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

Raja Jothi^1,5, S Balaji^2,5,a, Arthur Wuster³, Joshua A Grochow⁴, Jörg Gsponer³, Teresa M Przytycka², L Aravind² & M Madan Babu³

^aPresent 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

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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.

Figure 1

Network representation of a transcriptional regulatory cascade. Transcription factors (TFs), denoted as nodes in a network (red and green circles), represent several entities (gene, mRNA, and protein) and events (transcription, translation, degradation, etc) that are compressed in both space and time. Although a series of regulatory events can be conveniently represented as a node in the network, the dynamics of the entities and the biological processes that make up the node are not captured.

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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).

Figure 6

A schematic model describing the conceptual framework of differential utilization of the same underlying regulatory network by distinct members of a genetically identical cell population. (A) A toy regulatory network showing two regulatory pathways, which will be used to respond to two specific extracellular stimuli. The red, green, and blue nodes in the network represent transcription factors (TFs), symbolically representing the inferred top-, core-, and bottom-layer TFs in the hierarchical network, respectively. (B) Members of a clonal cell population responding to stimulus 1 (top panel). The variability in expression of top-layer TFs (shown as nodes in varying shades of red; middle panel) permits differential sampling of the same underlying network by distinct members of a genetically identical population of cells. TFs colored in gray are not expressed at necessary levels, and are shown as inactive nodes. Edges originating from inactive TFs are inactive (shown in gray). A noisy master-regulator TF at the top of the hierarchy would mean that only a subset of a population, in which this TF is expressed at necessary levels, will have this TF in active form. An inactive TF at the top of a hierarchical regulatory cascade will result in the non-expression per inactivation of all downstream TFs and TGs dependent on this TF. Members of a clonal population whose regulatory pathway for a specific extracellular stimulus is active will initiate an effective response when that stimulus is encountered. And, those members in whom this regulatory pathway is inactive will be unable to mount an effective response. Though all members in the population are sampling the part of the network necessary to respond to stimulus 1, only a few members (shown as purple and orange cells; bottom panel) are sampling (or poised to sample) the part of the network necessary to respond to stimulus 2. (C) A change in stimulus (from stimulus 1 to 2) causes only those cells that have an active regulatory response pathway for stimulus 2 to effectively respond and survive, whereas the others may mount a late response or will not survive. Alternatively, low expression of top-layer TFs might facilitate cell survival if the pathway regulated by such TFs leads to cell death (e.g., apoptosis). Thus, the presence of noisy TFs at the top of the hierarchical regulatory cascade might confer a selective advantage as this permits at least some members in a clonal population to respond to changing conditions.

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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.