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Fecal Microbiome and Volatile Organic Compound Metabolome in Obese Humans With Nonalcoholic Fatty Liver Disease
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY 2013;11:868 – 875
Fecal Microbiome and Volatile Organic Compound Metabolome in Obese Humans With Nonalcoholic Fatty Liver Disease MAITREYI RAMAN,* IFTIKHAR AHMED,‡ PATRICK M. GILLEVET,§ CHRIS S. PROBERT,储 NORMAN M. RATCLIFFE,¶ STEVE SMITH,¶ ROSEMARY GREENWOOD,‡ MASOUMEH SIKAROODI,§ VICTOR LAM,* PAM CROTTY,* JENNIFER BAILEY,* ROBERT P. MYERS,* and KEVIN P. RIOUX*,# *Department of Medicine, Division of Gastroenterology and Hepatology, #Department of Microbiology & Infectious Diseases, Gastrointestinal Research Group, Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada; ‡Bristol Royal Infirmary, Bristol, United Kingdom; § Department of Environmental Science and Policy, Microbiome Analysis Center, George Mason University, Manassas, Virginia; 储Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; and ¶Faculty of Health and Life Sciences, Institute of Biosensor Technology, University of the West of England, Bristol, United Kingdom
BACKGROUND & AIMS:
The histopathology of nonalcoholic fatty liver disease (NAFLD) is similar to that of alcoholic liver disease. Colonic bacteria are a source of many metabolic products, including ethanol and other volatile organic compounds (VOC) that may have toxic effects on the human host after intestinal absorption and delivery to the liver via the portal vein. Recent data suggest that the composition of the gut microbiota in obese human beings is different from that of healthy-weight individuals. The aim of this study was to compare the colonic microbiome and VOC metabolome of obese NAFLD patients (n ⴝ 30) with healthy controls (n ⴝ 30).
METHODS:
Multitag pyrosequencing was used to characterize the fecal microbiota. Fecal VOC profiles were measured by gas chromatography–mass spectrometry.
RESULTS:
There were statistically significant differences in liver biochemistry and metabolic parameters in NAFLD. Deep sequencing of the fecal microbiome revealed over-representation of Lactobacillus species and selected members of phylum Firmicutes (Lachnospiraceae; genera, Dorea, Robinsoniella, and Roseburia) in NAFLD patients, which was statistically significant. One member of phylum Firmicutes was under-represented significantly in the fecal microbiome of NAFLD patients (Ruminococcaceae; genus, Oscillibacter). Fecal VOC profiles of the 2 patient groups were different, with a significant increase in fecal ester compounds observed in NAFLD patients.
CONCLUSIONS:
A significant increase in fecal ester VOC is associated with compositional shifts in the microbiome of obese NAFLD patients. These novel bacterial metabolomic and metagenomic factors are implicated in the etiology and complications of obesity.
Keywords: Nonalcoholic Fatty Liver Disease; Obesity; Microbiota; High-Throughput Nucleotide Sequencing; Volatile Organic Compound; Metabolomics.
Podcast interview: www.gastro.org/cghpodcast. Also available on iTunes; see editorial on page 876; see related article, Wu and Lewis on page 774, in this issue of CGH.
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he gut microbiota has been implicated in obesity, diabetes, metabolic syndrome, and nonalcoholic fatty liver disease (NAFLD) through effects on caloric salvage, host energy metabolism, proinflammatory signaling, and via direct hepatotoxicity of bacterial products.1 Obese human beings may have a fundamentally different gut microbiota, with an overall reduction in diversity, but an increase in the abundance of members of phylum
Firmicutes and a proportionate decrease in Bacteroidetes, compared with lean individuals.2 The metagenomes of obese human beings are enriched in microbial energy-harvesting genes.3 Chronic excessive ingestion of ethanol produces hepatic steatosis, steatohepatitis, and cirrhosis. Gut microbes are an endogenous source of ethanol, which may be delivered to the liver
Abbreviations used in this paper: BMI, body mass index; GC-MS, gas chromatography–mass spectrometry; MTPS, multitag pyrosequencing; NAFLD, nonalcoholic fatty liver disease; PCR, polymerase chain reaction; VOC, volatile organic compound. © 2013 by the AGA Institute 1542-3565/$36.00 http://dx.doi.org/10.1016/j.cgh.2013.02.015
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in a continuous fashion and promote steatosis and liver injury.4 Microbes produce about 300 other volatile organic compounds (VOCs) in the human gut,5 the systemic effects of which are unknown. NAFLD is a significant public health problem because of the dramatic increase in the prevalence of obesity, which along with insulin resistance and type II diabetes are major risk factors for the pathogenesis of the disease. Given the association between obesity and NAFLD, we hypothesized that there are specific compositional and functional characteristics of the gut microbiome in obese NAFLD patients, which may be relevant to the pathophysiology of NAFLD. Our objective was to describe and compare the fecal microbiota and bacteria-derived VOC metabolites in obese NAFLD patients and healthy-weight controls.
Methods Study Design and Patients This was an observational case-control study. The study protocol was approved by the Conjoint Health Research Ethics Board of the University of Calgary and Alberta Health Services. There were no dietary restrictions imposed on any participant. Obese patients had not actively engaged in lifestyle modifications such as weight loss attempts through diet and exercise at the time of study enrollment. Obese patients with a body mass index (BMI) greater than 30 kg/m2 and clinically suspected NAFLD (n ⫽ 30) were recruited from outpatient clinics at Foothills Medical Centre. Patients with an unexplained increase of liver enzyme levels of at least 1.5 times the upper limit of normal and an echogenic liver on ultrasound were screened for study recruitment. Patients were excluded if they had more than 20 g/d alcohol consumption, decompensated liver disease, antibiotics or probiotics within the preceding 3 months, medications believed to contribute to abnormal liver enzyme levels, or had fatty liver suspected as the result of other causes. All patients underwent a detailed clinical history, physical examination, and anthropometric measurements that, together with the earlier-described clinical laboratory measurements and sonographic findings, led to a diagnosis of clinically suspected NAFLD. Healthy controls (n ⫽ 30) were recruited from the Forzani and MacPhail Colon Cancer Screening Center at Foothills Medical Centre. These were healthy individuals who were not taking any prescription medications at enrollment. Controls were excluded if they had a BMI of 25 kg/m2 or greater or consumed more than 20 g/d of alcohol. Controls had normal liver enzyme levels within 6 months before enrollment.
Stool Samples Patients provided a single stool sample at the time of study entry. For control subjects (patients undergoing colonoscopy for colon cancer screening), stool specimens were obtained before any of the dietary or laxative preparations required for colonoscopy. Subjects were instructed on methods for stool sample collection and all materials were provided in a convenient specimen collection kit (Protocult, Rochester, MN). Each patient provided 15 mL of stool, which was collected at home in a sterile container and immediately stored (⫺20°C). The specimens were transported to the laboratory within 7 days of collection on an ice pack. Experiments have shown that if samples are frozen within 6 hours of sample production, only
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minor losses of VOC occur.6 Stool specimens can be stored at ⫺20°C for several weeks before subsequent bacterial DNA extraction and microbiome characterization.7 Fecal samples were thawed in an ice-water bath and 0.15 mL were transferred to sterile 1.5-mL vials for DNA extraction. For gas chromatography–mass spectrometry (GC-MS) analyses of VOC, 2 mL of stool were transferred in a similar fashion to a headspace vial (Supelco, Mississauga, Canada). Clean and accurate transfer of the thawed stool specimens was achieved by backloading the cut end of a sterile syringe with the plunger set to the desired volume, which then was used to deliver the measured aliquot to end-use vials.
DNA Extraction Total DNA was extracted from approximately 150 mg of stool using the Qiagen QIAamp DNA Stool MiniKit (Qiagen, Mississauga, Canada), which included a 2-minute mechanical bead (0.1 mm zirconia:silica) beating step.7
Microbiome Characterization Using Multitag Pyrosequencing Multitag pyrosequencing (MTPS) was performed by the Microbiome Analysis Center at George Mason University (Manassas, VA) on the Roche GS Junior platform (Branford, CT) using tagged fusion primers, as previously described.8 This technique allows the rapid sequencing of multiple samples at one time, yielding thousands of sequence reads per sample. Specifically, we generated a set of 96 emulsion polymerase chain reaction (PCR) fusion primers that contain the 454 emulsion PCR linkers on the 27F primer (AGAGTTTGATCCTGGCTCA G-3=) and 355R’ (5=-GCTGCCTCCCGTAGGAGT-3=) and different 8-base barcode between the A adapter and the 27F primer.8 Thus, each fecal sample was amplified with unique bar-coded forward 16S ribosomal RNA primers, and then up to 96 samples were pooled and subjected to emulsion PCR and pyrosequenced.
Microbiome Analysis Custom pipelines9 were used for assigning taxonomic identification to the MTPS reads and formatting the data for subsequent analyses using Unifrac (version 1.25.0),10 and Metastats (version 1.20.0).11 Custom Perl scripts were used to demultiplex the MTPS data by sorting the sequences into bins based on the barcodes and taxa in the samples. We have noted that ligating tagged primers to PCR amplicons distorts the abundances of the communities, and thus it is critical to incorporate the tags during the original amplification step. Reads were filtered based on quality scores and length. We identified the taxa present in each sample using the Bayesian classifier tool in version 10 of the Ribosomal Database Project.12 The abundances of the bacterial identifications then were normalized using a custom PERL script, and taxa present at greater than 1% of the community were tabulated. We chose this cut-off value because of our a priori assumption that taxa present in less than 1% of the community vary between individuals and have a minimal contribution to the functionality of that community and that 2000 reads per sample will only reliably identify community components that are greater than 1% in abundance. A raw count data matrix was used for the Metastats analysis. Normalized abundance tables were used for
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Results Clinical Characteristics
Table 1. Patient Characteristics and Clinical Data Parameter
NAFLD (n ⫽ 30)
Control (n ⫽ 30)
P value
Female sex Age, y BMI Fasting glucose level Triglyceride level Hypertension Units of alcohol/wk ALT level GGT level
17 (55) 49 (34–57) 33 (29–35) 5.5 (5.1–6.6) 1.6 (1.2–2.4) 15 (50) 0.2 (0–2) 51 (31–82) 76 (44–111)
17 (57) 51 (47–56) 22 (21–24) 5.1 (4.8–5.5) 0.8 (0.7–1.1) 3 (10) 3 (0.3–7) 18 (14–23) 17 (12–23)
1.00 .21 ⬍.01 .04 ⬍.01 ⬍.01 .01 ⬍.01 ⬍.01
NOTE. Sex and hypertension are reported as n (%); all other values are reported as median and interquartile range. ALT, alanine aminotransferase; GGT, ␥-glutamyltransferase.
principal coordinate analysis. Unifrac analysis was performed using the pipeline in Qiime (available at: http://qiime.org/).13 This tool is used to compare the topology of phylogenetic trees constructed from the operational taxonomic units in all the samples in each disease class.
Gas Chromatography–Mass Spectrometry Analysis of Fecal Volatile Organic Compounds Stool samples were transferred to a headspace vial (Supelco, Mississauga, Canada), and VOC were analyzed by GC-MS using equipment and run conditions previously described in detail.5 All analyses were performed on a PerkinElmer Clarus 500 GC-MS quadrupole benchtop system (Beaconsfield, UK). Before extraction, all sample vials were placed in a water bath at 60°C for 1 hour. Carboxen/polydimethylsiloxane solid-phase microextraction fibers (Supelco, Poole, UK) were used to adsorb the VOC from the vial headspace above the feces for 30 minutes. The GC-MS system was fitted with a 60-m SPB-1 column (Supelco, Poole, UK). The oven temperature was maintained at 40°C for 2 minutes after the injection, then heated 6°C per minute to 220°C and maintained for 4 minutes. The carrier gas was 99.95% pure helium (BOC, Guilford, UK) at a constant linear velocity of 35 cm/s. The MS was operated in electron ionization mode scanning mass ions from 17 to 350 m/z. Compounds were identified by comparison with the National Institute of Standards and Technology (NIST08) mass spectral library (available: http://chemdata.nist.gov/) followed by manual visual inspection and retention time matching of selected standards. In interpreting the data, only compounds with a 90% or greater probability of a match to library standards were named.
