Amino acid metabolic remodeling is a hallmark of cancer, driving an increased nutritional demand for amino acids. Amino acids are pivotal for energetic regulation, biosynthetic support, and homeostatic maintenance to stimulate cancer progression. However, the role of phenylalanine in multiple myeloma (MM) remains unknown. Here, we demonstrate that phenylalanine levels in MM patients are decreased in plasma but elevated in bone marrow (BM) cells. After the treatment, phenylalanine levels increase in plasma and decrease in BM. This suggests that changes in phenylalanine have diagnostic value and that phenylalanine in the BM microenvironment is an essential source of nutrients for MM progression. The requirement for phenylalanine by MM cells exhibits a similar pattern. Inhibiting phenylalanine utilization suppresses MM cell growth and provides a synergistic effect with Bortezomib (BTZ) treatment in vitro and murine models. Mechanistically, phenylalanine deprivation induces excessive endoplasmic reticulum stress and leads to MM cell apoptosis through the ATF3–CHOP–DR5 pathway. Interference with ATF3 significantly affects phenylalanine deprivation therapy. In conclusion, we have identified phenylalanine metabolism as a characteristic feature of MM metabolic remodeling. Phenylalanine is necessary for MM proliferation, and its aberrant demand highlights the importance of low-phenylalanine diets as an adjuvant treatment for MM.

The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4⁺ T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αβ T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses.

Background: The Makorin ring finger protein 1 (MKRN1) gene, also called RNF61, is located on the long arm of chromosome 7 and is a member of the RING finger protein family. The E3 ubiquitin ligase MKRN1 is closely linked to tumour development, but the exact mechanism needs to be elucidated. In this study, we aimed to investigate the specific mechanism and role of MKRN1 in colorectal cancer (CRC) development. Methods: MKRN1 expression in CRC was analysed using the Cancer Cell Line Encyclopaedia and the Cancer Genome Atlas (TCGA) databases. Rectal tumour tissues were frozen to explore the MKRN1 expression in CRC and its clinical significance. The impact of MKRN1 on CRC cell proliferation and migration was observed using CCK8, colony formation, wound healing, and transwell assays. A combination of MKRN1 quantitative proteomics, ubiquitination modification omics analysis, and a string of in vitro and in vivo experiments revealed the potential mechanisms by which MKRN1 regulates CRC metastasis. Results: MKRN1 expression was significantly elevated in CRC tissues compared to paracancerous tissues and was positively linked with prognosis (P < 0.01). MKRN1 downregulation inhibits CRC cell proliferation, migration, and invasion. Conversely, MKRN1 overexpression promotes the proliferation, migration, and invasion of CRC cells. Mechanistically, MKRN1 induces epithelial-mesenchymal transition (EMT) in CRC cells via ubiquitination and degradation of Smad nuclear-interacting protein 1 (SNIP1). Furthermore, SNIP1 inhibits transforming growth factor-β (TGF-β) signalling, and MKRN1 promotes TGF-β signalling by degrading SNIP1 to induce EMT in CRC cells. Finally, using conditional knockout mice, intestinal lesions and metastatic liver microlesions were greatly reduced in the intestinal knockout MKRN1 group compared to that in the control group. Conclusions: High MKRN1 levels promote TGF-β signalling through ubiquitination and degradation of SNIP1, thereby facilitating CRC metastasis, and supporting MKRN1 as a CRC pro-cancer factor. The MKRN1/SNIP1/TGF-β axis may be a potential therapeutic target in CRC.

Background: It has been demonstrated that clusterin (CLU) is a protective protein involved in a variety of diseases and disorders. However, the role of CLU in diabetic atherosclerosis is not elucidative. The objective of this study is to investigate the role of CLU in diabetic atherosclerosis and the molecular mechanisms.Method: In in vivo experiments, Clu knockout and overexpressed murine models were used to investigate the role of Clu in diabetic atherosclerosis. Atherosclerotic plaque formation was determined by hematoxylin-eosin (H&E) staining and Oil Red O staining. F4/80 and CD68 levels were determined by immunohistochemical staining. Transmission electron microscopy was used to observe changes in cell pyroptosis morphology. NLRP3 and IL-1β levels were determined by Western blot and immunofluorescence staining. In in vitro experiments, TNF-α, IL-6, and IL-1β levels in THP-1 derived macrophages were determined by real-time qPCR and ELISA.Results: We found that Clu-overexpression reduced while Clu knockout promoted atherosclerotic plaque formation, macrophage infiltration and inflammatory factor expression in mouse aortic plaques. Consistently, CLU overexpression inhibits the production of TNF-α, IL-6, and IL-1β in THP-1 derived macrophages. Moreover, Clu inhibited the release of inflammatory factors and macrophage pyroptosis in diabetic atherosclerosis murine models.Conclusion: Our study revealed that CLU could ameliorate diabetic atherosclerosis via suppressing inflammatory factors release and pyroptosis of macrophage. CLU may be a promising therapeutic target for diabetic atherosclerosis.

