Communication between levels of transcriptional control improves robustness and adaptivity

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Abstract
Regulation of eukaryotic gene expression depends on groups of related proteins acting at the levels of chromatin organization, transcriptional initiation, RNA processing, and nuclear transport. However, a unified understanding of how these different levels of transcriptional control interact has been lacking. Here, we combine genome‐wide protein–DNA binding data from multiple sources to infer the connections between functional groups of regulators in Saccharomyces cerevisiae . Our resulting transcriptional network uncovers novel biological relationships; supporting experiments confirm new associations between actively transcribed genes and Sir2 and Esc1, two proteins normally linked to silencing chromatin. Analysis of the regulatory network also reveals an elegant architecture for transcriptional control. Using communication theory, we show that most protein regulators prefer to form modules within their functional class, whereas essential proteins maintain the sparse connections between different classes. Moreover, we provide evidence that communication between different regulatory groups improves the robustness and adaptivity of the cell. ### Synopsis The nucleus is the distinguishing feature of the eukaryotic cell. It provides added mechanisms for regulating gene expression at the levels of chromatin organization, RNA processing, and selective export via the nuclear pore complex. Groups of proteins that mediate these processes have been extensively characterized. We propose that these functional groups of proteins exhibit extensive connectivity within and between groups in order to establish and maintain the transcriptional and nuclear architecture. These groups include transcription factors (TFs), RNA processing and nuclear proteins (RPs), nuclear transport (import/export) proteins (NTs), nucleosome remodelers (NRs), histone modification (e.g. acetylation) states (HSs), and histone modifying proteins (HMs). Chromatin‐immunoprecipitation experiments in combination with microarrays (termed ChIP‐chip) have mapped the genomic occupancy of the aforementioned protein classes in living cells. Genome‐wide identification of binding sites has allowed for the inference of which genes are regulated by such factors. For example, various TFs, HMs, and NRs have been shown to regulate specific gene expression programs ([Lee et al , 2002][1]; [Ng et al , 2002][2]; [Robyr et al , 2002][3]; [Bar‐Joseph et al , 2003][4]; [Robert et al , 2004][5]; [Gelbart et al , 2005][6]). However, a unified model that incorporates all these different levels of transcriptional control remains undefined. Ideally, integrating ChIP‐chip data from different labs can predict novel connections between individual regulators while providing a systems‐level description of the greater transcriptional architecture. However, achieving such a model is hindered by the variability in microarray technologies and statistical analyses used by different labs. Here, we combined and normalized ChIP‐chip data for 317 regulators to gain an integrative view of the genome‐wide interplay between different regulatory groups in Saccharomyces cerevisiae . As labs used disparate microarray technologies, we integrated the heterogeneous data sets using an assignment algorithm that maps each ChIP‐chip measurement to its pertinent annotated gene. In addition, we developed a general method for standardizing the level of binding to P ‐values in order to normalize the data. With the normalized data, we inferred biological relationships between any two regulators based on similar co‐occupancy in the genome. Using communication theory, we identified all significant co‐occupancy relationships between different protein groups and built a transcriptional network ([Figure 2][7]). In the process, we introduced mutual information, filtered correlation, and semi‐supervised clustering approaches for analyzing genome‐wide binding data. Our resulting transcriptional network identified over 340 known biological relationships, including associations within and between protein complexes studied in different labs ([Figure 2][7]). Moreover, previously detected protein–protein interactions also confirmed a significant portion of our predictions. Our integrative approach also uncovered novel biological phenomena, including unexpected connections between actively transcribed genes and silencing proteins Sir2 and Esc1 ([Figure 2B][7]). We validated these associations using ChIP‐chip and quantitative PCR experiments. Sir2 and Esc1 are also known to localize to the nuclear periphery, suggesting a coupling between silencing and nuclear transport factors. The experiments demonstrate that our network predictions represent an in silico screen for discovering new biological processes. We also analyzed the topology of the network to gain an integrative, systems‐level description of the eukaryotic transcriptional architecture. We calculated several standard and new network statistics that describe the connectivity of each protein group. Our analysis formally shows that NTs, RPs, and NRs act as modular units that mediate general functions for large numbers of transcripts, whereas TFs and HMs are the specialists that provide gene target specificity. We found that sequential in silico removal of network regulators, analogous to in vivo biological deletions, caused the connectivity between TFs to disintegrate more rapidly in the TF subnetwork than in the overall network ([Figure 5B and C][8]). A disconnected regulator cannot exchange information with the rest of the network, which may lead to a cellular malfunction; hence, interconnectedness between different regulatory groups made the overall network more robust to sequential deletions. Further, we found that proteins preferred to form modular subunits within their own class and communicate with other regulatory groups...