Faculty Bibliography
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160
Peroxiredoxin 6 mediates protective function of astrocytes in Aβ proteostasis
BACKGROUND:) activities involved in repair of oxidatively damaged cell membrane lipids and cellular signaling. In the CNS, PRDX6 is uniquely expressed by astrocytes and its exact function remains unexplored. METHODS:AD transgenic mice were once crossed to mice overexpressing wild-type Prdx6 allele or to Prdx6 knock out mice. Aβ pathology and associated neuritic degeneration were assessed in mice aged 10 months. Laser scanning confocal microscopy was used to characterize Aβ plaque morphology and activation of plaque-associated astrocytes and microglia. Effect of Prdx6 gene dose on plaque seeding was assessed in mice aged six months. RESULTS:AD transgenic mice promotes selective enticement of astrocytes to Aβ plaques and penetration of plaques by astrocytic processes along with increased number and phagocytic activation of periplaque microglia. This effects suppression of nascent plaque seeding and remodeling of mature plaques consequently curtailing brain Aβ load and Aβ-associated neuritic degeneration. Conversely, Prdx6 haplodeficiency attenuates astro- and microglia activation around Aβ plaques promoting Aβ deposition and neuritic degeneration. CONCLUSIONS:We identify here PRDX6 as an important factor regulating response of astrocytes toward Aβ plaques. Demonstration that phagocytic activation of periplaque microglia vary directly with astrocytic PRDX6 expression level implies previously unappreciated astrocyte-guided microglia effect in Aβ proteostasis. Our showing that upregulation of PRDX6 attenuates Aβ pathology may be of therapeutic relevance for AD.
PMID: 32907613
ISSN: 1750-1326
CID: 4589342
White matter atrophy in cerebral amyloid angiopathy
OBJECTIVE:We postulated that cerebral amyloid angiopathy (CAA) is associated with white matter atrophy (WMA) and that WMA can be related to cognitive changes in CAA. METHODS:White matter volume expressed as percent of intracranial volume (pWMV) of prospectively enrolled patients without dementia diagnosed with probable CAA was compared to age-matched healthy controls (HC) and patients with Alzheimer disease (AD). Cognitive scores were also sought to understand the potential effects of WMA on cognitive function. RESULTS:= 0.003, respectively). All associations remained independent in multivariable analyses. Within the CAA cohort, higher pWMV independently correlated with better scores of executive function. CONCLUSIONS:Patients with CAA show WMA when compared to age-matched HC and patients with AD. WMA independently correlates with the number of lobar microbleeds, a marker of CAA severity. Consistent spatial patterns of WMA especially in posterior regions might be related to CAA. The association between WMA and measures of executive function suggests that WMA might represent an important mediator of CAA-related neurologic dysfunction.
PMCID:7455340
PMID: 32611644
ISSN: 1526-632x
CID: 5864692
The genetic architecture of the human cerebral cortex
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
PMID: 32193296
ISSN: 1095-9203
CID: 5864682
Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer's Pathophysiology
Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer's disease (AD), a complex progressive disease, as a model because the well-established evidence provides a "gold-standard" causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the "gold standard" graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible.
PMCID:7031278
PMID: 32076020
ISSN: 2045-2322
CID: 5864672
FAM222A encodes a protein which accumulates in plaques in Alzheimer's disease
Alzheimer's disease (AD) is characterized by amyloid plaques and progressive cerebral atrophy. Here, we report FAM222A as a putative brain atrophy susceptibility gene. Our cross-phenotype association analysis of imaging genetics indicates a potential link between FAM222A and AD-related regional brain atrophy. The protein encoded by FAM222A is predominantly expressed in the CNS and is increased in brains of patients with AD and in an AD mouse model. It accumulates within amyloid deposits, physically interacts with amyloid-β (Aβ) via its N-terminal Aβ binding domain, and facilitates Aβ aggregation. Intracerebroventricular infusion or forced expression of this protein exacerbates neuroinflammation and cognitive dysfunction in an AD mouse model whereas ablation of this protein suppresses the formation of amyloid deposits, neuroinflammation and cognitive deficits in the AD mouse model. Our data support the pathological relevance of protein encoded by FAM222A in AD.
