Global quantification of mammalian gene expression control

Transcription

1 ARTICE doi:1.138/nture198 Globl quntifiction of mmmlin gene expression control Björn Schwnhäusser 1, Dorothe Busse 1,Ni 1, Gunnr Dittmr 1, Johnnes Schuchhrdt 2, Jn Wolf 1, Wei Chen 1 & Mtthis Selbch 1 Gene expression is multistep process tht involves the trnscription, trnsltion nd turnover of messenger RNAs nd proteins. Although it is one of the most fundmentl processes of life, the entire cscde hs never been quntified on genome-wide scle. ere we simultneously mesured bsolute mrna nd protein bundnce nd turnover by prllel metbolic pulse lbelling for more thn 5, genes in mmmlin cells. Wheres mrna nd protein levels correlted better thn previously thought, corresponding hlf-lives showed no correltion. Using quntittive model we hve obtined the first genome-scle prediction of synthesis rtes of mrnas nd proteins. We find tht the cellulr bundnce of proteins is predominntly controlled t the level of trnsltion. Genes with similr combintions of mrna nd protein stbility shred functionl properties, indicting tht hlf-lives evolved under energetic nd dynmic constrints. Quntittive informtion bout ll stges of gene expression provides rich resource nd helps to provide greter understnding of the underlying design principles. The four fundmentl cellulr processes involved in gene expression re trnscription, mrna degrdtion, trnsltion nd protein degrdtion. It is now cler tht ech step of this cscde is controlled by gene-regultory events 1,2. Although ech individul process hs been intensively studied, little is known bout how the combined effect of ll regultory events shpes gene expression. The fundmentl question of how genomic informtion is processed t different levels to obtin specific cellulr proteome hs therefore remined unnswered. With regrd to quntittive description of gene expression, numerous previous studies compring mrna nd protein levels concluded tht the correltion is poor 3,4. owever, the vilble dt suffer from severl limittions. Most studies re limited to few hundred genes, minly due to the technicl chllenges involved in lrge-scle protein identifiction nd quntifiction. Also, protein levels mesured in one experiment re typiclly compred to mrna levels determined in different experiment performed t different time in different lbortory, mking it difficult to interpret why the correltion is low. Finlly, mrna nd protein levels result from coupled processes of synthesis nd degrdtion. Therefore, nlysis of mrna nd protein levels lone cnnot provide sufficient informtion to understnd gene expression comprehensively. mrna nd protein turnover cn be mesured with drugs to inhibit trnscription or trnsltion 5,6, but this hs severe side effects. Studies bsed on rtificil fusion proteins re problemtic becuse tgging cn ffect protein stbility 7. To overcome these limittions we sought to quntify cellulr mrna nd protein expression levels nd turnover in prllel in popultion of unperturbed mmmlin cells. Pulse lbelling with rdioctive nucleosides or mino cids is regrded s the gold stndrd method to determine mrna nd protein hlf-lives. Recently, vrints of this pproch bsed on non-rdioctive trcers hve been estblished 8 1. In stble isotope lbelling by mino cids in cell culture (SIAC), cells re cultivted in medium contining hevy stble-isotope versions of essentil mino cids 11. When non-lbelled (tht is, light) cells re trnsferred to hevy SIAC growth medium, newly synthesized proteins incorporte the hevy lbel while pre-existing proteins remin in the light form. This strtegy cn be used to mesure protein turnover or reltive chnges in protein trnsltion 15,16. Similrly, newly synthesized RNA cn be lbelled with the nucleoside nlogue 4-thiouridine (4sU). 4sU-contining mrna cn be purified nd compred with the preexisting frction to compute mrna hlf-lives 1. Pulse lbelling of proteins nd mrnas We used prllel metbolic pulse lbelling with mino cids nd 4sU to mesure simultneously protein nd mrna turnover in popultion of exponentilly growing non-synchronized NI3T3 mouse fibroblsts (Fig. 1). Protein smples were collected t three time points, mesured by liquid chromtogrphy nd online tndem mss spectrometry (C-MS/MS) nd nlysed with the MxQunt softwre pckge 17. We identified 84,676 peptide sequences nd ssigned them to 6,445 unique proteins (flse discovery rte,1% t the peptide nd protein level). A totl of 5,279 of these proteins ws quntified by t lest three hevy to light (/) peptide rtios (Fig. 1b). Tissuespecific mino cid precursor pools nd recycling rtes, pervsive problem for in vivo pulse lbelling experiments 9,18,19, did not pprecibly ffect our results (Supplementry Fig. 1). For constnt incorportion