Article pubs.acs.org/jpr
1
H NMR Spectroscopy of Fecal Extracts Enables Detection of Advanced Colorectal Neoplasia
Aurelien Amiot,*,†,‡ Anthony C. Dona,† Anisha Wijeyesekera,† Christophe Tournigand,§ Isabelle Baumgaertner,§ Yann Lebaleur,‡ Iradj Sobhani,‡ and Elaine Holmes† †
Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, U.K. ‡ Department of Gastroenterology, Henri-Mondor Hospital, APHP, EC2M3-Equipe Universitaire Paris Est-Créteil (UPRC)-Val de Marne, F-94010 Creteil, France § Department of Oncology, APHP, Henri-Mondor Hospital, F-94010 Creteil, France Creteil, AP-HP S Supporting Information *
ABSTRACT: Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its etiopathogenesis and earlier diagnosis. Here, we investigated the fecal metabolic phenotype of patients with advanced colorectal neoplasia and controls using 1H-nuclear magnetic resonance (NMR) spectroscopy and multivariate modeling. The fecal microbiota composition was assessed by quantitative real-time PCR as well as Wif-1 methylation levels in stools, serum, and urine and correlated to the metabolic profile of each patient. The predictivity of the model was 0.507 (Q2Y), and the explained variance was 0.755 (R2Y). Patients with advanced colorectal neoplasia demonstrated increased fecal concentrations of four short-chain fatty acids (valerate, acetate, propionate, and butyrate) and decreased signals relating to β-glucose, glutamine, and glutamate. The predictive accuracy of the multivariate 1 H NMR model was higher than that of the guaiac-fecal occult blood test and the Wif-1 methylation test for predicting advanced colorectal neoplasia. Correlation analysis between fecal metabolites and bacterial profiles revealed strong associations between Faecalibacterium prausnitzii and Clostridium leptum species with short-chain fatty acids concentration and inverse correlation between Faecalibacterium prausnitzii and glucose. These preliminary results suggest that fecal metabonomics may potentially have a future role in a noninvasive colorectal screening program and may contribute to our understanding of the role of these dysregulated molecules in the cross-talk between the host and its bacterial microbiota. KEYWORDS: colorectal cancer, 1H NMR, metabonomics, advanced colorectal neoplasia
■
homeostasis of the immune system.8,10,11 Accumulating evidence suggests that the presence of microbial pathogens or an imbalance in the equilibrium between the intestinal microbiome and its host could contribute to the development of CRC.2,12 High throughput sequencing technology has permitted pinpointing a specific shift in the bacterial phylogenetic core of CRC patients, including a significant increase in Bacteroides/Prevotella and Fusobacterium and a decrease in Faecalibacterium, Coriobacteridae, and Roseburia species.13−15 Animal experimental models have also supported the ability of several bacteria to promote colorectal carcinogenesis upon local inflammation, while other studies have demonstrated that the carcinogenic effect of these bacteria can be achieved by direct genotoxic effects or by inducing epigenetic changes.16−20 Recently, it has been estimated that 16% of cancers around the world may be due to microbes.21 However, the question remains as to whether the CRC-related dysbiosis is indicative of a contributive effect of the intestinal
INTRODUCTION Colorectal cancer (CRC) is a significant cause of morbidity and mortality in developed countries, with an estimated 1 million new cases diagnosed every year worldwide.1,2 The opportunity to remove polyps and prevent the progression of the adenoma to carcinoma sequence, thereby preventing the dramatic changes in patients’ outcome according to the CRC stage at the time of diagnosis (with a five-year survival rate of more than 80% in early stages and less than 10% in the case of metastasized cancer), has led health-care systems worldwide to set up mass screening programs mostly based on fecal occult blood test and/or colonoscopy.3−6 Previous studies have recently demonstrated that environmental factors, including dietary changes (elevated intake in red meat and fat as well as decreased in dietary fiber intake), constitute a major contributor of individuals’ susceptibility.2,7−9 Recent studies in both mice and humans have highlighted the symbiotic relationship of the intestinal microbiome with its host and its contribution to the regulation of physiological processes such as dietary energy harvest, processing of food constituents through various metabolic pathways, and maturation and © 2015 American Chemical Society
Received: March 31, 2015 Published: July 27, 2015 3871
DOI: 10.1021/acs.jproteome.5b00277 J. Proteome Res. 2015, 14, 3871−3881
Article
Journal of Proteome Research
adenomas with high-grade dysplasia or in situ carcinoma) or invasive CRC (invasion of malignant cells beyond the muscularis mucosae).
microbiome on the development of CRC or if it is an indicator of a specific shift in metabolic pathways of the tumor microenvironment. Indeed, although high throughput sequencing techniques have indicated substantial diversity in the gut communities, the functions of the core-microbiome have been shown to be highly conserved among individuals with differences largely only in minor functionally redundant species.22 Furthermore, contrary to expectation, it has been demonstrated that the composition of the microbiome colonizing CRC tissue and the adjacent nonmalignant mucosa confers a favorable microenvironment on the tumor.14 Therefore, it could be assumed that the dysbiosis observed in colorectal cancer patients could be more related to the metabolic changes in the tumor microenvironment rather than a primary contributor to CRC pathogenesis.14,19 Metabonomics represents an in vivo systems approach for studying the dynamic changes in hundreds to thousands of low molecular-weight metabolites in intact tissue or body fluid samples in relation to disease processes or other stimuli.23 Recent studies have demonstrated that high-resolution magic angle spinning nuclear magnetic resonance (NMR) of colonic tissue can provide accurate diagnostic and staging of CRC.24,25 In this setting, the use of 1H NMR to study biofluids such as fecal extracts could be a useful tool as a noninvasive screening test and may aid in the elucidation of etiopathogenic metabolic pathways involved in CRC. To clarify the role of fecal metabolites in the development of CRC, we investigated the performance accuracy of 1H NMR spectroscopy of fecal extracts for CRC diagnosis and its link to the composition of the fecal microbiome. The guaiac fecal occult blood test (FOBT), a current clinical indicator for CRC and the Wif-1 methylation test, since epigenetic silencing of Wif-1 has been associated with CRC, were used as comparators. The overall goal of the study was to ascertain if fecal metabolic phenotyping could enhance diagnostic screening for CRC or uncover new etiopathogenic mechanisms by mapping the association between the fecal microbiome and metabolome.
■
Sample preparation
Fecal extracts were prepared for NMR analysis by mixing thoroughly 100 mg of fecal material with 250 mL of a 25% acetonitrile solution. To improve metabolite extraction, 25 μg of 1.0 mm zirconia beads was added and the mixture was agitated for 10 s at 6370 g in a Precellys24 mini-Beadbeater (Precellys, Montigny le Bretonneux, France). Samples were then centrifuged for 30 min at 16,000g in 0.22 μm cellulose acetate filter centrifuge tubes, and the supernatants recovered. An aliquot of 50 μL of the supernatant was put into a glass vial, placed into a turbovap nitrogen flow for 1 h, and resuspended with 600 μL of potassium buffer (1.5 mM KH2PO4, 2 mM NaN3, 1 mM sodium 3-(trimethylsilyl)propionate-d4) in deuterium oxide (D2O), and vortexed and centrifuged at 16,000g for 1 min. To ensure that the amounts of water of each sample were similar, the coefficient of variation of the extraction protocol was calculated (0.0614). All consumables were obtained from Sigma (Gillingham, UK). Fecal extract samples were then transferred to a 5 mm outer diameter NMR tube for analysis (Wilmad-Lab glass, UK). Quantitation of methylation of Wif-1 gene by quantitative PCR
The methylation level of the Wif-1 gene was determined for each patient as previously described.26 Briefly, after total DNA extraction from stool, urine, and blood samples using a Qiamp DNA Mini kit (Qiagen) and sodium bisulfite conversion, the methylation level was quantified using using locus-specific PCR primers flanking a MGB probe (Table S1). Bacterial analysis of fecal samples
Bacterial DNA was extracted and amplified with real-time PCR using an ABI 7000 Sequence Detection System with software version 1.2.3 (Applied-Biosystems, Foster City, Ca, USA), as previously described.27 Briefly, total bacteria and bacterial groups and species in the microbiota were quantified and bacterial densities within the microbiome compared between patients with advanced colorectal neoplasia patients’ and controls. The primers and probe sequences have been described elsewhere (Table S2).27 To overcome the difference in water content between fecal samples, normalization was applied by subtracting the level found for each particular bacterial population from the all-bacteria content. Results are expressed as the log 10 of the number of bacteria per gram of stool.