Statistical Analyses of Volatile Organic Compounds Analyses of VOC data were performed using SPSS version 16 (IBM SPSS, Armonk, NY). A total of 220 VOC were identified in these experiments. A dichotomous variable was assigned based on the presence and absence of volatile compounds (ie, 1 if the volatile was present; 0 if the volatile was absent). Univariate analysis was performed on these binary data to identify specific volatile compounds that were statistically significant (P ⱕ .05) in discriminating between the 2 groups.
Obese (n ⫽ 30) and control (n ⫽ 30) subjects were recruited between January and August of 2009. Patient characteristics and clinical data are reported in Table 1. Results are presented as median and interquartile range. Seventeen (55%) female subjects were found in each of the study groups. There was a similar number of Asian subjects among the obese patients and healthy controls (n ⫽ 5 vs n ⫽ 4; P ⬍ .05). There was no significant difference in age between obese patients and healthy controls (49 y, 34 –57 y vs 51 y, 47–56 y; P ⫽ .21). Obese patients had a significantly higher BMI (33 kg/m2, 29 –35 kg/m2 vs 22 kg/m2, 21–24 kg/m2; P ⬍ .05), fasting glucose level (5.5 mmol/L, 5.1– 6.6 mmol/L vs 5.1 mmol/L, 4.8 –5.5 mmol/L; P ⬍ .05), and triglyceride level (1.6 mmol/L, 1.2–2.4 mmol/L vs 0.8 mmol/L, 0.7–1.1 mmol/L; P ⬍ .05) compared with controls. Hypertension, type II diabetes, and dyslipidemia were found in 15 (50%) vs 3 (10%), 6 (20%) vs 1 (3.3%), and 11 (37%) vs 2 (6.7%) obese patients compared with controls, respectively. All obese patients had sonographic evidence of hepatic steatosis, without evidence of cirrhosis or portal hypertension. Healthy controls did not have an abdominal ultrasound performed. Abnormal transaminase levels consistent with NAFLD were seen in obese subjects compared with controls.
Volatile Organic Compound Analysis A total of 220 distinct VOC were detected in our patients (Supplementary Table 1). Many of the identified VOC were present infrequently in individuals. In healthy controls, the median number of distinct VOC within an individual was 64 (range, 45– 88). There were 22 VOC that were present in 80% or more of healthy controls and 7 of these were ubiquitous, comprising a core fecal VOC constituency (Table 2). In patients with NAFLD, the median number of VOC was 72 (range, 49 –90), which was significantly greater than in controls (P ⬍ .01). There were 23 identifiable VOC that were present in 80% or more of NAFLD patients, 8 of which were ubiquitous. Most of the core and common fecal VOC were the same in the NAFLD and control groups (Supplementary Table 1). Ethanol, acetone,
Table 2. Common and Ubiquitous Fecal VOC in HealthyWeight Control Patients Sorted Alphabetically Core (ubiquitous)
Common (ⱖ80% prevalence)
Acetone
Acetaldehyde
Butanoic acid 2-Butanone Ethanol 2-Heptanone
Benzaldehyde Benzeneacetaldehyde Butanal, 2-methylButanal, 3-methyl-
2-Pentanone
Butanoic acid, 2methylButanoic acid, 3methylButanoic acid, methyl ester
Propanal, 2-methyl
Butanoic acid, ethyl ester Carbon disulfide Dimethyl trisulfide Pentanoic acid Pentanoic acid, methyl ester Pinene Propanoic acid, 2-methyl-
NOTE. There were 7 fecal VOC found ubiquitously in healthy individuals. In addition to the core compounds, there were 15 other VOC found commonly (ⱖ80% prevalence) in these subjects.
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Table 3. Univariate Analyses Indicating Fecal VOC That Distinguished Control and NAFLD Groups, Sorted According to Degree of Statistical Significance (All P ⱕ .05) Increased in NAFLD Butanoic acid, propyl ester Propanoic acid, propyl ester Acetic acid, ethyl ester Acetic acid, pentyl ester Cyclohexene, 4-ethenyl-4methyl-3-(1-methylethenyl)1-(1-methylethyl)-, (3Rtrans)Butanoic acid, 3-methyl-, butyl ester n-Propyl acetate Butanoic acid, butyl ester Phellandrene Propanoic acid, ethyl ester 1,6-Octadien-3-ol, 3,7dimethyl-
Decreased in NAFLD 2-Butanone Furan, 2-methylHeptanal 2(3H)-furanone, dihydro-5methyl2-Heptanone, 6-methyl-
2,3-pentanedione 1,6-Octadien-3-ol, 3,7-dimethyl-, 2-aminobenzoate Cyclohexanol, 5-methyl-2-(1methylethyl)2-Octene, 3,7-dimethyl-, (Z)3-Hexanone, 2-methylAcetic acid, (1,2,3,4,5,6,7,8octahydro-3,8,8trimethylnaphth-2-yl)methyl ester Cyclohexane, hexyl-
Myrcene Pentanoic acid, methyl ester Acetic acid, methyl ester 2-Propynoic acid methyl ester Butanoic acid, 3-methyl-, ethyl ester 1-Propanol Propanoic acid, 2-methyl-, propyl ester
NOTE. There were 18 fecal VOC that were statistically significantly more prevalent in obese NAFLD patients, of which 13 were classified as ester compounds and are indicated by shading.
heptanone, butanoic acid, and pentanoic acid were notable for their ubiquitous presence among fecal VOC identified in both study groups. By using binary data (ie, VOC peak presence or absence), univariate analyses were used to discern statistical differences between fecal VOC composition in obese and control subjects (Table 3). There were 12 fecal VOC that were significantly less common in NAFLD than in controls, representing a variety of chemical classes (eg, aldehydes, furans, ketones, and so forth). There were 18 VOC that were significantly more prevalent among obese patients than controls, the majority of which were ester compounds (eg, aliphatic esters of ethanoic, propanoic, butanoic, and pentanoic acids).
Deep Taxonomic Analysis Using Multitag Pyrosequencing The calculated relative abundance of the various taxa at the genus level in obese NAFLD and control groups is presented as taxonomy pie charts in Figure 1. As expected, Bacteroides and various genera within phylum Firmicutes were the dominant constituents of the fecal microbiota in both patient groups. Samples were rank-ordered based on the abundance of genus Bacteroides and plotted as stacked histograms (Figure 2). The histograms show that there is dramatic variability of the
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fecal microbiota between individuals in both control and NAFLD groups. Most obvious, for example, is the wide variation in Bacteroides, comprising 5%– 80% relative abundance in various individuals in each study group and, therefore, there was no statistically discernible difference in the proportion of Bacteroides between NAFLD and control. Within the NAFLD group, there was no correlation between BMI and relative abundance of Bacteroides or Firmicutes. Metastats analysis was performed on the MTPS relative abundance data to determine which taxa were statistically different between the control and obese samples at family and genus levels (Table 4). We selected only taxa that had a Metastats P value of less than .05 and a Q value (false-discovery rate) that was less than the P value. We observed several families that were increased in the obese NAFLD group (Table 4). The most statistically robust increase was observed in the Kiloniellaceae and Pasteurellaceae families, which are members of phylum Proteobacteria, which often are observed to increase in various states of intestinal dysbiosis. Three Firmicutes also were increased significantly in NAFLD: Lactobacillaceae, Veillonellaceae, and Lachnospiraceae families, known for various products of anaerobic metabolism. In contrast, there was another member of phylum Firmicutes, Ruminococcaceae, that was decreased along with Porphyromonadaceae, a member of phylum Bacteroidetes. This illustrates the disadvantage of using higher phylogenetic levels (ie, phylum) to distinguish disease states because, in this case, 3 members of phylum Firmicutes increased and 1 decreased, which would be obscured if only phylum-level data were considered. At the genus level, we observed a significant increase in NAFLD patients in 3 Lachnospiraceae (Robinsoniella, Roseburia, and Dorea), and an increase in Lactobacillus (Table 4). We only observed a statistically significant decrease in Oscillibacter in NAFLD, a Ruminococcaceae. The earlier-described Metastats analyses showed that increases in specific families of Proteobacteria were associated with NAFLD; these findings were highly statistically significant. At a deeper level of phylogenetic resolution, specific genera of Firmicutes also were increased in obese NAFLD patients. Unifrac analysis enabled further statistical comparison and illustration of the salient findings of the MTPS data. The Unifrac principal coordinate analysis (not shown) revealed some differential clustering of the disease and control states but this was diffuse and not dramatic. The Unifrac tree (not shown) also showed some clustering with a Unifrac P value of .03, indicating a significant shift in the microbiome between control and NAFLD.
Discussion In a selected group of obese patients with clinically suspected NAFLD, we have discovered a significantly altered fecal VOC profile and compositional shift in the fecal microbiome. Although others have studied the gut microbiota in obesity, we provide the first description of the gut microbiome in obese NAFLD patients using next-generation sequencing technologies, and extend this to include an important functional correlate of the fecal microbiome, the microbial VOC metabolites that have plausible links to NAFLD as hepatotropic or hepatotoxic factors. Ley et al have shown that the gut microbiome of obese human beings is distinct from healthy-weight individuals, with a lower proportion of Bacteroidetes and a higher proportion of
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Figure 1. Fecal microbiome of obese NAFLD and healthyweight control subjects. MTPS data were used to calculate the relative abundance of various bacterial taxa. The labels indicate family and genus.
Firmicutes in a given subject.2 Other groups also have shown dysbiosis of the distal gut in obese human beings, but the exact findings have been inconsistent.14 We did not find a significant difference in the proportions of Firmicutes and Bacteroidetes between obese and healthy weight individuals in our study. Such discordant results may be owing to methodologic differences or differences in the patient populations such as degree of obesity, ethnicity, diet, environment, and associated comorbidities such as metabolic syndrome and its treatments. Indeed, compared with the original description of the gut microbiome in human beings with classes III and IV obesity,2 our patients mainly had class I obesity. Although we did not replicate pre-
viously described phylum-level differences in the fecal microbiota, at the high level of taxonomic resolution afforded by deep sequencing, we observed small but significant differences between obese NAFLD patients and controls. An important caveat and limitation to recognize is the difficulty in taxonomic assignment and discrimination of similar types of bacteria or the common presence of unclassified bacteria in version 10 of the Ribosomal Database Project and similar databases. Spencer et al15 recently suggested that host genotype and specific genera of Gammaproteobacteria (Klebsiella species, Enterobacter species, and Escherichia species) and Erysipelotrichi are important bacterial predictors of susceptibility to choline deficiency–induced fatty
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Figure 2. Relative proportions of dominant taxa at genus level in obese NAFLD patients and healthy-weight controls.
liver disease. Consistent with this, we also found taxa belonging to Gammaproteobacteria to be increased significantly in NAFLD. The fact that we observed significant changes in the microbiota and VOC profiles of our patients with class I obesity attests to the possibility that even more pronounced changes may have been observed if we were to study patients with more advanced forms of NAFLD and obesity. Our study participants did not maintain food diaries during the study period, limiting our ability to attribute altered fecal VOC and microbiomes to specific dietary differences in NAFLD. Diet certainly has an impact on the gut microbiome and, in fact, long-term dietary macronutrient composition is a major determinant of the intestinal microbiome in human beings.16 Short-term manipulation of diet (eg, extremes of fat and fiber intake) impacts the intestinal microbiome, but the effect seems small, transient, and ineffectual in changing enterotype designation.16 We did not try to impose any particular diet on our study participants because the details of what constitutes an adequate control diet in terms of macronutrient composition
and duration of consumption are unknown. We did not attempt to control for other environmental factors (eg, smoking, ambient pollution exposure, stress), because their specific impact on the microbiome is largely unknown. We also did not systematically determine or impose recruitment of selected ethnic groups in our study because relatively little is known about the fecal microbiome cross-culturally. We showed that ester VOC were identified more frequently in fecal samples from obese NAFLD patients than controls. Most fecal VOC are a result of microbial action on gut luminal substrates.17 Although there is abundant literature about bacterial production of short-chain fatty acid VOC (eg, acetic, propionic, and butyric acids) and ethanol by various gut microbes, very little is known about the microbes and biochemical pathways that may be involved in production of volatile esters in the human gut. Most of the esters linked to NAFLD in our study are derivatives of short chain aliphatic alcohols and carboxylic acids. Bacterial esterases can catalyze esterification reactions involving such organic acids and alcohols,18 which may be
Table 4. Metastats Analysis Indicating Taxa That Significantly Distinguished Control and NAFLD Groups, Ranked According to Statistical Significance
Phylum_class_order_family Proteobacteria_Alphaproteobacteria_Kiloniellales_Kiloniellaceae Proteobacteria_Gammaproteobacteria_Pasteurellales_Pasteurellaceae Firmicutes_Bacilli_Lactobacillales_Lactobacillaceae Firmicutes_Clostridia_Clostridiales_Lachnospiraceae Firmicutes_Clostridia_Clostridiales_Ruminococcaceae Bacteroidetes_Bacteroidia_Bacteroidales_Porphyromonadaceae Firmicutes_Clostridia_Clostridiales_Veillonellaceae Family_genus Lactobacillaceae_Lactobacillus Ruminococcaceae_Oscillibacter Lachnospiraceae_Robinsoniella Lachnospiraceae_Roseburia Lachnospiraceae_Dorea NOTE. Taxa that were increased in NAFLD patients are highlighted in gray.