rtes the logrithm of / rtios should increse linerly with time (Fig. 1c). Ninety-three per cent of proteins showed excellent liner correltion indicted by vribility of the liner regression slope smller thn 1% (Fig. 1d). Protein bundnce did not influence / rtio mesurements (Supplementry Fig. 2). In totl, we obtined confident set of 5,28 protein hlf-lives clculted from the slope of the regression line. Cycloheximide chse experiments for selected proteins spnning representtive rnge of hlf-lives greed well with hlf-lives determined by pulsed lbelling nd mss spectrometry (Supplementry Fig. 3). In prllel, we pulse lbelled newly synthesized RNA for 2 h with 4sU. RNA smples were frctionted into the newly synthesized nd pre-existing frctions. Both frctions nd the totl RNA smple were nlysed by mrna sequencing nd quntified by mpping reds to their exonic region 2. We clculted mrna hlflives bsed on the rtios of newly synthesized RNA/totl RNA rtio nd the pre-existing RNA/totl RNA 1. 1 Mx Delbrück Center for Moleculr Medicine, Robert-Rössle-Str. 1, D-1392 Berlin, Germny. 2 MicroDiscovery Gmb, Mrienburger Str. 1, D-145 Berlin, Germny. 211 Mcmilln Publishers imited. All rights reserved 19 MAY 211 VO 473 NATURE 337

2 RESEARC ARTICE Proteins SIAC light SIAC hevy (t 1,t 2,t 3 ) Pre-existing Newly proteins synthesized / rtio proteins Pre-existing RNA Intensity mrnas Seprtion 4 μm 4sU (2 h) RNA isoltion nd biotinyltion Without seprtion Newly synthesized RNA Solex sequencing Totl RNA b c Reltive intensity Reltive intensity ln(rtio+1) t 1 (1.5 h) (APTNPSVEDEPR) / rtio = t 3 (13.5 h) ist1h1c t 1 (1.5 h) 1 t 2 (4.5 h) 1 (SEAAPAAPAAAPPAEK) ist1h1c ist1h1c 8 (SEAAPAAPAAAPPAEK) (SEAAPAAPAAAPPAEK) 8 / rtio =.63 / rtio =.5 6 / rtio = t 1/2 = 4.5 h R 2 = ist1h1c t 1/2 = 62.1 h R 2 =.99 t 2 (4.5 h) (APTNPSVEDEPR) / rtio = % d t 3 (13.5 h) (APTNPSVEDEPR) / rtio = Figure 1 Prllel quntifiction of mrna nd protein turnover nd levels., Mouse fibroblsts were pulse lbelled with hevy mino cids (SIAC, left) nd the nucleoside 4-thiouridine (4sU, right). Protein nd mrna turnover ws quntified by mss spectrometry nd next-genertion sequencing, respectively. b, Mss spectr of peptides from high- nd low-turnover protein revel Proteins were, on verge, five times more stble (medin hlf-life of 46 h) thn mrnas (9 h) nd spnned bigger dynmic rnge (Fig. 2). Becuse very long (.2 h) nd very short (,3 min) protein hlflives cnnot be ccurtely quntified from our three time points, the true dynmic rnge of protein stbilities my be even higher. Notbly, we found no correltion between protein nd mrna hlf-lives (Fig. 2c, R 2 5.2, log log scle). Absolute mrna nd protein copy numbers We clculted bsolute cellulr mrna copy numbers bsed on the number of sequencing reds in the unfrctionted smple in conjunction 1, mrna 8 medin: 9 h c 1, Protein hlf-life (h) Protein medin: 46 h , Averge cellulr hlf-life (h) d 1, b Protein copies per cell , Averge copies per cell 1 R 2 =.2 R 2 = , mrna hlf-life (h) mrna copies per cell , mrna medin: 17 Protein medin: 16, Figure 2 mrna nd protein levels nd hlf-lives., b, istogrms of mrna (blue) nd protein (red) hlf-lives () nd levels (b). Proteins were on verge 5 times more stble nd 9 times more bundnt thn mrnas nd spnned higher dynmic rnge. c, d, Although mrna nd protein levels correlted significntly, correltion of hlf-lives ws virtully bsent. t 1 t 2 t 3 rvesting time point Vribility of liner regression slope (%) incresing hevy to light (/) rtios over time. c, Protein hlf-lives were clculted from log / rtios t ll three time points using liner regression. d, Vribility of liner regression slopes ssessed by leve-one-out crossvlidtion ws smll. with informtion on cellulr mrna content 2. Absolute protein copy numbers cn be inferred from mss spectrometry dt 21,22. To this end, we used the sum of pek intensities of ll peptides mtching to specific protein. When divided by the number of theoreticlly observble peptides, this vlue provides n ccurte proxy for protein levels ( intensitybsed bsolute quntifiction or ibaq, see Supplementry Methods). evels of detected proteins spnned pproximtely five orders of mgnitude (Fig. 2b). Reltively few proteins hd less thn 1 copies per cell, indicting tht some proteins of low bundnce escped detection. Indeed, we observed moderte detection bis (Supplementry Fig. 4) nd therefore restricted our nlysis to genes tht were identified t both the mrna nd protein level. In this subset, proteins were, on verge,,9 times more bundnt thn corresponding trnscripts. Despite huge spred, mrna nd protein levels were clerly correlted (Fig. 2d, R , log log scle). This correltion is considerbly higher thn in ny previous study in mmmls 3,4,23. An