Metabolic Profiling Comparison of Human

r Biomar ula ke c rs le o & Watanabe et al., J Mol Biomark Diagn 2012, S:3 M D

f i

o Journal of Molecular

n DOI: 10.4172/2155-9929.S3-002

r s

i u s o

J Biomarkers & Diagnosis ISSN: 2155-9929

Research Article Open Access Metabolic Profiling Comparison of Human Pancreatic Ductal Epithelial Cells and Three Pancreatic Cancer Cell Lines using NMR Based Metabonomics Miki Watanabe1, Sulaiman Sheriff2, Kenneth B. Lewis1, Junho Cho1, Stuart L. Tinch1 Ambikaipakan Balasubramaniam2,3,4 and Michael A. Kennedy1* 1Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio, USA 2Department of surgery, University of Cincinnati Medical Center, Cincinnati, Ohio, USA 3Shriners Hospital for Children, Cincinnati, OH 45229, USA 4Cincinnati Veterans Affairs Medical Center, Cincinnati, OH 45220, USA

Abstract Metabolic profiles of hydrophilic and lipophilic cell extracts from three cancer cell lines, Miapaca-2, Panc-1 and AsPC-1, and a non-cancerous pancreatic ductal epithelial cell line, H6C7, were examined by proton nuclear magnetic resonance spectroscopy. Over twenty five hydrophilic metabolites were identified by principal component and statistical significance analyses as distinguishing the four cell types. Fifteen metabolites were identified with significantly altered concentrations in all cancer cells, e.g. absence of phosphatidylgrycerol and phosphatidylcholine, and increased phosphatidylethanolamine and cholesterols. Altered concentrations of metabolites involved in glycerophospholipid metabolism, lipopolysaccharide and fatty acid biosynthesis indicated differences in cellular membrane composition between non-cancerous and cancer cells. In addition to cancer specific metabolites, several metabolite changes were unique to each cancer cell line. Increased N-acetyl groups in AsPC-1, octanoic acids in Panc-1, and UDP species in Miapaca-2 indicated differences in cellular membrane composition between the cancer cell lines. Induced glutamine metabolism and protein synthesis in cancer cells were indicated by absence of glutamine other metabolites such as acetate, lactate, serine, branched amino acids, and succinate. Knowledge of the specifically altered metabolic pathways identified in these pancreatic cancer cell lines may be usefulfor identifying new therapeutic targets and studying the effects of potential new therapeutic drugs.

Keywords: Pancreatic cancer; Miapaca-2; Panc-1; AsPC-1; H6C7; chromatography mass spectrometry (GC-MS), liquid chromatography NMR; Metabonomics; Metabolic profiling; PCA mass spectrometry (LC-MS), and capillary electrophoresis mass spectrometry are a few of the techniques used for metabolic profiling Abbreviations: BMRB: Biological Magnetic Resonance Data Bank; in both research and clinical applications [4]. For example, GC-MS has FBS: Fetal Bovine Serum; GalNAc: N-Acetylgalactosamine; GC-MS: Gas been used to differentiate between invasive ovarian carcinomas and Chromatography Mass Spectrometry; GlcNAc: N-Acetylglucosamine; borderline tumors in flash frozen human tumor samples (reviewed in H6C7: Immortalized Human Pancreatic Ductal Epithelial Cell Line; Griffin et al.) [5]. Sarcosine was suggested as a potential biomarker for HPDE6-E6E76c7: HDPE Human Ductal Pancreatic Epithelial; LC- prostate cancer progression by LC-MS based metabonomics analysis MS, Liquid Chromatography Mass Spectrometry; NMR: Nuclear [6]. By using NMR spectroscopy, several metabolites such as lactate Magnetic Resonance; NAA: N-Acetylaspartic Acid; NOESY: Nuclear in blood plasma and creatinine and phenylacetylglycine in urine were Overhauser Effect Spectroscopy; PC: Principal Component; PCA: identified as potential biomarkers for lung cancer [7]. In pancreatic Principal Component Analysis; TCA cycle: Tricarboxylic Acid Cycle; cancer research, a number of studies have recently reported the TSP: Trimethylsilylpropionate; UDP: Uridine Diphosphate potential identification of early biomarkers in biofluids using metabolic profiling [8-11]. Despite these encouraging studies, individual Introduction variability remains a challenge for the discovery and validation of Pancreatic cancer is one of the most lethal human cancers. disease biomarkers in human samples. According to the cancer statistics from 2010, more than 40 thousand Metabonomics can be used not only for biofluids, but also for cell patients are expected to be diagnosed in coming year [1]. Pancreatic cultures and tissues. As is the case in most cancer research, the majority cancer has the lowest five-year survival rate among all types of cancers. One of the reasons for such high mortality is the lack of early detection and diagnostic methods [2]. Most diagnoses occur after the cancer has progressed to an advanced stage when a surgical cure is not feasible. *Corresponding author: Michael A. Kennedy, Miami University, 106 Hughes Laboratories, Department of Chemistry and Biochemistry, Miami University, Biopsies, tomography scans, endoscopic ultrasound and endoscopic Oxford, OH 45056,USA Tel: 513-529-8267; E-mail: [email protected] retrograde cholangiopancreatography are some of the current detection Received November 01, 2011; Accepted March 06, 2012; Published March 12, and diagnostic methods for pancreatic cancer [2,3]. However, these 2012 methods are highly invasive and not suitable for general population screening. Development of an early detection method for pancreatic Citation: Watanabe M, Sheriff S, Lewis KB, Cho J, Tinch SL, et al. (2012) Metabolic Profiling Comparison of Human Pancreatic Ductal Epithelial Cells and Three cancer is essential in order to improve the likelihood that diagnosis will Pancreatic Cancer Cell Lines using NMR Based Metabonomics. J Mol Biomark occur before the cancer is at a stage that is too advanced to treat. Diagn S3:002. doi:10.4172/2155-9929.S3-002 Metabonomics is a non-invasive method used to identify Copyright: © 2012 Watanabe M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits biomarkers in biofluids such as urine, serum, plasma, stool, saliva unrestricted use, distribution, and reproduction in any medium, provided the and blood. Nuclear magnetic resonance (NMR) spectroscopy, gas original author and source are credited

