Metabolomic profiling of amoebic and pyogenic liver abscesses: an in vitro NMR study

ORIGINAL ARTICLE

Metabolomic profiling of amoebic and pyogenic liver abscesses:an in vitro NMR study

Santosh Kumar Bharti • Virendra Jaiswal •

Ujjala Ghoshal • Uday Chand Ghoshal •

Sanjay S. Baijal • Raja Roy • Chunni Lal Khetrapal

Received: 19 May 2011 / Accepted: 9 July 2011 / Published online: 16 July 2011

� Springer Science+Business Media, LLC 2011

Abstract Pus samples obtained from 109 patients with

liver abscess were examined by NMR spectroscopy. To our

knowledge this is the first report on metabolic profiling of

liver abscesses. Fifty metabolites were identified by com-

bination of one (1D) and two-dimensional (2D) NMR

spectra. Metabolic derangements were evaluated for dif-

ferentiation between amoebic (ALA) and pyogenic liver

abscess (PLA). The NMR results indicate that aspartate,

asparagine and galactose, integral components of lipopro-

teophophoglycans (LPG) of the cell wall of Entamoeba

histolytica are metabolic biomarkers of ALA. On the other

hand, acetate, propionate, butyrate, succinate and formate,

the fermentation products the facultative anaerobes are

significantly prevalent in PLA. The NMR based metabolic

profile of ALA and PLA are evaluated taking polymerase

chain reaction (PCR) and bacterial culture as gold standard

method. However, when NMR results were compared with

culture and PCR methods, a correct diagnosis of 94.11% in

ALA (n = 85) and 100% in PLA (n = 10) cases were

observed. NMR spectroscopy in conjunction with PCR and

culture can expedite in differentiating ALA from PLA.

Keywords NMR spectroscopy � Amoebic liver abscess �Pyogenic liver abscess � Metabolic profiling � PCR �Culture � Principal Component Analysis

Abbrevations

CPMG Carr–Purcell–Meiboom–Gill

PCR Polymerase chain reaction

TOCSY Total correlation spectroscopy

DQF-COSY Double quantum filtered-correlation

spectroscopy

HSQC Heteronuclear single quantum coherence

spectroscopy

1 Introduction

Liver abscess is a collection of pus accumulated in a cavity

formed by localised hepatic infection in the liver com-

monly caused by infection with protozoan parasite Ent-

amoeba histolytica as amoebic liver abscess (ALA) or gut

derived bacteria such as Escherichia coli, Klebsiella

pneumoniae etc., as pyogenic liver abscess (PLA). Other

types of liver abscesses like fungal, tubercular etc. are also

found but rare or relatively less frequent (Huang et al.

1996; Rahmatulla et al. 2001). World Health Organization

(WHO) estimates that amoebiasis is one of the three most

common causes of death from parasitic diseases. Mortality

Santosh Kumar Bharti, Virendra Jaiswal are the authors have

contributed equally.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11306-011-0339-7) contains supplementarymaterial, which is available to authorized users.

S. K. Bharti � R. Roy � C. L. Khetrapal (&)

Centre of Biomedical Magnetic Resonance, Sanjay Gandhi

Postgraduate Institute of Medical Sciences Campus,

Raibarely Road, Lucknow, Uttar Pradesh 226014, India

e-mail: [email protected]

V. Jaiswal � U. Ghoshal

Department of Microbiology, Sanjay Gandhi Post Graduate

Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

U. C. Ghoshal

Department of Gastroenterology, Sanjay Gandhi Post Graduate


S. S. Baijal

Department of Radiodiagnosis, Sanjay Gandhi Post Graduate


123

Metabolomics (2012) 8:540–555

DOI 10.1007/s11306-011-0339-7

http://dx.doi.org/10.1007/s11306-011-0339-7

mainly results from extra-intestinal infections, ALA being

the commonest. Differentiation of ALA from PLA is

important as management and outcome of the two condi-

tions may differ (Lodhi et al. 2004). Clinical features and

conventional laboratory parameters of the two conditions

are similar and include fever, cough, right upper quadrant

abdominal pain, leukocytosis, enlarged liver and raised

right dome of the diaphragm on chest radiograph. There are

some specific sonographic feature of ALA that differ from

PLA but these differences are not sufficient for differenti-

ation (Ralls et al. 1987). Demonstration of E. histolytica,

the causative organism of ALA, on microscopy of pus

sample is infrequent (Haque et al. 2000). Laboratory

diagnosis of ALA is usually made by conventional serum

antiamoebic antibody test. However, it cannot differentiate

past infection from current infection and is also positive in

a proportion of healthy population in endemic areas

(Khairnar and Parija 2007). Detection of amoebic antigen

has low sensitivity, particularly in patients partly treated

with anti-amoebic drugs (Haque et al. 2000; Parija and

Khairnar 2007; Zeehaida et al. 2008). Several polymerase

chain reaction (PCR)-based methods have been developed

to amplify DNA of E. histolytica in liver pus and stool

specimens (Acuna-Soto et al. 1993; Khan et al. 2006;

Tannich and Burchard 1991; WHO 1997). PCR is one of

the methods having best sensitivity and specificity for

detection of E. histolytica in liver pus samples as compared

to other techniques (Fotedar et al. 2007a, b; Verweij et al.

2004) and also recommended by WHO (1997). The PCR is

elegant technique and frequently adopted by biological

scientists but routinely not applied in pathology laboratory

because of its complexity and difficult procedures (Latchman

1995). PCR was chosen because of its high sensitivity for

diagnosis of ALA and comparing the NMR metabolic

profile. PLA is conventionally diagnosed by positive bac-

terial culture in liver pus in absence of anti-amoebic anti-

body in serum. However, sensitivity of this criterion is low

if antibiotics have been used previously. The metabolomics

approach to provide insight into their metabolic status and

pathophysiology of pus formation has not been yet repor-

ted. On the other hand such an approach may provide

desired information on ALA as well as PLA. Metabolomics

allows the qualitative and quantitative measurement of all

metabolites present in cell, biofluids, pathological fluids,

tissue and tissue extract (Dunn and Ellis 2005; Lindon et al.

2000, 2003). The biochemical composition of ALAs and

PLAs may differ. Among all the other analytical techniques

used for metabolomics studies, high resolution NMR

spectroscopy is widely used for investigating the compo-

sition of body fluids, tissues extract, pathological fluids, as

a wide range of metabolites can be detected simultaneously

without separation of individual components. Therefore,

we hypothesize that metabolic profile of pus from liver

abscess would be different in patients with ALA and PLA.

Accordingly we undertook this study with the following

aims, (a) Metabolic profiling of liver abscess, (b) To find

out the metabolic differences between ALA and PLA

taking PCR and bacterial culture as gold standard methods.

2 Materials and methods

2.1 Subjects

One hundred nine patients undergoing drainage of liver

abscess in the Department of Radiodiagnosis in a tertiary

care centre during a 3 year period (January 2006 to

December 2009) were included in this study. Classification

of liver abscesses was based on result of E. histolytica-PCR

and bacterial culture which were previously described

(Virendra Jaiswal et al. 2010). Patients diagnosed to have,

mixed infection with E. histolytica and bacteria were

excluded. Informed consent was obtained from all the

patients and the protocol was approved by Institution’s

Ethics Committee (PGI/DIR/RC/957/2007).

2.2 Sample preparations for NMR

Five ml of liver pus specimen was collected from each

patient during drainage of abscess. An aliquot (approxi-

mately 2 ml) of liver pus was immediately stored in -80�C

for 1H NMR analysis and the remaining sample was pro-

cessed microbiologically for another study, which has been

reported previously (Virendra Jaiswal et al. 2010). Before

performing the NMR analysis, samples were thawed at

room temperature. The whole volume of sample was son-

icated to homogenise and the centrifuge at 12,000 rpm for

10 min at 4�C temperature to remove the suspended par-

ticulate matter. For 1H NMR experiments 250 ll of

supernatant was pipetted out and makeup to 500 ll with

deuterium oxide (D2O). For quantitative evaluation of

various metabolites, sample was taken in 5 mm NMR tube

with a Wilmad coaxial insert filled with known concen-

tration of TSP (Sodium salt of 3-trimethylsilyl-(2,2,3,3-d4)-

propionic acid) in deuterium oxide. All the chemicals used

for NMR analysis were purchased from Sigma Aldrich,

USA. All the samples were coded and randomly provided

for NMR analysis and PCR and Culture results were not

disclosed until complete NMR analysis.

2.3 Experimental 1H NMR spectroscopy

The NMR experiments were performed on a Bruker Avance

400 MHz spectrometer equipped with a 5 mm Broad Band

Inverse probe shielded with z-gradients. During the analy-

sis, sample temperature was 300 K. One dimensional (1D)

Metabolic profile of pus from liver abscesses 541

123

single pulse experiments and Carr–Purcell–Meiboom–Gill

(CPMG) sequence were used with presaturation pulse to

suppress the water signal. The parameters used are: 8 kHz

spectral width, 32K data points, 90� flip angle (10.6 ls), a

total repetition time of 19.04 s and total echo time of

269 ms (for CPMG experiments), number of scan 128,

dummy scan 8. The resultant spectra were processed using

exponential line broadening of 0.3 Hz before Fourier

transformation. Manual phase correction was performed

followed by automatic base line correction. Unambiguous

assignments of various metabolites were performed using

2D homonuclear and heteronuclear NMR spectroscopy.

Some of the resonances were also confirmed by spiking

experiments using standard compounds. The magnitude

mode double quantum filtered correlation spectroscopy

(DQF-COSY), total correlation spectroscopy (TOCSY) and1H–13C heteronuclear single quantum coherence (HSQC)

spectroscopy were performed for the assignments of reso-

nances. For DQF-COSY 2K data points were collected in t2domain over a spectral width of 8 kHz, water resonance was

presaturated during relaxation delay of 2.0 s, 512 t1 incre-

ments with 64 transient scans and 16 dummy scans. For

TOCSY, all basic parameters were same as in DQF-COSY.

Spin lock time used was 70 ms. 512 increments were col-

lected in t1 dimension using 88 transient scan. The resulting

2D data were Fourier transformed after zero filling in the t1dimension to 1,024 points and multiplying in both dimen-

sion by sine bell window function. All data acquisition and

processing were performed using XWINNMR 3.5 version.

