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1.  Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes 
A combinatorial depletion strategy is combined with biochemistry, quantitative proteomics and computational approaches to elucidate the structure of the SAGA/ADA complexes. The analysis reveals five connected functional modules capable of independent assembly.
A combinatorial approach of gene depletions with multiple bait proteins coupled with biochemical, proteomic and computational approaches can experimentally determine modules of stable multi-protein complexes.SAGA is a 19-subunit complex consisting of five connected modules with Spt20 being particularly important for the assembly of the intact complex.One of the modules, the HAT/Core module, is also shared with the distinct six-subunit complex ADA.Architectural models of large multi-protein complexes can be assembled using our approach, which is an alternative method to generate novel insight into the organization and architecture of multi-protein complexes.
Determining the architectures of protein complexes improves our understanding of protein cellular functions. In order to efficiently characterize the subunits of protein complexes assembled in vivo, affinity purification followed by proteomics mass spectrometry (APMS) strategies have been devised. Partial or whole protein complexes are first biochemically isolated using tagged components of the complex, followed by an identification of all co-purified proteins using mass spectrometry. However, those approaches are insufficient to provide information about the spatial arrangement and the interrelationship of the proteins of the respective complex.
In this study, we developed and applied a novel method utilizing biochemistry, quantitative proteomics and computational approaches in order to characterize the organization of proteins in a complex. The key of our method is the systematic purification of several tagged components of the protein complex in multiple genetic deletion strains, which serve to compromise the integrity of the complex. Using a series of computational methods, these raw quantitative values are next interpreted in order to determine the modular organization of the complex as well as the interrelationships between its subunits, which in turn can be used to predict a macromolecular model of the complex.
We tested this approach to obtain novel insights into the architecture of multi-protein complexes on the Saccharomyces cerevisiae Spt–Ada–Gcn5 histone acetyltransferase (HAT) (SAGA) and ADA complexes, which are conserved complexes involved in chromatin remodeling (Koutelou et al, 2010). Regular quantitative APMS strategies in wild-type backgrounds were not sufficient to separate tight protein complexes like SAGA/ADA into its distinct modules. However, after perturbing the system using genetic deletions of several subunits located in different topological parts of SAGA, hierarchical cluster analysis performed on 34 purifications (generated using 10 different TAP-tagged baits) resulted in a dissociation of the Gcn5 HAT complexes into five modules: (1) the SA_TAF module, (2) the SA_SPT module, (3) the DUB module, (4) the HAT/Core module and (5) the ADA module (Figure 2A and B).
The approach of purifying a protein in a deletion strain furthermore provides valuable information about the influence of the deleted subunit on the association and interdependency of the bait and the remaining preys. In order to quantify these associations, we calculated a probability between every prey and bait in the deletion strain purifications based on Bayes' theorem (Sardiu et al, 2008). In conjunction with preexisting interaction data obtained from yeast two-hybrid and genetic complementation assays, we finally used these probabilities to predict a low-resolution model for the architecture of the SAGA and ADA complexes (Figure 4).
This novel approach revealed that the SAGA/ADA complexes are composed of five distinct functional modules, of which two were not previously described (SA_SPT and SA_TAF). These modules, which are responsible for different functions of the SAGA complex, are capable of assembling independently from the remaining modules of the complex. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 HAT complexes. Compared with other structural studies, which mapped 9 of the 19 known SAGA subunits using single EM reconstruction (Wu et al, 2004) or resolved the structure of the 4 subunits of the DUB module using X-ray crystallography (Kohler et al, 2010; Samara et al, 2010), our approach is not limited to a maximum number of complex subunits. Consequently, we were able to construct a macromolecular model consisting of all 21 SAGA/ADA subunits, which bridges the gap between the previous limited EM analysis and focused X-ray crystallography analysis.
Despite the availability of several large-scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild-type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well-studied complexes, Spt–Ada–Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high-resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants.
PMCID: PMC3159981  PMID: 21734642
ADA; architecture; protein interaction network; quantitative proteomics; SAGA
2.  Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana 
A protein interactome focused towards cell proliferation was mapped comprising 857 interactions among 393 proteins, leading to many new insights in plant cell cycle regulation.A comprehensive view on heterodimeric cyclin-dependent kinase (CDK)/cyclin complexes in plants is obtained, in relation with their regulators.Over 100 new candidate cell cycle proteins were predicted.
The basic underlying mechanisms that govern the cell cycle are conserved among all eukaryotes. Peculiar for plants, however, is that their genome contains a collection of cell cycle regulatory genes that is intriguingly large (Vandepoele et al, 2002; Menges et al, 2005) compared to other eukaryotes. Arabidopsis thaliana (Arabidopsis) encodes 71 genes in five regulatory classes versus only 15 in yeast and 23 in human.
Despite the discovery of numerous cell cycle genes, little is known about the protein complex machinery that steers plant cell division. Therefore, we applied tandem affinity purification (TAP) approach coupled with mass spectrometry (MS) on Arabidopsis cell suspension cultures to isolate and analyze protein complexes involved in the cell cycle. This approach allowed us to successfully map a first draft of the basic cell cycle complex machinery of Arabidopsis, providing many new insights into plant cell division.
To map the interactome, we relied on a streamlined platform comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, TAP adapted for plant cells, and matrix-assisted laser desorption ionization (MALDI) tandem-MS for the identification of purified proteins (Van Leene et al, 2007, 2008Van Leene et al, 2007, 2008). Complexes for 102 cell cycle proteins were analyzed using this approach, leading to a non-redundant data set of 857 interactions among 393 proteins (Figure 1A). Two subspaces were identified in this data set, domain I1, containing interactions confirmed in at least two independent experimental repeats or in the reciprocal purification experiment, and domain I2 consisting of uniquely observed interactions.
Several observations underlined the quality of both domains. All tested reverse purifications found the original interaction, and 150 known or predicted interactions were confirmed, meaning that also a huge stack of new interactions was revealed. An in-depth computational analysis revealed enrichment for many cell cycle-related features among the proteins of the network (Figure 1B), and many protein pairs were coregulated at the transcriptional level (Figure 1C). Through integration of known cell cycle-related features, more than 100 new candidate cell cycle proteins were predicted (Figure 1D). Besides common qualities of both interactome domains, their real significance appeared through mutual differences exposing two subspaces in the cell cycle interactome: a central regulatory network of stable complexes that are repeatedly isolated and represent core regulatory units, and a peripheral network comprising transient interactions identified less frequently, which are involved in other aspects of the process, such as crosstalk between core complexes or connections with other pathways. To evaluate the biological relevance of the cell cycle interactome in plants, we validated interactions from both domains by a transient split-luciferase assay in Arabidopsis plants (Marion et al, 2008), further sustaining the hypothesis-generating power of the data set to understand plant growth.
With respect to insights into the cell cycle physiology, the interactome was subdivided according to the functional classes of the baits and core protein complexes were extracted, covering cyclin-dependent kinase (CDK)/cyclin core complexes together with their positive and negative regulation networks, DNA replication complexes, the anaphase-promoting complex, and spindle checkpoint complexes. The data imply that mitotic A- and B-type cyclins exclusively form heterodimeric complexes with the plant-specific B-type CDKs and not with CDKA;1, whereas D-type cyclins seem to associate with CDKA;1. Besides the extraction of complexes previously shown in other organisms, our data also suggested many new functional links; for example, the link coupling cell division with the regulation of transcript splicing. The association of negative regulators of CDK/cyclin complexes with transcription factors suggests that their role in reallocation is not solely targeted to CDK/cyclin complexes. New members of the Siamese-related inhibitory proteins were identified, and for the first time potential inhibitors of plant-specific mitotic B-type CDKs have been found in plants. New evidence that the E2F–DP–RBR network is not only active at G1-to-S, but also at the G2-to-M transition is provided and many complexes involved in DNA replication or repair were isolated. For the first time, a plant APC has been isolated biochemically, identifying three potential new plant-specific APC interactors, and finally, complexes involved in the spindle checkpoint were isolated mapping many new but specific interactions.
Finally, to get a general view on the complex machinery, modules of interacting cyclins and core cell cycle regulators were ranked along the cell cycle phases according to the transcript expression peak of the cyclins, showing an assorted set of CDK–cyclin complexes with high regulatory differentiation (Figure 4). Even within the same subfamily (e.g. cyclin A3, B1, B2, D3, and D4), cyclins differ not only in their functional time frame but also in the type and number of CDKs, inhibitors, and scaffolding proteins they bind, further indicating their functional diversification. According to our interaction data, at least 92 different variants of CDK–cyclin complexes are found in Arabidopsis.
In conclusion, these results reflect how several rounds of gene duplication (Sterck et al, 2007) led to the evolution of a large set of cyclin paralogs and a myriad of regulators, resulting in a significant jump in the complexity of the cell cycle machinery that could accommodate unique plant-specific features such as an indeterminate mode of postembryonic development. Through their extensive regulation and connection with a myriad of up- and downstream pathways, the core cell cycle complexes might offer the plant a flexible toolkit to fine-tune cell proliferation in response to an ever-changing environment.
Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.
PMCID: PMC2950081  PMID: 20706207
Arabidopsis thaliana; cell cycle; interactome; protein complex; protein interactions
3.  Quantification of mRNA and protein and integration with protein turnover in a bacterium 
Determination of the average cellular copy number of 400 proteins under different growth conditions and integration with protein turnover and absolute mRNA levels reveals the dynamics of protein expression in the genome-reduced bacterium Mycoplasma pneumoniae.
Our study provides a fine-grained, quantitative picture to unprecedented detail in an established model organism for systems-wide studies.Our integrative approach reveals a novel, dynamic view on the processes, interactions and regulations underlying the central dogma pathway and the composition of protein complexes.Simulations using our quantitative data on mRNA, protein and turnover show how an organism copes with stochastic noise in gene expression in vivo.Our data serve as an important resource for colleagues both within our field of research and in related disciplines.
A hallmark of Systems Biology is the integration of diverse, large quantitative data sets with the aim to gain novel insights into how biological processes work. We measured individual mRNA and protein abundances as well as protein turnover in the bacterium Mycoplasma pneumoniae. This human pathogen is an ideal model organism for organism-wide studies. It can be readily cultured under laboratory conditions and it has a very small genome with only 690 protein-coding genes. This comparably low complexity allows for the exhaustive analysis of major cellular biomolecules avoiding constrains introduced by limitations of available analysis techniques.
Using a recently developed mass spectrometry-based approach, we determined the average cellular copy number for over 400 individual proteins under different growth and stress conditions. The 20 most abundant proteins, including Elongation factor Tu, cellular chaperones, and proteins involved in metabolizing glucose, the major energy source of M. pneumoniae account for nearly 44% of the total cellular protein mass. We observed abundance changes of many expected and several unexpected proteins in response to cellular stress, such as heat shock, DNA damage and osmotic stress, as well as along batch culture growth over 4 days.
Integration of the protein abundance data with quantitative mRNA measurements revealed a modest correlation between these two classes of biomolecules. However, for several classical stress-induced proteins, we observed a correlated induction of mRNA and protein in response to heat shock. A focused analysis of mRNA–protein abundance dynamics during batch culture growth suggested that the regulation of gene expression is largely decoupled from protein dynamics in M. pneumoniae, indicating extensive post-transcriptional and post-translational regulation influencing the cellular mRNA–protein ratios.
To investigate the factors influencing the cellular protein abundance, we measured individual protein turnover rates by mass spectrometry using a label-chase approach involving stable isotope-labelled amino acids. The average half-life of a protein in M. pneumoniae is 23 h. Based on the measured quantitative mRNA data, the protein abundances and their half-lives, we established an ordinary differential equations model for the estimation of individual in vivo protein degradation and translation efficiency rates. We found out that translation efficiency rather than protein turnover is the dominating factor influencing protein abundance. Using our abundance and turnover data, we additionally performed stochastic simulations of gene expression. We observed that long protein half-life and low translational efficiency buffers gene expression noise propagating from low cellular mRNA levels in vivo.
We compared the abundance ratios of proteins associating into complexes in vivo with their expected functional stoichiometries. We observed that for stable protein complexes, such as the GroEL/ES chaperonin or DNA gyrase, our measured abundance ratios reflected the expected subunit stoichiometries. More dynamic protein complexes, such as the DnaK/J/GrpE chaperone system or RNA polymerase, showed several unusual subunit ratios, pointing towards transient interaction of sub-stoichiometric subunits for function. A detailed, quantitative analysis of the ribosome, the largest cellular protein complex, revealed large abundance differences of the 51 subunits. This observation indicates a multi-functionality for several, abundant ribosomal proteins.
Finally, a comparison of the determined average cellular protein abundances with a different pathogenic bacterium, Leptospira interrogans, revealed that cellular protein abundances closely reflect their respective lifestyles.
