For the chromosome spreads RPE-1 and trisomic RPE −1 cells were maintained at 37°C with 5% CO 2 in DMEM GlutaMax (GIBCO) containing 10% fetal bovine serum (FBS), 100U penicillin and 100U streptomycin. The cells were grown to 70%–80% confluency before being treated with 50 ng/ml colchicine for 3-5 hr.

For measurements of relative protein levels at steady state in RPE-1 and RPE-1 trisomic cells fully Heavy SILAC labeled RPE-1 and Light labeled RPE-1 trisomic cells were grown to 70% confluency in 10 cm plates as described above. A label swap experiment was also performed.

For SILAC pulse-chase the mouse fibroblasts were grown to 80% confluency in 15 cm plates in Light SILAC DMEM. Cells were washed three times in PBS before being pulsed in Heavy SILAC DMEM for 4 hr (or as annotated in fig. S4 F-H). Cells were then washed in PBS before being trypsinated for 2 min at 37°C. Cells were resuspended in PBS before half of the cells were transferred to a 10 cm plate containing Medium-heavy SILAC DMEM and the other half spun down and pellet then frozen.

The AHA pulse-chase of mouse fibroblasts in combination with inhibitor treatments experiments were performed in a manner similar to the AHA p-c experiments. Methionine starved cells were pulsed with 1 mM AHA for 1 hr before being washed twice in pre-warmed PBS and then chased in Light or Medium-heavy SILAC. In these experiments different inhibitors or the vector control DMSO (Biomol) were added as follows. Proteasomes were blocked using 20 μM (S)-MG-132 (MG132, Cayman chemical) and inhibition of autophagy was secured by a combination of 250 nM Bafilomycin A1 (Invivogen) and 500 nM wortmannin (Calbiochem) both treatments were added only during the chase. In contrast, 100 nM Actinomycin D (Sigma-Aldrich) was added both during the pulse and chase.

For the AHA pulse-chase experiments mouse and human cells were cultured in either Light SILAC DMEM, Medium-heavy SILAC DMEM or Heavy SILAC DMEM until fully labeled. Experiments were started when cells reached ∼25% density. For the first two biological replicates two 10 cm plates were used per time point and for the third biological replicate two 15 cm plates were used per time point to increase the starting material. After 1 hr in the respective Light, Medium-heavy or Heavy methionine starvation SILAC DMEM cells were labeled with 1 mM AHA for 1 hr. After the pulse Medium and Light cells were washed in PBS then Light or Medium-heavy SILAC DMEM, respectively, before being chased in the same medium.

For the enrichment specificity experiments mouse cells were cultured in a 15 cm plate with Heavy SILAC DMEM. The cells were washed twice in pre-warmed PBS before being starved of methionine for 1h in Heavy SILAC DMEM depleted of methionine (custom made from Biosera) referred to as “methionine starvation SILAC DMEM” below. The methionine starvation was followed by a 2.5 hr long incubation with 1mM AHA.

For the radioactive pulse-chase coupled to immunoprecipitation experiments the mouse cells were grown in 15 cm plates in Light SILAC DMEM. Cells were washed twice in pre-warmed PBS before being starved for 1 hr in methionine and cysteine starvation DMEM supplemented with 5% HEPES buffer (life technologies) added to compensate for the incubator lacking a CO 2 source. Cells were then pulsed for 1 hr in the same medium supplemented with radioactive 35 S methionine and cysteine (Perkin Elmer) at a 125 μCi/mL final concentration. After the pulse cells were washed twice in pre-warmed PBS before being chased in Light SILAC DMEM containing 10-fold cysteine and methionine.

For the radioactive pulse-chase in combination with AHA or methionine experiments cells were cultured in Light SILAC DMEM. Confluent cells in 6-well plate wells were washed in pre-warmed PBS before being starved of methionine and cysteine for 45 min in DMEM free of both amino acids (Sigma-Aldrich) supplemented with glutamine, 1% Penicillin and Streptomycin and 10% dialyzed fetal calf serum referred to as “methionine and cysteine starvation DMEM.” Cells were then pulsed for 1 hr with 80 μCi final concentration ofS-Cysteine (Perkin Elmer) in combination with either 1 mM AHA or 1 mM methionine (). Cells were washed twice in Light SILAC DMEM before either being directly lyzed or chased for 6 or 24 hr in “cold” medium with either 50 μM cycloheximide or 10-fold cysteine (Sigma-Aldrich) added to prevent re-incorporation of the radiolabeled amino acids.

Cells were, if nothing else is stated, cultured in SILAC DMEM (life technologies) complemented with glutamine (Glutamax, life technologies), 1% Penicillin and Streptomycin (life technologies) and 10% dialyzed fetal calf serum (Pan-Biotech). The SILAC DMEM was supplemented with standard L-arginine (Arg0, Sigma-Aldrich) and L-lysine (Lys0, Sigma-Aldrich) as in () and referred to as “Light SILAC DMEM” below. Alternatively, Arg6 and Lys4 or Arg10 and Lys8 were added in place of their light counterparts these media are referred to as “Medium-heavy SILAC DMEM” and “Heavy SILAC DMEM,” respectively (). Cells were cultured at 37°C and 5% CO

NIH 3T3 Mouse fibroblast cells were acquired from ATCC. The human retinal pigmented epithelium cell line RPE-1 hTERT, referred to as “RPE-1,” was a kind gift from Stephen Taylor (University of Manchester, UK). The trisomic cell line, referred to as “RPE-1 trisomic,” was generated using microcell-mediated chromosome transfer () but has spontaneously gained a part of chromosome 11 specific to this study.

Method Details

35S Cysteine Pulse-Chase in Combination with AHA or Methionine NIH 3T3 mouse fibroblast cells were grown in Light SILAC DMEM. Confluent cells in 6-well plate wells were washed in pre-warmed PBS before being starved of methionine and cysteine for 45 min in methionine starvation SILAC DMEM. Cells were then pulsed for 1h with 80 μCi final concentration of 35S-Cysteine in combination with either 1mM AHA or 1mM methionine. Cells were then washed twice in Light SILAC DMEM before either being directly lyzed or chased for 6 or 24 hr in “cold” medium with either 50 μM cycloheximide or 10-fold cysteine (Sigma-Aldrich) added to prevent re-incorporation of the radiolabeled amino acids. After chase, cells were scraped and lyzed in modified radio-immunoprecipitation buffer (50 mM Tris HCl (pH 7.4), 1 mM EDTA, 150 mM NaCl, 1% Nonidet P-40, 0.25% Na-deoxycholate and 0.1% SDS) containing 2-fold protease inhibitor cocktail (Roche). All samples were frozen at −80°C before being thawed on ice for 30 min in the presence of an endonuclease (Benzonase, Merck). Samples were spun down to clear cell debris. The resulting supernatant was diluted in LDS sample buffer (Invitrogen) complemented with DL-DTT (Sigma-Aldrich) before being boiled at 95°C for 5 min. Proteins were resolved by SDS-PAGE using a 10% polyacrylamide gel. Proteins were fixed in the gel by 5% Acetic acid and 50% methanol and then Commassie stained (colloidal blue stain kit, Novex). The gel was vacuum dried for 2 hr using a gel drying system (Bio Rad) at 75°C. Vacuum dried gels were scanned using a scanner (Canon) for quantification of total loaded protein. The radioisotope signal was measured by exposing the gel to a magnetic photostimulable phosphor plate overnight. The plate was then scanned on a phosphorimager (Typhoon FLA 9500, GE Healthcare). Radioactive and Coomassie images were quantified using the ImageQuant software (GE Healthcare). Each lane was quantified separately and background signal was estimated by marking a lane with no proteins loaded. The measured background was subtracted from the signal. The radioactive signal was then further normalized to total protein input estimated by the Coomassie staining. Three biological replicates were performed each containing technical triplicates. Statistics and plotting was performed in Excel (Microsoft).

