A Multi-Source Profile of TAAR3P: trace amine associated receptor 3, pseudogene as an IDG Orphan G Protein-Coupled Receptor

TAAR3P (trace amine associated receptor 3, pseudogene) is a gene classified within the Illuminating the Druggable Genome (IDG) program as a target. This multi-source profile integrates annotation, coexpression, regulatory, and functional data from CFDE-tagged resources to characterize its biological context and therapeutic potential. Our analysis reveals 1 PubMed co-mentions, identifying top coexpressed partners including none. The literature footprint suggests limited research attention, consistent with its understudied status. Enrichr gene-set lookup identifies Sensory Perception (GO:0007600) as a leading annotation. No GSFM functional predictions were available. No regulatory element data were available for this locus. Multi-dataset association analysis via the Harmonizome identifies 403 gene-set associations across multiple biological datasets.Together, these findings provide a comprehensive baseline for future investigation of TAAR3P as a druggable target.

Introduction

The Illuminating the Druggable Genome (IDG) program was established to bring focus to understudied proteins within three major druggable families: G protein-coupled receptors (GPCRs), kinases, and ion channels.¹Despite their therapeutic potential, many of these proteins remain poorly characterized in terms of ligand identity, signaling pathways, and disease associations.¹

TAAR3P is encoded by a gene with Entrez ID 9288 (Ensembl: ENSG00000179073). According to MyGeneInfo²: Predicted to enable trace-amine receptor activity. Predicted to be involved in sensory perception of chemical stimulus. Predicted to be located in plasma membrane. Predicted to be integral component of membrane. [provided by Alliance of Genome Resources, Apr 2022]

Methods

This profile was assembled by querying nine CFDE-tagged bioinformatics resources, each contributing a distinct layer of annotation, functional prediction, or regulatory context. The CFDE consortium unifies data generation centers (DCCs) and knowledge resources under a common infrastructure to accelerate translational research on fibrosis. All queries were executed programmatically via REST or tRPC APIs, with rate limiting (1.2 s inter-call delay) to respect server constraints. Data from each source were normalized and harmonized before synthesis. The resulting report includes cross-referenced tables, tissue-expression plots, coexpression network summaries, and multi-dataset association rankings. Figures were rendered by matplotlib and seaborn.

Data sources used in this profile:

1. MyGeneInfo (https://mygene.info) for gene annotation, functional summary, and genomic coordinates.²
2. IDG Classification API (https://maayanlab.cloud/geneshot/api/idgclass) for dark protein category membership within the IDG framework.
3. Geneshot (https://maayanlab.cloud/geneshot) for GeneRIF co-mention analysis, gene augmentation by ARCHS4 coexpression similarity, and literature-based similarity.³
4. Gene-Specific Functional Model (GSFM, https://gsfm.maayanlab.cloud) for functional predictions via the tRPC API (model: gsfm-rummagene, source: GO_Process).⁴
5. Enrichr (https://maayanlab.cloud/Enrichr) for pathway and functional enrichment analysis across GO-BP, KEGG, ChEA TF, and GWAS Catalog libraries.⁵
6. CFDE Linked Data Hub (https://ldh.genome.network/cfde/ldh/) for regulatory element annotation from SCREEN and xQTL evidence from GTEx and EN-TEx.
7. ARCHS4 (https://maayanlab.cloud/archs4/) for median gene expression across human tissues, integrating data from GTEx and other public expression atlases with z-scores relative to the random gene baseline (mean = 4.336, std = 3.921).
8. Metabolomics Workbench MetGENE (https://www.metabolomicsworkbench.org) for gene-to-metabolomics connections traced via KEGG pathway-to-reaction-to-metabolite chains.
9. Harmonizome (https://maayanlab.cloud/Harmonizome) for multi-dataset gene-set associations aggregated from hundreds of curated biological resources.⁶

Results

Literature Co-Mention Landscape

A total of 1 PubMed IDs mention TAAR3P in GeneRIF annotations³.

Literature-Based Gene Similarity

GeneRIF-based similarity identifies genes that share biological context with TAAR3P in curated gene-relation-inference statements compiled from the biomedical literature³.

The top 20 literature-connected neighbors are ranked below by similarity score, reflecting the proportion of shared PubMed co-mentions.