PMCID:6972869
PMID: 31964863
ISSN: 2041-1723
CID: 5134432
Detection of Cerebrovascular Loss in the Normal Aging C57BL/6 Mouse Brain Using in vivo Contrast-Enhanced Magnetic Resonance Angiography
Microvascular rarefaction, or the decrease in vascular density, has been described in the cerebrovasculature of aging humans, rats, and, more recently, mice in the presence and absence of age-dependent diseases. Given the wide use of mice in modeling age-dependent human diseases of the cerebrovasculature, visualization, and quantification of the global murine cerebrovasculature is necessary for establishing the baseline changes that occur with aging. To provide in vivo whole-brain imaging of the cerebrovasculature in aging C57BL/6 mice longitudinally, contrast-enhanced magnetic resonance angiography (CE-MRA) was employed using a house-made gadolinium-bearing micellar blood pool agent. Enhancement in the vascular space permitted quantification of the detectable, or apparent, cerebral blood volume (aCBV), which was analyzed over 2 years of aging and compared to histological analysis of the cerebrovascular density. A significant loss in the aCBV was detected by CE-MRA over the aging period. Histological analysis via vessel-probing immunohistochemistry confirmed a significant loss in the cerebrovascular density over the same 2-year aging period, validating the CE-MRA findings. While these techniques use widely different methods of assessment and spatial resolutions, their comparable findings in detected vascular loss corroborate the growing body of literature describing vascular rarefaction aging. These findings suggest that such age-dependent changes can contribute to cerebrovascular and neurodegenerative diseases, which are modeled using wild-type and transgenic laboratory rodents.
PMCID:7606987
PMID: 33192479
ISSN: 1663-4365
CID: 4671302
Canadian journal of statistics = Revue canadienne de statistique. 2019:47(1):108-131.DOI: 10.1002/cjs.11487
A Review of Statistical Methods in Imaging Genetics
With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.
PMCID:6605768
PMID: 31274952
ISSN: 0319-5724
CID: 5134412
A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative
Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.
PMCID:6570412
PMID: 30569583
ISSN: 1097-0193
CID: 5134422
Anti-prion Protein Antibody 6D11 Restores Cellular Proteostasis of Prion Protein Through Disrupting Recycling Propagation of PrPSc and Targeting PrPSc for Lysosomal Degradation
PrPSc is an infectious and disease-specific conformer of the prion protein, which accumulation in the CNS underlies the pathology of prion diseases. PrPSc replicates by binding to the cellular conformer of the prion protein (PrPC) expressed by host cells and rendering its secondary structure a likeness of itself. PrPC is a plasma membrane anchored protein, which constitutively recirculates between the cell surface and the endocytic compartment. Since PrPSc engages PrPC along this trafficking pathway, its replication process is often referred to as "recycling propagation." Certain monoclonal antibodies (mAbs) directed against prion protein can abrogate the presence of PrPSc from prion-infected cells. However, the precise mechanism(s) underlying their therapeutic propensities remains obscure. Using N2A murine neuroblastoma cell line stably infected with 22L mouse-adapted scrapie strain (N2A/22L), we investigated here the modus operandi of the 6D11 clone, which was raised against the PrPSc conformer and has been shown to permanently clear prion-infected cells from PrPSc presence. We determined that 6D11 mAb engages and sequesters PrPC and PrPSc at the cell surface. PrPC/6D11 and PrPSc/6D11 complexes are then endocytosed from the plasma membrane and are directed to lysosomes, therefore precluding recirculation of nascent PrPSc back to the cell surface. Targeting PrPSc by 6D11 mAb to the lysosomal compartment facilitates its proteolysis and eventually shifts the balance between PrPSc formation and degradation. Ongoing translation of PrPC allows maintaining the steady-state level of prion protein within the cells, which was not depleted under 6D11 mAb treatment. Our findings demonstrate that through disrupting recycling propagation of PrPSc and promoting its degradation, 6D11 mAb restores cellular proteostasis of prion protein.
PMID: 29987703
ISSN: 1559-1182
CID: 3191832
Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics
In the Alzheimer's disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.
PMCID:6381141
PMID: 30783207
ISSN: 2045-2322
CID: 5864622
