PROFILING THE METABOLOME CHANGES CAUSED BY CRANBERRY JUICES OR CRANBERRY PROCYANIDINS USING 1 H 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
213
Embed
PROFILING THE METABOLOME CHANGES CAUSED BY …ufdcimages.uflib.ufl.edu/UF/E0/04/90/90/00001/LIU_H.pdf · profiling the metabolome changes caused by cranberry juices or cranberry procyanidins
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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
1 A REVIEW: BIOACTIVITY AND BIOAVAILABILITY OF PROCYANIDINS IN CRANBERRIES ...................................................................................................... 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
Materials and Methods............................................................................................ 41 Chemicals and Materials .................................................................................. 41
Extraction, Purification and Characterization of Partially Purified Cranberry Procyanidins and Partially Purified Cranberry Procyanidins ......................... 42
Multivariate Data Processing and Statistical Analyses ..................................... 47 Results and Discussion........................................................................................... 49
Procyanidin Composition and Content in PPCP and PPAP ............................. 49
6
Quality Control of Multivariate Analyses ........................................................... 49
NMR Metabolomics Analysis of Rat Plasma .................................................... 50 LC-HRMS Metabolomics Analysis of Rat Plasma ............................................ 51
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
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
5 UHPLC-Q-ORBITRAP-HRMS-BASED GLOBAL METABOLOMICS REVEAL METABOLOME MODIFICATIONS IN PLASMA OF YOUNG WOMEN AFTER CRANBERRY JUICE OR APPLE JUICE CONSUMPTION .................................. 127
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
6 MODIFICATION OF URINARY METABOLOME IN YOUNG WOMEN AFTER CRANBERRY JUICE CONSUMPTION WERE REVEALED USING UHPLC-Q-ORBITRAP-HRMS-BASED GLOBAL METABOLOMICS APPROACH ................ 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
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
9
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
10
LIST OF FIGURES
Figure page 1-1 Structures of epicatechin and procyanidin oligomers isolated from
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
11
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
12
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
13
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
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
16
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.
18
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
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
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
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
Rat plasma after administering PPCP
-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
Rat plasma after administering PPAP
Rat plasma after administering PPCP
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]
Rat plasma after administering PPAP
Rat plasma after administering PPCP
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
Rat plasma after administering PPAP
Rat plasma after administering PPCP
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
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.
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).
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
]
89
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
93
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
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
10th day morning (8-10 am)
Collect first-morning urine samples Consume 1 bottle of cranberry or apple juice Collect blood samples
Wash out period for 2 weeks
25th day morning (8-10 am)
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
28th day morning (8-10 am)
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
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.
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
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
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.
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.
Materials and Methods
Chemicals and Materials
Cranberry juice cocktail (double strength, 54% juice) and 100% apple juice were
provided by Ocean Spray Cranberries, Inc. (Lakeville-Middleboro, MA, USA). LC-MS
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.
143
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)
Detected Mass [M-H]-
p[1] (contribution)a
p(corr)[1] (confidence) b
MSMS Putative Identification
Theoretical Mass [M-H]-
Mass Difference (Da)
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
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.
NO. Retention Time (min)
Detected Mass [M+H]+
p[1] (contribution) a
p(corr)[1] (confidence) b
MSMS Putative Identification Theoretical Mass [M+H]+
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)
Detected Mass [M-H]-
p[1] (contribution) a
p(corr)[1] (confidence) b
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)
Detected Mass [M-H]-
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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)
Detected Mass [M-H]-
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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)
Detected Mass [M+H]+
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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)
Detected Mass [M+H]+
p[1] (contribution) a
p(corr)[1] (confidence) b
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 ----
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.
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.
-30
-20
-10
0
10
20
30
-25 -20 -15 -10 -5 0 5 10 15 20t[1]
t [2
]A
B
-15
-10
-5
0
5
10
-25 -20 -15 -10 -5 0 5 10 15 20t[1]
t [2
]
C
-20
-15
-10
-5
0
5
10
15
-50 -40 -30 -20 -10 0 10 20 30t[1]
t [2
]
-15
-10
-5
0
5
10
-20 -15 -10 -5 0 5 10 15t[1]
t [2
]
D
PoolQC from apple juice group
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
Plasma after cranberry juice
-25
-20
-15
-10
-5
0
5
10
15
20
-30 -20 -10 0 10 20t[1]
t [2
]
-25
-20
-15
-10
-5
0
5
10
15
20
-30 -20 -10 0 10 20t[1]
t [2
]A B
154
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.
-25
-20
-15
-10
-5
0
5
10
15
20
-20 -15 -10 -5 0 5 10 15t[1]
-25
-20
-15
-10
-5
0
5
10
15
20
-20 -15 -10 -5 0 5 10 15t[1]
t [2
]
to [
1]
Baseline plasma before cranberry juice
Plasma after cranberry juice
A B
-15
-10
-5
0
5
10
-20 -15 -10 -5 0 5 10 15t[1]
-20
-15
-10
-5
0
5
10
15
-15 -10 -5 0 5 10t[1]
to [
1]
CD
t [2
]
155
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.
-10
-5
0
5
10
-10 -5 0 5 10tcv[1]
-15
-10
-5
0
5
10
-10 -8 -6 -4 -2 0 2 4 6 8 10tcv[1]
tcv[
2]
tocv
[1]
A B
Baseline plasma before cranberry juice
Plasma after cranberry juice
C D
-10
-8
-6
-4
-2
0
2
4
6
-10 -8 -6 -4 -2 0 2 4 6 8 10
tcv[1]
tcv[
2]
-12
-10
-8
-6
-4
-2
0
2
4
6
8
-10 -8 -6 -4 -2 0 2 4 6 8tcv[1]
tocv
[1]
156
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)
R2
, Q2
r(y, permuted y)
A B
157
Figure 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. A) PLS-DA model and B) OPLS-DA model.
R2
, Q2
r(y, permuted y)
R2
, Q2
r(y, permuted y)
A B
158
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.
-20
-15
-10
-5
0
5
10
15
-30 -20 -10 0 10 20t[1]
t [2
]
A
Plasma after apple juice
Plasma after cranberry juice
t [2
]
-25
-20
-15
-10
-5
0
5
10
15
20
-25 -20 -15 -10 -5 0 5 10 15 20t[1]
B
159
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.
t [2
]
-25
-20
-15
-10
-5
0
5
10
15
20
-20 -15 -10 -5 0 5 10 15
t[1]
-25
-20
-15
-10
-5
0
5
10
15
20
-20 -15 -10 -5 0 5 10 15t[1]
to [
1]
A B
Plasma after apple juice
Plasma after cranberry juice
-20
-15
-10
-5
0
5
10
15
-15 -10 -5 0 5 10t[1]
t [2
]
-20
-15
-10
-5
0
5
10
15
-15 -10 -5 0 5 10t[1]
to [
1]
C D
to [
1]
160
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.
-15
-10
-5
0
5
10
-10 -8 -6 -4 -2 0 2 4 6 8tcv[1]
tcv
[2]
-20
-15
-10
-5
0
5
10
-8 -6 -4 -2 0 2 4 6tcv[1]
tocv
[1]
A B
Plasma after apple juice
Plasma after cranberry juice
C D
-8
-6
-4
-2
0
2
4
6
8
10
-8 -6 -4 -2 0 2 4 6tcv[1]
tcv
[2]
-10
-5
0
5
10
-8 -6 -4 -2 0 2 4 6tcv[1]
tocv
[1]
161
Figure 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. A) PLS-DA model and B) OPLS-DA model.
R2
, Q2
r(y, permuted y)
R2
, Q2
r(y, permuted y)
A B
162
Figure 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. A) PLS-DA model and B) OPLS-DA model.
R2
, Q2
r(y, permuted y)
R2
, Q2
r(y, permuted y)
A B
163
Figure 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. 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.
1, 122
3
4
7 5
6
8
10
13
14
A
1
2, 10
4
5,7
6, 15
8
912, 13
B
164
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.
13 2
7
8, 9
10, 11
A
1
23
4
5, 6 12
B
7
8, 9, 10, 11
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.
Materials and Methods
Chemicals and Materials
Cranberry juice cocktail (double strength, 54% juice) and 100% apple juice were
provided by Ocean Spray Cranberries, Inc. (Lakeville-Middleboro, MA, USA). LC-MS
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.
178
Table 6-2. Identification of discriminant metabolites in human urine after drinking cranberry juice or apple juice by negative ionization analysis.
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.
179
Table 6-3. Identification of discriminant metabolites in human urine after drinking cranberry juice or apple juice by positive ionization analysis.
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.
180
Table 6-4. Unidentified discriminant metabolic features in human urine after cranberry juice or apple juice by negative ionization analysis.
Retention Tim (min)
Detected Mass [M-H]-
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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
Table 6-4. Continued. Retention Tim (min)
Detected Mass [M-H]-
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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)
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.
182
Table 6-5. Unidentified discriminant metabolic features in human urine after cranberry juice or apple juice by positive ionization analysis.
Detected Mass [M+H]+
p[1] (contribution) a p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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
Table 6-5. Continued. Retention Tim (min)
Detected Mass [M+H]+
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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
Table 6-5. Continued. Retention Tim (min)
Detected Mass [M+H]+
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
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
Table 6-5. Continued. Retention Tim (min)
Detected Mass [M+H]+
p[1] (contribution) a
p(corr)[1] (confidence) b
CJ vs. AJ c CJ vs. BS d
8.472 83.0863 0.050 (0.050) 0.691 (0.768)
8.786 213.0655 0.051 (0.066) 0.620 (0.758)
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.
186
Table 6-6. Summary of identified discriminant metabolites in rats and human. Discriminant Metabolites Cranberry vs. baseline Cranberry vs. apple Apple vs.
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
188
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
189
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.
-25
-20
-15
-10
-5
0
5
10
15
20
-150 -100 -50 0 50 100 150t[1]
t [2
]
PoolQC from apple juice group
PoolQC from Baseline group
PoolQC from cranberry juice group
Urine of baseline, cranberry and apple group
QC from internal standards
-30
-20
-10
0
10
20
30
-150 -100 -50 0 50 100t[1]
t [2
]
A B
-30
-20
-10
0
10
20
-50 -40 -30 -20 -10 0 10 20 30 40t[1]
t [2
]
-40
-30
-20
-10
0
10
20
-80 -60 -40 -20 0 20 40 60t[1]
t [2
]
C D
190
Figure 6-2. The PCA score plot of human baseline urine and human urine after cranberry juice from LC-HRMS
metabolomics. A) Data were acquired by negative ionization and B) data were acquired by positive ionization. Blue squares: baseline urine before drinking cranberry juice. Green squares: urine after drinking cranberry juice.
t [2
]
-30
-20
-10
0
10
20
-50 -40 -30 -20 -10 0 10 20 30 40t[1]
Baseline urine before cranberry juice
Urine after cranberry juice
-40
-30
-20
-10
0
10
20
30
-80 -60 -40 -20 0 20 40 60t[1]
t [2
]
A B
191
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.
