extensive in vivo metabolite-protein interactions …kevinyip/papers/smallmolecules_cell...1...
TRANSCRIPT
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Extensive In vivo Metabolite-Protein Interactions Revealed by
Large-Scale Systematic Analyses
Xiyan Li1,2, Tara A. Gianoulis3, Kevin Y. Yip4, Mark Gerstein3,4,5, Michael Snyder1,2,4,*
1 Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-
5120
2 Department of Molecular, Cellular and Developmental Biology, Yale University, New
Haven, CT 06520
3 Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT
06520
4 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven,
CT 06520
5 Department of Computer Science, Yale University, New Haven CT 06520
* Correspondence: [email protected]
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Summary
Natural small compounds comprise most cellular molecules and bind proteins as
substrates, products, cofactors and ligands. However, a large scale investigation of
in vivo protein-small metabolite interactions has not been performed. We developed
a mass spectrometry assay for the large scale identification of in vivo protein-
hydrophobic small metabolite interactions in yeast and analyzed compounds that
bind ergosterol biosynthetic proteins and protein kinases. Many of these proteins
bind small metabolites; a few interactions were previously known, but the vast
majority are novel. Importantly, many key regulatory proteins such as protein
kinases bind metabolites. Ergosterol was found to bind many proteins and may
function as a general regulator. It is required for the activity of Ypk1, a mammalian
AKT/SGK1 kinase homolog. Our study defines potential key regulatory steps in
lipid biosynthetic pathways and suggests small metabolites may play a more general
role as regulators of protein activity and function than previously appreciated.
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Introduction
Over the past decade considerable effort has been devoted to analyzing biological
networks, particularly protein-protein, expression, transcription factor binding and even
protein phosphorylation networks (reviewed in Snyder and Gallagher, 2009). These
studies have provided a wealth of information for understanding protein function, which
components work together and the basic principles of regulatory network organization.
In total numbers, small metabolites comprise the vast majority of cellular
components, and like proteins, they are present in a broad range of cellular concentrations
and participate in a wide variety of biochemical and regulatory functions. They serve as
metabolic components, cofactors for enzymes, forms of energy for biochemical reactions
and regulators of protein function (Forster et al., 2003). As regulators of protein function,
metabolites can act globally to control many proteins or specifically target a limited
number of proteins. Examples of the wide variety of small metabolite-protein
associations include the binding of galactose to a sensor protein (Yano and Fukasawa,
1997), steroid hormones to transcription factors (Evans, 1988) and second messengers
including phospholipids, cyclic nucleotides and arachidonic acids to specific cellular
targets. In spite of their importance in mediating protein function and regulation,
systematic approaches for analyzing in vivo interactions have not been performed. Such
information is expected to be valuable not only for elucidating the biochemical activities
and regulation of individual proteins, but also for assembling and understanding
regulatory networks and connections between biological pathways. Furthermore, since
metabolite levels can be adjusted by dietary intake of nutrients, understanding the
regulation of cellular processes by metabolites has potential therapeutic value in
correcting defects in biochemical pathways.
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Saccharomyces cerevisiae has served as an important model organism for many
large-scale studies including analysis of protein-protein interactions, phenotypes, genetic
interactions, protein localization, gene expression, and transcription factor binding
(reviewed in Horak and Snyder, 2002; Snyder and Gallagher, 2009). To date, over 682
metabolic compounds have been identified in yeast (Forster et al., 2003) and many are
known to be hydrophobic; 52% have a logP greater than methanol (Fig. S1A). Many
models of yeast metabolism have been generated and capable of predicting key
regulatory metabolic steps (Cascante and Marin, 2008; Herrgard et al., 2008).
Several previous studies have analyzed small molecules and small molecule-protein
interactions in yeast. Metabolites have been profiled from yeast extracts using mass
spectrometry (Allen et al., 2003), and limited studies to identify metabolites that bind
proteins have been performed (Lee et al., 2007). Protein and small molecule microarrays
have been used to discover several in vitro interactions (Beloqui et al., 2009; Kuruvilla et
al., 2002; Morozov et al., 2003; Zhu et al., 2001). Assays to examine small molecule-
protein interactions have been developed (Maynard et al., 2009; Tagore et al., 2008);
however, a systematic effort to identify the small metabolites that bind to large numbers
of proteins in vivo has not been performed. Thus, the number and types of proteins that
bind small molecules in the cell are not known. Such information is expected to both help
inform potential regulatory interactions and elucidate the function and regulation of
proteins and pathways.
Here we present a systematic large scale investigation of the endogenous protein-
metabolite interactome in yeast. We focused on the interaction of hydrophobic
metabolites with components of the ergosterol biosynthesis pathway and protein kinases.
Ergosterol biosynthetic enzymes were studied because we expected that these might bind
hydrophobic metabolites, and protein kinases were chosen because of their importance in
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global regulation of protein function. We found that a large number of proteins bound to
hydrophobic metabolites and described many novel interactions. Further analysis has
revealed that the yeast sterol, ergosterol, binds to many protein kinases, often with 1:1
stoichiometry, and is important for the activity of a highly conserved kinase, Ypk1, a
member of the Akt/SGK1 family, and for the protein levels of Ssk22, a MAPKKK
involved in osmotic responses. Ergosterol is the major sterol in yeast and, analogous to
cholesterol in mammals, it is an abundant component of plasma membranes. Overall, our
results demonstrate that a variety of small metabolite-protein interactions occur in
eukaryotes and suggesting an extensive role in the global regulation of protein activities.
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Results
A large scale assay to identify hydrophobic compounds associated with proteins:
application to ergosterol biosynthetic enzymes
We developed a sensitive and scalable method to systematically identify small
metabolites bound to proteins using affinity protein purification and mass spectrometry
(Fig. 1). Briefly, proteins tagged with an IgG-binding protein domain (Gelperin et al.,
2005) were isolated from lysates using magnetic beads. After washing, the small
metabolites were then extracted in methanol and analyzed using a reverse phase C18
column-equipped Ultra Performance Liquid Chromatography (UPLC) column coupled to
a Quadrupole-Time-of-flight (Q-TOF) mass spectrometer. As a negative control, parallel
experiments were performed using a yeast strain lacking the fusion protein (Y258). The
metabolites significantly enriched in the presence of the fusion relative to the control
strains and methanol solvent were identified. We focused on hydrophobic molecules
because they are less likely to be removed from proteins during washes and can readily
be detected by atmospheric pressure chemical ionization (APCI). The resultant mass
spectra are comprised mostly of the protonated precursor ions ([M+H]+) which readily
allow small metabolite identification. The mass spectrometry assay was first developed
and standardized using 12 diverse compounds, including lipid-soluble vitamins from A,
D, E and K families, and two sterols (ergosterol and lanosterol) which allowed us to
optimize the sensitive detection (~200 femtomole in a mixture in profile scan mode) and
separation of each of these compounds (Fig. S1B). We also found that this method
detected more than 340 features in a methanol extract of yeast cells (An LC profile and
list of peaks enriched in the extract relative to the solvent are in Figure S1C and Table
S1), indicating its scope is sufficiently broad to cover at least hundreds of metabolites in a
single experiment.
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We first established the profiling assay using a group of 21 enzymes involved in
ergosterol biosynthesis (Parks and Casey, 1995) whose known substrates and products,
most of which are non-polar hydrophobic molecules, are readily detectable by LC-APCI-
Q-TOF (Fig. 2A). The assays for the Erg proteins were performed using 2-3 separate
protein preparations (biological replicates) each containing 0.5-5 picomoles of protein;
for each sample preparation 4-6 technical replicates were analyzed in parallel. The mass
spectra data were analyzed in MarkerLynx or XCMS to identify molecules based on
retention times and accurate molecular mass (see Methods and Fig. S1D for details).
Since the background for the peak regions is very low, the correspondence between both
technical and biological replicates was extremely high (mean of r.s.d. = 7.5± 4% for 3
technical replicates; for biological replicates see next section). We therefore used a
stringent threshold for calling positive signals. Finally, protein purity was examined using
SDS-gel electrophoresis after metabolite extraction. In general a single or major band of
the expected size is present in the strain expressing the fusions relative to the negative
control, although 14% of preparations contained more than one band indicative of either
associated proteins or degradation products (Fig S2A, Fig. S3A).
Analysis of the LC-MS results revealed that 16 of the 21 purified Erg proteins
associated with small metabolites (Table 1). One example shown in Figure 2 contains
several compounds associated with Erg6 that eluted from the LC column at retention time
10.84 min (Fig. 2B); this peak contained 3 mass peaks which were significantly lower (t
test P value<0.01, >10 fold) in the Y258 yeast control or methanol solvent (Fig. 2C).
