Abstract
Protein N-terminal (Nt) acetylation is one of the most abundant
modifications in eukaryotes, covering ~50-80 % of the proteome,
depending on species. Cells with defective Nt-acetylation display a
wide array of phenotypes such as impaired growth, mating defects and
increased stress sensitivity. However, the pleiotropic nature of these
effects has hampered our understanding of the functional impact of
protein Nt-acetylation. The main enzyme responsible for Nt-acetylation
throughout the eukaryotic kingdom is the N-terminal acetyltransferase
NatA. Here we employ a multi-dimensional proteomics approach to analyze
Saccharomyces cerevisiae lacking NatA activity, which causes global
proteome remodeling. Pulsed-SILAC experiments reveals that
NatA-deficient strains consistently increase degradation of ribosomal
proteins compared to wild type. Explaining this phenomenon, thermal
proteome profiling uncovers decreased thermostability of ribosomes in
NatA-knockouts. Our data are in agreement with a role for
Nt-acetylation in promoting stability for parts of the proteome by
enhancing the avidity of protein-protein interactions and folding.
Subject terms: Proteomics, Proteomic analysis, Acetylation, Protein
quality control
__________________________________________________________________
N-terminal acetylation is a common modification with unclear function.
Here, using multidimensional proteomics, the authors found that
NatA-deficient yeast show increased ribosomal protein degradation and
decreased ribosome thermostability, suggesting that N-terminal
acetylation enhances proteome stability.
Introduction
Protein modifications are essential to modulate cellular protein
activity, stability, subcellular localization, and interactions^[38]1.
One of the most important protein modifications is acetylation, which
can occur co- or post-translationally at the ε-amino group of lysine
residues or the free α-amino group at the protein
N-terminus^[39]2,[40]3. The latter, known as Nt-acetylation, is among
the most abundant protein modifications in all eukaryotic
proteomes^[41]4, but its function remains poorly understood. Advances
in high-resolution mass spectrometry and molecular biology techniques
have lately helped to shed light on the molecular mechanisms and
essential biological processes, where Nt-acetylation and the enzymes
responsible for catalyzing this modification have a central
function^[42]5–[43]8.
Chemically, Nt-acetylation refers to a process that involves the
covalent addition of an acetyl group to the free amino group of the
α-carbon of the N-terminal residue in a protein. This process is
catalyzed by Nt-acetyltransferases (NATs) using acetyl coenzyme A
(Ac-CoA) as the main donor of the acetyl group^[44]9. Unlike lysine
acetylation, the most exhaustively studied acetylation type,
Nt-acetylation has been regarded as an irreversible and static
modification, as no deacetylase acting on N-termini has yet been
identified^[45]2,[46]4. Nevertheless, different reports suggested that
Nt-acetylation may be regulated by cellular signaling and its cellular
status can vary in different disease states and biological processes
such as cancer^[47]10, developmental disorders^[48]11,[49]12, drought
stress^[50]8,[51]13, calorie restriction and Ac-CoA
availability^[52]14,[53]15 or apoptotic fate^[54]16,[55]17. Thus,
Nt-acetylation has emerged as an important protein modification that is
involved in the regulation of different biological pathways.
To date, eight NATs have been reported in eukaryotes (NatA-H), of which
NatA, NatB and NatC act in a co-translational manner and perform most
Nt-acetylation in eukaryotic proteomes^[56]4. There are
well-established examples of how Nt-acetylation may steer protein
function: via protein stability and degradation, folding, subcellular
localization, and complex formation^[57]18. The effects of
NatA-mediated Nt-acetylation reported so far are very diverse, probably
reflecting the large number of NatA substrates^[58]6. The Saccharomyces
cerevisiae NatA complex was found to steer gene expression, most likely
in part due to Nt-acetylation of the silencers Sir3 and
Orc1^[59]7,[60]19,[61]20. Some impact on protein folding and
aggregation was also observed and could result from chaperones directly
steered by Nt-acetylation or by the co-operation of chaperones and NatA
at the ribosome^[62]7,[63]21,[64]22. Specific yeast proteins may be
targeted for degradation via the exposure of N-degrons. However, global
yeast analyses did not reveal Nt-acetylation as a major determinant for
protein stability^[65]7,[66]23. In human and plant cells, a subset of
NatA substrates are shielded from proteasomal degradation by
Nt-acetylation^[67]17,[68]24,[69]25. Thus, NatA has the potential to
regulate a number of cellular proteins and pathways, but a detailed
proteome-wide understanding of how the NatA complex activity may steer
proteostasis remains unclear.
For that reason, we applied proteome-wide multidimensional mass
spectrometry-based approaches on Saccharomyces cerevisiae, lacking NatA
complex activity, to explore the link between Nt-acetylation, protein
turnover, and thermostability at a proteome scale. Together, the
combined analysis of different properties of the proteome suggests that
abolishment of NatA complex activity promotes thermal instability of
cytosolic ribosomal proteins and increase their turnover. In agreement
with previous observations, our results support that Nt-acetylation has
an important role in the control of protein stability.
Results
Lack of NatA activity induce proteome remodeling in Saccharomyces cerevisiae
The NAT machinery in S. cerevisiae known to date is composed of five
NATs (NatA–NatE). The main contributor to the N-terminal (Nt) acetylome
is the NatA complex, which is evolutionarily conserved in
eukaryotes^[70]6. In S. cerevisiae, NatA is composed of two essential
subunits: a catalytic subunit Naa10 (Ard1), and a ribosome-anchoring
auxiliary subunit Naa15 (Nat1) as well as an auxiliary subunit without
a well-defined role, Naa50 (Nat5)^[71]6,[72]26,[73]27. Whereas the
substrate specificity of NatB and NatC complexes is determined by the
second amino acid after the initial methionine, the NatA complex
co-translationally acetylates small amino acids (Ala-, Thr-, Ser-,
Val-, and Gly) at the N-termini exposed after methionine
cleavage^[74]18,[75]27–[76]29. In yeast, NatA is estimated to
Nt-acetylate around 40% of the entire proteome^[77]6. The
loss-of-function of Naa10 is embryonic lethal in higher
eukaryotes^[78]8,[79]30,[80]31 such as Arabidopsis thaliana, Drosophila
melanogaster, Danio rerio, and Homo sapiens but not in S. cerevisiae.
In yeast, deletion of genes encoding either of the major NatA subunits,
Naa10 or Naa15, completely abolish NatA activity and cause similar
phenotypes^[81]32. We therefore reasoned that a naa10Δ yeast strain
would be a suitable model to determine the effect of lacking
Nt-acetylation on a proteome-wide scale. Furthermore, the naa10Δ strain
allows the study of the Nt-acetylome and proteome concurrently,
circumventing the compensatory effects of the Nt-acetylation backup
systems described in mice and in human^[82]30,[83]33.
The naa10Δ strain used here replicated the previously described
phenotypic responses to stressors and we found that loss of Naa10 had a
negative impact on cell growth in synthetic complete liquid medium
(Supplementary Fig. [84]1A). To be able to compare WT and naa10Δ
strains at similar growth stages, we compensated for the slower cell
doubling time by harvesting WT and naa10Δ cells when they reached an
optical density at 600 nm of ~1.8. To determine the effect of the loss
of Naa10 on the yeast proteome, we performed a differential global
protein expression analysis comparing the proteome differences between
WT and naa10Δ strains. As described in “Methods” and schematized in
Fig. [85]1A, we made use of offline high-pH reversed-phase
chromatography to fractionate peptide mixtures resulting from tryptic
digestion of yeast lysates prior to online low pH LC–MS/MS analysis of
each fraction in turn. The comprehensive yeast proteome presented in
this study contains 4113 and 3943 protein-coding genes for WT and
naa10Δ strains, respectively (False discovery rate; FDR < 1%), which
represents the detection of about 96% of the proteome expected to be
expressed during log phase^[86]34–[87]36 (Fig. [88]1B). With this
proteome depth, we were able to detect all four NATs (NatA, NatB, NatC,
and NatE) expressed during log-phase growth in S. cerevisiae
(Supplementary Fig. [89]1B). Unsurprisingly, we did not detect Naa40
(NatD), as it has been reported not to be expressed during log-phase
growth^[90]35. As expected, the catalytic subunit of the NatA complex
Naa10 was only present in the WT and not detected in the naa10Δ strain.
Noteworthy, the other components of the NatA complex, Naa15 and Naa50
(NatE), were also significantly down-regulated in the KO condition.
This indicates that lack of Naa10 disrupts the NatA complex formation
resulting in degradation of the Naa15 and Naa50 subunits, in agreement
with previous data^[91]26. Conversely, the NatB complex subunits Naa25
(Mdm20) and Naa20 (Nat3) as well as the NatC complex subunit Naa30
(Mak3) remained unaltered in the naa10Δ strain (Supplementary
Fig. [92]1B).
Fig. 1. Lack of NatA activity (naa10Δ) causes the downregulation of
mitochondrial and ribosomal proteins.
[93]Fig. 1
[94]Open in a new tab
A Scheme of the performed proteome profiling experiments. Yeast were
harvested at active growth phase and lysates were digested using the
proteome capture aggregation method (PAC) prior High-pH fractionation
(HpH). Quantification was performed by using label-free intensities
(LFQ; label-free quantitation). n = 3 replicate cultures per condition.
