Open Access Article
Caroline
Ballet‡
a,
Mário S. P.
Correia‡
a,
Louis P.
Conway
a,
Theresa L.
Locher
a,
Laura C.
Lehmann
b,
Neeraj
Garg
a,
Miroslav
Vujasinovic
c,
Sebastian
Deindl
b,
J.-Matthias
Löhr
cd and
Daniel
Globisch
*a
aDepartment of Medicinal Chemistry, Science for Life Laboratory, Uppsala University, Box 574, SE-75123 Uppsala, Sweden. E-mail: Daniel.globisch@scilifelab.uu.se
bDepartment of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Box 596, SE-75124 Uppsala, Sweden
cDepartment for Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden
dDepartment of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden
First published on 28th June 2018
Gut microbiota significantly impact human physiology through metabolic interaction. Selective investigation of the co-metabolism of bacteria and their human host is a challenging task and methods for their analysis are limited. One class of metabolites associated with this co-metabolism are O-sulfated compounds. Herein, we describe the development of a new enzymatic assay for the selective mass spectrometric investigation of this phase II modification class. Analysis of human urine and fecal samples resulted in the detection of 206 sulfated metabolites, which is three times more than reported in the Human Metabolome Database. We confirmed the chemical structure of 36 sulfated metabolites including unknown and commonly reported microbiota-derived sulfated metabolites using synthesized internal standards and mass spectrometric fragmentation experiments. Our findings demonstrate that enzymatic sample pre-treatment combined with state-of-the-art metabolomics analysis represents a new and efficient strategy for the discovery of unknown microbiota-derived metabolites in human samples. Our described approach can be adapted for the targeted investigation of other metabolite classes as well as the discovery of biomarkers for diseases affected by microbiota.
To allow for a detailed metabolic investigation we sought to develop a selective method for the discovery of unknown metabolites derived from the co-metabolism of microorganisms and their host. It has been reported that gut microbiota profoundly affect phase I and phase II conversions, which have evolved to clear xenobiotic metabolites in mammals.9 Phase I metabolism enzymes such as cytochrome P450 oxidases introduce polar groups into xenobiotics including microbiota derived metabolites. This process is followed by phase II conjugation to enhance hydrophilicity. This mechanism has evolved to excrete these metabolites from the human body, however, it can also lead to bioactivation and the generation of toxic compounds. In particular, O-sulfated metabolites have been identified as a major feature of the co-metabolism of microbes and their host.10 Metabolites in this class have been suggested to be key regulators of bacterial interaction with their host as sulfotransferases and sulfatases have been identified in the genomes of gut microbes.11,12 Two prominent examples are indoxyl sulfate (1) and p-cresyl sulfate (2), which were among the first identified metabolites of microbial and host co-metabolism (Scheme 1).12 Indole and cresol are produced from the aromatic amino acids, tryptophan and tyrosine, through digestion by gut microbes. These molecules are absorbed in the colon of the human host and further metabolized by phase I and II clearance processes. Both metabolites have since been described as uremic toxins as they are linked to the development of chronic kidney disease (CKD) as well as cardiovascular diseases, emphasizing the relevance of this metabolite class to human physiology.13,14
The discovery of unknown metabolic links between microbes and their human host is crucial to evaluate their impact on disease development as well as to identify new biomarkers or drug targets. Analytical tools for the selective analysis of microbial metabolites are not available. To overcome this limitation, we have developed a sulfatase assay for the facile and selective hydrolysis of sulfate esters under mild conditions (Scheme 1). This approach is in line with examples from proteome (proteases) and modified nucleic acid analyses (phosphatases) and represents a new strategy for targeted metabolomics analysis.
780g for 5 min) and dried in vacuo. The remaining pellet was dissolved in 60 μL water/acetonitrile (95/5, v/v), vigorously shaken for 30 s and transferred to HPLC-vials for UPLC-MS/MS analysis.
780g) and the supernatant was collected. This step was followed by protein precipitation with the addition of ice cold methanol (4 × volume of the fecal extract). Each sample was vigorously shaken for 30 s and cooled at −20 °C for 30 min.
