Reduction of the expression of the late-onset Alzheimer’s disease (AD) risk-factor BIN1 does not affect amyloid pathology in an AD mouse model

Andrew, Robert J.; Rossi, Pierre De; Nguyen, Phuong D.; Kowalski, Haley R.; Recupero, Aleksandra J.; Guerbette, Thomas; Krause, Sofia V.; Rice, Richard C.; Laury‐Kleintop, Lisa D.; Wagner, Steven L.; Wang, Shuai

doi:10.1074/jbc.ra118.006379

Cited by 38 publications

(38 citation statements)

References 58 publications

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“…For example, BIN1 and CLU were both found to be enriched among Oligodendrocytes, Excitatory and Inhibitory neurons in Amyloid Precursor Protein metabolism, and Amyloid Beta formation and metabolism. BIN1 was found to be the second most predominant loci involved in AD progression (following APOE) [88] and was not involved in later steps of amyloidogenic processing from later stage AD progression, falling in line with previously identified involved steps [89]. In addition, BIN1 has been found to link to microglia [25] and be commonly implicated in Tauopathy, further supporting the status of BIN1 as a high risk loci for AD [90].…”

Section: Gene Regulatory Network Associated With Functional Interplasupporting

confidence: 72%

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Jin

Rehani

Ying

et al. 2020

Preprint

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Strong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer's disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.Recent analyses have also revealed that brain disease risk variants are located in non-coding regulatory elements (e.g., enhancers) and that the risk genes likely have cell-type specific effects including neuronal and non-neuronal types [25,26]. In addition, recent single-cell studies 68 TFs,

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Section: Gene Regulatory Network Associated With Functional Interplasupporting

confidence: 72%

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Jin

Rehani

Ying

et al. 2020

Preprint

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“…Many studies have investigated the role of genes such as Phosphatidylinositol Binding Clathrin Assembly Protein ( PICALM ) 40 , Solute Carrier Family 24 Member 4 ( SLC24A4 ) 41 , Bridging Integrator 1 ( BIN1 ) 10, 42 , and Membrane Spanning 4-Domains A6A ( MS4A6A ) 43 in AD since their implication in the disease by GWAS. However, it remains unclear which polymorphisms drive these associations.…”

Section: Resultsmentioning

confidence: 99%

Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Corces

Shcherbina

Kundu

et al. 2020

Preprint

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42Genome-wide association studies (GWAS) have identified thousands of variants associated with 43 disease phenotypes. However, the majority of these variants do not alter coding sequences, making 44 it difficult to assign their function. To this end, we present a multi-omic epigenetic atlas of the 45 adult human brain through profiling of the chromatin accessibility landscapes and three-46 dimensional chromatin interactions of seven brain regions across a cohort of 39 cognitively healthy 47 individuals. Single-cell chromatin accessibility profiling of 70,631 cells from six of these brain 48 regions identifies 24 distinct cell clusters and 359,022 cell type-specific regulatory elements, 49 capturing the regulatory diversity of the adult brain. We develop a machine learning classifier to 50 integrate this multi-omic framework and predict dozens of functional single nucleotide 51 polymorphisms (SNPs), nominating gene and cellular targets for previously orphaned GWAS loci. 52These predictions both inform well-studied disease-relevant genes, such as BIN1 in microglia for 53 Alzheimer's disease (AD) and reveal novel gene-disease associations, such as STAB1 in microglia 54 and MAL in oligodendrocytes for Parkinson's disease (PD). Moreover, we dissect the complex 55 inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying ectopic enhancer-gene 56 contacts in neurons that increase MAPT expression and may mediate this disease association. This 57 work greatly expands our understanding of inherited variation in AD and PD and provides a 58 roadmap for the epigenomic dissection of noncoding regulatory variation in disease. 59 60 61 62Alzheimer's disease (AD) and Parkinson's disease (PD) affect ~50 and ~10 million individuals 63 world-wide, as two of the most common neurodegenerative disorders. Several large consortia have 64 assembled genome-wide association studies (GWAS) that associate genetic variants with clinical 65 diagnoses of probable AD dementia 1-4 or probable PD 5-7 , or with their characteristic pathologic 66 features. These efforts have led to the identification of dozens of potential risk loci for these 67 prevalent neurodegenerative diseases. One goal of these studies was to build more precise 68 molecular biomarkers of AD or PD, efforts that are beginning to yield encouraging results with 69 polygenic risk scores 8 . The other major goal was to gain deeper insight into the molecular 70 pathogenesis of disease and thereby inform novel therapeutic targets. Some of the risk loci contain 71 coding variants and so have credibility as putative disease mediators. However, most risk loci are 72 in noncoding regions and so it remains unclear if the nominated (often nearest) gene is the 73 functional disease-relevant gene, or if some other gene is involved 9 . Furthermore, even if the 74 nominated gene is a true positive, the noncoding risk locus might regulate additional genes. These 75 challenges remain a fundamental gap in interpreting the etiology of neurodegenerative diseases 76 and d...

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“…The loss of BIN1 expression in the cKO mice did not affect the expression of the paralog of BIN1, Amph1, and several other proteins in the brain ( Figure S2; Emx data not shown). Immunostaining of BIN1 using monoclonal antibody (mAb) 13463 under conditions that preferentially stain neuronal BIN1 (Andrew et al, 2019) revealed an almost complete loss of BIN1 in the hippocampus ( Figure 1B). Microdissection immunoblot analysis confirmed a significant decrease in both the BIN1:H and BIN1:L isoforms ( Figures 1C and S2), suggesting that hippocampal neurons express multiple isoforms of BIN1, including those lacking the brain-specific CLAP domain, as confirmed by immunoblotting using pAb B1415, an antibody that is specific for the longest BIN1 brain isoforms 1 and 2 (BIN1:H).…”

Section: Characterization Of Bin1 Neuronal Expression In the Brainmentioning

confidence: 99%

“…Microglial BIN1 also has been recently found to influence the release of Tau in extracellular vesicles (Crotti et al, 2019). The loss of BIN1 expression influences AD b-amyloid pathogenesis in cultured neurons, but this does not appear to be the case in vivo (Andrew et al, 2019;Miyagawa et al, 2016). Among older individuals without dementia, carriers of the BIN1 rs744373 risk allele were found to have similar amyloid pathology but increased Tau pathology and significantly impaired memory performance (Franzmeier et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Neuronal BIN1 Regulates Presynaptic Neurotransmitter Release and Memory Consolidation

et al. 2020

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Reduction of the expression of the late-onset Alzheimer’s disease (AD) risk-factor BIN1 does not affect amyloid pathology in an AD mouse model

Cited by 38 publications

References 58 publications

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Neuronal BIN1 Regulates Presynaptic Neurotransmitter Release and Memory Consolidation

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