Proteome turnover in the bloodstream and procyclic forms of Trypanosoma brucei measured by quantitative proteomics

Background: Cellular proteins vary significantly in both abundance and turnover rates. These parameters depend upon their rates of synthesis and degradation and it is useful to have access to data on protein turnover rates when, for example, designing genetic knock-down experiments or assessing the potential usefulness of covalent enzyme inhibitors. Little is known about the nature and regulation of protein turnover in Trypanosoma brucei, the etiological agent of human and animal African trypanosomiasis. Methods: To establish baseline data on T. brucei proteome turnover, a Stable Isotope Labelling with Amino acids in Cell culture (SILAC)-based mass spectrometry analysis was performed to reveal the synthesis and degradation profiles for thousands of proteins in the bloodstream and procyclic forms of this parasite. Results: This analysis revealed a slower average turnover rate of the procyclic form proteome relative to the bloodstream proteome. As expected, many of the proteins with the fastest turnover rates have functions in the cell cycle and in the regulation of cytokinesis in both bloodstream and procyclic forms. Moreover, the cellular localization of T. brucei proteins correlates with their turnover, with mitochondrial and glycosomal proteins exhibiting slower than average turnover rates. Conclusions: The intention of this study is to provide the trypanosome research community with a resource for protein turnover data for any protein or group of proteins. To this end, bioinformatic analyses of these data are made available via an open-access web resource with data visualization functions.


Introduction
The tsetse transmitted protozoan parasites of the species complex Trypanosome brucei cause human African trypanosomiasis (HAT) and the cattle disease Nagana 1 . The parasites undergo a complex lifecycle between their mammalian hosts and insect vectors. The parasites multiply as procyclic forms in the tsetse midgut, some of which migrate to the salivary glands where they differentiate to epimastigote forms and then into non-dividing metacylic trypomastigote forms. The latter are transmitted to the host during the insect bloodmeal, where they differentiate in into rapidly dividing slender trypomastigotes that colonise primarily the hemolymphatic system. Some parasites differentiate into non-dividing stumpy trypomastigotes that are pre-adapted to differentiate into procyclic forms if taken up by a tsetse in a bloodmeal, thus completing the lifecycle. It is the passage of slender trypomastigote forms across the bloodbrain barrier that leads to the severe neutrological symptoms of HAT. During this complex lifecycle, T. brucei achieves differentiation and responds to changes in its environment through adaptation of its proteome [2][3][4][5][6][7][8][9] . With the aim of gaining further insights into the proteome plasticity of this parasite, we set out a mass spectrometry strategy to define the global proteome turnover rate of T. brucei and compare the results between the BSF and PCF lifecycle stages.
Stable isotope labelling with amino acids in cell culture (SILAC) label-chase cell cultures All isotopes were from CK Isotopes, Cambridge Isotope Labs, USA. T. brucei PCF cells were labelled to steady state, i.e., during 8 to 9 cell divisions, using medium isotope SILAC SDM-79 medium, containing [U-13 C]-L-arginine.HCl and [ 2 H 4 ]-L-lysine.2HCl, known as R6K4, as described before 4,11 . Cells were recovered by centrifugation (600 × g for 10 min at room temperature) and transferred into pre-warmed (27°C) light isotope SDM-79 medium (chase medium), containing unlabelled L-arginine.HCl and L-lysine.2HCl, known as R0K0, to a final cell density of 1 × 10 7 cells/ml. Aliquots of 3 × 10 7 cells were removed in triplicate 0.25, 0.5, 1, 2, 4, 8, 20, and 28 h after transfer to chase medium. The 0-h chase time point was taken in triplicate just before cells were transferred to the light (R0K0) chase medium. Cell aliquots from each time point were placed on ice-water, counted using a Z2 Coulter counter (Beckman) and immediately diluted with 10 ml icecold PBS to stop the chase. The cells were then centrifuged (800 × g for 10 min at 4°C) and resuspended in 0.1 ml PBS with subsequent addition of equal volume of lysis buffer (freshly prepared 4% SDS in 0.1 M Tris-HCl pH 7.2 containing 0.1 M DTT), vortexed and heated for 20 min at 50°C. Aliquots were snap-frozen in liquid nitrogen and stored at -80°C until further processing. Before filter-aided sample preparation (FASP; see below), cell lysates from each time point were mixed 1:1 with equivalent lysates from cells labelled to steady-state using heavy isotope SILAC SDM-79 medium, containing [ 13 C 6 , 15 N 4 ]-L-Arginine.HCl and [ 13 C 6 , 15 N 2 ]-L-Lysine.2HCl, known as R10K8.