Our study represents an organism-wide, quantitative analysis of cellular protein abundances. Integrating our proteomics data with determined mRNA levels and protein turnover rates reveals insights into the dynamic interplay and regulation of mRNA and proteins, the central biomolecules of a cell.
Biological function and cellular responses to environmental perturbations are regulated by a complex interplay of DNA, RNA, proteins and metabolites inside cells. To understand these central processes in living systems at the molecular level, we integrated experimentally determined abundance data for mRNA, proteins, as well as individual protein half-lives from the genome-reduced bacterium Mycoplasma pneumoniae. We provide a fine-grained, quantitative analysis of basic intracellular processes under various external conditions. Proteome composition changes in response to cellular perturbations reveal specific stress response strategies. The regulation of gene expression is largely decoupled from protein dynamics and translation efficiency has a higher regulatory impact on protein abundance than protein turnover. Stochastic simulations using in vivo data show how low translation efficiency and long protein half-lives effectively reduce biological noise in gene expression. Protein abundances are regulated in functional units, such as complexes or pathways, and reflect cellular lifestyles. Our study provides a detailed integrative analysis of average cellular protein abundances and the dynamic interplay of mRNA and proteins, the central biomolecules of a cell.
PMCID: PMC3159969  PMID: 21772259
mRNA–protein; Mycoplasma pneumoniae; protein homeostasis; protein turnover; quantitative proteomics
4.  Quantitative analysis of regulatory flexibility under changing environmental conditions 
Day length changes with the seasons in temperate latitudes, affecting the many biological rhythms that entrain to the day/night cycle: we measure these effects on the expression of Arabidopsis clock genes, using RNA and reporter gene readouts, with a new method of phase analysis.Dusk sensitivity is proposed as a simple, natural and general mathematical measure to analyse and manipulate the changing phase of a clock output relative to the change in the day/night cycle.Dusk sensitivity shows how increasing the numbers of feedback loops in the Arabidopsis clock models allows more flexible regulation, consistent with a previously-proposed, general operating principle of biological networks.The Arabidopsis clock genes show flexibility of regulation that is characteristic of a three-loop clock model, validating aspects of the model and the operating principle, but some clock output genes show greater flexibility arising from direct light regulation.
The analysis of dynamic, non-linear regulation with the aid of mechanistic models is central to Systems Biology. This study compares the predictions of mechanistic, mathematical models of the circadian clock with molecular time-series data on rhythmic gene expression in the higher plant Arabidopsis thaliana. Analysis of the models helps us to understand (explain and predict) how the clock gene circuit balances regulation by external and endogenous factors to achieve particular behaviours. Such multi-factorial regulation is ubiquitous in, and characteristic of, living systems.
The Earth's rotation causes predictable changes in the environment, notably in the availability of sunlight for photosynthesis. Many biological processes are driven by the environmental input via sensory pathways, for example, from photoreceptors. Circadian clocks provide an alternative strategy. These endogenous, 24-h rhythms can drive biological processes that anticipate the regular environmental changes, rather than merely responding. Many rhythmic processes have both light and clock control. Indeed, the clock components themselves must balance internal timing with external inputs, because circadian clocks are reset daily through light regulation of one or more clock components. This process of entrainment is complicated by the change in day length. When the times of dawn and dusk move apart in summer, and closer together in winter, does the clock track dawn, track dusk or interpolate between them?
In plants, the clock controls leaf and petal movements, the opening and closing of stomatal pores, the discharge of floral fragrances, and many metabolic activities, especially those associated with photosynthesis. Centuries of physiological studies have shown that these rhythms can behave differently. Flowering in Ipomoea nil (Pharbitis nil, Japanese morning glory) is controlled by a rhythm that tracks the time of dusk, to give a classic example. We showed that two other rhythms associated with vegetative growth track dawn in this species (Figure 5A), so the clock system allows flexible regulation.
The relatively small number of components involved in the circadian clockwork makes it an ideal candidate for mathematical modelling. Molecular genetic studies in a variety of model eukaryotes have shown that the circadian rhythm is generated by a network of 6–20 genes. These genes form feedback loops generating a rhythm in mRNA production. A single negative feedback loop in which a gene encodes a protein that, after several hours, turns off transcription is capable of generating a circadian rhythm, in principle. A single light input can entrain the clock to ‘local time', synchronised with a light–dark cycle. However, real circadian clocks have proven to be more complicated than this, with multiple light inputs and interlocked feedback loops.
We have previously argued from mathematical analysis that multi-loop networks increase the flexibility of regulation (Rand et al, 2004) and have shown that appropriately deployed flexibility can confer functional robustness (Akman et al, 2010). Here we test whether that flexibility can be demonstrated in vivo, in the model plant, A. thaliana. The Arabidopsis clock mechanism comprises a feedback loop in which two partially redundant, myb transcription factors, LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), repress the expression of their activator, TIMING OF CAB EXPRESSION 1 (TOC1). We previously modelled this single-loop circuit and showed that it was not capable of recreating important data (Locke et al, 2005a). An extended, two-loop model was developed to match observed behaviours, incorporating a hypothetical gene Y, for which the best identified candidate was the GIGANTEA gene (GI) (Locke et al, 2005b). Two further models incorporated the TOC1 homologues PSEUDO-RESPONSE REGULATOR (PRR) 9 and PRR7 (Locke et al, 2006; Zeilinger et al, 2006). In these circuits, a morning oscillator (LHY/CCA1–PRR9/7) is coupled to an evening oscillator (Y/GI–TOC1) via the original LHY/CCA1–TOC1 loop.
These clock models, like those for all other organisms, were developed using data from simple conditions of constant light, darkness or 12-h light–12-h dark cycles. We therefore tested how the clock genes in Arabidopsis responded to light–dark cycles with different photoperiods, from 3 h light to 18 h light per 24-h cycle (Edinburgh, 56° North latitude, has 17.5 h light in midsummer). The time-series assays of mRNA and in vivo reporter gene images showed a range of peak times for different genes, depending on the photoperiod (Figure 5C). A new data analysis method, mFourfit, was introduced to measure the peak times, in the Biological Rhythms Analysis Software Suite (BRASS v3.0). None of the genes showed the dusk-tracking behaviour characteristic of the Ipomoea flowering rhythm. The one-, two- and three-loop models were analysed to understand the observed patterns. A new mathematical measure, dusk sensitivity, was introduced to measure the change in timing of a model component versus a change in the time of dusk. The one- and two-loop models tracked dawn and dusk, respectively, under all conditions. Only the three-loop model (Figure 5B) had the flexibility required to match the photoperiod-dependent changes that we found in vivo, and in particular the unexpected, V-shaped pattern in the peak time of TOC1 expression. This pattern of regulation depends on the structure and light inputs to the model's evening oscillator, so the in vivo data supported this aspect of the model. LHY and CCA1 gene expression under short photoperiods showed greater dusk sensitivity, in the interval 2–6 h before dawn, than the three-loop model predicted, so these data will help to constrain future models.
The approach described here could act as a template for experimental biologists seeking to understand biological regulation using dynamic, experimental perturbations and time-series data. Simulation of mathematical models (despite known imperfections) can provide contrasting hypotheses that guide understanding. The system's detailed behaviour is complex, so a natural and general measure such as dusk sensitivity is helpful to focus on one property of the system. We used the measure to compare models, and to predict how this property could be manipulated. To enable additional analysis of this system, we provide the time-series data and experimental metadata online.
The circadian clock controls 24-h rhythms in many biological processes, allowing appropriate timing of biological rhythms relative to dawn and dusk. Known clock circuits include multiple, interlocked feedback loops. Theory suggested that multiple loops contribute the flexibility for molecular rhythms to track multiple phases of the external cycle. Clear dawn- and dusk-tracking rhythms illustrate the flexibility of timing in Ipomoea nil. Molecular clock components in Arabidopsis thaliana showed complex, photoperiod-dependent regulation, which was analysed by comparison with three contrasting models. A simple, quantitative measure, Dusk Sensitivity, was introduced to compare the behaviour of clock models with varying loop complexity. Evening-expressed clock genes showed photoperiod-dependent dusk sensitivity, as predicted by the three-loop model, whereas the one- and two-loop models tracked dawn and dusk, respectively. Output genes for starch degradation achieved dusk-tracking expression through light regulation, rather than a dusk-tracking rhythm. Model analysis predicted which biochemical processes could be manipulated to extend dusk tracking. Our results reveal how an operating principle of biological regulators applies specifically to the plant circadian clock.
PMCID: PMC3010117  PMID: 21045818
Arabidopsis thaliana; biological clocks; dynamical systems; gene regulatory networks; mathematical models; photoperiodism
5.  Actin dynamics tune the integrated stress response by regulating eukaryotic initiation factor 2α dephosphorylation 
eLife  null;4:e04872.
Four stress-sensing kinases phosphorylate the alpha subunit of eukaryotic translation initiation factor 2 (eIF2α) to activate the integrated stress response (ISR). In animals, the ISR is antagonised by selective eIF2α phosphatases comprising a catalytic protein phosphatase 1 (PP1) subunit in complex with a PPP1R15-type regulatory subunit. An unbiased search for additional conserved components of the PPP1R15-PP1 phosphatase identified monomeric G-actin. Like PP1, G-actin associated with the functional core of PPP1R15 family members and G-actin depletion, by the marine toxin jasplakinolide, destabilised the endogenous PPP1R15A-PP1 complex. The abundance of the ternary PPP1R15-PP1-G-actin complex was responsive to global changes in the polymeric status of actin, as was its eIF2α-directed phosphatase activity, while localised G-actin depletion at sites enriched for PPP1R15 enhanced eIF2α phosphorylation and the downstream ISR. G-actin's role as a stabilizer of the PPP1R15-containing holophosphatase provides a mechanism for integrating signals regulating actin dynamics with stresses that trigger the ISR.
eLife digest
For a cell to build a protein, it must first copy the instructions contained within a gene. A complex molecular machine called a ribosome then reads these instructions and translates them into a protein. This translation process involves a number of steps. Proteins called eukaryotic translation initiation factors (or eIFs for short) coordinate the first step in the process, which is known as ‘initiation’.
The eIFs also provide the cell with ways to control how quickly it makes proteins. For example, when a cell is stressed, either by starvation or toxins, it adds a phosphate group onto part of an eIF protein, called eIF2α. This modification makes this eIF protein less able to initiate translation, and so the cell builds fewer proteins and conserves more of its resources during times of stress.
Once the stressful conditions are over, the phosphate group is removed from eIF2α by an enzyme called a phosphatase. This phosphatase contains two subunits: one that recognizes eIF2α and another that removes the phosphate group. However, experiments that attempted to recreate this phosphatase activity using just these two subunits in a test tube failed to generate a working enzyme that specifically targeted the phosphate group of eIF2α. This suggests that in cells this enzyme contains an additional unknown subunit. Now, Chambers, Dalton et al. (and Chen et al.) report the identity of a ‘missing’ third subunit as a protein known as globular-actin or G-actin.
Chambers, Dalton et al. engineered human and fruit fly cells to add ‘molecular handles’ on the two known subunits of the phosphatase enzyme. These handles could then be used to essentially pull these proteins out of the mixture of molecules within a cell and see what other proteins came along too. Both of the known subunits ‘pulled’ G-actin along with them; this suggested that it could be the missing part of the phosphatase enzyme.
Further experiments confirmed that G-actin works together with the other two subunits to specifically remove the phosphate group from eIF2α in mouse cells that had been stressed using a harmful chemical. Individual G-actin proteins can bind together to form long filaments, and signals that encourage a cell to divide or move also trigger the formation of actin filaments. This reduces the activity of the phosphatase enzyme by depriving it of a crucial component, i.e., free G-actin proteins. As such, the new mechanism described by Chambers, Dalton et al. suggests how growth and movement signals might also change a cell's sensitivity to stress. These findings may hopefully enable stressed cells to be targeted by drugs to treat disease; but future work is needed to clarify under what circumstances the integration of such signals into the stress response is beneficial to the cell.
PMCID: PMC4394351  PMID: 25774599
integrated stress response; actin; PPP1R15A; GADD34; PPP1R15B; CReP; D. melanogaster; human; mouse
6.  G-actin provides substrate-specificity to eukaryotic initiation factor 2α holophosphatases 
eLife  null;4:e04871.
