Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships
- Ian H McHardy1,
- Maryam Goudarzi3Email author,
- Maomeng Tong8,
- Paul M Ruegger4,
- Emma Schwager7,
- John R Weger4,
- Thomas G Graeber8,
- Justin L Sonnenburg6,
- Steve Horvath5,
- Curtis Huttenhower7,
- Dermot PB McGovern2,
- Albert J FornaceJr3,
- James Borneman4 and
- Jonathan Braun1Email author
© McHardy et al.; licensee BioMed Central Ltd. 2013
Received: 31 March 2013
Accepted: 12 May 2013
Published: 5 June 2013
Consistent compositional shifts in the gut microbiota are observed in IBD and other chronic intestinal disorders and may contribute to pathogenesis. The identities of microbial biomolecular mechanisms and metabolic products responsible for disease phenotypes remain to be determined, as do the means by which such microbial functions may be therapeutically modified.
The composition of the microbiota and metabolites in gut microbiome samples in 47 subjects were determined. Samples were obtained by endoscopic mucosal lavage from the cecum and sigmoid colon regions, and each sample was sequenced using the 16S rRNA gene V4 region (Illumina-HiSeq 2000 platform) and assessed by UPLC mass spectroscopy. Spearman correlations were used to identify widespread, statistically significant microbial-metabolite relationships. Metagenomes for identified microbial OTUs were imputed using PICRUSt, and KEGG metabolic pathway modules for imputed genes were assigned using HUMAnN. The resulting metabolic pathway abundances were mostly concordant with metabolite data. Analysis of the metabolome-driven distribution of OTU phylogeny and function revealed clusters of clades that were both metabolically and metagenomically similar.
The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites. The consistent relationship between inferred metagenomic function and assayed metabolites suggests that metagenomic composition is predictive to a reasonable degree of microbial community metabolite pools. The finding that certain metabolites strongly correlate with microbial community structure raises the possibility of targeting metabolites for monitoring and/or therapeutically manipulating microbial community function in IBD and other chronic diseases.
KeywordsMicrobiome Metabolome Inter-omic analysis
Bovine serum albumin
HMP unified metabolic analysis network
Inflammatory bowel disease
Kyoto Encyclopedia of Genes and Genomes
Operational taxonomic unit
Polymerase chain reaction
Phylotypic investigation of communities by reconstruction of unobserved states
Ultra performance liquid chromatography
Short-chain fatty acid
Solid phase extraction
Quantitative Insights into Microbial Ecology
Weighted correlation network analysis.
The intestinal mucosal surface is the site of a complex orchestration of immunologic, metabolic and ecological forces that drive microbial community structure. In most cases, these forces balance the composition of the gut microbiota with mucosal health, facilitating normal nutrient absorption, local and systemic endocrinology, angiogenesis, epithelial barrier function, brain development, liver function, immune development and gut homeostasis [1–7]. However, the immunological and functional state of the mucosa is influenced by the microbiota, and it is therefore susceptible to detrimental interactions with changes in luminal bacteria [8, 9]. The microbial composition is typically well controlled; however, in certain genetically and environmentally susceptible individuals, control of microbial composition is compromised, leading to (or resulting from) clinical manifestations in immune and inflammatory diseases [10–13].
The intestinal mucosal ecosystem harbors an assortment of host factors, microbiota, and metabolites. The microbial ecology in the context of this molecular milieu is an area of intense study, but to this point it has mainly been probed by the potential (versus expressed) functionality represented by the microbial metagenome [14–18]. A central goal and methodologic challenge in human-associated microbial ecology is to identify dietary, metabolic, and host and microbial factors that drive microbial community structure. Recent work by Jansson and colleagues [19, 20] and our group  indicates that components of the mucosal proteome correlate with certain microbial species and reveals intriguing differences between the potential and expressed biochemical pathways detected in microbial communities in vivo. In twin-pair studies, Crohn’s disease-associated differences in fecal metabolites have been detected in parallel with microbial compositional and metagenomic differences in this compartment, and represented biomarkers related to disease state, presumably in part as products of the disease-associated changes in microbial metagenomic function [22–24]. Identification of such relationships is fundamental for interventional strategies to alter microbiota composition in the context of dysbiosis, and have been highlights of recent landmark studies of environment and diet in human fecal microbial composition [11, 16, 25]. Indeed, direct analysis of metabolic output by and interactions between microbial species is a burgeoning investigative field, but challenging methodologically, particularly in vivo[26, 27].