Mean (NAFLD)
Mean (control)
P value
Q value
Interpretation
0.00000 0.00000 0.00000 0.16210 0.22077 0.04519 0.01934
0.00106 0.00572 0.01376 0.22631 0.15240 0.02062 0.04953
.00100 .00100 .00100 .01499 .02498 .04496 .04595
0.00001 0.00001 0.00001 0.00016 0.00025 0.00041 0.00041
Increase in NAFLD Increase in NAFLD Increase in NAFLD Increase in NAFLD Decrease in NAFLD Decrease in NAFLD Increase in NAFLD
0.00000 0.06947 0.00283 0.05526
0.01453 0.01693 0.00880 0.07610 0.04545
.00100 .00100 .01399 .02098 .02498
0.00043 0.00043 0.00571 0.00835 0.00969
Increase in NAFLD Decrease in NAFLD Increase in NAFLD Increase in NAFLD Increase in NAFLD
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a source of the ester VOC observed in our study. In addition, esters may be formed by nonenzymatic esterification reactions involving gut luminal or bacterial substrates. At the phylum level, we have identified increases in Proteobacteria, and at the family level, we have observed increases in Lactobacillus and 3 genera of Lachnospiraceae associated with NAFLD. We are not aware of any defined metabolic pathways that specifically link these bacteria individually or as a consortium to the production of the fecal ester compounds identified in the NAFLD group. Endogenous ethanol production by gut microbes deserves further mention in light of its recent link to nonalcoholic steatohepatitis. Zhu et al19 reported a significant increase in the abundance of Escherichia in patients with nonalcoholic steatohepatitis, which was associated with increased blood ethanol concentrations. In our study, ethanol was identified as a ubiquitous fecal VOC in both NAFLD and control groups, but we did not measure blood or breath alcohol concentrations and we did not observe an increase in Escherichia abundance in NAFLD. Because of the complexities, dynamics, redundancies, and interconnectedness of the human intestinal microbiota in terms of membership and function, it is difficult to causally link production of ethanol or ester VOC to numerically increased bacterial taxa. As shown in the present article and in previous work,5 there is a core group of fecal VOC representing approximately 60% of the identified compounds, which reflect essential functions of the resident fecal microbiota irrespective of day-to-day dietary influences. However, some fecal VOC may originate directly from dietary sources such as aldehydes, ketones, and terpenes, which give fruits and vegetables their characteristic aroma, and aliphatic compounds that are found in meat. Variations in the remaining fecal VOC are most likely owing to individual dietary practices and distinctions of microbiota. It remains possible that some of the observed differences in fecal VOC profiles between control and NAFLD patients are attributable to diet. Our study population was limited to obese NAFLD and healthy controls. Because we did not study obese non-NAFLD patients, it is difficult to conclude that the observed changes in VOC are a consequence of NAFLD rather than obesity. Recruitment of obese patients without NAFLD would require liver biopsy to exclude hepatic steatosis in obese patients with normal liver biochemistry. Liver biopsies were not performed in our NAFLD patients because this is not presently the standard of care at our institution and we could not justify the risk associated with liver biopsy in this pilot study. However, analyzing the fecal microbiome and VOC metabolome data with reference to histologic parameters may have provided insight into possible threshold effects with escalating disease severity. An abdominal ultrasound was not performed in our controls to exclude steatosis. Reports on the prevalence of lean NAFLD are not well documented. One recent study20 reported a prevalence of NAFLD (identified histologically) of 2.8% in individuals with a BMI less than 30 kg/m2. The majority of lean patients with NAFLD have associated metabolic risk factors, abdominal obesity, and Asian or East Indian ethnicity to which different parameters defining overweight and obesity are applied. All of our controls were Caucasian without these risk factors. Additional studies are required to establish the mechanistic relevance of our novel observations to the pathogenesis of NAFLD. Although several ester compounds were identified more commonly in NAFLD patients, ultimately the relevance to
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NAFLD likely depends on systemic uptake. To date, little is known about the presence of gut-derived VOC in peripheral or portal blood as a medium for the systemic effects of these compounds in human beings.17 Absorption of gut-derived VOC may occur by passive diffusion of lipophilic small molecules across the colonic epithelial lipid membrane according to partitioning coefficient, although this is likely to be an oversimplification of the in vivo process.21 Similar to ethanol, ester VOC also may have injurious effects on barrier or local immune functions in the gut. The link between obesity, NAFLD, and host intestinal microbiota is likely to be more sophisticated than just compositional shifts; complex functional aspects of host-microbiota interactions (eg, diet, environment) are likely at play. In subsequent work, we propose a metagenomic analysis of both microbiome composition and functional capacity (eg, using the Kyoto Encyclopedia of Genes and Genomes Database) correlated with host clinical parameters, energy homeostasis, and proinflammatory signaling in obese and NAFLD patients.
Supplementary Material Note: To access the supplementary material accompanying this article, visit the online version of Clinical Gastroenterology and Hepatology at www.cghjournal.org, and at http:// dx.doi.org/10.1016/j.cgh.2013.02.015. References 1. Parnell JA, Raman M, Rioux KP, et al. The potential role of prebiotic fibre for treatment and management of non-alcoholic fatty liver disease and associated obesity and insulin resistance. Liver Int 2012;32:701–711. 2. Ley RE, Turnbaugh PJ, Klein S, et al. Microbial ecology: human gut microbes associated with obesity. Nature 2006;444:1022– 1023. 3. Turnbaugh PJ, Ley RE, Mahowald MA, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006;444:1027–1031. 4. Cope K, Risby T, Diehl AM. Increased gastrointestinal ethanol production in obese mice: implications for fatty liver disease pathogenesis. Gastroenterology 2000;119:1340 –1347. 5. Garner CE, Smith S, De Lacy Costello B, et al. Volatile organic compounds from feces and their potential for diagnosis of gastrointestinal disease. FASEB J 2007;21:1675–1688. 6. Ahmed I, Smith S, Probert C. Volatile organic compounds as diagnostic faecal biomarkers in inflammatory bowel disease— method development. Gut 2009;58:A63. 7. Zoetendal EG, Heilig HG, Klaassens ES, et al. Isolation of DNA from bacterial samples of the human gastrointestinal tract. Nat Protoc 2006;1:870 – 873. 8. Gillevet P, Sikaroodi M, Keshavarzian A, et al. Quantitative assessment of the human gut microbiome using multitag pyrosequencing. Chem Biodivers 2010;7:1065–1075. 9. Naqvi A, Rangwala H, Spear G, et al. Analysis of multitag pyrosequence data from human cervical lavage samples. Chem Biodivers 2010;7:1076 –1085. 10. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71:8228 – 8235. 11. White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 2009;5:e1000352. 12. Cole JR, Wang Q, Cardenas E, et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 2009;37:D141–D145.
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13. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335–336. 14. Kallus SJ, Brandt LJ. The intestinal microbiota and obesity. J Clin Gastroenterol 2012;46:16 –24. 15. Spencer MD, Hamp TJ, Reid RW, et al. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology 2011;140:976 –986. 16. Wu GD, Chen J, Hoffmann C, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 2011;334: 105–108. 17. Probert CS, Ahmed I, Khalid T, et al. Volatile organic compounds as diagnostic biomarkers in gastrointestinal and liver diseases. J Gastrointest Liver Dis 2009;18:337–343. 18. Elgaali H, Hamilton-Kemp TR, Newman MC, et al. Comparison of long-chain alcohols and other volatile compounds emitted from food-borne and related Gram positive and gram negative bacteria. J Basic Microbiol 2002;42:373–380. 19. Zhu L, Baker SS, Gill C, et al. Characterization of the gut microbiome in non-alcoholic steatohepatitis (NASH) patients: a connection between endogenous alcohol and NASH. Hepatology 2013;57:601– 609. 20. Vos B, Moreno C, Nagy N, et al. Lean non-alcoholic fatty liver
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disease (lean-NAFLD): a major cause of cryptogenic liver disease. Acta Gastroenterol Belg 2011;74:389 –394. 21. Yazdanian M, Glynn SL, Wright JL, et al. Correlating partitioning and Caco-2 cell permeability of structurally diverse small molecular weight compounds. Pharm Res 1998;15:1490 –1494.
Reprint requests Address requests for reprints to: Maitreyi Raman, MD, MSc, FRCPC, Department of Medicine, Division of Gastroenterology and Hepatology, University of Calgary, 6D26, 3280 Hospital Drive, Calgary, Alberta, Canada T2N 4N1. e-mail: [email protected]; fax: (403) 5925090. Conflicts of interest The authors disclose no conflicts. Funding Maitreyi Raman and Kevin Rioux were supported by the Canadian Institutes of Health Research (Human Microbiome Catalyst Grant), and the Division of Gastroenterology and Hepatology at Foothills Medical Centre, University of Calgary.
July 2013
FECAL MICROBIOME AND VOC METABOLOME IN NAFLD
Supplementary Table 1. Index of Fecal VOC Identified in a Cohort of Obese NAFLD and Healthy Control Patients Compound
Normal ⫽ 30
NAFLD ⫽ 30
2-Butanone 2-Heptanone 2-Pentanone Acetone Butanoic acid Ethyl alcohol Pentanoic acid Propanal, 2-methylá-Pinene Butanoic acid, 2-methylUnknown compounds RT 33.3 Acetaldehyde Butanal, 2-methylButanal, 3-methylButanoic acid, 3-methylBenzaldehyde Butanoic acid, ethyl ester Benzeneacetaldehyde Propanoic acid, 2-methylBenzene, 1-methyl-2-(1-methylethyl)Pentanoic acid, methyl ester Butanoic acid, methyl ester Carbon disulfide Dimethyl trisulfide Hexanal Indole á-Phellandrene Compound-95 (RT-30.8) Propanal, 3-(methylthio)Disulfide, dimethyl Methyl propionate 1-Propanol 2,3-Butanedione Cyclohexene, 1-methyl-5-(1methylethenyl)-, (R)Heptanal Bicyclo[3.1.1]hept-2-ene, 2,6,6trimethyl-, (ñ)Acetic acid, methyl ester Ethanedioic acid, trimethyl ester Phenol, 4-methylUnknown compounds RT 35.7 2-Hexanone Benzene, 1,3-bis(1,1-dimethylethyl)Nonanal 2,3-Pentanedione Butanoic acid, 2-methyl-, methyl ester, (ñ)Phenol Propanoic acid Unknown compounds RT-35.4 Acetic acid Octanal Propanol, 2-methyl2-Heptanone, 5-methyl2-Nonanone Butanoic acid, 1-methylethyl ester Copaene Methanethiol 1-Butanol
30 30 29 30 30 30 30 30 29 29 29 28 28 28 28 27 27 26 26 23 25 24 24 24 23 23 19 22 22 21 21 20 20 20
21 30 30 30 30 30 30 28 30 29 30 25 23 28 24 26 29 26 25 26 30 28 27 18 21 22 27 17 18 18 28 27 21 20
20 19
11 15
18 18 18 18 17 17 17 16 16
26 15 21 19 10 18 15 8 21
16 16 15 15 15 14 14 14 14 14 14 13
22 18 15 21 16 11 16 18 12 21 12 15
875.e1
Supplementary Table 1. Continued Compound
Normal ⫽ 30
NAFLD ⫽ 30
Bicyclo[3.1.0]hexane, 4-methyl-1-(1methylethyl)-, didehydro Cyclohexene, 1-methyl-4-(1methylethylidene)Phenol, 3-methylCyclohexanol, 5-methyl-2-(1methylethyl)-, Furan, 2-methyl1,4-Cyclohexadiene, 1-methyl-4-(1methylethyl)Isopropyl alcohol Propanoic acid, butyl ester Trimethylene oxide (2-Aziridinylethyl)amine 1-propene, 1-(methylthio)-, à-Cubebene Hexanoic acid, methyl ester Pentanoic acid, ethyl ester Propanoic acid, 2-methyl-, methyl ester 2-Butanol 2-Heptanone, 6-methyl2-Hexanone, 5-methylAcetic acid ethenyl ester Cyclohexene, 4-ethenyl-4-methyl-3(1-methylethenyl)-1-(1methylethyl)-, (3R-trans)Eucalyptol Pentanoic acid, pentyl ester Propanoic acid, 2-methylpropyl ester Propanoic acid, ethyl ester 1,6-Octadien-3-ol, 3,7-dimethyl-, 2-aminobenzoate 5-Hepten-2-one, 6-methylD-limonene Phenyl-pentamethylUndecane 2(3H)-Furanone, dihydro-5-methyl2-Pentanone, 3-methyl4,8,12-Tetradecatrien-1-ol, 5,9,13trimethylCyclohexane, 1,1’-(1,2-dimethyl-1,2ethanediyl)Furan, 2-pentylPentane Propanal R(-)3,7-Dimethyl-1,6-octadiene 2,3-Dioxabicyclo[2.2.1]heptane, 1-methyl2-Decanone 2-Octene, 3,7-dimethyl-, (Z)3-Hexanone, 2-methylAcetic acid, (8-octahydro-3,8,8trimethylnaphth-2-yl)-methyl ester Butanoic acid, 2-methyl-, propyl ester Butanoic acid, 2-methylbutyl ester Butanoic acid, butyl ester Cyclohexane, 1-ethenyl-1-methyl2,4-bis(1-methylethenyl)-, Cyclohexane, hexyl-
13
17
13
20
13 12
9 5
12 11
3 13
11 11 11 10 10 10 10 10 10
12 17 11 11 12 10 11 9 15
9 7 9 9 9
12 1 7 9 20
9 9 9
9 13 10
9 8
19 2
8 8 8 8 7 7 7
8 9 9 9 1 8 8
7
8
7 7 7 7 6
9 5 6 8 7
6 6 6 6
9 1 1 6
6
11
6 6 6
5 16 12
6
1
875.e2
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CLINICAL GASTROENTEROLOGY AND HEPATOLOGY Vol. 11, No. 7
Supplementary Table 1. Continued
Supplementary Table 1. Continued
Compound
Normal ⫽ 30
NAFLD ⫽ 30
Compound
Normal ⫽ 30
NAFLD ⫽ 30
Cyclohexanecarboxylic acid, methyl ester Decane Dimethyl sulfide Naphthalene, 1-hexahydro-4,7dimethyl-1-(1-methylethyl)n-Propyl acetate Pentanal Propanoic acid, 1-methylethyl ester 1,3,5-Cycloheptatriene 1-Dodecanol, 3,7,11-trimethylAcetyl valeryl Butanoic acid, 2-methyl-, ethyl ester Butanoic acid, 2-methylpropyl ester Butanoic acid, 3-methyl-, ethyl ester Butanoic acid, 3-methyl-, methyl ester Butanoic acid, 3-methyl-, propyl ester Cyclohexanecarboxylic acid Cyclohexanecarboxylic acid, ethyl ester Furan, 3-methylHeptane, 5-ethyl-2,2,3-trimethylNonane Oxirane, (methoxymethyl)Pentanoic acid, butyl ester Phenol, 2,4-bis(1,1-dimethylethyl)Propanoic acid, propyl ester Thiopivalic acid 1,6-Octadien-3-ol, 3,7-dimethyl2,6-Dimethyldecane 2-Propenal 2-Undecanone 2-Undecanone, 6,10-dimethyl3,5-Heptadien-2-one, 6-methyl-, (E)3-Buten-2-one, 1-(2,3,6trimethylphenyl)3-Carene 3-Furaldehyde 3-Hexanone, 4-methyl5-Octen-2-yn-4-ol à-Calacorene Azulene, 1-octahydro-1,4-dimethyl-7(1-methylethenyl) Benzene, (2-methyloctyl)Camphene Caryophyllene Furan, 2,3-dihydro-3-methylOctane Octane, 4-methylo-Xylene S-methyl 3-methylbutanethioate Tetracyclo, nonane, 3,6,9-triethyl3,6,9-trimethylUnknown compound RT-32.1 1,6-Octadiene, 3,7-dimethyl-, (S)13-Tetradecynoic acid, methyl ester 1-Butanol, 3-methyl-, propanoate 1-Cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl1-Pentanol 2-Butenal, 3-methyl-
6
7
6 6 6
7 7 7
3 3 3 3
4 4 4 4
6 6 6 5 5 5 5 5 5 5
16 2 10 6 6 2 5 6 13 3
3 3 3 3 3
4 4 2 9 4
3 3 3 3 3 3
8 4 7 4 5 4
5
11
5 5
6 6
3 3 2
4 4 3
5 5 5 5 5 5 5 5 4 4 4 4 4 4 4
6 6 6 1 10 6 18 5 13 5 3 7 3 2 4
2 2 2 2
3 4 3 3
2
7
2 2 2
3 0 1
2 2 2
2 10 3
2
3
4 4 4 4 4 4
5 4 5 1 1 5
2 2 2
3 3 3
2
3
4 4 4 4 4 4 4 4 4
5 5 5 1 5 1 5 5 5
2 2 2
3 3 3
3 3 3 3 3
4 4 4 8 4
2 2 2 1 1 1 1 1 1
3 3 3 4 5 2 2 2 2
3 3
4 4
2-Octene, 2-methyl-6-methylene2-Pentene, 4-methyl-, (Z)3,5-Octadiene, 2,7-dimethyl3-Cyclohexen-1-ol, 4-methyl-1-(1methylethyl)-, acetate 3-Hexanone 3-Hexanone, 5-methyl5H-1-Pyrindine Benzene, 4-ethenyl-1,2-dimethylBicyclo[3.1.0]hexane, 4-methylene1-(1-methylethyl)Butyl 2-methylbutanoate Dodecanoic acid, 3-hydroxyFormic acid, butyl ester Methyl alcohol Pentanoic acid, 4-methylPentanoic acid, 4-methyl-, ethyl ester Phosphonic acid, (p-hydroxyphenyl)Toluene 1,5-Cyclodecadiene, 1,5-dimethyl-8(1-methylethenyl)1-Butanol, 2-methyl-, propanoate 1H-Indole, 3-methyl1H-Indole, 4-methyl2,2,4-Trimethyl-1,3-pentanediol diisobutyrate 3-Cyclohexen-1-ol, 4-methyl-1-(1methylethyl)-, (R)3-Penten-2-one 4-Hexen-3-one 4H-1-Benzopyran, 4,4,5,8tetramethyl5-Ethyl-1-nonene á-Myrcene Bicyclo[3.1.1]hept-2-ene, 2,6dimethyl-6-(4-methyl-3-pentenyl)Butanoic acid, 3-methyl-, 2methylpropyl ester Butanoic acid, 3-methylbutyl ester Cyclobutene, 2-propenylideneCyclohexanecarboxylic acid, propyl ester Cyclohexanol, 1-methyl-4-(1methylethyl)Decanal Hexanoic acid Pentanoic acid, 2-methylpropyl ester Sulfide, allyl methyl Tetradecane Tetradecane, 2,6,10-trimethyl1-Butanol, 3-methyl-, acetate 1-Hexadecanol, 2-methyl1R-à-Pinene 2,3-Dimethyldecane 2-Hydroxy-3-pentanone 3-Cyclohexene-1-methanol, à,à4trimethyl4-Heptanone Acetic acid, pentyl ester Bicyclo[3.1.1]heptane, 6,6-dimethyl2-methylene-, (1S) -
1 1 1
5 10 2
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY 2013;11:868 – 875
Fecal Microbiome and Volatile Organic Compound Metabolome in Obese Humans With Nonalcoholic Fatty Liver Disease MAITREYI RAMAN,* IFTIKHAR AHMED,‡ PATRICK M. GILLEVET,§ CHRIS S. PROBERT,储 NORMAN M. RATCLIFFE,¶ STEVE SMITH,¶ ROSEMARY GREENWOOD,‡ MASOUMEH SIKAROODI,§ VICTOR LAM,* PAM CROTTY,* JENNIFER BAILEY,* ROBERT P. MYERS,* and KEVIN P. RIOUX*,# *Department of Medicine, Division of Gastroenterology and Hepatology, #Department of Microbiology & Infectious Diseases, Gastrointestinal Research Group, Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada; ‡Bristol Royal Infirmary, Bristol, United Kingdom; § Department of Environmental Science and Policy, Microbiome Analysis Center, George Mason University, Manassas, Virginia; 储Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; and ¶Faculty of Health and Life Sciences, Institute of Biosensor Technology, University of the West of England, Bristol, United Kingdom
BACKGROUND & AIMS:
The histopathology of nonalcoholic fatty liver disease (NAFLD) is similar to that of alcoholic liver disease. Colonic bacteria are a source of many metabolic products, including ethanol and other volatile organic compounds (VOC) that may have toxic effects on the human host after intestinal absorption and delivery to the liver via the portal vein. Recent data suggest that the composition of the gut microbiota in obese human beings is different from that of healthy-weight individuals. The aim of this study was to compare the colonic microbiome and VOC metabolome of obese NAFLD patients (n ⴝ 30) with healthy controls (n ⴝ 30).
METHODS:
Multitag pyrosequencing was used to characterize the fecal microbiota. Fecal VOC profiles were measured by gas chromatography–mass spectrometry.
RESULTS:
There were statistically significant differences in liver biochemistry and metabolic parameters in NAFLD. Deep sequencing of the fecal microbiome revealed over-representation of Lactobacillus species and selected members of phylum Firmicutes (Lachnospiraceae; genera, Dorea, Robinsoniella, and Roseburia) in NAFLD patients, which was statistically significant. One member of phylum Firmicutes was under-represented significantly in the fecal microbiome of NAFLD patients (Ruminococcaceae; genus, Oscillibacter). Fecal VOC profiles of the 2 patient groups were different, with a significant increase in fecal ester compounds observed in NAFLD patients.
CONCLUSIONS:
A significant increase in fecal ester VOC is associated with compositional shifts in the microbiome of obese NAFLD patients. These novel bacterial metabolomic and metagenomic factors are implicated in the etiology and complications of obesity.
Keywords: Nonalcoholic Fatty Liver Disease; Obesity; Microbiota; High-Throughput Nucleotide Sequencing; Volatile Organic Compound; Metabolomics.
Podcast interview: www.gastro.org/cghpodcast. Also available on iTunes; see editorial on page 876; see related article, Wu and Lewis on page 774, in this issue of CGH.
