Vol 439 | 16 February 2006 | doi : 10.1038 / nature04473 LETTERS A bottom-up approach to gene regulation Nicholas J. Guido1 * , Xiao Wang2 * , David Adalsteinsson3 , David McMillen5 , Jeff Hasty6 , Charles R. Cantor1 , Timothy C. Elston4 & J. J. Collins1 The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models1 23 . If simple , well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components , we may begin to build an understanding of cellular regulatory processes from the ` bottom up ' . Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli . We studied its behaviour in synthetic gene networks under increasingly complex conditions : unregulated , repressed , activated , and simultaneously repressed and activated . We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system , and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback . The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division , which we confirm experimentally . This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger , more complex regulatory networks , and that this bottom-up approach can provide insights into gene regulation . We engineered the O RO lac promoter in E. coli as a tool for investigating the interactive effects of positive and negative transcriptional regulation . The basis for the engineered promoter is the P RM promoter of bacteriophage l . In the native promoter , the operator sites O R1 and O R2 cooperatively bind the l repressor protein , CI , which in turn acts as a transcriptional activator by recruiting RNA polymerase24 . In the engineered O RO lac promoter , the third operator site of the native P RM promoter , O R3 , was mutated to significantly reduce CI binding , thereby removing the repressive function of this site . A lac operator , which can bind the repressor protein LacI , was placed upstream of O R1 . The result is a promoter that can be repressed by LacI and activated by CI ; it thus enables the combined effects of repression and activation to be investigated . We inserted the O RO lac promoter in a high-copy plasmid that lacked the cI activator and lacI repressor genes to create an unregulated system -LRB- Fig. 1a -RRB- , to establish the promoter 's basal properties . The green fluorescent protein gene -LRB- gfp -RRB- was placed under the control of the O RO lac promoter as a readout for transcriptional induction . A repressor-only system was constructed by adding a strongly constitutively expressed lacI gene , under the control of the P LtetO1 promoter , to the unregulated system -LRB- Fig. 1b -RRB- . The LacI protein binds to the lac operator as a tetramer , which reduces the binding of the RNA polymerase , and may affect the binding of CI to its operator 1 site . When isopropyl-b-D - thiogalactopyranoside -LRB- IPTG -RRB- is added to the media , it binds to the LacI tetramer and weakens binding of the LacI protein to the O lac operator site . This allowed us to tune the degree of O RO lac repression . An activator-only system was constructed by adding the cI gene under the control of the pBAD promoter , which is activated by arabinose -LRB- Fig. 1c -RRB- . The CI protein forms a dimer that binds sequentially and cooperatively to the O R1 and O R2 sites of the O RO lac promoter . Finally , both repression and activation , as implemented in the individual modules above , were placed together on one plasmid , to create a repressor activator system -LRB- Fig. 1d -RRB- . A mathematical model was developed to account for the in vivo behaviour of this modular system , initially using equilibrium modelling of the underlying biochemical reaction scheme to capture the mean transcriptional response of the three regulated systems : the repressor-only , activator-only and repressor activator systems . These considerations allow us to derive explicit expressions for the probabilities of each of the six binding states as a function of the inducer levels -LRB- see Supplementary Information -RRB- . These expressions were globally fitted to the transcriptional induction data using the unregulated system as a baseline , leading to the mean fluorescence results -LRB- blue lines -RRB- shown in Fig. 1f h . The deterministic model was then extended to include stochastic effects25 . Preliminary investigations with the stochastic model revealed that fluctuations in the concentrations of transcription factors have very minor effects on the variability in the expression levels of the GFP reporter protein . Therefore , synthesis , degradation , and multimerization of CI and LacI were not included in our baseline model . All stochastic modelling was carried out using the BioNetS software26 , which uses a highly optimized version of the Gillespie Monte Carlo algorithm . To create the distributions of the GFP reporter protein , synthesis and degradation of GFP messenger RNA and protein were tracked explicitly . To include cell growth and division in the model , the cell volume was treated as a random variable that undergoes exponential growth26 with a mean doubling time of 20 minutes , as observed in the experimental system . At cell division , the volume is halved and the mRNA and GFP molecules are divided between daughter cells on the basis of a binomial distribution21 . Because the plasmid used in our experimental system has a highcopy ColE1 origin of replication , intercellular copy-number variability can be large27 . Rather than model the complex copy-number control system explicitly , we assumed a gamma distribution at the time of cell division -LRB- see Supplementary Information -RRB- . The mean was assumed to be 50 plasmid copies per cell28 . The variance was adjusted so that the model accurately captures the fluctuations in GFP levels observed in the activator-only system , and then kept at this value Department of Biomedical Engineering , Bioinformatics Program , Center for BioDynamics and Center for Advanced Biotechnology , Boston University , 44 Cummington Street , Boston , Massachusetts 02215 , USA . 2Department of Statistics and Operations Research , 3Department of Mathematics , 4Department of Pharmacology , University of North Carolina , Chapel Hill , North Carolina 27599 , USA . 5Institute for Optical Sciences and Department of Chemical and Physical Sciences , University of Toronto at Mississauga , 3359 Mississauga Rd , Mississauga , Ontario L5L 1C6 Canada . 6Department of Bioengineering , University of California , San Diego , 9500 Gillman Drive , La Jolla , California 92093 , USA . * These authors contributed equally to this work . 856 2006 Nature Publishing Group NATURE | Vol 439 | 16 February 2006 LETTERS when the repressor-only and repressor activator systems were considered . Comparison of the experimental results and the stochastic simulations in Fig. 1e and Fig. 1i k shows that the model can accurately capture the in vivo behaviour of the unregulated system and all three regulated systems . The coefficients of variation -LRB- CVs -RRB- of the three regulated systems are shown in the insets of Fig. 1f h . As can be seen , the stochastic model slightly underestimates the experimentally observed variability . This discrepancy of approximately 0.1 in CV for each system is largely due to the model 's inability to capture the long tails that extend into high fluorescence levels in the experimental flow cytometry histograms , and indicates that our relatively simple model does not account for all sources of variability . For example , while the model includes extrinsic noise5 that arises from cell growth and division and fluctuations in plasmid copy number , it does not account for cell-to-cell variability in the transcriptional and translational machinery . An important test for any mathematical model is its ability to predict the outcome of novel experiments . We designed and conducted several new experiments on the basis of the model 's predictions . First , the stochastic model results revealed that a large amount of the variability in GFP levels is attributable to fluctuations in the plasmid copy number . To test this prediction , we placed the repressor activator system on a low-copy plasmid that is tightly