Abstract

   The pattern of metabolites produced by the gut microbiome comprises a
   phenotype indicative of the means by which that microbiome affects the
   gut. We characterized that phenotype in mice by conducting metabolomic
   analyses of the colonic-cecal contents, comparing that to the
   metabolite patterns of feces in order to determine the suitability of
   fecal specimens as proxies for assessing the metabolic impact of the
   gut microbiome. We detected a total of 270 low molecular weight
   metabolites in colonic-cecal contents and feces by gas chromatograph,
   time-of-flight mass spectrometry (GC-TOF) and ultra-high performance
   liquid chromatography, quadrapole time-of-flight mass spectrometry
   (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of
   specimen, representing almost all known biochemical pathways related to
   the amino acid, carbohydrate, energy, lipid, membrane transport,
   nucleotide, genetic information processing, and cancer-related
   metabolism. A total of 115 metabolites differed significantly in
   relative abundance between both colonic-cecal contents and feces. These
   data comprise the first characterization of relationships among
   metabolites present in the colonic-cecal contents and feces in a
   healthy mouse model, and shows that feces can be a useful proxy for
   assessing the pattern of metabolites to which the colonic mucosum is
   exposed.

   Keywords: metabolite, mass spectrometry, colon, cecal contents, feces

1. Introduction

   The gastrointestinal tract, which is the site of the host’s first
   encounter with nutrients, microorganisms and other bioactive factors in
   foods, hosts a complex community of microorganisms. The role of that
   community in affecting health is becoming increasingly evident.
   Research has shown that differences in the nature of the gut microbiome
   are associated with risk to obesity [[34]1], colon cancer [[35]2], and
   type 2 diabetes mellitus [[36]3]. Such effects would appear to involve
   the production by the gut microbiome of metabolites used as substrates
   and/or signaling molecules by mucosal epithelial cells.

   Metabolites represent molecular read-outs of cells. The pattern of
   metabolites produced by the gut microbiome comprises a phenotype
   indicative of the means by which that microbiome affects the gut. Such
   metabolic phenotypes can be characterized using metabolomic approaches,
   which employ the capacities of tandem liquid/gas chromatography-mass
   spectrometry to profile metabolites.

   Two major platforms are available for metabolomic analyses: gas
   chromatography, time-of-flight mass spectrometry (GC-TOF) and liquid
   chromatography quadrapole, time-of-flight mass spectrometry (LC-Q-TOF).
   The former is useful for the analysis of thermally stable volatile
   compounds, typically >600 daltons, and yields high separation
   efficiency and reproducible retention times and mass spectra. The
   latter does not require derivatization, and is amenable for the
   analysis of polar and high molecular weight metabolites [[37]4,[38]5].
   Combining data produced by both platforms offers comprehensive insight
   into actual metabolomic phenotypes.

   Interrogating high-dimensional metabolomic data calls for several
   analytical tools: multiple hypothesis testing with false discovery rate
   (FDR) adjustment, hierarchical clustering, principal components
   analysis (PCA), partial least squares (PLS), biochemical pathway
   enrichment and network analysis [[39]6,[40]7]. With these tools,
   difficult and complex analyses of large amounts of data can be more
   easily accomplished and interpreted.

   Little is presently known about the metabolic phenotypes of the gut
   microbiome nor whether those can be estimated by analyzing feces, a
   more readily accessible specimen, particularly in clinical contexts.
   Therefore, we compared the metabolic profiles of the colonic-cecal
   luminal contents and recently collected feces of C57BL/6 mice fed a
   standard chow-type diet. These baseline data can inform studies of the
   host-gut environment and the whether fecal analyses may be informative
   in such studies.

2. Results and Discussion

2.1. Metabolomic Comparisons of Sample Types

   A total of 270 metabolites were identified in the colonic-cecal
   contents and feces. Of these 251 (93%) metabolites were found in both
   types of specimen. Only 19 showed metabolites unique patterns (eight
   found only in colonic-cecal contents; 11 found only in feces)
   ([41]Figure 1); however, 115 metabolites showed significantly different
   relative abundances in colonic-cecal contents vs. feces (p[adj] < 0.05)
   ([42]Figure 1, [43]Table S1). Metabolic pathway enrichment analysis
   revealed that these 115 metabolites fell into 21 biochemical pathways
   (as defined by Kyoto Encyclopedia of Genes and Genomes, KEGG [[44]8]),
   which were mainly related to amino acid, lipid metabolism, rare amino
   acids, cofactors, vitamins, signaling molecules, nitrogen, energy, and
   human disease/cancer.