J Mol Biomark Diagn Biomarkers Discovery & Validation ISSN:2155-9929 JMBD an open access journal Citation: Watanabe M, Sheriff S, Lewis KB, Cho J, Tinch SL, et al. (2012) Metabolic Profiling Comparison of Human Pancreatic Ductal Epithelial Cells and Three Pancreatic Cancer Cell Lines using NMR Based Metabonomics. J Mol Biomark Diagn S3:002. doi:10.4172/2155-9929.S3-002

Page 2 of 17 of pancreatic cancer studies are based on in vitro examinations using immortalized HDPE cell line HPDE6-E6E76c7 (H6C7) was obtained various cell lines. Cancer cell lines are widely used to study potential from Dr. Tsao (Ontario cancer institute/Princess Margaret hospital, therapeutic drugs, to identify proteins with altered expression levels, University Health Network). Miapaca-1 and Panc-1 cells were grown gene mutations, and inactivation or activation of metabolic pathways in high glucose Dulbecco’s Modified Eagle Medium (Hyclone) [12-14]. Several metabonomics studies have shown differences in supplemented with 10% fetal bovine serum (FBS), penicillin and metabolic profiles among different cancer cell lines [15] and the streptomycin. Cells were maintained in a 5% CO2, humidified metabolic processes that occur during apoptosis and cell cycle phases atmosphere at 37°C. AsPC1 cells were grown in Gibco® RPMI medium [16,17]. In addition, monitoring metabolic profiles can also be useful (Invitrogen) supplemented with 10% FBS, penicillin and streptomycin. to study the effect of various stimuli such as drug treatments [18,19]. H6C7 cells were maintained in keratinocyte serum free media supplemented with epidermal growth factor and bovine pituitary Even though findings from cancer cell line studies have been extract (Invitrogen). Cells were maintained in 5% CO , humidified presented as generally characteristic of the disease, heterogeneity in 2 atmosphere at 37°C. Confluent cells were trypsinized and seeded into both the pathology of patients and between pancreatic cancer cell lines 100 mm dishes and maintained. When cells were grown to 75-85% must be anticipated. Sipos et al. have categorized the cellular structure, confluence in 10cm culture dishes, the media was aspirated and cells population doubling time, and functional marker expressions of twelve were trypsinized using trypsin-EDTA solution (Invitrogen). Fresh pancreatic ductal adenocarcinoma cell lines [20]. In addition, Monti et growth media containing 10% FBS was added to the trypsinized cells al. [21] further showed differences in expression of immunorelevant (10:1 ratio) and centrifuged for 5 minutes at 1,500 rpm at 4°C. The molecules, secretion of immunomodulatory cytokines and susceptibility media was aspirated and the cell pellet was washed with 10 ml of cold to apoptosis and chemotherapeutic agents [21]. phosphate buffered saline, pH 7.4, and centrifuged again as above. This Three human pancreatic cancer cell lines, Miapaca-2, Panc-1 and step was repeated two more times and the cell pellets were stored at AsPC-1, were used in this study and table 1 provides a summary of the -80°C until removal for cell extraction. characteristics of the cell lines. According to these comparative studies, Cell extraction: Methanol-chloroform-water extraction was the Miapaca-2 cell line originated from a patient with a primary performed as previously described [23]. Briefly, cell pellets were adenocarcinoma tumor and has epithelial cell like morphology. These resuspended in 500 µL of ice-cold 2:1 (v/v) methanol:chloroform cells are poorly differentiated and have a large abundant cytoplasm. It solution and then transferred into a 1.5 ml Eppendorf tube. After has been shown that