T
he gut microbiota has been implicated in obesity, diabetes, metabolic syndrome, and nonalcoholic fatty liver disease (NAFLD) through effects on caloric salvage, host energy metabolism, proinflammatory signaling, and via direct hepatotoxicity of bacterial products.1 Obese human beings may have a fundamentally different gut microbiota, with an overall reduction in diversity, but an increase in the abundance of members of phylum
Firmicutes and a proportionate decrease in Bacteroidetes, compared with lean individuals.2 The metagenomes of obese human beings are enriched in microbial energy-harvesting genes.3 Chronic excessive ingestion of ethanol produces hepatic steatosis, steatohepatitis, and cirrhosis. Gut microbes are an endogenous source of ethanol, which may be delivered to the liver
Abbreviations used in this paper: BMI, body mass index; GC-MS, gas chromatography–mass spectrometry; MTPS, multitag pyrosequencing; NAFLD, nonalcoholic fatty liver disease; PCR, polymerase chain reaction; VOC, volatile organic compound. © 2013 by the AGA Institute 1542-3565/$36.00 http://dx.doi.org/10.1016/j.cgh.2013.02.015
Fecal Microbiome and Volatile Organic Compound Metabolome in Obese Humans With Nonalcoholic Fatty Liver Disease MAITREYI RAMAN,* IFTIKHAR AHMED,‡ PATRICK M. GILLEVET,§ CHRIS S. PROBERT,储 NORMAN M. RATCLIFFE,¶ STEVE SMITH,¶ ROSEMARY GREENWOOD,‡ MASOUMEH SIKAROODI,§ VICTOR LAM,* PAM CROTTY,* JENNIFER BAILEY,* ROBERT P. MYERS,* and KEVIN P. RIOUX*,# *Department of Medicine, Division of Gastroenterology and Hepatology, #Department of Microbiology & Infectious Diseases, Gastrointestinal Research Group, Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada; ‡Bristol Royal Infirmary, Bristol, United Kingdom; § Department of Environmental Science and Policy, Microbiome Analysis Center, George Mason University, Manassas, Virginia; 储Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; and ¶Faculty of Health and Life Sciences, Institute of Biosensor Technology, University of the West of England, Bristol, United Kingdom
BACKGROUND & AIMS:
The histopathology of nonalcoholic fatty liver disease (NAFLD) is similar to that of alcoholic liver disease. Colonic bacteria are a source of many metabolic products, including ethanol and other volatile organic compounds (VOC) that may have toxic effects on the human host after intestinal absorption and delivery to the liver via the portal vein. Recent data suggest that the composition of the gut microbiota in obese human beings is different from that of healthy-weight individuals. The aim of this study was to compare the colonic microbiome and VOC metabolome of obese NAFLD patients (n ⴝ 30) with healthy controls (n ⴝ 30).
METHODS:
Multitag pyrosequencing was used to characterize the fecal microbiota. Fecal VOC profiles were measured by gas chromatography–mass spectrometry.
RESULTS:
There were statistically significant differences in liver biochemistry and metabolic parameters in NAFLD. Deep sequencing of the fecal microbiome revealed over-representation of Lactobacillus species and selected members of phylum Firmicutes (Lachnospiraceae; genera, Dorea, Robinsoniella, and Roseburia) in NAFLD patients, which was statistically significant. One member of phylum Firmicutes was under-represented significantly in the fecal microbiome of NAFLD patients (Ruminococcaceae; genus, Oscillibacter). Fecal VOC profiles of the 2 patient groups were different, with a significant increase in fecal ester compounds observed in NAFLD patients.
CONCLUSIONS:
A significant increase in fecal ester VOC is associated with compositional shifts in the microbiome of obese NAFLD patients. These novel bacterial metabolomic and metagenomic factors are implicated in the etiology and complications of obesity.
Keywords: Nonalcoholic Fatty Liver Disease; Obesity; Microbiota; High-Throughput Nucleotide Sequencing; Volatile Organic Compound; Metabolomics.
Podcast interview: www.gastro.org/cghpodcast. Also available on iTunes; see editorial on page 876; see related article, Wu and Lewis on page 774, in this issue of CGH.
T
he gut microbiota has been implicated in obesity, diabetes, metabolic syndrome, and nonalcoholic fatty liver disease (NAFLD) through effects on caloric salvage, host energy metabolism, proinflammatory signaling, and via direct hepatotoxicity of bacterial products.1 Obese human beings may have a fundamentally different gut microbiota, with an overall reduction in diversity, but an increase in the abundance of members of phylum
Firmicutes and a proportionate decrease in Bacteroidetes, compared with lean individuals.2 The metagenomes of obese human beings are enriched in microbial energy-harvesting genes.3 Chronic excessive ingestion of ethanol produces hepatic steatosis, steatohepatitis, and cirrhosis. Gut microbes are an endogenous source of ethanol, which may be delivered to the liver
Abbreviations used in this paper: BMI, body mass index; GC-MS, gas chromatography–mass spectrometry; MTPS, multitag pyrosequencing; NAFLD, nonalcoholic fatty liver disease; PCR, polymerase chain reaction; VOC, volatile organic compound. © 2013 by the AGA Institute 1542-3565/$36.00 http://dx.doi.org/10.1016/j.cgh.2013.02.015
July 2013
in a continuous fashion and promote steatosis and liver injury.4 Microbes produce about 300 other volatile organic compounds (VOCs) in the human gut,5 the systemic effects of which are unknown. NAFLD is a significant public health problem because of the dramatic increase in the prevalence of obesity, which along with insulin resistance and type II diabetes are major risk factors for the pathogenesis of the disease. Given the association between obesity and NAFLD, we hypothesized that there are specific compositional and functional characteristics of the gut microbiome in obese NAFLD patients, which may be relevant to the pathophysiology of NAFLD. Our objective was to describe and compare the fecal microbiota and bacteria-derived VOC metabolites in obese NAFLD patients and healthy-weight controls.
Methods Study Design and Patients This was an observational case-control study. The study protocol was approved by the Conjoint Health Research Ethics Board of the University of Calgary and Alberta Health Services. There were no dietary restrictions imposed on any participant. Obese patients had not actively engaged in lifestyle modifications such as weight loss attempts through diet and exercise at the time of study enrollment. Obese patients with a body mass index (BMI) greater than 30 kg/m2 and clinically suspected NAFLD (n ⫽ 30) were recruited from outpatient clinics at Foothills Medical Centre. Patients with an unexplained increase of liver enzyme levels of at least 1.5 times the upper limit of normal and an echogenic liver on ultrasound were screened for study recruitment. Patients were excluded if they had more than 20 g/d alcohol consumption, decompensated liver disease, antibiotics or probiotics within the preceding 3 months, medications believed to contribute to abnormal liver enzyme levels, or had fatty liver suspected as the result of other causes. All patients underwent a detailed clinical history, physical examination, and anthropometric measurements that, together with the earlier-described clinical laboratory measurements and sonographic findings, led to a diagnosis of clinically suspected NAFLD. Healthy controls (n ⫽ 30) were recruited from the Forzani and MacPhail Colon Cancer Screening Center at Foothills Medical Centre. These were healthy individuals who were not taking any prescription medications at enrollment. Controls were excluded if they had a BMI of 25 kg/m2 or greater or consumed more than 20 g/d of alcohol. Controls had normal liver enzyme levels within 6 months before enrollment.
Stool Samples Patients provided a single stool sample at the time of study entry. For control subjects (patients undergoing colonoscopy for colon cancer screening), stool specimens were obtained before any of the dietary or laxative preparations required for colonoscopy. Subjects were instructed on methods for stool sample collection and all materials were provided in a convenient specimen collection kit (Protocult, Rochester, MN). Each patient provided 15 mL of stool, which was collected at home in a sterile container and immediately stored (⫺20°C). The specimens were transported to the laboratory within 7 days of collection on an ice pack. Experiments have shown that if samples are frozen within 6 hours of sample production, only
FECAL MICROBIOME AND VOC METABOLOME IN NAFLD
869
minor losses of VOC occur.6 Stool specimens can be stored at ⫺20°C for several weeks before subsequent bacterial DNA extraction and microbiome characterization.7 Fecal samples were thawed in an ice-water bath and 0.15 mL were transferred to sterile 1.5-mL vials for DNA extraction. For gas chromatography–mass spectrometry (GC-MS) analyses of VOC, 2 mL of stool were transferred in a similar fashion to a headspace vial (Supelco, Mississauga, Canada). Clean and accurate transfer of the thawed stool specimens was achieved by backloading the cut end of a sterile syringe with the plunger set to the desired volume, which then was used to deliver the measured aliquot to end-use vials.
DNA Extraction Total DNA was extracted from approximately 150 mg of stool using the Qiagen QIAamp DNA Stool MiniKit (Qiagen, Mississauga, Canada), which included a 2-minute mechanical bead (0.1 mm zirconia:silica) beating step.7
Microbiome Characterization Using Multitag Pyrosequencing Multitag pyrosequencing (MTPS) was performed by the Microbiome Analysis Center at George Mason University (Manassas, VA) on the Roche GS Junior platform (Branford, CT) using tagged fusion primers, as previously described.8 This technique allows the rapid sequencing of multiple samples at one time, yielding thousands of sequence reads per sample. Specifically, we generated a set of 96 emulsion polymerase chain reaction (PCR) fusion primers that contain the 454 emulsion PCR linkers on the 27F primer (AGAGTTTGATCCTGGCTCA G-3=) and 355R’ (5=-GCTGCCTCCCGTAGGAGT-3=) and different 8-base barcode between the A adapter and the 27F primer.8 Thus, each fecal sample was amplified with unique bar-coded forward 16S ribosomal RNA primers, and then up to 96 samples were pooled and subjected to emulsion PCR and pyrosequenced.
Microbiome Analysis Custom pipelines9 were used for assigning taxonomic identification to the MTPS reads and formatting the data for subsequent analyses using Unifrac (version 1.25.0),10 and Metastats (version 1.20.0).11 Custom Perl scripts were used to demultiplex the MTPS data by sorting the sequences into bins based on the barcodes and taxa in the samples. We have noted that ligating tagged primers to PCR amplicons distorts the abundances of the communities, and thus it is critical to incorporate the tags during the original amplification step. Reads were filtered based on quality scores and length. We identified the taxa present in each sample using the Bayesian classifier tool in version 10 of the Ribosomal Database Project.12 The abundances of the bacterial identifications then were normalized using a custom PERL script, and taxa present at greater than 1% of the community were tabulated. We chose this cut-off value because of our a priori assumption that taxa present in less than 1% of the community vary between individuals and have a minimal contribution to the functionality of that community and that 2000 reads per sample will only reliably identify community components that are greater than 1% in abundance. A raw count data matrix was used for the Metastats analysis. Normalized abundance tables were used for
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Results Clinical Characteristics
Table 1. Patient Characteristics and Clinical Data Parameter
NAFLD (n ⫽ 30)
Control (n ⫽ 30)
P value
Female sex Age, y BMI Fasting glucose level Triglyceride level Hypertension Units of alcohol/wk ALT level GGT level
17 (55) 49 (34–57) 33 (29–35) 5.5 (5.1–6.6) 1.6 (1.2–2.4) 15 (50) 0.2 (0–2) 51 (31–82) 76 (44–111)
17 (57) 51 (47–56) 22 (21–24) 5.1 (4.8–5.5) 0.8 (0.7–1.1) 3 (10) 3 (0.3–7) 18 (14–23) 17 (12–23)
1.00 .21 ⬍.01 .04 ⬍.01 ⬍.01 .01 ⬍.01 ⬍.01
NOTE. Sex and hypertension are reported as n (%); all other values are reported as median and interquartile range. ALT, alanine aminotransferase; GGT, ␥-glutamyltransferase.
principal coordinate analysis. Unifrac analysis was performed using the pipeline in Qiime (available at: http://qiime.org/).13 This tool is used to compare the topology of phylogenetic trees constructed from the operational taxonomic units in all the samples in each disease class.
Gas Chromatography–Mass Spectrometry Analysis of Fecal Volatile Organic Compounds Stool samples were transferred to a headspace vial (Supelco, Mississauga, Canada), and VOC were analyzed by GC-MS using equipment and run conditions previously described in detail.5 All analyses were performed on a PerkinElmer Clarus 500 GC-MS quadrupole benchtop system (Beaconsfield, UK). Before extraction, all sample vials were placed in a water bath at 60°C for 1 hour. Carboxen/polydimethylsiloxane solid-phase microextraction fibers (Supelco, Poole, UK) were used to adsorb the VOC from the vial headspace above the feces for 30 minutes. The GC-MS system was fitted with a 60-m SPB-1 column (Supelco, Poole, UK). The oven temperature was maintained at 40°C for 2 minutes after the injection, then heated 6°C per minute to 220°C and maintained for 4 minutes. The carrier gas was 99.95% pure helium (BOC, Guilford, UK) at a constant linear velocity of 35 cm/s. The MS was operated in electron ionization mode scanning mass ions from 17 to 350 m/z. Compounds were identified by comparison with the National Institute of Standards and Technology (NIST08) mass spectral library (available: http://chemdata.nist.gov/) followed by manual visual inspection and retention time matching of selected standards. In interpreting the data, only compounds with a 90% or greater probability of a match to library standards were named.
