Xiaojian Shi

Alzheimer's disease (AD) risk gene ApoE4 perturbs brain lipid homeostasis and energy transduction. However, the cell-type-specific mechanism of ApoE4 in modulating brain lipid metabolism is unclear. Here, we describe a detrimental role of ApoE4 in regulating fatty acid (FA) metabolism across neuron and astrocyte in tandem with their distinctive mitochondrial phenotypes. ApoE4 disrupts neuronal function by decreasing FA sequestering in lipid droplets (LDs). FAs in neuronal LDs are exported and internalized by astrocytes, with ApoE4 diminishing the transport efficiency. Further, ApoE4 lowers FA oxidation and leads to lipid accumulation in both astrocyte and the hippocampus. Importantly, diminished capacity of ApoE4 astrocytes in eliminating neuronal lipids and degrading FAs accounts for their compromised metabolic and synaptic support to neurons. Collectively, our findings reveal a mechanism of ApoE4 disruption to brain FA and bioenergetic homeostasis that could underlie the accelerated lipid dysregulation and energy deficits and increased AD risk for ApoE4 carriers.

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26−37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895–0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).