Dephosphorylation of eukaryotic translation initiation factor 2a (eIF2a) restores protein synthesis at the waning of stress responses and requires a PP1 catalytic subunit and a regulatory subunit, PPP1R15A/GADD34 or PPP1R15B/CReP. Surprisingly, PPP1R15-PP1 binary complexes reconstituted in vitro lacked substrate selectivity. However, selectivity was restored by crude cell lysate or purified G-actin, which joined PPP1R15-PP1 to form a stable ternary complex. In crystal structures of the non-selective PPP1R15B-PP1G complex, the functional core of PPP1R15 made multiple surface contacts with PP1G, but at a distance from the active site, whereas in the substrate-selective ternary complex, actin contributes to one face of a platform encompassing the active site. Computational docking of the N-terminal lobe of eIF2a at this platform placed phosphorylated serine 51 near the active site. Mutagenesis of predicted surface-contacting residues enfeebled dephosphorylation, suggesting that avidity for the substrate plays an important role in imparting specificity on the PPP1R15B-PP1G-actin ternary complex.
eLife digest
For a cell to build a protein, it must first copy the instructions contained within a gene. A complex molecular machine called a ribosome then reads these instructions and translates them into a protein. This translation process involves a number of steps. Proteins called eukaryotic translation initiation factors (or eIFs for short) coordinate the first step in the process, which is known as ‘initiation’.
The eIFs also provide the cell with ways to control how quickly it makes proteins. For example, when a cell is stressed, either by starvation or toxins, it adds a phosphate group onto part of an eIF protein, called eIF2α. This modification makes this eIF protein less able to initiate translation, and so the cell builds fewer proteins and conserves more of its resources during times of stress.
Once the stressful conditions are over, the phosphate group is removed from eIF2α by an enzyme called a phosphatase. This phosphatase contains two subunits: one that recognizes eIF2α and another that removes the phosphate group. However, experiments that attempted to recreate this phosphatase activity using just these two subunits in a test tube failed to generate a working enzyme that specifically targeted the phosphate group of eIF2α. This suggests that in cells this enzyme contains an additional unknown subunit. Now, Chen et al. (and Chambers, Dalton et al.) report the identity of a ‘missing’ third subunit as a protein known as globular-actin or G-actin.
First, Chen et al. looked at the three-dimensional structure of a two-subunit complex formed from the previously known subunits of the phosphatase enzyme, and confirmed that it could remove phosphate groups from a range of proteins and not just eIF2α. However, when a mixture of other proteins taken from mouse cells was added to this two-subunit complex, the complex could specifically remove the phosphate group on the eIF2α protein.
Further experiments revealed that G-actin was the protein in the mixture that, when added to the two-subunit complex, made it specifically target the eIF2α protein. Chen et al. then used a combination of biochemical and structural biology techniques to investigate the phosphatase activity of the three-subunit complex. These findings suggest a plausible molecular mechanism by which the three-subunit complex becomes selective for its target, but further refinements to the structural work will be needed to critically test these suggestions.
PMCID: PMC4394352  PMID: 25774600
rabbit; cell lines; mouse; E. coli; human
7.  Vaccinia Virus–Encoded Ribonucleotide Reductase Subunits Are Differentially Required for Replication and Pathogenesis 
PLoS Pathogens  2010;6(7):e1000984.
Ribonucleotide reductases (RRs) are evolutionarily-conserved enzymes that catalyze the rate-limiting step during dNTP synthesis in mammals. RR consists of both large (R1) and small (R2) subunits, which are both required for catalysis by the R12R22 heterotetrameric complex. Poxviruses also encode RR proteins, but while the Orthopoxviruses infecting humans [e.g. vaccinia (VACV), variola, cowpox, and monkeypox viruses] encode both R1 and R2 subunits, the vast majority of Chordopoxviruses encode only R2 subunits. Using plaque morphology, growth curve, and mouse model studies, we investigated the requirement of VACV R1 (I4) and R2 (F4) subunits for replication and pathogenesis using a panel of mutant viruses in which one or more viral RR genes had been inactivated. Surprisingly, VACV F4, but not I4, was required for efficient replication in culture and virulence in mice. The growth defects of VACV strains lacking F4 could be complemented by genes encoding other Chordopoxvirus R2 subunits, suggesting conservation of function between poxvirus R2 proteins. Expression of F4 proteins encoding a point mutation predicted to inactivate RR activity but still allow for interaction with R1 subunits, caused a dominant negative phenotype in growth experiments in the presence or absence of I4. Co-immunoprecipitation studies showed that F4 (as well as other Chordopoxvirus R2 subunits) form hybrid complexes with cellular R1 subunits. Mutant F4 proteins that are unable to interact with host R1 subunits failed to rescue the replication defect of strains lacking F4, suggesting that F4-host R1 complex formation is critical for VACV replication. Our results suggest that poxvirus R2 subunits form functional complexes with host R1 subunits to provide sufficient dNTPs for viral replication. Our results also suggest that R2-deficient poxviruses may be selective oncolytic agents and our bioinformatic analyses provide insights into how poxvirus nucleotide metabolism proteins may have influenced the base composition of these pathogens.
Author Summary
Efficient genome replication is central to the virulence of all DNA viruses, including poxviruses. To ensure replication efficiency, many of the more virulent poxviruses encode their own nucleotide metabolism machinery, including ribonucleotide reductase (RR) enzymes, which act to provide ample DNA precursors for replication. RR enzymes require both large (R1) and small (R2) subunit proteins for activity. Curiously, some poxviruses only encode R2 subunits. Other poxviruses, such as the smallpox vaccine strain, vaccinia virus (VACV), encode both R1 and R2 subunits. We report here that the R2, but not the R1, subunit of VACV RR is required for efficient replication and virulence. We also provide evidence that several poxvirus R2 proteins form novel complexes with host R1 subunits and this interaction is required for efficient VACV replication in primate cells. Our study explains why some poxviruses only encode R2 subunits and identifies a role for these proteins in poxvirus pathogenesis. Furthermore, we provide evidence that mutant poxviruses unable to generate R2 proteins may become entirely dependent upon host RR activity. This may restrict their replication to cells that over-express RR proteins such as cancer cells, making them potential therapeutics for human malignancies.
PMCID: PMC2900304  PMID: 20628573
8.  Dynamic interaction networks in a hierarchically organized tissue 
We have integrated gene expression profiling with database and literature mining, mechanistic modeling, and cell culture experiments to identify intercellular and intracellular networks regulating blood stem cell self-renewal.Blood stem cell fate in vitro is regulated non-autonomously by a coupled positive–negative intercellular feedback circuit, composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9).The antagonistic signals converge in a core intracellular network focused around PI3K, Raf, PLC, and Akt.Model simulations enable functional classification of the novel endogenous ligands and signaling molecules.
Intercellular (between cell) communication networks are required to maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the recognized importance of intercellular networks in regulating adult stem and progenitor cell fate, the specific cell populations involved, and the underlying molecular mechanisms are largely undefined. Although a limited number of studies have applied novel bioinformatic approaches to unravel intercellular signaling in other cell systems (Frankenstein et al, 2006), a comprehensive analysis of intercellular communication in a stem cell-derived, hierarchical tissue network has yet to be reported.
As a model system to explore intercellular communication networks in a hierarchically organized tissue, we cultured human umbilical cord blood (UCB)-derived stem and progenitor cells in defined, minimal cytokine-supplemented liquid culture (Madlambayan et al, 2006). To systematically explore the molecular and cellular dynamics underlying primitive progenitor growth and differentiation, gene expression profiles of primitive (lineage negative; Lin−) and mature (lineage positive; Lin+) populations were generated during phases of stem cell expansion versus depletion. Parallel phenotypic and subproteomic experiments validated that mRNA expression correlated with complex measures of proteome activity (protein secretion and cell surface expression). Using a curated list of secreted ligand–receptor interactions and published expression profiles of purified mature blood populations, we implemented a novel algorithm to reconstruct the intercellular signaling networks established between stem cells and multi-lineage progeny in vitro. By correlating differential expression patterns with stem cell growth, we predict cell populations, pathways, and secreted ligands associated with stem cell self-renewal and differentiation (Figure 3A).
We then tested the correlative predictions in a series of cell culture experiments. UCB progenitor cell cultures were supplemented with saturating amounts of 18 putative regulatory ligands, or cocultured with purified mature blood lineages (megakaryocytes, monocytes, and erythrocytes), and analyzed for effects on total cell, progenitor, and primitive progenitor growth. At the primitive progenitor level, 3/5 novel predicted stimulatory ligands (EGF, PDGFB, and VEGF) displayed significant positive effects, 5/7 predicted inhibitory factors (CCL3, CCL4, CXCL10, TNFSF9, and TGFB2) displayed negative effects, whereas only 1/5 non-correlated ligand (CXCL7) displayed an effect. Also consistent with predictions from gene expression data, megakaryocytes and monocytes were found to stimulate and inhibit primitive progenitor growth, respectively, and these effects were attributable to differential secretome profiles of stimulatory versus inhibitory ligands.
Cellular responses to external stimuli, particularly in heterogeneous and dynamic cell populations, represent complex functions of multiple cell fate decisions acting both directly and indirectly on the target (stem cell) populations. Experimentally distinguishing the mode of action of cytokines is thus a difficult task. To address this we used our previously published interactive model of hematopoiesis (Kirouac et al, 2009) to classify experimentally identified regulatory ligands into one of four distinct functional categories based on their differential effects on cell population growth. TGFB2 was classified as a proliferation inhibitor, CCL4, CXCL10, SPARC, and TNFSF9 as self-renewal inhibitors, CCL3 a proliferation stimulator, and EGF, VEGF, and PDGFB as self-renewal stimulators.
Stem and progenitor cells exposed to combinatorial extracellular signals must propagate this information through intracellular molecular networks, and respond appropriately by modifying cell fate decisions. To explore how our experimentally identified positive and negative regulatory signals are integrated at the intracellular level, we constructed a blood stem cell self-renewal signaling network through extensive literature curation and protein–protein interaction (PPI) network mapping. We find that signal transduction pathways activated by the various stimulatory and inhibitory ligands converge on a limited set of molecular control nodes, forming a core subnetwork enriched for known regulators of self-renewal (Figure 6A). To experimentally test the intracellular signaling molecules computationally predicted as regulators of stem cell self-renewal, we obtained five small molecule antagonists against the kinases Phosphatidylinositol 3-kinase (PI3K), Raf, Akt, Phospholipase C (PLC), and MEK1. Liquid cultures were supplemented with the five molecules individually, and resultant cell population outputs compared against model simulations to deconvolute the functional effects on proliferation (and survival) versus self-renewal. This analysis classifies inhibition of PI3K and Raf activity as selectively targeting self-renewal, PLC as selectively targeting survival, and Akt as selectively targeting proliferation; MEK inhibition appears non-specific for these processes.
This represents the first systematic characterization of how cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny. The complex intercellular communication networks can be approximated as an antagonistic positive–negative feedback circuit, wherein progenitor expansion is modulated by a balance of megakaryocyte-derived stimulatory factors (EGF, PDGF, VEGF, and possibly serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). This complex milieu of endogenous regulatory signals is integrated and processed within a core intracellular signaling network, resulting in modulation of cell-level kinetic parameters (proliferation, survival, and self-renewal). We reconstruct a stem cell associated intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular and intracellular signaling. These findings lay the groundwork for novel strategies to control blood stem cell self-renewal in vitro and in vivo.
Intercellular (between cell) communication networks maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the importance of intercellular networks in stem cell biology, their rules, structure and molecular components are poorly understood. Herein, we describe the structure and dynamics of intercellular and intracellular networks in a stem cell derived, hierarchically organized tissue using experimental and theoretical analyses of cultured human umbilical cord blood progenitors. By integrating high-throughput molecular profiling, database and literature mining, mechanistic modeling, and cell culture experiments, we show that secreted factor-mediated intercellular communication networks regulate blood stem cell fate decisions. In particular, self-renewal is modulated by a coupled positive–negative intercellular feedback circuit composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). We reconstruct a stem cell intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular, and intracellular signaling. This represents the first systematic characterization of how stem cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny.
PMCID: PMC2990637  PMID: 20924352
cellular networks; hematopoiesis; intercellular signaling; self-renewal; stem cells
9.  Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome 
First systematic analysis of the evolutionary conserved InR/TOR pathway interaction proteome in Drosophila.Quantitative mass spectrometry revealed that 22% of identified protein interactions are regulated by the growth hormone insulin affecting membrane proximal as well as intracellular signaling complexes.Systematic RNA interference linked a significant fraction of network components to the control of dTOR kinase activity.Combined biochemical and genetic data suggest dTTT, a dTOR-containing complex required for cell growth control by dTORC1 and dTORC2 in vivo.
