Abstract
Metabolic reactions play important roles in organisms such as providing
energy, transmitting signals, and synthesizing biomacromolecules.
Charting unknown metabolic reactions in cells is hindered by limited
technologies, restricting the holistic understanding of cellular
metabolism. Using mass spectrometry-resolved stable-isotope tracing
metabolomics, we develop an isotopologue similarity networking
strategy, namely IsoNet, to effectively deduce previously unknown
metabolic reactions. The strategy uncovers ~300 previously unknown
metabolic reactions in living cells and mice. Specifically, we
elaborately chart the metabolic reaction network related to
glutathione, unveiling three previously unreported reactions nestled
within glutathione metabolism. Among these, a transsulfuration
reaction, synthesizing γ-glutamyl-seryl-glycine directly from
glutathione, underscores the role of glutathione as a sulfur donor.
Functional metabolomics studies systematically characterize biochemical
effects of previously unknown reactions in glutathione metabolism,
showcasing their diverse functions in regulating cellular metabolism.
Overall, these newly uncovered metabolic reactions fill gaps in the
metabolic network maps, facilitating exploration of uncharted
territories in cellular biochemistry.
Subject terms: Metabolomics, Mass spectrometry, Data processing
__________________________________________________________________
IsoNet, an isotopologue similarity networking strategy based on mass
spectrometry-resolved stable-isotope tracing metabolomics, enables ab
initio elucidation of unknown metabolites and their reactions, which is
applied to the glutathione metabolic reaction network.
Introduction
Metabolic reactions in which metabolites are biosynthesized or
degraded, lay the foundation of cellular biochemistry by fulfilling
energy to cellular functions, metabolizing nutrients to building blocks
for macromolecule biosynthesis, and producing messengers for cellular
signaling^[42]1–[43]3. In living organisms, metabolites are connected
to each other through one or multiple metabolic reactions and form the
metabolic reaction network that affords the biochemical architecture of
cellular metabolism^[44]4. Dedicated databases such as Kyoto
Encyclopedia of Genes and Genomes (KEGG)^[45]5 and REACTOME^[46]6 have
collected known metabolic reactions of various organisms, providing
valuable resources to understand cellular metabolism. In our previous
work, we demonstrated that metabolic reaction network is essential for
both known and unknown metabolite discovery^[47]7,[48]8. However, more
than 30% of gene sequences are functionally unannotated with a large
portion of them being considered to encode metabolic
enzymes^[49]9,[50]10. Thus, the metabolic network interwoven with
different metabolic reactions is largely incomplete, which restricts
our knowledge on the scope of cellular biochemistry.
To this end, both computational and experimental approaches have been
developed to expedite the discovery of previously uncharacterized
metabolic reactions^[51]11,[52]12. Computational prediction plays a
crucial role in prediction of hypothetical metabolic reactions by
leveraging existing knowledge of metabolic networks, enzyme functions,
and chemical transformations, which includes the homology-based enzyme
function annotation^[53]13, constraint-based genome-scale metabolic
modeling^[54]14, and machine learning-based prediction^[55]15,[56]16.
While its strength in a high-coverage of predicted reactions, only a
very small portion of them can be experimentally validated in organisms
of interest. In addition, computational prediction usually confines to
known metabolites, which restricts the scope of previously unknown
reactions being predicted. As a comparison, in vitro enzyme
activity-based metabolomics profiling represents one of the most common
experimental technologies for discovering previously unknown metabolic
reactions^[57]12,[58]17. For example, Sévin et al. implemented this
technology to characterize 1275 uncharacterized proteins in Escherichia
coli, and discovered 241 enzymes that catalyze metabolic reactions.
Among them, 29 metabolic reactions with 12 enzymes were experimentally
validated^[59]17. However, a major drawback of this approach is the low
recovery rate with less than 20% of enzymes being successfully
characterized, due to the prerequisites of purifying proteins,
concentrations, and availabilities of metabolites^[60]17. In addition,
non-enzymatic reactions which are integral parts of the metabolic
networks with essential roles in cellular metabolism, cannot be
captured by the enzyme activity-based metabolomics approaches.
Alternatively, stable-isotope tracing metabolomics involves introducing
isotopically labeled substrates (e.g., ^13C-glucose) into a living
organism^[61]18–[62]20. This allows for the tracking of the
incorporation of stable-isotopes (e.g., ^13C) into metabolites produced
by cellular metabolism^[63]21. Although in piecemeal ways, this
technology has increasingly become the method of choice to verify the
newly discovered metabolites and metabolic reactions based on
preconception of metabolic routes^[64]22,[65]23. For example, Fox et
al. recently used stable-isotope tracing metabolomics with
^13C[5]-valine confirming multiple previously unknown conjugations
between 3-hydroxypropionate and amino acids, and discovered a
shunt-within-a-shunt metabolic pathway for propionate degradation in
Caenorhabditis elegans^[66]24. In practice, stable-isotope tracing
metabolomics generally requires a prior knowledge of possible
metabolite targets, leading to the ab initio elucidation of previously
unknown metabolic reactions a great challenge^[67]21,[68]25.
Specifically, the implementation of stable-isotope tracing metabolomics
with a data-driven and hypothesis-free strategy for discovering unknown
metabolites and associated metabolic reactions in one study has not
been realized by far. Under stable-isotope tracing experiments,
metabolic reactions that take place in a living organism result in
specific isotope labeling patterns in metabolite products, termed as
isotopologue patterns^[69]20,[70]21,[71]25,[72]26. The substrate and
product metabolites within one metabolic reaction usually share similar
isotopologue patterns (Fig. [73]1a), which leaves ample potential for
previously unknown reaction discovery. Thus, we reason that previously
unknown metabolic reactions can be deduced by comparing the
isotopologue pattern similarity between labeled substrate and product
metabolites.
Fig. 1. Reaction-paired metabolites tend to share similar isotopologue
patterns in stable-isotope tracing metabolomics.
[74]Fig. 1
[75]Open in a new tab
a Schematic illustration of generation of metabolic reaction-paired
metabolites and calculation of their isotopologue pattern similarity. b
Numbers of labeled metabolites and reaction pairs found in 293T cells
labeled with [U-^13C]-glutamine or [U-^13C]-glucose for 17 h. c
Isotopologue pattern similarity scores (S[ISO]) of reaction-paired
metabolites (asparagine/aspartate and aspartate/orotate) in 293T cells
labeled with [U-^13C]-glutamine (n = 6 biological replicates per
group). Values represent means ± SEM. d Isotopologue pattern similarity
scores (S[ISO]) of reaction-paired metabolites
(glutamate/α-ketoglutarate and α-ketoglutarate/cis-aconitate) in 293T
cells labeled with [U-^13C]-glucose (n = 6 biological replicates per
group). Values represent means ± SEM. e Percentages of high
isotopologue similarity scores of two metabolites (S[ISO] ≥ 0.7) in
reaction pairs (RPs) and non-reaction pairs (non-RPs), respectively, in
293T cells labeled with [U-^13C]-glutamine or [U-^13C]-glucose. The
metabolite pairs with more than 7 steps of reactions in metabolic
reaction network were defined as non-reaction pairs. Source data are
provided as Source Data files for Fig. 1b–e.
In this work, we develop an approach, namely isotopologue similarity
networking (IsoNet), for charting unknown metabolic reactions through
mass spectrometry-resolved stable-isotope tracing metabolomics. Using
IsoNet, we construct the isotopologue similarity networks for labeled
metabolites in live cells and mice, and discover hundreds of putative
unknown metabolic reactions. Specifically, we complete the metabolic
reaction network associated with glutathione metabolism, which includes
10 metabolic reactions. Most importantly, we uncover a previously
uncharacterized transsulfuration reaction by which γ-Glu-Ser-Gly was
synthesized directly via transsulfuration of glutathione. These
findings of unknown reactions fill the previously unexplored metabolic
network, complementing our understanding of the metabolic maps in
cellular biochemistry.
Results
Reaction-paired metabolites tend to share similar isotopologue patterns
The basic principle of our method is that the reaction-paired
metabolites have higher tendency to be similar in isotopologue patterns
in the mass spectrometry-resolved stable-isotope tracing metabolomics.