Sample collection and pre-processing
All enrolled subjects were consented under an approved Institutional Review Board (IRB) protocol from Cedars Sinai Medical Center prior to routine colonoscopy. All subjects underwent bowel preparation with Miralax® prior to colonoscopy. For each sample region, approximately 30ml of sterile water was endoscopically flushed onto the mucosal surface and recollected via aspiration. Samples were obtained from the cecum and sigmoid colon region of each subject. Samples were kept on ice for the duration of the pre-processing that immediately followed sample collection. Samples were centrifuged at 4,000 × g for 10 minutes at 4°C. The supernatant was aliquoted into three 50-ml tubes with equal volumes and frozen at −80°C. The pellets were resuspended in 2 ml of RNAprotect Bacteria Reagent (Qiagen, Valencia, CA, USA), aliquoted into three separate 15-ml conical tubes, centrifuged at 4,000 × g for 10 minutes at 4°C, separated from the supernatant and frozen at −80°C.
High-throughput 16S analysis
DNA was extracted from 93 samples using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA), and a 30-second beat-beating step using a Mini-Beadbeater-16 (BioSpec Products, Bartlesville, OK, USA). High-throughput sequencing analysis of bacterial rRNA genes was performed using extracted genomic DNA as the templates. One hundred-microliter amplification reactions were performed in an MJ Research PTC-200 thermal cycler (Bio-Rad Inc., Hercules, CA, USA) and contained 50 mM Tris (pH 8.3), 500 μg/ml BSA, 2.5 mM MgCl2, 250 μM of each deoxynucleotide triphosphate (dNTP), 400 nM of each primer, 4 μl of DNA template, and 2.5 units JumpStart Taq DNA polymerase (Sigma-Aldrich, St. Louis, MO, USA). The PCR primers (F515/R806) targeted a portion of the 16S rRNA gene containing the hypervariable V4 region, with the reverse primers including a 12-bp barcode (Additional file 1) . Thermal cycling parameters were 94°C for 5 minutes; 35 cycles of 94°C for 20 seconds, 50°C for 20 seconds, and 72°C for 30 seconds, and followed by 72°C for 5 minutes. PCR products were purified using a MinElute 96 UF PCR Purification Kit (Qiagen). DNA sequencing was performed using an Illumina HiSeq 2000 (Illumina, Inc., San Diego, CA, USA). Clusters were created using template concentrations of 1.9 pM and PhiX at 65 K/mm2, which is recommended by the manufacturer for samples with uneven distributions of A, C, G and T. One hundred base-sequencing reads of the 5’ end of the amplicons and seven base barcode reads were obtained using the sequencing primers listed in Additional file 1. De-multiplexing, quality control, and operational taxonomic unit (OTU) binning were performed using quantitative insights into microbial ecology (QIIME) . The total initial number of sequencing reads was 70,278,364. Low-quality sequences were removed using the following parameters: Q20, minimum number of consecutive high-quality base calls = 100 bp, maximum number of N characters allowed = 0, maximum number of consecutive low-quality base calls allowed before truncating a read = 3. Numbers of sequences removed using the aforementioned quality control parameters were: barcode errors (5,199,568), reads too short after quality truncation (5,545,570), and too many Ns (38,358). Then, 59,494,868 remaining reads were then used to pick OTUs from the GreenGenes reference database, which automatically bins OTUs at 97% identity, so that the resulting data were compatible with phylotypic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis: 1,536,002 reads were discarded during OTU picking due to alignment failure. After OTU picking, 57,958,866 reads remained.
Solid phase extraction (SPE)
Before cecum and sigmoid lavage aliquots were subjected to metabolomic analysis, they were cleaned with SPE due to the presence of a polymer presumably derived from bowel preparation (bowel preparation often involves polyethylene glycol). The SPE protocol was adopted, modified and made compatible for the downstream mass spectrometry (MS) analysis. MCX cartridges (Waters Corp. Milford, MA, USA) were conditioned with methanol and phosphoric acid prior to use. Each sample was diluted 1:2 in 2% phosphoric acid and loaded on to the MCX cartridge. Samples were incubated with the mix-mod polymer sorbent in the cartridges. The application of vacuum throughout the procedure was kept to the minimum to allow for ample sample/sorbent interaction. The sorbent was then washed with 2% formic acid in water and 10 ml of water. The metabolites were then eluted off the column by subsequent washes with methanol and 5% ammonium hydroxide, dried, and reconstituted in 2% acetonitrile in water.
