PROFILING THE METABOLOME CHANGES CAUSED BY...

PROFILING THE METABOLOME CHANGES CAUSED BY CRANBERRY JUICES OR CRANBERRY PROCYANIDINS USING 1H NMR AND UHPLC-Q-ORBITRAP-HRMS

GLOBAL METABOLOMICS APPROACHES

By

HAIYAN LIU

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2015

To my parents for their unconditional love; my partner for encouraging me at every step

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ACKNOWLEDGMENTS

I would like to express my gratitude to my major advisor, Dr. Liwei Gu, for his

patience, continuous encouragement and mentorship. Without his guidance and

support, this research could not be accomplished. I am grateful for my committee

members, Dr. Zhihua Su, Dr. Peggy R. Borum and Dr. Maurice R. Marshall for their

valuable time and suggestions.

I acknowledge all the assistance provided by Dr. Timothy J. Garrett, Dr. Arthur S.

Edison, Dr. Fariba Tayyari, Ramadan Ajredini and Sandi Batson Sternberg in the

Southeast Center for Integrated Metabolomics (SECIM). They’ve provided tremendous

help for this research project.

I cherished the friendship with my lab group members, Dr. Keqin Ou, Wei Wang,

Dr. Hanwei Liu, Dr. Amandeep K. Sandhu, Bo Zhao, Kaijie Song, Sara Marshall, Dr.

Zheng Li, Weixin Wang and Yajing Qi. They were always willing to offer helping hands.

The laughter we shared brought abundant joy and made our lives memorable.

Most of all, I would like to express my deepest gratitude to my parents for their

patience, constant love and unconditional support. I also would like give my heartful

thanks to my partner Qiuzhong Wu, who provided me the strength to succeed,

encouraged and guided me. Without their love and support, I would not be able to

successfully accomplish my graduate studies.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES ........................................................................................................ 10

LIST OF ABBREVIATIONS ........................................................................................... 14

ABSTRACT ................................................................................................................... 16

CHAPTER

1 A REVIEW: BIOACTIVITY AND BIOAVAILABILITY OF PROCYANIDINS IN CRANBERRIES ...................................................................................................... 18

Procyanidins in Cranberries .................................................................................... 18 Cranberries & Urinary Tract Infections .................................................................... 18

Intervention Studies and Clinical trials .............................................................. 18

Mechanisms ..................................................................................................... 22 Bioavailability of Procyanidins ................................................................................. 26

Absorption and Metabolism in Stomach and Small Intestine ............................ 26 Microbial Catabolism of Procyanidins in Colon ................................................. 30

Metabolomics Approach to Assess Food Specific Molecular Profiles and Biomarkers after Intake........................................................................................ 31

Assessment of Food Intake .............................................................................. 31

Metabolomics ................................................................................................... 33 Applications of Metabolomics for Discovery of Biomarkers of Dietary Intake ... 35

Research Objectives ............................................................................................... 37

2 PROFILING THE METABOLOME CHANGES CAUSED BY CRANBERRY PROCYANIDINS IN PLASMA OF FEMALE RATS USING 1H NMR AND UHPLC-Q-ORBITRAP-HRMS GLOBAL METABOLOMICS APPROACHES.......... 39

Background ............................................................................................................. 39

Materials and Methods............................................................................................ 41 Chemicals and Materials .................................................................................. 41

Extraction, Purification and Characterization of Partially Purified Cranberry Procyanidins and Partially Purified Cranberry Procyanidins ......................... 42

Animals and Experiment Design ...................................................................... 44 1H NMR Analyses ............................................................................................. 45 UHPLC-Q-Orbitrap-HRMS Analyses ................................................................ 45

Multivariate Data Processing and Statistical Analyses ..................................... 47 Results and Discussion........................................................................................... 49

Procyanidin Composition and Content in PPCP and PPAP ............................. 49

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Quality Control of Multivariate Analyses ........................................................... 49

NMR Metabolomics Analysis of Rat Plasma .................................................... 50 LC-HRMS Metabolomics Analysis of Rat Plasma ............................................ 51

Discriminant Metabolites Identification ............................................................. 53 Summary ................................................................................................................ 59

3 1H NMR-BASED METABOLOMICS REVEALS URINARY METABOLOME MODIFICATIONS IN FEMALE RATS BY CRANBERRY PROCYANIDINS ............ 74

Background ............................................................................................................. 74

Materials and Methods............................................................................................ 75 Chemicals and Materials .................................................................................. 75 Animal Experiment ........................................................................................... 76

1D 1H and 2D 1H-13C NMR analyses ................................................................ 76 Multivariate Statistical Analyses ....................................................................... 77

Results and Discussion........................................................................................... 78

Urinary Metabolome Modification after PPCP or PPAP ................................... 78 Discriminant Metabolites Identification ............................................................. 81

Summary ................................................................................................................ 84

4 A 1H NMR BASED APPROACH TO INVESTIGATE METABOLOMIC DIFFERENCES IN THE PLASMA AND URINE OF YOUNG WOMEN AFTER CRANBERRY JUICE OR APPLE JUICE CONSUMPTION .................................... 96

Background ............................................................................................................. 96

Materials and Methods............................................................................................ 97 Chemicals and Materials .................................................................................. 97

Total Phenolics, Total Anthocyanins, Procyanidin Composition and Content... 97 Sugar Analyses in Cranberry Juice and Apple Juice ........................................ 99 Subjects and Study Design .............................................................................. 99 1H NMR Metabolomics Analyses .................................................................... 100 Multivariate Data Processing .......................................................................... 101

Results and Discussion......................................................................................... 102 Juice Analyses ............................................................................................... 102 Quality Control Data ....................................................................................... 102

Multivariate Analyses of Plasma after Drinking Cranberry Juice vs. Drinking Apple Juice .................................................................................................. 103

Multivariate Analyses of Urine after Drinking Cranberry Juice vs. Drinking Apple Juice .................................................................................................. 104

Discriminant Metabolite Identification ............................................................. 105 Summary .............................................................................................................. 108

5 UHPLC-Q-ORBITRAP-HRMS-BASED GLOBAL METABOLOMICS REVEAL METABOLOME MODIFICATIONS IN PLASMA OF YOUNG WOMEN AFTER CRANBERRY JUICE OR APPLE JUICE CONSUMPTION .................................. 127

Background ........................................................................................................... 127

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Materials and Methods.......................................................................................... 127

Chemicals and Materials ................................................................................ 127 Subjects and Study Design ............................................................................ 127

UHPLC-Q-Orbitrap-HRMS Analyses .............................................................. 127 Multivariate Data Processing and Statistical Analyses ................................... 129

Results and Discussion......................................................................................... 130 Quality Control of Multivariate Analyses ......................................................... 130 Baseline Plasma vs. Plasma after Drinking Cranberry Juice .......................... 131

Plasma after Drinking Apple Juice vs. Plasma after Drinking Cranberry Juice ............................................................................................................ 134

Discriminant Metabolites Identification ........................................................... 135 Summary .............................................................................................................. 141

6 MODIFICATION OF URINARY METABOLOME IN YOUNG WOMEN AFTER CRANBERRY JUICE CONSUMPTION WERE REVEALED USING UHPLC-Q-ORBITRAP-HRMS-BASED GLOBAL METABOLOMICS APPROACH ................ 165

Background ........................................................................................................... 165

Materials and Methods.......................................................................................... 165 Chemicals and Materials ................................................................................ 165 Subjects and Study Design ............................................................................ 165

UHPLC-Q-Orbitrap-HRMS Analyses .............................................................. 165 Multivariate Data Processing and Statistical Analyses ................................... 167

Results and Discussion......................................................................................... 168 Quality Control of Multivariate Analyses ......................................................... 168

Baseline Urine vs. Urine after Drinking Cranberry Juice................................. 168 Urine after Drinking Apple Juice vs. Urine after Drinking Cranberry Juice ..... 170 Discriminant Metabolites Identification ........................................................... 171

Summary .............................................................................................................. 175

7 CONCLUSIONS ................................................................................................... 198

LIST OF REFERENCES ............................................................................................. 200

BIOGRAPHICAL SKETCH .......................................................................................... 213

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LIST OF TABLES

Table page 2-1 Content of procyanidins in PPCP and PPAP. ..................................................... 60

2-2 Summary of parameters for PCA, PLS-DA, and OPLS-DA models for rat plasma after administering PPCP or PPAP by oral gavage. ............................... 61

2-3 Identification of discriminant metabolites in rat plasma after administering PPCP or PPAP by oral gavage........................................................................... 62

2-4 Unidentified discriminant metabolic features for rat plasma after administering PPCP or PPAP by oral gavage. ................................................... 63

3-1 Summary of parameters for PLS-DA and OPLS-DA models for rat baseline urine and urine after administering PPCP or PPCP by oral gavage. .................. 86

3-2 Summary of the metabolite profile changes in rat baseline urine and urine after administering PPCP or PPCP by oral gavage. ........................................... 87

4-1 Timeline of intervention study on women. ........................................................ 109

4-2 Total phenolics, total anthocyanins, procyanidin composition and content of cranberry juice and apple juice. ........................................................................ 110

4-3 Summary of parameters for PCA, PLS-DA, and OPLS-DA models for human baseline plasma and plasma after drinking cranberry juice or apple juice. ....... 111

4-4 Summary of parameters for PCA, PLS-DA, and OPLS-DA models for human plasma after drinking cranberry juice or apple juice. ......................................... 112

4-5 Summary of parameters for PCA, PLS-DA, and OPLS-DA models for human urine after drinking cranberry juice or apple juice. ............................................ 113

4-6 Summary of metabolite profile changes in plasma and urine of young women after drinking cranberry juice and apple juice. .................................................. 114

5-1 Summary of parameters for PLS-DA and OPLS-DA models for human baseline plasma and plasma after drinking cranberry juice or apple juice. ....... 142

5-2 Identification of discriminant metabolites in human plasma after drinking cranberry juice or apple juice by negative ionization analysis. ......................... 143

5-3 Identification of discriminant metabolites in human plasma after drinking cranberry juice or apple juice by positive ionization analysis. ........................... 145

5-4 Unidentified discriminant metabolic features in human plasma after cranberry juice or apple juice by negative ionization analysis. .......................................... 147

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5-5 Unidentified discriminant metabolic features in human plasma after cranberry juice or apple juice by positive ionization analysis. ........................................... 150

6-1 Summary of parameters for PLS-DA or OPLS-DA model for human baseline urine and urine after drinking cranberry juice or apple juice. ............................ 177

6-2 Identification of discriminant metabolites in human urine after drinking cranberry juice or apple juice by negative ionization analysis. ......................... 178

6-3 Identification of discriminant metabolites in human urine after drinking cranberry juice or apple juice by positive ionization analysis. ........................... 179

6-4 Unidentified discriminant metabolic features in human urine after cranberry juice or apple juice by negative ionization analysis. .......................................... 180

6-5 Unidentified discriminant metabolic features in human urine after cranberry juice or apple juice by positive ionization analysis. ........................................... 182

6-6 Summary of identified discriminant metabolites in rats and human. ................. 186

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LIST OF FIGURES

Figure page 1-1 Structures of epicatechin and procyanidin oligomers isolated from

cranberries.......................................................................................................... 38

2-1 HPLC chromatogram of procyanidins in PPCP and PPAP using fluorescence detection.. ........................................................................................................... 64

2-2 The PCA score plot of rat plasma and quality control samples from 1H NMR metabolomics. .................................................................................................... 65

2-3 The PCA score plot of rat plasma from 1H NMR metabolomics after administering PPCP or PPAP. Red squares: rat plasma after administering PPCP. ................................................................................................................. 66

2-4 The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat plasma derived from 1H NMR metabolomics. ..................................................... 67

2-5 The PCA score plot of rat plasma and quality control samples from LC-HRMS metabolomics.. ........................................................................................ 68

2-6 The PCA score plot of rat plasma from LC-HRMS metabolomics after administering PPCP or PPAP. ............................................................................ 69

2-7 The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat plasma derived from LC-HRMS metabolomics.. ................................................. 70

2-8 Validation plot obtained from 200 permutation tests for the OPLS-DA model of rat plasma after administering PPCP or PPAP from LC-HRMS metabolomics. .................................................................................................... 71

2-9 S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of rat plasma after administering PPCP or PPAP. .................................. 72

2-10 VIP plot of variables with VIP score higher than 1. ............................................. 73

3-1 The PCA score plot of rat baseline urine and urine after administering PPCP or PPAP.. ............................................................................................................ 88

3-2 The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat baseline urine and urine after administering PPCP. ........................................... 89

3-3 The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat baseline urine and urine after administering PPAP. ........................................... 90

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3-4 The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat urine after administering PPCP or PPAP. ........................................................... 91

3-5 Validation plot obtained from 200 permutation tests for the OPLS-DA models of rat baseline urine and urine after administering PPCP or PPAP from 1H NMR metabolomics.. .......................................................................................... 92

3-6 S-line associated with the OPLS score plots of data derived from rat baseline urine and urine after PPCP or PPAP.. ................................................................ 93

4-1 Chromatograms of procyanidins extracted from cranberry juice and apple juice using fluorescence detection. ................................................................... 115

4-2 Chromatograms of sugar standards and juices using refractive index detector. ........................................................................................................... 116

4-3 The PCA score plot of human plasma and plasma quality control from 1H NMR metabolomics.. ........................................................................................ 117

4-4 The PCA and OPLS-DA score plots of human plasma after drinking cranberry juice or apple juice from 1H NMR metabolomics. .............................. 118

4-5 Model score plot and cross-validated score plot of OPLS-DA model for human plasma after drinking cranberry juice or apple juice from 1H NMR metabolomics. .................................................................................................. 119

4-6 Validation plot of 200 permutation tests for OPLS-DA model built for human plasma after drinking cranberry juice or apple juice from 1H NMR metabolomics. .................................................................................................. 120

4-7 The PCA and OPLS-DA score plot of human urine after drinking cranberry juice or apple juice from 1H NMR metabolomics. .............................................. 121

4-8 Cross-validated score plot of OPLS-DA model derived from human urine after drinking cranberry juice or apple juice from 1H NMR metabolomics. ........ 122

4-9 Validation plot of 200 permutation tests for OPLS-DA model built for human urine after drinking cranberry juice or apple juice from 1H NMR metabolomics. .................................................................................................. 123

4-10 S-line associated with the OPLS score plots of data derived from human plasma after cranberry juice or apple juice consumption. ................................. 124

4-11 S-line associated with the OPLS score plots of data derived from human urine after cranberry juice or apple juice consumption. .................................... 125

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4-12 Box-and-whisker plot of the NMR signal intensities of eight significant metabolites detected in human plasma or human urine of young women after drinking cranberry juice and apple juice. .......................................................... 126

5-1 The PCA score plot of human plasma and quality control samples from LC-HRMS metabolomics.. ...................................................................................... 152

5-2 The PCA score plot of human baseline plasma and human plasma after drinking cranberry juice from LC-HRMS metabolomics. ................................... 153

5-3 The PLS-DA and OPLS-DA score plots of human baseline plasma and human plasma after drinking cranberry juice from LC-HRMS metabolomics. .. 154

5-4 The PLS-DA and OPLS-DA cross-validated score plots of human baseline plasma and human plasma after drinking cranberry juice from LC-HRMS metabolomics.. ................................................................................................. 155

5-5 Validation plot obtained from 200 permutation tests for the PLS-DA and OPLS-DA models of human baseline plasma vs. human plasma after cranberry juice by negative ionization analysis. ................................................ 156

5-6 Validation plot obtained from 200 permutation tests for the PLS-DA and OPLS-DA models of human baseline plasma vs. human plasma after cranberry juice by positive ionization analysis. ................................................. 157

5-7 The PCA score plot of human plasma after drinking apple juice or cranberry juice from LC-HRMS metabolomics. ................................................................. 158

5-8 The PLS-DA and OPLS-DA score plots of human plasma after drinking apple juice or cranberry juice from LC-HRMS metabolomics. .................................... 159

5-9 The PLS-DA and OPLS-DA cross validated score plots of human plasma after drinking apple juice or cranberry juice from LC-HRMS metabolomics. ..... 160

5-10 Validation plot obtained from 200 permutation tests for the PLS-DA and OPLS-DA models of human plasma after apple juice vs. plasma after cranberry juice by negative ionization analysis. ................................................ 161

5-11 Validation plot obtained from 200 permutation tests for the PLS-DA and OPLS-DA models of human plasma after apple juice vs. after cranberry juice by positive ionization. ....................................................................................... 162

5-12 S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of human baseline plasma and plasma after cranberry juice or apple juice by negative ionization. .............................................................................. 163

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5-13 S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of human baseline plasma and plasma after cranberry juice or apple juice by positive ionization. ............................................................................... 164

6-1 The PCA score plot of human urine and quality control samples from LC-HRMS metabolomics. ....................................................................................... 189

6-2 The PCA score plot of human baseline urine and human urine after cranberry juice from LC-HRMS metabolomics.. ................................................................ 190

6-3 The PLS-DA, OPLS-DA score plots and cross-validated score plots of human baseline urine and urine after cranberry juice. .................................................. 191

6-4 Validation plot obtained from 200 permutation tests for the PLS-DA and OPLS-DA models of human baseline urine vs. human urine after cranberry juice. ................................................................................................................. 192

6-5 The PCA score plot of human urine after drinking apple juice or cranberry juice from LC-HRMS metabolomics. ................................................................. 193

6-6 The OPLS-DA score plots and cross-validated score plots of human urine after drinking apple juice or cranberry juice from LC-HRMS metabolomics.. .... 194

6-7 Validation plot obtained from 200 permutation tests for the OPLS-DA models of human urine after apple juice vs. human urine after cranberry juice.. .......... 195

6-8 S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of human baseline urine and urine after cranberry juice or apple juice by negative ionization.. ..................................................................................... 196

6-9 S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of human baseline urine and urine after cranberry juice or apple juice by positive ionization. ....................................................................................... 197

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LIST OF ABBREVIATIONS

µg Microgram

µL Microliter

μm Micrometer

µmol Micromole

AJ Apple juice

arb Arbitrary unit

BS Baseline

CJ Cranberry juice

CPMG Carr-Purcell-Meiboom-Gill

DP Degree of polymerization

DSS 2,2-dimethyl-2-silapentane-5-sulfonate

FLD

FT

Fluorescent detector

Fourier transformed

g Gram

g Relative centrifugal force

h Hour(s)

HESI Heated electrospray ionization

HPLC High performance liquid chromatography

HPHPA 3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid

HRMS High resolution mass spectrometer

HSQC Heteronuclear single quantum coherence

L Liter

min Minute (s)

mL Milliliter

15

mM Millimolar

mm Millimeter

MS Mass spectrometer

m/z Mass to charge ratio

nM Nanomolar

nm Nanometer

NMR Nuclear magnetic resonance

NOESY Nuclear overhauser effect spectroscopy

OPLS-DA Orthogonal projection on latent structure-discriminant analysis

OSC Orthogonal signal correction

PAFFT Peak alignment by fast fourier transform

PCA Principal component analysis

PHPAA p-Hydroxyphenylacetic acid

PLS-DA Projection on latent structure-discriminant analysis

PPAP Partially purified apple procyanidins

PPCP Partially purified cranberry procyanidins

PQN Probabilistic quotient normalization

psi Pounds per square inch

QC Quality control

SECIM Southeast Center for Integrated Metabolomics

TOF Time of flight

UHPLC Ultra high performance liquid chromatography

UTI Urinary tract infection

v Volume

VIP Variable Importance Projection

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

PROFILING THE METABOLOME CHANGES CAUSED BY CRANBERRY JUICES OR CRANBERRY PROCYANIDINS USING 1H NMR AND UHPLC-Q-ORBITRAP-HRMS

GLOBAL METABOLOMICS APPROACHES

By

Haiyan Liu

December 2015

Chair: Liwei Gu Major: Food Science

Cranberries are known to prevent urinary tract infections and other chronic

conditions. Procyanidins are thought to be the bioactive components. The objective of

this study was to identify specific molecular profiles and biomarkers of cranberry

procyanidin or cranberry juice intake in female rats or young women using 1H NMR and

UHPLC-Q-Orbitrap-MS global metabolomics approaches.

Twenty four female Sprague-Dawley rats were administered partially purified

cranberry (PPCP) or apple procyanidins (PPAP) by oral gavage for 3 times at 0, 12 and

24 hours using a 250 mg extracts/kg body weight dose each. A 24-h baseline urine

were collected before the 1st gavage. Second 24-hour urine were collected after the 1st

oral gavage. Six hours after the 3rd gavage, plasma samples of each rat were collected.

Urine and plasma were analyzed using 1H NMR and UHPLC-Q-Orbitrap-HRMS.

Multivariate analyses revealed that plasma and urinary metabolome in rats were

modified after administering PPCP or PPAP. A total of 36 metabolic features in rat

plasma were detected to be discriminant metabolites using UHPLC-Q-Orbitrap-HRMS

metabolomics. Among them, 11 exogenous metabolites originated from procyanidins

17

catabolism by gut microbiota were identified. Furthermore, urinary excretion of six

endogenous organic acids and three exogenous metabolites were changed after PPCP

or PPAP using 1H NMR metabolomics.

Seventeen young women were given either cranberry or apple juice for three

days using a randomized cross-over design. The metabolome in human plasma and

urine were modified following cranberry juice compared to baseline or apple juice. A

total of 26 and 18 metabolites were identified in human plasma and urine, respectively,

to differentiate cranberry juice consumption from baseline or apple juice consumption.

In conclusion, the plasma and urinary metabolome in female rats or young

women were changed after intake of cranberry procyanidins or cranberry juices. Food

specific metabolite profiles and biomarkers were identified in plasma and urine. These

biomarkers may be used to estimate cranberry juice or cranberry procyanidin intake.

The metabolomics differences between cranberry and apple procyanidins as well as the

differences between cranberry juices and apples juices may help to explain the unique

bioactivities of cranberry juice in mitigating urinary tract infections.

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CHAPTER 1 A REVIEW: BIOACTIVITY AND BIOAVAILABILITY OF PROCYANIDINS IN

CRANBERRIES

Procyanidins in Cranberries

Cranberries (Vaccinium macrocarpon) are a native crop in North America grown

commercially in Wisconsin, Massachusetts, New Jersey, Washington, and part of

Canada. Cranberries are a rich source of flavan-3-ols and procyanidins (Center, 2004).

Procyanidins are oligomeric or polymeric of flavan-3-ols linked through interflavan

bonds. Procyanidins are classified as B-type and A-type based on type of interflavan

bonds (Ou & Gu, 2014). B-type interflavan linkages are C4→ C8 and/or C4→ C6. A-

type procyanidins contain an additional ether bond C2→O→C7(Ou & Gu, 2014). Most

foods including apple juice, grapes, and cocoa contain exclusively B-type procyanidins.

Cranberries are one of a few foods that contain predominantly A-type procyanidins (L.

Gu, Kelm, Hammerstone, Beecher, Holden, Haytowitz, et al., 2003). Procyanidins have

various degree of polymerization (DP). Procyanidins with DP 1, 2-5, and >10 are is

monomer, oligomers, polymers and high polymers, respectively. At about 448 mg/100g

fresh fruits, cranberries contained highest amount of procyanidins compared to all other

fruits (Center, 2004). Over 75% procyanidins in cranberries are polymers and high

polymers (L. Gu, Kelm, Hammerstone, Beecher, Holden, Haytowitz, et al., 2004). The

structures of epicatechin and procyanidin oligomers isolated from cranberries are shown

in Figure 1-1.

Cranberries & Urinary Tract Infections

Intervention Studies and Clinical trials

Urinary tract infection (UTI) is diagnosed by the presence of bacteria in the urine.

It affects over 11 million women in the U.S., and costs over $ 1.6 billion each year (Fihn,

19

2003). Most patients have recurrent infection during their lifetime (Hooton, 2001). The

standard treatment of UTI is to use antibiotics. However, antibiotic-resistant bacteria

cause frequent treatment failure and relapse. Cranberries have been used for UTI

prevention and treatment for over 100 years (Blatherwick, 1914). An epidemiological

study found that drinking cranberry juice on a regular basis decreased the chance of

UTI (Foxman, Geiger, Palin, Gillespie, & Koopman, 1995). The study was retrospective

and examined the relationship between first time UTI and health/sex behavior. The

authors concluded that young and sexually active women may benefit from cranberries

consumption.

The first clinical study to investigate the effects of cranberry on UTI was

conducted in 1966. Sixty patients with bacteriuria were recruited and given 480 mL

cranberry juice daily. After 3 weeks, 53% of the patients had a positive response, but

most patients had a recurrence 6 weeks after stopping drinking juice (Sobota, 1984). In

an open, randomized, controlled trial, three groups of women were given 3 different

treatments: group 1 drank 50 mL of cranberry-lingonberry juice (contained 7.5 g

cranberry concentrate and 1.7 g lingonberry concentrate) daily; group 2 had 100 mL of

a lactobacillus drink; group 3 received no treatment (Kontiokari, Sundqvist, Nuutinen,

Pokka, Koskela, & Uhari, 2001). After 6 months, 16% of the subjects in cranberry group

had ≥ 1 recurrence of UTI, compared to 39% in the lactobacillus group and 36% in the

control group. After stopping drinking, the recurrence of UTI at 12 months in cranberry

group was significantly lower than that in control group and lactobacillus drink group.

Similar results were obtained by Stothers et al. in a placebo-controlled, double-blind

clinical trial, in which a total of 150 women with previous UTI were divided into 3 groups

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(Stothers, 2002). One group took placebo juice and placebo tablets; the second group

received cranberry juice and placebo tablets; the last group had placebo juice and

cranberry tables. The study lasted for 1 year and the results showed that 18% and 20%

of subjects in cranberry tablets and cranberry juice group experienced ≥ 1 recurrence of

UTI, lower than 32% recurrence rate in the control group. A double-blind crossover

study was done on 15 women with recurrent UTI (Walker, Barney, Mickelsen,

WALYON, & Mickelsen, 1997). All the subjects received either cranberry capsule or a

placebo capsule for 3 months. Then patients were switched to an alternative therapy for

another 3 months. It was found that cranberry treatment led to a lower recurrence rate.

Elderly women are more susceptible to UTI. In a randomized, double-blind study

(Avorn, Monane, Gurwitz, Glynn, Choodnovskiy, & Lipsitz, 1994), 153 asymptomatic

elderly women were provided with 300 mL of either cranberry juice cocktail or placebo

for 6 months. Urine sample were tested at baseline and at a 1-month interval for 6

months. There was no difference in the percentage of urine samples with bacteriuria at

the baseline and after 1 month of treatment (~20% at baseline and ~25% after 1

month). But from the 2-month, the percentage of urine sample with bacteriuria in

cranberry group was lower than that in placebo group. At the end of the study, urine

samples with bacteriuria in cranberry group was 42% less frequent than those in the

placebo group, suggesting that cranberry juice reduced the frequency of bacteriuria

(Avorn, Monane, Gurwitz, Glynn, Choodnovskiy, & Lipsitz, 1994). In another non-

blinded crossover study (Haverkorn & Mandigers, 1994), elderly patients received either

15 mL cranberry juice or the same amount of water twice daily. After 4 weeks, patients

switched to the other treatment for the next 4 weeks. At the end of the study, urine

21

samples with bacteriuria in 3 patients were observed during the entire study, and 7

patients had no bacteriuria in either cranberry or water treatment. In the rest of 7

patients’ urine samples, the chance of bacteriuria in cranberry treatment period was less

than that when receiving water.

However, several clinical trials showed that cranberry juices had no effect in

preventing UTI. In a double-blind, randomized, placebo-controlled trial, 319 college

women with an acute UTI were recruited (Barbosa-Cesnik, Brown, Buxton, Zhang,

DeBusscher, & Foxman, 2011). Participants received 240 mL of 27% low-calorie

cranberry juice cocktail or placebo juice twice a day for 6 months. At baseline, 3- and 6-

month time point, urine samples were collected from participants for uropathogens

assessment. The recurrence rate of UIT was 20% in cranberry juice group and 14% in

placebo group. The presence of urinary symptoms at 3 days, 1-2 weeks, and at>1

month was similar between study groups. In another double-blind, randomized,

placebo-controlled study, a total of 255 children (1-16 years old) treated for UTI were

randomized to receive 300 mL of either cranberry juice or placebo juice twice a day for

6 months (Salo, Uhari, Helminen, Korppi, Nieminen, Pokka, et al., 2012). The primary

end point was the occurrence of the first UTI episode during the 12-months follow-up.

Regular cranberry juice drinking reduced the number of UTI recurrence but did not

decrease the number of children experiencing at least 1 recurrence after initial UTI

episode. In a randomized, placebo-controlled, double-blind trial, 376 elderly patients in

hospital received 300 mL of either cranberry juice or placebo juice once daily (McMurdo,

Bissett, Price, Phillips, & Crombie, 2005). The primary outcome was time to the onset of

first UTI. A total of 5.6% of participants developed a symptomatic UTI, with 14/189 in the

22

placebo group and 7/187 in the cranberry juice group. These between-group differences

were not significant. The authors confirmed the acceptability of cranberry juice to elderly

people but not observed the effectiveness of cranberry juice in reducing UTI in elderly

hospital patients. When using cranberry products in clinical trials, participant adherence

is a challenge because of the bitterness and astringency of cranberry juices. Without

specific biomarkers, there is no effective way to evaluate participant compliance in a

clinical trial. Poor participant adherence, high withdrawal rate, and lack of sufficient

active ingredient have been attributed to the ineffectiveness of cranberry products in

preventing UTIs in some clinical trials (Jepson, Craig, & Williams, 2013).

Mechanisms

Although the exact mechanism remains unknown, several theories have been

proposed to explain the effects of cranberries in preventing UTI. Nearly 95% of UTIs are

caused by uropathogenic strain of Escherichia coli bacteria (Blatherwick & Long, 1923).

An earliest theory suggested that the acidity of cranberries played a key role in inhibiting

the activity of uropathogenic E.coli (Blatherwick & Long, 1923). However, latter research

dispelled this theory because a bacteriostatic pH in urine can hardly be achieved

following normal consumption of cranberry juices (Amy B Howell, 2007). A prevalent

theory nowadays is that the preventive effects of cranberries on UTI are due to ability of

cranberries to inhibit the adhesion of E.coli (Amy B Howell, 2007). Uropathogenic E.coli

adhere to the uroepithelium first, and then multiply and colonize the urinary tract,

resulting in UTI (Beachey, 1981). P-fimbriae and type-1 fimbriae of uropathogenic E.coli

adhere to the carbohydrate receptor on the surface of uroepithelium cells. These E.coli

are the virulence factors in the pathogenesis of UTI (Beachey, 1981). P-fimbriated E.coli

adhere to the oligosaccharide receptor sequences (Bond, Favero, Petersen, Gravelle,

23

Ebert, & Maynard, 1981) and typ-1 fimbriated E.coli adheres to mannose-like receptors

(Beachey, 1981).

Ex vivo and in vitro studies showed that cranberries juices and cranberry

capsules inhibited the adhesion of uropathogenic E.coli on urinary tract epithelium cells.

In a multicenter, randomized, double-blind, crossover study, 32 sexually active females

over 18 years old from 4 different countries were recruited (Amy B Howell, Botto,

Combescure, Blanc-Potard, Gausa, Matsumoto, et al., 2010). Eight subjects from

France and 8 subjects from Spain received treatment of 2 placebo capsules, or 1

placebo and 1 cranberry capsule, or 2 cranberry capsules during 3 treatment periods.