These 3 peaks were identified as episterol (381.353 atomic mass unit (amu)),
dimethylzymosterol (395.368 amu) and lanosterol (409.384 amu), respectively, by
elemental composition analysis and hydrophobicity matching in retention time with
known chemicals (See Fig. S1B). Nine other proteins also bound small metabolites
related to ergosterol biosynthesis. A large number of Erg and other proteins analyzed in
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this study that were just as abundant in the protein preparations as the metabolite binding
protein did not bind any metabolites (Fig. S3D). For each of the ten proteins that bound
ergosterol related metabolites a specific set of associating molecules were observed, and
similarly each metabolite had a distinct profile. For example, (S)-2,3-epoxysqualene and
5-cholesta-8,24-dien-3-one specifically associated only with Erg1 (Fig. 2D), whereas
lanosterol, ergosterol and episterol consistently co-purified with 5, 5 and 3 Erg proteins,
respectively, above the control (Table 1). One Erg proteins was found to bind known
substrates (e.g. Erg3 bound episterol) and three other Erg proteins bound known products
(e.g. (S)-2,3-epoxysqualene for Erg1) suggesting that these substrates and products are
tightly associated with their metabolizing/biosynthetic enzymes.
Importantly the majority of metabolite-protein interactions detected in our assay
were novel (e.g. dimethylzymosterol for Erg6) (Table 1). Of particular interest were
lanosterol and ergosterol which each bound 5 Erg proteins. Ergosterol bound its natural
synthesizing enzyme Erg4 and four other enzymes that control the last five steps in
ergosterol biosynthesis starting from zymosterol, suggesting a multi-step feedback
regulatory mechanism (Fig, S2B, and Table 1). Ergosterol was not detected with the
known ergosterol-regulated enzyme Hmg1, probably due to a low level of protein in the
protein preparation (Fig. S2A). Although low levels of protein may be an issue in several
instances, it is unlikely to be a major problem overall as the distribution of protein levels
of the 37 metabolite binding proteins identified in our entire study (Erg proteins and
protein kinases) is similar to the distribution of the level of the majority of the 124
proteins analyzed (Fig. S3D). Lanosterol also bound five enzymes; these enzymes are
located at different points in the biosynthetic pathway. Interestingly, unlike other erg
mutant strains, yeast lacking Erg7, the enzyme that produces lanosterol, fails to grow in
the absence of lanosterol (Karst and Lacroute, 1977), suggesting a major role for this
lipid in yeast that might include modulation of protein function. Overall, these results
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raise the possibility that, in addition to the known inhibitory regulation of Hmg1 by
ergosterol and of Erg13 by acetoacetyl-CoA (Parks and Casey, 1995), many steps in the
ergosterol biosynthesis pathway may be regulated by biosynthetic products of the
pathway (Fig. 2A).
Novel metabolites were discovered to bind yeast Erg proteins
In addition to well-characterized sterols and other lipids that bound Erg proteins was an
unexpected metabolite, pentaporphyrin I. Pentaporphyrin is a heme-related intermediate
that may bind noncovalently to proteins due to the lack of peripheral methyl groups. It
was detected in 7 of 21 Erg enzymes and was identified using a known standard (Fig. 2E-
F; Table 1). It is not clear whether this metabolite is “free” porphyrin or derived from a
bound form after loss of the central coordinated metal ion during LC-MS detection.
Nevertheless measurements of the binding affinity and stoichiometry revealed
dissociation constants (Kd) of 8 to 34 M and pentaporphyrin:protein stoichiometries of
approximately 1:1 (for Idi1 and Erg6) to 2:1 (for Erg27) (Fig. 2G). The discovery that
pentaporphyrin is associated with several ergosterol biosynthetic components may
explain the observation that elimination of heme synthesis results in ergosterol
auxotrophy (Parks and Casey, 1995) and suggests that pentaporphyrin-Erg protein
interactions are important for protein function. Thus, our systematic analyses of small-
protein interactions reveal novel interactions important for protein function and further
help explain phenotypes for yeast strains lacking these different proteins.
Large scale analysis of hydrophobic small metabolites bound to yeast protein
kinases
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Protein kinases control cellular processes and regulate protein function at many
levels. We next examined which of the yeast protein kinases bound hydrophobic
metabolites. 103 protein kinases representing all functional branches in yeast (Hunter and
Plowman, 1997) (Fig. 3A) were purified and analyzed as described above (Fig. S3A).
Two biological replicates were performed for all 103 kinases, with at least 3 technical
replicates per biological replicate. A total of 95 metabolite peaks (background and
specific peaks) were identified in both experiments, and these peaks were highly
correlated in both retention time and exact mass from the 2 batches (R2=0.78) (Fig. S3B).
Using a stringent threshold, a total of 10 different peaks were found to be associated with
21 protein kinases, but not with negative (Y258) or methanol solvent controls (Table S2
and Fig. S3C,E) or with many other abundant yeast proteins (Fig. S3D). The specific
peaks fell into two classes. One major class (11 of 14 analyzed) yielded reproducible
peak intensity signals (R2 >0.9) whereas another set (3 of 14) exhibited less correlation of
peak intensities (R2<0.75). It is likely that the compounds bound to the highly correlated
peaks represent strong steady-state interactions, and those with differing levels of
interacting metabolites interact transiently and/or weakly (Morozov et al., 2003).
An example of specific binding is shown in Fig. 3B-C for Kin4, a protein kinase
regulating mitotic exit (Caydasi and Pereira, 2009). The methanol extract from Kin4
contained a singly charged ion of 379.337 amu at retention time 10.98 min. Analysis of
standards revealed that this compound is ergosterol (dehydrated state) rather than
ergocalciferol, a compound of identical molecular mass (Fig. 3D).
The 10 specific binding metabolites represent different lipids and sterols; however
one kinase Ste20 was found to bind pentaporphyrin, albeit at reduced levels relative to
the ergosterol enzymes, raising the possibility that Ste20 is associated with this molecule.
The compound identified most often among different kinases was ergosterol which was
associated with 15 different protein kinases. None of these proteins was previously
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known to bind ergosterol, and, except for Yck2, none were known to be membrane-
associated. KEGG pathways analysis reveals an enrichment of ergosterol bound-kinases
in metabolism of inositol phosphate, starch and sucrose, nicotinate and nicotinamide, and
sphingoglycolipids (Table S3). The different ergosterol binding kinases belong to
different families of protein kinases (Fig. 3A), suggesting a potential regulatory role of
ergosterol in the regulation of many types of protein kinases and many different aspects
of yeast biology.
Ergosterol-protein kinase interactions have binding affinities and stoichiometries in
ranges expected for physiological relevance
To determine if the binding coefficients observed are likely to be physiologically
relevant, the stoichiometry and affinity of the ergosterol-protein kinase association was
determined for 3 kinases, Ypk1, Hal5, and Rck2, along with three non binding controls
Atg1, Psk2 and denatured Ypk1, using an in vitro binding assay we developed (Fig. 4A).
For a fixed amount of the ergosterol-bound protein kinases, ergosterol binding exhibited
a saturable curve over an increasing concentration of free ergosterol, indicating specific
binding. In contrast, the binding curve was close to linear for the same amount of
denatured protein or control proteins (Fig. S4), indicating non-specific adsorption. The
Kd for each ergosterol binding kinase was between 4.7 to 17.9 M (Fig. 4A), figures
significantly lower than the endogenous concentration of ~4.8 mM for ergosterol in yeast,
assuming a uniform distribution throughout the cell (Ejsing et al., 2009). Although
neither the kinase nor ergosterol is likely to be uniform in their cellular distribution, the
4.7-17.9 M binding constant found for Ypk1 and the other kinases is well within a
plausible range for biological relevance. The binding ratio of ergosterol to protein was
found to be close to 1 (0.98-1.1, 95% confidence interval) for each protein kinase
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suggesting that one small metabolite binds to one protein (Fig. 4A). This ratio is
consistent with an in vivo biological role for ergosterol in regulating kinase activity.
Ergosterol regulates the activity of a highly conserved kinase, Ypk1, and influences
the Ssk22 levels
To determine if ergosterol binding is important for kinase function, we tested the effect of
ergosterol on Ypk1 activity using in vitro kinase assays (Fig. 4B). Ypk1 is a yeast
homolog of the mammalian SGK/AKT protein kinases which are involved in many
important cellular processes and human disease (Brazil and Hemmings, 2001); yeast
Ypk1 has been implicated in receptor-mediated endocytosis and sphingolipid-mediated
signalling (Jacquier and Schneiter, 2010). Ypk1 was purified from wild type cells grown
in the absence and presence of 0.4 mM ergosterol and tested for stimulation of in vitro
kinase activity in the presence of increasing concentrations of ergosterol. Ypk1 activity
from cells grown in the absence of ergosterol is significantly (and reproducibly) elevated
in the presence of increasing amounts of ergosterol (Fig. 4B). Ypk1 activity was even
higher when cells were grown in the presence of ergosterol and could be stimulated to a
similar extent. Because Ypk1 was isolated from wild type cells which contain ergosterol,
it might already contain bound ergosterol. We therefore purified Ypk1 protein from an
erg4 strain which lacks ergosterol (Parks and Casey, 1995). Ypk1 protein levels were
similar to preparations from wild type cells (Fig. 4C-a), however, Ypk1 activity was very
low in erg4 strains (at least 5-fold lower) relative to wild type cells and could not be
stimulated (Fig, 4B). These results demonstrate that ergosterol stimulates Ypk1 kinase
activity. Since Ypk1 isolated from erg4 strains was low and could not be stimulated it is
likely that some ergosterol must be present during Ypk1 synthesis and activation.