B Right: Venn diagram depicting all proteins quantified in the deep
proteome profiling from this dataset compared to complete yeast
log-phase proteome reported in ref. ^[95]36. Left: Venn diagram
depicting numbers of all proteins quantified in the deep proteome
profiling, (Proteome, n = 4330). C Differential expression profiling of
the WT and naa10Δ strains in a volcano plot. Significant regulated
proteins at 1% and 5% false discovery rate (FDR) are delimited by
dashed and solid lines, respectively (FDR controlled, two-sided t test,
randomizations = 250, s0 = 0.1). Ribosomal proteins from large (RPL)
and small subunit (RPS) are colored in blue and green, respectively. D
Violin plot of the cellular component (CC) gene ontology annotation of
the log10 fold detected proteome ratios. Kruskal–Wallis test with
two-sided Wilcoxon post hoc test, multiple test correction according to
Benjamin–Hochberg, ns P > 0.05, *P < = 0.05, **P < = 0.01,
***P < = 0.001, ****P < = 0.0001; box bounds correspond to quartiles of
the distribution (center: median; limits: 1st and 3rd quartile;
whiskers: +/− 1.5 IQR). (n = 3, n = independent cultures per
condition). Source data are provided as a Source Data file (B–D).
The differential proteome expression analysis between WT and naa10Δ
strains highlighted that some members of the Arg/N-end rule and
ubiquitin-fusion degradation (UFD) pathways UBR1, UFD4, UFD2, NTA1, and
TOM1 together with the proteasome and autophagy markers such as ATG1,
ATG20, ATG11, and PEP4 were upregulated in the KO. In contrast,
cytosolic and mitochondrial ribosomal proteins from the small and large
subunit as well as mitochondrial proteins related to the electron
transport chain were down-regulated (Fig. [96]1C and Supplementary
Fig. [97]1C). In addition, the classification of the regulated proteome
by gene ontology (GO) analysis, showed a significant downregulation of
proteins associated with the cellular component terms mitochondrion,
cytosolic and mitochondrial ribosomes in the naa10Δ strain
(Fig. [98]1D). To investigate this observation further, we performed a
gene set enrichment analysis (GSEA) using KEGG pathways (Supplementary
Fig. [99]1D). The GSEA analysis revealed an overrepresentation of
autophagy and endocytosis pathways in the naa10Δ strain, while KEGG
pathways related to metabolic regulation, ribosomal structural
proteins, and oxidative phosphorylation were underrepresented. These
findings are in agreement with previous reports linking the loss of
NatA activity with impairment of mitochondrial degradation^[100]37, a
general effect on genome stability and metabolism, as well as the
upregulation of the UPS system upon loss of
Nt-acetylation^[101]7,[102]38.
Effect of abolished NatA-mediated Nt-acetylation on protein half-lives
The downregulation of ribosomal proteins observed in the naa10Δ strain
suggests that Nt-acetylation negatively affects the stability of these
proteins. Earlier studies in S. cerevisiae point to Nt-acetylation as a
mechanism for steering protein degradation as part of cellular quality
control^[103]39–[104]41, while other investigations did not uncover any
major impact on protein stability or degradation^[105]7,[106]23. Thus,
to elucidate the role of Nt-acetylation on protein half-lives, we
reasoned that a systematic and proteome-wide investigation was needed.
Global protein half-life were estimated using a pulsed stable isotope
labeling by amino acids in cell culture (pSILAC) chase labeling
approach quantifying the incorporation of light (^12C,^14N-enriched)
stable isotope labeled lysine, an essential amino acid, into newly
synthesized proteins, while pre-existing proteins remain in the
pre-labeled heavy stable isotope (^13C,^15N) form^[107]42. We analyzed
populations of WT and naa10Δ strains growing in log phase and sampled
them at six different time points, corresponding to approximately two-
and three-cell doubling times (Fig. [108]2A). Protein extracts were
digested with endoproteinase Lys-C to ensure quantification of
resulting peptides, which were independently fractionated into 12
fractions by offline high-pH reversed-phase chromatography and each
fraction was measured by online LC–MS/MS. A combined analysis
identified 47,270 peptide sequences and 4333 proteins (FDR < 1%) across
all experimental conditions. As protein degradation generally follows
first-order kinetics, assuming that a newly synthesized protein has the
same probability of being degraded as a pre-existing, protein loss
follows an exponential decay, and the log-transformed relative isotope
abundance (RIA) is therefore expected to display a linear behavior with
a negative slope in the time domain^[109]43. Based on this, we were
able to determine 3420 protein half-lives (T[1/2]) with high confidence
inferred from the calculated slopes of linear regression (Supplementary
Data [110]1). In addition, we were able to determine the half-life for
570 Nt-peptides, 411 for WT and 365 for naa10Δ (Fig. [111]2B and
Supplementary Data [112]2).
Fig. 2. Protein half-life and cell cycle time inference by a pulsed-SILAC
chase (pSILAC).
[113]Fig. 2
[114]Open in a new tab
A Schematic representation of the implemented Pulsed-SILAC chase
strategy (pSILAC). Yeast cells grown in heavy lysine were pulse-labeled
with light lysine and lysed after six time points. Lysates were
digested using only Lys-C as protease prior to High-pH fractionation
(HpH). H/L SILAC ratios were quantified, and the Relative Isotope
Abundance (RIA) calculated. n = 2 replicate cultures per condition. B
Venn diagram condensing numbers of all protein and Nt-peptides inferred
the half-lives inferred by pSILAC (Proteome, n = 3420, N-terminome,
n = 578). C Time-domain RIA incorporation into naa10Δ and WT system
proteins. Mean ± standard deviations are shown. The WT and naa10Δ
calculated dilution constant (Kdil) and dilution time (Tdil; cell
double time) are shown in blue and red, respectively. (n = 2,
n = independent cultures per condition). Source data are provided as a
Source Data file (B, C).
Since the balance between protein degradation and synthesis is a
regulated process involved in the coordination of multiple cellular
responses, including cell signaling, cell cycle progression,
etc.^[115]44,[116]45, protein synthesis and degradation rates depend on
cellular surveillance systems. Two main processes determine protein
half-life: (i) dilution due to cell growth and (ii) intracellular
degradation via the proteasome or lysosome^[117]46,[118]47. While
degradation is a selective process regulating protein half-life in a
specific manner^[119]48, the dilution is a global process effectively
reducing the cellular protein amount by fifty percent for every cell
cycle^[120]49. Thus, to account for the effect of the dilution on
protein half-life, the growth rate of WT and naa10Δ strains were
determined by estimating the median of RIA at each timepoint. The
reason for determining the cell cycle rate using this strategy instead
of optical density was that the difference in cell size between the
naa10Δ and WT would introduce errors in the OD measurements and also
because this technique does not distinguish between living and
potentially dead cells. By calculating the dilution constant (K[dil])
for WT and naa10Δ strains, we determined that the growth rate of the
naa10Δ strain is 28% slower than the corresponding WT strain
(Fig. [121]2C).
To establish if the observed growth rate difference between naa10Δ and
WT strains strongly affected the half-life estimations, we modeled the
effect of the 28% decrease of K[dil] in the naa10Δ on the WT protein
half-lives (Supplementary Fig. [122]2). The decrease in growth rate
shows that proteins with a longer half-life became even longer-lived,
whereas short-lived proteins were unaffected (Fig. [123]3A). The fact
that generally long-lived proteins became even longer-lived suggests
that delay in the cell cycle doubling time acts as confounder in
comparison of protein half-life estimations between conditions as an
apparent stabilization of proteins of long-lived proteins are
determined mainly by the difference in growth
rate^[124]46,[125]50,[126]51. Thus, to explore the effect of
NatA-dependent Nt-acetylation on protein half-life, we normalized the
turnover rate by the corresponding growth rate for WT and naa10Δ
strains, respectively (Fig. [127]3B). Using this strategy, we observed
that the normalized turnover rates of proteins are generally faster in
naa10Δ compared to the WT. Next, we compared the total distribution of
the normalized turnover rates of proteins with the corresponding
Nt-peptides determined in WT (Fig. [128]3C) and naa10Δ strains
(Fig. [129]3D). Interestingly, we found no statistical difference
between the normalized turnover rates of the proteome compared to
Nt-peptides in WT, but there was a statistically significant difference
in the corresponding turnover rates in naa10Δ cells. This observation
is to be expected since the inferred half-life of Nt-peptides from
pSILAC experiments corresponded mostly to acetylated substrates in the
WT and non-acetylated NatA in naa10Δ cells, respectively. These
findings were further confirmed by comparing the normalized turnover
rate distribution of NatA substrates from the naa10Δ strain against
NatA substrates from WT strain and naa10Δ full proteome (Supplementary
Fig. [130]3A, B). Finally, to investigate if the lack of NatA and thus
Nt-acetylation of NatA substrates affect their turnover rates in the
naa10Δ strain, we compared Nt-acetylated peptides identified in WT
against the corresponding free Nt-peptides in the naa10Δ strain,
representing NatA type substrates, which start with Ala Thr, Ser, Val,
or Gly at position 2+. This analysis revealed that the free Nt-peptides
have a significantly faster turnover rate compared to the Nt-acetylated
peptides (Fig. [131]3E, Supplementary Fig. [132]3C, and Supplementary
Data [133]3). This finding indicates that N-terminal protein
acetylation is a modification that promotes protein stability rather
than instability in the yeast proteome.
