Post-Deconvolution MS/MS Spectra Extraction with Data-Independent Acquisition for Comprehensive Profiling of Urinary Glucuronide-Conjugated Metabolome

Chemical Isotope Labeling and Dual-Filtering Strategy for Comprehensive Profiling of Urinary Glucuronide Conjugates

Glucuronidation, a crucial process in phase II metabolism, plays a vital role in the detoxification and elimination of endogenous substances and xenobiotics. A comprehensive and confident profiling of glucuronate-conjugated metabolites is imperative to understanding their roles in physiological and pathological processes. In this study, a chemical isotope labeling and dual-filtering strategy was developed for global profiling of glucuronide metabolites in biological samples. N,N-Dimethyl ethylenediamine (DMED-d0) and its deuterated counterpart DMED-d6 were used to label carboxylic acids through an amidation reaction. First, carboxyl-containing compounds were extracted based on a characteristic mass difference (Δm/z, 6.037 Da) observed in MS between light- and heavy-labeled metabolites (filter I). Subsequently, within the pool of carboxyl-containing compounds, glucuronides were identified using two pairs of diagnostic ions (m/z 247.1294/253.1665 and 229.1188/235.1559 for DMED-d0/DMED-d6-labeled glucuronides) originating from the fragmentation of the derivatized glucuronic acid group in MS/MS (filter II). Compared with non-derivatization, DEMD labeling significantly enhanced the detection sensitivity of glucuronides, as evidenced by a 3- to 55-fold decrease in limits of detection for representative standards. The strategy was applied to profiling glucuronide metabolites in urine samples from colorectal cancer (CRC) patients. A total of 685 features were screened as potential glucuronides, among which 181 were annotated, mainly including glucuronides derived from lipids, organic oxygen, and phenylpropanoids. Enzymatic biosynthesis was employed to accurately identify unknown glucuronides without standards, demonstrating the reliability of the dual-filtering strategy. Our strategy exhibits great potential for profiling the glucuronide metabolome with high coverage and confidence to reveal changes in CRC and other diseases.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Long short-term memory based quasi-targeted lipidomics reveals propane-1,2-diol expediting the digestion of lipids via mediating the α-helices to a random curl or β folding of lipase

Scandal of detecting 1,2-propanediol (PD) in milk brought a crisis to the trust of consumers in dairy industry, and investigations focused effect of PD on digestive behavior of milk were still restricted. Long short-term memory amalgamated to quasi-targeted lipidomics was applied to monitor dynamics changes of lipids during digestion and the pseudo-first-order kinetic model elucidated that PD elevated the digestibility of lipid with the degradation rate (S-1) ranged from 4440.31 to 5665.59 and mediated the transition of α-helices (26.46% to 19.07% of pancreatic lipase and 29.89% to 23.37% of gastric lipase) covering active center in lipase to random curl (48.25% to 51.17% of pancreatic lipase and 41.58% to 44.57% of gastric lipase) and β folding (9.14% to 4.67% of pancreatic lipase and 6.52% to 10.05% of gastric lipase), ultimately upregulating the lipase activity and further intervening lipid nutrients utilization in milk. This study provided a critical insight about the impact of PD contamination at trace concentrations on the nutritional value of milk fat during digestion.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

Conversion of soybean oil extraction wastes into high-performance wood adhesives based on mussel-inspired cation-π interactions

As a soybean oil extractive byproduct, high temperature defatted soy meal (HSM) presents great potential as a raw material for vegetable protein adhesives to replace aldehyde-based adhesives in the wood-based panel production. However, the application has been hindered by its poor cold-pressing adhesive performance. Herein, a novel HSM-based adhesive with excellent cold-pressing adhesion performance was developed based on mussel-inspired cation-π interactions. Highly reactive polyamidoamine-epichlorohydrin (PAE) and folic acid (FA) were added into an HSM-based adhesive to construct a dual-network system stabilized by strong cation-π interactions. The coacervate formed by PAE and FA served as an "internal adhesive" to bond HSM particles together, yielding high initial viscosity but easy sizing. As expected, the prepared adhesive exhibited an excellent cold-pressing bonding strength of 423 kPa, showing a 295% improvement compared to the soy protein (SP) adhesive. To improve the hot-pressing bonding strength of the adhesives, inorganic calcium carbonate (CaCO₃) particles were introduced into the adhesive system to build an organic-inorganic hybrid adhesive system. The wet shear strength of the SPAE-FA-CaCO₃ adhesive significantly improved from 0.63 MPa to 0.96 MPa, meeting the requirements for the practical application. This method provides a novel strategy to exploit high-performance vegetable protein-based wood adhesives.