spanned by the first neighbors of the 10 most connected TFs (GATA2, MAZ, NFE2L1, NFKB1, NKX25, NKX61, PAX4, PRRX2, TFCP2, and ZBTB7B), details of which are listed in Table 3; (C) Subnetwork of module-specific and muscle-expressed TFs; (D) Network among TFs with absolute correlation (r) >0.9 and number of common TGs (N) >100. Black and red edges correspond to positive and negative correlations, respectively. The network reveals the central role of TGIF1 which also contains a binding motif in the promoter region of myostatin (MSTN). Colors represent functional modules: cell cycle (green), fat (yellow), immune (purple), mitochondria (cyan), and muscle/glycolysis (red). Big and small nodes represent TFs with and without detectable expression in muscle, respectively.
Mentions: We used the log-odds ratio (LOD) to investigate the relationship between the co-expression correlation observed for genes pairs and the number of shared TFs. We observed that the LOD-value is dependent on the type of transcriptional regulatory motifs (TRMn) defining the motif of n common TFs jointly regulating the same set of TGs, and with n = 0, 1, 2,... up to n ≥ 10 (Additional File 2). These results corroborated and, to a degree, formally validated our previous observation that gene pairs sharing TFBS showed an increased co-expression correlation (Figure 2A). A similar result was observed for gene pairs sharing from more than 1 to more than 10 TFBSs (Additional File 2). Again, the trend is more pronounced when only positive correlations are considered, in line with a higher reliability for positive correlations [32]. As expected, the distribution of the co-expression correlations for pairs of genes with TRMs of 1, 5 or 10 genes shows an increased bias to highly positive correlations with increased size of the TRM (Figure 4B). Likewise, extreme positive correlations (i.e. within the interval {0.8,1.0}) are more frequent among high-order TRMs than extreme negative correlations (i.e. within the interval {-1.0,-0.8}) (Additional File 2).

Bottom Line: To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine.Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational and Systems Biology, CSIRO Food Futures Flagship and CSIRO Livestock Industries, 306 Carmody Rd, St. Lucia, Brisbane, Queensland 4067, Australia.

ABSTRACT

Background: Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.

Results: We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively.

Conclusion: The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

Show MeSH