Enrichment Specificity of AHA-Labeled Proteins Hou et al., 2015 Hou J.

Wang X.

McShane E.

Zauber H.

Sun W.

Selbach M.

Chen W. Extensive allele-specific translational regulation in hybrid mice. Rappsilber et al., 2003 Rappsilber J.

Ishihama Y.

Mann M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Kulak et al., 2014 Kulak N.A.

Pichler G.

Paron I.

Nagaraj N.

Mann M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Fully Heavy SILAC labeled mouse fibroblasts were cultured in a 15 cm plate until 50% confluent. The cells were washed twice in pre-warmed PBS before being starved of methionine in Heavy methionine starvation SILAC DMEM for 1 hr. The starvation was followed by a 2.5 hr long incubation with 1 mM AHA. The AHA-labeled Heavy cells were then washed and scraped in ice cold PBS, spun down and mixed 1:1 with fully Light labeled cells not labeled by AHA (see Figure S1 A for experimental design). Two reversed experiments were also performed in where the Light cells were AHA-labeled and the Heavy cells not. The cells were lyzed using urea lysis buffer from the Click-iT protein enrichment-kit supplemented with 2-fold protease inhibitors (Roche). The lysate was treated with benzonase for 10 min before being sonicated in a water bath. Samples were spun down at 20,000 rcf and supernatants were transferred to new tubes containing alkyne agarose beads. The click reaction was performed overnight following the “Click-iT protein enrichment kit”-protocol as described earlier (). Proteins were reduced by heating to 70°C in the presence of 10 mM DTT in SDS buffer and later alkylated by the addition of 40 mM iodoacetamide (Sigma-Aldrich) final concentration. Beads were sequentially washed in SDS buffer, 8 M Urea in 100 mM Tris (pH 8) and 80% acetonitrile by centrifugation and decanting supernatant. Proteins were digested “on bead” in 5% acetonitrile in ABC buffer first 3 hr by LysC and then over night with trypsin. The peptide solution was acidified by addition of trifluoroacetic acid (TFA, Sigma-Aldrich) before peptides were desalted and stored on StageTips (). In short, stageTips were prepared by inserting 3 discs of C18 material (3M) into 200 μl pipette tips. The C18 was activated by methanol. Organic solvents were washed away by Buffer A (5% acetonitrile and 0.1% formic acid) before peptides were loaded onto the stageTips. Salts were washed away by washing the retained peptides in Buffer A. Peptides were eluted using Buffer B (80% Acetonitrile and 0.1% formic acids) and organic solvent was evaporated using a speedvac (Eppendorf). Samples were diluted in Buffer A (5% acetonitrile and 0.1% formic acid) before being put on SCX tips (3 M) (). In brief, 3 discs of SCX material (3 M) were added to a 200 μl pipette tip and activated by sequentially washing in methanol, buffer B and 500 mM ammonium acetate in 15% acetonitrile and 0.5% formic acid. SCX tips were washed three times in 0 mM salt buffer (15% acetonitrile and 0.5% formic acid) before the samples were added. Flow through was collected and the remaining peptides were eluted by increasing the salt concentration in three steps using 50, 150 and 500 mM ammonium acetate. Eluted peptides were once again desalted on stageTips. Sheean et al., 2014 Sheean M.E.

McShane E.

Cheret C.

Walcher J.

Müller T.

Wulf-Goldenberg A.

Hoelper S.

Garratt A.N.

Krüger M.

Rajewsky K.

et al. Activation of MAPK overrides the termination of myelin growth and replaces Nrg1/ErbB3 signals during Schwann cell development and myelination. Puchades et al., 1999 Puchades M.

Westman A.

Blennow K.

Davidsson P. Removal of sodium dodecyl sulfate from protein samples prior to matrix-assisted laser desorption/ionization mass spectrometry. In parallel, peptides from the input to the click chemistry (lyzed cells mixed 1:1) were prepared by Wessel-Flügge precipitation and “on pellet” digest as previously described (). In short proteins from the input were precipitated by sequentially adding, MS-grade methanol, chloroform, and finally, water before spinning down the samples at 10,000 rcf (). The upper water phase was removed, methanol was added, the sample was centrifuged again and supernatant discarded. The retrieved protein pellet was air-dried before being resuspended in 6 M Urea, 2 M Thiourea in 10 mM HEPES (pH8). Proteins were denatured by adding 10 mM DTT in 50 mM ammoniumbicarbonate (ABC buffer) and then alkylated by the addition of 55 mM iodoacetamide in ABC buffer. Proteins were then digested by the addition of LysC for 3 hr. The sample was diluted in ABC buffer until the concentration of urea was less than 2 M before trypsin was added over night. Resulting peptides were desalted on stageTips. Input and on bead digested peptides were separated on a 2,000 mm monolithic column with a 100-μm inner diameter filled with C18 material that was kindly provided by Yasushi Ishihama (Kyoto University) (from now on referred to as “2 m monolithic column”) using a 4 hr linear gradient with a 300 nl/min flow rate of increasing Buffer B concentration on a High Performance Liquid Chromatography (HPLC) system (ThermoScientific). Peptides were ionized using an electrospray ionization (ESI) source (ThermoScientific) and analyzed on a Q Exactive mass spectrometer (ThermoScientific). The mass spectrometer was run in data dependent mode selecting the top 10 most intense ions in the MS full scans (Orbitrap resolution: 70,000; target value: 3,000,000 ions; maximum injection time of 20 ms) for higher energy collision induced dissociation. The resulting MS/MS spectra from the Orbitrap had a resolution of 17,500 after a maximum ion collection time of 60 ms with a target of reaching 1,000,000 ions. Cox and Mann, 2008 Cox J.

Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. The resulting raw files were analyzed using MaxQuant software version 1.5.1.2 (). Default settings were kept except that ‘match between runs’ was turned on. Lys8 and Arg10 were set as labels and oxidation of methionines, n-terminal acetylation and deamidation of aspargine and glutamine residues were defined as variable modifications. Carbamidomethyl of c-termini was set as fixed modification. The in silico digests of the mouse Uniprot database (2014-10) and a database containing common contaminants were done with Trypsin/P. The false discovery rate was set to 1% at both the peptide and protein level and was assessed by in parallel searching a database containing the reversed sequences from the Uniprot database. The resulting text files were filtered to exclude reverse database hits, potential contaminants, and proteins only identified by site. Plotting and statistics were done using R and figures were modified in Illustrator (Adobe). The median H/L ratios from the input samples were used to estimate the mixing ratio of the input and the H/L ratios after the enrichment were adjusted correspondingly.

AHA Pulse-Chase of SILAC-Labeled NIH 3T3 Mouse Fibroblasts Fully Light, Medium and Heavy SILAC labeled mouse fibroblast were grown as in the “enrichment efficacy experiment.” Experiments were performed when cells reached ∼25% cell density so that full confluency would not be reached during the 32 hr of chase time (See Figure 1 B for experimental design). For the first two replicates two 10 cm plates were used per time point and for the third replicate two 15 cm plates were used to increase the starting material. During the whole experiment cells were grown in the presence of arginine and lysine in their respective labeling sate (Light, Medium-heavy or Heavy). After 1 hr in methionine starvation SILAC DMEM cells were labeled with 1 mM AHA for 1h. After the pulse Medium and Light cells were washed first in PBS then SILAC DMEM before being chased in the same medium. Heavy cells used for time point 0 hr were instead washed in ice cold PBS before being scraped in the same and spun down, and cell pellets were frozen. After the chase the Medium and Light cells were also scraped and frozen. Eravci et al., 2014 Eravci M.

Sommer C.

Selbach M. IPG strip-based peptide fractionation for shotgun proteomics. Wiśniewski et al., 2009 Wiśniewski J.R.

Zougman A.

Mann M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. The frozen pellets were thawed and lyzed as described for “enrichment efficacy experiment.” Also the click reaction and washing of beads, denaturation, alkylation, and digestion was performed as above. In one, out of the three experiments, the peptides were pre-fractionated by isoelectric focusing into 12 fractions as described in (). In two experiments the peptides were separated using strong anion exchange (SAX). The SAX protocol was performed as in (). In short SAX material (3M) was put in 200 μl pipette tips and activated by methanol. The SAX tip was then washed by high pH buffer (20 mM Acetic acid, 20 mM phosphoric acid, 20 mM boric acid, pH was adjusted to 11 by titrating in 1 M sodium hydroxide) before peptides were loaded onto the SAX material. The peptides were then eluted stepwise by decreasing the pH of the buffer in discrete steps (pH of 11 (flow though), 8, 5 and 3 all prepares as above with the addition of 0.25 M NaCl to the pH 3 buffer). The eluted peptides were stored on stageTips. IEF and SAX fractionated peptides were separated on a HPLC system as described above by either 4 or 2 hr gradients with a 250 nl/min flow rate on a 15 cm column with an inner diameter of 75 μm packed in house with ReproSil-Pur C18-AQ material (Dr. Maisch, GmbH). Peptides were ionized using an ESI source and analyzed on a Q Exactive with the above described settings. The acquired raw-files were analyzed using MaxQuant with the same settings as for the enrichment specificity experiment but with Arg10 and Lys8 set as heavy labels and Arg6 and Lys4 as medium-heavy labels. For all downstream analysis we used non-normalized SILAC ratios (see below for normalization procedure) with a minimum of 2 SILAC counts. Reverse database hits, potential contaminants, and proteins only identified by site were all excluded.

Data Normalization Normalization is a common challenge for experiments measuring abundances – differences in starting material, labeling efficiency, instrument sensitivity, etc. are all contributors to deviations in the scale of measurements. A common normalization strategy is to normalize the data to the median value in each experiment or replicate, assuming that the median values should not change through the series of experiments. However, for pulse chase experiments, we expect the measured quantities (and thus also the median) to decay over time, thus this strategy cannot be used. In our data, we expect each time point to have an unknown and potentially different multiplicative factor which affects all measurements at that time point. Thus, we aim to estimate the average value of the multiplicative factor that affects the real data at each time point without assuming any protein degradation rates a priori while being robust to experimental errors. Schwanhäusser et al., 2011 Schwanhäusser B.

Busse D.

Li N.

Dittmar G.

Schuchhardt J.

Wolf J.

Chen W.

Selbach M. Global quantification of mammalian gene expression control. Toyama et al., 2013 Toyama B.H.

Savas J.N.

Park S.K.

Harris M.S.

Ingolia N.T.

Yates 3rd, J.R.

Hetzer M.W. Identification of long-lived proteins reveals exceptional stability of essential cellular structures. Our normalization scheme is based on the assumption that there are stable proteins within the pool of proteins measured, whose amounts decay very little during the time course of the experiment ( Figure S4 ) (). Without noise, the signals corresponding to these proteins would remain unchanged and equal to 100% left throughout the experiment. With noise, these very stable proteins can still be identified; the Medium/Heavy and Light/Heavy SILAC ratios of these proteins should be among the highest throughout the experiment. Using this method, we identify the most stable proteins and then calculate the multiplicative factor necessary to normalize the data for each time point such that the geometric mean of the measurements of these very stable proteins will have a signal of 100% (see Figure S2 for overview of normalization strategy). score i = ∑ t ∈ { 8,16,32 } PercentileRank i ( t )

where the index i denotes the protein and PercentileRank i (t) maps the rank of each protein’s signal strength (from smallest to largest, at time t) to the interval (0, 1). Proteins with higher signals at each time point will have higher scores. Thus for a protein who has the highest signal at all time points would have a score equal to the number of time points, which we call maxScore (i.e., the range of scores is (0, maxScore]). Each protein has up to 3 scores, one from each replica. From the three scores, we calculate the deviation of the score from the maximum score: d e v i = ∑ j ∈ # r e p l i c a s ( max Score− s c o r e i , j ) 2

Candidates for normalization are those proteins with the lowest deviations. This normalization scheme is based on 4 key assumptions: 1. All groups of cells (heavy/medium/light) produce and degrade proteins equally.