the doubling time of Miapaca-2 is about 40 hours. vortexing, the tubes were incubated on a mixer for 10 min at 4ºC. Then, Panc-1 cells are often used as an in vitro model of non-endocrine 250 μl of ice-cold H2O and 250 μl of chloroform were added to the cells pancreatic cancer for tumorigenicity studies. These cells were isolated and mixed using a vortex mixer. The tubes were sonicated in a water from a primary tumor in the pancreatic duct of an epithelioid carcinoma bath for 10 min and centrifuged for 10 min at 17949xg. The top layer, patient. Panc-1 also shows epithelial cell like morphology and is poorly i.e. the hydrophilic extract, and bottom layer, i.e. the lipophilic extract, differentiated. The doubling time of Panc-1 is around 56 hours. AsPC- were transferred into new Eppendorf tubes. The samples were dried 1 cells originated from pancreas ascites from an adenocarcinoma in a SpeedVac centrifuge for 2-3 h and stored at -20°C until further patient. These cells also have the epithelial cell like morphology but are preparation for NMR analysis. moderately to highly differentiated with the doubling time of about 58 hours. In this study, the metabolic profiles of these three pancreatic NMR spectroscopy: Hydrophilic cell extracts were resuspended in cancer cell lines were examined by NMR spectroscopy and compared 200 μL of buffer (150 mM potassium phosphate at pH 7.4, 1 mM NaN3, with immortalized human pancreatic ductal epithelial cells. 0.01 % trimethylsilylpropionate (TSP) in 100% D2O (the required quantity of buffer for each sample was originally prepared inH O, Because the majority (~90%) of human pancreatic cancers belong 2 lyophilized, the reconstituted in 100% D2O) and lipophilic cell extracts to an adenocarcinoma of ductal epithelial origin, a non-cancerous were resuspended in 200 μL deuterated chloroform. All NMR spectra immortalized human pancreatic ductal epithelial (HPDE) cell line were recorded on a Bruker AvanceTM III spectrometer operating at HPDE6-E6E76c7 (H6C7) [22] was used as a control in this study. 850.1 MHz. All experiments were conducted at 293 K using 3 mm NMR NMR spectra of hydrophilic and lipophilic extracts from three human tubes (Norell). A standard 1H 1D presaturation experiment (zgpr) and cancer cell lines, Miapaca-2, Panc-1, and AsPC-1 were compared to the the first increment of 1D NOESY (noesygppr1d) experiment were control to identify unique metabolic profiling characteristics of each cell recorded and processed for both hydrophilic and lipophilic samples as line. This study provides not only potential biomarkers for pancreatic previously reported [24]. All data were collected using a spectral width cancer, but also provides insight into the metabolic profiles of cell lines of 20.0 ppm, 64K points resulting in an acquisition time of 1.93 s, and often used for in vivo therapeutic studies. These metabonomic profiles on-resonance presaturation for solvent suppression during a 4 s recycle can be further used to study the effect of the potential therapeutic drugs. delay. The zgpr experiment was used to screen samples and check Materials and Methods shimming. For hydrophilic samples, 1D NOESY spectra were collected with 64 scans, 4 dummy scans, 10 ms mixing time, and presaturation at Pancreatic cancer cell line cultures: Human pancreatic the residual water frequency. The 90º pulse widths, measured for each adenocarcinoma cells, Panc-1, Miapaca-2 and AsPC1, were obtained sample using the automatic pulse calculation experiment (pulsecal) in from American Type Culture Collection (Manassas, VA). The TopSpin 2.1.1 (Bruker BioSpin, Billerica, MA), were between 8.3 and

