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Rapid advances in DNA synthesis techniques have made it possible to engineer viruses, biochemical pathways and assemble bacterial genomes. Here, we report the synthesis of a functional 272,871 bp designer eukaryotic chromosome, synIII, which is based on the 316,617 bp native Saccharomyces cerevisiae chromosome III. Changes to synIII include TAG/TAA stop-codon replacements, deletion of subtelomeric regions, introns, tRNAs, transposons and silent mating loci as well as insertion of loxPsym sites to enable genome scrambling. SynIII is functional in S. cerevisiae. Scrambling of the chromosome in a heterozygous diploid reveals a large increase in “a mater” derivatives resulting from loss of the MATα allele on synIII. The total synthesis of synIII represents the first complete design and synthesis of a eukaryotic chromosome, establishing S. cerevisiae as the basis for designer eukaryotic genome biology.

Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Understanding how midlife risk factors influence age-at-onset (AAO) of Alzheimer’s disease (AD) may provide clues to delay disease expression. Although midlife adiposity predicts increased incidence of AD, it is unclear whether it affects AAO and severity of Alzheimer’s neuropathology. Using a prospective population-based cohort, the Baltimore Longitudinal Study of Aging (BLSA), this study aims to examine the relationships between midlife body mass index (BMI) and (1) AAO of AD (2) severity of Alzheimer’s neuropathology and (3) fibrillar brain amyloid deposition during aging. We analyzed data on 1,394 cognitively normal individuals at baseline (8643 visits; average follow up interval 13.9 years), among whom, 142 participants developed incident AD. In two sub-samples of BLSA, 191 participants underwent autopsy and neuropathological assessment, and 75 non-demented individuals underwent brain amyloid imaging. Midlife adiposity was derived from BMI data at 50 years of age. We find that each unit increase in midlife BMI predicts earlier onset of AD by 6.7 months (P=0.013). Higher midlife BMI was associated with greater Braak neurofibrillary tangle but not CERAD neuritic plaque scores at autopsy overall and with greater fibrillar amyloid measured by global mean cortical distribution volume ratio and within the precuneus. In conclusion, midlife overweight predicts earlier onset of AD and greater burden of Alzheimer’s neuropathology. A healthy BMI at midlife may delay the onset of AD.

Understanding short-term cognitive decline in relation to Alzheimer's neuroimaging biomarkers in early stages of the development of neuropathology and neurodegeneration will inform participant recruitment and monitoring strategies in clinical trials aimed at prevention of cognitive impairment and dementia. We assessed associations among neuroimaging measures of cerebral amyloid pathology, a hallmark Alzheimer's neuropathology, hippocampal atrophy, and prospective cognition among 171 cognitively normal Baltimore Longitudinal Study of Aging participants (baseline age 56-95 years, 48% female, 562 cognitive assessments, 3.7 years follow-up). We categorized each individual based on dichotomous amyloid pathology (A) and hippocampal neurodegeneration (N) status at baseline: A-N-, A+N-, A-N+, A+N+. We conducted linear mixed effects analyses to assess cross-sectional and longitudinal trends in cognitive test z-scores by amyloid and neurodegeneration group. To investigate the effects of amyloid dose and degree of hippocampal atrophy, we assessed the associations of continuous mean cortical amyloid level and hippocampal volume with cognitive performance among individuals with detectable amyloid pathology at baseline. Individuals with amyloidosis or hippocampal atrophy had steeper longitudinal declines in verbal episodic memory and learning compared to those with neither condition (A+N- versus A-N-: β = - 0.069, P = 0.017; A-N+ versus A-N-: β = - 0.081, P = 0.025). Among individuals with hippocampal atrophy, amyloid positivity was associated with steeper declines in verbal memory (β = - 0.123, P = 0.015), visual memory (β = - 0.121, P = 0.036), language (β = - 0.144, P = 0.0004), and mental status (β = - 0.242, P = 0.002). Similarly, among individuals with amyloidosis, hippocampal atrophy was associated with steeper declines in verbal memory (β = - 0.135, P = 0.004), visual memory (β = - 0.141, P = 0.010), language (β = - 0.108, P = 0.006), and mental status (β = - 0.165, P = 0.022). Presence of both amyloidosis and hippocampal atrophy was associated with greater declines than would be expected by their additive contributions in visual memory (β = - 0.139, P = 0.036), language (β = - 0.132, P = 0.005), and mental status (β = - 0.170, P = 0.049). Neither amyloidosis nor hippocampal atrophy was predictive of declines in executive function, processing speed, or visuospatial ability. Among individuals with amyloidosis, higher baseline amyloid level was associated with lower concurrent visual memory, steeper declines in language, visuospatial ability, and mental status, whereas greater hippocampal atrophy was associated with steeper declines in category fluency. Our results suggest that both amyloid pathology and neurodegeneration have disadvantageous, in part synergistic, effects on prospective cognition. These cognitive effects are detectable early among cognitively normal individuals with amyloidosis, who are in preclinical stages of Alzheimer's disease according to research criteria. Our findings highlight the importance of e...

INTRODUCTION Individualized estimates of age at detectable amyloid-beta (Aβ) accumulation, distinct from amyloid positivity, allow for analysis of onset age of Aβ accumulation as an outcome measure to understand risk factors. METHODS Using longitudinal Pittsburgh compound B (PiB) PET data from Baltimore Longitudinal Study of Aging, we estimated the age at which each PiB+ individual began accumulating Aβ. We used survival analysis methods to quantify risk of accumulating Aβ and differences in onset age of Aβ accumulation in relation to APOE ε4 status and sex among 36 APOE ε4 carriers and 83 non-carriers. RESULTS Age at onset of Aβ accumulation for the APOE ε4− and ε4+ groups was 73.1 and 60.7, respectively. APOE ε4 positivity conferred a 3-fold risk of accumulating Aβ after adjusting for sex and education. DISCUSSION Estimation of onset age of amyloid accumulation may help gauge treatment efficacy in interventions to delay symptom onset in Alzheimer’s disease.

It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm3. These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. We apply our method to studying cortical β-amyloid deposition, a hallmark of preclinical Alzheimer's disease, as measured using positron emission tomography. Results on 104 individuals with a total of 300 visits suggest that precuneus is the earliest cortical region to accumulate amyloid, closely followed by the cingulate and frontal cortices, then by the lateral parietal cortex. The extracted progression scores reveal a pattern similar to mean cortical distribution volume ratio (DVR), an index of global brain amyloid levels. The proposed method can be applied to other types of longitudinal imaging data, including metabolism, blood flow, tau, and structural imaging-derived measures, to extract individualized summary scores indicating disease progression and to provide voxelwise trajectories that can be compared between brain regions.

Alzheimer’s disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease.