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Serum metabolomic alterations in multiple
myeloma revealed by targeted and untargeted
metabolomics approaches: a pilot study†
Venkatesh Chanukuppa,
ab
Tushar H. More,
ab
Khushman Taunk,
a
Ravindra Taware,
a
Tathagata Chatterjee,
c
Sanjeevan Sharma
d
and Srikanth Rapole *
a
Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid
proliferation of plasma cells, which leads to overproduction of antibodies. MM affects around 15% of all
hemato-oncology cases across the world. The present study involves identification of metabolomic
alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based
targeted and GC-MS based untargeted approaches. Several MM specific serum metabolomic signatures
were observed in this study. A total of 54 metabolites were identified as being significantly altered in MM
cohort, out of which, 26 metabolites were identified from LC-MRM/MS based targeted analysis, whereas
28 metabolites were identified from the GC-MS based untargeted analysis. Receiver operating
characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can
be projected as marker metabolites to discriminate MM subjects with higher specificity and sensitivity.
Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including
pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur
metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information
regarding MM induced serum metabolite alterations and their pathways, which could offer further
insights into this cancer.
1. Introduction
Multiple myeloma (MM) is reported as the second most preva-
lent hematological malignancy (15%) across the world.
1
MM is
characterized by rapid proliferation of plasma cells and their
subsequent accumulation in bone marrow which results in
overproduction of antibodies and eventually leads to bone
resorption.
2
It is one of the most dominant hematological
malignancies on the Indian subcontinent, where 4 out of every
100 000 people are detected with this cancer every year.
3
Development of MM is a complex process that is associated with
cytological abnormalities such as hyperdiploidy and chromo-
somal translocations.
4
Being a heterogeneous disease, MM can
be broadly categorized into the following types: monoclonal
gammopathy of undetermined clinical signicance (MGUS),
a benign form of MM; an intermediate stage called as smol-
dering myeloma and the symptomatic myeloma.
5
MGUS could
progress into smoldering myeloma, which nally turns into the
symptomatic myeloma. Despite recent developments in the
eld of hematological malignancy research, there is a signi-
cant gap regarding the exact nature of molecular changes that
occur during malignant evolution leading to MM.
It is well-known fact that malignant transformations are
associated with altered metabolic pathways mostly associated
with biosynthetic and bioenergetic processes.
6
Such metabolic
rearrangement or switching depicts an adjustment to aid cell
survival, tissue remodeling, tumor growth, as well as cancer
metastasis. Strikingly, as evident from available literature,
suggests that this metabolic adjustment is coordinated by
genomic programming and inveigled by the tumor microenvi-
ronment. Moreover, in some situations, the alteration in
metabolism plays a crucial role in oncogenesis.
6,7
Therefore, it is
of utmost importance to extract such metabolic deviations by
metabolomics approach and understand them in the context of
prognosis, therapy response as well as disease pathophysiology.
Metabolomics not only includes the utilization of various
analytical methodologies towards identication and quanti-
cation of the metabolites pertaining to a biological system, but
can monitor the changes at metabolites level in a variety of
clinical specimens such as serum, cells, tissue, urine and other
biological uids.
8–17
Earlier metabolomics studies in MM were
mostly carried out on in vitro models, such as cell lines, to
a
Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune-411007, MH,
India. E-mail: rsrikanth@nccs.res.in; Fax: +91-20-2569-2259; Tel: +91-20-2570-8075
b
Savitribai Phule Pune University, Ganeshkhind, Pune-411007, MH, India
c
Army Hospital (R&R), Dhaula Kuan, New Delhi-110010, DL, India
d
Armed Forces Medical College, Pune-411001, MH, India
†Electronic supplementary information (ESI) available. See DOI:
10.1039/c9ra04458b
Cite this: RSC Adv.,2019,9,29522
Received 13th June 2019
Accepted 11th September 2019
DOI: 10.1039/c9ra04458b
rsc.li/rsc-advances
29522 |RSC Adv.,2019,9, 29522–29532 This journal is © The Royal Society of Chemistry 2019
RSC Advances
PAPER
investigate metabolic alterations induced by the drug resis-
tance.
18,19
Apart from this, identication of potential targets
related to apoptosis
20
was also studied in context of MM.
Moreover, few clinical studies were also conducted to investi-
gate the serum metabolomics alterations upon disease remis-
sion.
21
However, to our knowledge, there is no report of
comprehensive serum metabolomic investigation using tar-
geted and untargeted metabolomic approaches for MM to
decipher the metabolic alterations crucial for understanding
the disease pathophysiology, which in future could be trans-
lated into disease diagnosis.
Therefore, in the present study, we have carried out an
elaborate MM serum metabolic proling of human patients
compared to healthy control subjects using both targeted and
untargeted approaches to identify metabolic ngerprint asso-
ciated with this disease.