To demonstrate this principle, we first analyzed 293T cell samples
labeled with isotopic tracers such as [U-^13C]-glutamine and
[U-^13C]-glucose using a high-resolution mass spectrometer. For
^13C-labeled metabolites, we retrieved their metabolic reaction
relationship from metabolic reaction network curated using the KEGG
database, which results in reaction pairs (RPs) between labeled
metabolites (Fig. [76]1a). Then, we extracted corresponding
isotopologues for individual metabolites and characterized the
isotopologue pattern similarity for the reaction-paired metabolites
(Fig. [77]1a). To calculate isotopologue similarity scores (S[ISO]), we
developed a scoring algorithm applicable for three types of metabolic
scenarios which are based on the numbers of carbon and labeled
isotopologues between metabolite pairs (Supplementary Fig. [78]1a–d;
see “Calculation of isotopologue pattern similarity” section in
“Methods”). As a result, we identified 174 ^13C-labeled metabolites
within 199 reaction pairs in 293T cells labeled with [U-^13C]-glutamine
(Fig. [79]1b and Supplementary Data [80]1). Likewise, when
[U-^13C]-glucose was used for labeling, 242 ^13C-labeled metabolites
and 445 reaction pairs were characterized in 293T cells (Fig. [81]1b
and Supplementary Data [82]1). Further examination showed that
reaction-paired metabolites, such as asparagine and aspartate,
aspartate and orotate, displayed similar isotopologue patterns with
S[ISO] being calculated as 0.83 and 0.87, respectively (Fig. [83]1c).
This observation was also seen in cells labeled with [U-^13C]-glucose.
For examples, reaction-paired metabolites such as glutamate and
α-ketoglutarate, α-ketoglutarate and cis-aconitate in TCA cycle, had
isotopologue similarity scores of 0.81 and 0.96, respectively
(Fig. [84]1d).
Next, we extended the isotopologue similarity calculation to all
^13C-labeled metabolites in RPs and non-RPs (see “Isotopologue
similarity between reaction-paired and non-reaction paired metabolites”
section in “Methods”). In cells labeled with [U-^13C]-glutamine, more
than 60.7% of labeled metabolites in reaction pairs have an
isotopologue similarity score larger than 0.7, while only 18.6% of
labeled metabolites in non-reaction pairs met this criterion
(Fig. [85]1e). The results in the [U-^13C]-glucose labeling experiment
showed the similar conclusion wherein ^13C-labeled metabolites in RPs
(45.7%) had higher chances of similar isotopologue patterns than those
in non-RPs (14.6%). Closer examination of the relationship between
isotopologue similarity and metabolic reaction steps revealed that the
isotopologue similarity score decreased as steps of reactions
increased, either in [U-^13C]-glutamine or [U-^13C]-glucose labeling
experiments (Supplementary Fig. [86]1e). Overall, these data
demonstrated that the reaction-paired metabolites in principle tend to
share similar isotopologue patterns in stable-isotope tracing
metabolomics.
Discovery of previously unknown metabolic reactions through isotopologue
similarity networking
We further developed an isotopologue similarity networking approach,
namely IsoNet, by combining mass spectrometry-resolved stable-isotope
tracing metabolomics and isotopologue similarity networking between
labeled metabolites (Fig. [87]2a). We demonstrated that IsoNet enables
to discover previously unknown metabolic reactions in living organisms.
The detailed description of the IsoNet algorithm is provided in “The
workflow of IsoNet” section of “Methods”. In brief, IsoNet includes
four major steps: (1) annotation of labeled metabolites; (2)
isotopologue similarity networking; (3) unknown reaction discovery; and
(4) elucidation of unknown reactions and structures (Supplementary
Fig. [88]2a–d). To demonstrate the IsoNet approach, we analyzed 293T
cells labeled with a mixture of ^13C-tracers including
[U-^13C]-glutamine, [U-^13C]-glucose, and [U-^13C]-acetate. Then, we
constructed an isotopologue similarity network containing 841 nodes and
1544 edges (Fig. [89]2b). Specifically, one node represents one
^13C-labeled metabolite, while an edge represents that two labeled
metabolites shared similar isotopologue patterns and MS/MS spectra
(i.e., structural similarity). Among all labeled metabolites, 109 of
them were known metabolites, while the rest 732 ones were unknowns
(Fig. [90]2c and Supplementary Data [91]2).
Fig. 2. Discovery of previously unknown metabolic reactions through
isotopologue similarity networking.
[92]Fig. 2
[93]Open in a new tab
a Workflow for the isotopologue similarity networking approach. Red dot
represents known metabolites and blue dot represents unknown
metabolites. b The isotopologue similarity network in 293T cells
labeled with mixed tracers ([U-^13C]-glutamine, [U-^13C]-glucose, and
[U-^13C]-acetate) for 17 h. Nodes represent labeled known (red) and
unknown (black) metabolites; edges represent that two metabolites
shared similar isotopologue patterns and MS/MS spectra. c Numbers of
known and unknown labeled metabolites in the network. d Numbers of
known and unknown reactions in the network. The reactions between known
and unknown metabolites were defined as unknown I. The reactions
between unknown metabolites were defined as unknown II. Red dot
represents known metabolites and blue dot represents unknown
metabolites. e Retrieval of 86 known reactions from KEGG: the recall
rate (left) and reaction step distribution (right). f Statistics of
reaction classes for unknown reactions. g Top 6 ranked reaction types
with indicated atom exchanges in unknown reactions. Source data are
provided as Source Data files for Fig. 2c–g.
To elucidate metabolic reactions revealed by the isotopologue
similarity networking, we curated a metabolic reaction network, a delta
mass library, and a reference reaction library from the KEGG database
(Supplementary Fig. [94]2e and Supplementary Datas [95]3 and [96]4).
First, 86 reactions were identified as known reactions which were
consisted of two known metabolites (Fig. [97]2d and Supplementary
Data [98]2). To validate these known reactions, we retrieved their
metabolic routes from KEGG metabolic reaction network. The results
showed that 91% of known reactions (n = 78) were successfully recalled
(Fig. [99]2e). In addition, we revealed that the majority of these
reactions were found to take place within 1–3 steps of metabolic
reactions (Fig. [100]2e). For example, an isotopologue similarity
subnetwork, which consisted of 15 nodes and 25 edges, uncovered known
metabolic reactions among uridine diphosphate (UDP)-glucuronate,
UDP-glucose/UDP-galactose, uridine triphosphate (UTP), and cytidine
triphosphate (CTP) (Supplementary Fig. [101]3). These results
demonstrated that the isotopologue similarity networking strategy
enables to identify metabolic reaction relationships with a high
accuracy in living cells.
Next, we matched the experimental mass differences (Δm/z) between
paired metabolites in the isotopologue similarity network (n = 1544)
with the delta mass library. Each delta mass in the library is
associated with atom differences and reaction classes (Supplementary
Fig. [102]2e and Supplementary Data [103]3). Thus, we successfully
annotated a total of 322 unknown metabolic reactions with information
on atom differences and reaction classes (Fig. [104]2d and
Supplementary Data [105]2). They were classified as two types: unknown
I and unknown II. Unknown reaction type I is of unreported metabolic
relationship between known metabolites and unknown metabolites, while
unknown reaction type II is that between unknown metabolites. As a
result, 99 and 223 reactions were characterized as unknown reaction
type I and unknown reaction type II, respectively (Fig. [106]2d). To
elucidate these unknown reactions, we first examined their reaction
classes obtained from the delta mass library. Reaction classes
including redox reaction, transfer reaction, hydrolysis, lytic
reaction, isomerization, ligation reaction, and others, were identified
both in unknown type I and type II reactions (Fig. [107]2f). In
particular, the class of redox reaction was the most common one, with
42 and 73 redox reactions being identified in unknown reactions type I
and type II, respectively (Fig. [108]2f). Then, the atom difference was
used to search the reference reaction library (Supplementary
Data [109]4) to retrieve the relevant reference reactions.
Consistently, closer examination of atom differences between the
reaction-paired metabolites revealed that the most common unknown
reaction involved the exchange of an oxygen atom (e.g., hydroxylation;
Fig. [110]2g). Other reactions, such as exchanges of atoms of [H + N]
(e.g., amination), [H + N − O] (e.g., hydrolysis of amide), [2H + C]
(e.g., methylation), [2H + O] (e.g., hydration) and [2H] (e.g.,
reduction), were also among the top-ranked unknown reactions
(Fig. [111]2g).
Collectively, these results proved that the isotopologue similarity
networking approach enables to discover and elucidate unknown metabolic
reactions from stable-isotope tracing metabolomics data, which provides
a great potential to characterize previously unknown metabolic routes
of endogenous metabolites. To further demonstrate the applicability of
the methods in other biological systems, the results of IsoNet approach
on global stable-isotope tracing metabolomics datasets of immortalized
bone marrow-derived macrophage (iBMDM) cells and mouse liver were also
provided. Their isotopologue similarity networks and discovered unknown
reactions were also demonstrated using IsoNet (Supplementary
Fig. [112]4 and Supplementary Datas [113]5 and [114]6).