Cell line Origin Morphology Est. doubling time Ref. Miapaca-2 primary adenocarcinoma tumor epithelial cell 40 hrs 45 Panc-2 primary tumor in the pancreatic duct of an epithelioid carcinoma epithelial cell 56 hrs 46 AsPC-1 pancreas ascites from an adenocarcinoma patient epithelial cell 58 hrs 47 H6C7 pancreatic ductal epithelial cell 17 Table 1: A summary of characteristics of cell lines used in the study.

Page 3 of 17

8.8 μs. For lipophilic samples, first increments of 1D NOESY spectra - 0). One-dimensional heat-map plots were generated using Microsoft were collected with 128 scans, 4 dummy scans and an offset frequency Excel as described previously [25]. Mean differences for bucket of 6194.14 Hz (7.36 ppm) in order to saturate the signal from residual intensities between groups were calculated by subtracting one group of chloroform. The 90º pulse widths for the lipophilic samples were normalized bucket intensity means from another group of normalized between 7.7 and 8.1 μs. bucket intensity means. Fold-changes were calculated by dividing the treated bucket means by the control bucket means. For fold-changes Multivariate statistical analysis of NMR spectra: Principal less than 1.0, the negative inverse of the treated to control ratios were components analyses (PCA) and statistical significance analyses were reported and the resulting values were referred to as “negative” fold- performed using AMIX (Bruker BioSpin, Billerica, MA). Only first changes. increments of 1D NOESY spectra were used for PCA. The numbers of cells were counted in each culture dish prior to trypsin treatment. Identification of metabolites: The statistical significance analysis Two different spectral normalization techniques were tested including described by Goodpaster et al. [25] was used to identify bucket normalization to total intensity and normalization to the total cell frequencies that showed statistically significant changes in intensity count. We assessed the performance of the two methods by evaluating in comparisons between cell lines. Metabolites corresponding to these the cluster spread of replicate samples in a PCA scores plot. We found resonances were then identified using chemical shift assignments of that normalization to total intensity produced tighter clustering spectra of hydrophilic cell extracts based on comparison with chemical compared to normalization by total cell count. Therefore, the shifts of metabolites in Biological Magnetic Resonance Data Bank normalization to total intensity technique was used and all spectra were (BMRB) (http://www.bmrb.wisc.edu/metabolomics/), in the ChenomX normalized to total intensity prior to PCA. An internal TSP standard of NMR Suite (ChenomX Inc., Edmonton, Alberta, Canada) and in other known concentration (580 µM) was included in all samples. Analysis published data [18,26-28]. For some tentatively assigned metabolites, of the TSP concentrations following normalization indicated that the an authentic standard sample was purchased (Sigma Aldrich), difference in the TSP concentrations among replicates of the same cell prepared in the hydrophilic buffer, and NMR spectra collected as line group, or between replicates of different cell line groups, was not above for comparison of chemical shifts and confirmation of tentative statistically significant (p > 10-1 for all TSP concentration comparisons). assignments. Chemical shifts of resonances in lipophilic extracts This analysis indicated that the normalization procedure did not were compared against the chemical shifts of resonances of known introduce significant systematic error into the peak intensities. For metabolites in the BMRB and other published data [18,19,23,29]. PCA, the Advanced Bucketing feature in Amix was used and up to five principal components had to be considered in order to account for 95% Determination of metabolite concentrations. Concentrations of the variance in the data (Table 2). Peaks that experienced less than of metabolites for each treatment group were determined in each 5% variance were excluded from PCA to simplify data analysis. The replicate using the ChenomX NMR Suite software package (http:// bucket width was optimized to match the width of the resonances at www.chenomx.com/) by quantitative comparison of metabolite peak the base of the peaks, so that the integrated bucket intensities reported intensities relative to that of the TSP added to each sample as both a accurately on the integrated peak areas. Spectra were binned into chemical shift and concentration reference standard. Specifically, 0.005 (hydrophilic) and 0.03 (lipophilic) ppm wide buckets over the the ChenomX software was used to adjust the intensity of the NMR region 0.01 - 10.0 ppm. A critical value (α-value) of 0.05 was selected spectrum of the metabolite stored in the reference database until it to ensure a false-positive rate of no more than 5% in determination of matched the intensity of the peaks in the experimental spectrum, significant changes in metabolite concentrations following treatment and concentrations of the metabolites were measured relative to the based on p-score evaluation. In order to maintain a constant family- internal TSP standard of known concentration, in this case, 580 mM. wise error, and to correct for making multiple simultaneous inferences Since the concentration measurements were based on quantification from each data set, a Bonferroni correction was applied by dividing α relative to an internal standard, the concentration determinations by the number of buckets used in the PCA. The Bonferroni-corrected were independent of any potential normalization artifacts. Only non- α-values using 0.005 ppm buckets for hydrophilic cell extracts, overlapped peaks were used to determine concentrations for each determined for each comparison, are listed in Table 2. The Bonferroni- metabolite. For metabolites containing multiple non-overlapped corrected α-values using 0.03 ppm buckets for lipophilic cell extracts peaks, the means and standard deviations of the concentrations were were determined for each comparison and are also listed in Table 2. calculated using all non-overlapped peaks and using all replicate data. Changes in metabolite concentrations with corresponding p-values Results and Discussion less than the Bonferroni-corrected α-value were considered statistically significant. The loading plot data points were color-coded according NMR data and spectral assignments: NMR data were collected on to bucket p-values: Black (>α-value, i.e. not statistically significant), six, or seven in the case of H6C7, replicate samples of hydrophilic and Blue (α-value >10-5), Green (10-5 - 10-6), Yellow (10-6 - 10-7), Red (10-7 lipophilic cell extracts from each cell line: H6C7, Miapaca-2, Panc-1 and

