Transcriptome architecture across tissues in the pig - PubMed (original) (raw)

Comparative Study

Transcriptome architecture across tissues in the pig

André L J Ferraz et al. BMC Genomics. 2008.

Abstract

Background: Artificial selection has resulted in animal breeds with extreme phenotypes. As an organism is made up of many different tissues and organs, each with its own genetic programme, it is pertinent to ask: How relevant is tissue in terms of total transcriptome variability? Which are the genes most distinctly expressed between tissues? Does breed or sex equally affect the transcriptome across tissues?

Results: In order to gain insight on these issues, we conducted microarray expression profiling of 16 different tissues from four animals of two extreme pig breeds, Large White and Iberian, two males and two females. Mixed model analysis and neighbor - joining trees showed that tissues with similar developmental origin clustered closer than those with different embryonic origins. Often a sound biological interpretation was possible for overrepresented gene ontology categories within differentially expressed genes between groups of tissues. For instance, an excess of nervous system or muscle development genes were found among tissues of ectoderm or mesoderm origins, respectively. Tissue accounted for ~11 times more variability than sex or breed. Nevertheless, we were able to confidently identify genes with differential expression across tissues between breeds (33 genes) and between sexes (19 genes). The genes primarily affected by sex were overall different than those affected by breed or tissue. Interaction with tissue can be important for differentially expressed genes between breeds but not so much for genes whose expression differ between sexes.

Conclusion: Embryonic development leaves an enduring footprint on the transcriptome. The interaction in gene x tissue for differentially expressed genes between breeds suggests that animal breeding has targeted differentially each tissue's transcriptome.

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Figures

Figure 1

Figure 1

NJ (left) and UPGMA trees (right) using the 1 - _r_2 distance. Each sample is named using the tissue acronym (four letters, Table 1), breed (LW or IB) and sex (M, male or F, female); LW males are indicated by open squares; LW females, by open circles; IB males, by black squares and IB females, by black circles.

Figure 2

Figure 2

NJ tree of tissues using the 1-rT2 Distance. The four groups in Table 1 are indicated by symbols: brain (open squares), endocrine (grey squares), structural (grey triangles) and metabolic (black circles).

Figure 3

Figure 3

Differential GO categories across embryo layers. Percentage of the most frequent GO categories within extreme genes for each embryonic layer (A, ectoderm; B, mesoderm; C, endoderm; D, all genes in A, B and C). The number in each category is the false discovery rate (FDR) that the category is over represented with respect to the GO frequency across all genes in the microarray. The FDR is shown only if < 0.20.

Figure 4

Figure 4

Relation between _z_-scores. Plot of _z_-scores of breeds and sexes (top), and between the breed _z_-scores and the standard deviation within probes of Probe × Tissue solutions (bottom).

Figure 5

Figure 5

Sample clustering using differentially expressed genes. Genes differentially expressed between sexes (top) and breeds (bottom).

Figure 6

Figure 6

Proportion of functional annotation categories. Percentage of the most frequent GO categories within the most significant differentially expressed genes between sexes (Table 3) and between breeds (Table 4). The number in each category is the false discovery rate (FDR) that the category is over represented with respect to the GO frequency across all genes in the microarray. The FDR is shown only if < 0.20.

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