👤 Clifford R Jack

White matter (WM) is a key substrate for interregional neural communication and cognitive function but the role of WM glucose metabolism in cognitive aging has been understudied. Using multimodal neuroimaging (MRI, FDG-PET, amyloid-PET) from 3142 participants (15,287 visits) across two studies, we examined the contribution of WM to cognition and identified divergent WM signatures. Higher glucose metabolism in expected WM (EWM; corpus callosum and cingulum) was associated with better cognition, whereas increased metabolism in atypical WM (AWM; corona radiata) was linked to worse cognition, indicating a compensatory mechanism. EWM metabolism declined with aging, Alzheimer's disease (AD) progression (amyloid-β and APOE-ε4 carrier), and white matter hyperintensities, while AWM metabolism increased with aging and vascular risk but was partially weakened by AD neuropathology. Longitudinally, higher EWM and lower AWM metabolism predicted slower cognitive decline. Divergent WM metabolic patterns shed light on the dynamic role of WM in maintaining cognitive function. This study emphasizes the complementary information provided by WM metabolism for predicting future cognitive decline and identifying cognitive resilience. Show less

Associations of Alzheimer's disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier vs later an individual's progression on plasma and PET Alzheimer's disease biomarkers is associated with earlier vs later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed effects models to estimate how much earlier vs later each individual progresses on plasma p-tau217, amyloid PET, tau PET, and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs, and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cutpoints, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R=0.38-0.47) in the MCSA and stronger (R=0.74-0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET AD biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer's disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer's disease co-pathologies. Show less

The genetic basis of sporadic early-onset Alzheimer's disease (EOAD) remains largely unknown, prompting evaluation of late-onset Alzheimer's disease (LOAD) polygenic risk in EOAD. A LOAD polygenic score (PGS) was calculated in the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) study and tested for associations with AD risk, cognitive performance, and imaging and fluid biomarkers. Though PGS was elevated in LOAD and EOAD, it was not a significant predictor of EOAD adjusting for APOE ε4 carrier status and was not associated with age of EOAD onset (p = 0.106) or with cognitive performance (p = 0.417). In LEADS, greater LOAD PGS was associated with differences in neuroimaging and fluid biomarkers, including elevated synaptosomal-associated protein 25 (SNAP-25) (p = 2.3 × 10 While LOAD polygenic risk contributed minimally to EOAD onset and cognitive dysfunction, PGS association with fluid biomarkers in LEADS suggests a role for LOAD polygenic risk in EOAD pathophysiology. LOAD PGSs were elevated in both LOAD and EOAD compared to controls; however, LOAD PGS did not significantly predict EOAD risk, age at onset, or cognitive performance independent of APOE ε4 in the LEADS. Higher LOAD PGS was associated with lower amyloid PET Centiloids (less brain amyloid deposition) as well as lower CSF biomarker Aβ42 in LEADS (proxy marker suggesting higher brain amyloid deposition) in LEADS; these contradictory findings support the need for larger studies to further investigate whether LOAD PGS is associated with increased amyloid deposition in EOAD. Higher LOAD PGS was also associated with higher levels of CSF synaptosomal-associated protein 25 (SNAP-25), a key component of the SNARE complex, suggesting that LOAD genetic factors may contribute to dysregulation of synaptic transmission and/or pathological protein aggregation in EOAD. Show less

Engagement in physical and cognitive activities is associated with a decreased risk of mild cognitive impairment (MCI) and dementia, but the association with Alzheimer disease (AD) neuroimaging biomarkers is less clear. We thus examined associations of physical and cognitive activities with longitudinal trajectories of AD neuroimaging biomarkers among older adults free of dementia. We conducted a longitudinal study within the population-based Mayo Clinic Study of Aging (mean follow-up durations 1.3-3.4 years). Participants were aged 50 years or older and were cognitively unimpaired or had MCI at baseline. Engagement in physical and cognitive activities during 12 months before baseline was assessed through questionnaires. Participants underwent AD neuroimaging biomarker assessments at 1 or more time points. We ran linear mixed-effect models to examine associations between physical and cognitive activity composite scores and trajectories of individual yearly change in amyloid deposition (Pittsburgh compound B [PiB]-PET centiloid), tau burden (tau-PET standardized uptake value ratio [SUVR]), and regional glucose hypometabolism (fluorodeoxyglucose [FDG]-PET SUVR), adjusted for age, sex, We included 1,176 participants (47% female; mean [SD] age, 68.7 [9.6] years) for PiB-PET trajectories, 399 participants (49% female; mean [SD] age, 71.9 [11.0] years) for tau-PET trajectories, and 983 participants (46% female; mean [SD] age, 67.9 [9.2] years) for FDG-PET trajectories. PiB-PET and tau-PET measures increased during follow-up (3.4 [SD 4.0] and 1.3 [SD 2.1] years, respectively), whereas FDG-PET values decreased over 2.9 (SD 3.5) years of follow-up. Participants with higher total physical activity (interaction estimate 0.0017; 95% CI 0.0003-0.0031; Physical activity was associated with less synaptic dysfunction and cognitive activity with less synaptic dysfunction and lower amyloid burden over time, albeit effect sizes were small. Further research is needed to validate findings and clarify causal inference between physical and cognitive activities and AD neuroimaging biomarkers. Show less

Fluorescent fusion proteins are widely used to study protein localization and interaction dynamics in living cells. However, to fully characterize proteins and to understand their function it is crucial to determine biochemical characteristics such as enzymatic activity and binding specificity. Here we demonstrate an easy, reliable and versatile medium/high-throughput method to study biochemical and functional characteristics of fluorescent fusion proteins. Using a new system based on 96-well micro plates comprising an immobilized GFP-binding protein (GFP-mulitTrap), we performed fast and efficient one-step purification of different GFP- and YFP-fusion proteins from crude cell lysate. After immobilization we determined highly reproducible binding ratios of cellular expressed GFP-fusion proteins to histone-tail peptides, DNA or selected RFP-fusion proteins. In particular, we found Cbx1 preferentially binding to di-and trimethylated H3K9 that is abolished by phosphorylation of the adjacent serine. DNA binding assays showed, that the MBD domain of MeCP2 discriminates between fully methylated over unmethylated DNA and protein-protein interactions studies demonstrate, that the PBD domain of Dnmt1 is essential for binding to PCNA. Moreover, using an ELISA-based approach, we detected endogenous PCNA and histone H3 bound at GFP-fusions. In addition, we quantified the level of H3K4me2 on nucleosomes containing different histone variants. In summary, we present an innovative medium/high-throughput approach to analyse binding specificities of fluroescently labeled fusion proteins and to detect endogenous interacting factors in a fast and reliable manner in vitro. Show less

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2. We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8 ± 2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain. Show less