ABSTRACT
Objective Reducing FODMAPs can be clinically beneficial in IBS but the mechanism is poorly understood. We aimed to detect microbial signatures that might predict response to the low FODMAP diet and assess whether microbiota compositional and functional shifts could provide insights into its mode of action.
Design We used metagenomics to determine high-resolution taxonomic and functional profiles of the stool microbiota from IBS cases and household controls (n=56 pairs) on their usual diet. Clinical response and microbiota changes were studied in 41 pairs after 4 weeks on a low FODMAP diet.
Results Unsupervised analysis of baseline IBS cases pre-diet identified two distinct microbiota profiles, which we refer to as IBSP (pathogenic-like) and IBSH (health-like) subtypes. IBSP microbiomes were enriched in Firmicutes and genes for amino acid and carbohydrate metabolism, but depleted in Bacteroidetes species. IBSH microbiomes were similar to controls. On the low FODMAP diet IBSH and control microbiota were unaffected, but the IBSP signature shifted towards a health-associated microbiome with an increase in Bacteroidetes (p=0.009), a decrease in Firmicutes species (p=0.004) and normalization of primary metabolic genes. The clinical response to the low FODMAP diet was greater in IBSP subjects compared to IBSH (p = 0.02).
Conclusion 50% of IBS cases manifested a ‘pathogenic’ gut microbial signature. This shifted towards the healthy profile on the low FODMAP diet; and IBSP cases showed an enhanced clinical responsiveness to the dietary therapy. The effectiveness of FODMAP exclusion in IBSP may result from the alterations in gut microbiota and metabolites produced. Microbiota signatures could be useful as biomarkers to guide IBS treatment; and investigating IBSP species and metabolic pathways might yield insights regarding IBS pathogenic mechanisms.
Significance of this study
What is already known on this subject?
What is already known on this subject?
IBS subjects often respond to a low FODMAP diet.
The gut microbiota has been implicated in IBS.
The microbiota in IBS subjects may change with diet.
What are the new findings
What are the new findings
We were able to stratify patients with IBS according to their gut microbiota species and metabolic gene signatures.
We identified a distinct gut microbiota subtype with an enhanced clinical response to a low FODMAP diet compared to other IBS subjects.
How might it impact on clinical practice in the foreseeable future?
How might it impact on clinical practice in the foreseeable future?
The potential development of a microbiota signature as a biomarker to manage IBS cases with a low FODMAP diet recommendation.
If the bacteria represented in the IBSP subtype are shown to play a pathogenic role in IBS, perhaps through the metabolic activity this provides a target for new therapies and an intermediate phenotype by which to assess them.
Introduction
Irritable Bowel Syndrome (IBS) affects 10-15% of the population worldwide[1]. It impacts quality of life[2] and incurs significant health economic cost[3]. The pathophysiology of IBS includes changes in visceral nerve sensitivity[4], intestinal permeability[5] and psychological factors[6]. Several lines of evidence suggest the gut microbiome as a key aetiological factor in IBS. For example, there is a six-fold increased risk of developing IBS following an episode of infective gastroenteritis[7], probiotics and dietary intervention can reduce the symptoms [8, 9] and faecal transplantation has reported efficacy in treating IBS[10]. Recent studies using 16S ribosomal RNA profiles (low resolution taxonomic profiling to genus level and no functional inference) have suggested an altered gut microbiota in IBS subjects compared to controls. Although the findings of earlier studies vary significantly recent studies more consistently indicate a reduction in Bacteroidetes in IBS cases vs controls[11-13]. However, mechanisms linking the gut microbiota and IBS symptoms remain poorly understood.
IBS symptoms can be treated with low fibre diets to reduce the colonic microbial fermentation that produces hydrogen and methane, leading to bloating[9]. More recently diets avoiding fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs) have demonstrated efficacy [14-17]. The mechanisms are debated[18], but potentially involve modulation of microbiota composition and metabolite production[19].
The low FODMAP diet is challenging for many patients to follow, reduces various ‘healthy’ foodstuffs (pulses, particular fruits and vegetables), and its long-term consequences on health are unknown. Thus there is a recognised need to better understand how low FODMAP diets work[20], and ideally identify biomarkers that predict response.
