Metabolism-Associated Epigenetic and Immunoepigenetic Reprogramming in Liver Cancer

Epigenetic regulation of inflammation: The metabolomics connection

Epigenetic factors are considered the regulator of complex machinery behind inflammatory disorders and significantly contributed to the expression of inflammation-associated genes. Epigenetic modifications modulate variation in the expression pattern of target genes without affecting the DNA sequence. The current knowledge of epigenetic research focused on their role in the pathogenesis of various inflammatory diseases that causes morbidity and mortality worldwide. Inflammatory diseases are categorized as acute and chronic based on the disease severity and are regulated by the expression pattern of various genes. Hence, understanding the role of epigenetic modifications during inflammation progression will contribute to the disease outcomes and therapeutic approaches. This review also focuses on the metabolomics approach associated with the study of inflammatory disorders. Inflammatory responses and metabolic regulation are highly integrated and various advanced techniques are adopted to study the metabolic signature molecules. Here we discuss several metabolomics approaches used to link inflammatory disorders and epigenetic changes. We proposed that deciphering the mechanism behind the inflammation-metabolism loop may have immense importance in biomarkers research and may act as a principal component in drug discovery as well as therapeutic applications.

Establishment of a novel glycolysis-immune-related diagnosis gene signature for endometriosis by machine learning

PURPOSE

The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model.

METHODS

A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-based semantic similarity and logistic regression model analyses were used to investigate core genes. Reverse real-time quantitative PCR (RT-qPCR) of 5 target genes was analysed.

RESULTS

The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test sets, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) prediction model was 0.777, 0.824, and 0.774. Additionally, there was a remarkable difference in the immune environment between the EMS and control groups. Eventually, the five target genes were verified by RT-qPCR.

CONCLUSION

The glycolysis-immune-based predictive model was established to forecast EMS patients' diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.

Proline metabolism reprogramming of trained macrophages induced by early respiratory infection combined with allergen sensitization contributes to development of allergic asthma in childhood of mice

Background

Infants with respiratory syncytial virus (RSV)-associated bronchiolitis are at increased risk of childhood asthma. Recent studies demonstrated that certain infections induce innate immune memory (also termed trained immunity), especially in macrophages, to respond more strongly to future stimuli with broad specificity, involving in human inflammatory diseases. Metabolic reprogramming increases the capacity of the innate immune cells to respond to a secondary stimulation, is a crucial step for the induction of trained immunity. We hypothesize that specific metabolic reprogramming of lung trained macrophages induced by neonatal respiratory infection is crucial for childhood allergic asthma.

Objective

To address the role of metabolic reprogramming in lung trained macrophages induced by respiratory virus infection in allergic asthma.

Methods

Neonatal mice were infected and sensitized by the natural rodent pathogen Pneumonia virus of mice (PVM), a mouse equivalent strain of human RSV, combined with ovalbumin (OVA). Lung CD11b⁺ macrophages in the memory phase were re-stimulated to investigate trained immunity and metabonomics. Adoptive transfer, metabolic inhibitor and restore experiments were used to explore the role of specific metabolic reprogramming in childhood allergic asthma.

Results

PVM infection combined with OVA sensitization in neonatal mice resulted in non-Th2 (Th1/Th17) type allergic asthma following OVA challenge in childhood of mice. Lung CD11b⁺ macrophages in the memory phage increased, and showed enhanced inflammatory responses following re-stimulation, suggesting trained macrophages. Adoptive transfer of the trained macrophages mediated the allergic asthma in childhood. The trained macrophages showed metabolic reprogramming after re-stimulation. Notably, proline biosynthesis remarkably increased. Inhibition of proline biosynthesis suppressed the development of the trained macrophages as well as the Th1/Th17 type allergic asthma, while supplement of proline recovered the trained macrophages as well as the allergic asthma.

Conclusion

Proline metabolism reprogramming of trained macrophages induced by early respiratory infection combined with allergen sensitization contributes to development of allergic asthma in childhood. Proline metabolism could be a well target for prevention of allergic asthma in childhood.

Biofluids Metabolic Profiling Based on PS@Fe3O4-NH2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening

Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH₂-coated polystyrene@Fe₃O₄ magnetic beads (PS@Fe₃O₄-NH₂ MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe₃O₄-NH₂ MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.