Hydrophilic extract (0.005ppm) Lipophilic extracts (0.03ppm) Total number of Significant Bonferroni-corrected Total number of Significant Bonferroni-corrected α buckets buckets α-values buckets buckets -values H6C7 – Miapaca-2 250 42 4.76x10-4 48 24 1.04x10-3 - Panc-1 174 41 5.56x10-4 53 26 9.43x10-4 - AsPC-1 81 43 6.17x10-4 47 20 1.06x10-3 Miapaca-2 – Panc-1 84 30 5.95x10-4 39 5 1.28x10-3 - AsPC-1 54 14 9.26x10-4 39 4 1.28x10-3 Panc-1 – AsPC-1 49 14 1.02x10-4 35 0 1.43x10-3 Table 2: Total number of buckets for each comparison (after excluding those with less than 5% variance), the number of significant buckets, and Bonferroni-corrected a-values of pair–wise PCA analysis for each comparison from hydrophilic and lipophilic extracts.

Page 4 of 17

AsPC-1. Representative spectra of hydrophilic and lipophilic extracts of Miapaca-2 to Panc-1, Miaapaca-2 to AsPC-1, and Panc-1 to AsPC- each cell line are shown in Figures 1&2, respectively. Expected water- 1. Scores plots and loadings plots of each analysis are shown in soluble cell metabolites such as amino acids, organic acids and bases Supplementary Figure S2. All comparisons indicated complete group were observed in spectra of hydrophilic extract samples and partial discrimination and separation of the groups into distinct clusters at assignments are listed in Table 3. Water-insoluble metabolites such as the 95% confidence intervals. All buckets with significant changes and fatty acids and cholesterol were observed in spectra of lipophilic extract their corresponding p-values are listed in (Table 3). One-dimensional samples and partial assignments are listed in Table 4. heat-map plots for the same comparisons (Figure 4) show how the Unsupervised principal component analysis (PCA): In order significant buckets were distributed across the spectra. Globally, to obtain a global sense of how the metabolic profiles of the cell lines similar numbers of significant buckets, (ranging from 41 to 43, (Table compared, an unsupervised PCA was carried out on the 25 normalized 2) were found in the comparisons between H6C7 cells and each of the 1D 1H-NMR spectra obtained from hydrophilic cell extracts from all cancer cell lines (Figure 4A-C). Very similar patterns of positive mean four cell lines. The PCA scores plot of the hydrophilic cell extracts using differences of bucket intensities in the aliphatic chemical shift range, the first two PCs showed complete separation of all cell lines at the 95% around 0.00 to 3.17 ppm, were observed in these comparisons. Fewer confidence intervals (Figure 3A). The loadings plot corresponding to significant buckets were detected ranging from 14 to 30, (Table 2) in the scores plot for the hydrophilic cell extracts is shown in Figure S1A. the comparisons between cancer cell lines (Figure 4D-F). The lipophilic extract data was analyzed in a similar manner. Distinct separation was found between normal pancreatic ductal epithelial Pair-wise PCA and statistical significant analysis of lipophilic cell cells and pancreatic cancer cells however no distinct separation was extracts: PCA scores plots for pair-wise comparisons of lipophilic cell observed among the cancer cell lines (Figure 3B).The loadings plot extracts showed complete separation between H6C7 and all cancer cell corresponding to the scores plot for the lipophilic cell extracts is shown line