ttempt to improve this correltion further by nonliner trnsformtion resulted only in mrginl increse (R , Supplementry Fig. 5). It seems tht for our dt set, this is bout the mximum correltion between mrna nd protein tht cn be chieved without dditionl informtion. Reproducibility To investigte the experimentl noise we performed second independent lrge-scle experiment nd mesured mrna nd protein levels nd hlf-lives gin. The overll correltion of hlf-lives nd levels between both replictes ws good (Supplementry Fig. 6 nd Supplementry Tble 1). Removing less-consistent dt points did not increse correltion between mrna nd protein levels or hlflives (Supplementry Fig. 7). Thus, noise hs little impct on the observed correltion between mrna nd protein levels nd hlf-lives. We lso vlidted bsolute mrna nd protein copy numbers using independent methods. For mrna copy numbers we used the NnoString technology, which cptures nd counts individul trnscripts without enzymtic rections 24. Correltion between sequencing nd NnoString dt ws high (r 5.79, see lso Supplementry Fig 8). Absolute protein quntifiction ws vlidted by spike-in 338 NATURE VO MAY Mcmilln Publishers imited. All rights reserved

3 ARTICE RESEARC experiments using mixture of 48 proteins with known concentrtions (Supplementry Fig. 8b). ibaq vlues correlted well with known bsolute protein mounts over t lest four orders of mgnitude nd hd higher precision nd ccurcy thn lterntive mesures of bsolute protein bundnce (dt not shown) 21,22. We lso ssessed degrdtion nd synthesis rtes for mrnas nd proteins by ctinomycin D nd cycloheximide tretment, respectively. For high turnover proteins nd mrnas we obtined results consistent with pulse lbelling dt (Supplementry Fig. 8c f). A quntittive model of gene expression Our dt llow us to clculte verge synthesis rtes of mrnas nd proteins for thousnds of genes using mthemticl model (Fig. 3 nd Supplementry Methods). The experimentl dt re bsed on popultion of non-synchronized cells. Therefore, our estimted rtes provide n verge over the popultion nd time. Averge cellulr trnscription rtes predicted by the model spnned two orders of mgnitude with medin of bout two mrna molecules per hour (Fig. 3b). An extreme exmple ws Mdm2 with more thn 5 mrnas per hour. A microscopic study on the cytomeglovirus (CMV) promoter reported trnscription termintion rtes of 5.8 to 8.7 mrnas per hour 25. These vlues re bove the medin of our predictions, s perhps expected for strong promoter system. Next, we clculted trnsltion rte constnts; tht is, how mny proteins re mde from ech mrna templte per hour (Fig. 3c). We find medin trnsltion rte constnt of bout 4 proteins per mrna per hour. Severl proteins involved in trnsltionl regultion such s the trnsltion initition fctor eif4g1, frgile X syndrome relted protein Fxr2 nd tuberin hd extremely low rte constnts nd were trnsltionlly repressed. Plotting trnsltion rte constnts ginst protein levels reveled tht bundnt proteins re trnslted bout 1 times more efficiently thn those of low bundnce (Fig. 3d). ence, different trnsltion efficiencies contribute to the higher dynmic rnge of proteins compred to mrnas (Fig. 2b). Intriguingly, trnsltion rte constnts sturted t round 18 protein copies per mrna per hour. To our knowledge, the mximl c v sr mrna k sp x [mrna] k dr x [mrna] k dp x [protein] protein 25 b v sr (mrnas per hour) d k sp (proteins per mrna per hour) , , k sp (proteins per mrna per hour) Protein copies per cell 5 1 Figure 3 Quntittive model of gene expression in growing cells., mrnas re synthesized with the rte v sr nd degrded with rte constnt k dr. Proteins re trnslted nd degrded with rte constnts k sp nd k dp, respectively. b, Clculted mrna trnscription rtes show uniform distribution. c, Clculted trnsltion rte constnts re not uniform. d, Trnsltion rte constnts of bundnt proteins sturte between pproximtely 12 nd 24 proteins per mrna per hour. Red line shows the loclly weighted fit (owess). Dshed lines indicte 95% confidence intervls of the owess mximum vlue clculted by bootstrpping. trnsltion rte constnt in mmmls is not known. On the bsis of ref. 1, the estimted mximl trnsltion rte constnt in se urchin embryos is 14 copies per mrna per hour, which is surprisingly close to the prediction of our model. Control of gene expression A long-stnding question is how much protein bundnce is controlled t the trnscriptionl, post-trnscriptionl, trnsltionl nd post-trnsltionl levels. Until now, this hs minly been ddressed indirectly by nlysing mrna nd protein sequence fetures. Fetures relted to trnsltion initition (for exmple, Shine Dlgrno, Kozk nd 39 untrnslted region (UTR) sequences), elongtion (for exmple, codon bis) nd protein