Gut microbiome and serum metabolome analyses identify molecular biomarkers and altered glutamate metabolism in fibromyalgia

Abstract

Background: Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition.

Methods: We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers.

Results: We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia.

Conclusions: Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

Keywords: Cytokines; Fibromyalgia; Gut microbiota; Metabolomics; Omics integration; Pain; miRNAs.

Conflict of interest statement

The authors declare no competing interests.

Copyright © 2019. Published by Elsevier B.V.

Figures

Fig. 1

Experimental design workflow, from patient recruitment and sample collection to the arrival of processed samples into the research centre and their examination using distinct omics techniques.

Fig. 2

Microbiome multivariate analysis. (A) Principal Component Analysis (PCoA) of the complete cohort. (B) Supervised Partial Least Squares Discriminant Analysis (PLS-DA) analysis, showing the discrimination between the sample groups. (C) Alpha-diversity indexes for each sample group, showing the adjusted p-value computed using Student's t-test.

Fig. 3

Core microbiome and genus-discriminant analyses. (A) The composition of core microbiome for each sample group and the comparison of bacterial ubiquity in the two groups. (B) Genera significantly different (adj p > .05) between the control and fibromyalgia samples, obtained using the protocols described in the Methods. Positive log2 fold changes (x-axis) indicate genera with positive fold difference between fibromyalgia and control. Negative log2 fold changes are shown as negative x values. Each point represents a single OTU, coloured by phylum. On the y-axis, the taxonomic genus level is indicated. Size of the points reflect the log-mean abundance of the sequence data. (C) qPCR results for the differential expression of bacterial genes related to glutamate bacterial degradation. Results are indicated in differential Cts count.

Fig. 4

Univariate metabolomics analysis. (A) Volcano plot of 1070 metabolic features detected in serum samples after background subtraction and removal of the features found in 2 FC indicates increased abundance in fibromyalgia patients. All p-values were adjusted using the Bonferroni method.

Fig. 5

Heatmap of scaled correlations between the bacteria whose abundance was altered in fibromyalgia and the identified metabolites. The dendrograms were unsupervised. Red arrows mark the bacteria with increased abundance in fibromyalgia, green arrows, with decreased abundance, and “equals” symbol indicates the OTUs with both increased and decreased abundance (A). Omics correlations with indexes used in fibromyalgia diagnostics, as defined by ACR 2010 criteria. Only significant correlations (p-value < .05) are coloured. Positive correlations are indicated in red and negative correlations, in blue. Correlations between circulating miRNA levels (B), circulating cytokine levels (C), identified serum metabolites (D) and microbiome composition (at genus level) (E). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Multi-omics integration. (A) sPLS-DA consensus plot for the combination of the 4 datasets, showing the nearly complete discrimination of the 71 samples (36 fibromyalgia and 35 control samples). (B) The individual contribution of each dataset to the sPLS-DA final model, in each case showing the score plots for the two first components, indicating the best separation capability for microbiome data, followed by cytokines, metabolomics and miRNAs. (C) ROC curves for each omics dataset, with the Area under the Curve (AUC) values.

Fig. S1

Rarefaction curves computed for observed OTUs (upper panel) and Shannon (lower panel) indexes. The vertical line at 12,000 reads indicates the value selected for rarefying the samples (A). PCoA analysis of unweighted UNIFRAC distances of the non-rarefied and the rarefied datasets. Axis titles indicate the variance explained by each component. Control samples are coloured in blue and fibromyalgia in red (B).

Fig. S2

Glutamate incorporation to bacterial cytoplasm and transformation pathways. Decreased genes are indicated with a red triangle.

Fig. S3

PCA analysis of full metabolomics data, coloured by the hospital sample origin.

Fig. S4

Scaled correlations between the full metabolomics dataset and the microbiome OTUs, at the genus level. Red colouring indicates positive correlations and blue, negative correlations.

Fig. S5

Volcano plots for the cytokine (A) and miRNA (B) profiling. All p-values were adjusted using the Bonferroni method.

Fig. S6

Multi-omics integration, employing DIABLO functionality in the mixOmics R package, using the full metabolomics feature dataset. (A) sPLS-DA consensus plot for the combination of the 4 datasets, showing the perfect discrimination between the two sets of samples (36 fibromyalgia and 35 control samples). (B) The individual contribution of each dataset to the final sPLS-DA model, showing the score plots for the two first components in each case. Using the metabolomics data gives the best separation capability, followed by the microbiome, cytokines and miRNAs. (C) ROC curves for each omics dataset, showing the AUC value for each set.