Glutathione-associated subnetwork reveals previously unknown metabolic
reactions
Glutathione (GSH) metabolism is instrumental for maintenance of
cellular redox. Aberrant glutathione metabolism leads to many diseases
such as cancer, Alzheimer’s disease, and diabetes^[115]27. IsoNet
analysis of 293T cells revealed a glutathione-associated isotopologue
similarity subnetwork in the stable-isotope tracing metabolomics data
collected in positive ionization mode (Fig. [116]3a). In this
subnetwork, two nodes are identified as known metabolites (glutathione
and S-lactoylglutathione), while the rest three nodes are unknown
metabolites which were elucidated through our IsoNet approach. In the
glutathione-associated subnetwork, we observed a key unknown reaction
in which the thiol group of glutathione was converted into a hydroxyl
group of M292T447 (Fig. [117]3b). To elucidate this unknown reaction,
we first examined the metabolite pair of glutathione and M292T447 that
they had a high isotopologue similarity score (S[ISO]) of 0.94 and a
mass difference (Δm/z) of 15.9771 Da (Fig. [118]3c). Matching the mass
difference to the delta mass library suggests that the atom difference
between two metabolites is [S − O]. Given the atom difference, the
related reference transsulfuration reaction ([119]R03923) was retrieved
from the reference reaction library, and used to elucidate the unknown
transformation between glutathione and M292T447. In the reference
reaction, L-cysteine is converted into L-serine, wherein a thiol group
being converted into a hydroxyl group (Fig. [120]3c). Such a
transsulfuration reaction also results in an atom difference of [S − O]
and a delta mass of 15.9772 Da. Inspired by this reference reaction, we
therefore speculated a similar transsulfuration reaction that taken
place between glutathione and the unknown metabolite M292T447
(Fig. [121]3c).
Fig. 3. Glutathione-associated isotopologue similarity subnetwork reveals
previously unknown metabolic reactions.
[122]Fig. 3
[123]Open in a new tab
a The glutathione-associated isotopologue similarity subnetwork in
positive ionization mode. The grey shad highlights the edge between
glutathione and M292T447. b The unknown metabolic reaction between
glutathione and M292T447. Orange points represent the ^13C-labeled
atoms. c Elucidation of the metabolic reaction between glutathione and
M292T447 (γ-Glu-Ser-Gly). The red shads highlight the variable
substructures. d Interpretation of MS/MS spectra between glutathione
and γ-Glu-Ser-Gly. The red shads highlight the variable substructures.
e Validation of γ-Glu-Ser-Gly using chemical standard (Std): retention
time match (top) and MS2 spectral match (bottom). f The relative levels
of γ-Glu-Ser-Gly in various mouse tissues (n = 3–4) quantified by
LC − MS. g Isotopologue pattern similarity between glutathione and
γ-Glu-Ser-Gly in mouse liver tissue. Mice were administered with
[U-^13C]-glucose for 24 h (n = 4 biological replicates per group). h
Intracellular γ-Glu-Ser-Gly levels in 293T and iBMDM cells after the
treatment of buthionine sulfoximine (BSO; 100 μM) for 12 h or 10 h,
respectively (n = 6 biological replicates for 293T cells per group,
p = 0.0070 for glutathione, p < 0.0001 for γ-Glu-Ser-Gly; n = 5
biological replicates for iBMDM cells per group p = 0.0093 for
glutathione, p < 0.0001 for γ-Glu-Ser-Gly). i Quantitative real-time
PCR analyses of mRNA levels of GCS gene expression in control group and
the siRNA-transfected 293T cells (n = 3 biological replicates per
group; p = 0.023, ctrl vs siRNA1; p = 0.025, ctrl vs siRNA2; p = 0.031,
ctrl vs siRNA3). j, k Intracellular levels of glutathione
(p = 0.000064, ctrl vs siRNA1; p = 0.0019, ctrl vs siRNA2; p = 0.0015,
ctrl vs siRNA3) ( j) and γ-Glu-Ser-Gly (p = 0.021, ctrl vs siRNA1;
p = 0.011, ctrl vs siRNA2; p = 0.015, ctrl vs siRNA3) (k) in control
group and siRNA-transfected 293T cells (n = 3 biological replicates per
group). P-values were determined by a two-tailed Student’s t-test. *,
p-value < 0.05; **, p-value < 0.01; ***, p-value < 0.001; ****,
p-value < 0.0001. Values represent means ± SD. Source data are provided
as Source Data files for Fig. 3c and f–k.
To validate the reaction product, we interpreted MS/MS spectra of
glutathione and M292T447, and jointly inferred the structure as
γ-Glu-Ser-Gly (Fig. [124]3d). Specifically, red, green, and blue
fragments shifted the similar delta mass as precursors
(Δm/z = 15.9771 Da), which was used to determine the variable
substructure highlighted as the red shads. Further, we synthesized the
chemical standard of γ-Glu-Ser-Gly, and matched retention times (RT)
and MS/MS spectra between M292T447 and the synthesized chemical
standard to confirm its identity (Fig. [125]3e). In addition to 293T
cells, we also measured the levels of γ-Glu-Ser-Gly across a variety of
mouse tissues (brown adipose tissue, brain, heart, intestine, liver,
lung, muscle, spleen, and white adipose tissue), with the spleen being
the most abundant organ (Fig. [126]3f). Subsequently, we conducted an
in vivo stable-isotope tracing experiment in mice using
[U-^13C]-glucose and collected liver tissue to examine the
incorporation of ^13C-tracer into the glutathione-associated reaction
network. As a result, γ-Glu-Ser-Gly in mouse liver was found as being
isotopically labeled and shared a high isotopologue similarity with
glutathione, which proved that γ-Glu-Ser-Gly could be endogenously
synthesized and had a close metabolic relationship with glutathione
(Fig. [127]3g). To verify that the newly found metabolic reaction links
to glutathione metabolism, we next treated cells with buthionine
sulfoximine (BSO), which is an inhibitor of gamma-glutamylcysteine
synthetase (GCS) in glutathione metabolism pathway^[128]28. As
expected, BSO treatment effectively decreased levels of glutathione and
γ-Glu-Ser-Gly in both of 293T and iBMDM cells (Fig. [129]3h). To
confirm that γ-Glu-Ser-Gly is synthesized directly from glutathione, we
further generated GCS knockdown 293T cells deficient in glutathione
biosynthesis using RNA interference (Fig. [130]3i). As results showed
in Fig. [131]3j, k, GCS knockdown dramatically decreased cellular
levels of not only glutathione but also γ-Glu-Ser-Gly, demonstrating
the direct link between γ-Glu-Ser-Gly biosynthesis and endogenous
glutathione level. Collectively, these results demonstrated a tangible
transsulfuration reaction of glutathione that generated γ-Glu-Ser-Gly
in the glutathione metabolism network.
Validation of a previously uncharacterized transsulfuration reaction with
glutathione
As in the reference transsulfuration reaction ([132]R03923), cysteine
is converted to serine by transferring the sulfur to tRNAs and
producing thiolated tRNAs, which play essential functions in regulating
cellular translational capacity and metabolic homeostasis^[133]29. In
the previously unknown transsulfuration reaction discovered above, we
found that glutathione is converted to γ-Glu-Ser-Gly, and also acts as
a “sulfur-donating” metabolite (Fig. [134]4a). The newly identified
metabolite γ-Glu-Ser-Gly belongs to the family of γ-glutamyl
tripeptide. However, the biosynthesis of γ-glutamyl tripeptide is
commonly reported through the ligation of glutamate with various amino
acids via gamma-glutamylcysteine synthetase (GCS) and glutathione
synthetase (GSS)^[135]28 (Fig. [136]4b). To demonstrate the finding of
a previously unknown transsulfuration reaction distinct from the known
ligation reaction, we first examined the isotopologue patterns of key
metabolite substrates such as glutamate, glycine, serine, and cysteine
in 293T cells labeled by a mixture of isotopic tracers
([U-^13C]-glutamine, [U-^13C]-glucose, and [U-^13C]-acetate)
(Fig. [137]4c). Joint probability can calculate the likelihood of
different events in one time when random variables are given
(Supplementary Fig. [138]5a). With inputs of experimental isotopologue
patterns for metabolite substrates, we then calculated the predicted
isotopologue patterns of γ-Glu-Ser-Gly in the scenarios of two
different reaction pathways using the joint probability (Fig. [139]4d,
e). If a ligation reaction is carried out, γ-Glu-Ser is first
synthesized from glutamate and serine followed by glycine ligation to
produce γ-Glu-Ser-Gly mainly with isotopologues of M + 5, M + 6, M + 7,
M + 8, and M + 10 (Fig. [140]4e), which significantly differs from its
experimental isotopologue pattern. The similarity score between
experimental isotopologue pattern and predicted isotopologue pattern
for γ-Glu-Ser-Gly is only 0.52 (Fig. [141]4d). As a comparison, in the
scenario of transsulfuration reaction, glutathione (M + 5 and M + 7) is
first biosynthesized via the ligation of glutamate with cysteine and
glycine. Then, transsulfuration of glutathione occurs which results in
the metabolite product γ-Glu-Ser-Gly mainly with isotopologues of
M + 5, and M + 7 (Fig. [142]4e). Next, we calculated the similarities
between experimental isotopologue patterns and predicted isotopologue
patterns for glutathione and γ-Glu-Ser-Gly in the previously unknown
pathway. The generated S[ISO] values were calculated as 0.67 and 0.89
for glutathione and γ-Glu-Ser-Gly, respectively (Fig. [143]4e). These
results demonstrated that the transsulfuration reaction from
glutathione is used for γ-Glu-Ser-Gly biosynthesis instead of the
conventional ligation reaction.
Fig. 4. Validation of a previously uncharacterized transsulfuration reaction
with glutathione.
[144]Fig. 4
[145]Open in a new tab
a Biosynthesis of γ-Glu-Ser-Gly via the transsulfuration reaction from
glutathione. b Biosynthesis of γ-Glu-Ser-Gly via the ligation reaction.
c The isotopologue patterns of glutamate, glycine, serine, and cysteine
in 293T cells labeled with mixed tracers of [U-^13C]-glutamine,
[U-^13C]-glucose, and [U-^13C]-acetate for 17 h. d The predicted
isotopologue pattern of γ-Glu-Ser-Gly by joint probability based on
ligation reactions in (b). e The predicted isotopologue patterns of
glutathione (left) and γ-Glu-Ser-Gly (right) by joint probability based
on transsulfuration reaction in (a). f, g The experimental isotopologue
patterns of glutathione (red) and γ-Glu-Ser-Gly (red) and predicted
γ-Glu-Ser-Gly (blue and green) in 293T cells labeled with
[U-^13C]-serine (f) or [U-^13C]-cysteine (g) for 6 h. Values represent
means ± SEM (n = 6 biological replicates per group). Source data are
provided as Source Data files for Fig. 4c–g.
Further, we carried out distinct stable-isotope tracing experiments in
293T cells to validate the newly found glutathione transsulfuration
route using [U-^13C]-serine, [U-^13C]-cysteine, and [U-^13C]-glycine as
tracers individually. Specifically, in [U-^13C]-serine labeling
experiment, M + 3 labeled serine was quickly converted into M + 2
labeled glycine (Supplementary Fig. [146]5b), which was further used to
synthesize glutathione. Therefore, the newly synthesized glutathione
had the expected M + 2 isotopologue from labeled glycine
(Fig. [147]4f), which is consistent with the isotopologue pattern of
glutathione labeled with [U-^13C]-glycine (Supplementary Fig. [148]5d,
e). Importantly, γ-Glu-Ser-Gly was measured only in M + 2 isotopologue
form, while M + 3 or M + 5 labeled isotopologues were absent
(Fig. [149]4f), suggesting that the newly labeled γ-Glu-Ser-Gly was
directly derived from glutathione through transsulfuration but not from
ligation with M + 3 labeled serine. Also, [U-^13C]-cysteine labeling
demonstrated the incorporation of labeled cysteine into both
glutathione and γ-Glu-Ser-Gly in M + 3 forms (Fig. [150]4g and
Supplementary Fig. [151]5c). Thus, cysteine but not serine was involved
into γ-Glu-Ser-Gly biosynthesis. Additionally, comparative analyses
demonstrated a high consistency between experimental isotopologue
pattern and predicted isotopologue pattern via the transsulfuration
reaction for γ-Glu-Ser-Gly. However, experimental isotopologue pattern
of γ-Glu-Ser-Gly was remarkedly different from the predicted
isotopologue pattern via the ligation reaction (Fig. [152]4f, g).
Finally, we demonstrated that the previously unknown transsulfuration
reaction from glutathione for γ-Glu-Ser-Gly synthesis was also present
in iBMDM cells through [U-^13C]-cysteine labeling experiment
(Supplementary Figs. [153]5f and [154]5g). Additionally, γ-Glutamyl
transferase (GGT), another enzyme that is involved in the biosynthesis
of γ-glutamyl tripeptide, catalyzes the transfer of γ-glutamyl group of
glutathione to amino acids or peptides. The likelihood of this
γ-glutamyl transfer reaction was excluded by [U-^13C]-serine labeling
experiment (Supplementary Fig. [155]5h). Collectively, above results
disclosed a previously uncharacterized transsulfuration reaction from
glutathione which revised the reported biosynthetic routes of
γ-glutamyl tripeptide.
IsoNet fulfils the glutathione metabolic reaction network
Next, we combined the isotopologue subnetworks of both positive and
negative ionization modes to chart an integrated reaction network for
glutathione metabolism (Fig. [156]5a). In total, 11 nodes and 12 edges
were included in the network with three known metabolites (glutathione,
glutathione disulfide, and S-lactoylglutathione). We therefore
attempted to deduce the rest nodes and edges using IsoNet. In addition
to γ-Glu-Ser-Gly, seven nodes were further identified as
S-acetylglutathione (Supplementary Fig. [157]6), glutathione
sulfinamide (Supplementary Fig. [158]7), deaminated glutathione
(Supplementary Fig. [159]8), γ-Glu-3-sulfamoyl-Ala-Gly (Supplementary
Fig. [160]9), glutathione sulfinic acid (Supplementary Fig. [161]10),
glutathione sulfonic acid (Supplementary Fig. [162]11), and
S-(2-succinyl)glutathione (Supplementary Fig. [163]12) by the
comparison of isotopologue similarity, relevance to reference
reactions, and interpretation of MS/MS spectra. These metabolite
identities were also validated with synthesized chemical standards or
products from in vitro reactions (Supplementary Figs. [164]6–[165]12).
Most of these metabolites were not included in neither KEGG nor HMDB
databases (Supplementary Table [166]1). In the network, a total of 10
reactions were identified, which includes distinct reaction classes
such as redox reaction, transfer reaction, hydrolysis reaction, and
ligation reaction. Among them, two reactions were recorded in the KEGG
database, five reactions were previously reported, and three previously
unknown reactions were firstly reported and characterized in this study
(Supplementary Table [167]2). Three previously unknown reactions
included the transsulfuration reaction to synthesize γ-Glu-Ser-Gly, the
acetylation reaction to synthesize S-acetylglutathione, and the
oxidation reaction to synthesize γ-Glu-3-sulfamoyl-Ala-Gly. Most
importantly, γ-Glu-3-sulfamoyl-Ala-Gly is a previously unreported
metabolite that not included in PubChem, KEGG or HMDB databases
(Supplementary Table [168]1). Finally, we also confirmed their
metabolism linked to glutathione metabolism pathway using in vivo
stable-isotope tracing metabolomics in live mice and BSO inhibition in
different cell lines (Supplementary Figs. [169]6–[170]12).
Fig. 5. IsoNet fulfils the glutathione metabolic reaction network.
[171]Fig. 5
[172]Open in a new tab
a An integrated glutathione metabolic network combined with datasets of
positive and negative ionization modes. b The glutathione metabolic
reaction network including 10 metabolic reactions. c PCA analysis of
individual metabolite treatments. Metabolite intensities were
normalized to their respective control group. d Significantly enriched
pathways of individual metabolite treatment experiments. Pathway
enrichment p-values were calculated using the hypergeometric test
(unadjusted). The circle size represents the number of metabolite hits
in the pathway. The color represents raw p-value of enrichment. Source
data are provided as Source Data files for Fig. 5c, d.
Further, we verified the metabolic reactions using stable-isotope
tracing technology and in vitro chemical reactions (Supplementary
Fig. [173]13). For example, S-acetylglutathione was found to be
synthesized through an acetyl transfer reaction between glutathione and
acetyl coenzyme A (acetyl-CoA) in live cells (Supplementary
Fig. [174]13a, b). Labeling cells with [U-^13C]-pyruvate and
[U-^13C]-acetate, which serve as the precursors of the acetyl group in
acetyl-CoA, produced S-acetylglutathione with isotopologue of M + 2
(Supplementary Fig. [175]13a). Also, we replicated the reaction by
directly mixing glutathione and acetyl-CoA without enzymes, and
observed a time-dependent production of S-acetylglutathione
(Supplementary Fig. [176]13b). Glutathione sulfinamide has been
reported as a specific biomarker for the exposure to
HNO^[177]30,[178]31. Indeed, we observed a significantly increased
level of glutathione sulfinamide in the redox reaction system involving
glutathione and Angeli’s salt (HNO source) (Supplementary
Fig. [179]13c). Additionally, we found that γ-Glu-3-sulfamoyl-Ala-Gly
was synthesized as a product in this reaction system (Supplementary
Fig. [180]13c). This result suggests that glutathione sulfinamide could
be metabolized into γ-Glu-3-sulfamoyl-Ala-Gly via an oxidative
reaction. Also, in the presence of reactive oxygen species (ROS),
glutathione undergoes oxidation to produce glutathione sulfinic acid
and glutathione sulfonic acid^[181]32. The two oxidative reactions were
confirmed with glutathione sulfinic acid and glutathione sulfonic acid
being detected in the reaction system of glutathione and hydrogen
peroxide (Supplementary Fig. [182]13d). The ligation reaction between
glutathione and fumarate producing S-(2-succinyl)glutathione was also
validated as a non-enzymatic reaction^[183]33 (Supplementary
Fig. [184]13e). S-(2-succinyl)glutathione was reported act as an
alternative substrate to glutathione reductase to decrease NADPH levels
and boost mitochondrial ROS and HIF-1 activation^[185]33. The discovery
of this reaction enhances our understanding of how fumarate functions
as a proto-oncometabolite. In summary, these results demonstrated that
the isotopologue similarity networking approach can resolve both
previously characterized and uncharacterized metabolic reactions
associated with glutathione metabolism, charting a complete
glutathione-centered metabolic reaction network (Fig. [186]5b).
To explore the biochemical effects of these reactions, we treated 293T
cells with S-acetylglutathione, S-lactoylglutathione, glutathione
sulfinic acid, γ-Glu-Ser-Gly, γ-Glu-3-sulfamoyl-Ala-Gly, and
glutathione sulfonic acid, respectively (Fig. [187]5c). Comprehensive
untargeted metabolomic analyses using LC–MS were performed on
individual treatments and controls (Supplementary Data [188]7).
Metabolite treatment increased the endogenous levels of individual
treated metabolites in each treatment group compared to the control
group (Supplementary Fig. [189]14a). Next, principle component analysis
(PCA) was conducted using 345 metabolites detected in all groups
(Fig. [190]5c and Supplementary Data [191]7). The results showed that
the metabolic changes were similar between treatments with
S-acetylglutathione and S-lactoylglutathione. Similar changes were also
observed between treatments with γ-Glu-3-sulfamoyl-Ala-Gly and
glutathione sulfonic acid. However, the changes induced by
γ-Glu-Ser-Gly and glutathione sulfinic acid were exceptionally distinct
among all of them. Further pathway enrichment analysis also
demonstrated disparate impacts on cellular metabolism elicited by
different glutathione metabolites (Fig. [192]5d and Supplementary
Table [193]3). For examples, S-acetylglutathione and
S-lactoylglutathione affected sphingolipid metabolism and unsaturated
fatty acid synthesis, leading to a decrease of sphingosine,
phytosphingosine, arachidonic acid, eicosapentaenoic acid, and
eicosatrienoic acid (Fig. [194]5d and Supplementary Fig. [195]14b).
γ-Glu-Ser-Gly treatment significantly decreased levels of nucleotides
and nucleotide derivatives (Supplementary Fig. [196]14c). Glutathione
sulfinic acid and γ-Glu-3-sulfamoyl-Ala-Gly primarily influenced amino
acid metabolism (Supplementary Fig. [197]14d, e). Specifically,
glutathione sulfonic acid treatment impacted not only amino acid
metabolism but also glyoxylate and dicarboxylate metabolism
(Supplementary Fig. [198]14f). In conclusion, these previously
uncharacterized metabolites and reactions nested in the glutathione
metabolic reaction network serve as important metabolic niches
modulating cellular biochemistry.
Itaconate-associated isotopologue similarity subnetwork disclose previously
unknown metabolites
Itaconate is a crucial anti-inflammatory metabolite, which modulates
immune responses upon infections and the pathogenesis of inflammatory
diseases^[199]34,[200]35. Next, we constructed an isotopologue
similarity network in the lipopolysaccharide (LPS)-treated iBMDM cells,
uncovering previously unknown reactions and metabolites related to
inflammation (Supplementary Data [201]8). In the itaconate-associated
isotopologue similarity subnetwork, we identified two previously
unknown metabolites and associated reactions (Supplementary
Fig. [202]15a, b). The metabolites produced by these reactions were
identified as itaconate-cysteine (Supplementary Fig. [203]15c–g) and
sulfoitaconate (Supplementary Fig. [204]15h–l) by isotopologue
similarity comparison, relevance to reference reactions, interpretation
of MS/MS spectrum, and validation using chemical standards. Neither
itaconate-cysteine nor sulfoitaconate is recoded in the KEGG or HMDB
databases. Moreover, both itaconate-cysteine and sulfoitaconate were
found to increase dramatically by LPS stimulation in iBMDM cells, which
were even more profound than the elevation of itaconate (Supplementary
Fig. [205]15f and k), suggesting their relevance with inflammation. In
conclusion, IsoNet facilitates the discovery of previously unknown
metabolites linked to inflammation, offering valuable potentials for
understanding anti-inflammatory processes.
Discussion
Cellular metabolism is a complex network intertwined with a myriad of
metabolic reactions which support various cellular processes such as
cell proliferation, signaling transduction, and growth. However,
complementing the map of metabolic reaction network remains non-trival
due to the intrinsic complexity of cellular metabolism and the lack of
powerful technologies. Combining global stable-isotope tracing
metabolomics and isotopologue similarity networking, the IsoNet
technology developed in this study allowed to uncover hundreds of
putative unknown metabolic reactions in cellular metabolism. The
findings of unknown reactions fill the previously unexplored networks
and complement our understanding of the metabolic maps in cellular
metabolism. This technology is particularly useful towards reaction
discovery in the case of unknown reaction type I. In this scenario,
IsoNet establishes the connection between a known metabolite and an
unknown metabolite. Given the experimental mass differences, IsoNet
deduces the atom difference thus a substructure of the unknown
metabolite, and then a potential reaction taken place by matching
against the reference reaction library. Indeed, we performed manual
inspection to deduce metabolic reactions between the labeled metabolite
pairs. With the advancement of artificial intelligence models in
metabolomics, it is plausible that in the near future, we may be able
to achieve automated deduction of unknown metabolic reactions with the
reaction-paired metabolites obtained from of IsoNet.
IsoNet calculates isotopologue pattern similarity based on biochemical
principles which directly aligns isotopologue pattern segments (defined
as “motifs”). The motif-based alignment offers better interpretability
compared with point-to-point alignment approaches such as dynamic
programing, since the isotopologue pattern represents a specific
substructure of metabolites from metabolic reactions. However, in
principle, isotopologue pattern similarity calculation using IsoNet is
highly similar to conventional dynamic programming. In addition, IsoNet
tends to discover large and composite unknown metabolites. This is
because that large, composite metabolites with multiple substructures
tend to display more diverse isotopologue patterns in the
stable-isotope labeling. Consequently, the motif-based isotopologue
pattern similarity networking is more likely to connect these
metabolites. Additionally, the diverse isotopologue patterns facilitate
the discovery and annotation of previously unknown metabolites and
related biochemical reactions. The discovery of glutathione-associated
previously unknown metabolites and reactions serves as a prominent
example. For small metabolites, such as glycolytic intermediates and
amino acids, IsoNet can also connect them through isotopologue pattern
similarity networking (see examples in Fig. [206]1c, d). However, due
to the limited structural information that can be obtained from the
isotopologue patterns or MS/MS spectra of small metabolites, the
annotation of unknown metabolites within these subnetworks remains a
challenge. We anticipate that the annotation functions within IsoNet
can be further improved to continue filling gaps in the metabolic
network. The coverage of metabolites and reactions in the isotopologue
similarity network is influenced by the MS ionization mode. To address
this, we therefore acquired data using both positive and negative
ionization modes to improve the coverage of detected metabolites. Then,
we used IsoNet and constructed an integrated isotopologue similarity
network which combines data from individual ionization modes. To
further expand coverage of metabolites in the isotopologue similarity
network, methods such as chemical derivatization and multidimensional
chromatography separation can also be employed. In our study, our
original focus was primarily on metabolomics. We believe that with
suitable experimental conditions designed for lipidomic analysis (e.g.,
optimized measurement settings and labeling protocols), IsoNet can also
be applicable for the discovery of unknown lipid species. Furthermore,
the reference reaction library within IsoNet was primarily designed for
metabolites and should be further developed to account for lipid
metabolism and reactions.