T. brucei BSF cells were labelled to steady state levels, between 8 to 9 cell divisions, using medium isotope (R6K4) SILAC HMI-11T medium, containing 120 μM [U-13C]-L-Arginine.HCl and 240 μM [ 2 H 4 ]-L-Lysine.2HCl as previously described 4 . When the culture reached 1 × 10 6 cells/ml, the labelled cells were harvested by centrifugation at room temperature and transferred into light isotope (R0K0) HMI-11T chase medium, containing 399 μM L-Arginine.HCl and 800 μM L-Lysine.2HCl, at a final cell density of 6.2 × 10 5 cells/ml. Aliquots of 1.5 × 10 7 cells were removed in triplicate at 0.5, 1, 2, 4, 8 and 12 h after transfer to chase medium. The 0 h chase time point was taken in triplicate just before cells were transferred to the light (R0K0) chase medium. Cell aliquots from each time point were placed on ice-water, counted in using Z2 Coulter counter (Beckman) and immediately diluted with 10 ml ice cold PBS to stop the chase. The cells were then centrifuged (800 × g for 10 min at 4°C) and resuspended in 0.05 ml PBS with subsequent addition of equal volume of lysis buffer (freshly prepared 4% SDS in 0.1 M Tris-HCl pH 7.2 containing 0.1 M DTT), vortexed and heated for 20 min at 50°C. Aliquots were snap frozen in liquid nitrogen and stored -80°C until further processing. Before FASP, see below, cell lysates from each time point were mixed 1:1 with equivalent lysates from cells labelled to steady-state in heavy isotope (R10K8) SILAC HMI-11 medium.

FASP
Tryptic peptides were prepared using the FASP method 12 with minor modifications. Briefly, BSF and PCF samples were defrosted, vortexed, reduced with 100 mM DTT by heating at 50°C for 20 min and combined 1:1 to the corresponding R10K8 labelled BSF or PCF lysate. Samples were mixed with 8 M urea in 0.1 M Tris-HCl pH 8.5, placed inside spin filters (Vivacon 500, 10,000 MWCO) and washed with this buffer as described in the original protocol. Alkylation was performed using 50 mM iodoacetamide (freshly prepared) in the same buffer for 20 min at room temperature in the dark. Further washes with 8 M urea buffer, followed by washes with 50 mM ammonium bicarbonate buffer, were performed as in the original protocol. Digestion with trypsin (trypsin modified, sequencing grade, Roche) was performed with enzyme protein ratio 1:100 overnight at 37°C inside a humid chamber. Tryptic peptides were eluted by centrifugation into low binding Eppendorf tubes. Spin filters were washed with 50 μL 0.5 M NaCl, combined with the first eluate and acidified with 10 μL 10% trifluoroacetic acid (TFA), then further diluted with 400 μL of 0.1% TFA, desalted using C18-RP microspin silica columns (Nest group, USA) and freeze-dried.

Fractionation of tryptic peptides and peptide LC-MS/MS analysis using MaxQuant
Aliquots of 50 μg of BSF and PCF freeze dried and desalted SILAC labelled tryptic peptides were re-dissolved in 50 μL of 5% formic acid and fractionated on an Xbridge BEH C18 column (130 Å, 3.5 μm, 4.6 × 150 mm) using a Dionex Ultimate 3000 HPLC system. Buffer A was composed of 2% acetonitrile in 10 mM ammonium formate (pH 9.0) and buffer B of 80% acetonitrile in 10 mM ammonium formate (pH 9.0). Columns were run at 1 mL/min at 30°C with a starting composition of 10% buffer B at 0 min, followed by an increase to 40% buffer B at 11 min, 100% at 12 min and down to 10% at 13 min until the end of the run at 20 min. Fractions (1 ml) were collected and subsequently pooled into 10 final fractions. The first 3 fractions were mixed with the final 3 fractions (i.e., 1 with 13, 2 with 12 and 3 with 11) dried using a GeneVac evaporator and redissolved in 50 μL of 5% formic acid. These peptide fractions in 5% formic acid were injected onto a C18 nano-trap column using a Thermo Scientific Ultimate 3000 nanoHPLC system, washed with 2% acetonitrile, 0.1% formic acid and resolved on a 150 mm × 75 μm C18 reverse phase analytical column using a gradient from 2% to 28% acetonitrile over 120 min at a flow rate of 200 nL/min. Peptides were ionised by nano-electrospray ionisation at 2.5 kV. Tandem mass spectrometry analysis was carried out on a QExactive+ mass spectrometer, using HCD fragmentation of precursor peptides. A data-dependent method was utilised, acquiring MS/MS spectra for the top 15 most abundant precursor ions.

