Conjugation reactions are of critical significance in human metabolism. Identification of these conjugated metabolites is still challenging. Here, we propose a strategy, post-deconvolution MS/MS spectra extraction with data-independent acquisition (PDMS2E-DIA), to comprehensively profile the glucuronide-conjugated metabolome. PDMS2E-DIA enables the identification of conjugated and unconjugated metabolite pairs through neutral loss filtering combined with a significant change in abundance after the deconjugation reaction. Purified DIA MS/MS spectra were constructed by extracting MS/MS fragments shared between spectra derived from conjugated and unconjugated metabolites. The feasibility of this approach was first demonstrated by the identification of two glucuronide-conjugated metabolite standards spiked in urine samples. For human urine samples, 479 features were structurally annotated as potential glucuronide-conjugated metabolites, resulting in the identification of 211 metabolites. Fragment peaks derived from interferents were found to be removed by PDMS2E-DIA, which increased about 6 times the number of identified urine metabolites compared with those calculated from raw DIA deconvoluted MS/MS spectra. This approach was found to have great potential for identifying glucuronide-conjugated metabolites, as well as other kinds of chemical conjugations.

Hepatocellular carcinoma (HCC) is among the ten most commonly diagnosed cancers and the fourth leading cause of cancer-related death. Patients with hepatitis B virus (HBV) infection are prone to developing chronic liver diseases (i.e., fibrosis and cirrhosis), and the HBV X antigen plays an important role in the development of HCC. The difficulty in detecting HCC at the early stages is one of the main reasons that the death rate approximates the incidence rate. The regulators controlling the downstream liver protein expression from HBV infection are unclear. Mass spectrometric techniques and customized programs were used to identify differentially expressed proteins which may be involved in the development of liver fibrosis and HCC progression in hepatitis B virus X protein transgenic mice (HBx mice). FSTL1, CTSB, and TGF-β enhanced the signaling pathway proteins during the pathogenesis of HBx. Missing proteins can be essential in cell growth, differentiation, apoptosis, migration, metastasis or angiogenesis. We found that LHX2, BMP-5 and GDF11 had complex interactions with other missing proteins and BMP-5 had both tumor suppressing and tumorigenic roles. BMP-5 may be involved in fibrosis and tumorigenic processes in the liver. These results provide us an understanding of the mechanism of HBx-induced disorders, and may serve as molecular targets for liver treatment.

Melissa officinalis (MO), known as lemon balm, is a popular ingredient blended in herbal tea. In recent decades, the bioactivities of MO have been studied in sub-health and pathological status, highlighting MO possesses multiple pharmacological effects. We previously showed that hot water MO extract exhibited anticancer activity in colorectal cancer (CRC). However, the detailed mechanisms underlying MO-induced cell death remain elusive. To elucidate the anticancer regulation of MO extract in colon cancer, a data-driven analysis by proteomics approaches and bioinformatics analysis was applied. An isobaric tandem mass tags-based quantitative proteome analysis using liquid chromatography–coupled tandem mass spectrometry was performed to acquire proteome-wide expression data. The over-representation analysis and functional class scoring method were implemented to interpret the MO-induced biological regulations. In total, 3465 quantifiable proteoforms were identified from 24,348 peptides, with 67 upregulated and 54 downregulated proteins in the MO-treated group. Mechanistically, MO impeded mitochondrial respiratory electron transport by triggering a reactive oxygen species (ROS)-mediated oxidative stress response. MO hindered the mitochondrial membrane potential by reducing the protein expression in the electron transport chain, specifically the complex I and II, which could be restored by ROS scavenger. The findings comprehensively elucidate how MO hot water extract activates antitumor effects in colorectal cancer (CRC) cells.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.