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Oxidative stress plays a pivotal role in the development of diabetic cardiomyopathy (DCM). Previous studies have revealed that inhibition of mitochondrial fission suppressed oxidative stress and alleviated mitochondrial dysfunction and cardiac dysfunction in diabetic mice. However, no research has confirmed whether mitochondria fission accentuates hyperglycemia‐induced cardiomyoblast oxidative stress through regulating fatty acid oxidation (FAO). We used H9c2 cardiomyoblasts exposed to high glucose (HG) 33 mM to simulate DCM in vitro. Excessive mitochondrial fission, poor cell viability, and lipid accumulation were observed in hyperglycemia‐induced H9c2 cardiomyoblasts. Also, the cells were led to oxidative stress injury, lower adenosine triphosphate (ATP) levels, and apoptosis. Dynamin‐related protein 1 (Drp1) short interfering RNA (siRNA) decreased targeted marker expression, inhibited mitochondrial fragmentation and lipid accumulation, suppressed oxidative stress, reduced cardiomyoblast apoptosis, and improved cell viability and ATP levels in HG‐exposed H9c2 cardiomyoblasts, but not in carnitine palmitoyltransferase 1 (CPT1) inhibitor etomoxir treatment cells. We also found subcellular localization of CPT1 on the mitochondrial membrane, FAO, and levels of nicotinamide adenine dinucleotide phosphate (NADPH) were suppressed after exposure to HG treatment, whereas Drp1 siRNA normalized mitochondrial CPT1, FAO, and NADPH. However, the blockade of FAO with etomoxir abolished the above effects of Drp1 siRNA in hyperglycemia‐induced H9c2 cardiomyoblasts. The preservation of mitochondrial function through the Drp1/CPT1/FAO pathway is the potential mechanism of inhibited mitochondria fission in attenuating oxidative stress injury of hyperglycemia‐induced H9c2 cardiomyoblasts. [ABSTRACT FROM AUTHOR]

Alzheimer disease (AD) is an irreversible neurodegenerative disease, and astrocytes play a key role in its onset and progression. The aim of this study is to analyze the characteristics of neurotoxic astrocytes and identify novel molecular targets for slowing down the progression of AD. Single-nucleus RNA sequencing (snRNA-seq) data were analyzed from various AD cohorts comprising about 210,654 cells from 53 brain tissue. By integrating snRNA-seq data with bulk RNA-seq data, crucial astrocyte types and genes associated with the prognosis of patients with AD were identified. The expression of neurotoxic astrocyte markers was validated using 5 × FAD and wild-type (WT) mouse models, combined with experiments such as western blot, quantitative real-time PCR (qRT-PCR), and immunofluorescence. A group of neurotoxic astrocytes closely related to AD pathology was identified, which were involved in inflammatory responses and pathways related to neuron survival. Combining snRNA and bulk tissue data, ZEP36L, AEBP1, WWTR1, PHYHD1, DST and RASL12 were identified as toxic astrocyte markers closely related to disease severity, significantly elevated in brain tissues of 5 × FAD mice and primary astrocytes treated with Aβ. Among them, WWTR1 was significantly increased in astrocytes of 5 × FAD mice, driving astrocyte inflammatory responses, and has been identified as an important marker of neurotoxic astrocytes. snRNA-seq analysis reveals the biological functions of neurotoxic astrocytes. Six genes related to AD pathology were identified and validated, among which WWTR1 may be a novel marker of neurotoxic astrocytes. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that threatens the population health of older adults. However, the mechanisms of how altered metabolism involves AD pathology are poorly understood. This study intends to provide novel insights into the role of metabolic alteration in the pathology and identify potential metabolic biomarkers for AD diagnosis Method: The Gas chromatography‐mass spectrometry (GC‐MS) was used to measure the concentrations of serum metabolites in a cohort of subjects with AD (n = 88) and cognitively normal control (CN) (n = 85). The patients were classified as very mild (n = 25), mild (n = 27), moderate (n = 25), and severe (n = 11). Serum metabolic profiles were analyzed by multivariate (principal component analysis) and univariate approaches (t‐tests, false discovery, receiver operating characteristic curves, and correlation analysis). The artificial neural network model (ANN) was applied to identify potential biomarkers of AD. Biofunctional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes. Result: The results revealed considerable separation between the AD and CN patients. Edetic acid, (S)‐3,4‐Dihydroxybutyric acid, and arachidonic acid were identified as potential AD diagnostic biomarkers using an ANN model. And the classification model could predict the risk for AD with high accuracy (AUC = 0.93). The most significant changes in the metabolite's composition occur during the very mild to the mild stage. The metabolic enrichment analysis revealed that carbohydrate metabolism deficiency and disturbance of amino acids, fatty acids, and lipids metabolism are involved in AD progression. Especially, pathway analysis highlighted that l−glutamate participated in four crucial nervous system pathways (including GABAergic synapse, glutamatergic synapse, retrograde endocannabinoid signaling and synaptic vesicle cycle). Conclusion: Serum metabolomic analysis demonstrated that the metabolic alterations had already arisen at the very mild stages of dementia. Disrupt energy metabolism may be the core of the vicious cycle in AD, resulting in a wide range of metabolic disorders, including abnormal glucose metabolism, TCA cycle impairments, amino acids, fatty acids, and lipids dysregulation. Furthermore, a metabolic diagnostic model of AD was constructed using ANN, which could robustly differentiate AD patients from CN. Our study identified new markers for AD diagnosis and highlighted the roles of metabolic changes on the progression of AD. [ABSTRACT FROM AUTHOR]