Statistical Analyses of Volatile Organic Compounds Analyses of VOC data were performed using SPSS version 16 (IBM SPSS, Armonk, NY). A total of 220 VOC were identified in these experiments. A dichotomous variable was assigned based on the presence and absence of volatile compounds (ie, 1 if the volatile was present; 0 if the volatile was absent). Univariate analysis was performed on these binary data to identify specific volatile compounds that were statistically significant (P ⱕ .05) in discriminating between the 2 groups.
Obese (n ⫽ 30) and control (n ⫽ 30) subjects were recruited between January and August of 2009. Patient characteristics and clinical data are reported in Table 1. Results are presented as median and interquartile range. Seventeen (55%) female subjects were found in each of the study groups. There was a similar number of Asian subjects among the obese patients and healthy controls (n ⫽ 5 vs n ⫽ 4; P ⬍ .05). There was no significant difference in age between obese patients and healthy controls (49 y, 34 –57 y vs 51 y, 47–56 y; P ⫽ .21). Obese patients had a significantly higher BMI (33 kg/m2, 29 –35 kg/m2 vs 22 kg/m2, 21–24 kg/m2; P ⬍ .05), fasting glucose level (5.5 mmol/L, 5.1– 6.6 mmol/L vs 5.1 mmol/L, 4.8 –5.5 mmol/L; P ⬍ .05), and triglyceride level (1.6 mmol/L, 1.2–2.4 mmol/L vs 0.8 mmol/L, 0.7–1.1 mmol/L; P ⬍ .05) compared with controls. Hypertension, type II diabetes, and dyslipidemia were found in 15 (50%) vs 3 (10%), 6 (20%) vs 1 (3.3%), and 11 (37%) vs 2 (6.7%) obese patients compared with controls, respectively. All obese patients had sonographic evidence of hepatic steatosis, without evidence of cirrhosis or portal hypertension. Healthy controls did not have an abdominal ultrasound performed. Abnormal transaminase levels consistent with NAFLD were seen in obese subjects compared with controls.
Volatile Organic Compound Analysis A total of 220 distinct VOC were detected in our patients (Supplementary Table 1). Many of the identified VOC were present infrequently in individuals. In healthy controls, the median number of distinct VOC within an individual was 64 (range, 45– 88). There were 22 VOC that were present in 80% or more of healthy controls and 7 of these were ubiquitous, comprising a core fecal VOC constituency (Table 2). In patients with NAFLD, the median number of VOC was 72 (range, 49 –90), which was significantly greater than in controls (P ⬍ .01). There were 23 identifiable VOC that were present in 80% or more of NAFLD patients, 8 of which were ubiquitous. Most of the core and common fecal VOC were the same in the NAFLD and control groups (Supplementary Table 1). Ethanol, acetone,
Table 2. Common and Ubiquitous Fecal VOC in HealthyWeight Control Patients Sorted Alphabetically Core (ubiquitous)
Common (ⱖ80% prevalence)
Acetone
Acetaldehyde
Butanoic acid 2-Butanone Ethanol 2-Heptanone
Benzaldehyde Benzeneacetaldehyde Butanal, 2-methylButanal, 3-methyl-
2-Pentanone
Butanoic acid, 2methylButanoic acid, 3methylButanoic acid, methyl ester
Propanal, 2-methyl
Butanoic acid, ethyl ester Carbon disulfide Dimethyl trisulfide Pentanoic acid Pentanoic acid, methyl ester Pinene Propanoic acid, 2-methyl-
NOTE. There were 7 fecal VOC found ubiquitously in healthy individuals. In addition to the core compounds, there were 15 other VOC found commonly (ⱖ80% prevalence) in these subjects.
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Table 3. Univariate Analyses Indicating Fecal VOC That Distinguished Control and NAFLD Groups, Sorted According to Degree of Statistical Significance (All P ⱕ .05) Increased in NAFLD Butanoic acid, propyl ester Propanoic acid, propyl ester Acetic acid, ethyl ester Acetic acid, pentyl ester Cyclohexene, 4-ethenyl-4methyl-3-(1-methylethenyl)1-(1-methylethyl)-, (3Rtrans)Butanoic acid, 3-methyl-, butyl ester n-Propyl acetate Butanoic acid, butyl ester Phellandrene Propanoic acid, ethyl ester 1,6-Octadien-3-ol, 3,7dimethyl-
Decreased in NAFLD 2-Butanone Furan, 2-methylHeptanal 2(3H)-furanone, dihydro-5methyl2-Heptanone, 6-methyl-
2,3-pentanedione 1,6-Octadien-3-ol, 3,7-dimethyl-, 2-aminobenzoate Cyclohexanol, 5-methyl-2-(1methylethyl)2-Octene, 3,7-dimethyl-, (Z)3-Hexanone, 2-methylAcetic acid, (1,2,3,4,5,6,7,8octahydro-3,8,8trimethylnaphth-2-yl)methyl ester Cyclohexane, hexyl-
Myrcene Pentanoic acid, methyl ester Acetic acid, methyl ester 2-Propynoic acid methyl ester Butanoic acid, 3-methyl-, ethyl ester 1-Propanol Propanoic acid, 2-methyl-, propyl ester
NOTE. There were 18 fecal VOC that were statistically significantly more prevalent in obese NAFLD patients, of which 13 were classified as ester compounds and are indicated by shading.
heptanone, butanoic acid, and pentanoic acid were notable for their ubiquitous presence among fecal VOC identified in both study groups. By using binary data (ie, VOC peak presence or absence), univariate analyses were used to discern statistical differences between fecal VOC composition in obese and control subjects (Table 3). There were 12 fecal VOC that were significantly less common in NAFLD than in controls, representing a variety of chemical classes (eg, aldehydes, furans, ketones, and so forth). There were 18 VOC that were significantly more prevalent among obese patients than controls, the majority of which were ester compounds (eg, aliphatic esters of ethanoic, propanoic, butanoic, and pentanoic acids).
Deep Taxonomic Analysis Using Multitag Pyrosequencing The calculated relative abundance of the various taxa at the genus level in obese NAFLD and control groups is presented as taxonomy pie charts in Figure 1. As expected, Bacteroides and various genera within phylum Firmicutes were the dominant constituents of the fecal microbiota in both patient groups. Samples were rank-ordered based on the abundance of genus Bacteroides and plotted as stacked histograms (Figure 2). The histograms show that there is dramatic variability of the
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fecal microbiota between individuals in both control and NAFLD groups. Most obvious, for example, is the wide variation in Bacteroides, comprising 5%– 80% relative abundance in various individuals in each study group and, therefore, there was no statistically discernible difference in the proportion of Bacteroides between NAFLD and control. Within the NAFLD group, there was no correlation between BMI and relative abundance of Bacteroides or Firmicutes. Metastats analysis was performed on the MTPS relative abundance data to determine which taxa were statistically different between the control and obese samples at family and genus levels (Table 4). We selected only taxa that had a Metastats P value of less than .05 and a Q value (false-discovery rate) that was less than the P value. We observed several families that were increased in the obese NAFLD group (Table 4). The most statistically robust increase was observed in the Kiloniellaceae and Pasteurellaceae families, which are members of phylum Proteobacteria, which often are observed to increase in various states of intestinal dysbiosis. Three Firmicutes also were increased significantly in NAFLD: Lactobacillaceae, Veillonellaceae, and Lachnospiraceae families, known for various products of anaerobic metabolism. In contrast, there was another member of phylum Firmicutes, Ruminococcaceae, that was decreased along with Porphyromonadaceae, a member of phylum Bacteroidetes. This illustrates the disadvantage of using higher phylogenetic levels (ie, phylum) to distinguish disease states because, in this case, 3 members of phylum Firmicutes increased and 1 decreased, which would be obscured if only phylum-level data were considered. At the genus level, we observed a significant increase in NAFLD patients in 3 Lachnospiraceae (Robinsoniella, Roseburia, and Dorea), and an increase in Lactobacillus (Table 4). We only observed a statistically significant decrease in Oscillibacter in NAFLD, a Ruminococcaceae. The earlier-described Metastats analyses showed that increases in specific families of Proteobacteria were associated with NAFLD; these findings were highly statistically significant. At a deeper level of phylogenetic resolution, specific genera of Firmicutes also were increased in obese NAFLD patients. Unifrac analysis enabled further statistical comparison and illustration of the salient findings of the MTPS data. The Unifrac principal coordinate analysis (not shown) revealed some differential clustering of the disease and control states but this was diffuse and not dramatic. The Unifrac tree (not shown) also showed some clustering with a Unifrac P value of .03, indicating a significant shift in the microbiome between control and NAFLD.
Discussion In a selected group of obese patients with clinically suspected NAFLD, we have discovered a significantly altered fecal VOC profile and compositional shift in the fecal microbiome. Although others have studied the gut microbiota in obesity, we provide the first description of the gut microbiome in obese NAFLD patients using next-generation sequencing technologies, and extend this to include an important functional correlate of the fecal microbiome, the microbial VOC metabolites that have plausible links to NAFLD as hepatotropic or hepatotoxic factors. Ley et al have shown that the gut microbiome of obese human beings is distinct from healthy-weight individuals, with a lower proportion of Bacteroidetes and a higher proportion of
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Figure 1. Fecal microbiome of obese NAFLD and healthyweight control subjects. MTPS data were used to calculate the relative abundance of various bacterial taxa. The labels indicate family and genus.
Firmicutes in a given subject.2 Other groups also have shown dysbiosis of the distal gut in obese human beings, but the exact findings have been inconsistent.14 We did not find a significant difference in the proportions of Firmicutes and Bacteroidetes between obese and healthy weight individuals in our study. Such discordant results may be owing to methodologic differences or differences in the patient populations such as degree of obesity, ethnicity, diet, environment, and associated comorbidities such as metabolic syndrome and its treatments. Indeed, compared with the original description of the gut microbiome in human beings with classes III and IV obesity,2 our patients mainly had class I obesity. Although we did not replicate pre-
viously described phylum-level differences in the fecal microbiota, at the high level of taxonomic resolution afforded by deep sequencing, we observed small but significant differences between obese NAFLD patients and controls. An important caveat and limitation to recognize is the difficulty in taxonomic assignment and discrimination of similar types of bacteria or the common presence of unclassified bacteria in version 10 of the Ribosomal Database Project and similar databases. Spencer et al15 recently suggested that host genotype and specific genera of Gammaproteobacteria (Klebsiella species, Enterobacter species, and Escherichia species) and Erysipelotrichi are important bacterial predictors of susceptibility to choline deficiency–induced fatty
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Figure 2. Relative proportions of dominant taxa at genus level in obese NAFLD patients and healthy-weight controls.
liver disease. Consistent with this, we also found taxa belonging to Gammaproteobacteria to be increased significantly in NAFLD. The fact that we observed significant changes in the microbiota and VOC profiles of our patients with class I obesity attests to the possibility that even more pronounced changes may have been observed if we were to study patients with more advanced forms of NAFLD and obesity. Our study participants did not maintain food diaries during the study period, limiting our ability to attribute altered fecal VOC and microbiomes to specific dietary differences in NAFLD. Diet certainly has an impact on the gut microbiome and, in fact, long-term dietary macronutrient composition is a major determinant of the intestinal microbiome in human beings.16 Short-term manipulation of diet (eg, extremes of fat and fiber intake) impacts the intestinal microbiome, but the effect seems small, transient, and ineffectual in changing enterotype designation.16 We did not try to impose any particular diet on our study participants because the details of what constitutes an adequate control diet in terms of macronutrient composition
and duration of consumption are unknown. We did not attempt to control for other environmental factors (eg, smoking, ambient pollution exposure, stress), because their specific impact on the microbiome is largely unknown. We also did not systematically determine or impose recruitment of selected ethnic groups in our study because relatively little is known about the fecal microbiome cross-culturally. We showed that ester VOC were identified more frequently in fecal samples from obese NAFLD patients than controls. Most fecal VOC are a result of microbial action on gut luminal substrates.17 Although there is abundant literature about bacterial production of short-chain fatty acid VOC (eg, acetic, propionic, and butyric acids) and ethanol by various gut microbes, very little is known about the microbes and biochemical pathways that may be involved in production of volatile esters in the human gut. Most of the esters linked to NAFLD in our study are derivatives of short chain aliphatic alcohols and carboxylic acids. Bacterial esterases can catalyze esterification reactions involving such organic acids and alcohols,18 which may be
Table 4. Metastats Analysis Indicating Taxa That Significantly Distinguished Control and NAFLD Groups, Ranked According to Statistical Significance
Phylum_class_order_family Proteobacteria_Alphaproteobacteria_Kiloniellales_Kiloniellaceae Proteobacteria_Gammaproteobacteria_Pasteurellales_Pasteurellaceae Firmicutes_Bacilli_Lactobacillales_Lactobacillaceae Firmicutes_Clostridia_Clostridiales_Lachnospiraceae Firmicutes_Clostridia_Clostridiales_Ruminococcaceae Bacteroidetes_Bacteroidia_Bacteroidales_Porphyromonadaceae Firmicutes_Clostridia_Clostridiales_Veillonellaceae Family_genus Lactobacillaceae_Lactobacillus Ruminococcaceae_Oscillibacter Lachnospiraceae_Robinsoniella Lachnospiraceae_Roseburia Lachnospiraceae_Dorea NOTE. Taxa that were increased in NAFLD patients are highlighted in gray.
