PPARG
peroxisome proliferator activated receptor gamma
This gene encodes a member of the peroxisome proliferator-activated receptor (PPAR) subfamily of nuclear receptors. PPARs form heterodimers with retinoid X receptors (RXRs) and these heterodimers regulate transcription of various genes. Three subtypes of PPARs are known: PPAR-alpha, PPAR-delta, and PPAR-gamma. The protein encoded by this gene is PPAR-gamma and is a regulator of adipocyte differentiation. Additionally, PPAR-gamma has been implicated in the pathology of numerous diseases including obesity, diabetes, atherosclerosis and cancer. Alternatively spliced transcript variants that encode different isoforms have been described.
provided by RefSeq
Biological Domains
Apoptosis, Autophagy, Epigenetic, Immune Response, Lipid Metabolism, Metal Binding and Homeostasis, Mitochondrial Metabolism, Myelination, Synapse, Vasculature
Pharos Class
Tclin
Also known as
ENSG00000132170 (Ensembl Release 113)
UNIPROTKB P37231
CIMT1, PPARgamma, NR1C3, PPARG5, PPARG2, GLM1, PPARG1
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 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.No dataNominated 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 PPARG
The TRS for PPARG, along with the component Genetic and Multi-omic Risk Scores, is shown here. The scores for PPARG 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 PPARG.
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 PPARG
Select a biological domain on the left to see the list of GO terms that link PPARG to it on the right. The percentage value displayed next to the currently selected biological domain indicates the proportion of PPARG'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 PPARG is annotated with.
BIOLOGICAL DOMAIN MAPPINGS
LINKING GO TERMS FOR LIPID METABOLISM (16/812)
- Arachidonic acid binding
- Cellular response to low-density lipoprotein particle stimulus
- Fatty acid metabolic process
- Fatty acid oxidation
- Lipid homeostasis
- Lipid metabolic process
- Lipoprotein transport
- Long-chain fatty acid transport
- Negative regulation of cholesterol storage
- Negative regulation of lipid storage
- Negative regulation of sequestering of triglyceride
- Positive regulation of cholesterol efflux
- Positive regulation of fatty acid metabolic process
- Positive regulation of fatty acid oxidation
- Regulation of cholesterol transporter activity
- Response to lipid
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 PPARG 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 PPARG 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 PPARG 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.
PPARG
This gene encodes a member of the peroxisome proliferator-activated receptor (PPAR) subfamily of nuclear receptors. PPARs form heterodimers with retinoid X receptors (RXRs) and these heterodimers regulate transcription of various genes. Three subtypes of PPARs are known: PPAR-alpha, PPAR-delta, and PPAR-gamma. The protein encoded by this gene is PPAR-gamma and is a regulator of adipocyte differentiation. Additionally, PPAR-gamma has been implicated in the pathology of numerous diseases including obesity, diabetes, atherosclerosis and cancer. Alternatively spliced transcript variants that encode different isoforms have been described. [provided by RefSeq, Jul 2008].
Proteomics
Proteomic analyses of post-mortem brains show whether protein products of PPARG 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 PPARG
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 PPARG 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 PPARG
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: The Roussos Lab at the Icahn School of Medicine at Mount Sinai
The Icahn School of Medicine at Mount Sinai AMP-AD team lead by Panos Roussos, Vahram Haroutunian, John Fullard, Gabriel Hoffman, Donghoon Lee, and Jaro Bendl focuses on changes of immune markers underlying the etiology of Alzheimer's disease.
Why was the target selected?
Genes are upregulated in AD based on single cell analysis of microglia subtypes
Predicted therapeutic direction
Antagonism predicted to reduce disease progression.
The type of data used and analyses done to identify target
scRNAseq and snRNAseq in microglia
Cohort study data: HBI_scRNAseq
Initial date of nomination
2023
Planned Experimental Validation
Not prioritized for experimental validation