Identification of Metabolite Biomarkers for Gout Using Capillary Ion

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Identification of novel metabolite biomarkers for gout using capillary ion chromatography with mass spectrometry Li Cui, Juan Liu, Xinmin Yan, and Shen Hu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b03232 • Publication Date (Web): 03 Oct 2017 Downloaded from http://pubs.acs.org on October 4, 2017

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Analytical Chemistry

Identification of novel metabolite biomarkers for gout using capillary ion chromatography with mass spectrometry

Li Cui 1, §, Juan Liu 2, §, Xinmin Yan 2, Shen Hu 1 1 School of Dentistry, University of California, Los Angeles, CA 90095, USA 2 Changzhou Second People’s Hospital, Nanjing Medical University, Changzhou 213000, China

§

Equal contribution

Correspondence: Shen Hu, PhD, MBA; Email: [email protected]; Tel: 310-2068834. Xinmin Yan, MD; Email:[email protected], Tel: +86-519-88104931.

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ABSTRACT Gout is a common form of inflammatory arthritis and the detailed pathogenic mechanisms for this metabolic disorder remain largely unknown. In this study, we first profiled the salivary metabolites in 8 patients with gout, 15 patients with hyperuricaemia (HUA), and 15 healthy individuals using capillary ion chromatography (CIC) with tandem mass spectrometry (MS/MS). Forty-nine salivary metabolites were found to be significantly changed between gout patient and healthy control groups and 26 salivary metabolites were significantly different between gout and HUA groups. Three metabolite biomarkers, uric acid, oxalic acid and L-homocysteic acid (HCA) were selected for validation in the saliva samples of 30 patients with gout, 30 patients with HUA, and 30 healthy control subjects. By using commercial assay kits for the measurements, salivary uric acid and oxalic acid levels were found to be significantly higher in gout patients than healthy controls whereas salivary HCA level was significantly higher in gout patients than both HUA patients and healthy controls. These assay measurements were in line with those obtained by CIC-MS/MS. In conclusion, we have demonstrated a new application of CIC-MS/MS for the discovery of novel metabolite biomarkers of gout. Validated biomarkers may be used for non-invasive, diagnostic and prognostic applications in gout. Future studies are warranted to investigate the clinical utility of these identified biomarkers for monitoring gout flare and/or treatment efficacy.


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INTRODUCTION Gout is a common metabolic disorder resulting from the deposition of monosodium urate (MSU) crystals which occurs predominantly in peripheral joints and surrounding tissues.1 The prevalence and incidence of gout have steadily increased over the past decades, particularly in the developed countries2. Major risk factors for gout include hyperuricaemia (HUA), genetics, dietary factors, medications and exposure to lead. Although HUA does not necessarily develop gout, it is considered one of the most important risk factors for gout. Both gout and HUA have been associated with a number of pathological conditions such as metabolic syndrome, cardiovascular diseases and renal diseases.3-6 Therefore, comprehensive understanding of the pathogenesis of gout and HUA is not only important for their treatment per se, but also might contribute to improving the clinical outcome of other diseases. Metabolomics is the systematic study of small molecule profiles in the context of physiological stimuli or disease states.7It has emerged as a powerful analytical methodology for studying metabolic pathways, gene networks and systems biology. With the newly evolved mass spectrometry (MS) and nuclear magnetic resonance spectroscopy methods, metabolomics has now become a valuable approach for screening novel biomarkers for disease diagnosis, prognosis, or treatment efficacy monitoring. Due to the highly complex nature and wide concentration range of the compounds, separation science plays a critical role in metabolomics in order to achieve a comprehensive profiling analysis.8 The strength of MS-based metabolomics is best



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realized when coupled to a separation technique, such as capillary electrophoresis, gas chromatography, or liquid chromatography (LC).9, 10 Ion chromatography (IC) represents an excellent complementary platform for separation of charged and polar compounds. It has been used for a wide range of applications such as analysis of environmental, pharmaceutical, food, and beverage samples. However, this technique was traditionally used for the separation of small inorganic ions. Due to a recent development of eluent suppression technology that allows continuous desalting and conversion of high-salt eluents into pure water, online coupling of IC with MS has been successful for analysis of metabolites such as carbohydrates, organic acids, sugar phosphates, and nucleotides in biological samples. We have demonstrated this IC method with Orbitrap MS for both global and targeted metabolomic analysis of head and neck cancer cells. Our studies indicate that IC with the eluent suppression offers high resolution for the separation of isomeric polar metabolites such as sugar phosphates.11, 12 Human saliva is a valuable body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. For patients, the noninvasive collection procedures for saliva dramatically reduce anxiety and discomfort, and simplify procurement of repeated samples for monitoring over time.13 Comprehensive analysis and identification of the deregulated metabolites in human saliva of gout patients will not only contribute to the understanding of disease pathogenesis, but may also reveal novel metabolomic biomarkers for clinical applications such as disease detection and prognosis prediction.


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In this study, we first profiled the saliva metabolomes of patients with gout, patients with HUA and healthy controls using capillary ion chromatography (CIC) with Orbitrap MS. A set of deregulated salivary metabolites that might be associated with progression of gout were identified by comparing their metabolomic profiles, and selected metabolite signatures were successfully validated in the saliva samples of independent patient and control cohorts. Our results demonstrated that CIC with tandem MS is a valuable methodology for the discovery of disease metabolite biomarkers.