stbility (for exmple, degrons) hve been nlysed nd reported to correlte prtilly with protein/mrna rtios in bcteri, yest nd mmmls 23,26,27. We lso observed sequence fetures chrcteristic of mrna nd protein stbility nd found tht mrnas with long 39 UTRs re, on verge, less stble (Supplementry Fig. 9). In ddition, the density of AU-rich elements nd binding motifs of specific RNA-binding protein (pumilio 2) correlted negtively with mrna stbility (Supplementry Fig. 1). ighly structured proteins were more stble thn unstructured ones (Supplementry Fig. 11). We lso identified mino cids over-represented in unstble proteins (Supplementry Fig. 11b). Sequence fetures re t best indirect proxies for mechnisms controlling protein bundnce. ow much efficiencies of different steps in the gene expression cscde contribute to vrince of cellulr protein copy numbers cn only be reveled by direct prllel genome-scle mesurements of mrna nd protein levels nd hlf-lives which were not vilble previously. In our dt the coefficient of determintion (R 2 ) between mrna nd protein copy numbers is.41 (Fig. 2d). Assuming the bsence of technicl nd biologicl noise, this mens tht,4% of the vrince in protein levels is explined by mrna levels considerbly more thn previously thought (Fig. 4). Most of this 4% is due to different trnscription rtes, wheres mrna stbility hs smller role. Considering trnsltion rte constnts mrkedly boosts R 2 to.95. Thus, trnsltion rte constnts hve the dominnt role for control of protein levels. Unexpectedly, the impct of protein degrdtion is rther smll. In the bove nlysis the sme experimentl dt were used to clculte synthesis rtes nd to estimte their impct on protein levels. To void this over-fit nd to ssess relibility of the model predictions we performed the sme nlysis with dt from the biologicl replicte experiment. In the replicte the coefficient of determintion between mrna nd protein levels ws.37 (Fig. 4b). We then used the model including the estimted prmeters from the first experiment to predict protein levels from mrna levels in the replicte dt. Predicted protein levels greed very well with mesured protein levels (R , Fig. 4c). Therefore, the model explins,85% of the vribility in protein copy numbers in n independent experiment. The correltion is very similr to the direct comprison of protein levels in both experiments (R , Supplementry Fig. 6d). We conclude tht technicl nd biologicl noise in our dt re low, nd tht the model fithfully predicts protein levels from mrna levels in mouse fibroblsts. It lso indictes tht the estimted impct of trnscription, mrna stbility, trnsltion nd protein stbility on protein bundnce is reproducible. We finlly ssessed how much of the efficiencies of the vrious steps in gene expression re retined in different cell type nd orgnism. To this end, we quntified mrna nd protein bundnce in the humn brest cncer cell line MCF7 by RNA-seq nd mss spectrometry, respectively. A totl of 2,3 humn genes from the MCF7 dt set hd orthologues in the mouse fibroblst dt. We then used rtes from the mouse fibroblst model to predict protein levels from mrna levels in humn brest cncer cells. In MCF7 cells, the model predicted,6% of the vribility in protein levels (Fig. 4). Although the frction explined by the model is smller thn in mouse fibroblsts, this indictes tht trnsltion nd degrdtion rtes re to 211 Mcmilln Publishers imited. All rights reserved 19 MAY 211 VO 473 NATURE 339

4 RESEARC ARTICE Predictive power (%) b Protein copies per cell, replicte Model dt R 2 =.37 NI3T3 replicte MCF , mrna copies per cell, replicte mrna trnscription (v sr ) mrna degrdtion (k dr ) mrna levels Protein trnsltion (k sp ) Protein degrdtion (k dp ) Noise/vribility some extent independent of the cell type nd conserved between mouse nd humn. It is noticeble, however, tht the drop in prediction is minly due to the fct tht the trnsltion prt of the model performs less well. lf-lives nd gene function Degrdtion of proteins is criticlly involved in mny cellulr processes including cell-cycle progression, signl trnsduction nd poptosis Similrly, mrna stbility is importnt for the temporl order of gene induction 1,31. Genes my hve evolved specific combintions of mrna nd protein hlf-lives under functionl constrints 1,31,32. We therefore sked if genes with specific combintions of mrna nd protein stbility hve distinct biologicl functions. We grouped genes ccording to their hlf-lives nd used gene ontology to find enriched biologicl processes (Fig. 5; see Supplementry Tble 2 for complete list). Genes with stble mrnas nd stble proteins were enriched in constitutive cellulr processes like trnsltion (tht is, ribosoml proteins), respirtion nd centrl metbolism (glycolysis, citric cid cycle). ence, mny housekeeping genes tend to hve stble mrnas nd proteins. In yest energy costs keep trnscription nd trnsltion rtes under selective pressure 33. We resoned tht energy constrints my explin why housekeeping genes tend to hve stble mrnas nd proteins. On the bsis of the model, we clculted the theoreticl energy required to mintin cellulr mrna nd protein levels by recycling from their building blocks (nucleotide monophosphtes nd mino cids, respectively) in terms of high energy phosphtes. This is conservtive estimte s splicing, folding nd trnsport re not included. Protein synthesis consumes more thn 9% of the energy wheres less thn 1% is needed for trnscription. A totl of 2% of the proteins consumed 8% of the energy for trnsltion (Preto principle or 8/2 rule). Consistent with optimiztion under energy constrints, bundnt proteins were significntly more stble thn less bundnt ones (Supplementry Fig. 12, P, 1 215, c Protein copies per cell replicte predicted from mrna levels replicte R 2 = Protein copies per cell, replicte Figure 4 Impct of different rtes nd rte constnts on protein bundnce., Protein levels re best explined by trnsltion rtes, followed by trnscription rtes. mrna nd protein stbility is less importnt (left br). b, In the replicte experiment mrna levels explined 37% of protein levels in NI3T3 cells (middle br in ). c, The model explins 85% of vrince in protein levels from mesured mrna levels (middle br in ). The mouse fibroblst model hs some predictive power for humn orthologous genes in MCF7 cells (right br in ). Error brs show 95% confidence intervls estimted by bootstrpping Protein hlf-life (h) , stble proteins unstble proteins mrna hlf-life (h) Stble mrnas/ stble proteins stble proteins Stble mrnas/ unstble proteins unstble proteins 1.5 Stble mrnas/ stble proteins Stble mrnas/ unstble proteins z-trnsformed log 1 P-vlue Genertion of precursor metbolites/energy Oxidtion reduction Purine nucleotide metbolic process Monoscchride metbolic process Cellulr respirtion Tricrboxylic cid cycle Glycolysis Secondry metbolic process Gluconeogenesis Trnsltion Chromtin orgniztion Chromtin modifiction Cell division Mitosis Cell cycle Trnscription Regultion of trnscription Ribosome biogenesis Regultion of cytokine production ncrna processing RNA splicing trna processing Dephosphoryltion mrna processing Regultion of cell prolifertion Defence response Glycogen metbolic process Cellulr iron ion homeostsis Integrin-medited signlling pthwy Cell dhesion Cellulr ction homeostsis Chemicl homeostsis Phosphoryltion Proteolysis Figure 5 Functionl chrcteristics of genes with different mrna nd protein hlf-lives. Genes were grouped ccording to their combintion of mrna nd protein hlf-lives nd nlysed for enriched gene ontology terms. A het mp of enrichment P-vlues revels functionl similrities of genes with similr combintions of hlf-lives. Wilcoxon test). This is not necessrily expected becuse the overll contribution of protein stbility to protein levels is very smll (Fig. 4). In ddition, bundnt proteins were significntly shorter (Supplementry Fig. 12b). Shuffling protein hlf-lives nd lengths mrkedly incresed theoreticl energy consumption (Supplementry Fig. 12c). Collectively, these observtions indicte tht mmmlin gene expression evolved under energy constrints. The subset of genes with unstble mrnas nd proteins ws strongly enriched in trnscription fctors, signlling genes, chromtin modifying enzymes nd genes with cell-cycle-specific functions (Fig. 5). Becuse mrnas nd proteins re informtion crriers, their degrdtion cn be NATURE VO MAY Mcmilln Publishers imited. All rights reserved

5 ARTICE RESEARC interpreted s built-in timer tht controls the persistence of genetic informtion 34. It therefore mkes intuitive sense tht mny regultory genes hve short mrna nd protein hlf-lives. owever, it must be stressed tht popultion-level dt cnnot provide informtion bout individul cells or molecules. The group of genes with stble proteins but unstble mrnas ws strongly enriched in terms relted to processing of mrnas, trnas nd non-coding RNAs. ence, mny mmmlin RNA-binding proteins re stble wheres their encoding trnscripts re short lived, s lso found in yest 35. Becuse mny RNA-binding proteins bind their own messge 36, this observtion is indictive of post-trnscriptionl negtive feedbck loop for RNA-binding proteins. Consistently, we found tht unstble mrnas re enriched for binding motifs of RNA-binding proteins (Supplementry Fig. 1). Finlly, the subset of genes with stble mrnas nd unstble proteins ws rich in extrcellulr proteins. This is expected, s secreted proteins hve short cellulr hlf-life. Additionlly, this group contins proteins involved in cellulr homeostsis, defence response nd proteolysis. This set contins two ferritin proteins tht re rpidly upregulted in response to iron 37. Ferritins re clssic exmples of trnsltionlly regulted genes. As trnsltionl regultion is not dependent on mrna hlf-lives, genes with stble mrnas cn still be dynmiclly regulted s long s their protein hlf-lives re short. It is tempting to speculte tht other homeostsis genes in this group re regulted t the level of trnsltion. Discussion Although gene expression is one of the most fundmentl processes in biology it hs never been quntified comprehensively. We provide the first nlysis of mrna nd protein levels, hlf-lives, trnscription rtes nd trnsltion rte constnts for thousnds of genes. In the future, dditionl methods like sequencing of nscent trnscripts nd ribosome profiling my further refine this picture 38,39. We found tht mrna levels explin round 4% of the vribility in protein levels. This frction is higher thn in previous studies on mmmls 3,4,23. We found tht in mouse fibroblsts, trnsltion efficiency is the single best predictor of protein levels. ence, protein bundnce seems to be predominntly regulted t the ribosome, highlighting the importnce of trnsltionl control 4,41. Whether this observtion is vlid in other cell types is not known. A recent study on embryonic stem cells reveled tht chnges in protein levels re not ccompnied by chnges in corresponding mrnas 42. It is lso not cler how much trnsltion rte constnts chnge under different conditions. Our observtion tht the mouse model cn to some degree predict levels of orthologous proteins in MCF7 cells suggests tht trnsltion efficiency is prtilly hrd-coded in the genome nd is not subject to chnge. Compred to trnsltionl control, protein stbility seems to hve minor role in cellulr protein bundnce in our system. This is surprising s protein degrdtion is involved in the regultion of mny cellulr processes From the globl perspective, the dominnce of trnsltionl regultion mkes sense given the high energy costs ssocited with protein synthesis. owever, it should lso be stressed tht our dt set represents verge vlues derived from popultion of dividing, non-synchronized cells. At the single cell level, the role of protein degrdtion for protein bundnce my be higher. Similrly, protein degrdtion my be more importnt upon perturbtion. Gene expression my follow certin design principles for optiml evolutionry fitness. Intriguingly, we found tht genes with certin combintions of mrna nd protein hlf-lives shre common functions, indicting tht they evolved under similr constrints. One of these constrints my be energy efficiency 33. Consistently, we observed tht the theoreticl energy needed for gene expression is much lower thn rndom. A second constrint my be the bility of genes to respond quickly to stimulus. We find tht mny trnscription fctors nd genes with cell-cycle-specific function hve unstble mrnas nd proteins, predisposing them to rpid trnscriptionl nd/or trnsltionl regultion. In ddition, genes with stble mrnas but unstble proteins cn be regulted quickly t the level of trnsltion. These observtions re consistent with the ide tht mny fst-responding genes hve short protein nd/or mrna hlf-lives 1,31,32,43. The globl picture is tht most mrnas nd especilly proteins re stble unless genes need to respond quickly to stimulus. Owing to the trde-off between dynmic regultion nd energy efficiency, this my be n optiml design. Our dt provide rich resource for the scientific community tht cn be mined in mny wys tht re beyond the scope of this study (see Supplementry Tble 3 for the entire dt set). For exmple, we provide by fr the lrgest dt set on protein copy numbers, which contins vluble informtion for modelling of cellulr processes nd stoichiometry of protein complexes 22. lf-lives of proteins nd mrnas cn be used to serch for properties of unstble mrnas or proteins, nd we provide first nlysis of chrcteristic sequence fetures (Supplementry Figs 9 nd 1). Genome-scle quntittive dt on bsolute mrna nd protein levels nd hlf-lives will certinly help to understnd the complex reltionships between thousnds of genes nd their products in biologicl systems. Note dded in proof: While this pper ws in revision, nother pper 44 reported tht chnges in mrna levels in dendritic cells re minly determined by trnscription rtes. This result is consistent with our findings in fibroblsts. Notbly, mrna hlf-lives reported in ref. 44 re considerbly shorter (see Supplementry Informtion for brief discussion). METODS SUMMARY NI3T3 cells grown in light () SIAC medium were simultneously pulselbelled with hevy () mino cids nd 4-thiouridine (4sU). For proteome