Figure 1.

   [45]Figure 1
   [46]Open in a new tab

   Overview of significant differences in metabolite profiles between
   colonic mucosal, colonic-cecal content and feces sample type
   comparisons.

   In this study, the chow diet contained 15.5% fiber (including crude and
   detergent fiber, e.g., cellulos, hemi-cellulose and lignin according to
   product data analysis (LabDiet cat#5015, St. Louis, MO). Certain
   dietary fibers are fermentable, and can alter gut microbiota
   composition. It is known that the gut microbiota is exposed to
   substrates that have escaped digestion in the upper gastrointestinal
   tract, or have been released into the lumen in the form of mucous and
   sloughed cells [[47]9,[48]10], and that their fermentation produced
   short chain fatty acids (SCFAs) [[49]10]. Accordingly, the metabolite
   patterns we found demonstrate the presence of metabolic pathways
   involved the metabolism of lipids and amino acids.

2.2. Metabolites Unique To Colonic-Cecal Contents or Feces

   Certain metabolites were found only in colonic-cecal contents or feces.
   Eight found only in colonic-cecal contents were related mostly to the
   metabolism of lipids, amino acids and nucleotides as well as to
   digestion and carcinogenesis ([50]Table 1). Eleven metabolites found
   only in feces were related to the metabolism of lipids, amino acids and
   some vitamins, as well as glycan biosynthesis, digestion system and
   carcinogenesis ([51]Table 1). The relative abundances of these unique
   metabolites were included in ([52]Table S1).

Table 1.

   Unique metabolites and their respective biochemical pathways in
   colonic-cecal contents and feces.
   Colon-cecal contents Feces
   Unique metabolites
   1-Hexadecanoyl-2-octadecadienoyl-sn-glycero-3-phosphocholine
   1H-Indole-3-carboxylic acid
   L-Alanyl-L-norleucine 1-Myristoyl-sn-glycero-3-phosphocholine
   Leu-Val 1-Octadecanoyl-sn-glycero-3-phosphocholine
   N-Palmitoylsphingosine Betaine
   Palmityl-L-carnitine L-Carnitine
   Pregnan-20-one, 17-(acetyloxy)-3-hydroxy-6-methyl-, (3b,5b,6a)-(A)
   Nicotinamide adenine dinucleotide (NAD)
   3a,12b-Dihydroxy-5b-cholanoic acid-(A) Oxyquinoline
   Deoxythymidine monophosphate (dTMP) Pregnan-20-one,
   17-(acetyloxy)-3-hydroxy-6-methyl-, (3b,5b,6a)-(B)
   13-hydroxy-9Z,11E-octadecadienoic acid
   3a,12b-Dihydroxy-5b-cholanoic acid-(B)
   UDP-N-acetyl-D-galactosamine
   Respective biochemical pathways
   Ether lipid metabolism Primary bile acid biosynthesis
   Glycerophospholipid metabolism Tryptophan metabolism
   Metabolic pathways Biosynthesis of secondary metabolites
   Choline metabolism in cancer Ether lipid metabolism
   Valine, leucine and isoleucine degradation / biosynthesis
   Glycerophospholipid metabolism
   Sphingolipid metabolism Metabolic pathways
   Bile secretion Choline metabolism in cancer
   Fatty acid degradation Glycine, serine and threonine metabolism
   Steroid hormone biosynthesis ABC transporters
   Secondary bile acid biosynthesis Bile secretion
   Pyrimidine metabolism Fatty acid degradation
   Folate biosynthesis
   Nicotinate and nicotinamide metabolism
   Quinolines
   Phenylalanine, tyrosine and tryptophan biosynthesis
   Steroid hormone biosynthesis
   Biosynthesis of unsaturated fatty acids
   Linoleic acid metabolism
   Secondary bile acid biosynthesis
   Mucin type O-Glycan biosynthesis
   Amino sugar and nucleotide sugar metabolism
   Mucin type O-Glycan biosynthesis
   [53]Open in a new tab