Overall, these results demonstrate that ergosterol is critical for Ypk1 activity.
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We also attempted to analyze the activity of Ssk22 (a kinase involved in
osmosensing (Posas et al., 1996)) in wild type and erg4 cells. In multiple independent
experiments we found that we could not purify Ssk22 from erg4 strains lacking
ergosterol (Fig. 4C-a). Addition of exogenous ergosterol to the medium restored Ssk22
levels in the mutant strain. To explore this further, Ssk22 levels were examined for up to
7 hrs after expression was induced from a GAL promoter. As shown in Fig. 4B-b,
copious amounts of Ssk22 protein are detected in wild type cells, but levels are
substantially reduced (6 to 20-fold) in erg4 cells (Fig. 4C-b). Although the levels of
Ssk22 were too low to measure kinase activity our results demonstrate that ergosterol is
important for maintaining Ssk22 protein levels in wild type yeast.
Growth of strains deleted for Ypk1 and other ergosterol binding proteins is affected
by ergosterol inhibitors
We next determined if ergosterol levels are important for the growth of yeast strains
lacking ergosterol-binding kinases. Yeast strains lacking Ypk1 were shown previously to
be sensitive to nystatin and fluconazole, two inhibitors of ergosterol biosynthesis (Gupta
et al., 2003; Hillenmeyer et al., 2008). Six strains deleted for different ergosterol binding
kinases as well as 4 strains deleted for kinases not found to bind ergosterol were grown in
the presence of different concentrations of fluconazole and cell density determined. As
shown in figure 4D, growth of ypk1 cells was inhibited by fluconazole, whereas ssk22
were resistant to fluconazole. Similarly, ypk1 cells were sensitive to nystatin (not
shown). The mutants of other 4 ergosterol-binding protein kinases behaved similarly to
the wild type cells and strains lacking non-ergosterol binding kinases. These results
indicate that genetic interactions are evident between ergosterol-bound kinases and the
ergosterol pathway.
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An integrated global small metabolite-protein network
To better view how our results might be connected with other regulatory interactions, we
next integrated the small metabolite-protein binding results with protein-protein
interaction data, genetic interaction data and metabolite networks constructed by others.
Our results suggest a highly connected network of interactions in which the small
metabolites add an extra dimension of regulatory information. We found extensive
interactions between the ergosterol-bound kinases, the ergosterol biosynthesis proteins
and a wide variety of other cellular components. Simplification of the network to only
those interactions that directly interact with the Erg pathway and ergosterol-bound
kinases reveals a bipartite pattern (Fig. 5A). Interestingly, the kinases are connected to
their interacting partners whereas ergosterol pathway proteins interact through genetic
and phenotypic interactions. We suggest that Erg pathway components either often
operate in the same pathways as the affected kinases and/or small metabolite products of
the pathway affect kinase regulators and/or substrates. Overall, our results demonstrate
close functional connections between ergosterol biosynthetic pathway components and
ergosterol-bound kinases.
Within the overall interaction network are a variety of interesting interactions. For
example, Erg20, an essential enzyme for both isoprenoid and ergosterol (Daum et al.,
1998) is indirectly phosphorylated by ergosterol-bound protein kinases Sat4 via Tpk1
whereas 5 other enzymes, Erg1, Erg4, Erg6, Erg7 and Erg26, are transcriptionally
regulated by 5 proteins (in the middle circle, Fig. 5A). In addition, 13 of 21 enzymes
(62%) in the ergosterol pathway and 3 ergosterol-binding kinases (Ypk1, Yak1, Mck1)
have physical interactions with the ubiquitin Ubi4. This figure is statistically significant
(p-value=1.5e-5 by Fisher's exact test), as only 18% of all yeast proteins have physical
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interaction with Ubi4 (Fig 5A bottom). Perhaps many components involved in ergosterol
biosynthesis are modified and/or degraded by the Ubi4-mediated ubiquitination pathway.
We next conducted gene function enrichment analyses for the 137 yeast genes
known to interact physically, genetically, or phenotypically with both one of the 21
ergosterol biosynthetic proteins and one of the 15 ergosterol-bound protein kinases (Fig.
5A top). Several categories of cell division and growth are particularly overrepresented,
such as cell cycle, stress responses, transcription, and general metabolism of lipids,
vitamins and carbohydrates (Table S3 for KEGG pathway and Fig. 5B for gene
ontology). These results further suggest that ergosterol can act through modulation of
protein kinase activation as a general regulator in coordinating various cellular biological
processes.
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Discussion
A large number of metabolite-protein interactions exist in eukaryotes
Inside a cell, most proteins presumably encounter various small metabolites, which are in
vast numerical excess as they constitute 5-8% of total cell weight (Alberts, 2002) and
possess a small molecular weight (<1000 Da). Although these encounters may not always
have functional consequences, it is likely that many of them will be important in
modulation of protein/enzyme activity. Our study is the first systematic analysis of in
vivo metabolite-protein interactions and has revealed many such interactions in a
eukaryotic cell. Approximately 70% of ergosterol biosynthetic proteins and 20% of
protein kinases were found to bind hydrophobic molecules. If a similar percentage exists
for the entire proteome as found for protein kinases, then >1200 soluble yeast proteins
would bind hydrophobic molecules. The analysis of hydrophilic small metabolites is
likely to increase the fraction of metabolite-binding proteins even further. Thus, a
substantial number of eukaryotic proteins are likely to bind metabolites.
One important advantage of an unbiased and systematic analysis of in vivo
protein-metabolite interactions is that many unexpected interactions are revealed, such as
interactions of protein kinases with sterols, the binding of ergosterol biosynthetic proteins
to different ergosterol-related metabolites and pentaporphyrin, a heme-related compound
that presumably binds noncovalently to proteins. In many cases the results can explain
previous unexplained observations. For example, yeast strains defective in heme
biosynthesis fail to grow in the absence of ergosterol (Parks and Casey, 1995). Likewise,
the finding that erg7 mutants fail to grow in the absence of ergosterol may be due to our
observation that lanosterol binds and likely regulates many key steps in the Erg pathway.
Finally, the observation that ypk1 mutants fail to grow in the presence of ergosterol
inhibitors is consistent with a role for ergosterol in Ypk1 pathway or related pathway
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function. Thus, the advantage of an unbiased screen reveals many novel interactions that
appear to be functional in vivo, and provides potential insights into previously
unexplained mutant phenotypes.
An assay for in vivo protein-small metabolite interactions
Investigation of protein-small metabolite interactions is difficult for two reasons: First,
sufficient quantities of proteins are required for detection, and many proteins can be
difficult to purify. Second, small metabolites vary in chemical properties, which prohibits
a universal detection method for untargeted profiling. We overcame these challenges by
using an epitope-tagged protein expression system for systematic analysis of large
numbers of proteins in yeast (Gelperin et al., 2005) and sensitive MS to detect trace
amounts of small metabolites at picomole scales. We further customized our MS
approach to target non-polar hydrophobic small metabolites because of the concern of
losing hydrophilic metabolites during protein purification and aqueous washes (Morozov
et al., 2003). The assay we developed will not detect non-volatile compounds and may
miss many phospholipids (albeit see Carrier et al., 2000) and perhaps other compounds as
well. Nonetheless, it is capable of detecting many diverse compounds (Tables 1,S2) and
yields not only reproducible and simple mass spectra amenable for further interpretation
(Fig. 2-3), but also meaningful results that could be validated using other assays (Fig. 4).
With modification, this method may also be used for profiling protein-bound hydrophilic
metabolites.
Our method differs significantly from several studies (Maynard et al., 2009;
Morozov et al., 2003; Tagore et al., 2008) in several important aspects: 1) we detect in
vivo bound small metabolites, which is analogous to co-immunoprecipitation, a gold
standard method for detecting in vivo protein-protein interactions; 2) the metabolomic
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composition exposed to each protein of study is at its in vivo physiological state, thus
avoiding bias due to overloading and/or disproportional composition of small molecules
that can occur in in vitro studies. 3) Finally the procedure we have established is highly
scalable and can be used to analyze large number of proteins.
Our current method cannot distinguish between physical and indirect association of
protein and small metabolites, as the metabolites we identify may purify with associated
proteins. Thus, the results are analogous to those for protein-protein interaction studies
that use affinity purification or two hybrid methods. Since the purified protein
preparations typically have a single overproduced peptide it is likely that most
interactions are direct, but indirect interactions are possible. In some cases the interaction
might be suggestive of a membrane association; for the cases of ergosterol-bound protein
kinases none have transmembrane domains and only one, Yck2, has been shown to be
membrane-associated through palmitoylation (Babu et al., 2004). Other limitations of our
assay are: 1) transient interactions, as might be expected in interactions with substrates
and products, may be difficult to observe. Those that are detected may prove to be
important limiting steps that control biochemical flux through pathways; 2) interactions
not normally present within the cell might be detected because the proteins are
overproduced; 3) false negative and false positive are difficult to assess. False negatives
may be transient interactions, have high off rate in aqueous phase or require particular
interaction conditions (e.g. protein modifications or interacting partners); apparent false
positives may be bona fide events that occur under conditions that have not been assessed
previously or due to nonspecific interactions.