MaxQuant analysis
Data was processed using MaxQuant version 1.5.8.3, which incorporates the Andromeda search engine 13 . Proteins were identified by searching a protein sequence database containing T. brucei brucei 927 annotated proteins (Version 32, downloaded from TriTrypDB 14 ) supplemented with frequently observed contaminants (porcine trypsin, bovine serum albumins and human keratins) and the Tb427.BES40.22 VSG protein as internal control. Search parameters specified an MS tolerance of 5 ppm, an MS/MS tolerance at 0.5 Da and full trypsin specificity, allowing for up to three missed cleavages. Carbamidomethylation of cysteine was set as a fixed modification and oxidation of methionine and N-terminal protein acetylation were allowed as variable modifications. Peptides were required to be at least 6 amino acids in length, and false discovery rates (FDRs) of 0.01 were calculated at the level of peptides, proteins and modification sites based on the number of hits against the reversed sequence database. A minimum of two peptides were quantified for each protein.

Bioinformatic pipeline
The output proteinGroup file of the MaxQuant program was used to extract the data. The protein groups annotated by the MaxQuant program as 'Only identified by site', 'Reverse' and 'Potential contaminant' were removed from the analysis. Moreover, protein groups identified with less than 2 unique peptides were removed from the analysis. To calculate the incorporation rate, we analysed the peptide.txt output file of MaxQuant as described in 15 at section 12. Briefly, we first distinguished between lysine-and arginine-containing peptides then, for each of these subsets, we determined the incorporation rate as 1-1/average ratio of the Heavy and Medium labels versus the Light label, using the non-normalized ratios outputs of Max- Quant To monitor parasite division, we determined the parasite density at each time point (Extended data, Table 1 with the limfit Python library that uses non-linear least squares 19,20 . The amplitude, offset and tau' parameters were initialised with the values of 0.9, 0.2 and 2, respectively. The parameters were set with a lower bound of zero. We used a weighted fitting, meaning that data points with smaller standard deviation had more importance for the curve fitting. The root mean square error (RMSE) between the estimated values and the predicted values were computed for all the fitted curves and reported to evaluate the quality of the fitting (Extended data, Tables 2 and 3 16 ). The half-life of the exponential decay models was computed by taking account of the cell division time. To this aim the tau ' was corrected with the formula: values were computed with the scipy python package 21 . The degradation linear motifs (degrons) were downloaded from the ELM database 22 . The regular expressions (REs) corresponding to the degrons were searched for in the primary protein sequence with a custom Python code. The half-life values were binned into decile groups. The degron REs were searched for in the 1st and 2nd decile groups for proteins with short half-lives and in the 9th and 10th decile groups for proteins with long half-lives. For each degron, we reported the fraction of proteins with at least one RE match in each decile bin under analysis.
The Gene Ontology (GO) term enrichment analysis was computed with the GOATOOLS python package, version 0.8.12 23 and the protein to GO term annotation was downloaded from TriTrypDB version 41 14 . Only GO terms with a p-value less than 0.01 and a minimum of 10 proteins annotated were reported. The localization information of the TrypTag project 24 was retrieved from the TriTrypDB web site using the download function of a pre-configured table and selecting Cellular Localization and Protein targeting and localization. The downloaded text file was parsed with a python script to extract the localization annotation from the HTML lines. For data visualization, only cellular compartments with more than 30 proteins annotated were used. Cell cycle regulated proteins were retrieved from Crozier et al. 25 . Only proteins with a fold change value greater than 1.3 were selected (see Supplementary Table 1 of Crozier et al. 25 ). The predicted protein complexes were retrieved from Crozier et al. 26 . We used the cumulative distribution of the half-life variance of proteins within the same predicted protein complexes and the residual protein amount variance of random complexes (the random complexes were equal in number and in size to the predicted protein complexes). The comparison was then repeated 1000 times, each time using a different seed for randomisation. We visualized the cumulative distribution of the variance between the predicted protein complexes and the random complexes and computed the p-value of the Kolmogorov-Smirnov test between the variances in the predicted and random protein complexes.
