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Title: Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions}
Article:
Abstract:
Essentially all biological processes depend on protein–protein interactions( PPIs). Timing of such interactions is crucial for regulatory function. Although circadian( ∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping( RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes( signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.
Introduction:
Circadian clocks are endogenous oscillators conserved in nearly all living organisms that drive ∼24 hour cycles in physiology and behavior. In mammals, the circadian oscillator is composed of interconnected transcriptional translational negative and positive feedback-loops which generate circadian rhythms at the molecular level. Within this gene-regulatory network, a precise timing of gene expression, protein–protein interactions( PPIs) as well as posttranscriptional and posttranslational modifications is essential for sustaining circadian rhythms with normal dynamics[1]–[3]. The interaction between the transcription factors CLOCK and BMAL1, which has been discovered in a yeast-two-hybrid( Y2H) screen[4], is crucial for the activation of the Period( Per1, Per2, Per3) and Cryptochrome( Cry1, Cry2) genes. PER and CRY proteins form large complexes that inhibit their own transcription by binding directly to the CLOCK/BMAL1 complex during the late night[5]. Circadian rhythms in gene expression are pervasive – 2–10% of the transcriptome in a given tissue is under circadian control[6],[7]. Consequently, also a large fraction of the proteome is thought to be regulated in a time-of-day dependent manner, although systems-wide studies of circadian protein abundance rhythms are still rare( however, see[8]). Cellular functions are increasingly recognized to be regulated by protein complexes or ‘modules’[9], thus PPIs and their timing are predicted to be crucial. In most cases, in which PPIs exert a regulatory function, such interactions are transient and occur only under specific conditions, e. g. as a response to a signal, after binding of a co-factor or when the expression of one or both partners is induced in response to a changing cellular condition. Circadian clock regulation of cellular functions via PPIs can be accomplished by restricting important interactions to specific times of the day. In the circadian oscillator, many of the known PPIs also happen predominantly at specific times of the day, e. g. PER/CRY complexes bind to CLOCK/BMAL1 in the late night to inhibit transactivation[5]. Here, the temporal binding profile correlates with the abundance profiles of PER and CRY proteins. While these examples demonstrate the fundamental importance of precisely timed PPIs for the circadian clockwork, we are still far from a comprehensive view of the PPI network among circadian oscillator proteins and their dynamics. Furthermore, the extent of a regulation of circadian output processes via time-of-day dependent PPIs is largely unknown. To elucidate unknown regulatory mechanisms within the circadian clockwork we have systematically mapped PPIs among 46 circadian components using high-throughput Y2H interaction experiments. We have identified 109 so far uncharacterized interactions and have successfully validated a sub-fraction via co-immunoprecipitation experiments in human cells. Among the novel PPIs we have identified modulators of CLOCK/BMLA1 function indicating a role for protein phosphatase 1( PPP1) in the dynamic regulation of BMAL1 abundance. Furthermore, to generate a more comprehensive circadian PPI network we have enriched and extended our experimental network with additional validated interactions and interaction partners from literature, some of which seem to be essential for normal circadian dynamics. The integration of circadian mRNA expression profiles from mouse liver allowed us to predict the interaction dynamics within our network in hepatocytes. Using systematic genetic perturbation studies( RNAi and overexpression in oscillating cells) we propose a crucial role of dynamic regulation( via rhythmic PPIs) for the molecular clockwork. Furthermore, we have extracted a dynamic modular organization as a pervasive circadian network feature possibly contributing to time-of-day dependent control of many cellular processes. Systems analysis on a global scale regarding circadian regulation of biological processes via rhythmic PPIs suggests a time-of-day dependent organization of the interactome. Altogether our data should provide a valuable resource of circadian PPIs within hepatocytes that are important not only for keeping the pace of the molecular clockwork but likely also for the control of cellular physiology.
Discussion:
Protein–protein interactions among circadian clock proteins are often time-of-day dependent, which is crucial for the function of the molecular circadian oscillator. While the recent years have witnessed the identification of an increasing number of clock proteins or modulators[15],[16],[21],[33],[34] a comprehensive analysis of PPIs within the circadian clockwork- in particular with respect to the timing of the PPIs- is still missing. Here, we identified 109 so far uncharacterized interactions within the circadian clockwork in yeast and have successfully validated a sub-fraction in mammalian cells. While our matrix screen design allowed us to perform independent replica experiments thereby reducing the risk of false positives and false negatives, it is clear that due to the obvious limitations of the Y2H system[35] our network is likely still far away from saturation. For example, interactions that depend on posttranslational modifications or on more than two proteins are difficult to detect in Y2H assays. Nevertheless, our screen showed a rather high sensitivity( ∼40% recovery of previously reported PPIs) compared to other Y2H reports or other PPI interaction methods[11]. In addition, we estimate to have only a low false-positive rate, since we could validate 86% of all CLOCK and BMAL1 interactions in mammalian cells. Interestingly, many of the new interactions occurred between core clock components and regulatory components such as kinases e. g., CSNK2β, phosphatases( e. g., PPP2, PPP1), and F-box proteins( e. g. FBXW11). Hence, our data should be a valuable resource for studying molecular events within the circadian system with so far uncharacterized posttranslational mechanisms being especially interesting. Whereas phosphorylation of clock proteins are increasingly recognized as crucial for circadian dynamics, de-phosphorylation events have not been studied as extensively[36]. Therefore, we characterized the newly discovered interaction between PPP1Cα and the CLOCK/BMAL1 heterodimer. Indeed, we could validate our in silico prediction of the daytime-dependence of this PPI, which negatively regulates BMAL1 abundance( see Figure 7 and Figure S7), whereas others propose PER proteins as substrates of PPP1[37],[38]. Further