In order to accurately link changes in gut microbiota structure with diet, including low FODMAP diets, detailed taxonomic profiling and quantification of microbial abundance is required. The gut microbiota of healthy adults is diverse, dominated by hundreds of bacterial species from the Bacteroidetes and Firmicutes phyla, with fewer species from Actinobacteria and Proteobacteria [21]. It is shaped by diet and impacts immunity, metabolism and cognition[22, 23]. While 16S rRNA studies have provided valuable insights into the gut microbiota and IBS, they cannot achieve taxonomic resolution to species level. Techniques of microbial culture and metagenomic sequencing now enable detailed taxonomic and functional characterisation [24].
The aim of the present study was to identify a biomarker of response to the low FODMAP diet and gain insights into microbial changes underlying treatment success using high resolution metagenomic and functional analysis of subjects with IBS and household controls before and while on a low FODMAP diet.
Materials and methods
Subjects
A prospective single centre case control study recruited participants from 2016 to 2019. We included adults (18-68 years of age) meeting the Rome IV criteria[25] for diarrhoea-predominant or mixed type IBS (IBS-D and -M respectively) with respective household controls. Subjects were recruited from outpatient clinics at Cambridge University Hospital in the UK and via a social media campaign.
We excluded cases with other gastrointestinal diseases, pregnancy, those already following a restrictive diet, and those taking probiotics or other medications within one month that could potentially modify the gut microbiota such as antibiotics, proton pump inhibitors, colonoscopy bowel preparation or metformin[26].
Ethics
approval was provided by Cambridge Central Research Ethics Committee reference 15/LO/2128.
Study procedures are summarised in Figure 1. Participants were assessed at baseline by a consultant. Three subsequent study visits were supervised by a dietitian when symptom severity scores were captured using the IBS Severity Scoring System (IBS-SSS)[27] and 7 day food intake diary used to assess FODMAP intake.
Stool samples
Participants and their household controls were asked to provide a stool sample at visit 1 while on their usual diet, after 4 weeks on a low FODMAP diet (at visit 2) and 12 weeks following FODMAP rechallenge in IBS subjects improving on the diet (visit 3, to identify individual FODMAP triggers), or a return to usual diet in IBS subjects not improving with the diet and in all household controls. Samples were sealed and immediately placed in the participant’s home freezer then courier transferred on dry ice to the Wellcome Sanger Institute within 48 hours for storage at -80°C prior to processing. DNA was extracted using the MP Biomedicals™ FastDNA™ SPIN Kit for Soil.
Metagenomic Sequencing
To profile the taxonomic composition of the stool samples from cases and controls we performed shotgun metagenomic sequencing using the Illumina Hi-Seq 4000 platform (read length 150bp, 450bp fragment size, average 12 million paired-end reads). Raw sequencing data were deposited under ENA Study Accession Number: XXXXXX. Paired-end read files were classified using a Kraken2 bespoke database containing 3,000 high-quality assemblies from 784 species associated with the human gut microbiome. Bracken[28] was applied to obtain refined species-level metagenomic profiles. An average of 10.2 million sequencing reads were classified at species rank by our platform, corresponding to a read classification rate of 86% (sup Fig 1). No difference in assigned read counts was observed between cases and controls (Wilcoxon P=0.7). Statistical analysis was performed using R language [29]. Taxonomic profiles were normalized using center log ratio (CLR) transform[30] after estimating zero values using the cmultRepl function from zCompositions R package [31].
For an unsupervised analysis to identify sub-populations of IBS cases, we applied a k-means clustering algorithm to CLR-transformed taxonomic profiles from baseline IBS case and household control samples. The optimal k value was obtained by minimizing within-group sum of squared metrics using the decostand function from the vegan R package[32].
A comparison of alpha diversity between groups was performed using a paired Wilcoxon test.
We used Aitchison distance[30] which is the Euclidean distance of the CLR transformed profiles to estimate beta diversity between samples. The significance of beta diversity difference was estimated using PERMANOVA test[33].
Associations between cluster assignment and clinical metadata were sought using Fisher’s exact test on the contingency table or Mann-Whitney test when appropriate (Table 1). We tested for correlation between microbial abundance and metadata (including IBS cluster and timepoint) by applying generalized linear mixed models (GLMMs) using MaAsLin2 software[34]. Random effects were modelled by matching IBS subject and household control. Vignette and source code for the analysis are available at http://github.com/kevinVervier/IBS.
2,754 high quality human gastrointestinal genomes (Attached Table 1) were downloaded from Human Gastrointestinal Bacteria Culture Collection[24], Culturable Genome Reference[35] and National Center for Biotechnology Information (NCBI). Maximum-likelihood trees were generated using FastTree v2.1.10[36] with default parameters, and protein alignments were produced by GTDB-Tk v1.3.0[37] with the classify_wf function and default parameters. Trees were visualized and annotated with Interactive Tree Of Life (iTOL) v5[38].
Functional metagenomic and genomic analysis
Functional profiling on each metagenome was conducted using HUMAnN3[39] with default parameters to quantify MetaCyc pathways[40]. Pathway enrichment was performed using MaAsLin2[34] (threshold at q-value < 0.1). Enriched pathways were classified in broad categories using the MetaCyc database.
To identify the genes present in an enriched MetaCyc pathway in a reference genome, we first collected the protein sequence corresponding to each gene in each pathway from the Metacyc database and UniProt[41]. BlastP[42] was then performed for each of these protein sequences against a protein database based on 544 genomes with a cut-off E-value of 1e-10. This genome collection of 544 genomes includes 420 genomes (56 species) of IBS associated bacteria representing cluster IBSP and 124 genomes (34 species) of health-associated bacteria representing cluster IBSH (see below for IBSP / IBSH description). Gene enrichment was calculated using one-sided Fisher’s exact test with p-value adjusted by Hochberg method.
Results
Cohort Summary
The cohort is summarised in Figure 1. Among cases, there was female predominance (73%) and IBS-M was the commonest subtype (59%). Fourteen cases (25%) reported symptom onset after an episode of gastroenteritis. The median IBS-SSS at baseline in the 56 cases was 272, with 45 cases (88.2%) scoring moderate (IBS-SSS>175 – n= 25) or severe (IBS-SSS> 300 – n=20). In controls the median IBS-SSS score was 7.5 (range: 0-196). Mean age of subjects was 38.7 (range 18-68) and controls 44.6 (range 18-74).
Comparison of gut microbiota from IBS cases and household controls
Metagenomic sequencing was carried out on 234 stool samples followed by reference genome mapping of sequence reads [24]. Our inclusion of household controls reduced confounding by environmental exposures (pets, prevailing diet, hygiene regime) and is important as gut microbes can frequently transmit between co-habiting humans[43]. Indeed, we observed that samples coming from the same household had a more conserved microbiota composition compared to the overall variability between all cases and all controls (sup Fig 2C, Wilcoxon p = 6.02E-05). To account for this potential confounder in subsequent analyses we applied pairwise comparisons where possible.
We first focused on understanding the compositional variation in bacterial species to identify potential pathogenic imbalances in IBS case gut microbiomes. We measured alpha diversity in baseline samples using the Chao1 index for the number of species (richness) and the Shannon index for the relative abundance of different species (evenness). The richness was not lower in IBS cases (Wilcoxon p=0.12) (sup Fig 2A), but the lower evenness suggested a more imbalanced species abundance distribution in favour of fewer bacterial species in IBS cases compared to controls (p=0.0092; sup Fig 2B). We also measured beta diversity between baseline microbiota samples using Aitchison distance and observed significantly more taxonomic variability within IBS cases compared to controls (sup Fig 2C, Wilcoxon p=1.3E-79).
Stratification of IBS patients based on gut microbiota compositional subtypes
The high variability in diversity observed within baseline microbiomes from IBS cases warranted exploration of possible stratification by microbiome profile, to identify distinguishing signals that went undetected during our initial analysis. We therefore performed unsupervised data clustering designed to identify microbiota subtypes in baseline samples from the 56 pairs of cases and household controls. Importantly, this analysis revealed two distinct microbiota taxonomic clusters (Fig.2A) with 28 cases assigned to each. Such clustering was not seen in household controls. Microbiome compositions in cluster 2 cases were similar to healthy controls whereas the cluster 1 cases were clearly separated. Compared to the overall variability previously observed across all IBS cases, microbiota diversity within each cluster was more conserved (sup Fig 3, Wilcoxon p=2.2E-08). We found no significant difference in age, gender, BMI, subtype of IBS, post-infectious IBS or concomitant medications between the two clusters (Table 1). Baseline symptom severity scores appeared modestly higher in cluster 1 than cluster 2 (median IBS-SSS=302 vs 249), but this was not statistically significant (Wilcoxon p=0.17).