Cellular growth is a fundamental process that requires constant adaptations to changing environmental conditions, like growth factor and nutrient availability, energy levels and more. Over the years, the insulin receptor/target of rapamycin pathway (InR/TOR) emerged as a key signaling system for the control of metazoan cell growth. Genetic screens carried out in the fruit fly Drosophila melanogaster identified key InR/TOR pathway components and their relationships. Phenotypes such as altered cell growth are likely to emerge from perturbed dynamic networks containing InR/TOR pathway components, which stably or transiently interact with other cellular proteins to form complexes and networks thereof. Systematic studies on the topology and dynamics of protein interaction networks become therefore highly relevant to gain systems level understanding of deregulated cell growth. Despite much progress in genetic analysis only few systematic protein interaction studies have been reported for Drosophila, which in most cases lack quantitative information representing the dynamic nature of such networks. Here, we present the first quantitative affinity purification mass spectrometry (AP–MS/MS) analysis on the evolutionary conserved InR/TOR signaling network in Drosophila. Systematic RNAi-based functional analysis of identified network components revealed key components linked to the regulation of the central effector kinase dTOR. This includes also dTTT, a novel dTOR-containing complex required for the control of dTORC1 and dTORC2 in vivo.
For systematic AP–MS analysis, we generated Drosophila Kc167 cell lines inducibly expressing affinity-tagged bait proteins previously linked to InR/TOR signaling. Bait expressing Kc167 cell lines were harvested before and after insulin stimulation for subsequent affinity purification. Following LC–MS/MS analysis and probabilistic data filtering using SAINT (Choi et al, 2010), we generated a quantitative network model from 97 high confidence protein–protein interactions and 58 network components (Figure 2). The presented network displayed a high degree of orthologous interactions conserved also in human cells and identified a number of novel molecular interactions with InR/TOR signaling components for future hypothesis driven analysis.
To measure insulin-induced changes within the InR/TOR interaction proteome, we applied a recently introduced label-free quantitative MS approach (Rinner et al, 2007). The obtained quantitative data suggest that 22% of all interactions in the network are regulated by insulin. Major changes could be observed within the membrane proximal InR/chico/PI3K signaling complexes, and also in 14-3-3 protein containing signaling complexes and dTORC1, a complex that contains besides dTOR all major orthologous proteins found also in human mTORC1 including the two dTORC1 substrates d4E-BP (Thor) and S6 Kinase (S6K). Insulin triggered both, dissociation and association of dTORC1 proteins. Among the proteins that showed enhanced binding to dTORC1 upon insulin stimulation we found Unkempt, a RING-finger protein with a proposed role in ubiquitin-mediated protein degradation (Lores et al, 2010). Besides dTORC1 our systematic AP–MS analysis also revealed the presence of dTORC2, the second major TOR complex in Drosophila. dTORC2 contains the Drosophila orthologous of human mTORC2 proteins, but in contrast to dTORC1 was not affected upon insulin stimulation. Interestingly, we also found a specific set of proteins that were not linked to the canonical TOR complexes TORC1 and TORC2 in dTOR purifications. These include LqfR (liquid facets related), Pontin, Reptin, Spaghetti and the gene product of CG16908. We found the same set of proteins when we used CG16908 as a bait, suggesting complex formation among the identified proteins. None of the dTORC1/2 components besides dTOR was identified in CG16908 purifications, indicating that these proteins form dTOR complexes distinct from dTORC1 and dTORC2. Based on known interaction information from other species and data obtained from this study we refer to this complex as dTTT (Drosophila TOR, TELO2, TTI1) (Horejsi et al, 2010; [18]Hurov et al, 2010; [20]Kaizuka et al, 2010). A directed quantitative MS analysis of dTOR complex components suggests that dTORC1 is the most abundant dTOR complex we identified in Kc167 cells.
We next studied the potential roles of the identified network components for controlling the activity of the dInR/TOR pathway using systematic RNAi depletion and quantitative western blotting to measure the changes in abundance of phosphorylated substrates of dTORC1 (Thor/d4E-BP, dS6K) and dTORC2 (dPKB) in RNAi-treated cells (Figure 5). Overall, we could identify 16 proteins (out of 58) whose depletion caused an at least 50% increase or decrease in the levels of phosphorylated d4E-BP, S6K and/or PKB compared with control GFP RNAi. Besides established pathway components, we found several novel regulators within the dInR/TOR interaction network. For example, RNAi against the novel insulin-regulated dTORC1 component Unkempt resulted in enhanced phosphorylation of the dTORC1 substrate d4E-BP, which suggests a negative role for Unkempt on dTORC1 activity. In contrast, depletion of CG16908 and LqfR caused hypo-phosphorylation of all dTOR substrates similar to dTOR itself, suggesting a positive role for the dTTT complex on dTOR activity. Subsequently, we tested whether dTTT components also plays a role in dTOR-mediated cell growth in vivo. Depletion of both dTTT components, CG16908 and LqfR, in the Drosophila eye resulted in a substantial decrease in eye size. Likewise, FLP-FRT-mediated mitotic recombination resulted in CG16908 and LqfR mutant clones with a similar reduced growth phenotype as observed in dTOR mutant clones. Hence, the combined biochemical and genetic analysis revealed dTTT as a dTOR-containing complex required for the activity of both dTORC1 and dTORC2 and thus plays a critical role in controlling cell growth.
Taken together, these results illustrate how a systematic quantitative AP–MS approach when combined with systematic functional analysis in Drosophila can reveal novel insights into the dynamic organization of regulatory networks for cell growth control in metazoans.
Using quantitative mass spectrometry, this study reports how insulin affects the modularity of the interaction proteome of the Drosophila InR/TOR pathway, an evolutionary conserved signaling system for the control of metazoan cell growth. Systematic functional analysis linked a significant number of identified network components to the control of dTOR activity and revealed dTTT, a dTOR complex required for in vivo cell growth control by dTORC1 and dTORC2.
Genetic analysis in Drosophila melanogaster has been widely used to identify a system of genes that control cell growth in response to insulin and nutrients. Many of these genes encode components of the insulin receptor/target of rapamycin (InR/TOR) pathway. However, the biochemical context of this regulatory system is still poorly characterized in Drosophila. Here, we present the first quantitative study that systematically characterizes the modularity and hormone sensitivity of the interaction proteome underlying growth control by the dInR/TOR pathway. Applying quantitative affinity purification and mass spectrometry, we identified 97 high confidence protein interactions among 58 network components. In all, 22% of the detected interactions were regulated by insulin affecting membrane proximal as well as intracellular signaling complexes. Systematic functional analysis linked a subset of network components to the control of dTORC1 and dTORC2 activity. Furthermore, our data suggest the presence of three distinct dTOR kinase complexes, including the evolutionary conserved dTTT complex (Drosophila TOR, TELO2, TTI1). Subsequent genetic studies in flies suggest a role for dTTT in controlling cell growth via a dTORC1- and dTORC2-dependent mechanism.
PMCID: PMC3261712  PMID: 22068330
cell growth; InR/TOR pathway; interaction proteome; quantitative mass spectrometry; signaling
10.  Boolean modeling of transcriptome data reveals novel modes of heterotrimeric G-protein action 
Classical mechanisms of heterotrimeric G-protein signaling are observed to function in regulation of the transcriptome. Conversely, many theoretical regulatory modes of the G-protein are not manifested in the transcriptomes we investigate.A new mechanism of G-protein signaling is revealed, in which the β subunit regulates gene expression identically in the presence or absence of the α subunit.We find evidence of cross-talk between G-protein-mediated and hormone-mediated transcriptional regulation.We find evidence of system specificity in G-protein signaling.
Heterotrimeric G-proteins, composed of α, β, and γ subunits, participate in a wide range of signaling pathways in eukaryotes (Morris and Malbon, 1999). According to the typical, mammalian paradigm, in its inactive state, the G-protein exists as an associated heterotrimer. G-protein signaling begins with ligand binding that results in a conformational change in a G-protein-coupled receptor (GPCR). Once activated by the GPCR, the Gα separates from the associated Gβγ dimer and the freed Gα and Gβγ proteins can then interact with downstream effector molecules, alone or in combination, to transduce the signal. Subsequent to signal propagation, Gα re-associates with the Gβγ dimer to reform the G-protein complex.
There are several classical routes for signal propagation through heterotrimeric G-proteins that have been categorized in mammalian systems (Marrari et al, 2007; Dupre et al, 2009). One route, which we designate classical I, requires the presence of both subunits, and can invoke one of two distinct mechanisms. In one mechanism, on GPCR activation, freed Gα and Gβγ each interact with downstream effectors to elicit the downstream response. In a related mechanism, Gα but not Gβγ interacts with downstream effectors, but the Gβγ dimer is nevertheless required to facilitate coupling of Gα with the relevant GPCR (Marrari et al, 2007). In a second route, which we designate classical II, it is solely the Gβγ dimer that interacts with downstream effectors; in this case, sequestration of Gβγ within the heterotrimer prevents signal propagation. In addition, a few non-classical G-protein regulatory modes have also been implicated in some systems, for example signaling by the intact heterotrimer in yeast (Klein et al, 2000; Frank et al, 2005). Observations such as these lead to a fundamental question, namely, which of all the theoretical regulatory modes of G-protein signaling are realized biologically. Our study answers this question in the context of the model plant Arabidopsis thaliana, and in addition analyzes the manner in which G-protein signaling couples with signaling by the plant hormone abscisic acid. The Arabidopsis genome encodes only one canonical Gα subunit, GPA1, and one canonical Gβ subunit, AGB1, and knockout mutants are available for each of these, allowing clear dissection of Gα- and Gβ-related phenotypes.
Abscisic acid (ABA) is a major plant hormone, which inhibits growth and promotes tolerance of abiotic stresses such as drought, salinity, and cold. ABA signaling is known to interact with heterotrimeric G-protein signaling in both developmental and stress responses in a complex manner, causing, for example, ABA hyposensitivity of guard cell stomatal opening in gpa1 and agb1 single mutants as well as agb1 gpa1 double mutants (Fan et al, 2008), but ABA hypersensitivity of the inhibition of seed germination and post-germination seedling development in the same mutants (Pandey et al, 2006). These experimental observations implicate G-proteins as one of the components of ABA signaling, but to date no systematic study has been conducted in either plant or metazoan systems to define the co-regulatory modes of a G-protein and a hormone.
In this study, we conduct genome-wide gene expression profiling in G-protein subunit mutants of A. thaliana guard cells and leaves, with or without treatment with ABA. By introducing one or more mediators acting downstream of the G-protein and ABA to control transcript levels, we propose nine G-protein/ABA signaling pathways including ABA-independent G-protein signaling pathways, G-protein-independent ABA signaling pathways, and seven distinct ABA–G-protein-coupled signaling pathways (Figure 1). We develop a Boolean modeling framework to systematically enumerate 14 possible theoretical regulatory modes of the G-protein and 142 co-regulatory modes of the G-protein and ABA, and then use a pattern matching approach to associate target genes with theoretical regulatory modes.
Our analysis shows that the G-protein regulatory mode that requires the presence of both Gα and Gβγ subunits (consistent with classical I mechanisms), is well represented in both guard cells and leaves. The G-protein regulatory mode that requires a freed Gβγ subunit (classical II G-protein regulatory mechanism) is well supported in guard cells and somewhat less so in leaves. In addition, a G-protein regulatory mode representing a non-classical regulatory mechanism is prevalent in guard cells but less so in leaves (Figure 5). In this regulatory mode, signaling by Gβ(γ) occurs, and this signaling is not regulated in any way by Gα.
By relating the target genes with the nine proposed G-protein/ABA signaling pathways, we are able to gauge the plausibility of regulatory modes of the G-protein and ABA at the pathway level. We find that G-protein-independent ABA signaling pathways are prevalent in both guard cells and leaves. The existence of an ABA-independent regulatory activity of the G-protein is well supported in guard cells, but not supported in leaves. Additive regulation by G-protein signaling plus G-protein-independent ABA signaling is rare in both guard cells and leaves. In addition, combinatorial cross-talk between G-protein signaling and ABA signaling and additive cross-talk between ABA–G-protein signaling and G-protein-independent ABA signaling are observed in both guard cells and leaves. Our transcriptome analysis indicates that in some cases, ABA definitely does not influence G-protein signaling, though it may do so in some other cases.
To investigate whether previously observed hypersensitivity or hyposensitivity of developmental and dynamic transient responses to ABA in G-protein mutants is recapitulated at the level of transcriptional regulation, we compare gene regulation by ABA in guard cells and leaves of the G-protein mutants versus wild type. We find that in guard cells, equal ABA hyposensitivity of all mutants combined is significant, although hyposensitivity in individual mutants is not. There is also a separate group of genes in guard cells that show ABA hypersensitivity in the gpa1 mutant, suggesting complex interactions between ABA and G-protein signaling in gene regulation in this cell type. In leaves, ABA hyposensitivity of gene expression in the three individual mutants and equal hyposensitivity in all mutants are strongly supported. In addition, several of the functional categories identified by our analysis of G-protein regulatory modes have been implicated in previous physiological analyses of G-protein mutants, providing validation to the biological interpretation of our results.
