Unraveling the formation mechanism of aroma compounds in pork during air frying using UHPLC-HRMS and Orbitrap Exploris GC-MS.

Lipids are the key matrix for the presence of odorants in meat products. The formation mechanism of odorants of air-fried (AF) pork at 230 °C was elucidated from the perspectives of lipids and heat transfer using physicochemical analyses and multidimensional statistics. Twenty-nine key aroma compounds were identified, with pyrazines predominantly contributing to the roasty aroma of air-fried roasted pork. Untargeted lipidomics revealed 1184 lipids in pork during roasting, with phosphatidylcholine (PC), phosphatidylethanolamine (PE), and triglyceride (TG) being the major lipids accounting for about 60 % of the total lipids. TG with C18 acyl groups, such as TG 16:1_18:1_18:2 and TG 18:0_18:0_20:3, were particularly significant in forming the aroma of AF pork. The OPLS-DA model identified seven potential biomarkers that differentiate five roasting times, including PC 16:0_18:3 and 2-ethyl-3,5-dimethylpyrazine. Notably, a lower specific heat capacity and water activity accelerated heat transfer, promoting the formation and retention of odorants in AF pork.

UBE2S promotes glycolysis in hepatocellular carcinoma by enhancing E3 enzyme-independent polyubiquitination of VHL.

Background/Aims: Ubiquitination is widely involved in the progression of hepatocellular carcinoma (HCC) by regulating various cellular processes. However, systematic strategies for screening core ubiquitin-related genes, clarifying their functions and mechanisms, and ultimately developing potential therapeutics for patients with HCC are still lacking. Methods: Cox and LASSO regression analyses were performed to construct a ubiquitin-related gene prediction model for HCC. Loss- and gain-of-function studies, transcriptomic and metabolomics analysis were used to explore the function and mechanism of UBE2S on HCC cell glycolysis and growth. Results: Based on 1423 ubiquitin-related genes, a four-gene signature was successfully constructed to evaluate the prognosis of patients with HCC. UBE2S was identified in this signature with the potential to predict the survival of patients with HCC. E2F2 transcriptionally upregulated UBE2S expression by directly binding to its promoter. UBE2S positively regulated glycolysis in a HIF-1α-dependent manner, thus promoting the proliferation of HCC cells. Mechanistically, UBE2S enhanced K11-linkage polyubiquitination at lysine residues 171 and 196 of VHL independent of E3 ligase, thereby indirectly stabilizing HIF-1α protein levels by mediating the degradation of VHL by the proteasome. In particular, the combination of cephalomannine, a small molecule compound that inhibits the expression of UBE2S, and PX-478, an inhibitor of HIF-1α, significantly improved the anti-tumor efficacy. Conclusions: UBE2S is identified as a key biomarker in HCC among the thousands of ubiquitin-related genes and promotes glycolysis by E3 enzyme-independent ubiquitination, thus serving as a therapeutic target for the treatment of HCC.

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning.

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies.