Edited by: Frederic Antonio Carvalho, INSERM U1107 Douleur et Biophysique Neurosensorielle (Neuro-Dol), France
Reviewed by: Maria Gazouli, National and Kapodistrian University of Athens, Greece; Evagelia C. Laiakis, Georgetown University, United States; Romain Villéger, University of Auvergne, France
This article was submitted to Microbiome in Health and Disease, a section of the journal Frontiers in Cellular and Infection Microbiology
†These authors have contributed equally to this work
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Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders, affecting 10–15% of the general population worldwide (Lovell and Ford,
The detailed pathophysiology of IBS is unknown but is thought to be heterogeneous. Previous studies of IBS mainly focused on the altered gastrointestinal motility, increased gut sensitivity, brain-gut interaction, and psychosocial distress (Kellow and Phillips,
Human gastrointestinal tract is a complex environment that includes a high diversity of inhabiting microorganisms, and complex interactions between microbes and the host (Yamashiro,
In order to investigate the interactions between gut microbiota and metabolome in IBS, both metabolite or microbe profiling analysis and correlative microbe–metabolite analysis were used in this study to analyze fecal samples from IBS patients or healthy people.
All participants were Chinese Han nationality. A total of 15 IBS patients meeting the Rome III criteria were recruited from the gastroenterology clinic in Beijing Friendship Hospital affiliated to Capital Medical University, Beijing, China. Fifteen healthy volunteers from physical examination center at Beijing Friendship Hospital were enrolled in the healthy control group These healthy volunteers had normal bowel movements without abdominal symptoms, coronary artery disease, inflammatory conditions and diabetes mellitus. Participants of IBS with diabetes, asthma, pregnancy, and earlier abdominal surgeries were excluded. Participants were asked not to take any antibiotics, eat spicy food, and smoke 4 weeks prior of sample collection. All participants have signed informed consent and the study was approved by the Ethics Committee of Beijing Friendship Hospital. Water ban was also required after midnight before collecting the samples in the morning. First early morning fecal samples were collected from each participant in sterile fecal specimen cups. Stool specimens were collected and handled by experienced clinicians and trained technicians. Each sample was divided into two tubes for metabolome and microbiota analysis and stored at −80°C until analysis.
By using a Power Soil® DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA), DNA was extracted from fecal pellets according to the manufacturer's instructions. The DNA samples were quantified by ultraviolet spectroscopy and stored at −80°C for further analysis. By using universal primers of U515(GTGCCAGCMGCCGCGGTAA)and E786 (GGACTACHVGGGTWTCTAAT), the V4 regions of the bacterial 16S rRNA gene were amplified. Individual amplification products were purified, barcoded and pooled to construct the sequencing library. Samples were sequenced with Illumina Miseq (Illumina HiSeq 2500) to generate pair-ended 150 × 150 reads. The sequenced raw data were then spliced and filtered to obtain the clean data. Thereafter, operational taxonomic units (OTUs) clustering and species classification were performed.
Fecal samples from two different groups (IBS patients and healthy people) were stored at −80°C before the process. The experiment was performed by using test kit—MicrobioMET (Metabo-Profile, Shanghai, P.R. China). MicrobioMET is a commercial total solution including standardized, quality-controlled sample preparation, rapid GC-TOFMS analysis, data analysis software, and project report. Briefly, samples were thawed on ice-bath and weighed 50 mg for preparation. Materials were then homogenized with 1 M NaOH (300 μL) solution and centrifuged at 13,500 rpm at 4°C for 20 min. Each 200 μL supernatant was transferred into an autosampler vial. The residue was further exacted with 200 μL methanol and centrifugated again under the same conditions. The resultant supernatant was combined with the first one in the autosampler vial. Then, the autosampler vial was capped and the extracts were submitted for automated sample derivatization using multipurpose sampler MPS2 (Gerstel, Muehlheim, Germany). After sample preparation, microbial metabolites were quantitated with a gas chromatography coupled to time-of-flight mass spectrometry (GC-TOFMS) (Pegasus HT, Leco Corp, USA), followed by metabolite annotation and identification. The total mass of the metabolites was determined by metabolite diversity analysis. The reference library was developed consisting of 132 methyl and ethyl chloroformate (MCF and ECF) derivatized compounds with their mass spectral and retention index information for metabolite identification. The type of capillary column used for gas chromatography is Rxi-5MS which is 30 m (length) × 250 μm I.D and 0.25-μm film thickness.