The parameters used for 1H–13C HSQC were: 2K data

points in t2 dimension over spectral width of 5,952 Hz, 500

t1 increments with 80 transients, relaxation delay of 2.0 s,

acquisition time of 170 ms and 90� pulse length. The

spectral width in t1 dimension was 20,124 Hz. The phase

sensitive data were obtained by the antiecho-time propor-

tional phase increments (Antiecho-TPPI) method. The

resulting data were zero-filled to 512 data points and were

weighted with 90� squared sine window functions in both

the dimensions prior to Fourier transformation.

2.4 PCR and bacterial culture of pus

Five ml of liver pus obtained during drainage of abscess

was examined for bacteria by Gram’s staining and bacterial

culture as per standard methods (Collee et al. 1996). PCR

analysis was performed on the sample for the detection of

E. histolytica in pus sample using Zaman et al. method

(2000).

2.5 Statistical analysis

Principal Component Analysis (PCA) on NMR Spectra:

The CPMG spectra obtained from ALA (n = 85) and PLA

(n = 10) were subjected for the multivariate PCA. The

spectra were reduced to 395 (between 0.5 and 9.0 ppm)

discrete chemical shift regions by digitization to produce a

series of sequentially integrated regions of 0.02 ppm width,

using Bruker AMIX software (Version 3.8.7, Bruker

GmbH, Germany). The region of 4.5–5.1 ppm was exclu-

ded from the analysis to remove the residual signal of

HOD, water and distorted region due to water suppression.

The data obtained was mean centered, scaled to Pareto

Scaling and then normalized by dividing each integral of

the segment by total area of the spectrum in order to

compensate for the differences in overall metabolite con-

centration between individual samples. The resulting data

matrix was further subjected for the PCA.

Univariate analysis of quantitative and categorical data:

Mann–Whitney U test and Fisher Exact test (SPSS 11.5)

was applied on the quantitative data and categorical data

respectively.

3 Results

Fourteen patients were excluded from the final analysis as a

definite diagnosis could not be made due to inadequate

work-up (n = 3), tubercular liver abscess (n = 1), fungal

liver abscess (n = 1), mixed infection (n = 9). Eighty-five

patients with ALA (85/104, 81.7%) and ten with PLA

(10/104, 9.6%) were finally included in this study. There-

fore the total sample size was from 95 patients.

Male were more affected by ALA (88%) as compared to

PLA (55%). Mean age of the ALA (41.1 ± 15.6 years) and

PLA (42.2 ± 23.1 years) patients were almost same and no

significant differences were found in our study. The mean

age of male and female in ALA group was 42.0 ± 16.4 and

40.5 ± 19.7 years respectively.

3.1 Identification of metabolites by NMR spectroscopy

The NMR spectra of liver abscesses were analyzed by the

combined use of 1D and 2D NMR spectroscopy. A typical

1D 1H NMR spectrum of ALA is shown in Fig. 1. The

typical 2D COSY, 1H–13C HSQC and TOCSY spectra used

for assignments of the compounds are shown in Figs. 2, 3

and Supplementary Fig. 1 respectively with resonance

assignments. The assignments were also carried out on the

basis of coupling pattern, coupling constant and chemical

shift reported in the literature (Bollard et al. 2009; Denkert

et al. 2008; Foxall et al. 1993; Gao et al. 2009; Govindaraju

et al. 2000; Grand et al. 1999; Gupta et al. 2001; Lai et al.

2005; Lindon et al. 1999; Nicholson et al. 1995; Nicholson

and Wilson 1989; Pinero-Sagredo et al. 2010; Rocha et al.

2010; Silwood et al. 2002; Sweatman et al. 1993), com-

parison with reference compounds present in Biological

542 S. K. Bharti et al.

123

Magnetic Resonance Data Bank and Human Metabolome

Data Base (Markley et al. 2007; Wishart et al. 2009). The

results of some of the spiking experiments (Supplementary

Fig. 2) and details procedure for comparing standard 1D

(Supplementary Fig. 3), COSY (Supplementary Fig. 4),

TOCSY (Supplementary Fig. 5), HSQC (Supplementary

Fig. 6) spectra with liver abscess spectra are presented in

supplementary materials. The 1H NMR spectra of liver pus

5.56.06.57.07.58.08.5

6X

ppm

1.01.52.02.53.03.54.04.5 ppm

4.6

Lysi

ne

Lac

tate

/ T

hreo

nine

Ala

nine

Ace

tate

Suc

cina

te

Ace

toac

etat

eGA

BA

GA

BA

GA

BA

/ Ly

sine

Eth

anol

Taur

ine

Met

hion

ine

Lac

tate

Thr

eoni

ne

Vali

ne

Glu

cose

Gal

acto

se

Gly

cine

Vali

ne/L

euci

ne/I

sole

ucin

e

Asp

arti

c A

cid

Asp

arag

ine

Taur

ine

Glu

cose

Ura

cil

UD

P / U

ridi

ne

Fum

aric

Aci

d

Gal

acto

se

Ura

cil /

Try

ptop

han

Tryp

toph

an

UD

P / U

ridi

ne

Ade

nine

Form

ate

Ura

cil

Tyro

sine

Tyro

sine

Phe

nyla

lani

neTr

ypto

phan

Glu

tam

ine

Cho

line

(A)

(B)

Fig. 1 Typical 1D 1H NMR spectrum of ALA showing metabolite assignments. Expansion of the spectrum from a 0.5–4.8 ppm and

b 5.0–9.0 ppm


123

samples were highly complex and it did not allow all the

assignment on the basis of chemical shift reported in lit-

eratures. 2D homonuclear NMR experiments were per-

formed to resolve the spectral complexity. Even resonance

overlapping was also observed in COSY and TOCSY

spectra in the chemical shift region of 3.0–4.25 ppm.

