Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture

Authors

  • Raja Jothi,

    Corresponding author
    1. Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
    • Corresponding authors. Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, 111 TW Alexander Drive, MD A3-03, Research Triangle Park, NC 27709, USA. Tel.:+1 919 316 4557; Fax:+1 301 541 4311; E-mail: jothi@mail.nih.govMRC Laboratory of Molecular Biology, Cambridge CB20QH, UK. Tel.:+44 (0)1223 402208; Fax:+44 (0)1223 213556; E-mail: madanm@mrc-lmb.cam.ac.uk

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    • These authors contributed equally to this work
  • S Balaji,

    1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
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    • Present address: Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
    • These authors contributed equally to this work
  • Arthur Wuster,

    1. MRC Laboratory of Molecular Biology, Cambridge, UK
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  • Joshua A Grochow,

    1. Department of Computer Science, University of Chicago, Chicago, IL, USA
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  • Jörg Gsponer,

    1. MRC Laboratory of Molecular Biology, Cambridge, UK
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  • Teresa M Przytycka,

    1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
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  • L Aravind,

    1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
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  • M Madan Babu

    Corresponding author
    1. MRC Laboratory of Molecular Biology, Cambridge, UK
    • Corresponding authors. Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, 111 TW Alexander Drive, MD A3-03, Research Triangle Park, NC 27709, USA. Tel.:+1 919 316 4557; Fax:+1 301 541 4311; E-mail: jothi@mail.nih.govMRC Laboratory of Molecular Biology, Cambridge CB20QH, UK. Tel.:+44 (0)1223 402208; Fax:+44 (0)1223 213556; E-mail: madanm@mrc-lmb.cam.ac.uk

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Abstract

Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.

Synopsis

Transcription factors (TFs), one of the key determinants of gene expression, regulate mRNA synthesis depending on intrinsic and extrinsic signals. Although the set of all regulatory interactions between TFs and their target genes (TGs) in a cell can be conveniently represented as nodes and edges in a network, it is important to note that each node in the network represents several entities (gene, mRNA, and protein) and events (transcription, translation, degradation, etc) that are compressed in both space and time (see Figure 1). Consequently, the dynamic nature of these events (synthesis and degradation of mRNA and protein molecules) and entities (steady state levels of mRNA and protein molecules) are expected to affect the regulatory interactions in the network. While we have a good understanding of the topology of regulatory networks, the dynamics of nodes (TFs and TGs) in these networks and its role in systems behavior remain largely unexplored. In this regard, several fundamental questions remain unanswered: for example, do transcription factors in the regulatory network have distinct dynamic properties (e.g., abundance, half-life, etc) characterizing their role in a regulatory cascade? More generally, does the position of a TF in the network structure relate to its dynamics? Although the richness of this detail is lost in the network representation, such questions can be addressed by integrating diverse genomic datasets encapsulating the dynamics of transcription and translation.

In this study, we investigated the dynamics of the yeast DNA-binding transcription factors by integrating diverse genome-scale datasets with the inherent hierarchical structure in the yeast transcription regulatory network. We used a novel method called vertex sort to classify DNA-binding TFs in the yeast regulatory network into three mutually exclusive hierarchical layers that we name as top, core, and bottom. Overlaying large-scale genomic datasets that measure transcript abundance, transcript half-life, translation efficiency, protein abundance, protein half-lives, and protein and transcription noise on the inferred hierarchical structure revealed that the dynamics of TFs in the regulatory network is not random. Rather, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. This indicates that the network topology and the nodal (TF) dynamics at the mRNA and the protein level are tightly linked. In particular, at the protein level, the top-layer TFs are relatively more abundant, long-lived, and show higher cell-to-cell variability compared to the core- and bottom-layer TFs.

Our observation that top-layer TFs display a relatively higher variability in protein abundance between individuals in a clonal population of cells suggests that such a behavior may confer a selective advantage to individuals as this permits at least some members in a population to respond effectively to changing conditions by triggering the relevant transcriptional cascade (Acar et al, 2008; Blake et al, 2006; Heath et al, 2008; Kaern et al, 2005; Lopez-Maury et al, 2008; McAdams and Arkin, 1999; Raj and van Oudenaarden, 2008; Ramsey et al, 2006; Rao et al, 2002; Raser and O'Shea, 2005; Samoilov et al, 2006; Shahrezaei and Swain, 2008a, 2008b; Spudich and Koshland, 1976). For instance, ABF1, which is a multifunctional transcription factor present in the in the top layer, is an abundant protein whose levels are noisy in a clonal population of cells. However, the activity of ABF1 depends on the availability of its co-activators (e.g., CDC6) and on its phosphorylation state, which is known to be regulated by several kinases (e.g., casein kinase 2) or phosphatases (Silve et al, 1992; Upton et al, 1995). The relatively higher noise in the abundance of ABF1 might ensure that at least some members in a population would respond rapidly during changing environments (i.e., when co-activators or kinases are activated in response to the altered external stimulus). We propose that high variability in the expression of key TFs, whose targets genes might contribute to phenotypic variation, might be a general strategy to facilitate adaptation to diverse environments (see Figure 6).

Further, our observation that the protein levels of the core-layer TFs and bottom-layer TFs are inherently tightly regulated suggests that such a tight regulation, along with other regulatory mechanisms such as post-translational modifications or physical interactions with other proteins, might act as a filter to minimize noise propagation down the hierarchy due to any ‘inadvertently’ triggered response. In other words, tight regulation of the core- and bottom-layer TFs via rapid degradation (i.e., shorter protein half-life) would ensure that such TFs are present only in low levels under normal conditions. Their presence in relatively lower levels might facilitate minimization of noise propagation because sufficient levels of TFs may not be present to trigger an appropriate response when transient signals ‘inadvertently’ activate them. Therefore, we suggest that the tight regulation of protein levels of the core- and bottom-level TFs might ensure fidelity and robustness in a regulatory cascade.

Taken together, our findings suggest that (i) the higher variability in abundance of top-layer TFs compared to core- and bottom-layer TFs in distinct members of a clonal cell population might permit differential utilization of the same underlying network (see Figure 6) and (ii) the tight regulation of core- and bottom-layer TFs might contribute to fidelity in gene expression. Thus, the interplay between the dynamics of individual nodes and the topology of the regulatory network would make the underlying network robust and permit at least some members in a population to effectively adapt to (or survive in) changing environments (see Figure 6).

Our findings have implications in synthetic biology experiments aimed at engineering gene regulatory circuits (Becskei and Serrano, 2000; Elowitz and Leibler, 2000; Gardner et al, 2000). In particular, the dynamics of TFs in terms of their abundance, half-life, and noise cannot be ignored as modulating these attributes could affect the outcome of a regulatory cascade. The proposed conceptual framework (see Figure 6) from our findings serves as a general model and also has important implications for a number of apparently different but related phenomena such as (i) bacterial persistence or adaptive resistance, (ii) differential cell-fate outcome in response to the same uniform stimulus, (iii) phenotypic variability in fluctuating environments, and (iv) cellular differentiation and development.

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