Visualization and comparison of metabolite profile were performed by using principal component analysis model with permutation testing algorithm, to detect metabolic variation between the two groups. The univariate statistical analysis was also employed to perform the inter-group comparisons.
An R package named weighted gene co-expression network analysis (WGCNA) was applied to analyse the relationship between clinical traits of IBS and microbiological as well as clinical traits of IBS and metabolomic data. Initially, Pearson's correlation of each pair metabolite–microbe was calculated, and an adjacency matrix was constructed based on the Pearson's results and a predefined soft-thresholding parameter. A topology overlap matrix was calculated, and hierarchical clustering was performed to find the modules. Modules with a correlation coefficient >0.7 were merged into one module. The first eigenvector of the module was used to represent each module. To determine the membership of each gene in the module, the average connectivity of the gene in the module was calculated.
Spearman correlation was analyzed to evaluate the correlation between microbes, metabolites and pathophysiological features.
Thirty adults were enrolled in this study, with 15 IBS patients meeting the Rome III criteria and another 15 healthy controls (HC). Among the IBS patients, 73.3% (11/15) had frequent abdominal pain (>10 times/week) and/or abdominal discomfort, and 60.0% (9/15) of them had increased bowel movements (3–5 times/day). Most of the IBS patients with abdominal pain may be relieved after bowel movement. All IBS patients enrolled in this study showed unformed (loose and watery) stools. The healthy controls had no abdominal pain and formed stool with normal bowel movements (
Demographic and clinical information of participants of this study.
Number of patients | 15 | 15 |
Mean age, year (SD) | 47.67 (14.24) | 28.27 (1.566) |
Female, |
5 (33.3%) | 7 (46.7%) |
Frequency of abdominal pain/week | ||
1–2 times |
2 (13.3%) | 0 (0.0%) |
3–5 times |
1 (6.7%) | 0 (0.0%) |
6–10 times |
1 (6.7%) | 0 (0.0%) |
>10 times |
11 (73.3%) | 0 (0.0%) |
Frequency of abdominal discomfort/week | ||
1–2 times |
2 (13.3%) | 2 (13.3%) |
3–5 times |
1 (6.7%) | 0 (0.0%) |
6–10 times |
1 (6.7%) | 0 (0.0%) |
>10 times |
11 (73.3%) | 0 (0.0%) |
Defecation times/day | ||
A (1–2 times) |
6 (40.0%) | 15 (100%) |
B (3–5 times) |
9 (60.0%) | 0 (0.0%) |
C (6–10 times) |
0 (0.0%) | 0 (0.0%) |
D (>10 times) |
0 (0.0%) | 0 (0.0%) |
Symptom duration | ||
1–2 years |
9 (60.0%) | 0 (0.0%) |
3–5 years |
3 (20%) | 0 (0.0%) |
>5 years |
3 (20%) | 0 (0.0%) |
Stool property | ||
Unshaped stool |
12 (93.3%) | 0 (0.0%) |
Mushy stool |
3 (6.7%) | 0 (0.0%) |
Soft stool |
0 (0.0%) | 15 (100.0%) |
Relief of abdominal pain after defecation | ||
Yes |
14 (93.3%) | 0 (0.0%) |
No |
1 (6.7%) | 0 (0.0%) |
Metabolite analysis was conducted by using MicrobioMET 1.0 technology. The Volcano Plots revealed that there were 31 significantly up-regulated metabolites (red dots) in the IBS patient group when compared with the healthy controls (
Metabolic profiling analysis in IBS and Healthy controls.
Major differentially abundant fecal metabolites (abundance%) in IBS patients and healthy control.