2. Materials and methods
2.1 Subject selection, sample collection and processing
The study cohort for targeted metabolomic analysis comprised
of 48 volunteers (MM ¼24, control ¼24) recruited at the Armed
Forces Medical College (AFMC), Pune. For untargeted metab-
olomics analysis, 40 subjects (MM ¼21, control ¼19) from the
same cohort were utilized. As we collected the samples in a long
duration of sample collection period because of the reason that
the availability of the samples was scarce for this disease, the
number of samples for the LC-MS and GC-MS are different. As
we performed the GC-MS analysis earlier, the sample cohort for
this analysis has 21 healthy control and 19 MM serum samples.
Whereas, later we collected some additional samples, which
made the total of 24 serum samples each for healthy controls
and MM for the LC-MS analysis. The inclusion criteria for MM
cohort consisted of only freshly diagnosed patients with
minimum 18 years of age and without any past or present
anticancer drug interventions. For the control cohort, age and
gender matched individuals devoid of hypertension or diabetes
was selected. ESI Table S1†summarizes the demographic
information of the study cohort used in this study. The study
cohort was advised overnight fasting and blood samples were
collected the following morning. 5 mL of peripheral blood was
collected in serum separator tubes (BD Vacutainer™SST™II
Advance Tubes). The peripheral blood samples were kept for
clotting at room temperature and serum separation from the
blood was achieved by centrifuging samples at 1500gfor 5 min
at 15 C. Serum samples were then transferred to cryo-vials,
labeled and subsequently liquid nitrogen were used to snap
freeze the samples. Post snap freezing, the serum samples were
stored at 80 C ultra-low temperature freezer until further use.
All experiments were performed in compliance with guidelines
mentioned in Declaration of Helsinki (DoH, 2013). Ethical
committees of National Centre for Cell Science (NCCS), Pune
and Armed Forces Medical College (AFMC), Pune approved this
study (IRB# NCCS/IEC/2016-I/8). The experimental aspects and
investigations to be undertaken were clearly explained to each
participants and written informed consent was obtained from
them. Each subject participated voluntarily for this study.
2.2 Targeted LC-MRM/MS based metabolomic proling
In-house built MRM library of 120 metabolites was used to
investigate the metabolic alterations in the serum of MM
study cohort. These metabolites were selected based on the
previous literature survey of various cancer metabolomic
studies.
22
Thechromatographyparametersanddetailsof
MRM library are discussed in our previous report
23
and the
same have been utilized for this study as well. Metabolites
extraction was executed by the addition of 200 mL methanol
to 25 mL of every serum samples, vortexed vigorously and
incubated at 20 C overnight. Moreover, d2L-phenylalanine
(40 ng) was added to the samples prior to extraction, which
served as an internal standard. Post incubation, the samples
were vortexed vigorously and subjected to 14 000gcentri-
fugation at 4 C for 10 min. The supernatant was then passed
through centrifugal lters (Corning® Costar® Spin-X®) with
0.22 mm nylon membrane. Filtrate obtained was then vacuum
dried and reconstituted in 25 mL of sample reconstitution
buffer (a mixture of 6.5 mL acetonitrile, 2.5 mL methanol,
1 mL water and 0.2 mL formic acid; all the solvents were LC-
MS grade) for acquiring the samples in positive ionization
mode mass spectrometry. In case of negative ionization
mode, the vacuum dried metabolites were reconstituted in 25
mL of ultrapure LC-MS grade water. Subsequently, 10 mLof
each sample was analyzed using a Shimadzu Prominence
HPLC system (Shimadzu Corporation, Japan) connected to
a triple quadrupole mass spectrometer, 4000 QTRAP (AB
SCIEX, USA). For positive mode ionization, metabolites were
separated through the XBridge™HILIC column (5 mm, 4.6
150 mm, Waters Corp, USA) and Atlantis™T3 column (5 mm,
4.6 150 mm, Waters Corp, USA) was used for negative
ionization mode. QC samples, prepared from the mixture of
pure metabolite standards, were run aer every 5 serum
metabolite sample injections to assess the uniform instru-
ment performance.
The LC buffers and chromatography parameters along with
MS sample acquisition parameters were adopted from our
earlier studies as mentioned elsewhere.
23
Positive ionization
chromatographic separation was carried out using 32 min
gradient program of mobile phase A (10 mM ammonium
formate with 0.1% formic acid in water) and mobile phase B
(acetonitrile with 0.1% formic acid) with 700 mLmin
1
elution
rate. The gradient started with 95% B and further decreased to
40% B over next 15 min and held constant for 6 min, and then
the column was gradually returned to 95% B and equilibrated
for another 9 min. The negative mode ionization chromatog-
raphy was carried out with elution rate of 500 mLmin
1
and
40 min gradient program consisting of 0% B at initial stage
andgraduallyincreasedto98%overnext24minandmain-
tained for 5 min in same state and eventually returned to 0% B.
Finally, the column was equilibrated with 100% solvent A for
9 min. Mass spectra were acquired using following set of
parameters; MS source temperature 400 Cwithinterface
heater on, curtain gas: 30 and entrance and exit potentials: 10,
two ion source gasses at 45 arbitrary units. LC-MRM/MS data
analysis was performed using Analyst 1.5 soware (Sciex, USA).
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9, 29522–29532 | 29523
Paper RSC Advances
Analyst quantitation wizard was used for the peak area inte-
gration. For elimination of biasness and to maintain quality
control measures, samples were randomized during sample
analysis and peak integration performed in a blinded manner.
Further, the peak area values obtained aer performing the
integration analysis were exported to a spreadsheet le for
further analysis.
2.3 Untargeted GC-MS-based metabolic proling
Metabolite extraction was performed as described in LC-MS
methodology. Additionally, two-step derivatization method was
applied to the dried metabolite extract to produce methyl and silyl
derivatives. First step was methylation, which was achieved by
addition of 30 mL of methoxyamine hydrochloride solution dis-
solved in pyridine (20 mg mL
1
) and incubating at 37 Cfor90min.
In the second step, silylation reaction was performed by adding 30
mL of BSTFA with 1% TMCS solution followed by 1 h incubation at
70 C. 1 mL of the derivatized sample was injected into the injection
port (250 C) of the Agilent 5975C GC system (Agilent Technologies,
USA) operating under splitless mode and connected to Agilent
5977A mass selective detector. Metabolites were separated on HP-5
MS ultra-inert fused silica capillary column (30 m 0.25 mm0.25
mm,Agilent,USA).UHPgradeheliumwasusedasthecarriergas
with a ow rate of 1 mL min
1
. Following parameters were used to
obtain the mass spectra; column inlet temperature 280 C, MS
quadrupole temperature 150 C, ion source temperature 230 C. GC
oven temperature settings are as follows; the gas chromatography
program started with initial oven temperature of 50 Cheldfor
2 min, then temperature was gradually increased to 100 Cat
3Cmin
1
rateandheldfor2minandtherampedupfurtherto
200 Cat3Cmin
1
and maintained for 2 min at this temperature.
The column temperature was lowered to 50 Cinnext5minand
equilibrated for 2 min. Full scan mode range from m/z50 to 550
was used for the metabolite data acquisition. Integration and
deconvolution of chromatograms were carried out by using the
AMDIS algorithm of Agilent Chemstation soware. Metabolites
identity was conrmed using NIST 11 standard mass spectral
database (NIST, Gaithersburg, MD). Peak areas of the metabolites
were exported in a matrix format to spreadsheet le aer chro-
matographic integration. Metabolic standards initiative (MSI)
guidelines were followed for performing the all metabolomic
experiments.
24
Fig. 1 Multivariate statistical analysis of targeted LC-MRM/MS MM serum metabolomics: (a) distribution of MM subjects (n¼24, blue) and healthy
controls (n¼24, green) in OPLS-DA score plot, (b) permutation validation of OPLS-DA score plot. Total 200 permutations were carried out on
OPLS-DA model and obtained R
2
¼0.978 and Q
2
¼0.952, where R
2
and Q
2
indicate the explained variance and predictive ability respectively, (c)
hierarchical clustering analysis depicting the segregation of MM (blue) and healthy controls (green).
29524 |RSC Adv.,2019,9, 29522–29532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
2.4 Data pre-processing and statistical analysis
The peak areas of identied metabolites in both targeted and
untargeted approaches were subjected to statistical analysis
usingMetaboanalyst4.0andSIMCA14(Umetrics,Umea,
Sweden). Metabolites having missing value >50% were dis-
carded from the datasets and those metabolites having <50%
missing value were subjected to missing value imputation by
half of the minimum positive value. Both datasets were
normalized to remove the major differences and make meta-
bolic features more comparable to each other.
22
The LC-MRM/
MS data-set was normalized to median, cube root transformed
and range scaling method was used for data scaling. Similarly,
GC-MS data was normalized to sum, cube root transformed
and auto-scaled.
25
Univariate and multivariate statistical
analyses were applied to these normalized datasets in order to
identify the differentially expressed metabolites that can
unambiguously discriminate MM subjects from their respec-
tive controls.
23,26
Univariate statistical methods such as Wil-
coxon rank t-test (p<0.05)andlog2foldchange($0.58/
#0.58) were used to select statistically signicant metabo-
lites in both the datasets. Furthermore, multivariate statistical
methods like principal component analysis (PCA), partial least
squares discriminant analysis (PLS-DA) and orthogonal partial
least squares discriminant analysis (OPLS-DA) were employed
to visualize the overall distribution of serum metabolites
among the study cohort as well as their efficacy in discrimi-
nation of MM subjects from the respective controls. Unlike
PCA, which is an unsupervised multivariate statistical
approach, PLS-DA and OPLS-DA are supervised multivariate
statistical methods and a cross validation is needed to avoid
over-tting of the data. The OPLS-DA model was veried by
building 200 random permutation models and comparing
their performance (R
2
-goodness of tandQ
2
-predictive ability
of the model) with the original model.
23
Similarly, PLSDA
model was also veried.Furthermore,variableimportancein
projection (VIP) score of OPLS-DA model was used to rank the
metabolites as per their contribution in segregation of MM
subjects from the controls in the score plot. To select the
marker metabolites, receiver operating characteristic (ROC)
curve analysis was carried out to identify the features with
highest specicity, sensitivity and accuracy to segregate the
MM subjects from controls.