The number of bacterial species (richness) appeared modestly lower in cluster 1 cases compared to cluster 2 microbiomes (Wilcoxon p=0.033), but no such difference was observed between respective controls (Wilcoxon p=0.57) (sup Fig 4A). Cases and controls from the same cluster show comparable richness (cluster 1 p=0.073, cluster 2 p=0.69). Shannon diversity (evenness) was clearly lower in IBS cluster 1 compared to cluster 2 cases (p=0.0002), but this difference was not seen between respective controls (p=0.078) (sup Fig 4B). Cases from cluster 1 had a lower evenness when compared to their household controls (p=0.0029), while this was not observed for cluster 2 (p=0.41). Overall, our findings suggest that cluster 1 case microbiomes are depleted in bacterial species and skewed towards specific bacteria compared to cluster 2.
Read abundance analysis identified distinct differences between bacterial species in the two IBS subtypes at baseline (MaAsLin2 q-value < 0.1; Attached Table 2). A total of 87 species were identified as significantly differentially abundant between the two IBS subtypes (56 up in cluster 1 and 31 up in cluster 2), and not observed between corresponding household controls. In IBS cluster 1 we observed a significant increase of bacteria from the Firmicutes phylum including known human pathogens (Clostridium difficile, Paeniclostridium sordellii, Clostridium Perfringens, Streptococcus anginosis) (sup Fig 4C) and a significant depletion of multiple Bacteroides and Parabacteroides species (sup Fig 4D). Phylogenetic analysis showed a clear distinction between the dominant species from the Firmicutes phylum in cluster 1 and the dominant species from the Bacteriodetes phylum in cluster 2 (Fig.2B). However, we did not observe a significant difference in abundance for these two phyla between groups (MaAsLin2 q-value: Firmicutes: 0.2, Bacteroidetes: 0.78) suggesting differences in a subset of species rather than an overall Firmicutes/Bacteroidetes imbalance.
Thus, we identified IBS subtypes with distinct microbiota signatures and clinical features at baseline: cluster 1 contained lower bacterial diversity, was depleted in commensal species from the Bacteroidetes phylum and enriched in species from the Firmicutes phylum, including human pathogens; and cluster 2 was indistinguishable from healthy household controls. We refer to cluster 1 as IBSP microbiome type for its pathogenic properties and cluster 2 as IBSH microbiome type due to its similarity to healthy household controls.
Enrichment of primary metabolism genes in gut microbiomes of IBSP patients
Bacterial species from the Bacteroidetes and Firmicutes phyla are evolutionarily and physiologically distinct, and contribute different core functions to the gut microbiome. Therefore, we reasoned that the functional capacity of IBSP microbiomes may contribute to IBS symptoms. To identify functional differences between the microbiomes of the two IBS subtypes, we performed an analysis of the functional capacity encoded in the metagenomes of baseline samples of IBSP and IBSH patients. This analysis was independent of the previous taxonomic analysis. We found a significant enrichment of 109 functional pathways and significant depletion of 13 functional pathways in IBSP microbiomes compared to IBSH microbiomes (Attached Table 3). Further functional classification indicated that the majority of enriched pathways in IBSP microbiomes (78.7%) could be classified to 5 major functional categories related to primary metabolism (Figure 3).
Since amino acid biosynthesis (25%) and carbohydrate metabolism (15.7%) were the two major functional categories that separate IBSP and IBSH cases, we next performed a targeted functional enrichment analysis in IBSP microbiomes at the species level. For amino acid biosynthesis this identified significant enrichment of genes involved in biosynthesis of tryptophan, threonine and histidine (sup Figure 5). Equivalent analysis of carbohydrate metabolism identified significant enrichment of genes involved in lactose metabolism, fructose metabolism, and trehalose metabolism, and biosynthesis of two short chain fatty acids (SCFA): butyrate and propionate (sup Figure 6).