The other 8 volunteers from Hungary and 8 from Japan received the same regimen but

with double dosage of cranberry powder. Urine samples before and after

placebo/cranberry capsules were collected and tested for the anti-adhesion activity.

Anti-adhesion activity was detected in urine samples collected from volunteers who

consumed cranberry powder, but not observed in urines collected from placebo group.

The inhibition of bacteria adhesion was dose-dependent, prolonged and increased with

the amount of procyanidins consumed. The authors concluded that consuming

cranberry powder at dosages of 72 mg of procyanidins offered protection against

bacterial adhesion (Amy B Howell, et al., 2010). In a different randomized, double-blind,

placebo-controlled and crossover study, 20 volunteers including 10 men and 10 women

were recruited (Di Martino, Agniel, David, Templer, Gaillard, Denys, et al., 2006).

Volunteers received 750 mL of drinks composed of (A) 250 mL placebo + 500 mL

mineral water; or (B) 750 mL of the placebo; or (C) 250 mL of the cranberry juice and

500 mL of mineral water; or (D) 750 mL of the cranberry juice. Each volunteer took the

24

four regimens successively in a random order, with a washout period of at least 6 days

between regimens. The first morning urine following cranberry or placebo consumption

was collected and used to test bacterial growth. Cranberry consumption caused a dose-

dependent decrease in bacterial adherence. Cranberry juice consumption provided anti-

adherence activity against different E. coli uropathogenic strains in the urine compared

with placebo (Di Martino, et al., 2006).

Procyanidins in cranberries were suggested to be the active compounds in

preventing the adhesion of E. coli. Cranberry procyanidins inhibited adhesion of only P-

fimbriated E.coli to urinary tract epithelial cells, but not type-1 fimbriae (Gupta, Chou,

Howell, Wobbe, Grady, & Stapleton, 2007). It was found that A-type procyanidins but

not B-type procyanidins in cranberries prevented the adhesion of P-fimbriated E.coli to

urinary tract epithelial cells (Foo, Lu, Howell, & Vorsa, 2000; Gupta, Chou, Howell,

Wobbe, Grady, & Stapleton, 2007). A-type procyanidins extracted from cranberry juice

and B-type procyanidins isolated from grape juice, apple juice, chocolate and green tea

were tested for their anti-adhesion activities towards P-fimbriated uropathogenic E.coli

in vitro. A-type procyanidins from cranberry had anti-adhesion activity in vitro at 60 µg

procyanidins/mL (Amy B. Howell, Reed, Krueger, Winterbottom, Cunningham, & Leahy,

2005). The threshold is 1200 µg/mL for grape juice. B-type procyanidins from other

dietary sources did not show anti-adhesion activity (Amy B. Howell, Reed, Krueger,

Winterbottom, Cunningham, & Leahy, 2005). In the same study, human subjects were

enrolled and provided with a single serving of each food containing same amount of

procyanidins. Urine samples were collected and tested for the anti-adhesion activity.

Bacterial anti-adhesion activity was detectable only in the urine of volunteers who

25

consumed cranberry juices (Amy B. Howell, Reed, Krueger, Winterbottom,

Cunningham, & Leahy, 2005).

The anti-adhesion mechanisms of cranberry procyanidins are not fully

understood. Studies suggested that A-type procyanidins may work as receptor analogs

to competitively inhibit adhesion of E.coli by binding to the fimbrial tips (Amy B Howell,

2007). A-type procyanidins in cranberries may alter cell surface properties of the

bacteria to reduce its adhesion capabilities (Foo, Lu, Howell, & Vorsa, 2000). It was also

observed that cranberry juice changed the conformation of surface macromolecules of

P-fimbriated E.coli and specifically reduced fimbrial length and density (Y. Liu, Black,

Caron, & Camesano, 2006). Other studies suggested that A-type procyanidins in

cranberries reduced fimbrial expression at the genetic level and changed the shape of

bacteria (Ahuja, Kaack, & Roberts, 1998; Y. Liu, Black, Caron, & Camesano, 2006).

A-type oligomers with DP 3-5 were the most effective procyanidins in preventing

adhesion of E. coli in ex vivo assays. A-type dimers were slightly active (Foo, Lu,

Howell, & Vorsa, 2000; Gupta, Chou, Howell, Wobbe, Grady, & Stapleton, 2007).

However, such activity may not explain the anti-adhesion activity of urine after cranberry

intake because the bioavailability of procyanidins was extremely low. Only trace amount

of A-type dimers were detected in human urine after cranberry juices intake (McKay,

Chen, Zampariello, & Blumberg, 2015; Zampariello, McKay, Dolnikowski, Blumberg, &

Chen, 2012). No A-type trimers or tetramers were detected in human urine or blood. It is

likely that anti-adhesion activities in urine is due to unknown metabolites from A-type

procyanidins. One objective of this research is to identify those metabolites.

26

Bioavailability of Procyanidins

Absorption and Metabolism in Stomach and Small Intestine

Flavan-3-ols were stable in simulated stomach juice (pH=1.8) in vitro (Spencer,

Chaudry, Pannala, Srai, Debnam, & Rice-Evans, 2000). Procyanidin oligomers from

chocolate were degraded completely to flavan-3-ol monomers under simulated gastric

juice (37oC, pH 2.0, 1–4 h, no digestive enzymes) in vitro (Spencer, Chaudry, Pannala,

Srai, Debnam, & Rice-Evans, 2000). However, in vivo study showed that procyanidins

were stable in human stomachs by analyzing gastric juice after ingestion of procyanidin-

rich cocoa beverages. No significant depolymerization or degradation of procyanidins

was observed in the gastric juice after cocoa ingestion (Rios, Bennett, Lazarus,

Rémésy, Scalbert, & Williamson, 2002). The authors suggested that the acid in the

stomach was buffered by the foods so that procyanidin exposed to much lower acidic

conditions. Additional studies supported that procyanidins from sorghum or grape seed

extracts were not depolymerized to monomeric flavan-3-ols in the gastrointestinal tract

in rodents (L. Gu, House, Rooney, & Prior, 2007; Tsang, Auger, Mullen, Bornet,

Rouanet, Crozier, et al., 2005).

Procyanidins are absorbed in the small intestine. Because no active transporters

were identified for procyanidins, passive diffusion appears to be the major transportation

route (Ou & Gu, 2013). Paracellular absorption is probably the predominate pathway for

procyanidins since passing the lipid bilayer via the transcellular pathway is not very

likely due to the large number of hydrophilic hydroxyl groups (Ou & Gu, 2013; Ou,

Percival, Zou, Khoo, & Gu, 2012). (-)-Epicatechin is a procyanidin monomer and a

constituent unit of procyanidin oligomers. Epicatechin is absorbed on the epithelium of

the upper portion of small intestine. Absorbed epicatechin undergoes extensive phase II

27

metabolism in the small intestine and liver (Crozier, 2013; Ou & Gu, 2013). The main

metabolites of (–)-epicatechin are (–)-epicatechin-3’-glucuronide, (–)-epicatechin-3’-

sulfate, and 3’-methyl-(–)-epicatechin-5-sulfate. Peak plasma concentration of the

metabolites was achieved 1-3 h after intake (Ottaviani, Momma, Kuhnle, Keen, &

Schroeter, 2012). Urinary excretion of (–)-epicatechin was 21% to 50% of (–)-

epicatechin intake in human (Crozier, 2013).

The absorption of oligomeric procyanidins differ from (-)-epicatechin and is

primarily affected by molecular sizes. A fraction of procyanidin oligomers with DP< 5 are

absorbed in the small intestine. Higher polymers were not absorbed at all. They bypass

small intestine and underwent microbial catabolism in the large intestine. Ou et al. (Ou,

Percival, Zou, Khoo, & Gu, 2012) investigated the transportation of cranberry A-type

procyanidin dimers, trimers, and tetramers on monolayers of Caco-2 cells. They found

that the transportation of A-type dimers, trimers and tetramers was rather low, at 0.6%,

0.4%, and 0.2%, respectively. No conjugated forms of A-type procyanidins were

detected (Ou, Percival, Zou, Khoo, & Gu, 2012). In a different study, A-type procyanidin

dimer A1 and A2 from peanut skin were detected in rat plasma after in situ perfusion of

small intestinal. The plasma concentration of A1 was 0.12 µmol/L after 30 min of

perfusion. A2 was not quantified. The absorption of A-type dimers was only 5-10% of

that of epicatechin (Appeldoorn, Vincken, Gruppen, & Hollman, 2009). A-type dimer A2

and a trimer was detected in rat urine after administering A-type oligomeric procyanidins

which were purified from pericarp of litchi. About 0.85% and 0.21% of ingested A2 and

trimers were excreted into the urine (Li, Sui, Xiao, Wu, Hu, Xie, et al., 2013).

28

More studies have been conducted to investigate the absorption rate of B-type

procyanidins. Deprez et al.(Deprez, Mila, Huneau, Tome, & Scalbert, 2001) found that

(+)-catechin, B-type procyanidin dimers and trimers had similar permeability coefficients

to that of mannitol on the human intestinal epithelia Caco-2 monolayers. In contrast, the

permeability of oligomeric procyanidins was 10-fold lower than dimers. Higher polymers

were not permeable at all (Deprez, Mila, Huneau, Tome, & Scalbert, 2001). Ou et al.

also found that procyanidin B2 was able to transport across the Caco-2 monolayers with

a transport rate of 3.0% (Ou, Percival, Zou, Khoo, & Gu, 2012). B-type procyanidin

dimers in grape seed extracts were absorbed in the small intestine using in site

perfusion, but the absorption rate was only 5-10% of that of epicatechin (Appeldoorn,

Vincken, Gruppen, & Hollman, 2009). Procyanidin dimer B2 and B5 from cocoa

transported from the lumen of isolated rat small intestines to the serosal side of

enterocytes. But the transportation rate was <1% of the total transferred flavonols-like

compounds (Spencer, Schroeter, Shenoy, S Srai, Debnam, & Rice-Evans, 2001).

Epicatechin, catechin, and procyanidin dimer B2 reached the highest plasma

concentration 2 h after cocoa consumption (Holt, Lazarus, Sullards, Zhu, Schramm,

Hammerstone, et al., 2002). Peak plasma concentration of epicatechin after chocolate

intake was observed at Tmax 2-3 h (Richelle, Tavazzi, Enslen, & Offord, 1999).

Two early in vivo studies concluded that procyanidin oligomers were not

bioavailable. In the first study (Donovan, Lee, Manach, Rios, Morand, Scalbert, et al.,

2002), catechin, procyanidin dimer B3 and a grapeseed extracts containing monomers

and a mixture of procyanidins were fed to rats in a single meal. Only catechin and

epicatechin conjugates were found in both plasma and urine after the catechin meal. No

29

procyanidins or conjugates were detected in plasma or urine after the procyanidin B3

meal. Procyanidins in the grapeseed extracts were not cleaved into monomers, and

therefore did not increase the monomers concentration in plasma or urine (Donovan, et

al., 2002). In the second study (Gonthier, Donovan, Texier, Felgines, Remesy, &

Scalbert, 2003), catechin, procyanidin dimer B2, timer C2 and polymers were fed to the

rats in a single meal, respectively. After the meal, methylated catechin and phenolic

acids were detected in both plasma and urine. No procyanidin dimers, trimers or any

conjugates were detected in plasma or urine. Only a very small amount of phenolic

acids (0.5%-0.7%) were found in the plasma and urine (Gonthier, Donovan, Texier,

Felgines, Remesy, & Scalbert, 2003). However, most other in vivo studies did detect the

procyanidin dimers or trimers in plasma or urine after procyanidins consumption in

rodent model. Trace amount of procyanidin dimer B1, B2, B3, B4, C2 and trimers were

detected in the urine of rats after consuming grapeseed extracts (Tsang, et al., 2005).

Dimer B2 and trimers were also found in the plasma of rats and urine of pigs after

feeding grapeseed extracts (Rzeppa, Bittner, Döll, Dänicke, & Humpf, 2012). Dimer B2

was detected in the plasma and urine of rats after intake of pure dimer B2 (Baba,

Osakabe, Natsume, & Terao, 2002). In these animal studies, only intact procyanidin

dimers or trimers were detected at a low concentration. No procyanidin conjugates were

detected at any level. Similar results were also found in human studies. Procyanidin

dimers were detected in plasma after one single cocoa drink which contained only B-

type procyanidins (Holt, et al., 2002). Two grams of grapeseed extracts in capsules

were ingested by 4 healthy volunteers, and procyanidin dimers were found in plasma 2

hours after ingestion (Sano, Yamakoshi, Tokutake, Tobe, Kubota, & Kikuchi, 2003).

30

Although these studies found procyanidin dimers or trimers in plasma or urine after

procyanidin-rich foods ingestion, the amount of excreted procyanidin oligomers in

plasma or urine was very low.

Microbial Catabolism of Procyanidins in Colon

A small portion of flavan-3-ols and procyanidin oligomers are absorbed in the

small intestine. Majority of ingested procyanidins reach the colon intact and are

metabolized by gut microflora. Griffith et al. (Griffiths, 1964) first discovered that

procyanidins were fermented by gut microflora to generate microbial metabolites. They

found 3-(3’-hydroxyphenyl)propionic acid in the urine of rats fed a diet containing (+)-

catechin (Griffiths, 1964). Deprez et al. (Déprez, Brezillon, Rabot, Philippe, Mila,

Lapierre, et al., 2000) found that polymeric procyanidins were completely degraded after

48 h incubation with freshly human fecal bacteria and the major metabolites were 3-(3’-

hydroxyphenyl)propionic acid, 4-hydroxyphenylacetic acid, 3-(4’-

hydryoxphenyl)propionic acid, and 3-phenylpropionic acid (Déprez, et al., 2000).

Procyanidin B2 was degraded by human microflora faster than epicatechin. Degradation

produced metabolites unique to procyanidin B2 including 5-(2’,4’-dihydroxy) phenyl-2-

ene valeric acid and 5-(3’,4’-dihydroxyphenyl) valeric acid (Stoupi, Williamson, Drynan,

Barron, & Clifford, 2010). The total yield of phenolic acids in colon decreases drastically

with the increase of degree of polymerization. About 10% and 7% of microbial

metabolites in rat guts were from monomers and dimers, whereas 0.7% and 0.5% were

generated from trimers and polymers, respectively (Gonthier, Donovan, Texier,

Felgines, Remesy, & Scalbert, 2003). A-type linkage in procyanidins is more rigid and

stable than B-type linkages due to an additional covalent bond (L. Gu, Kelm,

Hammerstone, Beecher, et al., 2003). About 80% of procyanidin A-type dimers and

31

40% of A-type trimers were degraded within 8 h of incubation with pig cecum microflora

(Engemann, Hubner, Rzeppa, & Humpf, 2012). A-type trimers produced hydroxylated

catabolites with more complicated patterns than A-type dimers. The metabolites for both

A-type dimer and trimer procyanidins included hydroxy- or dihydroxy-benzoic acids,

phenylacetic acids, phenylpropionic acids, and phloroglucinol (Engemann, Hubner,

Rzeppa, & Humpf, 2012). Gu et al. found that 50-80% of ingested procyanidins from

sorghum were degraded in the gastrointestinal tract of rats, and 11% of them were

excreted in 24-hour urine as phenolic acids. The major microbial metabolites found in

the serum of rats were 3,4-dihydroxybenzoic acid, vanillic acid, and 4-

hydroxyphenylacetic acid (L. Gu, House, Rooney, & Prior, 2007).

Metabolomics Approach to Assess Food Specific Molecular Profiles and Biomarkers after Intake

Assessment of Food Intake

Accurate assessment of food intake is critical in epidemiological studies to

associate the intake of certain foods with health outcomes. Current measurement of the

dietary intake uses food intake records and food composition database. Food intake

data are commonly obtained from food frequency questionnaires, diet diaries, diet

histories, multiple 24 h recalls, etc. These methods rely on self-reporting by the study

subjects. Consequently, the accuracy of this measurement remains uncertain (Manach,

Hubert, Llorach, & Scalbert, 2009; Spencer, Abd El Mohsen, Minihane, & Mathers,

2008). The limitation of food composition databases is the lack of systematic approach

to comprehensively identify and quantify nutrients in foods. This is particularly true for

phytochemicals, because phytochemical contents in foods are affected by genetics,

environmental conditions, cultivar differences, horticultural practices, and food

32

processing methods (Spencer, Abd El Mohsen, Minihane, & Mathers, 2008). The impact

of these factors on the phytochemical level in foods was not distinguished in the food

composition table, which makes the estimation of dietary intake of phytochemicals

inaccurate. Estimation of food or phytochemical intake using specific biomarkers in

urine or blood is expected to overcome these problem.

The positive correlation between doses of quercetin in fruit juice and urinary

quercetin level suggested that urinary quercetin may serve as a quantitative biomarker

of dietary quercetin intake (Young, Nielsen, Haraldsdóttir, Daneshvar, Lauridsen,

Knuthsen, et al., 1999). Plasma and urinary isoflavone levels are semi-quantitative

indicators of isoflavone intake (Setchell, Brown, Desai, Zimmer-Nechimias, Wolfe,

Jakate, et al., 2003). Flavonoids metabolites in plasma or urine could also be used as

potential biomarkers of fruits and vegetables intake. A positive correlation (r=0.86)

between total polyphenol metabolites in 24-h urine and fruits and vegetables intake was

observed in a dietary intervention study (Krogholm, Haraldsdóttir, Knuthsen, &

Rasmussen, 2004). Urinary quercetin and flavones were found to be higher after high-

vegetable diets. Fruit and vegetable intake positively correlated with changes in urinary

excretion of flavonoids (Krogholm, Haraldsdóttir, Knuthsen, & Rasmussen, 2004).

Positive correlation between the intake of polyphenols-rich foods and urinary excretion

of the corresponding metabolites from spot urine samples was found in several foods

(Mennen, Sapinho, Ito, Bertrais, Galan, Hercberg, et al., 2006). For example, apple

intake positively correlated with phloretin, grapefruits intake with naringenin, orange

consumption with hesperetin, citrus consumption with urinary excretion of hesperetin

and naringenin. Wine consumption positively associated with caffeic acid in human

33

plasma (Simonetti, Gardana, & Pietta, 2001). Black tea and coffee consumption

correlated with 24-h urinary excretion of 4-O-methylgallic and isoferulic acids in human,

respectively (Hodgson, Yee Chan, Puddey, Devine, Wattanapenpaiboon, Wahlqvist, et

al., 2004). These findings supported that polyphenol metabolites can be used as

specific biomarkers for the intake of polyphenol-rich foods (Ito, Gonthier, Manach,

Morand, Mennen, Rémésy, et al., 2005; Spencer, Abd El Mohsen, Minihane, & Mathers,

2008).

These traditional methods to identify and validate biomarkers for food intake was

based on the “one metabolite-one food” approach (Manach, Hubert, Llorach, & Scalbert,

2009). Candidate biomarker metabolites are often the predominant ones for particular

phytochemicals. In the case of procyanidins, no specific or predominant metabolite was

discovered in the urine or blood to serve as possible intake biomarkers. This issue can

be addressed by untargeted metabolomics approach because of its ability to

simultaneously analyze hundreds or thousands phytochemicals and their metabolites in

biofluids. Untargeted Metabolomics approach is useful to identify new and unexpected

biomarkers of phytochemicals intake.

Metabolomics

Metabolomics refers to the comprehensive analysis of low molecular-weight

metabolites in biological samples (Nicholson, Lindon, & Holmes, 1999). It is a system

biology approach to monitor systematic physiological effects following genetic

modification, pathophysiological changes, or exogenous challenges (Griffin, 2006;

Nicholson, Lindon, & Holmes, 1999). Diet plays a pivotal role to shape human

metabolome. Part of the ingested phytochemicals from foods are absorbed through gut

barrier and metabolized. The resultant exogenous phytochemical metabolites are part of

34

food metabolome and may alter the endogenous metabolites (Manach, Hubert, Llorach,

& Scalbert, 2009). Alteration of endogenous metabolites is considered the amplified

‘end-point’ output of changes down the biochemical pathways (S. Lin, Chan, Li, & Cai,

2010). Cranberries are known to affect gene expression, protein activity, and signaling

transduction (Deziel, Patel, Neto, Gottschall‐Pass, & Hurta, 2010; Kresty, Howell, &

Baird, 2011). Cranberry juice or procyanidin consumption may alter the profile of

endogenous metabolites. The microbial catabolites of A-type procyanidins and altered

profile of endogenous metabolites may contribute to the unique bioactivities of cranberry

juices or procyanidins.

Multiple analytical platforms are employed to efficiently generate the metabolic

profiles of biological samples. Nuclear magnetic resonance (NMR) spectroscopy and

mass spectrometry (MS) are the commonly used analytic techniques. NMR

spectroscopy has the advantage of being quantitative, highly reproducible, non-

selective and minimal sample preparation (Dunn, Broadhurst, Atherton, Goodacre, &

Griffin, 2011). MS coupled with chromatographic separation techniques are able to

detect a wider range of metabolites and identify compounds based on their unique

spectrums of mass fragments (H. M. Lin, Helsby, Rowan, & Ferguson, 2011).

Metabolomics strategy produces high-dimensional and complex data set. Multivariate

statistic techniques including principal component analysis (PCA), projection on latent

structure-discriminant analysis (PLS-DA), and orthogonal projection on latent structure-

discriminant analysis (OPLS-DA) are often used to reduce the dimensionality of the data

(Bylesjö, Rantalainen, Cloarec, Nicholson, Holmes, & Trygg, 2006). They are useful to

reveal patterns related to the physiological or pathological perturbation and to aid

35

biological interpretation (Marchesi, Holmes, Khan, Kochhar, Scanlan, Shanahan, et al.,

2007).

Applications of Metabolomics for Discovery of Biomarkers of Dietary Intake

Urinary metabolome modifications in human after cocoa consumption were

explored using a HPLC-Q-TOF-MS-based metabolomics approach (Llorach, Urpi-

Sarda, Jauregui, Monagas, & Andres-Lacueva, 2009). In a randomized, crossover

study, 5 women and 5 men were recruited and consumed either a single dose of cocoa

powder with milk or cocoa powder with water, or milk without cocoa. Urine samples

were collected at baseline, 0-6 h, 6-12h, and 12-24 h after cocoa consumption.

Multivariate statistic models including PCA, PLS-DA, and orthogonal signal correction

(OSC)-PLS-DA were built to reveal differences in urinary metabolome between three

diets. It was found that milk in the cocoa drinks had little influence on the urinary

metabolome. A segregation between cocoa with milk and only milk was observed on the

valid supervised models. A total of 27 compounds including alkaloid derivatives,

metabolites of flavan-3-ols and procyanidins were identified as the main discriminant

biomarkers. In another open, blind, randomized and placebo-controlled trial, 24

volunteers ingested either 10 capsules containing almond skin extract or 10 capsules

containing placebo (Llorach, Garrido, Monagas, Urpi-Sarda, Tulipani, Bartolome, et al.,

2010). Urine samples were collected at baseline, 0-6h, 6-12h, and 12-24 h. Samples

were analyzed using HPLC-Q-TOF-MS followed by multivariate statistics including PCA

and OPLS-DA models. Urinary metabolome of 4 different urine sampling times after

intake of almond skin extract were different from those after intake of placebo. A total of

34 microbial metabolites of procyanidins including flavonoid conjugates,

hydroxylphenylvalerolactone conjugates, 4-hydroxy-5-(phenyl)-valeric acid conjugates,

36

hydroxyphenyl-propionic acid conjugates, hydroxyphenylacetic acid conjugates, and

other phenolic acid conjugates were identified as the potential biomarkers of almond

polyphenol intake. Biomarkers of citrus intake was discovered using three study designs

(Pujos-Guillot, Hubert, Martin, Lyan, Quintana, Claude, et al., 2013). In the first design,

volunteers consumed an acute dose of orange of grapefruit juice. In the second design,

volunteers consumed orange juice regularly for one month. The third design used

volunteers from a large cohort study who reported high or low consumption of citrus

products. PCA and PLS-DA were used to reveal the urinary metabolome modifications

after intake of citrus products. Different discriminant markers were found in these three

studies. Many signals that increased after citrus intake in the acute study were not

found to be the contributing markers in the cohort study. Proline betaine, hydroxyproline

betaine, hesperetin and naringenin glucuronides were identified as sensitive

biomarkers. Additionally, two terpene metabolites were identified as candidate

biomarkers. The authors proposed that data-driven metabolomics profiling of urinary

metabolome in cohort subjects is a powerful approach to discover sensitive biomarkers

for a wide range of foods. The biomarkers of citrus intake were also investigated in

volunteers who consumed a standardized diet supplemented with mix-fruits

(Heinzmann, Brown, Chan, Bictash, Dumas, Kochhar, et al., 2010). Urinary metabolome

of study subjects were profiled using 1H NMR-based metabolomics approach. The

authors identified proline betaine as a putative biomarker of citrus consumption. This

biomarker was validated in an epidemiological study and it showed a sensitivity of

86.3% and a specificity of 90.6% using a receiver operating characteristic curve.

Metabolome modifications in male subjects after green tea or black tea consumption

37

were revealed using a 1H NMR-based metabolomics approach (S. Lin, Chan, Li, & Cai,

2010). Seventeen healthy male volunteers consumed black tea, green tea, or caffeine in

a randomized crossover study. It was found that urinary excretion of hippuric acid, 1, 3-

Dihydroxyphenyl-2-O-sulfate was increased after green tea or black tea consumption

compared to the control of caffeine. The intake of green teal and black tea had different

impact on the endogenous metabolites in urine and plasma. Green tea consumption

caused a greater increase in urinary excretion of several citric acid cycle intermediates.

Research Objectives

Cranberries are known to prevent urinary tract infections and other chronic

conditions. However, there is no effective way to assess cranberry intake in

epidemiological studies or clinical trials. The mechanism by which cranberries mitigate

UTI remains unknown in part because the systematic physiological effects of cranberry

intake in not clear.

The overall goal of this research is to identify specific molecular profiles and

biomarkers of cranberry intake and to help identify the mechanisms of cranberry juices

or procyanidins in mitigating urinary tract infections or other chronic diseases. We

hypothesized that the plasma and urinary metabolome of female rats and young women

are modified after cranberry procyanidins or cranberry juices. These research goals

were reached and hypotheses were tested by pursuing the following two specific aims:

1. To perform metabolomics profiling and fingerprinting (1H NMR & UHPLC-Q-Orbitrap-HRMS) of plasma and urinary metabolome in female Sprague-Dawley rats after administering partially purified procyanidins from cranberry powder or apples.

2. To perform metabolomics profiling and fingerprinting (1H NMR & UHPLC-Q-Orbitrap-HRMS) of plasma and urinary metabolome in young women following cranberry juice and to differentiate metabolites from those formed after apple juice consumption.

38

Figure 1-1. Structures of epicatechin and procyanidin oligomers isolated from

cranberries. 1, 2, 3, 4, 5 and 6 are epicatechin, epicatechin-(4β→8)-epicatechin (procyanidin B2), epicatechin-(4β→8, 2β→O→7)-epicatechin (procyanidin A2), epicatechin-(4β→6)-epicatechin-(4β→8, 2β→O→7)-epicatechin, epicatechin-(4β→8, 2β→O→7)-epicatechin-(4β→8)-epicatechin, and epicatechin-(4β→8)-epicatechin-(4β→8, 2β→O→7)-epicatechin, respectively (Foo, Lu, Howell, & Vorsa, 2000).

39

CHAPTER 2 PROFILING THE METABOLOME CHANGES CAUSED BY CRANBERRY

PROCYANIDINS IN PLASMA OF FEMALE RATS USING 1H NMR AND UHPLC-Q-ORBITRAP-HRMS GLOBAL METABOLOMICS APPROACHES

Background

Procyanidins are oligomers and polymers of (−)-epicatechin or (+)-catechin (L.

Gu, Kelm, Hammerstone, Beecher, et al., 2003). The molecular weight of procyanidins

is described by degree of polymerization (DP). Monomeric procyanidins are (−)-

epicatechin and (+)-catechin. Procyanidins with DP 2, 3, and 4 are dimers, trimers, and

tetramers, respectively. The most widely distributed procyanidins in foods are the B-

type, which are linked through C4→ C8 and/or C4→ C6 interflavan bonds (Ou & Gu,

2014). Examples of foods that contain exclusively B-type procyanidins are apples,

pears, blueberries, and cocoa. A-type procyanidins are rare in foods and they have an

additional ether interflavan bond C2→O→C7. Cranberries are among a few foods that

contain A-type procyanidins. A previous study showed that cranberry press cake with at

least one A-type bond accounted for more than 90% of the oligomers between trimers

and decamers (Feliciano, Krueger, Shanmuganayagam, Vestling, & Reed, 2012).

Studies suggested that A-type procyanidins may have greater or unique bioactivity

compared with B-type (Amy B. Howell, Reed, Krueger, Winterbottom, Cunningham, &

Leahy, 2005). Such activity was attributed to A-type procyanidins but not B-type ones

(Amy B. Howell, Reed, Krueger, Winterbottom, Cunningham, & Leahy, 2005).

Metabolomics have been widely applied in clinical, pharmaceutical and

toxicological studies for identification of biomarkers (Lindon, Holmes, & Nicholson,

2006). It assesses the metabolic changes in a global manner in order to monitor

biological function alteration due to genetic modification, pathophysiological changes, or

40

exogenous challenges (Griffin, 2006; Nicholson, Lindon, & Holmes, 1999).

Phytochemicals originating from foods are ingested, metabolized and absorbed in the

gastrointestinal tract generating a characteristic metabolome profile, which may further

alter endogenous metabolites. Metabolomics is an effective approach to distinguish the

metabolome differences caused by different diets (Llorach, Urpi-Sarda, Jauregui,

Monagas, & Andres-Lacueva, 2009). NMR and UHPLC-HRMS are the two most widely

used metabolomic platforms (Dunn, Broadhurst, Atherton, Goodacre, & Griffin, 2011).

Both techniques are able to detect hundreds of wide ranging metabolites in biological

samples. NMR spectroscopy has the advantage of being quantitative, highly

reproducible, non-selective (Dunn, Broadhurst, Atherton, Goodacre, & Griffin, 2011) and

minimal sample preparation (Beckonert, Keun, Ebbels, Bundy, Holmes, Lindon, et al.,

2007), while UHPLC-HRMS is highly sensitive and able to identify the chemical

structures of metabolites (Dunn, Broadhurst, Atherton, Goodacre, & Griffin, 2011). The

high-dimensional data produced by a metabolomics study is often processed using

multivariate statistical techniques such as PLS-DA and OPLS-DA to reduce the

dimensionality of the data (Bylesjö, Rantalainen, Cloarec, Nicholson, Holmes, & Trygg,

2006).