2.5 Pathway analysis
Both the datasets, targeted and untargeted were explored
towardsmetabolicpathwayanalysisforidentication of the
MM inducible metabolic pathway alterations. The analyses
were carried out by MetPA tool of the MetaboAnalyst 3.0 web
application (http://metpa.metabolomics.ca). Pathway impact
score was used as a measure to identify the altered metabolic
pathways. The pathway impact score was calculated based on
number of matched metabolites from datasets to a particular
metabolic pathway. The hypergeometric test was used for over-
representation analysis that tested for its enrichment in
Table 1 Statistically significant differentially regulated serum metabolites identified through the LC-MS based targeted approach
Sr. no. Metabolite VIP score p-Value FDR adjusted p-value Fold change AUC
Control
CV% MM CV%
1 NAD 1.69 1.28 10
23
7.97 10
22
17.56 0.97 16.49 13.84
2 Adenosine 1.69 1.99 10
23
7.97 10
22
13.78 0.97 23.65 18.12
3 CYTIDINE 1.63 1.78 10
16
3.56 10
15
5.56 0.97 23.68 23.49
4 Adenine 1.60 9.36 10
17
2.50 10
15
3.62 0.98 27.44 17.32
5 Oxaloacetic acid 1.55 9.02 10
16
1.44 10
14
0.01 0.99 18.23 17.46
6 Guanosine 1.51 5.17 10
14
6.89 10
13
3.03 0.97 21.45 25.68
7L-Methionine 1.38 2.41 10
9
2.75 10
8
0.60 0.94 24.58 27.84
8 Mono-ethylmalonate 1.38 3.06 10
9
3.06 10
8
0.63 0.92 27.96 21.52
9L-Leucine 1.34 4.65 10
8
4.13 10
7
0.67 0.91 14.94 16.75
10 L-Homoserine 1.31 2.56 10
7
2.05 10
6
0.64 0.90 22.34 25.73
11 L-Threonine 1.30 4.42 10
7
2.95 10
6
0.64 0.89 25.62 21.98
12 Thymine 1.28 3.68 10
6
1.92 10
5
0.62 0.87 18.82 16.72
13 Methyl malonate 1.18 3.48 10
7
2.53 10
6
1.63 0.90 12.57 15.64
14 L-Citrulline 1.17 3.02 10
6
1.86 10
5
0.52 0.86 18.93 21.88
15 Uracil 1.17 2.49 10
5
9.04 10
5
0.65 0.81 28.72 24.42
16 L-Cysteine 1.17 1.79 10
5
6.83 10
5
0.71 0.84 22.26 26.84
17 L-Arginine 1.15 1.79 10
5
6.83 10
5
0.65 0.82 15.82 19.68
18 Xanthosine dihydrate 1.09 0.000619 0.001415 0.62 0.76 25.68 18.94
19 Riboavin 1.09 4.14 10
6
1.95 10
5
2.90 0.87 19.84 23.46
20 UTP 1.08 4.98 10
5
0.000159 0.49 0.92 22.56 26.78
21 Succinic acid 1.07 4.09 10
5
0.000136 1.50 0.84 14.98 21.36
22 L-Glutamine 1.06 6.66 10
5
0.000197 0.67 0.81 18.96 19.14
23 Xylitol 1.04 0.000213 0.000567 1.73 0.86 22.46 21.58
24 Adonitol 1.04 0.000124 0.000355 1.81 0.87 18.92 27.65
25 Cytosine 1.01 6.42 10
5
0.000197 2.96 0.85 19.74 23.74
26 Thyroxine 1.00 0.000147 0.000406 0.60 0.79 29.76 19.52
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9, 29522–29532 | 29525
Paper RSC Advances
aspecic pathway and was compared according to random
hits. Further the genes associated with the differentially
expressed metabolites were identied using Kyoto Encyclo-
pedia of Genes and Genomes (KEGG) database and Human
Metabolome Database (HMDB).
3. Results
3.1 LC-MRM/MS based targeted metabolomic proling
In targeted approach, 81 metabolites were consistently detec-
ted in all samples out of 120 metabolites targeted. We have
built the LC-MRM/MS analysis method for this study by
nding the MRM transitions for each metabolite using pure
standards purchased from Sigma Aldrich. Representative
HILIC and T3 column chromatograms of LC-MRM/MS analysis
are presented in ESI Fig. S1.†Univariate statistical methods
were applied to identify metabolites that are signicantly
differing in their concentrations among MM subjects and
controls.Further,toextractthe MM inducible metabolomic
signature, multivariate statistical methods such as PCA, PLS-
DA and OPLS-DA were carried out. Modest segregation was
foundinthe2DscoreplotofthePCAindicatingthepresence
of concentration differences among the metabolites of MM
study cohort (ESI Fig. S2†). Moreover, a supervised multivar-
iate statistical approach such as OPLS-DA was used to sharpen
the differences between the MM subjects and their respective
controls. Robust group segregation was evident from the
OPLS-DA score plot which indicates the metabolic alterations
exist in the MM study cohort (Fig. 1a). The model based on
OPLS-DA statistics was further authenticated by the permuta-
tion test utilizing 200 permutations in between the data
groups to avoid biasness due to data over tting (Fig. 1b). The
permutation test yielded R
2
value of 0.978 and Q
2
value of
0.952 depicting that the original model has performed better
than the permutated models and assures of good prediction
ability. VIP score was used as criteria to select the metabolites
that had highest impact to group separation in score plot of
OPLS-DA. Combination of univariate and multivariate statis-
tical approaches (VIP > 1, FDR adjusted p<0.05,log2FC>
0.58/<0.58) were used to propose the panel of metabolites
that can discriminate MM subjects from the controls (Table 1).