In summary, by conducting a genome-wide gene expression profiling study in G-protein subunit mutants of A. thaliana guard cells and leaves and developing a Boolean modeling framework, we systematically evaluate the biological utilization of mechanisms of G-protein regulatory action and reveal novel regulatory modes of the G-protein. The results generate empirical evidence and insights regarding molecular events of G-protein signaling and response at the physiological level in both plants and mammals.
Heterotrimeric G-proteins mediate crucial and diverse signaling pathways in eukaryotes. Here, we generate and analyze microarray data from guard cells and leaves of G-protein subunit mutants of the model plant Arabidopsis thaliana, with or without treatment with the stress hormone, abscisic acid. Although G-protein control of the transcriptome has received little attention to date in any system, transcriptome analysis allows us to search for potentially uncommon yet significant signaling mechanisms. We describe the theoretical Boolean mechanisms of G-protein × hormone regulation, and then apply a pattern matching approach to associate gene expression profiles with Boolean models. We find that (1) classical mechanisms of G-protein signaling are well represented. Conversely, some theoretical regulatory modes of the G-protein are not supported; (2) a new mechanism of G-protein signaling is revealed, in which Gβ regulates gene expression identically in the presence or absence of Gα; (3) guard cells and leaves favor different G-protein modes in transcriptome regulation, supporting system specificity of G-protein signaling. Our method holds significant promise for analyzing analogous ‘switch-like' signal transduction events in any organism.
PMCID: PMC2913393  PMID: 20531402
abscisic acid; Arabidopsis thaliana; Boolean modeling; heterotrimeric G-protein; transcriptome
11.  Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network 
We present the first gene regulatory network (GRN) that pertains to post-developmental gene expression. Specifically, we mapped a transcription regulatory network of Caenorhabditis elegans metabolic gene promoters using gene-centered yeast one-hybrid assays. We found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs) compared with other gene-centered regulatory networks, and that these NHRs organize into functional network modules.The NHR family has greatly expanded in nematodes; C. elegans has 284 NHRs, whereas humans have only 48. We show that the NHRs in the metabolic GRN have metabolic phenotypes, suggesting that they do not simply function redundantly.The mediator subunit MDT-15 preferentially interacts with NHRs that occur in the metabolic GRN.We describe an NHR circuit that responds to nutrient availability and propose a model for the evolution and organization of NHRs in C. elegans metabolic regulatory networks.
Physical and/or regulatory interactions between transcription factors (TFs) and their target genes are essential to establish body plans of multicellular organisms during development, and these interactions have been studied extensively in the context of GRNs. The precise control of differential gene expression is also of critical importance to maintain physiological homeostasis, and many metabolic disorders such as obesity and diabetes coincide with substantial changes in gene expression. Much work has focused on the GRNs that control metazoan development; however, the design principles and organization of the GRNs that control systems physiology remain largely unexplored.
In this study, we present the first gene-centered GRN that includes ∼70 genes involved in C. elegans metabolism and physiology, 100 TFs and more than 500 protein–DNA interactions between them. The resulting metabolic GRN is enriched for NHRs, compared with other gene-centered regulatory networks. NHRs are well-known regulators of lipid meta-qj;bolism in mammals. The transcriptional activity of NHRs can be modified by diffusible ligands, which allows these TFs to function as molecular sensors and rapidly alter the expression of their target genes. Interestingly, NHRs comprise the largest family of TFs in nematodes; the C. elegans genome encodes 284 NHRs, most of which are uncharacterized. Furthermore, their organization in GRNs has not yet been investigated. In our study, we show that the C. elegans NHRs that we retrieved in the metabolic GRN organize into network modules, and that most of these NHRs function to maintain lipid homeostasis in the nematode. Interestingly, network modularity has been proposed to facilitate rapid and robust changes in gene expression. Our results suggest that the C. elegans metabolic GRN may have evolved by combining NHR family expansion with the specific modular wiring of NHRs to enable the rapid adaptation of the animal to different environmental cues.
NHRs can interact with transcriptional cofactors such as chromatin remodeling complexes and Mediator components. For instance, the C. elegans Mediator subunit, MDT-15, can interact with NHR-49 to regulate the expression of its target genes. To find all the TFs that MDT-15 can interact with, we performed systematic yeast two-hybrid assays with MDT-15 versus 755 full-length TFs. We found that MDT-15 preferentially associates with NHRs, and specifically with those NHRs that confer a metabolic phenotype and that occur in the metabolic GRN. This illustrates the central role of MDT-15 in the regulation of metabolic gene expression.
Using a variety of genetic and biochemical approaches, we characterized NHR-86 in more detail. NHR-86 participates in one of the two NHR modules, and has a high-flux capacity; that is it has both a high incoming and a high outgoing degree. We obtained an nhr-86 mutant and generated an NHR-86 antibody, and showed that NHR-86 functions as an auto-repressor in vivo and that nhr-86 mutant animals store abnormally high levels of body fat.
Finally, we discovered a novel NHR circuit that responds to nutrient availability. In this circuit NHR-45 regulates the activity of nhr-178 promoter in two distinct physiologically important tissues: the intestine and the hypodermis. Both of these NHRs are required to maintain lipid homeostasis in C. elegans. The expression of nhr-178 is responsive to the nutritional status of the animal, which switches between ON and OFF states in the hypodermis. We found that NHR-45 activity is necessary to control this switch in the hypodermis. Interestingly, NHR-45 has opposite effects on the activity of the nhr-178 promoter in these tissues: NHR-45 activates this promoter in the intestine, but represses it in the hypodermis.
Altogether our study leads to a model in which the expansion of the NHR family, TFs that have the capacity to act as fast molecular sensors, is combined with a modular network organization to enable rapid and robust responses to various environmental cues.
Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs that relate to metazoan development have been studied extensively. However, little is still known about the design principles, organization and functionality of GRNs that control physiological processes such as metabolism, homeostasis and responses to environmental cues. In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabolic genes. This network is enriched for nuclear hormone receptors (NHRs). The NHR family has greatly expanded in nematodes: humans have 48 NHRs, but C. elegans has 284, most of which are uncharacterized. We find that the C. elegans metabolic GRN is highly modular and that two GRN modules predominantly consist of NHRs. Network modularity has been proposed to facilitate a rapid response to different cues. As NHRs are metabolic sensors that are poised to respond to ligands, this suggests that C. elegans GRNs evolved to enable rapid and adaptive responses to different cues by a concurrence of NHR family expansion and modular GRN wiring.
PMCID: PMC2890327  PMID: 20461074
C. elegans; gene regulatory network; metabolism; nuclear hormone receptor; transcription factor
12.  MC EMiNEM Maps the Interaction Landscape of the Mediator 
PLoS Computational Biology  2012;8(6):e1002568.
The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors.
Author Summary
Phenotypic diversity and environmental adaptation in genetically identical cells is achieved by an exact tuning of their transcriptional program. It is a challenging task to unravel parts of the complex network of involved gene regulatory components and their interactions. Here, we shed light on the role of the Mediator complex in transcription regulation in yeast. The Mediator is highly conserved in all eukaryotes and acts as an interface between gene-specific transcription factors and the general mRNA transcription machinery. Even though most of the involved proteins and numerous structural features are already known, details on its functional contribution on basal as well as on activated transcription remain obscure. We use gene expression data, measured upon perturbations of various Mediator subunits, to relate the Mediator structure to the way it processes regulatory information. Moreover, we relate specific subunits to interacting transcription factors.
PMCID: PMC3380870  PMID: 22737066
13.  Role of Individual Subunits of the Neurospora crassa CSN Complex in Regulation of Deneddylation and Stability of Cullin Proteins 
PLoS Genetics  2010;6(12):e1001232.
The Cop9 signalosome (CSN) is an evolutionarily conserved multifunctional complex that controls ubiquitin-dependent protein degradation in eukaryotes. We found seven CSN subunits in Neurospora crassa in a previous study, but only one subunit, CSN-2, was functionally characterized. In this study, we created knockout mutants for the remaining individual CSN subunits in N. crassa. By phenotypic observation, we found that loss of CSN-1, CSN-2, CSN-4, CSN-5, CSN-6, or CSN-7 resulted in severe defects in growth, conidiation, and circadian rhythm; the defect severity was gene-dependent. Unexpectedly, CSN-3 knockout mutants displayed the same phenotype as wild-type N. crassa. Consistent with these phenotypic observations, deneddylation of cullin proteins in csn-1, csn-2, csn-4, csn-5, csn-6, or csn-7 mutants was dramatically impaired, while deletion of csn-3 did not cause any alteration in the neddylation/deneddylation state of cullins. We further demonstrated that CSN-1, CSN-2, CSN-4, CSN-5, CSN-6, and CSN-7, but not CSN-3, were essential for maintaining the stability of Cul1 in SCF complexes and Cul3 and BTB proteins in Cul3-BTB E3s, while five of the CSN subunits, but not CSN-3 and CSN-5, were also required for maintaining the stability of SKP-1 in SCF complexes. All seven CSN subunits were necessary for maintaining the stability of Cul4-DDB1 complexes. In addition, CSN-3 was also required for maintaining the stability of the CSN-2 subunit and FWD-1 in the SCFFWD-1 complex. Together, these results not only provide functional insights into the different roles of individual subunits in the CSN complex, but also establish a functional framework for understanding the multiple functions of the CSN complex in biological processes.
Author Summary
Protein degradation is precisely controlled in cells. The ubiquitin-mediated protein degradation pathway is highly conserved in eukaryotes, and the activity of ubiquitin ligases is regulated by the Cop9 signalosome (CSN), a multisubunit complex that is evolutionarily conserved from yeast to humans. Determining how the CSN complex functions biologically is crucial for understanding regulation of the ubiquitin-mediated protein degradation pathway. The filamentous fungus N. crassa is commonly used to study protein degradation. Its CSN complex contains seven subunits (CSN-1 to CSN-7). In this study, we generated knockout mutants of individual CSN subunits and observed the phenotypes of each mutant. We demonstrated that six of the seven CSN subunits were essential for cleaving the ubiquitin-like protein Nedd8 from cullin proteins (which act as scaffolds for ubiquitin ligases). In contrast, loss of the CSN-3 subunit had no effect on cullin neddylation. We also found that each CSN subunit had distinct roles in maintaining the stability of key components of cullin-based ubiquitin ligases. In summary, we systematically investigated the unequal contributions of CSN subunits to deneddylation and the maintenance of cullin-based ubiquitin ligases in N. crassa. Our work establishes a framework for understanding the function of CSN subunits in other eukaryotes.
PMCID: PMC2996332  PMID: 21151958
14.  Dissecting the Calcium-Induced Differentiation of Human Primary Keratinocytes Stem Cells by Integrative and Structural Network Analyses 
PLoS Computational Biology  2015;11(5):e1004256.
The molecular details underlying the time-dependent assembly of protein complexes in cellular networks, such as those that occur during differentiation, are largely unexplored. Focusing on the calcium-induced differentiation of primary human keratinocytes as a model system for a major cellular reorganization process, we look at the expression of genes whose products are involved in manually-annotated protein complexes. Clustering analyses revealed only moderate co-expression of functionally related proteins during differentiation. However, when we looked at protein complexes, we found that the majority (55%) are composed of non-dynamic and dynamic gene products (‘di-chromatic’), 19% are non-dynamic, and 26% only dynamic. Considering three-dimensional protein structures to predict steric interactions, we found that proteins encoded by dynamic genes frequently interact with a common non-dynamic protein in a mutually exclusive fashion. This suggests that during differentiation, complex assemblies may also change through variation in the abundance of proteins that compete for binding to common proteins as found in some cases for paralogous proteins. Considering the example of the TNF-α/NFκB signaling complex, we suggest that the same core complex can guide signals into diverse context-specific outputs by addition of time specific expressed subunits, while keeping other cellular functions constant. Thus, our analysis provides evidence that complex assembly with stable core components and competition could contribute to cell differentiation.
Author Summary
A key challenge in cellular network biology is to understand how protein complexes are cell-type or condition-specific assembled and disassembled. Cell differentiation is a major cellular reorganization bringing about fundamental changes in the new differentiated cell type. As many genes are expressed throughout all stages and only their expression levels differ, the question arises of how specific functions can be mediated. Here, focusing on the calcium-induced differentiation of primary human keratinocytes, we describe motifs of protein complex assemblies. We found that a large proportion of complexes contain both proteins expressed at similar levels in all stages of differentiation (non-dynamically expressed) and proteins with variable expression between (dynamically expressed). Using structural information we found that subunits tend to be replaced at structural overlapping surfaces of proteins. When applying our concepts to a manually annotated large TNF/NFkB signaling complex, we find a stable core associated with both a dynamically changing module and several stable modules. We propose this as a ‘constant signalosome ready to work,’ where a stable core is associated with a dynamic periphery. Altogether, our analysis highlights the importance of understanding the dynamic assembly and disassembly of complexes, taking 3D structural information into consideration, rather than only considering networks of individual proteins.
