Metabolomics Analysis of
Tuberculosis Drug Activity Using an
Agilent 6545 Q-TOF LC/MS
Application Note
Metabolomics

Authors

Abstract

Travis E. Hartman1 and Kyu Y. Rhee1,2Tuberculosis (TB) is both the leading cause of deaths due to an infectious

1

Division of Infectious Diseases,
Department of Medicine

2

Department of Microbiology and
Immunology, Weill Cornell Medical
College, New York, NY 10065, USAdisease and the leading cause of deaths due to a curable disease1. However,drug resistance1 is increasing while the pipeline of new drugs stagnates, andknowledge of existing drugs remains incomplete. Pyrazinamide (PZA) is a frontline
TB drug1 whose mechanism of action remains among the most poorly understood.
This Application Note presents a high-performance ion-pairing reversed-phase(IP-RP) Q-TOF LC/MS method that has enabled the biologically unbiased study

Yuqin Daiof the impact of PZA on the Mycobacterium tuberculosis metabolome. Coupled

Agilent Technologies, Inc.

with batch feature extraction and multivariate statistical analysis software, this

5301 Stevens Creek Blvd,

workflow enabled the discovery of activity-specific metabolic changes that may

Santa Clara, CA 95051 USA

help explain PZA’s unique metabolic effects.

Introduction

Experimental

Pyrazinamide (PZA) is a frontline drug
used in the treatment of tuberculosis(TB)1. Unfortunately, widespread
use has resulted in growing rates of
PZA resistance, while knowledge of
PZA’s mechanism of action remains
unresolved1. Existing knowledge indicatesthat PZA is a prodrug that undergoes
conversion to pyrazinoic acid (POA), thepyrazine analog of nicotinic acid, by the
NAD salvage pathway-specific enzyme
nicotinamidase (PncA)1. Knowledge of thedownstream biochemical consequencesof POA production is incomplete. NAD
biosynthesis and NAD‑dependent
reactions are two potentially direct, but
understudied, biochemical targets of PZA.
The reasons for this are both biologicaland experimental in nature due, in part,to limitations of existing analyticalmethodologies that have hindered
broad and sensitive detection of cellular
metabolites.

LC/MS Metabolomics workflow

This Application Note presents a highperformance Q-TOF LC/MS methodthat enabled the biologically unbiased
study of the impact of PZA on the
Mycobacterium tuberculosis metabolome.
In this method, the retention time
reproducibility and compound separationof the IP-RP chromatography2 combinedwith the high resolution and massaccuracy of the Agilent 6545 Q-TOF
improved selectivity, analytical sensitivity,
and reproducibility for metabolites in
profiling experiments. Furthermore,
the Agilent SWARM Autotune3 and
Jet Stream (AJS)4 technologies wereused to selectively enhance signalsof smaller, labile metabolites andchallenging phosphorylated metabolites
by optimizing the ion transmission of a
Q-TOF, and improving the efficiency of ion
generation and sampling.

LC/MS Method

A Q-TOF LC/MS metabolomics workflow(Figure 1) was developed. This workflow
includes the following steps:
1.

Acquire high quality data from the
tuberculosis cell extracts (threesamples/biological group of six
biological groups) using the Q-TOF
LC/MS with an AJS source innegative ion mode.

2.

Find metabolites from raw datausing the Profinder5 Batch Targeted
Feature Extraction algorithm witha user‑created Agilent Personal
Compound Database and Library(PCDL) file (.cdb) containing 111
metabolites.

3.