780g for 5 min, the supernatant containing the extracted fecal or urine metabolite mixture was dried in vacuo at ambient temperature. The residue was dissolved in 400 μL of 50 mM ammonium acetate buffer (300 μL for feces) and divided into two equal parts for enzymatic and control assays. An aliquot of the purified arylsulfatase was utilized in the enzymatic assay (11 U for urine/38 U for feces), while an aliquot of arylsulfatase solution was denatured by heating to 99 °C for 5 min and used as negative control. Both assays were shaken (300 rpm) for 17 h at 21 °C and subjected to protein precipitation by adding cold methanol (4 × the sample volume) for 15 min at 0 °C. After centrifugation (13
780g for 5 min), the supernatant was collected and dried in vacuo. Afterwards, the remaining pellet was dissolved in 150 μL of water/acetonitrile (95/5, v/v), vigorously shaken for 30 s and then centrifuged (13
780g for 5 min). Each supernatant was collected and transferred to a HPLC vial for UPLC-MS/MS analysis, alternating injection of control and assay samples to avoid biased results.
Encouraged by the substrate promiscuity of the arylsulfatase, we performed a non-tagged enzyme purification of the commercially available crude mixture. Throughout the four-step chromatographic purification, we tested the substrate specificity of all fractions in order to select those with high sulfatase and reduced glucuronidase activity as well as to evaluate the stability of the sulfatase during the purification process (see ESI† for details). 4-Methylumbelliferyl sulfate (4) and N-acetyltyramine-O,β-glucuronide (5) were selected to test sulfatase and glucuronidase activity, respectively (Fig. 1A). Analytical gel filtration of the purified arylsulfatase yielded a single peak consistent with a monomer in solution and a molecular weight of approximately 75 kDa as determined by SDS-PAGE (Fig. 1B and S2†). The purification procedure yielded the arylsulfatase in the required purity for our targeted metabolic analysis of human samples. Glucuronidase units activity was calculated following the described protocol in the ESI† using phenolphthalein (6) and phenolphthalein β-D-glucuronide (7). Glucuronidase background activity was reduced to less than 0.08% compared to the activity of the crude enzyme mixture (Fig. 1C). Importantly, this depleted activity does not interfere with our analysis.
Upon purification of the arylsulfatase, we treated a mixture of ten sulfate esters for 24 h using 19 U of sulfatase, including sulfated phenolic compounds [p-cresyl sulfate (2), ferulic acid 4-sulfate (3), 4-methylumbelliferyl sulfate (4), N-acetyltyramine-O-sulfate (8), L-tyrosine-O-sulfate (9), estrone-3-sulfate (10)], sulfated indole derivatives [indoxyl sulfate (1), N-acetylserotonine-O-sulfate (11)], and alkyl sulfates [D-mannose 6-sulfate (12), taurolithocholic acid 3-sulfate (13)] (Fig. 2). We obtained complete sulfate hydrolysis for six metabolites after 4 h incubation time (Fig. 2A and S3†). While two arylsulfate substrates [L-tyrosine-O-sulfate (9) and N-acetylserotonine-O-sulfate (11)] were not hydrolyzed completely after 4 h, they did exhibit conversion levels of 35% and 45% after 24 h, respectively, which is sufficient for detection of the sulfate ester and identification of its structure in our targeted metabolomics approach.
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| Scheme 2 Workflow for our described sulfatase-based mass spectrometric strategy for the analysis of human samples. Structure validation of ferulic acid 4-sulfate (3) is presented as an example. (Sulfatase crystal structure15). | ||
We obtained clear separation of both sample sets in a principle component analysis (Fig. S4†). Our detailed data analysis was focused on the identification of upregulated features in the control group in which the sulfate esters are unmodified. Over 3620 altered mass spectrometric features (fold-change > 1.5, p-value < 0.0001 and intensity > 30
000 ion count) were identified in negative mode analysis. To specifically analyze sulfates, we searched for features with a difference in m/z of 79.9568 Da (the difference between a sulfated metabolite and the corresponding hydrolyzed product) within a 10 ppm limit and obtained a total of 261 features as potential sulfated metabolites. We then applied selective MS/MS fragmentation to all 261 metabolites, which confirmed the presence of a sulfate ester in 188 metabolites. The total number of sulfated metabolites increased to 206 with metabolites identified in positive mass spectrometric mode analysis. This is about three times higher than the 65 sulfated metabolites reported in the Human Metabolome Database (HMDB), which demonstrates the strength of our selective method towards the identification of unknown metabolites.25 We derived the chemical formula and compared the MS/MS fragmentation for each metabolite with either synthetic standards or databases (HMDB and Sirius) to determine the molecular structure. Among the top hits, we readily identified the two most commonly reported microbiota-derived metabolites, indoxyl sulfate (1) and p-cresyl sulfate (2). Out of the identified 206 sulfated metabolites, we validated the chemical structure of 36 metabolites including eight, which were chemically synthesized for the unambiguous determination of their structure (Tables 1 and S1–S3†).
