Murat Bilgel

Total Synthesis of a Functional Designer Eukaryotic Chromosome

Designer Chromosome One of the ultimate aims of synthetic biology is to build designer organisms from the ground up. Rapid advances in DNA synthesis has allowed the assembly of complete bacterial genomes. Eukaryotic organisms, with their generally much larger and more complex genomes, present an additional challenge to synthetic biologists. Annaluru et al. (p. 55, published online 27 March) designed a synthetic eukaryotic chromosome based on yeast chromosome III. The designer chromosome, shorn of destabilizing transfer RNA genes and transposons, is ∼14% smaller than its wild-type template and is fully functional with every gene tagged for easy removal. A synthetic version of yeast chromosome III with every gene tagged can substitute for the original. 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–base pair designer eukaryotic chromosome, synIII, which is based on the 316,617–base pair native Saccharomyces cerevisiae chromosome III. Changes to synIII include TAG/TAA stop-codon replacements, deletion of subtelomeric regions, introns, transfer RNAs, 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 complete design and synthesis of synIII establishes S. cerevisiae as the basis for designer eukaryotic genome biology.

Self-Reported Sleep and β-Amyloid Deposition in Community-Dwelling Older Adults

IMPORTANCE Older adults commonly report disturbed sleep, and recent studies in humans and animals suggest links between sleep and Alzheimer disease biomarkers. Studies are needed that evaluate whether sleep variables are associated with neuroimaging evidence of β-amyloid (Aβ) deposition. OBJECTIVE To determine the association between self-reported sleep variables and Aβ deposition in community-dwelling older adults. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of 70 adults (mean age, 76 [range, 53-91] years) from the neuroimaging substudy of the Baltimore Longitudinal Study of Aging, a normative aging study. EXPOSURE Self-reported sleep variables. MAIN OUTCOMES AND MEASURES β-Amyloid burden, measured by carbon 11-labeled Pittsburgh compound B positron emission tomography distribution volume ratios (DVRs). RESULTS After adjustment for potential confounders, reports of shorter sleep duration were associated with greater Aβ burden, measured by mean cortical DVR (B = 0.08 [95% CI, 0.03-0.14]; P = .005) and precuneus DVR (B = 0.11 [0.03-0.18]; P = .007). Reports of lower sleep quality were associated with greater Aβ burden measured by precuneus DVR (B = 0.08 [0.01-0.15]; P = .03). CONCLUSIONS AND RELEVANCE Among community-dwelling older adults, reports of shorter sleep duration and poorer sleep quality are associated with greater Aβ burden. Additional studies with objective sleep measures are needed to determine whether sleep disturbance causes or accelerates Alzheimer disease.

Midlife adiposity predicts earlier onset of Alzheimer’s dementia, neuropathology and presymptomatic cerebral amyloid accumulation

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, 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 1394 cognitively normal individuals at baseline (8643 visits; average follow-up interval 13.9 years), among whom 142 participants developed incident AD. In two subsamples 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 but not CERAD (Consortium to Establish a Registry for Alzheimers Disease) neuritic plaque scores at autopsy overall. Associations between midlife BMI and brain amyloid burden approached statistical significance. Thus, higher midlife BMI was also associated with greater fibrillar amyloid measured by global mean cortical distribution volume ratio (P=0.075) and within the precuneus (left, P=0.061; right, P=0.079). 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.

Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample

The delineation of the relative temporal trajectories of specific cognitive measures associated with Alzheimers disease (AD) is important for evaluating preclinical markers and monitoring disease progression.

Extreme Deep White Matter Hyperintensity Volumes Are Associated with African American Race

Background: African Americans (AAs) have a higher prevalence of extreme ischemic white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) than do European Americans (EAs) based on the Cardiovascular Health Study (CHS) score. Ischemic white matter disease, limited to the deep white matter, may be biologically distinct from disease in other regions and may reflect a previously observed trend toward an increased risk of subcortical lacunar infarcts in AAs. We hypothesized that extreme deep WMH volume (DWMV) or periventricular volume (PV) may also have a higher prevalence in AAs. Thus, we studied extreme CHS scores and extreme DWMV and PV in a healthy population enriched for cardiovascular disease risk factors. Methods: We imaged the brains of 593 subjects who were first-degree relatives of probands with early onset coronary disease prior to 60 years of age. WMHs were manually delineated on 3-tesla cranial MRI by a trained radiology reader; the location and volume of lesions were characterized using automated software. DWMV and PV were measured directly with automated software, and the CHS score was determined by a neuroradiologist. Volumes were characterized as being in the upper 25% versus lower 75% of total lesion volume. Volumes in the upper versus the remaining quartiles were examined for AA versus EA race using multiple logistic regression (generalized estimating equations adjusted for family relatedness) and adjusted for major vascular disease risk factors including age ≥55 years versus <55, sex, current smoking, obesity, hypertension, diabetes and low-density lipoprotein >160 mg/dl. Results: Participants were 58% women and 37% AAs, with a mean age of 51.5 ± 11.0 years (range, 29-74 years). AAs had significantly higher odds of having extreme DWMVs (odds ratio, OR, 1.8; 95% confidence interval, CI, 1.2-2.9; p = 0.0076) independently of age, sex, hypertension and all other risk factors. AAs also had significantly higher odds of having extreme CHS scores ≥3 (OR, 1.3; 95% CI, 1.1-3.6; p = 0.025). Extreme PV was not significantly associated with AA race (OR, 1.3; 95% CI, 0.81-2.1; p = 0.26). Conclusions: AAs from families with early-onset cardiovascular disease are more likely to have extreme DWMVs (a subclinical form of cerebrovascular disease) and an extreme CHS score, but not extreme PV, independently of age and other cardiovascular disease risk factors. These findings suggest that this AA population is at an increased risk for DWMV and may be at an increased risk for future subcortical stroke. Longitudinal studies are required to see if DWMV is predictive of symptomatic subcortical strokes in this population.