regulated , maintaining 3 4 copies of the plasmid per cell28 . Experiments were then conducted using both arabinose and IPTG as an inducer , and as expected , the cells with the low copy number showed reduced expression levels . In the stochastic model , the plasmid copy number was fixed at three , and the concentration of LacI was proportionally reduced to reflect this change . The functional relationship between arabinose level and CI concentration was taken to be the same in the low-copy case as in the high-copy case , to match our experimental observation that reducing the plasmid copy number did not affect the shape of the system 's arabinose response curve -LRB- see Supplementary Information -RRB- . No additional changes were made to the model . As seen in Fig. 2 , the experimental distributions for GFP expression levels closely match those predicted by the model , indicating that the model accurately captures fluctuations in expression levels that are attributable to varying plasmid copy number . Figure 1 | Unregulated , repressor-only , activator-only and repressor activator systems on high-copy plasmids . Schematic designs , experimental data and model data are shown . a , The unregulated system with the engineered O RO lac promoter controlling the reporter gfp . The white boxes in the O RO lac promoter represent the operator sites at which CI and LacI proteins bind . The red cross at O R3 represents the point mutation that reduces CI binding at that operator site . b , The repressor-only system consists of the P LtetO1 constitutive promoter controlling lacI and the O RO lac promoter controlling gfp . c , The activator-only system consists of the pBAD promoter controlling cI and the O RO lac promoter controlling gfp . d , The repressor activator system represents a combination of the repressor-only and activator-only systems . e , Histograms of the unregulated system : experimental data -LRB- red -RRB- and stochastic model data -LRB- blue -RRB- . The x axis represents arbitrary fluorescence units from flow cytometry , and the y axis represents the frequency of cells producing the corresponding fluorescence level . f , Repressor-only system results : GFP expression represented as normalized fluorescence versus IPTG level ; red circles are experimental data and the blue lines are the results of the deterministic model . The inset shows CV versus IPTG level : experimental data -LRB- red -RRB- and stochastic model data -LRB- blue -RRB- . g , Activator-only system results : normalized fluorescence versus arabinose level , with an inset showing CV versus arabinose level . h , Repressor activator system results : normalized fluorescence versus arabinose level . The inset shows CV versus arabinose level , with 10 mM IPTG in each case . i , Histograms of normalized cell counts versus arbitrary fluorescence units , of experimental data -LRB- red -RRB- and stochastic model data -LRB- blue -RRB- for the repressor-only system . The solid lines in the histograms are the results for no inducer -LRB- IPTG in this case -RRB- and the dashed lines are the results for the highest level of IPTG . j , Histograms -LRB- as above -RRB- , showing results for no inducer -LRB- solid lines -RRB- and the highest level of arabinose -LRB- dashed lines -RRB- for the activator-only system . k , Histograms -LRB- as above -RRB- , showing results for no arabinose -LRB- solid lines -RRB- and the highest level of arabinose -LRB- dashed lines -RRB- , with 10 mM IPTG in each case for the repressor activator system . 857 2006 Nature Publishing Group LETTERS NATURE | Vol 439 | 16 February 2006 Figure 2 | Histograms of model data -LRB- blue lines -RRB- and experimental data -LRB- red lines -RRB- for the repressor activator system on a low-copy plasmid . The x axis represents arbitrary fluorescence units from flow cytometry , and the y axis represents the frequency of cells producing the corresponding fluorescence level for : a , 1 1026 % arabinose , no IPTG ; b , 5 1024 % arabinose , no IPTG ; c , 1 mM IPTG , no arabinose ; d , 50 mM IPTG , no arabinose . The inset in b shows CV versus arabinose level , and the inset in d shows CV versus IPTG level ; model data -LRB- blue lines -RRB- and experimental data -LRB- red lines -RRB- . The insets of Fig. 2b and d show a comparison of the CVs for the experimental and model results . The model accurately predicts the behaviour of the CV for the low-copy case , taking into account the 0.1 discrepancy discussed above . At very low inducer levels , the model predicts a larger CV than is observed experimentally . We attribute this discrepancy to autofluorescence in the experimental system . This has the effect of artificially increasing the mean fluorescence , thereby decreasing the CV . The effect is only important at low fluorescence levels , where autofluorescence makes up a significant portion of the measured mean value . To test the model 's predictive power in a more complex system , we added positive feedback to the repressor activator system . To accomplish this , we expanded the system so that the cI gene was transcribed polycistronically with gfp under the control of the O RO lac promoter -LRB- Fig. 3a -RRB- . Adding feedback to the stochastic model requires the explicit inclusion of the CI mRNA and protein molecular abundances and the biochemical processes that affect their levels -LRB- synthesis , degradation , multimerization -RRB- . The parameters that control these processes were chosen so that in the absence of feedback the expanded model produced CI protein levels identical to that of the simpler model -LRB- see Supplementary Information -RRB- . As can be seen in Fig. 3b g , the agreement between the behaviour predicted by the model and the experimental results is very good , validating the bottom-up approach to understanding gene regulatory networks . We also used the model to conduct a systematic analysis of how the different sources of noise contribute to the overall variability -LRB- Supplementary Fig. 7 -RRB- . Surprisingly , we found that if we run simulations with no cell growth or division using an ensemble of cells with varying plasmid copy number , the CV increases above the level at which cell growth and division proceed normally . This result was unexpected , because cell growth and division are generally thought to add variability to expression levels . A theoretical explanation of this counterintuitive prediction is provided in the Supplementary Information . In very general terms , this phenomenon can be understood as follows : in a population of cells with varying plasmid copy number , the intercellular variability increases as expression levels increase from new protein synthesis . Cell division not only limits the mean protein level , it also causes the distribution to be more tightly centred about the mean -LRB- Supplementary Fig. 8 -RRB- . This leads to an overall reduction of the variability in protein expression for the high-copy system where plasmid copy-number fluctuations account for a significant portion of the noise . In Figure 3 | Repressor activator system with positive feedback . a , Schematic of the positive feedback construct , where the cI gene is added to the repressor activator system on a high-copy plasmid . The cI gene is incorporated , along with gfp , in a polycistronic region controlled by the engineered O RO lac promoter . Model predictions and experimental results for the repressor-activator system with positive feedback : b , Normalized mean -LRB- arbitrary fluorescence units -RRB- versus arabinose level , where the red circles are experimental data and the blue diamonds are model predictions . The inset shows CV versus arabinose level . c , Normalized mean versus IPTG 858 level , with an inset showing CV versus IPTG level . d , Normalized mean versus arabinose level , with 50 mM IPTG in each case , with an inset showing CV versus arabinose level for 50 mM IPTG . e , Histograms , normalized cell counts versus arbitrary fluorescence units , of experimental data -LRB- red -RRB- and model predictions -LRB- blue -RRB- for 0.0001 % arabinose . f , Histograms of experimental data -LRB- blue -RRB- and model predictions -LRB- red -RRB- for 50 mM IPTG . g , Histograms of experimental data -LRB- blue -RRB- and model predictions -LRB- red -RRB- for 0.0001 % arabinose and 50 mM IPTG . 