PMCID: PMC4422705  PMID: 25946651
15.  Exploration of the Dynamic Properties of Protein Complexes Predicted from Spatially Constrained Protein-Protein Interaction Networks 
PLoS Computational Biology  2014;10(5):e1003654.
Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden dynamics within protein interaction networks.
Author Summary
Cells are complex dynamic systems, and a central challenge in modern cell biology is to capture information about interactions between the molecules underlying cellular processes. Proteins rarely act alone; more often they form functional partnerships that can specify the timing and/or location of activity. These partnerships are subject to dynamic changes, and thus protein interactions within complexes undergo continuous transitions. Genetic and biochemical evidence suggest that regulation or depletion of a single protein can alter the stability and activity of an entire protein complex. Experimental approaches that detect interactions within living cells provide critical information for the dynamical system that protein complexes represent; yet complexes are often depicted as static 2-dimensional networks. We have built a system that projects in vivo protein interaction datasets as 3-dimensional virtual protein complexes. By using this method to approximate the diffusion of complex components, we can predict transient conformational states and estimate their abundance in living cells. Our method offers biologists a framework to correlate experimental phenotypes with predicted complex dynamics such as short or long-range effects of a single perturbation to the function of the whole ensemble.
PMCID: PMC4038459  PMID: 24874694
16.  Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations 
PLoS Computational Biology  2007;3(4):e69.
To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.
Author Summary
Complex phenotypes such as common human diseases are caused by variations in DNA in many genes that interact in complex ways with a number of environmental factors. These multifactorial gene and environmental perturbations induce changes in molecular networks that in turn lead to phenotypic changes in the organism under study. The comprehensive monitoring of transcript abundances using gene expression microarrays in different tissues over a large number of individuals in a population can be used to reconstruct molecular networks that underlie higher-order phenotypes such as disease. The cost to generate these large-scale gene activity measurements over large numbers of individuals can be extreme. However, by integrating DNA variation and gene activity data monitored in each individual in a given population of interest, we demonstrate that the power to elucidate molecular networks that drive complex phenotypes can be significantly enhanced, without increasing the sample size. Using a biologically realistic simulation framework, we demonstrate that molecular networks reconstructed using the combined DNA variation and gene activity data are more accurate than molecular networks reconstructed from gene activity data alone, implying that adding DNA variation data might allow us to use fewer subjects to produce molecular networks that better explain complex phenotypes such as disease.
PMCID: PMC1851982  PMID: 17432931
17.  Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model 
Integrating molecular time-series data resulted in a more robust model of the plant clock, which predicts that a wave of inhibitory PRR proteins controls the morning genes LHY and CCA1.PRR5 is experimentally validated as a late-acting component of this wave.The family of sequentially expressed PRR proteins allows flexible entrainment of the clock, whereas a single protein could not, suggesting that the duplication of clock genes might confer this generic, functional advantage.The observed post-translational regulation of the evening protein TOC1 by interaction with ZTL and GI remains consistent with an indirect activation of TOC1 mRNA expression by GI, which was previously postulated from modelling.
Circadian rhythms are present in most eukaryotic organisms including plants. The core genes of the circadian clock are very important for plant physiology as they drive the rhythmic expression of around 30% of Arabidopsis genes (Edwards et al, 2006; Michael et al, 2008). The clock is normally entrained by daily environmental changes in light and temperature. Oscillations also persist under constant environmental conditions in a laboratory. The clock gene circuit in Arabidopsis is based on multiple interlocked feedback loops, which are typical of circadian genetic networks in other organisms (Dunlap and Loros, 2004; Bell-Pedersen et al, 2005). Mechanistic, mathematical models are increasingly useful in analysing and understanding how the observed molecular components give rise to the rhythmic behaviour of this dynamic, non-linear system.
Our previous model of Arabidopsis circadian clock (Locke et al, 2006) presented the core, three-loop structure of the clock, which comprised morning and evening oscillators and coupling between them (Figure 1). The morning loop included the dawn-expressed LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) genes, which negatively regulate their expression through activation of the inhibitor proteins, PSEUDO-RESPONSE REGULATOR 9 (PRR9) and PRR7. These were described by a single, combined model component, PRR9/7. The evening loop included the dusk-expressed gene TIMING OF CAB EXPRESSION 1 (TOC1), which negatively regulates itself through inhibition of a hypothetical activator, gene Y. The evening-expressed gene GIGANTEA (GI) contributes to Y function. The morning and evening loops were connected through inhibition of the evening genes by LHY/CCA1 and activation of LHY/CCA1 expression by a hypothetical evening gene X. Here, we extend the previous model of circadian gene expression (Locke et al, 2006) based on recently published data (Figure 1). The new model retains the good match of our previous model to the large volume of molecular time-series data, and improves the behaviour of the model clock system under a range of light conditions and in a wider range of mutants.
The morning loop was extended by adding a hypothetical clock component, the night inhibitor (NI), which acts together with PRR9 and PRR7 to keep the expression of LHY and CCA1 at low levels over a broad interval spanning dusk. This regulation is important to set the phase of LHY/CCA1 expression at dawn. Data from the literature suggested that the PRR5 gene was a candidate for NI, leading us to predict that the sequentially expressed PRR9, PRR7 and PRR5 proteins together formed a wave of inhibitors of LHY and CCA1. This hypothesis was tested under discriminating light conditions, in which the light interval is replaced with the dawn and dusk pulses of light to form a ‘skeleton photoperiod'. Combining this protocol with mutation of the PRR7 and/or PRR5 genes, our new experimental results validated the model predictions and confirmed that PRR5 contributes to the function that we modelled as NI. During revision of this paper, that result received further experimental support (Nakamichi et al, 2010).
Model simulations revealed the functional importance of the inhibitor wave in entraining the clock to the light/dark cycle. Separating PRR9 from the other inhibitors in the model showed how the strong light activation observed for this gene contributes to more rapid entrainment. The observed, post-translation regulation of all three inhibitor proteins by light (Farre and Kay, 2007; Ito et al, 2007; Kiba et al, 2007) was also included in the model. Light-regulated degradation provides a molecular mechanism to explain the later phase of LHY and CCA1 expression under long photoperiods compared with short photoperiods, in line with experimental observations.
The connection between evening and morning loops was revised by including the inhibition of the morning gene PRR9 by the evening component TOC1, based on the data on TOC1-overexpressing plants (Makino et al, 2002; Ito et al, 2005). This inhibition causes a delay of PRR9 expression relative to LHY/CCA1, which allows LHY/CCA1 to reach a higher expression level at dawn. Our simulations showed that a partial mutant that lacks this inhibition of PRR9 by TOC1 is sufficient to cause the higher level of PRR9 and the short circadian period observed in toc1 mutant plants.
The evening loop was extended by introducing the observed, post-translational regulation of the TOC1 protein by the F-box protein ZEITLUPE (ZTL) and stabilization of ZTL by its interaction with GI in the presence of light (Kim et al, 2007). GI's function in the clock model has thus been revised according to the data: GI promotes an inhibition of TOC1 protein function through positive regulation of ZTL. This results, together with negative regulation of Y by TOC1, in indirect activation of TOC1 mRNA expression by GI, which agrees with our earlier experimental data (Locke et al, 2006). Simulations showed that the post-translational regulation of TOC1 by ZTL and GI results in the observed long period of the ztl mutant and fast dampening of rhythms in the lhy/cca1/gi triple mutant.
This is the first mathematical model that incorporates the observed post-translational regulation into the genetic network of the Arabidopsis clock. In addition to specific, mechanistic insights, the model shows a generic advantage from the duplication of clock genes and their expression at different phases. Such clock gene duplications are observed in eukaryotes with larger genomes, such as the mouse. Analogous, functional duplication can be achieved by differential regulation of a single clock gene in distinct cells, as in Drosophila.
Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation.
PMCID: PMC2964123  PMID: 20865009
Arabidopsis thaliana; biological clocks; circadian rhythms; mathematical model; systems biology
18.  Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology 
PLoS Genetics  2011;7(1):e1001273.
Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.
Author Summary
Genome-wide association studies have uncovered hundreds of DNA changes associated with complex disease. The ultimate promise of these studies is the understanding of disease biology; this goal, however, is not easily achieved because each disease has yielded numerous associations, each one pointing to a region of the genome, rather than a specific causal mutation. Presumably, the causal variants affect components of common molecular processes, and a first step in understanding the disease biology perturbed in patients is to identify connections among regions associated to disease. Since it has been reported in numerous Mendelian diseases that protein products of causal genes tend to physically bind each other, we chose to approach this problem using known protein–protein interactions to test whether any of the products of genes in five complex trait-associated loci bind each other. We applied several permutation methods and find robustly significant connectivity within four of the traits. In Crohn's disease and rheumatoid arthritis, we are able to show that these genes are co-expressed and that other proteins emerging in the network are enriched for association to disease. These findings suggest that, for the complex traits studied here, associated loci contain variants that affect common molecular processes, rather than distinct mechanisms specific to each association.
PMCID: PMC3020935  PMID: 21249183
19.  The auxin signalling network translates dynamic input into robust patterning at the shoot apex 
We provide a comprehensive expression map of the different genes (TIR1/AFBs, ARFs and Aux/IAAs) involved in the signalling pathway regulating gene transcription in response to auxin in the shoot apical meristem (SAM).We demonstrate a relatively simple structure of this pathway using a high-throughput yeast two-hybrid approach to obtain the Aux/IAA-ARF full interactome.The topology of the signalling network was used to construct a model for auxin signalling and to predict a role for the spatial regulation of auxin signalling in patterning of the SAM.We used a new sensor to monitor the input in the auxin signalling pathway and to confirm the model prediction, thus demonstrating that auxin signalling is essential to create robust patterns at the SAM.
The plant hormone auxin is a key morphogenetic signal involved in the control of cell identity throughout development. A striking example of auxin action is at the shoot apical meristem (SAM), a population of stem cells generating the aerial parts of the plant. Organ positioning and patterning depends on local accumulations of auxin in the SAM, generated by polar transport of auxin (Vernoux et al, 2010). However, it is still unclear how auxin is distributed at cell resolution in tissues and how the hormone is sensed in space and time during development. A complex ensemble of 29 Aux/IAAs and 23 ARFs is central to the regulation of gene transcription in response to auxin (for review, see Leyser, 2006; Guilfoyle and Hagen, 2007; Chapman and Estelle, 2009). Protein–protein interactions govern the properties of this transduction pathway (Del Bianco and Kepinski, 2011). Limited interaction studies suggest that, in the absence of auxin, the Aux/IAA repressors form heterodimers with the ARF transcription factors, preventing them from regulating target genes. In the presence of auxin, the Aux/IAA proteins are targeted to the proteasome by an SCF E3 ubiquitin ligase complex (Chapman and Estelle, 2009; Leyser, 2006). In this process, auxin promotes the interaction between Aux/IAA proteins and the TIR1 F-box of the SCF complex (or its AFB homologues) that acts as an auxin co-receptor (Dharmasiri et al, 2005a, 2005b; Kepinski and Leyser, 2005; Tan et al, 2007). The auxin-induced degradation of Aux/IAAs would then release ARFs to regulate transcription of their target genes. This includes activation of most of the Aux/IAA genes themselves, thus establishing a negative feedback loop (Guilfoyle and Hagen, 2007). Although this general scenario provides a framework for understanding gene regulation by auxin, the underlying protein–protein network remains to be fully characterized.
In this paper, we combined experimental and theoretical analyses to understand how this pathway contributes to sensing auxin in space and time (Figure 1). We first analysed the expression patterns of the ARFs, Aux/IAAs and TIR1/AFBs genes in the SAM. Our results demonstrate a general tendency for most of the 25 ARFs and Aux/IAAs detected in the SAM: a differential expression with low levels at the centre of the meristem (where the stem cells are located) and high levels at the periphery of the meristem (where organ initiation takes place). We also observed a similar differential expression for TIR1/AFB co-receptors. To understand the functional significance of the distribution of ARFs and Aux/IAAs in the SAM, we next investigated the global structure of the Aux/IAA-ARF network using a high-throughput yeast two-hybrid approach and uncover a rather simple topology that relies on three basic generic features: (i) Aux/IAA proteins interact with themselves, (ii) Aux/IAA proteins interact with ARF activators and (iii) ARF repressors have no or very limited interactions with other proteins in the network.