extracts (Supplementary Figure S3). Pair-wise PCA on lipophilic in Fig ure S1 B. extracts of different cancer cells showed no separation between cancer cell lines (Supplementary Figure S3 D-F). One-dimensional heat-maps Pair-wise PCA and statistical significant analysis of hydrophilic showed similar patterns of significant buckets in the comparisons cell extracts: Six pair-wise PCA comparisons were conducted, between each cancer cell line and the normal pancreatic cells including H6C7 to Miapaca-2, H6C7 to Panc-1, H6C7 to AsPC-1, (Figure 5A-C). Few significant buckets were found in comparisons between cancer cells (Figure 5D-F). All significant buckets and their corresponding p-values are listed in (Table 4). A) Identification of metabolites and determination of fold- changes: Over 25 hydrophilic metabolites were identified as causing separation between healthy and cancer cell lines in PCA scores plots. B) Metabolites identities along with p-values and fold-changes for each significant bucket are summarized in (Table 3&4). Buckets that C) contained resonances from more than one metabolite were eliminated from this table. Peaks that clearly belonged to the same metabolite but

D) experienced some peak shifting between cell line extracts were aligned prior to statistical significance analysis. This was a particular problem for peaks belonging to myo-inositol, which had clustered multiplets ppm 9.0 8.0 7.0 4.0 3.0 2.0 1.0 that experienced slight shifts between cell line extracts. In order to Figure 1: Representative one-dimensional 850 MHz 1H NMR spectra of calculate p-values for myo-inositol peaks, wider 0.01 ppm buckets hydrophilic extracts of each cell line. A) H6C7, B) Miapaca-2, C) Panc-1, D) were used to measure the integrated intensity of the entire multiplet AsPC-1. Spectral range displayed is 0.75 – 4.75 ppm and 6.5 -9.70 ppm. structures and the peaks were then aligned prior to integration. Once the metabolite identities were confirmed, the concentrations of the metabolites were measured relative to the internal TSP standard. The average and standard deviations of the concentrations ofmost A) of the hydrophilic metabolites along with the fold-changes based on concentration measurements are reported in (Table 5). The fold- changes obtained from changes in peak intensities and concentration

B) estimations were consistent. It was impossible to calculate reliable fold- changes when metabolites were absent or nearly absent in one of the cell lines being compared (e.g. acetamide and glutathione). C) Although it was challenging to assign metabolites in lipophilic extracts due to lack of complete databases, some cellular membrane D) components such as lipids and cholesterol were identified (Table 4). By comparison with data from previous literature, singlets at 0.61 and 0.94 ppm were assigned to C19 and C18 cholesterols and multiple 7.0 6.0 5.0 4.0 3.0 2.0 1.0 (ppm) features from fatty acids were identified.[19,23,29] The presence of Figure 2: Representative one-dimensional 850 MHz 1H NMR spectra of phospholipids was confirmed by comparing spectra to standards lipophilic extracts of each cell line. A) H6C7, B) Miapaca-2, C) Panc-1, D) of phosphatidylcholine and phosphatidylethanolamine, which had AsPC-1. Spectral range displayed is 0.35 – 2.65 ppm and 2.65 -7.30 ppm. expected singlets at 3.35 and 3.27 ppm, respectively.