8.11.14.Eicosatrienoic.acid | 0.4015 (0.2588 to 0.7323) | 0.02651 (0.01306 to 0.03774) | <0.0001 | <0.0001 |
Capric.acid | 0.01297 (0.005925 to 0.02282) | 0.001884 (0.001097 to 0.003060) | <0.0001 | <0.0001 |
Gamma.Aminobutyric.acid | 0.9264 (0.5549 to 1.3016) | 0.07469 (0.03423 to 0.1132) | <0.0001 | 0.0005 |
L. Homoserine | 0.4402 (0.1948 to 0.7920) | 0.06821 (0.04100 to 0.1421) | <0.0001 | 0.0005 |
L. Isoleucine | 0.4013 (0.1336 to 1.1719) | 0.02851 (0.01412 to 0.04918) | <0.0001 | 0.0005 |
L. Leucine | 0.7815 (0.4295 to 2.2872) | 0.04603 (0.02629 to 0.08831) | <0.0001 | 0.0005 |
L. Methionine | 1.4862 (0.9226 to 11.4553) | 0.1517 (0.1288 to 0.3844) | <0.0001 | 0.0005 |
L. Norleucine | 0.2561 (0.1172 to 0.5144) | 0.01578 (0.007898 to 0.02883) | <0.0001 | 0.0005 |
L. Phenylalanine | 0.3267 (0.2405 to 0.7910) | 0.03863 (0.01623 to 0.07230) | <0.0001 | 0.0005 |
L. Tryptophan | 0.04805 (0.03404 to 0.09940) | 0.01058 (0.006317 to 0.02460) | <0.0001 | 0.0005 |
L. Valine | 0.5246 (0.2964 to 1.2419) | 0.04274 (0.01852 to 0.06188) | <0.0001 | 0.0005 |
N. acetyltryptophan | 0.06159 (0.05240 to 0.1753) | 0.01155 (0.007426 to 0.02833) | <0.0001 | 0.0005 |
Oxoadipic.acid | 1.7699 (0.8611 to 3.1822) | 0.1248 (0.06129 to 0.3714) | <0.0001 | 0.0005 |
Putrescine | 0.3064 (0.1681 to 0.5475) | 0.01038 (0.004429 to 0.05220) | <0.0001 | 0.0005 |
Ornithine | 0.2858 (0.1309 to 0.6154) | 0.02619 (0.01670 to 0.03983) | 0.0001 | 0.0005 |
Homocysteine | 15.5655 (10.8261 to 29.2755) | 3.4294 (0.7598 to 22.3389) | 0.0186 | 0.0569 |
16S rDNA amplification and sequencing was performed to detect and identify fecal microbiota. Volcano Plots showed that there were 19 significantly up-regulated (red dots) and 12 markedly down-regulated (green dots) microbial populations between the IBS patients and the healthy controls (
Microbial profiling analysis in IBS and healthy controls.
Major differentially abundant fecal microbes in IBS patients and healthy control.
0.06487 (0.04371 to 0.1329) | 0.02516 (0.01352 to 0.03376) | <0.0001 | 0.0015 | |
0.03701 (0.01935 to 0.09547) | 0.0008605 (0.0001639 to 0.003524) | <0.0001 | 0.0015 | |
0.003274 (0.002821 to 0.004217) | 0.0008195 (0.0002459 to 0.001065) | 0.0001 | 0.0015 | |
0.0007601 (0.0004166 to 0.001557) | 0.000082 (0.0000 to 0.0002459) | 0.0002 | 0.0021 | |
0.003625 (0.002492 to 0.004231) | 0.0003688 (0.0002459 to 0.0008195) | 0.0002 | 0.0021 | |
0.0003216 (0.0001535 to 0.0005408) | 0.001106 (0.0007376 to 0.001721) | 0.0003 | 0.0025 | |
0.002806 (0.001893 to 0.003661) | 0.0003688 (0.00008200 to 0.001065) | 0.0003 | 0.0025 | |
0.003245 (0.002806 to 0.005467) | 0.001803 (0.0009015 to 0.002295) | 0.0004 | 0.0030 | |
0.001374 (0.001169 to 0.002784) | 0.01057 (0.003442 to 0.02049) | 0.0006 | 0.0040 | |
0.0000585 (0.00002920 to 0.00008770) | 0.0004098 (0.0001639 to 0.0006556) | 0.0019 | 0.0100 | |
0.004677 (0.002361 to 0.007272) | 0.0009425 (0.0004098 to 0.003278) | 0.0019 | 0.0100 | |
0.002046 (0.00008772 to 0.003384) | 0 (0.0000 to 0.0002459) | 0.0050 | 0.0218 | |
0.03169 (0.01267 to 0.06433) | 0.004303 (0.001393 to 0.02811) | 0.0091 | 0.0336 | |
0.001988 (0.001352 to 0.002470) | 0.0008605 (0.0003278 to 0.001311) | 0.0100 | 0.0336 | |
0.0003508 (0.0001827 to 0.0004750) | 0.000123 (0.0000 to 0.0003278) | 0.0256 | 0.0663 |
A correlation matrix was generated to test the association within differentially abundant metabolites. The correlation matrix revealed that some metabolites were positively correlated with each other while some others were negatively correlated with each other (
The association between metabolomic profile and severity of IBS.
