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Reiman, E. M.

Publications and source records attributed to Reiman, E. M..

12 recordsLinked to original sources

Upregulation of the Proto-Oncogene Src Kinase in Alzheimer's Disease: From Molecular Interactions to Therapeutic Potential

Alzheimers disease (AD) is a progressive neurodegenerative disease, resulting in an irreversible deterioration of multiple brain regions associated with cognitive dysfunction. Phosphorylation of the microtubule-associated protein, Tau, is known to occur decades before symptomatic AD. The Src family of tyrosine kinases are known to phosphorylate select tyrosine sites on Tau and promote microtubule disassembly and subsequent neurofibrillary tangle (NFT) formation. Our data show that the proto-oncogene, non-receptor tyrosine kinase Src colocalizes with a range of late (PHF1) to early (MC1) AD-associated phosphorylated Tau epitopes. The strongest co-occurrence is seen with MC1 (probability of MC1 given Src =100%), an early AD-specific conformational dependent epitope. Single-cell RNA sequencing data of 101 subjects show that Src is upregulated in both AD inhibitory and excitatory neurons. The most significantly affected, by orders of magnitude, were excitatory neurons which are the most prone to pathological Tau accumulation. We measured Src phosphorylation by mass spectrometry across a cohort of 48 patient neocortical tissues and found that Src has increased phosphorylation on Ser75, Tyr187, and Tyr440 in AD, showing that Src kinase undergoes distinct phosphorylation alterations in AD. Through Brownian dynamics simulations of Src and Tau, we show that as Tau undergoes the transition into disease-associated paired helical filaments, there is a notable seven-fold increase in Src contact with Tau. These results collectively emphasize Src kinases central role in Tau phosphorylation and its close association with Tau epitopes, presenting a promising target for potential therapeutic intervention.

neuroscience↗

Interpretable deep learning framework towards understanding molecular changes in human brains with Alzheimer's disease: implication for microglia activation and sex differences in AD

INTRODUCTIONThe objective of this study is to characterize the dysregulation of gene expression in AD affected brain tissues through an interpretable deep learning framework. METHODSWe trained multi-layer perceptron models for the classification of neuropathologically confirmed AD vs. controls using transcriptomic data from three brain regions of ROSMAP study. The disease spectrum was then modeled as a progressive trajectory. SHAP value was derived to explain model predictions and identify significantly implicated genes for subsequent gene co-expression network analysis. RESULTSThe models achieved excellent performance in classification and prediction in two external datasets from Mayo RNA-seq cohort and Mount Sinai Brain Bank cohort. SHAP explainer revealed common and specific transcriptomic signatures from different brain regions. DISCUSSIONWe identified common gene signatures among different brain regions in microglia and sex specific modules in neurons implicated in AD. This work paves the way for utilizing artificial intelligence approaches in studying AD at the molecular level. Research-in-ContextO_LISystematic review: Postmortem brain transcriptomes have been analyzed to study the molecular changes associated with Alzheimers disease, usually by a direct contrast approach such as differential gene expression analysis. Nuanced gene regulatory networks thus cannot be easily pinpointed from convoluted data such as those from bulk-tissue profiling. We applied a novel interpretable deep learning approach to dissect the RNA-seq data collected from three different brain regions of a large clinical cohort and identified significant genes for network analysis implicated for AD. C_LIO_LIInterpretation: Our models successfully predicted neuropathological and clinical traits in both internal and external validations. We corroborated known microglial biology in addition to revealing novel sex chromosome-linked gene contributing to sex dimorphism in AD. C_LIO_LIFuture directions: The framework could have broad utility for interpreting multi-omic data such as those from single-cell profiling, to advance our understanding of molecular mechanisms of complex human disease such as AD. C_LI HighlightsO_LIWe applied novel interpretable deep learning methods to postmortem brain transcriptomes from three different brain regions C_LIO_LIWe interpreted the models to identify genes most strongly implicated in AD C_LIO_LINetwork analysis corroborated known microglial biology and revealed novel sex specific transcriptional factors associated with neuronal loss in AD C_LI

neuroscience↗

A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease

The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimers disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.

genomics↗

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons With and Without Significant Amyloid Plaques

BackgroundAmyloid-{beta} (A{beta}) plaques play a pivotal role in Alzheimers disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing A{beta} plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of A{beta}-plaques. ObjectiveTo develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma A{beta}42/40 ratio. MethodsWe extracted hippocampal multivariate morphometry statistics (MMS) from MR images and together with plasma A{beta}42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimers Institute (BAI), including prediction accuracy, precision, recall rate, F1 score and AUC score. ResultsResults from ADNI (mean age 72.6, A{beta}+ rate 49.5%) and BAI (mean age 66.2, A{beta}+ rate 36.9%) datasets revealed the integrated multimodal (IMM) models superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma A{beta}42/40 (0.73 and 0.81) predictors. ConclusionOur IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimers disease diagnostics, leveraging diverse pathologic features related to A{beta} plaques and structural MRI.

