Identification of an Endogenous Organosulfur Metabolite by Interpretation of Mass Spectrometric Data

Proposal and confirmation of N-(2-carboxyethyl)proline as a human endogenous metabolite

RATIONALE

Malondialdehyde, one of the peroxidation products of polyunsaturated fatty acids, has been widely reported as an oxidative stress biomarker in many diseases. However, malondialdehyde is inherently unstable in biological matrices, which renders its measurement unreliable with all the reported analytical methods. To find an alternative oxidative stress biomarker, we envisioned that N-(2-carboxyethyl)proline, a modified conjugate of malondialdehyde and proline, could be a stable candidate for this purpose.

METHODS

The proposed compound was chemically synthesized, and liquid chromatography-mass spectrometry methods were developed and used to search for the compound in human biological samples.

RESULTS

An endogenous metabolite in human feces and urine samples was found to match the synthetic N-(2-carboxyethyl)proline by chromatographic retention and the fragmentation pattern of its molecular ion.

CONCLUSION

The results confirmed that N-(2-carboxyethyl)proline was a new metabolite in human feces and urine samples. In addition, our results demonstrated a case of successful identification of true unknown metabolite by knowledge-based hypothesis of possible metabolites followed by experimental confirmation with a synthetic standard.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Admission Urinary and Serum Metabolites Predict Renal Outcomes in Hospitalized Patients With Cirrhosis

BACKGROUND AND AIMS

Acute kidney injury (AKI) has a poor prognosis in cirrhosis. Given the variability of creatinine, the prediction of AKI and dialysis by other markers is needed. The aim of this study is to determine the role of serum and urine metabolomics in the prediction of AKI and dialysis in an inpatient cirrhosis cohort.

APPROACH AND RESULTS

Inpatients with cirrhosis from 11 North American Consortium of End-stage Liver Disease centers who provided admission serum/urine when they were AKI and dialysis-free were included. Analysis of covariance adjusted for demographics, infection, and cirrhosis severity was performed to identify metabolites that differed among patients (1) who developed AKI or not; (2) required dialysis or not; and/pr (3) within AKI subgroups who needed dialysis or not. We performed random forest and AUC analyses to identify specific metabolite(s) associated with outcomes. Logistic regression with clinical variables with/without metabolites was performed. A total of 602 patients gave serum (218 developed AKI, 80 needed dialysis) and 435 gave urine (164 developed AKI, 61 needed dialysis). For AKI prediction, clinical factor-adjusted AUC was 0.91 for serum and 0.88 for urine. Major metabolites such as uremic toxins (2,3-dihydroxy-5-methylthio-4-pentenoic acid [DMTPA], N2N2dimethylguanosine, uridine/pseudouridine) and tryptophan/tyrosine metabolites (kynunerate, 8-methoxykyunerate, quinolinate) were higher in patients who developed AKI. For dialysis prediction, clinical factor-adjusted AUC was 0.93 for serum and 0.91 for urine. Similar metabolites as AKI were altered here. For dialysis prediction in those with AKI, the AUC was 0.81 and 0.79 for serum/urine. Lower branched-chain amino-acid (BCAA) metabolites but higher cysteine, tryptophan, glutamate, and DMTPA were seen in patients with AKI needing dialysis. Serum/urine metabolites were additive to clinical variables for all outcomes.

CONCLUSIONS

Specific admission urinary and serum metabolites were significantly additive to clinical variables to predict AKI development and dialysis initiation in inpatients with cirrhosis. These observations can potentially facilitate earlier initiation of renoprotective measures.