   Their largely similar metabolite profiles indicate the presence of the
   same functional pathways in both colonic-cecal contents and feces. Only
   7% of metabolites were unique to colonic-cecal contents or feces. These
   may have utility as signature molecules for these specimens. The
   presence only in colonic-cecal contents of peptides (Leu-Val,
   L-Alanyl-L-norleucine) and metabolites of bile acids, steroids, lipids
   and nucleotides
   (1-Hexadecanoyl-2-octadecadienoyl-sn-glycero-3-phosphocholine;
   Palmityl-L-carnitine;
   pregnan-20-one,17-(acetyloxy)-3-hydroxy-6-methyl-(3b,5b,6a)-(A); 3a,
   12b-dihydroxy-5b-cholanoic acid-(A); N-palmitoylsphingosine,
   deoxythymidine monophosphate) is consistent with the fact that the
   complete peptide-breakdown only occurs in colonic segment
   [[54]11,[55]12,[56]13,[57]14], and cecum also plays a critical role in
   bile secretion and steroid and lipid metabolism [[58]15]; whereas, the
   presence only in feces of metabolites of amino acids, bile acids and
   lipids (1H-Indole-3-carboxylic acid; betaine; oxyquinoline,
   1-Myristoyl-sn-glycero-3-phosphocholine,
   1-Octadecanoyl-sn-glycero-3-phosphocholine, L-Carnitine,
   3a,12b-Dihydroxy-5b-cholanoic acid-(B)), some fats, glycans and
   heterocylic compounds (NAD; 13-hydroxy-9Z, 11E-octadecadienoic acid;
   UDP-N-acetyl-D-galactosamine; oxyquinoline) is consistent with
   bacterial fermentation in the distal colon [[59]9,[60]16]. However, we
   cannot be sure the extent to which the latter metabolites may have been
   produced under aerobic conditions after defecation.

2.3. Metabolite Patterns and Networks

   Comparisons of the relative abundances of the 270 metabolites found in
   these specimens are shown as a heat map [[61]17] ([62]Figure 2). As the
   smaller bracket (at the top of [63]Figure 2) represents the higher
   similarity in abundance between individual samples, the data show that
   samples within a given sample type (e.g., cecal contents or feces)
   shared very similar patterns ([64]Figure 2). The cecal contents and
   feces also shared an overall similar metabolite profiles with only a
   few distinct patterns because feces were downstream-products of
   colonic-cecal contents via colonic fermentation ([65]Figure 2,
   [66]Table S1). Orthogonal signal correction partial least squares
   discriminant analysis (O-PLS-DA) [[67]18] was used to develop a
   multivariate classification model for these two sample types. The two
   latent variable (X and Y axis) for an O-PLS-DA model ([68]Figure 3),
   can be used to evaluate within- and between-group similarities between
   samples, and the smaller values of latent the more similar
   [[69]6,[70]18]. As latent variables of metabolites in cecal contents
   were smaller than that of feces ([71]Figure 3), it suggests that inter
   individual variance data within cecal content group are less different
   than that of feces samples ([72]Figure 3) [[73]19]. This suggests that
   future metabolite profiling studies would require fewer samples of
   colonic-cecal contents than that of feces within a treatment group.

Figure 2.

   [74]Figure 2
   [75]Open in a new tab

   The heatmap displays relative increase/decrease of metabolite contents
   and their similarities between individual samples. In these
   visualization columns represent samples and rows variables, and
   hierarchical cluster analysis (HCA) was used to group samples and
   metabolites based on similarities in auto-scaled values and
   correlations, respectively. Cluster identities represent differing
   experimental biological or analytical variability; colors (red,
   relative increase; blue, relative decrease).

Figure 3.

   [76]Figure 3
   [77]Open in a new tab

   Orthogonal signal correction partial least squares discriminant
   analysis (O-PLS-DA) was used to generate a multivariate classification
   model showing the relationship of the variance (in metabolites) and
   sample types (colonic-cecal content and feces). X and Y axis are
   O-PLS-DA sample scores for the first two latent variables. Color codes:
   pink, colonic-cecal contents; blue, feces.