2. Proteins degrade at different rates, which can be differentiated in the timescale of our experiments)

3. Proteins degrading the slowest have the highest Medium and Light to Heavy ratios (and thus lowest deviations)

4. The slowest degrading proteins do not degrade at all in the timescale of our experiment. Note that protein dilution due to cell division does not impact our data since we harvest the entire cell population. To find the most stable proteins, we consider proteins with data at all time points in all replica – one reason for this is that it ensures that all the potential candidates are able to contribute to the normalization factor. Furthermore, being in this subset suggests that these proteins are reliably measurable. For each of these proteins, we assign a score, defined aswhere the index i denotes the protein and PercentileRank(t) maps the rank of each protein’s signal strength (from smallest to largest, at time t) to the interval (0, 1). Proteins with higher signals at each time point will have higher scores. Thus for a protein who has the highest signal at all time points would have a score equal to the number of time points, which we call maxScore (i.e., the range of scores is (0, maxScore]). Each protein has up to 3 scores, one from each replica. From the three scores, we calculate the deviation of the score from the maximum score:Candidates for normalization are those proteins with the lowest deviations. This normalization scheme is based on 4 key assumptions: From the data, we find the population of proteins with the lowest score deviations (LSD, n = 200, < 5% of total population) and deem these to be the stable proteins (i.e., the LSD-proteins). This set is chosen intentionally large in order to mitigate the effects of outlying data points. In addition, based on the enrichment efficacy experiments ( Figures S1 A–S1C) described above we applied a stringent cut-off excluding all data points smaller than 10% protein remaining after normalization. Alternatively, we also tried to subtract protein specific background based on the fact that the background was highly reproducible ( Figure S1 C). This did not have any major impact on the protein classification and we therefore stayed with the simpler 10% cut-off.

Parameter Fitting A . The system is memoryless, meaning that the life expectancy for any single protein molecule does not change as the molecules age. For true exponential decay, the data should resemble a straight line when plotted in a log-linear plot. While the one-state model is a good approximation for some decay patterns, other decay patterns have dynamics that are not well described by a one-state model ( Deneke et al., 2013 Deneke C.

Lipowsky R.

Valleriani A. Complex degradation processes lead to non-exponential decay patterns and age-dependent decay rates of messenger RNA. In this study, we consider two simple models: a 1-state model (exponential decay, ED) and a 2-state model (non-exponential decay, NED). In the 1-state model, proteins are in state A just after synthesis. From state A, they are degraded at the rate k. The system is memoryless, meaning that the life expectancy for any single protein molecule does not change as the molecules age. For true exponential decay, the data should resemble a straight line when plotted in a log-linear plot. While the one-state model is a good approximation for some decay patterns, other decay patterns have dynamics that are not well described by a one-state model (). In the two-state model, proteins are in state A after synthesis. From state A, the molecule can immediately degrade at the rate of k A , or it can transition to state B with the rate k AB . Molecules that reach state B are degraded with rate k B . From the analysis point of view, one important distinction between the 1-state and 2-state models is that we lose the property of memorylessness; for 2-state models, the history of a specific molecule (which determines whether the molecule is in state A or state B) changes the expected residual life of the molecule. In short, the residual life of the molecule depends on the age of the molecule. In pulse-chase experiments, the duration of the pulse affects the composition of molecule ages at the beginning of the chase – for a very short pulse, the molecules synthesized in the pulse are likely to have the same age. However, for a longer pulse, molecules synthesized at the beginning of the pulse are “older”) while there are some molecules which are just newly synthesized. In short, the length of the pulse must be taken into account for the calculations to accurately uncover the dynamics of degradation. Deneke et al., 2013 Deneke C.

Lipowsky R.

Valleriani A. Complex degradation processes lead to non-exponential decay patterns and age-dependent decay rates of messenger RNA. Sin et al., 2016 Sin C.

Chiarugi D.

Valleriani A. Degradation parameters from pulse-chase experiments. The derivation of the mathematical description consists of two steps: First is to translate the single molecule dynamics model (e.g., the one-state or two-state model) into the degradation from steady state at the level of population averages. The translation of single molecule dynamics to population averages has been covered in (). The second step takes the pulse into account and returns the degradation curve of the population averages (e.g., the measurements from the experiments). Calculation of the response of the system resulting from a pulse has been covered in (). Λ ( t ) defines the theoretical decay pattern, namely the fraction of molecules left after a decay of t time units. This function is expressed in terms of parameters defined through the underlying degradation model. In our degradation model, we have assumed that the proteins follow either a 1-state model, in which there is only one degradation parameter, or a 2-state model, where there are three parameters. The equations used for fitting are as follows: Λ ( t ) = e − k A t for the 1 -state model

Λ ( t p + t ) = G ( t ) − G ( t p + t ) G ( 0 ) − G ( t p ) for the 2 -state model

where G ( t ) = k A B ( k A B + k A ) e − k B t + k B ( k A − k B ) e − ( k A B + k A ) t , and t is the measurement time after the end of the pulse whereas t p is the pulse length ( Sin et al., 2016 Sin C.

Chiarugi D.

Valleriani A. Degradation parameters from pulse-chase experiments. In our formalism, the functiondefines the theoretical decay pattern, namely the fraction of molecules left after a decay of t time units. This function is expressed in terms of parameters defined through the underlying degradation model. In our degradation model, we have assumed that the proteins follow either a 1-state model, in which there is only one degradation parameter, or a 2-state model, where there are three parameters. The equations used for fitting are as follows:where, and t is the measurement time after the end of the pulse whereas tis the pulse length (). Parameter estimation is performed by MATLAB through nonlinear fitting by minimizing the square deviation from the logarithm of the experimental data and the logarithm of the theoretical function. The routine employed for the nonlinear fit is fmincon. After parameter fitting we applied two quality criteria for selection of proteins for downstream analysis. First, only proteins which had measurements for more than four data points were kept. Second, profiles with RSS > 0.05 were not considered for downstream analysis.

Model Selection by the Akaike Information Criterion Burnham and Anderson, 2002 Burnham K.P.

Anderson D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. A I C = 2 k + n ln ( R S S n ) + 2 k ( k + 1 ) n − k −1

where n is the number of data points, k is the number of parameters, and R S S is the residual sum of squares. The AIC penalizes models with more parameters, worse fits, and less data. That is, the AIC quantifies the tradeoff between fit accuracy and model complexity. The Akaike Information Criterion (AIC) indicates the quality of the model for a given set of data (). Based on information theory, the AIC aims to find the model with minimal Kullback-Leibler distance between the proposed model and the “true” model (as assessed from the data). Models with more parameters have more degrees of freedom during the parameter estimation process, and can often deliver a more accurate fit to the data. However, a more accurate fit to the data does not necessarily imply a higher quality model – instead of a model describing the system’s dynamics, these “overfitted models” describe quantities not related to degradation, such as measurement noise. To decide which model we should adapt for each protein, we calculate the AIC for each model. The model resulting in the lowest AIC is the preferred model. We use the AIC with correction for small sample sizes to evaluate each of the two models fitted to each protein degradation pattern:where n is the number of data points,is the number of parameters, andis the residual sum of squares. The AIC penalizes models with more parameters, worse fits, and less data. That is, the AIC quantifies the tradeoff between fit accuracy and model complexity. i is the preferred one (relative to the other models we consider) by: Π A I C i = exp ( A I C min − A I C i 2 ) ∑ j exp ( A I C min − A I C j 2 ) .