PATIENTS AND METHODS
Study population and sample collection
Patients were selected from a cohort of 247 patients with an average risk of CRC (personal or familial history of colorectal polyp or cancer or any digestive symptom that required colonoscopy) referred to one of our two departments for screening colonoscopy between 2003 and 2007 as previously described.13,26 Fifty-five patients were randomly selected from this cohort for the present study. Exclusion criteria include previous history of colorectal surgery and inflammatory or infectious bowel diseases and patients with need for emergency colonoscopy. Patients were included after receiving information about the study and giving written informed consent. Medical history, any particular diets, and medications were collected during a personal interview. None of the study participants had taken antibiotics for at least 2 months prior to enrolment. Stool samples were collected 2 weeks to 3 days prior to colonoscopy and colon cleansing. The study was approved by the local ethical committee (number 2004-4 CCPPRB). The FOBT was performed using a guaiac Hemoccult test on all patients according to the manufacturer’s protocol.26 All participants underwent a complete colonoscopy with adequate colon cleansing. Patients were classified according to colonoscopy findings in two groups: patients with advanced colorectal neoplasia and normal individuals. Advanced colorectal neoplasia was defined as advanced adenomas (diameter >10 mm and/or villous component >20% and/or
1
H NMR spectroscopy of fecal water samples
1
H NMR spectra were acquired on a 600 MHz Bruker Avance spectrometer fitted with a 5 mm TXI probe and a 60 slot BACS autosampler (Bruker, Rheinstetten, Germany). Sample temperature was controlled at 300 K. Each spectrum consisted in 256 scans of 32K complex data points with a spectral width of 14 ppm (acquisition time 1.95 s). A standard 1-dimensional presaturation pulse sequence was used to suppress the residual water signal with low power selective irradiation at the water frequency during the recycle delay (2 s) and mixing time (0.15 s). A 90° pulse length of 8.8 μ was set for all samples after optimization of signal intensity on a representative sample. Spectra were transformed with 0.3 Hz line broadening and zero filling, manually phased, and baseline corrected using the TOPSPIN 2.0 software. Metabolites were identified using information found in the literature,28−30 on the web (Human Metabolome Database, http://hmdb.ca/), by use of chenoMx NMR suite 3872
DOI: 10.1021/acs.jproteome.5b00277 J. Proteome Res. 2015, 14, 3871−3881
Article
Journal of Proteome Research Table 1. Clinical Features of Study Subjects and Tumor Characteristics Number of subjects [n (%)] Age [median (IQR)] Male gender [n (%)] BMI [median (IQR)] (kg/m2) History of polyps [n (%)] Familial history of polyps/CRC Familial history of cancer Diabetes mellitus [n (%)] Dyslipidemia [n (%)] Specific diet [n (%)] Medication [n (%)] Colonoscopy indication [n (%)] Screening Symptoms
Table 2. List of Metabolites Found in Advanced Neoplasia and Control Patients n
Control patients (n = 22)
Advanced neoplasia (n = 33)
P
52.0 ± 12.0 15 (68%) 27.1 ± 13.5 5 (23%) 3 (14%) 13 (59%) 1 (5%) 1 (5%) 3 (14%) 13 (59%)
59.4 ± 6.9 25 (76%) 25.7 ± 3.9 4 (12%) 2 (6%) 11 (33%) 7 (21%) 9 (27%) 6 (18%) 22 (67%)
0.01 0.55 0.61 0.46 0.38 0.10 0.13 0.04 0.73 0.58
10 (45.5%) 12 (54.5%)
13 (39%) 30 (61%)
0.78 0.78
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
7.0 software (ChenoMx Inc.), and by use of statistical correlation spectroscopy (STOCSY) methods.31 Statistical analysis of 1H NMR data
Spectra were converted into statistical matrices using in-house tools developed in MATLAB (v.2013a), as previously described.25 Spectral regions containing water, ethanol, polyethylene glycol, and TSP signals were excluded from the analysis. Normalization of the spectra was performed to the median of the control samples, and spectra were scaled to unit variance. Unsupervised (PCA: primary component analysis) and supervised (orthogonal projections to latent structures discriminant analysis: OPLS-DA) multivariate analyses were performed using SIMCA-P + (v.13.0.1, Umetrics AB, Sweden) and MATLAB in order to identify the most discriminant signals between advanced colorectal neoplasia and control spectra (correlation value >0.2). The 7-fold within model cross validation with cross validated ANOVA testing and permutation testing (n = 10 000 replicates) were applied for internal validation of the Q2Y statistical parameter of the whole multivariate analyses. Receiver operator characteristic (ROC) curves were con structed using the cross-validated Y-values of the OPLS-DA model in SIMCA-P+. The areas under the ROC curves (AUROC) were calculated for OPLS-DA, FOBT, and Wif-1 methylation test models and compared as previously described.32 Hierarchical cluster analysis (HCA) was performed using SIMCA-P+. Metabolite concentrations were calculated using MATLAB and by use of chenoMx NMR suite 7.0 software as the AUC for each compound of interest. Metabolite concentration within groups was compared using a Mann−Whitney nonparametric test with SPSS software V17. Patients’ baseline characteristics of each group were compared using the Pearson chi-square test or Fisher’s exact test and the Wilcoxon nonparametric test whenever approriate. Bacterial populations were given for 1 SD of the log-transformed levels calculated for each control group. The Spearman’s two-tailed test was used to assess correlations between metabolites concentration and each group of bacteria as well as Wif-1 methylation tests. All comparisons were 2-sided.