2006 Nature Publishing Group NATURE | Vol 439 | 16 February 2006 LETTERS a stochastic model to experimental data from a modular , synthetic gene network , and then extend the model and use it to predict quantitatively the behaviour of a more complex network involving regulatory feedback . The model also accurately predicted the effects of changing plasmid copy numbers on the experimentally observed levels of variability in GFP expression and suggested that halting cell growth and division in cells with high-copy plasmids should increase fluctuations in protein expression , which was confirmed experimentally . This synthesis of theory and experiment has much to offer in investigations of cellular behaviour , and such approaches may eventually allow us to assemble a genuine bottom-up picture of the intricate processes of gene regulation . METHODS Plasmid construction . Plasmid construction began with the pZE21-MCS1 modular construct of Lutz and Bujard28 . This plasmid backbone contains the P LtetO1 promoter , T1T2 terminator , kanamycin resistance gene , ColE1 origin of replication and multiple cloning sites . The low copy origin of replication was from the pZS * 24-MCS1 plasmid28 . To create the O RO lac promoter , the O R region of bacteriophage l was altered with the G-to-Tor3-r2 point mutation in the O R3 operator site24 -LRB- greatly reducing its affinity for the CI protein -RRB- , and an O lac operator site was added upstream of the O R region . The promoter sequence is shown in Supplementary Fig. 12 . The point mutation and operator insertion were carried out by polymerase chain reaction -LRB- PCR ; using PfuTurbo DNA polymerase from Stratagene and an MJ Research PTC-100 thermal cycler -RRB- with primers designed such that the point mutation and operator site were placed in the primers . The lacI gene was PCR-amplified , with PfuTurbo and an MJ Research thermal cycler , from pTrc99a , the clts and O R region were from pGW7 -LRB- ATCC -RRB- , the gfpmut3 was from pJBA111 -LRB- J. B. Andersen , Technical University of Denmark -RRB- , and the pBAD promoter was from pBADHisA -LRB- Clontech -RRB- . Cell growth and expression experiments . All plasmids were inserted into the E. coli strain JM2 .300 -LRB- 2l , lacI22 rpsL135 -LRB- StrR -RRB- , thi-1 -RRB- via the transformation and storage solution heat-shock transformation protocol30 . Cells for experimentation were grown overnight and cultures were inoculated 1:300 in LB media and with 0.03 mg ml21 kanamycin antibiotic . Varying levels of IPTG and arabinose were added separately and in combination to affect the behaviour of the inducible promoters . Cultures were grown in a 37 8C incubator shaking at 300 r.p.m. for approximately three hours , at which point the cells reached a steady state -LRB- see Supplementary Information -RRB- , and the absorbance at 600 nm -LRB- A 600 nm -RRB- was between 0.3 and 0.4 . The minimal media cultures used the same conditions as LB cultures except that the minimal media cultures were grown in M9 minimal media with 2 % glucose . Data acquisition and analysis . Culture samples were spun down at 8,000 r.p.m. to pellet cells , and supernatant was removed . The samples were then resuspended in 0.75 ml of 1 PBS , and fluorescence measurements were taken via flow cytometry . The Becton Dickinson FACScalibur flow cytometer was used to measure 50,000 cells of each sample from a culture representing one level of inducer . Excitation of GFP was achieved via a 488-nm argon excitation laser and fluorescence measured with the 515 545 nm emission filter . The flow cytometer generates logscale values using a 10-bit analogue-to-digital converter , yielding integers in the range 0 to 1,023 for each of three measurements : fluorescence intensity , forward-scattering , and side-scattering . Cells were collected within a small forward-scatter and side-scatter gate , in the Cellquest software designed by BD Biosciences , to minimize fluorescence variation due to cell size . Modelling and stochastic simulations . The model parameters were estimated using Matlab 's least-squares fitting routine . The mean expression levels were fitted to the repressor-only , activator-only and repressor activator system results . To initialize the least-squares routine , we used 2,000 random initial guesses for the model parameters , generated from a uniform distribution within a biologically reasonable regime of parameter space . A discussion of the sensitivity of the model 's output to the values of the parameters is given in the Supplementary Information . The best parameter set was chosen from those 2,000 runs . All the fits were based on data normalized to the mean GFP expression level of the unregulated system . All stochastic simulations were done in BioNetS26 using the Gillespie algorithm . See Supplementary Information for details . Received 7 October ; accepted 18 November 2005 . 1 . Elowitz , M. B. & Leibler , S . A synthetic oscillatory network of transcriptional regulators . Nature 403 , 335 -338 -LRB- 2000 -RRB- . Figure 4 | CV versus arabinose levels for the repressor activator system . a , Model results for the high-copy case with cell growth and division -LRB- circles -RRB- and with no cell growth or division -LRB- stars -RRB- . b , Experimental results for the high-copy case for cells in rich media -LRB- circles -RRB- and minimal media -LRB- stars -RRB- . c , Modelling results for the low-copy case with cell growth and division -LRB- circles -RRB- and with no cell growth or division -LRB- stars -RRB- . d , Experimental results for the low copy case for cells in rich media -LRB- circles -RRB- and minimal media -LRB- stars -RRB- . contrast , for the low-copy case where plasmid copy number is tightly controlled , the model predicts that cell growth and division should not reduce noise levels and instead , halting cell growth and division is expected to decrease the variability . We considered ways to experimentally test these model predictions . Most methods of achieving arrest of cell growth and division -LRB- such as addition of the drug cephalexin29 -RRB- cause drastic changes in cell morphology or protein production , both of which would be detrimental to our experimental design . We found cell behaviour in minimal media to be the best approximation of our model conditions . In minimal media , cell growth and division slow considerably , while protein overproduction from plasmids can continue . Cells were placed in M9 minimal media and 2 % glucose with incubator conditions identical to those in the rich media experiments . Our model predicts that the variability in protein expression levels can increase if cells with the repressor activator system on a high-copy plasmid do not grow or divide -LRB- Fig. 4a -RRB- . The results in Fig. 4b show that the cells in minimal media with high-copy plasmids do in fact have a higher CV than those that are growing and dividing regularly in rich Lauria broth -LRB- LB -RRB- media . Additionally , our model predicts that the variability in expression levels for the low-copy case decreases when cell growth and division are stopped -LRB- Fig. 4c -RRB- . This effect is validated experimentally in Fig. 4d where the results show that cells with low-copy plasmids in minimal media have lower CVs than similar cells growing and dividing in rich media . -LRB- See Supplementary Information for similar results with stationary phase cells . -RRB- Previous work in synthetic biology1 23 has shown that one can develop a mathematical model for a given synthetic gene network and use the model to describe the behaviour of that network . These efforts set the stage for the possibility of using quantitative models of regulatory subsystems to predict the behaviour of larger , more complex regulatory networks , one of the main goals of synthetic biology . 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O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

gene
noise
expression
transcriptional regulation
synthetic biology
regulation
transcription
networks

development
molecular signaling
morphogenesis
evolution
regulatory cascades
gene expression


Vol

March

doi

ARTICLES

Proteome

survey

reveals

modularity

of

the

yeast

cell

machinery

Anne-Claude

Patrick

Paola

Roland

Markus

Martina

Christina

Lars

Juhl

Sonja

Birgit

Angela

Marie-Anne

Verena

Hoffman

Christian

Hoefert

Karin

Klein

Manuela

Anne-Marie

Malgorzata

Markus

Marita

Tatjana

Sean

Andreas

Tewis

Georg

Gerard

Gitte

Jens

M.

Bernhard

Peer

Robert

B.

Giulio

Protein

complexes

are

key

molecular

entities

that

integrate

multiple

gene

products

to

perform

cellular

functions

Here

we

report

the

first

genome-wide

screen

for

complexes

in

an

organism

budding

yeast

using

affinity

purification

and

mass

spectrometry

Through

systematic

tagging

of

open

reading

frames

-LRB-

ORFs

-RRB-

the

majority

of

complexes

were

purified

several

times

suggesting

screen

saturation

The

richness

of

the

data

set

enabled

a

de

novo

characterization

of

the

composition

and

organization

of

the

cellular

machinery

The

ensemble

of

cellular

proteins

partitions

into

complexes

of

which

are

novel

that

differentially

combine

with

additional

attachment

proteins

or

protein

modules

to

enable

a

diversification

of

potential

functions

Support

for

this

modular

organization

of

the

proteome

comes

from

integration

with

available

data

on

expression

localization

function

evolutionary

conservation

protein

structure

and

binary

interactions

This

study

provides

the

largest

collection

of

physically

determined

eukaryotic

cellular

machines

so

far

and

a

platform

for

biological

data

integration

and

modelling

Genomes

are

remarkable

in

that

they

encode

most

of

the

functions

necessary

for

their

interpretation

and

However

many

principles

as

to

how

individual

gene

products

form

the

structures

required

for

biological

activity

are

still

unknown

Biological

processes

such

as

the

cell

cycle

and

replication

require

precise

organization

of

molecules

in

time

and

space

Complexes

are

among

the

fundamental

units

of

macromolecular

They

are

thought

to

assemble

in

a

particular

order

and

often

require

energy-driven

conformational

changes

specific

post-translational

modifications

or

chaperone

assistance

for

proper

Their

composition

is

also

known

to

vary

according

to

cellular

requirements

Affinity

purification

methods

are

well

suited

for

studying

complexes

under

near-physiological

They

allow

macromolecules

physically

associated

with

a

tagged

bait

to

be

retrieved

and

identified

by

mass

These

methods

have

been

applied

as

large-scale

screens

in

prokaryotic

and

eukaryotic

cells

and

have

led

to

a

growing

collection

of

cellular

that

in

combination

with

large-scale

yeast

two-hybrid

are

powerful

integrators

of

additional

biological

However

in

the

absence

of

a

genome-wide

screen

where

many

complexes

are

retrieved

repeatedly

through

a

reverse

purification

process

assignment

of

a

component

to

a

particular

complex

relied

heavily

on

experimental

stringency

and

arbitrary

thresholds

Here

we

report

the

first

genome-wide

screen

for

complexes

to

investigate

the

underlying

organizational

principles

of

the

eukaryotic

cellular

machinery

Genome-wide

characterization

of

complexes

We

applied

the

tandem-affinity-purification

method

coupled

to

mass

spectrometry

-LRB-

TAP

MS

-RRB-

to

all

ORFs

of

Saccharomyces

cerevisiae

as

annotated

in

-LRB-

refs

Fig.

and

Supplementary

Information

-RRB-

We

employed

standardized

protocols

and

successfully

purified

unique

TAP-fusion

proteins

of

which

retrieved

at

least

one

partner

-LRB-

Fig.

Supplementary

Table

-RRB-

From

all

purifications

we

processed

samples

for

mass

spectrometry

and

identified

proteins

of

which

were

distinct

-LRB-

Fig.