Furthermore, we can calculate the probability that a particular modelis the preferred one (relative to the other models we consider) by: Aalen and Gjessing, 2001 Aalen O.O.

Gjessing H.K. Understanding the shape of the hazard rate: a process point of view (With comments and a rejoinder by the authors). Deneke et al., 2013 Deneke C.

Lipowsky R.

Valleriani A. Complex degradation processes lead to non-exponential decay patterns and age-dependent decay rates of messenger RNA. Aalen, 1994 Aalen O.O. Effects of frailty in survival analysis. Our modeling approach connects to a more general approach based on the hazard rate, which is the key functional in survival analysis (). In our models, the hazard rate is the age-dependent degradation rate (). Based on biological reasoning, we have interpreted the age-dependent rate as giving information on the aging of every individual molecule. We would like to point out that an alternative interpretation based on frailty theory () would give similar fitting quality.

Δ-Score Calculations y ( 4 hr ) = − ( log ( 100 % ) − tp 8 ) 8 ∗ 4 + log ( 100 % ) .

For proteins that decay exponentially, one can derive the relative protein abundance at any time point by drawing a straight line between time point 0 hr (100% protein left) and any other measurement. Making allowances for measurement noise and quantification errors, all other measurements should fall on this line. If a measurement does not fall on this line, the protein is non-exponentially degraded. We used this relationship to estimate the size and direction (increased or decreased stability with age of the molecule) of non-exponentiality of degradation for each protein. We used the median log “protein remaining [%]” at time point 8 hr (tp8) after chase to calculate the expected relative protein abundance at time point 4 hr assuming exponential degradation. For this we solved the linear equation (y = mx + c) for x = 4 hr, where the intercept c is log(100%), and the slope m is calculated using the value at tp8: Δ − s c o r e = y ( 4 hr ) − t p 4.

Finally, we calculated the distance from the measured median log “protein remaining [%]” at time point 4 hr (tp4), to the expected value, y(4 hr): This calculation was repeated for all proteins. The time points 4 and 8 hr were selected because of the observation that most of the initial degradation of NED proteins had already happened after 4 hr chase. Thereby we expected to be able to catch age-dependent stabilization (or destabilization) by comparing these two time points. Also, few proteins (see Table S1 ) had a half-life shorter than 2.5 hr and could thereby theoretically not be detected at the 8 hr time point. In addition, these short lived proteins were almost exclusively exponentially degraded according to the AIC call.

SILAC Pulse-Chase (Confirmation Experiment) To exclude issues related to using non-natural amino acids and to the enrichment process (e.g., background binders) we performed a pulse-chase experiment using only stable isotope labeled amino acids. Mouse fibroblasts were grown to 80% confluency in 15 cm plates in Light SILAC DMEM. Cells were washed three times in PBS before being pulsed in Heavy SILAC DMEM for 4 hr (or as annotated in Figure S4 ). Cells were then washed in PBS before being trypsinated for 2 min at 37°C. Cells were resuspended in PBS before half of the cells were transferred to a 10 cm plate containing Medium SILAC DMEM and the other half spun down and pellet then frozen. After the Medium chase (see Figures 4 and S4 , for different chase length) cells were spun down and frozen. In addition, “label-swap” experiments were also performed in this fashion. However, in the label-swap experiments the cells were pulsed with Medium-heavy and chased in Heavy amino acids. Cell pellets were lyzed and proteins denatured in 0.2% SDS, 0.1 M DTT and 50 mM ABC (pH 8) by boiling for 10 min at 95°C. After cooling, Benzonase was added for 10 min before cell lysates were spun down and supernatants were transferred to fresh tubes. Proteins were alkylated by adding iodoacetamide to a 0.25 M final concentration, in the dark, for 20 min. Proteins were precipitated by Wessel-Flügge precipitation as described above. The retrieved protein pellet was resuspended in 6 M Urea, 2 M Thiourea in 10 mM HEPES (pH8). Proteins were digested with LysC before being diluted in ABC buffer and trypsinated overnight. The resulting peptide solution was desalted on StageTips before being eluted in buffer B as described above. The peptides were resolved on a 4 m long monolithic column (2 × 2 m column combined) using a 12 hr gradient of increasing buffer B concentration and a flow rate of 500 nl/min. Peptides were ionized by ESI and analyzed on a Q-exactive orbitrap all with previous settings. Resulting raw-files were analyzed with MaxQuant with the same parameter settings as above. Plotting and statistics were performed using R and figures were modified in Illustrator.

35S Cysteine and Methionine Pulse-Chase Coupled with Immunoprecipitation NIH 3T3 mouse fibroblasts were grown in 15 cm plates as described above. Cells were washed twice in pre-warmed PBS before being starved in methionine starvation SILAC DMEM for 1 hr. Cells were then pulsed for 1 hr in the same medium supplemented with radioactive 35S methionine and cysteine (Perkin Elmer) at a 125 μCi/mL final concentration. After the pulse cells were either washed twice in pre-warmed PBS before being chased in medium containing 10-fold cysteine and methionine or scraped in ice cold PBS, spun down and pellets frozen (0 hr time point). After being chased, either for 4 or 8 hr, cells were collected as the 0 hr time point. Cell pellets were lyzed for 15 min in RIPA buffer (50 mM Tris (pH8), 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate, 1 mM EDTA, 1% NP40) supplemented with benzonase and 2-fold protease inhibitors. Lysates were spun down at 15 0000 rcf for 5 min and supernatant was transferred to new tubes. Lysates were precleared for 30 min by incubation with protein-A sepharose beads (Biovision) at 4°C while turning over head. Beads were spun down and supernatant was split into two fresh tubes (i.e., time point 0 hr were split in two, time point 4 hr was split in two and so forth). Polyclonal antibodies raised in rabbit against either VCP (LSBio, LS-C287469) or CCT3 (Proteintech, 10571-1-AP) were added overnight to one tube each. Thereby the VCP and CCT3 immunoprecipitations were performed on the same lysate this to limit differences in radioactive labeling. Two tubes had no antibody added and were subsequently used as bead controls. In the morning, protein-A sepharose beads were added to each tube and incubated while turning head over heel at 4°C for 3 hr. Afterward the beads were washed 3 times in lysis buffer. Supernatant was fully decanted and leftover liquid boiled off. Immunoprecipitated proteins were then eluted by cooking the beads in 1-fold LDS loading buffer with DTT. Beads were spun down and the same volume of supernatant was loaded onto a 4%–12% gradient SDS-polyacrylamide gel (ThermoFisher) and separated using electrophoresis as described above. The proteins were then further transferred to a PVDF membrane (Merck Millipore) using a wet western blot contraption (Invitrogen) set to a constant current of 250 mA for 2 hr. The radioactive signal from metabolically labeled proteins was detected by exposing a magnetic phosphor plate overnight and then measuring in a PhosphorImager as described above. As loading control the same membranes were also probed by the same antibodies as used for the IPs. Briefly the membranes were first blocked by incubating in 1% milk powder in Tris-buffered saline (TBS) and then incubated with the protein specific antibody diluted 1:5000 in 1% milk in TBS overnight while rotating at 4°C. Membranes were washed in TBS and 1% Tween before being incubated at RT for 1 hr with protein-A conjugated to horseradish peroxidase (Merck Millipore). Membranes were washed again before chemiluminescence substrate (PerkinElmer) was added and X-ray films (Fujifilm) were exposed to the membranes and developed using an Optimax 2010 machine (Protec). Two biological replicates were performed.

