Trends in Genetics
Volume 24, Issue 7, July 2008, Pages 319-323
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Genome Analysis
Comparison of transcription regulatory interactions inferred from high-throughput methods: what do they reveal?

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We compared the transcription regulatory interactions inferred from three high-throughput methods. Because these methods use different principles, they have few interactions in common, suggesting they capture distinct facets of the transcription regulatory program. We show that these methods uncover disparate biological phenomena: long-range interactions between telomeres and transcription factors, downstream effects of interference with ribosome biogenesis and a protein-aggregation response. Through a detailed analysis of the latter, we predict components of the system responding to protein-aggregation stress.

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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