-20
-15
-10
-5
0
5
10
15
B 8
B 1
0
B1
B 1
1
B 1
2
B 1
3
B 1
4
B 1
5
B 1
6
B 1
7
B 2
B 4
B 6
B 7 C1
C11
C12
C13
C14
C15
C16
C17
C2
C4
C6
C7
C8
Sample ID
t [1
]
-50
-40
-30
-20
-10
0
10
20
30
40
-40 -30 -20 -10 0 10 20 30
t[1]
to [
1]
A BBaseline Urine before cranberry juice
Urine after cranberry juice
-15
-10
-5
0
5
10
B 8
B 1
0
B1
B 1
1
B 1
2
B 1
3
B 1
4
B 1
5
B 1
6
B 1
7
B 2
B 4
B 6
B 7 C1
C11
C12
C13
C14
C15
C16
C17
C2
C4
C6
C7
C8
Sample ID
t [1
]
-25
-20
-15
-10
-5
0
5
10
15
20
25
-15 -10 -5 0 5 10
tcv[1]
tocv
[1]
C D
192
Figure 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. A) PLS-DA model by negative ionization and B) OPLS-DA model by positive ionization.
-0.2
0
0.2
0.4
0.6
0.8
-0.2 0 0.2 0.4 0.6 0.8 1
R2
, Q2
r(y, permuted y)
R2
, Q2
r(y, permuted y)
A BR2
Q2
193
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.
t [2
]
-30
-20
-10
0
10
20
-50 -40 -30 -20 -10 0 10 20 30 40t[1]
-40
-30
-20
-10
0
10
20
30
-80 -60 -40 -20 0 20 40 60t[1]
t [2
]
A BUrine after apple juice
Urine after cranberry juice
194
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.
-40
-30
-20
-10
0
10
20
30
-40 -30 -20 -10 0 10 20 30t[1]
to[1
]
-50
-40
-30
-20
-10
0
10
20
30
40
-30 -20 -10 0 10 20t[1]
to [
1]
tocv
[1]
-20
-15
-10
-5
0
5
10
15
-15 -10 -5 0 5 10tcv[1]
-30
-20
-10
0
10
20
-15 -10 -5 0 5 10tcv[1]
tocv
[1]
A B
C D
Urine after apple juice
Urine after cranberry juice
195
Figure 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. A) Data were acquired by negative ionization and B) data were acquired by positive ionization.
R2
, Q2
r(y, permuted y)
R2
, Q2
r(y, permuted y)
A B
196
Figure 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. 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.
2,5
3
4
68
9
A
B
1 4 5
2, 36
7
9
197
Figure 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. 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.
1
34
7
9A
5
2
B
10
2, 7
3
4 591 8
6
198
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
LIST OF REFERENCES
Ahuja, S., Kaack, B., & Roberts, J. (1998). Loss of fimbrial adhesion with the addition of Vaccinum macrocarpon to the growth medium of P-fimbriated Escherchia coli. The Journal of Urology, 159(2), 559-562.
Appeldoorn, M. M., Vincken, J.-P., Gruppen, H., & Hollman, P. C. (2009). Procyanidin dimers A1, A2, and B2 are absorbed without conjugation or methylation from the small intestine of rats. The Journal of Nutrition, 139(8), 1469-1473.
Avorn, J., Monane, M., Gurwitz, J. H., Glynn, R. J., Choodnovskiy, I., & Lipsitz, L. A. (1994). Reduction of bacteriuria and pyuria after ingestion of cranberry juice. JAMA, 271(10), 751-754.
Baba, S., Osakabe, N., Natsume, M., & Terao, J. (2002). Absorption and urinary excretion of procyanidin B2 [epicatechin-(4β-8)-epicatechin] in rats. Free Radical Biology and Medicine, 33(1), 142-148.
Barbosa-Cesnik, C., Brown, M. B., Buxton, M., Zhang, L., DeBusscher, J., & Foxman, B. (2011). Cranberry juice fails to prevent recurrent urinary tract infection: results from a randomized placebo-controlled trial. Clinical Infectious Diseases, 52(1), 23-30.
Beachey, E. H. (1981). Bacterial adherence: adhesin-receptor interactions mediating the attachment of bacteria to mucosal surfaces. Journal of Infectious Diseases, 143(3), 325-345.
Beckonert, O., Keun, H. C., Ebbels, T. M., Bundy, J., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2007). Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols, 2(11), 2692-2703.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 289-300.
Bingol, K., Li, D.-W., Bruschweiler-Li, L., Cabrera, O. A., Megraw, T., Zhang, F., & Bruschweiler, R. (2014). Unified and Isomer-Specific NMR Metabolomics Database for the Accurate Analysis of 13C–1H HSQC Spectra. ACS Chemical Biology, 10(2), 452-459.
Blatherwick, N. (1914). The specific role of foods in relation to the composition of the urine. Archives of Internal Medicine, 14(3), 409-450.
Blatherwick, N., & Long, M. L. (1923). Studies of urinary acidity II. The increased acidity produced by eating prunes and cranberries. Journal of Biological Chemistry, 57(3), 815-818.
201
Bond, W., Favero, M., Petersen, N., Gravelle, C., Ebert, J., & Maynard, J. (1981). Survival of hepatitis B virus after drying and storage for one week. The Lancet, 317(8219), 550-551.
Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., & Liu, P. (2013). The human urine metabolome. PloS one, 8(9), e73076.
Brindle, J. T., Antti, H., Holmes, E., Tranter, G., Nicholson, J. K., Bethell, H. W., Clarke, S., Schofield, P. M., McKilligin, E., & Mosedale, D. E. (2002). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine, 8(12), 1439-1445.
Burton, L., Ivosev, G., Tate, S., Impey, G., Wingate, J., & Bonner, R. (2008). Instrumental and experimental effects in LC–MS-based metabolomics. Journal of Chromatography B, 871(2), 227-235.
Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J.
(2006). OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification. Journal of Chemometrics, 20(8‐10), 341-351.
Caton, P. W., Pothecary, M. R., Lees, D. M., Khan, N. Q., Wood, E. G., Shoji, T., Kanda, T., Rull, G., & Corder, R. (2010). Regulation of Vascular Endothelial Function by Procyanidin-Rich Foods and Beverages†. Journal of Agricultural and Food Chemistry, 58(7), 4008-4013.