nlysis, proteins were extrcted, seprted by SDS polycrylmide gel electrophoresis (PAGE), trypsin-digested nd nlysed by C-MS/MS on high-resolution instruments (TQ-Orbitrp X nd Velos, Thermo Fisher). Rw files were processed by MxQunt (version ) for peptide/protein identifiction nd quntifiction. In totl 3,588,163 frgment spectr led to 972,333 peptide identifictions (84,676 unique peptide sequences) tht were ssigned to 6,445 unique proteins (flse discovery rte of 1% t the peptide nd protein level). Averge bsolute mss devition ws.29 prts per million (p.p.m.). Absolute protein mounts were clculted s the sum of ll peptide pek intensities divided by the number of theoreticlly observble tryptic peptides (intensity bsed bsolute quntifiction, or ibaq). RNA ws extrcted nd seprted into newly synthesized nd pre-existing frctions bsed on the incorported 4sU. Totl, pre-existing nd newly synthesized RNA smples were processed ccording to n mrna sequencing protocol (two rounds of oligo(dt) enrichment) nd nlysed on Solex GAIIX sequencing pltform (36 cycles). Reds were mpped to the mouse genome reference sequence (mm9, July 27) using SOAP2 with mximum of two mismtches llowed. Only uniquely mpped reds were retined. For more detils on dt cquisition, processing, nlysis nd modelling see Supplementry Methods. Received 16 November 21; ccepted 1 April Ben-Tbou de-eon, S. & Dvidson, E.. Modeling the dynmics of trnscriptionl generegultory networksforniml development. Dev. Biol. 325, (29). 2. Komili, S. & Silver, P. A. Coupling nd coordintion in gene expression processes: systems biology view. Nture Rev. Genet. 9, (28). 3. de Sous Abreu, R., Penlv,. O., Mrcotte, E. M. & Vogel, C. Globl signtures of protein nd mrna expression levels. Mol. Biosyst. 5, (29). 4. Mier, T., Guell, M. & Serrno,. Correltion of mrna nd protein in complex biologicl smples. FEBS ett. 583, (29). 5. Belle, A., Tny, A., Bitinck,., Shmir, R. & O She, E. K. Quntifiction of protein hlf-lives in the budding yest proteome. Proc. Ntl Acd. Sci. USA 13, (26). 6. Yng, E. et l. Decy rtes of humn mrnas: correltion with functionl chrcteristics nd sequence ttributes. Genome Res. 13, (23). 7. Yen,. C., Xu, Q., Chou, D. M., Zho, Z. & Elledge, S. J. Globl protein stbility profiling in mmmlin cells. Science 322, (28). 8. Gouw, J. W., Krijgsveld, J. & eck, A. J. Quntittive proteomics by metbolic lbeling of model orgnisms. Mol. Cell. Proteomics 9, (21). 9. Beynon, R. J. & Prtt, J. M. Metbolic lbeling of proteins for proteomics. Mol. Cell. Proteomics 4, (25). 211 Mcmilln Publishers imited. All rights reserved 19 MAY 211 VO 473 NATURE 341

6 RESEARC ARTICE 1. Friedel, C. C., Dolken,., Ruzsics, Z., Koszinowski, U.. & Zimmer, R. Conserved principles of mmmlin trnscriptionl regultion reveled by RNA hlf-life. Nucleic Acids Res. 37, e115 (29). 11. Mnn, M. Functionl nd quntittive proteomics using SIAC. Nture Rev. Mol. Cell Biol. 7, (26). 12. Doherty, M. K., mmond, D. E., Clgue, M. J., Gskell, S. J. & Beynon, R. J. Turnover of the humn proteome: determintion of protein intrcellulr stbility by dynmic SIAC. J. Proteome Res. 8, (29). 13. Milner, E., Brne, E., Beer, I. & Admon, A. The turnover kinetics of mjor histocomptibility complex peptides of humn cncer cells. Mol. Cell. Proteomics 5, (26). 14. m, Y. W., mond, A. I., Mnn, M. & Andersen, J. S. Anlysis of nucleolr protein dynmics revels the nucler degrdtion of ribosoml proteins. Curr. Biol. 17, (27). 15. Schwnhäusser, B., Gossen, M., Dittmr, G. & Selbch, M. Globlnlysis of cellulr protein trnsltion by pulsed SIAC. Proteomics 9, (29). 16. Selbch, M. et l. Widespred chnges inproteinsynthesis induced by micrornas. Nture 455, (28). 17. Cox, J. & Mnn, M. MxQunt enbles high peptide identifiction rtes, individulized p.p.b.-rnge mss ccurcies nd proteome-wide protein quntifiction. Nture Biotechnol. 26, (28). 18. Price, J. C., Gun, S., Burlingme, A., Prusiner, S. B. & Ghemmghmi, S. Anlysis of proteome dynmics in the mouse brin. Proc. Ntl Acd. Sci. USA 17, (21). 19. Wu, C. C., McCoss, M. J., owell, K. E., Mtthews, D. E. & Ytes, J. R. III. Metbolic lbeling of mmmlin orgnisms with stble isotopes for quntittive proteomic nlysis. Anl. Chem. 76, (24). 2. Mortzvi, A., Willims, B. A., McCue, K., Scheffer,. & Wold, B. Mpping nd quntifying mmmlin trnscriptomes by RNA-Seq. Nture Methods 5, (28). 21. u, P., Vogel, C., Wng, R., Yo, X. & Mrcotte, E. M. Absolute protein expression profiling estimtes the reltive contributions of trnscriptionl nd trnsltionl regultion. Nture Biotechnol. 25, (27). 22. Mlmström, J. et l. Proteome-wide cellulr protein concentrtions of the humn pthogen eptospir interrogns. Nture 46, (29). 