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Growth effects of N-acylethanolamines on gut bacteria reflect altered bacterial abundances in inflammatory bowel disease

March 2020
Nature Microbiology 5(5):1-12

Authors:

Broad Institute of MIT and Harvard

Inflammatory bowel diseases (IBD) are associated with alterations in gut microbial abundances and lumenal metabolite concentrations, but the effects of specific metabolites on the gut microbiota in health and disease remain largely unknown. Here, we analysed the influences of metabolites that are differentially abundant in IBD on the growth and physiology of gut bacteria that are also differentially abundant in IBD. We found that N-acylethanolamines (NAEs), a class of endogenously produced signalling lipids elevated in the stool of IBD patients and a T-cell transfer model of colitis, stimulated growth of species over-represented in IBD and inhibited that of species depleted in IBD in vitro. Using metagenomic sequencing, we recapitulated the effects of NAEs in complex microbial communities ex vivo, with Proteobacteria blooming and Bacteroidetes declining in the presence of NAEs. Metatranscriptomic analysis of the same communities identified components of the respiratory chain as important for the metabolism of NAEs, and this was verified using a mutant deficient for respiratory complex I. In this study, we identified NAEs as a class of metabolites that are elevated in IBD and have the potential to shift gut microbiota towards an IBD-like composition. Inflammatory bowel diseases (IBD) are associated with increased faecal N-acylethanolamines (NAEs), which are primarily host-produced signalling lipids, in patients and a mouse model of colitis. These metabolites can enhance the growth of bacterial species enriched in IBD faecal samples and are associated with the expression of respiratory chain genes necessary for microbial metabolism of NAEs.

Validation of metabolite screen results in dose assays Growth curves are shown for three strains using seven metabolites in multiple concentrations. Growth was monitored over time in a volume of 40 µL per well in 384-well plates. The final concentration of DMSO per treated and control well was 0.25%. Growth curves representative of three independent tests are shown and error bars in controls represent the standard deviation of the mean of six technical replicates.

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Changes in transcriptional activity (KEGG orthologies) in complex microbial communities in response to NAE treatment Log2 relative expression is shown for bacterial families contributing to a selection of KEGG orthologies in samples from chemostats A and B treated with DMSO control, individual NAEs, a combination of all four NAEs (denoted as NAE-mix), or oleic acid (OA). Family-level relative expression values were computed at the species level, averaged over replicates, and then averaged within-family while weighting by species abundance. Unknown (“x”) values represent cases where a function’s DNA and/or RNA abundance were zero for a given stratification (resulting in non-finite log2 relative expression). Col_sort refers to the measure (treatment, time or chemostat) used to order the metadata columns.

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Correlation between absolute and relative NAE abundances in stool from PRISM subjects NAEs detected in stool from PRISM Crohn’s disease (CD) patients (n=21) in absolute abundances (ng mg⁻¹) are plotted against their respective relative abundances⁸. Pearson correlation coefficients (r) are shown. Progenesis QI (nonlinear DYNAMICS) was used for the extraction of non-targeted LC-MS features and TraceFinder (Thermo Fisher Scientific) was used for the manual peak extraction of known metabolites on basis of their mass to charge ratio (m/z) and retention times determined using authentic standards.

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Growth effects of NAEs on intestinal bacteria elevated in IBD Palmitoylethanolamide (PEA), linoleoyl ethanolamide (LEA), oleoyl ethanolamide (OEA) and arachidonoyl ethanolamide (AEA) were added to growing cells (in the range of 10⁶ to 10⁸ CFU mL⁻¹) in three concentrations (0 µM, 50 µM and 100 µM), and growth was monitored in an absorbance reader in the anaerobic chamber over time. Controls contained 0.4% DMSO. Growth curves representative of two independent experiments are shown and error bars represent the standard deviation of the mean of three technical replicates. Source data

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Transcriptional responses of Bacteroides fragilis to linoleoyl ethanolamide (LEA) and arachidonoyl ethanolamine (AEA) a, Differential gene expression between three independent exponential cultures treated for 10 minutes with a sub-inhibitory concentration (25 µM) of LEA or AEA and controls (0.04% DMSO). Differential expression was determined with edgeR, and gene functions were defined using InterPro (EMBL), the NCBI Conserved Domain Database (CDD) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Selected significantly differentially expressed genes (|log2 fold-change (treated/control)|>0.5, FDR<0.05; Benjamini-Hochberg FDR values were derived from p-values calculated using the likelihood-ratio test) are shown in color. b, Genomic environment of the differentially expressed genes using colors that correspond with (a). Genes in white were not significantly differentially expressed. Coordinate maps refer to the genome of strain B. fragilis ATCC 25285. Gene products that have been experimentally shown to be associated with the outer membrane by LC-MS/MS analysis are in bold (Wilson, M. M., Anderson, D. E. & Bernstein, H. D. Analysis of the outer membrane proteome and secretome of Bacteroides fragilis reveals a multiplicity of secretion mechanisms. PLoS ONE10, e0117732 (2015)).