The metabolite-protein interactions are likely to be specific for several reasons.
First, only a limited number of metabolites were found to be bound to any protein and
distinct metabolites are bound to a particular protein and even between closely related
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proteins. In fact, the majority of yeast proteins do not bind ergosterol or other
metabolites, even though most are just as abundant in our preparations as the metabolite
binding proteins (Fig. S3D). Second, the stoichiometry of the protein-small metabolite
interaction is usually on the order of 1:1 (or 1:2 for Erg27), suggestive of specific
interaction. Third, for most of the interactions the signal intensities are highly
reproducible between experiments. These results are consistent with specific interactions
rather than nonspecific adsorption to protein surfaces. Thus, although some of the
interactions we detect may be nonspecific, our data indicate that many of them are
specific.
Small metabolites as general regulators of cellular processes
As demonstrated in this study, ergosterol, an important building material for new cell
membranes, regulates the activity of Ypk1 (Schmelzle et al., 2002) and the levels of
Ssk22. Ypk1 is important for stress response, cell wall integrity, lipid uptake and budding
(Jacquier and Schneiter, 2010) and Ssk22 is involved in the Hog1-mediated osmosensory
pathway (Posas et al., 1996). Since even low ergosterol concentrations can stimulate
Ypk1 activity it is possible that Ypk1 is a biomass sensor for ergosterol levels and help
coordinate cell wall synthesis and budding. The positions of both ergosterol and Ypk1 in
the integrated network (Fig. 5) are consistent with key roles in coordinating these
processes along with cell cycle control and other related biological processes (Table S3).
Furthermore, the role of ergosterol as a coordinator of many molecular and cellular
processes has general similarities to other key molecular regulators such as cAMP and
phospholipid derivatives (Alberts, 2002). Since ergosterol is a critical component of cell
membranes it is ideally suited to coordinate membrane synthesis with related biological
processes that depend upon membrane and cell integrity such as the stress response,
endocytosis, budding, cell division. The regulation by ergosterol may differ from
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classical signalling regulators in terms of magnitude and selectivity, because as a
structural component of membranes, ergosterol serves more diverse general roles than
small molecules made specifically for signalling. As such ergosterol is well situated for
simultaneously monitoring cell integrity, intracellular processes and environmental
interactions. In addition to possible roles as a structural component and signalling
molecule, ergosterol might also serve as a facilitator of protein folding as cells grown in
the absence of ergosterol have low Ypk1 kinase activity and low levels of Ssk22. Perhaps
ergosterol is important for the proper folding of these enzymes and homeostasis for
Ssk22.
Small metabolites and human disease
The interplay of metabolites and proteins may have profound importance in human
health. Strong associations between urine metabolites and human diseases have been
documented (e.g. Nicholson et al., 2008). Thus, the knowledge gained from this study
may pave the way, not only to more fully understand the molecular basis of disease, but
to potentially modulate specific biological pathways by manipulating the metabolite
concentration through nutrient uptake or intervention. Consistent with the latter
possibility, many key enzymatic steps are controlled by pharmaceuticals that are analogs
of cellular metabolites; these could be used to modulate pathways regulated by metabolite
interactions.
21
Experimental Procedures
Yeast strains and media Yeast MORF strains expressing tagged proteins were grown
and processed as described previously (Gelperin et al., 2005) Yeast knockout strains were
from Yeast Genome Deletion Project. Y258 is MATa pep4-3, his4-580, ura3-53, leu2-
3,112. The Y258 strain used as negative control in this study was integrated with a URA3
gene from pRS426 vector. For yeast transformation, MORF plasmids were rescued from
yeast MORF strains and then introduced into the yeast using standard methods (Gietz and
Woods, 2002). Standard YPAD or SC-URA media supplemented with glucose, raffinose,
or galactose were used. For ergosterol treatments, a suspension at 0.05g/ml in ethanol
was added to 300 volumes of a yeast raffinose culture.
Affinity purification and metabolite extraction Frozen yeast cell pellets from 150 ml
cultures were resuspended in 1ml lysis solution (200 mM NH4Ac, 1x Complete Protease
Inhibitor Cocktail (Roche), 1 mM EGTA). One volume of Zirconia silica beads was
added to facilitate cell lysis using a FastPrep 24 machine (MP Biomedicals) and a setting
of 60 s for 3 times at 6.5 m/s. The lysis was repeated once with another 1 ml of lysis
solution. The combined supernatants were then incubated with 50 µl of rabbit IgG-
crosslinked M-270 epoxy Dynabeads (Invitrogen) for 2 h at 4°C. The beads were washed
once in 300 mM NH4Ac and once in 150 mM NH4Ac; each wash lasted 5 min. To extract
the protein-bound metabolites, 60 µl of pure methanol was added to the beads and
incubated at RT for 10 min. The methanol extract was then immediately transferred to a
Waters max recovery glass vial and analyzed by a mass spectrometer the same day. The
beads were then boiled in 30 µl 2x SDS sample buffer for 10 min, 15 µl of the
supernatant were loaded on a 4-15% SDS-PAGE gel for separation and stained by
ProtoBlue Safe reagent (National Diagnostics).
22
UPLC-coupled APCI mass spectrometry The mass spectrometry system used in this
study was comprised of an Acquity UPLC system and a Micromass Q-TOF mass
spectrometer equipped with an APCI probe (Waters Co., Milford MA). The operating
software was MassLynx v4.1 (Waters Co., Milford, MA). For each run, 10 µl metabolite
extract was loaded on an Acquity UPLC BEH C18 column protected by a VanGuard pre-
column using a binary solvent gradient of 0 to 100% methanol in water for 10 min and
100% methanol for another 10 min. The collection mass range was 100-1500 m/z in
profile scan mode to avoid missing uncommon mass adducts. The probe and source
temperatures were 500 and 130 °C, respectively.
Mass spectrometry assay development and validation. Pure standards of highest
available purity were prepared in methanol to 10 M final concentration. Five l of the
solution was loaded on the same LC system using identical gradient settings. The results
were then compared with the mass peaks from real samples and other known compound
standards for matched retention time and monoisotopic exact mass patterns. Most
identifications were performed using MarkerLynx or XCMS. For ergosterol, lanosterol
and pentoporphyrin, standards were also analyzed by LC-MS (Figs. 2,3,S1).
In vitro small metabolite-protein binding assay Purified protein kinases (in 50 mM
Hepes pH 7.5, 150 mM NaCl, 0.1% Triton X-100, 30% glycerol) were either kept on ice
(native) or boiled for 10 min (denatured) before distribution in 10 l aliquots. One l of
ergosterol stock (in 10% DMSO) or pentaporphyrin stock (in methanol) was added from
a 2-fold dilution series. After incubation for 15 min at 25°C, the mixture was loaded to
Zeba desalting microcolumn (Pierce) for 2 min. Ninety l of methanol was then added to
the flowthrough and vortexed briefly before loading on mass spec for quantification. A
standard curve was constructed using the loading standards diluted 1000 fold in
methanol. The quantification method was optimized with 10 M ergosterol or
pentaporphyrin on a TSQ Vantage triple quad (Thermo) run on SRM mode. The
23
monitored reaction transition was 379 m/z to 239 m/z for ergosterol and 331 m/z to 282
m/z for pentaporphyrin, respectively.
In vitro Kinase activity assay Kinase activity assay was performed on Corning 384-well
plates using Z’-lyte Kinase Assay Kit-Ser/Thr 6 Peptide according to manual instruction
(Invitrogen). The incubation time was 2 h at 25°C. Protein and ATP concentrations (1
mM) were determined empirically by titration according to manufacturer’s instruction.
Data analyses The mass spectra were first analyzed in MarkerLynx SCN639 to generate
a table with peak intensity, peak elemental composition, and associated proteins. Further
analyses were conducted in Microsoft Excel for peak intensity averages and the two-
tailed t-test comparison assuming unequal variance. Stringent thresholds were used (see
Fig. S1D for the MarkerLynx settings; thresholds are in Fig. S3C legend and Table 1).
The initial cut-off value of signal to noise ratio was set to ≥5. For XCMS analysis (Smith
et al., 2006), mass spectra were first centroided and converted to netCDF format in
MassLynx. UPLC and Q-TOF specific peak calling and grouping parameters were used.
Construction of interaction network All networks were visualized using Cytoscape
(Shannon et al., 2003). The raw interaction network was created by including all genes
that interact with one of the ergosterol-bound kinases and one of the proteins in the Erg
pathway. The interactions were gathered from BioGRID (Breitkreutz et al., 2008),
transcription factor binding data (Teichmann and Babu, 2004) and phosphorylome data
(Ptacek et al., 2005).
Gene function enrichment analyses The GO enrichment graph was created using
the raw interaction network after filtering phosphorylation and transcription factor
binding data. The genes in the middle layer of the resulting network were analyzed by
BiNGO (Maere et al., 2005).