A key finding using IsoNet strategy is the discovery of a previously
uncharacterized transsulfuration reaction by which γ-Glu-Ser-Gly is
synthesized directly via glutathione transsulfuration. Cellular
transsulfuration play fundamental roles in the maintenance of redox
homeostasis and sulfur balance^[207]36,[208]37. Abnormalities in
transsulfuration reactions contribute to a broad spectrum of diseases,
such as neurodegenerative diseases, autism, and vascular
dysfunctions^[209]38,[210]39. The sulfur-containing metabolite cysteine
has been canonically regarded as the central metabolic hub for
transsulfuration reactions in cells. Our findings demonstrated that
glutathione acts as a “sulfur-donating” metabolite for a previously
unknown transsulfuration reaction with the metabolite γ-Glu-Ser-Gly
being biosynthesized. Inspired by the reaction products of cysteine
transsulfuration, we reasoned that the sulfur of glutathione is
probably transferred to tRNAs which produces thiolated tRNAs. In human,
there is a set of tRNA thiolation enzymes such as mitochondrial
tRNA-specific 2-thiouridylase 1 (MTU1) and cytoplasmic tRNA
2-thiolation protein 1/2 (CTU1/2), catalyzing the sulfur modification
on tRNAs^[211]40–[212]42. More importantly, the human tRNA-modification
genes remain largely unexplored. Approximately 23% of the modification
genes in human remain unknown^[213]42. Given that glutathione is a
critical cellular sulfur donor, it is highly plausible that these
unidentified genes and enzymes may also be involved in tRNA thiolation
and the transsulfuration reaction of glutathione. Thiolated tRNAs are
indispensable modules for cell growth through regulating cellular
translational capacity^[214]29. Importantly, metabolic homeostasis is
tightly controlled by thiolated tRNAs whose levels reciprocally
regulate nucleotide synthesis, amino acid metabolism and carbohydrate
metabolism^[215]29,[216]43. Indeed, we observed consistent metabolic
changes in these pathways, in particular the decreased levels of
nucleotides in cells with γ-Glu-Ser-Gly treatment. Therefore, we
surmise that the previously unknown transsulfuration reaction with
glutathione functions is potentially in synergy to determine thiolated
tRNAs levels, thus modulating cellular metabolism. Undeniably, more
mechanistic studies are warranted to understand how the previously
unknown glutathione transsulfuration reaction modulates tRNA thiolation
and balances translational capacity and cellular redox homeostasis.
IsoNet prioritizes reaction discovery based on stable-isotope tracing
metabolomics, but rather activity-based enzyme screening systems. One
limitation of our method is that IsoNet can not characterize specific
metabolic enzymes responsible for the previously unknown reactions
discovered in this study. This could be addressed by integrating IsoNet
analysis with functional metabolomics approach developed by Sévin et
al. who incubated purified protein and protein-overexpressing cell
lysate in metabolite cocktails. As such, metabolic enzymes associated
with reactions in specific models may be targeted to complete the
metabolic reaction network. Nevertheless, these findings of previously
unknown reactions though IsoNet fill the previously unexplored
metabolic network, and complement our understanding of the metabolic
maps in cellular metabolism.
Methods
Ethical statement
The animal experiments were compliant with the ethical guidelines of
the Institutional Animal Care and Use Committees of Interdisciplinary
Research Center on Biology and Chemistry, Shanghai Institute of Organic
Chemistry, Chinese Academy of Sciences (approval research project
number: ECSIOC_2023-23).
Chemicals and standards
LC–MS grade water (H[2]O) was purchased from Honeywell (Muskegon, MI,
USA). LC–MS grade acetonitrile (ACN) was purchased from Merck
(Darmstadt, Germany). Ammonium hydroxide (NH[4]OH) and ammonium acetate
(NH[4]OAc) were purchased from Sigma (St. Louis, MO, USA).
Stable-isotope tracers including [U-^13C]-glucose, [U-^13C]-glutamine,
[U-^13C]-acetate, [U-^13C]-serine, [U-^13C]-cysteine, [U-^13C]-glycine,
and [U-^13C]-pyruvate were purchased from Cambridge Isotope
Laboratories (MA, USA). DL-buthionine-sulfoximine (BSO) and
N-ethylmaleimide (NEM) were purchased from Sigma-Aldrich (St. Louis,
MO, USA). Catalase (C100456) and Crotalusadamanteus L-amino acid
oxidase (A128538), Angeli’s salt (A332354), and S-lactoylglutathione
(L121369) were purchased from Aladdin Bio-Chem Technology Co. LTD
(Shanghai, China).
Stable-isotope tracing in cells and sample preparation
Cells (293T, iBMDM cell lines) were plated in 6-cm dishes and cultured
in Dulbecco Modified Eagle’s Medium (DMEM) containing 10% dialyzed
fetal bovine serum (dFBS) and 1% penicillin/streptomycin (PS). When
cells were grown to 80% confluence, the culture medium was changed to a
fresh medium solution with the following stable-isotope tracers for
indicated times. For labeling experiments with a mixture of tracers,
the fresh medium contained 25 mM [U-^13C]-glucose, 4 mM
[U-^13C]-glutamine, 5 mM [U-^13C]-acetate in glucose-free and
glutamine-free DMEM. For labeling experiments with [U-^13C]-glucose,
the fresh medium contained 25 mM [U-^13C]-glucose in glucose-free DMEM.
For labeling experiments with [U-^13C]-glutamine, the fresh medium
contained 4 mM [U-^13C]-glutamine in glutamine-free DMEM. For labeling
experiments with [U-^13C]-serine, the fresh medium contained 0.6 mM
[U-^13C]-serine in DMEM. For labeling experiments with
[U-^13C]-glycine, the fresh medium contained 0.6 mM [U-^13C]-glycine in
DMEM. For labeling experiments with [U-^13C]-cysteine, the fresh medium
contained 0.4 mM [U-^13C]-cysteine in DMEM. For labeling experiments
with [U-^13C]-pyruvate, the fresh medium contained 4 mM
[U-^13C]-pyruvate in glucose-free DMEM. For labeling experiments with
[U-^13C]-acetate, the fresh medium contained 5 mM [U-^13C]-acetate in
DMEM.
Fast extraction of metabolites in cells was performed as follows. In
brief, the culture medium was quickly removed, and cells were washed
with PBS twice. Cell dishes were placed on dry ice and the precooled
metabolite extraction solution (MeOH:ACN:H[2]O = 2/2/1, v/v/v, 800 μL)
was added to dishes to quench metabolism. The dishes were then
incubated at −80 °C for 40 min. The cell contents were scraped and
transferred to a 1.5-mL Eppendorf tube. Another 400 μL extraction
solution was added to wash dish and transferred to the same EP tube.
The samples were vortexed for 1 min and centrifuged for 10 min at
16,200 × g and 4 °C to precipitate insoluble materials. For the
measurement of cysteine levels if needed, 50 μL supernatant was taken
out and 50 μL extraction solvent (MeOH: H[2]O, 4:1, v/v, containing
25 mM NEM and 10 mM ammonium formate, pH 7.0) was added followed by
incubation on ice for 30 min^[217]28,[218]44. The NEM-derivatized
metabolite extracts were then analyzed by LC-MS. The rest supernatant
was taken to a new 1.5-mL Eppendorf tube and evaporated to dryness at
4 °C using a vacuum concentrator. The dried extracts were kept in
−80 °C. Before LC–MS analysis, the dried extracts were reconstituted in
100 μL of ACN:H[2]O (1:1, v/v), sonicated for 10 min, and centrifuged
for 15 min at 16,200 × g and 4 °C to remove insoluble debris. The
supernatant was then transferred to HPLC vials for LC–MS analysis.
Stable-isotope tracing in mice and sample preparation
12-week-old male mice (C57BL/6J; n = 3/4) were group-housed in a
barrier facility at room temperature of 22 °C with 50% humidity and
12 h light/12 h dark cycles. Mice first received a liquid diet with
free access to drinking water for one week. The liquid diet was
composed of glucose (30 g), soy protein (10 g), coconut milk (34.3 mL)
from Nature’s Charm, and water per 100 mL volume. Then, mice were
fasted overnight prior to stable-isotope tracing. Tracing experiments
were conducted by switching to a liquid diet with replacement of
unlabeled glucose to [U-^13C]-glucose at 9 am. Mice were sacrificed at
9 am on the second day after 24 h dietary [U-^13C]-glucose tracing.