It revealed that 26 metabolites were signicantly differed
among MM study cohort, out of which 11 metabolites were up-
regulated while 15 metabolites were down-regulated in MM
subjects as compared to control (Table 1). Hierarchical
Fig. 2 Heat map of LC-MRM/MS based differential metabolites between healthy controls and MM serum samples. The colors from green to red
indicate the increased amount of metabolites (C1–C24: healthy controls, M1–M24: multiple myeloma).
29526 |RSC Adv.,2019,9,29522–29532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
clustering analysis depicted distinct clusters of MM and
control subjects, which demonstrate metabolic adaptation of
MM subjects is different when compare to healthy controls
(Fig.1c).Furtherheatmapforthedifferentially expressed
metabolites found through LC-MS analysis were showed in
Fig. 2 and S4.†
3.2 GC-MS based untargeted metabolomic proling
In untargeted approach, 153 metabolites were detected in MM
study cohort. Retention time and NIST library metabolite
matching score information of the altered metabolites identi-
ed using untargeted GC-MS approach is mentioned in ESI
Table S2†as per the MSI guidelines.
24,27
For untargeted data
analysis, the metabolites that had matching score >80% with
the NIST library search were only considered. ESI Fig. S3†
depicts a representative GC-MS spectrum for MM serum
metabolites. The same set of statistical treatments (univariate
and multivariate) mentioned above were performed for the
untargeted dataset in order to identify the metabolites that are
altered signicantly upon MM induction. Multivariate statis-
tical models like PCA and OPLS-DA were built from the untar-
geted dataset to detect the metabolic ngerprint unique to MM.
PCA 2D score plot depicted the fairly segregated MM and
control population, which indicates the intrinsic concentration
differences exist among the study cohort (ESI Fig. S4†).
Furthermore, distinct group separation was observed in the
OPLS-DA score plot and metabolic features contributing most to
the segregation were identied by the VIP score > 1 (Fig. 3a). A
permutation test with 200 permutations was performed to
validate the performance of the OPLS-DA model that was built
for the MM untargeted metabolite dataset. The permutation test
indicates the original model (R
2
value ¼0.967 and Q
2
value ¼
0.81) is better than permutated models and has not over tted
the data (Fig. 3b). Hierarchical cluster analysis was also in
accordance with our earlier ndings, revealing clear segregation
of study cohort into MM subjects and controls (Fig. 3c). Total 28
most signicant metabolites were discovered by using
Fig. 3 Multivariate statistical analysis of untargeted GC-MS MM serum metabolomics. (a) OPLS-DA score plot depicting the robust separation of
MM subjects (n¼19, blue) from healthy controls (n¼21, green), (b) permutation validation of OPLS-DA plot obtained after performing 200
random permutations which yielded R
2
¼0.967 and Q
2
¼0.81, (c) hierarchical clustering analysis representing good clustering of study subjects.
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9,29522–29532 | 29527
Paper RSC Advances
combination of univariate and multivariate approaches (VIP > 1,
FDR adjusted p< 0.05, log 2 FC > 0.58/<0.58) of which 16
metabolites were up-regulated and 12 metabolites were down-
regulated in MM as compared to control (Table 2). Further,
heat map for the differentially expressed metabolites found
through GC-MS analysis were showed in Fig. 4 and S5.†
3.3 Marker metabolite selection from targeted and
untargeted datasets
Both targeted and untargeted datasets were subjected to the
ROC curve analysis to select the metabolites with the highest
specicity, sensitivity and accuracy capable of discriminating
the MM subjects from respective controls. The area under the
curve (AUC) is used as a measure of the predictive ability of
a metabolite in ROC curve analysis. Based on AUC, six metab-
olites each from both the datasets were projected as marker
metabolites to discriminate the MM subjects from their
respective controls. In targeted LC-MRM/MS dataset, metabo-
lites such as NAD, adenosine, cytidine, adenine, oxaloacetic
acid and guanosine were identied as marker metabolites and
their expression pattern along with ROC curve plot is repre-
sented in Fig. 5a and b. Likewise, in GC-MS analysis, D-ribo-
hexitol, beta-D-glucopyranosiduronic acid, hexadecanoic acid
1,5-anhydro-D-sorbitol, uridine and D-talofuranose are consid-
ered as predictive metabolites and their ROC curve plot along
with expression pattern is depicted in Fig. 5c and d.