Top 10 literature-similar genes for TAAR3P, ranked by GeneRIF similarity score

Regulatory Element Landscape (CFDE Linked Data Hub)

The CFDE Linked Data Hub integrates regulatory element data from SCREEN, GTEx, and EN-TEx to map regulatory elements and quantitative trait loci associated with TAAR3P. Regulatory elements include enhancers, promoters, and insulators whose activity profiles overlap or target the TAAR3P locus.

No regulatory elements or xQTL evidence were identified for the TAAR3P locus, which may reflect incomplete annotation for this genomic region.

Tissue Expression Profile (ARCHS4)

ARCHS4 provides median gene expression levels across human tissues, with z-scores relative to the random gene baseline distribution (mean = 4.336, std = 3.921). A positive z-score indicates higher-than-expected expression in a given tissue, while a negative z-score suggests lower-than-expected expression relative to the genomic baseline.

Top tissues by absolute z-score:

Top 10 tissues ranked by expression z-score for TAAR3P, derived from ARCHS4 cross-tissue analysis (Source: ARCHS4)

Functional Predictions (GSFM)

The Gene-Specific Functional Model (GSFM) uses a generative AI approach to predict functional annotations for genes⁴. The model (gsfm-rummagene) was queried for GO Biological Process predictions for TAAR3P, returning predicted terms with associated probabilities, z-scores, and known-annotation status.

No GSFM predictions were returned for TAAR3P from the GO_Process source.

Metabolomics Connections (Metabolomics Workbench MetGENE)

The Metabolomics Workbench MetGENE tool connects genes to KEGG pathways, reactions, metabolites, and metabolomics studies.

No MetGENE pathway connections were found for TAAR3P. This is expected for genes without KEGG pathway annotations.

Multi-Dataset Associations (Harmonizome)

The Harmonizome platform aggregates gene associations from hundreds of curated biological datasets⁶.

A total of 403 gene-set associations were found for TAAR3P across 1 distinct datasets.

Data sources: LINCS L1000 CMAP Chemical Perturbation Consensus Signatures

TAAR3P -- Top Harmonizome associations (Source: Harmonizome)

Gene Annotation Lookup

The Enrichr genemap service was used to identify all curated gene sets and pathway terms that include TAAR3P. Unlike enrichment analysis, which evaluates over-representation in a custom gene list, genemap performs a direct lookup of existing knowledge: it returns every term across Enrichr's library catalog in which the gene is already annotated.⁵.

GO Biological Process terms:

TAAR3P -- GO Biological Process terms containing this gene

Discussion

The literature footprint of TAAR3P consists of 1 PubMed co-mentions, indicating limited research attention. Within the IDG framework, this level of coverage supports its classification as an understudied target¹.

Therapeutic Context

As a member of the protein family family, TAAR3P represents a potentially druggable target. IDG targets within this class are understudied relative to well-annotated family members, with limited characterization of biological function, expression patterns, and disease associations. The sparse annotation landscape of TAAR3P complicates therapeutic prioritization. Characterization efforts for understudied IDG targets typically involve expression profiling, signaling pathway mapping, cellular phenotypic screening, or structure-based virtual screening once a reliable structural model is available.

Literature Network Insights

GeneRIF-based similarity identifies 5_8S_rRNA as the most literature-connected neighbor, though similarity scores were low across the board, reflecting the sparse annotation landscape around TAAR3P.

Knowledge Gaps and Priorities

Key priorities for future investigation of TAAR3P include:

1. Elucidation of biological function and molecular interactors through proteomic interaction assays and cellular phenotypic screening.
2. Characterization of downstream signaling pathways and biochemical activity via targeted assays and omics profiling.
3. Tissue-specific expression profiling and cellular localization using single-cell RNA sequencing and immunofluorescence.
4. Genetic association studies to link TAAR3P variants to disease phenotypes using GWAS meta-analysis and Mendelian randomization.
5. Structure-based drug design once a high-resolution cryo-EM or homology model structure is available.
6. Multi-omics integration combining transcriptomics, proteomics, and metabolomics to contextualize TAAR3P within broader pathophysiological networks.

Supplementary Information

This section provides quick-reference summaries and resource links for TAAR3P. The GSFM model page can be revisited for up-to-date functional predictions as the model is retrained:

GSFM predictions for TAAR3P: https://gsfm.maayanlab.cloud/gene/TAAR3P

Data Summary

Key identifiers and annotation counts for TAAR3P are summarized below.

Data summary for TAAR3P