| Urine sample analysis | Feature no. |
|---|---|
| Identified features | 3620 |
| Potential sulfated metabolites | 261 |
| MS2-validation of sulfate ester | 206 |
| Identified by MS2 fragmentation | 36 |
| Identified by synthetic internal standard | 8 |
As an example, we highlight the structure validation procedure for p-cresyl sulfate (2) using a synthetic standard (Fig. 3). Each of these molecules was synthesized and co-injected for UPLC-MS analysis to confirm the correct chemical structure. The extracted ion chromatograms (EIC) of the natural and synthetic metabolite overlapped perfectly and eluted with the same retention time. Furthermore, spiking of the synthetic metabolite into the urine sample revealed a single peak (Fig. 3A). Mass spectrometric fragmentation experiments demonstrated the same fragments and fragmentation pattern for the synthesized and natural metabolite (Fig. 3B).
Our targeted analysis of these samples led to the unambiguous identification of 41 sulfated metabolites (Table S4†), including the microbiota-derived metabolites p-cresyl sulfate (2), ferulic acid 4-sulfate (3) and L-tyrosine-O-sulfate (9). These identified sulfated metabolites can either be produced by (i) human metabolism only, (ii) food derived metabolites, (iii) co-metabolism, or (iv) merely by microbiota metabolism. Our findings distinctly demonstrate the sensitivity of our targeted method as we identified a high number of sulfated metabolites in fecal samples, a sample set not commonly analyzed for the detection of phase II metabolites.
In this interdisciplinary study, we confirmed 36 metabolites using a combination of chemical synthesis and mass spectrometric fragmentation experiments. These molecules included at least seven microbiota-derived metabolites, which have been reported to directly impact human physiology (Table 2).30 Indoxyl sulfate (1) and p-cresyl sulfate (2) are well known biomarkers of chronic kidney disease and cardiovascular diseases, respectively.31,32 Microbiota-produced ferulic acid has been reported to prevent thrombosis and artherosclerosis.33 We also identified the sulfates of caffeic acid (14), vanillic acid (15) and 3-hydroxyphenyl-propionic acid (16), which have been associated with the neurodegenerative Parkinson's and Alzheimer's diseases.34,35 Another connection between microbiota-derived sulfated metabolites and human disease is the link between 4-ethylphenyl sulfate (17) and autism.36 To our best knowledge, comprehensive detection and analysis of these disease-related metabolites in a single study has not yet been reported. This profound metabolic correlation with human health demonstrates the importance of discovering unknown metabolites to evaluate their impact on disease development. Interestingly, we found that 9 out of these metabolites have previously only been reported as their unsulfated counterparts including the two examples chavicol sulfate and 2-methoxy-4-vinylphenol sulfate (Tables S1 and S2†). These metabolites require further investigation to identify their role in microbial virulence and impact on human health.
| Microbial metabolite | Link to disease |
|---|---|
| Indoxyl sulfate (1) | Chronic kidney disease (CKD) |
| p-Cresyl sulfate (2) | Cardiovascular diseases (CVD) |
| Ferulic acid 4-sulfate (3) | Thrombosis and artherosclerosis prevention |
| Caffeic acid 4-sulfate (14) | Alzheimer disease |
| Vanillic acid 4-sulfate (15) | Parkinson disease |
| 3-Hydroxyphenyl-propionic acid sulfate (16) | Parkinson disease |
| 4-Ethylphenyl sulfate (17) | Autism |
The discovery of more than three times higher number of sulfated metabolites than reported in the HMDB and the identification of hitherto undetected sulfate esters demonstrates the strengths of our enzymatic methodology coupled with the chemical synthesis of metabolites to confirm their molecular structures. The validation of structural regio- and stereochemistry through chemical synthesis is the initial step for the quantitative analysis of these metabolites to evaluate their potential as biomarkers for human disease.
Footnotes |
| † Electronic supplementary information (ESI) available: Human sample ethics, enzyme purification, enzymatic assay methods, workflow for metabolomics analysis, metabolite structure identification, synthetic protocols, and NMR spectra. See DOI: 10.1039/c8sc01502c |
| ‡ These authors contributed equally to this work. |
| This journal is © The Royal Society of Chemistry 2018 |