Supplementary

Figs

-RRB-

These

represent

about

of

the

estimated

proteome

for

exponentially

growing

and

cover

all

functional

classes

and

subcellular

localizations

The

absolute

abundances

of

the

identified

proteins

show

a

wide

range

from

to

copies

per

although

coverage

varied

considerably

being

highest

for

the

most

abundant

proteins

-LRB-

copies

per

cell

coverage

-RRB-

and

lowest

for

the

rarest

proteins

-LRB-

copies

coverage

-RRB-

-LRB-

Supplementary

Fig

-RRB-

We

measured

reproducibility

by

performing

purifications

in

duplicate

-LRB-

soluble

membrane

-RRB-

and

found

that

on

average

of

recovered

proteins

were

common

to

both

giving

an

approximation

of

false-positive

negative

rates

within

the

raw

data

However

as

complexes

are

retrieved

in

several

purifications

interactions

observed

repeatedly

are

more

likely

to

be

correct

-LRB-

see

below

-RRB-

The

purification

data

contains

of

known

complexes

from

the

Munich

Information

Center

for

Protein

Sequences

-LRB-

MIPS

-RRB-

-LRB-

complexes

-RRB-

and

our

own

literature

mining

-LRB-

complexes

-RRB-

We

found

no

evidence

for

known

complexes

possibly

because

they

may

not

assemble

under

our

growth

conditions

or

because

the

tag

interferes

with

complex

This

is

the

case

for

the

partially

recovered

CCT

-LRB-

chaperonin-containing

tailless

complex

Cellzome

AG

Meyerhofstrasse

Heidelberg

Germany

Meyerhofstrasse

Heidelberg

Germany

MPI-IB

Charite

Campus

Mitte

Schumannstrasse

Berlin

Germany

for

Molecular

Medicine

of

the

Austrian

Academy

of

Sciences

Lazarettgasse

Vienna

Austria

Present

address

EMBL

Meyerhofstrasse

Heidelberg

Germany

These

authors

contributed

equally

to

this

work

Nature

Publishing

Group

ARTICLES

NATURE

Vol

March

polypeptide

-RRB-

complex

the

carboxy

termini

of

the

eight

subunits

in

the

ring-like

core

of

the

complex

lie

on

interaction

However

these

situations

could

often

be

rescued

of

TAP-tagged

proteins

that

we

could

not

purify

were

detected

in

purifications

using

other

complex

components

We

used

a

modified

purification

procedure

for

membrane

proteins

and

successfully

purified

of

the

that

were

tagged

For

example

we

retrieved

the

Q/t-SNARE

complex

including

both

integral

membrane

components

of

the

trimeric

receptor

-LRB-

and

-RRB-

and

the

peripheral

membrane

machinery

-LRB-

-RRB-

required

for

We

also

detected

novel

links

such

as

that

between

the

palmitoyl

transferase

-LRB-

a

six-transmembrane-segment

protein

-RRB-

and

-LRB-

the

Gb

subunit

of

the

pheromone

receptor-coupled

G

protein

-RRB-

which

is

consistent

with

genetic

and

supports

a

role

for

protein

acylation

in

the

pheromone

response

De

novo

definition

of

protein

complexes

The

proportion

of

new

proteins

identified

per

purification

dropped

asymptotically

during

the

progression

of

the

screen

suggesting

that

the

procedure

was

to

near

saturation

-LRB-

Supplementary

Fig

-RRB-

We

also

observed

that

of

known

were

retrieved

several

times

resulting

in

a

high

coverage

of

known

components

-LRB-

Supplementary

Fig

-RRB-

We

exploited

this

redundancy

to

define

complexes

computationally

Current

approaches

for

defining

complexes

from

binary

were

not

deemed

appropriate

as

these

are

not

directly

inferable

from

purifications

We

also

explicitly

avoided

the

incorporation

of

prior

knowledge

to

circumvent

any

bias

towards

well-studied

proteins

We

first

derived

a

socio-affinity

index

-LRB-

see

the

Methods

-RRB-

that

quantifies

the

propensity

of

proteins

to

form

partnerships

It

measures

the

log-odds

of

the

number

of

times

two

proteins

are

observed

together

relative

to

what

would

be

expected

from

their

frequency

in

the

data

set

and

encompasses

both

the

spoke

and

the

matrix

models

for

assigning

binary

interactions

within

purifications

The

index

accounts

for

the

frequency

of

proteins

within

the

data

set

and

thus

naturally

discriminates

true

from

spurious

interactions

involving

very

promiscuous

partners

For

instance

which

was

seen

in

purifications

and

would

have

been

ignored

under

previous

high-frequency

filtering

showed

high

indices

only

with

proteins

it

is

known

to

associate

with

-LRB-

and

-RRB-

Generally

pairs

with

socio-affinity

indices

below

should

be

considered

with

caution

-LRB-

reproducibility

-RRB-

though

those

above

are

more

reliable

-LRB-

-RRB-

These

indices

capture

some

biochemical

properties

of

protein

protein

interactions

there

is

a

tentative

correlation

with

the

few

dissociation

constants

available

in

the

literature

-LRB-

P

-RRB-

and

protein

pairs

with

high

socio-affinity

indices

are

more

likely

to

be

in

direct

contact

as

measured

either

by

three-dimensional

structures

or

the

yeast

two-hybrid

system

-LRB-

Supplementary

Fig

-RRB-

To

our

knowledge

this

is

the

first

attempt

to

recreate

numbers

approximating

physical

measurements

purely

from

proteomics

data

If

each

protein

only

belonged

to

a

single

complex

we

could

generate

a

definitive

set

by

a

single

clustering

step

using

socio-affinity

indices

However

it

is

well

established

that

proteins

can

be

present

in

multiple

complexes

a

property

we

reasoned

could

be

captured

by

an

iterative

procedure

Briefly

we

first

used

the

socio-affinity

indices

to

form

a

matrix

for

all

pairs

of

proteins

studied

and

then

applied

cluster

analysis

to

generate

an

initial

list

of

complexes

We

then

subtracted

a

penalty

from

the

initial

matrix

values

and

repeated

clustering

Tight

associations

are

not

drastically

affected

by

the

penalty

while

looser

ones

are

gradually

eroded

and

can

be

replaced

by

others

not

present

initially

We

varied

the

clustering

parameters

-LRB-

number

of

iterations

clustering

type

penalty

values

and

so

on

-RRB-

over

a

sensible

range

to

produce

different

complex

sets

and

compared

each

to

a

manually

curated

group

of

known

complexes

used

for

structural

We

computed

both

coverage

-LRB-

that

is

the

fraction

of

proteins

in

known

complexes

that

we

retrieved

-RRB-

and

accuracy

-LRB-

that

is

the

fraction

of

the

retrieved

complexes

components

that

match

those

already

known

Fig.

-RRB-

The

best

conditions

generated

a

collection

of

complexes

with

coverage

and

accuracy

However

inspection

revealed

that

known

complex

Figure

Synopsis

of

the

genome-wide

screen

for

complexes

and

data

analysis

a

Summary

of

the

overall

experimental

strategy

MIPS/SGD

Munich

Information

Center

for

Protein

Sequences/Saccharomyces

Genome

Database

b

Definition

and

terminology

used

to

define

protein-complex

architecture

Nature

Publishing

Group

NATURE

Vol

March

ARTICLES

components

could

be

found

under

clustering

conditions

with

slightly

poorer

accuracy

or

coverage

Therefore

we

grouped

similar

complexes

from

conditions

with

coverage

and

accuracy

above

The

resulting

different

protein-complex

variations

were

termed

complex

isoforms

-LRB-

Fig.

-RRB-

This

procedure

increased

the

overall

coverage

to

The

inclusion

of

parameters

resulting

in

accuracy

coverage

below

did

not

increase

the

coverage

but

significantly

decreased

accuracy

-LRB-

data

not

shown

-RRB-

Comparison

with

the

complete

collection

of

known

complexes

-LRB-

from

MIPS

and

the

literature

-RRB-

showed

that

of

complexes

were

entirely

novel

and

just

of

those

previously

known

lacked

novel

components

-LRB-

Supplementary

Table

-RRB-

Of

the

known

complexes

not

recovered

by

the

procedure

above

were

partially

found

in

single

purifications

-LRB-

Supplementary

Table

-RRB-

but

produced

a

signal

too

weak

to

be

recovered

automatically

Modular

organization

of

the

cell

machinery

The

above

procedure

partitions

proteins

in

complexes

into

two

types

core

components

that

are

present

in

most

isoforms

and

attachments

present

in

only

some

of

them

-LRB-

Fig.

-RRB-

This

is

reminiscent

of

an

organization

structure

proposed

previously

that

was

based

on

a

small-scale

Complex

cores

ranged

from

proteins

in

size

-LRB-

average

-RRB-

Among

the

attachments

we

noticed

several

instances

where

two

or

more

proteins

were

always

together

and

present

in

multiple

complexes

which

we

call

modules

-LRB-

Supplementary

Table

on

average

associated

with

cores

-RRB-

We

tested

whether

this

organization

was

a

reflection

of

biological

phenomena

by

first

looking

at

transcriptional

control

of

the

complex

components

A

quality

controlled

set

of

differentially

expressed

genes

derived

from

microarray

showed

that

a

large

percentage

of

pairs

of

proteins

within

cores

were

coexpressed

at

the

Figure

Evidence

supporting

complex

organization

Proteins

in

each

organization

level

-LRB-

cores

and

so

on

-RRB-

are

referred

to

as

groups

a

Percentage

of

cell

cycle

co-regulated

genes

found

in

the

same

group

b

Percentage

of

coregulated

proteins

in

the

same

group

expressed

at

the

same

time

during

the

cell

cycle

c

d

are

as

for

a

b

but

for

sporulation

genes

e

Average

dispersion

ranges

for

protein

abundance

within

each

group

f

h

Percentage

of

groups

having

exactly

the

same

subcellular

localizations

cellular

functions

or

phylogenetic

conservation

respectively

i

j

Percentage

of

pairs

for

which

a

direct

interaction

is

known

from

three-dimensional

structures

or

yeast

twohybrid

experiments

respectively

Values

on

each

bar

show

the

total

number

of

counts

n.d.

not

determined

See

Supplementary

Information

for

further

details

Figure

Architecture

and

modularity

of

complexes

Proteins

are

coloured

according

to

their

The

line

attribute

corresponds

to

socioaffinity

indices

dotted

lines

dashed

lines

plain

lines

Bait

proteins

are

shown

in

bold

and

shaded

circles

around

groups

of

proteins

indicate

cores

and

modules

a

The

exosome

and

the

Ski

module

b

Stages

in

de-adenylation-dependent

mRNA

degradation

arrows

show

the

order

of

events

c

Two

distinct

families

of

cap-binding

proteins

the

nuclear

CBC

-LRB-

cap-binding

complex

-RRB-

and

the

cytoplasmic

Nature

Publishing

Group

ARTICLES

NATURE

Vol

March

Figure

Modularity

of

the

yeast

cellular

machinery

a

Modularity

matrix

across

cellular

function

The

x

and

y

axes

show

modules

and

cores

respectively

clustered

according

to

functional

categories

-LRB-

-RRB-

cell

cycle

cell

fate

cell

transport

defence

energy

environment

metabolism

protein

fate

protein

synthesis

transcription

signalling

and

unknown

Whenever

a

module

combines

with

a

core

the

intersection

is

highlighted

Dotted

lines

show

the

modularity

of

the

complexes

in

Fig.

b

Frequency

of

cross-talk

between

different

cellular

processes

The

thickness

of

the

lines

between

the

functional

classes

are

proportional

to

the

frequency

of

core

module

interactions

between

them

same

time

during

the

cell

cycle

and

sporulation

-LRB-

Fig.

d

-RRB-

consistent

with

the

view

that

core

components

represent

functional

units

Comparison

with

genome-wide

protein

abundance

and

localization

revealed

that

cores

and

modules

were

also

more

likely

to

be

expressed

at

a

similar

copy

number

-LRB-

Fig.

-RRB-

and

to

be

co-localized

in

the

cell

-LRB-

Fig.

-RRB-

Notably

attachments

showed

a

greater

heterogeneity

in

expression

levels

than

expected

from

random

supporting

the

notion

that

they

might

represent

non-stoichiometric

components

Cores

and

modules

showed

the

greatest

degree

of

similarity

in

terms

of

annotated

function

-LRB-

Fig.