   Biochemical network analysis (defined by KEGG) revealed that the
   abundance of most carbohydrate metabolites (e.g.,
   galactose-6-phosphase, erythrose), and amino acid metabolites (e.g.,
   glutamic acid, aspartic acid, leucine, valine) were greater in colonic
   cecal contents than that in feces ([78]Figure 4, [79]Table S1). This is
   in agreement with the fact that complex carbohydrates and poorly
   digested proteins may reach the hind gut to be available for colonic
   bacterial fermentation [[80]11,[81]12,[82]13,[83]14,[84]20]. In
   contrast, the contents of most fatty acid-related metabolites (e.g.,
   linoleic acid, jasmonic acid, and phospholipid) were greater in feces
   than in colonic-cecal contents ([85]Figure 4, [86]Table S1). The higher
   content of phospholipid in feces may reflect bacterial breakdown
   [[87]12,[88]21].

Figure 4.

   [89]Figure 4
   [90]Open in a new tab

   Biochemical and chemical similarity network highlighting important
   differences in metabolites abundance between colonic-cecal content and
   feces. Vertex size encodes the fold change in metabolite abundance
   between groups (when the metabolite abundance in colonic-cecal content
   was higher than that in feces, we defined it as “increase”; in
   contrast, if the metabolite abundance in colonic-cecal content was
   lower than that in feces, we defined it as “decrease”). Vertex shape
   (triangle, increase; “V”, decrease; ellipse, no change) and color
   (blue, decrease; red, increase; gray, insignificant change or p > 0.05
   after-adjustment for FDR) are used to encode the significance and
   relative direction of changes in metabolites between colonic-cecal
   content and feces comparison.

   Collectively, the metabolite detection, unique metabolites, relative
   abundance comparison and network analyses are the key methods to study
   the gut metabolome in our future nutritional intervention experiments.

3. Experimental Section

3.1. Animals, Diets, and Treatment

   This study was approved by the Animal Care and Use Committee of the
   Grand Forks Human Nutrition Research Center, and animals were
   maintained in accordance with NIH guidelines for the care and use of
   laboratory animals. Male C57BL/6 mice, 5 months old and body weight (30
   g), were individually housed in Plexiglas™ ventilated cages within a
   pathogen-free facility that maintained a 12-h light/dark cycle. Mice (n
   = 5) were given free access to deionized water, and a common
   practical-type, chow diet (LabDiet cat#5015, St. Louis, MO). This chow
   diet is a complete life cycle diet, in which 19.8% calories from
   protein, 26.1% calories from fat (ether extract) and 54.1% calories
   from carbohydrates. These mice had been in this diet for 5 months
   (their entire life time). At the termination of the experiment, mice
   were euthanized with a mixture of ketamine and xylazine by
   intraperitoneal injection. Feces were collected over a 24-h period
   prior to euthanasia. After euthanasia, the gut was prepared by
   dissection, the colonic and cecal contents were combined, and all
   samples were placed in sealed containers and held at −80 °C prior to
   analysis.

3.2. Sample Extraction

   Aliquots (10 mg) of colonic-cecal contents and feces were each thawed
   and extracted by agitation with 1mL of degassed acetonitrile:
   isopropanol: water (3:3:2) at –20 °C after which the soluble portion
   was recovered by centrifugation. Aliquots (450 µL) of that supernate
   were used for each GC-TOF and LC-Q-TOF analyses.

3.3. GC-TOF Analysis

   Extracts were derivatized as previously published [[91]22], and then
   analyzed using an Agilent 7890A gas chromatograph (Santa Clara, CA)
   coupled to a Leco Pegasus IV time-of-flight mass spectrometer. A
   Gerstel MPS2 automatic liner exchange system (ALEX) was used to
   eliminate sample cross-contamination during the GC-TOF analysis. The
   column was 30 m, 0.25 mm i.d. Rtx5Sil-MS with 0.25 µm 5% diphenyl film
   with a 10 m integrated guard column (Restek, Bellefonte PA)
   [[92]23,[93]24,[94]25]. The sample (0.5 µL) was injected at 50 °C
   ramped to 250 °C in splitless mode with a 25 s splitless time. The
   chromatographic gradient consisted of a constant flow of 1 ml/min,
   ramping the oven temperature from 50 °C for to 330 °C over 22 min. Mass
   spectrometry parameters were: 280 °C transfer line temperature,
   electron ionization at −70 V, and a 250 °C ion source temperature. Mass
   spectra were acquired at 1525 V detector voltage at m/z 85–500 with 17
   spectra/sec. Acquired spectra were further processed using the BinBase
   database [[95]25,[96]26,[97]27]. Data quality and instrument
   performance were monitored throughout the data acquisition using
   quality control and reference plasma samples (NIST), as previously
   described [[98]22].