Brown adipose tissue (BAT), brain (cortex), heart, intestine, kidney,
liver, lung, muscle, spleen and white adipose tissue (WAT) were
dissected and quickly frozen in liquid nitrogen immediately and stored
at −80 °C until metabolite extraction. The animal experiments were
compliant with the ethical guidelines of the Institutional Animal Care
and Use Committees of Interdisciplinary Research Center on Biology and
Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of
Sciences (approval research project number: ECSIOC_2023-23). Sex was
controlled by exclusively using male mice, thereby avoiding metabolic
variability introduced by periodic hormonal changes in females.
The mouse tissues were transferred into homogenizer tubes and
homogenized with H[2]O at a ratio of 200 μL H[2]O per 20 mg tissue and
ceramic beads using a homogenizer (JXFSTPRP-CL, Shanghai Jingxin
Experimental Technology) at the low-temperature condition. 200 μL
homogenized solution was taken out and 800 μL extraction solution (ACN:
MeOH = 1:1, v/v) was added for metabolite extraction. The mixture
solution was vortexed for 30 s, and sonicated for 10 min at 4 °C water
bath. After incubation for 1 h at −20 °C, the sample was centrifuged
for 15 min at 16,200 × g and 4 °C. The supernatant was taken to a new
1.5-mL EP tube and evaporated to dryness at 4 °C in a vacuum
concentrator. The following extraction processes were the same as those
for cell samples.
Metabolite treatment in cells
293T cells were plated in 6-cm dishes and cultured in Dulbecco Modified
Eagle’s Medium (DMEM) containing 10% fetal bovine serum (FBS) and 1%
penicillin/streptomycin (PS). When cells were grown to about 80%
confluence, the culture medium was changed to a fresh medium solution
with the following metabolites for 12 h. For treatment with
γ-Glu-Ser-Gly, the fresh medium contained 0.15 mM γ-Glu-Ser-Gly in
DMEM. For treatment with S-acetylglutathione, the fresh medium
contained 0.5 mM S-acetylglutathione in DMEM. For treatment with
S-lactoylglutathione, the fresh medium contained 0.5 mM
S-lactoylglutathione in DMEM. For treatment with
γ-Glu-3-sulfamoyl-Ala-Gly, the fresh medium contained 0.5 mM
γ-Glu-3-sulfamoyl-Ala-Gly in DMEM. For treatment with glutathione
sulfinic acid, the fresh medium contained 1 mM glutathione sulfinic
acid in DMEM. For treatment with glutathione sulfonic acid, the fresh
medium contained 2 mM glutathione sulfonic acid in DMEM. The fast
extraction of metabolites was used the same method as that of
stable-isotope tracing experiments in cells.
RNA interference, transfection, and quantitative real-time PCR analysis
siRNA duplexes were obtained from Genomeditech (Shanghai, China). The
siRNA sequences are designed to target GCLC (glutamate-cysteine ligase
catalytic subunit), the catalytic subunit of GCS. The detailed
sequences were 5′-GAAGGAGGCUACUUCUAUAtt-3′ (siRNA1),
5′-GGAUCAUAUUUACAUGGAUtt-3′ (siRNA2), and 5′-GAGCCAUUGAAGAACAAUAtt-3′
(siRNA3). The siRNA (5′-UUCUCCGAACGUGUCACGdTdT) was used as negative
control. Approximately 5 × 10^5 cells were plated the day before
transfection in 6-well plates and collected 2 mL of fresh growth medium
prior to transfection. siRNA was transfected using Lipofectamine
2000^TM Transfection Reagent (Thermo Fisher Scientific) according to
the manufacturer’s protocol. The final concentration of siRNAs was
100 nM. Cells were harvested 48 h later and processed further for
real-time PCR and LC-MS analyses.
The 293T cells were directly lysed and used for RNA extraction. Total
RNA was isolated using RNA Easy Fast Tissue/Cell Kit (TIANGEN, DP451)
according to the manufacturer’s instruction. The concentration and
purity of the extracted RNA was determined using NanoDrop (ThermoFisher
Scientific). First-strand cDNA was synthesized from 1 μg of RNA using
Reverse Transcriptase M-MLV (RNase H-) (Takara, Japan). The cDNAs were
analyzed by quantitative real-time PCR with the following primers: GCS,
5′-GCTGTTGCAGGAAGGCATTG-3′ and 5′-AGTTTGGAGGAGGGGGCTTA-3′; actin, 5′-
CTTCGCGGGCGACGAT-3′ and 5′- CCACATAGGAATCCTTCTGACC -3′. Real-time PCR
analysis was performed using the QuantStudio 6 Flex real-time PCR
system (ThermoFisher Scientific) with 2× SYBR Green qPCR Master Mix
(SelleckChem). The 2^−ΔΔCT method was used to calculate the gene
expression data, with the actin gene serving as the reference control.
LC−MS-based metabolomics
The metabolomics analysis protocol followed our previous publication
with minor modification^[219]7,[220]8. Metabolomics data of biological
samples were acquired using a Vanquish UHPLC coupled to an Orbitrap
Exploris 480 (ThermoFisher Scientific, United States) using Xcalibur
(version 4.4.16.14, Thermo Fisher Scientific, USA). A Waters ACQUITY
UPLC BEH amide column (particle size, 1.7 μm; 100 mm (length) × 2.1 mm
(i.d.)) and Phenomenex Kinetex C18 column (particle size, 2.6 μm;
100 mm (length) × 2.1 mm (i.d.)) were used for LC separation and the
column temperature was kept at 25 °C. The injection volume was 2 μL.
For the amide column, mobile phase A was water with 25 mM ammonium
hydroxide (NH[4]OH) and 25 mM ammonium acetate (NH[4]OAc), and B was
ACN for both positive (ESI+) and negative (ESI−) ionization modes. The
flow rate was 0.5 mL/min and the gradient was set as follows:
0–0.5 min, 95% B; 0.5–7 min, 95% B to 65% B; 7–8 min, 65% B to 40% B;
8–9 min, 40% B; 9–9.1 min, 40% B to 95% B; 9.1–12 min, 95% B. For the
C18 column, mobile phase A was 0.01% acetic acid in 100% water, and B
was acetonitrile/isopropanol (1/1; v/v) for both positive (ESI+) and
negative (ESI−) ionization modes. The flow rate was 0.3 mL/min and the
gradient was set as follows: 0.0–1.0 min, 1% B; 1.0–8.0 min, 1% B to
99% B; 8.0–9.0 min, 99% B; 9.0–9.1 min, 99% B to 1% B; 9.1–12 min, 1%
B.
ESI source parameters of the Orbitrap Exploris 480 were set as follows:
spray voltage, 3000 V or −3000 V, positive (ESI+) and negative (ESI−)
ionization modes, respectively; vaporizer temperature, 400 °C; sheath
gas, 50 arb; aux gas, 15 arb; sweep gas, 2 arb; ion transfer tube
temperature, 350 °C. LC–MS data acquisition was operated in full scan
with polarity switching mode for all samples. Additional ddMS2 scans
were performed on QC samples to acquire MS/MS spectra. The full scan
was set as: orbitrap resolution, 60,000; AGC target, 1e6; maximum
injection time, 100 ms; scan range, 70–1200 Da. The ddMS2 scan was set
as: orbitrap resolution, 30,000; AGC target, 1e5; maximum injection
time, 60 ms; scan range, 50–1200 Da; top N setting, 6; isolation width,
1.0. The collision energy was set as SNCE 20-30-40%. Dynamic exclusion
duration was set as 4 s and isotope exclusion was on.
Data processing of metabolite-treatment untargeted metabolomics
The metabolite annotation steps followed previous publication^[221]21.
Briefly, the raw data (.raw) was converted to.mzXML (for full scan
mode) and.mgf (for ddMS2 mode) format using ProteoWizard (version
3.0.20360). Then the mzXML data files were grouped for peak detection
and alignment using R package “xcms” (version 3.12.0). the generated
MS1 peak table and MS2 files were uploaded to MetDNA2^[222]8 (version
1.4.4; [[223]http://metdna.zhulab.cn/]) for metabolite annotation.
In each metabolite-treatment group, Unpaired two-tailed Student’s
t-test in R was performed to compare the treatment group with the
control group (Supplementary Data [224]7). Metabolites with a
p-value < 0.05 were selected as significantly changed metabolites. The
KEGG ID of those significantly changed metabolites was inputted for
pathway analysis. The pathway analysis was performed by MetaboAnalyst
6.0 [[225]https://www.metaboanalyst.ca/]. Enrichment method was set as
“Hypergeometric Test”. Topology analysis was set as
“Relative-betweeness Centrality”. The pathway was considered
significant at a threshold of p-value < 0.05.