Characterizing Multidevice Capillary Vibrating Sharp-Edge Spray Ionization for In-Droplet Hydrogen/Deuterium Exchange to Enhance Compound Identification

Multidevice capillary vibrating sharp-edge spray ionization (cVSSI) source parameters have been examined to determine their effects on conducting in-droplet hydrogen/deuterium exchange (HDX) experiments. Control experiments using select compounds indicate that the observed differences in mass spectral isotopic distributions obtained upon initiation of HDX result primarily from solution-phase reactions as opposed to gas-phase exchange. Preliminary studies have determined that robust HDX can only be achieved with the application of same-polarity voltage to both the analyte and the deuterium oxide reagent (D₂O) cVSSI devices. Additionally, a similar HDX reactivity dependence on the voltage applied to the D₂O device for various analytes is observed. Analyte and reagent flow experiments show that, for the multidevice cVSSI setup employed, there is a nonlinear dependence on the D₂O reagent flow rate; increasing the D₂O reagent flow by 100% results in only an ∼10-20% increase in deuterium incorporation for this setup. Instantaneous (subsecond) response times have been demonstrated in the initiation or termination of HDX, which is achieved by turning on or off the reagent cVSSI device piezoelectric transducer. The ability to distinguish isomeric species by in-droplet HDX is presented. Finally, a demonstration of a three-component cVSSI device setup to perform multiple (successive or in combination) in-droplet chemistries to enhance compound ionization and identification is presented and a hypothetical metabolomics workflow consisting of successive multidevice activation is briefly discussed.

Biomarker identification and pathway analysis of rheumatoid arthritis based on metabolomics in combination with ingenuity pathway analysis

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease worldwide, but understanding its pathogenesis is still limited. In this study, plasma untargeted metabolomics of a discovery cohort and targeted analysis of a verification cohort were performed by gas chromatograph mass spectrometry (GC/MS). Univariate and multivariate statistical analysis were utilized to reveal differential metabolites, followed by the construction of biomarker panel through random forest (RF) algorithm. The pathways involved in RA were enriched by differential metabolites using Ingenuity Pathway Analysis (IPA) suite. Untargeted metabolomics revealed eighteen significantly altered metabolites in RA. Among these metabolites, a three-metabolite marker panel consisting of L-cysteine, citric acid and L-glutamine was constructed, using random forest algorithm that could predict RA with high accuracy, sensitivity and specificity based on a multivariate exploratory receiver operator characteristic (ROC) curve analysis. The panel was further validated by support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) algorithms, and also verified with targeted metabolomics using a verification cohort. Additionally, the dysregulated taurine biosynthesis pathway in RA was revealed by an integrated analysis of metabolomics and transcriptomics. Our findings in this study not only provided a mechanism underlying RA pathogenesis, but also offered alternative therapeutic targets for RA.

Metabonomic-Transcriptome Integration Analysis on Osteoarthritis and Rheumatoid Arthritis

Purpose

This study is aimed at exploring the potential metabolite/gene biomarkers, as well as the differences between the molecular mechanisms, of osteoarthritis (OA) and rheumatoid arthritis (RA).

Methods

Transcriptome dataset GSE100786 was downloaded to explore the differentially expressed genes (DEGs) between OA samples and RA samples. Meanwhile, metabolomic dataset MTBLS564 was downloaded and preprocessed to obtain metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEG-metabolite relations. Finally, metabolic pathway enrichment analysis was performed to investigate the differences between the molecular mechanisms of OA and RA.

Results

A total of 976 DEGs and 171 metabolites were explored between OA samples and RA samples. The PCA and linear module analysis investigated 186 DEG-metabolite interactions including Glycogenin 1- (GYG1-) asparagine_54, hedgehog acyltransferase- (HHAT-) glucose_70, and TNF receptor-associated factor 3- (TRAF3-) acetoacetate_35. Finally, the KEGG pathway analysis showed that these metabolites were mainly enriched in pathways like gap junction, phagosome, NF-kappa B, and IL-17 pathway.

Conclusions

Genes such as HHAT, GYG1, and TRAF3, as well as metabolites including glucose, asparagine, and acetoacetate, might be implicated in the pathogenesis of OA and RA. Metabolites like ethanol and tyrosine might participate differentially in OA and RA progression via the gap junction pathway and phagosome pathway, respectively. TRAF3-acetoacetate interaction may be involved in regulating inflammation in OA and RA by the NF-kappa B and IL-17 pathway.