The mechanism by which cranberry procyanidins mitigate urinary tract infection

remains elusive. A-type procyanidins from cranberry juice inhibited the adhesion of

uropathogenic E. coli, whereas those from apple juice showed no activity. Anti-adhesion

activity in human urine was detected following cranberry juice cocktail consumption, but

not after consumption of apple juice (Amy B. Howell, Reed, Krueger, Winterbottom,

Cunningham, & Leahy, 2005). We hypothesized that the metabolome changes caused

41

by cranberry procyanidins in female rats may be different from those caused by apple

procyanidins. The objective of this study is to identify molecular profile and putative

biomarkers in plasma of female rats after intake of partially purified cranberry

procyanidins (PPCP) using both 1H NMR and UHPLC-Q-Orbitrap-HRMS based global

metabolomics approaches.

Materials and Methods

Chemicals and Materials

Freeze-dried cranberry powder was provided by Ocean Spray Cranberries, Inc.

(Lakeville-Middleboro, MA, USA). Fresh granny smith apples were purchased from a

local grocery store. LC-MS grade acetonitrile, methylene chloride, methanol, acetic acid,

formic acid, and acetone were purchased from Fischer Scientific Co. (Pittsburgh, PA,

USA). (-)-Epicatechin was purchased from Sigma Chemical Co. (St. Louis, MO, USA).

A mixture of partially pure procyanidin oligomers (monomers through nonamers) was

provided by Mars Inc. (McLean, VA, USA). D2O (99.9% D) was provided from

Cambridge Isotope Laboratories, Inc. (Tewksbury, MA, USA). Creatine-D3, L-leucine-

D10, L-tryptophan-2, 3, 3-D3, caffeine-D3 were obtained from CDN Isotopes Inc. (Pointe-

Claire, Quebec, Canada). Sephadex LH-20 resin was purchased from Sigma-Aldrich

(St. Louis, MO, USA). Amberlite FPX 66 resin was a product of Rohm and Haas Co.

(Philadelphia, PA, USA). Pooled quality control plasma samples used in NMR

metabolomics were purchased from the American Red Cross and were collected over a

period of about 2 weeks.

42

Extraction, Purification and Characterization of Partially Purified Cranberry Procyanidins and Partially Purified Cranberry Procyanidins

One hundred and twenty grams of freeze-dried cranberry powder was extracted

with 1 L of methanol. The cranberry-methanol mixture was put into a beaker sealed with

Parafilm M and sonicated for 30 min. After sonication the cranberry-methanol mixture

was placed in darkness at room temperature for 48 h. Extracts obtained after vacuum

filtration were combined and concentrated under a partial vacuum using a rotary

evaporator which was performed at 45 oC. The concentrated extract was re-suspended

in 20 mL of water and loaded onto a column packed with Amberlite FPX 66 resin.

Column was eluted with 3 L of de-ionized water to remove free sugars and organic

acids. Column was then eluted with 500 mL of methanol to recover cranberry

phytochemicals absorbed in the resin. Methanol was then evaporated using a

SpeedVac Concentrator (Thermo scientific ISS110, Waltham, MA) under a reduced

pressure to yield dry cranberry sugar-free extract (5.40 g). The sugar-free powder (5.40

g) was suspended in 100 mL of 30% methanol and loaded onto a column (5.8×28 cm)

packed with Sephadex LH-20, which was soaked in 30% methanol for over 4 hours

before use. The column was eluted with 30% methanol to remove anthocyanins and

phenolic acids, and then eluted with 70% acetone to yield partially purified cranberry

procyanidins (3.95 g). To extract procyanidins from fresh apples, 5000 g fresh granny

smith apples were used. Fresh apples were stored at -20 oC and divided into two

batches before the extraction. Each 2500 g frozen apples were cut into small pieces

and pulverized to apple puree using a blender. Apple puree was mixed with 2 L of

methanol and sonicated for 40 min on an ice bath. Then the apple puree-methanol

suspension was placed in darkness at -10 oC for 48 h. Two batches of extracts obtained

43

after vacuum filtration were combined and concentrated under a partial vacuum using a

rotary evaporator which was performed at 45 oC The concentrated extract was re-

suspended in 50 mL of water and loaded onto a column packed with Amberlite FPX 66

resins. Column was eluted with 3 L of de-ionized water to remove free sugars and

organic acids. Column was then eluted with 500 mL of methanol to recover apple

procyanidins absorbed in the resin. Methanol was then evaporated using a SpeedVac

Concentrator (Thermo scientific ISS110, Waltham, MA) under a reduced pressure to

yield partially purified apple procyanidins (5.30 g).

Quantitative and qualitative analyses of procyanidins followed a previous

publication (Hanwei Liu, Zou, Gao, & Gu, 2013). The HPLC-MSn system had an HCT

ion trap mass spectrometer (Bruker Daltonics, Billerica, MA, USA) coupled with an

Agilent 1200 HPLC (Palo Alto, CA, USA) equipped with a binary pump, an autosampler,

and a fluorescence detector. Separation of procyanidins was carried out on a Luna

Silica (2) column (250 × 4.6 mm, 5 μm particle size, Phenomenex, Torrance, CA, USA)

at a column temperature of 37 oC. The binary mobile phase consisted of (A) methylene

chloride/methanol/acetic acid/water (82:14:2:2, v: v: v: v) and (B) methanol/acetic acid/

water (96:2:2, v: v: v: v). The 70 min gradient was as follows: 0−20 min, 0.0−11.7% B

linear; 20−50 min, 11.7−25.6% B linear; 50−55 min, 25.6−87.8% B linear; 55−65 min,

87.8% B isocratic; 65−70 min, 87.8−0.0% B linear; followed by 5 min of column re-

equilibration before the next injection. Excitation and emission of the fluorescent

detector were set at 231 and 320 nm, respectively. Electrospray ionization at negative

mode was performed using nebulizer 50 psi, drying gas 10 L/min, drying temperature

350 °C, and capillary 4000 V. Mass spectra were recorded from m/z 150 to 2000. The

44

most abundant ion in full scan was isolated, and its product ion spectra were recorded.

Identification of A- and B-type procyanidins using tandem mass spectrometry followed a

previously published method (L. Gu, Kelm, Hammerstone, Zhang, Beecher, Holden, et

al., 2003).

Procyanidins in PPCP and PPAP were quantified based on a method

standardized by Mars Inc. (Robbins, Leonczak, Li, Johnson, Collins, Kwik-Uribe, et al.,

2012). This method uses (−)-epicatechin as a calibrant and relative response factors for

procyanidin dimers through nonamers, because at the same concentration the ratio of

fluorescent responses between an oligomer and (−)-epicatechin stay constant under the

same HPLC condition. The relative response factor of nonamers was used as the

response factor to quantify high polymers.

Animals and Experiment Design

Approval for animal study was sought through the Institutional Animal Care and

Use Committee at the University of Florida (IACUC Study #201307837). Female

Sprague Dawley (n=24, 220-280 g) were housed in the animal facility and acclimated

for 5 days using a purified diet free of flavonoid compounds (D10012G, Research diet

Inc., New Brunswick, NJ, USA). Two female rats were housed in a cage. After the

acclimation period female rats were randomly divided into two groups with 12 female

rats per group, and fasted for six hours before the metabolomics study. PPCP or PPAP

were dispersed in water and administered by oral gavage at 0 and 12 hours using a

dose of 250 mg extracts/kg body weight. Female rats had free access to food and water

after dosing. At 24 hours, female rats were gavaged for a third time. Six hours after the

3rd gavage, female rats were anesthetized and blood samples were collected by cardiac

puncture into vials containing sodium heparin using heparinized syringes. Blood

45

collection time point was selected based on a previous study which showed that [14C]

procyanidin B2 in rats reached a peak plasma concentration at Tmax 5-6 hours after oral

administration (Stoupi, Williamson, Viton, Barron, King, Brown, et al., 2010). Blood

samples were centrifuged at 2,000 g for 10 min at 4 oC to obtain plasma. All plasma

samples were aliquoted and kept in a -80 oC freezer until analyses.

1H NMR Analyses

Plasma samples were thawed at 4oC in a cold room. Four hundred µL of saline

solution (NaCl 0.9% in 10% D2O) was added to 200 µL of plasma. The mixtures were

vortexed for 1 minute and centrifuged at 16, 000 g for 15 min at 4oC and 550 µL of

supernatant was transferred into 5 mm Bruker NMR tubes (Z105684 Bruker 96 well

rack) using Gilson 215 Liquid Handler (Trilution software version 2.0). All 1H-NMR

spectra were collected on a 600 MHz Avance II NMR spectrometer (Bruker Biospin,

Rheinstetten, Germany) equipped with a 5 mm CryoProbe. A Bruker sampleJet

operated by IconNMR in Topspin was used to record spectra automatically. 1D CPMG-

presaturated spectra for plasma were recorded. Optimal probe tuning and matching, 90°

pulse length, water offset, and receiver gain were adjusted on the representative

sample. The probe was automatically locked to H2O+D2O (90%+10%) and shimmed for

each sample. All NMR data were acquired at 300 K.

UHPLC-Q-Orbitrap-HRMS Analyses

Frozen plasma samples (-80 oC) were thawed at room temperature. One plasma

sample (50 µL) was mixed with 400 µL acetonitrile: acetone: methanol (8:1:1, v: v: v) to

precipitate the proteins. Ten µL isotopically-labeled standard solution (40 µg/mL L-

tryptophan-D3, 4 µg/mL L-leucine-D10, 4 µg/mL creatine-D3, and 4 µg/mL caffeine-D3)

was added to the above extraction mixture as internal standards. The sample was then

46

vortexed and placed in a 4 oC refrigerator for 30 min to assist protein precipitation. Then

the sample was centrifuged at 20,000 g for 10 min at <10 oC to pellet the protein. One

hundred and twenty five µl of supernatant was transferred to a new 1 ml Eppendorf tube

and dried under a gentle stream of Nitrogen (Organomation Associates, Inc., Berlin,

MA, USA). Dried sample was reconstituted in 50 µL 0.1% formic acid in water and

vortexted. The sample solution was put on an ice bath for 10-15 min and centrifuged at

20,000 g for 5 min at <10 oC to remove debris. The supernatant was transferred into a

LC glass vial with fused glass insert for analyses. All 24 rat plasma samples were

prepared in the same manner. Three pooled quality control (QC) samples were

prepared by mixing an equal volume of the supernatant from 24 rat plasma extracts. In

addition, three neat QC samples were prepared by adding 20 µL of isotopically-labeled

standard solution directly to three LC glass vials, respectively. To monitor the

performance of data acquisition, run sequence was started with 3 blanks (0.1% formic

acid in water), one neat QC, and one pooled QC followed by every 10 plasma samples

to ensure instrument drift was minimal.

Chromatographic separation was performed on a Thermo Scientific-Dionex

Ultimate 3000 UHPLC using an ACE Excel 2 C18-PFP column, 100 mm x 2.1 mm i.d., 2

µm (Advanced Chromatography Technologies, Aberdeen, UK). The mobile phase

consisted of (A) water with 0.1% formic acid and (B) acetonitrile. The gradient was as

follows: 0−3 min, 100% A isocratic; 3−13 min, 0−80% B linear; 13−16 min, 80% B

isocratic; 16−16.5 min, 80-0% B linear; followed by 3 min of re-equilibration of the

column before the next run. The flow rate was 350 μL/min and the injection volume was

4 μL. Before starting the sequence, UHPLC column was rinsed using 100% acetonitrile

47

and then equilibrated using 100% 0.1% formic acid for 10 min. The UHPLC system was

coupled to a Q Exactive™ Hybrid Quadrupole-Orbitrap High Resolution Mass

Spectrometer (Thermo Fisher Scientific, San Jose, CA, USA). The MS acquisition was

performed in negative ionization with a mass resolution of 70,000 at m/z 200 and

separate injections were performed in a data-dependent (top 5) MS/MS mode with the

full scan mass resolution reduced to 35,000 at m/z 200. The m/z range for all full scan

analyses was 70–1000. Heated electrospray ionization (HESI) parameters were as

follows: sheath gas flow 45 arb, auxiliary gas flow 10 arb, sweep gas flow 1 arb, spray

voltage 3.5 kV, capillary temperature 320 °C, and probe temperature 350°C. In source

CID (Collision-Induced Dissociation) was 2 eV. The mass spectrometer was calibrated

using Pierce™ negative ion calibration solution (Thermo Fisher Scientific, San Jose CA,

USA). To avoid possible bias, the sequence of injections for plasma samples was

randomized.

Multivariate Data Processing and Statistical Analyses

All NMR spectra were phased and baseline corrected using NMRPipe (Delaglio,

Grzesiek, Vuister, Zhu, Pfeifer, & Bax, 1995) and then converted to FT (Fourier

transformed) files. The FT files were imported into MATLAB (R2013B, the Mathworks,

Inc., Natick, MA, USA). Spectra were referenced to the alanine peak at δ1.469 ppm and

water resonance region (4.66-4.95 ppm) was excluded. Then the spectra were aligned

and normalized in MATLAB. The resultant data set was imported into SIMCA (Version

13.0.3, Umetrics, Umea, Sweden) for multivariate statistical analysis. Data were mean-

centered and Pareto scaled before PCA, PLS-DA and OPLS-DA analyses in SIMCA.

LC-HRMS data were converted to mzXML using MSConvert from ProteoWizard

(Chambers, Maclean, Burke, Amodei, Ruderman, Neumann, et al., 2012) and then

48

processed using MZmine 2.12 (Pluskal, Castillo, Villar-Briones, & Orešič, 2010). Peaks

in each sample were extracted, deconvoluted, and deisotoped. Alignment using join

aligner algorithm was conducted with a 10 ppm tolerance for m/z values and 0.2 min

tolerance for retention time. Gap filling using peak finder algorithm was performed to fill

in missing peaks. The resultant data set was imported into SIMCA (Version 13.0.3,

Umetrics, Umea, Sweden) for multivariate statistical analysis. Data were mean-

centered, Pareto scaled and log-transformed before PCA analysis. Data were mean-

centered and log-transformed before PLS-DA and OPLS-DA analyses in SIMCA.

Unsupervised PCA model was performed to initially examine intrinsic variation in the

data set. Then supervised pattern recognition methods include PLS-DA and OPLS-DA

(Bylesjö, Rantalainen, Cloarec, Nicholson, Holmes, & Trygg, 2006) were used to extract

maximum information on discriminant compounds from the data. Validation of the model

was tested using 7-fold internal cross-validation and permutation tests for 200 times. To

further evaluate the predictive ability of the PLS-DA and OPLS-DA models, an external

validation procedure was performed (Brindle, Antti, Holmes, Tranter, Nicholson, Bethell,

et al., 2002; Llorach, et al., 2010). The LC-HRMS metabolomics data set was split into a

training set and a test set. Approximately 80% of the samples were randomly selected

as the training set and the remaining 20% were treated as the test set. PLS-DA and

OPLS-DA models were built based on the training set and obtained models were used

to blindly predict the classification of the samples in the test set. This procedure was

repeated 30 times and correct classification rate was calculated. For univariate

analyses, mass spectral intensity data of selected metabolites which have been mean-

centered and log-transformed were subjected to Welch’s t test. Benjamini–Hochberg

49

procedure at α=0.01 (Benjamini & Hochberg, 1995) was conducted to control false

discoveries. The univariate analyses were done using Microsoft Excel (Version 2010,

Microsoft Corporation, Seattle, WA, USA).

Results and Discussion

Procyanidin Composition and Content in PPCP and PPAP

Procyanidins were extracted and partially purified from cranberry powder or fresh

apples. Figure 2-1 shows the HPLC fluorescence chromatograms of procyanidins in

PPCP and PPAP. The content of total procyanidins in PPCP was 511 mg/g extracts,

lower than that in PPAP (690 mg/g extracts). Over 90% of procyanidin oligomers (dimer

to tetramers) in PPCP were A-type (Table 2-1). Our results were consistent with a

previous study which showed that procyanidins with at least one A-type bond accounted

for more than 90% of trimers through undecamers in cranberry press cake (Feliciano,

Krueger, Shanmuganayagam, Vestling, & Reed, 2012). Monomers through tetramers

accounted for 45% of total procyanidin, with the rest being high polymers. A- and B-type

pentamers and hexamers were identified but not quantified in PPCP due to peak

overlapping (Figure 2-1). PPAP contained exclusively B-type procyanidins. Content of

dimers through tetramers in PPAP were higher than those in PPCP (Table 2-1).

Monomer through tetramers accounted for 65% of total procyanidins in PPAP, with

about 13% being high polymers.

Quality Control of Multivariate Analyses

In this study, the concept of biology QC which uses biological samples including

plasma, urine or tissue as quality controls was adopted (Gika, Theodoridis, Wingate, &

Wilson, 2007). Biological QCs consisting of 4 replicates of pooled Red Cross plasma

were analyzed together with rat plasma to validate NMR acquisition method. The PCA

50

model was built to investigate the metabolome differences between QCs and rat

plasma. The mechanism was based on the ability of the PCA model to cluster samples

in an unsupervised approach. The PCA score plot (Figure 2-2) showed that the 4

replicates were segregated from experimental samples, indicating that the NMR data

acquisition method was valid. Since variations between LC-HRMS injections and

artifacts due to the order of acquisition and carry-over, sensitivity changes or ion

suppression could occur during the experimental period (Burton, Ivosev, Tate, Impey,

Wingate, & Bonner, 2008). Sample acquisition was randomized, and QC samples were

used to monitor the instrument performance. Pooled QCs were further examined using

multivariate statistic techniques. A PCA model was constructed to visualize any

separation between three QCs. PCA score plot (Figure 2-5) demonstrates that the three

QCs across the entire sequence were tightly clustered, suggesting a high quality of data

acquisition.

NMR Metabolomics Analysis of Rat Plasma

PCA model was built on NMR metabolomics data before supervised multivariate

analyses. PCA score plot showed a separation between PPCP and PPAP, with one

sample from PPAP group mixed with the group of PPCP (Figure 2-3). To further confirm

and validate the metabolome differences between PPCP and PPAP, PLS-DA and

OPLS-DA models were constructed. Two principal components were selected to build

PLS-DA model. One principal component and one orthogonal component were used to

construct OPLS-DA model. The R2X and R2Y of both models was 0.433 and 0.676,

respectively (Table 2-2). R2 represents the goodness of fit, and the results indicated that

about 43% of variance in X data matrix and 68% of variance in Y was explained by

PLS-DA and OPLS-DA models. The high R2 values indicated the robustness of the

51

supervised models (Llorach, Urpi-Sarda, Jauregui, Monagas, & Andres-Lacueva, 2009).

Overfitting arises in PLS and OPLS models when the number of variables is much

larger than that of observations. It could be a problem with any high-dimensional data.

Accidental correlation between one or more variables becomes common for

metabolomics data (Kemsley, Le Gall, Dainty, Watson, Harvey, Tapp, et al., 2007).

Internal cross-validation is thus the first step to test the predictability of the supervised

models. If Q2 calculated from the cross-validation has a low value, then conclusion

could be drawn that the supervised model does not have predictability. In the present

study, 7-fold internal cross validation was performed on both PLS-DA and OPLS-DA

models derived from NMR metabolomics data. Q2 obtained from cross-validation for

PLS-DA and OPLS-DA was 0.254 and 0.291, respectively (Table 2-2). They were much

lower than 0.5, a threshold value for a good multivariate model of metabolomics data

(Hawkins, Basak, & Mills, 2003). Although a segregation between PPCP and PPAP was

observed on the PLS-DA and OPLS-DA score plots (Figure 2-4A, 2-4B), the low Q2

values suggested that both models had poor predictability and the segregation was

most likely due to overfitting. Misclassification that occurred during cross-validation

(Figure 2-4C, 2-4D) also confirmed that NMR metabolomics data did not reveal a

metabolome difference in rat plasma between PPCP and PPAP.

LC-HRMS Metabolomics Analysis of Rat Plasma

Similarly, LC-HRMS metabolomics data was analyzed using supervised models

to reveal the metabolic changes of rat plasma after administering PPCP or PPAP.

Figure 2-7A and 2-7B showed a clear segregation between two groups on the score plot

of both PLS-DA and OPLS-DA models. The advantage of OPLS-DA over PLS-DA is

that the “structure noise” of data matrix which is unrelated to the variation of interest is

52

filtered and described only by the orthogonal component. The variation of scientific

interest is described in the predictive component. Therefore the interpretability of the

resulting model is increased (Fonville, Richards, Barton, Boulange, Ebbels, Nicholson,

et al., 2010). In the present study, PLS-DA models derived from LC-HRMS metabolomic

data had high quality parameters which was not improved by OPLS-DA, suggesting

reduced “structure noise” in the data set. PLS-DA had two principal components with an

overall value of R2X and R2Y of 0.428 and 0.995, respectively (Table 2-2). Similarly,

OPLS-DA generated one principal component and one orthogonal component. The R2X

and R2Y of OPLS-DA model was 0.428 and 0.995 (Table 2-2). It showed that about

42% of variance in X data matrix and 99% of variance in Y data matrix was explained by

both supervised models.

To test for overfitting and the validity of PLS and OPLS models derived from LC-

HRMS metabolomic data, three validation methods were used. Seven-fold internal

cross validation was initially performed on both PLS-DA and OPLS-DA models.

Predictability Q2 obtained from cross-validation was 0.982 and 0.974 for PLS-DA and

OPLS-DA model, respectively. The high Q2 indicated both supervised models had

excellent predictability. The cross-validated score plots (Figure 2-7C, 2-7D) showed that

no rat plasma from two groups was misclassified which was consistent with the internal

validation result. In order to further confirm the predictability of PLS-DA and OPLS-DA

models, permutation test was conducted. The class labels of PPCP and PPAP group

were permuted and randomly assigned to different observations. Then a classification

model was calculated with the permutated class labels. The procedure was repeated

200 times. R2 and Q2 within each model were calculated and a regression line was

53

drawn. Ideally, all R2 and Q2 calculated from the permutation data should be lower than

those from the actual data and the Q2-intercept value obtained from the regression line

should be lower than 0.05 (Kang, Choi, Kang, Kwon, Wen, Lee, et al., 2008). The

rationale behind the permutation test is that the newly constructed classification models

should not be able to predict the classes well with a wrong class label (Westerhuis,

Hoefsloot, Smit, Vis, Smilde, van Velzen, et al., 2008). Figure 2-8 showed that the

goodness of fit (R2) and predictive powder (Q2) of newly constructed models with

permuted class labels were decreased compared to the actual model, indicating the

supervised model was statistically valid and the achieved segregation between PPCP

and PPAP was not due to overfitting. Cross-validation and permutation test provide a

reasonable estimate of the predictability of a PLS or OPLS model (Eriksson, 2006).

However, external validation that uses an independent set of test data to evaluate

predictability of a supervised model that is built on the training set is a more scrupulous

and demanding method (Eriksson, 2006). The correct classification rates of 100% for

both PLS-DA and OPLS-DA models (Table 2-2) were obtained, indicating that the

supervised models based on LC-HRMS metabolomics data had excellent predictability

and were able to correctly predict the unknown samples. The validation tests suggested

that the UHPLC-HRMS metabolomics approach was able to reveal the metabolome

changes in female rats after administering PPCP compared with PPAP.

Discriminant Metabolites Identification

No modification of rat plasma metabolome was detected using 1H NMR-based

metabolomics approach although it was proven to be an effective tool for metabolomics

profiling in other studies(Graham, Holscher, & Green, 2014; Kang, et al., 2008). This

was likely due to the inherent low sensitivity of NMR technique that failed to detect

54

procyanidin metabolites in plasma. Untargeted UHPLC-HRMS metabolomics was a

more sensitive method to reveal the metabolome differences and contributing markers.

S-plot (Figure 2-9) is a statistical tool that visualizes the variable influence in a

projection-based model and discover the responsible metabolites. It is a scatter plot that

combines the covariance (magnitude) and correlation loading (reliability) for the model

variables (Wiklund, Johansson, Sjöström, Mellerowicz, Edlund, Shockcor, et al., 2008).

S-plot can be applied to projection-based models including OPLS, PLS or PCA. The x-

axis in the S-plot describes the magnitude of each variable. The y-axis represents the

reliability of each variable. The y-axis has a theoretical minimum of -1 and maximum of

+1. Unless the variable variance is uniform, otherwise the scatter plot will look like an S-

shape. At a significance level p=0.05, a p(corr) of 0.5 was used as an arbitrary cutoff

value to select the potential markers (Llorach, Urpi-Sarda, Jauregui, Monagas, &

Andres-Lacueva, 2009). The markers with higher absolute p[1] and p(corr) values which

are located on the upper right or lower left corner of the S-plot were the statistical

relevant variables for explaining the separation between PPCP and PPAP. The

variables in the middle of the S-plot did not show any relevance in the model. Variable

importance for projection (VIP) is another statistical tool used to summarize the

importance of X-variable both for X- and Y- models (Eriksson, 2006). It is used to

determine the relevance and importance of a variable in a projection-based model. The

influence on the response of each variable is summed over all components and

categorical responses, relative to the total sum of squares of the model. For a given

model, there will be only one VIP-vector summarizing all components and Y-variables.

This makes the VIP an appealing measure of the global effect of diet intervention. A

55

threshold of VIP value ≥ 1 is usually considered appropriate for a metabolomics study

(Eriksson, 2006). In the present study, S-plot is used as the primary statistical tool for

determining the significant metabolites. We compared the markers selected from S-plot

to those selected using a VIP plot. All variables with VIP score >1 were plotted in Figure

2-10. Variables selected as significant ones from S-plot were colored in red. It was

found that variables selected from S-plot had a higher VIP score (VIP>1.7). This result

supported the reliability and effectiveness of S-plot, and indicated that S-plot is a more

scrupulous method. These selected significant metabolites were then subjected to

Welch’s t test, and the p-value obtained for each marker was smaller than 0.01 (Table

2-3). Benjamini–Hochberg procedure (α=0.01) was conducted to control false

discoveries.

A total of 1186 metabolic features were detected in rat plasma, among which 36

features were found to be discriminant metabolites on the basis of multivariate analysis

(Figure 2-9). Eleven metabolites were identified based on their accurate masses and/or

product ion spectra (Table 2-3). The other 25 unidentified metabolites were listed in

Table 2-4. HMDB (Wishart, Tzur, Knox, Eisner, Guo, Young, et al., 2007) and/or

Phenol-Explorer (Neveu, Perez-Jimenez, Vos, Crespy, Du Chaffaut, Mennen, et al.,

2010) were searched to assist metabolite identification. One metabolite that was higher

in rat plasma after PPCP was the ion at m/z 137.0246 [M-H]- producing a product ion at

m/z 93.0339 [M-H-COO]- after MS/MS. It was tentatively identified as p-hydroxybenzoic

acid as it matched the same compound in HMDB (Δ=0.0002 Da) and a previous

publication (Chen, Bozzo, Freixas-Coutin, Marcone, Pauls, Tang, et al., 2014). The

compound producing a [M-H]- ion at m/z 93.0337 [M-H]- was tentatively identified as

56

phenol according to HMDB (Δ=0.0009 Da). The ion at m/z 172.9915 [M-H]- was

tentatively identified as phenol sulfate which agreed with HMDB (Δ=0.0001 Da). The

plasma level of catechol sulfate was elevated in female rats after administering PPCP.

This metabolite was previously identified in human urine after drinking blackcurrant juice

(Törrönen, McDougall, Dobson, Stewart, Hellström, Mattila, et al., 2012). Identification

of catechol sulfate was based on the accurate m/z 188.9863[M-H]-, product ion at m/z

109.0296 [M-H-sulphate]-and HMDB match (Δ=0 Da). 3, 4-Dihydroxyphenylvaleric acid

and 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide were also detected and

tentatively identified in rat plasma after PPCP intake. It should be noted that the

difference between detected mass and theoretical mass of 4'-O-methyl-(-)-epicatechin-

3'-O-beta-glucuronide was 0.0707 Da, which was relatively higher than other mass

error. However, 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide was tentatively

assigned because it was the only database match that was biologically relevant and no

standard was available for further identification. Furthermore, consumption of PPCP

also decreased the plasma level of five metabolites. The metabolite producing a [M-H]-

ion at m/z 479.1190 and a product ion at m/z 303.0885 [M-H-glucuronide]- was

assigned as O-methyl-(-)-epicatechin-O-glucuronide by comparing with HMDB

(Δ=0.0001 Da). A previous publication revealed that methylation of (-)-epicatechin

occurred at 3'- position in rats (Natsume, Osakabe, Oyama, Sasaki, Baba, Nakamura,

et al., 2003). Another study showed that glucuronidation of daidzein occurred at the 7

position after daidzein was incubated with Sprague-Dawley rat liver microsome (Zhang,

Song, Cunnick, Murphy, & Hendrich, 1999). The positions of glucuronidation and

methylation in rats were markedly different from humans, mice, pigs, etc. In the present

57

study, the substitution positions were not able to be further confirmed without NMR

spectra of purified compounds. The metabolite producing a [M-H]- ion at m/z 289.0384

was putatively identified as 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate. The

identification agreed with HMDB (Δ=0.0003 Da) and was described in a previous study

(Garcia‐Aloy, Llorach, Urpi‐Sarda, Jáuregui, Corella, Ruiz‐Canela, et al., 2014). 5-

(hydroxyphenyl)-Ƴ-valerolactone-O-sulphate was putatively identified based on its m/z

at 271.0287 [M-H]- and HMDB match (Δ=0.0005 Da). The metabolite having a [M-H]- ion

at m/z 184.0757 was putatively identified as 4-hydroxydiphenylamine according to the

HMDB (Δ=0.0011 Da). 4-hydroxydiphenylamine is a metabolite of diphenylamine and

found in stored apples (Rudell, Mattheis, & Fellman, 2005). The metabolite giving a [M-

H]- ion at m/z 461.9787 and product ion at m/z 264.0330 [M-H-hexose-H2O]- was

tentatively identified as peonidin-3-O-hexose. The exact type of hexose could not be

determined due to lack of standard comparison. Previous studies showed that both

peonidin-3-O-glactoside and peonidin-3-O-glucoside were found in rat plasma after they

were administered with anthocyanin-rich extracts (Ichiyanagi, Shida, Rahman, Hatano,

& Konishi, 2006). In the present study, the detection of peonidin-3-O-hexose in rat

plasma after administering PPAP was likely due to the residual anthocyanins in PPAP.

Procyanidins purified from cranberry powder were predominantly A-type while

exclusively B-type procyanidins were found in PPAP. Procyanidins had a very low

absorption rate in vivo and only a small portion of epicatechin and oligomeric

procyanidins (DP<5) were able to be absorbed in the small intestine (Ou & Gu, 2014).

The majority of A- and B-type procyanidin oligomers and polymers were degraded by

gut microbiota in the colon to produce microbial metabolites. More than half of identified

58

discriminant metabolites in the present study corresponded to the phase II and microbial

metabolites of procyanidins. A previous study demonstrated that B-type procyanidin

dimers were catabolized by microbial cleavage of C-ring and/or oxidation of A-ring, and

further degraded into hydroxyphenyl-Ƴ-valerolactone (Stoupi, Williamson, Drynan,

Barron, & Clifford, 2010). Phenylvalerolactones were then slowly dehydroxylated by

bacteria to form phenylvaleric acids (Sánchez-Patán, Cueva, Monagas, Walton, Gibson,

Martín-Álvarez, et al., 2012). 5-(hydroxyphenyl)-Ƴ-valerolactone-O-sulphate which was

found to be decreased after rat receiving PPCP was formed after phase II metabolism

of 5-(hydroxyphenyl)-Ƴ–valerolactone. 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-

sulphate, which was also decreased after ingesting PPCP was probably generated by

dehydroxylation of dihydroxyphenyl-Ƴ-valerolactone after further sulfation. 3, 4-

Dihydroxyphenylvaleric acid was probably a dehydroxylation product from

dihydroxyphenyl-Ƴ-valerolactone. p-hydroxybenzoic acid was likely to be formed by

progressive shortening the aliphatic chain by α-and β-oxidation of phenylvaleric acids

(Sánchez-Patán, et al., 2012). Compared to extensive investigation on B-type dimers

catabolism, limited data is available on the microbial catabolism of A-type procyanidins.