Fig. 3. Lack of N-terminal acetylation due to deletion of NAA10 promotes
protein degradation of NatA substrates in the yeast proteome.
[134]Fig. 3
[135]Open in a new tab
A A comparison between determined half-lives in WT system (T[1/2]) and
modeled effect of WT half-lives (T*[1/2]) with a reduced dilution
constant (Kdil[KO] = −0.26). Red line represents the predicted
half-lives due to the lack of naa10 and blue line the determined
half-lives in WT. Cell cycle time (Tcc = 1.9 h) of the WT system is
marked by an arrow (Black dot). Data were modeled using the equation
show on top and calculated according to ref. ^[136]46. B Cumulative
frequency plot of the normalized turnover rate (Kdeg/Kdil) determined
in naa10Δ and WT system (WT, blue; naa10Δ, red). Two-sided
Kolgomorov-Smirnov test, KS P = −2.2 e-16. C, D Violin plot of
normalized turnover rates of WT and naa10Δ proteome compared to their
corresponding N-terminome. Statistical significance was assessed using
two-sided Wilcoxon test, multiple test correction according to
Benjamini–Hochberg, ns = P > 0.05, *P < = 0.05, **P < = 0.01,
***P < = 0.001, ****P < = 0.0001; box bounds correspond to quartiles of
the distribution (center: median; limits: 1st and 3rd quartile;
whiskers: +/− 1.5 IQR). Overlap between the N-terminome and proteome
detected in the pSILAC, as well as the N-terminome acetylation status
and NAT substrate class are shown on top. (n = protein or peptide
normalized turnover rates derived from at least two independent
experiments per condition). E Same as (C, D), but comparing the
N-terminal acetylated peptides of the NatA type detected in the WT and
their corresponding unmodified N-terminal peptides detected in naa10Δ
cells. Source data are provided as a Source Data file (A–E).
In addition, to corroborate the Nt-acetylation status of proteins in
the naa10Δ strain, we took advantage of the high protein sequence
coverage provided by the high-pH fractionation in the pSILAC
experiments. We specifically analyzed the last timepoint of essentially
full light SILAC incorporation and complemented the detected
Nt-peptides with Nt-peptides detected by a specific N-terminal
enrichment strategy (Supplementary Fig. [137]4A). The reason for this
was that the pSILAC experiment and the N-terminal enrichment were
digested with complementary enzymes (Lys-C and Arg-C like digestion
with trypsin, respectively) allowing increased coverage of the
N-terminome. Collectively, we identified 1480 non-redundant Nt-peptides
(Supplementary Fig. [138]4B) considering only those covering the first
or second amino acid of annotated protein sequences retrieved from the
UniProt database^[139]52 depending on the cleavage of the initiating
methionine (Met^i). We found that 73% of the WT N-terminome was
acetylated and 60% of those acetylated peptides were substrates of the
NatA complex (Supplementary Fig. [140]4C), in agreement with previous
studies^[141]6,[142]15,[143]29. Moreover, the naa10Δ strain showed a
reduction of 56% of the acetylated N-terminome, which fits well with
the proportion of the N-terminome acetylated by the NatA complex
(Supplementary Fig. [144]4D). Reassuringly, most of the unmodified
Nt-peptides detected in the naa10Δ strain found to be Nt-acetylated in
WT, matched the known NatA substrate motif of N-terminal Ala-, Thr-,
Ser-, Val-, and Gly- after the Met^i has been removed by methionine
aminopeptidases. In contrast, Nt-peptides representing NatB type
substrates, thus starting with Met^i before Asp-, Asn-, Glu- or Gln- at
position +2 were unaffected between samples (Supplementary
Fig. [145]4E). Furthermore, our results correlate with the reported
likelihood of a protein to being Nt-acetylated based on the +2 amino
acid in its sequence, where proteins with Ser- and Thr- at +2 have a
higher probability to be acetylated compared to the ones with Gly- and
Val- at 2+^[146]29 (Supplementary Fig. [147]4F and Supplementary
Data [148]4).
Absence of NatA-dependent N-terminal acetylation increases the turnover rate
of ribosomal proteins in exponentially growing yeast cells
To facilitate the analysis of changes on turnover rate between the
experimental conditions, we decided to visualize them using a volcano
plot analysis and classified the t test significant proteins into two
groups designating if they had faster or slower turnover rate in the
naa10Δ strain compared to the WT (Fig. [149]4A). Interestingly,
cytosolic ribosomal proteins have faster turnover rates compared to the
WT, while the mitochondrial ribosomal proteins show mixed behavior.
Fig. 4. Lack of NatA activity promotes the enhanced degradation of cytosolic
ribosomal proteins.
[150]Fig. 4
[151]Open in a new tab
A Comparison of the naa10Δ and WT systems as a volcano plot to identify
significant changes in protein turnover rates. Significant regulated
proteins at 1% and 5% false discovery rate (FDR) are delimited by
dashed and solid lines, respectively (FDR controlled, two-sided t test,
randomizations = 250, s0 = 0.1). Significant proteins were classified
into two groups; Fast, red and slow, blue. B Ice logo diagram comparing
the first five sequence amino acids of the significant fast and slow
normalized turnover rate proteins (P < 0.05 unpaired two-tailed
Student’s t test). C GSEA-based KEGG pathway enriched analysis. P
values were calculated by a two-sided permutation test and multiple
hypothesis testing was FDR corrected. The significance threshold set at
FDR > 0.05. D Phenotypic growth of S. cerevisiae naa10Δ in the presence
of various stressors. The indicated yeast strains were grown to early
log phase and serial 1/10 dilutions containing the same number of cells
were spotted on various media and imaged the 6 following days. 30 °C,
incubated for 3 days on YPD at 30 °C; Caffeine, incubated for 3 days on
YPD + 0.2% caffeine; and CHX, incubated for 3 days on YPD + 0.2 μg/ml
cycloheximide. Source data are provided as a Source Data file (A).
In addition, we analyzed the sequence motif consensus of the first five
amino acid residues from the proteins classified as significant in the
two groups (Fig. [152]4B). Potential NatA substrates with the amino
acid residues Ser-, Ala-, and Gly- in the second position were
significantly overrepresented in the fast turnover rate group,
suggesting that the absence of Nt-acetylation due to loss of NatA
activity in the naa10Δ strain is responsible for the fast degradation
of these proteins. This result supports that Nt-acetylation promotes
protein stability and underpins the importance of this modification
across eukaryotes. Conversely, potential NatC (or NatE) substrate
proteins with Phe and Leu in the second position (Met-Phe and Met-Leu
N-termini) seem to have a slower turnover rate in the naa10Δ strain
compared to WT, indicating that the turnover of these proteins are not
directly affected by the lack of NatA but via downstream effects. The
overrepresentation of substrates of different NAT types, according to
the turnover classification, could suggest that the specificity of the
different NATs is connected to different biological functions, as also
indicated previously^[153]7. We confirmed this observation by
subgrouping the naa10Δ strain proteome into NatA-C substrates and
compared the normalized turnover rates of each subgroup against the
naa10Δ full proteome normalized turnover rates (Supplementary
Fig. [154]3D–F).
To further substantiate this observation, we performed a GSEA-based
KEGG pathway enrichment analysis of the fast and slow turnover rate
protein groups (Fig. [155]4C). We found that the faster-degraded
proteins are enriched for ribosome and ribosome biogenesis pathways.
Contrastingly, the slower degrading proteins are enriched for members
of pathways related to the biosynthesis of secondary metabolites,
purine metabolism, lysine degradation, and acetyl-CoA and fatty acid
metabolism.
Noteworthy, several cytosolic ribosomal proteins belonging to the 60 S
and 40 S subunits have been annotated as substrates of the NatA
complex^[156]53 (Supplementary Data [157]5). However, the functional
effects of the absence of Nt-acetylation in the ribosome are not well
understood. In general, Nt-acetylation has been associated with
decreased protein synthesis of the ribosome components and its
assembly. Interestingly, in the specific case of NatA-deficient cells,
polysome profiling experiments have shown a normal 60 S/80 S
ratio^[158]7,[159]53, but a decrease in translational fidelity in the
presence of protein synthesis inhibitors in naa10Δ strain compared to
WT^[160]53. Thus, this suggests that the reduced translational fidelity
in NatA-deficient cells is most likely due to defective activity or
structure of fully assembled 80 S ribosome. In support of this, the
naa10Δ strain exhibits normal ribosome biogenesis, addressed by
northern blotting analysis of pre-rRNA processing intermediate
(Supplementary Fig. [161]5A–C). Likewise, the naa10Δ strain showed
increased sensitivity to the protein translation elongation inhibitor
cycloheximide and caffeine (Fig. [162]4D)^[163]6. However, these
effects of Nt-acetylation on ribosomes are challenging to define, since
the fast degradation of ribosomal proteins in the naa10Δ strain could
conceivably be an indirect consequence of the fast degradation of NatA
substrates or other dysfunctionality caused by lack of Nt-acetylation
of ribosomal regulatory proteins.