Mass spectrometry analysis
A 5-μl aliquot of extracted metabolites from each sample was injected onto a reverse-phase 50 × 2.1 mm ACQUITY 1.7-μm C18 column (Waters Corp.) using an ACQUITY UPLC system (Waters Corp.) with a gradient mobile phase consisting of 2% acetonitrile in water containing 0.1% formic acid (A) and 2% water in acetonitrile containing 0.1% formic acid (B). Each sample was resolved for 10 minutes at a flow rate of 0.5 ml/minute. The gradient consisted of 100% A for 0.5 minutes, then a ramp of curve 6 to 100% B from 0.5 minutes to 10 minutes. The column eluent was introduced directly into the mass spectrometer by electrospray. MS was performed on a Q-TOF Premier (Waters Corp.) operating in either negative-ion (ESI-) or positive-ion (ESI+) electrospray ionization mode with a capillary voltage of 3,200 V, and a sampling cone voltage of 20 V in negative mode and 35 V in positive mode. The desolvation gas flow was set to 800 L/h and the temperature was set to 350°C. The cone gas flow was 25 L/h, and the source temperature was 120°C. Accurate mass was maintained by introduction of LockSpray interface of sulfadimethoxine (311.0814 (M+H) + or 309.0658 9M-H)−) at a concentration of 250 pg/μl in 50% aqueous acetonitrile and a rate of 150 μl/minute. Data were acquired in centroid mode from 50 to 850 m/z in MS scanning. Centroided and integrated MS data from the UPLC-TOFMS were processed to generate a multivariate data matrix using MarkerLynx (Waters Corp.). The data were normalized to total protein and processed using an array of statistical tools such as R, SIMCA P, and an in-house statistical script. The statistically significant metabolites were putatively identified using several online databses such as HMDB, MMCD, KEGG, and Lipidmaps.
Spearman inter-omic correlation analysis
All bioinformatic analysis of cleaned metabolomic and metagenomic data was performed in R. Microbiome and metabolome data from the same samples were merged by subject and thresholded such that analytes measured above background in fewer than 18% subjects were removed from all analyses. The cutoff value of 18% was selected such that similar numbers of observations were eliminated from both the cecum and sigmoid comparisons, and significant inter-omic correlations were not enriched for rare analytes. Inter-omic analysis involved simple Spearman correlation of analyte abundance with calculation of P-values using the R function cor.test. Spearman correlation was used for inter-omic analysis because it detects more complicated relationships that might otherwise go undetected using other metrics, such as Pearson correlation. Wherever mentioned, the R package qvalue was used to generate q-values for each spearman correlation. To quantify correlations with individual genera, the following steps were taken. First, OTUs from the correlation data (Additional file 2) were binned by genus assignment. Then, all duplicate metabolic correlations were removed so that even if all OTUs from a genus correlated with a single metabolite it was quantified only as a single interaction.
Microbial cluster generation
For heat map-based microbial clustering analysis, heat maps were created of Spearman correlation matrices (without q-value thresholding) using the heatmap.2 function from the gplots R package. Hierarchical clustering of bacteria was based on the Euclidian distance metric and the complete method of hierarchical clustering by metabolites (only those with ≥2 significant OTU correlations). Dendrograms were then extracted from the output of that function and cut using the base cut function. Cut height was determined by the number of significant modules as defined by prediction strength. We used k-means clustering to assess significance in the prediction strength calculations, which has been shown to be a robust strategy for determining optimal cluster number for hierarchical cluster modules . While twenty modules were predicted for the sigmoid data, only six were predicted for the cecal data. To facilitate comparison between regions, we cut the sigmoid dendrogram such that seven (no cut height allowed six) clusters were generated. Prediction strength was performed using the prediction.strength function from the fpc R package . To determine the similarity of microbial cluster composition, cluster assignment of shared OTUs was examined. Significance of overlapping OTUs between the cecal and sigmoid clusters was determined using one-sided (greater) Fisher’s exact test.
Coinertia analysis identifies successive axes of covariance between two datasets involving the same test subjects. Coinertia analysis was performed using the coinertia function from the ade4 R package, applied to eigenvalues of the metabolome and microbiome . The significance of RV scores, which are indicative of global similarity, was estimated using the RV.rtest function, which performs a Monte Carlo-based estimation on the sum of eigenvalues from a coinertia analysis.
Procrustes analysis analyzes the congruence of two-dimensional shapes produced from superimposition of principal component analyses from two datasets. To remain consistent, we performed Procrustes analysis on the Euclidian distances of eigenvalues for both the microbiome and metabolome using the Procrustes function in the vegan R package .