The Python code to reproduce the analysis pipeline and the figures reported in this paper are available as a series of Jupyter notebooks at https://github.com/mtinti/wor_turnover and on Zenodo 27 .

Results
A SILAC label-chase technique was used to study the turnover rate of the T. brucei bloodstream (BSF) and procyclic forms (PCF) proteomes ( Figure 1). Triplicate biological replicates were analysed for both BSF and PCF cultures. For BSF parasites, cells were grown in parallel in heavy (H) and medium (M) L-Arg and L-Lys containing media. After 8 to 9 cell divisions, the steady-state medium-labelled parasites were placed in light (L) culture media and aliquots were taken at times of 0, 0.5, 1, 2, 4, 8, and 12 h and the cells lysed in an SDS-Tris buffer. Each time-point lysate was mixed 1:1 with corresponding heavy labelled lysate to provide an internal standard for normalization of synthesis and degradation data 17 . Thus, the decrease of medium labelled peptides and increase of light labelled peptides relative to (constant) heavy labelled peptides over time were used to calculate rates of protein degradation and synthesis, respectively. Accurate cell counting was performed throughout the experiments to take into account changes in medium and light peptides relative to heavy peptides due to cell division during the chase period. Samples from each chase time-point were processed to tryptic peptides by FASP, separated into 10 sub-fractions by high-pH reversed-phase HPLC and analysed by LC-MS/MS. The same experiments were performed for PCF cells except that chasetimes of 0, 0.25, 0.5, 1, 2, 4, 8, 20 and 28 h were used. In both cases, tryptic peptides were quantified using MaxQuant as described before 4,11,13,28 and processed with an in-house developed pipeline. The MaxQuant program detected 6456 and 6466 protein groups in the BSF and PCF samples, respectively. These were filtered to remove protein groups annotated as: Only identified by site, Reverse hits and Potential contaminants. Further, we removed protein groups identified with less than two unique peptides. We evaluated the reproducibility of the biological replicates by computing the Spearman correlation coefficient between each data point of the BSF ( Figure 2A) and PCF ( Figure 3A) time course experiments using the M/L ratio of the MaxQuant protein group output. As expected, data points closer in time have a higher Spearman correlation coefficient relative to data points distant in time, creating the diagonal pattern visualized in the heatmaps of Figure 2A and Figure 3A. The Spearman correlation coefficient values between the time points of the 3 biological replicates is further visualized in Figure 2B for the BSF and Figure 3B for the PCF. Finally, we extracted normalized degradation profiles for 6023 and 5895 protein groups in the BSF and PCF respectively as described in the Methods section.
Comparison of protein turnover between BSF and PCF parasites For the comparison of degradation profiles between BSF and PCF we filtered the data to include only protein profiles with good quality and reproducibility of the fitting outputs. Thus, we only analysed protein groups with at least two independent measurements in each of 4 time points for BSF and PCF cells. We also applied a threshold of 0.1 for the RMSE of the curve fit ( Figure 4). This final filtering step produced a dataset for 4194 protein groups in the BSF and 3092 protein groups in the PCF, with 2600 protein groups in common between the two life-cycle stages.
Before the incorporation rate correction, the median of the normalized steady-state medium (R6K4) incorporation values prior the light (R0K0) chase were 0.88 for the BSF form and 0.95 for the PCF form. These apparently incomplete  incorporation values 29 are a function of the isotopic purities of the labelled amino acids (99% for R10, R6 and K6 and 96% for K4) and, most likely, the acquisition of some light Arg and Lys from the fluid phase endocytosis and lysosomal degradation of serum albumen and other serum proteins from the culture media. Consistent with this view is the fact that isotopic Lys incorporation appears to be lower than that of Arg, reflecting the lower levels of Arg versus Lys in bovine serum albumen ( Figure 5). Further, as BSF parasites have a much higher endocytic rate than PCF parasites, it makes sense that the apparent under-incorporation is significantly greater in BSF parasites 30 . To take into account these effects, we normalized the BSF values by a correction factor, as explained in the Methods.