Newly synthesized ribosomal proteins maintain their protein levels at log
phase despite their increased turnover rate
In S. cerevisiae, it has been reported that protein synthesis rate
decreases with decreasing growth rates^[164]54 and exponential growth
rates require ribosome synthesis^[165]55. Consequently, for balancing
the growth rate at log phase, we speculated that the slower growing
naa10Δ strain needs to adjust the synthesis of ribosomal proteins to
compensate for their faster degradation due to the lack of Naa10. To
investigate this, we estimated the relative abundance of the ribosomal
and ribosome-associated proteins between WT and naa10Δ by
intensity-based absolute quantification (iBAQ) analyzing only the light
stable isotope labeled lysine-containing peptides at 6 h after the
SILAC pulse as a proxy of the abundance of newly synthesized proteins
(Fig. [166]5A). This revealed that the relative abundance of the newly
synthesized cytosolic and mitochondrial ribosomal proteins did not
change between the WT and naa10Δ strains. This observation is in
agreement with previous reports showing that protein levels in
NatA-depleted eukaryotic models tend not to differ compared to WT at
the mid-log phase. However, the translation rate increases under
physiological conditions^[167]7,[168]24, pointing to the fact that
protein synthesis rates are adjusted in NatA-deficient cells to
maintain a functional concentration of ribosomes.
Fig. 5. NatA-defective yeast cells compensate for fast ribosomal turnover
rate by adjusting protein synthesis.
[169]Fig. 5
[170]Open in a new tab
A Scatterplot comparing log10 of the intensity-based absolute
quantification (IBAQ) of cytosolic and mitochondrial ribosomal proteins
between WT and naa10Δ strains. n = 3 replicate yeast cultures per
condition. B Same as (A) but comparing the normalized turnover rate
between conditions. n = 2 replicate cultures. Diagonal line in (A, B)
correspond to the identity function. Dashed lines indicate the cell
cycle time (Tcc). C Comparison of the WT and naa10Δ strains as a
volcano plot to identify newly synthesized protein abundance
significant changes. Significant regulated proteins at 1% and 5% false
discovery rate (FDR) are delimited by dashed and solid lines,
respectively (FDR controlled, two-sided t test, randomizations = 250,
s0 = 0.1). Source data are provided as a Source Data file (A–C).
In contrast, when comparing the turnover rate of ribosomal proteins,
those annotated as belonging to the cytosolic ribosomes have a faster
turnover rate in the naa10Δ compared to WT cells (Fig. [171]5B).
Conversely, the turnover rates of mitochondrial ribosomal proteins
showed a mixed behavior suggesting that enhanced synthesis of
mitochondrial ribosomal proteins is induced as a response of impaired
mitochondrial function. This phenomenon has also been described in
previous ribosome profiling and RNA-seq studies, which showed elevated
translation of nuclear-encoded genes of mitochondrial ribosomal
proteins in NatA-lacking strains^[172]7,[173]53,[174]56.
To visualize the global proteome abundance change of newly synthetized
proteins between naa10Δ and WT strains, we performed a volcano plot
analysis (Fig. [175]5C). This revealed that proteins related to the
ubiquitin–proteasome system (UPS), such as TOM1 and UFD2 were
upregulated in the naa10Δ strain, which aligns well with the
steady-state proteome analysis (Fig. [176]1B). UFD2 is a member of the
ubiquitin-fusion degradation (UFD) pathway, which elongates ubiquitin
moieties by lysine-ε-amino specific linkage of ubiquitin N-terminal
leading to their degradation by the proteasome^[177]39. Likewise, the
TOM1 protein, which is the homolog of human HUWE1, has been linked to
the degradation of excess histones^[178]57,[179]58, ribosomal proteins
made in excess and thus not assembled into mature ribosomes^[180]59 and
different pre-replicative complexes during G[1]^[181]60. Recently, it
was shown that TOM1 co-immunoprecipitates with UFD-like substrate
reporters, linking TOM1 with UFD pathway and degradation in
NAA10-deficient cells^[182]61. In addition, TOM1 has been linked to a
novel quality control pathway, which governs the homeostasis of
ribosomal proteins. This pathway is important for responding to
imbalances in production of ribosome components, which, if it is not
regulated, can exacerbate the temperature-sensitive growth^[183]53 and
precipitation of ribosomes^[184]59, a phenotype already described for
the naa10Δ strain^[185]7. The differential analysis of newly
synthesized proteins also disclosed that proteins related to one-carbon
metabolism (OCM) were down-regulated in the naa10Δ strain
(Fig. [186]5C). Interestingly, OCM protein members have been implicated
in the regulation of the crucial steps of protein synthesis, growth,
and translation processes^[187]62,[188]63. This observation is in
agreement with the naa10Δ phenotype as the OCM has been linked with the
control of protein synthesis through the regulation of the abundance of
key substrates such as formylated methionine (Met-tRNA^fMet) and amino
acids^[189]64.
Absence of NatA-dependent N-terminal acetylation decreases the
thermostability of ribosomal proteins in exponentially growing yeast cells
Given that Nt-acetylation is important for different protein
properties, such as quality control^[190]22,[191]41, protein
folding^[192]65, and protein–protein interactions^[193]66,[194]67, we
wondered if the mechanism behind the fast degradation of ribosomal
proteins in the naa10Δ strain was related to defects on protein folding
or protein–protein interactions. Thus, we explored structural
differences of the naa10Δ and WT proteomes under near-physiological
conditions by Thermal Proteome Profiling and Data Independent
Acquisition (TPP-DIA) (Fig. [195]6A).
Fig. 6. Ribosomal proteins are unstable and rapidly degraded in
NatA-defective yeast cells.
[196]Fig. 6
[197]Open in a new tab
A Schematic representation of the implemented TPP-DIA strategy. Yeast
cells grown in mid-log phase were harvested by centrifugation and
submitted to a temperature treatment. Lysates were digested using Lys-C
and trypsin. Quantification was performed by using label-free
intensities (LFQ; label-free quantitation). n = 6 replicate cultures
per condition. Melting curves were inferred by using a four-parameter
log-logistic model. B Venn diagram depicting numbers of all
protein-melting temperatures, (Meltome, n = 2689). C Comparison of the
WT and naa10Δ strains in a volcano plot to identify changes in
protein-melting temperature. Significant regulated proteins at 1% and
5% false discovery rate (FDR) are delimited by dashed and solid lines,
respectively (FDR controlled, two-slide t test, randomizations = 250,
s0 = 0.1). D GSEA-based KEGG pathway enriched analysis. P values were
calculated by two-sided permutation test and multiple hypothesis
testing was FDR corrected. Significance threshold set at FDR > 0.05
Significance threshold set at P.adjust <0.05, NA: no enriched terms at
the specified cutoff. E Scatterplot representing the overlap between
protein thermostability and degradation. Dashed red line delineate the
minimal fold change (s0, 0.1). Melting temperature and degradation
difference distribution are shown in the top and right plot border,
respectively. Source data are provided as a Source Data file (C, E).
The TPP approach is based on the principle that proteins denature and
become insoluble when exposed to heat. By measuring the abundance of
proteins in the soluble fraction through a gradient of temperatures,
the resulting melting curves reflect protein intra and
inter-interactions in the cellular milieu. Changes in protein
associations can therefore be inferred through thermal stability
readouts in a large-scale manner by mass spectrometry^[198]68,[199]69.
Using this strategy, we determined with high confidence 2689
protein-melting temperatures (T[m]), defining T[m] as the temperature
at which 50% of the protein is unfolded (Fig. [200]6B and Supplementary
Data [201]6).
To identify proteins with changes in their thermal stability between
the naa10Δ and WT strains, we performed a volcano plot analysis of the
protein-melting temperatures (Fig. [202]6C). This analysis showed that
ribosomal proteins of both the large and small ribosome subunits as
well as proteins of the proteasome are unstable with lower T[m] in the
naa10Δ condition compared to WT. Contrarily, E3 ubiquitin ligases such
as TOM1, CDC16, HRT1 and RSP5 were stabilized with higher T[m] in the
naa10Δ.
KEGG pathway enrichment analysis of proteins with significant changes
in thermostability showed that proteins related to the ribosome are
destabilized in the naa10Δ strain (Fig. [203]6D). These findings are in
agreement with the literature as TOM1, HRT1, and RSP5 are ubiquitin
ligases related with the degradation of defective and excess ribosomal
proteins. For example, RSP5 is linked to the maintenance of cytosolic
ribosome integrity under rich nutrient conditions^[204]70, while HRT1
and TOM1 are associated with the degradation of non-functional
ribosomes^[205]71,[206]72 and quality control pathway of ribosomes,
respectively^[207]59,[208]60.
To validate the results obtained by the DIA-TPP, we performed an
isothermal shift assay (ITSA)^[209]73. The ITSA approach simplifies the
DIA-TPP experiment while increasing the statistical power by enhancing
the identification sensitivity by quantifying the difference in soluble
and precipitated protein fractions at single temperatures
(Supplementary Fig. [210]6A). The individual temperatures were selected
according to the melting curves of ribosomal proteins obtained from
DIA-TPP ranging from 38.3 to 49.9 °C and divided into four different
temperatures (Supplementary Fig. [211]6B). To visualize proteins
showing changes in thermostability, we performed a volcano plot
analysis per temperature for the soluble and precipitated fractions,
respectively (Supplementary Fig. [212]6C). As expected, the ribosomal
proteins were destabilized in the naa10Δ strain when comparing the
soluble and precipitated fractions to WT across temperatures. Moreover,
since it has been observed that thermal destabilization of cytosolic
ribosomes occurs during mitosis in human cells^[213]74, we decided to
investigate the cell cycle distribution in the naa10Δ vs WT yeast by
flow cytometry to discard any bias caused by the enrichment of mitotic
cells. The actively growing WT and naa10Δ yeast cells showed no
significant difference between their cell cycle distribution profiles
(Supplementary Figs. [214]7A, B and [215]8). Thus, the reduced
proliferation rate of the naa10Δ strain is caused by an overall delay
of the cell cycle progression in general rather than in any particular
cell cycle stage. In addition, based on the forward scatter
measurements, we were able to corroborate that naa10Δ cells were 20%
larger than WT cells (Supplementary Fig. [216]7C), agreeing with what
has been reported earlier^[217]7.
