Browsing by Subject "Alzheimer’s Disease (AD)"

Now showing 1 - 5 of 5
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    A multiethnic transcriptome for Alzheimer Disease identifies cross‐ancestry and ancestry‐specific expression profiles
    (Wiley, 2025-01-03) Yang, Zikun; Cieza, Basilio; Reyes-Dumeyer, Dolly; Lee, Annie J.; Dugger, Brittany N.; Jin, Lee-Way; Murray, Melissa E.; Dickson, Dennis W.; Pericak-Vance, Margaret A.; Vance, Jeffery M.; Foroud, Tatiana M.; Teich, Andrew F.; Mayeux, Richard; Tosto, Giuseppe; Neurology, School of Medicine
    Background: Alzheimer’s Disease (AD) presents complex molecular heterogeneity, influenced by a variety of factors including heterogeneous phenotypic, genetic, and neuropathologic presentations. Regulation of gene expression mechanisms is a primary interest of investigations aiming to uncover the underlying disease mechanisms and progression. Method: We generated bulk RNA‐sequencing in prefrontal cortex from 565 AD brain samples (non‐Hispanic Whites, n = 399; Hispanics, n = 113; African American, n = 12) across six U.S. brain banks, and conducted differential gene expression and enrichment analyses. We sought to identify cross‐ancestry and ancestry‐specific differentially expressed genes (DEG) and pathways across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We validated our findings using the Religious Orders Study/Memory and Aging Project study (ROS/MAP, n = 1,095). Lastly, we validated top DEG using publically‐available human single‐nucleus RNA sequencing (snRNAseq) data. Result: AD‐known genes VGF (LFC = ‐0.661, padj = 3.78) and ADAMTS2 (padj = 1.21) were consistently differentially expressed across statistical models, ethnic groups, and replicated in ROS/MAP (Figure 1). Genes from the heat shock protein (HSP) family, e.g. HSPB7 (padj = 3.78), were the top DEG, also replicated in ROS/MAP. Ethnic‐stratified analyses prioritized TNFSF14 and SPOCD1 as top DEG in Hispanic samples. Gene set enrichment analysis highlighted several significantly pathways, including “TYROBP causal network in microglia” (WP3945; padj = 1.68) and “Alzheimer Disease” (WP5124; padj = 4.24). snRNAseq validated several DEG, including VGF downregulated in neurons (padj = 1.1). Conclusion: To our knowledge, this is the largest diverse transcriptome study for AD in post‐mortem tissue. We identified perturbated genes and pathways resulting in cross‐ethnic and ethnic‐specific findings, ultimately highlighting the importance of diversity in AD investigations.
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    Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning
    (Elsevier, 2020-06) Castillo-Barne, Diego; Su, Li; Ramírez, Javier; Salas-Gonzalez, Diego; Martinez-Murcia, Francisco J.; Illan, Ignacio A.; Segovia, Fermin; Ortiz, Andres; Cruchaga, Carlos; Farlow, Martin R.; Xiong, Chengjie; Graff-Radford, Neil R.; Schofield, Peter R.; Masters, Colin L.; Salloway, Stephen; Jucker, Mathias; Mori, Hiroshi; Levin, Johannes; Gorriz, Juan M.; Neurology, School of Medicine
    Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.
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    BrainAGE Estimation: Influence of Field Strength, Voxel Size, Race, and Ethnicity
    (medRxiv, 2023-12-05) Dempsey, Desarae A.; Deardorff, Rachael; Wu, Yu-Chien; Yu, Meichen; Apostolova, Liana G.; Brosch, Jared; Clark, David G.; Farlow, Martin R.; Gao, Sujuan; Wang, Sophia; Saykin, Andrew J.; Risacher, Shannon L.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across- MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.
