APP
amyloid beta precursor protein
This gene encodes a cell surface receptor and transmembrane precursor protein that is cleaved by secretases to form a number of peptides. Some of these peptides are secreted and can bind to the acetyltransferase complex APBB1/TIP60 to promote transcriptional activation, while others form the protein basis of the amyloid plaques found in the brains of patients with Alzheimer disease. In addition, two of the peptides are antimicrobial peptides, having been shown to have bacteriocidal and antifungal activities. Mutations in this gene have been implicated in autosomal dominant Alzheimer disease and cerebroarterial amyloidosis (cerebral amyloid angiopathy). Multiple transcript variants encoding several different isoforms have been found for this gene.
provided by RefSeq
Biological Domains
APP Metabolism, Apoptosis, Cell Cycle, Endolysosome, Epigenetic, Immune Response, Lipid Metabolism, Metal Binding and Homeostasis, Mitochondrial Metabolism, Oxidative Stress, Proteostasis, Structural Stabilization, Synapse, Tau Homeostasis, Vasculature
Pharos Class
Tclin
Also known as
ENSG00000142192 (Ensembl Release 113)
UNIPROTKB P05067
AAA, CVAP, CTFgamma, PN-II, PN2, ABETA, APPI, preA4, ABPP, AD1, alpha-sAPP
Summary of Evidence
This tab shows an overview of how the selected gene is associated with AD.
Genetic Association with LOAD
Indicates whether or not this gene shows significant genetic association with Late Onset AD (LOAD) based on evidence from multiple studies compiled by the ADSP Gene Verification CommitteeTrueBrain eQTL
Indicates whether or not this gene locus has a significant expression Quantitative Trait Locus (eQTL) based on an AMP-AD consortium studyTrueRNA Expression Change in AD Brain
Indicates whether or not this gene shows significant differential expression in at least one brain region based on AMP-AD consortium work. See ‘EVIDENCE’ tab.TrueProtein Expression Change in AD Brain
Indicates whether or not this gene shows significant differential protein expression in at least one brain region based on AMP-AD consortium work. See ‘EVIDENCE’ tab.TrueNominated Target
Indicates whether or not this gene has been submitted as a nominated target to Agora.True
AD Risk Scores
About AD Risk Scores
The TREAT-AD Center at Emory-Sage-SGC has developed a Target Risk Score (TRS) to objectively rank the potential involvement of specific genes in AD. The TRS is derived by summing two component risk scores, the Genetic Risk Score and the Multi-omic Risk Score, each of which is derived from a meta-analysis of multiple harmonized data sets. More information about the methodology used to define these risk scores is available here.
AD Risk Scores for APP
The TRS for APP, along with the component Genetic and Multi-omic Risk Scores, is shown here. The scores for APP are superimposed on the genome-wide score distributions. If No Data is Currently Available is displayed for a score, that score was not calculated for APP.
Biological Domain Classification
About Biological Domains
A biological domain represents a standardized area of biology defined by a set of discrete, biologically coherent GO terms. The TREAT-AD Center at Emory-Sage-SGC has defined nineteen biological domains associated with AD, and objectively mapped genes to those biological domains using GO term annotations. More information about the methodology used to define AD biological domains, and to generate genome-wide biological domain mappings, is available here.
Biological Domains for APP
Select a biological domain on the left to see the list of GO terms that link APP to it on the right. The percentage value displayed next to the currently selected biological domain indicates the proportion of APP's total unique GO terms that map to the biological domain. The ratio displayed on the right indicates how many of the biological domain's total GO terms APP is annotated with.
BIOLOGICAL DOMAIN MAPPINGS
LINKING GO TERMS FOR SYNAPSE (38/894)
- Associative learning
- Axo-dendritic transport
- Axon
- Axon midline choice point recognition
- Axonogenesis
- Collateral sprouting in absence of injury
- Dendrite development
- Dendritic shaft
- Dendritic spine
- Growth cone
- Ionotropic glutamate receptor signaling pathway
- Learning
- Learning or memory
- Modulation of excitatory postsynaptic potential
- Negative regulation of long-term synaptic potentiation
- Neuromuscular junction
- Neuron projection development
- Neuron projection maintenance
- Neuron remodeling
- Positive regulation of excitatory postsynaptic potential
- Positive regulation of long-term synaptic potentiation
- Postsynapse
- Presynapse
- Presynaptic active zone
- Receptor complex
- Regulation of acetylcholine-gated cation channel activity
- Regulation of dendritic spine maintenance
- Regulation of long-term neuronal synaptic plasticity
- Regulation of NMDA receptor activity
- Regulation of presynapse assembly
- Regulation of spontaneous synaptic transmission
- Regulation of synapse structure or activity
- Signaling receptor binding
- Synapse
- Synapse organization
- Synaptic assembly at neuromuscular junction
- Synaptic vesicle
- Visual learning
RNA Expression
The results shown on this page are derived from a harmonized RNA-seq analysis of post-mortem brains from AD cases and controls. The samples were obtained from three human cohort studies across a total of nine different brain regions.