Therefore 1H–13C HSQC was recorded and it provided

better resolution for assignments of metabolites due to

more dispersion of chemical shift in 13C dimension.

Aspartic acid, asparagine and galactose were observed in

low concentrations and therefore assigned by spiking

experiments (Supplementary Fig. 2). Whereas, acetic acid,

formic acid, succinate and acetoacetate were assigned by

the 13C chemical shifts obtained from the HSQC spectra as

well as by spiking with known standard compounds in the1H NMR spectra. Different classes of metabolites such as

carbohydrates, organic acids, aliphatic and aromatic amino

acids (AAA), bacterial fermentation products and lipids

Fig. 2 2D 1H–1H COSY spectrum of ALA along with the assignment

of the metabolites. (Ileu isoleucine, Leu leucine, Val valine, Lyslysine, Glu glutamate, Gln glutamine, Meth methionine, Arg arginine,

Eth ethanol, Pro proline, Ala alanine, Lac lactate, Thre threonine, Citcitrate, Asp aspartic acid, Asn asparagine, Tyr tyrosine, His histidine,

Phe phenylalanine, Tau taurine, Try tryptophan, Gluc glucose)


123

were detected in the 1H NMR spectra liver pus. The

detailed 1H NMR chemical shift assignment of the

metabolites observed in the liver pus samples are presented

in Table 1. GABA was observed in very high concentration

in some of the samples and it was assigned by TOCSY as

shown in (Supplementary Fig. 1).

3.2 PCA of NMR spectra

Multivariate analysis (PCA) was performed on the CPMG1H NMR spectra of ALA and PLA as it provides better

baseline. A seven-component model explained 98.0% of the

variance, with the first two components explaining 93.5.0%

ppm

1.01.52.02.53.03.54.04.5 ppm

15

20

25

30

35

40

45

50

55

60

65

70

75

Leucine

LeucineLeucine

Leucine

Leucine

Valine

Valine

Valine

Valine

Isoleucine

Isoleucine

Isoleucine

Isoleucine

Isoleucine

Lactate

Threonine

Threonine

Lactate

Aspartic Acid

Asparagine

Methionine

Methionine

Methionine

Methionine

Lysine

Lysine

Lysine

LysineGlutamic Acid

Glutamic Acid

Tryptophan

Histidine

Tyrosine

Phenylalanine

Tryptophan

PhenylalanineTyrosine

Histidine

Proline

Proline

Citrate

Proline

Glycine

Citrulline

Citrulline

Alanine

Choline

Choline

Ornithine

Ornithine

Ornithine

Serine

Serine

Betaine

Myo-Inositol

Myo-InositolMyo-Inositol

Gluconic Acid

Gluconic Acid

Gluconic Acid

Glucose

Glucose

Glucose

Glucose

Glucose

Glucose

Alanine

Aspartic Acid

Asparagine

Proline

Glycerol

Glycerol

Ornithine

Fig. 3 2D 1H–13C HSQC spectrum of ALA. The assignments of the metabolites are labelled


123

Table 1 1H chemical shift assignments of the metabolites observed in the NMR spectra of ALAs and PLAs