Plotting the median protein degradation values against cell doubling time to normalise for the different doubling times of BSF and PCF cells ( Figure 6) suggests that protein turnover in BSF cells is significantly faster in this lifecycle stage (Figure 7). This finding is supported by the distribution analysis of the fitted parameters. In the exponential decay model, the offset value (horizontal asymptote) can be used as a proxy for the residual amount of protein left after one round of cell division and the tau value can be used as a proxy for how fast the protein reaches this offset value. As both the offset and the tau values are, on average, smaller in the BSF relative to the PCF protein groups (Figure 8), it is possible to conclude that the BSF proteome is more rapidly turned over than the PCF proteome, and this is also apparent from the computed median protein group half-lives corrected for the cell duplication ( Figure 7C). While higher rates of protein turnover might be expected in BSF cells from their 10°C higher growth temperature alone, it is also clear that other factors are also at play. Thus, the correlation of half-life values for proteins in common between BSF and PCF cells is quite low, with a Pearson correlation coefficient of 0.55 and r 2 value of 0.3 ( Figure 9). Such mechanisms might include factors leading to changes in the rate of protein synthesis, for example, mRNA stability and/or mRNA access to polysomes, and/or factors leading to changes in the rate of protein degradation, for example, ubiquitylation and proteasome-mediated proteolysis.
We next analysed potential links between cellular function and the stability of trypanosome proteins by binning the protein half-lives into decile groups and computing the GO term enrichment in each bin. A heatmap of the BSF and PCF GO annotations that were discovered in no more than 4 of the selected bins, with a p-value of 0.01 or less, is shown in (Figure 10). In both lifecycle stages, the quantiles containing the shortest-lived proteins are enriched for terms that are related to regulation of gene expression and nucleolar localization, whereas the quantiles with the most stable proteins contain terms related to proteins and protein complexes localized to cellular compartments such as the mitochondria, the glycosome or the flagellum. The Trypanosome Go annotation does not contain data for short linear motifs. On the other hand, degrons (short linear motifs targeting proteins to degradation) have been found to influence the turnover in the human proteome 31,32 . For this reason, we wondered about the importance of degrons for the turnover of the T. brucei proteome. As illustrated in Figure 11, we could not find evidence for linear degradation motif enrichment in the primary sequences of the shortest-lived proteins (1 and 2 half-lives decile groups) relative to the more stable proteins (9 and 10 half-lives decile groups).
To further analyse the relationship between protein localization and stability, we took advantage of the high-throughput imaging localization data of the TrypTag resource 24 deposited at TriTrypDB 14 . From these data, we extracted the localization information for the 575 proteins annotated with single location descriptors from C-terminal tagging only (to minimise potential artefacts from disrupting N-terminal signal peptides). From this list, we removed cell compartments represented by <30 annotated proteins. Finally, we added the glycosomal proteome described in Guther et al. 6 . We chose to add this experimentally determined glycosome subset of proteins (n=159) rather than rely on TrypTag localisation data for this organelle as many glycosomal proteins have C-terminal and/or N-terminal Peroxisomal Targeting Signal sequences 11 . The assembled localisation dataset (Extended data, Table 4 16 ) was used to visualise the distribution of protein half-lives in each cell compartment. To better compare the BSF and PCF life stages, we transformed the protein half-lives into z scores. As illustrated in Figure 12, the glycosome and axoneme compartments contained proteins with generally higher stability than the average, while proteins targeted to the nucleolus were the least stable in both BSF and PCF.
The balance between protein synthesis and degradation defines proteostasis and protein abundance. This prompted us to asses any potential relationship between protein abundance and half-life. The scatter plots reported in Figure 13A and B shows that there is a poor correlation between abundance and protein half-life in both the BSF and PCF life stages. However, we noted that most of the proteins with longer half-lives have an intensity value greater than 1e8 in both BSF and PCF ( Figure 13A and B). Further, we observed that the protein abundance tends to increase with the protein half-life, with a more pronounced trend in the PCF relative to the BSF life stage (Figure 13C and D).