Next, we compared the changes in protein turnover with the
corresponding melting point differences determined by pSILAC and
DIA-TPP, respectively (Fig. [218]6E). We found that ribosomal proteins
exhibiting faster turnover rate, also have a significant shift in their
thermostability. In contrast, proteasomal proteins show a significant
shift in thermostability but not a significant change in turnover rate.
This is consistent with Nt-acetylation of catalytic core proteasomal
protein members having been linked to NatB complex with the majority of
proteins composing the 20 S proteasome are substrates of that NAT
complex. However, it has also been reported that eight subunits of the
19 S proteasome are NatA substrates and that lack of NatA did not
result in a significant change in chymotrypsin-like activity of the
26 S proteasome but a higher activity and accumulation level of the
catalytic core particle of proteasome 20 S in absence of
SDS^[219]61,[220]75,[221]76. To verify this observation, we plotted the
previously confirmed NatA substrates from our N-terminome profiling
experiments in the thermostability and turnover rate space
(Supplementary Fig. [222]9A, B). Reassuringly, the global changes in
thermostability and turnover rate of NatA substrates resembled what we
observed at the protein level. These findings suggest that
Nt-acetylation may affect the structure of the 19 S proteasome, which
aligns well with our results, suggesting that Nt-acetylation of
ribosomal and proteasomal proteins might be implicated in folding or
interaction between proteins of their respective complexes.
Taken together, our data indicate that lack of Naa10 promotes the
defective or delayed folding of ribosomal proteins and consequently,
increases the probability of their degradation, especially under
perturbations such as heat and possibly other stress conditions.
Lack of NatA-dependent N-terminal acetylation promotes ubiquitination of
ribosomal proteins in exponentially growing yeast cells
NatA mutant strains display a pleiotropic phenotype affecting different
cellular processes linked to the impairment of protein–protein
interactions, transcriptional alterations, and impairment of chaperone
systems^[223]7,[224]18,[225]29. Therefore, it is likely that the UPS
system is required to be active to eliminate the damage caused by lack
of NatA and maintain cellular proteostasis. According to this
hypothesis, the lack of NatA has been related with an increased
activity of the UPS system^[226]24,[227]61. In addition, we found that
the ubiquitin ligases involved in the N-degron pathway TOM1, UFD2,
UFD4, and UBR1^[228]40, as well as proteasomal proteins were
upregulated in the naa10Δ strain. Consequently, we investigated the
role of NAA10 deletion on protein ubiquitination by using a diGly
enrichment approach and DIA-MS (Fig. [229]7A).
Fig. 7. Fast-degraded proteins are ubiquitinated in NatA-defective cells.
[230]Fig. 7
[231]Open in a new tab
A Schematic representation of the implemented ubiquitinome enrichment
strategy. Yeast cells grown to mid-log phase were harvested and
digested using Lys-C and trypsin prior to peptide clean-up and digly
peptide enrichment. Quantification was performed by using label-free
intensities (LFQ; label-free quantitation). n = 3 replicate cultures
per condition. B Comparison of the naa10Δ and WT systems in a volcano
plot to identify ubiquitinated peptide abundance changes (diGly
peptides). Significantly regulated proteins at 1% and 5% false
discovery rate (FDR) are delimited by dashed and solid lines,
respectively (FDR controlled, two-sided t test, randomizations = 250,
s0 = 0.1). Fast turnover rate proteins determined by pSILAC are
highlighted in blue. C Same as (B) but highlighting large and small
subunit ribosomal proteins. Source data are provided as a Source Data
file (B, C).
To check if there is a link between fast turnover proteins detected in
the pSILAC experiment and UPS system, we overlapped the fast turnover
proteins in a volcano plot comparing diGly sites between naa10Δ and WT
strains (Fig. [232]7B). As expected, the majority of the fast turnover
proteins were more ubiquitinated in the naa10Δ strain compared to WT.
As shown previously, the ribosomal proteins were unstable and
faster-degraded in the naa10Δ strain. To investigate this further, we
mapped the proteins of the large and small ribosomal subunits to the
differentially expressed ubiquitinome analysis (Fig. [233]7C and
Supplementary Data [234]7). We found that the ribosomal proteins were
more often ubiquitinated in the naa10Δ strain. In addition, lysine
residues that have been reported not to be accessible in the structure
of the mature ribosome and common substrates of TOM1^[235]59, as well
important ubiquitination events related to RQC (ribosome-associated
protein quality control) and NRD (non-functional rRNA decay) pathways
such as RPS3-K212^[236]77,[237]78 and RPS20-K8^[238]79 were found in
our analysis. This observation suggests that the ubiquitin ligase TOM1
is active in the naa10Δ strain, and further that the ribosomal proteins
are actively being ubiquitinated and thereby marked for degradation by
the UPS system. To test this hypothesis, we created a double KO strain
(naa10Δ tom1Δ) and tried to rescue ribosomal proteins from proteasomal
degradation (Supplementary Fig. [239]10A, B). Although only four
ribosomal proteins from the large ribosome subunit (RPL7, RPL4, RPL42,
and RPL37) were upregulated in the naa10Δ tom1Δ compared to naa10Δ
strain, this result suggests that additional ubiquitin ligases such as
UFD2, UFD4 and UBR1 as well as proteasomal proteins, which were
consistently upregulated in the different dimensions of the naa10Δ
proteome, are likely involved in the fast turnover of ribosomal
proteins in NatA-lacking cells.
Discussion
The alteration of the Nt-acetylome results in a pleiotropic phenotype
as a consequence of changes in intrinsic properties of the
Nt-acetylated proteins, such as lifespan, folding and binding^[240]18.
However, the effect of Nt-acetylation seems to be dependent on the
cellular context and the identity of the Nt-acetylated proteins. Our
results suggest that the lack of Nt-acetylation carried out by the NatA
complex in Saccharomyces cerevisiae promotes the fast turnover of a
number of NatA substrates. This finding is in agreement with recent
studies in other species^[241]17,[242]24,[243]80,[244]81 supporting the
concept that across the eukaryotic kingdom, Nt-acetylation increases
proteome stability rather than destabilizing it. In particular, we
found that cytosolic ribosomal proteins, which in general are
substrates of NatA^[245]53, were consistently affected by the lack of
NatA. Briefly, this set of proteins shows a fast turnover and lower
thermostability at mid-log phase, as well as a downregulation of some
ribosomal proteins at steady state. These results indicate that
Nt-acetylation might be involved in protein folding or protein–protein
interactions, affecting protein complexes such as the ribosome. On the
other hand, proteasomal proteins showed lower thermostability but no
change in turnover rate at mid-log phase in the naa10Δ strain,
suggesting that the lack of Nt-acetylation might affect the proteasome
complex but not to a degree, which compromises its stability. This
aligns with previous observations arguing that protein degradation
requires not just the absence of Nt-acetylation but also other
intrinsic features of the target proteins^[246]23. Noteworthy, the
ribosome biogenesis in the naa10Δ strain compared to WT showed a normal
profile, but increased sensitivity to temperature, as well as protein
translation inhibitors such as caffeine and cycloheximide. These
results indicate that the Nt-acetylation might contribute to the
optimal activity and assembling of the ribosome. In fact, a recent
study on pathogenic variants of NAA15 found these to cause congenital
heart disease^[247]82 which could be mechanistically related with our
results. Briefly, patient-derived cells expressing pathogenic NAA15
variants displayed decreased Nt-acetylation of NatA substrates and
defects in cardiomyocyte differentiation. Interestingly, the
NAA15-defective human cells displayed a very specific downregulation of
cytosolic ribosomal proteins that could not be mechanistically
explained. Given the current data in yeast establishing a functional
link between NatA-mediated Nt-acetylation and ribosomal protein
stability, the same mechanism is likely to be at play in the cells of
humans with heart disease caused by defective NatA. Our results suggest
that the lack of NatA promotes the defective or delayed folding of
ribosomal proteins or the disruption of their interactions in the
full-assembled 80 S ribosome, decreasing the active fraction of
ribosomes and increasing their probability of degradation, particularly
under stress conditions, which could explain the temperature-sensitive
and slow-growth phenotypes described in NatA-lacking cells.
The ubiquitinome analysis revealed the active ubiquitination of the
fast-degraded proteins in the NatA-lacking cells. In addition,
ubiquitination events on ribosomal proteins from both ribosome subunits
and specific ribosomal large subunit sites, which have been described
as concealed in the mature ribosomes, suggest that TOM1 is active in
the naa10Δ strain. Furthermore, we found ubiquitination events
associated to NRD and RQC pathways (RPS3-K212^[248]78,[249]79 and
RPS20-K8^[250]79, respectively) in the naa10Δ ubiquitinome. In
contrast, despite observing upregulated autophagy markers such as ATG19
and ATG38 in naa10Δ strain, ubiquitination of RPL25-K74^[251]83,[252]84
was observed. This ubiquitination event has previously been reported to
prevent the degradation of the 60 S subunit by ribophagy. Therefore, it
is likely that the degradation of ribosomal proteins in naa10Δ strain
occurs mostly through the UPS system. However, the observed impairment
of mitochondrial ribosomal protein degradation can be explained by the
critical role of NatA in mitophagy^[253]37.