S. no. Name of metabolites Chem. shift Resonances Assignment methods

1 Acetic acidb 1.92 CH3 1D/HSQC/spiking

2 Acetoacetate 2.23 (s) CH3 1D/spiking

3 Adenine 8.19 (s) 3CH/8CH 1D

8.21 (s) 3CH/8CH

4 Alanine 1.48 (d) b-CH3 1D/COSY/HSQC

3.78 (q) a-CH

5 Arginine 1.71 (m) c-CH2 1D/COSY/HSQC

1.91 (m) b-CH2

3.25 (d) d-CH2

3.77 (t) a-CH

6 Asparaginea 2.87 (dd) b-CH 1D/COSY/HSQC/spiking

2.95 (dd) b0-CH

4.01 (dd) a-CH

7 Aspartic acida 2.69 (dd) b-CH 1D/COSY/HSQC/spiking

2.82 (dd) b0-CH

3.90 (dd) a-CH

8 Betaine 3.26 N–CH3 1D/HSQC

3.90 CH2

9 Beta-hydroxybutyrate 1.20 (d) CH3 1D/COSY/HSQC

2.31 (dd) b-CH

2.43 (dd) b0-CH

4.13 a-CH

10 n-Butyric acidb 0.90 (t) CH3 1D/TOCSY/HSQC

1.57 (m) 3CH2

2.18 (t) 2CH2

11 Choline 3.21 (s) N(CH3)3 1D/HSQC

3.53 N–CH2

4.07 O–CH2

12 Citric acid 2.53 (d) CH2 1D/COSY/HSQC

2.67 (d) CH2

13 Citrulline 3.75 a-CH 1D/TOCSY/HSQC

3.15 (t) d-CH2

1.88 (m) c-CH2

1.57 (m) b-CH2

14 Creatine 3.03 (s) CH3 1D/HSQC

3.93 CH2

15 Cysteine 3.06 b-CH2 1D/HSQC

3.94 a-CH

16 Dimethyl amine 2.75 (s) CH3 1D/HSQC/spiking

17 Ethanol 1.18 (t) CH3 1D/COSY/HSQC

3.62 CH2

18 Formic acidb 8.46 (s) CH 1D/HSQC/spiking

19 Fumaric acid 6.51 (s) CH 1D

20 Galactosea 4.60 (d) 1CH 1D/spiking

21 GABA 1.90 (m) 3CH2 1D/COSY/HSQC

2.30 (t) 2CH2

3.02 (t) 4CH2


123

Table 1 continued


22 a-Glucose 3.41 C4H 1D/COSY/TOSCY/HSQC

3.54 C2H

3.71 C3H

3.83 C6H

3.85 C5H

5.24 (d) C1H

23 b-Glucose 3.24 C2H 1D/COSY/TOSCY/HSQC

3.41 C4H

3.47 C5H

3.49 C3H

3.74 C6H

3.91 C60H

4.65 (d) C1H

24 Gluconic acid 4.11 2CH 1D/HSQC

4.02 3CH

3.81 6CH2

3.65 6CH2

3.75 4CH

3.75 5CH

25 Glutamate 2.09 b-CH2 1D/COSY/HSQC

2.35 (m) c-CH2

3.77 a-CH

26 Glutamine 2.13 (m) b-CH2 1D/COSY/TOSCY/HSQC

2.45 (m) c-CH2

3.77 a-CH

27 Glycine 3.56 CH2 1D/HSQC

28 Glycerol 3.57 1,3CH2 1D/COSY/HSQC

3.66 1,3CH2

3.79 2CH

29 Histidine 3.18 b-CH 1D/HSQC

3.25 b0-CH

3.99 a-CH

7.13 (s) C4H-ring

7.98 (s) C2H-ring

30 Isoleucine 0.94 (t) d-CH3 1D/COSY/TOSCY/HSQC

1.01 (d) c-CH3

1.26 (m) c-CH

1.47 (m) c0-CH

1.98 (m) b-CH

3.68 (d) a-CH

31 Lactate 1.33 (d) b-CH3 1D/COSY/HSQC

4.12 (q) a-CH

32 Leucine 0.96 (d) d-CH3 1D/COSY/TOSCY/HSQC

0.97 (d) d0-CH3

1.71 (m) c-CH/b-CH2

3.75 a-CH

33 Lipids 0.90/0.96 CH3 1D/COSY/TOCSY

1.29 –(CH2)n–


123

Table 1 continued


1.58 CH2–CH2–CO

2.04/2.07 CH=CH–CH2

2.25 CH2–CO

2.81 CH=CH–CH2–CH=CH

5.32 CH=CH

34 Lysine 1.47 (m) c-CH2 1D/COSY/TOSCY/HSQC

1.72 (m) b-CH2

1.9 (m) d-CH2

3.02 N–CH2

3.74 a-CH

35 Methionine 2.13 (s) S–CH3 1D/COSY/HSQC

2.16 (m) b-CH2

2.64 (t) c-CH2

3.85 a-CH

36 Myo-inositol 3.28 (t) C2H-ring 1D/COSY/HSQC

3.54 (d) C1,3H-ring

3.62 (t) C5H-ring

4.06 (t) C4,6H-ring

37 Ornithine 1.81 (m) c-CH2 1D/COSY/HSQC

1.92 (m) b-CH2

3.05 (t) d-CH2

3.78 a-CH

38 Phenylalanine 3.12 b-CH 1D/COSY/HSQC

3.28 b0-CH

3.98 a-CH

7.32 (d) C2H, C6H-ring

7.37 (m) C4H-ring

7.41 (m) C3H, C5H-ring

39 Propionic acidb 1.05 (t) CH3 1D/COSY/HSQC

2.18 (q) CH2

40 Proline 2.01 (m) c-CH2 1D/COSY/HSQC

2.08 (m) b-CH

2.35 (m) b0-CH

3.35 d-CH

3.42 d0-CH

4.13 a-CH

41 Serine 3.85 (m) b-CH2 1D/COSY/HSQC

3.97 (m) a-CH

42 Succinateb 2.46 (s) CH2 1D/HSQC/spiking

43 Scyllo-inositol 3.35 (s) CH 1D/HSQC

44 Taurine 3.25 (t) S–CH2 1D/COSY/HSQC

3.41 (t) N–CH2

45 Threonine 1.34 (d) c-CH3 1D/COSY/HSQC

3.6 (d) a-CH

4.25 (m) b-CH

46 Tryptophan 3.29 (dd) b-CH 1D/COSY/HSQC

3.47 (dd) b0-CH


123

of the total variance. The clear clustering between ALA and

PLA in the PCA of spectra demonstrated significant meta-

bolic variations in ALA and PLA groups (Fig. 4). Exami-

nation of PC1, PC2 and PC3 loadings showed that the cluster

separation arising mainly due to acetate, succinate, formate,

asparagine, aspartic acid, glucose, branch chain amino acids

(BCA), AAA, lactate, alanine etc. (Fig. 5). The analysis of

2D loading plot showed that PLA samples were separated

from ALA mainly due to acetate, succinate and formate

(Supplementary Fig. 7). Dispersion in the ALA group was

observed due variations in BCA, AAA, lactate, aspartic acid,