Turnover analysis of cell cycle regulated proteins
We next sought to analyse the stability of proteins involved in the regulation of the cell cycle. To this end, we retrieved the cell cycle regulated proteins in PCF trypanosomes identified in a recent publication from our laboratory 25 . As for the localization analysis, we decided to use the z-score to transform the protein residual amount and we could extract data for 197 and 120 proteins in the BSF and PCF respectively. As illustrated in Figure 14, the cell cycle regulated proteins are enriched for low z-score values in the PCF and BSF, suggesting that cell cycle regulated proteins are likely to have a faster turnover rate than the total proteome (Extended data, Table 5 16 ).

Turnover analysis of protein complexes
To investigate the relationship between protein stability and membership of a protein complex, we took advantage of a recent publication from our laboratory aimed at the identification of soluble cytoplasmic protein complexes in T. brucei. In Croizer et al. 26 , we defined a set of 234 high confidence protein complexes (Supplementary Table 2 of 13) based on protein elution profiles using two size exclusion and one ion   exchange chromatography system. The residual protein value variance of the protein complex subunits shows a statistically significant difference, with the protein complexes having a smaller variance than seen for random protein complexes, for both BSF ( Figure 15A) and PCF ( Figure 15B). These findings support the hypothesis that proteins associating in the same complex are turned over at similar rates 33 .

Web resource
All of the processed MS data and turnover analyses are freely available via a searchable web application that can be browsed at http://134.36.66.166:8082/turnover; code for the web application is available from GitHub and Zenodo 27 . The web application displays two interactive search interfaces that visualise the turnover data for PCF and BSF cells, allowing a direct   comparison between the two life stages. The first application at the home page allows the comparison of one protein at a time ( Figure 16). By clicking on any row of the Selection Table, the BSF and PCF normalised M/H degradation data points will appear in Plot panels on the right, with the best exponential fit for the data. The Selection Table is fully searchable by protein identifier and protein descriptions in the search field on the top of the table. A summary table (Fitted Parameters) at the bottom of the plots highlights the fitted parameters of the exponential decay model for both BSF and PCF. The link "alter" on the top of the BSF and PCF data plots opens a new window that contains interactive plots (Figure 17). This new visualisation allows the modification of the exponential decay model parameters (amplitude, tau and offset) and updates the results of the newly fitted model. The second web application is found through the "Multi Plot" link and allows the comparison of multiple proteins at the same time ( Figure 18). The data to visualise can be uploaded with the Selection Table on the left of the application by ticking the boxes on the right of protein identifiers and descriptions. The Selection Table is fully searchable by protein identifier and protein descriptions with the search field on the top of the table. The protein(s) can be uploaded and visualised one by one or as a group. To search and upload a group of proteins, it is necessary to insert a search term in the search box and add the relevant proteins one by one.

Discussion
This paper describes the first proteome-wide analyses of protein turnover in the BSF and PCF life-cycle stages of T. brucei.
We hope this open data resource will be useful to the trypanosome research community.
The doubling times of the BSF and PCF cells in these experiments were approximately 12 h and 22 h, respectively ( Figure 6 and Extended data, Table 1   PCF doubling time is longer than the normal range (12 to 14 h). We think that these relatively long doubling times are partly due to the necessary use of dialysed foetal bovine serum to perform the isotopic Arg/Lys labelling 4 and partly due to the effects of the unavoidable centrifugation step required to transfer the cells from medium-to light-isotope media to perform the chase, the stress of which seems to cause to growth arrest for about 2 h. Interestingly, the four PCF time points under 2 hours (0, 0.25, 0.5 and 1 h) show the lowest correlation compared all the other time points (Figure 2 and Figure 3).   Despite this lag-phase, the subsequent logarithmic growth suggests that the parasites are reasonably healthy for the majority of the chase period.
