Microbiome-derived metabolites are important for the microbiome-gut-brain axis and the discovery of new disease treatments. d-Alanine (d-Ala) is found in many animals as a potential co-agonist of the N-methyl-d-aspartate receptors (NMDAR), receptors widely used in the nervous and endocrine systems. The gut microbiome, diet and putative endogenous synthesis are the potential sources of d-Ala in animals, although there is no direct evidence to show the distribution and racemization of gut-absorbed l-/d-Ala with regards to host-microbe interactions in mammals. In this work, we utilized germ-free mice to control the interference from microbiota and isotopically labeled l-/d-Ala to track their biodistribution and racemization in vivo. Results showed time-dependent biodistribution of gut-absorbed d-Ala, particularly accumulation of gut-absorbed d-Ala in pancreatic tissues, brain, and pituitary. No endogenous synthesis of d-Ala via racemization was observed in germ-free mice. The sources of d-Ala in mice were revealed as microbiota and diet, but not endogenous racemization. This work indicates the importance of further investigating the in vivo biological functions of gut-microbiome derived d-Ala, particularly on NMDAR-related activities, for d-Ala as a potential signaling molecules in the microbiome-gut-brain axis.

Chemical profiles of medicinal plants could be dissimilar depending on the cultivation environments, which may influence their therapeutic efficacy. Accordingly, the regional origin of the medicinal plants should be authenticated for correct evaluation of their medicinal and market values. Metabolomics has been found very useful for discriminating the origin of many plants. Choosing the adequate analytical tool can be an essential procedure because different chemical profiles with different detection ranges will be produced according to the choice. In this study, four analytical tools, Fourier transform near‑infrared spectroscopy (FT-NIR), ¹H-nuclear magnetic resonance spectroscopy (¹H‑NMR), liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectroscopy (GC-MS) were applied in parallel to the same samples of two popular medicinal plants (Gastrodia elata and Rehmannia glutinosa) cultivated either in Korea or China. The classification abilities of four discriminant models for each plant were evaluated based on the misclassification rate and Q² obtained from principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS‑DA), respectively. ¹H-NMR and LC-MS, which were the best techniques for G. elata and R. glutinosa, respectively, were generally preferable for origin discrimination over the others. Reasoned by integrating all the results, ¹H-NMR is the most prominent technique for discriminating the origins of two plants. Nonetheless, this study suggests that preliminary screening is essential to determine the most suitable analytical tool and statistical method, which will ensure the dependability of metabolomics-based discrimination.

Black ginseng has various pharmacological activities, but only few studies have compared its pharmacological effects with those of red ginseng. We conducted an integrative systematic literature evaluation and developed a non-inferiority test based on the multivariate modeling approach to compare the pharmacological effects of red ginseng and black ginseng. We searched reported studies on the pharmaceutical effects and composition of ginsenosides and assigned numeric scores using nonlinear principal component analysis, based on discretization measures for the included publications. Downstream weighted linear regression models were constructed to study the eight major biological activities that are generally known to be exhibited by red ginseng. Our statistical model, based on available ordinal information gathered from previous literature, helped in comparing the overlapping effects of black ginseng. Black ginseng showed antioxidant effects comparable to those of red ginseng; however, this variant was inferior to red ginseng in enhancing immunity, relieving fatigue, alleviating depression/anxiety, decreasing body fat, and reducing blood pressure. We have showed a cost-efficient method to indirectly evaluate the biological effects of ginseng products using data from published articles. This method can also be used to compare the nutritional and medicinal value of herbal medicines that share similar compositions of bioactive compounds.

Particulate matter 2.5 (PM2.5) is associated with reproductive health and adverse pregnancy outcomes. However, studies evaluating biological markers of PM2.5 are lacking, and identifying biomarkers for estimating prenatal exposure to prevent pregnancy complications is essential. Therefore, we aimed to explore urine metabolites that are easy to measure as biomarkers of exposure. In this matched case-control study based on the PM2.5 exposure, 30 high PM2.5 group (>15 μg/m³) and 30 low PM2.5 group (<15 μg/m³) were selected from air pollution on pregnancy outcome (APPO) cohort study. We used a time-weighted average model to estimate individual PM exposure, which used indoor PM2.5 and outdoor PM2.5 concentrations by atmospheric measurement network based on residential addresses. Clinical characteristics and urine samples were collected from participants during the second trimester of pregnancy. Urine metabolites were quantitatively measured using gas chromatography-mass spectrometry following multistep chemical derivatization. Statistical analyses were conducted using SPSS version 21 and MetaboAnalyst 5.0. Small for gestational age and gestational diabetes (GDM) were significantly increased in the high PM2.5 group, respectively (P = 0.042, and 0.022). Fifteen metabolites showed significant differences between the two groups (P < 0.05). Subsequent pathway enrichment revealed that four pathways, including pentose and glucuronate interconversion with three pentose sugars (ribose, arabinose, and xylose; P < 0.05). The concentration of ribose increased preterm births (PTB) and GDM (P = 0.044 and 0.049, respectively), and the arabinose concentration showed a tendency to increase in PTB (P = 0.044). Therefore, we identified urinary pentose metabolites as biomarkers of PM2.5 and confirmed the possibility of their relationship with pregnancy complications.

Background/Objectives: Nitrosamine drug substance-related impurities (NDSRIs) are a class of potent genotoxic impurities that pose a critical risk to patient safety, thereby necessitating the stringent control of pharmaceutical products. Nortriptyline (NORT) and sertraline (SERT) are two widely prescribed antidepressants that form highly potent NDSRIs, N-nitroso-nortriptyline (NNORT) and N-nitroso-sertraline (NSERT), respectively. Despite these risks, a substantial gap exists in terms of the validated analytical methods for surveillance. Accordingly, this study addressed this need by developing a liquid chromatography–tandem mass spectrometry method for the quantification of NNORT and NSERT in drug products. Methods: A comparative evaluation of two reversed-phase columns (general-purpose C18 column and phenyl-hexyl column) was performed to achieve optimal chromatographic resolution of the parent active pharmaceutical ingredients (APIs). Results: The phenyl-hexyl column demonstrated superior separation for both NDSRI/API pairs by leveraging π-π interactions to markedly enhance the resolution. This was particularly critical for SERT. The method was fully validated according to the International Council for Harmonization guideline Q2(R1) and demonstrated excellent linearity (r² = 0.998 for both NNORT and NSERT) with limits of quantitation of 20 ng/g for NNORT and 125 ng/g for NSERT. Accuracy was confirmed with recoveries of 96.6–99.4% for NNORT and 98.6–99.4% for NSERT, and precision was acceptable, with relative standard deviation below 3.9% and 1.9%, respectively. The application of this method to commercial products subjected to accelerated stress testing revealed NNORT formation in NORT products with an average concentration of 190 ng/g, as well as NSERT formation in SERT products resulted in an average concentration of 172 ng/g. Conclusions: This validated method provides a reliable tool for routine quality control, thereby enabling pharmaceutical manufacturers and regulatory agencies to ensure safety and compliance with widely used antidepressant medications.