asparagine, alanine etc. One of the ALA sample was over-

lapped with PLA group (Fig. 4) and detail spectral analysis

of this sample showed high content of acetate, succinate and

formate which are key metabolites in PLA. Aspartic acid and

asparagine were also present in this sample but their inten-

sities were relatively low when compared with acetate,

succinate and formate, proving their major role in separating

PLA from ALA. Acetate, succinate and formate were also

observed in two other ALA samples but in less concentration

and therefore classified in the cluster of ALA group (Fig. 5).

3.3 NMR spectral assignments, quantitation

and statistical analysis

The stack plots of the two representative PLA and ALA

pus samples 1H NMR spectra are shown in Fig. 6 so as to

highlight the major differences in the metabolites. The

absence of asparagine, aspartic acid and galactose reso-

nances was very straightforward in the PLA spectrum.

-2.5-2.0

-1.5-1.0

-0.50.0

0.51.0

1.5

PC-1PC-2

PC-3

-2.0-1.5

-1.0-0.5

0.00.5

1.0-1.0

-0.5

0.0

0.5

1.0

1.5

ALA (n=85)PLA (n=10)

Fig. 4 PCA of CPMG 1H NMR spectra of ALA and PLA. Scores

plot PCA discriminating ALA from PLA based on selected metab-

olites. ALA cases mark by circle, have acetate, succinate and formate

in the NMR spectra

Table 1 continued


4.04 (dd) a-CH

7.19 (t) C5H-ring

7.26 (t) C6H-ring

7.30 (s) C2H-ring

7.53 (d) C4H-ring

7.72 (d) C7H-ring

47 Tyramine 2.92 (t) CH2 1D/COSY/HSQC

3.23 N–CH2

6.88 (d) 3,5CH

7.21 (d) 2,5CH

48 Tyrosine 3.06 (dd) b-CH 1D/COSY/HSQC

3.19 (dd) b0-CH

3.95 (dd) a-CH



49 Uracil 5.8 (s) C5H-ring 1D/COSY/HSQC

7.54 (d) C6H-ring

50 Valine 0.99 (d) c-CH3 1D/COSY/TOCSY/HSQC

1.04 (d) c0-CH3

3.62 (d) b-CH

2.28 a-CH

a Metabolites specific to ALA and bmetabolites specific to PLA. All other metabolites were common in both ALA and PLA samples


123

While in ALA, absence of acetate, propionate, butyrate,

succinate and formate signals were observed. A doublet at

4.60 ppm arising due to anomeric proton signal of galact-

ose was observed only in *40% ALA pus samples.

A known concentration of TSP was used in coaxial insert

to avoid the quantitative error arising due to interaction of

TSP with proteins present in the sample. A relaxation time of

19.04 s was given to ensure the full relaxation of all reso-

nances. Relative integral of metabolites resonances were

normalise to TSP signal. Concentration of the TSP present in

coaxial insert was validated by known concentration of

glycine before quantification of the metabolites (Larive et al.

1997). Quantitation of only 17 metabolites was performed

from patients with ALA and PLA. However, quantitation of

propionate and butyrate could not be performed due to

overlapping of the resonances with other signals. Quantita-

tive variability of the common metabolites was found to be

statistically insignificant between ALA and PLA (Table 2).

The presence of aspartic acid, asparagine and/or galactose

was found to be fingerprint metabolites in ALA cases, while

acetate, succinate, propionate, butyrate and formate were

only observed in PLA cases. Sample size in PLA group was

quite low as compared to ALA. Therefore, in order to verify

the bacterial fermentation products specific to PLA, an in

vitro study has been performed by incubating bacterial

clinical isolates (K. pneumoniae) in bacteria free liver

abscess (ALA) having no previous fermentation product as

observed by NMR spectroscopy. K. pneumoniae was chosen

because it the most common isolates of PLA (Chang et al.

2000; Cheng et al. 1989; Lee et al. 2001; Rahimian et al.

2004). The ALA sample mixed with K. pneumoniae sus-

pension in phosphate buffered saline and incubated for 18 h

at 37�C. Detail procedure and results are reported in sup-

plementary material. After 18 h incubation liver abscess

sample showed bacterial fermentation products acetate,

formate, succinate, propionate etc. in very high concentra-

tion (Supplementary Fig. 8) as already reported in all the 10

PLA samples.