Despite those caveats, we think the data reported here are valuable. Some of the proteome-wide half-life values from our study appear to be in line with several specific examples reported in literature, whereas others differ. For example: BSF ISG75 and ISG65 are reported to have half-lives <3 h 34,35 , and our estimates for ISG75 (Tb927.5.360) and ISG65 (Tb927.2.3270) are 2.4 and 2.5 h, respectively. PCF CYC3ty (Tb927.6.1460) and CYC2ty (Tb927.11.14080) are reported to have half-lives >6 and >9 h, respectively 36 , and our estimates (albeit in BSF) are 4.9 and 5.6 h, respectively. The turnover of the glycosomal protein fructose bisphosphate aldolase (Tb927.10.5620) has been determined with a pulse-chase experiment to be greater than 3 h 37 and our dataset shows a turnover rate of 7.8 h. A similar pulse-chase experiment has determined a rapid turnover (as short as 30 min) for aldolase and two other glycosomal proteins (D-glyceraldehyde-3-phosphate dehydrogenase and NADdependent glycerol-3-phosphate dehydrogenase) in PCF cells 38 . Our data suggest a much longer half-life for those proteins (>7 h), as also suggested in a different study by Clayton in 1988 39 . The half-life of the main VSG (Tb427.BES40.22) has been reported in the order of 72 h 40,41 . However, our estimate of the main VSG half-life is 5.3 h, which is in good agreement with a recent determination of the VSG coat replacement (4.6 h) determined by flow cytometry 42 .   The median half-lives of the BSF and PCF proteomes were 5.6 and 13 h, respectively, versus doubling times of 12 and 22 h, respectively ( Figure 6 and Figure 7). By contrast, recent studies on protein turnover in human and yeast cells showed median turnovers similar to the their respective cell doubling times 17,43,44 . This suggest that in trypanosome protein replacement is not only driven by dilution due to cell division but also other by other active mechanisms, presumably including proteasome-mediated destruction. Interestingly, and consistent with this, proteasome inhibition has been shown to be highly toxic to trypansomatids 45,46 .
To get a better insight into the turnover of BSF and PCF proteome we divided the proteins into classes based on their relative turnover rates and we analysed those classes with a GO term enrichment strategy. A class of rapid turnover proteins is shared between BSF and PCF and is enriched for the "posttranscriptional regulation of gene expression" term ( Figure 10). The annotation of this set of proteins derives mostly from two high throughput screenings aimed at the characterization of the mRNA-binding proteome 47,48 . For this reason, it is not surprising that this class contains proteins with RNA binding domains. This finding suggests that the RNA binding proteins, which appear to be the primary modulators of gene expression in trypanosomes 49,50 , need to be switched on and off promptly as the case for the cell cycle-regulated proteins (see below). Also, the cell compartment "nucleolus", contains rapid turnover proteins, as identified by both the GO enrichment and the TrypTag localization analyses (Figure 10 and Figure 12). Several proteins targeted to this cell compartment are involved in ribosome subunit biogenesis (Extended data, Table 5 16 ) and previous studies have observed a higher turnover rate for nucleolar proteins in human and yeast cells as well 17,43 .
Another class of rapid turnover proteins in both BSF and PCF are cell cycle regulated proteins (Figure 14). It is interesting to note that three of the identified BSF proteins (Tb927.11.15800: Tip Of Extending FAZ protein 1 or Cytokinesis initiation factor 1, Tb927.11.8220: aurora B kinase and Tb927.9.14290: Cytokinesis initiation factor 2) have been identified in a signal cascade that initiates cytokinesis. In particular, the phosphorylation of cytokinesis initiation factor 1 by polo-like kinase (Tb927.7.6310) targets it to the anterior tip of the new flagellum attachment zone filament, where it subsequently recruits Aurora B kinase to initiate cytokinesis 51 . Localisation studies of these three proteins suggested a temporal relationship of appearance and co-localisation during the cell cycle, and our study provides evidence that this is achieved by a mechanism of synthesis and degradation. In a follow-up study, cytokinesis initiation factor 2 was found to interact with the cytokinesis initiation factor 1 52 . This study provided evidence that both overexpression or depletion of the cytokinesis initiation factor 2 inhibited cytokinesis, further suggesting that tight co-regulation of protein synthesis and degradation occurs for the proteins involved in this pathway.