Inhibitor Treatments + Controls Inhibitor treatment experiments were performed as the AHA p-c experiments but only with three time points (0, 4 and 8 hr). In addition to pulsing the cells with 1 mM AHA different inhibitors or vector control DMSO (Biomol) were added. Proteasomes were blocked using 20 μM MG132 (Cayman chemical) and a robust inhibition of autophagy was secured by a combination of 250 nM Bafilomycin A1 (Invivogen) and 500 nM wortmannin (Calbiochem). Both treatments were added only during the chase. 100 nM Actinomycin D (Sigma-Aldrich) was added both during the pulse and chase. Sury et al., 2015 Sury M.D.

McShane E.

Hernandez-Miranda L.R.

Birchmeier C.

Selbach M. Quantitative proteomics reveals dynamic interaction of c-Jun N-terminal kinase (JNK) with RNA transport granule proteins splicing factor proline- and glutamine-rich (Sfpq) and non-POU domain-containing octamer-binding protein (Nono) during neuronal differentiation. Inhibition of autophagy by Bafilomycin A1/Wortmannin was monitored by in parallel taking samples for western blotting as previously described (). In short, scraped cells were spun down and directly lyzed in LDS sample buffer supplemented with DTT. Samples were run on 4%–12% Bis-Tris gradient gels (NuPAGE, Invitrogen) before being blotted onto PVDF membrane (Immobilion-P, Millipore) using a wet blotting contraption (Invitrogen). The Autophagy blocked cells were probed against LC3-II and afterwards the membrane was stripped at 37°C for 15 min in stripping buffer (2% SDS (Roth), 2% β-mercaptoethanol in 65 mM Tris Base (pH 6.7, Roth)) before being re-blotted using an anti-β-actin antibody. Treated cells for mass spec analysis were scraped, lyzed, and had their AHA labeled proteins clicked to alkyne-agarose beads as described above. Proteins were reduced with DTT and alkylated before beads were washed all as in the main AHA p-c experiment. Proteins were digested “on bead” by LysC and then trypsinated overnight. Peptide solution were put on 4 mm/1 ml C18 columns (Empore, 3 M) and washed in buffer A. Peptides were eluted in buffer B and vacuum dried. Motoyama et al., 2007 Motoyama A.

Xu T.

Ruse C.I.

Wohlschlegel J.A.

Yates 3rd, J.R. Anion and cation mixed-bed ion exchange for enhanced multidimensional separations of peptides and phosphopeptides. Kulak et al., 2014 Kulak N.A.

Pichler G.

Paron I.

Nagaraj N.

Mann M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Hou et al., 2015 Hou J.

Wang X.

McShane E.

Zauber H.

Sun W.

Selbach M.

Chen W. Extensive allele-specific translational regulation in hybrid mice. Kulak et al., 2014 Kulak N.A.

Pichler G.

Paron I.

Nagaraj N.

Mann M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. MG132 treated samples were separated using an online SCX/WAX approach. Samples were loaded on a column packed first with C18 material “trap” and then with a 2:1 mixture of WAX 3 μm beads (PolyLC PolyWAX LP) and 3 μm SCX beads (PolyLC PolyWAX LP) (). The peptides were subsequently eluted with increasing salt concentration (ammonium acetate in 4, 8, 16, 32, 64 and 500 mM steps) onto the C18 trap part of the pre-column. Each fraction eluted from the SAX/SCX material where then separated as normal on a 15 cm C18 column with 2 hr gradients of increasing buffer B concentration with a 250 nl/min flow rate. Bafilomycin A1/ Wortmannin treated samples were put on SCX tips and washed in no salt buffer, as described above, to minimize polymer contamination. Samples were then eluted with 500 mM ammonium acetate before being desalted on stageTips (). Samples were eluted from stageTips by Buffer B, vacuum dried, re-suspended in Buffer A and then separated on a HPLC system using a 2 m column and an 8 hr gradient as previously described (). Actinomycin D samples were also put on SCX tips but further manually fractionated by eluting in steps by increasing salt concentration (8, 16, 32 and 500 mM ammonium acetate) before being put back on stageTips (). Peptides eluted from stageTips were vacuum dried and resuspended in buffer A before being separated on a 15 cm C18 column as described above. In all three cases above, eluted peptides were ionized using an ESI source and analyzed on a Q Exactive with the above described settings. ESI and mass spectrometer settings were, for all samples, as described above. The resulting raw files were analyzed by MaxQuant with the same settings as the standard AHA p-c experiment. Timelines were reassembled from non-normalized protein ratios, resulting into three time points (0, 4 and 8 hr) for each inhibitor treatment and the corresponding DMSO control. Proteins were filtered for being represented by at least two peptide identification events. Each time point was normalized to the geometric mean of the identified intersection of the LSD proteins that were identified in the mouse dataset used for the mathematical modeling and then values below 10% were removed. From this normalized dataset Δ-scores were calculated for each, treatment and control, as described above. The difference of the Δ-scores between treatment and DMSO control was compared for the three protein subsets identified as NED, ED and UN. Differences between distributions were tested using the Wilcoxon rank-sum test. The corresponding p value is reported in the figure legend for each treatment.

Degradation Profile Prediction from Different Protein Features Ori et al., 2016 Ori A.

Iskar M.

Buczak K.

Kastritis P.

Parca L.

Andrés-Pons A.

Singer S.

Bork P.

Beck M. Spatiotemporal variation of mammalian protein complex stoichiometries. Ori et al., 2016 Ori A.

Iskar M.

Buczak K.

Kastritis P.

Parca L.

Andrés-Pons A.

Singer S.

Bork P.

