Gut microbiota plays important roles in the host health. The host and symbiotic gut microbiota coproduce a large number of metabolites during the metabolism of food and xenobiotics. The analysis of fecal metabolites can provide a noninvasive manner to study the outcome of the host-gut microbiota interaction. Herein, we reported the comprehensive profiling of fecal metabolome of mice by an integrated chemical isotope labeling combined with liquid chromatography-mass spectrometry (CIL-LC-MS) analysis. The metabolites are categorized into several submetabolomes based on the functional moieties (i.e., carboxyl, carbonyl, amine, and thiol) and then analysis of the individual submetabolome was performed. The combined data from the submetabolome form the metabolome with relatively high coverage. To this end, we synthesized stable isotope labeling reagents to label metabolites with different groups, including carboxyl, carbonyl, amine, and thiol groups. We detected 2302 potential metabolites, among which, 1388 could be positively or putatively identified in feces of mice. We then further confirmed 308 metabolites based on our established library of chemically labeled standards and tandem mass spectrometry analysis. With the identified metabolites in feces of mice, we established mice fecal metabolome database, which can be used to readily identify metabolites from feces of mice. Furthermore, we discovered 211 fecal metabolites exhibited significant difference between Alzheimer's disease (AD) model mice and wild type (WT) mice, which suggests the close correlation between the fecal metabolites and AD pathology and provides new potential biomarkers for the diagnosis of AD.

Fatty acid esters of hydroxy fatty acids (FAHFAs) are a new class of lipid mediators with promising anti-diabetic and anti-inflammatory properties. Comprehensive screening and identification of FAHFAs in biological samples would be beneficial to the discovery of new FAHFAs and enable greater understanding of their biological functions. Here, we report the comprehensive screening of FAHFAs in rice and  Arabidopsis thaliana by chemical isotope labeling-assisted liquid chromatography-mass spectrometry (CIL-LC-MS). Multiple reaction monitoring (MRM) was used for screening of FAHFAs. With the proposed method, we detected 49 potential FAHFA families, including 262 regioisomers, in tissues of rice and Arabidopsis thaliana, which greatly extends our knowledge of known FAHFAs. In addition, we proposed a strategy to identify FAHFA regioisomers based on their retention on a reversed-phase LC column. Using the proposed identification strategy, we identified 71 regioisomers from 11 FAHFA families based on commercial standards and characteristic chromatographic retention behaviors. The screening technique could allow for the discovery of new FAHFAs in biological samples. The new FAHFAs identified in this work will contribute to the in-depth study of the functions of FAHFAs.

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Chemical labeling (CL) in combination with liquid chromatography-mass spectrometry (LC-MS) analysis has been demonstrated to be a promising technology in metabolomic analysis. However, identification of chemically labeled metabolites remains to be challenging. Retention time (RT) is one of the most important parameters for the identification of metabolites, but it could vary greatly in LC-MS analysis. In this work, we developed a chemical labeling-based HPLC retention index (CL-HPLC RI) strategy to facilitate the identification of metabolites. In this CL-HPLC RI strategy, a series of 2-dimethylaminoethylamine (DMED)-labeled fatty acids were used as calibrants to establish RIs for DMED-labeled carboxylated compounds and a series of 4-( N, N-dimethylamino)phenyl isothiocyanate (DMAP)-labeled fatty amines were used as calibrants for DMAP-labeled amine compunds. To calculate the RIs, the whole LC chromatogram was divided into 24 time intervals by 23 DMED-labeled fatty acid standards or 15 time intervals by 14 DMAP-labeled fatty amine standards. Then, we established the RIs of 854 detected DMED-labeled carboxylated metabolites and 1057 DMAP-labeled amine metabolites in fecal samples and demonstrated that RIs were highly reproducible under different elution gradients, columns, and instrument systems. Finally, we applied this strategy to the identification of metabolites in human serum. Using RIs, 267 DMED-labeled carboxylated metabolites and 273 DMAP-labeled amine metabolites in human serum matched well with the fecal metabolome database. Taken together, the developed CL-HPLC RI strategy was demonstrated to be a promising method to facilitate the identification of metabolites in metabolomic analysis.

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