-RRB-

When

considering

orthologous

proteins

in

other

species

cores

and

modules

were

least

likely

to

be

present

partially

that

is

if

one

component

was

present

-LRB-

or

absent

-RRB-

the

others

usually

were

also

-LRB-

Fig.

-RRB-

Finally

proteins

within

cores

and

modules

were

most

likely

to

be

in

direct

physical

contact

as

assessed

both

by

three-dimensional

structures

-LRB-

Fig.

-RRB-

and

the

yeast

two-hybrid

system

-LRB-

Fig.

-RRB-

Overall

the

greatest

degree

of

functional

similarity

and

physical

association

was

found

between

proteins

within

cores

or

modules

thus

strongly

supporting

the

model

Examples

of

protein-complex

architecture

The

analysis

was

able

to

capture

architectural

details

of

known

complexes

Attachments

often

specify

a

particular

function

for

a

complex

The

exosome

contains

the

complete

Ski

complex

among

its

attachments

-LRB-

Fig.

-RRB-

supporting

previous

reports

that

this

association

is

required

for

cytoplasmic

messenger

RNA

The

modular

architecture

can

also

capture

sequential

events

associated

with

pathways

providing

a

dynamic

view

of

cellular

processes

Complex

captured

three

discrete

functional

stages

in

de-adenylation-dependent

RNA

degradation

-LRB-

Fig.

-RRB-

The

core

of

the

complex

binds

to

de-adenylated

mRNAs

a

module

-LRB-

known

as

the

mRNA

de-capping

complex

-RRB-

removes

the

cap

and

the

attachment

protein

-LRB-

a

exonuclease

-RRB-

digests

the

We

identified

mutually

exclusive

modules

in

complexes

Of

these

appeared

to

be

related

to

differences

in

subcellular

locations

and

might

thus

specify

subtle

differences

in

function

Among

them

two

mutually

exclusive

cap-binding

modules

were

in

different

isoforms

of

complex

-LRB-

Fig.

-RRB-

The

first

-LRB-

or

-RRB-

is

cytoplasmic

and

essential

for

cap-dependent

translation

while

the

second

is

nuclear

and

plays

a

direct

role

in

pre-mRNA

processing

and

Other

architectures

hinted

at

novel

regulatory

mechanisms

Complex

formed

around

the

yeast

protein

contained

three

metabolic

enzymes

involved

in

the

heat

stress

a

neutral

trehalase

and

the

serine

palmitoyltransferase

complex

contained

three

predicted

motifs

and

formed

a

core

with

The

presence

of

as

a

module

suggested

the

assembly

of

alternative

complexes

around

A

common

control

mechanism

for

and

might

ensure

the

coordinated

production

of

two

metabolites

central

to

the

heat

shock

response

trehalose

and

sphingolipids

Similar

coordinated

control

of

metabolic

enzymes

through

phosphorylation

and

subsequent

binding

to

is

established

in

and

has

recently

been

proposed

for

human

A

modularity

matrix

across

functions

We

derived

a

matrix

representing

a

global

view

of

the

connections

between

cores

and

modules

-LRB-

Fig.

-RRB-

There

was

a

strong

tendency

for

modules

to

combine

with

cores

in

the

same

functional

category

suggesting

coherence

in

our

assignment

of

core

and

module

composition

Using

the

guilt-by-association

principle

it

is

possible

to

suggest

functions

for

modules

For

example

the

novel

module

-LRB-

and

-RRB-

combined

with

several

cores

involved

in

ribosome

biogenesis

suggesting

a

role

in

this

process

Module

-LRB-

and

-RRB-

associated

with

the

translation

initiation

complex

supporting

previous

genetic

evidence

for

a

role

in

RNA

The

degree

of

core

module

cross-talk

between

functional

categories

-LRB-

Fig.

-RRB-

highlights

many

known

connections

such

as

that

between

protein

synthesis

transcription

and

the

cell

cycle

in

addition

to

others

less

well

established

For

instance

the

many

links

between

metabolism

and

transcription

are

supported

by

recent

findings

of

roles

for

metabolic

enzymes

in

transcriptional

Similarly

strong

links

between

cell

metabolism

and

defence

argue

for

a

re-evaluation

of

yeast

metabolic

pathways

as

targets

for

anti-fungal

drug

discovery

Complexes

as

a

scaffold

for

genetic

data

Interaction

networks

have

been

used

previously

to

study

the

effect

of

gene

knockouts

for

example

showing

that

proteins

central

in

networks

tend

to

be

lethal

when

More

recently

studies

have

systematically

monitored

the

effects

of

loss

of

function

under

a

series

of

different

leading

to

phenotypic

profiles

which

are

ideal

for

probing

protein-complex

architecture

-LRB-

Fig.

-RRB-

We

found

Nature

Publishing

Group

NATURE

Vol

March

ARTICLES

Figure

Phenotypic

data

mapped

to

complexes

a

Novel

complex

b

HOPS

-LRB-

homotypic

fusion

and

vacuole

protein

sorting

-RRB-

c

adaptor

complex

e

amphiphysin-like

complex

and

the

module

f

Pyruvate

and

a-ketoglutarate

dehydrogenase

g

complex

Details

are

as

for

Fig.

d

Phenotypic

effect

of

deletion

of

complex

Shaded

cells

indicate

a

growth

defect

-LRB-

slow

growth

or

no

growth

relative

to

the

control

-RRB-

those

boxed

in

red

represent

the

phenotypic

signature

of

the

complex

Similarities

-LRB-

mean

number

of

phenotypes

shared

by

components/total

number

of

phenotypes

-RRB-

were

calculated

for

complexes

Sensitivity

phenotypes

-LRB-

-RRB-

paraquat

ethanol

hygromycin-B

caffeine

rapamycin

cycloheximide

hydroxyurea

galactose

high

salt

raffinose

glycerol

lactate

benomyl

and

low

phosphate

complexes

with

at

least

two

proteins

present

in

a

data

set

of

yeast

of

which

showed

similar

phenotypic

patterns

-LRB-

Fig.

random

behaviour

would

predict

only

five

-RRB-

In

one

case

profile

similarity

supported

the

authenticity

of

a

novel

complex

-LRB-

Fig.

-RRB-

In

others

there

is

evidence

that

shared

proteins

play

wider

roles

than

the

individual

complexes

they

are

part

of

For

example

the

pyruvate

and

a-ketoglutarate

dehydrogenase

complexes

show

similar

phenotypes

but

the

lipoamide

dehydrogenase

subunit

-LRB-

-RRB-

shared

between

them

has

other

phenotypes

suggesting

that

it

could

have

additional

functions

-LRB-

Fig.