Our results suggest specific functions involved in amino acid biosynthesis and metabolism of simple dietary sugars are distinct features in bacteria of the IBSP cluster at baseline, which are underrepresented in bacteria of the IBSH cluster. Correlating the compositional (Figure 2B) and functional (Figure 3) features identified a subset of candidate species associated with the IBSP cluster (Figure 3) and enriched in significant pathways. A strong positive correlation was observed between the abundance of these pathways and abundance of the bacterial species with known pathogenic capabilities (C. difficile, P. sordellii, C. perfringens) and a pathobiont associated with ulcerative colitis (Faecalicatena gnavus, previously named Ruminococcus gnavus[44]). Commensal species depleted in IBSP patients did not encode these pathways.
Low FODMAP dietary intervention corrects IBSP microbiomes
A total of 41 IBS cases and their household controls followed a low FODMAP diet for 4 weeks and provided a stool sample while on the diet. There was a significant reduction in the IBS-SSS on and after the completion of the low FODMAP diet (mean IBS-SSS pre-diet = 278, on diet = 128, post diet = 117) (Figure 4A). A decrease in IBS-SSS scores was seen on the low FODMAP diet in patients harbouring IBSP and IBSH-type microbiomes (Figure 4B) but was more pronounced in IBSP patients (Δ IBS-SSS in IBSP =194 vs IBSH =114; p=0.02) (Figure 4C).
Comparison of taxonomic profiles between baseline (pre-diet) stool samples and those obtained while on the low FODMAP diet for four weeks revealed a significant shift in the microbiota composition of IBSP cases but not IBSH cases nor healthy controls (Figure 5A). Compared to the differences seen between IBSP and IBSH at baseline, beta diversity analysis showed the microbiome profiles from IBSP cases became more similar to those seen in IBSH cases and healthy controls while on the low FODMAP diet. This was apparent as a decreased variability in microbiome composition within all IBS cases (IBSP + IBSH combined) on diet compared to pre-diet (Supp. Fig. 7, Wilcoxon test p=1E-19). Within both IBSP and IBSH cases it was also evident that the diet produced a greater shift in microbiota composition in IBSP compared to IBSH, with a bigger distance between sample profiles from the same case at the two timepoints (baseline and on-diet) (Supp. Fig. 7, Wilcoxon test p=0.03).
Diet intervention shifted the taxonomic composition of IBSP cases by increasing Bacteroides levels (B. cutis, B. stercorirosoris), and decreasing pathobiont levels (including C. difficile, Streptococcus parasanguinis, Paeniclostridium sordelli) towards those seen in IBSH (Fig. 5 B-C) and household controls (Supplementary Figure 8). The functional profile of the IBSP microbiome was also impacted by the diet intervention, for example producing a decrease in degradation of the FODMAP trehalose (Fig. 5D) and a decrease in glycolysis to levels comparable to those in IBSH patients and healthy controls (Fig. 5E).
After the low FODMAP diet ended participants returned to a normal diet, albeit with cases excluding foods identified as triggering their symptoms. After 3 months there was no significant shift in the microbiota diversity of the cases in the two clusters compared to while on full dietary restriction (Supp. Fig 9, PERMANOVA p=0.998) and no significant change in the abundance of any bacterial taxa between these timepoints. Thus, the shift in the IBSP microbiota to a heathy profile appeared stable for at least 3 months and correlated with continuing symptomatic well-being (Fig. 4A).
Discussion
We defined two gut microbiome subtypes in IBS cases with distinct signatures based on species and encoded microbial functions, and differential clinical responses to a low FODMAP diet intervention. Although the early IBS microbiome literature is rather inconsistent regarding taxa implicated and the presence of subtypes [11], our work is congruent with the observations of Jeffery et al.[12, 13] who used shotgun, 16S rRNA gene microbiome profiling, and metabolomics to provide evidence of IBS microbiome subtypes identifying Lachnospiraceae species and enrichment in amino acid biosynthesis. Not only do our results replicate this stratification within IBS in a larger cohort, but being based on shotgun metagenomics data they benefit from both greater taxonomic resolution - identifying an increase in selected Firmicutes species and depletion of Bacteroidetes species in one subgroup - and the ability to analyse the functions encoded in the microbiome. Furthermore, the dietary intervention allowed us to characterize the clinical responses of each patient subtype; and inclusion of household controls, following the same dietary intervention, was a unique feature of our study designed to correct known confounding environmental effects[45].