Page 5 of 17

Bucket Miapaca-2 Fold Panc-1 Fold AsPC-1 Fold MP Fold MA Fold PA Fold Metabolites (ppm) P-Value change P-Value change P-Value change P-Value change P-Value change P-Value change

Adenine /AMP 8.611 2.70E-03 -5.30 2.04E-10 -4.40 5.54E-14 -5.81 *** *** ***

ATP/ADP 8.538 6.00E-08 3.00 5.50E-02 2.73 *** *** 6.44E-05 -1.98 ***

3-hydroxyisovaleric acid 1.254 3.40E-12 <-10 5.74E-12 <-10 4.90E-12 <-10 *** *** ***

Acetamide 2.023 6.05E-14 6.99 1.00E-10 4.52 *** 4.70E-07 -1.54 7.32E-13 -5.97 6.65E-02 -3.86

Acetate 1.921 1.55E-05 3.23 6.79E-07 4.90 2.29E-06 2.38 1.02E-02 1.52 4.47E-02 -1.36 2.57E-04 -2.06

N-acetlyglucosamine 2.04 *** *** 4.55E-02 5.21 *** 5.47E-02 4.37 2.42E-15 7.38

Alanine 1.489 2.20E-01 -3.49 6.90E-15 <-10 2.70E-03 -2.06 9.12E-01 <-10 5.71E-01 1.94 4.84E-01 >10

1.481 5.65E-08 -1.77 3.35E-02 <-10 3.23E-09 -1.42 6.92E-07 -3.19 *** 2.51E-12 4.00

Aspartate 3.908 *** *** 8.50E-14 3.36 *** *** ***

3.901 *** *** 3.89E-13 4.73 *** *** ***

3.894 *** *** 1.59E-13 3.51 *** *** ***

Choline 3.209 3.17E-01 1.72 1.05E-09 -3.52 5.60E-11 >10 4.02E-02 -6.04 3.95E-03 8.50 3.45E-10 >10

Creatine 3.933 8.62E-01 >10 4.86E-08 1.89 *** 8.51E-01 <-10 6.66E-01 1.37 1.05E-05 -1.51

3.04 1.16E-01 1.07 5.50E-02 1.67 *** 7.23E-06 1.58 *** 2.76E-06 -1.62

Creatine phosphate 3.953 *** 1.10E-08 2.77 *** 7.36E-08 3.10 *** 4.94E-07 -2.21

3.046 1.52E-02 -1.55 *** *** 6.12E-08 4.24 *** 7.45E-01 >10

3.053 2.70E-03 -6.14 3.09E-07 2.06 2.70E-03 -16.70 *** *** 6.65E-02 -7.68

Glutamate 2.348 6.31E-01 -1.05 *** 1.27E-08 -2.06 8.29E-01 -1.00 1.10E-03 <-10 3.35E-07 -1.94

2.358 8.60E-01 1.01 *** 2.70E-03 -1.89 6.66E-01 1.10 2.06E-04 -1.92 6.65E-02 -2.10

*2.162 5.56E-13 -2.21 1.20E-07 -1.86 4.22E-13 -2.18 4.02E-02 -2.84 *** ***

*2.155 2.34E-12 -3.09 3.35E-02 -4.37 8.06E-13 -5.19 *** *** ***

*2.144 3.80E-14 -3.63 3.35E-02 -4.53 6.05E-15 -7.05 *** *** ***

*2.137 5.28E-11 -2.14 5.88E-12 -2.79 2.00E-13 -4.96 *** *** ***

*2.127 1.62E-08 -1.70 3.35E-02 -1.75 2.51E-12 -3.28 *** *** ***

*3.785 *** 3.35E-02 -2.34 1.04E-10 -2.18 *** 3.52E-06 -2.99 ***

*3.776 2.70E-03 -2.17 5.61E-13 -4.85 3.51E-08 -5.18 1.80E-03 1.41 9.94E-02 -1.48 ***

*3.769 2.70E-03 -2.03 9.94E-07 -1.40 8.32E-11 -2.12 *** *** ***

Glutamine 2.476 1.22E-12 -5.44 4.89E-01 <-10 4.15E-01 <-10 *** *** ***

2.47 7.47E-14 -6.42 4.89E-01 <-10 4.15E-01 <-10 *** *** ***

2.458 1.65E-13 -7.26 3.06E-01 <-10 1.52E-14 <-10 *** *** ***

2.45 2.31E-14 -6.11 3.06E-01 <-10 7.15E-17 <-10 *** *** ***

2.44 2.85E-13 -5.77 3.34E-15 <-10 3.58E-16 <-10 *** *** ***