Targeted brain proteomics uncover multiple pathways to Alzheimer's dementia

Yu, Lei; Petyuk, Vladislav; Gaiteri, Chris; Mostafavi, Sara; Young‐Pearse, Tracy L.; Shah, Raj C.; Buchman, Aron S.; Schneider, Julie A.; Piehowski, Paul; Sontag, Ryan; Fillmore, Thomas; Shi, Tujin; Smith, Richard; Jager, Philip L. De; Bennett, David A.

doi:10.1002/ana.25266

Cited by 100 publications

(114 citation statements)

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“…These transcriptional AD-related module clusters represent an attractive mechanism to support translational research. Predictions of genes with an important role within an AD-related module cluster have been validated experimentally in vitro and ex vivo (Mostafavi et al, 2018; Yu et al, 2018; Zhang et al, 2013b). Within model systems, gene signatures for human AD clusters can serve as readouts to evaluate consequences of target engagement that are known to be relevant to human disease (Wan et al, submitted).…”

Section: Discussionmentioning

confidence: 99%

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Logsdon

Perumal

Swarup³

et al. 2019

Preprint

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101

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52Alzheimer's disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related 53 biological processes. This complexity contributes, in part, to existing difficulties in the identification of 54 successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to 55 characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular 56 models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-57 analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression 58 methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research 59 studies. From this analysis, five consensus clusters were identified that described the major sources of 60 AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. 61AD genetic associations, previously studied AD-related biological processes, and AD targets under active 62 investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated 63 strong heterogeneity between males and females in AD-related expression that was consistently observed 64 across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams 65 were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, 66 which was specific for genes that showed the strongest association with changes in AD-related gene 67 expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules 68 that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been 69 previously identified and was enriched for genes related to proteostasis. This study provides an atlas to 70 map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery 71 efforts.

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Section: Discussionmentioning

confidence: 99%

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Logsdon

Perumal

Swarup³

et al. 2019

Preprint

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101

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“…To do this, we utilized a targeted proteomics dataset generated from the same sample type and sample collection as our multi-omic data: DLPFC samples of ROS/MAP participants (Synapse ID: syn10468856). The targeted proteins were candidate genes from previous AD studies [64] and measured by liquid chromatography-selected reaction monitoring [65]. We applied the same case/control definition as for the main analysis resulting in 393 AD cases and 214 control subjects.…”

Section: Validation In Independent Datasetsmentioning

confidence: 99%

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

et al. 2020

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Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

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“…This contrasts with several studies reporting Aβ‐related atrophy (de Flores et al, ) but concurs with other studies in cognitively normal individuals that reported increased volume and outward deformation in the thalamus and hippocampus in association with cortical and regional PiB retention (Chetelat et al, ; Schroeder et al, ). The exact role of Aβ dyshomeostasis in AD pathogenesis remains to be understood (Chételat, ), especially in light of other candidates such as tau tangles and other novel proteomics targets associated with cognitive decline but independent from Aβ (Xia et al, ; Yu et al, ). In addition, a very high level of heterogeneity in neuropathologic comorbidity has been observed at a person‐specific level, with more than 200 distinct combinations of neuropathologies being reported and with 78% of participants showing mixed neuropathology (Boyle et al, ).…”

Section: Discussionmentioning

confidence: 99%

Subcortical amyloid load is associated with shape and volume in cognitively normal individuals

Rahayel

Bocti

Dupont

et al. 2019

Human Brain Mapping

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Amyloid‐beta (Aβ) deposition is one of the main hallmarks of Alzheimer's disease. The study assessed the associations between cortical and subcortical 11C‐Pittsburgh Compound B (PiB) retention, namely, in the hippocampus, amygdala, putamen, caudate, pallidum, and thalamus, and subcortical morphology in cognitively normal individuals. We recruited 104 cognitive normal individuals who underwent extensive neuropsychological assessment, PiB–positron emission tomography (PET) scan, and 3‐T magnetic resonance imaging (MRI) acquisition of T1‐weighted images. Global, cortical, and subcortical regional PiB retention values were derived from each scan and subcortical morphology analyses were performed to investigate vertex‐wise local surface and global volumes, including the hippocampal subfields volumes. We found that subcortical regional Aβ was associated with the surface of the hippocampus, thalamus, and pallidum, with changes being due to volume and shape. Hippocampal Aβ was marginally associated with volume of the whole hippocampus as well as with the CA1 subfield, subiculum, and molecular layer. Participants showing higher subcortical Aβ also showed worse cognitive performance and smaller hippocampal volumes. In contrast, global and cortical PiB uptake did not associate with any subcortical metrics. This study shows that subcortical Aβ is associated with subcortical surface morphology in cognitively normal individuals. This study highlights the importance of quantifying subcortical regional PiB retention values in these individuals.

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Targeted brain proteomics uncover multiple pathways to Alzheimer's dementia

Abstract: Using targeted proteomics, this work identified cortical proteins involved in Alzheimer's dementia and begins to dissect two different molecular pathways: one affecting β-amyloid deposition and another affecting resilience without a known pathological footprint. Ann Neurol 2018;83:78-88.

Cited by 100 publications

References 48 publications

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

Subcortical amyloid load is associated with shape and volume in cognitively normal individuals

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