We refer to the IBS microbiome subtypes as IBSp (pathogenic) and IBSH (healthy). Overall, 75% of IBS cases in our study improved on a low FODMAP diet as measured by a decrease in IBS-SSS but higher levels of symptom response were seen in cases with IBSP compared to IBSH microbiomes. IBSP microbiomes were notably different from the microbiome of IBSH cases and healthy household controls, with an enrichment of distinct bacterial species and gene families seen in IBSP that allows us to propose potential pathogenic mechanisms.
Within the dysbiotic IBSP microbiomes we saw a significant enrichment of a broad range of evolutionarily distinct Firmicutes species, including known human pathogens (Clostridium difficile, C. sordellii and C. perfringens), a pathobiont associated with ulcerative colitis (Faecalicatena gnavus, previously named Ruminococcus gnavus[44]) and known gut species not previously identified as human pathobionts (C. clostridioforme and Fusicatenibacter saccharivorans). Interestingly, we also saw an enrichment in IBSP microbiomes of the lactic acid bacteria Streptococcus parasanguinsis and S. timonensis, that are usually found in the oral cavity.
IBSP microbiomes are enriched in genes and pathways involved in metabolising simple sugars that are recognised FODMAPs commonly found in dairy products (lactose), fruit (fructose) and food additives (trehalose, lactose and fructose). Lactose and fructose are known triggers of IBS so our analysis provides a list of candidate bacteria for further investigation (sup Figure 5). Interestingly, trehalose is found in mushrooms (excluded in the low FODMAP diet) and was introduced as a food additive in the 1990s, since when specific lineages of C. difficile have evolved to avidly metabolize it and in so doing increase their abundance [46]. Trehalose could trigger IBS symptoms by fuelling the growth of specific ‘pathogenic’ bacterial species.
Microbial metabolism of hexoses derived from FODMAP carbohydrates produce pyruvate by anaerobic glycolysis in the gut. Pyruvate is a key metabolite that feeds in to short chain fatty acid (SCFA) production[47]. Our pathway analysis (sup Figure 5) predicts that several bacterial species enriched in IBSP microbiomes contain genes for converting pyruvate to butyrate (classical pathway) and/or propionate (acrylate pathway)[48]. Butyrate and propionate are major metabolites in the colon that bind to GPR receptors 41, 43 (propionate) and 109A (butyrate): these short chain fatty acids regulate tryptophan hydrolase gene transcription in enterochromaffin cells facilitating the production of 5 hydroxytryptamine (5HT) from tryptophan; 5HT is postulated as a key agent in the production of IBS symptoms[49, 50]. Moreover, in IBSP microbiomes, we observed an enrichment of genes for tryptophan biosynthesis which would facilitate this mechanism.
We also found enrichment in IBSP microbiomes for the genes coding for the biosynthesis of amino acids including histidine, arginine, ornithine, tryptophan, alanine and threonine (supp Figure 6). Interestingly, Lee et al.[51] found elevated levels of threonine, tryptophan, and phenylalanine, as well as amino acid metabolites cadaverine and putrescine, in stool samples of IBS patients, providing direct evidence of altered amino acid metabolism. Histidine is a precursor to histamine, implicated in the generation of IBS symptoms following its release from mast cells; histamine can itself also activate these cells [42].