EXPERIMENTAL SECTION Patients and Samples This study was approved by the Institutional Review Board Committee, and all patients and controls had given informed consent. Collection of clinical specimens was performed at the Changzhou Second People's Hospital. The information pertaining to the human samples was recorded in a manner such that the subjects could not be identified, directly or through identifiers linked to the subjects. Gout was typically diagnosed using similar clinical criteria from the American College of Rheumatology. Hyperuricaemia was defined as the level of serum uric acid >420 µmol/L in males and>360 µmol/L in females.14 None of the patients had chronic renal failure, hypertension, diabetes, abnormal liver function, thyroid dysfunction or other complications. All the healthy controls were recruited from the Medical Examination Center, Changzhou Second People’s Hospital, and also diagnosed to be free from HUA and gout. The clinical information of the enrolled participants was summarized in the supplementary tables (Tables S1 and S2). 5 ACS Paragon Plus Environment


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The details about sample collection and processing are described in the Supplementary information. Extraction of salivary metabolites was performed using a similar method described earlier.11 In brief, 0.8 mL of ice cold MeOH/CHCl3 (9:1) was immediately added to 0.2 mL of each saliva supernatant sample. Extracts were transferred to microcentrifuge tubes and pelleted at 4 °C for 5 min at 10000g. Supernatants were then transferred to new microcentrifuge tubes for CIC-MS analysis. CIC-MS for metabolite profiling A Thermo Scientific Dionex ICS-4000 CIC system consisting of a capillary pump, an eluent generator with a capillary KOH cartridge, and a suppressor was used in this study for IC separation. The ACES 300 suppressor was operated in an external-water mode with ultrapure water, and regenerant was delivered by an external AXP pump at a flow rate of 40 µL/min. The eluent of the IC system was converted to pure water after the suppressor and connected to a divert valve that directs the flow to the MS source. Detailed experimental parameters for CIC separation has been described in our previous study 13. Briefly, the CIC was performed with the IonPac AS11HC-4 µm, 0.4 × 250 mm columns (2000 Å) at 25 µL/min (at 35 °C). The gradient was as follows: started with an initial 2 mM KOH, increased to 20 mM at 12.0 min, then to 95 mM at 22.0 min, held 95 mM for 6 min, followed by a decrease to 2 mM within 0.1 min, and held for 5.0 min to re-equilibrate the column. A Thermo Scientific Q Exactive Orbitrap mass spectrometer was operated under ESI negative mode (spray voltage, 3400V) for all detections. Data-dependent MS/MS were acquired on “Top 5” most abundant ions in the full scans 11. Clinical Validation 6 ACS Paragon Plus Environment

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Selected metabolite biomarkers, uric acid, oxalic acid and L-homocysteic acid (HCA), were then validated in both discovery and validation cohorts. The validation cohort was separate and independent from the discovery cohort. The levels of these metabolites in the saliva samples were measured with the uric acid ELISA kit (Mybiosource, San Diego, CA, USA), oxalic acid colorimetric assay kit (Mybiosource) and HCA ELISA kit (Cusabio Biotech, Co., Ltd., Wuhan, China), respectively. The saliva samples from all the participants were measured in duplicates using a 96-well plate reader (Biotek, Winooski, VT, USA) and their concentrations were determined based on the calibration curves of included standards of the kits. Data Analysis Differential analyses of the metabolomics data and validation data were both performed with one way ANOVA. Analysis of the CIC-MS data was initially performed with the Compound Discoverer 2.0 software (Thermo Fisher), which does metabolite identification/quantitation, chromatography peak alignment, mass spectrum visualization and statistical analysis. Metabolites of interest were further searched in the METLIN Database using the observed m/z with mass error constraint of 3 ppm at negative mode (http://metlin.scripps.edu/), and experimental MS/MS spectra were compared to available reference MS/MS spectra in the METLIN. Validation data from the assay kit measurements were processed with the MedCalc statistical software (MedCalc Software Co., Ostend, Belgium).The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to evaluate the predictive power using all three metabolites. P values less than 0.05 were considered as statistically significant.



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RESULTS AND DISCUSSION Global Profiling of Salivary Metabolites in Gout Patients, HUA Patients and Healthy Controls To discover potential metabolite biomarkers, we first profiled saliva samples from 8 gout patients, 15 HUA patients and 15 healthy control subjects using CIC with Orbitrap MS. In total, 13240 metabolic features were found to be present in the participants of three study groups. As shown in Figure 1, CIC-MS/MS with the Compound Discoverer 2.0 software allows to detect 3158 significantly changed (p value2) metabolic features (1299 down-regulated and 1859 up-regulated) between gout and HUA patients, and 5551 significantly altered metabolic features (2225 down-regulated and 3326 up-regulated) between gout patients and healthy controls, and 803 significantly changed metabolic features (341 down-regulated and 462 up-regulated) between HUA patients and healthy controls, respectively. Figure 2 shows the number of identified metabolites with significant changes among the three two-group comparisons. All the identified metabolites with significant changes between HUA and healthy control groups were also significantly altered between gout and control groups. Among the 26 significantly changed salivary metabolites between gout and HUA patients, 24 of them were also significantly changed between gout patients and healthy controls. Supplementary Table S3 and Table S4 present a partial list of salivary metabolites that are significantly different (p

Identification of Metabolite Biomarkers for Gout Using Capillary Ion

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