Perform differential analysis using
Mass Profiler Professional (MPP)(B.14.0), and map the differential
metabolites to KEGG pathways togain insight into the mechanistic
details of PZA activity.
Acquire high quality
LC/MS data

The ion-pairing reversed-phase (IP-RP)
chromatography method2 was used toachieve a wide coverage of metaboliteclasses and improve retention time
reproducibility. The previous LC gradient2
was slightly modified by holding 99 %B
for an additional 2 minutes to ensure
all coenzyme A (CoA) metabolites were
eluted. LC/MS analyses were performed
using an Agilent 1290 Infinity UHPLC
system coupled to an Agilent 6545 Q-TOF
with either Dual Electrospray (ESI) or
Dual AJS source. The LC consisted of abinary pump with seal wash (G4220A), an
autosampler (G4226A) with thermostat(G1330B), a thermostatted column
compartment (G1316C), and an isocraticpump (G1310A) with a 100:1 splitter(G1607-60000) for reference mass
addition. Dynamic mass axis calibration
was achieved by continuous infusion ofa reference mass solution. Tables 1 and2 summarize the optimized LC and MS
conditions.

Batch targetedfeature extraction

Differential analysis andpathway mapping

Profinder

MPP

Version B.08.00
Build 8.0.888.0

Version 14.0
Build 225823

Figure 1. LC/MS metabolomics workflow for high quality data acquisition and analyses.

2Preparation of metabolite
standards and TB cell extracts

For analytical method optimization, allmetabolite standards were preparedindividually in either MeOH/H2O (50/50)
or MeOH (for CoA metabolites). The
mixtures of metabolite standards wereprepared before LC/MS analysis.
For the tuberculosis study, virulent
M. tuberculosis cells were grown onhydrophilic Durapore membrane filters,
and transferred to plates containing
bacteriostatic or a bactericidal dose of
PZA. In addition to the dose-response,
experiments were controlled by altering
the pH of the experimental media frompH 6.6 (wherein PZA has little or no
activity) to pH 5.5 (wherein PZA is active).
At various time points, filters were
harvested for extraction. Metabolism
was quenched by plunging the filters
into 2:2:1 acetonitrile:methanol:water at–20 °C.
Quenched cells were lysed by beadbeating in a Precellys homogenizer(Bertin Instruments), and lysates weresterile filtered twice and snap frozen
on dry ice. At the time of analysis,
samples were vortexed, centrifuged, andtransferred to the HPLC vials for direct
injection into the LC/MS system. Table 3
summarizes the sample information of
six biological groups. Each sample wasinjected twice as technical replicates.

Table 1. LC parameters.
Agilent 1290 Infinity LC System
Analytical column

Agilent ZORBAX Extend C18, 2.1 × 150 mm, 1.8 µm (p/n 759700-902)

Guard column

Agilent ZORBAX SB-C8, 2.1 mm × 30 mm, 3.5 µm (p/n 873700-936)

Column temperature

40 °C

Injection volume

5 µL

Autosampler temperature

4 °C

Mobile phaseA) 97 % water/3 % methanol containing 5 mM TBA and 5.5 mM aceticacidB) Methanol containing 5 mM TBA and 5.5 mM acetic acid

Seal wash solvent

Isopropanol : H2O (1:1 v/v)

Flow rate

0.25 mL/min

Gradient

Time (min) %B
0
0
3.5
0
4
30
7.5
30
8
35
15
35
20
99
24
99
24.5
0

Stop time

24 minutes

Post time

5 minutes

Table 2. MS parameters.
Agilent 6545 Q-TOF
Ion mode

Negative

SourceAgilent Dual ESI or AJS

Capillary voltage

3,500 VDry gas temperature

250 °CDry gas flow

13 L/min

Nebulizer pressure

35 psiSheath gas temperature

225 °C or 350 °C for AJS

Sheath gas flow

12 L/minNozzle voltage

2,000 V

Fragmentor

Optimized by SWARM Autotune technology

Skimmer

Optimized by SWARM Autotune technology

Oct 1 RF Vpp

Optimized by SWARM Autotune technology

MS range

60–1,600 m/z

MS acquisition rate

1.5 spectra/sec

Reference mass

62.0327 and 983.0347

Reference pump flow

0.5 mL/min

Reference delivery

Agilent 1100 isocratic pump with 100:1 splitter (p/n G1607-60000)

Instrument mode

Extended dynamic range (2 GHz)

Table 3. Summary of the sample information.
pH\Treatment

Untreated samples

pH 6.6(little or no activity)

Control pH6.6 (triplicate) PZA(s)_pH6.6 (triplicate) PZA(c)_pH6.6 (triplicate)pH 5.5 (activity)

Control pH5.5 (triplicate) PZA(s)_pH5.5 (triplicate) PZA(c)_pH5.5 (triplicate)

3

PZA (bacteriostatic)

PZA (bacteriocidal)

×106
4.0

Agilent SWARM Autotune enables
faster and easier optimization of
ion transmission

Peak height

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Figure 2. Easy optimization of ion transmission using Agilent SWARM Autotune algorithm and dual ESI.
×106
3.5

AJS_SGT225_SGF12
AJS_SGT350_SGF12

3.0

ESI

Peak height

2.5
2.0
1.5
1.0
0.5

PH
Ac
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Co
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AJS technology uses thermal gradientfocusing ESI, and has demonstrated a
substantial boost (5–15x) in analyticalsensitivity for thermally stable molecules4.
In this study, we assessed a set of
endogenous metabolite standards
including small labile ones. Figure
3 shows the signal responses of 17
representative metabolites obtained witheither AJS or a conventional ESI source.
AJS clearly provided better analyticalsensitivity than the conventional
ESI, especially for those challenging
phosphorylated metabolites. We also
found that signal enhancement is highly

50–1,700 m/z ion tuning

3.0

ct

AJS Technology improves
analytical sensitivity across a wide
range of metabolites

50–750 m/z stablie ion tuning

3.5

La

Agilent SWARM autotune uses arobust multidimensional optimization
algorithm to optimize up to 21 parameterssimultaneously. This algorithm allows
researchers to automatically optimize
ion transmission 4x faster than previous
Agilent Q-TOFs. The optimization options
for small molecules include three mass
ranges: 50–1,700 m/z, 50–750 m/z,
and 50–250 m/z. For the lower mass
ranges of 50–750 m/z and 50–250
m/z, ion optics can be optimized topreserve labile compounds that mayotherwise suffer from low sensitivity
due to unintended fragmentation. To
determine the optimal tuning condition
for analyzing TB cell extracts, weevaluated the signal responses for aset of metabolite standards in the mass
ranges of 50–1,700 m/z and 50–750 m/z
with fragile and stable ion tuning. Figure 2
shows the optimal analytical sensitivity
obtained under three tuning conditions.
It is evident that 50–750 m/z tuning
provides better analytical sensitivity than
the 50–1,700 m/z tuning, and that thefragile ion tuning further enhanced thesmaller, labile metabolite signals. Thus,
50–750 m/z fragile ion tuning was chosen
for subsequent analyses.

50–750 m/z fragile ion tuningic

Results and Discussion

Figure 3. AJS enhances metabolite signals compared to a conventional ESI.

dependent on the thermal stability of ametabolite and sheath gas temperature.
Labile metabolites such as lactic acidand maleic acid respond better at a lower
sheath gas temperature (225 °C versus
350 °C). To maximize the analyticalsensitivity for CoA and nucleotide
metabolites, a sheath gas temperatureof 350 °C was used for analyzing the cell
extract samples.
4

The Agilent 6545 Q-TOF delivers
accurate mass and isotope ratio
measurement

Accurate mass measurement is criticalfor compound confirmation and molecular
formula generation for unknown
compounds, but is not sufficient byitself6. Isotope ratios provide essential
orthogonal information that can greatly
reduce the number of plausible molecularformulas, and increase confidence in the
result. To deliver excellent mass accuracy
and accurate isotope ratio measurements,
Agilent TOF-based mass spectrometers
have incorporated several innovative
hardware designs:

A thermally stable flight tube toensure a uniform flight path that
resists variations in lab temperature•

Low-noise high-voltage power
supplies to maintain precise voltages•

Proprietary ion pulser and detector
ADC designs with thermalcompensation circuitry to maintainstable mass accuracy

Figure 4 demonstrates the excellent
mass accuracy of the Agilent 6545
Q-TOF. Mass errors of less than ±1 ppm
were achieved for 54 out of 64 massmeasurements under four different tuning
conditions. Based on the accurate massmeasurements, the relative isotope
abundance (RIA) ratios were further
calculated from the same dataset. The
isotopic ion abundance ratio (RIA) isdefined as M+1/M (13C1/12C), where
M represents the monoisotopic ion.
Agilent MassHunter Qualitative Analysis
software was used to calculate thetheoretical RIAtheo values. The RIA errors
were calculated as shown in Equation 1:

50–1,700 m/z ion tuning, high sensitivity slicer
50–750 m/z stabile ion tuning, high resolution slicer

32
Mass error (ppm)•

A dual ion source for reference
mass addition to ensure consistentreal‑time calibration of every
spectrum

50–750 m/z stabile ion tuning, high sensitivity slicer

410≤±1 ppm

-1
-2
-3
-4
-5

0

100

200

300

400

500
m/z

600

700

800

900

Figure 4. The Agilent 6545 Q-TOF provides excellent mass accuracy, with mass errors <±1 ppm for 54 outof 64 mass measurements.
20
15

A

10
RIA errors (%)•

50–750 m/z fragile ion tuning, high sensitivity slicer

55

RIA errors ≤±10 %

0
-5

-10
-15
-20
1,000
20

RIA error (%) = 100 × (RIAexp – RIAtheo)/
RIAtheo

15
10

Figure 5 shows the RIA errors from thedata acquired using extended dynamic
range mode with the high resolution
slicer. Overall, the RIA errors were
below (10 %) for monoisotopic ions (M)with peak intensities in the range of
3.8 × 103 ~ 3.5 × 105 (Figure 5A), andthe first isotopic ions (M+1) with peakintensities in the range of 1.8 × 102 ~4.3 × 104 (Figure 5B). The mean RIA error
was –3.69 %. Similar results were also
obtained for the data acquired under thehigh-sensitivity slicer mode. These results

5

RIA errors (%)

Equation 1.

100,000

200,000
Intensity M

300,000

400,000

B

RIA errors ≤±10 %

0
-5

-10
-15
-20
100

10,00020,00030,000Intensity M+1

40,00050,000Figure 5. The Agilent 6545 Q-TOF delivers accurate relative isotope abundance
measurements. A) Intensity M represents the peak height of the monoisotopic ion(12C). B) Intensity M+1 represents the peak height of the isotopic ion (13C1).

5

clearly demonstrate that the 6545 Q-TOF
delivers superior relative isotopic
abundance measurements compared toan orbital-trapping FTMS7.

Data analysis
Agilent MassHunter Profinder –Batch Targeted Feature Extraction
Profinder was used for batch processingof the accurate mass LC/MS data.
The Profinder Batch Targeted Feature
Extraction provides a useful workflow
for biological pathway-driven data
analysis. In this study, Agilent Pathways
to PCDL (B.07.00) software was used
to create an Agilent PCDL file (.cdb)

73 Totalfrom KEGG metabolic pathways specificto M. tuberculosis strain H37Rv. The
created PCDL file was then edited inAgilent PCDL Manager software to
remove and add some metabolites basedon the study needs. The final database
containing 111 metabolites was used asa target list for Batch Targeted Feature
Extraction in MassHunter Profinder
software. After batch targeted feature
extraction, the results were reviewed
and manually curated as necessary.
Figure 6 illustrates that 73 out of 111
metabolites were found from the raw
data. By selecting a metabolite, forexample UDP-Glucose, in the Compound
Groups panel, the Compound Details,

List of compounds

Chromatogram Results, and MS
Spectrum Results for that metabolite aredisplayed. The compound-centric viewin Profinder provides useful insight into
the quality of the analytical data acrossdifferent biological groups as well as
the reproducibility of replicates within asample group. The Profinder batch results
can be exported as Compound Exchange
Format files that could be imported into
MPP (B.14.0) for statistical and pathway
analyses.
Although targeted data mining ispresented here, the same set of data canreadily be analyzed using untargeted data
mining with subsequent identification ofnovel metabolites.

Chromatographic results

Ctrl_pH6.6

UDP-Glucose
C15H24N2O17P2
Mass error = 0.2 ppm
Score(TgT) = 99.9

Ctrl_pH5.5

Compound details

PZA(s)_pH6.6

PZA(s)_pH5.5

PZA(c)_pH6.6

PZA(c)_pH5.5

Figure 6. Targeted feature extraction results from the cell extracts in the control and PZA-treated biological groups.

6

MS results

MPP – differential analysis of
PZA‑specific metabolic changes

Control, pH 5.5
Control, pH 6.6

MPP is a powerful chemometrics
software with many statistical tools thatallow researchers to get biological insightfrom complex data faster and easier.
Principal Component Analysis (PCA) is
one of the most common unsupervised
data analysis tools used to identify data
patterns and quality. Figure 7 illustrates
that PCA clearly detected the separationof biological groups with cell growth at
two different pH conditions. Furthermore,
PCA captures the overall variability inthe metabolome of actively dividing cells
taken from neutral pH in comparison withthe lower variability of the nongrowing
condition (pH 5.5).

PZA(c), pH 6.6
PZA(s), pH 5.5
PZA(s), pH 6.6

pH = 5.5

X-axis (component 1: 46.19 %)Y-axis (component 2: 20.5 %)Z-axis (component 3: 8.56 %)Figure 7. The PCA plot can clearly detect the separation of the treatment groups at two different
pH conditions.
43–Log10(corrected Pvalue)

A volcano plot provides a pair-wisecomparison between two different
biological groups where the results
can be visualized by fold-change andsignificance simultaneously. In thisstudy, PZA-treated samples showeddose‑response efficacy at pH = 5.5, whileat pH = 6.6 (bacteriostatic dose), efficacy
was completely lost. To gain insight into
PZA’s mechanism of action, we performed
differential analyses using MPP’s Filter
on Volcano Plot feature to compare thecontrol versus PZA‑treated samples
at pH 5.5, as well as the PZA‑treatedsamples with growth conditions at
pH 5.5 versus pH 6.6. Figure 8 shows
the volcano plot of the metabolites in
the PZA(s)-treated samples versus thecontrol at pH 5.5 with a cutoff of p <0.05
and fold change (FC) >1.5. Fifty-four of
the 73 metabolites displayed statisticalsignificance, and 28 of them showed atleast 1.5 fold changes in the abundance in
the PZA(s)-treated samples compared tothe control at pH 5.5. Using this approach,we were able to detect statisticallysignificant metabolites among thetreatments. For example, 25 differential
metabolites were detected with a p <0.05
and FC >1.5 for the PZA(s)‑treated groupat pH 5.5 compared to that at pH 6.6.

PZA(c), pH 5.5

pH = 6.6

2

1

-4

-2

0
Log2(fold change)

2

4

Figure 8. The volcano plot of the metabolites in the PZA-treated samples versus the control at pH 5.5.
Red dots represent those metabolites whose abundances are higher in the PZA-treated samples than in
the control (P <0.05, FC >1.5). Blue dots represent those metabolites whose abundances are lower in the
PZA-treated samples than the control (P <0.05, FC >1.5).

7

Cluster analysis is a powerful method
to organize entities and conditions ina dataset into clusters based on thesimilarity of their abundance profiles.
Hierarchical clustering is one of the mostwidely used clustering techniques for
unsupervised analysis of mass abundance
data, in which the most similarabundance profiles are joined to form agroup. These are further organized in atree diagram, until all data form a single
group. Figure 9 shows the hierarchicalclustering result performed on bothconditions and entities. Each column
represents a condition. In this case, acontrol or a treatment group, and eachrow represents an entity or a metabolite.
The metabolites are displayed as a heatmap, and the relative abundance isindicated by color. The tree branchingclearly distinguishes the metabolic
profile of PZA-treated cells incubated at
pH 5.5 from those incubated at pH 6.6.
Consistent with PZA’s selective activity
at pH 5.5, we observed a grouping of
PZA(s)‑treated cells at pH 6.6 with thoseof the control samples.