-RRB-

These

examples

highlight

the

promise

for

the

molecular

machinery

described

here

to

provide

a

molecular

rationale

for

gene-to-phenotype

relationships

Discussion

This

analysis

represents

only

a

snapshot

of

the

proteome

averaged

over

all

phases

of

the

cell

cycle

Nevertheless

this

is

the

first

screen

for

complexes

run

to

saturation

and

as

such

it

serves

as

a

guide

for

the

future

exploration

of

protein

interactions

under

other

physiological

states

For

example

we

do

not

expect

protein-complex

cores

to

vary

extensively

under

different

conditions

whereas

we

expect

significant

changes

to

occur

in

attachment

proteins

Extrapolation

based

on

the

fraction

of

known

complexes

recovered

suggests

that

there

may

be

an

additional

core

machines

leading

to

a

total

of

in

yeast

In

a

rough

approximation

based

on

the

ratio

of

gene

numbers

between

species

we

estimate

some

core

human

complexes

The

number

of

protein-complex

cores

is

small

compared

to

the

many

cellular

processes

mediated

by

them

and

shuffling

functional

modules

provides

an

efficient

means

to

multiply

functionality

and

simplify

temporal

and

spatial

regulation

The

modularity

is

highly

reminiscent

of

that

seen

elsewhere

in

nature

for

example

the

combinatorial

use

of

amino

acids

to

build

polypeptides

or

domains

to

create

proteins

with

complex

biochemical

properties

Modularity

might

very

well

represent

a

general

attribute

of

living

matter

with

de

novo

invention

being

rare

and

reuse

the

norm

Genome

sequencing

and

functional

genomics

have

provided

a

parts-list

and

partial

knowledge

of

how

these

parts

are

arranged

in

space

and

time

The

next

challenge

is

to

integrate

these

data

into

rational

models

of

entire

systems

Our

analysis

makes

some

first

steps

in

this

direction

providing

a

collection

of

individual

integrative

subsystems

the

machines

but

also

a

view

on

how

they

might

coordinate

cellular

functions

through

sharing

functional

modules

As

such

it

may

be

a

very

useful

platform

for

systems

biology

and

indeed

new

applications

in

nano

and

synthetic-biology

that

seek

to

re-engineer

the

cellular

machinery

towards

new

processes

METHODS

Experimental

procedures

We

created

a

library

of

strains

with

TAP-tag

cassettes

at

the

end

of

each

ORF

by

homologous

recombination

We

prepared

protein

extracts

from

exponentially

growing

haploid

yeast

strains

grown

in

l

of

complete

medium

Tandem-affinity

purification

-LRB-

TAP

-RRB-

mass

spectrometry

-LRB-

MS

-RRB-

characterization

of

complexes

was

performed

as

previously

For

membrane

proteins

we

used

a

special

protocol

provided

as

Supplementary

Information

Socio-affinity

and

iterative

clustering

to

generate

protein-complex

sets

We

defined

a

socio-affinity

index

-LRB-

A

-LRB-

i

j

-RRB-

-RRB-

that

quantifies

the

tendency

for

proteins

to

identify

each

other

when

tagged

-LRB-

the

spoke

model

S

-RRB-

and

to

co-purify

when

other

proteins

are

tagged

-LRB-

the

matrix

model

M

-RRB-

ni

jji

bait

A

i

j

Si

jji

bait

Si

jjj

bait

M

i

j

Si

jji

bait

log

bait

prey

prey

f

i

nbait

f

j

ni

bait

prey

ni

j

B

C

M

i

j

log

prey

prey

P

A

fi

fj

nprey

nprey

all

baits

Nature

Publishing

Group

ARTICLES

NATURE

Vol

March

For

the

spoke

model

terms

-LRB-

S

-RRB-

ni

jji

bait

is

the

number

of

times

that

protein

i

retrieves

j

when

i

is

tagged

f

bait

is

the

fraction

of

purifications

where

protein

i

i

prey

was

bait

f

j

is

the

fraction

of

all

retrieved

preys

that

were

protein

j

nbait

is

the

prey

total

number

of

purifications

-LRB-

that

is

baits

-RRB-

and

ni

bait

is

the

number

of

preys

prey

retrieved

with

protein

i

as

bait

For

the

matrix

model

term

-LRB-

M

-RRB-

ni

j

is

the

number

of

times

that

proteins

i

and

j

are

seen

in

purifications

with

baits

other

prey

prey

than

i

or

j

f

i

and

f

j

are

as

above

and

nprey

is

the

number

of

preys

observed

with

a

particular

bait

-LRB-

excluding

itself

-RRB-

We

used

socio-affinity

indices

to

populate

the

upper-diagonal

of

a

pair-wise

matrix

-LRB-

that

is

one

value

for

each

pair

of

proteins

in

the

data

set

-RRB-

We

assigned

a

value

of

zero

to

all

pairs

of

proteins

that

had

never

been

seen

together

We

generated

a

first

set

of

clusters

using

the

OC

program

-LRB-

G.

Barton

University

of

Dundee

-RRB-

and

then

subtracted

a

penalty

from

each

pair-wise

value

associated

with

the

set

We

then

repeated

the

cluster

generation

a

number

of

times

each

time

adding

any

new

clusters

to

a

growing

list

To

generate

different

sets

of

complexes

using

this

procedure

we

varied

the

number

of

iterations

-LRB-

-RRB-

the

socio-affinity

threshold

to

define

clusters

-LRB-

-RRB-

the

penalty

value

-LRB-

or

-RRB-

and

the

type

of

clustering

-LRB-

UPGMA

single

or

complete

linkage

-RRB-

Received

October

accepted

December

Published

online

January

Hood

L.

Heath

J.

R.

Phelps

M.

E.

Lin

B.

Systems

biology

and

new

technologies

enable

predictive

and

preventative

medicine

Science

-LRB-

-RRB-

Alberts

B

The

cell

as

a

collection

of

protein

machines

preparing

the

next

generation

of

molecular

biologists

Cell

-LRB-

-RRB-

Goh

C.

S.

Milburn

D.

Gerstein

M.

Conformational

changes

associated

with

protein

protein

interactions

Curr

Opin

Struct

Biol

-LRB-

-RRB-

Kemmeren

P.

et

al.

Protein

interaction

verification

and

functional

annotation

by

integrated

analysis

of

genome-scale

data

Mol

Cell

-LRB-

-RRB-

Edwards

A.

M.

et

al.

Bridging

structural

biology

and

genomics

assessing

protein

interaction

data

with

known

complexes

Trends

Genet

-LRB-

-RRB-

Puig

O.

et

al.

The

tandem

affinity

purification

-LRB-

TAP

-RRB-

method

a

general

procedure

of

protein

complex

purification

Methods

-LRB-

-RRB-

Rigaut

G.

et

al.

A

generic

protein

purification

method

for

protein

complex

characterization

and

proteome

exploration

Nature

Biotechnol

-LRB-

-RRB-

Gavin

A.

C.

et

al.

Functional

organization

of

the

yeast

proteome

by

systematic

analysis

of

protein

complexes

Nature

-LRB-

-RRB-

Ho

Y.

et

al.

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identification

of

protein

complexes

in

Saccharomyces

cerevisiae

by

mass

spectrometry

Nature

-LRB-

-RRB-

Bouwmeester

T.

et

al.

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physical

and

functional

map

of

the

human

TNF-a

NF-kB

signal

transduction

pathway

Nature

Cell

Biol

-LRB-

-RRB-

Butland

G.

et

al.

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network

containing

conserved

and

essential

protein

complexes

in

Escherichia

coli

Nature

-LRB-

-RRB-

Uetz

P.

et

al.

A

comprehensive

analysis

of

protein

protein

interactions

in

Saccharomyces

cerevisiae

Nature

-LRB-

-RRB-

Rual

J.

F.

et

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Towards

a

proteome-scale

map

of

the

human

protein

protein

interaction

network

Nature

-LRB-

-RRB-

Aloy

P.

et

al.

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assembly

of

protein

complexes

in

yeast

Science

-LRB-

-RRB-

de

Lichtenberg

U.

Jensen

L.

J.

Brunak

S.

Bork

P.

Dynamic

complex

formation

during

the

yeast

cell

cycle

Science

-LRB-

-RRB-

Kelley

R.

