Brown adipose tissue CoQ deficiency activates the integrated stress response and FGF21-dependent mitohormesis.

Coenzyme Q (CoQ) is essential for mitochondrial respiration and required for thermogenic activity in brown adipose tissues (BAT). CoQ deficiency leads to a wide range of pathological manifestations, but mechanistic consequences of CoQ deficiency in specific tissues, such as BAT, remain poorly understood. Here, we show that pharmacological or genetic CoQ deficiency in BAT leads to stress signals causing accumulation of cytosolic mitochondrial RNAs and activation of the eIF2α kinase PKR, resulting in activation of the integrated stress response (ISR) with suppression of UCP1 but induction of FGF21 expression. Strikingly, despite diminished UCP1 levels, BAT CoQ deficiency displays increased whole-body metabolic rates at room temperature and thermoneutrality resulting in decreased weight gain on high-fat diets (HFD). In line with enhanced metabolic rates, BAT and inguinal white adipose tissue (iWAT) interorgan crosstalk caused increased browning of iWAT in BAT-specific CoQ deficient animals. This mitohormesis-like effect depends on the ATF4-FGF21 axis and BAT-secreted FGF21, revealing an unexpected role for CoQ in the modulation of whole-body energy expenditure with wide-ranging implications for primary and secondary CoQ deficiencies.

Assess Alzheimer's Disease via Plasma Extracellular Vesicle-derived mRNA.

Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern, particularly due to the increasing aging population. Recently, it has been revealed that extracellular vesicles (EVs) originating from neurons play a critical role in AD pathogenesis and progression. These neuronal EVs can cross the blood-brain barrier and enter peripheral circulation, offering a less invasive means for assessing blood-based AD biomarkers. In this study, we analyzed plasma EV-derived messenger RNA (mRNA) from 82 subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls, using next-generation sequencing (NGS) to profile their gene expression for functional enrichment and pathway analysis. Based on the differentially expressed genes identified in both MCI and AD groups, we established a diagnostic model by implementing a machine learning classifier. The refined model demonstrated an average diagnostic accuracy over 98% and showed a strong correlation with different AD stages, suggesting the potential of plasma EV-derived mRNA as a promising non-invasive biomarker for early detection and ongoing monitoring of AD.