Color range
-2.9

0

2.9

Control
pH 5.5Control
pH 6.6

PZA(s)
pH 6.6

PZA(c)
pH 6.6

PZA(c)
pH 5.5PZA(s)
pH 5.5Figure 9. Hierarchical clustering on conditions and entities. The color range indicates the peak intensity of each metabolite for each condition. Metabolites with
low intensity are shown in blue; those with intermediate intensity are shown in yellow, and those with high intensity are shown in red. The color range bar
indicates the relative intensity of each metabolite.

8

Pathway Analysis using Pathway
Architect in MPP
Pathway analysis was performed using
Pathway Architect, providing a means
for the visual analysis of experimental
metabolite abundances. By mapping thedifferential metabolites and overlaying
the abundance results onto curated

pathways, for example KEGG pathways,
it is possible to view pathways enrichedwith metabolites. This facilitates aquick view of whether the pathway wassignificantly affected in response to thedrug treatments or cell growth conditions.
In this study, we found 23 pathways
with at least five or more statisticallysignificant metabolites. Figure 10 shows
the pantothenate and CoA biosynthesis
pathway in which 15 metabolites were
found. The individual heat strips adjacent
to each metabolite are the normalized
signal value.

Control, pH 6.6
PZA(s), pH 6.6
PZA(c), pH 6.6
Control, pH 5.5
PZA(s), pH 5.5
PZA(c), pH 5.5

Figure 10. Fifteen significantly different metabolites were mapped to the pantothenate and CoA biosynthesis pathway.

9Conclusions

References

Using a robust IP-RP Q-TOF
LC/MS method and Agilent MassHunter
Profinder and MPP software enabled
the identification of a specificmetabolic biosignature for PZA-treated
Mycobacterium tuberculosis. The
biosignature is derived from a broad
survey of pathways. Importantly, theresults were consistent with phenotypic
data from reports in the scientific
literature8,9, and may help to explain PZA’s
unique metabolic effects.

1. Zhang, Y.; Mitchison, D. The curious
characteristics of pyrazinamide: areview. Int. J. Tuberc. Lung Dis. 2003,
7(1), 6-21.
2. Dai, Y. Profiling of Endogenous
Metabolites Using Time-of-Flight
LC/MS with Ion-Pair Reverse Phase
Chromatography, Agilent Technologies
Application Note, publication number
5991-7802EN, 2017.
3. 6545 Q-TOF Brochure, Agilent
Technologies, publication number
5991-5468EN, 2015.
4. Alex Mordehai, ASMS 2009, Poster
number: MPM 331.
5. MassHunter Profinder: Batch
Processing Software for High
Quality Feature Extractionof Mass Spectrometry Data,
Agilent Technologies Technical
Overview, publication number
5991‑3947EN, 2014.

6. Kind, T.; Fiehn, O. Metabolomics
database annotation via query ofelemental compositions: Massaccuracy is insufficient even at lessthan 1 ppm. BMC Bioinformatics 2006,
7, 234.
7. Xu, Y.; et al. Evaluation of accuratemass and relative isotopic abundance
measurements in the LTQ-Orbitrap
Mass Spectrometer for Future
Metabolomics Database Building.
Anal. Chem. 2010, 82, 5490-5501.
8. Zhang, Y.; et al. Mechanisms ofPyrazinamide Action and Resistance.
Microbiol. Spectr. 2013, 2(4), 1-12.
9. Gopal, P.; et al. Pyrazinamide
resistance is caused by two distinct
mechanisms: prevention of coenzyme
A depletion and loss of virulence
factor synthesis. ACS Infect. Dis. 2016,
2, 616-626.

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For Research Use Only. Not for use in diagnostic
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This information is subject to change without notice.
© Agilent Technologies, Inc., 2017
Published in the USA, July 18, 20175991-7970EN