Metabolite module generation
We defined metabolic modules using soft-thresholded Pearson correlation analysis in combination with a topological overlap distance metric and average hierarchical clustering (R package: weighted correlation network analysis (WGCNA)) . Since our goal was to only group metabolites that were highly correlated with each other, we chose to use the more stringent Pearson method for generating modules. Soft-thresholding powers were defined using the pickSoftThreshold function of the WGCNA R package. For metabolic module generation, the data were first thresholded such that only metabolites present in at least 18% of samples were included for the module generation pipeline. There are many approaches to such clustering [35–38]. WGCNA uses a measure of shared metabolite neighbors based on topological overlap as input of hierarchical clustering. The height in the dendrogram is a measure of dissimilarity based on the topological overlap matrix; modules are defined as branches of a hierarchical cluster tree [34, 39]. WGCNA was attractive for this study since it provides module preservation statistics that allowed us to assess the reproducibility of modules across different data sets; provides a measure of intramodular connectivity that can be used to define intramodular hub genera ; and allows us to summarize each module by its module eigenvalue. The resulting modules were then validated using silhouette width and cophenetic distance metrics and compared with two independent module generating approaches: 1) average hierarchical clustering based only on Pearson correlation dissimilarity (1 – Pearson coefficient), and 2) K means using unthresholded Pearson correlation analysis. Both independent methods generated strongly similar module composition and distribution.
Using this approach on individual metabolites, the cecum metabolites (soft-thresholding power = 22, minimum module size = 10 metabolites) organized into 20 modules with 121 un-clustered metabolites, while the sigmoid metabolites (soft-thresholding power = 32, minimum module size = 10 metabolites) organized into 15 modules with 170 un-clustered metabolites. For each dataset, the abundance values of the un-clustered analytes were combined with module eigenvalues to facilitate downstream analyses.
Putative metabolite identity determination
An in-house script called StandAlone BioIdentifier was used to putatively identify ions based on their biological relevance via incorporation of four major small molecule databases: KEGG, HMDB, LipidMaps, and BioCyc. This metabolomic tool has the unique ability to distinguish mammalian metabolites from those of bacterial and plant origin providing an extra degree of confidence in the ions’ putative IDs. This user-friendly script allows one to choose from several positive and negative adducts at a user-predefined mass tolerance. For our UPLC/MS setup we chose H+ and Na+ adducts for the ESI+ mode and H- and Cl- for the ESI- mode at a predefined mass window of 20 ppm.
Quantifying metabolite overlap between the cecum and sigmoid
An R script was written that stringently identified identical metabolites measured in two datasets based on two parameters. The two parameters were defined such that two metabolites must: 1) have a mass difference ≤0.005 m/z, and 2) have a difference in retention time that was ≤0.04 minutes (2.4 seconds).
PICRUSt is a well documented tool designed to impute metagenomic information based on 16S input data (http://picrust.github.com/picrust/). To use the tool to impute metagenomes of microbial OTUs, we created a synthetic OTU table such that each OTU was represented by a single count in a single column. The synthetic table was then input into the PICRUSt pipeline using the terminal interface of a QIIME virtual machine running the Ubuntu operating system. The resulting metagenomic data was input into the HMP unified metabolic analysis network (HUMAnN) pipeline  using the same computational platform to sort individual genes into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways representing varying proportions of each generated metagenome.
Defining community structure of metabolite-associated microbes
Abundance data for groups of bacteria that significantly correlated with common metabolites were extracted from the thresholded OTU table (that is, only including bacteria present in at least six samples) and formatted for SparCC, a new tool developed for metagenomic data that simultaneously removes compositional effects while calculating correlation matrices for given OTU tables . For each group of bacteria tested, 1,000 permutations of randomly selected bacteria without replacement (from the same thresholded OTU table) were applied to the same analytic pipeline. For each correlation matrix produced, the average positive and average negative correlation was calculated and compared with the cumulative averages from the permuted datasets. Significance values were the calculated ratios of permuted correlation matrices with stronger positive or negative correlations than the correlation matrix of interest. Technically identical analysis was performed for correlations >0.2 or <−0.2 to supplement the analyses. Total branch lengths were calculated in R using the compute.brlen function from the ape R package.
