The architecture of the gene regulatory networks of different tissues

Abstract

Summary: The great variety of human cell types in morphology and function is due to the diverse gene expression profiles that are governed by the distinctive regulatory networks in different cell types. It is still a challenging task to explain how the regulatory networks achieve the diversity of different cell types. Here, we report on our studies of the design principles of the tissue regulatory system by constructing the regulatory networks of eight human tissues, which subsume the regulatory interactions between transcription factors (TFs), microRNAs (miRNAs) and non-TF target genes. The results show that there are in-/out-hubs of high in-/out-degrees in tissue networks. Some hubs (strong hubs) maintain the hub status in all the tissues where they are expressed, whereas others (weak hubs), in spite of their ubiquitous expression, are hubs only in some tissues. The network motifs are mostly feed-forward loops. Some of them having no miRNAs are the common motifs shared by all tissues, whereas the others containing miRNAs are the tissue-specific ones owned by one or several tissues, indicating that the transcriptional regulation is more conserved across tissues than the post-transcriptional regulation. In particular, a common bow-tie framework was found that underlies the motif instances and shows diverse patterns in different tissues. Such bow-tie framework reflects the utilization efficiency of the regulatory system as well as its high variability in different tissues, and could serve as the model to further understand the structural adaptation of the regulatory system to the specific requirements of different cell functions.

Contact: edgar.wingender@bioinf.med.uni-goettingen.de; jwang@nju.edu.cn

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Standard deviation of relative frequency (σ_RF) of hubs in real relative to random networks versus SI; (a) for in-hubs; (b) for out-hubs

Fig. 2.

Box plots of active ratio (i.e. the active connections among all the possible connections pertaining to them in the reference network) (a) for in-hubs; (b) for out-hubs. STG_TF/STG_miRNA/STG_NTF refers to strong TF/miRNA/non-TF hubs, POS_WK_TF/POS_WK_miRNA/POS_ WK_NTF to the positive case that a weak TF/miRNA/non-TF hub acts as a hub, and NEG_WK_TF/NEG_WK_miRNA/NEG_WK_NTF the negative case that a weak TF/miRNA/non-TF hub is not a hub anymore

Fig. 3.

Bow-tie structure of motif instances. (a) Distribution of TFs, miRNAs and non-TFs; (b) sketch of bow-tie structure. In (a), significance (P < 0.05) compared with randomized networks is indicated by one or two (significant in some or all tissues) asterisks; red asterisks: over-, green asterisks under-representation. The horizontal arrow in (b) represents the degrees between three gene sets. Its length is proportional to the value of degree. The red/green coloring indicates that the degree is significantly higher/lower than in the random cases

Fig. 4.

Three patterns of bow-tie structure. (a) Size ratio of input, core and output layer in eight tissues; (b) illustration of patterns