neuroscience↗

APOE4/4 is linked to damaging lipid droplets in Alzheimer's microglia

Several genetic risk factors for Alzheimers Disease (AD) implicate genes involved in lipid metabolism and many of these lipid genes are highly expressed in glial cells. However, the relationship between lipid metabolism in glia and AD pathology remains poorly understood. Through single-nucleus RNA-sequencing of AD brain tissue, we have identified a microglial state defined by the expression of the lipid droplet (LD) associated enzyme ACSL1 with ACSL1-positive microglia most abundant in AD patients with the APOE4/4 genotype. In human iPSC-derived microglia (iMG) fibrillar A{beta} (fA{beta}) induces ACSL1 expression, triglyceride synthesis, and LD accumulation in an APOE-dependent manner. Additionally, conditioned media from LD-containing microglia leads to Tau phosphorylation and neurotoxicity in an APOE-dependent manner. Our findings suggest a link between genetic risk factors for AD with microglial LD accumulation and neurotoxic microglial-derived factors, potentially providing novel therapeutic strategies for AD.

neuroscience↗

FEDERATED MORPHOMETRY FEATURE SELECTION FOR HIPPOCAMPAL MORPHOMETRY ASSOCIATED BETA-AMYLOID AND TAU PATHOLOGY

Amyloid-{beta} (A{beta}) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimers disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of A{beta}/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with A{beta}/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two ADNI cohorts to understand hippocampal alterations that relate to A{beta}/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI) and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89x speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal A{beta}/tau. As potential biomarkers for A{beta}/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between A{beta}/tau burden and hippocampal morphometry.

neuroscience↗

Deep Learning-Based Brain Transcriptomic Signatures Associated with the Neuropathological and Clinical Severity of Alzheimer's Disease

Brain tissue gene expression from donors with and without Alzheimers disease (AD) have been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyze RNA-seq data from 1,114 brain donors from the AMP-AD consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-{beta} plaque, tau neurofibrillary tangles, and clinical severity in multiple AD dementia populations. Starting from the cross-sectional data in the ROSMAP cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors AD progression. A severity index (SI) was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this dataset, SIs were found to be very closely correlated with all AD neuropathology biomarkers (R [~] 0.5, p < 1e-11) and global cognitive function (R = -0.68, p < 2.2e-16). We then applied the model to additional transcriptomic datasets from different brain regions (MAYO, n = 266; MSBB, n = 214), and observed that the model remained significantly predictive (p < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with AD co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with AD, other age-related neurological disorders and controls; and revealed the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyze heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like AD.

genomics↗

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,125 Individuals from the ADNI and OASIS Databases

Biomarker-assisted preclinical/early detection and intervention in Alzheimers disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (A{beta}) plaques in the human brain. However, current methods to detect A{beta} pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain A{beta} burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each subject. Then we apply these individual representations and a binary random forest classifier to predict brain A{beta} positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate A{beta} positivity in people with mild cognitive impairment (MCI) (Accuracy (ACC)=0.89 (ADNI)) and in cognitively unimpaired (CU) individuals (ACC=0.79 (ADNI) and ACC=0.81 (OASIS)). These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM), and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

bioinformatics↗

A Consensus Proteomic Analysis of Alzheimer’s Disease Brain and Cerebrospinal Fluid Reveals Early Changes in Energy Metabolism Associated with Microglia and Astrocyte Activation

Our understanding of the biological changes in the brain associated with Alzheimers disease (AD) pathology and cognitive impairment remains incomplete. To increase our understanding of these changes, we analyzed dorsolateral prefrontal cortex of control, asymptomatic AD, and AD brains from four different centers by label-free quantitative mass spectrometry and weighted protein co-expression analysis to obtain a consensus protein co-expression network of AD brain. This network consisted of 13 protein co-expression modules. Six of these modules correlated with amyloid-{beta} plaque burden, tau neurofibrillary tangle burden, cognitive function, and clinical functional status, and were altered in asymptomatic AD, AD, or in both disease states. These six modules reflected synaptic, mitochondrial, sugar metabolism, extracellular matrix, cytoskeletal, and RNA binding/splicing biological functions. The identified protein network modules were preserved in a community-based cohort analyzed by a different quantitative mass spectrometry approach. They were also preserved in temporal lobe and precuneus brain regions. Some of the modules were influenced by aging, and showed changes in other neurodegenerative diseases such as frontotemporal dementia and corticobasal degeneration. The module most strongly associated with AD pathology and cognitive impairment was the sugar metabolism module. This module was enriched in AD genetic risk factors, and was also highly enriched in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from the sugar metabolism module were increased in cerebrospinal fluid from asymptomatic AD and AD cases, highlighting their potential as biomarkers of the altered brain network. In this study of >2000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brain that may serve as therapeutic targets and fluid biomarkers for the disease.