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Articles

https://doi.org/10.1038/s41564-019-0655-7

1Broad Institute of MIT and Harvard, Cambridge, MA, USA. 2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

3Chemical Biology and Therapeutics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA, USA. 4Departments of Medicine, Microbiology

and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 5Center for Microbiome Informatics and Therapeutics, Massachusetts

Institute of Technology, Cambridge, MA, USA. 6Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston,

MA, USA. 7Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

8These authors contributed equally: Eric A. Franzosa, Jason Bishai. *e-mail: hera@broadinstitute.org; xavier@molbio.mgh.harvard.edu

Inflammatory bowel diseases (IBD), including ulcerative colitis

(UC) and Crohn’s disease (CD), are conditions of chronic gas-

trointestinal inflammation resulting from genetic predisposition

and perturbed interactions between gut microorganisms and host

immunity1,2. Many studies focus on microbial taxonomic and func-

tional changes during IBD3–5; however, the gut metabolome (com-

prising diet-, host- and microorganism-derived metabolites) is an

equally important contributor to intestinal health6–8.

Certain gut bacteria metabolize dietary fibre into short-chain

fatty acids such as butyrate that nourish colonocytes, promote

regulatory T-cell expansion and have immunosuppressive func-

tions9,10. Butyrate concentration and butyrate-producing bacte-

ria are depleted in the IBD gut11,12. Other bacteria (for example,

Lactobacillus spp., Bacteroides spp. and Clostridium sporogenes) con-

vert tryptophan into indole derivatives that promote healthy intes-

tinal barrier function and immune tolerance13. Indole producers are

also depleted in IBD8,12. Urease activity, predominantly contributed

by Proteobacteria, shifts the microbiome towards the imbalanced

state seen in IBD and worsens disease in a murine colitis model14.

Host-derived metabolites likewise affect microbiota composition:

bile acids, enriched in the IBD gut8, promote growth of bile acid-

metabolizing bacteria and inhibit growth of bile-sensitive bacteria15.

Inferring covariations between metabolites and bacteria that

are differentially abundant in IBD can functionally implicate gut

metabolites and microorganisms in intestinal health7,16. Two recent

studies combined microbial metagenomics and untargeted mass

spectrometry of metabolites to identify associations between stool

bacterial species and metabolites8,12. The first was a cross-sectional

study of UC, CD and non-IBD subjects within the Prospective

Registry in IBD Study at MGH (PRISM) cohort, and the sec-

ond was the longitudinal integrative Human Microbiome Project

(iHMP). Both concluded that microbial taxonomic changes associ-

ated with IBD, such as blooms of facultative anaerobes including

Proteobacteria4,5, are accompanied by significant shifts in metabo-

lite composition.

Here, we investigated the effects of intestinal metabolites that are

differentially abundant in IBD on the growth of gut bacteria that are

also differentially abundant in IBD, finding that metabolites includ-

ing amines and fatty acids strongly affect bacterial growth. Linoleoyl

ethanolamide (LEA), an NAE, impacted growth in ways that reflect

altered bacterial abundances in the IBD microbiome. We show that

LEA and three structurally related NAEs—palmitoylethanolamide

(PEA), oleoyl ethanolamide (OEA) and arachidonoyl ethanolamide

(AEA)—are enriched in stool from IBD patients and a T-cell trans-

fer model of colitis. These NAEs share common receptors and are

part of the endocannabinoid system, although only AEA is con-

sidered a true endocannabinoid as it binds the cannabinoid recep-

tors CB1 and CB217. We treated monocultures of bacteria that shift

Growth effects of N-acylethanolamines on gut

bacteria reflect altered bacterial abundances in

inflammatory bowel disease

Nadine Fornelos1, Eric A. Franzosa 1,2,8, Jason Bishai 1,8, John W. Annand3, Akihiko Oka 4,

Jason Lloyd-Price 1,2, Timothy D. Arthur1, Ashley Garner1, Julian Avila-Pacheco1, Henry J. Haiser3,

Andrew C. Tolonen1, Jeffrey A. Porter3, Clary B. Clish 1, R. Balfour Sartor4, Curtis Huttenhower 1,2,

Hera Vlamakis 1* and Ramnik J. Xavier 1,5,6,7*

Inflammatory bowel diseases (IBD) are associated with alterations in gut microbial abundances and lumenal metabolite

concentrations, but the effects of specific metabolites on the gut microbiota in health and disease remain largely unknown.

Here, we analysed the influences of metabolites that are differentially abundant in IBD on the growth and physiology of gut

bacteria that are also differentially abundant in IBD. We found that N-acylethanolamines (NAEs), a class of endogenously

produced signalling lipids elevated in the stool of IBD patients and a T-cell transfer model of colitis, stimulated growth of

species over-represented in IBD and inhibited that of species depleted in IBD invitro. Using metagenomic sequencing, we

recapitulated the effects of NAEs in complex microbial communities ex vivo, with Proteobacteria blooming and Bacteroidetes

declining in the presence of NAEs. Metatranscriptomic analysis of the same communities identified components of the respira-

tory chain as important for the metabolism of NAEs, and this was verified using a mutant deficient for respiratory complex I.