Center, B. (2004). USDA database for the proanthocyanidin content of selected foods. Washington, DC: US Department of Agriculture.
Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., & Egertson, J. (2012). A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology, 30(10), 918-920.
Chen, P. X., Bozzo, G. G., Freixas-Coutin, J. A., Marcone, M. F., Pauls, P. K., Tang, Y., Zhang, B., Liu, R., & Tsao, R. (2014). Free and conjugated phenolic compounds and their antioxidant activities in regular and non-darkening cranberry bean (< i> Phaseolus vulgaris</i> L.) seed coats. Journal of Functional Foods.
Crozier, A. (2013). Absorption, metabolism, and excretion of (−)-epicatechin in humans: an evaluation of recent findings. The American Journal of Clinical Nutrition, 98(4), 861-862.
Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., & Bax, A. (1995). NMRPipe: a multidimensional spectral processing system based on UNIX pipes. Journal of biomolecular NMR, 6(3), 277-293.
202
Déprez, S., Brezillon, C., Rabot, S., Philippe, C., Mila, I., Lapierre, C., & Scalbert, A. (2000). Polymeric proanthocyanidins are catabolized by human colonic microflora into low-molecular-weight phenolic acids. The Journal of Nutrition, 130(11), 2733-2738.
Deprez, S., Mila, I., Huneau, J.-F., Tome, D., & Scalbert, A. (2001). Transport of proanthocyanidin dimer, trimer, and polymer across monolayers of human intestinal epithelial Caco-2 cells. Antioxidants and Redox Signaling, 3(6), 957-967.
Deziel, B. A., Patel, K., Neto, C., Gottschall‐Pass, K., & Hurta, R. A. (2010). Proanthocyanidins from the American Cranberry (Vaccinium macrocarpon) inhibit
matrix metalloproteinase‐2 and matrix metalloproteinase‐9 activity in human prostate cancer cells via alterations in multiple cellular signalling pathways. Journal of Cellular Biochemistry, 111(3), 742-754.
Di Martino, P., Agniel, R., David, K., Templer, C., Gaillard, J., Denys, P., & Botto, H. (2006). Reduction of Escherichia coli adherence to uroepithelial bladder cells after consumption of cranberry juice: a double-blind randomized placebo-controlled cross-over trial. World journal of urology, 24(1), 21-27.
Donovan, J. L., Lee, A., Manach, C., Rios, L., Morand, C., Scalbert, A., & Rémésy, C. (2002). Procyanidins are not bioavailable in rats fed a single meal containing a grapeseed extract or the procyanidin dimer B3. British Journal of Nutrition, 87(04), 299-306.
Dunn, W. B., Broadhurst, D. I., Atherton, H. J., Goodacre, R., & Griffin, J. L. (2011). Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chemical Society Reviews, 40(1), 387-426.
Engemann, A., Hubner, F., Rzeppa, S., & Humpf, H.-U. (2012). Intestinal metabolism of two A-type procyanidins using the pig cecum model: detailed structure elucidation of unknown catabolites with Fourier transform mass spectrometry (FTMS). Journal of Agricultural and Food Chemistry, 60(3), 749-757.
Eriksson, L. (2006). Multi-and megavariate data analysis: MKS Umetrics AB.
Feliciano, R. P., Krueger, C. G., Shanmuganayagam, D., Vestling, M. M., & Reed, J. D. (2012). Deconvolution of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry isotope patterns to determine ratios of A-type to B-type interflavan bonds in cranberry proanthocyanidins. Food Chemistry, 135(3), 1485-1493.
Fihn, S. D. (2003). Acute uncomplicated urinary tract infection in women. New England Journal of Medicine, 349(3), 259-266.
203
Fonville, J. M., Richards, S. E., Barton, R. H., Boulange, C. L., Ebbels, T., Nicholson, J. K., Holmes, E., & Dumas, M. E. (2010). The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. Journal of Chemometrics, 24(11‐12), 636-649.
Foo, L. Y., Lu, Y., Howell, A. B., & Vorsa, N. (2000). A-Type Proanthocyanidin Trimers from Cranberry that Inhibit Adherence of Uropathogenic P-Fimbriated Escherichia c oli. Journal of Natural Products, 63(9), 1225-1228.
Foxman, B., Geiger, A. M., Palin, K., Gillespie, B., & Koopman, J. S. (1995). First-time urinary tract infection and sexual behavior. Epidemiology, 6(2), 162-168.
Fuleki, T., Pelayo, E., & Palabay, R. B. (1994). Sugar composition of varietal juices produced from fresh and stored apples. Journal of Agricultural and Food Chemistry, 42(6), 1266-1275.
Garcia‐Aloy, M., Llorach, R., Urpi‐Sarda, M., Jáuregui, O., Corella, D., Ruiz‐Canela, M., Salas‐Salvadó, J., Fitó, M., Ros, E., & Estruch, R. (2014). A metabolomics‐driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free‐living subjects from the PREDIMED study. Molecular Nutrition & Food Research.
Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. (2007). Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine. Journal of Proteome Research, 6(8), 3291-3303.
Giusti, M. M., & Wrolstad, R. E. (2001). Characterization and Measurement of Anthocyanins by UV-Visible Spectroscopy: John Wiley & Sons, Inc.
Gonthier, M.-P., Donovan, J. L., Texier, O., Felgines, C., Remesy, C., & Scalbert, A. (2003). Metabolism of dietary procyanidins in rats. Free Radical Biology and Medicine, 35(8), 837-844.
Graham, S. F., Holscher, C., & Green, B. D. (2014). Metabolic signatures of human Alzheimer’s disease (AD): 1H NMR analysis of the polar metabolome of post-mortem brain tissue. Metabolomics, 10(4), 744-753.