Samples and subjects used for analysis
Crohn's Disease, number
Average age, years
Cecum samples, number
Sigmoid samples, number
Overview of the measured mucosal microbiome and metabolome
Construction of metabolic modules based on metabolite co-occurrence
Before proceeding with inter-omic analysis, we first collapsed highly correlated metabolites into modules to streamline and facilitate downstream analyses. Since metabolites associated by biochemical pathway are expected to co-occur, we constructed a network of co-occurrent metabolites, and interrogated the network for modules that might reveal such pathway representation, and would also simplify and strengthen downstream analysis by reducing dimensionality (Methods). The metabolite co-occurrence network was constructed by Pearson correlation, where the edge connecting each pair of nodes was the co-occurrence estimate inferred from the relative abundance profiles of metabolites. Metabolite modules were then identified in the network by an adaptation of WGCNA. The modules generated by WCGNA were validated by independent approaches (Methods). Un-clustered metabolites were combined with module centroids (eigenmetabolites defined as the first singular vector) for each sample. This resulted in a set of 21 and 15 modules and 121 and 170 un-clustered metabolites for the cecum and sigmoid data, respectively. The complete list of metabolites, their module organization, and module dendrograms are available in Additional file 5. In the following phases of this study, datasets containing module eigenvalues and un-clustered metabolites for each separate colonic region were used as the inputs for metabolite-microbial inter-omic analyses.
Inter-omic network analysis reveals enrichment for shared metabolite associations
Network features of inter-omic analysis
Total interactions, number
Unique microbial nodes, number
Unique metabolic nodes, number
Ratio of Firmicutes OTUs with metabolic correlations
Ratio of Bacteroidetes OTUs with metabolic correlations
Ratio of Proteobacteria OTUs with metabolic correlations
Ratio of Actinobacteria OTUs with metabolic correlations
Ratio of Tenericutes OTUs with metabolic correlations
Average number of interactions per OTU
Average number of interactions per metabolite
Genera with the most metabolic correlations ( q <0.2) from the cecum and sigmoid data
Cecum interactions, number
Sigmoid interactions, number
Average % abundance in cecum
Average % abundance in sigmoid
Concordance of putative metabolite IDs and functional metagenomic predictions
Having observed strong correlation between individual microbes and metabolites, we were curious about the nature of the correlation. Correlation between microbes and metabolites could arise due to either catabolism/anabolism of metabolites by microbes or stimulation/inhibition of microbial growth by metabolites. To help determine whether catabolic or anabolic reactions might be responsible for any of the observed correlations, we sought to determine whether metabolic associations were concordant with genomic enrichment/depletion of cognate metabolic pathways. The underlying expectation was that observable metabolic differences between organisms would be concomitant with metagenomic enrichments/depletions of the corresponding metabolic pathways.
We were able to impute metagenomes for each OTU using the bioinformatic tool PICRUSt, which allows one to build metagenomes for each OTU using closest-related genomes of cluster OTUs available in the GreenGenes reference database. This bioinformatic method has been productively used in a recent study of functional microbial traits associated with IBD and is robust for large datasets despite the noise introduced through imputation . Accordingly, a metagenome was created for each OTU (representing imputed genes from the closest pre-sequenced GreenGenes OTU) and the relative genomic proportion of each functional pathway was determined using HUMAnN .
We then selected all metabolites that were significantly correlated with at least five OTUs (and thus, likely to be more biologically relevant) and had at least one putative molecular ID for this analysis. This resulted in a total of 64 metabolites, and due to multiple possible putative IDs for some metabolites, represented 111 molecules with KEGG pathway associations. For each putative metabolite ID, we generated two vectors: one containing every metabolite-OTU Spearman correlation coefficient for the metabolite in question, and another vector with the imputed metagenomic abundance of the putative KEGGpathway that produces the metabolite in question for every OTU. The two vectors were aligned by OTUs and then compared using Pearson correlation (Additional file 6). The rationale was that bacterial correlation with a single metabolite should be concordant with the corresponding metagenomic abundance of the source metabolic pathway in each bacterium in cases where catabolism or anabolism is the source of the OTU-metabolite correlation. This analysis resulted in 41 significant (Bonferroni P <0.05) positive correlations and 31 significant negative correlations; 39 correlations resulted in non-significant rho values (Additional file 7). Therefore, given the significance of the relationships, these data suggested some metabolic associations were probably due to microbial catabolic or anabolic reactions. However, more detailed in vitro or in vivo analyses are necessary to further explore these implications.