A Multivariate Nonlinear Mixed Effects Model for Longitudinal Image Analysis: Application to Amyloid Imaging

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 mm(3). 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 Alzheimers 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.

Individual estimates of age at detectable amyloid onset for risk factor assessment

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.

β-Amyloid Burden Predicts Lower Extremity Performance Decline in Cognitively Unimpaired Older Adults

Background Motor slowing is associated with risk of Alzheimers disease. Whether β-amyloid (Aβ) burden is associated with motor decline, independent of cognitive decline, is unknown. Methods About 59 cognitively unimpaired older participants had baseline PET-PiB scans and repeated measures of lower (usual gait speed, 400-m time, Health ABC Physical Performance Battery (HABCPPB) score, total standing balance time) and upper (mean tapping time) extremity performance during a mean follow-up of 4.7 years. Linear mixed effect models examined the relationship between baseline Aβ burden and motor decline, adjusting for age, sex, body mass index, cardiovascular risk, APOE ɛ4 status, memory decline, depressive symptoms, ankle-arm index, processing speed, executive function, and cerebrovascular disease. Results Higher mean cortical Aβ burden was associated with greater declines in gait speed and HABCPPB score and a greater increase in 400-m time. Higher Aβ of putamen was associated with declines in all lower extremity measures, including balance. Higher Aβ of dorsolateral prefrontal cortex and lateral temporal lobe was associated with declines of gait speed and 400-m time, and of precuneus with a greater increase in 400-m time. Associations remained similar after further adjustment. Conclusions In cognitively unimpaired older adults, Aβ burden overall and in specific brain regions are risk factors for lower extremity motor decline, independent of memory function. These findings provide the first empirical evidence that Aβ burden is a risk factor for mobility decline in older adults.

Temporal trajectory and progression score estimation from voxelwise longitudinal imaging measures: Application to amyloid imaging

Abstract. Cortical β-amyloid deposition begins in Alzheimers disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.

Voxelwise Relationships Between Distribution Volume Ratio and Cerebral Blood Flow: Implications for Analysis of β-Amyloid Images

Quantification of β-amyloid (Aβ) in vivo is often accomplished using the distribution volume ratio (DVR), based on a simplified reference tissue model. We investigated the local relationships between DVR and cerebral blood flow (CBF), as well as relative CBF (R1), in nondemented older adults. Methods: Fifty-five nondemented participants (mean age, 78.5 y) in the Baltimore Longitudinal Study of Aging underwent 15O-H2O PET CBF and dynamic 11C-PiB PET. 15O-H2O PET images were normalized and smoothed using SPM. A simplified reference tissue model with linear regression and spatial constraints was used to generate parametric DVR images. The DVR images were regressed on CBF images on a voxel-by-voxel basis using robust biologic parametric mapping, adjusting for age and sex (false discovery rate, P = 0.05; spatial extent, 50 voxels). DVR images were also regressed on R1 images, a measure of the transport rate constant from vascular space to tissue. All analyses were performed on the entire sample, and on high and low tertiles of mean cortical DVR. Results: Voxel-based analyses showed that increased DVR is associated with increased CBF in the frontal, parietal, temporal, and occipital cortices. However, this association appears to spare regions that typically show early Aβ deposition. A more robust relationship between DVR and CBF was observed in the lower tertile of DVR, that is, negligible cortical Aβ load, compared with the upper tertile of cortical DVR and Aβ load. The spatial distributions of the DVR–CBF and DVR–R1 correlations showed similar patterns. No reliable negative voxelwise relationships between DVR and CBF or R1 were observed. Conclusion: Robust associations between DVR and CBF at negligible Aβ levels, together with similar spatial distributions of DVR–CBF and DVR–R1 correlations, suggest that regional distribution of DVR reflects blood flow and tracer influx rather than pattern of Aβ deposition in those with minimal Aβ load. DVR–CBF associations in individuals with a higher DVR are more likely to reflect true associations between patterns of Aβ deposition and CBF or neural activity. These findings have important implications for analysis and interpretation of voxelwise correlations with external variables in individuals with varying amounts of Aβ load.