Acute pancreatitis (AP) represents a significant global health challenge. Despite recent advances in medical treatment, the development of novel therapeutic strategies remains crucial. Rhein, a natural compound of the Chinese herb Rheum, shows promise in the treatment of AP. However, the exact mechanism underlying its therapeutic effect is still not fully understood. To investigate the association between the rhein-related gut microbiota and AP, we conducted antibiotic-mediated microbiota depletion experiments, fecal microbiota transplantation (FMT), and in vitro bacterial culture experiments. Concurrently, we performed 16S rRNA gene sequencing, metagenomic sequencing, and liquid chromatography‒mass spectrometry (LC‒MS) analyses on mouse fecal samples to characterize alterations in the microbiota and metabolome. Transcriptomic studies were also performed to elucidate the mechanisms underlying acinar cell inflammation. Rhein alleviated AP by modulating the gut microbiota, as demonstrated by changes in the gut microbiota composition and improvements in AP after FMT in rhein-treated mice compared with those in cerulein-induced AP mice. Specifically, rhein is concentrated mainly in the stomach and intestines, where it exerts anti-inflammatory effects on acinar cells by antagonizing the TLR4/NF-κB/NLRP3 signaling pathway activated by trimethylamine-N-oxide (TMAO). This mechanism is associated with lipid peroxidation and necrosis mediated by oxidative stress. Clinically, disease severity in patients with AP is positively correlated with serum TMAO concentration. Rhein alleviates AP by modulating the intestinal microbiota to reduce TMAO production, thereby suppressing TMAO-induced activation of the TLR4/NF-κB/NLRP3 signaling pathway and inhibiting acinar cell inflammation.

Diacylglycerol kinases (DGKs) are crucial lipid-signaling enzymes that convert diacylglycerol (DAG) into phosphatidic acid (PA) through phosphorylation. This reaction modulates the DAG–PA axis, which is central to signal transduction, metabolic homeostasis, and immune responses. Mammals express ten DGK isoforms, classified into five subtypes on the basis of their structural features. These isoforms exhibit subtype-specific characteristics and distinct subcellular localization patterns, underpinning their diverse physiological functions. Accumulating evidence implicates DGKs in the pathogenesis of metabolic disorders, cancer, and cardiovascular and neurological diseases, where they may play either pathogenic or protective roles depending on the cellular context. This isoform-specific functionality renders DGKs promising yet underexploited therapeutic targets. Notably, one DGKζ inhibitor (ASP1570) has entered clinical trials, while other candidates, such as DGKα inhibitors (e.g., CU-3), remain at the preclinical stage. This review systematically summarizes the structural classification, catalytic mechanisms, and pathological roles of DGKs, with a particular emphasis on therapeutic targeting strategies and the associated technical challenges.

This study investigates the metabolic changes induced by endurance exercise, specifically race walking, in a cohort of 19 athletes. Blood samples were collected at four time points: pre-exercise (REST), immediately post-exercise (STAT), 3 hours into recovery (REC3), and 22 hours post-exercise (REC22). A total of 859 metabolites were identified through the untargeted method, and 465 metabolites and 411 lipids were identified through the targeted methods. Rigorous quality control measures were implemented throughout the study to ensure data reliability. The comprehensive dataset, which is publicly available on the Metabolomics Workbench website, offers valuable insights into the systemic metabolic shifts triggered by endurance exercise. This resource may prove instrumental in uncovering biomarkers associated with athletic performance, providing a foundation for future research in exercise physiology and metabolic health.

Background: The reprogramming of lipid metabolism, especially glycerolipid metabolism (GLM), plays a key role in cancer progression and response to therapy. However, the role and molecular characterization of GLM in pancreatic cancer (PC) remain unclear. Methods: A pan-cancer analysis of glycerolipid metabolism-related genes (GMRGs) was first conducted to assess copy-number variants, single-nucleotide variations, methylation, and mRNA expression. Subsequently, GLM in PC was characterized using lipidomics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomic analysis. A cluster analysis based on bulk RNA sequencing data from 930 PC samples identified GLM-associated subtypes, which were then analyzed for differences in prognosis, biological function, immune microenvironment, and drug sensitivity. To prioritize prognostically relevant GMRGs in PC, we employed a random forest (RF) algorithm to rank their importance across 930 PC samples. Finally, the key biomarker of PC was validated using PCR and immunohistochemistry. Results: Pan-cancer analysis identified molecular features of GMRGs in cancers, while scRNA-seq, spatial transcriptomics, and lipidomics highlighted GLM heterogeneity in PC. Two GLM-associated subtypes with significant prognostic, biofunctional, immune microenvironmental, and drug sensitivity differences were identified in 930 PC samples. Finally, ALDH2 was identified as a novel prognostic biomarker in PC and validated in a large number of datasets and clinical samples. Conclusions: This study highlights the crucial role of GLM in PC and defines a new PC subtype and prognostic biomarker. These findings establish a novel avenue for studying prognostic prediction and precision medicine in PC patients.

The profiling of metabolites provides an immediate snapshot that depicts crucial physiological information, holding immense potential for the early diagnosis and prognosis of diseases, including diabetes. Herein, we proposed an optimized and in-depth target-based metabolome platform through an integration of six distinct conditions, including a normal phase, a pre-column chemical derivatization and four reversed phase separation methods for the quantification of a total of 1609 small molecules (32 sub-classes) in serum after normalization using isotope-labeled internal standards. After undergoing rigorous methodological validation and comprehensive comparison with untargeted strategies, we present a new dataset of metabolomic profile encompassing a cohort of 200 healthy individuals and 100 newly diagnosed Type 2 diabetes mellitus (T2DM) patients from the northern region of China. The overall differential analysis results indicated obvious metabolic disturbance of amino acid, fatty acids, lysophosphatidyl-choline and triacylglycerol in T2DM. We hereby make these technical validation results and the profiling dataset publicly available to the scientific community, showcasing its exceptional sensitivity and robustness as an invaluable tool for the comprehensive targeted metabolome analysis.

The present study investigated the distinct metabolic profiles of two primary traditional Chinese medicine (TCM) syndromes in patients with cholelithiasis—liver Qi stagnation (LQS) and liver-gallbladder damp-heat (LGDH). By integrating untargeted and targeted metabolomics, we aimed to identify objective serum and urine biomarkers for syndrome differentiation and to elucidate their underlying biological basis. In total, 91 participants, comprising 33 healthy controls (HCs), 30 cholelithiasis patients with LQS syndrome, and 28 cholelithiasis patients with LGDH syndrome, were enrolled. Untargeted analysis of serum and urine samples quantified 602 and 636 metabolites, respectively. To strengthen the robustness of the dataset, complementary targeted metabolomics was conducted as an orthogonal validation step, providing reliable quantification for 210 serum and 204 urinary. The present findings provide molecular insights into the TCM principle of syndrome differentiation (Bian Zheng Lun Zhi). The identified biomarker panel may offer a basis for developing objective tools for the stratification of cholelithiasis syndromes and may support future efforts towards personalized therapeutic strategies.