Overall Expression of APP Across Brain Regions
This plot depicts the median expression of the selected gene across brain regions, as measured by RNA-seq read counts per million (CPM) reads. Meaningful expression is considered to be a log2 CPM greater than log2(5), depicted by the red line in the plot.
Differential Expression of APP Across Brain Regions
After selecting a statistical model, you will be able to see whether the selected gene is differentially expressed between AD cases and controls. The box plot depicts how the differential expression of the selected gene of interest (purple dot) compares with expression of other genes in a given tissue. Summary statistics for each tissue can be viewed by hovering over the purple dots. Meaningful differential expression is considered to be a log2 fold change value greater than 0.263, or less than -0.263.
AD Diagnosis (males and females)
Consistency of Change in Expression
This forest plot indicates the estimate of the log fold change with standard errors across the brain regions in the model chosen using the filter above. Genes that show consistent patterns of differential expression will have similar log-fold change value across brain regions.
AD Diagnosis (males and females)
Correlation of APP with Hallmarks of AD
This plot depicts the association between expression levels of the selected gene in the DLPFC and three phenotypic measures of AD. An odds ratio > 1 indicates a positive correlation and an odds ratio < 1 indicates a negative correlation. Statistical significance and summary statistics for each phenotype can be viewed by hovering over the dots.
Similarly Expressed Genes
The network diagram below is based on a coexpression network analysis of RNA-seq data from AD cases and controls. The network analysis uses an ensemble methodology to identify genes that show similar coexpression across individuals.
The color of the edges and nodes indicates how frequently significant coexpression was identified. Each node represents a different gene and the amount of edges within the network. Darker edges represent coexpression in more brain regions.
APP
This gene encodes a cell surface receptor and transmembrane precursor protein that is cleaved by secretases to form a number of peptides. Some of these peptides are secreted and can bind to the acetyltransferase complex APBB1/TIP60 to promote transcriptional activation, while others form the protein basis of the amyloid plaques found in the brains of patients with Alzheimer disease. In addition, two of the peptides are antimicrobial peptides, having been shown to have bacteriocidal and antifungal activities. Mutations in this gene have been implicated in autosomal dominant Alzheimer disease and cerebroarterial amyloidosis (cerebral amyloid angiopathy). Multiple transcript variants encoding several different isoforms have been found for this gene. [provided by RefSeq, Aug 2014].
Proteomics
Proteomic analyses of post-mortem brains show whether protein products of APP are differentially expressed between AD cases and controls. Each box plot depicts how the differential expression of the protein(s) of interest (purple dot) compares with expression of other proteins in a given brain region. Summary statistics for each tissue can be viewed by hovering over the purple dots.
Targeted SRM Differential Protein Expression
Selected Reaction Monitoring (SRM) data was generated from the DLPFC region of post-mortem brains of over 1000 individuals from multiple human cohort studies.
Note that only a single SRM result is available for a given gene, as the probes used for this experiment were designed to match multiple protein products derived from each targeted gene.
Genome-wide Differential Protein Expression
Select a protein from the dropdown menu to see whether it is differentially expressed between AD cases and controls.
The assay-specific box plots below depict how the differential expression of the selected protein of interest (purple dot) compares with expression of other proteins in each brain region that was assayed. Assay-specific summary statistics for each brain region can be viewed by hovering over the purple dot.
Multiple proteins may map to a single gene. Results from both TMT and LFQ assays are provided, however results for some proteins may be available for only one of the assays.
TMT Differential Protein Expression
Tandem mass tagged (TMT) data was generated from the DLPFC region of post-mortem brains of 400 individuals from the ROSMAP cohort.
Note that proteins may not be detected in this brain region; for these proteins, the plot will show no data.
LFQ Differential Protein Expression
Liquid-free quantification (LFQ) data was generated from post-mortem brains of more than 500 individuals. Samples were taken from four human cohort studies, representing four different brain regions.