Butyrate and propionate which are specifically found in

PLA did not demonstrated significant role in PCA clus-

tering of spectra. This may due to their low concentration

and overlapping with other signals. Similarly, a doublet

signal at 4.60 ppm for galactose was found to be insig-

nificant in the PCA loading plots when tried with including

galactose region. This may due to its very low concentra-

tion and was observed in only *40% of the samples from

ALA. Therefore, it was thought worthwhile that detailed

analysis of individual spectra followed by univariate

analysis may help for better discrimination.

On the basis of these distinct resonances, ALA and PLA

were classified and compared with PCR and culture results

and the details are presented in (Table 3). The data presented

is quite intriguing, the ALA samples can be 94.11% correctly

classified by NMR. Three patients had bacterial fermentation

products in their pus samples along with asparagine and

aspartic acids detected by NMR spectroscopy. All the ten

PLA samples were correctly identified by NMR spectros-

copy. Two ALA patients could not be classified by NMR

spectroscopy as it neither showed bacterial fermentation

products nor aspartic acid and asparagine in their pus samples.

4 Discussion

The results of 1H NMR spectroscopy of liver pus speci-

mens provide evidence that the metabolic profile of ALA is

different from that of PLA. Unsupervised multivariate PCA

also clearly demonstrated the differential metabolic profile

1.02.03.04.05.06.07.08.09.0

-0.4

-0.2

0.0

0.2

0.4

Ace

tate

Succ

inat

e

Form

ate

BC

A

Ala

nine

Asp

arag

ine

Asp

artic

Aci

d

Glu

cose

AA

A

1.02.03.04.05.06.07.08.09.0

-0.4

-0.2

0.0

0.2

0.4 PC-1 Loading

PC-2 Loading

BC

A

AA

A

Form

ate

Wat

er R

egio

n

Lac

tate

Ace

tate

Lac

tate

Suc

cina

te

Ala

nine

1.02.03.04.05.06.07.08.09.0

-0.4

-0.2

0.0

-0.2

-0.4PC-3 Loading

AA

A

Glu

cose

Gly

cine

BC

A

Lac

tate

Lac

tate

Fig. 5 PCA of CPMG 1H NMR spectra of ALA and PLA. 1D

loading plot of a PC 1, b PC 2 and c PC 3 generated from PCA shown

in Fig. 4. PC 1 and PC 2 are the major component for group

separation


123

of ALA and PLA. Even the NMR based metabolic profile

of ALA was found to be different when compared with

other pyogenic abscesses like brain abscesses and abdom-

inal abscesses (Grand et al. 1999; Lai et al. 2002, 2005).

This indicates that metabolism of E. histolytica is entirely

different from bacterial metabolism. One of the charac-

teristic features of E. histolytica is the presence of cell wall

rich surface antigens, lipoproteophophoglycans (LPG), rich

in aspartic acid, serine, glutamic acid, glucose and galact-

ose (Sue Moody-Haupt 2000). The presence of aspartic

acid and galactose in ALA may therefore be attributed to

the constituent of the cell wall of the E. histolytica in the

pus samples. Another plausible source of aspartic acid,

asparagine and galactose may be related to its specific

metabolism or due to different mode of pathogenesis of

E. histolytica (http://www.genome.jp/kegg-bin/show_pathway?

ehi00052) which is entirely different from the bacterial

metabolism (Anderson and Loftus 2005). In the absence of

glucose E. histolytica utilizes several amino acids such as

asparagine, aspartate, threonine, tryptophan, methionine,

homocysteine etc. for generation of ATP (Anderson and

Loftus 2005). Proton NMR spectra of ALA suggests that

glucose was present in a considerable amount and thus

amino acids were not utilized by E. histolytica. However,

glucose utilization by E. histolytica is lower as compared to

bacteria as confirmed by quantitative analysis (Table 2).

The metabolism predicted on the basis of genome sequence

of E. histolytica also suggests the production of ethanol and

propionate (Loftus et al. 2005). Ethanol was observed in

amoebic liver pus but not specific to ALA samples only.

Whereas, propionate was observed along with acetate and

succinate in PLA samples indicating end products of the

bacterial fermentation.

Metabolic profile of PLA showed additional resonances

such as acetate, succinate, propionate, formate and butyrate

and absence of aspartate, asparagine and galactose. These

metabolites present in the PLA are well known fermenta-

tion product of bacterial metabolism (Clark 1989; Murarka

et al. 2008; Wang et al. 2010). Our results of in vitro fer-

mentation liver abscess with K. pneumoniae also substan-

tiate that succinate, acetate, formate, propionate etc. are the

end product of bacterial fermentations. The presence of

6.07.08.0 1.02.03.04.05.0 ppm

Form

ate

Succ

inat

e

Ace

tate

Asp

/ A

sn

Asp

artic

Aci

d

Asp

artic

Aci

dM

ethi

onin

e

Asp

arag

ine

Asp

arag

ine

2.72.9 2.8 2.63.0

(A)

(B)

(C)

Glu

cose

Glu

cose

Eth

anol

Fig. 6 A stack plot of typical 1H NMR spectra of a PLAs and b ALAs showing difference in the metabolic profile. c Expansion of the spectra

between 2.6 to 3.0 ppm


123

http://www.genome.jp/kegg-bin/show_pathway?ehi00052

http://www.genome.jp/kegg-bin/show_pathway?ehi00052

their strong signal in liver abscess spectra clearly indicates

the abscess with pyogenic origin. Earlier, in vivo and in

vitro 1H NMR investigations had revealed the presence of

acetate and succinate as bacterial fermentation product in

the spectra of pyogenic brain abscesses. These signals had

been used as a diagnostic biomarker for the differentiation

Table 2 Evaluation of statistical significance of metabolites quantified and specific metabolites as observed in the 1H NMR spectra of ALA and

PLA

Metabolites ALA (mg/dl) (n = 85) PLA (mg/dl) (n = 10) P values

Median (range) Median (range)

Tryptophan 1.88 (0.00–29.62) 1.94 (0.00–17.92) NS*

(70%)a (80%)

Phenylalanine 16.32 (0.00–145.40) 9.39 (1.10–114.66) NS

(98.82%) (100%)

Histidine 10.18 (0.00–77.96) 4.66 (0.00–24.94) NS

(95.3%) (90%)

Tyrosine 12.73 (0.00–101.92) 6.68 (0.76–63.60) NS

(98.82%) (100%)

Uracil 0.67 (0.00–8.74) 0.48 (0.00–15.04) NS

(62.35%) (90%)

Glucose 30.90 (0.00–442.06) 10.63 (0.00–77.56) NS

(97.64%) (80%)

Threonine 23.34 (150.10) 10.38 (0.66–103.18) NS

(97.64%) (100%)

Valine 22.52 (0.00–141.62) 8.83 (1.40–156.58) NS

(98.82%) (100%)

Glycine 12.35 (0.19–78.88) 5.23 (1.00–95.14) NS

(100%) (100%)

Lysine 81.69 (1.28–628.88) 52.23 (6.00–747.00) NS

(100%) (100%)

Alanine 26.59 (0.43–157.42) 11.92 (1.66–198.42) NS

(100%) (100%)

Lactate 82.82 (7.65–319.90) 60.54 (9.30–293.16) NS

(100%) (100%)

Acetate 0.00 (2.10–32.08) 32.23 (4.34–130.72) 0.001

(3.52%) (100%)

Succinate Not detected 5.10 (0.95–155.13) 0.001

(3.52%) (1.95–28.0) (100%)

Formate Not detected 6.83 (1.53–17.77) 0.001

(3.52%) (1.10–12.34) (100%)

Aspartic Acid 10.24 (0.40–64.44) Not detected 0.001

(100%) (0.00%)

Asparagine 3.74 (0.00–26.23) Not detected 0.001

(98.82%) (0.00%)

Galactose Not quantified Not detected 0.014

(38.50%) (0.00%)

Propionate Not detected Not quantifiedb 0.001

(0.00%) (100%)

Butyrate Not detected Not quantified 0.001

(0.00%) (100%)

NS* not significant i.e. P values [0.05a Percentage defines the presence of metabolites in respective group. Example: Tryptophan was present in the 70% of the ALA i.e. in 30% ALA

sample it was absent. bNot quantified means metabolites detected in respective group but we are unable to quantify because of signal overlap


123

of pyogenic abscesses from tumors and tuberculous brain

abscesses (Garg et al. 2004; Grand et al. 1999; Gupta et al.

2001; Kim et al. 1997; Lai et al. 2002, 2005). These fer-

mentation products had also been utilized for the monitoring

of treatment. (Burtscher and Holtas 1999). Therefore, the

presence of strong signals of acetate, succinate, formate, and

propionate in liver pus samples clearly demonstrate the

presence of pyogenic infection.

Three ALA cases were classified as mixed infection by

NMR, the plausible reason may be the patients had

received antibiotic treatment prior to drainage of liver

abscess. Hence, their culture results were sterile but their

fermentation metabolites persisted in pus samples. NMR

spectroscopy was unable to classify two ALA cases and

this is may be because of low concentration of asparagine

and aspartate which was beyond the NMR detection limit.

Moreover, bacterial fermentation products were also not

observed in these cases, indicating absence of bacterial

infection. Therefore, large sample size is required to ensure

the role of these metabolites for differentiation in liver

abscess especially in mixed infection.

5 Conclusion

On the basis of our results of 1H NMR spectroscopic analysis

of liver pus, it may be concluded that the differential met-

abolic profile of ALA and PLA reflect the different meta-

bolic pathways involved in the infection of E. histolytica and

bacterial pathogens. These differential metabolic profiles of

ALA and PLA can be used to investigate the molecular

pathogenesis of E. histolytica and bacteria. The in vitro

NMR analysis of liver pus suggests that acetate, formate and

succinate can be used as distinguishing marker for PLA from

ALA in in vivo MRS studies. We believe that NMR spec-

troscopy of pus samples may be useful in conjunction with

routine diagnostic methods for better discrimination with

ALAs. For the differentiation with other liver abscesses like

fungal, tubercular etc., verification of metabolic profile of

such abscesses is required using NMR spectroscopy.

Acknowledgment Financial assistance from the Department of

Science and Technology, Govt. of India and Sanjay Gandhi Post

Graduate Institute of Medical Sciences, Lucknow, India are gratefully

acknowledged.

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Metabolomic profiling of amoebic and pyogenic liver abscesses: an in vitro NMR study

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