Beck M. Spatiotemporal variation of mammalian protein complex stoichiometries. Schwanhäusser et al., 2011 Schwanhäusser B.

Busse D.

Li N.

Dittmar G.

Schuchhardt J.

Wolf J.

Chen W.

Selbach M. Global quantification of mammalian gene expression control. Sormanni et al., 2015 Sormanni P.

Camilloni C.

Fariselli P.

Vendruscolo M. The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. The following features were selected to test each for prediction power of protein degradation profiles. The “Part of a Complex” feature distinguished proteins that are part of a complex from proteins that are not part of a complex (). Proteins were defined as being part of a complex, if they are listed in a published manually curated protein complex database (unfiltered version;). Protein Length refers to the protein sequence length and was taken from the UniProt fasta table (version 10.2011). “Protein abundances in steady state” refer to average protein copy numbers per cell () mapped by Uniprot accessions and gene names if the Uniprot accession was not mapped. The feature “Low Complexity Region” were obtained from the “mmusculus_gene_ensembl” dataset from the biomart database (status 14.10.2015). Listed lengths of Low Complexity regions were summed up per protein. Disordered, Helix and Beta Sheet fractions per protein were obtained by secondary structure prediction using the s2d method (). All structural features (“Low complexity,” “Disorder,” “Helix” and “Beta Sheet”) were normalized to protein length. For each feature a ROC-curve was generated and the area under the curve calculated using the pracma R-package. The robustness of the calculated AUCs was tested by running 200 bootstrap repetitions. The 90% confidence intervals of the resulting AUCs are shown as error bars in the corresponding bar plot. Each feature prediction was tested for being absent or present by reversing the sorting vector and the AUC > 0.5 was reported.

Protein Structural Dataset Winn et al., 2011 Winn M.D.

Ballard C.C.

Cowtan K.D.

Dodson E.J.

Emsley P.

Evans P.R.

Keegan R.M.

Krissinel E.B.

Leslie A.G.

McCoy A.

et al. Overview of the CCP4 suite and current developments. Marsh et al., 2013 Marsh J.A.

Hernández H.

Hall Z.

Ahnert S.E.

Perica T.

Robinson C.V.

Teichmann S.A. Protein complexes are under evolutionary selection to assemble via ordered pathways. Wells et al., 2016 Wells J.N.

Bergendahl L.T.

Marsh J.A. Operon gene order is optimized for ordered protein complex assembly. Starting from the entire set of protein structures in the Protein Data Bank on 2016-02-24, we searched for all polypeptide chains with > 70% sequence identity to a human or mouse gene. For genes that map to multiple chains, we selected a single chain sorting by sequence identity, then number of unique subunits in the complex, and then the number of atoms present in the chain. Pairwise interfaces were calculated between all pairs of subunits using AREAIMOL (). The normalized assembly order was calculated for all complexes, excluding those containing nucleic acid chains, by first predicting the (dis)assembly pathway as previously described using all the pairwise interfaces from each heteromeric complex () and implemented in the assembly-prediction package (). For subunits with multiple copies within a single complex, the average assembly order of each subunit type was considered. The normalized assembly order was defined so that the first subunit to assemble has a value of 0, the last has a value of 1, and the average value for all unique subunits in a complex is equal to 0.5.

Non-structural Dataset Ruepp et al., 2008 Ruepp A.

Brauner B.

Dunger-Kaltenbach I.

Frishman G.

Montrone C.

Stransky M.

Waegele B.

Schmidt T.

Doudieu O.N.

Stümpflen V.

Mewes H.W. CORUM: the comprehensive resource of mammalian protein complexes. To complement the analysis of protein complexes of known structure, we also performed coexpression analyses on the non-redundant “core” set of mammalian complexes from CORUM () (downloaded 2015-10-20). As CORUM preferentially uses human complexes in its non-redundant set, homologous mouse versions of each complex were generated by replacing each subunit/gene with its mouse counterpart, provided sequence identity was at least 70%. Sequence identities were calculated by collecting all mouse sequences for which NED/ED classifications were available and running BLAST on these against all genes in the CORUM core set. In cases where the identity of a subunit was ambiguous (as defined by CORUM), the first possible subunit for which homology data were available was selected.

Coexpression Analyses Okamura et al., 2015 Okamura Y.

Aoki Y.

Obayashi T.

Tadaka S.

Ito S.

Narise T.

Kinoshita K. COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Coexpression data were downloaded from COXPRESdb () (mouse dataset: Mmu.v13-01.G20959-S31479; human dataset: Hsa.v13-01.G20280-S73083). For each complex, the mean coexpression of each available subunit was calculated, using all other subunits in the complex. Cases where fewer than three unique subunits were present in the complex were discarded, due to calculations of average coexpression being superficially identical.

Estimation of Relative Protein Abundance after Pulse (iBAQ) Schwanhäusser et al., 2011 Schwanhäusser B.

Busse D.

Li N.

Dittmar G.

Schuchhardt J.

Wolf J.

Chen W.

Selbach M. Global quantification of mammalian gene expression control. To estimate the protein abundance after the pulse (i.e., the relative amount of newly synthesized proteins) we used intensity based absolute quantification (iBAQ, ()). First, all the intensities reported directly after the pulse, i.e., the H-intensities, for each protein group were divided by the number of observable peptides to correct for observability biases. Second, all the corrected H-intensities were normalized by using the median H-intensities for the LSD-proteins (see normalization strategy above). This allowed the combination of experiments. Finally, we reported the median H-Intensity from all experiments as the relative abundance. The median was used to avoid counting highly abundant proteins which show up in all replicates multiple times. Ori et al., 2016 Ori A.

Iskar M.

Buczak K.

Kastritis P.

Parca L.

Andrés-Pons A.

Singer S.

Bork P.

Beck M. Spatiotemporal variation of mammalian protein complex stoichiometries. For a complex centered analysis of the relative protein abundances after pulse, identified proteins from the mouse dataset were mapped to a filtered version of a protein complex database ()(see previous section) using gene names. Protein abundances were normalized in a complex centered manner: First all proteins that mapped to a complex were extracted. Second, abundances of all proteins of a complex were normalized to the average abundance of each complex. Subsequent filtering was applied to complex-centered values. For each protein only the average value derived from the complex(es) with the highest number of subunits is reported. The resulting filtered complex-centered abundances were compared between the protein subsets ED, NED and UN. Only proteins from complexes with at least one ED or one NED subunit but with at least two different categories (ED, NED or UN) were considered for the analysis.