work is needed to identify the respective regulatory subunits that may mediate substrate specificity. Our circadian PPI network is very densely connected( Figure 2) with a high clustering. How can such a network function? We analyzed both the predicted temporal organization, which separate PPIs in time as well as modular organization, which organize the network in functional complexes. To investigate temporal organization, we have integrated circadian expression profiles from mouse liver for the interacting pairs of proteins assuming that co-expression on transcript level can represent individual protein abundance probably as one limiting factor for physical interaction. De Lichtenberg et al. have pioneered the analysis of dynamic protein–protein interactions with a specific focus on cell-cycle stages in yeast also integrating transcription data[39] and Atwood et al. predicted the interaction time of circadian co- and antiphasic expressed proteins[40]. However, our analysis is not restricted to a specific process or specific circadian phases, but provides a systems-wide view of circadian PPI dynamics. Our transcript-based analysis led to the construction of a dynamic circadian( albeit only liver-specific) PPI network, in which PPIs are formed at all circadian phases( see Figure 4B). Obviously, our analysis harbors several limitations, since PPIs in vivo depend on a variety of factors such as spatial restrains, restriction to specific tissues, relative protein abundance, mRNA processing, stoichiometry and interaction kinetics, complex formation and posttranslational modifications. All these parameters are not represented by the corresponding mRNA profiles of interaction partners. However, our assumption that indeed dynamic binding events can be approximated by such an approach is supported by( i) our finding that co-expression of transcripts at similar circadian phases more often occurs among interacting proteins( see Figure 4B and Figure S6B),( ii) known interaction dynamics between components of the circadian system can be reproduced( see Figure 4B), e. g. the circadian phase-specific CLOCK/CRY1 interaction[5], and( iii) the in silico predicted time-of-day dependent interaction between PPP1Cα and CLOCK/BMAL1 could be validated with endogenous liver components. Nevertheless, it should be noted, that on a systems-wide scale it is still largely unknown, whether and to which extent genes with rhythmically expressed transcripts also display circadian protein levels. While recent comparisons between transcript levels and protein levels have shown a rather good correlation[41],[42], our circadian PPI network should still be considered as a prediction. Dynamic ‘hubs’( proteins predicted in many rhythmic PPIs) seem to be especially important for circadian rhythms( see Figure 5C) as revealed by our genetic perturbation analyses. Thus, apparently not the absolute number of interactions is crucial for the importance of a clock protein but the degree of dynamic PPIs. This may be not too surprising, since precisely timed interactions between activators and their repressors is the fundamental principle of the circadian negative feedback mechanism. Interestingly, this principle may be translated to a global scale: we find that proteins with a rhythmic transcript have significantly more interaction partners than non-rhythmic proteins( p<10−10, Wilcoxon Rank test). In addition, proteins that qualify as regulatory components( as defined by their GO category ‘regulation of biological process’) have significantly more interaction partners than non-regulatory proteins( p<10−15, Wilcoxon Rank test; see also Text S1). Together, this suggests that rhythmic control of PPIs is an essential feature of biological networks. While such analyses are only of correlative nature, it would be interesting in future studies to analyze directly whether a particular PPI or the rhythmicity of a particular PPI is required for normal rhythms. To this end, however, novel( perhaps pharmacological) tools are needed to specifically disrupt the PPI without interfering with the abundance or other PPIs the component might execute. In the last decade transcriptome analysis were successfully used to study circadian dynamics on a systems-wide level[6],[7] with mRNA rhythms serving as indicators for output control. Corresponding comprehensive studies on the level of the proteome are still largely missing. To get novel insights into the time-of-day dependent regulation of cellular processes we propose a new strategy to predict circadian regulation at the level of protein complexes rather than looking at mRNA profiles of individual components. Based on this dynamic interactome we have constructed a ‘process network’ with many processes( represented by corresponding GO terms) strongly connected by predicted dynamic PPIs( see Figure 6D and Figure S6C). While this concept has obvious limitations( ambiguous GO assignments, predictive nature of rhythmic PPIs, etc.) it allows a first, systems-wide glance on how cellular processes might be regulated in a time-of-day specific manner beyond circadian transcription. Future studies are needed to investigate to what extent and on what mechanistic bases rhythmic PPIs contribute to the dynamic modulation of cellular processes. Overall, we propose a global view on the circadian control of protein–protein interactions important not only for the circadian oscillator but also for the temporal orchestration of many essential cellular processes.
Circadian clocks are endogenous oscillators that drive daily rhythms in physiology , metabolism , and behavior . In mammals , circadian rhythms are generated within nearly every cell; and , although dysfunction of circadian clocks is associated with various diseases ( including diabetes and cancer ) , the molecular mechanisms linking the clock machinery with output pathways are little understood . Since essentially all biological processes depend on protein–protein interactions , we investigated here on a systems-wide level how time-of-day-specific protein–protein interactions contribute to the temporal organization of cellular physiology . We constructed a circadian interactome using experimentally generated protein–protein interaction data and made this network dynamic by the incorporation of time-of-day-dependent expression data . Interestingly , systematic genetic network perturbation ( RNAi and overexpression ) suggests a crucial role for circadian components involved in dynamic interactions . Systems analysis of a global network revealed that interacting proteins are in the liver significantly more expressed at similar daytimes likely to restrict regulatory interactions to specific circadian phases within cells . Overall , circadian protein–protein interactions are predicted to dynamically connect important cellular processes ( signal transduction , cell cycle , etc . ) using—very often—protein modules with components co-expressed in time , shedding new light on the daily organization of cellular physiology .