In this study, we identified NAEs as a class of metabolites that are elevated in IBD and have the potential to shift gut microbiota

towards an IBD-like composition.

NATURE MICROBIOLOGY | VOL 5 | MARCH 2020 | 486–497 | www.nature.com/naturemicrobiology

486

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Streptococcus mutans is a cariogenic bacterium in the oral cavity involved in plaque formation and dental caries. The endocannabinoid anandamide (AEA), a naturally occurring bioactive lipid, has been shown to have anti-bacterial and anti-biofilm activities against Staphylococcus aureus. We aimed here to study its effects on S. mutans viability, biofilm formation and extracellular polysaccharide substance (EPS) production. S. mutans were cultivated in the absence or presence of various concentrations of AEA, and the planktonic growth was followed by changes in optical density (OD) and colony-forming units (CFU). The resulting biofilms were examined by MTT metabolic assay, Crystal Violet (CV) staining, spinning disk confocal microscopy (SDCM) and high-resolution scanning electron microscopy (HR-SEM). The EPS production was determined by Congo Red and fluorescent dextran staining. Membrane potential and membrane permeability were determined by diethyloxacarbocyanine iodide (DiOC2(3)) and SYTO 9/propidium iodide (PI) staining, respectively, using flow cytometry. We observed that AEA was bactericidal to S. mutans at 12.5 µg/mL and prevented biofilm formation at the same concentration. AEA reduced the biofilm thickness and biomass with concomitant reduction in total EPS production, although there was a net increase in EPS per bacterium. Preformed biofilms were significantly affected at 50 µg/mL AEA. We further show that AEA increased the membrane permeability and induced membrane hyperpolarization of these bacteria. AEA caused S. mutans to become elongated at the minimum inhibitory concentration (MIC). Gene expression studies showed a significant increase in the cell division gene ftsZ. The concentrations of AEA needed for the anti-bacterial effects were below the cytotoxic concentration for normal Vero epithelial cells. Altogether, our data show that AEA has anti-bacterial and anti-biofilm activities against S. mutans and may have a potential role in preventing biofilms as a therapeutic measure.

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Nov 2017
BIOINFORMATICS

bioBakery is a meta'omic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured analysis modules also joined into workflows for reproducibility. Availability: bioBakery (http://huttenhower.sph.harvard.edu/biobakery) is publicly available for local installation as individual modules and as a virtual machine image. Each individual module has been developed to perform a particular task (e.g. quantitative taxonomic profiling or statistical analysis), and they are provided with source code, tutorials, demonstration data, and validation results; the bioBakery virtual image includes the entire suite of modules and their dependencies pre-installed. Images are available for both Amazon EC2 and Google Compute Engine. All software is open source under the MIT license. bioBakery is actively maintained with a support group at biobakery-users@googlegroups.com and new tools being added upon their release. Contact: chuttenh@hsph.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Therapeutic Opportunities in Inflammatory Bowel Disease: Mechanistic Dissection of Host-Microbiome Relationships

Article

Aug 2019
CELL

The current understanding of inflammatory bowel disease (IBD) pathogenesis implicates a complex interaction between host genetics, host immunity, microbiome, and environmental exposures. Mechanisms gleaned from genetics and molecular pathogenesis offer clues to the critical triggers of mucosal inflammation and guide the development of therapeutic interventions. A complex network of interactions between host genetic factors, microbes, and microbial metabolites governs intestinal homeostasis, making classification and mechanistic dissection of involved pathways challenging. In this Review, we discuss these challenges, areas of active translation, and opportunities for development of next-generation therapies.

Microbial genes and pathways in inflammatory bowel disease

Article

Jun 2019
NAT REV MICROBIOL

Perturbations in the intestinal microbiome are implicated in inflammatory bowel disease (IBD). Studies of treatment-naive patients have identified microbial taxa associated with disease course and treatment efficacy. To gain a mechanistic understanding of how the microbiome affects gastrointestinal health, we need to move from census to function. Bacteria, including those that adhere to epithelial cells as well as several Clostridium species, can alter differentiation of T helper 17 cells and regulatory T cells. Similarly, microbial products such as short-chain fatty acids and sphingolipids also influence immune responses. Metagenomics and culturomics have identified strains of Ruminococcus gnavus and adherent invasive Escherichia coli that are linked to IBD and gut inflammation. Integrated analysis of multiomics data, including metagenomics, metatranscriptomics and metabolomics, with measurements of host response and culturomics, have great potential in understanding the role of the microbiome in IBD. In this Review, we highlight current knowledge of gut microbial factors linked to IBD pathogenesis and discuss how multiomics data from large-scale population studies in health and disease have been used to identify specific microbial strains, transcriptional changes and metabolic alterations associated with IBD. Perturbations in the intestinal microbiome are implicated in inflammatory bowel disease (IBD). In this Review, Xavier and colleagues highlight current knowledge of gut microbial factors linked to IBD pathogenesis and discuss how multiomics data from large-scale population studies in health and disease have been used to identify specific microbial strains, transcriptional changes and metabolic alterations associated with IBD.

Cross sectional evaluation of the gut-microbiome metabolome axis in an Italian cohort of IBD patients.

Article