3.4 Identication of altered MM metabolome associated
pathways and genes
To evaluate the impact of MM on metabolic pathways, metab-
olites from both the MRM based LC-MRM/MS and GC-MS
analysis were subjected for pathway analysis using the MetPa
tool of MetaboAnalyst 3.0. Pathway analysis results are
demonstrated in Fig. 6. Top pathways emerged as pyrimidine
metabolism; alanine, aspartate and glutamate metabolism;
glycine, serine and threonine metabolism; cysteine and methi-
onine metabolism; purine metabolism; nitrogen metabolism;
sulfur metabolism; and citrate cycle (ESI Table S3†). In order to
identify the MM differentially regulated metabolites associated
genes, 54 serum metabolites were mapped using Kyoto Ency-
clopedia of Genes and Genomes (KEGG) and human metabolic
data base. This analysis resulted in identication of 676 genes
which could be related to the MM differentially expressed
metabolites (ESI Table S4†).
4. Discussion
At present, detection of MM is majorly dependent on morpho-
logical criteria and its multi-symptomatic nature makes diagnosis
and prognosis challenging. This demands the identication of
novel clinically relevant molecular markers and potential targets
for developing new therapies for MM. Cancer and metabolism
share close connectivity as malignant cells undergoes profound
Table 2 Statistically significant differentially regulated serum metabolites identified through the GC-MS based untargeted approach
Sr. no. Metabolite VIP score p-Value FDR adjusted p-value Fold change AUC
Control
CV% MM CV%
1D-Ribo-hexitol 2.30 4.20 10
20
6.43 10
18
7.08 1.00 19.36 23.58
2 Beta-D-glucopyranosiduronic acid 2.25 1.81 10
18
1.39 10
16
5.81 1.00 28.92 23.74
3 Hexadecanoic acid 1.93 8.82 10
8
4.50 10
6
1.80 0.95 18.62 14.85
4 1,5-Anhydro-D-sorbitol 1.84 3.49 10
7
1.07 10
5
0.25 0.89 26.35 28.61
5 Pseudo uridine 1.83 2.35 10
7
8.97 10
6
6.11 0.95 12.32 16.78
6D-Talofuranose 1.77 1.51 10
6
3.84 10
5
0.37 0.91 29.84 26.74
7 Glucofuranoside 1.66 1.54 10
5
3.36 10
4
2.73 0.91 23.48 25.68
8N-Acetyl glucosamine 1.52 2.39 10
4
3.03 10
3
0.56 0.82 19.46 23.42
9 Purine 1.51 1.15 10
4
1.88 10
3
0.45 0.84 26.59 28.32
10 Beta-D-galactopyranoside 1.51 1.23 10
4
1.88 10
3
0.42 0.85 21.65 25.38
11 Pyrimidine 1.50 6.16 10
5
1.18 10
3
0.40 0.83 20.28 22.07
12 2-Piperidinecarboxylic acid 1.50 1.60 10
4
2.22 10
3
0.44 0.81 14.63 16.09
13 D-Lactose 1.42 3.18 10
3
2.01 10
2
1.64 0.75 12.59 16.83
14 D-Xylofuranose 1.42 4.41 10
4
4.49 10
3
2.80 0.77 15.97 16.23
15 L-Threonine 1.42 2.06 10
3
1.50 10
2
0.20 0.69 15.86 14.93
16 Nonadecanoic acid 1.41 8.80 10
4
8.42 10
3
2.36 0.83 24.46 26.66
17 L-Threitol 1.40 3.58 10
4
3.92 10
3
2.24 0.81 26.32 27.39
18 D-Glucopyranose 1.39 3.00 10
3
2.00 10
2
2.39 0.81 22.71 25.80
19 2-Alpha-mannobiose 1.38 5.87 10
3
3.10 10
2
1.50 0.73 27.28 29.84
20 Cystathionine 1.33 3.51 10
3
2.07 10
2
0.63 0.80 23.40 14.67
21 Stearate 1.27 2.82 10
3
1.96 10
2
2.11 0.87 15.82 13.26
22 Inositol 1.25 1.42 10
3
1.15 10
2
1.54 0.80 18.54 19.23
23 L-Asparagine 1.20 1.94 10
3
1.48 10
2
0.49 0.85 25.63 27.52
24 Arachidonic acid 1.20 1.13 10
2
4.93 10
2
1.60 0.71 16.92 20.84
25 Beta-D-galactofuranose 1.19 7.39 10
3
3.54 10
2
2.04 0.84 24.36 19.56
26 D-Xylopyranose 1.18 1.31 10
3
1.15 10
2
0.23 0.79 25.87 27.32
27 Stigmasterol 1.18 1.38 10
3
1.15 10
2
0.54 0.80 28.39 27.65
28 Maltitol 1.10 7.65 10
3
3.55 10
2
3.51 0.74 26.57 28.34
29528 |RSC Adv.,2019,9, 29522–29532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
metabolic rearrangements to support its proliferative nature and
bioenergetics. Hence, in this study, we attempted to explore the
serum metabolomic alterations in MM using LC-MRM/MS based
targeted as well as GC-MS based untargeted metabolomics. The
study showed profound changes in serum metabolites of MM
patients as compared to controls. The major metabolic changes
identied are discussed further herewith.
Targeted LC-MRM/MS analysis unveiled signicant alter-
ations of 26 metabolites in MM serum as compared to control.
These differentially expressed metabolites consist of 11 up-
regulated and 15 down-regulated metabolites. NAD was
observed to be up-regulated in MM subjects as compared to the
controls, which is considered as a key molecule in various
biological processes including energy metabolism and enzy-
matic regulation.
28
It is also the substrate for non-redox reac-
tions that affect gene expression such as Ca
2+
transport and
apoptosis.
29
NAD is involved in the important post-translational
modications of proteins such as acylation and deacylation by
SIRTs enzymes.
30
Moreover, the aberrant NAD metabolism is
reported in various cancers.
31
Adenosine, which is found to be
increased in MM serum, is an important immunosuppressive
molecule.
32
Extracellular adenosine bind to adenosine receptors
of immune cells suppressing the pro-inammatory activity of
the cells.
32
Furthermore, the activation of adenosine receptors
increases inammatory molecules and immunoregulatory cells
governing a long-lasting immunosuppressive environment.
32
This leads to the discouragement of any antitumor immune
responses induced by the body. Oxaloacetic acid (OAA), a key
intermediate in the TCA cycle involved in energy production is
downregulated in MM subjects when compared with controls.
The reason for depleted levels of oxaloacetic acid in serum
could be explained by the fact that oxaloacetate along with
citrate is used to generate acetyl COA and aspartate that is used
in the synthesis of nucleotides and lipids quite extensively in
proliferating cells. It is also in agreement with the earlier report
that OAA can induce apoptosis in hepatocellular carcinoma
cells via inhibition of glycolysis and restoring oxidative phos-
phorylation.
33
Thus, decreased levels of OAA in MM serum
samples also suggest a prevalence of glycolysis over oxidative
phosphorylation in MM subjects. Guanosine is formed as an
intermediate of the nucleic acid breakdown.
34
During purine
salvage, guanosine can be cleaved further to release guanine,
which is salvaged back to GMP by the enzyme hypoxanthine-
guanine phosphoribosyl transferase (HGPRT).
34
Increased
concentrations of guanosine in MM serum imply that the
purine salvage is highly active in MM. In addition, elevated
levels of cytidine, and adenine in MM represents an overall
increase in nucleotide metabolism to support proliferation.
34
Fig. 4 Heat map of GC-MS based differential metabolites between healthy controls and MM serum samples. The colors from green to red
indicate the increased amount of metabolites (C1–C21: healthy controls, M1–M19: multiple myeloma).
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9, 29522–29532 | 29529
Paper RSC Advances
In untargeted GC-MS analysis, signicant changes in 28
metabolites were observed that included 16 up-regulated and 12
down-regulated metabolites. Concentration of hexadecanoic acid
(palmitic acid) was found to be increased in MM serum as
compared to controls. It is reported that increased palmitic acid
level is associated with TL4 mediated invasiveness in pancreatic
cancer.
35
Moreover, palmitic acid supplementation has shown to
rescue adiponectin-induced apoptosis in MM cells.
36
Therefore,
our observation of increased palmitic acid in MM might be
linked withits role in invasiveness and proliferationthat needs to
be further investigated. Further, decrement in 1,5-anhydro-D-
sorbitol was observed in MM serum, which is an indicator of
glycaemic control. Its levels manifest the activity of polyol path-
ways that are mostly functional in hyper glycaemic conditions.
37
Increase in uridine levels reects active involvement of pyrimi-
dine metabolism which is common metabolic alteration
observed in cancer.
34
It is well-known fact that, N or O linked N-
acetylglucosamine (GlcNAc) is one of the prominent post-
translational modications observed on proteins displayed on
the cell surface of malignant cells. Moreover, it is now established
that GlcNAc modied proteins aid in cell migration and inva-
sion.
38
GlcNAc is reported to be mediating cell signaling directly
and indirectly.
39
The modication of epithelial cadherin (E-
cadherin) with O-linked b-GlcNAc is reported to impair cell
Fig. 5 Marker metabolites selected by ROC curve analysis (a) concentration differences between significant metabolites from targeted analysis of MM
(red) and healthy control (green) samples demonstrated by box-and-whiskerplots,(b)theROCcurveanalysisofmarkermetabolitesviz. NAD (AUC ¼
0.97, sensitivity ¼0.95, specificity ¼0.96, CI ¼95%), adenosine (AUC ¼0.97, sensitivity ¼0.95, specificity ¼0.92, CI ¼95%), cytidine (AUC ¼0.97,
sensitivity ¼0.91, specificity ¼0.96, CI ¼95%), adenine (AUC ¼0.98, sensitivity ¼0.95, specificity ¼0.96, CI ¼95%), oxaloacetic acid (AUC ¼0.99,
sensitivity ¼0.92, specificity ¼0.97, CI ¼95%) and guanosine (AUC ¼0.97, sensitivity ¼0.87, specificity ¼0.88, CI ¼95%) from targeted analysis, (c)
concentration differences between significant metabolites from untargeted analysis of MM (red) and healthy control (green) samples demonstrated by
box-and-whisker plots, (d) the ROC curve analysis of marker metabolites viz. D-ribohexitol (AUC ¼1, sensitivity ¼0.94, specificity ¼0.95, CI ¼95%),
beta-D-glucopyranosiduronic acid (AUC ¼1, sensitivity ¼0.94, specificity ¼0.95, CI ¼95%), hexadecanoic acid (AUC ¼0.95, sensitivity ¼0.84,
specificity ¼0.85, CI ¼95%), anhydro-D-sorbitol (AUC ¼0.89, sensitivity ¼0.80, specificity ¼0.85, CI ¼95%), uridine (AUC ¼0.95, sensitivity ¼0.84,
specificity ¼0.90, CI ¼95%) and D-talofuranose (AUC ¼0.91, sensitivity ¼0.75, specificity ¼0.79, CI ¼95%) from GC-MS analysis.
29530 |RSC Adv.,2019,9, 29522–29532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
adhesion and promote tumour cell invasion.
40
We have observed
decreased levels of GalNAc in MM serum which strengthens the
hypothesis of its preferred utilization for post-translational
protein glycosylation in malignant condition. For rapid and
continuous synthesis of cell constituting basic machinery,
proliferating cancerous cells have high demands of nucleotides.
This gets achieved by either nucleotide salvage pathways of
purines and pyrimidines or de novo synthesis or a combination of
both.
34
Nucleotide salvage pathway utilizes downstream compo-
nents of degraded nuclear material like DNA or RNA in the form
of purines and pyrimidines to resynthesize the dNTPs for DNA/
RNA formation.
34
Decrement of both purines and pyrimidines
in GC-MS analysis reinforce hyperactivity of the nucleotide
salvage pathway to full the proliferative demand.
The signicant metabolites obtained from both approaches
were further evaluated for their role in various physiological
pathways. The metabolic pathway analysis revealed perturba-
tions in alanine, aspartate and glutamate metabolism, pyrimi-
dine metabolism, cysteine and methionine metabolism,
glycine, serine and threonine metabolism, purine metabolism,
nitrogen metabolism, sulfur metabolism, and citrate cycle (TCA
cycle). Several metabolites from purine and pyrimidine metab-
olism have been deregulated which is in good agreement with
previous reports from various cancer metabolomics studies.
41
Sulfur metabolism is important in the generation of sulfur-
containing amino acids that perform many vital functions.
42
Alterations in the TCA cycle, as observed in this study, has been
demonstrated to play a vital role in carcinogenesis.
43
Our results
demonstrate that certain key metabolic pathways including
NAD metabolism, TCA cycle, nucleotide metabolism and
sulphur metabolism etc. gets altered in MM and targeting these
pathways and its intermediary metabolites may help in devel-
oping novel targets for treating MM.
5. Conclusions
Overall, the present study demonstrates the potential of serum
metabolomics approach towards the segregation of MM cohort
from healthy controls. Increased metabolite coverage was ob-
tained with the use of different metabolomics platforms such as
LC-MRM/MS and GC-MS. The results obtained are noteworthy
and helpful in discrimination of MM individuals from healthy
controls. We believe that these results may be useful in future
diagnostic, prognostic and therapeutic development. In addi-
tion, these results will help to understand the disease patho-
physiology of MM in the context of altered cancer metabolism.
However, thorough validation of the inferences drawn from this
study in a large cohort of samples is required before its imple-
mentation in the clinical setting. Validation in large cohort of
patients for the metabolite based biosignature, identied in this
study, can help diagnose MM in early stages. Early diagnosis
will eventually lead to proper on-time therapeutic interventions
from the clinicians, which could help in better management of
this disease, thereby improving the patient life and survival
expectancy.
Author contributions
Conceived the study: VC, TC, SS, SR; designed the study: VC, TC,
SS, SR; performed the experiments: VC, THM; compiled and
analyzed data: VC, THM, KT, RT, SR; statistical analysis: VC,
THM, SR; provided clinical samples: TC, SS; provided chemicals
and reagents: SR. All authors reviewed the manuscript and
contributed in writing.
Conflicts of interest
Authors declare that they do not have any conict of interests.
Acknowledgements
General: We thank all the volunteers for participating in this
study. Funding: authors would like to acknowledge Department
of Biotechnology (DBT), Govt. of India, grant no. BT/PR10855/
BRB/10/1330/2014 and NCCS intramural funding. VC and RT
acknowledge Council of Scientic and Industrial Research
(CSIR), New Delhi, India for fellowship.
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