Preparation of Chromosome Spreads and Chromosome Painting Cells were grown to 70%–80% confluency before treatment with 50 ng/ml colchicine for 3-5 hr. Subsequently, cells were collected by trypsinization and centrifuged at 250rcf for 10min. Pellets were then resuspended in 75 mM KCl and incubated for 10-15 min at 37°C. After centrifugation at 150 rcf for 10 min, cell pellets were resuspended in 3:1 methanol/acetic acid for fixation. Finally, cell pellets were washed several times in 3:1 methanol/acetic acid, spread on a wet glass slide and air-dried at 42°C for 5 min. Each sample was labeled with probes for two different chromosomes. Probes (Chrombios GmbH, Raubling, Germany) for chromosomes 5 and 11 were tagged with FITC and TAMRA, respectively. The chromosomes were labeled according to the manufacturer’s instructions and counterstained with DAPI. Images were obtained by a fully automated Zeiss inverted microscope.

Genomic DNA Sequencing and Copy-Number Estimation of RPE-1 and RPE-1 Trisomic Cells XT Target Enrichment System for Illumina Paired-End Multiplexed Sequencing Library Protocol (Agilent Technologies, Publication Number G7530-90000). Genomic DNA sequencing library was prepared with 100 ng sheared genomic DNA using TruSeq ChIP Library Prep Kit according to the manufacturer’s guidance (Illumina). The libraries were sequenced in 1x 100 nt manner on HiSeq 2000 platform with a depth of ∼30 million reads per library (Illumina). Sequencing reads were aligned to the human reference genome (hg19) using Bowtie (version 2.1.0) with default parameters, and only uniquely mapped reads were kept for downstream analysis. With a sliding window of size 100Kb and a step size of 50-Kb, mapped reads in each window were then counted and used for copy-number estimation. With the assumption that most genomic regions for the cells were diploid, we took C i given by the following formula as the copy-number estimates for genomic location at the ith window: C i = 2 × R i median j ∈ I R j

where R i is the read counts of the ith window. To avoid underestimating copy numbers for regions with multi-aligned reads, we adjusted for mappability based on mapping of simulated reads with uniform coverage across the genome. The original read counts were divided by the read counts in the same window obtained from the simulation data, and the adjusted read counts were instead used for copy-number estimation. DNA was isolated using the Blood and Cell Culture DNA kit (QIAGEN) according to the manufacturer’s recommendations. 1 μg genomic DNA was sheared following the SureSelectTarget Enrichment System for Illumina Paired-End Multiplexed Sequencing Library Protocol (Agilent Technologies, Publication Number G7530-90000). Genomic DNA sequencing library was prepared with 100 ng sheared genomic DNA using TruSeq ChIP Library Prep Kit according to the manufacturer’s guidance (Illumina). The libraries were sequenced in 1x 100 nt manner on HiSeq 2000 platform with a depth of ∼30 million reads per library (Illumina). Sequencing reads were aligned to the human reference genome (hg19) using Bowtie (version 2.1.0) with default parameters, and only uniquely mapped reads were kept for downstream analysis. With a sliding window of size 100Kb and a step size of 50-Kb, mapped reads in each window were then counted and used for copy-number estimation. With the assumption that most genomic regions for the cells were diploid, we tookgiven by the following formula as the copy-number estimates for genomic location at the ith window:whereis the read counts of the ith window. To avoid underestimating copy numbers for regions with multi-aligned reads, we adjusted for mappability based on mapping of simulated reads with uniform coverage across the genome. The original read counts were divided by the read counts in the same window obtained from the simulation data, and the adjusted read counts were instead used for copy-number estimation.

AHA Pulse-Chase of SILAC-Labeled RPE-1 and RPE-1 Trisomic Cells RPE-1 and RPE-1 trisomic cells were grown, methionine starved, AHA pulsed, chased, and lyzed as described for the mouse fibroblast. Experiments were started when cells reach ∼30% confluency and two 15 cm plates were used per time point. Click chemistry, denaturation, alkylation, washing, and digestion were performed as described for the mouse cells. Peptides were stageTipped on 4 mm/1 ml C18 columns (3M). Peptides were eluted using 500 μl buffer B and dried in a speed-vac until a few μl liquid was left. For two of the samples Buffer A was added to 10 μl final volume. 5 μl of sample was loaded onto a 15 cm column and 5 μl onto a 2 m monolithic column using a HPLC system. The 15 cm column and 2 m monolithic column samples were analyzed on a Q-Exactive orbitrap system, as described above, deploying 4 and 6 hr gradients of increasing Buffer B, respectively. For one sample the peptides were further SCX fractionated into 2 fractions eluted with 125 mM and 500 mM ammonium acetate as described above. These samples were analyzed using 4 hr gradients of increasing Buffer B concentration over a 15 cm column. The resulting raw files were analyzed using MaxQuant with the previously described parameter settings with the exception that the Andromeda search engine was matching the acquired MS/MS spectra to the human Uniprot database (2014-10). 3 biological replicates were performed per cell line. Normalization, fitting of models, Δ-score and abundance after pulse calculations were performed as for the mouse fibroblasts. For all downstream analysis proteins derived from genes located on autosomes were used except when from chromosome 10 (fully trisomic in both parental and trisomic cell line) and chromosome 12 (clonal expansion of trisomic cells among control cells).

Relative Protein Levels at Steady State in RPE-1 and RPE-1 Trisomic Cells Fully Heavy SILAC labeled RPE-1 and Light labeled RPE-1 trisomic cells were grown to 70% confluency in 10 cm plates. Cells were scraped in ice cold PBS before being spun down at 1000rcf and PBS decanted. Cell pellets were lyzed in 1.3% SDS, 0.1 M DTT in 50 mM ammonium bicarbonate solution. Samples were heated to 95°C for 10 min. After cooling the samples, Benzonase was added for another 10 min. The samples from the two cell lines were then mixed 1:1 and spun down at 20,000 rcf to clear cell debris. Proteins in supernatant were alkylated by the addition of 0.25 M iodoacetamide, final concentration, and left in the dark at room temperature for 20 min. After alkylation proteins were directly precipitated, to get rid of SDS, by Wessel-Flügge precipitation as described above. The resulting protein pellet was solubilized by shaking the sample in 6 M Urea/2 M thiourea in 10 mM HEPES (ph8). Proteins were digested “on pellet” by Lys-C for 3 hr at room temperature before the sample was diluted in ABC buffer and Trypsin was added overnight. Peptides were acidified by triflouroacetic acid before being stored on stageTips. Peptides were prepared for HPLC as described above and analyzed using a 6 hr gradient on a 15 cm column packed with C18 material as described above. The Q-exactive was run with standard setting and the raw files were analyzed as described for the AHA enrichment specificity experiment. MaxQuant output was filtered as described above but this time normalized SILAC ratios were used for downstream analysis. A label swap experiment was also performed in where RPE-1 cells were grown in light SILAC medium and RPE-1 trisomic cells were grown in heavy SILAC medium. The analysis used the average SILAC ratio for the two experiments.