Mean (NAFLD)
Mean (control)
P value
Q value
Interpretation
0.00000 0.00000 0.00000 0.16210 0.22077 0.04519 0.01934
0.00106 0.00572 0.01376 0.22631 0.15240 0.02062 0.04953
.00100 .00100 .00100 .01499 .02498 .04496 .04595
0.00001 0.00001 0.00001 0.00016 0.00025 0.00041 0.00041
Increase in NAFLD Increase in NAFLD Increase in NAFLD Increase in NAFLD Decrease in NAFLD Decrease in NAFLD Increase in NAFLD
0.00000 0.06947 0.00283 0.05526
0.01453 0.01693 0.00880 0.07610 0.04545
.00100 .00100 .01399 .02098 .02498
0.00043 0.00043 0.00571 0.00835 0.00969
Increase in NAFLD Decrease in NAFLD Increase in NAFLD Increase in NAFLD Increase in NAFLD
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a source of the ester VOC observed in our study. In addition, esters may be formed by nonenzymatic esterification reactions involving gut luminal or bacterial substrates. At the phylum level, we have identified increases in Proteobacteria, and at the family level, we have observed increases in Lactobacillus and 3 genera of Lachnospiraceae associated with NAFLD. We are not aware of any defined metabolic pathways that specifically link these bacteria individually or as a consortium to the production of the fecal ester compounds identified in the NAFLD group. Endogenous ethanol production by gut microbes deserves further mention in light of its recent link to nonalcoholic steatohepatitis. Zhu et al19 reported a significant increase in the abundance of Escherichia in patients with nonalcoholic steatohepatitis, which was associated with increased blood ethanol concentrations. In our study, ethanol was identified as a ubiquitous fecal VOC in both NAFLD and control groups, but we did not measure blood or breath alcohol concentrations and we did not observe an increase in Escherichia abundance in NAFLD. Because of the complexities, dynamics, redundancies, and interconnectedness of the human intestinal microbiota in terms of membership and function, it is difficult to causally link production of ethanol or ester VOC to numerically increased bacterial taxa. As shown in the present article and in previous work,5 there is a core group of fecal VOC representing approximately 60% of the identified compounds, which reflect essential functions of the resident fecal microbiota irrespective of day-to-day dietary influences. However, some fecal VOC may originate directly from dietary sources such as aldehydes, ketones, and terpenes, which give fruits and vegetables their characteristic aroma, and aliphatic compounds that are found in meat. Variations in the remaining fecal VOC are most likely owing to individual dietary practices and distinctions of microbiota. It remains possible that some of the observed differences in fecal VOC profiles between control and NAFLD patients are attributable to diet. Our study population was limited to obese NAFLD and healthy controls. Because we did not study obese non-NAFLD patients, it is difficult to conclude that the observed changes in VOC are a consequence of NAFLD rather than obesity. Recruitment of obese patients without NAFLD would require liver biopsy to exclude hepatic steatosis in obese patients with normal liver biochemistry. Liver biopsies were not performed in our NAFLD patients because this is not presently the standard of care at our institution and we could not justify the risk associated with liver biopsy in this pilot study. However, analyzing the fecal microbiome and VOC metabolome data with reference to histologic parameters may have provided insight into possible threshold effects with escalating disease severity. An abdominal ultrasound was not performed in our controls to exclude steatosis. Reports on the prevalence of lean NAFLD are not well documented. One recent study20 reported a prevalence of NAFLD (identified histologically) of 2.8% in individuals with a BMI less than 30 kg/m2. The majority of lean patients with NAFLD have associated metabolic risk factors, abdominal obesity, and Asian or East Indian ethnicity to which different parameters defining overweight and obesity are applied. All of our controls were Caucasian without these risk factors. Additional studies are required to establish the mechanistic relevance of our novel observations to the pathogenesis of NAFLD. Although several ester compounds were identified more commonly in NAFLD patients, ultimately the relevance to
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NAFLD likely depends on systemic uptake. To date, little is known about the presence of gut-derived VOC in peripheral or portal blood as a medium for the systemic effects of these compounds in human beings.17 Absorption of gut-derived VOC may occur by passive diffusion of lipophilic small molecules across the colonic epithelial lipid membrane according to partitioning coefficient, although this is likely to be an oversimplification of the in vivo process.21 Similar to ethanol, ester VOC also may have injurious effects on barrier or local immune functions in the gut. The link between obesity, NAFLD, and host intestinal microbiota is likely to be more sophisticated than just compositional shifts; complex functional aspects of host-microbiota interactions (eg, diet, environment) are likely at play. In subsequent work, we propose a metagenomic analysis of both microbiome composition and functional capacity (eg, using the Kyoto Encyclopedia of Genes and Genomes Database) correlated with host clinical parameters, energy homeostasis, and proinflammatory signaling in obese and NAFLD patients.
Supplementary Material Note: To access the supplementary material accompanying this article, visit the online version of Clinical Gastroenterology and Hepatology at www.cghjournal.org, and at http:// dx.doi.org/10.1016/j.cgh.2013.02.015. References 1. Parnell JA, Raman M, Rioux KP, et al. The potential role of prebiotic fibre for treatment and management of non-alcoholic fatty liver disease and associated obesity and insulin resistance. Liver Int 2012;32:701–711. 2. Ley RE, Turnbaugh PJ, Klein S, et al. Microbial ecology: human gut microbes associated with obesity. Nature 2006;444:1022– 1023. 3. Turnbaugh PJ, Ley RE, Mahowald MA, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006;444:1027–1031. 4. Cope K, Risby T, Diehl AM. Increased gastrointestinal ethanol production in obese mice: implications for fatty liver disease pathogenesis. Gastroenterology 2000;119:1340 –1347. 5. Garner CE, Smith S, De Lacy Costello B, et al. Volatile organic compounds from feces and their potential for diagnosis of gastrointestinal disease. FASEB J 2007;21:1675–1688. 6. Ahmed I, Smith S, Probert C. Volatile organic compounds as diagnostic faecal biomarkers in inflammatory bowel disease— method development. Gut 2009;58:A63. 7. Zoetendal EG, Heilig HG, Klaassens ES, et al. Isolation of DNA from bacterial samples of the human gastrointestinal tract. Nat Protoc 2006;1:870 – 873. 8. Gillevet P, Sikaroodi M, Keshavarzian A, et al. Quantitative assessment of the human gut microbiome using multitag pyrosequencing. Chem Biodivers 2010;7:1065–1075. 9. Naqvi A, Rangwala H, Spear G, et al. Analysis of multitag pyrosequence data from human cervical lavage samples. Chem Biodivers 2010;7:1076 –1085. 10. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71:8228 – 8235. 11. White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 2009;5:e1000352. 12. Cole JR, Wang Q, Cardenas E, et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 2009;37:D141–D145.
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13. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335–336. 14. Kallus SJ, Brandt LJ. The intestinal microbiota and obesity. J Clin Gastroenterol 2012;46:16 –24. 15. Spencer MD, Hamp TJ, Reid RW, et al. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology 2011;140:976 –986. 16. Wu GD, Chen J, Hoffmann C, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 2011;334: 105–108. 17. Probert CS, Ahmed I, Khalid T, et al. Volatile organic compounds as diagnostic biomarkers in gastrointestinal and liver diseases. J Gastrointest Liver Dis 2009;18:337–343. 18. Elgaali H, Hamilton-Kemp TR, Newman MC, et al. Comparison of long-chain alcohols and other volatile compounds emitted from food-borne and related Gram positive and gram negative bacteria. J Basic Microbiol 2002;42:373–380. 19. Zhu L, Baker SS, Gill C, et al. Characterization of the gut microbiome in non-alcoholic steatohepatitis (NASH) patients: a connection between endogenous alcohol and NASH. Hepatology 2013;57:601– 609. 20. Vos B, Moreno C, Nagy N, et al. Lean non-alcoholic fatty liver
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disease (lean-NAFLD): a major cause of cryptogenic liver disease. Acta Gastroenterol Belg 2011;74:389 –394. 21. Yazdanian M, Glynn SL, Wright JL, et al. Correlating partitioning and Caco-2 cell permeability of structurally diverse small molecular weight compounds. Pharm Res 1998;15:1490 –1494.
Reprint requests Address requests for reprints to: Maitreyi Raman, MD, MSc, FRCPC, Department of Medicine, Division of Gastroenterology and Hepatology, University of Calgary, 6D26, 3280 Hospital Drive, Calgary, Alberta, Canada T2N 4N1. e-mail: [email protected]; fax: (403) 5925090. Conflicts of interest The authors disclose no conflicts. Funding Maitreyi Raman and Kevin Rioux were supported by the Canadian Institutes of Health Research (Human Microbiome Catalyst Grant), and the Division of Gastroenterology and Hepatology at Foothills Medical Centre, University of Calgary.
