Browsing by Author "Novotny, Brenna C."

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    Metabolomic and lipidomic signatures in autosomal dominant and late-onset Alzheimer's disease brains
    (Wiley, 2023) Novotny, Brenna C.; Fernandez, Maria Victoria; Wang, Ciyang; Budde, John P.; Bergmann, Kristy; Eteleeb, Abdallah M.; Bradley, Joseph; Webster, Carol; Ebl, Curtis; Norton, Joanne; Gentsch, Jen; Dube, Umber; Wang, Fengxian; Morris, John C.; Bateman, Randall J.; Perrin, Richard J.; McDade, Eric; Xiong, Chengjie; Chhatwal, Jasmeer; Dominantly Inherited Alzheimer Network (DIAN) Study Group; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's Disease Metabolomics Consortium (ADMC); Goate, Alison; Farlow, Martin; Schofield, Peter; Chui, Helena; Karch, Celeste M.; Cruchaga, Carlos; Benitez, Bruno A.; Harari, Oscar; Neurology, School of Medicine
    Introduction: The identification of multiple genetic risk factors for Alzheimer's disease (AD) suggests that many pathways contribute to AD onset and progression. However, the metabolomic and lipidomic profiles in carriers of distinct genetic risk factors are not fully understood. The metabolome can provide a direct image of dysregulated pathways in the brain. Methods: We interrogated metabolomic signatures in the AD brain, including carriers of pathogenic variants in APP, PSEN1, and PSEN2 (autosomal dominant AD; ADAD), APOE ɛ4, and TREM2 risk variant carriers, and sporadic AD (sAD). Results: We identified 133 unique and shared metabolites associated with ADAD, TREM2, and sAD. We identified a signature of 16 metabolites significantly altered between groups and associated with AD duration. Discussion: AD genetic variants show distinct metabolic perturbations. Investigation of these metabolites may provide greater insight into the etiology of AD and its impact on clinical presentation. Highlights: APP/PSEN1/PSEN2 and TREM2 variant carriers show distinct metabolic changes. A total of 133 metabolites were differentially abundant in AD genetic groups. β-citrylglutamate is differentially abundant in autosomal dominant, TREM2, and sporadic AD. A 16-metabolite profile shows differences between Alzheimer's disease (AD) genetic groups. The identified metabolic profile is associated with duration of disease.
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    Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers
    (Springer Nature, 2023-04-21) Brase, Logan; You, Shih-Feng; D'Oliveira Albanus, Ricardo; Del-Aguila, Jorge L.; Dai, Yaoyi; Novotny, Brenna C.; Soriano-Tarraga, Carolina; Dykstra, Taitea; Fernandez, Maria Victoria; Budde, John P.; Bergmann, Kristy; Morris, John C.; Bateman, Randall J.; Perrin, Richard J.; McDade, Eric; Xiong, Chengjie; Goate, Alison M.; Farlow, Martin; Dominantly Inherited Alzheimer Network (DIAN); Sutherland, Greg T.; Kipnis, Jonathan; Karch, Celeste M.; Benitez, Bruno A.; Harari, Oscar; Neurology, School of Medicine
    Genetic studies of Alzheimer disease (AD) have prioritized variants in genes related to the amyloid cascade, lipid metabolism, and neuroimmune modulation. However, the cell-specific effect of variants in these genes is not fully understood. Here, we perform single-nucleus RNA-sequencing (snRNA-seq) on nearly 300,000 nuclei from the parietal cortex of AD autosomal dominant (APP and PSEN1) and risk-modifying variant (APOE, TREM2 and MS4A) carriers. Within individual cell types, we capture genes commonly dysregulated across variant groups. However, specific transcriptional states are more prevalent within variant carriers. TREM2 oligodendrocytes show a dysregulated autophagy-lysosomal pathway, MS4A microglia have dysregulated complement cascade genes, and APOEε4 inhibitory neurons display signs of ferroptosis. All cell types have enriched states in autosomal dominant carriers. We leverage differential expression and single-nucleus ATAC-seq to map GWAS signals to effector cell types including the NCK2 signal to neurons in addition to the initially proposed microglia. Overall, our results provide insights into the transcriptional diversity resulting from AD genetic architecture and cellular heterogeneity. The data can be explored on the online browser (http://web.hararilab.org/SNARE/).