Note that proteins may not be detected in all four brain regions; for these proteins, the plot will show fewer than four brain regions.
Metabolomics
The results shown on this page are derived from an analysis of metabolite levels from AD cases and controls. The samples were obtained from approximately 1400 individuals from the ADNI study. Metabolites are associated with genes using genetic mapping and the metabolite with the highest genetic association is shown for each gene.
Mapping of Metabolites to APP
No metabolomic data is currently available.
Levels of Metabolite by Disease Status
This plot shows differences in metabolite levels in AD cases and controls.
Target Enabling Resources
Use these links to discover the Target Enabling Resources for APP that are currently available, under development, or planned.
Drug Development Resources
These external sites provide information and resources related to drug development.
Additional Resources
These external sites provide additional information about therapeutic targets for AD and related dementias.
Evidence Supporting the Nomination of APP
This gene has been nominated as a potential target for AD. Nominated targets are obtained from several sources, including the National Institute on Aging's Accelerating Medicines Partnership in Alzheimer's Disease (AMP-AD) consortium. Targets have been identified using computational analyses of high-dimensional genomic, proteomic and/or metabolomic data derived from human samples.
TREAT-AD: Emory University - Sage Bionetworks - Structural Genomics Consortium
The mission of the Emory-Sage-Structural Genomics Consortium (SGC) TREAT-AD center is to diversify the portfolio of drug targets in Alzheimer's disease (AD). We aim to catalyze research into biological pathways that have been associated with disease from deep molecular profiling and bioinformatic evaluation of AD in the human brain within the National Institute on Aging's (NIA) Accelerating Medicines Partnership-Alzheimer's Disease (AMP-AD) consortium. Many of these potential AD drug targets are predicted to reside among the thousands of human proteins that historically have received little attention and for which there are few reagents, such as quality-verified antibodies, cell lines, assays or chemical probes. To catalyze their investigation, we are developing and openly distributing experimental tools, including chemical probes, for broad use in the evaluation of a diverse set of novel AD targets.
Why was the target selected?
This target is found within a TMT proteomics network module that was highly correlated with cognition. This module (Module 42) contains several novel AD targets, including SMOC1 and MDK.
Predicted therapeutic direction
Unknown.
The type of data used and analyses done to identify target
TMT proteomics network.
Cohort study data: Banner, ROSMAP, MSBB, Emory
Initial date of nomination
2024
Planned Experimental Validation
validation studies ongoing
Learn more about the target nomination processExperimental Validation of APP
Nominating teams provided details on experimental validation studies they performed to examine a role for the target in AD.
AMP-AD : Columbia University - Rush University
The Columbia-Rush AMP-AD team, led by Philip De Jager and David Bennett, focuses on taking a systems biology approach to mine a unique set of deep clinical, paraclinical, pathologic, genomic, epigenomic, transcriptomic, proteomic, metabolic and single cell brain and blood data from more than 1000 subjects from two prospective cohort studies of aging and dementia. They use these data to identify genes, proteins, and pathways with critical roles in a range of traits that influence the function of the aging brain, including susceptibility Alzheimer's disease, the accumulation of aging-related neuropathologies, cognitive decline in older age, as well as resilience to the brain pathologies. Finally, the team then interrogates those genes in ex vivo and in vitro systems to determine their therapeutic potential.
Hypothesis Being Tested
APP overexpression contributes to AD-relevant findings in Trisomy 21
Species and Model System
Human; iPSC-neuron
Outcome Measure
Biochemical; Measurements of Abeta and tau, RNAseq, proteomics
Summary of Findings
Contributors
Young-Pearse, Bennett, De Jager, Seyfried
Published?
No
Date of Report
12/1/20
AMP-AD : Emory University
The Emory AMP-AD team, led by Allan Levey, focuses on the generation and analysis of proteomic data to understand neurodegenerative disease. Targets nominated by the Emory team have been identified through the analysis of differential protein expression and co-expression network analysis.
Hypothesis Being Tested
Does knock-down or ectopic expression of the fly ortholog appl enhance or suppress tau toxicity in Drosophila
Species and Model System
Drosophila; Robotic Negative Geotaxis (climbing) assay
Outcome Measure
Behavioral; Locomotor speed (negative geotaxis)
Summary of Findings
Experiment in progress
Contributors
Joshua Shulman, David Li-Kroeger
Published?
No
Date of Report
12/1/20