We then constructed the same correlation matrix to test the correlations within differentially abundant microbes (
The association between microbial profile and severity of IBS.
To investigate the correlation between metabolites and microbes, a correlation matrix was generated by calculating the Pearson's correlation coefficient. Based on the correlation matrix (
The networks of gut microbiome and metabolites in IBS patients.
The underlying mechanisms contributed to IBS are thought to be heterogeneous. Many factors have been reported to play a role in the development of IBS, such as “brain-gut axis,” immune regulation, defection, risk gene mutations, and altered gastrointestinal motility (Louis et al.,
In this study, we demonstrated that the fecal metabolome differed significantly between IBS patients and HC. The four clusters including 31 metabolites were significantly upregulated in the IBS group as compared to the healthy controls. Among these metabolites, we detected higher levels of L-methionine and homocysteine in IBS samples. Methionine and homocysteine are common amino acid obtained from food. Both methionine and homocysteine are generally considered to play an important role in intestinal health (Townsend et al.,
Interestingly, we also found that some differentially abundant metabolites were positively correlated with each other and some were negatively correlated with each other, suggesting that further studies were needed to investigate the intrinsic links between these metabolites. For example, L-leucine was positively correlated with adipic acid (
In our study, 16S rDNA amplicon sequencing analysis showed that 19 microbial populations were significantly up-regulated, and 12 microbial populations were markedly down-regulated between IBS group and healthy controls. Among these gut microbes, the levels of
The intestinal metabolites or microbiota in IBS patients has also been investigated by other studies, and various alterations of specific metabolites or microbes have been reported (Krogius-Kurikka et al.,
Based on WGCNA, we obtained two modules of metabolites which were significantly associated with IBS clinical traits. Among these, the module turquoise was positively correlated with abdominal pain, duration of symptom, abdominal discomfort, and stool form. To our interest, we found that γ-aminobutyric acid (GABA) was included in module turquoise. It has been reported that the disruption of GABA leaded to neurological disease and enhancing GABA inhibition could alleviated sleep disorders, chronic pain and anxiety which have higher prevalence in IBS patients (Adeghate and Ponery,
The association studies of differentially abundant microbes and metabolites showed that microbes were significantly correlated with each other, and 4 metabolites (homocysteine, putrescine, glycine, and ornithine) were strongly associated with several microbes (
Recently, we have reported a study on gut microbiota and metabolites of water-avoidance stress induced rats IBS model (Liu et al.,
Inflammatory Bowel Disease (IBD) was also a common disorder of digestive tract. Studies have reported that IBD was also associated with the dramatic changes of gut microbiota and metabolome. Thus, both 16S rDNA amplification sequencing and metabolite profiling analyses were widely used in the research of IBD as well. Most studies on IBD have reported a decreased α-diversity of microbiota, which were similar with that of IBS (Dovrolis et al.,
We acknowledged the following limitations in the present study. First, this is a single-center study with a limited sample size; however, it is comparable to previous studies that have attempted to illuminate the changes of metabolites or microbiota in IBS patients (Carroll et al.,
Taken together, in this exploratory study, we identified specific metabolites or microbes in fecal samples that could be potential biomarkers for differentiation IBS patients from healthy controls. We also found that some fecal metabolites or microbes were associated with the clinical traits of IBS. In addition, some of these metabolites were strongly associated with microbes, suggesting these metabolites were likely to be originated from microbial metabolism. However, further studies are needed to investigate the members of differential abundant metabolites and microbiota; It is also important to elucidate their roles in gut metabolic processes and their potential interplay with inflammation and nervous systems.
The datasets generated for this study can be found in Sequence Read Archive using the accession number
The studies involving human participants were reviewed and approved by the Ethics Committee of Beijing Friendship Hospital. The patients/participants provided their written informed consent to participate in this study.
SZha and LM: designed the experiments. SZhu, SL, LC, HL, and ZZ: performed the experiments. SL, LM, JG, YZ, and QZ: analyzed the data. SL and SZhu: wrote the manuscript. SZha and LM: revised the manuscript. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The Supplementary Material for this article can be found online at:
The heatmap of significantly differential abundant metabolites or microbes.
Correlation of fecal metabolites with clinical traits of IBS.
Correlation of fecal microbes with clinical traits of IBS.
Major differentially abundant fecal metabolites in IBS patients and healthy control.