These results potentially link the lack of Nt-acetylation with quality
control mechanisms important for regulating multiplex subunit complex
and protein folding through the UPS system such as the excess ribosomal
protein quality control (ERISQ) pathway^[254]59. Nevertheless, the
upregulation of multiple ubiquitin-conjugating enzymes such as TOM1,
UFD2, UFD4, and UBR1 and proteasomal proteins in the NatA-lacking cells
suggest that the fast turnover of NatA substrates is the result of a
systemic upregulation of the UPS system.
Combined, our results contribute to elucidate the mechanism behind the
role of NatA-mediated Nt-acetylation on proteostasis. Specifically, how
this modification might be coupled to the protein folding and complex
formation as well as the activity of the UPS system, supporting the
concept of Nt-acetylation as an avidity enhancer of protein–protein
interactions and folding. However, structural and biochemical studies
are needed to shed light on the mechanism behind the rules dictating
the evolutionarily conserved interactions promoted by Nt-acetylation
within the full-assembled ribosome and thus its impact on ribosome
function.
Methods
Yeast strains and sample preparation
The S. cerevisiae strains used were S288C isogenic yeast strains (MATα)
wild-type, BY4742 ([255]Y10000, EUROSCARF; internal reference
Arnesenlab yTA36); and the thereof modified naa10∆, YHR013C-∆::kanMX4
([256]Y10976, EUROSCARF; internal reference Arnesenlab yTA42).
Yeast phenotyping on agar plates were performed as previously^[257]6.
Briefly, yeast strains were grown to the early log phase and serial
1:10 dilutions containing the same number of cells were spotted on agar
plates containing various stressors.
For the pSILAC experiment, S. cerevisiae strains were grown in
synthetic medium containing 6.7 g/l yeast nitrogen base, 2% glucose,
2 g/l dropout mix (Yeast Synthetic Drop-out Medium Supplements without
lysine, Y1896 Sigma-Aldrich) containing all amino acids except lysine.
For heavy pre-labeling, heavy [^13C[6]/^15N[2]] l-lysine (608041,
Sigma-Aldrich) was added to a final concentration of 30 mg/l or
0.436 mM. Triplicate cultures of each strain were precultured three
successive times in medium containing heavy lysine overnight at 30 °C.
After preculture for SILAC labeling, cells were diluted to
OD[600] = 0.4 and cultured in triplicates of 400 ml, still in heavy-Lys
medium. After 90 min, cells were transferred to medium containing light
lysine (L5501, Sigma-Aldrich), via three rapid and gentle washes in
30 °C pre-heated medium without lysine at room temperature. At this
point OD[600] was readjusted to 0.4 for all samples. Cells were
harvested at given time points by centrifugation (10,000×g for 5 min at
4 °C) and OD[600] was measured for each harvest point. Cell pellets
were washed twice with ice-cold water, snap-frozen in liquid N[2] and
stored at −80 °C. As a control, prior to the transfer from heavy to
light lysing medium, 25 ml of heavy-Lys culture was kept and harvested
along with the last timepoint.
For all other sample preparations, yeast were grown in complete
synthetic medium at 30 °C. Overnight preculture was diluted to
OD[600] = 0.5. Cultured yeast were harvested at OD[600] ~1.8 by
centrifugation (10,000×g for 5 min at 4 °C), washed two times with cold
water and stored at −80 °C. For the ubiquitinome analysis, triplicate
cultures of yeast WT and naa10Δ were grown starting from OD[600] = 0.4
in synthetic complete medium. 0.003% SDS and DMSO were added after 3 h
and cells were incubated for further 4 h. Cells were harvested by
10 min centrifugation at 3000×g followed by three washes in cold water.
Yeast flow cytometry with SYTOX Green
Yeast were grown in SC medium and harvested at the same conditions as
for the other analyses, before fixation and staining with the
DNA-binding dye SYTOX Green (Invitrogen™ #S7020), which has been shown
to outperform propidium iodide providing more reliable stain with
improved linearity between DNA content and fluorescence^[258]85. At
harvest, 1 ml culture was centrifuged at 4000×g for 10 min, before the
cells were resuspended in 1.5 ml Milli-Q water. The cells were fixed by
drop-wise addition of 3.5 ml 100% ethanol at 1400 rpm vortexing and
incubated on a rotating wheel (15 rpm) overnight at 4 °C. The cells
were washed in 1 ml Milli-Q water and incubated in 500 μl heat-treated
RNase solution (Qiagen #19101) for 4 h at 37 °C. The cells were
pelleted, treated with 200 μl pepsin protease solution (Roche
#10108057001) for 15 min at 37 °C, before resuspension in 500 μl 50 mM
Tris, pH 7.5 and storage at 4 °C. In all, 50 μl cell solution was mixed
with 1 ml SYTOX Green solution in a dark microtube and then sonicated
at 20 kHz for 5 × 2 s pulses on ice. The DNA content reported by the
SYTOX Green signal intensity was measured using Accuri C6 (Flow
cytometry core facility, Bergen) and the FL1 detector with a standard
530/30 band pass filter. The limit was set to 5000 cells, and fluidics
speed was set to fast. Three independent experiments with seven replica
cultures in total were run. Data was processed, analyzed and visualized
using FlowJo. Cell cycle analysis was performed using the cell cycle
tool in FlowJo applying the Watson (Pragmatic) model and equal range
for C1 and C2, optimized for the lowest possible RMSD. Cell cycle
distribution % values were exported and statistical testing was
performed using two-tailed t test with unequal variance.
Northern blotting analysis of rRNA processing intermediates
Ten µg whole cell RNA from WT and naa10Δ strains (biological
triplicates) was separated by denaturing gel electrophoresis on a 1%
agarose formaldehyde denaturing gel. The RNA was subsequently
transferred to a positively charged nylon membrane (BrightStar-Plus,
Ambion) by capillary blotting, followed by cross-linking using
UV-light. Probes targeting ITS1 (CGGTTTTAATTGTCCTA), ITS2
(TGAGAAGGAAATGACGCT), 18 S (AATTCTCCGCTCTGAGATGG), 5.8 S
(GCAATGTGCGTTCAAAGA), and 25 S (GATCAGACAGCCGCAAAAAC), 10 pmol each,
were labeled with [γ-32-P]-ATP using T4 polynucleotide kinase (Thermo
Fisher Scientific) and hybridized to the membrane in hybridization
buffer (4× Denhardts solution, 6× SSC, and 0.1% SDS), at 45 °C for
16 h. Subsequently, the membranes were washed four times in washing
buffer (3× SSC and 0.1% SDS) and then exposed to a Phosphor Imager (PI)
screen overnight for the ITS1 and ITS2 probe and 10 minutes for the
18 S, 5.8 S, and 28 S probes to avoid saturation. The PI screens were
scanned using a Typhoon scanner (GE Healthcare) and analyzed by
Fiji-ImageJ software.
Preparation of samples for LC–MS/MS analysis
For global proteome profiling and pSILAC, yeast cells were resuspended
1:2 in lysis buffer composed of 100 mM Tris(hydroxymethyl)aminomethane
(Tris), pH 8.5, 5 mM, Tris(2-carboxyethyl)phosphine hydrochloride
(TCEP), 10 mM chloroacetamide (CAA) and 2% sodium dodecyl sulfate
(SDS). Cells were lysed by eight rounds of bead beating (1 min beating,
1 min rest, 66 Hz) in a Precellys 24 homogenizer with 400 µm silica
beads (2:1, resuspended cells: silica beads). The extracted protein
lysates were heated to 95 °C during 10 min, briefly sonicated and
centrifuged at 16,000×g, 4 °C. Afterwards, the protein concentration
was approximated using the BCA assay (Pierce^TM). The resulting samples
were digested overnight using the protein aggregation capture (PAC)
protocol^[259]86. The proteolytic digestion for pSILAC was performed by
addition of lysyl endopeptidase (Lys-C, Wako), 1:50 enzyme to protein
ratio, and incubated at room temperature overnight. For all other
sample preparations, lysyl endopeptidase (Lys-C, Wako) and trypsin
(Tryp, Sigma-Aldrich) were added 1:300 and 1:100 enzyme to protein
ratio, respectively and incubated at 37 °C overnight. The digestion was
quenched by the addition of trifluoroacetic acid (TFA, Sigma-Aldrich)
to final concentration of 1%. The resulting peptide mixtures were
desalted and stored on Sep-Pak columns (Waters) at 4 °C until further
use. Three independent cultures by condition were analyzed.
Offline High pH Reversed-Phase HPLC Fractionation.100 µg of peptides
were separated by HpH reversed-phase chromatography using a Waters
XBridge BEH130 C18 3.5 μm 4.6 × 250 mm column on an Ultimate 3000
high-pressure liquid chromatography (HPLC) system (Dionex, Sunnyvale,
CA, USA) operating at a flow rate of 1 ml/min with three buffer lines.