A former study employed a pig cecum model and showed that, similar as B-type dimers

catabolism, A-type procyanidins degradation was initiated by cleavage of C-ring

followed by generation of various phenolic acids (Engemann, Hubner, Rzeppa, &

Humpf, 2012). A-type procyanidins oligomers exhibited a more complicated pattern of

hydroxylated catabolites probably due to their more rigid and complex interflavan ether

bonds (Engemann, Hubner, Rzeppa, & Humpf, 2012). However, in the present study we

failed to detect any metabolites that retain this unique ether linkage.

59

Summary

Female Sprague-Dawley rat plasma metabolome differences between PPCP and

PPAP were detected using an untargeted UHPLC-Q-Orbitrap-HRMS metabolomics

approach but not a 1H NMR metabolomics approach. This study is one of few

publications that use two metabolomics tools. Compared to 1H NMR metabolomics,

UHPLC-Q-Orbitrap-HRMS metabolomics is more effective to reveal the overall rat

plasma metabolome modifications caused by PPCP or PPAP and identify the

contributing makers. Discriminant metabolites including p-hydroxybenzoic acid, phenol,

phenol-sulfate, catechol sulphate, 3, 4-dihydroxyphenylvaleric acid, 4'-O-methyl-(-)-

epicatechin-3'-O-beta-glucuronide were significantly higher in rat plasma after PPCP

intake. On the contrary, plasma level of several metabolites including O-methyl-(-)-

epicatechin-O-glucuronide, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-

(hydroxyphenyl)-Ƴ-valerolactone-O-sulphate, peonidin-3-O-hexose and 4-

hydroxydiphenylamine were increased after rats were gavaged with PPAP.

60

Table 2-1. Content of procyanidins in PPCP and PPAP. Procyanidins Partially Purified Cranberry

procyanidins (mg/g extracts) Partially Purified Apple procyanidins (mg/g extracts)

Monomer 8.64±0.36 61.2±1.59 Dimers 71.7±3.48(60.9±0.98)* 161±5.41

Trimers 71.6±0.40(60.9±0.66)* 101±4.28

Tetramers 75.9±0.22(75.9±0.22)* 125±6.31

Pentamers UQ 95.4±5.37

Hexamers UQ 58.1±3.30

High polymer 283±13.1 88.1±8.34

Total 511±17.6 690±34.6

Data are expressed as mean ± standard deviation. UQ: detected as mixture of A- and B-type oligomers, but not quantified due to peak overlapping. *Numbers in the parentheses represent the content of A-type procyanidins. Numbers out of parenthesis are total procyanidins.

61

Table 2-2.Summary of parameters for PCA, PLS-DA, and OPLS-DA models for rat plasma after administering PPCP or PPAP by oral gavage.

1H NMR metabolomics LC-HRMS metabolomics

PCA PLS-DA OPLS-DA PCA PLS-DA OPLS-DA

Na

5 2 1Pc+1Od 4 2 1Pc+1Od

R2

X(cum)b

0.783 0.433 0.433 0.516 0.428 0.428

R2

Y(cum)b

--- 0.676 0.676 --- 0.995 0.995

Q2

(cum)b

0.513 0.254 0.291 0.521 0.982 0.974

*Correct Classification Rate

--- --- --- --- 100%±0 100%±0

a N: number of components. b R2X (cum)and R2Y (cum) are the cumulative modeled variations in the X and Y matrix, respectively. Q2Y (cum) is the cumulative predicted variation in the Y matrix.

c Predictive component. d Orthogonal component.

62

Table 2-3. Identification of discriminant metabolites in rat plasma after administering PPCP or PPAP by oral gavage. NO. Detected

Mass [M-H]-

(MSMS)

Retention Tim (min)

p[1] (contribution)

p(corr)[1] (confidence)

VIP Welch t test a

Metabolites Putative Identification

Theoretical Mass [M-H]-

Mass Difference (Da)

Database ID

PPCPvs. PPAP b

1 137.0246 (93.0339 [M-H-COO]-)

9.231 0.063 0.931 2.16 <0.01 p-hydroxybenzoic acid c 137.0244 0.0002 HMDB 00500

2 172.9915 6.855 0.051 0.609 1.74 <0.01 phenyl sulfate 172.9914 0.0001 HMDB 60015

3 188.9863 (109.0296) [M-H-sulphate]-)

6.566 0.139 0.933 4.73 <0.01 catechol sulphate c 188.9863 0.0000 HMDB 61713

4 209.0904 9.525 0.057 0.587 1.99 <0.01 3, 4-dihydroxyphenylvaleric acid

209.0819 0.0085 HMDB 29233

5 493.2059 7.572 0.098 0.584 3.22 <0.01 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide

493.1352 0.0707 HMDB 29180

6 93.0337 6.854 0.096 0.653 3.25 <0.01 phenol 93.0346 0.0009 HMDB 00228

7 479.1194(303.0885[M-H-glucuronide]-)

7.463 -0.065 -0.608 2.24 <0.01 3'-O-methyl-(-)-epicatechin-7-O-glucuronide c

479.1195 0.0001 HMDB 41659

8 289.0384

7.095 -0.088 -0.725 3.06 <0.01 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate c

289.0387 0.0003 HMDB 59976

9 271.0287 7.793 -0.134 -0.812 4.68 <0.01 5-(hydroxyphenyl)-gamma-valerolactone-O-sulphate

271.0282 0.0005 HMDB 599993

10 184.0757 9.448 -0.236 -0.991 8.16 <0.01 4-hydroxydiphenylamine 184.0768 0.0011 HMDB 32597

11 461.9787(264.0330[M-H-hexose-H2O]-)

9.481 -0.249 -0.994 8.60 <0.01 peonidin-3-O-hexose 462.1168 0.1380 Phenol-Explorer

a Benjamini–Hochberg procedure was conducted to control false discoveries at α=0.01 b Arrows indicated a decrease or increase in metabolite level in rats plasma after administering PPCP compared to PPAP. c Identification agrees with those in Chen et al. (Chen, et al., 2014), Törrönen et al. (Törrönen, et al., 2012), Natsume et al. (Natsume, et al.,

2003), and Garcia-Aloy et al. (Garcia‐Aloy, et al., 2014).

63

Table 2-4. Unidentified discriminant metabolic features for rat plasma after administering PPCP or PPAP by oral gavage.

Detected Mass [M-H]-

Retention Tim (min)

p[1] (contribution)

p(corr)[1] (confidence)

Welch t test a PPCP vs. PPAP b

174.9867 6.854 0.057 0.616 <0.01

190.9822 6.584 0.163 0.920 <0.01

203.0021 7.173 0.173 0.943 <0.01

205.0112 9.232 0.101 0.960 <0.01

240.9789 6.853 0.057 0.590 <0.01

257.9695 6.853 0.069 0.614 <0.01

304.0135 6.606 0.083 0.876 <0.01

308.9664 6.851 0.088 0.600 <0.01

419.1698 7.616 0.097 0.576 <0.01

157.0871 8.324 -0.083 -0.835 <0.01

213.0193 9.293 -0.100 -0.956 <0.01

213.0196 9.194 -0.122 -0.944 <0.01

215.0383 9.457 -0.219 -0.984 <0.01

235.0823 5.094 -0.110 -0.573 <0.01

264.0338 9.451 -0.370 -0.991 <0.01

266.0291 9.547 -0.287 -0.985 <0.01

280.0286 7.824 -0.116 -0.944 <0.01

332.0207 9.463 -0.056 -0.982 <0.01

332.9666 7.560 -0.049 -0.682 <0.01

349.0116 9.448 -0.253 -0.995 <0.01

393.9909 9.464 -0.057 -0.981 <0.01

400.008 9.459 -0.282 -0.995 <0.01

467.9958 9.473 -0.244 -0.991 <0.01

529.9661 9.465 -0.069 -0.968 <0.01

535.9839 9.464 -0.071 -0.985 <0.01

a Benjamini–Hochberg procedure was conducted to control false discoveries at α=0.01. b Arrows indicated a decrease or increase in metabolite level in rats plasma after administering PPCP compared to PPAP.

64

Figure 2-1. HPLC chromatogram of procyanidins in PPCP and PPAP using

fluorescence detection. A) Partially purified cranberry procyanidins and B) partially purified apple procyanidins. Peak identification was performed using MSn. The numbers beside the peaks indicate the degree of polymerization. 2b-6b designate the peaks of B-type procyanidin dimers through hexamers. 2a-6a designate the peaks of A-type procyanidin dimers through hexamers with one A-type linkage.

65

Figure 2-2. The PCA score plot of rat plasma and quality control samples from 1H NMR

metabolomics. Green squares: 4 replicates of Red Cross pooled plasma. Red squares: rat plasma after administering PPCP. Blue squares: rat plasma after administering PPAP. Each square represents an individual rat.

66

Figure 2-3. The PCA score plot of rat plasma from 1H NMR metabolomics after

administering PPCP or PPAP. Red squares: rat plasma after administering PPCP. Blue squares: rat plasma after administering PPAP. Each square represents an individual rat.

-5

-4

-3

-2

-1

0

1

2

3

4

-8 -6 -4 -2 0 2 4 6

t[1]

t[2

]Rat plasma after administering PPAP

Rat plasma after administering PPCP

67

Figure 2-4. The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat plasma derived from 1H NMR metabolomics. A) PLS-DA score plot, B) OPLS-DA score plot, C) PLS-DA cross-validated score plot and D) OPLS-DA cross-validated score plot. Red squares: rat plasma after administering PPCP. Blue squares: rat plasma after administering PPAP. Each square represents an individual rat.

-4

-3

-2

-1

0

1

2

3

-8 -6 -4 -2 0 2 4 6

t[1]

t[2

]

-4

-3

-2

-1

0

1

2

3

-6 -4 -2 0 2 4

t[1]

to [

1]

A B

Rat plasma after administering PPAP


-2

-1.5

-1

-0.5

0

0.5

1

1.5

-4 -3 -2 -1 0 1 2 3

tcv[1]

tcv

[2]

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-3 -2 -1 0 1 2

tcv[1]

tocv

[1]

C D

68

Figure 2-5. The PCA score plot of rat plasma and quality control samples from LC-

HRMS metabolomics. Green squares: pooled plasma samples as quality control. Red squares: rat plasma after administering PPCP. Blue squares: rat plasma after administering PPAP. Each square represents an individual rat.

t[2]

-20

-15

-10

-5

0

5

10

15

-25 -20 -15 -10 -5 0 5 10 15 20

t[1]

QC



69

Figure 2-6. The PCA score plot of rat plasma from LC-HRMS metabolomics after

administering PPCP or PPAP. Red squares: rat plasma after administering PPCP. Blue squares: rat plasma after administering PPAP. Each square represents an individual rat.

t[2]

-20

-15

-10

-5

0

5

10

15

-25 -20 -15 -10 -5 0 5 10 15 20

t[1]



70

Figure 2-7. The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat plasma derived from LC-HRMS

metabolomics. A) PLS-DA score plot, B) OPLS-DA score plot, C) PLS-DA cross-validated score plot and D) OPLS-DA cross-validated score plot. Red squares: rat plasma after administering PPCP. Blue squares: rat plasma after administering PPAP. Each square represents an individual rat.

to[1

]

-15

-10

-5

0

5

10

-20 -15 -10 -5 0 5 10 15

t[1]

t[2]

-15

-10

-5

0

5

10

-20 -15 -10 -5 0 5 10 15

t[1]

A B



tocv

[1]

-10

-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6

tcv[1]

-10

-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6tcv[1]

tcv[2

]

C D

71

Figure 2-8. Validation plot obtained from 200 permutation tests for the OPLS-DA model

of rat plasma after administering PPCP or PPAP from LC-HRMS metabolomics.

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-0.2 0 0.2 0.4 0.6 0.8 1

R2Q2

R2

, Q2

r(y, permuted y)

72

Figure 2-9. S-plots associated with the OPLS-DA score plot of data derived from LC-

HRMS of rat plasma after administering PPCP or PPAP. p[1] is the loading vector of covariance in the first principal component. p(corr)[1] is loading vector of correlation in the first principal component. Variables with |p| ≥ 0.05 and |p(corr)| ≥ 0.5 are considered statistically significant. Significant variables in blue color were identified and numbered according to Table 3-3. Unidentified significant variables in red color were listed in Table 3-4. Non-significant variables were in green color.

1

2, 46

3

5

7

8

9

1011

73

Figure 2-10. VIP plot of variables with VIP score higher than 1. Variables selected as significant ones from S-plot were marked in red with a VIP score > 1.7.

Variables

74

CHAPTER 3 1H NMR-BASED METABOLOMICS REVEALS URINARY METABOLOME MODIFICATIONS IN FEMALE RATS BY CRANBERRY PROCYANIDINS

Background

Cranberries (Vaccinium macrocarpon) are known to have various health benefits

including preventing urinary tract infection (Amy B. Howell, Reed, Krueger,

Winterbottom, Cunningham, & Leahy, 2005), delaying aging process (Wilson, Singh,

Vorsa, Goettl, Kittleson, Roe, et al., 2008), decreasing the risk of cardiovascular

diseases (Caton, Pothecary, Lees, Khan, Wood, Shoji, et al., 2010), inhibiting the

glycation of human hemoglobin and serum albumin (Haiyan Liu, Liu, Wang, Khoo,

Taylor, & Gu, 2011). Many of these health-promoting properties of cranberries were

attributed to their procyanidins content. Procyanidins are oligomers and polymers of (−)-

epicatechin or (+)-catechin with various degree of polymerization (L. Gu, Kelm,

Hammerstone, Beecher, Cunningham, Vannozzi, et al., 2002). Procyanidins are

classified as A-type and B-type according to their interflavan bonds. Apples contain

exclusively B-type procyanidins while over 65% procyanidins in cranberries are A-type

(L. Gu, et al., 2004).

Untargeted metabolomics employ high-throughput analytical platforms to

investigate the metabolic changes in a global manner. NMR spectroscopy is able to

detect hundreds of metabolites in biological samples. This technique has the advantages

of being quantitative, highly reproducible, non-selective and minimal sample preparation

(Dunn, Broadhurst, Atherton, Goodacre, & Griffin, 2011). Multivariate statistic techniques

are very helpful to reduce the dimensionality of the high-dimensional data produced by

metabolomics study (Bylesjö, Rantalainen, Cloarec, Nicholson, Holmes, & Trygg, 2006).

75

The urinary metabolome modifications in female Sprague-Dawley rats after

administering partially purified cranberry procyanidins (PPCP) or partially purified apple

procyanidins (PPAP) were investigated. We hypothesized that cranberry or apple

procyanidins will modify urinary metabolome and metabolome changes caused by

cranberry procyanidins will be different from those caused by apple procyanidins. The

objective of this chapter is to test this hypothesis using a 1H NMR-based metabolomics

approach.



Freeze-dried cranberry powder was provided by Ocean Spray Cranberries, Inc.

(Lakeville-Middleboro, MA, USA). Fresh granny smith apples were purchased from a

local grocery store. HPLC-grade methanol, acetone, sodium phosphate dibasic

anhydrous, sodium phosphate monobasic anhydrous, sodium hydroxide and sodium

chloride were purchased from Fischer Scientific Co. (Pittsburgh, PA, USA). D2O (99.9%

D), 2, 2-dimethyl-2-silapentane-5-sulfonate (DSS, 98%) was a product from Cambridge

Isotope Laboratories, Inc (Tewksbury, MA, USA). Sephadex LH-20 resin was purchased

from Sigma-Aldrich (St. Louis, MO, USA). Amberlite FPX 66 resin was obtained from

Rohm and Haas Co. (Philadelphia, PA, USA).

Partially purified cranberry procyanidins (3.95 g) was extracted from freeze-dried

cranberry power and purified using column chromatography on Amberlite FPX 66 resins

and Sephadex LH-20. Partially purified apple procyanidins (5.30 g) were extracted and

purified from fresh granny smith apples using a similar method. PPCP contained a

mixture of A- type and B- procyanidin oligomers and polymers. The total procyanidin

content was 51.1% (w/w) with high polymer (DP>10) content being 28.3% (w/w). PPAP

76

contained exclusively B-type procyanidin oligomers and polymers. The total procyanidin

content was 69.1% (w/w) with 8.8% (w/w) being high polymers with DP>10. Detailed

extraction procedure and compositional data was described in Chapter 2.

Animal Experiment

Approval for animal study was sought through the Institutional Animal Care and

Use Committee at the University of Florida. Female Sprague Dawley (n=24, 220-280 g)

were acclimated in the animal facility for 5 days using a purified diet free of flavonoid

compounds (D10012G, Research diet Inc., New Brunswick, NJ, USA). After the

acclimation period, rats were housed individually in a metabolic cage for 24 hours to

collect 24-hour baseline urine. Afterwards, rats were randomly divided into two groups

with 12 rats per group, and fasted for six hours before metabolomics study. PPCP or

PPAP were dispersed in water and administered by oral gavage at 0 and 12 hour using a

dose of 250 mg extracts/kg body weight. Rats had free access to food and water after

the gavage. The 24-hour urine of each rat was collected starting from 0 hour after the 1st

gavage. All urine samples were aliquoted and kept in a -80 oC freezer until analysis. One

rat did not produce any urine sample during 24-hour urine collection period after the

gavage with PPAP. Therefore this rat was excluded from this study.

1D 1H and 2D 1H-13C NMR analyses

Urine samples were thawed at 4oC in a cold room and then were centrifuged at

16, 000 g for 15 min. Gilson 215 Liquid Handler (Trilution software version 2.0) was used

to transfer urine (540 µL) into a 5 mm Bruker NMR tube (Z105684 Bruker 96 well rack)

having 60 µL of 1.5 M phosphate buffer (pH 7.4) containing 1 mM DSS in 10% D2O. The

mixture in each NMR tube was vortexed for 30 seconds. All 1H-NMR spectra were

collected on a 600 MHzAvance II NMR spectrometer (Bruker Biospin, Rheinstetten,

77

Germany) equipped with a 5 mm cryo probe. A sample changer (SampleJet, Bruker

BioSpin, Rheinstetten, Germany) operated under IconNMR software (Bruker BioSpin,

Rheinstetten, Germany) was used for automation. All NMR data were acquired at 300K.

Probe tuning and matching were optimized for the representative sample. A 90° pulse

length, the offset of the water signal, water suppression and receiver gain for a data set

were also determined on the representative sample in each run. The probe was

automatically locked to H2O+D2O (90%+10%) and shimmed for each sample. 1D

NOESY-presaturated spectra for all urine samples were recorded. 2D 1H-13C

heteronuclear single quantum coherence (HSQC) were obtained on selected samples to

aid metabolite identification.

Multivariate Statistical Analyses

Raw data from the spectrometer were zero filled, Fourier transformed (FT) and

phase corrected using NMRPipe (Delaglio, Grzesiek, Vuister, Zhu, Pfeifer, & Bax, 1995).

1D FT files were imported into MATLAB (R2013B, the Mathworks, Inc., Natick, MA,

USA) for referencing, removing residual water, baseline correction, alignment, and

normalization. Chemical shifts were referenced to the left peak of lactate at 1.47 ppm.

Peaks were aligned using peak alignment by fast Fourier transform (PAFFT) method and

spectra were normalized using probabilistic quotient normalization (PQN) method. The

resultant data set was imported into SIMCA (Version 13.0.3, Umetrics, Umea, Sweden)

for multivariate statistical analysis. Data were mean-centered and Pareto scaled before

PCA, PLS-DA and OPLS-DA analyses in SIMCA. Unsupervised PCA model was

performed to initially examine intrinsic variation in the data set. Then supervised pattern

recognition methods include PLS-DA and OPLS-DA (Bylesjö, Rantalainen, Cloarec,

Nicholson, Holmes, & Trygg, 2006) were used to extract maximum information on

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discriminant compounds from the data. Validation of the model was tested using 7-fold

internal cross-validation and permutation tests for 200 times. To further evaluate the

predictive ability of the PLS-DA and OPLS-DA models, an external validation procedure

was performed (Brindle, et al., 2002; Llorach, et al., 2010). The NMR metabolomics data

set was split into a training set and a test set. Approximately 70% of the samples were

randomly selected as training set and the remaining 30% were treated as test set. PLS-

DA and OPLS-DA models were built based on the training set and obtained models were

used to blindly predict the classes of the samples in the test set. This procedure was

repeated 30 times and correct classification rates were calculated.

2D 1H-13C HSQC FT files were imported to MestReNova software (Version 9.0,

Mestrelab Research S.L., A Coruña, Spain) for peak picking. 1H-13C HSQC peak lists

were transferred to the COLMAR 13C-1H HSQC query web server (Bingol, Li,

Bruschweiler-Li, Cabrera, Megraw, Zhang, et al., 2014) for metabolite identification.


Urinary Metabolome Modification after PPCP or PPAP

The score plot of PCA in Figure 3-1 showed that rat baseline urine clustered on

upper left of the graph. They were partially separated from urine after administering

PPCP or PPAP. It suggested that urinary metabolome was modified after administering

procyanidins from cranberries or apples. However, no segregation of rat urine between

PPCP and PPAP was observed on PCA score plot. To further examine the metabolic

changes, supervised multivariate statistic techniques were used. PLS-DA and OPLS-DA

models were constructed on the urine samples in three comparisons: baseline vs. PPCP,

baseline vs. PPAP, PPCP vs. PPAP. For all three comparisons, two principal

components were generated to build PLS-DA model. One principal component and one

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orthogonal component were constructed to build OPLS-DA model. R2 was calculated to

evaluate the performance of the supervised models and the goodness of fit. For baseline

vs. PPCP comparison, the R2Y of both PLS-DA and OPLS-DA models was 0.966 (Table

3-1), indicating that about 97% of variance in Y was explained by these supervised

models. For baseline vs. PPAP comparison, both models had a R2Y of 0.969 (Table 3-1)

suggesting that approximate 97% of variance of Y was explained by the models.

Similarly, for PPCP vs. PPAP comparison, supervised models with R2Y of 0.889 were

obtained (Table 3-1), suggesting that both models had a valid goodness of fit. A clear

segregation between baseline urine and urine after administering PPCP (Figure 3-2A, 3-

2B), baseline urine and urine after administering PPAP (Figure 3-3A, 3-3B), urine after

administering PPCP or after PPAP (Figure 3-4A, 3-4B) was observed on the score plot

of PLS-DA and OPLS-DA models for all three comparisons. Furthermore, validation

methods were conducted to validate these models. When processing multivariate data

with hundreds or thousands variables, one should take extreme caution about the

possibility of overfitting. Accidental correlations between one or more variables may

result in the unreliable segregation between groups (Kemsley, et al., 2007). To avoid this

caveat, three methods including internal cross-validation, permutation test and external

validation were conducted to confirm the validity and predictability of the supervised

models. For baseline vs. PPCP comparison, Q2 obtained from cross-validation for PLS-

DA and OPLS-DA was 0.853 and 0.852, respectively (Table 3-1). They were much

higher than 0.5, a thresh hold value for a good multivariate model of metabolomics data.

Furthermore, the score plots of PLS-DA and OPLS-DA from the cross-validation also

showed a clear discrimination between two groups and no misclassification was

80

observed during cross-validation (Figure 3-2C, 3-2D), indicating the segregation

observed on the score plot was not due to overfitting. Similarly, for the baseline vs.

PPAP comparison, cross-validation showed a Q2 of 0.757 and 0.777 for PLS-DA and

OPLS-DA models, respectively (Table 3-1). The cross-validation score plots of PLS-DA

and OPLS-DA models showed a clear separation between two groups and no

misclassification was observed (Figure 3-3C, 3-3D). As for the PPCP vs. PPAP

comparison, the urinary metabolic profiles of rats were modified by both PPCP and

PPAP. Therefore the magnitude of differences in urinary metabolome between PPCP

and PPAP was lower than that between baseline and PPCP or baseline and PPAP

group. This was demonstrated by the relatively lower Q2 of 0.656 and 0.629 obtained

from PLS-DA and OPLS-DA models in the cross-validation (Table 3-1). However, the Q2

of 0.635 and 0.613 were still higher than 0.5, indicating the supervised models were

valid. The cross-validated score plots of PLS-DA and OPLS-DA showed a separation

between two groups, although three samples had cross validation score of 0 on the

OPLS-DA cross-validate score plot (Figure 3-4D).

The second validation method used was the permutation test. The class labels of

tested groups were permuted and randomly assigned to different observations. With the

permutated class labels, 200 new supervised models were built, respectively. R2 and Q2

within each model was calculated and a regression line was drawn. The Q2-intercept

value obtained from the regression line should be lower than 0.05 for a valid model.

Permutation regression line was obtained from the OPLS-DA model derived from

baseline vs. PPAP comparison (Figure 3-5A), baseline vs. PPCP comparison (Figure 3-

5B), and PPCP vs. PPAP comparison (Figure 3-5C). The negative Q2 intercept

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suggested a good predictability of the OPLS-DA models. However, the relatively high R2

intercept indicated somewhat overfitting of the supervised models. To further confirm the

validity and predictability of PLS-DA and OPLS-DA models, external validation which is a

more scrupulous and demanding method was used (Eriksson, 2006). The results

showed that the correct classification rate of 99.4% and 95.3% was obtained for baseline

vs. PPAP comparison and baseline vs. PPAP comparison, respectively (Table 3-1). As

for the PPCP vs. PPAP comparison, correct classification rate of 95.8% and 96.4% was

calculated for PLS-DA and OPLS-DA model, respectively (Table 3-1). The external

validation results demonstrated that supervised models derived from rat urine NMR data

had excellent predictability and were able to correctly predict the unknown urine samples

with a correct classification rate of above 95%. All three validation tests suggested that

1H NMR global metabolomics approach was effective to reveal the urinary metabolome

modification in female rats after administering PPCP or PPAP.


The statistical S-lineTM is the tailored S-plot plot for NMR spectroscopy data and

was used to identify the potential metabolites that contribute to the urinary metabolome

modification by PPCP or PPAP. S-line combines the covariance (magnitude) and

correlation (reliability) for the model variables (Wiklund, et al., 2008) and visualizes both

in one graph. The p(ctr) is the centered loading vector of the first principal component. It

was colored according to the absolute value of the correlation loading p(corr). A

p(corr)>0.5 was selected as a significance level. The advantage of the S-line plot over S-

plot is that it displays the predictive loading in a form resembling the original NMR

spectra. The discriminant metabolites were identified by comparing their NMR spectra

with reported spectra (Bouatra, Aziat, Mandal, Guo, Wilson, Knox, et al., 2013), Human

82

Metabolome Database (Wishart, et al., 2007) and the COLMAR 13C-1H HSQC query

(Bingol, et al., 2014). These discriminant metabolites were summarized in Table 3-2.

The HPLC chromatograms of PPCP and PPAP depicted in Chapter 2 showed that

PPCP contained both A-type and B-type procyanidins, while PPAP had exclusively B-

type procyanidins. Majority of ingested procyanidins are not absorbed in small intestine.

They reach the colon intact and are degraded by gut microbiota (Ou & Gu, 2014). The

resultant exogenous procyanidins metabolites are part of food metabolome and may also

change the endogenous metabolome. A total of 17 metabolites were modified in the

urine of female rats after PPCP or PPAP. The urinary level of hippuric acid, succinic

acid, lactic acid, unknown metabolite 1 at 7.30-7.35 ppm, and unknown metabolite 2 at

7.37-7.42 ppm were increased after rats were administered with PPCP compared to

baseline urine. Endogenous metabolites including α-ketoglutaric acid and citric acid were

decreased after administering PPCP compared to baseline samples. Similarly, rats after

PPAP had a lower urinary level of α-ketoglutaric acid, citric acid and creatinine compared

to baseline urine. PPAP caused a stronger increase of several metabolites including D-

maltose, α-D-glucose, formic acid, 3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid, p-

hydroxyphenylacetic acid, phenol, unknown metabolite 1 at 7.30-7.35 ppm, and unknown

metabolite 2 at 7.37-7.42 ppm, unknown metabolite 3 at 6.77 (s) ppm, unknown

metabolite 4 at 6.73 (dd) ppm, and unknown metabolite 5 at 7.04 (s) ppm. By comparing

the urinary metabolite profile of rats after PPCP and after PPAP, it was found that

hippuric acid, unknown metabolite 1 at 7.30-7.35 ppm, and unknown metabolite 2 at

7.37-7.42 ppm increased after PPCP. Metabolites including D-maltose, 3-(3’-

hydroxyphenyl)-3-hydroxypropanoic acid, p-hydroxyphenylacetic acid, phenol, unknown

83

metabolite 3 at 6.78 (s) ppm, and unknown metabolite 4 at 6.74 (dd) ppm and unknown

metabolite 5 at 7.04 (s) ppm decreased after PPCP. The most important metabolites that

were responsible for the separation between baseline vs. PPCP were hippuric acid,

unknown metabolite 1 at 7.30-7.35 ppm, and unknown metabolite 2 at 7.37-7.42 ppm.

The correlation loadings p(corr) of these three metabolites were 0.94, 0.91, and 0.73,

which were much higher than the statistically significant level of 0.5. For baseline vs.

PPAP, metabolites including 3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid, p-

hydroxyphenylacetic acid, phenol, formic acid, unknown metabolite 1 at 7.30-7.35 ppm,

and unknown metabolite 2 at 7.37-7.42 ppm, unknown metabolite 3 at 6.78 (s) ppm,

unknown metabolite 4 at 6.74 (dd) and unknown metabolite 5 at 7.04 (s) ppm had the

highest correlation loadings p(corr) of 0.79, 0.84, 0.90, 0.78, 0.82, 0.73, 0.86, 0.83 and

0.92, respectively. By comparing PPCP vs. PPAP, the most important metabolite for the

separation was an increased hippuric acid after PPCP with a correlation loading p(corr)

of 0.94. This result was consistent with a previous rat study which showed that

consumption of cranberry powder caused an increase in urinary excretion of hippuric

acid. Its quantity in urine was higher than any other urinary phenolic acids (Prior, Rogers,

Khanal, Wilkes, Wu, & Howard, 2010). In addition, several exogenous metabolites

including 3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid, p-hydroxyphenylacetic acid,

phenol, unknown metabolite 3 at 6.78 (s) ppm, unknown metabolite 4 at 6.74 (dd) ppm

and unknown metabolite 5 at 7.04 (s) ppm also contributed to the segregation of

metabolite profiles between PPCP and PPAP.