Relationships between the metabolome and microbial composition and function
We next attempted to determine the differential metabolic functions represented by each microbial cluster. For this, we chose to focus on imputed metagenomic content of microbes in each cluster due to the rich data made available through PICRUSt. We performed Kruskal-Wallis one-way analysis of variance on all pairs of microbial clusters and imputed metagenomic KEGG pathways and selected some pathways that were significantly different (Bonferroni, P <0.05) in at least one cluster. Figures 5C and 6C highlight the genomic representation patterns in the clusters for these pathways. For example, cluster 1 (from both the cecum and sigmoid data) primarily contained Proteobacteria and was enriched for fatty acid metabolism and depleted for several amino acid metabolism pathways. The fact that this microbial cluster contained many Proteobacteria and was enriched for fatty acid metabolism suggests that it may have relevance for IBD, as many studies have shown increases of certain Proteobacteria and decreases of short-chain fatty acids (SCFA) in IBD and other inflammatory diseases [11, 52–55]. Conversely, cluster 4 (from both cecum and sigmoid data) were not enriched for fatty acid metabolism but were instead enriched for fatty acid biosynthesis, which would presumably provide SCFA-mediated protection from IBD [56, 57]. Indeed, genus level analysis of this cluster revealed enrichment for Roseburia and Faecalibacterium genera, which have been shown to produce SCFA and are depleted in IBD [11, 58–60]. Unsurprisingly, representation of carbon utilization pathways also differentiated the clusters. For example, clusters containing significant amounts of Proteobacteria were depleted for genes encoding starch, sucrose, amino sugar and nucleotide metabolism. Furthermore, genes encoding glutathione metabolism also strongly differentiated the clusters. Among its many functions, glutathione is involved in intracellular oxidative stress control and thus, the differing representation of these genes could be indicative of varying levels of oxidative stress tolerance . In addition, genes involved in lipopolysaccharide (LPS) biosynthesis varied between clusters, likely reflecting the varying composition of Gram-negative bacteria in each cluster.
Microbes with shared metabolite associations exhibit significant microbial community structure
Analysis of metabolite-associated microbial communities
Metabolite mass and retention time
Correlated OTUs, number (q<0.2)
Total branch length
Average positive correlation
Average negative correlation
Number of correlations >0.2
Number of correlations <−0.2
Green yellow module
342.2631 m/z RT = 4.2272
386.2897 m/z RT = 4.3619
230.1845 m/z RT = 0.4078
596.3475 m/z RT = 3.04
595.3469 m/z RT = 3.0469
593.3309 m/z RT = 3.0247
613.3011 m/z RT = 3.0293
605.3348 m/z RT = 3.3185
(23S)-23,25-dihdroxy-24-oxovitamine D3 23-(beta-glucuronide)
591.3203 m/z RT = 3.0304
100.0761 m/z RT = 0.3093
22% Negative, 78% positive
673.3209 m/z RT = 3.3132
434.1867 m/z RT = 0.6621
30% Positive, 70% negative
619.3474 m/z RT = 3.6896
124.0395 m/z RT = 0.3735
615.3154 m/zRT = 3.0227
Midnight blue module
Close examination of the metabolites with multiple q <0.2 microbial correlations revealed three classes of metabolites: 1) those with almost exclusively positive correlations with bacteria; 2) those with almost exclusively negative correlations with bacteria; and 3) those with multiple positive and negative correlations with bacteria. Two examples of the latter are shown in Additional file 2, Figure S2 and Additional file 6, Figure S6 and are analyzed further in the Discussion. Unfortunately, due to the partial transitive nature of correlations, we could not conclude that the observed significance of metabolite-associated microbial community structure was ecologically relevant. Lacking tools to disambiguate the transitive features, we can only postulate that metabolites associated with communities with significant community structure may have ecologic influence.
With the advent of next-generation sequencing platforms, a major influx of studies have sought to identify microbial composition differences in various habitats. However, such studies rarely consider environmental variables, such as metabolites or proteins, resulting in incomplete systemic clarity and potentially erroneous assumptions. This study represents one of the first successful attempts to integrate components of the adult gut mucosal ecosystem. We chose to perform analysis on two distinct colonic regions to ensure reproducibility of findings. Notably, all mucosal samples were collected from subjects who had undergone bowel preparation. While standard for both clinical and research endoscopy, bowel preparation is known to alter microbial alpha and beta diversity . Accordingly, such depletion of mucosal microbiota is likely to reduce the scope of detectable inter-omic relationships. However, we reason that the observed relationships are representative of the native mucosa. Also, bowel preparation should result in less dietary and enteric secretion input from the proximal intestine, thereby increasing biogeographic resolution and decreasing noise from dietary metabolites. Nonetheless, it is possible that bowel preparation introduces metabolic changes in the microbial community that elicits non-physiologic inter-omic relationships. Therefore, the scope and quality of these inter-omic relationships merit additional assessment in undisturbed mucosal sites.