From the GO term analyses, it is possible to extract a group of slow turnover proteins enriched for terms related to the flagella localization (axoneme, intraciliary transport particle, cilium motility, ciliary plasm) among which appear the components of the Rab family and intraflagellar transport (IFT) complex ( Figure 10 and Extended data, Table 2 16 ). The Rab family contains membrane-associated proteins responsible for vesicle trafficking 53 while the IFT protein complex is responsible for the formation of the flagella and transport of protein from the base of flagella to their tip and back 54 . The IFT protein complex itself contains members of the Rab family 55 . The higher stability of these two protein classes compared to the proteins involved in cell cycle regulation might be explained by their localization. The membrane anchored proteins are generally recycled by an endocytosis mechanism 56 . This mechanism might be slower than the ubiquitin-mediated proteasome degradation that acts on cytoplasmic proteins. On the other hand, the proteins of the IFT complex might be stabilized by their localization in the flagella, a very stable structure in T. brucei also during mitosis 57 . This is supported by the observation that components of the axoneme are among the most stable proteins in BSF and PCF ( Figure 12).
The general hypothesis of localisation-dependent protein stability is further supported by the observed relatively slow turnover of proteins localized to the mitochondria and glycosomes ( Figure 10 and Figure 12). T. brucei contains a single mitochondrion that is replicated during cell cycle 58 . It is likely that the proteins localized in this organelle are recycled/ degraded by a different mechanism, i.e., mitophagy, with slower kinetics than experienced by cytosolic proteins 59 . Many mitochondrial proteins have signal sequences at their N termini that are necessary and sufficient for import of the proteins into this organelle. The different turnover rates of individual mitochondrial proteins might then reflect the different rates at which these proteins are translocated to the organelle and their propensity for proteasome-dependent turnover prior to import. A similar situation could explain the range turnover rates seen for glycosomal proteins ( Figure 12): Thus, their relatively slow median turnover values would be consistent with peroxisomal autophagy (or 'pexophagy') as the principal mechanism of turnover for these organelles 60-62 , superimposed with proteinspecific pre-import quality-control turnover mechanisms 62 .
Previous turnover analyses in human cells showed that rapid turnover proteins contain specific sequence elements that may serve as degradation signals, such as PEST domains or cleavage sites and are enriched for disordered regions 17,31,32 . More recently, it has been found that PEST domains or cleavage site degradation signals have little importance for the turnover of yeast proteins 43 . Similarly, we did not find any evidence of enrichment for degrons in the BSF and PCF rapid turnover proteins ( Figure 11). It is possible to speculate that those degradation signals are less critical for protein turnover in unicellular eukaryotes such as yeast and trypanosomes, relative to multicellular eukaryotes.
Our analyses suggest that protein localisation and function contribute to determining the stability of the trypanosome proteome. On the other hand, the correlation between abundance and protein half-lives, as determined for the PCF and BSF life stages, shows that highly abundant proteins topically have long half-lives. This trend could be consistent with a model whereby steady-state protein levels in trypanosomes are controlled primarily by post-translational rates of degradation, rather than rates of synthesis. • Table 4. Protein half-life and sub-cellular localization analysis. This table reports the localization analysis for the protein identified in the TrypTag database. The column protein_id shows the top protein identification in the MaxQuant output. The column locs shows the name of the cell compartment locations taken from the TrypTag database. The columns BSFPCF report the protein half-life values as computed from the fitted parameters. The columns BSF Z score and PCF Z score report the z-scores for columns C and D, respectively. The column desc shows the protein descriptors of the protein groups from the MaxQuant output.

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. 1.
differentially regulated protein subsets to uncover some new biology, perhaps.
It might have been valuable to have a correlation between mRNA abundance (and decay) and protein abundance (and decay). I think there's plenty of evidence that these can correlate poorly but it would be valuable to understand how these regulatory levels interact. Since several mRNA datasets (including decay studies) exist, this could be a useful follow up study.
The data are all publicly accessible but most use will be derived through availability in TriTrypDb (or at least as a link from the gene code in TriTryp DB to the web application for viewing decay rates). I assume the authors are coordinating with TriTrypDB curators to incorporate this information into forthcoming TriTrypDB data releases? A minor technical point relates to the cell lines used. The bloodstream form strain used is 427. I assume the procyclic forms are of the same strain? As it stands the PCF are described as 29.13.6 cells but I think these assignations relate to plasmid insertions not the parasite strain itself.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results? Yes
No competing interests were disclosed.

Competing Interests:
Reviewer Expertise: Trypanosome molecular cell biology and development.
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