systems biology↗

Severe Hyposmia Distinguishes Neuropathologically Confirmed Dementia with Lewy Bodies from Alzheimer’s Disease Dementia

Due to the absence of core clinical features, many subjects with neuropathologically-confirmed dementia with Lewy bodies (DLB) are never diagnosed as such during life. Most of these are diagnosed with Alzheimers disease dementia (ADD) or unspecified dementia. Unrecognized DLB therefore is a critical impediment to clinical studies and treatment trials of both ADD and DLB. There are numerous published studies that suggest that olfactory function tests may be able to differentiate some neurodegenerative conditions from each other and from normal subjects, but there are very few studies with neuropathological confirmation of diagnosis. We compared University of Pennsylvania Smell Identification Test (UPSIT) results in 209 subjects: 1) 29 concurrently meeting intermediate or high consensus clinicopathological criteria for both DLB and ADD 2) 96 meeting criteria for ADD without DLB 3) 84 control subjects that were non-demented and without parkinsonism at death. The DLB subjects had significantly lower (one-way ANOVA p < 0.0001, pairwise Bonferroni p < 0.05) first and mean UPSIT scores (13.7 and 13.2) than ADD (23.3 and 22.2) or controls (29.6 and 28.9). For subjects with first and mean UPSIT scores less than 20 and 17, respectively, Firth logistic regression analysis, adjusted for age, gender and mean MMSE score, conferred statistically significant odds ratios of 17.5 and 18.0 for predicting a DLB vs ADD diagnosis, as compared to 3.3 for the presence or absence of visual hallucinations throughout the clinical observation period. To our knowledge, this is the largest study to date comparing olfactory function in subjects with neuropathologically confirmed DLB and ADD. Olfactory function testing may be a convenient and inexpensive strategy for enriching dementia studies or clinical trials with DLB subjects, or conversely, reducing the inclusion of DLB subjects in ADD studies or trials.

neuroscience↗

Large-scale proteomic analysis of human prefrontal cortex identifies proteins associated with cognitive trajectory in advanced age

In advanced age, some individuals maintain a stable cognitive trajectory while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by traditional neurodegenerative pathologies. Hence, to identify new processes underlying variation in cognitive trajectory, we perform an unbiased proteome-wide association study of cognitive trajectory in a discovery (n=104) and replication cohort (n=39) of initially cognitively unimpaired, longitudinally assessed older-adult brain donors. We find 579 proteins associated with cognitive trajectory after meta-analysis. Notably, we present novel evidence for increased neuronal mitochondrial activities in cognitive stability regardless of the burden of traditional neuropathologies. Furthermore, we provide additional evidence for increased synaptic activities and decreased inflammation and apoptosis in cognitive stability. Importantly, we nominate proteins associated with cognitive trajectory, particularly the 38 proteins that act independently of neuropathologies and are also hub proteins of protein co-expression networks, as promising targets for future mechanistic studies of cognitive trajectory.

neuroscience↗

Faster Cognitive Decline in Dementia due to Alzheimer Disease with Clinically Undiagnosed Lewy Body Disease

Neuropathology has demonstrated a high rate of comorbid pathology in dementia due to Alzheimers disease (ADD). The most common major comorbidity is Lewy body disease (LBD), either as dementia with Lewy bodies (AD-DLB) or Alzheimers disease with Lewy bodies (AD-LB), the latter representing subjects with ADD and LBD not meeting neuropathological distribution and density thresholds for DLB. Although it has been established that ADD subjects with undifferentiated LBD have a more rapid cognitive decline than those with ADD alone, it is still unknown whether AD-LB subjects, who represent the majority of LBD and approximately one-third of all those with ADD, have a different clinical course. Subjects with dementia included those with \"pure\" ADD (n = 137), AD-DLB (n = 64) and AD-LB (n = 114), all with two or more complete Mini Mental State Examinations (MMSE) and a full neuropathological examination. Linear mixed models assessing MMSE change showed that the AD-LB group had significantly greater decline compared to the ADD group ({beta} = -0.69, 95% CI: -1.05, -0.33, p<0.001) while the AD-DLB group did not ({beta} = -0.30, 95% CI: -0.73, 0.14, p = 0.18). Of those with AD-DLB and AD-LB, only 66% and 2.1%, respectively, had been diagnosed with LBD at any point during their clinical course. The probable cause of LBD clinical detection failure is the lack of a sufficient set of characteristic core clinical features. Core DLB clinical features were not more common in AD-LB as compared to ADD. Compared with clinically-diagnosed AD-DLB subjects, those that were clinically undetected had significantly lower prevalences of parkinsonism (p = 0.046), visual hallucinations (p = 0.0008) and dream enactment behavior (0.013). Clinical identification of ADD with LBD would allow stratified analyses of ADD clinical trials, potentially improving the probability of trial success.

neuroscience↗