This study revealed significant inter-omic structure in both the cecum and sigmoid colon that was independent of age or disease status. While the relationship between the microbiome and metabolome appeared strongest in the cecum by Procrustes and coinertia analysis, a larger number of significant correlations were observed in the sigmoid compared to the cecum, possibly reflecting the known biogeographic differences of both microbes and metabolites [11, 45, 51]. Despite this biogeographic dissimilarity, we observed significant overlap in findings between the cecum and sigmoid data. While only 342 metabolites were measured in both colonic regions, several observations remained consistent between datasets. As highlighted in Figures 5 to 7, the cecum and sigmoid microbial clusters were very similar in composition and function. Furthermore, as shown in Table 2, the relative ratios of inter-omic correlations were nearly identical at the phylum level. However, some biogeographic differences were observed. For example, the sigmoid data had nearly double the number of significant inter-omic correlations involving Proteobacteria and Actinobacteria, which might suggest differing biogeographic functional roles. Furthermore, using prediction strength, six microbial clusters were predicted for the cecum data while twenty were predicted for the sigmoid, suggesting that microbial function in the sigmoid is much more distinct than in the cecum.
This study also identified microbial clusters that were both metabolically and metagenomically concordant. Using observed metabolic correlations to govern cluster assignment of microbes revealed similarities among diverse groups of bacterial phyla that might not otherwise have phylogenetic or genomic associations. The metabolic relationships defined by these clusters may provide a new avenue to consider in vivo microbial function and host response. As noted above, species of Firmicutes, including Faecalibacterium, Phascolarctobacterium, and Roseburia tend to be depleted in IBD while Proteobacteria species tend to be enriched . All three IBD-depleted genera were substantially confined to cluster 4 of both colonic regions, which was enriched for fatty acid biosynthetic genes and depleted of fatty acid metabolism genes. Fatty acids with the most relevance are SCFA, which are produced as fermentative byproducts. Multiple dietary inputs can be used by bacteria to produce SCFA, including microbial or dietary-derived starch, acetate, lactate, linoleic acid, and fiber . Fatty acid biosynthesis is particularly relevant to the host because SCFA, like butyrate, can 1) act as energy sources for colonocytes; 2) inhibit Nuclear Factor-κB activation in human colonic epithelial cells, resulting in decreased levels of inflammatory cytokines; and 3) stimulate mucin production, which could result in increased barrier protection . Accordingly, reduction in SCFA availability to the host, which could occur if cluster 4 bacteria were depleted, could increase mucosal propensity for and susceptibility to inflammation. However, cluster 4 is also highly enriched for various amino acid metabolic and biosynthetic pathways that could also influence the environmental availability of such molecules and thereby contribute to mucosal homeostasis in ways that have not yet been examined (Figures 5C and 6C). Furthermore, cluster 4 was depleted of genes from the KEGG glutathione metabolism pathway, which includes both biosynthetic and metabolic genes. Glutathione is important for mitigating oxidative stress and acts as a powerful redox buffer . Therefore, depletion of genes involved in production and metabolism of glutathione could indicate that bacteria in cluster 4 were more susceptible to oxidative stress and would therefore be at a selective disadvantage in oxidative inflammatory conditions like those found in IBD. Conversely, cluster 1, which contained many Proteobacteria, was significantly enriched with genes from the glutathione metabolism pathway, which could explain why such bacteria tend to more abundant in IBD . Therefore, grouping metabolically similar bacteria into clusters aids functional analyses by 1) reducing the dimensionality of data; 2) allowing assignment of potential functions to bacteria that might not be culturable in vitro; and 3) defining collective relationships between bacteria that might not be overtly related by phylogenetic sequences.
Another central finding of this study was the rich network of significant correlations between the microbiome and metabolome. Such correlation structure likely arises from a combination of two general processes: 1) catabolism and anabolism of metabolites by microbes, and 2) stimulation and inhibition of microbial growth by metabolites. Indeed, it is widely accepted that dietary alteration is accompanied by shifts in gut microbiome composition and that microbial composition influences the intestinal metabolome [16, 25, 26]. However, the metabolites and metabolic pathways involved in such processes are unknown. Therefore, while it is difficult to conclusively assign cause and effect to correlation data, a central goal of this study was to determine whether bioinformatic signatures of either process could be detected.