Calculation of isotopologue pattern similarity
We calculated isotopologue pattern similarity scores of metabolite
pairs stratified by the numbers of carbons and labeled isotopologues
(Supplementary Fig. [226]1a). In the type I scenario, metabolite pairs
had the same number of carbons in their structures and an isotopologue
pattern similarity score was calculated by normalized Manhattan
distance Eq. ([227]1) (Supplementary Fig. [228]1b).
[MATH: SISO=1/1+∑i=0
nIa,<
mi>i−Ib,i :MATH]
1
Where
[MATH:
Ia,i :MATH]
is the labeled fraction of isotopologue
[MATH: Mi
:MATH]
from metabolite/motif A,
[MATH:
Ib,i :MATH]
is the labeled fraction of isotopologue
[MATH: Mi
:MATH]
from metabolite/motif B,
[MATH: n :MATH]
is the minimum carbon number of the metabolite A or B.
In the type II scenario, metabolite pairs had different carbon numbers
and only one isotopologue was labeled (Supplementary Fig. [229]1c). For
example, the only isotopologue of M + 3 was observed in both
metabolites A and B. Then, a motif was generated in which the carbon
number of motifs was the minimum number of that in metabolite pairs.
Finally, an isotopologue pattern similarity score S[ISO] from the motif
match was calculated by Eq. ([230]1).
In the type III scenario, metabolite pairs had different carbon numbers
and more than one isotopologue were labeled (Supplementary
Fig. [231]1d). First, the metabolite with less carbon number (e.g.,
metabolite A) was used as a reference to generate motifs. Motifs used
for match follows three criteria: (1) exclusion of M0; (2) carbon
numbers of motifs should be larger or equal to half of that for the
metabolite with more carbon number (e.g., metabolite B); and (3) the
maximum generated motif number is 999. Then, the generated motifs
(motifs in A) and the metabolite with more carbon number (B) formed
motif-metabolite pairs. Each motif-metabolite pair was matched
sequentially starting from M1, with a step length of 1. Isotopologue
pattern similarity scores (S[ISO]) from the motif-metabolite match were
then calculated by Eq. ([232]1). Finally, the maximum S[ISO] and the
optimal motif were exported.
The workflow of IsoNet
The IsoNet workflow includes four steps: (1) annotation of labeled
metabolites; (2) isotopologue similarity networking; (3) unknown
reaction discovery; and (4) elucidation of unknown reactions and
structures (Supplementary Fig. [233]2a–d).
Annotation of labeled metabolites
Raw mass spectrometry data files (.raw) acquired from the orbitrap mass
spectrometer were first converted to.mzXML (for full scan mode) and.mgf
(for ddMS2 mode) format using ProteoWizard (version 3.0.20360). The R
package “xcms” (version 3.12.0) was used for peak detection and
alignment of mzXML data files of unlabeled samples. Key parameters were
set as follows: method, “centWave”; ppm, 10; mzwid, 0.006; snthresh, 6;
peakwidth, c(5, 30); minfrac, 0.5. The intermediate xcmsSet object from
‘xcms’ after peak detection was exported as a xcmsSet file (.Rda). The
generated MS1 peak table and MS2 files were then uploaded to MetDNA2
(version 1.4.1; [[234]http://metdna.zhulab.cn/]) for metabolite
annotation^[235]7,[236]8. Key parameters in MetDNA2 were set as
follows: instrument, ThermoExploris; column, “HILIC” or “RP” according
to LC separation; ce, SNCE20_30_40%; method_lc, “Amide12min” or
“RP12min” according to LC separation; mz_tol, 15 ppm. Metabolite
annotations with metabolomics standards initiative (MSI) level 1 and
level 2 were initially assigned as known metabolites. For remaining
unknown features, peak annotation from CAMERA^[237]45 and the peak
correlation network in MetDNA2 were first used to remove redundancies
including isotopes, adducts, in-source fragmentation (ISF), and neural
losses. Then, formulas of the remaining features were predicated by
Genform^[238]46 (download on June 28th, 2020;
[[239]https://sourceforge.net/projects/genform/]). Key parameters of
Genform were set as: adduct forms [M + H]^+ in positive and [M-H]^- in
negative modes; tolerance of m/z error, 5 ppm; elements included, C, H,
N, O, P, S. The resultant top five formula candidates were kept.
Finally, a metabolite annotation table (.csv file) including both known
and unknown metabolites was generated as a target list for subsequent
isotopologue extraction. MetDNA also outputted a MS/MS spectral data
file (.msp).
The above metabolite annotation table, a previously generated xcmsSet
file, unlabeled data (.mzXML), and labeled data (.mzXML) were subjected
to our recently published R package “MetTracer” for global extraction
of labeled metabolites^[240]21. The parameters for MetTracer were set
as follows: rt.extend, 15; value, “maxo”; equipment, “Orbitrap”; ppm,
10; res.define, 200; resolution, 60000; d.extract, “labelled”;
correct.iso, “TRUE”; adj.contaminate, “TRUE”. In the labeled samples,
if the labeled fraction of one isotopologue (except M0) in one
metabolite is larger than 0.02 in >50% of samples, the metabolite was
considered to isotopically labeled. For data processing with manual
check, the m/z and RT information of each isotopologue, and the raw
data files (.raw) were imported into Skyline (v21.1.0.278). Integration
range for each isotopologue was manually adjusted to ensure accurate
quantification. Key parameters in Skyline were set as: ion match
tolerance, 0.01 m/z; precursor mass analyzer, orbitrap; resolving
power, 60000 at 200 m/z. Then, the quantification result was corrected
using a R package “Accucor” (v0.3.0)^[241]47 for natural isotope
correction. Key parameters in Accucor were set as: resolution, 60000;
resolution_defined_at, 200. Finally, labeled metabolites with
normalized intensities of isotopologues were obtained as a csv file.
Isotopologue similarity networking
To perform isotopologue pattern similarity networking, we developed an
R package “IsoNet”. The input data for IsoNet analysis includes a
metabolite annotation table (.csv), a quantification table of labeled
metabolites(.csv), and a MS/MS spectral data file (.msp). First, IsoNet
calculates the isotopologue pattern similarity scores between two
labeled metabolites by the method as described above. The MS/MS
spectral similarity scores were calculated via a modified cosine score
function reported in GNPS^[242]48. The tolerance of m/z error was
15 ppm and the least matched fragment was 1. Key parameters for IsoNet
include: mid_cutoff, 0.7; mid_fc, 20; mid_isoDegree, 0.1;
mid_min_motifLen, 0.5; ms2_score_cutoff, 0.5; mid_max_motif, 1000;
mass_diff_freq_cut_off, 4; ignore_max, TRUE. Finally, the isotopologue
similarity network was generated in which one node represents one
isotopically labeled metabolite and an edge represents the two
metabolites had an isotopologue pattern similarity score >0.7 and a
similar MS/MS spectral similarity score >0.5. The isotopologue pattern
network was visualized in Cytoscape (v3.9.1) and the subnetworks
containing lipids were removed.
Unknown reaction discovery
We categorized metabolic reactions in the isotopologue pattern network
into three types, including known reactions, unknown reaction type I,
and unknown reaction type II. Among them, known reactions are of
characterized metabolic relationship between two known metabolites. For
known reactions, their reaction steps were calculated based on the
knowledge-based metabolic reaction network curated from the KEGG
database reported in our previous work of MetDNA^[243]8. This reaction
network comprises 6397 metabolites and 8129 reactions (Supplementary
Fig. [244]2e). The reaction step represents the minimum number of
metabolic reactions occurred between two metabolites in the metabolic
reaction network. Calculation of reaction distance between two
metabolites was realized by using a distance function in R package
“igraph” (v1.2.9), which determines the number of connections between
any two nodes within the network. By inputting KEGG IDs, the number of
reaction steps between two metabolites can be derived from the MRN
network. To annotate unknown reactions, we curated a delta mass library
(Supplementary Fig. [245]2 and Supplementary Data [246]3). We matched
the experimental mass differences (Δm/z) of metabolite pairs in the
isotopologue pattern network with a list of delta masses that are
associated with atom differences and reaction classes in the delta mass
library. The tolerance of delta mass match was set as 2 mDa. Annotated
information for each unknown reaction included the atom difference and
the reaction class. These annotated unknown reactions were classified
into type I and type II. Unknown reaction type I is of unreported
metabolic relationship between one known metabolite and one unknown
metabolite, while unknown reaction type II is that between two unknown
metabolites.
Elucidation of unknown reactions and structures
We used the known metabolic reactions in the reference reaction library
as references to deduce the structures of unknowns in the unknown