Griffin, J. L. (2006). The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? Philosophical Transactions of the Royal Society B: Biological Sciences, 361(1465), 147-161.
Griffiths, L. (1964). Studies on flavonoid metabolism. Identification of the metabolites of (+)-catechin in rat urine. Biochemical Journal, 92(1), 173.
Gu, L., House, S. E., Rooney, L., & Prior, R. L. (2007). Sorghum bran in the diet dose dependently increased the excretion of catechins and microbial-derived phenolic acids in female rats. Journal of Agricultural and Food Chemistry, 55(13), 5326-5334.
204
Gu, L., Kelm, M., Hammerstone, J. F., Beecher, G., Cunningham, D., Vannozzi, S., & Prior, R. L. (2002). Fractionation of polymeric procyanidins from lowbush blueberry and quantification of procyanidins in selected foods with an optimized normal-phase HPLC-MS fluorescent detection method. Journal of Agricultural and Food Chemistry, 50(17), 4852-4860.
Gu, L., Kelm, M. A., Hammerstone, J. F., Beecher, G., Holden, J., Haytowitz, D., Gebhardt, S., & Prior, R. L. (2004). Concentrations of proanthocyanidins in common foods and estimations of normal consumption. The Journal of Nutrition, 134(3), 613-617.
Gu, L., Kelm, M. A., Hammerstone, J. F., Beecher, G., Holden, J., Haytowitz, D., & Prior, R. L. (2003). Screening of foods containing proanthocyanidins and their structural characterization using LC-MS/MS and thiolytic degradation. Journal of Agricultural and Food Chemistry, 51(25), 7513-7521.
Gu, L., Kelm, M. A., Hammerstone, J. F., Zhang, Z., Beecher, G., Holden, J., Haytowitz, D., & Prior, R. L. (2003). Liquid chromatographic/electrospray ionization mass spectrometric studies of proanthocyanidins in foods. Journal of Mass Spectrometry, 38(12), 1272-1280.
Gu, Y., Hurst, W. J., Stuart, D. A., & Lambert, J. D. (2011). Inhibition of key digestive enzymes by cocoa extracts and procyanidins. Journal of Agricultural and Food Chemistry, 59(10), 5305-5311.
Gupta, K., Chou, M., Howell, A., Wobbe, C., Grady, R., & Stapleton, A. (2007). Cranberry products inhibit adherence of p-fimbriated Escherichia coli to primary cultured bladder and vaginal epithelial cells. The Journal of Urology, 177(6), 2357-2360.
Haverkorn, M. J., & Mandigers, J. (1994). Reduction of bacteriuria and pyuria using cranberry juice. JAMA, 272(8), 590-590.
Hawkins, D. M., Basak, S. C., & Mills, D. (2003). Assessing model fit by cross-validation. Journal of Chemical Information and Computer Sciences, 43(2), 579-586.
Heinzmann, S. S., Brown, I. J., Chan, Q., Bictash, M., Dumas, M.-E., Kochhar, S., Stamler, J., Holmes, E., Elliott, P., & Nicholson, J. K. (2010). Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. The American Journal of Clinical Nutrition, 92(2), 436-443.
Hodgson, J. M., Yee Chan, S., Puddey, I. B., Devine, A., Wattanapenpaiboon, N., Wahlqvist, M. L., Lukito, W., Burke, V., Ward, N. C., & Prince, R. L. (2004). Phenolic acid metabolites as biomarkers for tea-and coffee-derived polyphenol exposure in human subjects. British Journal of Nutrition, 91(02), 301-305.
205
Holt, R. R., Lazarus, S. A., Sullards, M. C., Zhu, Q. Y., Schramm, D. D., Hammerstone, J. F., Fraga, C. G., Schmitz, H. H., & Keen, C. L. (2002). Procyanidin dimer B2 [epicatechin-(4β-8)-epicatechin] in human plasma after the consumption of a flavanol-rich cocoa. The American Journal of Clinical Nutrition, 76(4), 798-804.
Hooton, T. M. (2001). Recurrent urinary tract infection in women. International Journal of Antimicrobial Agents, 17(4), 259-268.
Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., & Aoshima, K. (2010). MassBank: a public repository for sharing mass spectral data for life sciences. Journal of Mass Spectrometry, 45(7), 703-714.
Howell, A. B. (2007). Bioactive compounds in cranberries and their role in prevention of urinary tract infections. Molecular Nutrition & Food Research, 51(6), 732-737.
Howell, A. B., Botto, H., Combescure, C., Blanc-Potard, A.-B., Gausa, L., Matsumoto, T., Tenke, P., Sotto, A., & Lavigne, J.-P. (2010). Dosage effect on uropathogenic Escherichia coli anti-adhesion activity in urine following consumption of cranberry powder standardized for proanthocyanidin content: a multicentric randomized double blind study. BMC Infectious Diseases, 10(1), 94.
Howell, A. B., Reed, J. D., Krueger, C. G., Winterbottom, R., Cunningham, D. G., & Leahy, M. (2005). A-type cranberry proanthocyanidins and uropathogenic bacterial anti-adhesion activity. Phytochemistry, 66(18), 2281-2291.
Ichiyanagi, T., Shida, Y., Rahman, M. M., Hatano, Y., & Konishi, T. (2006). Bioavailability and tissue distribution of anthocyanins in bilberry (Vaccinium myrtillus L.) extract in rats. Journal of Agricultural and Food Chemistry, 54(18), 6578-6587.
Ito, H., Gonthier, M.-P., Manach, C., Morand, C., Mennen, L., Rémésy, C., & Scalbert, A. (2005). Polyphenol levels in human urine after intake of six different polyphenol-rich beverages. British Journal of Nutrition, 94(4), 500-509.
Jepson, R., Craig, J., & Williams, G. (2013). Cranberry products and prevention of urinary tract infections. JAMA, 310(13), 1395-1396.
Kang, J., Choi, M.-Y., Kang, S., Kwon, H. N., Wen, H., Lee, C. H., Park, M., Wiklund, S., Kim, H. J., & Kwon, S. W. (2008). Application of a 1H nuclear magnetic resonance (NMR) metabolomics approach combined with orthogonal projections to latent structure-discriminant analysis as an efficient tool for discriminating between Korean and Chinese herbal medicines. Journal of Agricultural and Food Chemistry, 56(24), 11589-11595.
Kemsley, E. K., Le Gall, G., Dainty, J. R., Watson, A. D., Harvey, L. J., Tapp, H. S., & Colquhoun, I. J. (2007). Multivariate techniques and their application in nutrition: a metabolomics case study. British Journal of Nutrition, 98(1), 1-14.
206
Kontiokari, T., Sundqvist, K., Nuutinen, M., Pokka, T., Koskela, M., & Uhari, M. (2001). Randomised trial of cranberry-lingonberry juice and Lactobacillus GG drink for the prevention of urinary tract infections in women. BMJ, 322(7302), 1571.
Kresty, L. A., Howell, A. B., & Baird, M. (2011). Cranberry proanthocyanidins mediate growth arrest of lung cancer cells through modulation of gene expression and rapid induction of apoptosis. Molecules, 16(3), 2375-2390.
Krogholm, K. S., Haraldsdóttir, J., Knuthsen, P., & Rasmussen, S. E. (2004). Urinary total flavonoid excretion but not 4-pyridoxic acid or potassium can be used as a biomarker for the intake of fruits and vegetables. The Journal of Nutrition, 134(2), 445-451.
Lee, Y. A., Cho, E. J., Tanaka, T., & Yokozawa, T. (2007). Inhibitory activities of proanthocyanidins from persimmon against oxidative stress and digestive enzymes related to diabetes. Journal of Nutritional Science and Vitaminology, 53(3), 287-292.
Li, S., Sui, Y., Xiao, J., Wu, Q., Hu, B., Xie, B., & Sun, Z. (2013). Absorption and urinary excretion of A-type procyanidin oligomers from< i> Litchi chinensis</i> pericarp in rats by selected ion monitoring liquid chromatography–mass spectrometry. Food Chemistry, 138(2), 1536-1542.
Liang, J., Xu, F., Zhang, Y.-Z., Huang, S., Zang, X.-Y., Zhao, X., Zhang, L., Shang, M.-Y., Yang, D.-H., & Wang, X. (2013). The profiling and identification of the absorbed constituents and metabolites of Paeoniae Radix Rubra decoction in rat plasma and urine by the HPLC–DAD–ESI-IT-TOF-MS n technique: A novel strategy for the systematic screening and identification of absorbed constituents and metabolites from traditional Chinese medicines. Journal of Pharmaceutical and Biomedical Analysis, 83, 108-121.
Liebich, H., & Först, C. (1990). Basic profiles of organic acids in urine. Journal of Chromatography B: Biomedical Sciences and Applications, 525, 1-14.
Lin, H. M., Helsby, N. A., Rowan, D. D., & Ferguson, L. R. (2011). Using metabolomic analysis to understand inflammatory bowel diseases. Inflammatory Bowel Diseases, 17(4), 1021-1029.
Lin, S., Chan, W., Li, J., & Cai, Z. (2010). Liquid chromatography/mass spectrometry for investigating the biochemical effects induced by aristolochic acid in rats: the plasma metabolome. Rapid Communications in Mass Spectrometry, 24(9), 1312-1318.
Lindon, J. C., Holmes, E., & Nicholson, J. K. (2006). Metabonomics techniques and applications to pharmaceutical research & development. Pharmaceutical Research, 23(6), 1075-1088.
207
Liu, H., Liu, H., Wang, W., Khoo, C., Taylor, J., & Gu, L. (2011). Cranberry phytochemicals inhibit glycation of human hemoglobin and serum albumin by scavenging reactive carbonyls. Food & Function, 2(8), 475-482.
Liu, H., Zou, T., Gao, J.-m., & Gu, L. (2013). Depolymerization of cranberry procyanidins using (+)-catechin,(−)-epicatechin, and (−)-epigallocatechin gallate as chain breakers. Food Chemistry, 141(1), 488-494.
Liu, Y., Black, M. A., Caron, L., & Camesano, T. A. (2006). Role of cranberry juice on
molecular‐scale surface characteristics and adhesion behavior of Escherichia coli. Biotechnology and Bioengineering, 93(2), 297-305.
Llorach, R., Garrido, I., Monagas, M., Urpi-Sarda, M., Tulipani, S., Bartolome, B., & Andres-Lacueva, C. (2010). Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) DA Webb) skin polyphenols. Journal of Proteome Research, 9(11), 5859-5867.
Llorach, R., Urpi-Sarda, M., Jauregui, O., Monagas, M., & Andres-Lacueva, C. (2009). An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. Journal of Proteome Research, 8(11), 5060-5068.
Loke, W. M., Jenner, A. M., Proudfoot, J. M., McKinley, A. J., Hodgson, J. M., Halliwell, B., & Croft, K. D. (2009). A metabolite profiling approach to identify biomarkers of flavonoid intake in humans. The Journal of Nutrition, 139(12), 2309-2314.
Manach, C., Hubert, J., Llorach, R., & Scalbert, A. (2009). The complex links between dietary phytochemicals and human health deciphered by metabolomics. Molecular Nutrition & Food Research, 53(10), 1303-1315.
Marchesi, J. R., Holmes, E., Khan, F., Kochhar, S., Scanlan, P., Shanahan, F., Wilson, I. D., & Wang, Y. (2007). Rapid and noninvasive metabonomic characterization of inflammatory bowel disease. Journal of Proteome Research, 6(2), 546-551.