3.4. LC-Q-TOF Data Collection and Analysis

   Extracts were evaporated to dryness under reduced pressure, and
   reconstituted in 50 µL of water:acetonitrile (98:2 v:v), sonicated (5
   min) and, then, centrifuged. Supernates (40 µL) were transferred to
   amber glass vials (National Scientific-C4000-2W) fitted with
   micro-inserts (Supelco 27400-U).

   Polar extracts were analyzed using an Agilent 1290 A Infinity Ultra
   High Performance Liquid Chromatography system coupled to an Agilent
   Accurate Mass-6530-QTOF in both positive and negative modes. The column
   (40 °C) was a Waters Acquity BEH Shield RP18 Column (150 mm length x
   2.1 mm internal diameter; 1.7 µM particles). Mobile Phase A consisted
   of 100% LCMS grade water containing 0.1% formic acid; mobile phase B
   consisted of 100% acetonitrile containing 0.1% formic acid. The
   gradient started from 0 min 2% (B), 0–0.5 min 2% (B), 0.5–12 min 95%
   (B), 12–12.5 min 95% (B), 12.5–12.6 min 2% (B), and 12.6 min–17 min 2%
   (B). The flow rate was 0.6 mL/min; an injection volume was 5 µL for
   both ESI (+/−) mode acquisitions with an ESI capillary voltage was +
   3.5 kV and −3.5 kV for positive and negative mode, respectively. For
   MSMS, collision energies were 10, 20 and 40 eV for both positive and
   negative acquisition modes. Data were collected over a mass range of
   m/z 60–1200 Da with a spectral acquisition speed of 2 spectra per sec.

   Data were processed using MZmine 2.10. All peak intensities are
   representative of peak heights. Annotations were completed by matching
   experimental accurate mass MS/MS spectra to MS/MS libraries, including
   Metlin-MSMS, NIST12, and LipidBlast. Spectral matching was accomplished
   using MSPepSearch manually curated using The NIST Mass Spectral Search
   Program Version 2.0g. Metabolite libraries were created, in positive
   and negative ionization modes, containing all confirmed identified
   compounds. MZmine’s Custom Database Search tool was used to assign
   annotations based on accurate mass and retention time matching.

3.5. Data Analysis

   Data collected by GC-TOF and LC-Q-TOF were merged for both types of
   sample. GC-TOF values are reported for metabolic features measured on
   both platforms with analysis limited to annotated features. Missing
   values for the LC-Q-TOF were imputed as zeros. Statistical analyses
   were conducted on natural logarithm-transformed metabolic parameters,
   and data summaries are presented for raw values. Multivariate analyses
   (clustering, PCA, O-PLS-DA) were implemented on natural
   logarithm-transformed and auto-scaled values (mean centered and scaled
   by the standard deviation, z-scaled).

   Statistical analyses were implemented in R v3.01. Independent sample
   t-Tests were used to test for differences in metabolite profiles
   between colonic-cecal contents and feces. The probability or
   significance level for the test statistics (i.e., p-values) were
   adjusted for the multiple hypotheses tested [[99]28], and the false
   discovery rate (FDR)-adjusted p-values are reported as “p[adj]”. The
   FDR was also directly estimated as the q-value, for all comparisons
   [[100]29]. Hierarchical cluster analysis (HCA) was used to group
   samples and metabolites based on similarities in auto-scaled values and
   correlations, respectively [[101]6]. Principal components analysis
   [[102]6] was used to evaluate the sample class structure including
   similarities and differences. Orthogonal signal correction partial
   least squares discriminant analysis (O-PLS-DA) [[103]18] was used to
   develop a multivariate classification model for the two types of
   samples. In order to visualize the biological interrelationship of the
   270 known metabolites in colonic cecal contents and feces, biochemical
   and chemical similarity network analysis was used to generate networks
   vertices based on biochemical relationships (KEGG RPAIR Database).

4. Conclusions

   In summary, we have conducted the first comprehensive comparison of the
   metabolite profiles of colonic-cecal contents and feces in the mouse.
   Our results show that these specimens have very similar metabolite
   profiles, indicating the suitability of feces, which can be sampled
   non-invasively, as proxies for estimating the metabolite patterns of
   hindgut microbiome. These findings should inform future research on the
   role of diet and the gut microbiome in supporting health.

Acknowledgments