PPCP increased the urinary excretion of lactic acid, succinic acid and hippuric

acid. Both hippuric acid and succinic acid are the intermediates of phenylalanine

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metabolism, suggesting that intake of PPCP altered phenylalanine metabolism pathway

at gene or protein levels. Both lactic acid and succinic acid participate in propanoate

metabolism, indicating an upregulation of propanoate metabolism by PPCP. Citric acid

and α-ketoglutaric acid are key intermediates in citrate cycle. Both compounds also

participate in the metabolism of glyoxylic acid and dicarboxylic acid. A reduction of citric

acid and α-ketoglutaric in rat urine indicated a downregulation of citrate cycle and

glyoxylic acid metabolism by both PPCP and PPAP. Compared to baseline, PPAP

increased the urinary excretion of D-glucose and D-maltose that are intermediates in

starch and sucrose metabolism, suggesting that PPAP had an impact on the metabolism

of carbohydrate in rats. Previous research suggested that procyanidins prevented or

alleviated type 2 diabetes in part by inhibiting enzymes in starch digestion (Y. Gu, Hurst,

Stuart, & Lambert, 2011; Lee, Cho, Tanaka, & Yokozawa, 2007). Phenol, p-

hydroxyphenylacetic acid and 3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid also

increased after PPAP. They are microbial metabolites of procyanidins by gut microbiota

(Ou & Gu, 2014). Phenol and p-hydroxyphenylacetic acid may also originate from

tyrosine metabolism. Phenol is a metabolite degraded directly from tyrosine. p-

Hydroxyphenylacetic acid is an intermediate converted from 4-

hydroxyphenylacetaldehyde which is oxidized from p-tyramine in the pathway of tyrosine

metabolism. An increase of formic acid after PPAP suggested an alternation of pyruvate

metabolism pathway.

Summary

Female Sprague-Dawley rat urinary metabolome modifications after administering

PPCP or PPAP were detected using a global 1H NMR metabolomics approach. PPCP

caused an increase of hippuric acid, lactic acid, succinic acid, but a decrease of citric

85

acid and α-ketoglutaric acid in rat urine after administering PPCP compared to baseline

urine. The urinary level of α-D-glucose, D-maltose, 3-(3’-hydroxyphenyl)-3-

hydroxypropanoic acid, p-hydroxyphenylacetic acid and phenol were increased but citric

acid, α-ketoglutaric acid and creatinine were decreased after administering PPAP

compared to baseline urine. The metabolite profile differences between PPCP and PPAP

were observed. Discriminate metabolite included hippuric acid which was higher in rat

urine after PPCP. D-maltose, 3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid, p-

hydroxyphenylacetic acid and phenol were lower after PPCP.

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Table 3-1. Summary of parameters for PLS-DA and OPLS-DA models for rat baseline urine and urine after administering PPCP or PPCP by oral gavage.

Baseline vs. PPCP Baseline vs. PPAP PPCP vs. PPAP

PLS-DA OPLS-DA PLS-DA OPLS-DA PLS-DA OPLS-DA

Na

2 1Pc+1Od 2 1Pc+1Od 2 1Pc+1Od

R2

X(cum)b

0.248 0.248 0.326 0.326 0.278 0.278

R2

Y(cum)b

0.966 0.966 0.969 0.969 0.889 0.889

Q2

(cum)b

0.853 0.852 0.757 0.777 0.656 0.629


0.950±0.089 0.956±0.075 0.967±0.068 0.967±0.068 0.900±0.143 0.922±0.105


c Predictive component. d Orthogonal component. * Correct classification rate was obtained from external validation procedure repeated for 30 times.

87

Table 3-2. Summary of the metabolite profile changes in rat baseline urine and urine

after administering PPCP or PPCP by oral gavage.

a Arrows indicated a decrease or increase in metabolites detected in rat urine after PPCP compared to baseline.

b Arrows indicated a decrease or increase in metabolites detected in rat urine after PPAP compared to baseline.

c Arrows indicated a decrease or increase in metabolites detected in rat urine after PPCP compared to PPAP.

*Compound was identification by COLMAR 13C-1H HSQC query (Bingol, et al., 2014).

Metabolites Chemical shift (multiplicity)

PPCP vs. Baseline a

PPAP vs. Baseline b

PPCP vs. PPAP c

lactic acid* 1.32 (d) ---- ----

succinic acid* 2.39 (s) ---- ----

citric acid* 2.52 (d), 2.67 (d) ----

α-ketoglutaric acid* 2.43 (t), 2.99 (t) ----

creatinine* 3.03 (s), 4.04 (s) ---- ----

α-D-glucose* 5.23 (d), 4.65 (d) ---- ----

D-maltose* 3.26 (dd), 3.42 (t), 3.58 (m), 3.62 (m), 3.71 (m), 3.76 (m), 3.83 (m), 3.89 (m), 3.96 (m), 5.40 (d), 5.25 (d)

----

phenol 6.94 (d) ----

p-hydroxyphenylacetic acid (PHPAA)*

7.15 (d), 6.85 (d) ----

3-(3’-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA)*

5.05 (dd), 6.84 (dd), 6.97 (d), 7.22 (t)

----

hippuric acid* 7.53 (t), 7.62 (t), 7.82 (d)

----

formic acid* 8.45 (s) ---- ----

unknown metabolite 1 7.30-7.35

unknown metabolite 2 7.37-7.42

unknown metabolite 3 6.77 (s) ----

unknown metabolite 4 6.73 (dd) ----

Unknown metabolite 5 7.04 (s) ----

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Figure 3-1. The PCA score plot of rat baseline urine and urine after administering PPCP

or PPAP. Green squares: rat baseline urine. Red squares: rat urine after administering PPAP. Blue squares: rat urine after administering PPCP. Each square represents an individual rat.

Rat baseline urine

Rat urine after PPCP

Rat urine after PPAP

-8

-6

-4

-2

0

2

4

6

-8 -6 -4 -2 0 2 4 6t[1]

t [2

]

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Figure 3-2. The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat baseline urine and urine after

administering PPCP. A) PLS-DA score plot, B) OPLS-DA score plot, C) PLS-DA cross-validated score plot and D) OPLS-DA cross-validated score plot. Green squares: rat baseline urine before administering PPCP. Blue squares: rat urine after administering PPCP. Each square represents an individual rat.

Rat baseline urine before administering PPCPRat urine after administering PPCP

B

C

A

D

-6

-4

-2

0

2

4

-10 -8 -6 -4 -2 0 2 4 6 8t[1]

t [2

]

to [

1]

-8

-6

-4

-2

0

2

4

6

-8 -6 -4 -2 0 2 4 6t[1]

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-4 -3 -2 -1 0 1 2 3tcv[1]

tcv

[2]

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2tcv[1]

tocv

[1]

90

Figure 3-3. The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat baseline urine and urine after

administering PPAP. A) PLS-DA score plot, B) OPLS-DA score plot, C) PLS-DA cross-validated score plot and D) OPLS-DA cross-validated score plot. Green squares: rat baseline urine before administering PPAP. Red squares: rat urine after administering PPAP. Each square represents an individual rat.

Rat baseline urine before administering PPAP

Rat urine after administering PPAP

A B

C D

t [2

]

-6

-4

-2

0

2

4

-10 -8 -6 -4 -2 0 2 4 6 8t[1]

to [

1]

-6

-4

-2

0

2

4

-10 -8 -6 -4 -2 0 2 4 6 8t[1]

-1.5

-1

-0.5

0

0.5

1

-5 -4 -3 -2 -1 0 1 2 3 4tcv[1]

-2

-1

0

1

2

-4 -3 -2 -1 0 1 2 3tcv[1]

tcv

[2]

tocv

[1]

91

Figure 3-4. The PLS-DA and OPLS-DA score plots and cross-validated score plots of rat urine after administering PPCP

or PPAP. A) PLS-DA score plot, B) OPLS-DA score plot, C) PLS-DA cross-validated score plot and D) OPLS-DA cross-validated score plot. Blue squares: rat urine after administering PPCP. Red squares: rat urine after administering PPAP. Each square represents an individual rat.

Rat urine after administering PPCPRat urine after administering PPAPA B

C D

t [2

]

-8

-6

-4

-2

0

2

4

6

-8 -6 -4 -2 0 2 4 6t[1]

-10

-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6t[1]

to [

1]

tocv

[1]

-2

-1

0

1

2

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2tcv[1]

tocv

[1]

-4

-3

-2

-1

0

1

2

3

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5tcv[1]

92

Figure 3-5. Validation plot obtained from 200 permutation tests for the OPLS-DA models

of rat baseline urine and urine after administering PPCP or PPAP from 1H NMR metabolomics. A) Rat baseline urine vs. urine after administering PPAP, B) rat baseline urine vs. urine after administering PPCP and C) urine after administering PPCP vs. after PPAP.

R2,

Q2

r(y, permuted y)R

2, Q

2r(y, permuted y)

R2,

Q2

r(y, permuted y)

BA

CR

2, Q

2R

2, Q

2

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Figure 3-6. S-line associated with the OPLS score plots of data derived from rat baseline urine and urine after PPCP or

PPAP. A) Baseline urine vs. urine after PPCP, B) baseline urine vs. urine after PPAP and C) urine after PPCP vs. urine after PPAP. The x-axis is chemical shift derived from NMR spectra. The y-axis p(ctr)[1] is the centered loading vector of the first principal component. p(ctrl)[1] is colored according to the absolute value of the correlation loading p(corr). p(corr)>0.5 is selected as a significance level.

94

Figure 3-6. Continued.

95

Figure 3-6. Continued.

96

CHAPTER 4 A 1H NMR BASED APPROACH TO INVESTIGATE METABOLOMIC DIFFERENCES IN

THE PLASMA AND URINE OF YOUNG WOMEN AFTER CRANBERRY JUICE OR APPLE JUICE CONSUMPTION

Background

Cranberries (Vaccinium macrocarpon) are a native crop in North America. Fresh

cranberries have a tart taste, therefore majority of them are processed into juice for

consumption. Cranberry procyanidins are oligomeric or polymeric of flavan-3-ols linked

through interflavan bonds. B-type interflavan linkage is C4→ C8 and/or C4→ C6. A-type

procyanidins contain an additional ether bond C2→O→C7 (Ou & Gu, 2014). Most foods

including apples or apple juice contain exclusively B-type procyanidins, while

cranberries or cranberry juice contains both A and B-type procyanidins (L. Gu, Kelm,

Hammerstone, Beecher, et al., 2003). Ingestion of cranberry juice has long been

associated with prevention of urinary tract infection (UTI) (Blatherwick, 1914). Studies

showed that A-type procyanidins from cranberry juice inhibited the adhesion of

uropathogenic E. coli, whereas B-type procyanidins from apple juice showed no activity

(Amy B. Howell, Reed, Krueger, Winterbottom, Cunningham, & Leahy, 2005). Anti-

adhesion activity in human urine was detected following cranberry juice cocktail

consumption, but not after consumption of the apple juice (Amy B. Howell, Reed,

Krueger, Winterbottom, Cunningham, & Leahy, 2005). A-type trimers demonstrated

anti-adhesion activity, whereas epicatechin and a B-type dimer showed no such effect

(Foo, Lu, Howell, & Vorsa, 2000).

We hypothesized that cranberry juice consumption may have a different impact

on human metabolome compared to apple juice. A 1H NMR-based metabolomics

97

approach with multivariate statistic techniques was applied to analyze the overall

metabolic impact by cranberry juice and differentiate that from apple juice consumption.



Cranberry juice cocktail (double strength, 54% juice) and 100% apple juice was

provided by Ocean Spray Cranberries, Inc. (Lakeville-Middleboro, MA). Gallic acid,

HPLC-grade acetonitrile, methylene chloride, methanol, acetic acid, Folin−Ciocalteau

reagent, sodium carbonate, sodium phosphate dibasic anhydrous, sodium phosphate

monobasic anhydrous, sodium hydroxide, sodium chloride, sodium azide, sucrose,

glucose, and fructose were purchased from Fischer Scientific Co. (Pittsburgh, PA,

USA). D2O (99.9% D), 2, 2-dimethyl-2-silapentane-5-sulfonate (DSS, 98%) were from

Cambridge Isotope Laboratories, Inc. (Tewksbury, MA, USA). Sephadex LH-20 resin

was purchased from Sigma-Aldrich (St. Louis, MO, USA). Amberlite FPX 66 resin was a

product of Rohm and Haas Co. (Philadelphia, PA, USA). Pooled plasma used as quality

control samples were purchased from American Red Cross and they were collected

over a period of about 2 weeks.

Total Phenolics, Total Anthocyanins, Procyanidin Composition and Content

Two hundred and fifty mL of cranberry juice or apple juice was loaded onto a

column packed with Amberlite FPX 66 resins. Column was eluted with 3 L of

deionization water to remove sugars and ascorbic acids. Column was eluted with 300-

400 mL of methanol to yield cranberry juice sugar-free extract (890 mg) or apple juice

sugar-free extract (393 mg). The total phenolic content of juice sugar-free extracts were

determined by Folin-Ciocalteu assay (Singleton & Rossi Jr, 1965). A pH differential

assay was used to determine the total anthocyanin content (Giusti & Wrolstad, 2001).

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Another 250 mL of cranberry juice or apple juice was loaded onto a column (5.8×28 cm)

packed with Sephadex LH-20. The column was eluted with 30% methanol to remove

anthocyanins and phenolic acids, and then eluted with 70% acetone to yield cranberry

juice procyanidin extract (273 mg) or apple juice procyanidin extract (18.5 mg). The

procyanidin composition and content were analyzed on an Agilent 1200 HPLC system

(Palo Alto, CA) equipped with a binary pump, an autosampler, a fluorescence detector,

and a HCT ion trap mass spectrometer (Bruker Daltonics, Billerica, MA). Separation of

procyanidins was carried out on a Luna Silica (2) column (250 × 4.6 mm, 5 μm particle

size, Phenomenex, Torrance, CA) at a column temperature of 37 oC. The binary mobile

phase consisted of (A) methylene chloride/methanol/acetic acid/water (82:14:2:2, v: v: v:

v) and (B) methanol/acetic acid/ water (96:2:2, v: v: v: v). The 70 min gradient was as

follows: 0−20 min, 0.0−11.7% B linear; 20−50 min, 11.7−25.6% B linear; 50−55 min,

25.6−87.8% B linear; 55−65 min, 87.8% B isocratic; 65−70 min, 87.8−0.0% B linear;

followed by 5 min of column re-equilibration before the next injection. Excitation and

emission of the fluorescent detector were set at 231 and 320 nm, respectively.

Electrospray ionization at negative mode was performed using nebulizer 50 psi, drying

gas 10 L/min, drying temperature 350 °C, and capillary 4000 V. Mass spectra were

recorded from m/z 150 to 2000. The most abundant ion in full scan was isolated, and its

product ion spectra were recorded.

Procyanidins in PPCP and PPAP were quantified based on a method

standardized by Mars Inc.(Robbins, et al., 2012). This method uses (-)-epicatechin as a

calibrant and relative response factors for procyanidin dimers through nonamers,

because at the same concentration fluorescent signal response ratio between an

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oligomer and (−)-epicatechin stays constant under the same HPLC condition. The

relative response factor of nonamers was used as the response factor of high polymers

Sugar Analyses in Cranberry Juice and Apple Juice

Sugar analysis was conducted on an Agilent 1200 HPLC system consisting of an

autosampler, a binary pump, and a refractive index detector (Agilent Technologies, Palo

Alto, CA). Separation was carried out on a Restek ultra amino column (5 μm, 250 × 4.6

mm). The column temperature was maintained at 30 °C and a 5 μL of sample was

injected. Acetonitrile/water (80:20, v: v) was used as the mobile phase at a constant

flow rate of 1.0 mL/min. The optical unit temperature was set at 35 °C and the refractive

index detector signal was monitored in positive polarity. The run time for each sample

was 15 min followed by 5 min post time before the next run. Calibration curves were

constructed by use of pure standards of glucose, fructose, and sucrose.

Subjects and Study Design

Human study was approved by Institutional Review Boards at University of

Florida. Eighteen healthy female college students between 21-29 years old with a

normal BMI of 18.5-25 were recruited. Each subject was provided with a list of foods

that contained significant amount of procyanidins, such as cranberries, apples, grapes,

blueberries, chocolate and plums. They were advised to avoid these foods during the 1-

6th day and the rest of the study. On the morning of the 7th day, a first-morning baseline

urine sample and blood sample were collected from all human subjects after overnight

fasting. Participants were then randomly allocated into two groups (n=9 for each group)

to consume either cranberry juice or apple juice. Six bottles (250 mL/bottle) of juice

were given to participants to drink in the morning and evening of the 7th, 8th, and 9th day.

On the morning of 10th day, all subjects returned to clinical unit to provide a first-morning

100

urine sample after overnight fasting. The blood sample was also collected from

participants 30-60 min later after they drank another bottle of juice in the morning. After

two-weeks of wash out period, participants switched to the alternative regimen and

repeat the protocol. The timeline of the trial was summarized in Table 4-1. Blood

samples were centrifuged at 2,000 g for 10 min at 4 oC to obtain plasma. All urine and

plasma samples were aliquot and kept in a -80 oC freezer until analysis. One human

subject was dropped off this study because she missed part of her appointments.

Another two human subjects were removed from urine metabolomics analyses because

they failed to provide required urine samples.

1H NMR Metabolomics Analyses

Plasma or urine samples were taken out of -80 oC freezer to thaw at 4 oC in a

cold room, and then centrifuged at 5,220 g for 5 min. Plasma (400 µL) was mixed with

200 µL of saline solution (0.9% NaCl in D2O). Urine (400 µL) was mixed with 200 µL of

phosphate buffer (pH 7.4, DSS added). Both plasma and urine samples were

transferred into 5 mm Bruker NMR tubes (Z105684 Bruker 96 well rack) using Gilson

215 Liquid Handler (Trilution software version 2.0). All 1H-NMR spectra were collected

on a 600 MHz Avance II NMR spectrometer (Bruker Biospin, Germany) equipped with a

5mm cryo probe. Instrument had a samples changer (Sample Xpress Lite Autosampler)

under Icon-NMR. 1D NOESY-presaturated spectra for urine and 1D CPMG-

presaturated spectra for plasma were recorded. All NMR data were acquired at 25°C.

Probe tuning and matching were optimized for the first sample in each run. A 90° pulse

length, the offset of the water signal, water suppression and receiver gain for a data set

were also determined on the first sample in each run. The probe was automatically

locked to H2O+D2O (90%+10%) and shimmed for each sample.

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Multivariate Data Processing

All NMR spectra were phased and baseline corrected using NMRPipe and then

converted to FT files. The FT files were imported into MATLAB (R2013B, the

Mathworks, Inc., Natick, MA). Water regions (4.6-5.1 ppm) and DSS region (-0.2-0.5

ppm) were removed. Then the spectra were aligned and normalized in MATLAB. The

resultant data set was imported into SIMCA (Version 13.0.3, Umetrics, Umea, Sweden)

for multivariate statistical analysis. Data were mean-centered, Pareto scaled before

PCA, PLS-DA and OPLS-DA analysis in SIMCA. Unsupervised PCA model was

performed to initially examine intrinsic variation in the data set. Then supervised pattern

recognition methods PLS-DA and OPLS-DA (Bylesjö, Rantalainen, Cloarec, Nicholson,

Holmes, & Trygg, 2006) were used to extract maximum information on discriminant

compounds from the data. Validation of the model was tested using 7-fold internal

cross-validation and permutation tests for 200 times. To further evaluate the predictive

ability of the PLS-DA and OPLS-DA models, an external validation procedure was

performed (Brindle, et al., 2002; Llorach, et al., 2010). The whole data set was split into

a training set and a test set. Approximately 70% of the samples were randomly selected

as training set and the remaining 30% were treated as test set. PLS-DA and OPLS-DA

models were built based on the training set and obtained models were used to blindly

predict the classification of the samples in the test set. This procedure was repeated 30

times and the correct classification rate was calculated. For univariate analyses,

Welch’s t test was carried out on the NMR signal intensity of selected metabolites which

were considered to be responsible for the separation between treatments from

multivariate analyses. Benjamini–Hochberg (1995) procedure (α=0.01) was conducted

to control false discoveries. Box-and-whisker plot was drawn to display variations in

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samples within each treatment. The univariate analyses were done using Excel

Microsoft (Version 2007, Microsoft Corporation., Seattle, WA, USA).


Juice Analyses

Figure 4-1 showed HPLC chromatograms of procyanidins extracted from

cranberry juice and apple juice using fluorescence detection. Both A- and B-type

procyanidins were detected in cranberry juice, while only B-type procyanidins and their

oxidized forms were found in apple juice. Oligomeric procyanidins with DP 1-5 and high

polymeric procyanidins in juice were identified and quantified by HPLC-MSn. The total

quantifiable procyanidin content was 566 µg/mL in cranberry juice and 9.68 µg/mL in

apple juice. The total phenolics and total anthocyanins of apple juice (124 µg gallic acid

/mL, 0.12 µg cyanidin 3, 5-diglucoside/mL) were lower than those in cranberry juice

(913 µg gallic acid/mL, 59.2 µg cyanidin 3,5-diglucoside/mL) (Table 4-2). It should be

noted that ascorbic acid was removed using a chromatographic method so it was not

counted as part of total phenolics.

Sugar composition and content were analyzed on HPLC using a refractive index

detector. Glucose, fructose and sucrose were found in apple juice, while only glucose

and fructose appeared in cranberry juice (Figure 4-2). The results were consistent with

previously reported findings (Fuleki, Pelayo, & Palabay, 1994). Table 4-2 showed total

sugar content in apple juice was 10 times higher than that in cranberry juice, with

fructose as the dominant type of sugar.

Quality Control Data

To validate NMR acquisition method, quality control samples consisting of 17

replicates of pooled plasma collected from American Red Cross were analyzed along

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with experimental samples. PCA model was built to analyze the metabolic differences

between quality control and experimental samples. The mechanism was based on the

ability of PCA model to cluster samples in an unsupervised approach. PCA score plot

(Figure 4-3) showed that the 17 replicates were tightly clustered, suggesting that our

data acquisition method was valid.

Multivariate Analyses of Plasma after Drinking Cranberry Juice vs. Drinking Apple Juice

PCA, PLS-DA and OPLS-DA models were built to analyze the metabolic patterns

of plasma and urine. Neither PCA, PLS-DA or OPLS-DA models was able to detect

metabolic differences in baseline plasma and plasma after cranberry or apple juice

consumption (Table 4-3). Q2 calculated from cross-validation was all below 0.5

indicating the poor predictability of these supervised models. However, overall

metabolic profiles of plasma and urine after cranberry juice consumption were found to

be different from those after apple juice consumption. Figure 4-4 showed that two

groups of plasma samples were segregated on the score plot of OPLS-DA model but

not on a PCA model. Compared with PLS-DA model, OPLS-DA performed the analysis

with orthogonal filtration of matrix X on a vector Y. The variance in the X matrix was split

by OPLS model into predictive and orthogonal variance. “Structure noise” of data matrix

which was unrelated to the variation of interest such as genetic background, age,

physical activity, stress, etc., was filtered and described only by the orthogonal

component. The variation of scientific interest was only observed in the predictive

component, which is the first component. Therefore the interpretability of the resulting

model was increased (Fonville, et al., 2010). Plasma after drinking cranberry juice and

apple juice were clearly segregated on the score plot of OPLS-DA. One predictive

104

component and seven orthogonal components were generated in the OPLS-DA. This

model obtained high-quality parameters with an overall value of R2X, R2Y, and Q2 of

0.856, 0.979, and 0.652, respectively (Table 4-4). It indicated that about 85% in X data

set and 98% of variance in Y data set was explained by this model. Model validation

was performed by permutation test (n=200) and 7-fold internal validation. Q2 of 0.652

calculated from cross-validation was higher than 0.5, which was considered as good for

metabolomics data. Q2 indicated the OPLS-DA model had a good predictability. The Q2-

intercept values from permutation test (Figure 4-6) were lower than 0.05, indicating that

the achieved segregation was not due to overfitting. OPLS-DA score plot and cross-

validated score plot were visualized in Figure 4-5. Although one plasma sample after

cranberry juice consumption and two plasma samples after apple juice consumption

were misclassified during internal validation, the rest of samples were correctly

classified into two groups.

Multivariate Analyses of Urine after Drinking Cranberry Juice vs. Drinking Apple Juice

Similar results were obtained from urine metabolomics data. As an unsupervised

technique, PCA reveals the main structure in the data without considering a special

direction or type of information. The score plot of PCA in Figure 4-7A showed some

segregation between two groups of urine samples. By using a supervised pattern

recognition technique, a much more clear segregation was observed on the score plot

of OPLS-DA (Figure 4-7B). One predictive component and two orthogonal components

were generated from the OPLS-DA model. An overall value of R2X, R2Y, and Q2 of

0.548, 0.853, and 0.503 indicated good quality of the OPLS-DA model (Table 4-5).

Model validation was performed by permutation test (n=200) and 7-fold internal

105

validation. Q2 of 0.503 suggested that the model had a good predictive ability. The

regression line generated from permutation test (Figure 4-9) suggested the OPLS-DA

model was valid. The cross-validated score plot (Figure 4-8) was another way to

visualize the 7-fold internal validation. Both plasma and urine metabolomics data

suggested a differentiation between cranberry juice and apple juice consumption was

obtained.

Discriminant Metabolite Identification

To identify the contributing metabolites that are responsible for the separation of

cranberry juice and apple juice consumption, a S-lineTM technology, which is the tailored

S-plot (Llorach, Urpi-Sarda, Jauregui, Monagas, & Andres-Lacueva, 2009; Wiklund, et

al., 2008) for NMR spectroscopy data was used. It visualizes both the covariance and

the correlation structure between the X-variables and the predictive score. The p(ctr) is

the centered loading vector of the first principal component. It was colored according to

the absolute value of the correlation loading p(corr). A p(corr)>0.5 was selected as

significance level. The advantage of the S-line plot is that it displays the predictive

loading in a form resembling the original NMR spectra. A list of makers detected in the

S-line was then subjected to Welch’s t test, and the p-value obtained for each marker

was smaller than 0.01 (Table 4-6). Both multivariate and univariate analyses concluded

eight significant metabolites were responsible for the separation between drinking

cranberry juice and apple juice consumption. The contributing metabolites were

identified by comparing their NMR spectra with published papers (Bouatra, et al., 2013;

Psychogios, Hau, Peng, Guo, Mandal, Bouatra, et al., 2011) and those registered in the

Human Metabolome Database. These markers found from plasma and urine

metabolomic data were summarized in Table 4-6.

106

A-type procyanidins in cranberry juice or B-type procyanidins in apple juice have

very low absorption rate in vivo. Only a small portion of epicatechin and procyanidin

oligomers with DP<5 were absorbed in small intestine (Ou & Gu, 2014). The absorption

rate was below 5% (Gonthier, Donovan, Texier, Felgines, Remesy, & Scalbert, 2003;

Rzeppa, Bittner, Döll, Dänicke, & Humpf, 2012; Tsang, et al., 2005). The majority of

procyanidin oligomers and polymers reach colon, where both A- and B-type

procyanidins are degraded by gut microflora to form various microbial metabolites. Part

of the microbial metabolites were low molecular weight phenolic acids and

phenylvalerolactones (Ou & Gu, 2014). In the present study, the detection of these

microbial metabolites by NMR spectroscopy was limited due to their low levels in urine

or plasma. However, by using a global metabolomics approach we were able to find

several endogenous metabolites that are responsible for the separation of cranberry

juice and apple juice consumption. These metabolites were marked on the S-line in

Figure 4-10 and Figure 4-11. Cranberry juice and apple juice consumption had different

impact on endogenous metabolites in urine and plasma. The plasma level of citric acid

was considered to be increased after consumption of cranberry juice according to its

loading profile. Although its relatively low magnitude makes it not an ideal case, its

correlation loading p(corr) >0.5 is accepted for being statistically significant. Lactate, D-

glucose and two unidentified metabolites in plasma were higher after consumption of

apple juice. One unidentified metabolite with chemical shift at 3.56 (m) ppm and 4.01

(m) ppm was first identified as quinic acid by matching its NMR spectrum with published

data in Human Metabolome Database. We then spiked the plasma samples with pure

quinic acid to disprove the identification. Cranberry juice consumption caused a stronger

107

increase in urinary excretion of hippuric acid and one unidentified metabolites. The

result was consistent with our previous finding that urinary level of hippuric acid in

female rats was greatly increased after intake of cranberry procyanidins. Hippuric acid is

formed by the conjugation of benzoic acid with glycine in the liver, and then excreted in

urine. Production of hippuric acid is mainly from two routes. One is from the

consumption of foods containing benzoic acid. The other one is from the metabolism of

polyphenols into benzoic acid by the gut microflora (Walsh, Brennan, Pujos-Guillot,

Sébédio, Scalbert, Fagan, et al., 2007). Procyanidins were degraded by the gut

microflora into benzoic acid in colon and benzoic acid was converted to hippuric acid in

the liver (Rechner, Kuhnle, Bremner, Hubbard, Moore, & Rice-Evans, 2002). A previous

animal study showed that consumption of cranberry powder caused a strong increase in

urinary excretion of hippuric acid. Its quantity in urine were higher than any other urinary

phenolic acids (Prior, Rogers, Khanal, Wilkes, Wu, & Howard, 2010). Citric acid is an

intermediate in the citric acid cycle intermediates. Plasma level of citric acid was

elevated after cranberry juice consumption, suggesting an increased oxidative energy

metabolism. Reduction in plasma level of lactate suggested that cranberry juice

consumption may be associated with anaerobic glycolysis reduction (S. Lin, Chan, Li, &

Cai, 2010).

Box-and-whisker plots of signal intensity of these eight metabolites were used to

display their differences in plasma or urine level following juice consumption (Figure 4-

12). The median intensity of lactate, glucose, unknown 1 (singlet at 2.36 ppm), and

unknown 2 (multiplet at 4.01 ppm) in plasma following cranberry juice consumption

were about two times lower than those after drinking apple juice. It was consistent with

108

Welch’s t test, confirming their significantly low level in plasma after drinking cranberry

juice (Table 4-6). It is interesting to notice that the whiskers on the box plots of hippuric

acid and unknown 4 (singlet 2.11 ppm) following apple juice were considerably smaller

compared to those following cranberry juice, indicating that contents of hippuric acid

and unknown 4 (singlet 2.11 ppm) were consistently low throughout all urine samples

following apple juice consumption. The results were consistent with both univariate and

multivariate analyses that these two metabolites had significantly higher quantities in

subjects’ urine after drinking cranberry juice.

Summary

This study showed that global 1H NMR metabolomics was a very effective

approach to differentiate metabolic impact of cranberry juice from those of apple juice.

The metabolic differences observed in the present study were consistent with our

previous findings in female rats. Cranberry juice consumption caused a higher urinary

excretion of hippuric acid, while apple juice intake increased the plasma concentration

of lactate and D-glucose. Several health benefits were associated with consumption of

cranberry juices; however, the mechanisms remain unclear. The metabolic differences

observed in this study may help to explain the physiological activities of procyanidin-rich

cranberry juices.

109

Table 4-1. Timeline of intervention study on women. Volunteers 18 healthy females college students

Treatment A: Cranberry juice B: Apple juice

1st -6th day and the rest of the study

Avoid procyanidins-rich foods

7th day morning (8-10 am)

Collect first-morning baseline urine samples Collect baseline blood samples Consume 1 bottle of cranberry or apple juice

7th day evening

Consume 1 bottle of cranberry or apple juice

8th-9th day Consume 1 bottle of cranberry or apple juice in the morning and evening


Collect first-morning urine samples Consume 1 bottle of cranberry or apple juice Collect blood samples

Wash out period for 2 weeks


Collect first-morning baseline urine samples Collect baseline blood samples. Consume 1 bottle of cranberry or apple juice

25th day evening

Consume 1 bottle of cranberry or apple juice

26th-27th day Consume 1 bottle of cranberry or apple juice in the morning and evening


Collect first-morning urine samples Consume 1 bottle of cranberry or apple juice Collect blood samples.

