Dataset Information

Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer's Disease Progression.

ABSTRACT: Alzheimer's disease (AD) displays a long asymptomatic stage before dementia. We characterize AD stage-associated molecular networks by profiling 14,513 proteins and 34,173 phosphosites in the human brain with mass spectrometry, highlighting 173 protein changes in 17 pathways. The altered proteins are validated in two independent cohorts, showing partial RNA dependency. Comparisons of brain tissue and cerebrospinal fluid proteomes reveal biomarker candidates. Combining with 5xFAD mouse analysis, we determine 15 A?-correlated proteins (e.g., MDK, NTN1, SMOC1, SLIT2, and HTRA1). 5xFAD shows a proteomic signature similar to symptomatic AD but exhibits activation of autophagy and interferon response and lacks human-specific deleterious events, such as downregulation of neurotrophic factors and synaptic proteins. Multi-omics integration prioritizes AD-related molecules and pathways, including amyloid cascade, inflammation, complement, WNT signaling, TGF-? and BMP signaling, lipid metabolism, iron homeostasis, and membrane transport. Some A?-correlated proteins are colocalized with amyloid plaques. Thus, the multilayer omics approach identifies protein networks during AD progression.

SUBMITTER: Bai B

PROVIDER: S-EPMC7318843 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the minimalRNN with "model filling" compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.

Project description:Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. However, despite extensive clinical and genomic studies, the molecular basis of AD development and progression remains elusive.To elucidate molecular systems associated with AD, we developed a large scale gene expression dataset from 1053 postmortem brain samples across 19 cortical regions of 125 individuals with a severity spectrum of dementia and neuropathology of AD. We excluded brain specimens that evidenced neuropathology other than that characteristic of AD. For the first time, we performed a pan-cortical brain region genomic analysis, characterizing the gene expression changes associated with a measure of dementia severity and multiple measures of the severity of neuropathological lesions associated with AD (neuritic plaques and neurofibrillary tangles) and constructing region-specific co-expression networks. We rank-ordered 44,692 gene probesets, 1558 co-expressed gene modules and 19 brain regions based upon their association with the disease traits.The neurobiological pathways identified through these analyses included actin cytoskeleton, axon guidance, and nervous system development. Using public human brain single-cell RNA-sequencing data, we computed brain cell type-specific marker genes for human and determined that many of the abnormally expressed gene signatures and network modules were specific to oligodendrocytes, astrocytes, and neurons. Analysis based on disease severity suggested that: many of the gene expression changes, including those of oligodendrocytes, occurred early in the progression of disease, making them potential translational/treatment development targets and unlikely to be mere bystander result of degeneration; several modules were closely linked to cognitive compromise with lesser association with traditional measures of neuropathology. The brain regional analyses identified temporal lobe gyri as sites associated with the greatest and earliest gene expression abnormalities.This transcriptomic network analysis of 19 brain regions provides a comprehensive assessment of the critical molecular pathways associated with AD pathology and offers new insights into molecular mechanisms underlying selective regional vulnerability to AD at different stages of the progression of cognitive compromise and development of the canonical neuropathological lesions of AD.

Dataset Information

Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer's Disease Progression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets