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A gene expression atlas of the domestic pig.

Freeman TC, Ivens A, Baillie JK, Beraldi D, Barnett MW, Dorward D, Downing A, Fairbairn L, Kapetanovic R, Raza S, Tomoiu A, Alberio R, Wu C, Su AI, Summers KM, Tuggle CK, Archibald AL, Hume DA - BMC Biol. (2012)

Bottom Line: The analysis presented here provides a detailed functional clustering of the pig transcriptome where transcripts are grouped according to their expression pattern, so one can infer the function of an uncharacterized gene from the company it keeps and the locations in which it is expressed.In particular, we discuss the expression signatures associated with the gastrointestinal tract, an organ that was sampled at 15 sites along its length and whose biology in the pig is similar to human.As an important livestock animal with a physiology that is more similar than mouse to man, we provide a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, EH25 9PS, UK. tom.freeman@roslin.ed.ac.uk

ABSTRACT

Background: This work describes the first genome-wide analysis of the transcriptional landscape of the pig. A new porcine Affymetrix expression array was designed in order to provide comprehensive coverage of the known pig transcriptome. The new array was used to generate a genome-wide expression atlas of pig tissues derived from 62 tissue/cell types. These data were subjected to network correlation analysis and clustering.

Results: The analysis presented here provides a detailed functional clustering of the pig transcriptome where transcripts are grouped according to their expression pattern, so one can infer the function of an uncharacterized gene from the company it keeps and the locations in which it is expressed. We describe the overall transcriptional signatures present in the tissue atlas, where possible assigning those signatures to specific cell populations or pathways. In particular, we discuss the expression signatures associated with the gastrointestinal tract, an organ that was sampled at 15 sites along its length and whose biology in the pig is similar to human. We identify sets of genes that define specialized cellular compartments and region-specific digestive functions. Finally, we performed a network analysis of the transcription factors expressed in the gastrointestinal tract and demonstrate how they sub-divide into functional groups that may control cellular gastrointestinal development.

Conclusions: As an important livestock animal with a physiology that is more similar than mouse to man, we provide a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells. The data and analyses are available on the websites http://biogps.org and http://www.macrophages.com/pig-atlas.

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Network visualization and clustering of the pig transcriptome. A.Three-dimensional visualization of a Pearson correlation graph of data derivedfrom analysis of pig tissues and cells. Each node (sphere) in the graph representsan individual probeset on the array and the edges (lines) correspond tocorrelations between individual measurements above the defined threshold. Thegraph is comprised of 20,355 nodes (probesets) and 1,251,575 edges (correlations≥0.8). The complex topology of the graph is a result of groups ofco-expressed genes forming cliques of high connectivity within the graph.Clustering of the graph using the MCL algorithm was used to assign genes to groupsbased on coexpression. By inspection of the underlying profiles, areas of thegraph can be associated with genes expressed by specific tissue or cellpopulations. Plots of the average expression profile of genes in selected clustersare given on the right: B. profile of cluster 4 genes whose expression isrestricted to brain and spinal cord; C. profile of cluster 7 genes whoseexpression is highest in blood; D. profile of cluster 10 genes whoseexpression is restricted to skeletal muscle; E. profile of cluster 22 geneswhose expression is highest in the adrenal gland. MCL, Markov clusteralgorithm.
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Figure 1: Network visualization and clustering of the pig transcriptome. A.Three-dimensional visualization of a Pearson correlation graph of data derivedfrom analysis of pig tissues and cells. Each node (sphere) in the graph representsan individual probeset on the array and the edges (lines) correspond tocorrelations between individual measurements above the defined threshold. Thegraph is comprised of 20,355 nodes (probesets) and 1,251,575 edges (correlations≥0.8). The complex topology of the graph is a result of groups ofco-expressed genes forming cliques of high connectivity within the graph.Clustering of the graph using the MCL algorithm was used to assign genes to groupsbased on coexpression. By inspection of the underlying profiles, areas of thegraph can be associated with genes expressed by specific tissue or cellpopulations. Plots of the average expression profile of genes in selected clustersare given on the right: B. profile of cluster 4 genes whose expression isrestricted to brain and spinal cord; C. profile of cluster 7 genes whoseexpression is highest in blood; D. profile of cluster 10 genes whoseexpression is restricted to skeletal muscle; E. profile of cluster 22 geneswhose expression is highest in the adrenal gland. MCL, Markov clusteralgorithm.

Mentions: We used BioLayout Express3D to analyze the pig transcriptome datagenerated using the Snowball array (all normalized expression data is provided inAdditional file 2). From a pairwise transcript-to-transcriptcorrelation matrix a weighted, undirected network graph was constructed using a Pearsoncorrelation threshold cut-off of r ≥ 0.80. The resultant graph was large andhighly structured (Figure 1, Additional file 3) with one large component of 19,708 nodes and 90 smaller components(unconnected networks of correlations) of between 57 and 5 nodes (20,352 probesets intotal, that is, just under half the transcripts represented on the array). The topologyof the graph contained localized areas of high connectivity and high correlation(representing groups of genes with similar profiles), dominated by groups of genes thatare coexpressed and form highly connected cliques within the network (Figures 1 and 2). Nodes representing differentprobesets designed to the same gene were generally highly correlated and connected toeach other in the graph, confirming the validity of the probeset annotation andapproach.


A gene expression atlas of the domestic pig.

Freeman TC, Ivens A, Baillie JK, Beraldi D, Barnett MW, Dorward D, Downing A, Fairbairn L, Kapetanovic R, Raza S, Tomoiu A, Alberio R, Wu C, Su AI, Summers KM, Tuggle CK, Archibald AL, Hume DA - BMC Biol. (2012)

Network visualization and clustering of the pig transcriptome. A.Three-dimensional visualization of a Pearson correlation graph of data derivedfrom analysis of pig tissues and cells. Each node (sphere) in the graph representsan individual probeset on the array and the edges (lines) correspond tocorrelations between individual measurements above the defined threshold. Thegraph is comprised of 20,355 nodes (probesets) and 1,251,575 edges (correlations≥0.8). The complex topology of the graph is a result of groups ofco-expressed genes forming cliques of high connectivity within the graph.Clustering of the graph using the MCL algorithm was used to assign genes to groupsbased on coexpression. By inspection of the underlying profiles, areas of thegraph can be associated with genes expressed by specific tissue or cellpopulations. Plots of the average expression profile of genes in selected clustersare given on the right: B. profile of cluster 4 genes whose expression isrestricted to brain and spinal cord; C. profile of cluster 7 genes whoseexpression is highest in blood; D. profile of cluster 10 genes whoseexpression is restricted to skeletal muscle; E. profile of cluster 22 geneswhose expression is highest in the adrenal gland. MCL, Markov clusteralgorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814290&req=5

Figure 1: Network visualization and clustering of the pig transcriptome. A.Three-dimensional visualization of a Pearson correlation graph of data derivedfrom analysis of pig tissues and cells. Each node (sphere) in the graph representsan individual probeset on the array and the edges (lines) correspond tocorrelations between individual measurements above the defined threshold. Thegraph is comprised of 20,355 nodes (probesets) and 1,251,575 edges (correlations≥0.8). The complex topology of the graph is a result of groups ofco-expressed genes forming cliques of high connectivity within the graph.Clustering of the graph using the MCL algorithm was used to assign genes to groupsbased on coexpression. By inspection of the underlying profiles, areas of thegraph can be associated with genes expressed by specific tissue or cellpopulations. Plots of the average expression profile of genes in selected clustersare given on the right: B. profile of cluster 4 genes whose expression isrestricted to brain and spinal cord; C. profile of cluster 7 genes whoseexpression is highest in blood; D. profile of cluster 10 genes whoseexpression is restricted to skeletal muscle; E. profile of cluster 22 geneswhose expression is highest in the adrenal gland. MCL, Markov clusteralgorithm.
Mentions: We used BioLayout Express3D to analyze the pig transcriptome datagenerated using the Snowball array (all normalized expression data is provided inAdditional file 2). From a pairwise transcript-to-transcriptcorrelation matrix a weighted, undirected network graph was constructed using a Pearsoncorrelation threshold cut-off of r ≥ 0.80. The resultant graph was large andhighly structured (Figure 1, Additional file 3) with one large component of 19,708 nodes and 90 smaller components(unconnected networks of correlations) of between 57 and 5 nodes (20,352 probesets intotal, that is, just under half the transcripts represented on the array). The topologyof the graph contained localized areas of high connectivity and high correlation(representing groups of genes with similar profiles), dominated by groups of genes thatare coexpressed and form highly connected cliques within the network (Figures 1 and 2). Nodes representing differentprobesets designed to the same gene were generally highly correlated and connected toeach other in the graph, confirming the validity of the probeset annotation andapproach.

Bottom Line: The analysis presented here provides a detailed functional clustering of the pig transcriptome where transcripts are grouped according to their expression pattern, so one can infer the function of an uncharacterized gene from the company it keeps and the locations in which it is expressed.In particular, we discuss the expression signatures associated with the gastrointestinal tract, an organ that was sampled at 15 sites along its length and whose biology in the pig is similar to human.As an important livestock animal with a physiology that is more similar than mouse to man, we provide a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, EH25 9PS, UK. tom.freeman@roslin.ed.ac.uk

ABSTRACT

Background: This work describes the first genome-wide analysis of the transcriptional landscape of the pig. A new porcine Affymetrix expression array was designed in order to provide comprehensive coverage of the known pig transcriptome. The new array was used to generate a genome-wide expression atlas of pig tissues derived from 62 tissue/cell types. These data were subjected to network correlation analysis and clustering.

Results: The analysis presented here provides a detailed functional clustering of the pig transcriptome where transcripts are grouped according to their expression pattern, so one can infer the function of an uncharacterized gene from the company it keeps and the locations in which it is expressed. We describe the overall transcriptional signatures present in the tissue atlas, where possible assigning those signatures to specific cell populations or pathways. In particular, we discuss the expression signatures associated with the gastrointestinal tract, an organ that was sampled at 15 sites along its length and whose biology in the pig is similar to human. We identify sets of genes that define specialized cellular compartments and region-specific digestive functions. Finally, we performed a network analysis of the transcription factors expressed in the gastrointestinal tract and demonstrate how they sub-divide into functional groups that may control cellular gastrointestinal development.

Conclusions: As an important livestock animal with a physiology that is more similar than mouse to man, we provide a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells. The data and analyses are available on the websites http://biogps.org and http://www.macrophages.com/pig-atlas.

Show MeSH
Related in: MedlinePlus