24
Acknowledgements
We thank E. Davidov and X. Zhou for technical assistance, and Drs. H. Im, M. Bruno and L. Wu for
comments on the manuscript. This work was supported by grants from the NIH (M.S. and M.G.).
Author contributions The conceptual design for the project was developed by X.L. and M.S. Laboratory
experiments were performed by X.L. Data analyses were performed by X.L., T.G., K.Y. and M.G. The
manuscript was prepared by X.L. and M.S.
The data associated with this manuscript may be downloaded from ProteomeCommons.org Tranche using the following hashes: 3p4ChAFPxhPlDfJgNaNcwVkKDQB1mRUaC50kAIVOHpcnJBwKRuMEdHbKtOZ3EA7MAukmjVpJSU+iN+eEi/CV484usqMAAAAAAAdwfg== EYjiZQUEARIJ+bdkINP7NPioduy5VXECtZ8VGQNKcsCxIXUfz6XCP1lNzWeuYUjowgxjR7Ifax92C+20XOBo1676S3AAAAAAAAeCiw== BA0YXtmWY5hLUiccG9ZymmsSWCdWVK9u29B1L8l2qtMJaoRejYtR38ytdvxfrNcxwvQ8dy5G883wP4W+FCOiFTzycLsAAAAAAAMXdw== KRu5hE39SdTOOMcB/BL0oiY2hit1phPbiBvOHK3o302RiXW5WGW0YgHFkKwNK8xJ3PjGr7rvttu53A0kKHBCQj8LV3EAAAAAAAGkHw==
25
Figure Legends
Figure 1. Flow chart for the identification small metabolites bound to proteins.
Molecules bound to a strain expressing a protein of interest relative to a control stain are
identified using the scheme presented. See also Figure S1. and Table S1.
Figure 2. Identification of small metabolites associated with ergosterol biosynthetic
proteins.
(A) An overview of ergosterol biosynthesis pathway. Substrates and products of the yeast
ergosterol biosynthetic pathway (retrieved from MetaCyc with modification) are in blue
whereas protein enzymes are in black (included in this study) or gray (not included in this
study). Known interactions are linked by red curve labelled with Θ for inhibitory effects.
Interactions discovered in this study are indicated by green arrows from a metabolite to a
binding protein.
(B) LC plots of the small metabolites extracted from a protein (Erg6, red), the negative
control (Y258, purple), and the methanol solvent (green), respectively. Base peak
intensity (BPI, %) is plotted with retention time (in minute) of corresponding mass
spectra (shifted by 1% for clarity). Note BPI peaks are composite, not a good indicator of
the intensity of single molecular masses. The 100% BPI in counts is indicated on the
graph. All traces were smoothed by the Savitzky-Golay method using 2 passes of window
size of 3 scans.
(C) Combined average mass spectra of the 10.80-11.20 min region in B (indicated by a
blue block arrow). The masses of 3 Erg5-bound small metabolites are indicated along
with their chemical identities. The X-axis is the peak mass (amu); the Y-axis is the peak
intensity (%).
26
(D) Summary of the average peak intensity of two small metabolites listed in Table 1
extracted from each of the 21 ergosterol biosynthetic proteins (n=5). * indicates statistical
significance (two tail t-test, P value<0.01) in comparison with the negative control Y258.
Error bar = SEM.
(E) An LC plot showing detection of pentaporphyrin I (311.100 amu at 9.40 min) from
Mvd1 (red) but not from Y258 (purple) or methanol samples (green). Indent shows the
LC of pure pentaporphyrin I.
(F) Combined mass spectra of the LC peak region in E. Color labels are as in E. X-axis is
shifted by 0.05 amu for clarity. The indent profile shows the mass spectrum of pure
pentaporphyrin.
(G) In vitro binding curves of pentaporphyrin and Erg proteins. Each binding curve was
subject to fitting comparison (P value< 0.01) to a saturable binding curve (specific) or a
straight line (non-specific). Binding constants Kd, Bmax and curve fitting R2 are
indicated (in M) on each graph. Stoichiometry (metabolite:protein) is also indicated.
See also Fig. S2.
Figure 3. S. cerevisiae protein kinase-small metabolite interaction.
(A) A total of 103 of 129 kinases were analyzed in this study. Kinases not tested are
indicated in gray. The 20 kinases that bound small metabolites are in red.
(B) An LC plot of the small metabolites extracted from a kinase (Kin4, red), the negative
control (Y258, purple), and the methanol (green). The focused retention time region (in
minute, zoomed in 4x) is indicated above the trace. Graph labels are as in Fig. 2B.
27
(C) Combined average mass spectra of the 10.9 to 11.1 min region in B. Graph label is as
in Fig. 2C (n=3 for Kin4; n=9 for Y258 and methanol). The peak corresponding to
ergosterol is marked by *.
(D) An example showing identification of a bound metabolite as ergosterol. The mass
spectra of pure ergosterol, pure ergocalciferol, and one of the small metabolites extracted
from protein kinase Ypk1 shown on right and their respective UPLC on left. The
elemental composition is indicated respective mass peaks. Graph label is as in B,C.
See also Figure S3 and Table S2.
Figure 4. Detailed analysis of several kinase-small metabolite interactions.
(A) In vitro binding analysis of ergosterol and several protein kinases. The curve-fitting
was done in GraphPad Prism 5. Error bars are SEM (n=3). Statistic comparison of curve
fitting between a straight line for non-specific binding (null) vs. one-site specific binding
was used to determine specific binding pattern (P value<0.05). The R2 (unweighted) was
0.918, 0.908, and 0.933 for Hal5, Rck2, and Ypk1 respectively. The ergosterol-binding
characteristics of their protein kinases are listed below.
(B) Protein kinase activity of Ypk1 is stimulated by the addition of ergosterol. Ypk1
protein was purified from wild type (BY4741) cells grown in the presence or absence of
2 mM ergosterol during galactose induction of protein expression, or from ergosterol
deficient yeast (erg4). Equal amounts of purified protein were tested in each assay. The
relative activity was determined using a Sgk1-specific kinase assay. Error bars=SEM,
n=4.
(C) Levels of Ssk22 and Ypk1 in yeast. a) Ssk22 and Ypk1 were purified from equal
amounts of wild type (BY4741) and ergosterol-lacking mutant (erg4) cells with or
without 0.4 mM ergosterol. In 3 independent experiments, Ssk22 cannot be detected in
28
erg4. b) Immunoblot of Ssk22 and Ypk1 from yeast cell lysates over 7-hour after
galactose induction. Proteins were probed with rabbit IgG (1:10,000 dilution of 10 mg/ml
stock). Equal amounts of protein were loaded; erg4 strains produce less protein as
indicated by relative abundance listed below (% of wild type).
(D) Cell growth (absorption at 600 nm) is affected by the ergosterol-repressing drug
fluconazole in mutants of ergosterol binding protein kinases. Error bars are SEM, n=8.
Dotted lines and gray legends are mutants of protein kinases that did not bind ergosterol
in this study.
See also Fig. S4.
Figure 5. Integrative interactomes of proteins and small metabolites.
(A) top, A protein-protein interaction network showing intermediate proteins (green
circles) that link ergosterol-binding protein kinases (purple diamonds) from this study
and ergosterol biosynthetic enzymes through known physical or genetic interactions or
common phenotypes (red hexagons), The edge colors denote interaction types: green for
physical, blue for genetic and phenotypic, purple for phosphorylation, red for metabolic,
olive for transcription factor binding; Middle, A subnetwork showing a group of
intermediate proteins phosphorylated by ergosterol-bound protein kinases and regulate
ergosterol enzymes through transcription and phosphorylation; Bottom, A subnetwork
showing only ubiquitin (Ubi4)-interacting intermediate proteins and ergosterol enzymes.
(B) Gene ontology (GO) enrichment analysis of the 137 intermediate proteins in A top (P
value <0.01 for hypergeometric test against GOSlim_Yeast).
See also Table S3.
29
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Tab
le 1
. Sum
mar
y of
iden
tifi
ed s
mal
l met
abol
ites
ass
ocia
ted
wit
h e
rgos
tero
l bio
syn
thet
ic p
rote
ins.
Er
gost
erol
5
,7,2
4(2
8)-
ergo
stat
rien
ol
lan
oste
rol
4,4
-dim
eth
yl-
zym
oste
rol
5a-
chol
esta
-8
,24
-die
n-3
-on
e ep
iste
rol
(S)-
2,3
-ep
oxyl
squ
alen
e P
enta
por
ph
yrin
R
T (
min
) 1
0.8
4
13
.47
1
1.0
6
11
.03
1
1.8
9
10
.99
9
.95
9
.40
M
ass
(am
u)
37
9.3
37
3
79
.33
7
40
9.3
84
3
95
.36
8
38
3.3
28
3
81
.35
3
42
5.3
79
3
11
.10
0
Elem
ent
com
pos
itio
n
C28
H4
3*
C28
H4
3*
C30
H4
9*
C29
H4
7*
C27
H4
3O
C2
8H
45
* C3
0H
49
O
C20
H1
5N
4
Erg1
P
Erg2
P
Erg3
P
S
Erg4
P
Erg5
S
Er
g6
Erg7
P
S
Erg9
Er
g11
S
Er
g24
P
Erg2
5
S
Erg2
6
P
Er
g27
H
mg1
Id
i1
Mvd
1
S
Erg
enzy
mes
not
bou
nd
to
any
kn
own
inte
rmed
iate
s or
pen
tap
orp
hyr
in:
Erg 8 Erg10 Erg12 Erg13 Erg20
Gra
y sh
ade
deno
tes
a sm
all m
etab
olite
-pro
tein
ass
ocia
tion
iden
tifie
d in
this
stu
dy. “
P” a
nd “S
” ind
icat
es k
now
n pr
oduc
t and
sub
stra
te, r
espe
ctiv
ely.