The results of our interaction analysis suggest a model for the Aux/IAA-ARF signalling pathway in the SAM, where transcriptional activation by ARF activators would be negatively regulated by two independent systems, one involving the ARF repressors, the other the Aux/IAAs. The presence of auxin would remove the inhibitory action of Aux/IAAs, but leave the ARF repressors to compete with ARF activators for promoter-binding sites. To explore the regulatory properties of this signalling network, we developed a mathematical model to describe the transcriptional output as a function of the signalling input that is the combinatorial effect of auxin concentration and of its perception. We then used the model and a simplified view of the meristem (where the same population of Aux/IAAs and ARFs exhibit a low expression at the centre and a high expression in the peripheral zone) for investigating the role of auxin signalling in SAM function. We show that in the model, for a given ARF activator-to-repressor ratio, the gene induction capacity increases with the absolute levels of ARF proteins. We thus predict that the differential expression of the ARFs generates differences in auxin sensitivities between the centre (low sensitivity) and the periphery (high sensitivity), and that the expression of TIR1/AFB participates to this regulation (prediction 1). We also use the model to analyse the transcriptional response to rapidly changing auxin concentrations. By simulating situations equivalent either to the centre or the periphery of our simplified representation of the SAM, we predict that the signalling pathway buffers its response to the auxin input via the balance between ARF activators and repressors, in turn generated by their differential spatial distributions (prediction 2).
To test the predictions from the model experimentally, we needed to assess both the input (auxin level and/or perception) and the output (target gene induction) of the signalling cascade. For measuring the transcriptional output, the widely used DR5 reporter is perfectly adapted (Figure 5) (Ulmasov et al, 1997; Sabatini et al, 1999; Benkova et al, 2003; Heisler et al, 2005). For assaying pathway input, we designed DII-VENUS, a novel auxin signalling sensor that comprises a constitutively expressed fusion of the auxin-binding domain (termed domain II or DII) (Dreher et al, 2006; Tan et al, 2007) of an IAA to a fast-maturating variant of YFP, VENUS (Figure 5). The degradation patterns from DII-VENUS indicate a high auxin signalling input both in flower primordia and at the centre of the SAM. This is in contrast to the organ-specific expression pattern of DR5::VENUS (Figure 5). These results indicate that the signalling pathway limits gene activation in response to auxin at the meristem centre and confirm the differential sensitivity to auxin between the centre and the periphery (prediction 1). We further confirmed the buffering capacities of the signalling pathway (prediction 2) by carrying out live imaging experiments to monitor DII-VENUS and DR5::VENUS expression in real time (Figure 5). This analysis reveals the presence of important temporal variations of DII-VENUS fluorescence, while DR5::VENUS does not show such global variations. Our approach thus provides evidence that the Aux/IAA-ARF pathway has a key role in patterning in the SAM, alongside the auxin transport system. Our results illustrate how the tight spatio-temporal regulation of both the distribution of a morphogenetic signal and the activity of the downstream signalling pathway provides robustness to a dynamic developmental process.
A comprehensive expression and interaction map of auxin signalling factors in the Arabidopsis shoot apical meristem is constructed and used to derive a mathematical model of auxin signalling, from which key predictions are experimentally confirmed.
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.
PMCID: PMC3167386  PMID: 21734647
auxin; biosensor; live imaging; ODE; signalling
20.  microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma 
A moderate loss of miR-122 function correlates with up-regulation of seed-matched genes and down-regulation of mitochondrially localized genes in both human hepatocellular carcinoma and in normal mice treated with anti-miR-122 antagomir.Putative direct targets up-regulated with loss of miR-122 and secondary targets down-regulated with loss of miR-122 are conserved between human beings and mice and are rapidly regulated in vitro in response to miR-122 over- and under-expression.Loss of miR-122 secondary target expression in either tumorous or adjacent non-tumorous tissue predicts poor survival of heptatocellular carcinoma patients.
Hepatocellular carcinoma (HCC) is one of the most aggressive human malignancies, common in Asia, Africa, and in areas with endemic infections of hepatitis-B or -C viruses (HBV or HCV) (But et al, 2008). Globally, the 5-year survival rate of HCC is <5% and about 600 000 HCC patients die each year. The high mortality associated with this disease is mainly attributed to the failure to diagnose HCC patients at an early stage and a lack of effective therapies for patients with advanced stage HCC. Understanding the relationships between phenotypic and molecular changes in HCC is, therefore, of paramount importance for the development of improved HCC diagnosis and treatment methods.
In this study, we examined mRNA and microRNA (miRNA)-expression profiles of tumor and adjacent non-tumor liver tissue from HCC patients. The patient population was selected from a region of endemic HBV infection, and HBV infection appears to contribute to the etiology of HCC in these patients. A total of 96 HCC patients were included in the study, of which about 88% tested positive for HBV antigen; patients testing positive for HCV antigen were excluded. Among the 220 miRNAs profiled, miR-122 was the most highly expressed miRNA in liver, and its expression was decreased almost two-fold in HCC tissue relative to adjacent non-tumor tissue, confirming earlier observations (Lagos-Quintana et al, 2002; Kutay et al, 2006; Budhu et al, 2008).
Over 1000 transcripts were correlated and over 1000 transcripts were anti-correlated with miR-122 expression. Consistent with the idea that transcripts anti-correlated with miR-122 are potential miR-122 targets, the most highly anti-correlated transcripts were highly enriched for the presence of the miR-122 central seed hexamer, CACTCC, in the 3′UTR. Although the complete set of negatively correlated genes was enriched for cell-cycle genes, the subset of seed-matched genes had no significant KEGG Pathway annotation, suggesting that miR-122 is unlikely to directly regulate the cell cycle in these patients. In contrast, transcripts positively correlated with miR-122 were not enriched for 3′UTR seed matches to miR-122. Interestingly, these 1042 transcripts were enriched for genes coding for mitochondrially localized proteins and for metabolic functions.
To analyze the impact of loss of miR-122 in vivo, silencing of miR-122 was performed by antisense inhibition (anti-miR-122) in wild-type mice (Figure 3). As with the genes negatively correlated with miR-122 in HCC patients, no significant biological annotation was associated with the seed-matched genes up-regulated by anti-miR-122 in mouse livers. The most significantly enriched biological annotation for anti-miR-122 down-regulated genes, as for positively correlated genes in HCC, was mitochondrial localization; the down-regulated mitochondrial genes were enriched for metabolic functions. Putative direct and downstream targets with orthologs on both the human and mouse microarrays showed significant overlap for regulations in the same direction. These overlaps defined sets of putative miR-122 primary and secondary targets. The results were further extended in the analysis of a separate dataset from 180 HCC, 40 cirrhotic, and 6 normal liver tissue samples (Figure 4), showing anti-correlation of proposed primary and secondary targets in non-healthy tissues.
To validate the direct correlation between miR-122 and some of the primary and secondary targets, we determined the expression of putative targets after transfection of miR-122 mimetic into PLC/PRF/5 HCC cells, including the putative direct targets SMARCD1 and MAP3K3 (MEKK3), a target described in the literature, CAT-1 (SLC7A1), and three putative secondary targets, PPARGC1A (PGC-1α) and succinate dehydrogenase subunits A and B. As expected, the putative direct targets showed reduced expression, whereas the putative secondary target genes showed increased expression in cells over-expressing miR-122 (Figure 4).
Functional classification of genes using the total ancestry method (Yu et al, 2007) identified PPARGC1A (PGC-1α) as the most connected secondary target. PPARGC1A has been proposed to function as a master regulator of mitochondrial biogenesis (Ventura-Clapier et al, 2008), suggesting that loss of PPARGC1A expression may contribute to the loss of mitochondrial gene expression correlated with loss of miR-122 expression. To further validate the link of miR-122 and PGC-1α protein, we transfected PLC/PRF/5 cells with miR-122-expression vector, and observed an increase in PGC-1α protein levels. Importantly, transfection of both miR-122 mimetic and miR-122-expression vector significantly reduced the lactate content of PLC/PRF/5 cells, whereas anti-miR-122 treatment increased lactate production. Together, the data support the function of miR-122 in mitochondrial metabolic functions.
Patient survival was not directly associated with miR-122-expression levels. However, miR-122 secondary targets were expressed at significantly higher levels in both tumor and adjacent non-tumor tissues among survivors as compared with deceased patients, providing supporting evidence for the potential relevance of loss of miR-122 function in HCC patient morbidity and mortality.
Overall, our findings reveal potentially new biological functions for miR-122 in liver physiology. We observed decreased expression of miR-122, a liver-specific miRNA, in HBV-associated HCC, and loss of miR-122 seemed to correlate with the decrease of mitochondrion-related metabolic pathway gene expression in HCC and in non-tumor liver tissues, a result that is consistent with the outcome of treatment of mice with anti-miR-122 and is of prognostic significance for HCC patients. Further investigation will be conducted to dissect the regulatory function of miR-122 on mitochondrial metabolism in HCC and to test whether increasing miR-122 expression can improve mitochondrial function in liver and perhaps in liver tumor tissues. Moreover, these results support the idea that primary targets of a given miRNA may be distributed over a variety of functional categories while resulting in a coordinated secondary response, potentially through synergistic action (Linsley et al, 2007).
Tumorigenesis involves multistep genetic alterations. To elucidate the microRNA (miRNA)–gene interaction network in carcinogenesis, we examined their genome-wide expression profiles in 96 pairs of tumor/non-tumor tissues from hepatocellular carcinoma (HCC). Comprehensive analysis of the coordinate expression of miRNAs and mRNAs reveals that miR-122 is under-expressed in HCC and that increased expression of miR-122 seed-matched genes leads to a loss of mitochondrial metabolic function. Furthermore, the miR-122 secondary targets, which decrease in expression, are good prognostic markers for HCC. Transcriptome profiling data from additional 180 HCC and 40 liver cirrhotic patients in the same cohort were used to confirm the anti-correlation of miR-122 primary and secondary target gene sets. The HCC findings can be recapitulated in mouse liver by silencing miR-122 with antagomir treatment followed by gene-expression microarray analysis. In vitro miR-122 data further provided a direct link between induction of miR-122-controlled genes and impairment of mitochondrial metabolism. In conclusion, miR-122 regulates mitochondrial metabolism and its loss may be detrimental to sustaining critical liver function and contribute to morbidity and mortality of liver cancer patients.
PMCID: PMC2950084  PMID: 20739924
hepatocellular carcinoma; microarray; miR-122; mitochondrial; survival
21.  Different sets of QTLs influence fitness variation in yeast 
We have carried out a combination of in-lab-evolution (ILE) and congenic crosses to identify the gene sets that contribute to the ability of yeast cells to survive under alkali stress.Each selected line acquired a different set of mutations, all resulting in the same phenotype. We identified a total of 15 genes in ILE and 17 candidates in the congenic approach, and studied their individual contribution to the phenotype.The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.None of the genes identified encode structural components of the pH machinery. Instead, most encode regulatory functions, such as ubiquitin ligases, chromatin remodelers, GPI anchoring and copper/iron sensing transcription factors.
The majority of phenotypes in nature are complex traits affected by multiple genes [usually called quantitative trait loci (QTLs)], as well as by environmental factors. Many traits with practical importance such as crop yield in plants and susceptibility to various diseases in humans fall under this category. Understanding the architecture of complex traits has become the new frontier of genetic research, and many studies have greatly contributed to this field. However, to date, the genetic basis of only a few of these traits has been identified, and many questions regarding the architecture of complex traits and the accumulation of QTLs during evolution still remain unanswered. Among them are: How many QTLs affect complex phenotypes? What is the effect of each QTL? How do complex traits change during evolution? Is the adaptation process repeatable?, etc. In order to identify the QTLs that affect one of the important components of fitness variability in yeast, and to answer some of the questions above, we combined in-lab evolution (ILE) with the construction of congenic lines to isolate and map several gene sets that contribute to the ability of yeast cells to survive under alkali stress.
We carried out an ILE experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. This process was followed by hybridizations to tiling arrays to identify the mutations acquired during the laboratory selective process. The ILE procedure revealed mutations in 15 genes, thus defining the QTLs and mechanisms that affect, in a quantitative fashion, the ability to cope with alkali stress. Our results indicate that during ILE several populations acquired different sets of QTLs that conferred the same phenotype. We identified each individual mutation in these strains, and validated and estimated their contribution to the phenotype. The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.
In addition to the ILE, we have studied the mechanisms regulating fitness under alkali stress at natural habitats. We used a clinically isolated strain able to grow at high pH and a standard laboratory strain with a limited ability to sustain high pH as the parents of series of backcrosses to construct congenic lines up to the 8th generation. Seventeen genomic intervals that are candidates to contain QTLs were thus identified. In order to detect the contributing QTL in each interval, a predictive algorithm was applied, which scored the candidate genes in each genomic interval based on their interactions and similarity to the ILE genes. The algorithm was validated by testing the effect of the predicted candidate gene's deletions on the phenotype. Twelve out of 29 deletions were found to affect the trait (P-value 0.023).
