Trends in Genetics
UpdateGenome AnalysisComparison of transcription regulatory interactions inferred from high-throughput methods: what do they reveal?
Section snippets
Reconstruction of transcriptional regulatory networks
Deciphering the complete transcriptional regulatory program of organisms is an important goal in molecular biology. Identification of the spatial and temporal regulatory interactions between transcription factors (TFs) and their target genes is an important step toward this goal (Box 1; Figure 1a). For this purpose, different high-throughput methods (see Figure S1), are currently used to infer transcription regulatory interactions in various organisms. Although these methods aim to identify
Comparison of the local and global structure of the inferred networks
The three distinct TRNs have several interesting similarities and differences in terms of their global and local structure. At the global level, TFs in the TRNCC and TRNGRD have similar distributions in terms of the number of target genes regulated by a given TF (i.e. out-degree distribution), a trend best approximated by a power-law decay [2] (Figure 1a). This implies the presence of global regulators or hubs (traditionally defined as the top 20% of TFs with the greatest number of target
Interference with translation, the telomere effect and a response to protein aggregation influence the different TRNs
We examined the TF hubs in TRNGRD and found that ∼40% of the regulatory interactions in this network were caused by the top four of the five major hubs (i.e. Gcr1p, Cst6p, Sfp1p and Mcm1p). None of these four was identified as a hub in TRNCC. These hubs, with the exception of Mcm1p, have regulatory interactions with numerous target genes (∼100) encoding ribosomal components (Figure S5a; Table S7). Furthermore, Sfp1p is a well-characterized major regulator of genes involved in ribosomal
Concluding remarks
We identified additional effects captured by the high-throughput methods, highlighting for the need of post facto analysis to discriminate functionally relevant regulatory interactions from such effects. The major secondary effects in the three networks, the telomere effect (in TRNCC), the ribosomal gene effect (in TRNGRD) and the role of the protein misfolding and/or aggregation response (in TRNGROE), provide leads, some of which were previously unsuspected, to understand disparate biological
Acknowledgements
S.B., L.M.I., and L.A. are funded by the Intramural research program of National Institutes of Health, USA. M.M.B. is funded by the Medical Research Council UK, Darwin College and Schlumberger. We thank Arthur Wuster, colleagues at the Laboratory of Molecular Biology, the editor and the anonymous referees for helpful feedback on previous versions of this manuscript.
Glossary
- TRGGROE
- The transcriptional network reconstructed from analysis of gene expression on overexpression of the relevant transcription factors (TFs). Nodes represent TFs or target genes (TGs). A TF is linked to a target gene if it is differentially expressed on overexpression of the TF.
- TRNCC
- The transcriptional network reconstructed from large-scale chromatin immunoprecipitation-chip (ChIP-chip) experiments. Nodes represent TFs or TGs and edges represent direct binding of the TF in the promoter
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