Ideker

T.

Systematic

interpretation

of

genetic

interactions

using

protein

networks

Nature

Biotechnol

-LRB-

-RRB-

Mewes

H.

W.

et

al.

MIPS

A

database

for

genomes

and

protein

sequences

Nucleic

Acids

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-LRB-

-RRB-

Kumar

A.

et

al.

An

integrated

approach

for

finding

overlooked

genes

in

yeast

Nature

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-LRB-

-RRB-

Ghaemmaghami

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et

al.

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analysis

of

protein

expression

in

yeast

Nature

-LRB-

-RRB-

Huh

W.

K.

et

al.

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analysis

of

protein

localization

in

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yeast

Nature

-LRB-

-RRB-

Washburn

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Wolters

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Yates

J.

R.

III

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analysis

of

the

yeast

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protein

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technology

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Biotechnol

-LRB-

-RRB-

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H.

W.

et

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MIPS

Analysis

and

annotation

of

proteins

from

whole

genomes

Nucleic

Acids

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-LRB-

-RRB-

Liou

A.

K.

Willison

K.

R.

Elucidation

of

the

subunit

orientation

in

CCT

-LRB-

chaperonin

containing

-RRB-

from

the

subunit

composition

of

CCT

microcomplexes

EMBO

J.

-LRB-

-RRB-

Kraynack

B.

A.

et

al.

and

the

novel

-LRB-

-RRB-

protein

are

required

for

the

stability

of

the

Q/t-SNARE

complex

at

the

endoplasmic

reticulum

in

yeast

Mol

Biol

Cell

-LRB-

-RRB-

Kao

L.

R.

Peterson

J.

Ji

R.

Bender

L.

Bender

A.

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between

the

ankyrin

repeat-containing

protein

and

the

pheromone

response

pathway

in

Saccharomyces

cerevisiae

Mol

Cell

Biol

-LRB-

-RRB-

Bader

G.

D.

Hogue

C.

W

An

automated

method

for

finding

molecular

complexes

in

large

protein

interaction

networks

BMC

Bioinformatics

-LRB-

-RRB-

Dezso

Z.

Oltvai

Z.

N.

Barabasi

A.

L.

Bioinformatics

analysis

of

experimentally

determined

protein

complexes

in

the

yeast

Saccharomyces

cerevisiae

Genome

Res

-LRB-

-RRB-

Orban

T.

I.

Izaurralde

E.

Decay

of

mRNAs

targeted

by

RISC

requires

the

Ski

complex

and

the

exosome

RNA

-LRB-

-RRB-

Sheth

U.

Parker

R.

Decapping

and

decay

of

messenger

RNA

occur

in

cytoplasmic

processing

bodies

Science

-LRB-

-RRB-

Fortes

P.

et

al.

The

yeast

nuclear

cap

binding

complex

can

interact

with

translation

factor

and

mediate

translation

initiation

Mol

Cell

-LRB-

-RRB-

McKendrick

L.

Thompson

E.

Ferreira

J.

Morley

S.

J.

Lewis

J.

D.

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of

eukaryotic

translation

initiation

factor

with

the

nuclear

capbinding

complex

provides

a

link

between

nuclear

and

cytoplasmic

functions

of

the

guanosine

cap

Mol

Cell

Biol

-LRB-

-RRB-

Jenkins

G.

M.

Hannun

Y.

A.

Role

for

de

novo

sphingoid

base

biosynthesis

in

the

heat-induced

transient

cell

cycle

arrest

of

Saccharomyces

cerevisiae

J.

Biol

Chem

-LRB-

-RRB-

Sehnke

P.

C.

Ferl

R.

J.

Plant

Omnipotent

metabolic

phosphopartners

Sci

STKE

-LRB-

-RRB-

-LRB-

doi

stke

-RRB-

Pozuelo

Rubio

M.

et

al.

purification

of

over

human

phosphoproteins

reveals

new

links

to

regulation

of

cellular

metabolism

proliferation

and

trafficking

Biochem

J.

-LRB-

-RRB-

Winstall

E.

Sadowski

M.

Kuhn

U.

Wahle

E.

Sachs

A.

B

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Saccharomyces

cerevisiae

RNA-binding

protein

functions

in

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mRNA

metabolism

J.

Biol

Chem

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Shi

Y.

Shi

Y.

Metabolic

enzymes

and

coenzymes

in

transcription

a

direct

link

between

metabolism

and

transcription

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Genet

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Jeong

H.

Mason

S.

P.

Barabasi

A.

L.

Oltvai

Z.

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and

centrality

in

protein

networks

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-RRB-

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A.

M.

Janse

D.

M.

Tanay

A.

Shamir

R.

Church

G.

M

A

global

view

of

pleiotropy

and

phenotypically

derived

gene

function

in

yeast

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Syst

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published

online

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-LRB-

doi

-RRB-

Said

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R.

Begley

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J.

Oppenheim

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V.

Lauffenburger

D.

A.

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L.

D.

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network

analysis

of

phenotypic

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protein

networks

and

toxicity

modulation

in

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cerevisiae

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Natl.

Acad

Sci

USA

-LRB-

-RRB-

Bader

G.

D.

Hogue

C.

W.

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yeast

protein

protein

interaction

data

obtained

from

different

sources

Nature

Biotechnol

-LRB-

-RRB-

Subramanian

S.

Woolford

C.

A.

Jones

E.

W

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protein

functions

at

the

endosome

and

the

vacuole

of

Saccharomyces

cerevisiae

Mol

Biol

Cell

-LRB-

-RRB-

Friesen

H.

Colwill

K.

Robertson

K.

Schub

O.

Andrews

B.

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of

the

Saccharomyces

cerevisiae

cortical

actin

patch

protein

with

proteins

involved

in

ER

to

golgi

vesicle

trafficking

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-LRB-

-RRB-

Ross

J.

Reid

G.

A.

Dawes

I.

W

The

nucleotide

sequence

of

the

gene

encoding

lipoamide

dehydrogenase

in

Saccharomyces

cerevisiae

comparison

between

eukaryotic

and

prokaryotic

sequences

for

related

enzymes

and

identification

of

potential

upstream

control

sites

J.

Gen.

Microbiol

-LRB-

-RRB-

Supplementary

Information

is

linked

to

the

online

version

of

the

paper

at

Acknowledgements

We

thank

C.

Cohen

S.

Artavanis-Tsakonas

B.

Seraphin

and

L.

Serrano

for

support

and

suggestions

throughout

the

work

and

F.

Weisbrodt

for

assistance

with

the

graphics

Author

Information

Purification

and

complex

data

have

been

deposited

at

the

IntAct

database

-LRB-

-RRB-

with

accession

numbers

-LRB-

purifications

-RRB-

and

-LRB-

author

inferred

complexes

-RRB-

The

data

including

the

MS

protein

identifications

are

accessible

at

and

the

yeast

strains

are

available

from

Euroscarf

-LRB-

http

-RRB-

Reprints

and

permissions

information

is

available

at

npg.nature.com/reprintsandpermissions

The

authors

declare

no

competing

financial

interests

Correspondence

and

requests

for

materials

should

be

addressed

to

G.S.-F

-LRB-

gsuperti@cemm.oeaw.ac.at

-RRB-

R.B.R.

-LRB-

russell@embl-heidelberg.de

-RRB-

Nature

Publishing

Group

O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

protein network complexes yeast modularity proteomics