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Supplementary Table 1. Index of Fecal VOC Identified in a Cohort of Obese NAFLD and Healthy Control Patients Compound
Normal ⫽ 30
NAFLD ⫽ 30
2-Butanone 2-Heptanone 2-Pentanone Acetone Butanoic acid Ethyl alcohol Pentanoic acid Propanal, 2-methylá-Pinene Butanoic acid, 2-methylUnknown compounds RT 33.3 Acetaldehyde Butanal, 2-methylButanal, 3-methylButanoic acid, 3-methylBenzaldehyde Butanoic acid, ethyl ester Benzeneacetaldehyde Propanoic acid, 2-methylBenzene, 1-methyl-2-(1-methylethyl)Pentanoic acid, methyl ester Butanoic acid, methyl ester Carbon disulfide Dimethyl trisulfide Hexanal Indole á-Phellandrene Compound-95 (RT-30.8) Propanal, 3-(methylthio)Disulfide, dimethyl Methyl propionate 1-Propanol 2,3-Butanedione Cyclohexene, 1-methyl-5-(1methylethenyl)-, (R)Heptanal Bicyclo[3.1.1]hept-2-ene, 2,6,6trimethyl-, (ñ)Acetic acid, methyl ester Ethanedioic acid, trimethyl ester Phenol, 4-methylUnknown compounds RT 35.7 2-Hexanone Benzene, 1,3-bis(1,1-dimethylethyl)Nonanal 2,3-Pentanedione Butanoic acid, 2-methyl-, methyl ester, (ñ)Phenol Propanoic acid Unknown compounds RT-35.4 Acetic acid Octanal Propanol, 2-methyl2-Heptanone, 5-methyl2-Nonanone Butanoic acid, 1-methylethyl ester Copaene Methanethiol 1-Butanol
30 30 29 30 30 30 30 30 29 29 29 28 28 28 28 27 27 26 26 23 25 24 24 24 23 23 19 22 22 21 21 20 20 20
21 30 30 30 30 30 30 28 30 29 30 25 23 28 24 26 29 26 25 26 30 28 27 18 21 22 27 17 18 18 28 27 21 20
20 19
11 15
18 18 18 18 17 17 17 16 16
26 15 21 19 10 18 15 8 21
16 16 15 15 15 14 14 14 14 14 14 13
22 18 15 21 16 11 16 18 12 21 12 15
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Supplementary Table 1. Continued Compound
Normal ⫽ 30
NAFLD ⫽ 30
Bicyclo[3.1.0]hexane, 4-methyl-1-(1methylethyl)-, didehydro Cyclohexene, 1-methyl-4-(1methylethylidene)Phenol, 3-methylCyclohexanol, 5-methyl-2-(1methylethyl)-, Furan, 2-methyl1,4-Cyclohexadiene, 1-methyl-4-(1methylethyl)Isopropyl alcohol Propanoic acid, butyl ester Trimethylene oxide (2-Aziridinylethyl)amine 1-propene, 1-(methylthio)-, à-Cubebene Hexanoic acid, methyl ester Pentanoic acid, ethyl ester Propanoic acid, 2-methyl-, methyl ester 2-Butanol 2-Heptanone, 6-methyl2-Hexanone, 5-methylAcetic acid ethenyl ester Cyclohexene, 4-ethenyl-4-methyl-3(1-methylethenyl)-1-(1methylethyl)-, (3R-trans)Eucalyptol Pentanoic acid, pentyl ester Propanoic acid, 2-methylpropyl ester Propanoic acid, ethyl ester 1,6-Octadien-3-ol, 3,7-dimethyl-, 2-aminobenzoate 5-Hepten-2-one, 6-methylD-limonene Phenyl-pentamethylUndecane 2(3H)-Furanone, dihydro-5-methyl2-Pentanone, 3-methyl4,8,12-Tetradecatrien-1-ol, 5,9,13trimethylCyclohexane, 1,1’-(1,2-dimethyl-1,2ethanediyl)Furan, 2-pentylPentane Propanal R(-)3,7-Dimethyl-1,6-octadiene 2,3-Dioxabicyclo[2.2.1]heptane, 1-methyl2-Decanone 2-Octene, 3,7-dimethyl-, (Z)3-Hexanone, 2-methylAcetic acid, (8-octahydro-3,8,8trimethylnaphth-2-yl)-methyl ester Butanoic acid, 2-methyl-, propyl ester Butanoic acid, 2-methylbutyl ester Butanoic acid, butyl ester Cyclohexane, 1-ethenyl-1-methyl2,4-bis(1-methylethenyl)-, Cyclohexane, hexyl-
13
17
13
20
13 12
9 5
12 11
3 13
11 11 11 10 10 10 10 10 10
12 17 11 11 12 10 11 9 15
9 7 9 9 9
12 1 7 9 20
9 9 9
9 13 10
9 8
19 2
8 8 8 8 7 7 7
8 9 9 9 1 8 8
7
8
7 7 7 7 6
9 5 6 8 7
6 6 6 6
9 1 1 6
6
11
6 6 6
5 16 12
6
1
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Supplementary Table 1. Continued
Supplementary Table 1. Continued
Compound
Normal ⫽ 30
NAFLD ⫽ 30
Compound
Normal ⫽ 30
NAFLD ⫽ 30
Cyclohexanecarboxylic acid, methyl ester Decane Dimethyl sulfide Naphthalene, 1-hexahydro-4,7dimethyl-1-(1-methylethyl)n-Propyl acetate Pentanal Propanoic acid, 1-methylethyl ester 1,3,5-Cycloheptatriene 1-Dodecanol, 3,7,11-trimethylAcetyl valeryl Butanoic acid, 2-methyl-, ethyl ester Butanoic acid, 2-methylpropyl ester Butanoic acid, 3-methyl-, ethyl ester Butanoic acid, 3-methyl-, methyl ester Butanoic acid, 3-methyl-, propyl ester Cyclohexanecarboxylic acid Cyclohexanecarboxylic acid, ethyl ester Furan, 3-methylHeptane, 5-ethyl-2,2,3-trimethylNonane Oxirane, (methoxymethyl)Pentanoic acid, butyl ester Phenol, 2,4-bis(1,1-dimethylethyl)Propanoic acid, propyl ester Thiopivalic acid 1,6-Octadien-3-ol, 3,7-dimethyl2,6-Dimethyldecane 2-Propenal 2-Undecanone 2-Undecanone, 6,10-dimethyl3,5-Heptadien-2-one, 6-methyl-, (E)3-Buten-2-one, 1-(2,3,6trimethylphenyl)3-Carene 3-Furaldehyde 3-Hexanone, 4-methyl5-Octen-2-yn-4-ol à-Calacorene Azulene, 1-octahydro-1,4-dimethyl-7(1-methylethenyl) Benzene, (2-methyloctyl)Camphene Caryophyllene Furan, 2,3-dihydro-3-methylOctane Octane, 4-methylo-Xylene S-methyl 3-methylbutanethioate Tetracyclo, nonane, 3,6,9-triethyl3,6,9-trimethylUnknown compound RT-32.1 1,6-Octadiene, 3,7-dimethyl-, (S)13-Tetradecynoic acid, methyl ester 1-Butanol, 3-methyl-, propanoate 1-Cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl1-Pentanol 2-Butenal, 3-methyl-
6
7
6 6 6
7 7 7
3 3 3 3
4 4 4 4
6 6 6 5 5 5 5 5 5 5
16 2 10 6 6 2 5 6 13 3
3 3 3 3 3
4 4 2 9 4
3 3 3 3 3 3
8 4 7 4 5 4
5
11
5 5
6 6
3 3 2
4 4 3
5 5 5 5 5 5 5 5 4 4 4 4 4 4 4
6 6 6 1 10 6 18 5 13 5 3 7 3 2 4
2 2 2 2
3 4 3 3
2
7
2 2 2
3 0 1
2 2 2
2 10 3
2
3
4 4 4 4 4 4
5 4 5 1 1 5
2 2 2
3 3 3
2
3
4 4 4 4 4 4 4 4 4
5 5 5 1 5 1 5 5 5
2 2 2
3 3 3
3 3 3 3 3
4 4 4 8 4
2 2 2 1 1 1 1 1 1
3 3 3 4 5 2 2 2 2
3 3
4 4
2-Octene, 2-methyl-6-methylene2-Pentene, 4-methyl-, (Z)3,5-Octadiene, 2,7-dimethyl3-Cyclohexen-1-ol, 4-methyl-1-(1methylethyl)-, acetate 3-Hexanone 3-Hexanone, 5-methyl5H-1-Pyrindine Benzene, 4-ethenyl-1,2-dimethylBicyclo[3.1.0]hexane, 4-methylene1-(1-methylethyl)Butyl 2-methylbutanoate Dodecanoic acid, 3-hydroxyFormic acid, butyl ester Methyl alcohol Pentanoic acid, 4-methylPentanoic acid, 4-methyl-, ethyl ester Phosphonic acid, (p-hydroxyphenyl)Toluene 1,5-Cyclodecadiene, 1,5-dimethyl-8(1-methylethenyl)1-Butanol, 2-methyl-, propanoate 1H-Indole, 3-methyl1H-Indole, 4-methyl2,2,4-Trimethyl-1,3-pentanediol diisobutyrate 3-Cyclohexen-1-ol, 4-methyl-1-(1methylethyl)-, (R)3-Penten-2-one 4-Hexen-3-one 4H-1-Benzopyran, 4,4,5,8tetramethyl5-Ethyl-1-nonene á-Myrcene Bicyclo[3.1.1]hept-2-ene, 2,6dimethyl-6-(4-methyl-3-pentenyl)Butanoic acid, 3-methyl-, 2methylpropyl ester Butanoic acid, 3-methylbutyl ester Cyclobutene, 2-propenylideneCyclohexanecarboxylic acid, propyl ester Cyclohexanol, 1-methyl-4-(1methylethyl)Decanal Hexanoic acid Pentanoic acid, 2-methylpropyl ester Sulfide, allyl methyl Tetradecane Tetradecane, 2,6,10-trimethyl1-Butanol, 3-methyl-, acetate 1-Hexadecanol, 2-methyl1R-à-Pinene 2,3-Dimethyldecane 2-Hydroxy-3-pentanone 3-Cyclohexene-1-methanol, à,à4trimethyl4-Heptanone Acetic acid, pentyl ester Bicyclo[3.1.1]heptane, 6,6-dimethyl2-methylene-, (1S) -
1 1 1
5 10 2
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY 2013;11:868 – 875
Fecal Microbiome and Volatile Organic Compound Metabolome in Obese Humans With Nonalcoholic Fatty Liver Disease MAITREYI RAMAN,* IFTIKHAR AHMED,‡ PATRICK M. GILLEVET,§ CHRIS S. PROBERT,储 NORMAN M. RATCLIFFE,¶ STEVE SMITH,¶ ROSEMARY GREENWOOD,‡ MASOUMEH SIKAROODI,§ VICTOR LAM,* PAM CROTTY,* JENNIFER BAILEY,* ROBERT P. MYERS,* and KEVIN P. RIOUX*,# *Department of Medicine, Division of Gastroenterology and Hepatology, #Department of Microbiology & Infectious Diseases, Gastrointestinal Research Group, Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada; ‡Bristol Royal Infirmary, Bristol, United Kingdom; § Department of Environmental Science and Policy, Microbiome Analysis Center, George Mason University, Manassas, Virginia; 储Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom; and ¶Faculty of Health and Life Sciences, Institute of Biosensor Technology, University of the West of England, Bristol, United Kingdom
BACKGROUND & AIMS:
The histopathology of nonalcoholic fatty liver disease (NAFLD) is similar to that of alcoholic liver disease. Colonic bacteria are a source of many metabolic products, including ethanol and other volatile organic compounds (VOC) that may have toxic effects on the human host after intestinal absorption and delivery to the liver via the portal vein. Recent data suggest that the composition of the gut microbiota in obese human beings is different from that of healthy-weight individuals. The aim of this study was to compare the colonic microbiome and VOC metabolome of obese NAFLD patients (n ⴝ 30) with healthy controls (n ⴝ 30).
METHODS:
Multitag pyrosequencing was used to characterize the fecal microbiota. Fecal VOC profiles were measured by gas chromatography–mass spectrometry.
RESULTS:
There were statistically significant differences in liver biochemistry and metabolic parameters in NAFLD. Deep sequencing of the fecal microbiome revealed over-representation of Lactobacillus species and selected members of phylum Firmicutes (Lachnospiraceae; genera, Dorea, Robinsoniella, and Roseburia) in NAFLD patients, which was statistically significant. One member of phylum Firmicutes was under-represented significantly in the fecal microbiome of NAFLD patients (Ruminococcaceae; genus, Oscillibacter). Fecal VOC profiles of the 2 patient groups were different, with a significant increase in fecal ester compounds observed in NAFLD patients.
CONCLUSIONS:
A significant increase in fecal ester VOC is associated with compositional shifts in the microbiome of obese NAFLD patients. These novel bacterial metabolomic and metagenomic factors are implicated in the etiology and complications of obesity.
Keywords: Nonalcoholic Fatty Liver Disease; Obesity; Microbiota; High-Throughput Nucleotide Sequencing; Volatile Organic Compound; Metabolomics.
Podcast interview: www.gastro.org/cghpodcast. Also available on iTunes; see editorial on page 876; see related article, Wu and Lewis on page 774, in this issue of CGH.
T
he gut microbiota has been implicated in obesity, diabetes, metabolic syndrome, and nonalcoholic fatty liver disease (NAFLD) through effects on caloric salvage, host energy metabolism, proinflammatory signaling, and via direct hepatotoxicity of bacterial products.1 Obese human beings may have a fundamentally different gut microbiota, with an overall reduction in diversity, but an increase in the abundance of members of phylum
Firmicutes and a proportionate decrease in Bacteroidetes, compared with lean individuals.2 The metagenomes of obese human beings are enriched in microbial energy-harvesting genes.3 Chronic excessive ingestion of ethanol produces hepatic steatosis, steatohepatitis, and cirrhosis. Gut microbes are an endogenous source of ethanol, which may be delivered to the liver
Abbreviations used in this paper: BMI, body mass index; GC-MS, gas chromatography–mass spectrometry; MTPS, multitag pyrosequencing; NAFLD, nonalcoholic fatty liver disease; PCR, polymerase chain reaction; VOC, volatile organic compound. © 2013 by the AGA Institute 1542-3565/$36.00 http://dx.doi.org/10.1016/j.cgh.2013.02.015