NETosis plays a pivotal role in the innate immune response after diabetic myocardial infarction (MI), exerting a profound influence on the overall pathological process and potential recovery outcomes. The metabolism of diabetic cardiomyocyte actively creates a specialized micro environment for the innate immune response after MI. However, the mechanism by which cardiac metabolism drives NETosis remains unclear. Utilizing public databases of human MI sc-RNA datasets, we discovered that cardiomyocyte PDK4 expression mediates the intensification of glycolysis, which is strongly correlated with NETosis. Through mass spectrometry imaging and phenotype assessment, we ascertained that specific knockout of PDK4 in cardiomyocytes (PDK4^fl/flMyh6^Cre, male, 6 weeks) led to a reduction in NETosis by restraining micro environmental lactate (LA) production. In addition, the role of LA in promoting NETosis has been further corroborated by in vivo/in vitro experiments involving LA supplementation and its absence. Moreover, LA redirects neutrophil metabolic flux from glycolysis to the pentose-phosphate pathway (PPP). Mechanistically, LA triggers metabolic remodeling through the PRMT9-mediated methylation of PFKL at the R301 residue, resulting in PFKL inactivation and the consequent restriction of glycolysis. Our findings reveal the crucial role of cardiomyocyte metabolism in NETosis, shedding light on the role of LA as a vital signaling molecule in the crosstalk between cardiomyocytes and neutrophils. Importantly, we screened pitavastatin, a potential inhibitor of PDK4 among the FDA-approved drugs, and verified that it can alleviate NETosis in diabetic MI, which provides a rationale for drug selection in diabetic MI patients.

Objective: The appropriate use of statins plays a vital role in reducing the risk of atherosclerotic cardiovascular disease (ASCVD). However, due to changes in diet and lifestyle, there has been a significant increase in the number of individuals with high cholesterol levels. Therefore, it is crucial to ensure the rational use of statins. Adverse reactions associated with statins, including liver enzyme abnormalities and statin-associated muscle symptoms (SAMS), have impacted their widespread utilization. In this study, we aimed to develop a predictive model for statin efficacy and safety based on real-world clinical data using machine learning techniques.Methods: We employed various data preprocessing techniques, such as improved random forest imputation and Borderline SMOTE oversampling, to handle the dataset. Boruta method was utilized for feature selection, and the dataset was divided into training and testing sets in a 7:3 ratio. Five algorithms, including logistic regression, naive Bayes, decision tree, random forest, and gradient boosting decision tree, were used to construct the predictive models. Ten-fold cross-validation and bootstrapping sampling were performed for internal and external validation. Additionally, SHAP (SHapley Additive exPlanations) was employed for feature interpretability. Ultimately, an accessible web-based platform for predicting statin efficacy and safety was established based on the optimal predictive model.Results: The random forest algorithm exhibited the best performance among the five algorithms. The predictive models for LDL-C target attainment (AUC = 0.883, Accuracy = 0.868, Precision = 0.858, Recall = 0.863, F1 = 0.860, AUPRC = 0.906, MCC = 0.761), liver enzyme abnormalities (AUC = 0.964, Accuracy = 0.964, Precision = 0.967, Recall = 0.963, F1 = 0.965, AUPRC = 0.978, MCC = 0.938), and muscle pain/Creatine kinase (CK) abnormalities (AUC = 0.981, Accuracy = 0.980, Precision = 0.987, Recall = 0.975, F1 = 0.981, AUPRC = 0.987, MCC = 0.965) demonstrated favorable performance. The most important features of LDL-C target attainment prediction model was cerebral infarction, TG, PLT and HDL. The most important features of liver enzyme abnormalities model was CRP, CK and number of oral medications. Similarly, AST, ALT, PLT and number of oral medications were found to be important features for muscle pain/CK abnormalities. Based on the best-performing predictive model, a user-friendly web application was designed and implemented.Conclusion: This study presented a machine learning-based predictive model for statin efficacy and safety. The platform developed can assist in guiding statin therapy decisions and optimizing treatment strategies. Further research and application of the model are warranted to improve the utilization of statin therapy.