MSN
moesin
Moesin (for membrane-organizing extension spike protein) is a member of the ERM family which includes ezrin and radixin. ERM proteins appear to function as cross-linkers between plasma membranes and actin-based cytoskeletons. Moesin is localized to filopodia and other membranous protrusions that are important for cell-cell recognition and signaling and for cell movement.
provided by RefSeq
Biological Domains
Immune Response, Lipid Metabolism, Structural Stabilization, Synapse, Vasculature
Pharos Class
Tbio
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 CommitteeFalseBrain eQTL
Indicates whether or not this gene locus has a significant expression Quantitative Trait Locus (eQTL) based on an AMP-AD consortium studyFalseRNA 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 MSN
The TRS for MSN, along with the component Genetic and Multi-omic Risk Scores, is shown here. The scores for MSN 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 MSN.
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 MSN
Select a biological domain on the left to see the list of GO terms that link MSN to it on the right. The percentage value displayed next to the currently selected biological domain indicates the proportion of MSN'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 MSN is annotated with.
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 MSN 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.
Filter the following charts by statistical model
Differential Expression of MSN 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.
Consistency of Change in Expression
This forest plot indicates the estimate of the log fold change with 95% confidence interval 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.
Correlation of MSN 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.
Proteomics
Proteomic analyses of post-mortem brains show whether protein products of MSN 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 MSN
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 MSN 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 MSN
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.
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.
Why was the target selected?
MSN was identified as a potential driver protein based on protein coexpression analysis. The group of proteins coexpressed with MSN is conserved across the 3 datasets considered, is enriched for inflammatory processes, and for protein products of genes near loci previously associated with AD risk. MSN has increased abundance in AD across all 3 cohorts examined, and progressively increases in asymptomatic (prodromal) AD to symptomatic AD, and also correlates with both hallmark AD pathology scores (CERAD for amyloid burden; and Braak for Tau extent of spread). MSN is highly expressed as a marker of disease-associated microglia and/or endothelial cell types.
Predicted therapeutic direction
Antagonism predicted to reduce disease progression. Phosphorylation downstream of Rho/Rock influences actin, focal adhesion binding; may have redundancy with EZR and RDX, complicating targeting. MSN-directed therapeutics that improve microglial motility and/or phagocytosis competence would reduce abeta/amyloid plaque burden.
The type of data used and analyses done to identify target
Discovery quantitative proteomics of FrCx, WPCNA of multiple and consensus cohorts, ANOVA
Cohort study data: ACT, BLSA, Banner
Initial date of nomination
2018
Planned Experimental Validation
validation studies ongoing
Learn more about the target nomination processAMP-AD: The Zhang Lab at the Icahn School of Medicine at Mount Sinai
The Icahn School of Medicine at Mount Sinai AMP-AD team lead by Eric Schadt, Bin Zhang, Jun Zhu, Michelle Ehrlich, Vahram Haroutunian, Samuel Gandy, Koichi Iijima, and Scott Noggle focuses on developing a multiscale network approach to elucidating the complexity of Alzheimer's disease.
Why was the target selected?
The protein expression of MSN was up-regulated in the brain parahippocampal area in AD vs. NL across various AD clinic traits (Braak score, CDR, CERAD, Plaque-mean). It was identified as an important key driver protein in the Bayesian network analysis.
Predicted therapeutic direction
Agonism predicted to reduce disease progression. MSN silencing by siRNA decreased A-beta phagocytosis and increased lipopolysaccharide-induced production of the pro-inflammatory cytokine, tumor necrosis factor (TNF)
The type of data used and analyses done to identify target
Differential protein expression analysis, and Bayesian network analysis derived from proteomics profiling and WGS genotypes.
Cohort study data: MSBB
Initial date of nomination
2020
Planned Experimental Validation
not prioritized for experimental validation
Learn more about the target nomination processAMP-AD: The Zhang Lab at the Icahn School of Medicine at Mount Sinai
The Icahn School of Medicine at Mount Sinai AMP-AD team lead by Eric Schadt, Bin Zhang, Jun Zhu, Michelle Ehrlich, Vahram Haroutunian, Samuel Gandy, Koichi Iijima, and Scott Noggle focuses on developing a multiscale network approach to elucidating the complexity of Alzheimer's disease.
Why was the target selected?
MSN up-regulated in the parahippocampal area of CERAD definite AD brains. It was identified in the Bayesian network analysis as an important key driver of up-regulation of genes associated with AD severity.
Predicted therapeutic direction
Antagonism predicted to reduce disease progression. MSN was significantly up-regulated in the parahippocampal area of AD (p=6.943054e-07, FC=1.351137) / Demented (p=4.371646e-05, FC=1.372884) / severly Plaqued (p=5.965625e-06, FC=1.422039) brains compared to the normal brains.
The type of data used and analyses done to identify target
Bayesian network analysis derived from RNAseq and WGS genotypes.
Cohort study data: MSBB
Initial date of nomination
2018
Planned Experimental Validation
not prioritized for experimental validation
Learn more about the target nomination processCommunity Contributed: The Chang Lab at the University of Arizona
The Chang Team at the University of Arizona, led by Rui Chang, develops novel computational systems biology models to discover drug targets for in-silico precision medicine for AD. The target nominations are based on work performed by the Chang Team at the Icahn School of Medicine at Mt. Sinai.
Why was the target selected?
MSN was identified as an important key driver by predictive network analysis in dorsolateral prefrontal cortex in ROSMAP cohort, and frontal and temporal cortex in KRONOSII cohort, and Brodmann area 10 in MSBB cohort.
Predicted therapeutic direction
Antagonism predicted to reduce disease progression
The type of data used and analyses done to identify target
Predictive network analysis derived from RNAseq, Proteomics and GWAS genotypes
Cohort study data: ROSMAP, Kronos, MSBB
Initial date of nomination
2019
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 LFQ proteomics network module that was highly correlated with cognition. This module (Module 4) contains several novel AD targets, including MSN and CD44.
Predicted therapeutic direction
Antagonism predicted to reduce disease progression. We propose that inhibiting the interaction between CD44 and MSN would reverse harmful activities of microglia and provide beneficial outcomes for AD patients.
The type of data used and analyses done to identify target
LFQ proteomics network.
Cohort study data: ACT, BLSA, Banner
Initial date of nomination
2022
Experimental Validation of MSN
Nominating teams provided details on experimental validation studies they performed to examine a role for the target in AD.
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
Gene target regulates Tau or Ab induced neurodegeneration
Species and Model System
Drosophila, Human; Tau gain-of-function model
Outcome Measure
Imaging; Eye cellular toxicity, cell expression
Summary of Findings
MSN inhibits neurotoxicity in Tau GOF model.
Contributors
Joshua Shulman, Katherine Allison, Tom Lee TREAT AD Ranjita Betarbet (IHC)
Published?
No
Date of Report
12/1/20