Buffer A H[2]O, Buffer B C[2]H[3]N (ACN) and Buffer C 25 mM
NH[4]HCO[3], pH 8 (Ammonium bicarbonate). The separation was performed
by a linear gradient from 5% B to 35% B in 62 min followed by a linear
increase to 60% B in 5 min, and ramped to 70% B in 3 min. Buffer C was
constantly introduced throughout the gradient at 10%. A total of 12
fractions were collected at 60 s intervals. Samples were acidified
after digestion to final concentration of 1% trifluoroacetic acid
(TFA). In total, 250 ng of each sample were loaded into Evotips
(Evosep) for LC–MS/MS analysis.
TPP-DIA and ITSA LC–MS/MS analysis. Yeast pellets were resuspended in
1 ml lysis buffer1 composed of 0.1% nonyl phenoxypolyethoxylethanol
(NP-40) and Phosphate-Buffered Saline (PBS, 137 mM NaCl, 2.7 mM KCl,
10 mM Na[2]HPO[4] and KH[2]PO[4], Sigma-Aldrich) supplemented with
protease inhibitor (cOmplete™ Protease Inhibitor Cocktail, Roche) at
room temperature (RT). In all, 400-µm silica beads were added to 200 µl
of the resuspended cells 2:1 suspension to beads ratio. The cells were
lysed by eight rounds of bead beating (1 min beating, 3 min rest,
66 Hz) at 4 °C. The protein concentration was approximated using the
BCA assay (Pierce^TM). In total, 100 µl of lysate at 2 µg/µl of each
sample were transferred to a 96-well plate and keep to room temperature
for 10 min. The samples were transferred to specific wells according to
the desired temperature. The cell lysates were heated at their
respective temperatures in a thermocycler for 4 min and immediately
incubated at RT for an additional 4 min. The resulting cell lysates
were transferred to a 1.5 ml microcentrifuge tubes and centrifuged at
20,000×g, 4 °C for 1 h. 50 µL of the supernatant were transferred to a
deep well plate. Afterward, 50 µl of lysis buffer2 composed of 200 mM
Tris, pH 8.5, 10 mM TCEP, 20 mM CAA and 4% SDS. The resulting protein
lysates were heated at 95 °C for 10 min. The samples were digested
overnight by a 96-well format automatized PAC^[260]86 workflow
optimized for the KingFisher^TM Flex robot (Thermo Fisher Scientific).
Briefly, the 96-well comb was stored in plate #1, the sample in plate
#2 in a final concentration of 70% acetonitrile and with 50 µl of
magnetic Amine beads (ReSyn Biosciences) in a protein-to-bead ratio of
1:2. Protein aggregation capture was performed in two steps of 1 min
mixing and 10 min pauses. The sequential washes were executed in
2.5 min. Washing solutions are in plates #3–5 (95% Acetonitrile (ACN))
and plates #6–7 (70% Ethanol). Plate #8 contains 300 μl digestion
solution of 50 mM ammonium bicarbonate (ABC), 0.5 µg of Lys-C (Wako)
and 1 µg trypsin (Sigma-Aldrich). The digestion was quenched by the
addition of trifluoroacetic acid (TFA, Sigma-Aldrich) to a final
concentration of 1%. Six independent cultures per condition were
analyzed.
For the precipitate analysis by ITSA, the remaining supernatant after
centrifugation was discarded, and 100 µl fresh lysis buffer1 were added
to the 1.5-ml microcentrifuge tubes. The resulting suspension was
centrifuged at 20,000×g, 4 °C for 1 h. This operation was repeated two
times. Afterward, the supernatant was discarded, and the precipitate
was solubilized with lysis buffer and heated at 95 °C during 10 min.
The samples were digested overnight by a 96-well format automatized
PAC^[261]86 as stated previously.
For the TTP-DIA experiment, the peptide concentration for the two
lowest temperatures was spectrophotometrically determined at 280 nm
using a NanoDrop instrument (Thermo Scientific) and the average used as
the concentration for all samples. For ITSA, the peptide concentration
for each sample was determined. An equivalent of 500 ng of each sample
were loaded into Evotips (Evosep) for LC–MS/MS analysis.
N-terminal-enrichment. The enrichment was performed as outlined
previously^[262]87 with some modifications. Briefly, the yeast cells
were lysed, and the protein concentration quantified as in yeast
proteome profiling experiment. Afterward, magnetic SiMAG-Sulfon beads
(Chemicell, 1202) were added to protein lysate to beat ratio of 1:10
ratio (w/w). Pure 100% ACN was added to a final volume 70% v/v to
initiate binding. After 10 min incubation at RT, the mixture was kindly
vortexed and incubated for additional 10 min. The supernatant was
removed with help of a magnet and the beads were rinsed with 1 ml ACN
and 1 ml 70% ethanol. Beads were resuspended in 100 μl 100 mM HEPES, pH
8. 2 M folmadehyde (Sigma-Aldrich) and 1 M sodium cyanoborohydride
(Sigma-Aldrich) were added to a final concentration of 30 mM and 15 mM,
respectively. The lysate was incubated at 37 °C for 1 h. Following
this, fresh labeling reagents were added, and the lysate was incubated
for an additional hour. The reaction was quenched by the addition of
4 M Tris, pH 6.8 to a final concentration of 500 mM and incubated for
3 h. Additional magnetic beads were added at a 1:5 ratio and protein
bound by addition of 100% ACN to a final concentration of 70% v/v.
Beads were settled on a magnetic stand after 20 min incubation at RT
and vortexed each 10 min. The supernatant was removed, and the beads
rinsed with 1 mL ACN and 1 ml 70% ethanol. The beads were resuspended
in 300 μl of 200 mM HEPES buffer, pH 8.0. The digestion was performed
with trypsin (Tryp, Sigma-Aldrich) 1:100 enzyme to protein ratio and
incubated 37 °C overnight. After the digestion, 100% ethanol was added
to the proteome digest to a final concentration of 40% v/v before the
addition of undecanal (EMD Millipore) at an undecanal to peptide ratio
of 20:1 w/w and addition of 1 M sodium cyanoborohydride to a final
concentration of 30 mM. The mixture was incubated at 37 °C for 1 h.
Afterwards, the mixture was bounded to a magnetic rack and the
supernatant transferred to a low-binding tube. The supernatant was
acidified to pH=2 with 0.1% TFA in 40% ethanol to a loading volume of
500 μl and loaded on a Sep-Pak column (Waters). The Sep-Pak columns
were conditioned with 1 ml methanol followed by 3 ml 0.1% TFA in 40%
ethanol. The flow-through was collected in 1.5-ml protein Low-bind
tubes and concentrated by vacuum centrifugation. Finally, the resulting
peptides were resuspended in 0.1% TFA and desalted using Sep-Pak
columns (Waters). The resulting peptides were storage using Sep-Pak
columns (Waters) until use at 4 °C. Four independent cultures per
condition were measured.
DiGly peptide enrichment. Yeast cells were resuspended 1:2 in lysis
buffer composed of 100 mM Tris, pH 8.5, 5 mM, TCEP, 1 mM
N-ethylmaleimide (NEM) and 2% SDS. Cells were lysed by bead beating and
the resulting lysate was supplemented with CAA to a final concentration
of 5.5 mM. DiGly peptide enrichment was performed using the PTMScan®
Ubiquitin Remnant Motif (K-ɛ-GG) Kit (Cell Signaling Technology (CST)).
Briefly, 1 mg peptides per sample were reconstituted in 1.5 ml PTMScan
HS IAP Bind Buffer and 20 µl of cross-linked antibody magnetic beads
were added to each sample tube. The tubes were incubated on an
end-over-end rotator for 2 h at 4 °C. Afterward, the tubes were spun at
2000×g for 5 s and placed in a magnetic rack for 10 s. The supernatant
was discarded and 1 ml HS IAP Wash Buffer was mix with the beads. The
tubes were placed again in a magnetic rack and the supernatant
discarded. This operation was repeated four times. Subsequently, the
tubes were washed with LC–MS water two times. Finally, the DiGLY
peptides were eluted form the magnetic beads by adding 50 µl of IAP
Elution Buffer (0.15% TFA) to the beads for 10 min at room temperature
and 500 rpm. Tube was placed in a magnetic rack and the supernatant was
transferred to a new microcentrifuge tube. This operation was repeated
two times. The peptide concentration was spectrophotometrically
determined at 280 nm using a NanoDrop instrument (Thermo Scientific),
and the equivalent of 500 ng of each sample were loaded onto Evotips
(Evosep) for LC–MS/MS analysis. Three independent cultures per
conditioned were analyzed.
LC–MS/MS analysis
All samples except those with Nt-enrichment were analyzed on the Evosep
One system (Evosep) using a 15 cm, in-house packed, reversed-phase
column (150 μm inner diameter, ReproSil-Pur C18-AQ 1.9 μm resin [Dr.
Maisch GmbH]). The column temperature was controlled at 60 °C using a
using an integrated column oven (PRSO-V1, Sonation, Biberach, Germany)
and binary buffer system, consisting of buffer A (0.1% formic acid
(FA), 5% ACN) and buffer B (100% ACN) and interfaced online with the
Orbitrap Exploris 480 MS (Thermo Fisher Scientific, Bremen, Germany)
using Xcalibur (tune version 3.0). pSILAC and proteome profiling
experiment was measured with the pre-programmed gradient for 60 samples
per day (SPD). For all other experiments, the pre-programed gradient
correspond to 30 SPD was used.