Boun
d pr
otei
ns fo
r erg
oste
rol a
nd p
enta
porp
hyrin
had
an
enric
hmen
t gre
ater
than
3-fo
ld a
nd 1
000-
fold
, res
pect
ivel
y, a
nd th
e re
mai
nder
wer
e
enric
hed
grea
ter t
han
5-fo
ld.
All
boun
d m
etab
olite
s ha
d a
t-tes
t (tw
o ta
iled
uneq
ual v
aria
nce
in re
lativ
e to
the
nega
tive
cont
rol)
p-va
lue
belo
w 0
.05;
for e
rgos
tero
l and
pen
tapo
rphy
rin p
-val
ues
of le
ss th
an 0
.01
and
1E-1
0 w
as u
sed.
* in
dica
tes
a de
hydr
ated
form
of t
he e
xpec
ted
form
ula
1) Methanol extraction
Wash
beadbead
Lysis + IgG beads
2) SDS extraction
bead
SampleSample
UPLC-APCI-MS
TIC
%
m/zm/z 100100--15001500
* * *
SDS-PAGE
IgG
ControlControl
time
LC
MS
TTime8.00 9.00 10.00 11.00 12.00
%
0
100 Erg6TOF MS AP+
BPI1.91e4
10.01
9.52
9.93
10.45 11.69
11.52 11.878.31 10.84
Y258MeOH
Overlay step 1%
m/z385 390 395 400 405 410 415
%
0
100%
0
100
%
0
100Erg6 TOF MS AP+
513395.368dimethylzymosterol381.353
episterol 409.384lanosterol
Y258 TOF MS AP+ 513
MeOH TOF MS AP+ 513
Hmg2
-
Erg20
Erg20
-
-
Erg27
Erg4
Erg25
Erg25
Erg2
Erg25
Erg26
Erg5
Erg25
Erg6
Erg25
Erg25
Erg3
Erg27
Erg26
Idi1
2 dimethylallyl diphosphate
Mvd1
4,4-dimethyl-5--cholesta-8,14,24-trien-3--ol
Hmg1
lanosterol
Erg13
2 acetoacetyl-CoA
2 all-trans-farnesyl diphosphate
Erg82 mevalonate-5-phosphate
Erg11
2 mevalonate-diphosphate
Erg9
2(S)-3-hydroxy-3-methylglutaryl-CoA
4 acetyl-CoA
Erg24
2 isopentenyl diphosphate
(S)-2,3-epoxysqualene
Erg10
squalene
Erg7
presqualene diphosphate
2 geranyl-diphosphate
4,4-dimethylzymosterol
Erg12
2(R)-mevalonate
Erg9
5,7,24(28)-ergostatrienol
episterol
-formyl-4-methyl-5-cholesta-8,24-dien-3-ol
ergosterol
3-keto-4-methylzymosterol
4--methylzymosterol
4-carboxy-4-methyl-5-cholesta-8,24-dien-3-ol
zymosterol
-hydroxymethyl-4-methyl--cholesta-8,24-dien-3-ol
-carboxy-5-cholesta-8,24-dien-3-ol
-formyl-5-cholesta-8,24-dien-3-ol
fecosterol
4-hydroxymethyl--5-cholesta-8,24-dien-3-ol
5-cholesta-8,24-dien-3-one
Erg1
A
5,7,22,24(28)-ergostatetraenol
B
C
DE
F
G
m/z310 311 312
%
0
100Mvd1TOF MS AP+
535
311.100
312.14
Y258MeOH
311.08
312.06
313.12
Time (min)9.00 9.20 9.40 9.60 9.80
%
0
100
Mvd1TOF MS AP+ 311.1 0.10Da
120
9.40
Y258 MeOH
9.45pentaporphyrin
contro
lErg
1Erg
2Erg
3Erg
4Erg
5Erg
6Erg
7Erg
8Erg
9Erg
10Erg
11Erg
12Erg
13Erg
20Erg
24Erg
25Erg
26Erg
27Hmg1
Idi1Mvd
10
50
100
150
200
5-cholesta-8,24-dien-3-one*
contro
lErg
1Erg
2Erg
3Erg
4Erg
5Erg
6Erg
7Erg
8Erg
9Erg
10Erg
11Erg
12Erg
13Erg
20Erg
24Erg
25Erg
26Erg
27Hmg1
Idi1Mvd
10
50
100
150
200
(S)-2,3-epoxysqualene*
Cou
nts
Idi1
0 200 400 600
BMAX KD
0.02431.53
R2 0.98
Loading porphine (μM)
Cou
nts
(x10
)
Erg6
0 50 100 150
R2 0.98
BMAX KD
0.0228.06
Erg27
00
BMAX KD
0.04634.51
R2 0.96 Erg12
0
R2 0.97
200 400 600200 400 600
Loading porphine (μM)
Loading porphine (μM) Loading porphine (μM)
6
Cou
nts
(x10
)6
Cou
nts
(x10
)6
Cou
nts
(x10
)6
5.0
4.0
3.0
2.0
1.0
0
2.5
2.0
1.5
1.0
0.5
0
6.0
4.0
2.0
8.0
6.0
4.0
2.0 0
(0.74:1) (1.05:1)
(2.05:1)
CDC5
ATG1MPS1
CD
C7
RCK1
RCK2
PRR1
YKL171W
HRR25ELM1
IRE1
GCN2
KSP1
PKH1PKH2 RIM
15IP
L1
SWE1 PTK2
PTK1
AKL1
HSL1
MEK1KIN2KIN1
YCK3ENV7ISR1YPL150WSKY1
RIO2RIO1
CAK1
SIP1
YAK1
CBK1
TDA
1PKC1
RA
D53 VH
S1SK
S1
PRK1ARK1
SNF1
CMK2CMK1
KIN3MKK1MKK2STE7PBS2
YCK2YCK1
SAK1
TOS3
BCK1
BUD32
ALK2
SMK1
RIM11MRK1
MCK1YGK3
KNS1IME2
DBF20DBF2
YBR028C
PKH3
CKA2CKA1
CHK1
DUN1
SKM
1
CLA4
STE20SPS1
CDC15STE11
SLN1ALK1
TAF1
CTK1
PHO85
FPK1KIN82
KK
Q8
HA
L5 SAT4
PRR2
KCC4GIN4
PKP1TEL1
PSK1PSK2
YKL161C
SLT2
HOG
1
SGV1
KIN28SSN3CDC28
YPK2YPK1
SCH9
YPL141
C
KIN4
HRK1
YDL025C
KSS1
FUS3
TPK2
CTK3BUB1
VP
S34
TPK1TPK3
SNF4
PKP2
VPS1
5
CTK2
MEC1
SIP2GAL83
TOR1TOR2
SSK
22
SSK
2
CK1
AGC
CMGC
CAMK
‘Yeast’
STE
STE
CAMK
B100Kin4
Y258
MeOH
Time (min)
%
0
100
%
0
100
%
0
TOF MS AP+ BPI
3.42e3
TOF MS AP+ BPI
3.42e3
TOF MS AP+ BPI
3.42e3
8.00 10.00 12.00
10.98
Kin4TOF MS AP+
583
379.339
Y258 TOF MS AP+ 583
MeOHTOF MS AP+
583
*
385.219
m/z (amu)250 275 300 325 350 375 400 425 450
%
0
100
%
0
100
%0
100
x4
C
D
m/z (amu)
%
0
100
%
0
100
%
0
100Ergocalciferol
3.31e4
397.348379.339
Ergosterol
3.85e3
379.339397.35
Ypk1
425
379.339
[C28H42-H2O+H +][C28H44O+H +]
Time (min)
%
0
100
%
0
100
%
0
100Ergocalciferol
TOF MS AP+ 379.34 0.01Da
2.69e3
10.91
Ergosterol
681
11.01
Ypk1
80.5
10.99
LC MS
A
A B
D
10 -1 10 0 10 1 10 21.0.0
1.5.5
2.0.0
2.5
3.0.0WT - ergosterolWT + ergosterol
erg4 - ergosterol
Ergosterol (M)
Rel
ativ
e ac
tivity
(fol
ds)
0 5 10 15 200.0
0.2
0.4
0.6
0.8
1.0
40 80
WT atg1dun1hal5
kcc4 psk2rck2ssk22
swe1yak1ypk1
Fluconazole (g/ml)
A 600
at 4
2 hC
kD (M)Bmax
(ergosterol: protein)Hal5 6.771±1.055 0.982±0.068Rck2 4.739±0.744 1.036±0.067Ypk1 17.90±3.54 1.100±0.119
0 10 20 30 40 50
0.0
0.5
1.0Hal5Rck2Ypk1
Loading ergosterol (M)
Bin
ding
ratio
(erg
oste
rol :
pro
tein
)
ERG5
ERG6 ERG9ERG8ERG7
HMG1
ERG1
MVD1
IDI1
HAL5
SSK22
MCK1
YCK2
PRR2
SAT4
ERG26
ERG20ERG24
ERG11
ERG12ERG25
ERG13
ERG10
ERG2
GZF3
MGA1ERG27
ERG4YAP6
MBP1
ACE2
TPK1
ERG3
A
B
AFT1PLP2GET2
MNN4
SHR3YPS1
ERP2
ESA1
UFD4
ERV25
RPN4
CLA4
ARP4
UPC2
PDR3
GUP1
RIM101
POP2
SAC1
SUT1
PIL1
UBX7
VPS1
FUS3
TEX1
YAF9
RTT109
BST1
MMS22
SWI4
CSM3
ABF1
GIM4RAD52GAS1CLB3