Although we detect higher levels of specific pathogens in IBSP microbiomes we have no evidence to suggest they are causing IBS symptoms through known toxin virulence factors. Instead, the data suggest an enrichment of primary metabolic pathways in diverse Firmicutes species. Our analysis indicates a potential for increased production of amino acids; and SCFA through metabolizing FODMAP carbohydrates. It is possible that such metabolites and their derivatives could be noxious at high levels within the colon, or be pathological if produced within the wrong intestinal niche, a type of metabolic virulence, leading to IBS symptoms. One key finding from our work is that IBSP and IBSH microbiomes have distinct bacterial community responses to low FODMAP dietary intervention, providing a basis to define a mode of action. Thus it is possible that removal of the eliciting dietary component starves the pathobionts leading to reduction in their growth and metabolism and a consequent decrease in symptoms, accompanied by an expansion of commensal or symbiotic species leading to a health associated microbiome. Although the number of case/control pairs (n=21) who provided follow-up samples at 12 weeks after rechallenge with FODMAPs was relatively modest, and some continued to exclude specific FODMAP-containing foods, it was interesting to note that both their symptoms (Fig 4A) and microbiomes (sup Fig 9) remained notably stable. This corroborates and perhaps helps to explain the durable benefit that can be seen from a low FODMAP diet.
We observed a differential response of IBSP and IBSH microbiome subtypes to the low FODMAP diet, suggesting that some gut microbiomes are more influenced by dietary interventions. Based on our analysis it is not obvious how or whether IBSH microbiomes contribute to IBS symptoms since they are indistinguishable from household control microbiomes and did not significantly alter in response to the low FODMAP diet. That symptoms in IBSH cases still improved somewhat on FODMAP exclusion suggests either that the response is linked to a non-bacterial component of the microbiome, such as viruses, or is unconnected mechanistically to the microbiota, perhaps instead reflecting a direct effect of dietary constituents and their metabolites on gut neuronal function or osmotic load.
The presence of microbially-defined IBS subtypes with differing responses to dietary intervention has been suggested by some previous studies. In one the microbiome in children responding to a low FODMAP diet appeared enriched at baseline with taxa such as Bacteroides, Ruminococcaceae and Faecalibacterium prausnitzii[14]. In other studies, stool microbial profiles assessed by a commercial kit correlated with differing responses to a low FODMAP diet[52]; and the profile of faecal volatile organic compounds, postulated as reflecting microbiome differences, predicted response to a low FODMAP diet or probiotics[53].
Our study has limitations. The sample size was relatively modest: the strict inclusion criteria, the restriction of concomitant medications and the required participation of household controls needing to follow the low FODMAP diet hindered recruitment. Dietary information was limited to the last week of the interventional phase of the low FODMAP diet: participants could have been tempted to follow a more rigorous diet on the week they had to report their dietary intake. With the design of the study, it was impossible to exclude other factors, apart from diet, that could have impacted the benefit observed, including the psychological impact of being assessed within a research study, the placebo effect that has been described in other studies, and referral bias. Our findings of distinct IBS clusters based on microbiome profiles, the shift on the low FODMAP diet and the clinical responses, should be validated in other populations from different geographical distributions and exposed to different dietary habits.
The identification of a microbial signature ‘biomarker’ that correlates with improved response to a low FODMAP diet may, if validated, allow better stratification and selection of patients likely to benefit from the diet. It also opens the door to trying other therapeutic strategies that manipulate the microbiota in the same direction and achieve the same symptomatic improvement but without the need to undergo the same stringent dietary restrictions. Further, closer study of the implicated microbes may give the opportunity to better understand the interaction between diet, microbiota, metabolites and the human gut-brain axis that leads to the development of IBS symptoms in more than 10% of the world’s population.
Competing Interests
TR has received research/educational grants and/or speaker/consultation fees from Abbvie, Arena, AstraZeneca, BMS, Celgene, Ferring, Galapagos, Gilead, GSK, LabGenius, Janssen, Mylan, MSD, Novartis, Pfizer, Sandoz, Takeda and UCB. SM has received research/educational grants and/or speaker/consultation fees from Abbvie. MP has received research/educational grants and/or speaker/consultation fees from Takeda. TDL is the co-founder and CSO of Microbiotica.
Funding
This research was co-funded by Addenbrooke’s Charitable Trust (ACT), Cambridge and the Wellcome Sanger Institute and supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014).
Figure legends
Acknowledgements
We would like to thank the patients and household controls who participated in the study. Also Tracy Papworth, Max Delvincourt, Chris Cederwall and You Yi Hong who contributed to patient identification and recruitment. This research was co-funded by Addenbrooke’s Charitable Trust (ACT), Cambridge and the Wellcome Sanger Institute This research was also supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.