McKay, D. L., Chen, C.-Y. O., Zampariello, C. A., & Blumberg, J. B. (2015). Flavonoids and phenolic acids from cranberry juice are bioavailable and bioactive in healthy older adults. Food Chemistry, 168, 233-240.
McMurdo, M. E., Bissett, L. Y., Price, R. J., Phillips, G., & Crombie, I. K. (2005). Does ingestion of cranberry juice reduce symptomatic urinary tract infections in older people in hospital? A double-blind, placebo-controlled trial. Age and Ageing, 34(3), 256-261.
Mennen, L. I., Sapinho, D., Ito, H., Bertrais, S., Galan, P., Hercberg, S., & Scalbert, A. (2006). Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. British Journal of Nutrition, 96(01), 191-198.
208
Moffett, J. R., & Namboodiri, M. A. (2003). Tryptophan and the immune response. Immunology and Cell Biology, 81(4), 247-265.
Natsume, M., Osakabe, N., Oyama, M., Sasaki, M., Baba, S., Nakamura, Y., Osawa, T., & Terao, J. (2003). Structures of (−)-epicatechin glucuronide identified from plasma and urine after oral ingestion of (−)-epicatechin: differences between human and rat. Free Radical Biology and Medicine, 34(7), 840-849.
Neveu, V., Perez-Jimenez, J., Vos, F., Crespy, V., Du Chaffaut, L., Mennen, L., Knox, C., Eisner, R., Cruz, J., & Wishart, D. (2010). Phenol-Explorer: an online comprehensive database on polyphenol contents in foods. Database, 2010, bap024.
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29(11), 1181-1189.
Ottaviani, J. I., Momma, T. Y., Kuhnle, G. K., Keen, C. L., & Schroeter, H. (2012). Structurally related (−)-epicatechin metabolites in humans: assessment using de novo chemically synthesized authentic standards. Free Radical Biology and Medicine, 52(8), 1403-1412.
Ou, K., & Gu, L. (2013). Absorption and metabolism of proanthocyanidins. Journal of Functional Foods.
Ou, K., & Gu, L. (2014). Absorption and metabolism of proanthocyanidins. Journal of Functional Foods, 7, 43-53.
Ou, K., Percival, S. S., Zou, T., Khoo, C., & Gu, L. (2012). Transport of cranberry A-type procyanidin dimers, trimers, and tetramers across monolayers of human intestinal epithelial Caco-2 cells. Journal of Agricultural and Food Chemistry, 60(6), 1390-1396.
Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010). MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11(1), 395.
Prior, R. L., Rogers, T. R., Khanal, R. C., Wilkes, S. E., Wu, X., & Howard, L. R. (2010). Urinary Excretion of Phenolic Acids in Rats Fed Cranberry†. Journal of Agricultural and Food Chemistry, 58(7), 3940-3949.
Psychogios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., Sinelnikov, I., Krishnamurthy, R., Eisner, R., & Gautam, B. (2011). The human serum metabolome. PloS one, 6(2), e16957.
209
Pujos-Guillot, E., Hubert, J., Martin, J.-F., Lyan, B., Quintana, M., Claude, S., Chabanas, B., Rothwell, J. A., Bennetau-Pelissero, C., & Scalbert, A. (2013). Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. Journal of Proteome Research, 12(4), 1645-1659.
Rechner, A. R., Kuhnle, G., Bremner, P., Hubbard, G. P., Moore, K. P., & Rice-Evans, C. A. (2002). The metabolic fate of dietary polyphenols in humans. Free Radical Biology and Medicine, 33(2), 220-235.
Richelle, M., Tavazzi, I., Enslen, M., & Offord, E. (1999). Plasma kinetics in man of epicatechin from black chocolate. European Journal of Clinical Nutrition, 53(1), 22-26.
Rios, L. Y., Bennett, R. N., Lazarus, S. A., Rémésy, C., Scalbert, A., & Williamson, G. (2002). Cocoa procyanidins are stable during gastric transit in humans. The American Journal of Clinical Nutrition, 76(5), 1106-1110.
Robbins, R. J., Leonczak, J., Li, J., Johnson, J. C., Collins, T., Kwik-Uribe, C., & Schmitz, H. H. (2012). Determination of flavanol and procyanidin (by degree of polymerization 1–10) content of chocolate, cocoa liquors, powder (s), and cocoa flavanol extracts by normal phase high-performance liquid chromatography: collaborative study. Journal of AOAC International, 95(4), 1153-1160.
Rudell, D. R., Mattheis, J. P., & Fellman, J. K. (2005). Evaluation of diphenylamine derivatives in apple peel using gradient reversed-phase liquid chromatography with ultraviolet–visible absorption and atmospheric pressure chemical ionization mass selective detection. Journal of Chromatography A, 1081(2), 202-209.
Rzeppa, S., Bittner, K., Döll, S., Dänicke, S., & Humpf, H. U. (2012). Urinary excretion and metabolism of procyanidins in pigs. Molecular Nutrition & Food Research, 56(4), 653-665.
Salo, J., Uhari, M., Helminen, M., Korppi, M., Nieminen, T., Pokka, T., & Kontiokari, T. (2012). Cranberry juice for the prevention of recurrences of urinary tract infections in children: a randomized placebo-controlled trial. Clinical Infectious Diseases, 54(3), 340-346.
Sánchez-Patán, F., Cueva, C., Monagas, M., Walton, G. E., Gibson, G. R., Martín-Álvarez, P. J., Moreno-Arribas, M. V., & Bartolomé, B. (2012). Gut microbial catabolism of grape seed flavan-3-ols by human faecal microbiota. Targetted analysis of precursor compounds, intermediate metabolites and end-products. Food Chemistry, 131(1), 337-347.