SLX8MOB1BEM2CDC37
RMI1
YPT6HFI1
RPC40
RSC8CSF1
ISC1MET18
RAD27
SHP1SEC28
CTF8
ERG13
ERG11
ERG12
ERG8
IDI1
ERG1
MVD1
HMG1
ERG2
ERG9
ERG10
ERG20
ERG27
ERG26
ERG25
ERG6
ERG5
ERG3
ERG4
ERG7
ERG24
HEK2
TPK1
SAS10
SLT2
CAX4
RAD3
MED8
ARO1
TAF12
PAF1
RPB3
PMT1
BRE1
RVB2
MNN1
ORM2
SSD1
GET1
GIM3
ACE2
PPH22
STP1
PTK2
TOR1
STE20
NCS2
FAR3SLX5BRE2TOS3 HAP4BRE5SOH1 PHO80
PHO85
AVT4
SPF1
RTN2
SSA1
SNL1
SLA1
BUL1
HCM1
INO2
AGE1
MST27
SWI6
YAP6
CDC13
LEO1
MGA1
SEC2
YNL010W
SKN7
MBP1
MSN2MSN4PCL9SNF1
STE12
YBR159W
HSC82
SUR4
SCS7
RIC1
RGP1
INO4
MCK1KIN4
PRR1
KCC4
KIN82
PRR2
YPK1
SAT4
HAL5
YCK2
YAK1SCH9
RCK2
CBK1
SSK22
HSP82YPC1
UBI4TEC1
PRP6
NAP1
APL6
APS3
ARV1
RTG3
APM3
PDR5
TLG2
SWI5
OPI3
GZF3
SEC14
INP54
(21) (15)
(137)
transferase activity
transcription regulator activity
protein kinase activity
DNA binding
MFprotein modification process
vesicle−mediated transport
transport
signal transduction
response to stress
BP
transcription
GO
cell cycle
endomembrane system
nucleus
cytoplasm
endoplasmic reticulum
membrane
Golgi apparatus
CC
1.00E-2 <1.00E-7
VPS1
VPS27
YCF1
YDR266C
YGR012W
YGR130C
YLR241W
YCK1
YGR126W
YBR159W
UFD4UBX7
TRM3TRP1
UME6
UBX5UBR1
URA8
UBP15
VMA5
RVS167
SAC6
SEC2
SEC28
SIP5
SIT4
SLA1
SLM1
SHE3
RTN2
STE20STE23
STB2
STE5 TPS2TEC1STP22STT4 TOR2
SSA1
SNF1SOG2
SPT16SRO7
SMF1
MVD1
ERG12
ERG13
ERG9
ERG11
ERG1
IDI1
ERG26
ERG27
ERG3
ERG6
ERG5
ERG4
UBI4ADP1
AKR1
ZRC1
AVT1
AVT3
BMH2
CAT8
CBF5
CDC12
CDC28
CDC39
YMR115W
YMR295C
YNL045W
YPK2
ARP2
AMD1
YPR091C
ALY2
YNL217W
ATG9
HSF1HRT1
HOG1HEF3
HSL1
GLY1GPH1GRE3GSY2GUP1GUS1GZF3HAS1
HSP42HUL4
INP54
KEM1
IRC6
IST2
ECM10
ERV25FOL2
FZO1GCD7
ERP2
GDB1
PRR1
MCK1
KIN82
HAL5
KCC4
KIN4
DRE2
DCS2
CYC7
CHS1
DNM1
DCS1
CMK1
PRR2
RCK2
SAT4
SCH9
SSK22YAK1
YCK2
CBK1
RPN3
RET1
PGA2
YPK1
PFK26
PHO13
RPN1
PTP2
PFK2
ROD1
PIL1
PDR5
OLA1
NSR1
MYO2
MSN2
MIH1
MCM2
KSP1
OPI1
PDX3
(13)(15)
(119)
-4 -2 0 2 4 6 8 10 120
400
800
1200
1600
methanol
chloroform
318 171172
total=661
LogP value
Mol
ecul
ar W
eigh
t (D
alto
n)A B
C
Time (min)2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00
%
0
100 TOF MS AP+ BPI
1.10e4
10.84
0.28
10.45
10.039.27
6.299.91
11.40
12.60
11.6911.87
12.92
13.38
D
2 4 6 8 10 129
10
11
12
pentaporphyrin
cyclosporin A
ergosterol
VitD2
VitD3
-tocotrienol
-tocotrienol
-tocotrienol
lanosterol
-tocopherol
VitK1Squalene
xLogP value (Hydrophobicity)
Peak
Ren
tent
ion
Tim
e (m
in)
A
Time (min)9.50 10.00 10.50 11.00 11.50 12.00 12.50
%
0
100TOF MS AP+
379.338 0.10Da455
10.84 Erg4
Y258
B
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028C
YCK1YCK2
YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047W
YPK1YPK2
YPL141C
YPL150W
0
20
40
60
80 R11.67M339.29
Env7
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028C
YCK1YCK2
YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047W
YPK1YPK2
YPL141C
YPL150W
0
50
100
150 R11.01M379.34
Cbk1Hal5 Kcc4
Kin4
Mck1 Prr1
Prr2
Rck2
Sat4
Sch9Ssk22
Yak1 Yck2
Ypk1
Kin82
ergosterol
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028C
YCK1YCK2
YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047W
YPK1YPK2
YPL141C
YPL150W
0
20
40
60
80
100 R11.32M547.48
Env7
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028C
YCK1YCK2
YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047W
YPK1YPK2
YPL141C
YPL150W
0
20
40
60
80
100 R11.54M549.49
Env7
Vhs1Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2
RIM11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028CYCK1
YCK2YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047WYPK1
YPK2
YPL141C
YPL150W
0
20
40
60 R11.57M575.51
Cbk1 Hal5 Sat4
Prr1
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028CYCK1
YCK2YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047WYPK1
YPK2
YPL141C
YPL150W
0
50
100
150 R11.7M577.52Env7
Kcc4Kin82 Pkh3
Prr1
Rck2
Sat4
Vhs1Ypk1
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2
RIM11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028CYCK1
YCK2YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047WYPK1
YPK2
YPL141C
YPL150W
0
5
10
15
Kin82
R11.29M663.46
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028C
YCK1YCK2
YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047W
YPK1YPK2
YPL141C
YPL150W
0
20
40
60 R11.18M688.50
Env7
Hal5
Pkh3
Prk1
Rck2
Sat4Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2RIM
11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE20STE7SWE1
TAF1TOS3
TPK2TPK3
VHS1YAK1
YBR028C
YCK1YCK2
YCK3
YDL025C
YKL161C
YKL171W
YMR291W
YNR047W
YPK1YPK2
YPL141C
YPL150W
0
20
40
60
80 R11.37M716.53
Env7
Kin4
Prr1 Prr2
Sat4
Sch9
Sky1
Vhs1Ypk1
Cou
nts
AKL1ALK2
ARK1ATG1
BCK1BUB1
BUD32CAK1
CBK1
CDC28CDC5
CDC7CHK1
CKA1CKA2
CLA4CMK1
CTK2CTK3
DBF2
DBF20DUN1
ENV7FUS3
GAL83GCN2
GIN4
HAL5HOG1
HRR25IM
E2IPL1
IRE1ISR1KCC4
KIN1
KIN28KIN
3KIN
4KIN
82KKQ8
KNS1KSP1
KSS1MCK1
MEK1MKK1
MPS1MRK1
PBS2PKH2
PKH3PKP1
PRK1PRR1
PRR2PSK2
PTK1PTK2
RCK1RCK2
RIM11RIO
1RIO
2SAK1
SAT4SCH9
SGV1SIP1
SIP2SKM1
SKS1SKY1
SLT2SMK1
SNF1SNF4
SPS1SSK2
SSK22SSN3
STE200
500
1000
1500
Ste20
R9.45M311.10
pentaporphyrin I
Cou
nts
A
B C
0.0 1.0 2.0 3.0 4.0 5.00
10
20
30
40
log (Protein abundance) (a.u.)