23. Vogel, C. et l. Sequence signtures nd mrna concentrtion cn explin twothirds of protein bundnce vrition in humn cell line. Mol. Syst. Biol. 6, 4 (21). 24. Geiss, G. K. et l. Direct multiplexed mesurement of gene expression with colorcoded probe pirs. Nture Biotechnol. 26, (28). 25. Drzcq, X. et l. In vivo dynmics of RNA polymerse II trnscription. Nture Struct. Mol. Biol. 14, (27). 26. Arv, Y., Bos, F. E., Brown, P. O. & erschlg, D. Dissecting eukryotic trnsltion nd its control by ribosome density mpping. Nucleic Acids Res. 33, (25). 27. Wu, G., Nie,. & Zhng, W. Integrtive nlyses of posttrnscriptionl regultion in the yest Scchromyces cerevisie using trnscriptomicndproteomic dt. Curr. Microbiol. 57, (28). 28. Kirkptrick, D. S., Denison, C. & Gygi, S. P. Weighing in on ubiquitin: the expnding role ofmss-spectrometry-bsed proteomics. Nture CellBiol. 7, (25). 29. ershko, A. & Ciechnover, A. The ubiquitin system. Annu. Rev. Biochem. 67, (1998). 3. King, R. W., Deshies, R. J., Peters, J. M. & Kirschner, M. W. ow proteolysis drives the cell cycle. Science 274, (1996). 31. o, S. & Bltimore, D. The stbility of mrna influences the temporl order of the induction of genes encoding inflmmtory molecules. Nture Immunol. 1, (29). 32. egewie, S., erzel,., Westerhoff,. V. & Bluthgen, N. Recurrentdesign ptternsin the feedbck regultion of the mmmlin signlling network. Mol. Syst. Biol. 4, 19 (28). 33. Wgner, A. Energy constrints on the evolution of gene expression. Mol. Biol. Evol. 22, (25). 34. Pedrz, J. M. & Pulsson, J. Effects of moleculr memory nd bursting on fluctutions in gene expression. Science 319, (28). 35. Mittl, N., Roy, N., Bbu, M. M. & Jng, S. C. Dissecting the expression dynmics of RNA-binding proteins in posttrnscriptionl regultory networks. Proc. Ntl Acd. Sci. USA 16, (29). 36. ogn, D. J., Riordn, D. P., Gerber, A. P., erschlg, D. & Brown, P. O. Diverse RNAbinding proteins interct with functionlly relted sets of RNAs, suggesting n extensive regultory system. PoS Biol. 6, e255 (28). 37. entze, M. W., Muckenthler, M. U. & Andrews, N. C. Blncing cts: moleculr control of mmmlin iron metbolism. Cell 117, (24). 38. Ingoli, N. T., Ghemmghmi, S., Newmn, J. R. & Weissmn, J. S. Genome-wide nlysis in vivo of trnsltion with nucleotide resolution using ribosome profiling. Science 324, (29). 39. Churchmn,. S. & Weissmn, J. S. Nscent trnscript sequencing visulizes trnscription t nucleotide resolution. Nture 469, (211). 4. Gebuer, F. & entze, M. W. Moleculrmechnisms of trnsltionl control. Nture Rev. Mol. Cell Biol. 5, (24). 41. Sonenberg, N. & innebusch, A. G. Regultion of trnsltion initition in eukryotes: mechnisms nd biologicl trgets. Cell 136, (29). 42. u, R. et l. Systems-level dynmic nlyses of fte chnge in murine embryonic stem cells. Nture 462, (29). 43. Rosenfeld, N., Elowitz, M. B. & Alon, U. Negtive utoregultion speeds the response times of trnscription networks. J. Mol. Biol. 323, (22). 44. Rbni, M. et l. Metbolic lbeling of RNA uncovers principles of RNA production nd degrdtion dynmics in mmmlin cells. Nture Biotechnol. doi:1.138/ nbt.1861 (24 April 211). Supplementry Informtion is linked to the online version of the pper t Acknowledgements We thnk N. Rjewsky nd. Dölken for fruitful discussions nd C. Sommer for technicl ssistnce. M.S. nd W.C. re supported by the elmholtz Assocition, the Germn Ministry of Eduction nd Reserch (BMBF) nd the Sente of Berlin by funds imed t estblishing the Berlin Institute of Medicl Systems Biology (BIMSB) (grnt number A). J.W. is supported by the ForSys-progrmme of the Germn Ministry of Eduction nd Reserch (grnt number ); D.B. by the elmholtz Allince on Systems Biology/MSBN; nd N.. by the Chin Scholrship Council CSC. Author Contributions M.S. conceived, designed nd supervised the experiments. B.S. performed wet-lb experiments, mss spectrometry nd proteomic dt nlysis. D.B. nd J.W. developed nd employed the mthemticl model. N.. performed RNA-seq experiments. W.C. designed nd supervised RNA-seq experiments. B.S., D.B., J.S., W.C. nd M.S. nlysed genome-wide dt. G.D. helped in cycloheximide chse experiments nd dt nlysis. B.S., D.B., J.S., J.W., W.C. nd M.S. interpreted the dt. M.S. wrote the mnuscript. Author Informtion Sequences hve been deposited in the Sequence Red Archive under ccession code SRA3871. Reprints nd permissions informtion is vilble t The uthors declre no competing finncil interests. Reders re welcome to comment on the online version of this rticle t Correspondence nd requests for mterils should be ddressed to J.W. (jn.wolf@mdc-berlin.de, for mthemticl modelling), W.C. (wei.chen@mdc-berlin.de, for trnscriptomics) or M.S. (mtthis.selbch@mdc-berlin.de, for proteomics). 342 NATURE VO MAY Mcmilln Publishers imited. All rights reserved