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    Generation and validation of anti‐TREM2 agonistic antibodies to enable the advancement of drug targets in the TREM2/DAP12 signaling pathway for the treatment of Alzheimer Disease
    (Wiley, 2025-01-09) Moussaif, Mustapha; Javens-Wolfe, June; Palkowitz, Alan D.; Richardson, Timothy I.; Pharmacology and Toxicology, School of Medicine
    Background: TREM2 signaling has been implicated in Alzheimer’s Disease (AD). TREM2 regulates microglial states and functions such as phagocytosis. The most prominent TREM signaling adapter is DAP12, encoded by TYROBP. Understanding functional changes of this complex, and downstream effectors such as SHIP1, PLCG2 and the Scr family kinases Lyn and Hck, is required to evaluate a broad range of therapeutic hypotheses and drug targets for prioritization and enablement. The lack of available, well validated, and openly distributed experimental tools can limit early drug discovery efforts. Therefore, the IUSM Purdue TREAT‐AD Center has generated and validated TREM2 activating antibodies to enable the advancement of drug targets in the TREM2/DAP12 signaling pathway. Method: To establish and validate anti‐TREM2 agonist antibodies, heavy and light chain variable sequences were identified from multiple publications including patent applications. Antibodies were formatted as either human IgG1, Fc null mutant IgG1 or antibody transport vehicle (ATV) Fc null mutant IgG1. They were expressed in mammalian ExpiCHO cells and tested ex vivo for agonism based on their ability to activate AKT and Syk phosphorylation in THP1 cells and TREM2/DAP12 overexpressing cells respectively. The strongest agonistic candidate was scaled, purified, and further characterized biophysically and functionally. Result: Several agonistic antibodies were identified. AL2p31 antibody showed binding specificity to human versus murine TREM2. Biophysical characterization using biolayer interferometry showed that binding kinetic parameters (KD, Kon, and Koff) were not significantly affected in LALAPG null mutant Fc background. AL2p31 specifically induced Syk phosphorylation in comparison to an isotype control. Analysis of antibodies formatted as bispecific IgG1 targeting both TREM2 and the human transferrin receptor (hTfR), confirmed that RS9‐F6 can bind both human and murine TREM2 and revealed the ATV 35‐21‐16 variant sequence as a binder for the hTfR. Conclusion: The mission of the IUSM Purdue TREAT‐AD Center is to enable and advance the next generation of drug targets for the treatment of AD. The validation of anti‐TREM2 agonistic antibodies as research tools will enable comprehensive studies of the TREM2/DAP12 signaling and potential drug targets within the pathway including SHIP1, PLCG2 and the Scr family kinase Lyn and Hck.
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    Impact of Knowledge About Alzheimer’s Disease on Interest in Participation in Biomarker Research
    (Wiley, 2025-01-09) Etchison, Taylor; Eliacin, Johanne; Polsinelli, Angelina J.; Richards, Ralph; Richards, Mollie; Campbell, Christopher; Shaw, Pamella; Gao, Sujuan; Van Heiden, Sarah; Risacher, Shannon L.; Hendrie, Hugh C.; Saykin, Andrew J.; Wang, Sophia; Radiology and Imaging Sciences, School of Medicine
    Background: Previous studies suggest limited knowledge about Alzheimer’s Disease (AD) is a barrier to underrepresented group participation in AD research. Connections between knowledge of AD and factors like social determinants of health or confidence in biomarker research have not been carefully examined. We hypothesized perceived knowledge about AD would be associated with research hesitancy independent of sociodemographics and trust of researchers. Method: The AD‐REACH study surveyed 399 research‐naïve non‐Hispanic Black and white adults 55 years and older living in Indianapolis, Indiana. Participants reported perceived knowledge of AD on a 5‐point scale (5 = “I know what [AD] is, what causes it, and how to manage and prevent it;” 1 = “I know nothing at all”). Knowledge was dichotomized into higher (≥ 3) and lower (< 3) levels. Demographics were compared using chi‐squared tests and t‐tests. Ordinal logistic regression models examined the association between perceived knowledge about AD and outcome variables (e.g., hesitancy towards research participation) and were adjusted for sociodemographics and very high trust of researchers. Result: Those who reported being Black, male, from a higher Area Deprivation Index, or having less than 16 years of education had lower perceived knowledge about AD (Table 1). Trust of researchers was not associated with perceived knowledge. Those reporting lower AD knowledge described more hesitancy to participate in biomarker research (53.0% vs 72.7%, OR 2.50, 95% CI 4.35‐1.39, p = 0.002) and blood draw (62.3% vs 80.9%, OR 2.17, 95% CI 4.00‐1.19, p = 0.012) but not neuroimaging procedures (Table 2). Those with lower perceived knowledge of AD were more likely to need additional information to make decision about whether to participate in research (58.1% vs 29.9%, OR 3.70, 95% CI 9.09‐1.49, p = 0.005) (Table 3). Conclusion: Populations with health disparities report lower knowledge of AD, and lower perceived knowledge is associated with research hesitancy, especially for biomarker procedures. Perceived AD knowledge is also independent from trust, suggesting the feasibility of a two‐pronged intervention to foster diverse participation in AD biomarker research. Future studies will need to confirm these findings in other cohorts and examine how culturally tailored strategies to increase AD knowledge may reduce research hesitancy among underrepresented groups.