Sano, A., Yamakoshi, J., Tokutake, S., Tobe, K., Kubota, Y., & Kikuchi, M. (2003). Procyanidin B1 is detected in human serum after intake of proanthocyanidin-rich grape seed extract. Bioscience, Biotechnology, and Biochemistry, 67(5), 1140-1143.
210
Setchell, K. D., Brown, N. M., Desai, P. B., Zimmer-Nechimias, L., Wolfe, B., Jakate, A. S., Creutzinger, V., & Heubi, J. E. (2003). Bioavailability, disposition, and dose-response effects of soy isoflavones when consumed by healthy women at physiologically typical dietary intakes. The Journal of Nutrition, 133(4), 1027-1035.
Simonetti, P., Gardana, C., & Pietta, P. (2001). Plasma levels of caffeic acid and antioxidant status after red wine intake. Journal of Agricultural and Food Chemistry, 49(12), 5964-5968.
Singleton, V., & Rossi Jr, J. (1965). Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. American Journal of Enology and Viticulture, 16(3), 144.
Sobota, A. (1984). Inhibition of bacterial adherence by cranberry juice: potential use for the treatment of urinary tract infections. The Journal of Urology, 131(5), 1013-1016.
Spencer, J. P., Abd El Mohsen, M. M., Minihane, A.-M., & Mathers, J. C. (2008). Biomarkers of the intake of dietary polyphenols: strengths, limitations and application in nutrition research. British Journal of Nutrition, 99(01), 12-22.
Spencer, J. P., Chaudry, F., Pannala, A. S., Srai, S. K., Debnam, E., & Rice-Evans, C. (2000). Decomposition of cocoa procyanidins in the gastric milieu. Biochemical and Biophysical Research Communications, 272(1), 236-241.
Spencer, J. P., Schroeter, H., Shenoy, B., S Srai, S. K., Debnam, E. S., & Rice-Evans, C. (2001). Epicatechin is the primary bioavailable form of the procyanidin dimers B2 and B5 after transfer across the small intestine. Biochemical and Biophysical Research Communications, 285(3), 588-593.
Stothers, L. (2002). A randomized trial to evaluate effectiveness and cost effectiveness of naturopathic cranberry products as prophylaxis against urinary tract infection in women. Canadian Journal of Urology, 9, 1558-1562.
Stoupi, S., Williamson, G., Drynan, J. W., Barron, D., & Clifford, M. N. (2010). A
comparison of the in vitro biotransformation of (–)‐epicatechin and procyanidin B2 by human faecal microbiota. Molecular Nutrition & Food Research, 54(6), 747-759.
Stoupi, S., Williamson, G., Viton, F., Barron, D., King, L. J., Brown, J. E., & Clifford, M. N. (2010). In vivo bioavailability, absorption, excretion, and pharmacokinetics of [14C] procyanidin B2 in male rats. Drug Metabolism and Disposition, 38(2), 287-291.
211
Törrönen, R., McDougall, G. J., Dobson, G., Stewart, D., Hellström, J., Mattila, P., Pihlava, J.-M., Koskela, A., & Karjalainen, R. (2012). Fortification of blackcurrant juice with crowberry: Impact on polyphenol composition, urinary phenolic metabolites, and postprandial glycemic response in healthy subjects. Journal of Functional Foods, 4(4), 746-756.
Tsang, C., Auger, C., Mullen, W., Bornet, A., Rouanet, J.-M., Crozier, A., & Teissedre, P.-L. (2005). The absorption, metabolism and excretion of flavan-3-ols and procyanidins following the ingestion of a grape seed extract by rats. British Journal of Nutrition, 94(02), 170-181.
Walker, E. B., Barney, D. P., Mickelsen, J. N., WALYON, R., & Mickelsen, R. (1997). Cranberry concentrate: UTI prophylaxis. Journal of Family Practice, 45(2), 167-168.
Walsh, M. C., Brennan, L., Pujos-Guillot, E., Sébédio, J.-L., Scalbert, A., Fagan, A., Higgins, D. G., & Gibney, M. J. (2007). Influence of acute phytochemical intake on human urinary metabolomic profiles. The American Journal of Clinical Nutrition, 86(6), 1687-1693.
Westerhuis, J. A., Hoefsloot, H. C., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J., van Duijnhoven, J. P., & van Dorsten, F. A. (2008). Assessment of PLSDA cross validation. Metabolomics, 4(1), 81-89.
Wiklund, S., Johansson, E., Sjöström, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., Gottfries, J., Moritz, T., & Trygg, J. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry, 80(1), 115-122.
Wilson, T., Singh, A. P., Vorsa, N., Goettl, C. D., Kittleson, K. M., Roe, C. M., Kastello, G. M., & Ragsdale, F. R. (2008). Human glycemic response and phenolic content of unsweetened cranberry juice. Journal of Medicinal Food, 11(1), 46-54.
Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., & Sawhney, S. (2007). HMDB: the human metabolome database. Nucleic Acids Research, 35(suppl 1), D521-D526.
Young, J. F., Nielsen, S. E., Haraldsdóttir, J., Daneshvar, B., Lauridsen, S. T., Knuthsen, P., Crozier, A., Sandström, B., & Dragsted, L. O. (1999). Effect of fruit juice intake on urinary quercetin excretion and biomarkers of antioxidative status. The American Journal of Clinical Nutrition, 69(1), 87-94.
Zampariello, C. A., McKay, D. L., Dolnikowski, G., Blumberg, J., & Chen, C. (2012). Determination of cranberry proanthocyanidin A2 in human plasma and urine using LC-MS/MS. The FASEB Journal, 26, 124.128.
212
Zhang, Y., Song, T. T., Cunnick, J. E., Murphy, P. A., & Hendrich, S. (1999). Daidzein and genistein glucuronides in vitro are weakly estrogenic and activate human natural killer cells at nutritionally relevant concentrations. The journal of nutrition, 129(2), 399-405.
213
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.