End

110

Table 4-2. Total phenolics, total anthocyanins, procyanidin composition and content of cranberry juice and apple juice.

Cranberry Juice Apple Juice

Procyanidins content (µg/mL juice)

Monomer 6.39±0.19 Not Detected

Dimers 53.8±0.1 0.225±0.170

Trimers 49.2±0.7 0.445±0.012

Tetramers 58.5±0.5 1.26±0.07

Pentamers 34.4±1.8 1.28±0.01

High Polymers 364±14 6.46±0.41

Total 566±17 9.68±0.52

Total phenolics (µg gallic acid equivalents/mL juice)*

Total phenolics 913±7 124±1

Total anthocyanins (µg cyanidin 3,5-diglucoside equivalents/mL juice)

Total anthocyanins 59.2±2.4 0.12±0.00

Sugar Composition and Content (mg/mL juice)

Fructose 3.46±0.12 157±8

Glucose 22.3±0.3 76.1±2.3

Sucrose Not Detected 41.6±1.5

Total 25.8±0.4 275±12

Data are expressed as mean ± standard deviation. *Ascorbic acid was not counted as total phenolics

111

Table 4-3. Summary of parameters for PCA, PLS-DA, and OPLS-DA models for human baseline plasma and plasma after drinking cranberry juice or apple juice.

Baseline vs. Cranberry Juice Baseline vs. Apple Juice

PCA PLS-DA OPLS-DA PCA PLS-DA OPLS-DA

Na

4 2 1Pc+1Od 4 2 1Pc+1Od

R2

X(cum)b

0.553 0.278 0.278 0.751 0.274 0.274

R2

Y(cum)b

--- 0.598 0.598 --- 0.656 0.656

Q2

(cum)b

0.346 -0.207 -0.478 0.633 0.105 0.403

a N: number of components. b R2X (cum)and R2Y (cum) are the cumulative modeled variations in the X and Y matrix, respectively. Q2Y (cum) is the cumulative predicted variation in the Y matrix. c Predictive component. d Orthogonal component.

112

Table 4-4. Summary of parameters for PCA, PLS-DA, and OPLS-DA models for human plasma after drinking cranberry juice or apple juice.

Model Na

R2

X(cum)b

R2

Y(cum)b

Q2

(cum)b

Correct classification Rate*

PCA 3 0.683 ---- 0.521 ----

PLS-DA 3 0.571 0.716 0.414 0.803±0.098

OPLS-DA 1Pc

+7Od

0.856 0.979 0.652 0.803±0.091

a N: number of components. b R2X (cum)and R2Y (cum) are the cumulative modeled variations in the X and Y matrix, respectively. Q2Y (cum) is the cumulative predicted variation in the Y matrix. c Predictive component. d Orthogonal component. *Correct classification rate was obtained from external validation procedure repeated for 30 times.

113

Table 4-5. Summary of parameters for PCA, PLS-DA, and OPLS-DA models for human urine after drinking cranberry juice or apple juice.

Model Na

R2

X(cum)b

R2

Y(cum)b

Q2

(cum)b

Correct classification Rate*

PCA 2 0.505 ---- 0.414 ----

PLS-DA 3 0.548 0.853 0.547 0.802±0.108

OPLS-DA 1Pc

+2Od

0.548 0.853 0.503 0.802±0.101


c Predictive component. d Orthogonal component. *Correct classification rate was obtained from external validation procedure repeated for 30 times.

114

Table 4-6. Summary of metabolite profile changes in plasma and urine of young women after drinking cranberry juice and apple juice.

Metabolites Chemical shift (multiplicity) p-value a Cranberry juice vs. apple juice b

Plasma lactate 1.32 (d), 4.11 (q) <0.01

D-glucose 3.22 (dd), 3.40 (q), 3.46 (m), 3.70 (m), 3.83 (m), 3.90 (m), 5.23 (d)

<0.01

citric acid 2.52 (d), 2.62 (d) <0.01

unknown 1 2.36 (s) <0.01

unknown 2 4.01 (m) <0.01

unknown 3 3.56 (m) <0.01

Urine hippuric acid 3.96 (d), 7.54 (t),7.63 (t), 7.82 (d), 8.53 (br,s)

<0.01

unknown 4 2.11 (s) <0.01

a p-value obtained from Welch’s t test. Benjamini–Hochberg procedure was conducted to control false discoveries and conclude that all these variables are significant different at α=0.01.

b Arrows indicated a decrease or increase in metabolite level in plasma or urine after cranberry juice consumption compared to apple juice.

115

Figure 4-1. Chromatograms of procyanidins extracted from cranberry juice and apple

juice using fluorescence detection. A) Cranberry juice and B) apple juice. Identification was performed using HPLC-FLD-MSn .The numbers beside the peaks indicate the degree of polymerization of B-type procyanidins. 2a-4a designates the peaks of procyanidins dimers through pentamers with one A-type linkage. O3-O5 designates the peaks of oxidized B-type procyanidins trimer, tetramers, and pentamers found in apple juice.

min0 10 20 30 40 50 60

LU

10

20

30

40

50

60

1

2a

2b3a

3a3 4a

4a4a 5a

3

High polymer

A

min0 10 20 30 40 50 60

LU

5

10

15

20

High polymer

2

O3

O3

O3O4

O4 O4

O5B

116

Figure 4-2. Chromatograms of sugar standards and juices using refractive index

detector. A) Sugar standards, B) apple juice and C) cranberry juice.

min0 2 4 6 8 10 12 14

nRIU

0

50000

100000

150000

A

FructoseGlucose Sucrose

min0 2 4 6 8 10 12 14

nRIU

0

20000

40000

60000

B Fructose

GlucoseSucrose

min0 2 4 6 8 10 12 14

nRIU

0

20000

40000

60000C

Fructose Glucose

117

Figure 4-3. The PCA score plot of human plasma and plasma quality control from 1H

NMR metabolomics. Green squares: plasma after drinking cranberry juice. Blue squares: plasma after drinking apple juice. Red squares: 17 replicates of pooled plasma samples.

-8

-6

-4

-2

0

2

4

6

-15 -10 -5 0 5 10

t[1]

Plasma after cranberry juice

Plasma after apple juice

17 replicates of pooled plasma

t [2]

118

Figure 4-4. The PCA and OPLS-DA score plots of human plasma after drinking

cranberry juice or apple juice from 1H NMR metabolomics. A) PCA score plot and B) OPLS-DA score plot. Green squares: human plasma after cranberry juice. Blue squares: human plasma after apple juice.

-6

-4

-2

0

2

4

-10 -8 -6 -4 -2 0 2 4 6 8

t[1]

t [2]

A

-8

-6

-4

-2

0

2

4

6

-3 -2 -1 0 1 2 3

t[1]

to [1

]

Plasma after cranberry juicePlasma after apple juice

B

119

Figure 4-5. Model score plot and cross-validated score plot of OPLS-DA model for

human plasma after drinking cranberry juice or apple juice from 1H NMR metabolomics. Circles: model scores of plasma. Squares: cross-validated scores of plasma. Green color: plasma after cranberry juice. Blue color: plasma after apple juice.

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

B15

'

B5

'

B17'

D4

'

D17

'

D3'

B13

'

D9

'

D11

'

B16'

D7

'

B11

'

D5'

D12

'

D16

'

D8'

D2

'

B12

'

B4

'

B8'

D1

'

D13

'

B14'

B10

'

D10

'

B9'

B2

'

B6

'

D14

'

B1'

B7

'

D15

'

B3'

D6

'

Sample ID

Cross-validated scores of plasma after cranberry juice

Cross-validated scores of plasma after apple juice

Model scores of plasma after cranberry juice

Model scores of plasma after apple juice

t[1]

, tcv

[1]

120

Figure 4-6. Validation plot of 200 permutation tests for OPLS-DA model built for human plasma after drinking cranberry juice or apple juice from 1H NMR metabolomics.

r(y, permuted y)

R2

, Q2

121

Figure 4-7. The PCA and OPLS-DA score plot of human urine after drinking cranberry

juice or apple juice from 1H NMR metabolomics. A) PCA score plot and B) OPLS-DA score plot. Green squares: human urine after cranberry juice. Blue squares: human urine after apple juice.

t [2]

Urine after cranberry juiceUrine after apple juice

-0.06

-0.04

-0.02

0

0.02

0.04

-0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06t[1]

A

-0.06

-0.04

-0.02

0

0.02

0.04

-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04t[1]

t o [

1]

B

122

Figure 4-8. Cross-validated score plot of OPLS-DA model derived from human urine

after drinking cranberry juice or apple juice from 1H NMR metabolomics. Green squares: urine after cranberry juice. Blue squares: urine after apple juice.

Urine after cranberry juice

Urine after apple juice

tocv

[1]

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

-0.03 -0.025 -0.02 -0.015 -0.01 -0.005 0 0.005 0.01 0.015 0.02

tcv[1]

123

Figure 4-9. Validation plot of 200 permutation tests for OPLS-DA model built for human

urine after drinking cranberry juice or apple juice from 1H NMR metabolomics.

r(y, permuted y)

R2

, Q2

124

Figure 4-10. S-line associated with the OPLS score plots of data derived from human plasma after cranberry juice or

apple juice consumption. The x-axis is chemical shift derived from NMR spectra. The y-axis p(ctr)[1] is the centered loading vector of the first principal component. p(ctr)[1] is colored according to the absolute value of the correlation loading p(corr). p(corr)>0.5 is selected as significance level.

125

Figure 4-11. S-line associated with the OPLS score plots of data derived from human urine after cranberry juice or apple

juice consumption. The x-axis is chemical shift derived from NMR spectra. The y-axis p(ctr)[1] is the centered loading vector of the first principal component. p(ctr)[1] is colored according to the absolute value of the correlation loading p(corr). p(corr)>0.5 is selected as significance level.

126

Figure 4-12. Box-and-whisker plot of the NMR signal intensities of eight significant

metabolites detected in human plasma or human urine of young women after drinking cranberry juice and apple juice.

127

CHAPTER 5 UHPLC-Q-ORBITRAP-HRMS-BASED GLOBAL METABOLOMICS REVEAL METABOLOME MODIFICATIONS IN PLASMA OF YOUNG WOMEN AFTER

CRANBERRY JUICE OR APPLE JUICE CONSUMPTION

Background

The objective of this study is to investigate the plasma metabolome modifications

of young women after drinking cranberry juice or apple juice and to identify putative

biomarkers using an UHPLC-Q-Orbitrap-HRMS-based metabolomics profiling method.



Cranberry juice cocktail (double strength, 54% juice) and 100% apple juice were

provided by Ocean Spray Cranberries, Inc. (Lakeville-Middleboro, MA, USA). LC-MS

grade acetonitrile, methylene chloride, methanol, acetic acid, formic acid and acetone

were purchased from Fischer Scientific Co.(Pittsburgh, PA, USA). Creatine-D3, L-

leucine-D10, L-tryptophan-2, 3, 3-D3, caffeine-D3 were from CDN Isotopes Inc. (Pointe-

Claire, Quebec, Canada). Pooled plasma from American Red Cross were collected over

a period of about 2 weeks.



Florida. Detailed protocol of the human study was described in Chapter 4. The timeline

was summarized in Table 4-1.


Frozen plasma samples (-80 oC) were thawed at room temperature. One plasma

(100 µL) was mixed with 800 µL acetonitrile: acetone: methanol (8:1:1, v: v: v) to

precipitate the proteins. Twenty µL isotopically-labeled standard solution (40 µg/mL L-

128

tryptophan-D3, 4 µg/mL L-leucine-D10, 4 µg/mL creatine-D3, and 4 µg/mL caffeine-D3)

was added to the above extraction mixture as internal standards. The sample was

vortexed and placed in a 4 oC refrigerator for 30 min to assist protein precipitation. This

sample was then centrifuged at 20,000 g for 10 min at <10 oC to pellet the protein. Two

hundred and fifty µL of supernatant was transferred to a new 1 mL Eppendorf tube and

dried under a gentle stream of Nitrogen (Organomation Associates, Inc., Berlin, MA,

USA). Dried sample was reconstituted in 100 µL 0.1% formic acid in water and

vortexted. The sample solution was put on an ice bath for 10-15 min and centrifuged at

20,000 g for 5 min at <10 oC to remove debris. The reconstituted sample was

transferred into a glass vial with fused glass inserts for analyses. All 34 human plasma

samples were prepared in the same manner. Four groups of quality control (QC)

samples including pooled plasma from baseline group, cranberry juice group, apple

juice group and Red Cross group were prepared and analyzed with experimental

plasma samples to monitor the stability and validity of instrumental acquisition. Running

sequence started with 3 blanks (0.1% formic acid in water), one Red Cross QC, one

pooled QC from baseline group, one pooled QC from cranberry juice group and one

pooled QC from apple juice group, followed by 17 plasma samples to ensure instrument

drift was minimal.






129


column before the next run. The flow rate was 350 μL/min. The UHPLC system was



performed using both negative and positive ionization with a mass resolution of 70,000

at m/z 200. Separate injections were performed in a data-dependent (top 5) MS/MS

mode with full scan mass resolution reduced to 35,000 at m/z 200. The injection volume

was 4 μL for negative ionization and 2 μL for positive ionization acquisition. The m/z

range for all full scan analyses was 70–1000. Heated electrospray ionization (HESI)

parameters were as follows: sheath gas flow 45 arb (arbitrary units) auxiliary gas flow

10 arb, sweep gas flow 1 arb, spray voltage 3.5 kV, probe temperature 350°C, capillary

temperature 320 °C for negative ionization and 325 °C for positive ionization. In source

CID (Collision-Induced Dissociation) was 2 eV. The mass spectrometer was calibrated

using Pierce™ negative and positive ion calibration solution (Thermo Fisher Scientific,

San Jose CA, USA). To avoid possible bias, the sequence of injections for plasma

samples was randomized.



(Chambers, et al., 2012) and then processed using MZmine 2.12 (Pluskal, Castillo,

Villar-Briones, & Orešič, 2010). Peaks in each sample were extracted, deconvoluted,

and deisotoped. Alignment using join aligner algorithm was conducted with a 10 ppm

tolerance for m/z values and 0.2 min tolerance for retention time. Gap filling using peak

finder algorithm was performed to fill in missing peaks. The resultant data set was

imported into SIMCA (Version 14.0, Umetrics, Umea, Sweden) for multivariate statistical

130

analysis. Data acquired using negative ionization were mean-centered, Pareto scaled

and log-transformed before multivariate statistical analyses. Data obtained using

positive ionization were mean-centered, Pareto scaled and log-transformed before

building PCA model; mean-centered and log-transformed before PLS-DA and OPLS-DA

analyses. Unsupervised PCA model was performed to initially examine intrinsic

variation in the data set. Then supervised pattern recognition methods include PLS-DA

and OPLS-DA (Bylesjö, Rantalainen, Cloarec, Nicholson, Holmes, & Trygg, 2006) were

used to extract maximum information on discriminant compounds from the data.

Validation of the model was tested using 7-fold internal cross-validation and permutation

tests for 200 times. To further evaluate the predictive ability of the PLS-DA or OPLS-DA

models, an external validation procedure was performed (Brindle, et al., 2002; Llorach,

et al., 2010). The LC-HRMS metabolomics data set was split into a training set and a

test set. Approximately 70% of the samples were randomly selected as the training set

and the remaining 30% were treated as the test set. PLS-DA and OPLS-DA models

were built based on the training set and then blindly predicted the classes of the

samples in the test set. This procedure was repeated 30 times and a correct

classification rate was calculated.



Due to the variations between LC-HRMS injections and artifacts caused by the

order of acquisition and carry-over, sensitivity changes or ion suppression could occur

during the experimental period (Burton, Ivosev, Tate, Impey, Wingate, & Bonner, 2008).

Sample acquisition was randomized. QCs created from baseline group, cranberry juice

group, apple juice group, and Red Cross group were analyzed along with experimental

131

plasma samples to monitor the instrument performance. The PCA model was built to

investigate the metabolome difference between QCs and experimental samples. The

mechanism was based on the ability of the PCA model to cluster samples in an

unsupervised approach. QCs from Red Cross plasma clustered together and separated

from experimental plasma on the PCA score plots (Figure 5-1A, 5-1B). It confirmed the

stability of instrumental analysis. PCA score plots based on experimental samples and

QCs from cranberry group, apple group and baseline group (Figure 5-1C, 5-1D) showed

that replicates of pooled QCs from each group tended to cluster together across the

entire sequence indicating a good quality of data acquisition.

Baseline Plasma vs. Plasma after Drinking Cranberry Juice

Two sets of data acquired by LC-HRMS negative ionization and positive

ionization were subjected to multivariate analyses, respectively. No segregation

between baseline plasma and plasma after cranberry juice was observed on the PCA

score plots for either negative ionization or positive ionization acquisition (Figure 5-2A,

5-2B). Compared to unsupervised PCA model, supervised multivariate statistic

techniques including PLS-DA and OPLS-DA successfully segregated two groups of

plasma samples in both negative and positive ionization data acquisition. Score plots of

PLS-DA (Figure 5-3A, 5-3C) and OPLS-DA (Figure 5-3B, 5-3D) demonstrated a clear

separation of baseline plasma vs. plasma after drinking cranberry juice. Two principal

components were selected to build PLS-DA model. A principal component and an

orthogonal component were used to construct OPLS-DA model. R2 represents the

goodness of fit. The R2Y of supervised models based on negative ionization and

positive ionization was 0.901 and 0.951, respectively (Table 5-1). The results indicated

that above 90% of variance in Y data matrix was explained by both PLS-DA and OPLS-

132

DA models. The advantage of OPLS-DA over PLS-DA is that the “structure noise” of

data matrix which is unrelated to the variation of interest is filtered and described only

by the orthogonal component. The variation of scientific interest is described in the

predictive component. Therefore the interpretability of the resulting model is increased

(Fonville, et al., 2010). In this study the performance of PLS-DA was similar to OPLS-

DA, indicating little “structure noise” existed in the LC-HRMS data. Overfitting is

possible when analyzing high-dimensional data with thousands of variables. This is due

to accidental correlations between one or more variables. Therefore, validation of

supervised model was applied to detect overfitting. Three validation methods were used

to test the validity and predictability of PLS-DA and OPLS-DA models. Internal cross-

validation was the first step to test the predictability of the supervised models. Q2

calculated from the cross-validation higher than 0.5 indicates a good multivariate model.

Q2 higher than 0.9 suggests excellent metabolomics data. Seven-fold internal cross

validation was performed on both PLS-DA and OPLS-DA models. Q2 obtained from

PLS-DA and OPLS-DA model derived from negative ionization mode was 0.627 and

0.641, respectively. Q2 of 0.764 and 0.679 was calculated from PLS-DA and OPLS-DA

model derived from positive ionization mode, respectively (Table 5-1). These Q2 values

indicated that supervised models derived from both negative and positive ionization had

a good predictability and the segregation was not due to overfitting. Cross-validated

PLS-DA and OPLS-DA score plots (Figure 5-4) also showed a separation between two

groups of plasma. Three or four plasma samples were misclassified during cross

validation. Misclassification would not occur if a supervised model has an excellent

predictability with Q2 higher than 0.9. To further test the predictability of PLS-DA and

133

OPLS-DA models, permutation tests were conducted. The class labels of baseline and

cranberry juice drinking group were permuted and randomly assigned to different

observations. Then a new supervised model was calculated with the permutated class

labels. The procedure was repeated 200 times. R2 and Q2 of each newly constructed

model were calculated and a regression line was drawn. In an ideal case, all R2 and Q2

calculated from the permutation data should be lower than those from the actual data,

and the Q2-intercept value obtained from the regression line should be lower than 0.05.

The rationale behind the permutation test is that the newly constructed classification

models that are built based on permutated class labels should not be able to correctly

predict the class (Westerhuis, et al., 2008). Although the R2 calculated from permeated

models were higher than 0.7, the corresponding Q2 were smaller than 0.4 (Figure 5-5,

Figure 5-6), suggesting that the classification models based on permuted class labels

had poor predictability compared to actual model. Therefore, the achieved segregation

between baseline plasma and plasma after drinking cranberry juice was not likely due to

overfitting. Cross-validation and permutation test provided a reasonable estimation of

the predictability of a PLS or OPLS model (Eriksson, 2006). External validation uses an

independent set of test data to evaluate predictability of a supervised model and

therefore is a more scrupulous and demanding method (Eriksson, 2006). Correct

classification rate of 85% and 90% for supervised models derived from negative and

positive ionization analyses were obtained from external validation procedure (Table 5-

1). These results indicated that both PLS-DA and OPLS-DA models had excellent

predictabilities and were able to correctly predict unknown plasma samples with an error

rate smaller than 15%. These three validation tests confirmed that there were true

134

changes of plasma metabolome in young women after drinking cranberry juice. UHPLC-

HRMS metabolomics was an effective approach to reveal the metabolome modification.

Plasma after Drinking Apple Juice vs. Plasma after Drinking Cranberry Juice

Similarly, projection pattern techniques were applied to investigate the

metabolome differences in human plasma after drinking apple juice or cranberry juice.

Compared to unsupervised PCA model which did not segregate the two groups of

plasma (Figure 5-7), PLS-DA and OPLS-DA were able to separate human plasma after

drinking apple juice from those after cranberry juice consumption (Figure 5-8). Both

PLS-DA and OPLS-DA models derived from negative ionization and positive ionization

had high quality parameters. The R2Y of four supervised models were higher than

0.950, suggesting that more than 95% of the variance of Y data matrix was explained by

these models. Q2 calculated from 7-fold cross-validation for PLS-DA and OPLS-DA

models derived from negative ionization were 0.846 and 0.809, respectively, (Table 5-1)

indicating a very good predictability of supervised models. Q2 were higher than those

obtained from positive ionization (Table 5-1), suggesting that a better performance of

supervised models was achieved based on LC-HRMS data acquired using negative

ionization. The cross-validated score plots (Figure 5-9) showed a clear segregation of

two groups of plasma. No misclassification occurred during the cross validation.

Compared to supervised models derived from baseline plasma vs. plasma after

cranberry juice, the same models derived from plasma after apple juice vs. plasma after

cranberry juice had higher Q2 and a zero misclassification. It suggested that plasma

metabolome differences between apple juice and cranberry juice consumption were

more robust than those between baseline and cranberry juice consumption.

135

Permutation test and external validation test were conducted to further

investigate the validity and predictability of supervised models. Figure 5-10 and Figure

5-11 showed that the Q2 of each model constructed based on permutated class labels

was smaller than 0.4, suggesting a good predictability. Therefore, the achieved

segregation between plasma after apple juice or cranberry juice was not likely due to

overfitting. External validation test generated correct classification rates of 97.7% and

98% for supervised models derived from negative and positive ionization (Table 5-1),

indicating that both PLS-DA and OPLS-DA models had excellent predictability and were

able to correctly predict the unknown human plasma samples with an error rate smaller

than 3%. These results confirmed that the plasma metabolome of young women after

drinking cranberry juice were different than those after drinking apple juice.


S-plot is a statistical tool that visualizes the variable influence in a projection-

based model. It was used to discover the discriminant metabolites. At a significance

level p= 0.05, a p(corr) of 0.5 was used as an arbitrary cutoff value to select the

potential biomarkers (Llorach, Urpi-Sarda, Jauregui, Monagas, & Andres-Lacueva,

2009). Metabolites with higher absolute p[1] and p(corr) values were located on the

upper right or lower left corner of the S-plot. They were the statistically significant

variables contributing to the separation between apple and cranberry juice consumption.

Figure 5-12A and 5-13A show that a total of 57 and 28 metabolic features in negative

mode and positive mode, respectively, were discriminant metabolites that separated

baseline and cranberry juice consumption. Among them, 14 features in negative mode

and 8 features in positive mode were identified based on their accurate masses and/or

product ion spectra. Similarly, Figure 5-12B and 5-13B demonstrate that 39 and 42

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metabolic features in negative mode and positive mode, respectively, were discriminant

metabolites that separated apple juice and cranberry juice consumption. A total of 12

metabolic features in both negative mode and positive mode were identified using

accurate masses and/or product ion spectra. These identified metabolites were

numbered in the Figure 5-12, 5-13 and summarized in Table 5-2 and Table 5-3.

Unidentified metabolic features were listed in Table 5-4 and Table 5-5. HMDB (Wishart,

et al., 2007), mzCloud, Metlin and Mass Bank (Horai, Arita, Kanaya, Nihei, Ikeda, Suwa,

et al., 2010) were searched to assist metabolite identification.

Seven exogenous metabolites were higher in human plasma after drinking

cranberry juice compared to baseline plasma. One metabolite producing a [M-H]- ion at

m/z 188.9854 and a product ion at m/z 109.0294 [M-H-sulfate]-. It was tentatively

identified as catechol sulfate as it matched the same metabolite in HMDB (Δ=0.0009

Da). This metabolite was also higher in rat plasma after intake of cranberry procyanidins

in Chapter 2. The compound producing a [M-H]- ion at m/z 151.0389 was tentatively

identified as hydroxyphenyl acetic acid (Δ=0.0012Da). It generated product ions at m/z

107.0499 [M-H-COO]-, m/z 64.8080, and m/z 59.0130. The fragmentation pattern

matched hydroxyphenylacetic acid in mzCloud. However, the position of hydroxyl group

(3- or 4- ) could not be determined based on product ion spectra. A metabolite

producing a [M-H]- ion at m/z 242.9967 was putatively identified as coumaric acid

sulfate (Δ=0.0002 Da). The product ion at m/z 163.0400 [M-H-sulfate]- was observed in

its MS2 spectra. Other product ions including m/z 146.9611, m/z 119.0503 and m/z

174.9560 matched the fragmentation pattern of coumaric acid (Liang, Xu, Zhang,

Huang, Zang, Zhao, et al., 2013). The compound with [M-H]- ion at m/z 273.0077 and a

137

major product ion at m/z 193.0504 [M-H-sulfate]- was tentatively identified as ferulic acid

sulfate (Δ=0.0003 Da). Characteristic fragment ions at m/z 178.0270, m/z 149.0606 and

m/z 134.0372 were in high accordance with ferulic acid (Liang, et al., 2013). 3, 4-

Dihydroxyphenyl ethanol sulfate (Δ=0.0004 Da) was detected and tentatively identified

in human plasma after drinking cranberry juice. It had a [M-H]- ion at m/z 233.0121 and

a product ion at m/z 153.0149 [M-H-sulfate]-. Two isomers of trihydroxybenzoic acid

were detected and tentatively identified based on their accurate m/z 171.0264 [M+H]+

and m/z 171.0265 [M+H]+. The product ions of one isomer included m/z 152.0704, m/z

148.9768, m/z 125.9610, m/z 88.0713 and m/z 84.9602. The fragment ions of the other

isomer were m/z 148.9768, m/z 125.9608, m/z 84.0812, m/z 89.0713 and m/z

109.0761. The fragmentation patterns and accurate masses of these two isomers

matched those of trihydroxybenzoic acid in mzCloud (Δ=0.0024 Da).

Furthermore, 11 endogenous metabolites were higher in human plasma after

drinking cranberry juice compared to baseline plasma. The metabolite producing a [M-

H]- ion at m/z 178.0501 and a fragment ion at m/z 134.0612 was tentatively identified as

hippuric acid (Δ=0.0008 Da) by comparing its spectra with those in mzCloud. This

metabolite also generated a [M+H]+ ion at m/z 180.0656 and a fragment ion at m/z

105.0337 in positive mode, which matched the same compound in HMDB and mzCloud.

2-Hydroxyhippuric acid (Δ=0.0008 Da) was detected and tentatively identified based on

the [M-H]- ion at m/z 194.0451 and product ions at m/z 150.0506, m/z 194.0456, and

m/z 93.0342. The fragmentation pattern matched those in mzCloud and HMDB. The

compound producing a [M+H]+ ion at m/z 196.0395 was tentatively identified as

hydroxyhippuric acid (Δ=0.0209 Da) by searching HMDB. However, the position of

138

hydroxyl group could not be determined for this compound because no MS2 spectra

was collected from the positive ionization analysis. The metabolite generating a [M-H]-

ion at m/z 168.0292 and a [M+H]+ ion at m/z 170.0449 was tentatively identified as 2-

furoyglycine. The product ions at m/z 124.0404 and m/z 67.0183 in negative mode

matched those in mzCloud (Δ=0.0010 Da). The MS2 spectra in positive mode showed

ions at m/z 95.0131, m/z 123.9658, m/z 146.9814, m/z 88.0398 matching the

fragmentation pattern of 2-furoyglycine in Metlin database (Δ=0.0001 Da).

Vanilloylglycine was detected and tentatively identified based on its accurate m/z

224.0561[M-H]-. Searching m/z 224.0561[M-H]- in HMDB yielded only vanilloylglycine

with Δ<0.001 Da. Hippuric acid, hydroxyhippuric acid, 2-furoyglycine, vanilloylglycine

belong to acyl glycine and they are formed by conjugation of benzoic acid,

hydroxybenzoic acid, vanillic acid or furan derivatives with glycine. These phenolic acids

were likely generated from procyanidin catabolism in colon by the gut microflora (Ou &

Gu, 2014). A previous animal study showed that consumption of cranberry powder

caused a strong increase in urinary excretion of hippuric acid. Its quantity were higher

than any other phenolic acids (Prior, Rogers, Khanal, Wilkes, Wu, & Howard, 2010). A

compound produced a [M-H]- ion at m/z 191.0554 and a [M+H]+ ion at m/z 193.0708. It

was identified as quinic acid (Δ=0.0007 Da, Δ=0.0001 Da) because its accurate mass

and MS2 spectra matched those of quinic acid standard. Retention time and mass

spectra of quinic acid were curated in the in-house database at SECIM. The metabolite

producing a [M-H]- ion at m/z 147.0287 was tentatively identified as citramalic acid by

comparing its fragment ions at m/z 129.0195, m/z 102.9458, m/z 873.9248 and m/z

58.9582 with those in HMDB (Δ=0.0012 Da). Citramalic acid is an analog of malic acid

139

and an inhibitor of malic acid production. The compound generating a [M-H]- ion at m/z

173.0092 and product ions at m/z 128.8782, m/z 99.9255, m/z 116.9285, m/z 118.9659

and m/z 85.0290 was tentatively identified as aconitic acid by searching HMDB and

mzCloud (Δ=0.0009 Da). However, the trans- or cis-form of aconitic acid could not be

determined based on only MS2 spectra. Cis- and trans- aconitic acids belong to

tricarboxylic acids. Cis-aconitic acid is an intermediate produced by the dehydration of

citric acid in citrate cycle. The compound producing a [M-H]- ion at m/z 180.0657 was

tentatively identified as tyrosine. Its product ions were m/z 163.0100, m/z 135.0452, and

m/z 119.0502, matching those in HMDB and mzCloud (Δ=0.0039 Da).

Hydroxyoctadecanoic acid, a hydroxyl fatty acid, was putatively identified based on its

accurate [M-H]- ion at m/z 299.2592 (Δ=0.00003 Da). Glycerol 3-phosphate generating

an accurate m/z 173.0236 [M+H]+ was tentatively identified according to HMDB

(Δ=0.0026 Da). Glycerol 3-phosphate is a metabolite in both glycerolipid and

glycerophospholipid metabolism pathway. The compound producing a [M+H]+ ion at m/z

162.0551 was tentatively assigned as dihydroxyquinoline. The positions of two hydroxyl

groups could not be determined. Among various isomers of dihydroxyquinoline, 4, 6-

and 4, 8-dihydroxyquinoline are products after conversion of 5-hydroxykynurenamine or

3-hydroxykynurenamine by monoamine oxidase. They are catabolites of tryptophan

through the kynurenine metabolic pathway. This pathway is employed by immune

system to modulate the balance between responsiveness to pathogens and tolerance to

non-harmful antigens (Moffett & Namboodiri, 2003). It was proposed that tryptophan

catabolism facilitates immune tolerance by suppressing T cell proliferation due to a

reduction of this critical amino acid. Another theory proposed was that catabolites of

140

tryptophan can suppress certain immune cells (Moffett & Namboodiri, 2003). Indole-3-

acetaldehyde is also an intermediate in tryptophan metabolism and its level increased

after drinking cranberry juice compared to apple juice consumption. Decarboxylation of

tryptophan generates tryptamine which is further catabolized by monoamine oxidase to

indole-3-acetaldehyde.

Most of these metabolites higher in human plasma following cranberry juice

compared to baseline also increased compared to apple juice consumption (Table 5-2,

5-3). A few exceptions were aconitic acid, tyrosine, and hydroxyphenyl acetic acid. 2-

Furoyglycine was found to be decreased in human plasma following drinking cranberry

juice compared to apple juice. Moreover, metabolites including vanilloloside, 5-

(trihydroxyphenyl)-ϒ-valerolactone, 3-(hydroxyphenyl) propionic acid, 4-acetamido-2-

aminobutanoic acid and indole-3-acetaldehyde increased following cranberry juice

compared to apple juice. Vanilloloside is a phenolic glucoside and was tentatively

assigned based on its accurate m/z 315.1088 [M-H]- matching HMDB (Δ= 0.0003 Da). A

compound generating a [M+H]+ ion at m/z 225.0733 was tentatively identified as 5-

(trihydroxyphenyl)-ϒ-valerolactone as it matched the same metabolite in HMBD (Δ=

0.0024 Da). 3-(Hydroxyphenyl) propionic acid was tentatively assigned because its

accurate m/z 167.0705 [M+H]+ and fragment ion at m/z 120.0808 matched those in

HMDB and mzCloud (Δ=0.0023 Da). Vanilloloside, 5-(trihydroxyphenyl)-ϒ-valerolactone

and 3-(hydroxyphenyl) propionic acid were exogenous metabolites and derived from

microbial degradation of procyanidins. 4-Acetamido-2-aminobutanoic acid belongs to

the family of alpha amino acids and derivatives. It was tentatively assigned according to

its accurate m/z 161.0921 [M+H]+ (Δ= 0.0004 Da).

141

More than two thirds of discriminant metabolites identified in the present study

were endogenous metabolite, with the rest being phenolic acids derived from

catabolism of procyanidins by gut microbiota. These identified endogenous metabolites

corresponded to quinic acids and derivatives, hydroxyl fatty acids, acyl glycine,

glycerophosphates, indoles and derivatives. They are intermediates or products in a

range of metabolic pathways.

Summary

This study showed that metabolite profiles of young women after drinking

cranberry juice were different from those before drinking cranberry juice or after apple

juice consumption. Compared to baseline condition, cranberry juice consumption

caused a greater increase of metabolites including catechol sulfate, 3, 4,-

dihydroxyphenyl ethanol sulfate, hydroxyphenyl acetic acid, coumaric acid sulfate,

ferulic acid sulfate, quinic acid, citramalic acid, aconitic acid, hippuric acid,

hydroxyhippuric acid, 2-furoylgycine, vanilloylglycine, tyrosine, hydroxyoctadecanoic

acid, trihydroxybenzoic acid, dihydroxyquinoline and glycerol 3-phosphate. Moreover,

compared to apple juice consumption, drinking cranberry juice increased the plasma

level of catechol sulfate, 3, 4,-dihydroxyphenyl ethanol sulfate, coumaric acid sulfate,

ferulic acid sulfate, 5-(trihydroxyphenyl)-ϒ-valerolactone, 3-(hydroxyphenyl) propionic

acid, vanilloloside, quinic acid, citramalic acid, hippuric acid, hydroxyhippuric acid,

vanilloylglycine, 4-acetamido-2-aminobutanoic acid, hydroxyoctadecanoic acid,

trihydroxybenzoic acid, glycerol 3-phosphate, dihydroxyquinoline and indole-3-

acetaldehyde. The plasma level of 2-furoylgycine was decreased following cranberry

juice compared to apple juice consumption.

142

Table 5-1. Summary of parameters for PLS-DA and OPLS-DA models for human baseline plasma and plasma after drinking cranberry juice or apple juice.

Negative Ionization Analyses Positive Ionization Analyses

Baseline vs. Cranberry Juice Cranberry Juice vs. Apple Juice

Baseline vs. Cranberry Juice

Cranberry Juice vs. Apple Juice

PLS-DA OPLS-DA PLS-DA OPLS-DA PLS-DA OPLS-DA PLS-DA OPLS-DA

Na

2 1Pc+1Od 2 1Pc+1Od 2 1Pc+1Od 2 1Pc+1Od

R2

X(cum)b

0.235 0.235 0.253 0.253 0.211 0.211 0.232 0.253

R2

Y(cum)b

0.901 0.901 0.951 0.951 0.951 0.951 0.959 0.951

Q2

(cum)b

0.627 0.641 0.846 0.809 0.764 0.679 0.796 0.776


0.853±0.094 0.857±0.097 0.977±0.043 0.977±0.043 0.900±0.095 0.900±0.095 0.980±0.048 0.980±0.048


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Table 5-2. Identification of discriminant metabolites in human plasma after drinking cranberry juice or apple juice by negative ionization analysis.

NO. Retention Time (min)


p[1] (contribution)a

p(corr)[1] (confidence) b

MSMS Putative Identification



Reference

CJ vs AJ c

CJ vs BS d

1 0.898 191.0554 0.085 (0.115)

0.781 (0.860)

---- Quinic acid 191.0561 0.0007 HMDB, in-house DB

2 2.805 147.0287 0.089 (0.080)

0.827 (0.848)

129.0195, 102.9485, 87.9248, 58.9582

Citramalic acid 147.0299 0.0012 HMDB

3 2.990 173.0083 0.092 0.718 128.8782, 99.9255, 116.9285, 118.9659

Aconitic acid 173.0092 0.0009 HMDB mzCloud

----

4 6.722 168.0292 -0.080 (0.093)

-0.730 (0.741)

124.0404,67.0183 2-Furoylglycine 168.0302 0.0010 mzCloud

5 7.206 188.9854 0.070 (0.085)

0.754 (0.816)

109.0294 Catechol sulfate

188.9863 0.0009 HMDB

6 7.312 233.0121 0.053 (0.060)

0.567 (0.560)

153.0149 3,4-Dihydroxyphenyl ethanol sulfate

233.0125 0.0004 Liang et al. e

7 7.419 194.0451 0.072 (0.085)

0.767 (0.868)

150.0506, 194.0456, 93.0342

2-Hydroxyhippuric acid

194.0458 0.0008 HMDB mzCloud

8 7.739 224.0561 0.094 (0.099)

0.879 (0.884)

---- Vanilloylglycine 224.0564 0.0035 HMDB

9 7.887 315.1088 0.133 0.892 ---- Vanilloloside 315.1085 0.0003 HMDB ----

10 8.069 178.0501 0.086 (0.100)

0.831 (0.836)

134.0612 Hippuric acid 178.0510 0.0008 HMDB mzCloud

11 8.165 180.0657 0.064 0.525 163.0400, 135.0452, 119.0502

Tyrosine 180.0666 0.0039 HMDB mzCloud

----

12 8.279 242.9967 0.104 (0.118)

0.850 (0.871)

163.0400, 146.9611, 119.0503, 174.9560

Coumaric acid sulfate

242.9969 0.0002 Liang et al. e

144

Table 5-2. Continued. NO. Retention

Time (min)







Reference

CJ vs AJ c

CJ vs BS d

13 8.313 273.0077 0.104 (0.124)

0.831 (0.907)

193.0504, 178.0270, 149.0606, 134.0372

Ferulic acid sulfate

273.0074 0.0003 Liang et al. e

14 10.660 151.0389 0.052 0.599 107.0499, 64.8080, 59.0130

Hydroxyphenyl acetic acid

151.0401 0.0012 mzCloud ----

15 14.588 299.2592 0.059 0.500 ---- Hydroxyoctadecanoic acid

299.2592 0.00003 HMDB

a Number inside the parentheses is the p[1] value obtained from OPLS-DA model based on cranberry juice vs. baseline. b Number inside the parentheses is the p (corr) [1] value obtained from OPLS-DA model based on cranberry juice vs. baseline c Arrows indicated a decrease or increase in metabolite level in human plasma after drinking cranberry juice compared to apple juice. d Arrows indicated a decrease or increase in metabolite level in human plasma after drinking cranberry juice compared to baseline. e Identification of compounds were referred to publication by Liang et al. (Liang, et al., 2013).

145

Table 5-3. Identification of discriminant metabolites in human plasma after drinking cranberry juice or apple juice by positive ionization analysis.


Detected Mass [M+H]+

p[1] (contribution) a


MSMS Putative Identification Theoretical Mass [M+H]+


Reference

CJ vs AJ c

CJ vs BS d

1 0.846 193.0708 -0.108 (0.113)

-0.839 (0.890)

111.0442, 129.0544, 95.0459, 83.0496

Quinic acid 193.0707 0.0001 mzCloud in-house DB

2 2.911 171.0264 -0.254 (0.252)

-0.954 (0.958)

152.0704, 148.9768, 125.9610,89.0713, 84.9602

Trihydroxybenzoic acid

171.0288 0.0024 HMDB mzCloud

3 3.337 171.0265 -0.269 (0.284)

-0.930 (0.958)

148.9768, 125.9608, 84.0812,89.0714,109.0761


171.0288 0.0024 HMDB mzCloud

4 3.678 161.0921 -0.085 -0.504 ---- 4-Acetamido-2-aminobutanoic acid

161.0921 0.00004 HMDB ----

5 5.318 225.0733 -0.075 -0.540 ---- 5-(Trihydroxyphenyl)-gamma-valerolactone

225.0757 0.0024 HMDB ----

6 5.325 167.0705 -0.075 -0.531 120.0808 3-(Hydroxyphenyl)propionic acid

167.0723 0.0023 HMDB mzCloud

----

7 6.728 170.0449 0.083 (0.129)

0.661 (0.795)

95.0131, 123.9658, 146.9814, 88.0398

2-Furoyglycine 169.0375 0.0001 HMDB Metlin

8 8.059 180.0656 -0.076 (0.086)

-0.847 (0.853)

105.0337 Hippuric acid 179.0582 0.0008 mzCloud

9 8.066 196.0395 -0.073 (0.092)

-0.838 (0.842)

---- Hydroxyhippuric acid 196.0604 0.0209 HMDB

10 8.067 162.0551 -0.083 (0.114)

-0.832 (0.828)

139.9819, 116.9662

Dihydroxyquinoline 162.055 0.0002 MMDB KEGG

146

Table 5-3. Continued. NO. Retentio

n Time (min)





Theoretical Mass [M+H]+


Reference

CJ vs AJ c

CJ vs BS d

11 8.067 173.0236 -0.076 (0.108)

-0.811 (0.806)

---- Glycerol 3-phosphate

173.0209 0.0026 HMDB

12 10.664 160.0758 -0.063 -0.531 118.0651, 132.0812, 146.9600

Indole-3-acetaldehyde

160.0757 0.0001 Mass Bank

----

a Number inside the parentheses is the p[1] value obtained from OPLS-DA model based on cranberry juice vs. baseline. b Number inside the parentheses is the p (corr) [1] value obtained from OPLS-DA model based on cranberry juice vs. baseline c Arrows indicated a decrease or increase in metabolite level in human plasma after drinking cranberry juice compared to apple juice. d Arrows indicated a decrease or increase in metabolite level in human plasma after drinking cranberry juice compared to baseline.

147

Table 5-4. Unidentified discriminant metabolic features in human plasma after cranberry juice or apple juice by negative ionization analysis.

Retention Time (min)




CJ vs. AJ c CJ vs. BS d

0.762 183.0866 0.054 0.608 ----

0.869 189.0397 0.064 0.626 ----

0.887 249.0148 0.093 (0.111) 0.644 (0.715)

0.895 377.0860 0.080 0.500 ----

0.898 191.0554 0.115 0.860 ----

0.918 495.0007 0.088 0.524 ----

0.919 493.0039 0.088 0.527 ----

1.751 231.0619 0.051 0.500 ----

1.955 177.9805 0.119 (0.137) 0.783 (0.887)

2.182 159.0652 0.093 (0.099) 0.718 (0.764)

2.280 181.0125 0.052 0.534 ----

2.799 129.0181 0.083 (0.111) 0.766 (0.889)

2.805 147.0287 0.089 (0.080) 0.827 (0.848)

2.977 159.0652 0.098 (0.115) 0.769 (0.782)

3.425 147.0651 0.068 0.599 ----

3.487 129.0181 0.060 0.568 ----

6.058 291.9445 0.188 (0.187) 0.935 (0.936)

6.064 229.9737 0.192 (0.173) 0.925 (0.935)

6.067 161.9855 0.196 (0.217) 0.851 (0.884)

6.088 177.9806 0.190 (0.195) 0.930 (0.927)

6.579 143.0337 0.076 0.782 ----

7.205 400.9614 0.125 (0.132) 0.744 (0.761)

7.205 190.9813 0.070 (0.086) 0.746 (0.812)

148

Table 5-4. Continued. Retention Time (min)





7.206 188.9854 0.085 0.816 ----

7.209 272.9386 0.082 (0.099) 0.720 (0.776)

7.209 256.9735 0.078 (0.099) 0.721 (0.784)

7.284 201.0761 0.051 0.513 ----

7.312 233.0121 0.060 0.560 ----

7.405 232.9759 0.110 (0.125) 0.931 (0.853)

7.419 194.0451 0.085 0.868 ----

7.557 264.9849 0.072 (0.077) 0.602 (0.600)

7. 739 224.0561 0.099 0.884 ----

7.887 315.1088 0.136 0.906 ----

8.068 246.0382 0.070 (0.087) 0.807 (0.823)

8.069 179.0470 0.087 (0.103) 0.800 (0.846)

8.069 134.0599 0.086 (0.102) 0.826 (0.841)

8.069 178.0501 0.100 0.837 ----

8.069 308.0092 0.072 (0.088) 0.811 (0.828)

8.069 276.0275 0.083 (0.095) 0.829 (0.841)

8.072 379.0911 0.108 (0.134) 0.805 (0.817)

8.073 263.0287 0.069 0.709 ----

8.195 143.0703 0.052 0.504 ----

8.279 242.9967 0.118 0.871 ----

8.313 273.0077 0.124 0.907 ----

8.426 182.0814 0.070 0.583 ----

149

Table 5-4. Continued. Retention Time (min)





9.189 257.0125 0.076 0.644 ----

9.499 266.8968 0.088 0.625 ----

9.501 268.8948 0.090 0.635 ----

a Number inside the parentheses is the p[1] value obtained from OPLS-DA model based on cranberry juice vs. baseline.

b Number inside the parentheses is the p (corr) [1] value obtained from OPLS-DA model based on cranberry juice vs. baseline

c Arrows indicated a decrease or increase in metabolite level in human plasma after drinking cranberry juice compared to apple juice.

d Arrows indicated a decrease or increase in metabolite level in human plasma after drinking cranberry juice compared to baseline.

150

Table 5-5. Unidentified discriminant metabolic features in human plasma after cranberry juice or apple juice by positive ionization analysis.

Retention Time (min)





0.792 183.0866 0.054 0.608

0.890 215.0526 -0.139 (0.185) -0.785 (0.884)

0.897 365.1054 0.102 0.539 ----

0.929 211.5523 0.121 0.515 ----

0.931 443.0483 0.118 0.509 ----

1.370 143.0316 0.054 0.645 ----

1.590 220.9743 0.126 0.831 ----

1.620 109.5209 0.109 0.813 ----

1.634 177.0072 0.093 0.665 ----

1.638 205.0021 0.110 0.816 ----

1.785 118.5261 0.080 0.816 ----

4.159 127.0756 -0.054 (0.078) -0.524 (0.675)

5.324 150.5415 -0.062 -0.513 ----

6.046 151.5097 -0.338 (0.302) -0.924 (0.942)

6.389 199.1077 0.054 0.601 ----

6.426 158.1176 -0.114 (0.112) -0.794 (0.846)

6.728 170.0449 0.083 0.661 ----

6.983 127.0756 0.054 0.633 -----

7.192 204.9820 -0.141 (0.138) -0.805 (0.801)

7.352 121.0287 -0.068 (0.077) -0.824 (0.896)

7.497 155.0780 -0.086 -0.535 ----

8.062 118.0655 -0.082 (0.098) -0.823 (0.842)

151

Table 5-5. Continued. Retention Tim (min)




CJ vs. AJ c

CJ vs. BS d

8.066 155.0128 -0.072 (0.094) -0.827 (0.840)

8.067 150.5366 -0.068 (0.089) -0.833 (0.837)

8.067 219.5525 -0.155 (0.170) -0.807 (0.850)

8.067 105.0339 -0.078 (0.100) -0.825 (0.840)

8.067 171.0499 -0.077 (0.094) -0.846 (0.834)

8.067 159.5418 -0.103 (0.120) -0.824 (0.813)

8.067 292.0141 -0.084 (0.111) -0.789 (0.796)

8.826 153.0258 0.082 0.555 ----

12.820 467.2621 0.060 0.488 ----

12.833 445.2799 0.068 0.515 ----

14.409 666.4347 0.078 0.520 ----

15.587 333.1514 -0.069 -0.516 ----

16.066 350.1780 -0.063 -0.499 ----





152

Figure 5-1. The PCA score plot of human plasma and quality control samples from LC-HRMS metabolomics. A) PLS-DA

score plot of negative ionization data, B) OPLS-DA score plot of positive ionization data, C) PLS-DA score plot of negative ionization without QC from Red Cross and D) OPLS-DA score plot of positive ionization data without QC from Red Cross.

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PoolQC from Baseline group

PoolQC from cranberry juice group

Plasma of baseline, cranberry and apple group

PoolQC from Red Cross plasma

153

Figure 5-2. The PCA score plot of human baseline plasma and human plasma after drinking cranberry juice from LC-

HRMS metabolomics. A) Data were acquired by negative ionization and B) data were acquired by positive ionization. Blue squares: baseline plasma before drinking cranberry juice. Green squares: plasma after drinking cranberry juice.

Baseline plasma before cranberry juice


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Figure 5-3. The PLS-DA and OPLS-DA score plots of human baseline plasma and human plasma after drinking cranberry

juice from LC-HRMS metabolomics. A) PLS-DA score plot by negative ionization, B) OPLS-DA score plot by negative ionization, C) PLS-DA score plot by positive ionization and D) OPLS-DA score plot by positive ionization. Blue squares: baseline plasma before drinking cranberry juice. Green squares: plasma after drinking cranberry juice.

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Figure 5-4. The PLS-DA and OPLS-DA cross-validated score plots of human baseline plasma and human plasma after

drinking cranberry juice from LC-HRMS metabolomics. A) PLS-DA cross-validated score plot by negative ionization, B) OPLS-DA cross-validated score plot by negative ionization, C) PLS-DA cross-validated score plot by positive ionization and D) OPLS-DA cross-validated score plot by positive ionization. Blue squares: baseline plasma before drinking cranberry juice. Green squares: plasma after drinking cranberry juice.

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Figure 5-5. Validation plot obtained from 200 permutation tests for the PLS-DA and

OPLS-DA models of human baseline plasma vs. human plasma after cranberry juice by negative ionization analysis. A) PLS-DA model and B) OPLS-DA model.

R2

, Q2

r(y, permuted y)

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, Q2

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OPLS-DA models of human baseline plasma vs. human plasma after cranberry juice by positive ionization analysis. A) PLS-DA model and B) OPLS-DA model.

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, Q2

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Figure 5-7. The PCA score plot of human plasma after drinking apple juice or cranberry juice from LC-HRMS

metabolomics. A) Data were acquired by negative ionization and B) data were acquired by positive ionization. Purple squares: plasma after drinking apple juice. Green squares: plasma after drinking cranberry juice.

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Figure 5-8. The PLS-DA and OPLS-DA score plots of human plasma after drinking apple juice or cranberry juice from LC-

HRMS metabolomics. A) PLS-DA score plot by negative ionization, B) OPLS-DA score plot by negative ionization, C) PLS-DA score plot by positive ionization and D) OPLS-DA score plot by positive ionization. Purple squares: plasma after drinking apple juice. Green squares: plasma after drinking cranberry juice.

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Figure 5-9. The PLS-DA and OPLS-DA cross validated score plots of human plasma after drinking apple juice or

cranberry juice from LC-HRMS metabolomics. A) PLS-DA cross-validated score plot by negative ionization, B) OPLS-DA cross-validated score plot by negative ionization, C) PLS-DA cross-validated score plot by positive ionization and D) OPLS-DA cross-validated score plot by positive ionization. Purple squares: plasma after drinking apple juice. Green squares: plasma after drinking cranberry juice.

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OPLS-DA models of human plasma after apple juice vs. plasma after cranberry juice by negative ionization analysis. A) PLS-DA model and B) OPLS-DA model.

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OPLS-DA models of human plasma after apple juice vs. after cranberry juice by positive ionization. A) PLS-DA model and B) OPLS-DA model.

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HRMS of human baseline plasma and plasma after cranberry juice or apple juice by negative ionization. A) Human baseline plasma vs. plasma after cranberry juice and B) human plasma after cranberry juice vs. plasma after apple juice. p[1] is the loading vector of covariance in the first principal component. p(corr)[1] is loading vector of correlation in the first principal component. Variables with |p| ≥ 0.05 and |p(corr)| ≥ 0.5 are considered statistically significant. Significant variables in blue color were identified and numbered according to Table 5-2. Unidentified significant variables in red color were listed in Table 5-4. Non-significant variables were in green color.

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Figure 5-13. S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of human baseline plasma and plasma after cranberry juice or apple juice by positive ionization. A) Human baseline plasma vs. plasma after cranberry juice and B) human plasma after cranberry juice vs. plasma after apple juice. p[1] is the loading vector of covariance in the first principal component. p(corr)[1] is loading vector of correlation in the first principal component. Variables with |p| ≥ 0.05 and |p(corr)| ≥ 0.5 are considered statistically significant. Significant variables in blue color were identified and numbered according to Table 5-3. Unidentified significant variables in red color were listed in Table 5-5. Non-significant variables were in green color.

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165

CHAPTER 6 MODIFICATION OF URINARY METABOLOME IN YOUNG WOMEN AFTER

CRANBERRY JUICE CONSUMPTION WERE REVEALED USING UHPLC-Q-ORBITRAP-HRMS-BASED GLOBAL METABOLOMICS APPROACH

Background

The objective of this study is to investigate the urinary metabolome modifications

and identify putative biomarkers in young women after drinking cranberry juice or apple

juice using an UHPLC-Q-Orbitrap-HRMS-based metabolomics approach.



Cranberry juice cocktail (double strength, 54% juice) and 100% apple juice were

provided by Ocean Spray Cranberries, Inc. (Lakeville-Middleboro, MA, USA). LC-MS

grade acetonitrile, methylene chloride, methanol, acetic acid, formic acid, sodium azide

and acetone were purchased from Fischer Scientific Co.(Pittsburgh, PA, USA).

Creatine-D3, L-leucine-D10, L-tryptophan-2, 3, 3-D3, caffeine-D3 were from CDN

Isotopes Inc. (Pointe-Claire, Quebec, Canada).



Florida. Detailed protocol of the human study was described in Chapter 4. The timeline

was summarized in Table 4-1.


Frozen urine samples (-80 oC) were thawed at room temperature. One urine (50

µL) was transferred to a clean, labeled microcentrifuge-filter tube. Twenty µL

isotopically-labeled standard solution (40 µg/mL L-tryptophan-D3, 4 µg/mL L-leucine-

D10, 4 µg/mL creatine-D3, and 4 µg/mL caffeine-D3) was added to the above tube as

166

internal standards. The urine sample was then diluted with 400 µL water: acetonitrile

(98:2, v: v) with 0.1% sodium azide. The diluted urine sample was vortexed and

centrifuged at 20,000 g for 10 min <10 oC to pellet debris. After centrifuge, the filter was

discarded and supernatant was transferred into a glass vial with a fused glass insert for

analyses. Three groups of triplicate quality control (QC) samples including pooled urine

from baseline group, pooled urine from cranberry juice group, and pooled urine from

apple juice group were prepared and analyzed concurrently with experimental urine

samples to monitor the stability and validity of instrumental acquisition. In addition, three

neat QC samples were prepared by adding 20 µL of isotopically-labeled standard

solution to three LC glass vials, respectively. Running sequence started with 3 blanks

(0.1% formic acid in water), one neat QC, one pooled QC from baseline group, one

pooled QC from cranberry juice group and one pooled QC from apple juice group,

followed by every 10 urine samples.







column before the next run. The flow rate was 350 μL/min. The UHPLC system was



performed in both negative and positive ionization with a mass resolution of 70,000 at

167

m/z 200. Separate injections were performed in a data-dependent (top 5) MS/MS mode

with the full scan mass resolution reduced to 35,000 at m/z 200. The injection volume

was 4 μL for negative ionization and 2 μL for positive ionization acquisition. The m/z

range for full scan analyses was 70–1000. Heated electrospray ionization (HESI)

parameters were as follows: sheath gas flow 45 arb, auxiliary gas flow 10 arb, sweep

gas flow 1 arb, spray voltage 3.5 kV, probe temperature 350°C, capillary temperature

320 °C for negative ionization and 325 °C for positive ionization. In source CID was 2

eV. The mass spectrometer was calibrated using Pierce™ negative and positive ion

calibration solution (Thermo Fisher Scientific, San Jose CA, USA). To avoid possible

bias, the sequence of injections for urine samples was randomized.



(Chambers, et al., 2012) and then processed using MZmine 2.12 (Pluskal, Castillo,

Villar-Briones, & Orešič, 2010). Peaks in each sample were extracted, deconvoluted,

and deisotoped. Alignment using join aligner algorithm was conducted with a 10 ppm

tolerance for m/z values and 0.2 min tolerance for retention time. Gap filling using peak

finder algorithm was performed to fill in missing peaks. This dataset was imported into

MetaboAnalyst. Normalization was conducted on each observation using its specific

gravity as the normalization factor. Specific gravity of each urine sample was measured

using a LED refractometer (Fisher Scientific, Pittsburgh, PA, USA). The resultant

dataset was exported from MetaboAnalyst and then imported into SIMCA (Version 14.0,

Umetrics, Umea, Sweden) for multivariate statistical analysis. Data acquired using both

negative and positive ionization were mean-centered, Pareto scaled and log-

transformed before PCA modelling; mean-centered and log-transformed before PLS-DA

168

or OPLS-DA analyses. Unsupervised PCA model was performed to initially examine

intrinsic variation in the data set. Then supervised pattern recognition methods include

PLS-DA and OPLS-DA were used to extract maximum information on discriminant

compounds from the data (Bylesjö, Rantalainen, Cloarec, Nicholson, Holmes, & Trygg,

2006). Validation of the models was tested using 7-fold internal cross-validation and

permutation tests for 200 times. To further evaluate the predictive ability of the PLS-DA

or OPLS-DA models, an external validation procedure was performed (Brindle, et al.,

2002; Llorach, et al., 2010). The LC-HRMS metabolomics data set was split into a

training set and a test set. Approximately 70% of the samples were randomly selected

as the training set and the remaining 30% were treated as the test set. PLS-DA and

OPLS-DA models were built based on the training set and then blindly predicted the

classes of the samples in the test set. This procedure was repeated for 30 times and

correct classification rates were calculated.



QCs created from internal standard group clustered tight and completely

separated from the experimental samples on the PCA score plots (Figure 6-1A, 6-1B). It

confirmed the stability of instrumental analyses and quality of sample preparation

method. PCA score plots also revealed that triplicates of pooled QCs from cranberry,

apple and baseline group clustered tight across the entire sequence (Figure 6-1C, 6-1D)

suggesting a good quality of data acquisition.

Baseline Urine vs. Urine after Drinking Cranberry Juice

LC-HRMS data of urine samples acquired using both negative and positive

ionization were subjected to multivariate analyses. A partial segregation of urine sample

169

between two groups was obtained on PCA score plot (Figure 6-2A) with two samples

from cranberry juice group clustered with baseline group. Using a PLS-DA model, a

complete separation between two groups was achieved (Figure 6-3A). The cross-

validated score plot also demonstrated a clear separation except that one sample from

cranberry juice group was misclassified into baseline group (Figure 6-3C). The R2Y of

the PLS-DA model was 0.939 indicating that about 94% of variance in the Y data matrix

was explained by the model. Q2 calculated from the 7-fold cross-validation was 0.873

suggesting a good predictability of the PLS-DA model (Table 6-1). All R2 and Q2

calculated from 200 times permutation tests (Figure 6-4A) were smaller than those from

actual model, supporting the results from cross-validation that the segregation between

two groups of urine samples was not due to overfitting. The external validation test

generated correct classification rate of 97.1% for the PLS-DA model derived from

negative ionization analyses. This model was able to correctly classify an unknown

human urine sample with an error rate of 2.9%. Similarly, a valid segregation between

baseline urine and human urine after drinking cranberry juice was obtained based on

positive ionization data. Both score plot and cross-validated score plot of OPLS-DA

model showed a clear separation between two groups (Figure 6-3B, 6-3D). Similar to

the model derived from negative ionization data, one urine sample from cranberry juice

group was misclassified into baseline group during the cross-validation. Validation plot

from 200 permutation tests showed that Q2 of each model built based on permutated

class labels were smaller than 0.4. Both cross-validation and permutation test showed a

good predictability of the OPLS-DA model and confirmed the segregation of two groups

of urine samples was not due to overfitting. Unknown urine samples collected from

170

similar studies could be correctly classified using this OPLS-DA model with an error rate

of 5% (Table 6-1). The urinary metabolome modifications of young women caused by

drinking cranberry juice were revealed using a UHPLC-HRMS metabolomics approach.

Urine after Drinking Apple Juice vs. Urine after Drinking Cranberry Juice

Projection pattern techniques were used to analyze the overall urinary

metabolome differences in young women after drinking apple juice or cranberry juice.

OPLS-DA models were built on the LC-HRMS data collected from both negative and

positive ionization. Approximately 92% and 94% of variance in Y data matrix were

explained by two OPLS-DA models, respectively, indicating an excellent goodness of fit

(Table 6-1). Figure 6-6 showed a clear separation between two groups of urine samples

on the score plots and cross-validated score plots of OPLS-DA models. One sample

from the cranberry juice group was misclassified into baseline group during cross-

validation. All other observations were correctly classified during the 7-fold internal

validation. Q2 calculated from the cross-validation of two OPLS-DA models was 0.825

and 0.762, respectively, indicating good predictabilities. To detect the possibility of

overfitting in the OPLS-DA models, 200 permutation tests were conducted and

validation plots were drawn in Figure 6-7. The results showed that all Q2 calculated from

the models with permutated class labels were smaller than 0.4. Therefore the achieved

segregation between two groups of urine samples were not likely due to overfitting.

Furthermore, correct classification rate of 95% was obtained for both OPLS-DA models

after external validation. Therefore, an unknown urine sample collected from a similar

study would be correctly classified with an error rate of 5% (Table 6-1).

171


S-plots were used to visualize the variable influence in OPLS-DA models derived

from LC-HRMS data of baseline urine vs. cranberry juice (Figure 6-8A, 6-9A) and

cranberry juice vs. apple juice (Figure 6-8B, 6-9 B). At a significance level of p = 0.05, a

p(corr) of 0.5 was used as an arbitrary cutoff value to select the potential biomarkers.

Discriminant metabolites located on the upper right or lower left corner of the S-plot had

higher absolute p[1] and p(corr) values.

Figure 6-8A and Figure 6-9A showed a total of 45 and 53 metabolic features in

negative mode and positive mode, respectively. They were discriminant metabolites that

separated baseline urine and urine after cranberry juice consumption. Among them, 7

features in negative mode and 6 in positive mode were identified based on their

accurate masses and/or product ion spectra. Similarly, Figure 6-8B and 6-9B showed

that 48 and 75 metabolic features in negative mode and positive mode, respectively,

were discriminant metabolites that separated urine after apple juice and urine after

cranberry juice consumption. Up to 8 metabolic features in negative mode and 9

features in positive mode were identified using accurate masses and/or product ion

spectra. These identified metabolites were numbered in the Figure 6-8, 6-9 and

summarized in Table 6-2 and Table 6-3. Unidentified metabolic features were listed in

Table 6-4 and Table 6-5. HMDB (Wishart, et al., 2007), KEGG (Liebich & Först, 1990),

mzCloud and Metlin were searched to assist metabolite identification.

Compared to apple juice consumption, 9 exogenous metabolites increased in

human urine after drinking cranberry juice. A metabolite producing a [M-H]- ion at m/z

289.0371 was tentatively identified as 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-

sulfate (Δ=0.0016 Da). Its product ions included m/z 209.0461 [M-H-sulfate]-, m/z

172

191.0561 [M-H-sulfate-H2O]-, and m/z 96.9598. The compound producing a [M-H]- ion

at m/z 287.0409 was tentatively identified as 5-(dihydroxyphenyl)-ϒ-valerolactone-O-

sulfate. Its product ions in negative mode included m/z 207.0209 [M-H-sulfate]-, m/z

171.0776 [M-H-sulfate-2H2O]-, m/z 97.0290, m/z 141.0550 and m/z 74.0241. HMDB

was searched for this identification (Δ=0.0178 Da). 5-(Dihydroxyphenyl)-ϒ-valerolactone

was detected and tentatively assigned according to HMDB (Δ=0.0021 Da). One

metabolite generating a [M-H]- ion at m/z 285.0620 and a major fragment ion at m/z

109.0294 [M-H-glucuronide]- was tentatively identified as diphenol glucuronide. It

matched the same compound in HMDB (Δ=0.0004 Da). The compound producing a

[M+H]+ ion at m/z 167.0303 and a fragment ion at m/z 121.0808 was tentatively

assigned as 3-(hydroxyphenyl)propionic acid according to HMDB (Δ=0.0003 Da).This

metabolite was found to be higher in human plasma following cranberry juice in Chapter

5. Coumaric acid (Δ=0.0002 Da) was detected and tentatively identified based on its

accurate m/z 165.0548 [M+H]+ and product ions at m/z 147.0439 and 119.0492. The

sulfated coumaric acid was previously detected in human plasma after drinking

cranberry juice in Chapter 5. Trihydroxybenzoic acid and O-methylgallic acid were

detected and tentatively assigned. Trihydroxybenzoic acid produced a [M+H]+ ion at m/z

171.0360 and fragment ions at m/z 148.9768, m/z 95.0131, m/z 125.9610, and m/z

107.9506. The product ions match those of trihydroxybenzoic acid in HMDB (Δ=0.0072

Da). However, the positons of three hydroxyl groups could not be determined unless the

retention time was compared to those of all possible isomers. Gallic acid could be one

possible isomer. O-Methylgallic acid was tentatively assigned because its accurate m/z

185.0447 [M+H]+ and product ions at m/z 125.9610, m/z 95.0131, m/z 148.9768 and

173

m/z 107.9506 matched those in Metlin and HMDB (Δ=0.0002 Da). The metabolite

producing a [M+H]+ ion at m/z 169.0862 were assigned as 1, 3, 5-trimethoxybenzene

according to HMDB (Δ=0.0003 Da). A previous study suggested that gallic acid, 4-O-

methylgallic acid and 1, 3, 5-trimethoxybenzene were candidate urinary biomarkers

after epigallocatechin gallate intake (Loke, Jenner, Proudfoot, McKinley, Hodgson,

Halliwell, et al., 2009).

In addition to these exogenous metabolites, a total of 6 endogenous metabolites

were detected and tentatively identified. A metabolite produced a [M-H]- ion at m/z

219.0512 and product ions at m/z 174.9699 [M-H-COO]-, m/z 115.9207, m/z 111.0087,

m/z 128.9645 and m/z 100.9333. It was tentatively assigned as 3-hydroxy-3-

carboxymethyl adipic acid according to HMDB (Δ=0.0002 Da). This metabolite belongs

to the family of tricarboxylic acids and derivatives (Liebich & Först, 1990). Pimelic acid

(Δ=0.0012 Da, Δ=0.00006 Da) that belongs to the family of dicarboxylic acids was

tentatively identified based on its accurate m/z 159.0651 [M-H]- and m/z 161.0810

[M+H]+. Its product ions in negative mode included m/z 115.0402, m/z 97.0290, m/z

141.0550 and m/z 74.0241. Its fragmentation pattern matched that in Metlin. A

metabolite generated a [M-H]- ion at m/z 175.0600 and product ions at m/z 73.0289, m/z

85.0290, m/z 87.0083 and m/z 132.0666. Its accurate mass and fragmentation pattern

matched those of 2- or 3-isopropylmalate in HMDB, mzCloud and Metlin (Δ=0.0012 Da).

The exact positions of hydroxyl group could not be determined based on MS2 spectra.

2- and 3-Isopropylmalate belong to hydroxyl fatty acids. 2-Isopropylmalate is an

intermediate in pyruvate metabolism. The compound generating a [M-H]- ion at m/z

205.0344 and a [M+H]+ ion at m/z 207.0499 was tentatively assigned as homocitric acid

174

according to HMDB and KEGG (Δ=0.0010 Da, Δ=0.0009 Da). Homocitric acid is a citric

acid analogue in human urine. It is an intermediate in pyruvate metabolism. Hippuric

acid was detected and tentatively identified based on its accurate m/z 178.0435 [M-H]-

and HMDB search (Δ=0.00747 Da). Elevated level of hippuric acid in human urine

following cranberry juice consumption was found using 1H NMR metabolomics

approach in Chapter 4. Quinic acid was increased in human urine after cranberry juice

consumption. Similar observation was made in human plasma in Chapter 5. The

accurate mass, retention time and fragmentation pattern of quinic acid standard was

curated as part of in-house database in SECIM. The identification of quinic acid was

based on the in-house database.

Compared to baseline urine, urinary excretion of two additional metabolites were

increased following cranberry juice consumption. One exogenous metabolite was

tentatively identified as 3, 4-dihydroxyphenyl propionic acid. It produced a [M-H]- ion at

m/z 181.0589 and products ions at m/z 137.0609, m/z 59.0131, m/z 109.0294 and m/z

121.0296, which matched those of 3, 4-dihydroxyphenyl propionic acid in mzCloud and

HMDB (Δ=0.0083 Da). One endogenous metabolite was tentatively assigned as N-

acetyl-L-glutamate 5-semialdehyde. This compound gave rise to a [M+H]+ ion at m/z

174.0733 which matched the same compound in HMDB and KEGG (Δ=0.0028 Da). N-

Acetyl-L-glutamate 5-semialdehyde belongs to the group of N-acyl-aliphatic-alpha

amino acids. It is an intermediate in the pathway of oxaloacetate metabolism.

Cranberry juice consumption caused a higher urinary level of metabolites that

belong to the family of di- or tri-carboxylic acids. Metabolites that are intermediates in

the pathway of 2-oxocarboxylic acids (oxaloacetate, pyruvate, etc.) metabolism also

175

increased after cranberry juice consumption. Carboxylic acids and 2-oxocarboxylic

acids supply energy to living cells. The results suggested that energy metabolism in

young women was changed by cranberry juice consumption.

All 53 identified discriminant metabolites in plasma and urine of human or rats

were summarized in Table 6-6. Among them, 35 metabolites contributed to the group

separation of plasma or urine among cranberry, apple and baseline. 3-(Hydroxyphenyl)

propionic acid, trihydroxybenzoic acid, quinic acid, hippuric acid were biomarkers of

cranberry intake in both human plasma and human urine. Catechol sulphate, 4-hydroxy-

5-(hydroxyphenyl)-valeric acid-O-sulphate and hippuric acid were detected in plasma or

urine of both human and rats after cranberry intake. Hippuric acid was the most

prominent biomarker detected in human plasma, human urine and rat urine.

Summary

This study demonstrated that the overall urinary metabolome in young women

were altered following cranberry juice consumption. Compared to baseline condition,

cranberry juice consumption caused a greater urinary excretion of metabolites including

5-(dihydroxyphenyl)-ϒ-valerolactone and its sulfated form, 3,4-dihydoxyphenyl propionic

acid, 3-(hydroxyphenyl) propionic acid, trihydroxybenzoic acid, 3-hydroxy-3-

carboxymethyl adipic acid, 2 or 3-isopropylmalate, pimelic acid, homocitric acid, hippuric

acid, quinic acid, and N-acetyl-L-glutamate 5-semialdehyde. Furthermore, urinary

metabolites discriminating cranberry juice and apple juice consumption included 4-

hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(dihydroxyphenyl)-ϒ-

valerolactone and its sulfated form, diphenol glucuronide, 3-(hydroxyphenyl) propanic

acid, O-methylgallic acid, trihydroxybenzoic acid, 1,3,5-trimethoxybenzene, coumaric

acid, 2-or 3-isopropylmalate, pimelic acid, homocitric acid, hippuric acid and quinic acid.

176

Elevated level of carboxylic acids and intermediates in 2-oxocarboxylic acids

metabolism suggested that cranberry juice consumption changed energy metabolism in

young women.

177

Table 6-1. Summary of parameters for PLS-DA or OPLS-DA model for human baseline urine and urine after drinking cranberry juice or apple juice.

Negative Ionization Analyses Positive Ionization Analyses





PLS-DA OPLS-DA OPLS-DA OPLS-DA

Na

2 1Pc+1Od 1Pc+1Od 1Pc+1Od

R2

X(cum)b

0.219 0.392 0.331 0.363

R2

Y(cum)b

0.939 0.920 0.951 0.941

Q2

(cum)b

0.873 0.825 0.847 0.762


0.971±0.054 0.938±0.064 0.954±0.061 0.958±0.060


178

Table 6-2. Identification of discriminant metabolites in human urine after drinking cranberry juice or apple juice by negative ionization analysis.

No. Retention Time (min)







Reference

CJ vs. AJ c

CJ vs. BS d

1 0.948 289.0371 0.050 0.513 191.0561, 209.0461, 96.9598

4-Hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate

289.0387 0.0016 HMDB ----

2 1.047 219.0512 0.057 (0.072)

0.729 (0.789)

174.9699, 115.9207, 111.0087, 128.9645, 100.9333

3-Hydroxy-3-carboxymethyl-adipic acid

219.0510 0.0002 HMDB

3 1.108 175.0600 0.063 (0.067)

0.750 (0.836)

73.0289, 85.0290, 87.0083, 132.0666

(2)3-Isopropylmalate

175.0610 0.0012 HMDB mzCloud Metlin

4 2.087 159.0651 0.065 (0.065)

0.693 (0.753)

115.0402, 97.0290, 141.0550, 74.0241

Pimelic acid 159.0663 0.0012 HMDB Metlin

5 3.490 287.0409 0.061 (0.067)

0.664 (0.749)

207.0209, 171.0776, 142.0511

5-(Dihydroxyphenyl)-ϒ-valerolactone sulfate

287.0231 0.0178 HMDB

6 5.970 205.0344 0.086 (0.076)

0.902 (0.926)

---- Homocitric acid 205.0354 0.0010 HMDB

7 7.771 285.0620 0.053 0.748 109.0294, 113.0243, 85.0290, 59.0131

Diphenol glucuronide

285.0616 0.0004 HMDB ----

8 8.017 181.0589 0.052 0.799 137.0609, 59.0131, 109.0294, 121.0296

3,4-Dihydroxyphenyl propionic acid

181.0506 0.0083 HMDB mzCloud

----

9 8.092 178.0435 0.069 (0.058)

0.660 (0.640)

---- Hippuric acid 178.0510 0.0075 HMDB


179

Table 6-3. Identification of discriminant metabolites in human urine after drinking cranberry juice or apple juice by positive ionization analysis.






Theoretical Mass [M+H]+


Reference

CJ vs AJ c

CJ vs BS d

1 0.890 193.0709 0.067 (0.050)

0.801 (0.733)

111.0442 129.0546, 95.0495, 83.0496, 69.0341

Quinic acid 193.0707 0.0002 HMDB in-house DB

2 2.025 161.0810 0.065 0.734 ---- Pimelic acid 161.0808 0.00006

HMDB

3 6.044 207.0490 0.093 (0.093)

0.814 (0.922)

139.0024, 143.0339, 157.0128, 129.0180, 119.0340

Homocitric acid 207.0499 0.0009 HMDB KEGG

4 6.107 167.0703 0.093 (0.087)

0.933 (0.937)

121.0808 3-(Hydroxyphenyl) propionic acid

167.0703 0.0003 HMDB

5 7.024 209.0787 0.116 (0.111)

0.936 (0.957)

149.0597, 166.0863, 84.9601

5-(Dihydroxyphenyl)-ϒ-valerolactone

209.0808 0.0021 HMDB

6 7.198 185.0447 0.070 0.691 126.0913, 125.1073, 143.1177

4-O-Methylgallic acid

185.0444 0.0002 HMDB Metlin

----

7 7.214 171.0360 0.071 (0.057)

0.741 (0.584)

148.9768, 95.0131, 125.9610, 107.9506


171.0288 0.0072 HMDB

8 7.865 169.0862 0.067 0.816 ---- 1,3,5-Trimethoxybenzene

169.0859 0.0003 HMDB ----

9 8.420 174.0733 0.059 (0.055)

0.739 (0.800)

146.0598 N-Acetyl-L-glutamate 5-semialdehyde

174.0761 0.0028 HMDB KEGG

----

10 9.103 165.0548 0.050 0.659 147.0439, 119.0492 Coumaric acid 165.0550 0.0002 HMDB mzCloud

----


180

Table 6-4. Unidentified discriminant metabolic features in human urine after cranberry juice or apple juice by negative ionization analysis.

Retention Tim (min)





0.912 192.0586 0.070 0.732 ----

0.997 179.9963 0.183 (0.174) 0.931 (0.953)

2.129 205.0711 0.076(0.078) 0.665 (0.721)

2.969 190.9646 0.064 (0.068) 0.610 (0.722)

3.732 200.0921 0.080 0.691 ----

4.167 141.0545 0.073 0.677 ----

5.485 162.9879 0.156 (0.160) 0.835 (0.905)

5.586 163.9890 0.163 (0.156) 0.936 (0.973)

5.620 82.0283 0.171 (0.164) 0.924 (0.965)

5.623 291.9446 0.177 (0.176) 0.909 (0.956)

5.658 346.9621 0.199 (0.185) 0.926 (0.953)

5.668 163.9816 0.189 (0.191) 0.908 (0.960)

5.692 273.9641 0.160 (0.156) 0.935(0.973)

5.736 177.9806 0.181 (0.185) 0.891 (0.958)

5.744 161.9856 0.188 (0.207) 0.842 (0.941)

5.746 162.9882 0.163 (0.169) 0.905 (0.958)

5.858 77.9635 0.155 (0.162) 0.922(0.952)

5.898 346.9621 0.212 (0.200) 0.947 (0.970)

5.905 163.9889 0.162 (0.159) 0.930 (0.967)

5.905 163.9815 0.175 (0.190) 0.899 (0.965)

5.929 161.9856 0.176 (0.190) 0.833 (0.931)

5.954 77.9635 0.156 (0.157) 0.917 (0.946)

181






5.995 177.9808 0.161 (0.147) 0.902 (0.892)

6.012 291.9443 0.154 (0.148) 0.915 (0.919)

6.054 82.0283 0.145 (0.140) 0.926 (0.953)

6.466 344.0305 0.084 (0.075) 0.813 (0.862)

6.562 432.0720 0.106 (0.108) 0.874 (0.928)

6.626 360.0609 0.088 (0.087) 0.699 (0.726)

7.105 229.9741 0.099 (0.074) 0.700 (0.688)

7.270 220.0535 0.089 0.649 ----

7.449 185.0811 0.082 (0.057) 0.826 (0.829)

7.568 199.0605 0.076 (0.066) 0.908 (0.886)

7.625 229.1076 0.082 (0.071) 0.826(0.783)

8.025 204.9968 0.062 0.628 -----

8.040 204.0045 0.056 (0.053) 0.769 (0.821)

8.099 181.0698 0.064 0.738 ----

8.117 274.0755 0.066 (0.059) 0.797 (0.847)

8.231 443.0101 0.109 (0.126) 0.799 (0.909)

8.239 430.9833 0.099 (0.126) 0.781 (0.884)

8.261 432.9802 0.101 (0.132) 0.750 (0.891)

8.269 441.0156 0.111 (0.117) 0.833 (0.914)

8.285 434.9778 0.155 (0.135) 0.796 (0.912)





182

Table 6-5. Unidentified discriminant metabolic features in human urine after cranberry juice or apple juice by positive ionization analysis.


p[1] (contribution) a p(corr)[1] (confidence) b


197.1020 -0.071 -0.738 ----

179.0915 -0.093 -0.761 ----

196.0293 0.067 (0.072) 0.736 (0.816)

143.0704 0.057 (0.063) 0.744 (0.763)

264.0900 0.086 (0.080) 0.763 (0.750)

125.0601 0.055 0.826 ----

129.5364 0.073 (0.061) 0.744 (0.645)

120.5311 0.105 (0.108) 0.845 (0.836)

143.0704 0.064 (0.077) 0.816 (0.801)

202.108 0.061 0.690 ----

129.5364 0.074 (0.069) 0.800 (0.812)

285.0811 0.103 (0.103) 0.714 (0.632)

127.0757 0.059 (0.051) 0.692 (0.602)

149.0072 0.131 (0.133) 0.897 (0.927)

237.9691 0.197 (0.201) 0.922 (0.943) ----

255.9796 0.182 (0.188) 0.925 (0.944)

253.9415 0.158 (0.159) 0.929 (0.923)

265.9643 0.167 (0.181) 0.909 (0.937)

276.9577 0.175 (0.172) 0.935 (0.926)

140.0017 0.167 (0.176) 0.917 (0.930)

151.5098 0.202 (0.213) 0.900 (0.914)

260.9852 0.161 (0.161) 0.905 (0.922)

183






5.855 130.9966 0.121 (0.120) 0.926 (0.921)

5.881 142.5044 0.132 (0.146) 0.910 (0.924)

5.895 229.0314 0.105 (0.103) 0.933 (0.954)

5.947 152.5208 0.111 (0.109) 0.917 (0.920)

6.034 207.0620 0.124 (0.112) 0.904 (0.950)

6.080 141.5363 0.116 (0.109) 0.921 (0.945)

6.222 164.0288 0.089 (0.089) 0.902 (0.942)

6.573 116.5105 0.071 (0.091) 0.522 (0.581)

6.752 247.0131 0.072 (0.080) 0.618 (0.653)

6.895 133.5387 0.121 (0.114) 0.942 (0.952)

6.907 201.0752 0.051 0.838 ----

7.043 311.0009 0.076 (0.058) 0.722 (0.598)

7.068 288.1806 0.062 0.675 ----

7.144 149.5338 0.055 0.826 ----

7.245 129.0184 0.065 0.716 ----

7.257 155.5507 0.051 0.740 ----

7.296 159.5291 0.070 0.741 ----

7.320 243.0476 0.092 (0.067) 0.728 (0.601)

7.348 187.0966 0.058 0.832 ----

7.364 150.5233 0.060 0.698 ----

7.414 143.5701 0.063 0.605 ----

7.455 142.5424 0.064 (0.053) 0.861 (0.806)

7.682 213.1122 0.062 0.792 ----

7.717 204.0420 0.059 0.756 ----

184






7.777 183.5290 0.074 (0.070) 0.767 (0.798)

7.888 203.544 0.068 (0.073) 0.788 (0.824)

8.032 404.5529 0.066 (0.077) 0.755 (0.831)

8.033 591.0945 0.071 (0.068) 0.689 (0.790)

8.038 576.1292 0.056 (0.069) 0.690 (0.797)

8.038 413.0433 0.063 (0.057) 0.672 (0.776)

8.039 421.0505 0.051 0.636 ----

8.041 180.0144 0.050 (0.060) 0.663 (0.796)

8.045 415.0440 0.071 0.782 ----

8.051 181.2448 0.056 (0.060) 0.584 (0.583)

8.052 178.8979 0.065 (0.057) 0.561 (0.559)

8.060 204.1052 0.084 (0.108) 0.748 (0.810)

8.064 236.1807 -0.059 -0.545 ----

8.079 243.5619 -0.062 -0.699 ---

8.094 364.0349 -0.075 -0.762 ----

8.094 181.5549 -0.050 -0.755 ----

8.097 172.5497 -0.050 -0.774 ----

8.102 180.5359 -0.069 -0.753 ----

8.104 193.5648 -0.056 -0.692 ----

8.108 193.0628 -0.053 -0.737 ----

8.142 201.0492 -0.051 -0.728 ----

8.274 238.5014 0.126 (0.146) 0.913 (0.949)

8.408 153.5600 0.052 0.649 ----

185






8.472 83.0863 0.050 (0.050) 0.691 (0.768)

8.786 213.0655 0.051 (0.066) 0.620 (0.758)





186

Table 6-6. Summary of identified discriminant metabolites in rats and human. Discriminant Metabolites Cranberry vs. baseline Cranberry vs. apple Apple vs.

baseline Rat

urine Human plasma

Human urine

Rat urine

Rat plasma

Human plasma

Human urine

Rat urine

p-Hydroxybenzoic acid X

Coumaric acid X X

Coumaric acid sulfate X X

Ferulic acid sulfate X X

Phenol X X X

Phenyl sulfate X

Diphenol glucuronide X

Catechol sulphate X X X

3, 4-Dihydroxyphenylvaleric acid

X

3,4-Dihydroxyphenyl propionic acid

X

Hydroxyphenyl acetic acid

X X

p-Hydroxyphenylacetic acid

X X

3-(3’-Hydroxyphenyl)-3-hydroxypropanoic acid

X X

3-(Hydroxyphenyl) propionic acid

X X X

4-Hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate

X X

5-(Hydroxyphenyl)-ϒ-valerolactone-O-sulphate

X

187

Table 6-6. Continued. Discriminant Metabolites Cranberry vs. baseline

Cranberry vs. apple

Apple vs. baseline

Rat urine

Human plasma

Human urine

Rat urine

Rat plasma

Human plasma

Human urine

Rat urine

5-(Dihydroxyphenyl)-ϒ-valerolactone

X X

5-(Dihydroxyphenyl)-ϒ-valerolactone sulfate

X X

5-(Trihydroxyphenyl)-ϒ-valerolactone

X

3,4-Dihydroxyphenyl ethanol sulfate

X X

4'-O-Methyl-(-)-epicatechin-3'-O-beta-glucuronide

X

3'-O-Methyl-(-)-epicatechin-7-O-glucuronide

X

4-O-Methylgallic acid X

1,3,5-Trimethoxybenzene X

Trihydroxybenzoic acid X X X X

4-Hydroxydiphenylamine X

Peonidin-3-O-hexose X

Quinic acid X X X X

Lactic acid X X

Succinic acid X

Citric acid X X X

α-Ketoglutaric acid X

Aconitic acid X

Citramalic acid X X

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Table 6-6. Continued. Discriminant Metabolites Cranberry vs. baseline

Cranberry vs. apple

Apple vs. baseline

Rat urine

Human plasma

Human urine

Rat urine Human plasma

Human urine

α-D-glucose X X

D-maltose X X

Creatinine X

2-Furoylglycine X X

Hippuric acid X X X X X X X

Hydroxyhippuric acid X X

Vanilloylglycine X X

Vanilloloside X

Tyrosine X

Hydroxyoctadecanoic acid

X

4-Acetamido-2-aminobutanoic acid

X

Glycerol 3-phosphate X X

Indole-3-acetaldehyde X

Dihydroxyquinoline X X

3-Hydroxy-3-carboxymethyl-adipic acid

X X

Pimelic acid X X

Homocitric acid X X

(2)3-Isopropylmalate X X

N-Acetyl-L-glutamate 5-semialdehyde

X

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Figure 6-1. The PCA score plot of human urine and quality control samples from LC-HRMS metabolomics. A) PLS-DA

score plot of negative ionization data, B) OPLS-DA score plot of positive ionization data, C) PLS-DA score plot of negative ionization without QC of internal standards and D) OPLS-DA score plot of positive ionization data without QC of internal standards.

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Figure 6-2. The PCA score plot of human baseline urine and human urine after cranberry juice from LC-HRMS

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Figure 6-3. The PLS-DA, OPLS-DA score plots and cross-validated score plots of human baseline urine and urine after

cranberry juice. A) PLS-DA score plot by negative ionization, B) OPLS-DA score plot by positive ionization, C) PLS-DA cross-validated score plot by negative ionization and D) OPLS-DA cross-validated score plot by positive ionization. Blue squares: baseline urine before drinking cranberry juice. Green squares: urine after drinking cranberry juice.

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OPLS-DA models of human baseline urine vs. human urine after cranberry juice. A) PLS-DA model by negative ionization and B) OPLS-DA model by positive ionization.

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Figure 6-5. The PCA score plot of human urine after drinking apple juice or cranberry juice from LC-HRMS metabolomics.

A) Data were acquired by negative ionization and B) data were acquired by positive ionization. Purple squares: urine after drinking apple juice. Green squares: urine after drinking cranberry juice.

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Figure 6-6. The OPLS-DA score plots and cross-validated score plots of human urine after drinking apple juice or

cranberry juice from LC-HRMS metabolomics. A) OPLS-DA score plot by negative ionization, B) OPLS-DA score plot by positive ionization, C) OPLS-DA cross-validated score plot by negative ionization and D) OPLS-DA cross-validated score plot by positive ionization. Purple squares: urine after drinking apple juice. Green squares: urine after drinking cranberry juice.

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Figure 6-7. Validation plot obtained from 200 permutation tests for the OPLS-DA models

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HRMS of human baseline urine and urine after cranberry juice or apple juice by negative ionization. A) Human baseline urine vs. urine after cranberry juice and B) human urine after cranberry juice vs. urine after apple juice. p[1] is the loading vector of covariance in the first principal component. p(corr)[1] is loading vector of correlation in the first principal component. Variables with |p| ≥ 0.05 and |p(corr)| ≥ 0.5 are considered statistically significant. Significant variables in blue color were identified and numbered according to Table 6-2. Unidentified significant variables in red color were listed in Table 6-4. Non-significant variables were in green color.

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HRMS of human baseline urine and urine after cranberry juice or apple juice by positive ionization. A) Human baseline urine vs. urine after drinking cranberry juice and B) human urine after cranberry juice vs. urine after apple juice. p[1] is the loading vector of covariance in the first principal component. p(corr)[1] is loading vector of correlation in the first principal component. Variables with |p| ≥ 0.05 and |p(corr)| ≥ 0.5 are considered statistically significant. Significant variables in blue color were identified and numbered according to Table 6-3. Unidentified significant variables in red color were listed in Table 6-5. Non-significant variables were in green color.

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CHAPTER 7 CONCLUSIONS

1H NMR and UHPLC-Q-Orbitrap-HRMS based metabolomics methods were

developed and employed to discover that plasma and urinary metabolome of both

female rats and young women were changed after intake of cranberry procyanidins or

cranberry juices. Our project is among few metabolomics studies that combined both 1H

NMR and UHPLC-Q-Orbitrap-HRMS analytical techniques. Although 1H NMR was

successfully applied to metabolomics studies on rat urine, human urine and human

plasma, the study in Chapter 2 demonstrated that UHPLC-Q-Orbitrap-HRMS

metabolomics was more effective to reveal the plasma metabolome modifications in rats

caused by cranberry procyanidins. A list of exogenous compounds corresponding to

microbial metabolites of procyanidins were the major contributing markers in the rodent

model. Similarly, the plasma and urinary metabolite profiles of young women were

changed after drinking cranberry juice compared to their baseline profiles. The

metabolome in young women after cranberry juice consumption were different from

those after apple juice consumption. Both endogenous and exogenous metabolites

were discovered and putatively identified as discriminant biomarkers.

Pattern projection techniques were successfully applied in this metabolomics

study. Supervised PLS-DA and OPLS-DA models were developed to segregate rats or

human subjects that received different types of procyanidins or juice. These supervised

PLS-DA and OPLS-DA models had good predictability and could be used to predict the

class of unknown samples from similar studies with low error rates.

The incompleteness of in-house database prevented accurate identification of

new or unknown metabolites in this and other metabolomics studies. All putatively

199

identified metabolites need to be confirmed in the future when the in-house metabolome

database is complete.

This metabolomics research resulted in the identification of specific molecular

profiles and biomarkers of cranberry procyanidin intake in rats and cranberry juice

intake in human for the first time. The discriminant metabolites suggested that many

metabolic pathways were affected by cranberry juice or cranberry procyanidin intake.

The changes in metabolite profiles were likely caused by the ability of cranberries to

impact gene transcription and protein expression. This is also the first time that the

systematic physiological effects of cranberry juice intake was depicted at metabolite

levels. Findings made in this research will help to provide an effective way to assess

cranberry juice or procyanidin intake in epidemiological studies or clinical trials. This

knowledge will help to elucidate the mechanisms of cranberry juices or procyanidins in

mitigating urinary tract infections or other chronic diseases.

200

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BIOGRAPHICAL SKETCH

Haiyan Liu was from Xi’an, China. She received her B.S. degree in food safety

and security from China Agricultural University in 2008. She was admitted into a master

program in the Food Science and Human Nutrition Department at the University of

Florida in 2009. She received her M.S. degree in 2011. Afterwards she continued her

study and joined the food science doctoral program. Haiyan received her Ph.D. degree

from the University of Florida in December 2015. She published four research papers

and presented her research at national conferences in 2009, 2011, 2014 and 2015.

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