■
21
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
RESULTS
Study population
Metabolite
δ1H (ppm)
Leucine Isoleucine Valine Lactate Threonine Alanine Lysine Arginine Acetate Proline
3.72(t), 1.69(m), 0.97(d), 0.94(d) 0.93(t), 1.00(d), 1.28(m), 1.47(m), 1.96(m), 3.68(d) 3.59(d), 2.25(m), 0.98(d), 1.03(d) 4.11(q), 1.33(d) 3.60(d), 4.26(m), 1.33(d) 3.81(q), 1.48(d) 3.77(t), 1.92(m), 1.73(m), 1.47(m), 3.05(t) 3.76(t), 1.89(m), 1.63(m), 3.23(t) 1.92(s) 1.99(m), 2.05(m), 2.36(m), 3.34(m), 3.45(m), 4.14(m) Glutamate 3.78(m), 2.06(m), 2.36(m) Methionine 3.87(m), 2.16(m), 2.65(dd), 2.15(s) Glutamine 3.78(m), 2.15(m), 2.46(m) Aspartate 3.92(m), 2.70(m), 2.81(m) Asparagine 4.01(m), 2.87(dd), 2.96(dd) Ethanolamine 3.15(t), 3.78(t) Glycine 3.55(s) Uracil 5.81(d), 7.59(d) Tyrosine 3.06(dd), 3.16(dd), 3.94(dd), 6.87(d), 7.20(d) Phenylalanine 7.44(m), 7.39(m), 7.33(m), 3.17(dd), 3.30(dd), 3.99(dd) α-Glucose 3.42(t), 3.54(dd), 3.71(t), 3.72(dd), 3.83(dd), 3.84(m), 5.23(d) β-Glucose 3.24(dd), 3.40(t), 3.47(dd), 3.90(dd), 4.64(d) Formate 8.46(s) Tryptophan 7.79(d), 7.56(d), 7.34(s), 7.29(t), 7.21(t), 4.06(dd), 3.49(dd), 3.31(dd) n-Butyrate 0.90(t), 2.16(t), 1.56(m) Isocaproate 0.92(d), 1.54(q), 1.61(m), 2.36(t) Propionate 2.19(q), 1.06(t) α-Hydroxyisovalerate 3.85(d), 2.02(m), 0.97(d), 0.84(d), 1.36(s) Myoinositol 3.53(dd), 4.06(t), 3.28(t), 3.63(t) Fumarate 6.53(s) Succinate 2.42(s) 5-Aminovalerate 3.02(t), 2.24(t), 1.65(m) Phenylacetic acid 3.52(s), 7.29(t), 7.36(t) Malonate 3.13(s) Trimethylamine 2.88(s) Methylmalonate 1.23(d), 3.16(q) Methylsuccinate 1.09(d), 2.13(dd), 2.53(dd), 2.63(m) Isovalerate 0.91(d), 1.96(m), 2.06(d) n-Valerate 0.89(t), 1.31(m), 1.53(m), 2.18(t) Uridine 4.36(t), 5.91(d), 5.92(d), 7.88(d) Glycerol 3.57(dd), 3.67(dd), 3.79(m Taurine 3.27(t), 3.43(t) Cadaverine 1.48(m), 1.73(m), 3.02(t) Putrescine 1.77(m), 3.06(m) Choline 3.20(s), 3.53(m) N-Acetyl-cysteine 2.06(s), 2.92(m), 4.38(m) Cytidine 3.81(dd), 3.92(m), 4.11(m), 4.20(t), 4.30(t), 5.89(d), 6.05(d), 7.83(d) Caprate 0.85(s), 1.27(s), 1.53(s), 2.16(s) Citrate 2.53(d), 2.65(d) Thymine 1.86(s), 7.37(s) Serine 3.85(dd), 3.96(dd), 4.00(dd) Homoserine 2.01(m), 2.16(m), 3.77(m), 3.85(dd) Isobutyrate 1.07(d), 2.39(m) Cholate 0.71(s), 1.02(s)
adenomas (simater