Interestingly, our results show that almost all beneficial mutations affected regulatory genes, and not structural components of the pH homeostasis machinery (such as proton pumps, which control the cell's pH). The genes identified affect global regulators, such as ubiquitin ligases, proteins involved in GPI anchoring, copper sensing and chromatin remodelers. Thus, we show that adaptive changes tend to occur in genes with wide influence, rather than in genes narrowly affecting the phenotype selected for.
One example of genes identified both in the ILE and in the congenic lines is the copper-sensing transcription factor MAC1, and its downstream targets CTR1 and CTR3, which encode copper transporters. Different mutations at the same residue (Cys 271) were found in four out of five independent ILE lines. These mutations inactivate a copper-sensing region of Mac1 and cause up-regulation of its target genes. The CTR1 and CTR3 genes were identified in the congenic lines. Moreover, we found that a Ty transposable element is responsible for the decreased expression of CTR3 in some strains, and its excision caused transcriptional activation, affecting the ability to thrive at high pH.
This work provides insights on both evolutionary and genetic issues (such as the appearance of adaptive mutations and the architecture of complex traits), while at the same time providing information about the mechanisms that contribute to growth at high pH, a subject with ramifications for cell physiology, pathogenicity, and stress response.
Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in-lab evolution (ILE) experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. The populations acquired different sets of affecting alleles, showing that evolution can provide alternative solutions to the same challenge. We measured the contribution of each allele to the phenotype. The sum of the effects of the QTLs was larger than the difference between the ancestor phenotype and the evolved strains, suggesting epistatic interactions between the QTLs. In parallel, a clinical isolated strain was used to map natural QTLs affecting growth at high pH. In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein-interaction networks, candidate genes were defined and validated by gene disruption. Many of the QTLs found by both methods are not directly implied in pH homeostasis but have more general, and often regulatory, roles.
PMCID: PMC2835564  PMID: 20160707
congenic lines; growth on alkali; in-lab evolution; QTL mapping; Saccharomyces cerevisiae
22.  Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes 
PLoS Computational Biology  2013;9(10):e1003278.
Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks.
Author Summary
Intracellular signaling networks are central to a cell's ability to adapt to its environment. Developing the capacity to effectively manipulate such networks would have a wide range of applications, from cancer therapy to synthetic biology. This requires a thorough understanding of the mechanisms of signal transduction, particularly the kinds of protein complexes that are formed during transmission of extracellular information to the nucleus. Traditionally, signaling complexes have been largely perceived (albeit often implicitly) as machine-like structures. However, the number of molecular complexes that could theoretically be formed by complex signaling networks is astronomically large. This has led to the pleiomorphic ensemble hypothesis, which posits that diverse and rapidly changing sets of transient protein complexes can transmit and process information. Our goal was to use computational approaches, specifically rule-based modeling, to test these hypotheses. We constructed a model of the prototypical yeast mating pathway and found significant ensemble-like behavior. Our results thus demonstrated that ensembles can in fact transmit extracellular signals with minimal noise. Additionally, a comparison of this model with one tailored to generate machine-like complexes displayed notable phenotypic differences, revealing potential advantages for ensemble-like signaling. Our demonstration that ensembles can function effectively will have a significant impact on how we conceptualize signaling and other processes inside cells.
PMCID: PMC3794900  PMID: 24130475
23.  Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range 
Quantitative analysis of time-resolved data in primary erythroid progenitor cells reveals that a dual negative transcriptional feedback mechanism underlies the ability of STAT5 to respond to the broad spectrum of physiologically relevant Epo concentrations.
A mathematical dual feedback model of the Epo-induced JAK2/STAT5 signaling pathway was calibrated with extensive time-resolved quantitative data sets from immunoblotting, mass spectrometry and qRT–PCR experiments in primary erythroid progenitor cells.We show that the amount of nuclear phosphorylated STAT5 integrated for 60 min post Epo stimulation directly correlates with the fraction of surviving cells 24 h later.CIS and SOCS3 were identified as the most relevant transcriptional feedback regulators of JAK2/STAT5 signaling in primary erythroid progenitor cells. Applying the model, we revealed that CIS-mediated inhibitory effects are most important at low ligand concentrations, whereas SOCS3 inhibition is more effective at high ligand doses.The distinct modes of inhibition of CIS and SOCS3 at various Epo concentrations provide a strategy for achieving control of JAK2/STAT5 signaling over the entire range of physiological Epo concentrations.
Cells interpret information encoded by extracellular stimuli through the activation of intracellular signaling networks and translate this information into cellular decisions. A prime example for a system that is exposed to extremely variable ligand concentrations is the erythroid lineage. The key regulator Erythropoietin (Epo) facilitates continuous renewal of erythrocytes at low basal levels but also secures compensation in case of, e.g., blood loss through an up to 1000-fold increase in hormone concentration. The Epo receptor (EpoR) is expressed on erythroid progenitor cells at the colony forming unit erythroid (CFU-E) stage. Stimulation of these cells with Epo leads to rapid but transient activation of receptor and JAK2 phosphorylation followed by phosphorylation of the latent transcription factor STAT5. Although STAT5 is known to be an essential regulator of survival and differentiation of erythroid progenitor cells, a quantitative link between the dynamic properties of STAT5 signaling and survival decisions remained unknown. STAT5-mediated responses in CFU-E cells are modulated by multiple attenuation mechanisms that operate on different time scales. Fast-acting mechanisms such as depletion of Epo by rapid receptor turnover and recruitment of the phosphatase SHP-1 control the initial signal amplitude at the receptor level. Transcriptional feedback regulators such as suppressor of cytokine signaling (SOCS) family members CIS and SOCS3 operate at a slower time scale. Despite the ample knowledge of the individual components involved, only little is known about the specific contributions of these regulators in controlling dynamic properties of STAT5 in response to a broad range of input signals. Therefore, dynamic pathway modeling is required to understand the complex regulatory network of feedback regulators.
To address these questions, we established a dual negative feedback model of JAK2/STAT5 signaling in primary erythroid progenitor cells isolated from mouse fetal livers. We provide a large data set of JAK2/STAT5 signaling dynamics employing quantitative immunoblotting, mass spectrometry and quantitative RT–PCR measured under different perturbation conditions to calibrate our model (Figure 3). The structure of our model was constructed to comprise the minimal number of parameters necessary to explain the data. Thereby, we aimed at a model with fully identifiable parameters that are essential to obtain high predictive power. Parameter identifiability was analyzed by the profile likelihood approach. Applying this method, we could establish a dual negative feedback model of JAK2-STAT5 signaling with structurally and in most cases practically identifiable parameters.
A major bottle-neck in combining signal transduction events with cellular phenotypes is the discrepancy in the time scale and stimuli concentrations that are applied in the different experiments. The sensitivity of biochemical assays to determine phosphorylation events within minutes or hours after stimulation is usually lower than the threshold of sensitivity in assays to determine the physiological response after one or more days. Facilitated by the model, we were able to compute the integrated response of JAK2/STAT5 signaling components for experimentally unaddressable Epo concentrations. Our results demonstrate that the integrated response of pSTAT5 in the nucleus accurately correlates with the experimentally determined survival of CFU-E cells. This provides a quantitative link of the dependency of primary CFU-E cells on pSTAT5 activation dynamics. By correlation analysis, we could identify the early signaling phase (⩽1 h) of STAT5 to be the most predictive for the fraction of surviving cells, which was determined ∼24 h later. Thus, we hypothesize that as a general principle in apoptotic decisions, ligand concentrations translated into kinetic-encoded information of early signaling events downstream of receptors can be predictive for survival decisions 24 h later.
After the first hour of stimulation, it is important to constrain signaling to a residual steady-state level. Constitutive phosphorylation of the JAK2/STAT5 pathway has a crucial role in the onset of polycythemia vera (PV), a disease associated with Epo-independent erythroid differentiation. The two identified transcriptional feedback proteins, CIS and SOCS3, are responsible for adjusting the phosphorylation level of STAT5 after 1 h of stimulation. Since the Epo input signal can vary over a broad range of ligand concentrations, we asked how CIS and SOCS3 can facilitate control of STAT5 long-term phosphorylation levels over the entire physiological relevant hormone concentrations. By using model simulations, we revealed that the two feedbacks are most effective at different Epo concentration ranges. Predicted by our mathematical model, the major role of CIS in modulating STAT5 phosphorylation levels is at low, basal Epo concentrations, whereas SOCS3 is essential to control the STAT5 phosphorylation levels at high Epo doses (Figure 6). As a potential molecular mechanism of this dose-dependent inhibitory effect, we could identify the quantity of pJAK2 relative to pEpoR that increases with higher Epo concentrations. Since SOCS3 can inhibit JAK2 directly via its KIR domain to attenuate downstream STAT5 activation, SOCS3 becomes more effective with the relative increase of JAK2 activation. Hence, CIS and SOCS3 act in a concerted manner to ensure tight regulation of STAT5 responses over the broad physiological range of Epo concentrations.
In summary, our mathematical approach provided new insights into the specific function of feedback regulation in STAT5-mediated life or death decisions of primary erythroid cells. We dissected the roles of the transcriptionally induced proteins CIS and SOCS3 that operate as dual feedback with divided function thereby facilitating the control of STAT5 activation levels over the entire range of physiological Epo concentrations. The detailed understanding of the molecular processes and control distribution of Epo-induced JAK/STAT signaling can be further applied to gain insights into alterations promoting malignant hematopoietic diseases.
Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.
PMCID: PMC3159971  PMID: 21772264
apoptosis; erythropoietin; mathematical modeling; negative feedback; SOCS
24.  Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration 
PLoS Computational Biology  2015;11(6):e1004295.
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.
Author Summary
Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.
PMCID: PMC4456145  PMID: 26042810
25.  A Functional Portrait of Med7 and the Mediator Complex in Candida albicans 
PLoS Genetics  2014;10(11):e1004770.
Mediator is a multi-subunit protein complex that regulates gene expression in eukaryotes by integrating physiological and developmental signals and transmitting them to the general RNA polymerase II machinery. We examined, in the fungal pathogen Candida albicans, a set of conditional alleles of genes encoding Mediator subunits of the head, middle, and tail modules that were found to be essential in the related ascomycete Saccharomyces cerevisiae. Intriguingly, while the Med4, 8, 10, 11, 14, 17, 21 and 22 subunits were essential in both fungi, the structurally highly conserved Med7 subunit was apparently non-essential in C. albicans. While loss of CaMed7 did not lead to loss of viability under normal growth conditions, it dramatically influenced the pathogen's ability to grow in different carbon sources, to form hyphae and biofilms, and to colonize the gastrointestinal tracts of mice. We used epitope tagging and location profiling of the Med7 subunit to examine the distribution of the DNA sites bound by Mediator during growth in either the yeast or the hyphal form, two distinct morphologies characterized by different transcription profiles. We observed a core set of 200 genes bound by Med7 under both conditions; this core set is expanded moderately during yeast growth, but is expanded considerably during hyphal growth, supporting the idea that Mediator binding correlates with changes in transcriptional activity and that this binding is condition specific. Med7 bound not only in the promoter regions of active genes but also within coding regions and at the 3′ ends of genes. By combining genome-wide location profiling, expression analyses and phenotyping, we have identified different Med7p-influenced regulons including genes related to glycolysis and the Filamentous Growth Regulator family. In the absence of Med7, the ribosomal regulon is de-repressed, suggesting Med7 is involved in central aspects of growth control.
Author Summary
In this study, we have investigated Mediator function in the human fungal pathogen C. albicans. An initial screening of conditionally regulated Mediator subunits showed that the Med7 of C. albicans was not essential, in contrast to the situation noted for S. cerevisiae. While loss of CaMed7 did not lead to loss of viability under normal growth conditions, it dramatically influenced the pathogen's ability to grow in different carbon sources, to form hyphae and biofilms, and to colonize the gastrointestinal tracts of mice. We used location profiling to determine Mediator binding under yeast and hyphal morphologies characterized by different transcription profiles. We observed a core set of specific and common genes bound by Med7 under both conditions; this specific core set is expanded considerably during hyphal growth, supporting the idea that Mediator binding correlates with changes in transcriptional activity and that this binding is condition specific. Med7 bound not only in the promoter regions of active genes but also of inactive genes and within coding regions and at the 3′ ends of genes. By combining genome-wide location profiling, expression analyses and phenotyping, we have identified different Med7 regulons including genes related to glycolysis and the Filamentous Growth Regulator family.
PMCID: PMC4222720  PMID: 25375174

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