Nt-enrichment was analyzed in an EASY-nLC 1200 system (Thermo Fisher
Scientific), using a 15 cm, in-house packed, coupled online with the
Orbitrap Q Exactive HF-X MS (Thermo Fisher Scientific, Bremen,
Germany), nanoflow liquid chromatography, at a flow rate of 250 nl/min.
The total gradient was 60 min followed by a 17 min washout and
re-equilibration. Briefly, the flow rate started at 250 nl/min and 8%
ACN with a linear increase to 24% ACN over 50 min followed by 10 min
linear increase to 36% ACN. The washout flow rate was set to 500 nl/min
at 64% ACN for 7 min followed by re-equilibration with a 5 min linear
gradient back down to 4% ACN. The flow rate was set to 250 nl/min for
the last 5 min.
For the DDA experiments. The Orbitrap Q Exactive HF-X MS was operated
in Top6 mode with a full scan range of 375–1500 m/z at a resolution of
60,000. The automatic gain control (AGC) was set to 3e6 with a maximum
injection time (IT) of 25 ms. Precursor ion selection width was kept at
1.4 m/z and peptide fragmentation was achieved by higher-energy
collisional dissociation (HCD) (NCE 28%). Fragment ion scans were
recorded at a resolution of 30,000, an AGC of 1e5 and a maximum fill
time of 54 ms. Dynamic exclusion was enabled and set to 30 s. The
Orbitrap Exploris 480 MS was operated in Top12 mode with a full scan
range of 350–1400 m/z at a resolution of 60,000. AGC was set to 300 at
a maximum IT of 25 ms. Precursor ion selection width was kept at
1.3 m/z and fragmentation was achieved by HCD (NCE 30%). Fragment ion
scans were recorded at a resolution of 15,000. Dynamic exclusion was
enabled and set to 30 s.
For the DIA experiments. The Orbitrap Exploris 480 MS was operated at a
full MS resolution of 120,000 at m/z 200 with a full scan range of
350–1400 m/z. The full MS AGC was set to 300 with an IT 45 ms. Fragment
ion scans were recorded at a resolution of 15,000 and IT of 22 ms. In
all, 49 windows of 13.7 m/z scanning from 361 to 1033 m/z were used
with an overlap of 1 Th. Fragmentation was achieved by HCD (NCE 27%).
Raw MS data analysis
For publication, all the raw files corresponding to pSILAC,
Nt-enrichment and pSILAC-6h were analyzed with MaxQuant (1.6.7.0) and
searched against a UniProt’s yeast protein sequence database as
follows. For pSILAC, a database composed of the canonical isoforms of
S. cerevisiae proteins as downloaded from UniProt in 2019, which was
customized by removing all signal peptides as annotated in UniProt. For
Nt-enrichment and pSILAC-6 h, a data database composed of the canonical
isoforms of S. cerevisiae proteins, as downloaded from UniProt in 2019
was used ([263]https://www.uniprot.org/). For pSILAC analysis, the
multiplicity was set to two allowing the detection of light (K0) and
heavy (K8)-labeled peptides. Cysteine carbamylation was set as a fixed
modification, whereas methionine oxidation and protein N-termini
acetylation were set as variable modifications. Match between runs
(MBR) was enabled. For Nt-enrichment and pSILAC-6h the default settings
were kept, and MBR was disabled.
TPP-DIA, ITSA, and Ubiquitinome raw files were analyzed using
Spectronaut v15 (Biognosys) with a library-free approach (directDIA)
using a database composed of the canonical isoforms of Saccharomyces
cerevisiae proteins as downloaded from UniProt in 2019, which was
customized by removing all signal peptides as annotated in UniProt.
This customized data based was supplemented with a common contaminant
database. For TPP-DIA and ITSA, cysteine carbamylation was set as a
fixed modification, whereas methionine oxidation and protein N-termini
acetylation were set as variable modifications. For the Ubiquitinome
experiment, DiGLY (K,T,S) was defined as an additional variable
modification and PTM localization was enabled and set to 0.5. Precursor
filtering was set as Q-value, and cross-run normalization was turned
off for TPP-DIA. ITSA analysis was performed with Spectronaut using
default settings. For ubiquitination, the imputation setting was
disabled. Further processing analyses were performed either in R
(v4.2.1), Prostar (v1.28.0)^[264]88 or Perseus (v1.6.7.0).
Bioinformatic data analysis
For all analyses, common contaminants and proteins hitting the reverse
decoy database were filtered out prior to analysis. The pSILAC
experimental data analysis was performed using R (v4.2.1). Briefly, all
protein identifications identified with less than two unique peptides
and detected in less than four time points were discarded. The
heavy-label incorporation was calculated from MaxQuant heavy-to-light
intensity ratios. The relative isotope abundance (RIA) was calculated
as follows:
[MATH: RIAt=ratioH/L/(1+ratioH/L) :MATH]
1
As the RIAt collected in the time domain follows an exponential curve
of the form:
[MATH: RIAt=Ae−kt :MATH]
2
The exponential curve was linearized for the derivation of protein
turnover parameters, using RIAt values up to 6 h. The linearization was
performed by taking the natural logarithm of both sides of the equation
and rearranging:
[MATH: lnRIAt=−kt+lnA :MATH]
3
By comparing the Eq. ([265]3), to a linear model y = mx + b, the slope
corresponds to k and the intercept corresponds to ln A. We calculate
the half-life (t½) of each protein as the time when the protein is
half-labeled (i.e., RIA = 0.5). Thus, t ½ was calculated as follows:
[MATH: t1/2=ln(2
)/∣k∣ :MATH]
4
For N-terminal peptide analysis from the pSILAC experiment, all
identified peptides were filtered to all keep those covering the first
or second amino acid of annotated protein sequences retrieved from the
UniProt database. Afterward, the identified peptides were grouped by
condition and the protein turnover parameters were determined as
mentioned previously. Afterward, the median absolute deviation (MAD) of
the calculated Kdil condition and replicate was calculated. Kdil values
outside the MAD range relative to the median were deemed as outliers.
TPP-DIA experiment was performed in R (v4.2.1). Briefly, data was
grouped by condition and treatment prior log2 transformation. For
normalization, to equal out differences in samples that result from
unequal sample concentration, normalization was performed using VSN
approach (Variance stabilizing Optimization) from the VSN package
(v3.15) and implemented in the Prostar pipeline^[266]88. Afterward, all
protein identifications per condition and replicate were treated
separately. Low-intensity values that were not part of distribution and
not valid values were filtered out. Only proteins with at least eight
data points were used for fitting. For fitting the melting curve
trajectories of each protein, a four-parameter logistic curve model was
used as follows:
[MATH:
[Protein]=d+<
/mo>a−d1+(<
mrow>TT50
)−d
:MATH]
5
Where:
T= temperature
[Protein] = Protein intensity
a = estimated [Protein] at minimum value of T
d = estimated [Protein] at maximum value of T
T[50] = mid-range T.
b = slope at the inflection point.
Afterward, the median absolute deviation (MAD) of the calculated T[50]
per protein and condition was calculated. T[50] values outside the MAD
range relative to the median were set as outliers.
For calculating the maximum number of protein-melting temperatures and
half-lives, the identified proteins were grouped by condition, and the
T[50] and T[1/2] were calculated as stated above. For statistical
analysis, each replicate were treated separately.
For the ITSA, the protein groups table output from Spectronaut v15 was
analyzed. The lowest temperature of each condition (25 °C) was used to
calculate the soluble and precipitated fraction for different
conditions. For ubiquitinome, the analysis was performed as
previously^[267]89. Briefly, DiGLY values were filtered to contain >50%
valid values in at least one experimental condition. Missing values
were imputed based on a normal distribution width and downshift of 1.8
and a width of 0.3.
The GO term annotation was performed using the R packages: GO.db
(v3.8.2) and the genome-wide annotation for Yeast database package
Org.Sc.sgd.sd (v3.8.2). The gene set enrichment analysis using KEGG
terms was performed with the function gseKEGG from the Clusterprofiler
R package (v3.15). The icelogo plots were built using the iceLogo web
tool found ([268]https://iomics.ugent.be/icelogoserver/). The
statistical analyses were conducted using the Krustal–Wallis,
Kolmogorov–Smirnov and Wilcoxon test. The P values were corrected
according to Benjamin–Hochberg. For volcano plots, P values were
calculated by unpaired two-tailed Student’s t test. Statistical
significance is indicated in the figure legends.
Reporting summary
Further information on research design is available in the [269]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[270]Supplementary information^ (2.5MB, pdf)
[271]41467_2023_40224_MOESM2_ESM.pdf^ (170.1KB, pdf)
Additional supplementary files description
[272]Supplementary Dataset 1^ (2.9MB, xlsx)
[273]Supplementary Dataset 2^ (159.6KB, xlsx)
[274]Supplementary Dataset 3^ (42.5KB, xlsx)
[275]Supplementary Dataset 4^ (468.6KB, xlsx)
[276]Supplementary Dataset 5^ (41.6KB, xlsx)
[277]Supplementary Dataset 6^ (284.9KB, xlsx)
[278]Supplementary Dataset 7^ (38.7KB, xlsx)
[279]Supplementary Dataset 8^ (1.4MB, zip)
[280]Reporting Summary^ (307.2KB, pdf)
[281]Peer Review File^ (5MB, pdf)
Acknowledgements