To observe whether catabolic or anabolic reactions contributed to the inter-omic correlation structure, KEGG pathway representation of imputed OTU metagenomes was correlated with the correlation coefficients from the pair-wise microbe-metabolite comparisons. While roughly half of these comparisons had significant positive correlation coefficients, indicating concordance between observed metabolite abundance and metagenomic abundance, a large proportion of correlations were insignificant or significantly negative (Additional file 6). While the mechanisms remain unclear, multiple possibilities exist for the differing directionality of observed correlations. For example, a metabolic end product might have a positive correlation with the OTU (and thus the originating pathway) that produces and exports it, but a negative correlation with the OTU (and thus the originating pathway) that imports and processes it in a downstream pathway. Unfortunately, this also means that some metabolites might have less significant correlation curves due to organisms that encode enzymes producing such metabolites, but are not correlated with the metabolites because they are not exported, which was required for us to measure the association. Given the immature understanding of gut metabolic pathways and the imperfect nature of putative metabolite ID picking, we were not able to resolve this issue. Regardless, these data suggest that our predicted metagenomic and putative metabolite ID data were concordant. This was an important finding because defining metabolite IDs using biochemical methods is subject to numerous limitations that could be simplified with metagenomic data. To try and detect signatures of microbial inhibition or stimulation, we attempted to quantify the significance of community structure between microbes with shared metabolite correlations. While significant community structure was observed between microbes, we were unable to deconvolute the transitive effects of correlation and thus, could not conclude that metabolite-mediated microbial stimulation/inhibition occurred. However, we provided two examples of communities that appeared to have exceptional structure, even compared against other metabolite-associated microbial communities. Additional file 10 shows the structure of a microbial community that was defined by common correlation with a cecal metabolite (mass = 230.1845, retention time = 0.4078 minutes). This metabolite negatively correlated with 17 bacteria. When correlated with each other, these bacteria formed two tight clusters with extremely well-defined co-exclusion structure. An appealing explanation for this type of behavior is that the two communities actively compete with each other for consumption of the metabolite. The metabolite-associated community in Additional file 11 involved a sigmoid metabolite (mass = 434.1867, retention time = 0.6621 minutes). This metabolite was negatively correlated with one Tenericute and eighteen Proteobacteria OTUs and positively correlated with seven Firmicutes OTUs. When correlated with each other, these bacteria also formed two distinct communities that were extremely co-exclusive; one community contained all the Firmicutes, and the other community contained most of the Proteobacteria. This behavior could be indicative of a Firmicutes-produced metabolite that is inhibitory to Proteobacteria. While it is possible that the observed structure was due to the transitive nature of the correlations, these observations would seem to suggest that metabolites might be driving microbial community structure and may directly or indirectly modulate inter-species competition.
Regardless of the mechanism of correlation, the strong correlation between individual metabolites and microbes has numerous potential implications for future innovations. For example, strong correlative relationships may have value as biomarkers for individual or groups of analytes. An obvious application would be the use of specific metabolites as indicators of the presence of certain bacteria, which would presumably be faster than culture- or sequence-based approaches. Furthermore, knowledge of the relationships between metabolites and bacteria may prove useful in either direct (therapeutic) or indirect (dietary) interventions for chronic disorders with microbial compositional shifts, such as IBD.
The data presented here reveal significant interdependence of the mucosal metabolome and microbiome. Evidence was presented that suggests the microbiome and metabolome have bi-directional influence, with bacteria influencing metabolite composition and metabolites contributing to microbial community architecture. The results also suggest that metabolites should be more deeply interrogated as direct mediators of microbial-associated disease activity and that metabolites may be a direct target for monitoring and therapeutically manipulating microbial community function in IBD and other microbiome-associated intestinal diseases.
This work was supported by NIH DK46763 (DPBM, JB), AI078885 (JB), DK062413 (DPBM), DK084554 (D.P.B.M.), NCRR and NCATS UL1TR000124 (JB), the Crohn’s and Colitis Foundation of America 3153 (JB and DPBM), the Burroughs Wellcome Fund Inter-school Program in Metabolic Diseases Fellowship (MT), Cedars-Sinai F Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute Research Funds (DPBM), and the Helmsley Foundation (DPBM). The authors would like to acknowledge Georgetown University's Proteomics and Metabolomics shared resource partially supported by NIH/NCI grant P30-CA051008. They also recognize the efforts of Sebastian Anizan (affiliated with Lombardi Comprehensive Cancer Center, Georgetown University) for contributing to metabolite processing.
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