Num
ber
Total (124)
Ergosterol Binders (20)All Binders (37)Non-binders (86)
Gaussian distribution fit Best-fit values Total Ergosterol binders All Binders Non-binders AMPLITUDE 36±3.7 8.4±0.65 15±1.1 23±2.2 MEAN 2.6±0.07 2.7±0.04 2.8±0.035 2.5±0.07 SD 0.57±0.07 0.41±0.04 0.41±0.035 0.66±0.07 R2 0.91 0.96 0.97 0.92
Batch 1
Batc
h 2
Batch 1
Batc
h 2
r=0.7845P<0.0001
r=0.6679P<0.0001
D
E
0 10 20 300
50
100
150
Ypk2Denatured Ypk1
Loading ergosterol (M)
Cou
nts
denatured Atg1 Psk2 Ypk2 Ypk1
r 0.73 0.91 0.94 0.92
1
Figure S1. Characteristics of the LC-MS assay, related to Figure 1.
(A) The hydrophobicity distribution of yeast metabolites. ALOGPS is used to calculate
the theoretic logP values of yeast metabolites based on a yeast metabolic network
constructed previously (Herrgard et al., 2008). The logP values of methanol and
chloroform are used to separate the whole metabolome into 3 regions. The number of
metabolites in each region is labelled.
2
(B) A plot of compound hydrophobicity and their retention time peaks on UPLC. A
mixture of 10-100 picomoles of each of 11 metabolites in methanol was resolved;
pentaporphyrin was analyzed separately. Retention time peaks were determined by
MarkerLynx after mass peak identification. An average from 3 runs is plotted with SEM.
Also loaded but not detected are retinol A and -carotene.
(C) An LC-MS chromatogram of yeast total extract with the method used in this study.
Red trace, yeast total extract; green, solvent.
(D) Peak calling settings in MarkerLynx. A screen shot shows the MarkerLynx parameter
settings used in this study.
3
Figure S2. Targeted peak enrichment and purification of ERG enzymes, related to
Figure 2.
(A) Coomassie Blue stained gels of the ergosterol biosynthetic proteins purified in this
study. The proteins are indicated at the top and their expected sizes (in kDa) at the
bottom. The negative control (Y258) is shown in red. The IgG heavy chain (H) eluted
from the beads is indicated (red triangle).
(B) An overlaid LC plot showing detection of ergosterol (379.338 amu at 10.84 min)
bound Erg4 relative to the negative control Y258. X-axis indicates retention time (in
minute) and Y-axis is the intensity (in %).
4
Figure S3. Untargeted profiling of small metabolites bound to yeast protein kinases,
related to Figure 3.
5
(A) Coomassie Blue-stained gels of 14 protein kinases purified in this study after
extraction of small metabolites. The proteins are indicated at the bottom. The IgG heavy
chain (H) eluted from the beads is indicated (red triangles).
(B) An intensity scatter plot of the 96 matched mass peaks between biological replicates
batch 1 and 2. The linear correlation coefficient and its P value are indicated on the
graph.
(C) An intensity scatter plot of the 48 scored protein kinase-small metabolite interactions
(unequal variance t-test P value < 0.01, signal/noise > 1000). Graph label is as in B.
(D) An histogram plot of the abundance distribution of metabolite-binding proteins. All
proteins used in this study are plotted. Protein abundance was measured in ImageJ
(Rasband, 1997-2009). The band intensity relative to the 55 kD marker band was divided
by the expected molecular weight to give arbitrary unit (a.u.). Curve fit to a Gaussian
distribution was done in Graphpad Prism 5 with statistic characteristics listed below. The
distribution is not significantly different between any of two groups (P value<0.05,
Bonferroni's Multiple Comparison Test after one-way ANOVA test).
(E) An overview of the specific interactions of small metabolite and protein kinase found
in this study. The mass spectral peak intensity of protein kinase-bound small metabolites
is plotted across 103 protein kinases included in this study. The average intensity of 3
replicates is shown with s.d. as error bars. The corresponding peak intensity from the
negative control was 0 for all ten peaks.
6
Figure S4. In vitro non-specific ergosterol binding curves of non-ergosterol binding
proteins, related to Figure 4. The binding assay was set up as described in Fig. 4 except
the Ypk1 protein was boiled 10 min before testing. Curve fit to a straight line indicates
non-specific binding. The r-values are listed below.
7
Table S1. The list of mass features detected by the UPLC-QTOF method in this
study, related to Figure 1.
The metabolites were extracted in pure methanol from Y258 yeast cells grown in SC-
URA medium supplemented with glucose. Methanol was used as a negative control.
The LC chromatogram is show at the top as an average of 10 technical replicates. The
mass spectra were processed in MarkerLynx using the same settings stated before. Out
of 538 features in total, only enriched features relative to control are listed after a
statistical t-test analyses (two tailed unequal variance, P value<0.01).
8
Table S2. Molecular characteristics of the 10 protein kinase-bound mass peaks
identified in this study, related to Figure 3.
Peaks
Element Composition
top hit PPM Identity Bound Kinases R9.61M311.099 C20H15N4 -9.3 Pentaporphyrin I [M+H]+ Ste20 R11.67M339.291 C21H39O3
C20H39N2O2
3
28
Monoacylglycerol [M+H‐H2O]+ Heneicosanedioic acid [M+H‐H2O]+ -Linolenoyl ethanolamide
Env7
R11.01M379.339 C28H43 7.6 Ergosterol [M+H‐H2O ]+ Cbk1 Hal5 Kcc4 Kin4 Kin82 Mck1 Prr1 Prr2 Rck2 Sat4 Sch9 Ssk22 Yak1 Yck2 Ypk1
R11.32M547.475 C35H63O4 C38H58O2
4
43
Diacylglycerol [M+H‐H2O]+ All-trans-retinyl linolate [M+H]+
Env7
R11.54M549.49 C35H65O4
3 Diacylglycerol [M+H‐H2O]+
Env7 Vhs1
R11.57M575.505 C37H67O4 1 Diacylglycerol [M+H‐H2O]+ Cbk1 Hal5 Sat4 Prr1 R11.7M577.523 C37H69O4 5 Diacylglycerol [M+H‐H2O]+ Env7 Kcc4 Kin82 Pkh3
Prr1 Rck2 Sat4 Vhs1 Ypk1
R11.29M663.458 C35H70NO8P C43H67O5
38
60
Phosphocholine (11:1(10E)/16:0) [M+H]+ Diacylglycerol [M+H]+
Kin82
R11.18M688.498 C37H70NO8P 9 Phosphatidylethanolamine [M+H]+ Env7 Hal5 Pkh3 Prk1 Rck2 Sat4
R11.37M716.529 C39H74NO8P 9 Phosphatidylethanolamine [M+H]+ Env7 Kin4 Prr1 Prr2 Sat4 Sch9 Sky1 Vhs1 Ypk1
The mass peaks are identified by their retention time (in minute) and exact atomic mass unit (in Dalton).
The top elemental composition hit, the mass accuracy (in ppm), the molecule identity, and the bound
protein kinases are also listed. Validated metabolites are in bold. Note the mono- and di-acylglycerides are
ambiguous in molecular identity. See Supplemental Experiment Procedure for metabolite assignment
method.
9
Table S3. KEGG pathway enrichment analysis of proteins that link ergosterol-
bound protein kinases and ergosterol biosynthetic proteins, related to Figure 5.
Genome Fraction
Sample Fraction e-score Description
KEGG Pathway sce00562 0.015334 9/137 0.004647 Inositol phosphate metabolism sce00600 0.016227 9/137 0.007119 Sphingoglycolipid metabolism sce00760 0.015334 8/137 0.022767 Nicotinate and nicotinamide metabolism sce00500 0.020545 9/137 0.038897 Starch and sucrose metabolism
Results summarized from GeneMerge analysis, see Supplemental Experiment Procedure for details.
10
Supplemental Experimental Procedures
Chemicals, reagents and labware Reagents used in the study were LC-MS grade or the
highest grade when necessary. All microcentrifuge tubes were protein LoBind tubes from
Eppendorf. Nitrile gloves were used for handling mass spec-related samples.
KEGG enrichment analysis The KEGG enrichment table using the same genes was
produced by GeneMerge (Castillo-Davis and Hartl, 2003). The enrichment cutoff (e-
score) was set to 0.05. The actual number of proteins with available annotation was 17
for KEGG pathway analysis (Kanehisa et al., 2008).
Metabolite assignment Accurate mass was used to search on METLIN database
(http://metlin.scripps.edu), with a tolerance of 0.01 Da for positive charged adducts
[M+H]+ and [M -H2O+H]+.
11
Supplemental references Castillo-Davis, C.I., and Hartl, D.L. (2003). GeneMerge--post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19, 891-892. Herrgard, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Bluthgen, N., Borger, S., Costenoble, R., Heinemann, M., et al. (2008). A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 26, 1155-1160. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., et al. (2008). KEGG for linking genomes to life and the environment. Nucleic Acids Res 36, D480-484. Rasband, W.S. (1997-2009). ImageJ (Bethesda, Maryland, USA,, National Institutes of Health).